,Company,Fichier,Content
0,ey,ey-execs-double-down-on-ai-explore-5-ai-adoption-strategies-for-success.pdf,"Execs double down
on AI: explore 5 AI
adoption strategies
for success
AI Pulse Survey — July 2024
About the survey
The EY AI Pulse Survey, conducted in May 2024, captures the investment trends
and attitudes toward artificial intelligence (AI) among 500 senior US executives as
they prepare to scale AI technologies in 2024. The survey highlights a significant
projected increase in AI investments, emphasizing the technology’s growing
importance in corporate strategy. The findings suggest that successful AI
adoption requires a holistic approach, including strategic diversified AI investments,
responsible AI practices and workforce upskilling. The insights offered aim to
guide executives in navigating the complexities of AI integration, with strategic
recommendations for those seeking to lead in the AI-driven business landscape.
2 | Execs double down on AI: explore 5 AI adoption strategies for success
Artificial intelligence is redefining the However, despite the forecast investment boom,
competitive business landscape, with our findings also indicate that many leaders are
leaders actively investing to capitalize on ignoring the foundational functions AI needs in
its transformative promise. To investigate order to thrive. Successful AI adoption demands
top-tier investment trends and perceptions more than just technological integration; it’s
in AI technology adoption among corporate about adapting to a new paradigm whereby
leaders, as well as uncover the state of AI in AI reshapes every aspect of the enterprise.
the US, we commissioned a survey among From building a scalable data infrastructure
500 senior executives across a spectrum of to fostering a workforce fluent in emerging
industries. Survey findings indicate a projected technologies, the research emphasizes the
nearly twofold increase in AI investments, need for a holistic approach to AI adoption.
exceeding US$10 million or more in the next As we stand on the cusp of an AI-driven era,
year, among those who are already investing, the message is clear: Those who invest wisely
signaling AI’s shift to a central role in corporate in AI today will be the industry trailblazers
growth strategies. of tomorrow.
This sentiment follows a year in which AI This article illuminates the essential strategies
investments had already significantly increased executives should deploy to navigate the
in pace. Just three years ago, about half of complexities of AI adoption as their investments
senior leaders said their organization spent less increase, including five key takeaways:
than 5% of its total budget on AI investments.
In contrast, today, 88% of those same leaders Adopt diversified AI investment
1
spend 5% or more of their total budget on AI. strategies.
It’s a number that is set to grow even higher, as
half of senior leaders said they would dedicate 2 Prioritize return on investment
25% or more of their total budget toward AI (ROI)-driven AI deployment.
investments in the coming year.
Align your business and AI maturity
3
At the same time, those already doubling roadmap.
down on investments are seeing the impact.
While nearly all are investing in AI, our findings
Invest in responsible AI as a
4
indicate a divergence between companies competitive edge.
experimenting in small ways and those making
larger investments. Senior leaders whose Embrace talent development as a
5
organizations are investing in AI and whose value driver.
current budget for AI investments is 5% or more
of their total budget saw higher rates of positive
return across dimensions surveyed compared
with those who spend less than 5%.
3 | Execs double down on AI: explore 5 AI adoption strategies for success
Leaders should adopt diversified
AI investment strategies
In the search for operational excellency, Figure 1: Leaders should adopt diversified AI
investment strategies
businesses are turning to AI as a transformative
technology. Custom AI development stands
out for its ability to enhance an enterprise’s With 95% of senior leaders
saying their organization is
operations, delivering peak efficiency and
currently investing in AI.
intelligent workflow management tailored to the
intricate needs of the business. Simultaneously,
the allure of pre-built AI technologies lies in 95%
their ability to offer immediate implementation
and a more favorable cost structure. Discerning
businesses should undertake a comprehensive
analysis of their operational requirements,
56%
competitive landscape and long-term goals
to determine the optimal blend of in-house
56%
developed and off-the-shelf AI solutions.
By doing so, they position themselves to
The key investment focus
leverage the full spectrum of AI benefits, lies in balancing custom
The acquisition of AI development.
ensuring a strategic advantage in the rapidly
ready-made AI products.
evolving marketplace.
This strategy allows for tailored solutions where necessary
while also leveraging the speed and cost efficiency of pre-built
AI technologies.
4 | Execs double down on AI: explore 5 AI adoption strategies for success
Prioritize ROI-driven AI deployment
Forward-thinking enterprises are looking to Figure 2: Prioritize ROI-driven AI deployment
AI as a catalyst for business transformation.
Strategic deployment of AI is crucial for firms
aiming to strengthen their performance About a third (34%) of senior
leaders say their organization is
and realize cost efficiencies. By focusing
tracking the impact of AI initiatives
on AI solutions that improve operational fully and at scale.
workflows and enhance employee productivity,
organizations can convert traditional business
34%
models into intelligent, AI-powered operations.
This advancement goes beyond simple task
improvement — it calls for a radical redesign of
77%
business processes to be AI-centric. By doing
so, companies are not just automating; they are
innovating, ensuring that their investments in AI
74%
yield measurable financial returns and solidify
their standing for the future.
The survey shows that
among organizations
investing in AI, those
investments are delivering
Employee positive returns, especially
productivity (74%). in areas like operational
efficiencies (77%).
5 | Execs double down on AI: explore 5 AI adoption strategies for success
33
Align your business and AI
maturity roadmap
Capturing the full potential of AI requires more Figure 3: Align your business and AI maturity roadmap
than just technological investment; it demands a
strategic alignment that integrates AI initiatives About 1/3 (34%) of senior
executives report that their
with the core objectives of the business. A
organization is aligning 34%
robust and well-structured data infrastructure AI strategy with business
objectives fully and at scale.
is critical as it underpins intelligent operations
and aligns with the company’s strategic
A third (36%) of senior
pursuits. This alignment paves the way for
leaders report that their
enhanced decision-making capabilities and organization is investing
36% in data infrastructure (i.e.,
a fertile environment for innovation. By
quality, accessibility and
achieving strategic AI maturity, organizations governance of data) fully
and at scale,
can transition into “superfluid” entities,
characterized by their seamless decision-making
processes and a relentless drive for innovation. and 35% report that their
In this way, a strong data foundation not only organization is creating a 35%
roadmap for AI implementation
supports AI but also propels businesses toward
fully and at scale.
their goals with unprecedented efficiency
and insight.
A superfluid enterprise is a highly agile
and adaptable organization, leveraging
digital innovation to swiftly respond to
market shifts, optimize processes and drive
continuous growth, ensuring sustained
competitive advantage.
6 | Execs double down on AI: explore 5 AI adoption strategies for success
44
Champion responsible AI
as a competitive edge
The surge in executive interest toward Figure 4: Champion responsible AI as a competitive edge
responsible AI marks a pivotal shift in business
strategy, placing ethical considerations at
the forefront of AI adoption. To navigate About a third (34%) of senior
this new terrain, companies should invest in leaders say their organization
is building an AI governance
comprehensive AI governance frameworks and
framework fully and at scale.
strategies for mitigating bias, thereby ensuring
that their AI systems uphold fairness and
transparency. Firms that excel in responsible AI
not only distinguish themselves in a competitive 34%
marketplace but also fortify themselves against
future regulatory issues. In addition, ethical
AI practices are a linchpin in the creation of
32%
a “superfluid” enterprise, where stakeholder
trust is strengthened, compliance is effortlessly 53%
maintained and operational friction is reduced,
all of which propels innovation. Pioneers in this About as many (32%)
senior leaders say their
are setting a new industry standard, providing
organization is addressing
services that are both transparent and With 53% of senior leaders bias in AI models fully and
equitable and charting the course for the future whose organization is at scale.
investing in AI reporting
of AI-powered businesses. There is clear interest
increased organizational
in responsible AI, but
interest in responsible
leaders are not taking the
AI over the past year,
necessary steps to realize
businesses should prioritize
this interest.
ethical considerations in
their AI strategies.
7 | Execs double down on AI: explore 5 AI adoption strategies for success
55
Embrace talent development
as a value driver
The scarcity of AI skills in the job market is a Figure 5: Embrace talent development as a value driver
clarion call for businesses to invest in extensive
employee upskilling programs. By cultivating AI
There is clearly a gap and talent is
skills within their existing workforce, companies hard to find, but only 4 in 10 (40%)
senior leaders are encouraging
can not only expedite the adoption of AI Additionally, only 37% of
employees to embrace AI fully and
senior leaders say their
technologies but also secure a vital competitive at scale.
organization is training/
advantage. Developing an internal pipeline of AI upskilling employees on
AI fully and at scale.
talent is essential for fostering a workforce that
is not just proficient but superfluid — adaptable, 40%
innovative and fully equipped to leverage AI
37%
for maximum impact. Moreover, by placing a
premium on attracting and nurturing AI-savvy
employees, organizations can establish that 83%
their operations are driven by professionals that
can unlock the full spectrum of AI’s capabilities, 78%
positioning the business at the forefront of
technological advancement.
With 83% of senior
leaders prioritizing
attracting workers who The difficulty in finding
are knowledgeable of AI, employees with the
businesses must recognize AI skill set needed for
the importance of building their organization (78%)
an AI-competent workforce. underscores the need for
comprehensive upskilling
programs.
8 | Execs double down on AI: explore 5 AI adoption strategies for success
Conclusion
The burgeoning influence of AI on the business landscape is undeniable, with our survey of 500
senior executives revealing a significant uptick in AI investments. This is not merely a trend but a
strategic imperative; companies that do not actively engage with AI risk being left behind in a market
that increasingly rewards innovation and agility. As we have seen, the future belongs to those who
recognize AI’s potential to redefine every facet of their operations — from process improvement to
decision-making — and invest accordingly.
A diversified AI investment strategy is paramount. Companies must balance the allure of ready-made
AI technology solutions with the bespoke advantages of custom development to create a hybrid model
that aligns with their unique business needs. This approach enables organizations to harness AI’s
full potential while maintaining flexibility in a dynamic market. In addition, the focus must be on ROI-
driven AI deployment. Investments in AI should not be made for the sake of technology alone; they
must be tied to clear, measurable business outcomes. Organizations that prioritize AI applications
with direct impact on operational efficiency and productivity will not only see immediate benefits but
also set the stage for long-term financial success.
It’s important to note that championing responsible AI is not just an ethical mandate but a competitive
differentiator. As AI becomes more widespread, companies that lead with transparency, fairness and
governance will build trust and resilience, positioning themselves favorably in the eyes of consumers
and regulators alike.
And aligning business and AI maturity roadmaps is crucial. Organizations must verify that their data
infrastructure and AI initiatives are in lockstep with their strategic goals. This synergy will enable
them to make smarter decisions faster and foster an environment ripe for continuous innovation.
Finally, talent development is a critical value driver in the AI equation. The scarcity of AI competencies
necessitates a proactive approach to upskilling and attracting top talent. Companies that build a
robust internal pipeline of AI skills will not only accelerate technology integration but also secure a
lasting competitive edge.
9 | Execs double down on AI: explore 5 AI adoption strategies for success
Methodology
Ernst & Young LLP commissioned a third party to conduct the 2024 EY AI Pulse Survey. The
online survey was conducted among n=500 US-employed decision-makers (SVP+) in the health;
life sciences, energy, technology, media and telecommunications (TMT); government and public
sector; consumer products and retail; advanced manufacturing and mobility (AMM); financial
services; private equity; and real estate, hospitality and construction (RHC) industries (i.e., n=50
per industry). The survey was fielded between April 29 and May 6, 2024. The margin of error
for the total sample is +/- 4 percentage points.
Ernst & Young LLP contacts
Dan Diasio
EY Global Artificial Intelligence Consulting Leader
dan.diasio@ey.com
Traci Gusher
EY Americas Data and Analytics Leader
traci.gusher@ey.com
Samta Kapoor
EY Americas Energy AI and Trusted AI Leader
samta.kapoor@ey.com
10 | Execs double down on AI: explore 5 AI adoption strategies for success
EY | Building a better working world
EY exists to build a better working world, helping
to create long-term value for clients, people and
society and build trust in the capital markets.
Enabled by data and technology, diverse EY
teams in over 150 countries provide trust
through assurance and help clients grow, transform
and operate.
Working across assurance, consulting, law,
strategy, tax and transactions, EY teams ask better
questions to find new answers for the complex
issues facing our world today.
EY refers to the global organization, and may refer to one or more, of the
member firms of Ernst & Young Global Limited, each of which is a separate
legal entity. Ernst & Young Global Limited, a UK company limited by guarantee,
does not provide services to clients. Information about how EY collects and
uses personal data and a description of the rights individuals have under data
protection legislation are available via ey.com/privacy. EY member firms do not
practice law where prohibited by local laws. For more information about our
organization, please visit ey.com.
Ernst & Young LLP is a client-serving member firm of
Ernst & Young Global Limited operating in the US.
© 2024 Ernst & Young LLP.
All Rights Reserved.
2406-4556497
ED None
This material has been prepared for general informational purposes only and is not intended to be relied
upon as accounting, tax, legal or other professional advice. Please refer to your advisors for specific
advice.
ey.com"
1,ey,ey-ukc-short-report-ai-and-productivity.pdf,"How can AI
augment your
people to realise
their full potential?
Contents
Chapter One: Setting the scene 1
What’s the value of AI innovation? 1
What tasks will AI augment? 2
Chapter two: How can AI enhance productivity 3
for UK business?
What magnitude of productivity savings could AI bring 3
to the UK economy?
Benefits of AI adoption for the workforce 4
What are the potential risks? 4
How can organisations retain and protect female talent? 4
Chapter three: AI and the UK regions 5
Where does this leave the regions? 6
Chapter four: Balancing rapid adoption with 8
ethical innovation
Cultural and operational risks 8
How can you create the right environment for AI innovation? 8
Chapter five: Create the right conditions 9
for enhanced productivity
Contacts
Catriona Campbell
Client Technology & Innovation Officer, Ernst & Young LLP
https://www.ey.com/en_uk/people/catriona-campbell
Harvey Lewis
Partner, Client Technology & Innovation, Ernst & Young LLP
https://www.ey.com/en_uk/people/harvey-lewis
Sofia Ihsan
EY Global Responsible AI, Consulting Leader, Ernst & Young LLP
https://www.ey.com/en_uk/people/sofia-ihsan
Chapter one
Chapter one:
Setting the scene
Policymakers worldwide recognise that artificial How is AI impacting
intelligence (AI) has the potential to drive enormous gains productivity?
in productivity and growth, with forecasts suggesting a
Implemented effectively,
contribution of between $13 trillion and $15.7 trillion to
AI could effectively add
the global economy by 2030.1 9.8 million
workers to the
In findings published by EY and Liberty Global in the report, Wired for AI,2 from a UK workforce
labour market perspective, 50% of jobs in the US, EU, UK and Switzerland could
be complemented by AI because the latest technology can help people become Create additional
more efficient in at least half of their tasks. productivity
equivalent to
As the UK economy takes its first tentative steps towards a more buoyant $7 trillion
economic outlook in 2024, AI has the potential to accelerate economic recovery,
in wages
thanks to the vast productivity and efficiency gains on offer to reinvigorate GDP,3
arising from AI’s ability to enhance work output and quality.
46%
Indeed, assuming the maximum potential efficiency gains for all workers, the of UK
total additional ‘productive capacity’ that could be unlocked within the combined jobs could be
complemented by AI
economies of the US, UK and Europe by AI is equivalent to 124 million workers:
around 62 million in Europe, 51 million in the US, 9.8 million in the UK and
1.4 million in Switzerland. The total value of this additional productive capacity
equates to approximately $7 trillion in yearly wages.
What’s the value of AI innovation?
Around 400 million people are employed across the US, EU, UK and Switzerland.
Of that figure, EY and Liberty Global analysis4 suggests that 50% of these jobs
could be complemented by AI because the technology can help people become
more efficient in at least half of their tasks. This means there are benefits on
offer for the majority of businesses in all sectors and markets. Put into the
context of workforce output, this acceleration of productivity is equal to adding
124 million more workers into the economies of the US, EU, UK and Switzerland.
Our results are consistent with estimates published by the IMF, who suggest that
60% of jobs in advanced economies could be impacted by AI, 40% in emerging
markets and 26% in low-income countries.5 The research suggests that AI could
have the greatest impact in Luxembourg, where nearly 56% of jobs could be
complemented; in the UK, that figure currently stands at 46%. With the impact of
AI set to target such a significant proportion of the UK workforce, understanding
its true benefit is crucial.
1
Chapter one
Percentage of jobs that can be complimented by AI
Figure 1. Percentage of jobs that can be complemented by AI and GenAI in the US, UK, Switzerland and
individual countries in Europe, showing the contribution from highly network dependent jobs
Source: EY and Liberty Global21
What tasks will AI augment?
As a general-purpose technology, AI’s principal impacts are likely to be felt
in improved efficiency and new business models across industries, providing
opportunities for business transformation and job creation. In the US, EY estimates
that generative AI (GenAI) is set to provide a substantial lift to productivity, likely
delivering a boost worth $650 billion over the next decade and lifting real GDP by
nearly 2.5% by 2033.6 Moreover, Goldman Sachs indicates that further progress
in the field of GenAI could add an extra $7 trillion to global output over the next
decade, as innovative tools like ChatGPT become increasingly woven into the fabric
of business and society.7
With the World Economic Forum predicting that 44% of roles will be disrupted
in the next five years, there is, of course, a fear that AI will displace workers in
sectors which are unable to adapt quickly.8 Yet, this is not the only possible future.
As economists Erik Brynjolfsson and Gabriel Unger suggest, “There is a scenario
in which AI leads to a higher-productivity-growth future. AI might be applied
to a substantial share of the tasks done by most workers and massively boost
productivity in those tasks.” 9
This report will explore where AI will have the biggest impact, how business leaders
can prepare their workforce for the new reality of AI augmented work, and the
regulatory and ethical watch outs — particularly when it comes to supporting women
in the workplace — that businesses should be wary of to ensure AI innovation does
not eclipse the needs of the workforce.
Responsible quantum computing for everyone | 22
Chapter two
Chapter two:
How can AI enhance
productivity for UK business?
In 2024, business leaders and policymakers alike will need
to address urgent issues in the labour market, namely the
number of people in work and the skill levels across the
workforce. As we enter a new epoch of technology innovation
in our workplaces, businesses will need more widespread tech
skill than ever before.
With AI presenting opportunities for net gains in employment figures, business
leaders and policy makers would be wise to focus on developing AI skills amongst
the existing workforce to prevent employee loss and provide opportunities for those
out of work to access AI skills courses. This can expedite their return to work, future
proof their skills and prevent unnecessary delays to AI innovation.
Addressing inactivity by encouraging people back into work through the creation of
AI-related roles could help contribute to closing some of the disparities in regional
growth performance. In a study conducted by the Institute for the Future of Work,
although 47% of respondents said AI and automation had eliminated positions within
their company, almost 67% reported the technology had created new positions.10
What magnitude of productivity savings could AI bring to the UK economy?
An accurate assessment of AI’s potential effect on productivity is difficult to
establish because it is a broad and rapidly evolving field. Considerable uncertainty
also remains about how it will be adopted by people and integrated into established
business processes. However, there are ways to assess its impact on the workforce
by considering how it can help people to carry out tasks more efficiently.
For organisations looking to retain top talent in a sluggish talent market, AI
innovation can be instrumental in improving workforce conditions, particularly in a
marketplace where budgets for learning and development are contracting; findings
from the recent EY CEO Outlook survey11 indicate 96% of UK leaders are considering
restructures or hiring freezes, a reduced focus on learning and development and
a move from permanent to contract workers. By prioritising opportunities for AI
innovation in areas where the technology can help to reduce the workload, free up
workers to enjoy more skillful work and embrace a better work-life balance; AI could
be the vehicle needed to redress potential workforce issues before firmer tactics,
such as redundancy, are adopted.
3
Chapter two
What are the potential risks?
Despite the evident benefits of integrating AI into the everyday lives of the
workforce, it’s essential that organisations remain aware of the potential damage AI
can have on diversity and inclusion, particularly in how it impacts women or lower
skilled workers.
In findings published by McKinsey Global12, the industries expected to shrink
as a direct result of AI automation include food services, customer service and
sales, and office support- all industries in which women are disproportionately
overrepresented. In the UK, women account for 53% of workers in food service13,
and over 60% of total workers across the service and administrative sectors.
Seniority also adds to the burden placed on women; there’s well established evidence
that women hold more lower paying jobs than men: currently only 41% of managerial
roles are held by women, this figure decreases to 38%14 when looking at senior
business leading positions. And with AI poised to drive operational efficiencies that
reduce administrative and repetitive workstreams, those in more junior positions-
who are predominantly women — also stand to be more affected.
How can organisations retain and protect female talent?
Considering AI’s potential to unduly damage the careers of women, it’s essential that
workplaces invest in comprehensive training and development programmes to upskill
workers and provide opportunities for growth into roles that are augmented by AI
rather than subsumed by it. By fostering a culture of continuous learning, companies
can ensure that all individuals remain at the forefront of AI advancements. As a
result, businesses can reduce potential layoffs and the exacerbation of skill gaps that
prevent employees from advancing, therefore avoiding an abyss of talent with those
with AI skills on one side, and those without stranded on the other.
Despite the gloomy outlook in some sectors, there will also real opportunities for
women thanks to AI innovation. McKinsey Global15 reports that in spite of real
challenges to workplace equality, AI innovation will generate a demand for work and
workers, which will only increase as economies grow, facilitated by AI: by 2030, the
same research indicates there will be a 17% increase in women’s jobs gained in the
UK as a result of AI. Men will also experience the same uplift.
Benefits of AI adoption for the workforce:
• Achieve an improved work- • Improve delivery times for
life balance, which may work, without an increase in
reduce attrition and work- stress or demand for overtime.
related stress. • Introduce lower skilled
• Perform other meaningful individuals to the workforce,
work, which increases output thanks to AI tools being able
quality or enhances value. to take the strain of more
• Spend more time with their complicated tasks.
clients, which increases client • Reducing burden of repetitive
satisfaction and may lead to or administrative tasks.
growth in future sales.
• Foster innovation because
creative thinking requires time.
• Increase work quality since
they have more time for
4
each task.
Chapter three
Chapter three:
AI and the UK regions
Despite AI’s potential to generate huge opportunities for the
UK — it has already delivered £3.7bn in gross revenue and
created 54,000 jobs — a staggering 75% of all AI activity is
taking place in just three regions: London, the South East
and the East of England, according to the Department for
Science, Innovation & Technology, leaving other UK regions
vulnerable to slower economic recovery and less opportunity
for productivity enhancement.16 Whilst ‘the golden triangle’ is
generating value for the UK economy, activity across the rest
of the UK is sluggish, particularly in the North, Midlands and
South West.
In the recently published EY Regional Economic Forecast17, it was found that London
and the southern regions of the UK are expected to lead the economic recovery,
thanks to a still strong labour market, a recovery in consumer spending, and robust
growth expected in information and communication, professional services, and a
recovering retail sector.
London’s success continues to be driven by the distinctiveness of its economy,
which is characterised by knowledge-based sectors such as professional services,
information and communication, and the concentration of high-skilled workers in
these sectors. AI is undoubtedly supporting that surge in economic recovery.
The UK Sectors Most Impacted by AI
Finance, IT and professional services will be most impacted by AI
Finance & insurance Information & Professional, Property Public Education
communication scientific & administration
technical & defence
Source: Department for Education, Unit for Future Skills, the impact of AI on UK jobs and training report
5
Chapter three
In research undertaken by the Department for Education18, London was the city
identified as most likely to the experience the earliest impacts and benefits of AI
innovation due to its high concentration of roles in these sectors. When considered
alongside the fact that 75% of AI focussed organisations are based in London, and
the density of professions that will be most quickly and intensely impacted by AI, it’s
unsurprising that London is currently at the forefront of AI innovation and is able to
realise its benefits before other UK regions.
Where does this leave the regions?
Whilst AI has the potential to exacerbate existing regional inequalities due to
disparities in AI preparedness, harnessing the technology could galvanise economic
growth across the regions, and help upskill workers cross-sector. But it will
take investment.
A report published by the Institute for the Future for Work19, expounds the
importance of developing workforce skills rather than focussing on AI alone,
emphasising that training and upskilling will have the biggest impact on regions
currently displaying the lowest levels of AI preparedness. The report says:
“Investments in training, complemented by the sharing of information about new
technologies, consultation on technology adoption, and an orientation towards
empowerment and autonomy, are expected to influence whether new technologies
have a positive or negative impact on work and workers. First, a highly skilled
workforce will be more likely to understand the need for the new technology, its
technical aspects, and its benefits, and feel less threatened by it (as noted by the
OECD in 2023), this will facilitate approaches to AI adoption in which labour is
complemented by technology.”
By upskilling workers, and providing opportunities for their personal development,
the fast-moving organisations can successfully use these technologies, and by
preparing the workforce first, and at scale, regional leaders can better enhance the
overall preparedness of their regions.
Nurturing high-value sectors can boost resilience in tough times and
accelerate growth in better years, but doing so requires regions to
build their own tailored growth plans that consider which industries
are set to flourish and how to cultivate them locally. High-value
sectors will require a high-value workforce, so building in-demand
skillsets and competencies with latest technology should help a
region attract investment while bolstering the local economy. For
example, Generative AI has the potential to enhance regional and
UK productivity rates, but will require a shift in skills to ensure the
workforce can collaborate with and complement the technology.
Rohan Malik, EY UK&I Managing Partner for Government & Infrastructure, EY UK regional economic forecast.20
66
Chapter three
Despite the south dominating AI growth, EY research projects that, between 2024-
2027, Manchester will experience the greatest Gross Value Add (GVA) growth at
2.2% compared to any other city in the UK, including London (0.6%)21. Whilst the
northern city undoubtedly lags behind the ‘golden triangle’ when it comes to AI,
economic value in Manchester is accelerating faster than any other, as investment
pours in. With initiatives such as the newly established AI Foundry helping SMEs
in the area make headway with AI innovation, and investment in AI research at
Manchester University increasing — this February the university received £12 million
in funding for AI research22 — it’s only a matter of time until this vibrant city closes
the gap on its southern counterparts.
Whilst there’s obvious work to be done to help the North embrace
all that AI has to offer, Manchester is undoubtedly benefiting from
a boost in GVA, that other cities across the country can’t rival.
With more spending power to invest in emerging technologies, it’s
essential that cities in the north embrace AI to help continue this
positive outlook, and to close the gap on the ‘golden triangle’.
Stephen Church, EY UK&I North Markets Leader & Manchester Office Managing Partner
77
Chapter four
Chapter four:
Balancing rapid adoption
with ethical innovation
Cultural and operational risks
The rapid evolution of AI technologies poses its own set of challenges. As AI technologies
advance, keeping up with the latest developments and understanding which innovations
are most applicable to individual businesses and sectors becomes increasingly complex.
This rapid progression can lead to a misalignment between AI capabilities and business
needs, potentially resulting in investments in technologies that are either outdated shortly
after implementation or do not deliver the expected value.
The fundamental challenge of business adoption lies not just in the successful
implementation of AI pilots and projects but also in cultivating an environment where
innovation is nurtured, and the workforce is ready and prepared to adapt alongside
these advancements.
Companies often struggle with rigid organisational and cultural structures that can
significantly impede the speed and success of AI implementations. Such structures
typically foster siloed departments and a resistance to change, making it challenging to
embrace the collaborative and agile methodologies required for effective AI integration.
Centralised approaches, while offering streamlined decision-making, may lack the
flexibility and localised insights necessary for innovative AI solutions. Conversely,
federated structures can encourage autonomy and innovation at the departmental level
but may suffer from a lack of cohesion and unified risk management or strategic direction.
To support both workplaces and the workforce towards embracing AI innovation in their
day-to-day work, creating the right environment for innovation will be crucial.
How can you create the right environment for AI innovation?
There is a critical need for clear and robust guidelines on the ethical use
of AI in the workplace, both in how the workforce interact with AI and how
1 Provide clients’ and consumer data is treated. Policymakers must formulate and
guardrails for enforce regulations like the EU’s AI Act, while businesses should establish
AI’s use comprehensive governance frameworks. This will ensure that AI is used
responsibly, with a focus on data privacy, fairness and transparency.
Companies should prioritise organisational agility to adapt swiftly to the
Nurture
2 changing AI landscape and to nurture curiosity that cuts across teams and
curiosity and
functions. Emphasising flexible, collaborative work environments and a culture
increase agility of continuous innovation will be key. This approach will enable businesses to
respond effectively to new AI advancements and market demands.
Keep As AI transforms the UK workforce, targeted investment in skill development
3 upskilling at and workforce training is imperative. Businesses should focus on equipping
the heart of AI their employees with the necessary skills to navigate and leverage AI
transformation technologies. Policymakers can support this initiative by providing incentives and
frameworks for continual learning and skill enhancement in the AI field.
8
Chapter five
Chapter five:
Create the right conditions
for enhanced productivity
As AI transforms the UK workplace, targeted investment in skills development and
workforce training is imperative to realise the 46% productivity growth potential on
offer for the UK.23
To rise to the challenge, companies should focus on equipping their employees with
the necessary skills to navigate and leverage AI technologies. Businesses can do this
by creating the necessary guardrails to create business environments in which AI can
be used safely whilst still promoting greater creativity and efficiency.
Policymakers can support business leaders by providing incentives and frameworks
for continual learning and skill enhancement in the AI field, that not only incentivise
individual businesses but help accelerate both productivity and output across the
whole of the United Kingdom.
The productivity boosts that are enabled will be significant for both the UK’s
economy and skills market as more workers upskill in preparation for a future
of work enabled by AI. But capitalising on AI’s full potential demands targeted
investment across the whole of the UK, cultivating new skills and strategic
organisational realignment. In essence, the UK is primed for a new digital
transformation, but leaders must prioritise developing AI skills to realise the promise
of a more productive future that benefits everyone.
Questions for those charged with leading AI innovation:
• What opportunities am I providing the workforce to upskill in AI technology?
• Do I have the right guardrails in place to guide AI innovation?
• Where is the potential in my organisation to use AI for greater impact?
• What does success look like for my organisation?
Acknowledgements
This report was written by Dr Harvey Lewis, Partner at EY and Catherine Jones from the Ernst
& Young LLP, Brand, Marketing and Communications team with support from EY ITEM club.
Some of the content in this report has been taken from the Wired for AI report, written in
conjunction with Liberty Global, published in February 2024.
The proprietary EY sourced analysis in this report was undertaken by Dr Harvey Lewis
and Timea Ivacson, Manager, from the Ernst & Young LLP, Data and AI team. Additional
contributions were gratefully received from Gareth Shier, Director in the Ernst & Young LLP
Econometrics and Modelling team, Sofia Ihsan, Director in the Ernst & Young LLP, Technology
Risk team, and Dr Ansgar Koene, Director and Global Leader for AI Ethics and Regulation.
The report has been designed by the Ernst & Young LLP, Creative Services Group.
9
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work-transitions-in-the-age-of-automation, accessed 23 April
10
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ey.com/uk"
2,ey,ey-european-ai-barometer-2024.pdf,"From challenges
to opportunities:
How EY and AI work
hand in hand
EY European AI Barometer
June 2024
From challenges to opportunities: How EY and AI work hand in hand
Contents
3
Introduction
1 4
The AI revolution
2 6
Adoption
3 8
Benefits
4 10
Impact on workforce
5 14
Capability building
6 18
Future of AI technology
7 19
Use cases
22
Study design
23
Contacts
2
From challenges to opportunities: How EY and AI work hand in hand
Introduction
In a world shaped by relentless technological progress, companies that fail
to evolve could disappear from the map. Artificial intelligence (AI) is sweeping
through the business landscape with fierce intensity – reshaping industries
and economies at an unprecedented pace. As AI advances to the forefront
of technological innovation, some brace themselves for the inevitable
challenges a step change of this magnitude will bring, while others fervently
seek to unlock the immense opportunities it promises.
Irrespective of AI technology’s many facets and manifestations, one thing
is certain: it will fundamentally redefine the way we work, the way we live and the
way we interact. Businesses need to give careful consideration to some bedrock
questions: should they embrace AI without reservation or proceed with caution?
What pitfalls and paybacks can they expect? How will AI impact the world of work?
And what regulatory frameworks do companies need to observe and how?
Nobody can lay claim to having all the answers in this rapidly evolving new
reality. But since the inception of the AI revolution, our EY teams have stepped
up to the challenge and have been helping clients chart their course for successful
transformation, pinpointing where they can best invest their resources
to extract value from AI for their respective businesses – and creating a better
working world for all stakeholders in the process.
This report seeks to share our experience with, and shed light on, the
multifaceted impact of AI in its many manifestations, examining how managers
and non-executive employees view the challenges and opportunities ahead.
We deep-dive into different sectors to understand the latest approaches to
harnessing the power of AI, with a particular focus on Europe, while maintaining
a global perspective. Our overarching goal is to unlock AI’s potential to create
positive impact in our economies and our communities, advocating for
a responsible, people-centered approach that prioritizes value
creation for everybody.
3
From challenges to opportunities: How EY and AI work hand in hand
1.
The AI revolution
Unlike past technological revolutions that largely involved adequately equipped by their employers to tackle the AI
the automation of manual labor, AI marks a paradigm shift transformation process with suitable training? Are they
in its focus on assisting and automating complex cognitive seeing a meaningful evolution of their job profiles and tasks?
functions, with unavoidable consequences for knowledge
workers. Entire industries and all manner of professions Around the world, regulators too are shifting their attention
are on the cusp of profound change. toward AI and its implications for the economy and for
society more broadly. Government commissions and tasks
AI has the potential to enhance workers’ efficiency and forces are investigating the likely impact on all sectors, from
unearth productivity gains throughout the economy. Our healthcare and financial services through to transportation,
AI survey of multidisciplinary professionals across levels, as they seek to address a host of concerns: citizens’ privacy,
sectors – and across Europe – already provides insights bias in algorithms, job displacement – the list goes on.
of the depth and scale of the AI-driven productivity In May 2024, for instance, the EU introduced the Artificial
boost in major economies, and an indication of its potential Intelligence Act (AIA), which aims to regulate the
contribution to the global economy. That said, unlocking development and use of AI systems within its borders,
productivity gains that AI promises will likely take time, to protect the safety, security and fundamental rights
effort, and wise strategy. of its people. To navigate the global regulatory complexity
that is rapidly emerging, companies will need to designate
One use case we are already seeing gaining traction is the compliance responsibilities for AI deployment and
deployment of AI as a powerful knowledge tool. By distilling use across their entire organization, not just
key insights from vast volumes of data, AI is already helping technical departments.
businesses and their teams make more accurate and in turn
better decisions. Forward-thinking executives are wasting Notwithstanding all the advancements promised, one thing
no time and have set to work exploring how AI can empower can never change: humans need to remain at the heart of AI
informed decision-making, while breaking down silos and development. Moving forward, the emphasis must be on AI
allowing more voices to be heared than ever before. But how empowering employees across all industries to work smarter,
exactly is Europe faring in the AI revolution? Do employees better and more efficiently. It’s not just a question of bringing
here feel that they are a part of the change and are to market better products and solutions: AI holds the
potential to craft a more sustainable global economy – for
people and for the environment.
AI
4
From challenges to opportunities: How EY and AI work hand in hand
Getting AI right
With a human-centered approach to AI, we help hone
technology to maximize talent, driving efficiency and
Most companies recognize the need to accelerate their AI productivity gains across business functions. EY teams
initiatives to gain a competitive edge. Yet concerns persist of leading multi-disciplinary professionals spanning risk,
as regards the pace of AI adoption and the maturity of strategy, technology, and transformation work hand in hand
solutions. Some caution against overinvesting in tools or use with clients to assist them in an implementation process that
cases likely to become obsolete all too soon. Others question is aligned with their purpose, culture, values, and key
whether the timeline touted by AI visionaries is realistic to stakeholders so that AI drives positive human impact.
unfold a truly transformative impact.
In the following, we gauge the current state of AI in European
There is no shortage of questions, and problems still remain businesses across a range of industries and determine the
unresolved. There is no one-size-fits-all model when it comes level and challenges of adoption, the perceived and captured
to AI. Drawing on the rich experience we have already gained benefits, the impact on the workforce, and approaches
working shoulder-to-shoulder with the clients across a broad to capability building, and cast a glance ahead at the future
spectrum of industries and a huge variety of use cases, we of this rapidly evolving technology.
are convinced that it is possible to create meaningful value
by taking a broad approach to AI and by augmenting people
potential to drive extraordinary outcomes.
Key takeaways
Adoption
Many organizations are still struggling with the operationalization of AI.
Barriers need to be removed; a clear tone from the top is needed.
Benefits
Cost benefits are already evident. The frame needs to be expanded to include
other benefits, including creating more meaningful and attractive work profiles
and hence improved employer branding.
Impact on workforce
AI is certain to have a huge and imminent impact on the workforce across all
sectors and professions. Upskilling is key.
Capability building
Organizations need to accelerate their investment in AI capabilities and make
sure they place their bets on the right technologies in a field that is undergoing
fast-paced innovation and whose future is difficult to predict. Appropriate
training programs are key.
Future of AI technology
AI technology is advancing along many different avenues. Tomorrow’s winners
are already making bold moves today.
5
From challenges to opportunities: How EY and AI work hand in hand
2.
Adoption
Beyond the specifics of where AI can be used, what a lot of decision-
makers want to know is the broader success factors for getting the
most long-term value from implementation. First and foremost, they
want to determine the technological foundations required for AI, with
a strong focus on data and the cloud, and their attention is on making
investments that deliver value. Stakeholders also note the importance
of workforce buy-in and adoption to ensure success at scale, with the
primary focus typically on building employees’ confidence to use AI
to improve their day-to-day efficiency.
The adoption of AI in European businesses continues to Security, accuracy, and explainability are viewed as crucial
evolve, albeit with its fair share of challenges. While the factors on the road to success when implementing AI – more
potential benefits are undeniable, organizations are still so with respect to business viability than ethics. At the top
grappling with operationalization. Barriers to adoption of the agenda are security and privacy, discussed from a
include internal policies, external regulation, and the technical as well as a responsibility angle. Secure internal
complexity of the new technologies. GPT models as well as the need to comply with regulation
(current and future) around data and AI are of high
Although some knowledge workers have been keen to importance to stakeholders and decision-makers.
experiment with AI, others are still reticent. In a recent
survey, EY teams asked members of the workforce from
Hurdles to adoption
all over Europe to share their experience with AI.
Almost three-quarters of all respondents (73%) already have
hands-on experience with AI technologies. Most of them use Organizations face a number of hurdles in the
AI in their private lives (38%), rather than at work (12%). operationalization of AI. For one thing, companies
The remaining respondents (23%) have AI experience themselves often impose restrictive policies on AI use by
in both spheres. employees. In some countries, only a relatively low share
of employees in the EY survey report being permitted by
From a regional perspective, the share of AI early-adopters their employer to use AI applications, most notably in
is highest in Spain (84%), followed by Switzerland (82%) Germany (42%) and Austria (46%). That contrasts markedly
and Italy (77%). On the other end of the scale, early with the situation in Switzerland (73%) and Spain (63%),
adopters are less common in the Netherlands (66%) and where most employees are permitted by their employers
Germany (67%). Men (75%) have experience using AI to use AI applications in their work. The share is likewise
applications more often than women (70%). Differences are relatively high in Portugal (58%), Belgium (57%), Italy (56%),
also evident between ranks, with more than 84% of managers and France (55%), all of which exceed the European average
saying they use or have used AI applications, compared with of 52%.
just 67% of respondents among non-executive employees.
In the following we look at some of the most common Complexity is another issue organizations are grappling
barriers to AI adoption in a work setting. with. Six out of ten respondents (67%) point to the
complexity of the implementation process for AI systems
in their organization. Adding another layer of complexity
6
From challenges to opportunities: How EY and AI work hand in hand
subject to increasing regulatory scrutiny, and Europe “systemic risks”. For instance, low-risk AI such as chatbots
is no exception (see EU AI Act). used in customer service will be subject to few requirements
beyond notifying users that they are interacting with AI.
Another important factor is the tone from the top. In AI intended for high-risk application areas that may impact
Switzerland, most respondents (56%) give their employer health, safety, or fundamental rights of people will have to
a good report card when it comes to the extent to which comply with stricter controls,while some applications areas,
management has a positive attitude as regards making such as subliminal manipulation of vulnerable groups,
progress with AI applications. However, only 5% describe are outright prohibited.
their employer as very open when it comes to implementation.
Switzerland ranks lowest in the category, together To comply with the AI Act, companies will need to clearly
with Germany. assign within their organizations responsibilities for
overseeing AI deployment and compliance. The mandated
The level of adoption of AI varies across Europe, with some responsibility extends beyond technical departments to
countries and sectors embracing it more readily than others. encompass the entire corporate fabric. Non-compliance
However, regardless of geographical location or industry, a exposes companies to severe risks, including heavy penalties
clear tone from the top is essential for successful integration. with maximum fines that even surpass the maximum fines
Leaders need to champion AI initiatives, fostering a culture under the EU’s General Data Protection Regulation (GDPR).
that encourages experimentation and innovation while
Compliance strategy and adaptation
addressing justified concerns about job, displacement,
ethical and legal considerations.
A strategic approach to implementation of AI Act compliance
Regulatory framework
begins with companies identifying gaps in their current
practices and outlining a meticulous plan customized to the
The European Union’s AI Act unifies how AI is regulated specific manifestation of AI deployment in their organizations.
across the single market of the 27 EU member states. It also The approach involves an as-is assessment encompassing
has important extraterritorial implications, as it covers all AI current procedures, employee training levels, and a technical
systems impacting people in the EU, regardless of where understanding of AI solutions, including an exhaustive
these systems are developed or deployed from. The AI Act inventory of the AI solutions deployed in the organization,
aims to standardize the use of AI across all its member what they are used for by whom.
states. Ratified by the European Parliament on 13 March
2024 and approved by the Council of the European Member Given the phased transition period, with enforcement of the
States on 14 May 2024, the act is expected to enter into AI Act prohibitions taking effect within 6 months, obligations
force in June 2024. It introduces a new regulatory for General Purpose AI mode developers starting after 1 year
framework for AI technology focused on the protection of and most of the obligations for high-risk AI applications
safety, security and fundamental rights of people in the EU. coming into force after 2 years, companies must hasten
to adjust their operations and implement the required
Risk and compliance framework
changes in a phased, monitored process. Not only is initial
compliance by the end of the implementation deadline
The AI Act adopts a risk-based approach to compliance critical, it also needs to be accompanied by a sustained
obligations, categorizing AI systems by application areas commitment to adapt to ongoing legislative amendments
and target groups into distinct risk levels. In this tiered and to provide staff with appropriate training at regular
compliance framework most requirements fall upon the intervals. In this way, companies can align with the EU’s
developers and deployers of AI systems classified as goal of ensuring safe AI use without stifling innovation.
“high-risk”, and on general-purpose AI models (including
foundation models and generative AI) deemed to pose
7
From challenges to opportunities: How EY and AI work hand in hand
3.
Benefits
How companies can add value by embedding AI into their products
is a major topic for managers in all sectors. Delivering convenient and
enjoyable experiences, using GenAI to improve chatbots, including
virtual try-on, or current checkout-free stores are prominent examples
that help make AI success stories and progress tangible and visible. In
addition, strategies are often directly linked to revenue generation.
Despite the challenges, the benefits of AI adoption are By function, the use cases in which AI has been
already evident, most notably as measured by cost savings. operationalized vary widely, from streamlining supply chain
However, the narrative surrounding AI benefits needs to be operations to optimizing marketing strategies and enhancing
expanded beyond just financial gains. While cost improvement customer experiences. At present, organizations are seeing
remains a primary driver, AI also enables organizations to the greatest benefits in IT (35%), followed closely by
improve decision-making processes, unlock new revenue marketing (30%) and cybersecurity (27%). Interestingly,
streams, and raise their employer brand value. legal and compliance departments see little scope for
AI implementation at present (see figure 2). That said,
First and foremost, what executives invariably want to know with little more than initial inroads made so far in AI
is their return on AI investment. Across Europe, almost half implementation and operationalization, most eyes are
of managers (45%) say that AI use has allowed them to save still fixed on future iterations of the technology.
costs, increase profits – or both (see figure 1). Measured
by these two criteria, AI deployment to date has been most Aside from cost and efficiency improvements, embracing
successful in Switzerland, where 81% of managers have AI allows businesses to automate repetitive tasks, freeing
had a positive experience with the technology. The share up employees to focus on more strategic and creative
of satisfied managers is also above average in Spain (60%) endeavors. Indeed, most respondents expect artificial
and Italy (58%). On the other hand, respondents intelligence to take over parts of their work (65%), with
in the Netherlands, Austria, and Germany (all 34%) some anticipating that they’ll be handing over some of their
are less impressed. workload to AI in the very near future (14%). If they get
it right, organizations have a tremendous opportunity to
leverage AI to enhance job descriptions. A shift toward
AI
more intellectually stimulating work profiles would not only
improve employee satisfaction but also enhance employer
branding, attracting top talent in a fiercely competitive
labor market. That said, all stakeholders need to address
legitimate concerns about job displacement, an issue we
investigate in the following section.
8
From challenges to opportunities: How EY and AI work hand in hand
Figure 1
?
Has AI already led to cost savings or increased profits within your company?
Switzerland 22% 14% 45% 12% 7%
Spain 20% 21% 19% 26% 14%
Italy 14% 26% 18% 26% 17%
Belgium 17% 17% 13% 38% 15%
France 12% 18% 16% 36% 18%
Portugal 14% 10% 15% 41% 20%
Germany 11% 11% 12% 33% 33%
Austria 12% 12% 10% 32% 34%
Netherlands 8% 12% 14% 38% 28%
Europe West 13% 16% 16% 32% 23%
Yes, we did save costs Yes, we increased revenues Yes, both It is too early to say that No, neither
Figure 2
?
In which area do you think AI can already help improve your business? (up to three answers)
IT 34,5%
Marketing 30,1%
Cyber security 26,6%
Emplyomee support 21,9%
Sales 21,7%
Human ressources 18,7%
Operations 18,7%
Legal/compliance 8,4%
Other 5,1%
I do not think AI can help my business 13,4%
9
From challenges to opportunities: How EY and AI work hand in hand
4.
Impact on workforce
Most of the leading minds in business AI say that employees will
be empowered by the new technology to work smarter and more
effectively. Speed and time savings are emphasized a lot. You often hear
talk of augmenting and freeing up employees – typically in conjunction
with reassurances that AI will not replace them and highlighting how
it will allow them to spend more time on value-added, creative, and
collaborative tasks. While employee efficiency gets the greatest
attention, improvement in other areas is also noted.
As AI technologies continue to advance, they are having Figure 4 shows the general consensus among respondents
an ever-increasing impact on the workforce. Job losses across the nine analyzed European countries, with more than
due to automation are a legitimate concern, particularly one in two (53%) stating that AI applications will influence
in industries with routine, repetitive tasks. However, the their work – or are already doing so. In Italy and Switzerland
broader impact extends beyond displacement, with AI (59% each), the figure is almost six out of ten. The
reshaping job profiles and necessitating new skill sets. proportion is also above average in the Netherlands (57%),
and Austria and Germany (56%). On the other hand, it is
When asked whether the use of AI will lead to job losses, below average in France (47%), Belgium (48%), as well as
respondents’ views vary greatly across European countries. in Spain and Portugal (both 49%).
Overall, slightly more than two out of three respondents
(68%) say that they expect fewer employees will be needed As discussed in the previous section, most respondents
as AI systems become more established and the number and expect artificial intelligence to take over elements of their
scope of use cases increases (see figure 3). The proportion work and redefine their job profiles. Analyzed by country,
is particularly high in Portugal (80%), Spain (78%), Italy more than three out of four respondents in Switzerland
(76%), and Belgium (74%). In contrast, there is somewhat (76%) assume that artificial intelligence will take over some
less concern about job losses as a consequence of AI of their tasks. This if followed by Spain and Portugal (72%
in Switzerland (57%), Germany (59%), and the each), Italy (70%), and Belgium (68%), where employees are
Netherlands (64%). likewise sure that – sooner or later – some of their tasks will
be taken over by applications from the field of AI.
One in three respondents in Italy (34%) expects that the The average among all respondents is 65%. In Germany
new technology will replace human labor on a large scale. (57%) and Austria (59%), the figure is below average.
The figure is similarly high in Portugal (31%). In contrast,
the proportion is significantly lower among respondents in Viewed by rank, managers (72%) are more likely to assume
Germany (14%), Switzerland (16%), and Austria (17%). that they will hand over tasks to AI-powered programs and
machines in the future than non-management employees
(61%). From a sector perspective, oil and gas (91%),
technology, media and telecommunications (81%),
financial services (81%), and insurance (81%) stand out.
10
From challenges to opportunities: How EY and AI work hand in hand
Figure 3
?
Do you think the use of Al will lead to companies needing fewer staff?
Portugal 25% 55% 18% 2%
Spain 18% 60% 20% 2%
Italy 20% 56% 21% 3%
Belgium 22% 52% 19% 7%
France 21% 48% 26% 5%
Austria 20% 45% 26% 9%
Netherlands 15% 49% 28% 8%
Germany 16% 43% 31% 10%
Switzerland 13% 44% 39% 5%
Europe West 19% 49% 26% 6%
Yes, definitely Yes, rather No, not so much No, definitely
Figure 4
?
Do you think your job is affected by the developments around artificial intelligence?
Italy 14% 45% 27% 14%
Switzerland 9% 50% 28% 13%
Netherlands 10% 47% 31% 12%
Austria 12% 44% 31% 13%
Germany 12% 44% 29% 15%
Portugal 11% 38% 30% 21%
Spain 10% 39% 33% 18%
Belgium 10% 38% 33% 19%
France 11% 36% 26% 27%
Europe West 11% 42% 29% 18%
Yes, very strongly Yes, partially No, hardly No, not at all
11
From challenges to opportunities: How EY and AI work hand in hand
On average in Europe, almost one in five respondents (19%) Upskilling and reskilling initiatives are of paramount
say that AI is already influencing their work – in Italy, it is importance to mitigate any negative consequences of
almost one in four (24%), while in Belgium, it is just over AI on employment. Organizations must invest in training
one in ten respondents (12%). programs to equip their workforce with the necessary
competencies to thrive in an AI-driven economy. Additionally,
A sizable 38% of all respondents expect to see a noticeable fostering a culture of lifelong learning is essential to ensure
increase in the influence of AI applications on their jobs that employees remain adaptable and resilient in the face
within the next three years. Here, respondents in of technological disruptions. According to our survey results,
Switzerland (54%) clearly stand out. not enough respondents are satisfied with the level of
training on AI they get at work. Switzerland leads the way,
That said, an interesting dichotomy is evident in that a not where 36% say their employer is providing enough training.
insignificant number of respondents think it unlikely that Employers in other countries need to do a lot better, most
artificial intelligence will take over parts of their work (35%). notably in Portugal, where only 14% of employees are
And of those who do anticipate having to hand over some satisfied with the current level of AI training they are
of their workload to AI, the vast majority don’t see that receiving. Most employees want live training and workshops
happening anytime soon (see figure 5). It appears that a (43%), followed by online courses (38%). In the following
substantial section of the workforce still believes that AI is section, we take a closer look at the investment priorities
not an imminent concern or it’s something that happens to of organizations in AI capabilities, including training.
somebody else. Either way, organizations clearly need to do
more to sensitize sections of the workforce about the scale
and scope of the AI revolution, an area in which training has
critical role to play.
AI
12
From challenges to opportunities: How EY and AI work hand in hand
Figure 5
?
How likely is it in your opinion, that parts of your tasks on the job will be done by programs and applications from the
field of artificial intelligence?
Switzerland 12% 64% 19% 6%
Spain 16% 56% 22% 5%
Portugal 21% 51% 22% 6%
Italy 16% 54% 23% 7%
Belgium 12% 56% 22% 10%
Netherlands 9% 56% 24% 11%
France 16% 47% 27% 10%
Austria 13% 46% 31% 10%
Germany 10% 47% 31% 13%
Europe West 14% 51% 26% 9%
Likely and very soon Likely but it will take some time Unlikely That is not going to happen
Figure 6
?
How likely is it in your opinion, that parts of your tasks on the job will be done by programs and applications from the
field of artificial intelligence?
Europe West Female Male
62,9% 65,7%
9%
14%
Likely and very soon
26% Likely but it will take some time
Management Non-management
Unlikely
That is not going to happen
51%
72,3% 60,7%
13
From challenges to opportunities: How EY and AI work hand in hand
5.
Capability building
With the rapid development of AI in mind, many decision-makers
across various sectors emphasize the need to accelerate AI initiatives
to gain a competitive edge, and are increasing investment accordingly.
Discussion of partnerships to accelerate innovation is common, while
a few are pursuing equity investments in AI specialists. However,
several companies also express concerns regarding the pace and
maturity of AI development, including both those investing and others
taking a more cautious approach. Some warn against overinvesting in
tools or use cases that could soon become obsolete.
To fully leverage the potential of AI, organizations must Analyzed by sector, employees in private equity (71%),
prioritize capability building. Assessing AI readiness is crucial financial services (66%), the energy sector (62%), and
to identify gaps and allocate resources effectively. Holistic advanced manufacturing and mobility (62%) are confident
capability building involves not only investing in cutting-edge of their employers’ ability to pursue the AI
technologies but also cultivating a data-driven culture and transformation journey.
nurturing talent with expertise in AI in all its manifestations,
from machine learning to large language models. Most employees in Switzerland (57%) expect AI to be a top
investment priority in the coming year, followed by Spain
Taking a look at the current situation, employees in (54%). Prospects for AI investment are bleaker in Germany,
Switzerland (58%) are most confident about the where only 25% of respondents expect AI to be prioritized
possibilities for AI implementation in their company. and Austria with a mere 22%.
In Italy, too, a majority (52%) confirm that their employer
has the knowledge and the will to tackle the AI Respondents see new software (35%) and employee
transformation. Employees in Germany (34%) and Portugal qualification (33%) as top investment priorities for their
(35%), on the other hand, are more skeptical about their organization when it comes to AI. Interestingly, forecasting
company’s ability to implement and leverage AI capabilities rank lowest in the list of investment priorities.
(see figure 7). That might seem surprising given the possibilities already
demonstrated by AI-driven high-precision forecasting in
many sectors.
14
From challenges to opportunities: How EY and AI work hand in hand
Figure 7
?
Do you feel that your company has sufficient knowledge to implement and use AI effectively and start the
transformation process that comes with it? Percentage of respondents who answered “yes”.
Switzerland 58,0%
Italy 51,7%
France 45,2%
Spain 41,4%
Belgium 40,4%
Netherlands 39,4%
Austria 37,3%
Portugal 35,2%
Germany 34,0%
Europe West 41,7%
15
From challenges to opportunities: How EY and AI work hand in hand
Taking a closer look at the people factor, managers in In many instances, employees are taking the initiative and
Switzerland (72%) are most confident that their people have availing themselves of self-learning opportunities, be it
adequate training to work effectively with AI or are ready for privately, professionally, or a combination of the two.
the transformation process ahead. This compares with 56% Self-education in the field of AI is most widespread in
in Belgium, 54% in Italy, and 51% in Spain. At the other end Switzerland (60%), Italy (54%), and Spain (54%). Employees
of the scale are Austria and Germany both with 34%. in Germany are least likely to be engaged in self-education
activities (37%), indicating a clear need to sensitize the
Broken down by sector, managers in advanced workforce there as to the importance of AI skills for the
manufacturing (69%) are most confident that their people future of work and their career prospects.
have adequate training to work effectively with AI or are
ready for the transformation process ahead. This compares AI can be a powerful tool in the hands of skilled and
with 65% in financial services, 65% in agriculture, and 63% well-trained employees, promising massive productivity
in private equity. Lagging well behind at only 19% is the gains. Companies need to adopt an active role in training
public sector practice. and upskilling their people. Among other initiatives,
strategic partnerships with academic institutions and
Training programs tailored to the specific needs of each technology providers can also facilitate knowledge
region, sector, and function are essential for ensuring the exchange and accelerate innovation. By investing
successful integration of AI into business operations. in AI capabilities today, organizations can position
Employees are beginning to recognize the imperative themselves as leaders in an increasingly
of honing their AI acumen for their careers, with 44% of competitive landscape.
respondents stating that they are educating themselves
in the field of AI. Revealing a concerning gender bias,
our survey indicates that male employees (49%) are
more likely to be brushing up on their AI skills than their
female colleagues (40%).
AI
16
From challenges to opportunities: How EY and AI work hand in hand
Figure 8
?
Which specific field will be a top investment priority over the next year for your company when it comes to AI?
(up to five answers)
New software 34,5%
Employee qualification 33,0%
Cyber security 26,2%
Optimzing/automating current processes 25,7%
(Data)Analytics 23,3%
New hardware 23,2%
Logistics 20,1%
Manufacturing 17,5%
Analyzing customer/client data 16,6%
Customer contact/services 14,2%
Analyzing in general 12,7%
Accounting 12,7%
Copy writing 12,4%
Analyzing processes within the company 11,4%
Human ressources 10,2%
Controlling 8,7%
Procurement 8,6%
Knowledge management 8,6%
Forecasting 6,4%
Other fields 1,2%
Figure 9
?
Are you educating yours"
3,ey,ey-gl-adobe-genai-marketing-guide-06-2024.pdf,"GUIDE
Leading generative
AI deployment for
marketing.
Overcoming three hurdles in
generative AI adoption.
1
Contents
Executive Summary 3
Thought leaders in generative AI 4
Dial up transparency as you improve the relevance of customer experiences. 4
Be transparent while building your first-party data. 5
Match generative AI to customer expectations. 5
Make customer benefits central to decisions. 6
Amplify creativity without replacing human judgement. 6
Transform skeptical and novice employees to empowered generative AI pros. 6
Prioritize upskilling at all levels. 7
Begin with content creation. 8
Use short-term comparison metrics. 8
Appoint generative AI pioneers. 9
Drive generative AI innovation with confident governance. 9
Map and mitigate novel generative AI risks. 10
Establish a single point of control. 11
Organize your goals into the right sequence. 11
A checklist to start now. 12
Conclusion 13
Methodology 14
Sources 14
About Adobe 15
About EY 15
2
Executive Summary
Generative AI is defining the next generation of marketing We spoke with leading executives around the world across
today. Delivering hyper-personalized, multi-channel customer marketing, creative, CX, data, legal, risk, and compliance.
experiences at a fraction of the time and cost. Helping you glean
insights from your data in an instant. Detecting and responding to We uncovered three primary challenges to generative AI
conversion opportunities in real time. Experimenting to enhance adoption: managing customer privacy and experience
customer experience and deliver results at pace. expectations, transforming employees concerned about their jobs
into champions and innovators, and establishing governance that
This year, 98% of CEOs will invest in their company’s generative enables generative AI innovation to flourish.
AI capability. But 66% remain uncertain of the optimal adoption
path for their organization.1 To assist, Adobe collaborated with Through our interviews with early adopters, we found
the EY organization to undertake a series of structured, these consistent challenges and uncovered resolutions to
qualitative interviews to learn from generative AI first movers. overcome them.
Customers
80
Dial up transparency as you improve the relevance of customer experiences.
%
With generative AI, customers expect improved personalization from brands—but their trust
in organizations to use their data responsibly is limited. Generative AI can help you please your
of customers prioritize
customers with relevant and timely experiences. But to stand out in a crowded field, your focus
knowing when they are
on their needs must be tangible at every touchpoint.
talking to a human being
Resolution: Design every step in your generative AI journey for transparency and accountability or a bot.2
to customers to deliver meaningful experiences they trust.
Employees
Transform skeptical and novice employees to empowered, generative AI pros.
81
% Early adopters are making their first returns on investment in generative AI by automating
lower-value, repetitive tasks, for example, in content production. However, this is also where
employees will be most anxious about role reductions. To make progress, organizations should
Employees expect AI to
reassure and incentivize employees to master the tools, to experiment, and to contribute toward
free them to focus on
the future of their function.
higher-value tasks.3
Resolution: Prove the value of generative AI to employees, demonstrating job enrichment, time
savings, new opportunities, and career advancement.
Organization
#2 Priority
Drive generative AI innovation with confident governance.
Innovation in generative AI can drive efficiency and deliver new opportunities for revenue, but the
Data security and AI
pace at which you realize these gains is dependent on governance. Your external vendors and partners
should offer not just innovative tools, but also responsibly developed ones. You’ll also need your internal governance frameworks
stakeholders to flag the right opportunities, share data, and collaborate on new governance processes to are second only to
work at pace with your vendors.
employee skills in execs’
2024 priorities for AI.4
Resolution: Level up your leadership oversight and governance processes and focus on commercially
safe solutions that help you manage risk while taking advantage of the opportunities generative AI offers.
3
Thought leaders in generative AI.
Adobe and EY specialists are privileged to work with a wide range of organizations around the world,
facilitating their deployment of generative AI especially in the domain of customer experience (CX).
The world’s leading brands and agencies are partnering with Adobe to drive greater efficiency
in their organizations, applying our natively integrated generative AI in Creative Cloud and
Experience Cloud today to empower their teams to boost productivity and deliver personalization
at scale. We believe 2024 will be a watershed moment in developing customer experience.”
Eric Hall
SVP and Chief Marketing Officer,
Digital Experience, Adobe
Far from taking away creative work, we see generative AI supercharging it, creating exponential
value, and putting a new palette of CX capabilities at the fingertips of your whole team, which
further builds confidence. Customer expectations will change in 2024 through exposure to hyper-
personalized experiences. We are inspired by this generational opportunity, and the extraordinary
uses our clients are already making of it, keeping people at the center.”
Laurence Buchanan
Global Customer and Growth Leader, EY
From this experience and discussion with industry leaders we’ve distilled insights to support marketing
and CX leaders as they evaluate, implement, and harness the power of generative AI. This guide
concludes with a checklist to help you assess and refine your immediate priorities this year.
90
%
of $5 billion+ revenue companies remain at proof-of-concept or isolated capabilities in generative AI
Source: May 2023: EY Innovation Realized pulse survey, C-suite executives from majority $5bn+ global companies
4
Dial up transparency as you improve the relevance
of customer experiences.
Adobe’s State of Digital Customer Experience research revealed that 56% of consumers believe that generative AI
will make digital experiences more personalized, 54% believe content will be more relevant to their preferences,
and 53% expect to see an increase in product and service innovation.6
However, generative AI also poses new questions about privacy, transparency, and control. Consumers are
wary—79% are concerned or very concerned about how companies are using their personal data.7 So, CX leaders
must bring the voice of the customer to every part of the business that’s experimenting with this technology.
Marketing leaders must validate that every touchpoint that makes up the brand experience remains meaningful
and authentic. The organization will need their leadership to keep the focus on differentiating the brand and
building trust, regardless of function or touchpoint. On this solid foundation, you can push forward to deliver the
personalization that customers value.
To help resolve the tension between privacy and relevance, take these
actions that we see first movers doing:
Be transparent while building your
first-party data.
Organizations are adapting to a cookieless future by
expanding and enriching their own first-party customer Customer acquisition costs through digital
data, with the right permissions and consents. This also marketing are still pretty high right now. One
means working with your data partners to determine change that motivates is to really think about
how to collaborate to enrich data without relying on your first-party data—what data do we want to
third-party cookies. own and how do we get that data with the right
consent and permissions to be able to use it?”
As you shift to a clear first-party data strategy you must
also set clear expectations as you gather data. One CDO
Laurence Buchanan
at a global consumer packaged goods (CPG) business
Global Customer and Growth Leader, EY
explained to us that offering customers clarity in the
moment about how the company will hold and use
data is essential to winning trust and the consents they
need to engage customers with their augmented reality
experiences.
5
Match generative AI to customer As a creative team, we decided from day one
expectations. that we need to make sure that we’re upfront
about when we’re using AI versus not. Part of
Organizations need guidelines for disclosure around the
why we work with Adobe is because of their
use of generative AI. Regulators and governments do
not always keep up with the rapid pace of change, so stance on ethics. The idea that they’re making
marketing leaders need to champion the best interests sure that they’re pulling everything from their
of their customers. own stock images which are sourced ethically,
that was a big deal for us. They are also
An example of this is to take extra care when leveraging working on creating watermarking, so viewers
generative AI to represent human appearance or voice— know when it was generated by AI, that we can
customers may be upset by mistaking a generative AI then adapt in our marketing material.”
experience for an actual human being. A global CPG
organization commits not to use generative AI for any
Bridget Esposito
front-of-packaging images of people—employing it instead
Vice President, Head of Creative, Brand,
for close-ups where customers expect to see illustrations or
Prudential
infographics. A US top-5 insurer has set similar guidelines
that permit the use of generative AI for creating product
images, but never for images of humans.
Make customer benefits central to decisions.
It’s vital to look beyond immediate business value to target
potential benefits for customers. How are you considering
We have some special opportunities
customer preferences? Could the generative AI you’re
and possibilities to create quite stunning
deploying today make a customer experience more
digital experiences for our customers, or
empathetic, more accessible, or more timely?
to have a much more immersive shopping
experience. That is a super big moon shot
As you weigh your priority initiatives, factor in satisfaction
ratings or other customer experience indicators—to start but you can imagine a tool that would
where the real customer value is and to monitor the generate how a product would look in your
impact on your customer experiences. space. These are real possibilities.”
One multinational retail organization measures the Stefan Esping
impact of their generative AI chatbot not only through Data & Machine Learning Domain Manager,
customer satisfaction ratings, but also by the percentage Ingka
of conversations contained within the chatbot rather than
being transferred to an employee, and any increases in
sales following a chatbot interaction.
Amplify creativity without replacing human judgement.
Have a clear strategy to validate that any generative AI content is true to your brand, creatively enriching, and never generic.
As you free up creative teams from lower-value tasks such as image variation, the team can refocus on larger, more impactful
creative work for your brand.
Additionally, it is critical to apply generative AI via a custom model that can be trained on your own brand content, tone, style,
images, and standards so the outputs retain your brand’s unique traits.
6
First movers tell us they retain a human in the loop to keep consistency in brand messaging, imagery, and tone of voice. This
helps you to guarantee meaningful customer experiences every time.
A technology executive for a CPG company explained that generative AI is already deployed in drafting a significant
proportion of the product copy that’s displayed on retail websites in the US and the UK to engage consumers with their
products’ features and benefits. None of it goes live without human approval. In a global fashion brand, generative AI
develops prompts and visualizations for product designers, drawing on trends harvested from customer sentiment analysis.
Transform skeptical and novice employees to
empowered generative AI pros.
Demonstrate generative AI as a creative, career-building opportunity.
Generative AI creates real opportunity for professional development and career enhancement, but it’s only
natural that some employees may feel anxious around generative AI initiatives within their organizations. This
tension may be the most important blocker you face. You need your teams to have appetite for exploring
generative AI, before they can begin to capture the new opportunities it brings.
98 12
% to %
Reduction in proportion of employees concerned about generative AI after participating in pilot.
Source: January 2024: EY generative AI tool deployment – internal study
The senior executives surveyed in Adobe Digital Trends 2024 cited “advanced AI skills training for key staff” and
“basic AI understanding for all employees” as their top two priorities for preparing their employees to work
effectively with generative AI.8 Assigning pilot projects and letting insight grow organically is an essential first
step. Marketing leaders need to make these opportunities visible and relevant to each team member, to build the
“what’s in it for me” of the technology.
As one leader explained, on average, employees can expect their roles to become more strategic.
Our interviewees recommend content creation and content workflows as tasks that allow you to move swiftly to
demonstrate benefits for employees and customers. In an analysis of a generative AI deployment undertaken by
EY LLP, a 12-week pilot in a specific use case dramatically improved employees’ ability to grasp the opportunity
beyond the risks 9:
■ Understanding the potential of generative AI grew from 37% to 84%
■ Concerns about using generative AI fell from 99% to 12%
■ Confidence in personal ability to work with generative AI improved from 28% to 77%
7
To turn uncertain novices into empowered professionals, marketing leaders need to:
A digital executive in a CPG organization spoke about
Prioritize upskilling at all levels.
building a Center of Excellence. They provided education for
Success depends on the readiness of your teams to leverage
every one of their 10,000 employees to create a common
generative AI tools and processes. To enable them to become
baseline of understanding across every employee, and
fluent in generative AI, define and deliver training programs
published white papers about generative AI on the company
for all employees, from executives to practitioners.
portal. One Global Chief Marketing Officer in professional
services holds “promptathons,” a series of prompting
One marketing leader at a global professional services
sessions to upskill her team in the “art of the prompt.”
firm recommends creating “learning labs” with access to
generative AI tools, giving employees guidance and hands-
Adobe has established an AI Center of Excellence that
on experience with tools to reduce uncertainty. Another
oversees strategic alignment, compliance, and governance of
organization has an internal portal where employees can
AI initiatives. To ensure employees are equipped to apply AI
request a license for pre-approved generative AI tools.
in their roles, Adobe has comprehensive training programs,
resources, and podcasts to upskill the workforce and
personalize career development.
At Shiseido, we prioritize the continuous learning and development of our employees. Through our internal
Digital Academy, we provide accessible programs and certifications in data and AI advancements.
This education is crucial for our team’s success.”
Angelica Munson
Global Chief Digital Officer,
Shiseido
Begin with content creation.
We consistently hear that optimizing content creation or
content workflows is a powerful first move. It’s where your
employees are spending a lot of time and does not require
a lot of data connectivity to get started. Starting points for
generative AI usage in content workflows include: If you think about the previous world, you
■ Creative concepting and ideation would have a great concept, a great idea.
That would take time to really bring to life
■ Copy drafting and iterations
to share with your ‘buyer’. Now generative
■ Image drafting and refinement
AI can bring that idea very quickly to some
■ Production of content variations for testing across: sort of visualization. That speed is a clear
■ different channels benefit.”
■ different markets
■ different personas Duncan Avis
Americas Customer & Growth Leader, EY
Generative AI helps teams overcome the content
scalability challenge, boosting the quality, quantity,
velocity, findability, and reusability of the content you need
to drive personalization across multiple channels.
8
Use short-term comparison metrics. Choose metrics that build confidence:
■ Workplace satisfaction
Several first-movers report success using comparison
metrics to motivate employees and engage budget-
■ Time saved
holders with the before-and-after progress they’re making.
For example, 82% of employees taking part in a global
professional services generative AI pilot reported faster ■ Volume of content created
task completion.10
■ People required
■ Cost per asset
■ Speed to launch
Appoint generative AI pioneers.
In some areas, activating the organization to adopt
generative AI will be similar to change programs you
may have led in the past. The network of early adopters
in your org needs to be experienced in their professional
disciplines and able to mentor others.
To identify your generative AI pioneers across the
organization, start with employees who:
What has worked well for us is taking
■ Have a direct interest in AI capabilities, from the
perspectives of the business, marketing, tech, and risk an employee ‘influencer’ approach by
identifying people who are hungry to
■ Have the skills and appetite to communicate the benefits change, hungry to learn, and building out
and to positively influence employee culture the process with those employees. This
will then be cascaded throughout the
■ Hold aspirational, mid-level roles with a degree of organization more broadly.”
decision-making, managing more junior levels in the
organization
Chris Chesebro
Chief Digital Officer, Wella
■ Are commercially aware and risk-informed, capable of
assessing innovations from both perspectives
Collaborate with them to research and propose a set
of generative AI design principles and equip them to
experiment. Their example and enthusiasm can inspire the
team to move faster and move past any uncertainties.
One CPG organization has chosen 30 employees from
middle management to take part in the first generative
AI pilot in a sandbox environment. They were tasked to
identify risks and share learnings.
9
Drive generative AI innovation with
confident governance.
Develop generative AI controls and partners that can help you navigate risk
and opportunity.
To deliver business outcomes such as cost savings or content acceleration with generative AI, companies must
choose solutions that are built for business use cases. The right generative AI tools will need to meet some
unique criteria and have the right controls in place:
■ The base model must give you transparency into the data provenance and be designed for commercial safety.
■ You must be able to apply custom models that are trained on your own data to keep outputs relevant for
your brand and your business.
■ Your data must be secure and private, not shared with other businesses or used to train a publicly
available model.
■ Your partners should prioritize ethical, responsible AI development to protect your brand.
In addition to careful selection of the generative AI solutions that fit your business, companies must optimize
governance of those tools within their organization. Your existing internal controls framework will need to evolve.
AI governance is not just about setting rules, it’s about striking the right balance. It’s about
fostering creativity and innovation while ensuring accountability, responsibility, and transparency.
At the heart of AI governance is the commitment to respect our customers and align with our
values. It’s about turning AI potential into real-world applications, responsibly and ethically.”
Cynthia Stoddard
CIO of Adobe
10
To unleash the full potential of innovating in generative AI,
leaders have learned how to:
Map and mitigate novel generative AI risks.
Use an evaluation framework for generative AI tools that
screen for solutions with responsibility engineered into
their tools, including:
■ Clear intellectual property rights accounted for and
indemnification provided to minimize lawsuit risk
I don’t want to use an AI that’s been trained on
■ Robust security and privacy of your data non-licensed materials. We expect our bigger
■ Fairness and bias controls built-in agencies to self-certify for responsible practices
and we will write it into their contracts.”
■ Transparency in how models are built
Select vendors and partners who are passionate about IT Engineering Director,
preserving intellectual property and content credentials Global packaged good organization
and are helping to guide global regulation. Check if they
participate in industry standard-setting, for example
in the Content Authenticity Initiative, the NIST AI Risk
Management Framework or the EU AI Elections Accord. By
thinking ahead of regulations, these vendors will help to
future-proof your developing generative AI capability.
Establish a single point of control.
As marketing organizations move generative AI from
pilot to production, they need “air traffic control”—a
team comprising marketing, compliance, and technology
It’s crucial for marketing to be positioned
heads—to coordinate and direct generative AI development
at the heart of an AI control tower strategy,
across the organization. They will:
serving as a central hub that coordinates with
■ Define and communicate a governance framework for
legal, cybersecurity, privacy, and technology
generative AI
stakeholders to harness and action data
■ Assess risk for new generative AI vendors and proposals insights effectively. This centralized approach
helps establish that the CMO and marketing
■ Prioritize for customer and commercial relevance
teams are integral to the collaborative network,
■ Direct capital investment in generative AI
facilitating a unified direction and decision-
making across the various departments, to
This control function should be a distinct practice of an
overall delivery-focused generative AI Center of Excellence, move at the speed of business while mitigating
whose scope it is to govern: risk.”
■ The business model—generative AI opportunities
Tom Edwards,
for product portfolios, value proposition, growth
Managing Director,
opportunities
Applied & Generative AI Lead, EY
■ The operating model—generative AI potential to reduce
cost, accelerate, evolve the organization
■ Risk management—identifying and mitigating novel
11
risks, such as data privacy, bias, IP, and so on
11
Organize your goals into the right sequence.
A key role for leaders governing generative AI in their organization is to recognize the different ways it affects customers
and employees and to sequence your projects to suit.
Aim to prove concepts and cultivate skill and insight within the team before taking on more complex use cases.
The typical order, from simple to complex, will be:
Integrate vendor tools into your content creation
workflows to add creative uplift, scale, and
accelerate content production.
In parallel to your content creation
opportunities, kick off work to audit, connect,
clean, and structure your data. This will help
you prepare for more data-heavy generative
Develop customized content using generation AI use cases like personalization.
models trained on proprietary content, brand
guidelines, and historic campaigns.
Personalize marketing campaigns—build tailored messaging, content, and journeys
across channels for each customer.
Harness unstructured data by using generative AI to query, gather, and democratize insights from broad
datasets. This also helps you strengthen your personalized marketing campaigns noted above.
12
A checklist to start now.
To get started now and deliver on the full potential of generative AI in marketing and CX, organizations
should focus on the following key areas identified from our research with marketing leaders and subject
matter experts:
1. Customer trust
■ Do we have a list of customer pain points?
■ Is customer experience fully visible and factored into the way we assess generative AI priorities?
■ Have we defined specific customer-centric principles for uses of generative AI?
■ Does our existing research gather data on customer attitudes to generative AI?
■ Have we reviewed current brand guidelines to fit with generative AI applications?
2. Employee empowerment
■ Do we have a cross-functional list of employee pain points?
■ Do we have the right communications plan and training resources in place?
■ Do we have experiments up and running—and are we capturing what we learn?
■ Have we created space for open-ended innovation during generative AI discovery and experimentation?
■ Does the team have a mandate to discover its own metrics as projects progress?
■ Are we investing in generative AI training for all levels?
■ Have we created simple, accessible ways for employees to access and familiarize themselves with generative AI tools?
■ Are we refining roles and responsibilities to keep a human in the loop?
■ Have we defined scope and nominated advocates for a network of generative AI champions?
3. Organizational opportunity
■ Do we have an evaluation process in place to screen tools for risk mitigation?
■ Do we understand where generative AI is being assessed or implemented across the organization?
■ Is there a team in place applying a common framework or governance to align and maximize benefits?
■ Have we defined an efficient process to evaluate and implement generative AI technology in partnership with our
technology and legal peers?
■ Are we clear how our vendors and strategic partners’ generative AI initiatives map to our needs? Have we made full use
of their advice and resources?
■ Have we considered our customers and employees in the sequencing of our generative AI initiatives?
■ Is our mid- to long-term data transformation plan defined?
13
Conclusion
Leading the marketing function in the era of
generative AI.
Generative AI is here to stay as a transformative force across every part of the organization. But it has special
relevance for marketing and CX. In some capacity, 83% of creative professionals are already using generative AI
tools in their work. Among Gen-Zs, it’s above 90%.11
As a marketing or CX leader, applying generative AI means designing a plan for the marketing function that helps
drive profitable demand, inspires your employees, and enriches the customer experiences you deliver. It’s critical
to keep these three challenges in mind at every step: for your organization, your employees, and your customers.
Methodology
Structured interviews were conducted with participants in 30-, 45-, or 60-minute sessions with external
organizations (n=11) and subject matter experts (n=10).
Sample consisted of participants from across marketing, CX, digital, data, legal, and creative.
Focus of the discussion looked to explore relevant use cases, partnerships, and lived experiences from individuals
in the support of, exploration, and deployment of generative AI within a commercial context to gather lived
experiences and practical advice from participants.
Sources
1 EY CEO Imperatives quarterly update, January 2024
2 Adobe Digital Trends 2024, March 2024
3 EY - US, How organizations can stop skyrocketing AI use from fueling anxiety, October 2023
4 Adobe Digital Trends 2024, March 2024
5 EY Innovation Realized pulse survey, C-suite executives from majority $5bn+ global companies, May 2023
6 Adobe, The State of Digital Customer Experience Report 2023, October 2023
7 Adobe Trust Report - Customer trust is earned or broken with every experience, March 2022
8 Adobe Digital Trends 2024, March 2024
9 EY generative AI tool deployment – internal study, January 2024
10 EY generative AI tool deployment – internal study, January 2024
11 Adobe Blog - Creative pros are leveraging Generative AI to do more and better work, February 2024 14
ABOUT ADOBE
Adobe Experience Cloud is the most comprehensive suite of customer experience management
tools on the market. With solutions for data, content delivery, commerce, personalization, and
more, this marketing stack is created with the world’s first platform designed specifically to
create engaging customer experiences. Each product has built-in artificial intelligence and works
seamlessly with other Adobe products. And they integrate with your existing technology and
future innovations, so you can consistently deliver the right experience every time.
ABOUT EY
EY exists to build a better working world, helping create long-term value for clients, people and
society and build trust in the capital markets. Enabled by data and technology, diverse EY teams
in over 150 countries provide trust through assurance and help clients grow, transform and
operate. Working across assurance, consulting, law, strategy, tax and transactions, EY teams ask
better questions to find new answers for the complex issues facing our world today.
EY refers to the global organization, and may refer to one or more, of the member firms of Ernst
& Young Global Limited, each of which is a separate legal entity. Ernst & Young Global Limited, a
UK company limited by guarantee, does not provide services to clients. Information about how
EY collects and uses personal data and a description of the rights individuals have under data
protection legislation are available via ey.com/privacy. EY member firms do not practice law
where prohibited by local laws. For more information about our organization, please visit ey.com.
© 2024 EYGM Limited. All Rights Reserved.
EYG no. 005649-24Gbl
This material has been prepared for general informational purposes only and is not intended to be relied upon as accounting, tax, legal or other professional advice.
Please refer to your advisors for specific advice. The views of third parties set out in this publication are not necessarily the views of the global EY organization or its
member firms. Moreover, they should be seen in the context of the time they were made.
15"
4,ey,ey-idc-maketscape-worldwide-ai-services-2023-vendor-assessment.pdf,"IDC MarketScape
IDC MarketScape: Worldwide Artificial Intelligence Services
2023 Vendor Assessment
Jennifer Hamel
THIS IDC MARKETSCAPE EXCERPT FEATURES EY
IDC MARKETSCAPE FIGURE
FIGURE 1
IDC MarketScape Worldwide Artificial Intelligence Services Vendor Assessment
Source: IDC, 2023
Please see the Appendix for detailed methodology, market definition, and scoring criteria.
May 2023, IDC #US49647023e
IN THIS EXCERPT
The content for this excerpt was taken directly from IDC MarketScape: Worldwide Artificial Intelligence
Services 2023 Vendor Assessment (Doc # US49647023). All or parts of the following sections are
included in this excerpt: IDC Opinion, IDC MarketScape Vendor Inclusion Criteria, Essential Guidance,
Vendor Summary Profile, Appendix and Learn More. Also included is Figure 1, 2 and 3.
IDC OPINION
This IDC study represents a vendor assessment of the 2023 artificial intelligence (AI) services market
through the IDC MarketScape model. IDC last assessed this market in 2021. In the past two years, we
have revised our evaluation criteria and buyer perception survey instrument to refine our assessment
methodology and reflect market evolution. Thriving vendors in today's AI services market can both
clearly articulate their strategies for enabling clients' adoption of AI solutions and readily demonstrate
their current capabilities and proof points through existing client engagements.
Organizations increasingly look to AI solutions to drive revenue and profit growth as well as improve
outcomes in areas such as customer satisfaction, operational efficiency, sustainability, process speed
and accuracy, and speed to market for new products and services. However, many challenges persist,
including employees' lack of data literacy and technology training, technical complexity, lack of
resources to support end users and maintain AI systems, and issues related to security, privacy, and
governance. Professional services firms remain a critical source of expertise, skills, and tools to
incorporate AI into digital business strategies, build production-grade solutions, and realize ROI.
In this assessment, IDC evaluated AI services vendors across scoring criteria and collected feedback
from customers on their perception of the key characteristics and the capabilities of these vendors.
Key findings include:
▪ The most critical vendor attribute for successful AI services engagements, according to IDC's
Artificial Intelligence Services Buyer Perception Survey, remains ""ability to achieve business
outcomes."" The perceived priority of this attribute over all others was unchanged from the
2021 study.
▪ When buyers were asked about the primary business objective driving their engagement of
their artificial intelligence services vendor, at a worldwide level, the most frequent responses
were ""improve operational efficiency,"" ""build capability for tomorrow's business,"" and ""drive
higher revenue growth, gain market share."" Nearly 30% of the buyers we surveyed said they
achieved 30% or greater improvement in measurable KPIs from their AI services engagement.
▪ The top-rated vendor attribute, in aggregate, was the ability to ""integrate vendor project team
with internal team."" This aligns with IDC's evaluation of client adoption strategies around
workshops and stakeholder alignment and AI program enablement as top areas of strength on
average across AI services vendors.
IDC MARKETSCAPE VENDOR INCLUSION CRITERIA
This research includes analysis of AI services providers with global scale and broad portfolios
spanning IDC's research coverage. This assessment is designed to evaluate the characteristics of
each firm — as opposed to its size or the breadth of its services. In determining the group of vendors for
analysis in this IDC MarketScape, IDC considered the following set of inclusion criteria:
©2023 IDC #US49647023e 2
▪ Worldwide AI services revenue of at least $100 million over the last calendar year, with
revenue generated in each major geographic region (i.e., Americas, EMEA, and Asia/Pacific)
▪ Offerings across the life cycle of AI business and IT services (e.g., project-based, managed,
support, and training)
▪ AI services offerings and solutions addressing a range of industry verticals and business
functions
▪ Go-to-market alliances with a range of AI software providers
ADVICE FOR TECHNOLOGY BUYERS
▪ Maturity assessment. Challenges exist at every stage of the AI adoption journey that often
require expert advice to navigate. Look for services firms to assess your organization's AI
maturity, readiness, talent, and data needs and assist you with creating or refining AI
strategies and operating models to achieve specific business objectives and prepare you for
the next stage of adoption. Even organizations with previously established AI programs may
find your strategies and governance frameworks need adjustment to consider new implications
(ethical, regulatory, or otherwise) of generative AI capabilities and to incorporate appropriate
guardrails for developing and using the technology.
▪ Use case development. In today's economic climate, there is a heightened need to connect AI
solution innovation to real business outcomes. Seek a services partner that can provide
frameworks, methodologies, and tools to help you source innovation ideas from within your
business, discover and prioritize use cases, define KPIs for measuring business value, create
a strong innovation foundation across your organization, and produce deployable and scalable
AI solutions. As several of the customer reference interviews IDC conducted for this study
indicated, vendors' industry and functional domain knowledge gained from experience working
with many different customers helps accelerate the process of identifying and developing
impactful AI use cases.
▪ Skills. AI talent gaps are neither new nor abating for organizations anytime soon. IDC research
suggests that organizations will not solve their AI talent issues by merely hiring more data
scientists. Seek a services partner that can provide expertise not only in core AI model
development and your chosen AI platform but also in scaling and operationalizing AI models
(whether custom-developed algorithms or repurposed ""off the shelf"" solutions) and in
empowering your business end users to leverage AI-driven insights in their roles. Also,
consider guidance and support from services partners beyond staff augmentation to help you
build AI skills in your organization. Ask for best practices, recruiting resources, access to on-
demand AI talent pools, and pod-based or build-operate-transfer models that enable your
employees to learn AI skills while working with expert teams.
▪ Innovation and delivery accelerators. The fundamental value that AI services vendors offer is
helping customers achieve ROI from AI more quickly than they would on their own. Consider
the proprietary assets that vendors may propose as part of their AI services offerings, which
can include pretrained industry- or function-specific models, reusable component repositories,
curated and annotated training data sets, developer tools and microservices, and even full-
fledged products and platforms. These assets can fill gaps in commercial software products,
address specific business domain or technical challenges (such as integrating legacy
enterprise systems with new AI capabilities), or industrialize AI solution development and
management. Also consider the ecosystem of partners that AI services vendors collaborate
with to provide access to innovation that benefits your organization.
©2023 IDC #US49647023e 3
▪ Stakeholder alignment. According to IDC's Artificial Intelligence Services Buyer Perception
Survey, the most common project sponsors for AI services engagements were CIOs/CTOs,
information technology (IT) directors and managers, chief analytics/data officers, and line-of-
business (LOB) heads. Choose a vendor that can work across IT, LOB, and data teams to
ensure solutions address key stakeholder priorities. Buyers also rated ""knowledge
transfer/training for our internal team"" as one of the top 10 most critical attributes for AI
services engagement success. Seek out vendors that not only speak with budget holders but
also communicate effectively with end users, who will be interacting with and supporting AI
solutions, through workshops and change management programs.
▪ Data and AI governance. Strong foundations for data quality and privacy, responsible AI, and
MLOps are critical for enterprise-grade AI solutions that are both functional for business needs
and compliant with regulatory and risk management requirements. Seek services providers
that offer thought leadership and frameworks for data privacy, responsible AI, and MLOps and
proactively help you consider these issues as early as possible in the design process, as well
as through the deployment and monitoring of solutions, to mitigate potential risks.
▪ Vendor selection. Use this IDC MarketScape in contract negotiations and as a tool to not only
short list vendors for AI services bids but also evaluate vendors' proposals and oral
presentations. Make sure you understand where these players are truly differentiated and take
advantage of their expertise, technical, industry base, or otherwise.
VENDOR SUMMARY PROFILES
This section briefly explains IDC's key observations resulting in a vendor's position in the IDC
MarketScape. While every vendor is evaluated against each of the criteria outlined in the Appendix,
the description here provides a summary of each vendor's strengths and challenges.
EY
According to IDC analysis and buyer perception, EY is positioned in the Leaders category in this 2023
IDC MarketScape for worldwide artificial intelligence services.
EY places data and AI at the core of its Transformation Realized approach, which aims to enable
clients to envision their future business models and then design transformations that develop technical
capability at scale and manage organizational change. The firm's offerings cover both direct expansion
of large-scale AI programs and infusion of AI into transformational programs driven by C-suite buyer
agendas. Increasingly, EY integrates resources from its AI practice with strategy consultants from EY-
Parthenon to engage with boards and to shape AI strategies and has recently launched a generative
AI strategy and road map offering. The firm also continues to invest in proprietary technology
capabilities on the EY Fabric Intelligence ecosystem to provide responsible AI solutions (ShEYzam
including fairness as a service, NLP as a service) and reusable assets (e.g., EY Lighthouse). EY has
also created a collection of prebuilt AI solutions made available to clients through a marketplace called
EY ASpace. EY also leverages strategic partnerships with AI technology providers such as Microsoft,
SAP, IBM, Databricks, and Snowflake to codevelop solutions in quickly evolving areas such as
fairness, sustainability, and generative AI.
Strengths
According to customers, EY's strengths are the company's ability to deliver across the life cycle of AI
services, provide solutions using their preferred AI technology providers, integrate EY's project team
with their internal team, deliver AI-enabled automation services, and resolve problems or issues
©2023 IDC #US49647023e 4
related to customer service. IDC considers EY's strategies around offerings, platform-based delivery,
client adoption, sales enablement, alliances, growth, innovation and R&D, technology skills, and
employee retention as key strengths. EY also showcased strengths in achieving business outcomes
for clients with AI services.
Challenges
IDC believes EY's go-to-market strategy, though strong overall, could be improved further by more
collaboration with specialist AI software providers and data annotation services or crowdsourcing
providers on go-to-market initiatives for AI services. EY could also benefit from continued investment
in new asset-based AI services.
APPENDIX
Reading an IDC MarketScape Graph
For the purposes of this analysis, IDC divided potential key measures for success into two primary
categories: capabilities and strategies.
Positioning on the y-axis reflects the vendor's current capabilities and menu of services and how well
aligned the vendor is to customer needs. The capabilities category focuses on the capabilities of the
company and product today, here and now. Under this category, IDC analysts will look at how well a
vendor is building/delivering capabilities that enable it to execute its chosen strategy in the market.
Positioning on the x-axis, or strategies axis, indicates how well the vendor's future strategy aligns with
what customers will require in three to five years. The strategies category focuses on high-level
decisions and underlying assumptions about offerings, customer segments, and business and go-to-
market plans for the next three to five years.
The size of the individual vendor markers in the IDC MarketScape represents the market share of each
individual vendor within the specific market segment being assessed.
IDC MarketScape Methodology
IDC MarketScape criteria selection, weightings, and vendor scores represent well-researched IDC
judgment about the market and specific vendors. IDC analysts tailor the range of standard
characteristics by which vendors are measured through structured discussions, surveys, and
interviews with market leaders, participants, and end users. Market weightings are based on user
interviews, buyer surveys, and the input of IDC experts in each market. IDC analysts base individual
vendor scores, and ultimately vendor positions on the IDC MarketScape, on detailed surveys and
interviews with the vendors, publicly available information, and end-user experiences in an effort to
provide an accurate and consistent assessment of each vendor's characteristics, behavior, and
capability.
Market Definition
IDC defines AI as systems that learn, reason, and self-correct. These systems hypothesize and
formulate possible answers based on available evidence, can be trained through the ingestion of vast
amounts of content, and automatically adapt and learn from their mistakes and failures.
Recommendations, predictions, and advice based on this AI provide users with answers and
assistance in a wide range of applications and use cases.
©2023 IDC #US49647023e 5
AI services are utilized to assess, plan, design, implement, and operate the following:
▪ AI platforms facilitate the development of artificial intelligence models and applications,
including intelligent assistants that may mimic human cognitive abilities.
▪ AI applications include process and industry applications that automatically learn, discover,
and make recommendations or predictions.
Detailed definitions of the software tools and platforms that are relevant for AI services engagements
are available in IDC's Worldwide Software Taxonomy, 2023 (IDC #US50513623, April 2023). The
underlying data services are a critical component to AI systems, serving as the basis upon which initial
analysis and learning are conducted. Data services are highly specific to the function and process of
the AI system and may come from a wide range of sources, both unstructured and structured. These
data services include the processes needed to ingest, organize, cleanse, and utilize the data within the
AI-enabled applications.
AI services providers engage with clients to build AI capabilities through business services and IT
services (see Figure 2). For a detailed definition of the services markets illustrated in Figure 2, see
IDC's Worldwide Services Taxonomy, 2022 (IDC #US47769222, July 2022).
FIGURE 2
Artificial Intelligence Services
Source: IDC, 2023
Customer Perceptions of AI Services Vendors
A significant and unique component of this evaluation is the inclusion of the perceptions of AI services
buyers of both the key characteristics and the capabilities of the vendors evaluated. The buyers
participating in IDC's Artificial Intelligence Services Buyer Perception Survey have partnered with at
©2023 IDC #US49647023e 6
least one of the participating vendors directly on an AI services engagement within their company. The
survey findings highlight key areas where buyers expect AI services providers to showcase a range of
capabilities. The buyers consider these capabilities a must-have for AI services to be able to fulfill the
requirements of many business and IT issues that challenge the buyers.
Figure 3 illustrates the order of factors important for a successful AI services engagement for the AI
services customers surveyed in 2023. Survey findings suggest that the ability to achieve desired
business outcomes by the consulting and delivery teams working on an AI services engagement is the
most critical factor for the successful completion of the engagement. Customers also indicated a
vendor's ability to create quality data sets and pipelines for AI model training, provide quality skills in
and knowledge of AI, provide technical insights and competency, and provide security and governance
of AI algorithms, APIs, and training data to be among the most critical attributes for an engagement's
success.
©2023 IDC #US49647023e 7
FIGURE 3
Top 10 Factors for Successful Artificial Intelligence Services Engagements, 2023
Q. In order for an AI services engagement to be successful, please indicate the importance of
each of the following characteristics.
n = 116
Note: Mean scores are based on a scale of 1–5, where 1 is highly detrimental to success and 5 is essential to success.
Source: IDC's Artificial Intelligence Services Buyer Perception Survey, 2023
©2023 IDC #US49647023e 8
LEARN MORE
Related Research
▪ Artificial Intelligence Services Findings from Enterprise Intelligence Services Survey, 2022
(IDC #US49230423, January 2023)
▪ IDC FutureScape: Worldwide Artificial Intelligence and Automation 2023 Predictions (IDC
#US49748122, October 2022)
▪ Market Analysis Perspective: Worldwide Analytics and Intelligence Automation Services, 2022
(IDC #US48206022, September 2022)
▪ Worldwide Artificial Intelligence Services Forecast, 2022–2026 (IDC #US48206222, August
2022)
▪ Worldwide and U.S. Artificial Intelligence Services Market Shares, 2021: Adapting to Evolving
Client Needs (IDC #US48206622, August 2022)
▪ IDC's Worldwide Services Taxonomy, 2022 (IDC #US47769222, July 2022)
▪ IDC MarketScape: Worldwide Artificial Intelligence Services 2021 Vendor Assessment (IDC
#US46741921, May 2021)
Synopsis
This IDC study represents a vendor assessment of the artificial intelligence (AI) services market
through the IDC MarketScape model. This assessment discusses both quantitative and qualitative
characteristics that explain success in the AI services market. This IDC MarketScape covers a variety
of vendors participating in the AI services space. The evaluation is based on a comprehensive and
rigorous framework that assesses vendors relative to the criteria and to one another and highlights the
factors expected to be the most influential for success in the market in both the short term and the long
term.
""With rising public awareness of AI capabilities, spurred most recently by the ability to interact with
free, web-based generative AI tools, organizations are feeling pressure to move faster to incorporate
AI into digital business strategies or risk being left behind by competitors,"" says Jennifer Hamel,
research director, Analytics and Intelligent Automation Services at IDC. ""Successful AI services
providers continue to evolve their portfolios to meet ever-evolving client needs while remaining trusted
advisors to cut through hype and hysteria, set reasonable expectations for what AI can and should do
for their businesses, and develop road maps for adopting and managing AI solutions at scale.""
©2023 IDC #US49647023e 9
About IDC
International Data Corporation (IDC) is the premier global provider of market intelligence, advisory
services, and events for the information technology, telecommunications and consumer technology
markets. IDC helps IT professionals, business executives, and the investment community make fact-
based decisions on technology purchases and business strategy. More than 1,100 IDC analysts
provide global, regional, and local expertise on technology and industry opportunities and trends in
over 110 countries worldwide. For 50 years, IDC has provided strategic insights to help our clients
achieve their key business objectives. IDC is a subsidiary of IDG, the world's leading technology
media, research, and events company.
Global Headquarters
140 Kendrick Street
Building B
Needham, MA 02494
USA
508.872.8200
Twitter: @IDC
blogs.idc.com
www.idc.com
Copyright and Trademark Notice
This IDC research document was published as part of an IDC continuous intelligence service, providing written
research, analyst interactions, telebriefings, and conferences. Visit www.idc.com to learn more about IDC
subscription and consulting services. To view a list of IDC offices worldwide, visit www.idc.com/offices. Please
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Copyright 2023 IDC. Reproduction is forbidden unless authorized. All rights reserved."
5,ey,ey-the-aidea-of-india-2025-how-much-productivity-can-genai-unlock-in-india.pdf,"How much
productivity
can GenAI
unlock in India?
The AIdea of India: 2025
The AIdea of India: 2025 1
2 The AIdea of India: 2025
stnetnoC
Foreword
Chapter 1
Generative AI:
Shaping tomorrow
Executive
summary
Chapter 2
Pivoting to
AI-first digital
transformation
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Chapter 3
Chapter 6
Transforming work
Policy agenda
with GenAI
for India
Chapter 4
Industries in
transformation Annexures
Chapter 5
Government
Services
The AIdea of India: 2025 3
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Foreword
Over the past few years, innovation possible to use open-source models for as
in Generative AI (GenAI) has low as a few thousand rupees a month
progressed at an extraordinary in India.
pace, reaffirming its transformative
Yet, amidst all this innovation, enterprise
potential across a number of domains.
adoption rates of GenAI remain very low.
The possibilities are vast and hold the
Our survey shows that 36% of Indian
promise of profound changes on the
enterprises have allocated budgets and
horizon for millions of Indian citizens.
begun investing in GenAI, while another
Technical breakthroughs have been jaw 24% are testing its potential. Technology
dropping. We have quickly moved from sector clients are leading the way, with
auto-complete chatbots to reasoning Life Sciences and Financial Services
machines capable of spinning out following suit. Despite this, the business
credible, human like ‘Chains of Thought’ value remains limited, with just
(CoT) to find solutions to complex 15% having GenAI workloads in
problems. Today, multi-modal large production and only 8% able to fully
language models (LLMs) can enable measure and allocate AI costs.
seamless processing of text, audio,
This is not surprising – it takes time for
image, and video. Emerging trends like
innovation to be packaged and made
Agentic AI are enabling autonomous
ready for enterprise adoption. Enterprises
entities capable of taking actions. The
need clarity on ROI and guarantees
evolution of new hardware platforms
around issues like hallucination, data
and new AI accelerators has ensured
privacy and algorithmic bias as they craft
the computational power to support
their digital transformation roadmaps.
increasingly sophisticated models,
Over the next few years, we expect an
having even a trillion parameters and
explosion of enterprise adoption as these
groundbreaking efficiency.
issues are addressed and AI and GenAI
Along the way, the cost of intelligence models make their way into the
has fallen, driven by the open-source enterprise mainstream.
movement and the trend to use purpose
Just as during the earlier era of mobile
specific small language models (SLMs).
disruption, fintech and healthtech
This is making AI accessible to smaller
enterprise adoption will lead to the birth
businesses and very soon it may become
4 The AIdea of India: 2025
of AI-first companies with new business potential as the use case and data capital
models and revamped economics. These of the world. The focus will need to be on
firms will compete with digital interfaces enhancing data accessibility and compute
powered by chat, voice and regional infrastructure, fostering AI research
language models. Algorithms and new and innovation through initiatives like
datasets will help drive population-scale localized LLMs, and addressing challenges
operations. AI-driven apps will transform in responsible governance, intellectual
knowledge work. property rights, and data protection.
On the other hand, we need to address
The coming wave of change has
the coming potential job dislocation in
significant implications for India.
the workforce by implementing
In industries like financial services,
aggressive skilling programs and
healthcare and retail, we expect AI
apprentice schemes.
to reshape basic processes including
customer acquisition, operations and This report is an in-depth exploration
service. Industries including IT/ITeS and of GenAI’s current state in Indian
BPO will undergo more dramatic changes. enterprises, key trends shaping its
Next-generation industries like biotech, future, and implications for Indian
advanced manufacturing and renewables enterprises and policymakers.
will have the potential to leapfrog to
I hope you find this report valuable
AI-first business models.
- happy reading!
Our analysis reveals that, at a macro
level, the AI platform shift will impact
38 million employees, potentially
driving a 2.61% boost in productivity
by 2030 in the organized sector.
Enterprises will need to reorient Rajiv Memani
themselves rapidly to deal with this
Chairman and CEO,
coming impending tides of change.
EY India
There will also be significant pressure
on India’s policy agenda. On one hand,
there is the imperative to realize India’s
The AIdea of India: 2025 5
6 The AIdea of India: 2025
Executive
summary
Th e entire earth will be be paired with practical applications that solve
converted into a huge real-world problems, empower users, and bridge gaps in
brain, as it were, capable digital access and infrastructure.
of response in every one of its
parts.” This was Nikola Tesla, in
1904, predicting the impact of Innovation in GenAI continues at
the radio on the world.
a scorching pace
Every generation believes
it stands on the brink of
Innovation in GenAI surged in 2024, marking a
transformation, fueled by the
transformative year for the technology.
technologies of its time. Today,
as we contemplate the AI era, There was rapid progress in Multimodal AI, integrating
it feels like one of those pivotal text, images, audio and video into unified models that
moments. On one hand, there significantly enhance real-world usability. This was
is exponential innovation — particularly evident in the incorporation of these models
AI’s promise is vast, with the into AI-powered phones and emerging form factors like
potential to revolutionize smart glasses, enabling seamless and intuitive interactions
industries, redefine work, across diverse applications.
and unlock unprecedented
The open-source movement gathered steam. Leading
creativity and productivity.
open-source large language models (LLMs) such as
Breakthroughs in GenAI have
Meta’s Llama 3 and Mistral Large set new benchmarks for
been astounding, and the
performance while addressing critical concerns about data
possibilities appear limitless.
privacy and security. Simultaneously, there was a growing
Yet, there is the critical challenge realization that smaller, domain-specific models could
of making this transformation often outperform their larger counterparts in
relevant and accessible to targeted tasks.
consumers and enterprises. For
Year 2024 also saw breakthroughs in reasoning. Models
AI to truly deliver on its promise,
such as OpenAI’s GPT-4o31, and Google’s AlphaProof2
cutting-edge innovation needs to
1. https://openai.com/index/deliberative-alignment/
2. https://www.ebi.ac.uk/training/online/courses/alphafold/inputs-and-outputs/a-high-level-overview
The AIdea of India: 2025 7
GenAI in India: The current state of play
EY India’s C-suite GenAI survey
We conducted an in-depth GenAI survey covering more than 125 C-suite executives across India. They
represent diverse sectors, including Financial Services, Retail, Life sciences, Media and Entertainment,
Technology, Automotive, Industrials and Energy.
GenAI journey GenAI strategy: Direction and alignment
Integration with existing software means enterprises’ More than half of the enterprises have a GenAI strategy
exposure to GenAI is high. However, only a few have the but only some have a fully integrated strategy with clear
technology in production. execution plans
Fully integrated strategy
12% 22% with clear execution plans
36%
11% Strategy aligned with
business goals, but
34% 15% 18% execution plans are lacking
8% Strategy exists, but not
30% 9% aligned with business
goals
Basic understanding, no
POCs completed Productionalization in progress formal strategy
POCs in progress AI adopted
No POCs done No clear impact 39% No clear strategy
Architecture: GenAI platform and Implementation: Buy versus build
integration approach approach
Architecture integration is limited and enterprises are Approximately one in four have defined approach but
looking at ways to increase application application is uneven
11% Fully integrated and 16% 10% Well-defined and
19% optimized platform c ao pn ps rois at ce hntly applied
Integrated architecture in
4% place; facing utilization Defined approach, but
challenges not consistently applied
Platform selected; Preliminary approach, not
integration just started fully defined or executed
Platforms identified, but 21% Aware of options, but no
no integrated architecture clear decision framework
43% 23% N ino te p gl ra at tfo ior nm a s pe pl re oc ate cd h / 21% 32% N vso . c bo un ils di d ye er tation of buy
defined
Data: Platform readiness for GenAI Talent: Resource availability for GenAI
adoption adoption
Enterprises in India are at different stages of data AI expertise is a key need for most enterprises as they
readiness, with only a few at a mature level undergo GenAI transformation
3% 16% Fully ready and mature
3% 16% Extensive expertise and
Mostly ready, minor gaps resources for effective
deployment
Partially ready, requires Talent exists but
enhancements insufficient to support all
initiatives
Needs significant
23% improvements Have some skills but need
significant investment
22%
Not ready Aware of skills but lack
them and have no
19% 39% acquisition strategy
Have not thought about
35%
24% specific GenAI skill
requirement
8 The AIdea of India: 2025
GenAI in India: The current state of play achieved remarkable progress in solving GenAI in India: Shaping tomorrow
complex problems across disciplines like science,
mathematics and programming, consistently
India will chart a unique path as this technology
surpassing previous benchmarks. These advanced
evolves. We see five key trends that will
capabilities started to get packaged into agentic AI
significantly influence India’s AI evolution.
systems which aim to independently plan, reason,
and execute tasks by dynamically leveraging tools
and resources. Though still in its infancy, this
agent-driven paradigm promises to fundamentally 01 Chat, voice, regional languages
reshape our understanding of work and the way we augment digital interfaces
design software systems.
Hardware innovations continued to underpin these
02 Agents enable the transformation
advancements in GenAI. NVIDIA maintained its
of knowledge work
leadership with the Blackwell platform, enabling
trillion-parameter models while competitors drove
significant breakthroughs in AI accelerators.
03 LLMs are not all you need:
Toward compound AI systems
Moving from demos and labs to
enterprise grade capabilities
04
The falling cost of AI
Yet, despite these breakthroughs there is also
increasing doubt about the pace and magnitude
of the impact of GenAI. Goldman Sachs, for 05 The evolution of an
instance, has highlighted the imbalance between Indic AI ecosystem
the massive investments being funneled into AI and
the uncertain returns. In a June 2024 report titled
“Gen AI: Too Much Spend, Too Little Benefit?”,
the firm projected that tech giants and other
‘good enough’ for scaling across many use cases.
companies are set to invest nearly US$1 trillion
Our survey of Indian enterprises suggests that
in AI-related expenditures over the coming years,
customer service, operations and sales & marketing
spanning data centers, specialized hardware, and
functions are already leading the way in adoption.
infrastructure upgrades. Despite these staggering
Over the next few years, as these teething issues
sums, the tangible benefits remain elusive.
are addressed, AI and GenAI models make their
Our survey of Indian enterprises suggests that 36% way into the enterprise mainstream across all
of enterprises have budgeted and started investing functions and departments.
in GenAI while another 24% are experimenting with
it. Technology sector clients have been leading
the way with Life Sciences and Financial Services AI augmented interfaces will transform
following suit. At the same time business value
consumer apps
delivered is relatively low with only 15% of Indian
enterprises report having GenAI workloads in AI-powered chat, voice and regional language
production, and just 8% being able to fully measure tools are already making an impact and this trend
and allocate AI costs. will accelerate as digital models diffuse across
the Indian consumer, enterprise and government
This is not surprising. Packaging innovation into
landscape. GenAI native interfaces will also serve
products and services that enterprises can use
as front doors to onboard less digitally savvy users
is a time-consuming process. Enterprises need
into the digital economy. Solutions like NPCI’s Hello!
clarity on ROI and guarantees around issues like
UPI and IRCTC’s AskDisha chatbot demonstrate
hallucination, data privacy and algorithmic bias as
this shift, enhancing inclusivity for underserved
they craft their digital transformation roadmaps.
populations in semi-urban and rural areas.
Rapid advancements to date have already made AI
The AIdea of India: 2025 9
Agents will transform knowledge work A rich Indic AI ecosystem will evolve to
cater to unique Indian needs
The rapid integration of AI Agents into sectors
like information technology, finance, customer There has already been a mushrooming of Indic
service and healthcare will reshape traditional LLMs that leverage open-source models fine-tuned
ways of working, presenting both opportunities with Indian language datasets. A key initiative in
and challenges for Indian professionals. Our this space is Bhashini, a government-led AI project
analysis (more on this in ‘Transforming work with aimed at creating an open-source Indic language
GenAI’) indicates potentially large productivity dataset to expand internet and digital service
improvements that will begin to manifest accessibility in Indian languages. Going forward,
themselves and companies will begin to gear up to AI will increasingly become part of the India stack
help employees manage the coming transition to and available as digital public infrastructure to build
new ways of working. next generation platforms.
A burgeoning GenAI start-up ecosystem and local
Enterprises will start to move to an
AI infrastructure will help drive adoption in
AI-embedded tech stack Indian enterprises.
Enterprises will learn to treat LLMs as but one part
of an evolving AI enabled tech stack. AI adoption
Pivoting to AI-first digital
will accelerate as enterprises integrate LLM
capabilities with classical AI techniques, new transformation
modes of automation and the emerging modern
data stack.
Similar to the transformative impact of the digital
revolution, the accelerating shift toward AI-
AI costs will continue to fall
driven platforms is poised to reshape every factor
influencing a company’s EBITDA. Across Indian
The cost of using AI models has already
enterprises, AI-first approaches are steadily taking
plummeted, making them increasingly accessible to
root, embedding themselves throughout the value
enterprises. OpenAI’s GPT API costs, for example,
chain to enhance operational efficiency and unlock
have dropped nearly 80% in two years, while
new avenues of value creation.
open-source releases like Meta’s Llama are
unlocking new capabilities. This cost is expected At a foundational level, AI automates workflows,
to fall to around INR120 per hour* or lower as detects patterns, and delivers real-time predictions,
India specific LLMs offerings become viable. creating a closed-loop system for continuous
(*Assuming that the cost is US$4 per million tokens learning. This will help companies optimize value
and the application uses 100 tokens per second chains, enhance revenue streams through improved
continuously, the enterprise would spend channels and pricing, and transform delivery with
US$1.44 per hour.) new interfaces.
10 The AIdea of India: 2025
The agenda for enterprises
Reimagine the Rethink the tech Move to AI-ready Getting your Confronting the
business and stack data people ready for AI changing frontier of
operating model risk
A new AI-powered tech stack is emerging, and workforce adaptability. Change management
combining foundational models with specialized bridges the gap between innovation and
tools. Enterprises are increasingly adopting execution, enabling organizations to thrive
SLMs for domain-specific tasks due to their cost in an AI-driven world.
efficiency, precision, and ability to run on edge
AI-first strategies introduce risks related to bias,
devices. Enterprise software providers such as
cybersecurity and explainability. Organizations
SAP, Salesforce and Oracle are embedding AI
are mitigating these by adopting automated
into their platforms, accelerating adoption with
compliance systems, real-time anomaly detection,
ready-to-deploy AI tools. Meanwhile, traditional
and explainable AI models. Regulatory frameworks
Robotic Process Automation (RPA) is evolving
like India’s Digital Personal Data Protection
into intelligent automation by integrating GenAI,
Act (DPDP Act 2023) further emphasize the
enabling systems to adapt dynamically to changes
importance of responsible AI practices, especially in
without manual intervention.
sensitive sectors like healthcare and finance.
A solid data foundation is pivotal to enterprise
AI success. Enterprises are implementing robust
governance frameworks, addressing challenges Transforming work with GenAI
related to data quality, diversity and sensitivity.
Modern data stacks, including cloud platforms
and scalable data lakes, enable real-time ingestion In India GenAI has the potential to drive
and processing, essential for AI implementation. productivity gains, impacting millions of workers
Companies that nurture proprietary datasets and redefining the future of work.
are gaining competitive advantages by achieving
EY conducted a study of over 10,000 tasks in
superior model performance.
critical industries that contribute to the Indian
Preparing people for AI is crucial to unlocking its economy. To assess GenAI’s impact on productivity,
full potential, ensuring both technological adoption tasks were analyzed based on exposure (potential
The AIdea of India: 2025 11
impact of GenAI), complementarity (human due to its higher labor share in gross output, while
oversight needed) and intensity (frequency of tasks manufacturing and construction will see smaller
analyzed in granular time units). A ‘Productivity impacts. However, even in these sectors, AI can
Uplift’ Indicator was created, to quantify drive efficiencies through better capital deployment
this potential impact in terms of Automation and resource utilization, ultimately lowering labour
(elimination of the task), Augmentation expenses and improving overall cost efficiency.
(doing the same task better using GenAI) and
Realizing this potential requires reimagining
Amplification (enhancing the nature of the task
processes, redefining workflows and reskilling
and making it richer).
the workforce. The successful adoption of GenAI
This allowed us to analyze productivity gains at requires clear strategies, piloting use cases, and
job role, functional and organizational levels. scaling solutions, alongside reimagining processes,
Our analysis reveals that 24% of tasks can be redefining KPIs, and targeted reskilling. Large-scale
fully automated, while time spent on another upskilling initiatives, supported by public-private
42% can be significantly reduced, freeing up partnerships and AI-focused training programs, are
8-10 hours per week for corporate workers. This crucial to bridging the skill gap. With investments
translates to a productivity boost of 2.61% by in skills, data and infrastructure, GenAI can drive
2030 in the organized sector affecting 38 million economic productivity and ensure a future-ready
Indian employees and an additional 2.82% in the workforce for India.
unorganized sector. The largest productivity gains
from GenAI are expected in the services sector
Productivity gains across key sectors
This graph illustrates the labor cost as a percentage of gross output on the x-axis and the percentage
productivity improvement through AI on the y-axis. The size of the bubble represents the potential labor
efficiencies created by AI for the industry.
EY India jobs study: Transforming work with GenAI
12 The AIdea of India: 2025
laitnetop
ytivitcudorP
50%
45%
IT | 19%
40%
Retail | 5%
Banking | 9%
35%
Pharma | 2%
Insurance | 8%
30%
Telecom | 5%
Automobile | 2%
25% Metals and Mining | 4%
Healthcare | 13%
20%
Media and Entertainment | 5%
15%
10%
0% 10% 20% 30% 40% 50% 60%
Labor cost by gross output
secnahne
IA
secnahne
IA
yltnacfiingis
ytivitcudorp
yllanigram
ytivitcudorp
Labor plays Labor plays a
a smaller role larger role
A policy agenda for India To ensure Responsible AI, the government has
prioritized transparency, fairness and safety
through consultations and oversight. Plans include
India’s AI policy landscape reflects a balanced
forming a National Committee on Responsible
approach to fostering innovation while ensuring
and Trustworthy AI, addressing bias, privacy and
responsible deployment. The IndiaAI Mission stands
accountability. The DPDP Act requires
at the forefront, with a financial commitment
businesses to adopt privacy-preserving AI tools,
of over INR10,000 crore to develop India’s AI
anonymization protocols, and compliant
ecosystem across seven pillars, including access
workflows, aligning AI development with evolving
to high-quality datasets, expanded compute
data protection standards.
infrastructure, and responsible AI governance. Key
initiatives include establishing the India Dataset India’s strategic AI policies, anchored in inclusivity,
Platform for organized, sector-specific data access, data sovereignty and accountability, aim to position
deploying 10,000 GPUs to scale AI research, the country as a global AI leader while mitigating
and promoting AI solutions in critical sectors like risks, promoting innovation, and ensuring ethical AI
healthcare and agriculture through R&D incentives adoption across public and private sectors.
and innovation challenges.
The AIdea of India: 2025 13
C h a p t e r 1
Generative AI:
Shaping tomorrow
14 The AIdea of India: 2025
Generative AI:
Shaping tomorrow
The AIdea of India: 2025 15
Chapter 1
Generative AI:
Shaping tomorrow
The promise still holds
Multimodal AI advancements,
agent-driven systems, and hardware Over the past few years, innovation in GenAI has
progressed at an extraordinary pace, reaffirming
advancements like NVIDIA’s Blackwell
its transformative potential across a number of
are reshaping global applications,
domains. The possibilities are vast and hold the
moving GenAI from labs to promise of profound changes on the horizon. In
the domain of healthcare, AI could accelerate
enterprise-grade solutions
breakthroughs in biology, enabling the rapid
development of cures for diseases like cancer and
The rise of open-source LLMs and the Alzheimer’s while extending human lifespans. In
neuroscience, it offers hope for understanding
success of smaller, domain-specific
and treating mental illnesses such as depression
models are addressing privacy,
and schizophrenia, while also enhancing human
efficiency, and targeted use-case needs cognition and emotional well-being. Economically,
AI promises to potentially uplift billions out of
poverty by optimizing resource distribution and
The rapidly falling costs of AI
revolutionizing industries like agriculture and
solutions, like 80% drop in the price clean energy. In governance, AI might strengthen
of OpenAI’s APIs over two years, governance by enhancing public services and
reducing corruption. Finally, in education and work,
are making advanced capabilities
AI can democratize knowledge access and redefine
increasingly accessible to enterprises meaningful human contributions, ensuring an
inclusive future where technology enriches, rather
than replaces, human purpose.
India is leveraging GenAI for
regional language accessibility,
But GenAI is not without its
digital inclusivity, and transformative
consumer apps skeptics
As Indian enterprises adopt Yet, as with all transformative technologies, GenAI
has its share of doubters. While its promise is vast,
AI-embedded tech stacks;
concerns about the pace and magnitude of its
start-ups and SaaS companies will impact linger. Goldman Sachs, for instance, has
lead the charge, driving innovation highlighted the imbalance between the massive
investments being funneled into AI and the
and integration across industries
uncertain returns. In a June 2024 report titled
in the coming years “Gen AI: Too Much Spend, Too Little Benefit?”,
the firm projected that tech giants and other
companies are set to invest nearly US$1 trillion
16 The AIdea of India: 2025
in AI-related expenditures over the coming years, Emerging
spanning data centers, specialized hardware, and
trends
infrastructure upgrades. Despite these staggering
sums, the tangible benefits remain elusive. Adding such
to the tempered outlook, MIT economist and Nobel
as Agentic AI and synthetic data
laureate Daron Acemoglu provides a cautious
generation expanded AI’s capabilities
evaluation of AI’s economic impact. His research
suggests that contrary to ambitious forecasts of by enabling autonomous, multi-step
transformative productivity gains, AI may yield
tasks and addressing data scarcity
GDP growth of a more modest 0.93% to 1.16% over
the next decade, with the possibility of reaching
1.56% under optimal conditions. These critiques
underscore the need to balance enthusiasm with
realism, tempering grand visions with practical reasoning and accuracy. Landmark achievements,
assessments of AI’s current capabilities and including Nobel-recognized contributions to
its path forward. protein structure prediction (AlphaFold2) and
industry-specific LLMs for domains like healthcare
and finance, highlighted the technology’s potential.
The year of exponential Global investment in GenAI surged, driven by
tech giants like Google, OpenAI and Microsoft.
breakthroughs
Record-breaking funding rounds and open-source
contributions from Meta and others intensified
Year 2024 proved to be one of phenomenal competition, while advancements in hardware,
advancement in the field of GenAI culminating such as Nvidia’s Blackwell platform, provided the
with the announcement of OpenAI’s o3 class of computational power to support increasingly
models, which promise to offer a quantum leap in sophisticated models. Emerging trends, such
foundational LLM capabilities and reasoning. Earlier as Agentic AI and synthetic data generation,
in the year, the transition to multi-modality allowed expanded AI’s capabilities, enabling autonomous,
seamless handling of diverse data formats, while multi-step tasks and addressing data scarcity.
advancements like expanded context windows and SLMs offered cost-effective solutions for smaller
retrieval-augmented generation (RAG) improved enterprises. Despite concerns like overfitting
and model collapse, GenAI’s strides in reasoning,
multimodality and adaptability cemented its
position as a key driver of innovation and
productivity across sectors.
Every once in a while,
We are still early in the game
a new technology, an
Despite challenges, even today’s innovations in
GenAI offer immense enterprise value. The focus
old problem, and a
is not just on GenAI but also on integrating AI,
data, and automation to build tailored solutions.
big idea turn into an Rapid advancements have made AI ‘good enough’
for scaling across many use cases. Techniques like
innovation RAG and CoT address issues like hallucination,
while guardrails secure data privacy and safety. The
cost of AI has also dropped significantly, promising
Dean Kamen returns on existing investments.
Engineer and entrepreneur
The AIdea of India: 2025 17
Human-like adaptability of AI Agents
AI Agents operate, within an enterprise context, to achieve specific goals. They can be instructed in natural
language and act autonomously on behalf of users. Users specify objectives in terms of ‘what’ or task
goals, leaving the AI agent to figure out ‘how’ this is to be accomplished using available tools. An agentic
architecture represents a fundamentally new approach to building computer systems. If successful, it
signifies a leap forward as the focus is on outcomes rather than processes.
A key innovation is that much of the control logic in an AI Agent is driven by LLMs. This approach introduces
dynamic, non-deterministic behavior – similar to human decision-making – with its associated benefits and
challenges. Decision making, with AI agents, is no longer limited to rigid programming. Agents can adapt to
contexts and improve outcomes dynamically.
Applications of AI agents across contexts
Personal assistants Reasoning Agents
Advanced personal assistants, such as Apple’s OpenAI’s O1 (and now O3) models exemplify
AI-driven assistant, showcase how AI can AI’s growing reasoning capabilities. Using
handle complex, context-dependent queries. chain-of-thought methodology, O1 formulates
For instance, when asked about a family step-by-step plans to solve problems, improving
member’s flight arrival and dinner plans, the both accuracy and transparency. Users, too,
assistant seamlessly integrates information can trace the model’s logic, identify errors and
from emails, messages, maps, calendars and make corrections. Notably, O1 has achieved over
third-party apps. These systems build a semantic 80% accuracy in solving complex mathematical
model of the user, which enables navigation problems, marking a substantial advancement
across applications to respond accurately. As over previous models. Reasoning agents
AI becomes more embedded in devices and highlight the potential for AI to bring clarity and
productivity tools, personal assistants are poised reliability to intricate problem-solving tasks.
to adeptly manage digital lives, streamlining user
interactions and enhancing productivity.
Functional Agents Agents in the real world
Salesforce’s Agentforce platform brings Anthropic’s research on AI Agents emphasizes
agentic architectures into the enterprise realm. their ability to interact dynamically with the
These autonomous AI Agents personalize world to accomplish tasks and learn from
customer interactions, streamline support and those interactions. This vision extends beyond
orchestrate actions across multiple channels. the digital realm to where agents can control
This innovation shifts traditional business physical tools, robots or laboratory equipment,
models toward outcome-based pricing – where or even design equipment for specific tasks.
costs are tied to completed tasks rather than However, such dynamic systems bring
per-user licenses. Such a model aligns software challenges in ensuring safety, reliability, and
costs more closely with business outcomes, predictability – an essential focus
offering enterprises a flexible and value-driven for developers.
approach.
18 The AIdea of India: 2025
However, adoption remains low. Our survey of 02Open source LLMs
Indian enterprises suggests that 36% of enterprises
have budgeted and started investing in GenAI
The emergence of open source LLMs (OS LLMs)
while another 24% are experimenting with it.
from organizations like Meta and Mistral intensified
Technology sector clients have been leading the
competition, prompting closed-source providers
way with Life Sciences and Financial Services
such as Anthropic and OpenAI to enhance
following suit. At the same time business value
their offerings to justify premium pricing. For
delivered is relatively low with only 15% of Indian
instance, DeepSeek v3 has been able to surpass
enterprises report having GenAI workloads in
OpenAI’s GPT-4o in performance across several
production, and just 8% being able to fully measure
industry benchmarks.
and allocate AI costs. The survey highlights the
need for packaged solutions to bridge the gap and The shift also benefited hardware providers like
accelerate adoption. As innovations mature, they NVIDIA. Demand for GPUs expanded to include
will drive a new wave of digital transformation, organizations deploying OS LLMs privately, leading
unlocking extraordinary business benefits. At them to invest in NVIDIA hardware to run models
the same time, global trends positively influence like Meta’s Llama 3.1 405B internally,
GenAI developments in India through collaboration, rather than relying on API-based access to
investment and research. closed-source models. This diversification of"
6,ey,catalyzing-economic-growth-through-ai-investment.pdf,"Catalyzing economic
growth through capital
investment in GenAI
Catalyzing economic growth through capital investment in GenAI
1
Economic impact of AI: This EY-Parthenon macroeconomic article series
provides insights on the economic potential of GenAI and actionable
considerations. Discover more
In this installment, we delve into the realm of capital investment in generative AI
(GenAI). As GenAI has emerged as one of the key components of economic impact,
business leaders today find themselves at a crossroads. The October 2023 EY CEO
survey indicates a striking dilemma: while a significant 62% of business leaders
acknowledge the urgency of acting on GenAI to prevent competitors from gaining
Gregory Daco a strategic edge, an almost equal percentage (61%) express reservations due to the
EY-Parthenon Chief Economist uncertainties surrounding the formulation and execution of an AI strategy.
New York, NY
The survey further reveals an “adoption paradox.” It highlights that two-thirds of
organizations that have successfully launched at least one AI initiative anticipate
that AI will revolutionize their entire business and operational models within a mere
two-year span. In contrast, organizations with more extensive AI experience, defined
as those having completed five or more AI-related initiatives, project a more cautious
timeline of three to five years for AI to wield similar transformative effects.
This disparity in expectations underscores the presence of ‘“unknown unknowns” in
AI adoption, particularly in determining the nature and extent of capital investment
required for laying a robust AI foundation.
Catalyzing economic growth through capital investment in GenAI
2
In assessing the potential economic impact of GenAI from a capital investment
perspective, we examined the near-term boost to growth from increased investment
in research and development, infrastructure, software creation and company
adoption. Drawing parallels with the IT revolution in the period of 1980-2000, our
two main findings are:
• Significant boost to demand: Assuming trend growth around 8.5% in
investment categories where GenAI will be most significantly captured, we
estimate that capital investment in GenAI will contribute about 0.1 percentage
points (ppt) to US GDP growth annually over the next five years. Our baseline,
however, is that business investment will likely be 25% faster, leading to an
incremental boost to short-term growth of 0.1 percentage points of GDP per
year, worth over $150bn after five years. A more optimistic scenario could see
50% faster business investment growth, leading to an incremental boost to short-
term growth of 0.2ppt of GDP per year, worth a cumulative $325bn by 2028.
• Long-term boost from supply: In our baseline where business investment
is 25% faster than the current trend growth, the potential growth rate of the
economy would rise by 0.1ppt per year in the 2028-2033 period, lifting real GDP
by nearly 1% over the baseline by 2033, or the equivalent of a $250bn boost
over a decade. Assuming capital investment in AI technology grows 50% faster
than the 2017-2022 trend pace over the next five years, the annual capital
contribution to long-term GDP growth in the 2028-2033 period would rise by
0.2ppt. This stronger tech-driven trajectory would lift real GDP by more than 2%
over the baseline by 2033, or the equivalent of a $500bn boost over a decade.
Percent Billions (USD, 2017)
US Real GDP boost from 1.4 350
GenAI investment
1.2 300
1.0 250
0.8 200
0.6 150
0.4 100
0.2 50
0.0 0
Baseline — Optimistic — Baseline — Optimistic —
Boost Boost Cumulative Cumulative
per year per year� boost by 2028 boost by 2028
Source: Bureau of Economic Analysis; EY-Parthenon
Additional chart notes: This chart shows the GDP boost from GenAI investment on an average annual basis between
2023 and 2028 as well as the cumulative boost over the same time frame; both include baseline and optimistic
scenarios. Baseline assumes business investment in categories where GenAI will be most significantly captured
is 25% faster than trend growth; optimistic assumes business investment in categories where GenAI will be most
significantly captured is 50% faster than trend growth. Data from this chart is discussed in the article
Catalyzing economic growth through capital investment in GenAI
3
Looking across major economies, the contributions from greater GenAI investment
could also be significant. While the US market is likely to remain the leader in GenAI
technologies investment, China and Europe will be following closely behind. We
estimate that the lift to global GDP could total between $300bn and $600bn over the
next five years. The boost to global potential GDP could amount to between $500bn
and $1tn over the next decade.
In this installment of our “Economic impact of AI” series, we will focus on the
business investment and capital accumulation dimension and leave the productivity
dimension of accelerating processes, optimizing operations and unlocking new
capabilities to the next article in our series.
We will discuss investment in GenAI and associated capital accumulation by taking a
deeper look at the following:
• Back to basics: demand and supply
• The demand perspective: near-term contribution of capital investment in GenAI
to GDP
• The supply perspective: a strong capital foundation to promote more sustainable
growth
1. Back to basics: demand and supply
Capital investment in GenAI can spur stronger capital
accumulation and productivity, boosting the global
economy’s growth rate.
In an era where technological innovation is the cornerstone of economic prowess,
GenAI has the potential to reshape the contours of businesses and the broader
economy. This installment delves into the burgeoning role of increased capital
investment in AI, underscoring its potential to be a significant driver of near-term
economic growth.
It’s important to consider that GenAI investment is not just a technological upgrade
but a strategic economic lever to redefine business models, markets, industries and
the very fabric of the global economy. By dissecting the dynamics of AI investment,
we aim to unveil how it can propel economic activity, observed through the dual
prisms of demand and supply.
From the demand perspective, investment in GenAI is seen as a new frontier for
capital allocation, influencing various sectors from health care to finance, and
energizing them with innovative capabilities. The investment fuels the industries it
permeates, leading to an uptick in overall economic activity and consumer demand.
On the supply side, investment in AI will be a catalyst for stronger capital
accumulation as well as productivity growth, lifting the global economy’s potential
growth rate.
Catalyzing economic growth through capital investment in GenAI
4
As we noted in the first installment of our series, prior general-purpose
technologies have had a significant impact on economic activity, but that impact
has generally lagged.
Some of the main reasons for that lag are implementation and diffusion delays,
learning and adjustment periods due to the time it takes to effectively use new
technologies and delays in the development of complementary innovations or
infrastructure for the technology to be fully effective.
To establish GenAI as a cornerstone of modern industry, substantial capital
investment may be required.
• Research and development (R&D): Building and refining AI models necessitate
a significant influx of resources. The data-intensive nature of GenAI calls
for investment in gathering, storing and processing data, as well as in the
computational power needed to train sophisticated models.
• Infrastructure providers: Investment in the physical and digital infrastructure
necessary to support AI technologies forms another cornerstone of this economic
transformation. This encompasses everything from data centers to advanced
networking capabilities and even cybersecurity. The adequacy of this infrastructure
directly impacts the efficiency and effectiveness of AI solutions.
• Software creation: The investment in AI applications across various business
sectors is perhaps the most visible aspect of AI’s economic influence. From
finance to manufacturing, AI applications are revolutionizing traditional business
processes, enhancing customer experiences and opening new revenue streams.
These investments are not merely about automating routine tasks but are
also about leveraging AI to uncover insights, predict trends and create more
personalized and efficient services.
• Corporate adoption: It’s essential for businesses to invest in integrating GenAI into
their operations. This includes not only the technology itself but also the training
of personnel and restructuring of processes to fully capitalize on AI’s potential. The
widespread adoption of AI by businesses could have a notable economic impact
as it leads to increased operational efficiencies, reduced costs and enhanced
competitive capabilities. Moreover, as AI becomes more ingrained in business
operations, it will likely drive the demand for skilled workers and AI-related
services, and, consequently, it will probably stimulate job creation and economic
activity in related sectors.
Catalyzing economic growth through capital investment in GenAI
5
2. The demand perspective: near-term contribution of
capital investment in GenAI to GDP
Rising capital investment in GenAI is positioned to
increase quickly and prompt GDP growth.
In assessing the potential economic impact of GenAI from a demand perspective, it is
instructive to draw parallels with the investment dynamics of previous technological
revolutions. In the early 1990s, business investment in information processing
equipment and software totaled about 3% of GDP, or $155 billion.
As businesses invested in the physical and human infrastructure necessary to
support, implement and reshape business processes in the computer age, that
share of investment rapidly grew to 4.5% of GDP, or $400 billion by the early 2000s.
Percentage points
US business investment in
9.0
Historical Forecast
GenAI as a share of GDP
(percent)
8.0
7.0
6.0
5.0
4.0
3.0
2.0
8 9 0 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8
200 200 201 201 201 201 201 201 201 201 201 201 202 202 202 202 202 202 202 202 202
Optimistic — real Optimistic — nominal
Baseline — real Baseline — nominal
Trend — real Trend — nominal
Source: Bureau of Economic Analysis, EY-Parthenon; author’s calculation
Additional chart notes: Trend refers to trend growth around 8.5% per annum in investment categories where GenAI
will be most significantly captured. Optimistic assumes business investment is 50% faster than trend growth.
Catalyzing economic growth through capital investment in GenAI
6
We are likely on the cusp of a similar trend with GenAI, where burgeoning investment
in AI technology is poised to increase rapidly and boost GDP growth. Specifically, we
isolated the following investment categories likely to capture AI technology:
• Software
• R&D in semiconductor and other electric components manufacturing, other
computer and electronic product manufacturing, scientific services and
software publishers
• Computers and peripheral equipment
• Communication equipment
Scenario analysis
The categories where new investment in GenAI will be most significantly captured
totaled about $750 billion in 2017, or about 3.8% of real US GDP. By 2022,
investment had grown to just over $1.1 trillion, or about 5.2% of GDP.
• Trend growth: Assuming trend growth in line with economic momentum from
2017 to 2022, investment would be expected to grow around 8.8% per year from
2023 to 2028 and represent 7.6% of real GDP by 2028, or $1.9 trillion. While this
would mean that investment in AI technology would contribute about 0.4ppt to
GDP growth per year, it would not represent an increase in the growth contribution
relative to recent past.
• Baseline expectations: If, instead, we assume that nominal capital investment in
AI technology grows 25% faster than the 2017-2022 trend pace over the next five
years, then investment represents 8.1% of real GDP by 2028, or $2 trillion. This
would translate into an incremental contribution of GenAI technology investment
to GDP growth of 0.1ppt per year (for a total contribution of 0.5ppt) and, by 2028,
a boost to real GDP worth $150bn, or 0.6%.
• Reason for optimism: Still, there may be reason to be even more confident
about the outlook. Assuming capital investment in AI technology grows 50%
faster than the 2017-2022 trend pace over the next five years — which is akin
to the acceleration in business investment in information processing equipment
and software in the late 1990s — then investment would grow about 11% annually
from 2023 to 2028 and represent 8.7% of GDP by 2028, or $2.1 trillion. This
would constitute an incremental short-term contribution to GDP growth of 0.2ppt
per year (for a total contribution of 0.6ppt) and, by 2028, a boost to real GDP
worth $325bn, or 1.3%.
The potential uplift to global GDP from increased GenAI investment could also
be substantial. With the US expected to continue leading in GenAI technology
investment, closely followed by Europe, Japan and China, global GDP could see
an augmentation of between $300 billion (in our baseline scenario) and $600
billion (in the optimistic case) over the next five years. This significant boost would
reflect the accelerated adoption and integration of GenAI technologies across major
economies, underlining the transformative impact of AI.
Catalyzing economic growth through capital investment in GenAI
7
3. The supply perspective: a strong capital foundation
to promote more sustainable growth
Past tech disruptions and our scenario analysis
provide a case for optimism about GenAI’s ability to
drive long-term growth.
At the heart of AI’s transformative potential on the supply side of the economy is
its capacity to drive greater capital accumulation and stronger productivity growth.
Capital investment in AI is not just an expenditure; it’s a strategic allocation of
resources that acts as the foundation for developing and deploying AI solutions and
seeds future productivity enhancements.
While we will delineate the long-term growth implication from GenAI-driven
productivity growth in a subsequent article, we believe it is essential to dissect the
impact of greater capital accumulation first.
Capital accumulation in AI involves investing in various components such as AI
models (through building and refining), physical and digital infrastructure, software,
AI applications, and AI integration and adoption. Just like physical capital, these
investments in AI technologies act as the foundation that allows for stronger
economic potential.
Capturing longer-term impact from greater capital
investment in AI technology
The surge in business investment in information processing equipment and software
through the 1990s did not just lead to a direct boost to GDP growth, but it also led to
increased capital accumulation that then supported stronger long-term GDP growth.
To put things in perspective, the US economy’s potential GDP growth rate was
estimated to be around 2.5% from 1990 to 1995, but subsequently it accelerated
to 3.8% in the 1995-2000 period. Taking all drivers of growth into consideration,
the capital contribution to potential GDP growth nearly doubled from 0.7ppt in the
early 1990s to 1.3ppt in the 1995-2000 period. At the same time, the contribution
of productivity also rose from 1.1ppt to 1.7ppt from 1995 to 2000 and remained
elevated around 1.5ppt from 2000 to 2005.
Catalyzing economic growth through capital investment in GenAI
8
This confirms our findings from our first installment, which indicated a five- to 10-
year delay between the development of new technologies and their more sustainable
impact on productivity and growth potential.
Percentage points
US Average annual 4.0
contribution to real
3.5
potential GDP growth
3.0
2.5
2.0
1.5
1.0
0.5
0.0
1990–1995 1995–2000 2000–2005
Labor and productivity Capital Potential GDP
Source: Bureau of Economic Analysis, EY-Parthenon; author’s calculation
Additional chart notes: Trend refers to trend growth around 8.5% per annum in investment categories where GenAI
will be most significantly captured. Baseline assumes business investment is 25% faster than trend growth
Optimistic assumes business investment is 50% faster than trend growth
Scenario analysis
Using the same three scenarios, which analyzed the potential short-term economic
impact of greater capital investment in AI technologies, we can infer the likely boost
to potential GDP growth in the five years from 2028 to 2033.
• Trend growth: Assuming business investment in AI technology continues to grow
in line with its moderate 2017-2022 trend, the annual capital contribution to
long-term GDP growth in the 2028-2033 period would likely be around 0.5ppt.
• Baseline expectations: If, instead, we assume that capital investment in AI
technology grows 25% faster than the 2017-2022 trend pace over the next five
years, the capital contribution to long-term GDP growth in the 2028-2033 period
would rise from 0.5ppt annually to 0.6ppt — thereby lifting the potential growth
rate of the economy by 0.1ppt. This may appear to be a small difference, but by
lifting the economy’s potential growth rate, this stronger tech-driven trajectory
would lift GDP by nearly 1% over the baseline by 2033, or the equivalent of a
$230bn boost over a decade ($360bn in nominal terms).
• Reason for optimism: As we noted earlier, there is reason to be more confident
still about the potential capital accumulation contribution to long-term growth.
Assuming capital investment in AI technology grows 50% faster than the 2017-
2022 trend pace over the next five years — which is akin to the acceleration in
business investment in information processing equipment and software in the
late 1990s — the capital contribution to long-term GDP growth in the 2028-2033
period would rise from 0.5ppt annually to 0.7ppt, thereby lifting the potential
growth rate of the economy by 0.2ppt. This stronger tech-driven trajectory would
lift real GDP by nearly 2% over the baseline by 2033, or the equivalent of a $475bn
boost over a decade.
Catalyzing economic growth through capital investment in GenAI
9
In the long run, the potential upside to global GDP from greater capital investment
could be quite significant. How significant? Factoring stronger investment in Europe,
Japan and China and slower investment across emerging markets, we estimate a
boost to potential GDP growth worth between 0.5% and 1% by 2033, representing
between $500bn and $1tn.
Percentage points
US average annual capital 0.8
contribution to real potential 0.7
GDP growth
0.6
0.5
0.4
0.3
0.2
0.1
0.0
2020–2025 2025–2030 2030–2033
Trend Baseline Optimistic
Source: Bureau of Economic Analysis, EY-Parthenon
Additional chart notes: Trend refers to trend growth around 8.5% per annum in investment categories where GenAI
will be most significantly captured. Optimistic assumes business investment is 50% faster than trend growth.
Catalyzing economic growth through capital investment in GenAI
10
Breakdown of AI capital investment across sectors
When thinking about the sector-specific benefits from the GenAI revolution, we
often omit the investments that may be required to shift how industries operate.
By fostering innovation, enhancing productivity and creating new markets and
opportunities, the capital investments described above may be instrumental in
driving potential GDP growth.
Retail sector: AI’s role in retail is multifaceted, ranging from personalized shopping
experiences to inventory management. Capital investments in AI enable retailers to
better understand consumer behavior, optimize supply chains and enhance customer
service, leading to increased sales and market expansion. This sectoral growth is a
key contributor to overall economic development because it could boost retail sector
productivity while also stimulating consumer spending, a major component of GDP.
Health care sector: Investment in AI within health care is revolutionizing patient
care and medical research. AI-driven tools are being used to enhance diagnostic
precision, streamline patient treatment plans and personalize health care services.
This not only improves health outcomes but also helps optimize resource utilization,
reducing costs and contributing to economic growth. Additionally, AI in health care
is spearheading innovations in drug discovery and disease prediction, opening new
markets and avenues for growth.
Automotive industry: The automotive sector’s investment in AI is pivotal in
advancing the development of autonomous vehicles. This not only transforms the
concept of transportation but also stimulates investment in adjacent industries
like logistics and urban planning. The ripple effects of such advancements could
contribute significantly to GDP growth by fostering new business models, enhancing
supply chain efficiencies and creating demand in related sectors such as sensor
manufacturing and AI-driven navigation systems.
Manufacturing industry: In manufacturing, AI investment focuses on automation,
predictive maintenance and supply chain enhancement. This not only increases
production efficiency but also improves product quality, reduction of waste and
operational costs. The resultant increase in competitiveness and productivity of
the manufacturing sector could significantly contribute to GDP growth, while also
fostering an ecosystem of innovation and technological advancement.
Financial services: AI investments in financial services are reshaping banking,
insurance and investment sectors through enhanced risk assessment, fraud detection
and personalized financial planning services. This could increase the efficiency and
resilience of financial systems, supporting economic stability and growth.
Energy sector: Investment in AI within the energy sector is pivotal in transforming
how we generate, distribute and consume energy. AI technologies are being
integrated to help optimize energy production, enhance grid management and
facilitate the shift to renewable sources. Additionally, AI applications in predictive
maintenance of infrastructure may further boost economic efficiency. The
innovations driven by AI in the energy sector are crucial in supporting the transition
to a low-carbon economy, promoting sustainable economic development.
Catalyzing economic growth through capital investment in GenAI
11
Five recommendations for business leaders
By focusing on the following areas, stakeholders can better navigate the
complexities of AI capital investments and harness their full potential to drive
meaningful business transformation.
Strategic alignment with business goals
• Insight: It’s essential for AI investments to be closely aligned with the overarching
business goals and objectives. This alignment helps ensure that AI initiatives directly
contribute to the company’s strategic priorities, whether it’s improving customer
experience, optimizing operational efficiency or driving innovation.
• Recommendation: Conduct a thorough analysis to understand how AI can address
specific business challenges or opportunities. Establish clear KPIs to measure the
impact of AI initiatives on business outcomes.
Leveraging data as a strategic asset
• Insight: High-quality, relevant data is the fuel that powers AI systems. The ability of
a business to collect, process and analyze data effectively is a critical determinant
of AI success.
• Recommendation: Prioritize the establishment of a robust data infrastructure and
governance model. This may help ensure data quality, accessibility and scalability
to support AI initiatives.
Acquiring the right talent and partnering
• Insight: Successful AI implementation may require a combination of the right talent,
including data scientists, AI engineers and domain experts.
• Recommendation: Invest in building internal AI capabilities and work with
organizations that can bring the necessary professional skills and knowledge.
Continuous training and development programs are crucial to keep the team up
to date with the latest AI advancements.
Catalyzing economic growth through capital investment in GenAI
12
Fostering a culture of innovation and adaptability
Key contact
• Insight: The fast-evolving nature of AI technology makes it essential for businesses
Gregory Daco to be agile and adaptable.
EY-Parthenon Chief Economist
• Recommendation: Encourage a culture of innovation where experimentation with
New York, NY
AI is supported. This involves fostering an environment where learning from failures
is seen as a stepping stone to innovation, and where employees are encouraged to
think creatively about applying AI to solve business problems.
Understanding and managing risks
• Insight: AI projects come with their own set of risks, including data privacy
concerns, ethical considerations and potential biases in AI models.
• Recommendation: Develop a robust risk management framework that addresses
these challenges. This includes investing in data security, helping ensure compliance
with relevant regulations and implementing ethical AI practices. But for large-
scale transformation to happen, businesses may need to make significant upfront
investment in physical, digital and human capital to acquire and implement new
technologies and reshape business processes.
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Subtitle Ilis et volorepro quia simustis
about how EY collects and uses personal data and a description of the
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7,ey,ey-how-to-ask-corporate-vendors-the-right-questions-when-it-comes-to-rai.pdf,"How to ask corporate
vendors the right
questions when it comes
to responsible AI
As artificial intelligence (AI), which Most EY client vendors have probably started to embed AI
into their products and processes or are charting a roadmap
includes generative AI (GenAI) and
for an AI-powered future. Evaluating a vendor company’s AI
agentic AI, becomes part of business portfolio to assess current and future AI capabilities isn’t simple,
but it is important, especially in cases where the package
as usual, leaders are establishing and
selection, system implementation and maintenance process are
decentralized.
implementing strategies for their own
RAI inventory requires linking of data, models and use cases
organizations’ responsible AI (RAI)
for inventory and tracking purposes, which even large tech
adoption. The next phase is gaining an companies don’t necessarily have the capabilities for yet. So
vendors should be evaluated to minimize risk and facilitate
understanding of business vendor and
positive, transparent business relationships.
partner AI use, which could extend its
reach into regulatory compliance.
How to ask corporate vendors the right questions when it comes to responsible AI 1
When evaluating third-party RAI, it is a Technical documentation:
company’s responsibility to thoroughly assess:
 Whether the third party provides risk-assessment
 RAI framework adoption documentation at the enterprise level as well as the individual
model or system level
 Risk assessment
 What specific data and security regulations the AI system must
 Data management
comply with, including by regulation or jurisdiction
 Technical documentation
 What specific metrics are used to measure AI performance
 Post-production monitoring throughout the AI development lifecycle
 Disclosure practices  Specific limitations of the AI system and the necessary human
activities to address them
 Incident response
 Insurance coverage Post-production monitoring:
 Responsibilities of deployer  The process for post-production monitoring of AI solutions
 Contractual obligations to ensure AI systems are secure,  How the company will be informed when AI systems perform
compliant and ethically managed outside of the expected range
 Whether logging will be available for transactions interacting
with sensitive data
What companies should consider when Disclosure and communication:
evaluating third-party vendors for responsible AI:
 Whether the third party provides sufficient and specific
information on each of the AI systems to facilitate the safe
The RAI framework:
use of AI (think of food labels, included in the Singapore AI
 What framework or system the vendor has adopted verification requirements)
 Whether a SOC report equivalent report will be available on
Risk assessment:
the performance and compliance for AI systems
 The vendor risk assessment process and criteria
Incident response and recovery:
 What determines higher vs. lower risk
 Communication protocol if an incident occurs, including
 What governance addresses fairness, bias and accountability
threshold to disclose and resolution process
Access and data management:  Third party’s incident response capability and disaster
recovery plans in case of AI system failures or security
 Specific rights and data management protocols in place to
breaches, from incident management to crisis management
secure the AI system’s access to the environment
 Extent of the third party’s insurance coverage
 How the third party will use data and transactions to train its
models to clarify the ownership of any intellectual property
The company’s responsibilities:
that may result from the use of the AI systems, including
patents, trademarks and copyrights  What specific deployer responsibilities the third party is
passing on (think of it as the CUECs (complementary user
 Whether the third party has access to the vendor’s
entity controls) for a SOC report)
environment and/or data, and if so, how it is managed and
logged  Any specific training and support needed to use the AI systems
How to ask corporate vendors the right questions when it comes to responsible AI 2
3
Contractual obligations: The above considerations are easier to manage in new vendor
relationships. But many existing vendors have already started
 The right to audit the third-party AI systems
to embed AI into their products or plan to roll out new features
 Specific data use restrictions using AI in the near future, so the picture can get murky.
 Communication/escalation service-level agreements (SLAs) It’s often more challenging to evaluate a company’s current
portfolio of vendor systems and flag those that have AI
 Define clear accountability and liability terms related to the AI
capabilities or will implement AI in the next six months,
solution’s performance and impact
especially in cases where the package selection, system
implementation and maintenance process is not consistently
Access and data management:
centralized.
 What specific access rights and data management protocols
When evaluating third-party RAI, companies should use the
does the vendor need to put in place to secure the AI system’s
above checklist to thoroughly assess the vendor’s RAI and
access to the environment?
gain confidence that all AI systems are secure, compliant and
 Will the third party use data and transactions to train its
ethically managed.
models to clarify the ownership of any intellectual property
that may result from the use of the AI systems, including
patents, trademarks and copyrights?
 Will the third party have access to the vendor’s environment
and/or data? If so, how is the access managed? Is it logged?
EY contacts:
Chris Watson
Sarah Y Liang
EY Americas Risk and Supplier Services
EY Global Responsible AI Leader
Solution Leader
sarah.liang@ey.com
christopher.watson@ey.com
EY | Building a better working world
EY is building a better working world by creating new value for EY refers to the global organization, and may refer to one or more, of the member firms
of Ernst & Young Global Limited, each of which is a separate legal entity. Ernst & Young
clients, people, society and the planet, while building trust in
Global Limited, a UK company limited by guarantee, does not provide services to clients.
capital markets. Information about how EY collects and uses personal data and a description of the
rights individuals have under data protection legislation are available via ey.com/privacy.
Enabled by data, AI and advanced technology, EY teams help EY member firms do not practice law where prohibited by local laws. For more information
clients shape the future with confidence and develop answers for about our organization, please visit ey.com.
the most pressing issues of today and tomorrow. Ernst & Young LLP is a client-serving member firm of Ernst & Young Global Limited
operating in the US.
EY teams work across a full spectrum of services in assurance, © 2024 Ernst & Young LLP.
consulting, tax, strategy and transactions. Fueled by sector All Rights Reserved.
US SCORE no.
insights, a globally connected, multidisciplinary network and
2411-10227. ED none.
diverse ecosystem partners, EY teams can provide services in
This material has been prepared for general informational purposes only and is not intended to be relied
more than 150 countries and territories. upon as accounting, tax, legal or other professional advice. Please refer to your advisors for specific
advice.
All in to shape the future with confidence.
ey.com
How to ask corporate vendors the right questions when it comes to responsible AI 3"
8,pwc,ai_adopion_study.pdf,"AI Adoption in the
Business World:
Current Trends and
Future Predictions
1
April 2023
Agenda
1 Background and focus areas
2 Current state
3 Risk and Obstacles
4 Future state
5 Key Takeaways & Implementation Guideline
2 2
Background
33
PwC | Tomorrow’s audit, 3
today
Terms & Definitions
“Artificial intelligence” - Coined by emeritus Stanford Professor John McCarthy in 1955, was defined by him as “the science and
engineering of making intelligent machines”. In other words - AI is the ability of a machine to perform cognitive functions typically
associated with human minds, such as learning, reasoning, interacting with the environment, and solving problems.
“Machine learning” Machine Learning (ML) is the part of AI studying how computer agents can improve their perception, knowledge,
thinking, or actions based on experience or data. For this, ML draws from computer science, statistics, psychology, neuroscience,
economics and control theory
“Automation” is the conversion of a work process, a procedure, or equipment to automatic rather than human operation or control.
Automation does not simply transfer human functions to machines, but involves a deep reorganization of the work process, during
which both the human and the machine functions are redefined.
“Synthetic Data” is a class of data that is artificially generated rather than obtained from direct observations of the real world. Data can
be generated using different methods, such as statistically rigorous sampling from real data, semantic approaches and generative
adversarial networks or by creating simulation scenarios where models and processes interact to create completely new datasets of
events.
“Digital Twins” is a dynamic virtual copy of a physical asset, process, system or environment that looks like and behaves identically to
its real-world counterpart. A digital twin ingests data and replicates processes so you can predict possible performance outcomes and
issues that the real-world product might undergo.
44
Sources: Professor Christopher Manning, September 2020 (Stanford University), Michael R. Lowery, Massachusetts Institute of Technology, PwC US, Gartner
Caveats
● There is a lot of hype surrounding AI, and it is crucial to approach the topic with a critical eye. AI is not a panacea for all business
problems, and it is essential to have realistic expectations about what it can and cannot do.
● Not many people and companies that claim to use AI actually use AI. Many companies do so as a marketing tactic in order to
appear innovative, even if they don’t have any actual AI technology in use, or from lack of understanding of what AI is and how it
works. We acknowledge the data presented in this study may involve the participation of such companies which could impair the
accuracy of the results.
● The study does not offer real distinction between off-the-shelf AI tools (e.g. pre-built software packages such as NLP) that comes
with pre-trained models and AI models that require extensive customization and development to work for a specific business use
case.
● The data used for this study may not be reflective of current trends, as certain tools were not available when the data was
collected (e.g. ChatGPT, Midjourney etc). Also certain trends may shift or change in the intervening years, making the data less
relevant or less reflective of the current state of the market as this industry is changing very rapidly.
● Findings from this study may not be generalizable to all contexts, as AI adoption can vary widely depending on the industry,
business size, and other factors
● It can be challenging to establish causality in a study such as this, as other factors could be influencing the outcome, so our
advice is to treat the outcomes of this study with a degree of caution.
55
Sources: PwC US, Forrester
AI is the big one. I don't think Web3 was that big or
”
that metaverse stuff alone was revolutionary but AI
Background is quite revolutionary
- Bill Gates
We are all aware of the paradigm shift in the use of AI. Examples such as Netflix recommendations systems, ChatGPT and other
chatbots, Generative Art (Text-to-Image/Video), chatbots that impersonate customer services agents online are rising and taking over
our day-to-day lives, and companies constantly looking for new ways to exploit the new paradigm. These efforts are the new value
creation engines of many leading companies in almost every industry.
The adoption of AI was low during last years because technology and infrastructure supporting technology were adequate and not
very useful. However, the tides have changed and we are witnessing increasing adoption in current days. According to Forrester’s Data
and Analytics Survey (2022) 73% of data and analytics decision makers are building AI technologies and 74% seeing a positive impact
from AI technologies in their organization. AI is definitely been starting to be adopted in a wide range of industries, including
healthcare, finance, transportation, manufacturing, marketing, education and retail.
We believe that we stand at a very important time in history where AI will play a big role in companies’ transformation and daily
operation, and the faster companies will be able to embrace the change the more advantage they will have versus their competitors.
Given the enormous potential of AI, it is not surprising that adoption of the technology is growing rapidly, so we went to find what is
the current and future state of adopting AI technology. The study aims to provide an overview of the current state of AI adoption, the
benefits and challenges, and the future trends and predictions for AI adoption.
66
Sources: PwC US, Forrester
Current State
77
PwC | Tomorrow’s audit, 7
today
Current Impact Of AI In the Business World
Data analysis efforts improvement and Streamlining processes and reducing
provision of better and faster insights the time and resources required to
that can promote strategic decision and Decision Efficiency
complete certain tasks
Making
overall performance
Current AI
Impact on
Customer Revenue
Businesses
Scaling and supporting Service Growth Business are able to develop new
companies’ customer services, products and services faster, or enter
Cost
from chatbots and virtual new markets. Companies may be
Reduction
assistants which are equipped able to upsell in more efficient ways
with NLP techniques, to with personalized offers or
predictive and sentiment suggestions
Cost reduction made available by automating
analytics
manual tasks and reducing redundant labor
costs
88
AI Adoption and Areas Of Focus
Companies that are advanced with AI (“Leaders”) Companies are more focused on leveraging
are pioneering widespread adoption of AI in AI for productivity, decision making, customer
comparison to other companies in the market experience, product/services innovation and
employee experience
Q. To what extent is your company looking to integrate AI Q. To what extent is your company looking to integrate AI
technologies into its operations? Source: PwC 2022 AI Business technologies into its operations? Source: PwC 2022 AI Business
Survey, March 2022: Leader base of 364; Other base of 631 Survey, March 2022: Leader base of 364; Other base of 631
99
Sources: PwC US
Which Industries Currently Utilize AI the most?
Companies reported they Companies reported using
42% 35%
are exploring AI AI in their business
Financial
Tech Healthcare
Services
Fraud detection, risk Machine learning, cognitive Patient diagnosis, treatment
management, and investment computing, and robotics planning, and drug
analysis development
Automotive
Retail
& Assembly
Route optimization, demand Personalized recommendations,
forecasting, and autonomous and automated inventory
vehicles management
1100
Sources: PwC US, Forrester, IBM
Adoption Rate
Industry/Function HR Manufacturing Sales & Product/service Risk Service Strategy & Supply chain
Marketing development operations corporate management
finance
Automotive & Assembly 11% 26% 20% 15% 4% 18% 6% 17%
Retail 2% 18% 22% 17% 1% 15% 4% 18%
Financial services 10% 4% 24% 20% 32% 40% 13% 8%
Healthcare/pharma 9% 11% 14% 29% 13% 17% 12% 9%
High tech/telecom 12% 11% 28% 45% 16% 34% 10% 16%
Cross Industry average 9% 12% 20% 23% 13% 25% 9% 13%
Companies that use AI are Plan/execute
motivated by three factors: the 66% applying AI for Benefits from using
ability to cut expenses, develop sustainability goals AI to automate IT,
faster, and grow profitability. 54% business or network
However, each industry’s processes, including
approach to AI applications, as Utilizing AI tools for cost savings and
well as its problems and 53% better customers efficiencies
outcomes, may differ experience 1111
Sources:
Statista 2023
AI Adoption In Practice By Categories (1/2)
Gen Z Gen X Millennials
Younger generations tend to adopt AI technology in their
29% 28% 27% professional life easier and faster
1122
Sources: Fishbowl
AI Adoption In Practice By Categories (2/2)
Key Adoption Considerations
Companies are currently or planning to apply AI to
address their sustainability goals
66%
Companies see benefits from using AI to automate
IT, business or network processes
54%
Global AI spending coming from the US
74%
AI spend out of the global total software spend by
2025
6%
1133
Sources: IBM Global AI Adoption Index 2022,
Forrester
The Global AI Index
Country Talent Infrastructure Operating Research Development Government Total
Environment Strategy Rank
USA 1 4 35 1 1 17 1
China 24 1 6 2 2 2 2
United Kingdom 3 23 24 5 11 11 3
Canada 7 15 5 10 10 1 4
Israel 5 29 14 7 9 45 5
Singapore 4 8 55 4 14 15 6
South Korea 28 6 32 12 3 7 7
The Netherlands 6 9 10 15 8 33 8
Germany 11 13 30 6 12 10 9
1144
Sources: “Israel Innovation authority”
Current availability of AI technology
Perception-based AI Cognition-based AI Customer Experience
Improved customer
Analyze and understand Analyze and interpret data
experience with faster
Computer visual data (e.g. images) to support business strategy
Decision
delivery decisions
Vision
Making
Understand and analyze Understand, learn and percept
human language (e.g. text) data to make predictions and Scale and Efficiency
NLP
Predictive trends New levels of productivity
and cost savings through
Find best solutions to
Recognize and extract text
automated processes
problems with various
from an image or scanned
OCR techniques (e.g. ML models)
document Optimization
Create and generate new New Ways of Working
Build interfaces for
information and context based
Advanced applications to understand Re-thinking of the operating
Generative on user input and large
text or speech
Recognition model to enable intelligent
datasets
delivery
1155
stfieneB
tnerruC
AI, in its current state, is primarily at the feature level rather than at the infrastructure level. AI technologies are being used to add new features to
existing products and services, such as voice recognition or image recognition. These features are built on top of existing infrastructure and use AI
algorithms to perform specific tasks. While there have been some attempts to integrate AI more deeply into the infrastructure level, such as with edge
computing, the majority of AI usage is still at the feature level.
Current Prominent Local AI Use Cases
Automation of IT Marketing and Sales Fraud Detection AI Monitoring &
processes Business Analytics or Financial Planning & Governance
Security and Threat Intelligence Analysis Conversational AI or
Detection Virtual Assistants
1166
Sources: IBM Global AI Adoption Index 2022
Risks and
Barriers
1177
PwC | Tomorrow’s audit, 17
today
Biggest Challenges when Adopting AI
Fragmented Technology Stack No AI Methodology The definition of AI's
standard for deploying AI systems. The AI proven playbooks, including designs,
community has not converged yet on best practices, and technology pipelines.
formats and interfaces across the AI/ML
stack
Misguided Strategy Performance AI-Business Alignment Clear
cannot be guaranteed on an ongoing definitions of KPIs and KRis which are
basis. Lack of clear definitions of subject to ongoing assessment,
business goals and inflated evaluation and re-design
expectations
New Business Requirements
Evolving AI Regulation The technology
Identifying new requirements and
environment is rapidly changing. Lack of
insights as they evolve. Embrace
GRC (government, risk & compliance)
uncertainty
standards
1188
Source – Data Science Group
Current Regulation Highlights
1199
dna
sutatS
sthgilhgiH
evitcejbO
➔ The focus is on ethical, legal, and technical ➔ New York joined a number of states, ➔Several states in the US have passed
aspects of its use. including Illinois and Maryland, in general data privacy legislation that goes
➔ For high-risk AI applications there are regulating automated employment into effect at various times in 2023. These
additional requirements also for a decision tools (AEDTs) that leverage AI to laws contain provisions governing
conformity assessment. However, the AI Act make, or substantially assist, candidate “automated decision-making,” which
has no specific guidelines on how such screening or employment decisions includes technology that facilitates
conformity shall be demonstrated in ➔ The Equal Opportunity Employment AI-powered decisions
practice. Commission (EEOC) launched an initiative ➔AI-focused bills have been introduced in
➔ The GDPR requires heightened compliance on “algorithmic fairness” in employment Congress
when companies use technology like AI to ➔AI regulation appear to be potentially
solely make automated decisions that emerging from the Federal Trade
produce “legal … or similarly significant” Commission (FTC)
impacts on a consumer
noitalugeR
EU AI Act / GDPR US Regulation Future Regulation
Proposed legislation, which aim to No direct legislation as of now. Initial Future regulation may emerge in
accelerate the development and uptake of approach to AI regulation emerge, focused 2023-2024, mainly pertaining to model
AI, and to ensure that its use is according on specific AI-use cases bias, user rights, transparency and AI
to EU values governance
Types of Risks
❏ Lack of
accountability
❏ Rogue AI detection
Control
❏ Adversarial attacks
❏ Privacy protection
Security Enterprise
Performance Social ❏ Reputational
❏ Financial
performance
❏ Legal compliance
Economic
❏ Missinformation
❏ Errors
❏ Public manipulation
❏ Bias & Discrimination
❏ Opaqueness / Lack of
traceability ❏ Job displacement
❏ Enhancing inequality
2200
What’s Hindering AI Adoption?
Main barriers of AI adoption reported by companies
24% 24%
25%
29%
34%
Projects are too complex or Lack of tools or platforms to Limited AI skills, expertise
Too much data complexity Price is too high
difficult to integrate and scale develop models or knowledge
Majority of organizations haven’t taken steps to ensure trustworthiness and responsible AI adoption
Can’t explain their Not tracking performance Not making enough efforts
61% 68% 74%
AI-powered decisions variations and model drift on reducing data bias
2211
Sources: IBM Global AI Adoption Index 2022
AI Risk Categorization
❏ Datasets shift/skew
❏ Patterns of Drifts
❏ Business Problem ❏ Structural Changes
❏ Formulation Anatomy of ❏ Generative Changes
DS Process
❏ Uncertain Realm ❏ Datasets versioning
❏ Experimental/Exploration ❏ Models versioning
❏ Pipeline versioning
❏ Evaluation measures
versioning
❏ Data Quality Dimensions &
Granularity ❏ Regulation
❏ Imbalanced Datasets ❏ Fairness / Bias
❏ Data Lineage & Provenance ❏ Auditability
❏ Data Preprocessing & EDA
❏ AI Cybersecurity
❏ Model
❏ Algorithm Aversion
Selection/Architecture
❏ Transparency
❏ Model Inference latency
❏ Causality
❏ Model Hyperparameters
Tuning
❏ Overfitting/Generalization Technical Debt 2222
(OOD)
Source – Data Science Group
How to Mitigate Potential Risks?
Identify unique Control your data Keep governance Validate Diversify your
vulnerabilities Pay special attention up to speed independently - team
Determine where bias to issues in historical Governance should be and continuously Building diverse teams
could creep into your data and data continuous and You can use wither an helps reduce the
datasets and acquired from third enterprise-wide. Set internal independent potential risk of bias
algorithms and where parties. This includes frameworks, toolkits team or a third party falling through the
it could cause major biased correlations and controls to help to analyze your cracks. People from
damage between variables. spot problems before algorithms for different racial and
they may proliferate. fairness. gender identities and
economic
backgrounds will
notice different biases.
2233
Sources: “Understanding algorithmic bias and how to build trust in AI” - PwC US
Auditing & QA of AI Models for Verified Development &
Deployment
AI Audit Modeling
Auditing AI models before deployment is crucial to
avoid unintended consequences. As organizations
deploy AI systems for core functions, they face new
AI Audit Process
risks due to statistical uncertainty, which can
result in dire consequences and business failures.
The AI auditing process formalizes a pipeline to
Without clear AI standards, audits, risk
minimize potential AI risks and human error in
management strategies, and GRC standards,
decision-making processes. It leverages domain
organizations can mitigate major AI disasters, as
expertise to validate AI systems, applications,
already seen in many major institutions.
technology infrastructure, and standards for quality,
reliability, and consistency. The process ensures
alignment with business and governance
standards, generating human-readable specifications
that are useful to end-users.
2244
Source – Data Science Group
Future State
2255
PwC | Tomorrow’s audit, 25
today
The Rise of Generative AI
Companies growth opportunities in the AI generative
space: what’s next?
New product and services creation
❏ Idea generation
❏ Prototyping
❏ Market Research
R ❏ Customization Faster decision making
G
CA ❏ Process Optimization
% ❏ Predictive Analytics
7
33. ❏ Customer Insights
Enhanced customer demand ❏ Risk Assessment
forecasting ❏ Processes
Automation
❏ Inventory Optimization
❏ Sales Forecasting
❏ Supply Chain Improve efficiency of marketing
Three major areas that will be deeply impacted by Optimization campaigns
generative AI in the near future ❏ Personalization
❏ Better Customer
Segmentation
❏ Ad Optimization
New Drug Material Financial
❏ Faster Content Generation
Discovery Science Services
2266
Sources: Forrester
Near-Future Value Capture Opportunities
Decision Making Simulation Data Analysis
Computer-based processes The ability to process AI nowadays can help
that uses AI to mimic and significant amount of historical automate and streamline
simulate real-world scenarios data and produce and analyze the data analysis process
or system, allowing for the future optional decision trees (e.g. cleansing) as well as
testing and optimization of scenarios. Adoption rates are significantly improve
various strategies, outcomes, significant when it comes to predictive models and
and performances. areas of technology, operations analysis of unstructured
and maintenance, and also CX data.
and strategy.
2277
Sources: PwC US
Seize Current AI Value
Subject Matter Value Capture Proposal
ML models require huge amounts of data, which simulation
Synthetic data
models can create. Synthetic data can turbocharge other AI
and analytics initiatives. Simulation
To help make sense of your various data sets in the context
Digital Twin
of your business, consider making Digital Twins a platform
capability.
Simulating real-world scenarios or systems, allowing for the
Predictions and
testing and optimization of various strategies, outcomes,
Scenario modeling
and performances.
Data Analysis
Utilize AI abilities to clean and organize your data which
Labor cost
can save you time and resources put into this effort.
Bring your data specialists (e.g. Data Scientists, Engineers)
Data Specialist together before initiating any endeavour in order to align
methodology and goals.
Decision-Making
Consider combination between AI and other immersive
Other immersive technologies and especially Blockchain related technology
technologies which can help overcome data related problems (e.g. bias,
privacy, security).
2288
Sources: PwC US
Where Will Adoption Increase Most?
2299
D a t a - c e n t r i c A I
M o d e l - c e n
pA
nec
t r i c A
tacilp
A cirt
I
oiI n s -
Fusion of AI techniques
(composite AI)
Synthetic Data
Massive increase in adoption as
Expected to reach mainstream adoption
synthetic data, mainly to tackle
in two to five years, the business benefits
cost and timing problems
of composite AI are likely to be
pertaining to ML development.
transformational, enabling new ways of
doing business across industries that will
result in major shifts in industry
dynamics.
AI trust, risk and security Improved Business Cases
management
Organizations will need to integrate digital ethics
into their AI strategies to bolster their influence
and reputation among customers, employees,
partners and society.
Sources: Gartner
H u m a n - c e n t r i c A I
Decision intelligence and edge AI are both
expected to reach mainstream adoption in
two to five years and have transformational
business benefits.
Key Takeaways &
Implementation Guideline
3300
PwC | Tomorrow’s audit, 30
today
ROI Measurement Considerations
Companies are now increasingly able to predict AI
implementation ROI thanks to new assessment methods.
Qs. How confident are you These can capture not just “hard” returns, such as increased
in your company’s ability
to Assess ROI of current AI productivity, but also “soft” costs, such as new hardware
initiatives? How confident
spending
are you in your company’s
ability to accurately
Predict ROI of AI initiatives
in the next 12 months
❏ Time Savings ❏ Better Experience
❏ Cost Savings ❏ Talent Retention Soft Returns
Hard Returns
❏ Productivity Increase ❏ Team Agility
❏ Revenue Increase Benefits / Returns (capabilities)
ROI =
❏ Data Investments
Investment Costs
❏ Licenses ❏ Compute and Storage
Hard Costs ❏ Resources ❏ SME (Subject Matter Soft Costs
Experts)
❏ Data Science training
3311
Sources: “Solving AI’s ROI problem. It’s not that easy” - PwC US
Labor Considerations – Human-Centric AI
Despite AI talent shortage, companies that take holistic approach to AI are far more advanced than those taking a
piecemeal approach. Such companies also 1.5x more likely than other companies to plan on leveraging more
third-party vendors with their scalable AI workforce.
Highest value use of AI Reducing the need for The AI talent shortage can
in the labor market rote work will make be mitigated by taking
today is to help people employees life more in-house specialists that
to do better work, easier and more possess some of the skills
reducing the press to fill engaging you need, and provide them
hard-to-fill positions with the rest
Organizations will use AI to address labor or skill shortages in three main ways
Reducing manual or Increasing employee Improving recruiting and
65% 50% 45%
repetitive tasks Learning & Development human resources
processes
3322
Sources: IBM Global AI Adoption Index 2022, PwC US
The Future of Working with AI - Key Takeaways
Trends
Ethics & Governance
❏ AI is recession-resilient and continued AI
❏ Algorithmic and data biases will likely be
investments will continue in 2023,
regulated in the near future and will
particularly among business impacted by
create uncertainty regarding usage of
economic and supply chain disruptions
certain AI models
❏ In 2023 low/no code AI tools will be more
❏ Increased AI models regulations will force
involved in the software development
change in many companies’ systems and
lifecycle
infrastructure
❏ Image editing is going to be changed
❏ Applications of AI will not be fully
dramatically
implemented into enterprises until
business cases and expected ROI will be Workforce
fully understood
❏ In the short term - AI will free up employees
❏ Industries with more consumer
to focus on value-add tasks and will improve
regulatory pressure will have lower AI
job satisfaction
adoption rates
❏ AI applications still require human
❏ AI governance will likely join
supervision and therefore it is unlikely
cybersecurity as a board-level topic
that we will see dramatic HR changes in
3333
Sources: World Economic Forum. IBM Global AI Adoption index 2022, Source:The state of Machine Learning the near future, except certain functions
at the end of 2022 (cnvrg.io)
The Future of Working with AI - Key Takeaways
Management Adoption
❏ Management’s decision making ❏ AI adoption will probably still remain
processes will not change low in 2023
significantly in the short term ❏ 10% of Fortune 500 enterprises will
generate content with AI tools in 2023
❏ 25% of tech executives (e.g. CTO/CIO) will
❏ Company’s ability to completely
report to board/committee on AI
change its processes will be hard, and
governance
therefore we expect adoption to be
slow. It will be likely to be easier for
companies to integrate AI into their
Additional Opportunities
core processes if they can spin off
certain functions or form brand new
❏ Big opportunities of utilizing AI exist in M&A, the
business units
process itself and also ESG matters which
❏ AI models still considered as black box
companies invest a vast amount of time and
for non-technological employees
resources into them both
which will require training and
upskilling
❏ AI can improve and transform the way companies
❏ AI infrastructure challenges will
manage their databases and documentation
surpass data associated issues as the
biggest challenge for scaling AI/ML 3344
Sources: Experts Interviews, PwC Israel, Forrester Predictions 2023: Artificial Intelligence, run:ao 2023 state
of AI Infrastructure
Implementation Do’s and Don'ts
AI tools tend to be highly accurate, but they are definitely not perfect and can make bizarre mistakes. Maintaining human oversight
during the implementation and afterwards is crucial to ensuring quality, both for model training and for the final correction of the
output in downstream processes. Leaders must stay vigilant about the potential risks and cognizant of the need for proper training
and corporate governance
Action Implementation Guidelines
● Relevant data requirements should be identified as a first step, then evaluate the sources currently
Define the Problem
available
● Success criteria should be clearly defined and be as measurable as possible
● Stakeholders should be involved in the process of defining the problem (inc. external stakeholders
and customers)
● The program should be focused on digital and analytic understanding, awareness and
Develop Training
understanding of the flaws and advantages of the tools
Program ● All employees will need to be upskilled (inc. CEO)
● Make sure employees understand basic concepts of AI technologies
● Companies should ensure that employees are conversant with current technologies and this
transformation will take hold only if the entire workforce is brought along
● Anyone for whom a substantial portion of daily tasks will essentially be eliminated should be
Anticipate Impact
monitored (*that is the reality of automation)
● The message to communicate to such employees is that AI will free them to focus on
3355
harder-to-solve problems which demand human judgement or creativity
Sources: PwC US
Implementation Do’s and Don'ts
Action Implementation Guidelines
● Tactical level incentives to use AI tools and the new platform can create better engagement
● Incentives are dependant on corporate culture, but should include KPIs for performance reviews,
Offer Incentives
bonuses or coupons
● Employees will be compelled when they start seeing their productivity enhanced
● Appoint top-down champions who consistently communicate the benefits of the AI
Promote Cultural implementation
● Communicate the message that using AI tools is not only good for customers but also for the
Change
company’s growth
● Build trust by focusing on competence, consistency, dependability and transparency
● Make sure compliance with relevant regulations and appropriate requirements
Ensure Safe and ● Understand the background technology behind the AI tools
● Develop ethical and user policies
Trustworthy Use
● Establish internal audit team to monitor abusive, illegal and/or inappropriate usage
● The platform should combine data management, automation tools, and AI applications, and keep
people at the loop
Establish Platform
● The platform could be enterprise-level portal, wherein data could be stored and exchanged, and
applications uploaded and downloaded
● The platform should be accessible to all employees and be receptive to employee-led innovations
● Make sure the democratization of these powerful technologies are utilized responsibly 3366
Sources: PwC US"
9,pwc,nextgen-survey-2024.pdf,"PwC’s Global NextGen Survey 2024
Vietnam report
NextGen Vietnam
Succeeding in an AI-driven world
1 | PwC’s Global NextGen Survey 2024 - Vietnam report
2 | PwC’s Global NextGen Survey 2024 - Vietnam report
Table of Contents
Foreword 5
1. Embracing Leadership in the Digital Era 6
Rising generational involvement in family businesses 7
Setting a clear vision for the digital future 8
Leading the reinvention imperative in family businesses 10
2. Exploring Generative AI (“GenAI”) and Emerging Technologies 12
Attuned to emerging technologies 13
Ambition to lead AI innovation in family businesses 14
Navigating the gap between NextGen ambition and organisational AI 15
readiness
Harnessing the advantages of AI in the next three years 16
3. Maximising NextGen’s contribution to family businesses 21
Fostering alignment between generations 22
Governance and trust as bedrock for growth and innovation 23
Next steps 24
About PwC NextGen survey 28
Contact us 30
3 | PwC’s Global NextGen Survey 2024 - Vietnam report
44 || PPwwCC’’ss GGlloobbaall NNeexxttGGeenn SSuurrvveeyy 22002244 -- VViieettnnaamm rreeppoorrtt
Foreword
In the ever-evolving landscape of business and technology, the emergence
of generative artificial intelligence (“GenAI”) stands as a transformative
force, reshaping industries and redefining the very nature of leadership.
PwC’s NextGen 2024 Survey sheds light on Vietnamese NextGen’ pivotal
position in shaping the future of family businesses amidst the digital age’s
transformative tide. The NextGen in Vietnam are not mere spectators to
this paradigm shift, but are poised to step into leadership roles, armed
with a keen understanding of the significance of AI and its implications
for future strategies. More than ever, they have the capacity to shape their
family businesses and make an impact as they take up responsibilities as
stewards of their businesses.
We encourage you to share the insights from this survey with family
members, top management and peers, and join us on this exciting and
transformational journey.
Johnathan Ooi Siew Loke
Entrepreneurial and Private
Business Leader
PwC Vietnam
55 || PPwwCC’’ss GGlloobbaall NNeexxttGGeenn SSuurrvveeyy 22002244 -- VViieettnnaamm rreeppoorrtt
Embracing
Leadership in the
Digital Era
66 || PPwwCC’’ss GGlloobbaall NNeexxttGGeenn SSuurrvveeyy 22002244 -- VViieettnnaamm rreeppoorrtt
Rising generational involvement
in family businesses
Our ongoing analysis of family businesses Moreover, the number of respondents feeling
and the members of the next generation disengaged from their family businesses has
(“NextGen”) in Vietnam since 2019 has plummeted to just 12%, down from 34%
shown increasing generational involvement in 2022. This indicates a notable surge in
and transition within these enterprises. the contribution of NextGen to their family
enterprises.
In our latest report, the PwC Global
NextGen Survey 2022 – Vietnam’s NextGen The current business environment, post-
in Focus, we shed light on the Vietnamese pandemic, presents an opportune moment
NextGen who were emerging as leaders for NextGen to take the reins. With ever
in waiting and becoming particularly greater disruptions and complexities arising
noticeable during the pandemic. With from technological advancements and global
aspirations to lead and a readiness to shifts, there exists a wealth of opportunities
assume their roles, Vietnamese NextGen for innovation and entrepreneurship – areas
are now rising to the challenge, actively where the new generation of leaders excel.
participating in shaping the future of their
As Vietnam’s NextGen leaders step up
family businesses.
to the plate, they are poised to drive the
Two years on, now 52% of NextGen evolution of family businesses, striving for
respondents from our survey are already growth, resilience, and success in the face of
occupying leadership positions, a unprecedented challenges.
substantial increase from 29% in 2022.
Vietnam NextGen Current Job Role / Position (%)
52
Leadership role
29
15
Shareholder/beneficiary
11
15
Governance role
8
Employee/intern 12 2024
16
18 2022
Intrapreneur
5
9
Entrepreneur
16
Other 3
12
Not engaged yet
34
7 | PwC’s Global NextGen Survey 2024 - Vietnam report
Setting a clear vision for the digital
future
NextGen in Vietnam feel positive about their career opportunities and
ambitions
With a strong emphasis on personal and professional development, 76% of NextGen within
family businesses in Vietnam prioritise opportunities for learning and growth within their
companies. Moreover, they demonstrate a clear understanding of both the career aspirations set
by the current generation and their own ambitions for future roles within the family business.
NextGen’ future in the business (%)
76
I feel I have the opportunity to learn and grow within
the family business
75
73
I am clear on what the current generation’s goals
are for my career path in the family business
59
70
I have a clear idea about my personal ambitions
for a future role in the family business
70
Vietnam
Global
88 || PPwwCC’’ss GGlloobbaall NNeexxttGGeenn SSuurrvveeyy 22002244 -- VViieettnnaamm rreeppoorrtt
8
Aside from business growth priority, talent management and technology adoption are top
priorities for NextGen in the next two years
As Vietnamese NextGen step into leadership Additionally, they emphasise the importance
roles amidst the dawn of technological of strengthening the technological foundation
disruption, their focus remains steadfast (36%) and ensuring that employees possess
on securing the prosperity of their family the necessary skills to embrace new
businesses. A notable 39% prioritise growth as technologies (33%). This acute understanding
a top business imperative, underscoring their of the key ingredients for business success
commitment to driving forward momentum. demonstrates NextGen’ clear vision of what
drives success in the new era.
Recognising the vital role of human capital in
fortifying their businesses and the associated In navigating the challenges faced by
technological infrastructure, Vietnamese businesses and society today, Vietnamese
NextGen prioritise initiatives aimed at NextGen recognise the importance of
building and nurturing the talent pools of their a human-led, tech-powered approach.
businesses (39%). Their forward-thinking mindset reflects
their commitment to building resilient and
innovative businesses that can thrive in an
ever-evolving landscape.
Key priorities over the next two years (Top 3) - Vietnam
Global: Global:
Top 3 NextGen
priorities engagement
39
Achieving business growth
39 52 55
Talent management -
39
attracting/retaining the best
18 36 30
young talent
36
Adopting new technologies
39 25 33
Upskilling the digital capabilities 33
of our workforce 30 17 23
Reconsidering our asset allocation 27
and investments 18 19 25
Improving the working conditions/ 21
15 26
practices of our employees 24
Increasing our focus on investments 21
18 21
for sustainability and impact 21
Expanding into new sectors or 21
37 42
markets 15
Ensuring we offer the right
18 22 28
products and services for
24
today’s customers
9 19 21
Investing in innovation and R&D
18
Top 3 priority Engaged or likely to be engaged
9 | PwC’s Global NextGen Survey 2024 - Vietnam report
Leading the reinvention imperative
in family businesses
NextGen in Vietnam are in line with Reshaping management and
the sentiment of CEOs across the business strategy
Asia Pacific region regarding the
reinvention imperative The top areas where Vietnamese NextGen
seek to add value are in professionalising
In light of PwC’s 27th Annual Global CEO and modernising management practices.
Survey - Asia Pacific; where 69% of CEO Additionally, they want to play a key role in
respondents were from privately owned facilitating the separation of family ownership
businesses and from management. This highlights the
63% importance of external ideas and talent in the
success and longevity of family business.
Moreover, Vietnamese NextGen see
Asia Pacific CEOs expressed
themselves as better suited than their
scepticism about the economic
predecessors in developing a business
viability of their companies
strategy tailored for the digital age. With
within a decade on their current
a focus on fortifying the company’s
trajectories, technological foundation, this again
highlights the pivotal role that they are to
play in shaping the future trajectory of their
a pressing dual imperative emerges: organisations. Their proficiency, particularly
addressing immediate profitability challenges in navigating AI disruptions, is poised to be
while concurrently reinventing businesses for instrumental in ensuring the company’s long-
future sustainability. term success.
Vietnamese NextGen bring their distinct
perspectives to the forefront, identifying
avenues where they can drive substantial value
within their enterprises, alongside articulating
clear business objectives. Positioned as
catalysts for change, they recognise their key
roles in reshaping the future sustainability of
their family businesses.
Where NextGen feel they can add the most value to the business (%)
Having a business Having a clearly Separating family
strategy fit for the defined purpose, i.e. ownership from
digital age ensuring the business management
is not just about
making profits
Vietnam: 24% Vietnam: 18% Vietnam: 15%
Global: 21% Global: 10% Global: 8%
1100 || PPwwCC’’ss GGlloobbaall NNeexxttGGeenn SSuurrvveeyy 22002244 -- VViieettnnaamm rreeppoorrtt
Thriving on change
The story of Biti’s
Miss Vuu Le Quyen
CEO of Biti’s
In the dynamic realm of business, where As Biti’s starts integrating emerging
technology constantly reshapes the technologies such as GenAI into its
landscape, the story of Ms. Vuu Le Quyen, operations, Ms. Quyen emphasises the
CEO of Biti’s, offers valuable insights into importance of involving the founding
navigating change and preparing for an AI- generation through sharing compelling
driven future. use cases with her parents, who founded
the company, demonstrating the tangible
While driving business growth remains the
benefits and opportunities of new
company’s top priority, Ms. Quyen is deeply
technologies, thus fostering support and
committed to a people-centric approach.
understanding.
Initiatives such as “Happy Biti’s” underscore
her dedication to fostering a positive and “Reflecting on Biti’s current early-stage
transparent work culture that prioritises adoption of technologies like GenAI, I see the
employee well-being. Recognising the imperative for action. In the ever-changing
inevitable impact of emerging technologies business and technological landscape,
on the future workforce, she emphasises the adaptation is vital for survival. By identifying
need for organisational readiness to embrace gaps, assessing risks and opportunities,
change for the company’s advancement. and investing in areas that yield competitive
advantages, we can attain sustained growth
Acknowledging the natural resistance to
and success, with our people leading the
change within organisations, Ms. Quyen
charge.” - Ms. Quyen.
highlights the importance of effective change
management. In addition to providing
processes and training, she values the
significance of shifting mindsets. Clear
communication and engagement from
leadership are vital in helping employees
grasp the benefits of adaptation. Once
employees recognise the value and benefits,
they become proactive in self-learning,
experimentation, and integrating new
knowledge into their daily tasks.
1111 || PPwwCC’’ss GGlloobbaall NNeexxttGGeenn SSuurrvveeyy 22002244 -- VViieettnnaamm rreeppoorrtt
Exploring Generative AI
(“GenAI”) and Emerging
Technologies
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Attuned to emerging technologies
Most NextGen in Vietnam are very interested in
GenAI
According to the 2024 PwC Asia Pacific CEO Survey,
the emergence of climate change and technological
advancements, notably led by innovations like GenAI,
82%
has intensified the call for CEOs to adapt their strategies.
An overwhelming 77% of business leaders in the region
anticipate substantial shifts in how their companies
of NextGen in Vietnam
generate, deliver and capture value within the next three
are personally
years, all due to the influence of GenAI.
interested in GenAI
Echoing this sentiment, Vietnamese NextGen are
(Global: 82%)
increasingly intrigued by the transformative potential
of GenAI. Our research reveals an impressive 82% of
Vietnamese NextGen expressing personal enthusiasm for
exploring GenAI, reflecting a widespread recognition of its
power among the younger cohort of leaders.
Vietnamese NextGen see themselves as
knowledgeable on GenAI
55%
This shows a confidence in GenAI’s ability to drive
positive change and innovation, mirroring a broader
of NextGen in Vietnam
trend of embracing cutting-edge technologies in
feel personally
Vietnam’s evolving business landscape.
knowledgeable
This knowledge on GenAI makes Vietnamese NextGen
about GenAI
well positioned to derive insights and unlock the
(Global: 53%)
creative possibilities and business potential of AI and
emerging technologies, from AI-generated content to
AI-driven analytics.
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Ambition to lead AI innovation in family
businesses
67%
Personally, nearly two third (58%) also
believe that championing AI initiatives will
Vietnamese NextGen share a strong
enable them to make a name for themselves
consensus regarding GenAI’s
and allow them to become leaders within
potential as a powerful tool for
their businesses.
business transformation.
They view themselves as instrumental in
driving the adoption of GenAI, advocating
for its integration across various business
They perceive GenAI not merely as a
functions and processes. And over half (55%)
technological innovation, but as a catalyst
are keen to guide their businesses through
for redefining business operations,
the complexities of adopting these new
strategies and customer experience.
technologies.
There is widespread recognition among
Their proactive stance towards embracing
Vietnamese NextGen (67%) that AI
GenAI reflects not only their readiness to
represents a significant opportunity for
adapt but also their commitment to driving
family businesses to assume a leading
transformative change in their family
role in the responsible use of technology
businesses and the broader business
and AI. They envision their businesses not
landscape.
only adapting to the digital age but also
pioneering ethical and sustainable practices
in AI implementation.
Agreement with statements (%)
AI is a powerful force for 67
business transformation 73
There is an opportunity for family
67
businesses to take a leading role in the
responsible use of technology and AI 50
Vietnam
Being an AI champion will help me 58
Global
move into a leadership position
40
I feel I can personally help the
55
business to navigate emerging
technologies / AI 42
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Navigating the gap between NextGen
ambition and organisational AI readiness
However, a significant disparity arises 76%
between the personal ambition of
Vietnamese NextGen for GenAI and the
Vietnamese NextGen respondents
readiness of organisations to implement
say that their business is likely to be
such initiatives.
involved in AI-related initiatives in the
Vietnamese family businesses are very future,
much in the early stage of adopting these
new technologies, with a majority (63%) of
family businesses in Vietnam having yet to showing an optimistic outlook towards
explore AI. the adoption of this emerging technology
within the Vietnamese business landscape.
Nevertheless, there are still promising signs
Crafting an “early-stage” GenAI strategy is
of change. Nearly one third revealed that
essential for family businesses to maintain
they are currently exploring and piloting
a competitive edge amidst the rapidly
AI-related initiatives (27%), with a smaller
accelerating imperative for business
proportion having already implemented
reinvention.
such initiatives (9%). In addition, 27% of
Vietnamese family businesses (compared to
Read more:
the global average of 14%) have dedicated
Craft an “early-stage” GenAI
personnel or teams responsible for GenAI
strategy for leading adoption
initiatives within their organisations. and success.
Business’s current level of adoption of GenAI (%)
64% 24% 3% 9%
No activity/ Exploration Test/ Pilot Implementation
Not sure (Global: 30%) (Global: 7%) in a few or many areas
(Global: 55%) (Global: 7%)
36% already exploring/implementing AI
Read more:
GenAI: Bridging the gap
between intent and adoption
15 | PwC’s Global NextGen Survey 2024 - Vietnam report
Harnessing the advantages of AI
in the next three years
When exploring about the impact of GenAI on
family businesses,
39% agree that GenAI has already
begun influencing company strategy.
However, opinions diverge on its immediate effects
such as reducing headcount (24% believe this will
NextGen expect a
happen) and increasing profitability (18%).
rising impact from
A significant consensus is found in the acknowledgment
GenAI on business
that GenAI will necessitate a fundamental shift in workforce
skills (73%). Vietnamese NextGen widely agree that
employees must develop new competencies to effectively
harness this technology. Additionally, 64% share the
understanding that GenAI will reshape how companies
operate, from value creation to delivery and capture.
Impact from GenAI on family business in the next three years
Last 12 months Next 12 months Next 3 years
39% 24% 73%
say that AI has think that GenAI will think GenAI will require most of their
already changed their result in headcount workforce to develop new skills
company strategy reduction (Global: 48%)
(Global: 15%) (Global: 18%) 64%
18%
think GenAI will significantly change
think GenAI will increase the way their companies generate,
the profitability of their deliver, and capture value
company (Global: 44%)
(Global: 21%)
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Seizing opportunities presented
by AI, particularly in regard to the
workforce and skills
Vietnamese NextGen view GenAI as a catalyst Through leveraging AI-driven insights and
for business transformation, anticipating automation, businesses can effectively equip
benefits like increased employee productivity their teams with the digital skills necessary for
(39%), cost savings (39%), and enhanced success.
customer experience (36%). This highlights
These insights again highlight that Vietnamese
the strategic significance of embracing AI in
NextGen recognise the importance of a human-
today’s competitive landscape, positioning
led, tech-powered approach, where new
family businesses for success in the digital age.
technologies such as GenAI can greater power
In discussions about workforce upskilling, human potential.
Vietnamese NextGen reaffirm the critical role
of AI. Over half believe that GenAI will play a
key role in enhancing the digital capabilities of
their workforce, surpassing the global average
of 41%.
Top
opportunities or benefits
that NextGen believe AI
can/will bring to family
business
Enhanced
customer
experience
36%
Cost saving
Increased 39%
(Global: 25%)
employee
productivity (Global: 36%)
39%
(Global: 46%)
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Acknowledging challenges
in AI advancements
In line with their global counterparts, Vietnamese
NextGen recognise the challenges inherent in AI
advancements. Over two thirds of them acknowledge
the rapid evolution of AI technologies, which poses a
significant challenge for businesses striving to keep
pace with these advancements. Additionally, nearly half
of Vietnamese NextGen find it challenging to capitalise
on AI, indicating the complexity involved in effectively
leveraging this technology for business purposes.
Looking ahead,
73%
Vietnamese NextGen foresee that AI will
intensify competition in the market over the
next three years, highlighting the growing
pressure to stay ahead in an increasingly
AI-driven landscape.
These findings indicate the multifaceted challenges and
competitive dynamics associated with AI innovation,
emphasising the need for strategic adaptation and
proactive measures to navigate this rapidly evolving terrain.
Agreement with statements (%)
In the next three years, AI will lead to more
73
competitor pressure (e.g., new products, entrants,
64
price changes)
Vietnam
AI seems to evolve so quickly that it’s hard 70
to keep up 66
Global
48
It’s difficult to know how to capitalise on AI
51
18 | PwC’s Global NextGen Survey 2024 - Vietnam report
Advocating for a governance
framework for Responsible AI
Over half of NextGen in
Vietnam express concerns
Consequently, Vietnamese NextGen are
about the potential increase in
increasingly acknowledging the necessity of
cybersecurity risks associated governance structures for utilising AI within
with GenAI adoption. family businesses, with 69% recognising the
imperative of defining such frameworks. Clear
governance structures will play a pivotal role
in guiding ethical AI practices, mitigating risks,
and fostering trust among stakeholders.
Within this group, only 21% have taken
This reflects a heightened awareness of tangible steps. However, a further 48%
the vulnerabilities that may arise from the believe they need to do so, signalling a
use of these new technologies, such as growing awareness of the importance of
data breaches, privacy infringements, and proactive governance in AI adoption. There
cyber attacks. As GenAI systems often rely is a need for family businesses to prioritise
on vast amounts of sensitive data, there’s the establishment of robust governance
a growing need for robust cybersecurity frameworks to deploy AI technologies ethically
measures to mitigate these risks and and effectively.
safeguard critical assets.
Agree that GenAI is likely to increase the following risks in your company in
the next 12 months
Cybersecurity risk 58
(e.g., phishing attacks, data breaches)
48
52
Spread of misinformation
33 Vietnam
Global
52
Legal liabilities and reputational risks
29
Bias towards specific groups of 39
customers or employees
25
19 | PwC’s Global NextGen Survey 2024 - Vietnam report
SAudcocpetsins gst GoreinesAI responsibly
AI has the power to greatly transform our lives and work, but
we need to manage its risks to unlock its full potential in a
secure manner. When integrating AI into business processes,
it’s crucial to understand how decision making is supported
to ensure accuracy and fairness, as well as protect privacy
while fostering growth and innovation. That’s where
Responsible AI comes in.
Responsible AI is about managing risks in AI-based
solutions. It means evaluating our current practices or
creating new ones to ensure we use AI responsibly.
Organisations worldwide recognise the importance of
Responsible AI, though they may be at different stages of
implementation. By investing in Responsible AI from the
start, we gain a competitive edge. We can actively assess
related risks and establish an effective AI governance
framework using a standardised AI risk taxonomy and
toolkit.
As AI may significantly impact current family businesses in
the near future, NextGen leaders in Vietnam should consider
how AI could disrupt their current business models. They
need to seize new opportunities that AI can offer and invest
in the right resources and expertise to foster a culture of
adaptability. By embracing Responsible AI throughout their
AI adoption journey, they can harness its transformative
power while safeguarding their core values.
With this approach, family businesses in Vietnam can
navigate and shape a sustainable future of success within
the evolving AI landscape.”
Pho Duc Giang
Director
Data Trust and Cybersecurity
Services
PwC Vietnam
20 | PwC’s Global NextGen Survey 2024 - Vietnam report
20
Maximising NextGen’
contribution to family
businesses
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Fostering alignment between generations
Vietnamese NextGen seek to be agents of This highlights the barrier to adopting
change in order to bring innovation and change new ideas and innovation within family
within family businesses. However, in order for businesses, as the NextGen seek to drive
them to play their role as innovation catalysts, transformative change while encountering
there will need to be alignment between the internal resistance.
perspectives of the two generational cohorts, to
Furthermore, there is disparity in
make sure that business and family are united
perceptions regarding digital capabilities
in carrying out these transformative tasks.
between CurrentGen and NextGen.
A notable finding reveals that Interestingly,
almost half of 45% 42%
Vietnamese NextGen
perceive a lack of comprehensive a greater proportion of CurrentGen
understanding among the current have a positive view of their
generation (“CurrentGen”) company’s digital capabilities
regarding the opportunities linked
compared to NextGen (27%).
to technology transformation within
the business,
This suggests a potential gap in
a figure notably higher than the global understanding or prioritisation of digital
average (29%). initiatives between the two generational
cohorts. Closing this gap and fostering
alignment in perspectives will be crucial
Additionally, for leveraging the full potential of NextGen
as innovation accelerators within family
a substantial majority 61%
businesses.
of Vietnamese
NextGen encounter
resistance within their organisations
towards embracing change,
compared to the CurrentGen (39%).
Agree with statements - Vietnam (%) ?
There is a resistance within the 61
company
39
NextGen
27
CurrentGen
We have strong digital capabilities
42
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Governance and trust as bedrock for
growth and innovation
Strengthening family governance structure Additionally, over half of NextGen (52%)
and trust within family businesses struggle to understand the criteria for family
employment and board member/executive
Strong governance and internal trust serve as
selection, indicating a transparency gap.
the bedrock for family businesses venturing
into the era of emerging technologies. Moreover, trust issues pose a significant
However, Vietnamese NextGen show less challenge, with NextGen less likely to perceive
optimism in these crucial areas compared high levels of trust between family members.
to the perspectives of the CurrentGen, as Particularly, only 27% of NextGen believe
revealed in our Family Business Survey 2023 there are high levels of trust between them
- Vietnam Report. and the CurrentGen, compared to 31% of the
CurrentGen.
45%
Addressing this “trust gap” is paramount for
Vietnamese NextGen are likely the future growth of family businesses. Our
Family Business 2023 survey emphasises the
to express their clarity on the
importance of establishing a formal family
governance structure of their
governance structure and fostering transparent
business,
communication to manage conflicts effectively
compared to 50% of the CurrentGen. and ensure the longevity of family enterprises.
Similarly, perceptions about roles and
responsibilities are less defined among
NextGen, with only 48% feeling clarity
compared to 53% of the CurrentGen.
Agree with statements - Vietnam (%) ?
There are clear roles and
48
responsibilities for those involved in
53
running the business
45
We have a clear governance structure
50
High level of trust between NextGen
27
family members and the current
generation 31
High level of trust between family
15 NextGen
members outside the business and family
members working in the business 25
CurrentGen
High level of trust between family 15
owners and non-management
28
High level of trust between family
18
members and non-family members
within the business 22
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Involvement in succession
Compared to our NextGen 2022 survey, it is encouraging
to note a growing awareness among NextGen regarding
succession planning, with 72% indicating involvement
in the process (up from 58% in 2022), and 39% actively
participating in plan development (up from 26% in 2022).
72% 39%
indicating involvement actively participating in
in the process plan development
(up from 58% in 2022) (up from 26% in 2022)
Regarding succession hurdles, 36% of Vietnamese
NextGen find the readiness of the CurrentGen to retire a
difficult aspect, while 58% anticipate challenges in proving
24
themselves as new leaders or board members. However,
these figures reflect greater optimism compared to our
2022 survey results, where 42% and 61%, respectively,
expressed similar sentiments.
Awareness of succession plan - Vietnam
6
29
21
13
33
26
39
32
2024 2022
Yes, and we have developed the plan together
Yes, but I was not involved in its development
No, there is no plan
I don’t know if there is a plan
24 | PwC’s Global NextGen Survey 2024 - Vietnam report
Next steps
Turbocharging innovation in
family businesses
For
For NextGen
CurrentGen
Seek out dialogue Listen and put innovation in your
CEO and board agenda
If you feel well equipped to
understand the related opportunities Actively listen to concerns and have
and risks, don’t be afraid to talk to clear communication with NextGen
the current generation of leadership on innovation decisions. The decision
and raise your questions or concerns to implement innovation in the
about technological transformation. It business should be a debate about
will be for your mutual benefit. strategy, not about functions, tools or
technology.
Understand your business and
initiate or volunteer pilot projects Involve NextGen in AI
You need to be knowledgeable about You can start by engaging NextGen
the current business model and the in low-risk, high-return GenAI pilot
financial and organisational limits of programmes. This will support the
the company. Embrace opportunities generational transition and prepare
to learn, try and fail through pilot them for future roles in the business.
projects in order to develop your skills Treat them as a valuable resource
and talents with great passion. with which to build your AI strategy
and firm-wide AI capabilities.
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Next steps
Co-managing generational transition
in an AI-driven world
For CurrentGen
For NextGen
Get your licence to earn trust Use NextGen and their enthusiasm
to strengthen the board
You want to earn recognition and
respect in your own right and not Combine NextGen’ acumen for
depend on your birthright. A solid technology with the business
education and relevant working experience of older board members.
experience outside the family Invite them as guests to your board
business will give you the confidence meetings, and allow them to listen,
to contribute and the ability to learn and contribute their fresh ideas
earn the trust necessary for future and perspectives.
leadership or board positions.
Align family values, business
Strike the right balance purpose and governance
Finding the balance among respect, Family businesses can be conscious
continuity, disruption and change adopters of GenAI, but it is important
is key to making the business for everyone in the family to
ready for a digital future. Your role understand what this means and how
is to challenge the status quo in a it aligns with the family values and
constructive and respectful manner. business purpose. Reviewing your
family and corporate governance as
well as your leadership and board
composition will help to get this right.
2266 || PPwwCC’’ss GGlloobbaall NNeexxttGGeenn SSuurrvveeyy 22002244 -- VViieettnnaamm rreeppoorrtt
Generational alignment
in digital era
Aligning NextGen and current leaders’ understanding of
digital initiatives is vital for unlocking NextGen’ innovation
potential in family businesses. Fostering governance and
trust is key.
NextGen need to learn how to combine the legacy and
values of their family business with their own ambitions, as
well as having transparent communication on innovation
ideas. Meanwhile, the current generation can help the next
generation prepare for leadership by involving them in
leadership decision-making and letting them manage lower-
stakes innovation projects where they can prove themse"
10,pwc,the-emerging-threat-of-ai-powered-fraud_20.pdf,"The emerging threat o f AI-powered fraud
Artificial Intelligence (AI) is changing the landscape in our world today. ChatGPT, DALL-E and
other GenAI tools have ushered in a new era of advancements, allowing users to boost
productivity. A 2023 survey by IBM revealed that 42% of enterprise-scale companies have
actively deployed AI into their business model with an additional 40% currently exploring and
experimenting with AI1. This tells one story— AI is poised to transform businesses and will soon
become critical for businesses.
However, the widespread adoption of AI by businesses also
“
means that the technology will attract malicious actors such as
fraudsters and cybercriminals. As legitimate businesses seek to
grow using this new technology, fraudsters are also exploring
how to use AI to advance their “business”. As a result,
understanding the strategic opportunities and the inherent fraud
risks that comes with AI is now of paramount importance for
today’s business leaders.
42% of enterprise-scale companies
have actively deployed AI into their
business model.
Source: IBM Global AI Adoption Index 2023
1. BM Newsroom. 2024. “Data Suggests Growth in Enterprise Adoption of AI is Due to Widespread Deployment by Early Adopters, But Barriers Keep 40% in the Exploration and Experimentation Phases.” IBM Newsroom.
https://newsroom.ibm.com/2024-01-10-Data-Suggests-Growth-in-Enterprise-Adoption-of-AI-is-Due-to-Widespread-Deployment-by-Early-Adopters.
2
The emerging threat o f AI-powered fraud
History of fraud and technology
Historically, adoption of technology by individuals and businesses resulted in a corresponding adoption by fraudsters.
Emails led to the rise of phishing emails and email scams, social media platforms led to the rise of fake profiles for
scamming as well as outright theft of online identity, and e-commerce and digital payment platforms resulted in high
rates of online payment fraud. A recent study estimates that global cumulative merchant losses to online payment
fraud will exceed $343 billion between 2023 and 2027.2
USSD banking introduced in Nigeria also resulted in a surge in sim swap frauds enabling fraudsters to access funds,
obtain loans and make other transactions on victims accounts. Gift cards and reward programmes have led to a
wave of scams, forcing many establishments to either scrap them entirely or implement stricter regulations. In 2022,
Bloomberg reported that PayPal shut down 4.5 million accounts linked to exploiting their incentive and rewards
program.
These examples show that technological advancements have also led to new frauds and vulnerabilities to businesses
and individuals. Consequently, the prevalent adoption of AI also means that AI-powered fraud is coming.
2. Maynard, Nick. 2022. “Online Payment Fraud Losses to Exceed $343 Billion Globally Over the Next 5 Years | Press.” Juniper Research. https://www.juniperresearch.com/press/online-payment-fraud-losses-to-
exceed-343bn/.
3
The emerging threat o f AI-powered fraud
How businesses are using GenAI
“
Companies are integrating AI into their business strategies. PwC’s
27th Annual Global CEO Survey West Africa report found that 51%
of companies have adopted GenAI across their companies and
47% believe that GenAI will improve the quality of their company’s
products and services3. Businesses are using AI to automate
customer service, screen candidates in the recruitment process,
predict market trends, optimise energy distribution etc. In Nigeria,
companies in the financial services industry have integrated GenAI
in customer service with the introduction of chatbots.
When adopting new technology, businesses need to consider the
risks adoption exposes them to. There have been instances where
researchers manipulated AI and convinced chatbot users to visit a
51% of companies have adopted GenAI
website containing malware or a phishing text in order to get credit across their companies.
card details4. Another paper on Large Language Models (LLM)
found random words that when fed to chatbots, will cause them to Source: PwC’s 27th Annual Global CEO Survey
West Africa report
ignore their boundaries, resulting in these chatbots providing
instructions for building an explosive device and manipulating
elections5.
This leads to an important question - how are companies managing the fraud risks and other threats arising from this
new technology? Business leaders should mandate their teams to conduct extensive risk assessment to ascertain
how AI exposes their business to vulnerabilities or fraud. This is not only important for entities that are adopting or
plan to adopt AI. It is equally important for those entities who are yet to embark on any AI journey. PwC’s CEO
Survey further highlights that business leaders are concerned GenAI adoption will expose their businesses to
cybersecurity and misinformation risks.
3. PwC Nigeria. 2024. “PwC's 27th Annual Global CEO Survey - West Africa.” PwC. https://www.pwc.com/ng/en/publications/pwc-ceo-survey.html.
4. Greshake, Kai, Sahar Abdelnabe, Shailesh Mishra, Christoph Endres, Thorsten Holz, and Mario Fritz. 2023. “Not What You've Signed Up For: Compromising Real-World LLM-Integrated Applications with Indirect
Prompt Injection.” AISec '23: Proceedings of the 16th ACM Workshop on Artificial Intelligence and Security, (November), 79-90. https://doi.org/10.1145/3605764.3623985.
5. Zhou, Andy, Zifan Wang, Nicholas Carlini, Milad Nasr, J. Z. Kolter, and Matt Fredrikson. 2023. Universal and Transferable Adversarial Attacks on Aligned Language Models.
https://doi.org/10.48550/arXiv.2307.15043.
4
The emerging threat o f AI-powered fraud
How fraudsters are using GenAI
The developments in artificial intelligence have the potential to increase the volume and sophistication of fraud. Here
are some possible exploitations of AI for fraud6.
Generating text and image content.
GenAI can be used to create tailored emails, instant messages and image content
as the bait to hook potential scam victims, for example, in phishing and smishing
attempts, or through fraudulent adverts. GenAI can also make these scams harder
to detect by eliminating the traditional ‘tells’ such as poor spelling and grammar.
There have been instances where AI generated images (e.g. of damaged property)
were used to support insurance claims.
AI-enabled chatbots.
Fraudsters are leveraging elements of AI in chatbots that converse with victims to
manipulate them in a scam. Chatbots have the potential to amplify fraudsters’ ability
to reach victims, delivering volumes of scams that would previously have required a
large team of individuals operating in a scam centre.
Sophisticated targeting of victims.
Other instances where AI tools may be of benefit to cybercriminals is in the review
of large volumes of data to identify potential victims and tailor scam content to an
individual’s specific vulnerabilities. For example, using online content to identify an
individual’s employment details, family circumstances, where they have recently
been on holiday etc. GenAI could make it easier for fraudsters to analyse large
sets of data for their pig-butchering scams and perform them at scale.
6. PwC UK. 2024. “Written evidence submitted by PwC.” Committees. https://committees.parliament.uk/writtenevidence/125808/pdf/
5
The emerging threat o f AI-powered fraud
Deep fake videos.
Deep fakes are now used as ‘click bait’ to direct users onto malicious websites (where
their credit card information may then be harvested) or which use a trusted persona to
encourage investment in a scam. In April 2024, a video posted on social media
featured a Channels Television news anchor and Nigerian business mogul, Aliko
Dangote. In the video, Dangote appears to be promoting a cryptocurrency investment
scheme. Channels Television subsequently released a statement where it clarified that
the video was doctored using existing footage and a generated voiceover.
Voice cloning.
Deep fake technology can copy voices to an increasingly high degree of accuracy.
Currently, voice clones potentially require as much as an hour of training data to
perfect, but that requirement is reducing all the time. Voice clones can then be used
to trick individuals into making payments and can be used to break through
systems where voice biometrics are used for ID verification. In the upcoming 2024
US election, there are growing concerns about the implications of deepfakes as AI-
imitation of Joe Biden’s voice was used to discourage voters in New Hampshire7.
7. SWENSON, ALI, and WILL WEISSERT. 2024. “Fake Biden robocall being investigated in New Hampshire.” AP News. https://apnews.com/article/new-hampshire-primary-biden-ai-deepfake-robocall-f3469ceb6dd613079092287994663db5.
6
The emerging threat o f AI-powered fraud
The key impact of AI will be to enable
fraudsters to create content at greater speed
and in greater volume, and to make scams
more believable. For example, a fraudster who
has stolen the sim or WhatsApp profile of a
business leader could clone the owner’s voice
and use it to authorise payments or use AI to
generate content and use it to defraud the
business leader’s networks etc.
In February 2024, a finance officer in Hong
Kong had a video conference call with his Chief
Financial Officer and other team members. On
the call, he was directed to pay out $25 million.
The Hong Kong police reported that after
checking with the head office, the employee
discovered that everyone on the multi-person
conference call was (deep) fake8. In May 2024,
Financial Times identified the company as UK-
based engineering group, Arup9.
In May 2024, the CEO of WPP was the target
of a deepfake scam. Fraudsters created a fake
WhatsApp account using his image and set up
a Microsoft Teams meeting with an agency
leader, impersonating the CEO and another
senior executive. They used AI voice cloning
and YouTube footage to make the scam more
convincing. The scammers attempted to trick
the agency leader into setting up a new
business and revealing sensitive information10.
WPP noted that the scam attempt was
unsuccessful.
These reports show that businesses are
vulnerable to AI fraud.
8. Chen, Heather, and Kathleen Magramo. 2024. “Finance worker pays out $25 million after video call with deepfake ‘chief financial officer.’” CNN. https://edition.cnn.com/2024/02/04/asia/deepfake-cfo-scam-hong-
kong-intl-hnk/index.html.
9. Financial Times. 2024. “Arup lost $25mn in Hong Kong deepfake video conference scam.” Financial Times. https://www.ft.com/content/b977e8d4-664c-4ae4-8a8e-eb93bdf785ea.
10. Robins, Niick. 2024. “CEO of world's biggest ad firm targeted by deepfake scam.” The Guardian. https://www.theguardian.com/technology/article/2024/may/10/ceo-wpp-deepfake-scam.
7
The emerging threat o f AI-powered fraud
How businesses can prepare for AI fraud
Perform proactive fraud risk assessment.
Businesses should conduct periodic and proactive fraud risk
assessments to protect their organisation from AI-powered fraud.
Such risk assessments should start from reviewing existing
processes and identifying how malicious actors can leverage GenAI
to exploit the processes. This would entail the business staying
updated on AI developments.
In addition to this, businesses should incorporate fraud risk
assessment into their internal frameworks and mechanisms for
launching new products or adopting a new technology. This will help
them identify how the new technology and product could create new
risks or exacerbate existing ones, giving the advancement in AI at
the time of launch.
Businesses will become aware of fraud risks they face in the light of
changes in AI and should be able to take proactive measures to
mitigate such risks.
Review and update the anti-fraud strategy and framework.
Most businesses have not updated their anti-fraud policies, despite the rapidly changing business
environment powered by an equally rapid advancement in technology. Businesses should update their
anti-fraud policies to reflect current state realities and incorporate how the organisation intends to deal
with emerging fraud risks. In defining what constitutes fraud and misconduct, each organisation’s anti-
fraud policy should detail examples of use cases of AI and GenAI that would constitute fraud by
employees, vendors and other stakeholders.
ACFE’s 2024 Anti-fraud Technology Report highlighted that 83% of organisations plan to adopt GenAI in
their anti-fraud strategy11. Similarly, 69% of respondents to PwC’s 2024 Digital Trust Insight survey noted
that they plan to use generative AI for cyber defence in 2024, and nearly half (47%) are already using it for
cyber-risk detection and mitigation12.
11. Association of Certified Fraud Examiners and SAS. 2024. “2024 Anti-Fraud Technology Benchmarking Report.” ACFE. https://www.acfe.com/-/media/files/acfe/pdfs/sas_benchmarkingreport_2024.pdf.
12. PwC. 2023. “2024 Global Digital Trust Insights Survey.” PwC. https://www.pwc.com/us/en/services/consulting/cybersecurity-risk-regulatory/library/global-digital-trust-insights.html.
8
The emerging threat o f AI-powered fraud
Businesses should adopt the use of GenAI in their fraud prevention and detection efforts. This would
involve in-depth reviews of business needs and requirements before identifying the technology/system to
adopt or deploy. This should include outlining the roles and responsibilities of AI systems and human
analysts, establishing clear protocols for handling AI-generated alerts and insights, and ensuring
compliance with relevant regulatory requirements.
The fraud awareness and training programme should be updated to include modules that educate
employees on AI-enabled fraud and the potential impact to them and the organisation as a whole. Such
sessions should provide employees with clear steps and procedures to take when they suspect or
become aware of such fraud.
9
The emerging threat o f AI-powered fraud
Empower anti-fraud teams with the right skillset and tools.
Businesses should invest in building the capacity generative AI, and creating a controlled sandbox
of their anti-fraud teams to deal with and environment where employees can freely
respond to Ai-enabled fraud. This includes experiment and test innovative ideas without risk.
providing them with access to AI training as well Government can support businesses by raising
as investing in the right tools for investigating AI- awareness among individuals and law
powered fraud. enforcement agencies. This is essential to
combat GenAI-powered fraud effectively. The
In the light of the advances in AI, every government can initiate online campaigns to
organisation’s anti-fraud team must have digital inform the public about AI-driven fraud.
forensics capabilities (i.e. training and tools)
which will serve as a foundation for investigating In addition, the government should promote
and gathering evidence related to AI-enabled collaboration between the players in public and
fraud. Earlier this year, ACFE’s Anti-fraud private sectors to address the emerging threat of
Technology Report indicated that only 29% of AI-powered fraud. By working together, these
organisations have an anti-fraud program that institutions can pool resources, share insights,
involves digital forensics or e-discovery software. and develop coordinated strategies to effectively
This number would be significantly lower in fight fraud. Governments can encourage this
Nigeria and other countries in Sub-Saharan collaboration by:
Africa.
● Providing incentives for industry-led initiatives
Business leaders should balance their aimed at strengthening and enhancing fraud
enthusiasm for GenAI with a clear understanding detection capabilities; and,
of fraud and other risks that would be involved ● Creating regulatory frameworks to enable
with its use. They should think through the information-sharing and cooperation,
controls they can implement to mitigate those ultimately creating a more fraud-resilient
unique risks. They can consider developing ecosystem.
comprehensive training programs and guidelines
to ensure the ethical and responsible utilisation of
10
The emerging threat o f AI-powered fraud
Conclusion
The increased reports of deepfake scams and attempted fraud schemes send a clear message and
warning: AI-powered fraud is here. Business executives must update their fraud framework, perform risk
assessment on areas of their businesses that are vulnerable to GenAI-enabled scams and ensure their
anti-fraud team have the right skills and tools to detect and respond to threats from AI. Additionally,
governments, industries and law enforcement should collaborate to develop a framework for preventing
and responding to AI-enabled fraud.
11
The emerging threat o f AI-powered fraud
Contact us
Habeeb Jaiyeola
Partner and Forensics S ervices Leader,
PwC Nigeria
habeeb.jaiyeola@pwc.com
+234(0)803-394-5167
Adeola Adekunle
Associate Director, Forensics Services,
PwC Nigeria
adeola.adekunle@pwc.com
+234(0)806-486-3890
At PwC, our purpose is to build trust in society and solve important problems. We’re a network of
firms in 151 countries with more than 360,000 people who are committed to delivering quality in
assurance, advisory and tax services. Find out more and tell us what matters to you by visiting us at
www.pwc.com.
©2024 PwC. All rights reserved. PwC refers to the PwC network and/or one or more of its member
firms, each of which is a separate legal entity. Please see www.pwc.com/structure for further details.
The emerging threat o f AI-powered fraud"
11,pwc,s-b_on_PwC_early-days-generative-AI-strategy.pdf,"07 DECEMBER 2023
Do you have an
“early days”
generative AI
strategy?
Organizations at the forefront of generative
AI adoption address six key priorities to set
the stage for success.
by Scott Likens and Nicole Wakefield
www.pwc.com/generative-ai-strategy
Scott Likens Nicole Wakefield
is PwC’s Global AI and Innovation is PwC’s Global Advisory Clients and
Technology Leader. He is a principal Markets Leader. She is a partner
with PwC US. with PwC UK.
In a recent conversation about generative AI with one of our colleagues, the CIO
of a major healthcare company laid out a wide range of issues that concerned
her: risk protocols, use case development, cybersecurity, ethics and bias, train-
ing and development, and many more. After a few minutes, our colleague asked
the client to take a step back: “How clear are you on what you are trying to ac-
complish, and why? In other words, do you have a strategy?” These questions
stopped the CIO, leading her to call a series of meetings with key leaders, and
ultimately the board, to create a sharper set of objectives. What emerged was
a group of priorities that collectively formed what might be termed an “early
days” AI strategy.
Early days, because—let’s face it—that’s exactly where we are with gener-
ative AI. It was only in November 2022 that the consumer release of ChatGPT
captured the world’s imagination. Since then, organizations have been strug-
gling to keep up with the pace and potential they see in this new, general-pur-
pose technology application. Some organizations are doing better than others,
and it’s not too soon to start taking stock of early leaders that are leveraging
generative AI to capture value and pull ahead. Across industries, we’re seeing
these leaders tackling a number of critical priorities:
• They’re navigating tensions between the need for prudence and risk
mitigation, and the importance of moving quickly to grab emerging
opportunities.
• They’re aligning their new generative AI strategy with their existing digi-
tal and AI strategies, building on these foundations to guide their thinking
2 | www.pwc.com/strategy-business
rather than starting from scratch.
• They’re thinking big—encouraging experimentation across their organi-
zations, with a focus on identifying use cases that can scale.
• Rather than simply looking for ways to improve productivity, they’re look-
ing strategically at their options for putting productivity gains to use.
• Relatedly, they’re considering impacts on workers, roles, and skills-build-
ing, determining how best to both prepare employees to take advantage
of the new tools available and include employees in shaping the compa-
ny’s generative AI journey.
• They’ve realized that with such a potentially disruptive technology, team-
ing up and collaborating with their ecosystems can be a truly transforma-
tive route to a radical rethink of their value chains and business models.
In many cases, these priorities are emergent rather than planned, which is
appropriate for this stage of the generative AI adoption cycle. Leaders and orga-
nizations are learning as they go.
Priority 1: Manage the AI risk/reward tug-of-war
There’s a fascinating parallel between the excitement and anxiety generated by
AI in the global business environment writ large, and in individual organiza-
tions. At the same time that surging market capitalizations for early AI leaders
are providing financial evidence of the opportunity investors and markets see
in generative AI, a number of experts in the field are voicing existential angst
about the potentially significant unintended consequences that could emerge as
the reach of AI grows. Similarly, in many companies we know, there’s a tug-of-
war going on between the executives and managers seeking to rapidly tap the
potential of generative AI for competitive advantage and the technical, legal,
and other leaders striving to mitigate potential risks. Although such tension,
when managed effectively, can be healthy, we’ve also seen the opposite—dis-
agreement, leading in some cases to paralysis and in others to carelessness, with
large potential costs.
Achieving healthy tension often starts with a framework for adopting AI re-
sponsibly. At PwC, we developed such an approach several years ago, and we
3 | www.pwc.com/strategy-business
continue evolving it with the changing nature of AI opportunities and risks.
Practical safeguards and guidelines help organizations move forward faster, and
with more confidence. Open-minded, agile leadership also is critical: risk-mind-
ed leaders deliver better, faster guidance as they internalize the momentous sig-
nificance of the generative AI revolution. Opportunity-seekers are well-served
by spending time immersing themselves in what can go wrong to avoid costly
mistakes. And both groups need a healthy dose of appreciation for the priorities
and concerns of the other.
One company we know recognized it needed to validate, root out bias, and
ensure fairness in the output of a suite of AI applications and data models that
was designed to generate customer and market insights. Given the complexity
and novelty of this technology and its reliance on training data, the only inter-
nal team with the expertise needed to test and validate these models was the
same team that had built them, which the company saw as an unacceptable con-
flict of interest. The near-term result was stasis.
Another company made more rapid progress, in no small part because of ear-
ly, board-level emphasis on the need for enterprise-wide consistency, risk-appe-
tite alignment, approvals, and transparency with respect to generative AI. This
intervention led to the creation of a cross-functional leadership team tasked with
thinking through what responsible AI meant for them and what it required. The
result was a set of policies designed to address that gap, which included a core
set of ethical AI principles; a framework and governance model for responsible
AI aligned to the enterprise strategy; ethical foundations for the technical ro-
bustness, compliance, and human-centricity of AI; and governance controls and
an execution road map for embedding AI into operational processes.
For this company, in short, addressing risk head-on helped maintain momen-
tum, rather than hold it back.
Priority 2: Align your generative AI strategy with your digital
strategy (and vice versa)
If you’re anything like most leaders we know, you’ve been striving to digitally
transform your organization for a while, and you still have some distance to go.
4 | www.pwc.com/strategy-business
The rapid improvement and growing accessibility of generative AI capabilities
has significant implications for these digital efforts. Generative AI’s primary out-
put is digital, after all—digital data, assets, and analytic insights, whose impact
is greatest when applied to and used in combination with existing digital tools,
tasks, environments, workflows, and datasets. If you can align your generative
AI strategy with your overall digital approach, the benefits can be enormous.
On the other hand, it’s also easy, given the excitement around generative AI and
its distributed nature, for experimental efforts to germinate that are disconnect-
ed from broader efforts to accelerate digital value creation.
To understand the opportunity, consider the experience of a global consumer
packaged goods company that recently began crafting a strategy to deploy gen-
erative AI in its customer service operations. Such emphasis has been common
among companies. The chatbot-style interface of ChatGPT and other generative
AI tools naturally lends itself to customer service applications. And it often har-
monizes with existing strategies to digitize, personalize, and automate custom-
er service. In this company’s case, the generative AI model fills out service tick-
ets so people don’t have to, while providing easy Q&A access to data from reams
of documents on the company’s immense line of products and services. That all
helps service representatives route requests and answer customer questions,
boosting both productivity and employee satisfaction.
As the initiative took hold, leaders at the company began wondering wheth-
er generative AI could connect with other processes they had been working to
digitize, such as procurement, accounts payable, finance, compliance, HR, and
supply chain management. It turned out that similar generative AI models, with
refinement and tailoring for specific business processes, could fill out forms, as
well as provide Q&A access to data and insights in a wide range of functions.
The resulting gains, in total, dwarfed those associated with customer service,
and were possible only because the company had come up for air and connect-
ed its digital strategy and its generative AI strategy. In this case, the alterna-
tive would have been a foregone opportunity to turbocharge existing digital ef-
forts. In the extreme, siloed digitization and generative AI efforts might even
work at cross-purposes. Given how much companies have already invested in
5 | www.pwc.com/strategy-business
digitization, and the significance of generative AI’s potential, there’s no substi-
tute for the hard work of bringing the two together.
A fringe benefit of connecting digital strategies and AI strategies is that the
former typically have worked through policy issues such as data security and
the use of third-party tools, resulting in clear lines of accountability and deci-
sion-making approaches. Such clarity can help mitigate a challenge we’ve seen
in some companies, which is the existence of disconnects between risk and legal
functions, which tend to advise caution, and more innovation-oriented parts of
businesses. This can lead to mixed messages and disputes over who has the final
say in choices about how to leverage generative AI, which can frustrate every-
one, cause deteriorating cross-functional relations, and slow down deployment
progress. These disconnects are easily avoided, though. At another financial
services company we know that was seeking to exploit generative AI in the HR
function, the CHRO, the CIO, and the CISO came together quickly to assess the
new opportunities against the company’s existing data, tech, and cybersecurity
policies, providing helpful guidance that maintained momentum.
Priority 3: Experiment with an eye for scaling
The C-suite colleagues at that financial services company also helped extend
early experimentation energy from the HR department to the company as a
whole. Scaling like this is critical for companies hoping to reap the full benefits
of generative AI, and it’s challenging for at least two reasons. First, the diversity
of potential applications for generative AI often gives rise to a wide range of pi-
lot efforts, which are important for recognizing potential value, but which may
lead to a “the whole is less than the sum of the parts” phenomenon. Second, se-
nior leadership engagement is critical for true scaling, because it often requires
cross-cutting strategic and organizational perspectives.
Experimentation is valuable with generative AI, because it’s a highly versa-
tile tool, akin to a digital Swiss Army knife; it can be deployed in various ways
to meet multiple needs. This versatility means that high-value, business-spe-
cific applications are likely to be most readily identified by people who are al-
ready familiar with the tasks in which those applications would be most useful.
6 | www.pwc.com/strategy-business
Centralized control of generative AI application development, therefore, is like-
ly to overlook specialized use cases that could, cumulatively, confer significant
competitive advantage. Certainly, our experience at PwC—where internal hack-
athons have identified value creation opportunities comprising 1 to 2% of reve-
nue in some of our service lines—has underscored the importance of engaging
individual workers and departments in experimentation and exploration.
Powerful as pilots like this are for spotting business-specific trees of opportu-
nity, they run the risk of missing the forest (at best) or (at worst) veering toward
the “pilot purgatory” state in which many corporate advanced data analytics ef-
forts found themselves a few years ago, with promising glimmers generating
more enthusiasm than value. The above-mentioned financial services company
could have fallen prey to these challenges in its HR department, as it looked for
means of using generative AI to automate and improve job postings and employ-
ee onboarding.
Fortunately, the CHRO’s move to involve the CIO and CISO led to more than
just policy clarity and a secure, responsible AI approach. It also catalyzed a re-
alization that there were archetypes, or repeatable patterns, to many of the HR
processes that were ripe for automation. Those patterns, in turn, gave rise to a
lightbulb moment—the realization that many functions beyond HR, and across
different businesses, could adapt and scale these approaches—and to broader
dialogue with the CEO and CFO. They began thinking bigger about the impli-
cations of generative AI for the business model as a whole, and about patterns
underlying the potential to develop distinctive intellectual property that could
be leveraged in new ways to generate revenue.
This same sort of pattern recognition also was important to scaling at the
consumer packaged goods company we mentioned earlier. In that case, it soon
became clear that training the generative AI model on company documenta-
tion—previously considered hard-to-access, unstructured information—was
helpful for customers. This “pattern”—increased accessibility made possible
by generative AI processing—could also be used to provide valuable insights to
other functions, including HR, compliance, finance, and supply chain manage-
ment. By identifying the pattern behind the single use case initially envisioned,
7 | www.pwc.com/strategy-business
the company was able to deploy similar approaches to help many more func-
tions across the business.
As leaders make such moves, they also need to take a hard look at them-
selves: What skills does the organization need to succeed at scale with AI, and
to what extent do those capabilities already reside somewhere in the company?
What’s the plan for filling skills gaps, and on what time frame? Failure to pose
questions like these can lead to problems down the road—and they’re much
better answered in the context of early experiments than in the abstract.
Priority 4: Develop a productivity plan
Generative AI’s ability to find relevant information, perform repetitive pattern
tasks quickly, and integrate with existing digital workflows means the increased
efficiency and productivity it can deliver can be almost instant, both within in-
dividual departments and organization-wide. Such opportunities aren’t unique
to generative AI, of course; a 2021 s+b article laid out a wide range of AI-en-
abled opportunities for the pre-ChatGPT world.
Generative AI has boosted the awareness and interest of many leaders in
AI-enabled productivity gains, which companies can do three things with:
• Reinvest them to boost the quality, volume, or speed with which goods
and services are produced, generating greater output, broadly defined,
from the same level of input.
• Keep output constant and reduce labor input to cut costs.
• Pursue a combination of the two.
PwC firms in mainland China and Hong Kong followed the first approach in
small-scale pilots that have yielded 30% time savings in systems design, 50% ef-
ficiency gains in code generation, and an 80% reduction in time spent on inter-
nal translations. When generative AI enables workers to avoid time-consuming,
repetitive, and often frustrating tasks, it can boost their job satisfaction. Indeed,
a recent PwC survey found that a majority of workers across sectors are positive
about the potential of AI to improve their jobs.
Generative AI’s ability to create content—text, images, audio, and video—
means the media industry is one of those most likely to be disrupted by this new
8 | www.pwc.com/strategy-business
technology. Some media organizations have focused on using the productivity
gains of generative AI to improve their offerings. They’re using AI tools as an
aid to content creators, rather than a replacement for them. Instead of writing
an article, AI can help journalists with research—particularly hunting through
vast quantities of text and imagery to spot patterns that could lead to interest-
ing stories. Instead of replacing designers and animators, generative AI can help
them more rapidly develop prototypes for testing and iterating. Instead of decid-
ing that fewer required person-hours means less need for staff, media organiza-
tions can refocus their human knowledge and experience on innovation—per-
haps aided by generative AI tools to help identify new ideas.
It’s also important to consider that when organizations automate some of
the more mundane work, what’s left is often the more strategic work that con-
tributes to a greater cognitive load. Many studies show burnout remains a prob-
lem among the workforce; for example, 20% of respondents in our 2023 Glob-
al Workforce Hopes and Fears Survey reported that their workload over the 12
months prior frequently felt unmanageable. Organizations will want to take
their workforce’s temperature as they determine how much freed capacity they
redeploy versus taking the opportunity to reenergize a previously overstretched
employee base in an environment that is still talent-constrained.
Other companies may focus more on cost savings, which can be substan-
tial, but which also carry with them risks—for example, worker unrest (as we
saw in Hollywood), or the hollowing out of the capabilities that companies
need to differentiate themselves from competitors. Some organizations may
decide these risks are worth taking; the right approach will obviously vary
from industry to industry, company to company, and even department to de-
partment. What’s crucial is to have a plan: What is the relative importance of
speed, quality, and cost improvements? What time horizon are you solving
for? What will you do with employees whose skills have become redundant
as a result of new generative AI capabilities? Getting clarity on the answers
to questions like these is an important starting point for focusing your plan.
9 | www.pwc.com/strategy-business
Priority 5: Put people at the heart of your generative AI strategy
Regardless of the productivity path you choose to pursue, considering its impact
on your workforce and addressing it from the start will make or break the suc-
cess of your initiatives.
Our 26th Annual Global CEO Survey found that 69% of leaders planned to
invest in technologies such as AI this year. Yet our 2023 Global Workforce Hopes
and Fears Survey of nearly 54,000 workers in 46 countries and territories high-
lights that many employees are either uncertain or unaware of these technol-
ogies’ potential impact on them. For example, few workers (less than 30% of
the workforce) believe that AI will create new job or skills development oppor-
tunities for them. This gap, as well as numerous studies that have shown that
workers are more likely to adopt what they co-create, highlights the need to put
people at the core of a generative AI strategy.
Companies are investing in AI, but most workers aren’t sure what
that means for them
69% of CEOs say their company is Impact workers expect AI to have on their career in the
investing in advanced technologies next five years
such as AI
Positive Negative Neutral
AI will help me increase my
productivity/efficiency at work 31% 52% of
respondents
AI will create opportunities for me
27% selected at least
to learn new skills
one positive
AI will create new job opportunities for me 21% statement
AI will require me to learn new skills that I’m
18% 35% of
not confident I have the capacity to learn
respondents
AI will change the nature of
14% selected at least
my work in a negative way
one negative
statement
AI will replace my role 13%
I don’t think AI will impact my job 22%
AI will impact my job in other ways not listed 11%
Don’t know 10%
Sources: PwC’s 26th Annual Global CEO Survey and Global Workforce Hopes and Fears Survey 2023
10 | www.pwc.com/strategy-business
To ensure your organization is positioned to capitalize on the promise of gen-
erative AI, prioritize steps to engage employees in the creation and selection of
AI tools, invest in AI education and training, foster a culture that embraces hu-
man–AI collaboration and data-driven decision-making, and support innova-
tion. To this end, we suggest several key strategies:
• Engage your people early and often. Continually communicate why AI is
important and how it fits into the company’s goals. Explain how AI can make
employees’ jobs better and not replace them, and highlight that amassing AI
skills will be critical for workers to succeed in their careers going forward.
But remember that communication should be a two-way street. Provide
mechanisms to gather feedback from employees about their AI experienc-
es, and use it to refine tools and training programs and address any con-
cerns or challenges.
• Offer customized training and upskilling. Assess your emploees’
current AI skills and knowledge, and provide role-specific training
programs, learning resources, and certifications to address the gaps.
Consider teaming up with educational institutions or AI training pro-
viders to offer these programs. Create mentorship opportunities that
give employees guidance on their AI journey, and provide a way for
them to get advice and feedback from AI experts within your company.
And although it’s still difficult to predict many of the new roles that
generative AI could give rise to, we know they’ll materialize. Preparing
employees for these roles and highlighting the opportunities can energize
those looking for career growth and tamp down workers’ fears of replace-
ment. Prompt engineering is a much-discussed role, though it may prove
to be a short-term one as generative tools advance. Many other emerging
roles involving AI ethics and training will become more prevalent, along
with unforeseen roles.
• Promote a growth mindset. Create a workplace where learning and
trying new things with AI is encouraged by recognizing and rewarding
those who do so. And, importantly, make it clear that, with proper guard-
rails and protections in place, failures mark innovation and are expected,
11 | www.pwc.com/strategy-business
and even celebrated. One financial services firm we know, for exam-
ple, highlights at least one instance of failure on a weekly stand-up call
among its designers to make visible that these occurrences are accept-
able and incur no punitive measures. Unfortunately, this organization
remains in the minority—in our 2023 Annual Global CEO Survey, 53%
of respondents said leaders in their company don’t often tolerate small-
scale failures (and employees think that figure is closer to two-thirds).
Fostering a growth culture also includes encouraging employees to
share their learnings with each other as they begin working with these
tools. Some companies we know are establishing prompt libraries, for
example.
• Advocate and enable ethical AI use. Provide clear guidelines that ar-
ticulate how your organization defines the ethical use of generative AI,
and ensure that employees understand the importance of fairness, trans-
parency, and responsible AI practices. At PwC, for example, we’ve created
an internal microsite articulating the generative AI tools approved for em-
ployee use, acceptable business use cases, restrictions on the nature of in-
formation employees can input into these tools, requirements for human
oversight and quality checks, and more.
• Measure impact. Knowing what’s working and what isn’t requires not
only worker feedback but also measurement. Implement key performance
indicators to assess the impact of AI on productivity, innovation, and cus-
tomer satisfaction; and actively promote the results. Some companies we
know are conducting controlled experiments, such as by having software
engineers use coding assistants, to measure productivity improvements.
By following these strategies, organizations can systematically equip and
empower their workforce to position themselves, and the organization, for suc-
cess in an AI-driven world.
Priority 6: Work with your ecosystem to unlock even bigger
benefits
Recent PwC analysis has found that companies with a clear ecosystem strategy
12 | www.pwc.com/strategy-business
are significantly more likely to outperform those without one. It’s important,
as you experiment with AI, to look outside the four walls of your company: Do
you know how your suppliers, service providers, customers, and other partners
are planning to leverage this technology to improve their service proposition?
What implications does their use of AI have for your early days strategy? Will it
impose new conditions and demands? Could closer collaboration on AI lead to
fresh opportunities to develop stronger propositions?
The holy grail of healthcare and pharmaceutical firms, for instance, is the
ability to access patient records at scale and identify patterns that could uncov-
er routes to more effective treatments. Yet information sharing between orga-
nizations has long been restricted by privacy issues, local regulations, the lack
of digitized records, and concerns about protecting intellectual property—all of
which limit the scope and power of ecosystem collaboration.
Meanwhile, the use of AI has already become widespread across the indus-
try. Medical institutions are experimenting with leveraging computer vision
and specially trained generative AI models to detect cancers in medical scans.
Biotech researchers have been exploring generative AI’s ability to help identify
potential solutions to specific needs via inverse design—presenting the AI with
a challenge and asking it to find a solution. This AI-supported treatment discov-
ery approach is already being used for both precision medicine (via genetic and
healthcare record analysis to identify the best treatments given an individual’s
specific circumstances) and drug development (via protein and chemical model
synthesis that can create custom antibodies).
Until recently, the true potential of AI in life sciences was constrained by the
confinement of advances within individual organizations. Today, organizations
can combine generative AI’s ability to help create and manage records with its
capacity for creating statistically reliable, yet fully anonymized, synthetic data-
sets to enable safe, secure, large-scale data-sharing and data-pooling among
healthcare organizations and their partners. That larger pool of information in-
creases the opportunity for medical breakthroughs by helping researchers iden-
tify commonalities that can reveal more effective treatments—as well as new
opportunities for collaboration between organizations, new business models,
13 | www.pwc.com/strategy-business
and new ways to capture value along with improved patient outcomes.
Use cases have come up several times as we’ve described these priorities. That
makes sense, because generative AI is a general-purpose technology, suitable
for an enormous range of business activities; it’s hardly surprising that emerging
leaders are emphasizing the search for smart, targeted applications. Here again,
though, it’s important to underscore that it’s still early days. To understand how
early, consider another general-purpose technology: electricity. Beginning with
lighting in the 1870s, electricity began permeating a range of industrial settings
and applications, bringing with it a variety of productivity improvements in the
decades that followed. Electricity was the force behind a key feature of Henry
Ford’s automated assembly line—the overhead monorail conveyor system that
made it possible to move parts and materials smoothly throughout the plant.
Looking back, no one talks about Ford’s “electricity strategy.” Rather, the fo-
cus is on the moving assembly line. We suspect the same will be true with gen-
erative AI, which will give rise to revolutionary business innovations that are
beyond our imagination today. That makes early days AI strategies and prior-
ities like the ones we’ve described even more important. They won’t just yield
near-term business benefits; they’ll also build muscle and generate valuable
experience that sets up today’s leaders to achieve much bigger breakthroughs
to make product, process, and service innovations that represent the assembly
lines of the future.
The authors would like to thank Lois Geraldo and Julia Lamm
for their contributions to this article.
14 | www.pwc.com/strategy-business
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12,pwc,agentic-ai-the-new-frontier-in-genai-an-executive-playbook.pdf,"Agentic AI
– the new
frontier in
GenAI
An executive playbook
Harnessing AI isn’t just about
technology— it’s about unleashing
unprecedented potential.
In an era where speed, efficiency, and customer centricity dictate market leadership, organisations need to
harness every tool at their disposal. Over the past couple of years, artificial intelligence (AI) has exploded onto
the world stage, with companies and individuals across the globe rapidly adopting the technology. The GCC is
playing a lead role in the space, with business leaders in the region exploring ways of integrating this rapidly
developing technology into their operations.
Generative AI (GenAI) is being recognised as a game-changer for innovation in the region, empowering
enterprises by automating routine tasks, enhancing customer experiences and assisting in critical
decision-making processes. Insights from our 27th Annual CEO Survey: Middle East findings have shown that
73% of CEOs in the Middle East believe GenAI will significantly change the way their company creates, delivers
and captures value over the next three years1. GenAI is poised to make a significant economic impact, with
estimates indicating that it could contribute between $2.6 trillion and $4.4 trillion annually to global GDP across
various industries by 2030. In specific sectors, such as energy, investments in GenAI are expected to triple,
from $40 billion in 2023 to over $140 billion by the end of the decade. This surge in investment reflects the
transformative potential of GenAI, particularly in enhancing productivity, streamlining business processes, and
reshaping value chains across industries2.
Against this backdrop, multimodal GenAI agentic frameworks has emerged as transformative catalysts,
enabling businesses to accelerate process automation at an unprecedented scale. This technology involves
multiple AI agents working together, each specialising in different tasks or data types, to solve complex
problems and automate processes. By collaborating and constantly learning, these agents enhance
decision-making, optimise processes, and drive innovation. It combines range of advanced AI techniques to
process diverse data types and automate complex tasks.
The central question isn’t whether to adopt this technology, but how swiftly organisations can integrate it to
stay ahead of the competition. This executive playbook explores how organisations can leverage this
technology to boost operational efficiency, enhance customer experience, and drive revenue growth. It provides
real-world success stories spanning industry sectors and organisational functions, strategic insights, tactical
blueprints, and best practices to guide your journey into this revolutionary landscape.
Key insights
● Agentic AI, differentiated by its advanced human-like reasoning and interaction capabilities, is
transforming the manufacturing, healthcare, finance, retail, transportation, and energy sectors, among
others.
● Organisations’ AI strategies should leverage multimodal GenAI capabilities while ensuring ethical AI
safeguards to drive autonomous process re-engineering and enhanced decision-making across all lines
of business.
● Integrated effectively, agentic AI can enhance efficiency, lower costs, improve customer experience, and
drive revenue growth.
What is agentic AI?
Agentic AI generally refers to AI systems that
possess the capacity to make autonomous
decisions and take actions to achieve specific
goals with limited or no direct human
intervention3.
Key aspects of agentic AI
Autonomy: Agentic AI systems Goal-oriented behaviour: These Environment interaction: An
can operate independently, AI agents are designed to pursue agentic AI interacts with its
making decisions based on their specific objectives, optimising surroundings, perceiving changes
programming, learning, and their actions to achieve the and adapting its strategies
environmental inputs. desired outcomes. accordingly.
Learning capability: Many Workflow optimisation: Agentic Multi-agent and system
agentic AI systems employ AI agents enhance workflows and conversation: Agentic AI
machine learning or business processes by integrating facilitates communication
reinforcement learning techniques language understanding with between different agents to
to improve their performance over reasoning, planning, and construct complex workflows. It
time. decision-making. This involves can also integrate with other
optimising resource allocation, systems or tools, such as email,
improving communication and code executors, or search
collaboration, and identifying engines, to perform a variety of
automation opportunities. tasks.
Environment Learning
interaction capability
Workflow
Goal-oriented
optimisation
behaviour
Multi-agent
and system
Autonomy conversation
Evolution to multimodal GenAI agents
In AI, the only constant is change—embrace a culture of perpetual innovation.
The journey of agentic frameworks began as simple, rule-based systems designed to perform specific tasks. Over time,
these systems have evolved into sophisticated, multimodal agents capable of processing and integrating information from
various sources, such as text, images, and audio. Multimodality capabilities allow AI agents to understand, employ
reasoning, and interact like humans, enhancing their effectiveness and versatility to solve a wide range of business
problems4.
The evolution can be broken down into three key phases:
(20001s.) Integration of Machine Learning (ML)
○ Learning from data: The integration of ML allowed agents to learn from large datasets, improving their ability
to make decisions and perform tasks. This was a significant step forward from rule-based systems, as agents
could now adapt to new information and improve over time.
○ Natural Language Processing (NLP) enabled user interactions: Advances in NLP enabled agents to
understand and generate human language more effectively, making interactions more natural and intuitive.
(20102s.) Introduction of multimodality
○ Combining text, images, and audio: Multimodal agents emerged, capable of processing and integrating
information from various sources. For instance, an agent could analyse a text description, recognise objects in
an image, and understand spoken commands. This multimodality made agents more versatile and capable of
handling complex tasks.
○ Enhanced user interactions: Multimodal agents could interact with users in more dynamic ways, such as
providing visual aids in response to text queries or understanding context from a combination of spoken and
visual inputs.
20203s.- Advanced autonomy and real-time interactions
present ○ Advanced autonomy: Agents can operate independently, rationalise and set their own goals, develop path(s)
to attain these goals, and make independent decisions without constant human intervention, leveraging data
from multiple sources or synthetic datasets. In a multi-agentic orchestration system, the first set of agents
focus on mimicking human behaviour (e.g. ChatGPT-4o), that is, thinking fast to come up with solution
approach, while the second set of agents focus on slow reasoning (e.g. ChatGPT-1o) to come up with a vetted
solution5. Combining thinking fast and slow reasoning, agents can process information and make optimal
decisions in real-time – crucial for applications like autonomous vehicles, real-time customer service, and
various mission-critical business processes. This autonomy makes agentic AI particularly powerful in dynamic
and complex real-world environments.
○ User interactions within an ethical and responsible AI-controlled environment: With increased
capabilities, there has also been a focus on ensuring that agentic systems operate ethically and responsibly,
considering factors such as bias, transparency, and accountability.
Integration of ML (2000s)
NLP enabled user
Learning from data
interactions
Integration of Machine Learning (2000s)
Introduction of multimodality (2010s)
Natural Language Processing
LCeoamrnbiningi nfrgo mte xDta, tiamages, and (NLP) Enabled User
Enhanced user interactions
audio Interactions:
Goal-oriented
AI agent I An dte vg anra ct eio dn a o uf t oM na oc mh yin ae n L de ra er an li -n tg im (2 e0 i0 n0 tes r) actions (2020s-present)
behaviour
Natural Language Processing
User interactions within an
LHeuamrnainng-l ifkroem re Daastoaning and (NLP) Enabled User
ethical and responsible AI-
advanced autonomy Interactions:
controlled environment
Why organisations should pay attention
In the fast lane of technological evolution, missing the AI turn today means being outpaced tomorrow.
Agentic AI offers significant advantages in efficiency, decision-making, and customer interaction. By automating routine
tasks and providing intelligent insights, agentic AI can help organisations save time, reduce cost, and improve overall
productivity. Moreover, organisations who adopt an agentic AI system can gain a competitive advantage by leveraging its
capabilities to innovate and enhance their business operations. Lower cost to entry and economies of scale makes it
favourable for organisations to fully harness the capabilities it offers compared to its predecessors like traditional ML and
Robotic Process Automation (RPA)-driven automations.
Agentic AI systems can significantly enhance an organisation’s competitive edge by automating complex workflows,
reducing operational costs, and improving decision-making processes. These systems are designed to adapt to changing
business environments, driving higher productivity and enabling organisations to stay competitive. For example, agentic AI
can predict market trends and customer preferences, allowing businesses to tailor their strategies proactively. This
adaptability not only improves efficiency but also fosters innovation, giving companies a significant edge over competitors6.
Moreover, agentic AI systems can handle large volumes of data and extract actionable insights, which can be used to
optimise operations and enhance customer experiences. By automating routine tasks, these systems free up human
resources to focus on more strategic initiatives, thereby increasing overall organisational agility and responsiveness7.
Enhanced decision-making
Agentic AI systems can analyse vast amounts of data quickly and accurately, providing valuable insights to inform better
decision-making. Businesses can leverage these insights to optimise revenue and operations, identify market trends, and
make data-driven decisions. For instance, in the financial sector, AI can analyse market data to predict trends, inform
investment strategies, and boost investment ROI. In retail, it can streamline inventory management by predicting demand
and optimising stock levels.
Boosted efficiency and productivity
Agentic AI can significantly enhance business efficiency and productivity by automating routine tasks and processes. This
allows employees to focus on more strategic and creative activities. For example, in customer service, agentic AI can
handle common inquiries, freeing up human agents to tackle more complex issues. In manufacturing, AI-driven robots can
manage repetitive tasks with precision and consistency, reducing errors and increasing output.
Improved customer experience
By integrating agentic AI, businesses can offer personalised and responsive customer experiences. AI-driven chatbots and
virtual assistants can provide instant support, answer queries, and even recommend products based on customer
preferences and dynamic interactions. This improves customer satisfaction, builds loyalty, and drives sales. For example,
e-commerce platforms use AI to recommend products based on browsing history and purchase behaviour.
Agentic AI systems are redefining customer service centres and are gaining popularity as a game-changing capability
for both government entities and private sector organisations. While traditional rule-based chatbots
(software-as-a-service) provided basic 24/7 support, and Retrieval Augmented Generated (RAG)-based chatbots
enhanced human-like interactions (enhanced software-as-a-service), agentic AI surpasses both in terms of accuracy,
contextual coherence, and problem-solving ability.
In terms of accuracy, rule-based chatbots are limited to programmed responses, causing inaccuracies when queries
fall outside of predefined rules. RAG-based chatbots depend on retrieved data that may not match user intent. In
contrast, the novel approach of agentic AI allows it to understand nuances in language, generating accurate responses
even to complex or unseen queries. Its ability to learn from vast datasets enhances precision and adaptability, making
it superior for customer interactions.
One of the biggest limitations of chatbots has been contextual coherence. Rule-based chatbots struggle to maintain
context in extended interactions due to linear scripting, leading to disjointed responses that harm customer
experience. RAG-based chatbots may produce inconsistent replies if retrieval mechanisms don't consider previous
interactions. Whereas agentic AI’s orchestration capability helps it excel at tracking conversation history,
understanding dialogue flow, ensuring responses remain contextually appropriate and coherent, significantly boosting
customer engagement.
Thus far, both rule-based and RAG-based chatbots have limited autonomous problem-solving ability. The former can't
handle problems outside their scripts while the latter provide information but can't synthesise data and prepare the
human-live problem-solving logic to solve complex issues across integrated sources such as CRMs, ERP, or IVR
systems. The agentic AI performs dynamic reasoning and decision-making, leveraging a series of autonomous agents,
analysing customer issues, considering multiple factors, and applying learned knowledge to resolve problems more
efficiently. The outcome is quicker, solution-oriented, and fluid conversations that enhance customer experience and
set new standards for efficiency and responsiveness in automated customer service.
stnega-orciM
Customer support agent
Customer support agent
User Issue Feedback
Status updates
experience FAQ agent resolution collection Nth agent
agent
agent agent agent
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How to conceptualise agentic AI
solutions for future business operations
Agentic AI business imperatives
Organisations managing day-to-day operations stand to gain significantly from agentic AI systems, embracing the
emerging ""service-as-a-software"" model. This innovative approach transforms manual labour into automated, AI-driven
services. Rather than purchasing traditional software licences or subscribing to cloud-based software-as-a-service
(SaaS), businesses can now pay for specific outcomes delivered by AI agents. For example, a company might employ
AI customer support agents like Sierra to resolve issues on their websites, paying per resolution rather than maintaining
a costly human support team. This model allows organisations to access a wider range of services – whether it’s legal
support from AI-powered lawyers, continuous cybersecurity testing by AI penetration testers, or automated CRM
management – at a fraction of the cost. This not only drives efficiency but also significantly reduces operational
overheads.
By leveraging the service-as-a-software model, businesses can automate both routine and highly specialised tasks that
were once time-consuming, required skilled professionals, and typically involved expensive software licences or cloud
solutions. AI applications with advanced reasoning capabilities can now handle complex tasks, from software
engineering to running customer care centres, enabling companies to scale their operations without a proportional
increase in cost. This transition expands the services available to organisations of all sizes, freeing them to focus on
strategic priorities while AI systems manage the operational burden. Adopting these AI-driven services positions
businesses to stay competitive in an ever-evolving marketplace8.
Transitioning from copilot to autopilot models
Service-as-a-software represents an outcome-focused, strategic shift, enabling organisations to transition from their
current state to operating in ""copilot"" and ultimately ""autopilot"" modes. Sierra, for instance, offers a safety net by
escalating complex customer issues to human agents when necessary, ensuring a seamless customer experience.
While not all AI solutions offer this built-in fallback, a common strategy is to initially deploy AI in a ""copilot"" role
alongside human workers. This human-in-the-loop approach helps organisations build trust in AI capabilities over time.
As AI systems demonstrate their reliability, businesses can confidently transition to an ""autopilot"" mode, where AI
operates autonomously, enhancing efficiency and reducing the need for human oversight. GitHub Copilot is a prime
example of this, assisting developers and potentially automating more tasks as it evolves.
Outsourcing work through AI services
For organisations with high operational costs, outsourcing specific tasks to AI services that guarantee concrete
outcomes is an increasingly attractive option. Take Sierra, for example: businesses integrate Sierra into their customer
support systems to efficiently manage customer queries. Instead of paying for software licences or cloud-based
services, they pay Sierra based on the number of successful resolutions. This outcome-based model aligns costs
directly with the results delivered, allowing organisations to harness AI for specific tasks and pay solely for the
outcomes achieved.
This shift from traditional software licences or cloud SaaS to service-as-a-software is transformative in several ways:
Targeting service profits: Traditional SaaS focused on selling user seats, whereas service-as-a-software taps into
service profit pools, delivering solutions that focus on specific business outcomes.
Outcome-based pricing: Instead of charging per user or seat, service-as-a-software adopts a pricing model based on
the actual outcomes achieved, directly aligning costs with results.
High-touch delivery models: Service-as-a-software offers a top-down, highly personalised approach, providing
trusted, tailored solutions that meet the specific operational needs of businesses.
Why should organisations consider early
adoption and avoid being late movers?
Early adopters Late movers
Set industry benchmarks
Market Struggle to catch up and miss out on
and gain first-mover market advantage.
position creating competitive advantage.
Leverage AI to innovate business Slow to innovate business processes and
Innovation processes, deploy the AI solutions take full advantage of AI solutions to create
effectively and create differentiation. differentiation.
Build deeper customer relationships
Customer Play catch-up to match the personalised
through personalised and newer
relationships services of early adopters.
experiences.
Operational Streamline operations and reduce Higher lost opportunity cost due to late entry
efficiency operational cost early on. and adoptions.
Benefit from the initial learning curve and Miss out on early learning opportunities and
Learning curve
shape industry standards. industry influence.
Increase market share and profitability
Market share Struggle to achieve similar market share.
through early adoption.
Barriers to Create barriers for competitors through Face higher barriers to entry due to
entry deep AI integration. established competitors.
Pay relatively higher cost of entry and
Pay relatively lower cost of entry and lower
Cost to entry iterative test-and-learn due to new AI
learning and experiments.
solutions.
Real-world success stories
Catalysing change across all industries
Manufacturing: Siemens AG
Siemens transformed its maintenance operations by deploying AI models that analyse sensor data from machinery. The
system predicts equipment failures before they occur, scheduling maintenance proactively. The multimodal framework
processes data from various sources – vibration, temperature, and acoustic signals – providing a holistic view of
equipment health and proactive maintenance orchestrated by the agentic AI models.
Technology stack: Financial impact: Non-financial benefits:
● AI models: Regression and deep ● Savings: Reduced maintenance ● Enhanced equipment reliability
learning models costs by 20% ● Improved worker safety
● Platforms: Siemens ● Revenue growth: Increased
MindSphere9 production uptime by 15%
● Tools: Scikit-learn, TensorFlow,
Keras, IoT sensors
Healthcare: Mayo Clinic
By integrating AI into its radiology workflows, Mayo Clinic allows for quicker and more accurate diagnoses. The multimodal
AI processes imaging data alongside patient history and lab results, offering comprehensive insights that aid radiologists in
decision-making, automating documentation and process automation across the radiology value chain.
Technology stack: Financial impact: Non-financial benefits:
● AI Models: Regression and ● Efficiency gains: Reduced ● Improved diagnostic accuracy
Convolutional Neural Networks diagnostic times by 30% ● Enhanced patient outcomes
(CNNs) models ● Cost reduction: Lowered
● Frameworks: NVIDIA Clara unnecessary procedures by
platform10 15%
● Tools: Scikit-learn, PyTorch,
Medical Imaging Data
Finance: JPMorgan Chase
JPMorgan’s Contract Intelligence (COiN) platform uses AI to analyse legal documents, extracting key data points in
seconds. The multimodal framework interprets complex legal language, images, and tables, streamlining a process that
once took thousands of human hours.
Technology stack: Financial impact: Non-financial benefits:
● AI models: NLP with Generative ● Savings: Saved 360,000 hours ● Enhanced accuracy in
Pre-trained Transformers (GPT) of manual review annually document analysis
● Frameworks: COiN platform11 ● Risk mitigation: Significantly ● Improved employee productivity
● Tools: Python, Hadoop reduced compliance risk
Retail: Amazon
Amazon leverages AI to analyse browsing behaviour, purchase history, and even visual preferences. Multimodal AI models
generate personalised recommendations, orchestrate tasks across order fulfilment value chains, and enhance the
shopping experience to drive sales.
Technology stack: Financial impact: Non-financial benefits:
● AI models: Regression and deep ● Revenue boost: Increased sales ● Enhanced customer satisfaction
learning Models by 35% through personalised ● Increased engagement time on
● Frameworks: Amazon recommendations and one-click the platform
Personalise12 and Amazon order fulfilment
Order Fulfilment ● Customer retention: Improved
● Tools: AWS SageMaker loyalty rates by 20%
Transportation and logistics: DHL
DHL utilises AI models to predict and orchestrate shipping demands, optimise routes, and manage warehouse operations.
The system processes data from various sources, including traffic patterns, weather conditions, and order volumes.
Technology stack: Financial impact: Non-financial benefits:
● AI models: ML models and route ● Cost savings: Reduced ● Enhanced customer satisfaction
optimisation algorithms operational costs by 15% ● Reduced carbon footprint
● Frameworks: DHL Resilient ● Efficiency gains: Improved
supply chain platform13 delivery times by 20%
● Tools: IoT devices, ML models
Energy: BP (British Petroleum)
BP uses AI to analyse seismic data, generating 3D models of subterranean structures. The multimodal approach combines
geological, geophysical, and historical data to identify favourable drilling sites and orchestrate drilling equipment settings
for optimal outcomes.
Technology stack: Financial impact: Non-financial benefits:
● AI models: Regression and ● Savings: Reduced exploration ● Reduced environmental impact
GenAI models costs by 20% ● Improved safety measures
● Frameworks: Azure cloud ● Revenue growth: Increased
services14 successful drilling operations by
● Tools: Microsoft AI 15%
Education: Pearson
Pearson’s AI models tailor educational content to individual learner needs, adjusting difficulty levels and content types
based on performance and engagement data.
Technology stack: Financial impact: Non-financial benefits:
● AI models: Adaptive learning ● Revenue increase: Boosted ● Improved student outcomes
algorithms course enrollment by 25% ● Enhanced user engagement
● Frameworks: Multimodal content ● Cost reduction: Lowered
delivery systems15 content development costs by
● Tools: Python, TensorFlow 15%
Media and entertainment: Netflix
Netflix uses AI models to recommend and orchestrate content by analysing viewing habits, ratings, and even visual
content features. The multi-modal AI ensures that users find content that resonates with their preferences, keeping them
engaged.
Technology stack: Financial impact: Non-financial benefits:
● AI models: ML and GenAI ● Subscriber growth: Increased ● Personalised user experiences
models retention rates by 10% ● Improved content strategy
● Frameworks: Netflix multimodal ● Revenue boost: Enhanced
user interaction analysis16 engagement leading to higher
● Tools: AWS, Apache Spark subscription renewals
Telecommunications: AT&T
AT&T’s AI models analyse and orchestrate network performance data and customer interactions to optimise network
operations and personalise customer service through chatbots.
Technology stack: Financial impact: Non-financial benefits:
● AI models: ML for network ● Cost savings: Reduced ● Enhanced network reliability
analytics operational expenses by 15% ● Improved customer satisfaction
● Frameworks: Edge computing ● Revenue growth: Improved
with multimodal data inputs17 upselling through personalised
● Tools: AI chatbots, data offers
analytics platforms
Government and public sector: Singapore Government
Singapore utilises AI models to orchestrate and manage traffic flow, energy consumption, and public safety. The
multi-modal system processes data from various sensors and citizen feedback mechanisms to make real-time decisions.
Technology stack: Financial impact: Non-financial benefits:
● AI models: ML and GenAI ● Efficiency gains: Reduced ● Improved public services
models administrative costs by 25% ● Enhanced quality of life
● Frameworks: Smart Nation ● Economic growth: Attracted for citizens
platform18 US$12 billion in foreign
● Tools: IoT sensors, cloud investment
computing
Real-world success stories
Innovation within business functions
Human resources: Unilever
Unilever uses AI to screen candidates by analysing video interviews and responses, allowing recruiters to focus on the
most promising applicants.
Technology stack: Financial impact: Non-financial benefits:
● AI models: NLP and facial ● Cost reduction: Saved over ● Enhanced diversity in hiring
recognition algorithms US$1 million annually in ● Improved candidate experience
● Frameworks: Multimodal recruitment costs
candidate assessment ● Efficiency gains: Reduced hiring
platforms19 time by 75%
● Tools: HireVue AI platform
Customer service: Bank of America
Erica, an AI virtual agent, handles over a million customer queries daily – including snapshots of month-to-date spending
and flagging recurring charges – providing instant assistance and freeing human agents to tackle more complex issues.
Technology stack: Financial impact: Non-financial benefits:
● AI models: GenAI for ● Cost savings: Reduced ● Improved customer satisfaction
conversational interfaces customer service costs by 10% ● 24/7 customer support
● Frameworks: Multimodal ● Revenue growth: Increased availability
customer interaction platforms20 product cross-selling by 5%
● Tools: Erica, the virtual assistant
Marketing: Coca-Cola
Coca-Cola uses AI to generate marketing content, analyse consumer trends, and personalise advertising, resulting in more
effective campaigns.
Technology stack: Financial impact: Non-financial benefits:
● AI models: Generative ● Efficiency gains: Reduced ● Innovative marketing strategies
Adversarial Networks (GANs) content creation time by 50% ● Enhanced customer
● Frameworks: Multimodal data ● Revenue increase: Boosted engagement
analysis for consumer insights21 campaign ROI by 20%
● Tools: Custom AI platforms
Supply chain management: Walmart
Walmart employs AI to predict product demand, optimise stock levels, and streamline logistics, ensuring products are
available when and where customers need them.
Technology stack: Financial impact: Non-financial benefits:
● AI Models: Predictive analytics ● Cost Reduction: Decreased ● Reduced waste
for demand forecasting inventory costs by 15% ● Enhanced supplier relationships
● Frameworks: Multi-modal data ● Revenue Growth: Improved
integration from sales, weather, product availability leading to
and events22 higher sales
● Tools: Data lakes, Machine
Learning models
Research and development: Insilico
Medicine
Insilico Medicine, a biotechnology company focused on longevity, has developed inClinico, an AI platform that
predicts phase II clinical trial outcomes to enhance drug discovery and development.
Technology stack: Financial impact: Non-financial benefits:
● AI Models: In-house-developed ● Cost Reduction: 35% ● Accelerated drug discovery and
multimodal foundation model nine-month ROI in an clinical trials process
● Platforms: Multi-modal investment application ● 79% accuracy for clinical trials
integration of omics, text, ● Time Efficiency: Reduced drug
clinical trials, small molecule development time
properties, and disease
targets23
● Tools: Transformer-based,
in-house-trained AI model and
platform
Legal: Hogan Lovells
The AI platform analyses large sets of contracts and legal documents, extracting critical information, and identifying
risks.
Technology stack: Financial impact: Non-financial benefits:
● AI models: NLP and ML ● Efficiency gains: Increased ● Improved accuracy
● Frameworks: Kira Systems review speed by 40% ● Enhanced client satisfaction
platform with multimodal data ● Cost savings: Reduced billable
processing24 hours for clients
● Tools: Kira AI
Procurement: Coupa
Coupa’s AI-driven spend management platform optimises supplier selection, contract management, and spend analytics,
transforming procurement processes into a strategic function.
Technology stack: Financial impact: Non-financial benefits:
● AI models: Predictive analytics, ● ROI: Achieved an impressive ● Increased compliance and risk
machine learning, and spend 276% return on investment management.
forecasting. (ROI). ● Improved supplier performance
● Frameworks: Coupa ● Efficiency gains: Reduced and relationships
Source-to-Pay, Coupa Business procurement cycle and
Spend Management (BSM).25 significantly enhancing process
● Tools: Cloud computing, speed.
advanced sourcing optimisation,
real-time spend visibility.
IT Operations: Microsoft
Microsoft uses AI to monitor IT systems, predict failures, and automate support tickets, ensuring seamless operations.
Technology stack: Financial impact: Non-financial benefits:
● AI Models: Anomaly detection ● Cost Savings: Reduced IT ● Enhanced employee productivity
and predictive maintenance support costs by 20% ● Proactive issue resolution
algorithms ● Efficiency Gains: Improved
● Frameworks: Azure AI with system uptime by 15%
multi-modal data inputs26
● Tools: AI chatbots, Monitoring
tools
Sales: Salesforce
Salesforce’s AI analyses customer interactions, market trends, and sales data to provide actionable insights for sales
teams.
Technology stack: Financial impact: Non-financial benefits:
● AI models: Predictive analytics ● Revenue growth: Increased ● Improved customer relationships
with ML sales by 15% ● Enhanced decision-making
● Frameworks: Salesforce Einstein ● Efficiency gains: Reduced sales
with multimodal data cycle times by 25%
processing27
● Tools: CRM systems
Key GenAI agentic tools and their
differentiation
Commercial solutions Open-source solutions
LangGraph28 AutoGen29
● Target audience: Startups and established ● Target audience: Developers and researchers
enterprises ● Open-source framework: Facilitates cooperation
● Support: Offers robust customer support and among multiple AI agents
professional services ● Simplification: Orchestrates, automates, and
● Integration: Seamlessly integrates with existing optimises complex LLM workflows
enterprise systems ● Human-in-the-loop: Supports human-in-the-loop
● Customisation: High level of customisation and workflows for enhanced performance
control over workflows ● Community-driven: Encourages innovation and
● Features: Advanced features like statefulness collaboration within the community
(having a perfect memory or knowledge of previous
calls or requests), streaming support, and
moderation loops
CrewAI30 AutoGPT31
● Target audience: Fortune 500 companies and large ● Target audience: AI enthus"
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1 Introduction
3
2 Executive Summary
6
3 Purpose for this Report
9
4 Overview of the Thematic Insight Areas
12
5 Thematic Insights: Strategy and Adoption
14
6 Thematic Insights: Governance
18
7 Thematic Insights: Investment
21
8 Thematic Insights: Market Perspectives
25
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Table of
contents
Malta AI Business Survey Report 2024
Introduction
Introduction
Introduction
The 2024 PwC Malta AI Business Survey was an endeavour of PwC Digital Services with as generative AI, organisations can facilitate tasks like software development and system
the objective of assessing the current state of AI adoption among organisations in Malta. transformation. Furthermore, organisations that are integrating AI into their operations, can
As a means to inform this current state, the survey was designed to elicit feedback in achieve a human-led, tech-powered strategy that not only addresses current challenges
targeted thematic areas, within which several capability areas were explored. but also positions them towards future success.
This survey is grounded in extensive research and aligns with PwC’s broader studies on In fact, artificial intelligence is revolutionising modern businesses by offering unparalleled
AI’s business impacts such as PwC’s Global AI study, which found that AI has the potential value across various dimensions. From a business perspective, AI enables organisations
to contribute up to $15.7 trillion to the global economy, and an increased 26% boost in to uncover deep insights from vast quantities of data, driving operational efficiency
GDP for local economies by 2030. and transforming products and services to better meet customer needs. In terms of
experience, AI enhances customer interactions by providing personalised and seamless
Technologically, AI empowers businesses to innovate and stay competitive. According to experiences, as seen in PwC’s AI-driven solutions for contact centres that improve self-
our latest CEO Survey 2024, 45% of CEOs believe their organisation will not be viable in service and live engagements.
ten years if it remains on its current path. By leveraging advanced models and tools, such
Overview of
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Insight Areas
At the European Union (EU) level, we are seeing significant focus
being placed on how our approach to AI will define the world we
will eventually live in, and from the EU’s perspective, technology,
especially Artificial Intelligence, is seen as a crucial driver in sustaining
economic growth, societal well-being, and digital sovereignty. The
European AI strategy emphasises the importance of setting global
standards and strengthening digital infrastructure to ensure that Europe
remains competitive and resilient in the face of the rapidly advancing
technologies that surround us. In fact, the EU’s approach to artificial
intelligence aims to make the EU a global hub for AI by promoting
research, innovation, and industrial capacity and ensuring the safe and
ethical use of AI systems.
At a national level Malta’s AI Strategy named “Strategy and Vision for
Artificial Intelligence in Malta 2030” sets out a long-term vision aimed
at transforming the country into an AI pioneer within the European
economy, by enhancing AI education, research, and commercial
applications of AI. By design, the national AI strategy is built on
three strategic pillars focusing on boosting investment, innovation,
and adoption, with a further three enablers cutting across the
aforementioned areas. These strategic enablers being:
1) education and workforce;
2) ethical and legal; and
3) ecosystem infrastructure.
In this frame, we are excited to link the findings of the PwC Malta AI
Business Survey with such global insights, to provide a holistic view of
AI’s transformative potential and its implications for businesses in Malta.
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Insight Areas
Malta AI Business Survey Report 2024
Executive
ve Summar y
While it is still early days for Malta in terms of the adoption of AI technologies, we believe Executive Summary
this report underlines the need for organisations to put AI high on the agenda at a strategic
level. This can be done by ensuring they have robust governance frameworks in place, and
strategically aligning their goals and objectives, where possible, with AI enabled initiatives in
order to fully harness the mass potential AI is capable of bringing.
The below provides an executive summary that highlights the key insights and takeaways on
the thematic areas explored in this survey’s report:
Organisations are finding it challenging to 1
manage the generative AI wave
From an AI strategy and adoption perspective, 56% of organisations responded that they do
not have an individual or team dedicated to assessing potential AI use cases. This suggests a
significant gap in structured AI evaluation and implementation.
While a priority area, the set-up of governance structures
2
for the responsible implementation of AI is a key
limiting factor for organisations
Our survey showed that 75% of organisations identified themselves as having no AI
governance framework in place, indicating a substantial lack of oversight and control over
AI initiatives and related regulatory obligations. In particular, the lack of a robust governance
framework may result in ethical, legal, and operational risks, as well as misalignment with
organisational goals and standards.
Organisations view AI enablement efforts to bring the largest benefits 3
in internal-facing operations to gain operational efficiency benefits
41% of organisations view the potential for AI in having the greatest impact on their
internal operations, suggesting a focus on improving efficiency and productivity. Meanwhile,
29% are looking to enhance customer experience through AI, and 21% aim to improve their
data-driven, decision-making capabilities.
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Insight Areas
No AI Novices
4
None of the participants scored as an AI Novice, providing
a strong indication that all the respondents are familiar with AI
technologies where the lowest scoring firms were AI Explorers
(15% of the respondents). None of the participants scored as an AI
Novice, providing a strong indication that all the respondents are
familiar with AI technologies where the lowest scoring firms were AI
Explorers (15% of the respondents).
While the majority of respondents recognise the value 5
of AI, organisations remain early in their AI
enablement journey
More than half of the respondents fell into the Visionary category, which speaks to
the strong intent of these organisations to be actively involved in AI from design to
implementation in their respective operating models. While these firms recognise the
value of AI enablement, they find challenges in, among others, finding a dedicated
talent with subject matter expertise to support a responsible AI implementation.
Prioritising the human-in-the-loop is
6
enabling the Leaders & Trailblazers to
leverage AI ahead of their peers
Those organisations that scored among the Leaders (24%) and
Trailblazers (10%) distinguished themselves across the thematic
areas being assessed for the maturity. Here, our analysis shows
that these organisations are investing into AI technologies, while
prioritising the human-in-the-loop philosophy via: (I) pursuing internal
AI initiatives such as upskilling, and (ii) allocating internal resources
to drive adoption of the latest generative AI product offerings.
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Insight Areas
Malta AI Business Survey Report 2024
Purpose for
this Repor t
Purpose for this Report
The objective of the PwC Malta AI Business Survey was to assess the current state of Local businesses were invited to participate through public promotions distributed on
AI adoption in Malta, identify the key drivers and barriers organisations are facing to PwC’s website and social media platforms, through a number of published thought
AI implementation and to make aware and understand the potential benefits and risks leadership articles, as well as through an email invitation sent out by PwC Malta’s
that may emerge. The survey explores several critical areas to provide a comprehensive marketing team. Overall, 59 participants completed the survey on behalf of their
understanding of AI’s impact on businesses in Malta. Each dimension was carefully organisation, between March and September 2024. The survey targeted individuals who
chosen to address critical aspects of AI adoption and its implications for organisations, are most engaged with AI practices in their organisation, such as, but not limited to the
outlined by a number of questions spanning over four distinct aspects, all of which shall following roles: Chief Executive Officer, Chief Financial Officer, Chief Legal Officer, Chief
be further explored in the following sections. Information Officer, Chief Technology Officer, IT Manager/s, or similar. In the absence of
such a role, the person responsible for overseeing technological development was asked
The online self-administered survey consisted of 20 questions covering four AI thematic to fill in the survey.
areas, at the end of which, respondents were each provided a maturity score.
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Insight Areas
In interpreting the results of this survey, it is important to note
that this study is not a representative one, in that it does not
accurately represent a larger population. Furthermore, efforts
were made to directly reach out to potential participants
through email and/or phone calls.
An even share of the participants was observed to be from
organisations with a revenue stream of more than €50M
(27%) and in the €1M - €10M range (27%), with a further
20% having a revenue stream in the €10M - €50M range.
The profiles that contributed to the survey were among a
variety of business functions including C-suite (44%); IT and
Cybersecurity (31%) and Finance Professionals (8%). The
remaining 17% was made up of respondents coming from
Sales, Compliance, Human Capital, and Operations.
From an industry perspective, the respondents were from a
wide range of industries, including financial services (19%),
government and public services (15%), technology, media
and communications (15%), consumer markets, distribution
and retail (7%) and professional services (5%). The remaining
39% fell under a plethora of different industries such as food
and beverage, leasing, real estate, pharmaceutical, aviation,
logistics and mobility, and multi-sector service.
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Insight Areas
Malta AI Business Survey Report 2024
Overview of
of the Thematic
Insight Areas
I. Strategy and Adoption II. Governance
This thematic area examines how businesses in Malta are adopting AI Effective governance is essential for managing AI initiatives. This
technologies. It covers responses related to the usage, perception, and theme explores the structures and framework implemented by
implementation of AI solutions within organisations. By understanding how businesses to oversee AI projects. It highlights the importance of
AI is being integrated into business operations, we can gauge the current having robust governance mechanisms to ensure that AI is
state of AI adoption and identify trends and patterns in its usage. implemented ethically and responsibly, aligning with organisational goals
and regulatory requirements.
III. Investment IV. Market Perspective
The survey also investigates the financial commitment of businesses This thematic area captures the general views and benefits realisation
towards AI. This thematic area provides insights into the budget expected from the use of AI technology. It includes perspectives on the
allocations for AI efforts, reflecting the level of investment and prioritisation potential advantages AI can bring to businesses, such as increased
given to AI initiatives. Understanding the financial landscape helps in efficiency, improved decision-making, and enhanced customer
assessing the readiness and willingness of businesses to embrace AI. experiences. By analysing these views, we can better understand the
market sentiment towards AI and its perceived value.
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Insight Areas
Malta AI Business Survey Report 2024
Thematic Insights
Strategy and
Adoption
Strategy and Adoption
Key Insights
Use case discovery and identification remains a go-to initiative for many
organisations, and our results suggest that organisations are at different
stages in performing such a strategic exercise. The results indicate that a
significant percentage of respondents - 58% - are at the beginning of their
AI enablement journey. For these organisations, a focus may be to first
understand the business impact to be realised from AI into their organisations,
as a means to inform where a use case for AI may be explored.
To what extent is your company actively considering AI (e.g.
GenAI) technlogy as a tool (e.g. leveraging prebuilt tolls like
ChatGPT or Microsoft Copilot) in your business operations?
Not yet Using but Considering 29%
Limited Use Cases of AI 29%
Researching Internal Use Cases 19%
Integrated into Our Processes 12%
Performing Proof of Concepts 10%
0% 10% 20% 30%
% of Responses
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Insight Areas
The rate of AI enablement remains dependent on the availability of specialised talent.
Currently, it is clear from our analysis that organisations view their readiness to adopt
and successfully maintain AI technology use as reliant on their internal resources. 39%
of respondents believe there is a lack of subject matter expertise available within their
firm to be able to champion AI initiatives, be that an optimisation to transformation of
operating procedures.
In your opinion, what are the main challenges your organisation anticipates
encountering in the adoption of AI technologies (e.g. GenAI)?
7% Lack of Internal Expertise or Skills
14%
Integration with Existing Systems
39%
Data Privact and Security Concerns
19%
Ethical Considerations
22% We have not adopted /
considered adopting AI
Interestingly, data privacy and security risk from AI deployment is not a leading concern
for the participants. Ethical concerns from the use of AI technologies are the lowest
barrier for adoption, with the more conventional dimensions to digital transformation
being represented in: (i) integration of AI solutions with the organisation systems and
applications; and (ii) management of data privacy and security.
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Insight Areas
What’s next
The below are the recommended next steps that should be taken by organisations
to enhance their AI strategy and organisational adoption:
• Establish clear AI adoption goals and a manageable timeline to ensure a
successful implementation. Furthermore, the board should encourage cross-
departmental collaboration and produce a list of potential use cases that
would align with the long-term strategic objectives of the organisation.
• Prioritise the formation of an internal team overseeing all AI initiatives, use
case formulations and ensuring accountability while driving initiatives to
upskill employees in AI usage and other integrated tools to ultimately foster a
knowledgeable workforce.
• Secure investment to enable AI initiatives within the organisation, prioritising
governance practices while ensuring employees and the end-customer
experience a meaningful impact.
• Explore generative AI tools for contract drafting and review, legal research,
claims processing and vendor management. While questions surrounding
other fields including data protection and confidentiality may subsist, the CLO
should plan ahead and assess which solution can unlock more value for their
business without compromising on the essential security and privacy aspects.
• Support the implementation and experimentation of AI pilot projects to
demonstrate the value and impact AI has on the workforce and streamlining
processes to strive for a broader approach and not segmented. The CTO/CIO
should also ensure the implementation of a robust AI governance framework
to maintain data integrity and security.
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Insight Areas
Malta AI Business Survey Report 2024
Thematic Insights
Gover nan
ance
Governance
Key Insights
Undoubtedly, organisations are being asked to reinvent themselves to the With an ever-increasing list of legal and regulatory obligations, keeping a finger
operating model potential of the 21st century technologies being brought on the pulse of the AI regulatory landscape can be challenging. As of August
about through AI tools such as generative AI capability. Still, businesses must 2024, the regulatory clock via the EU AI Act has started and businesses will
be mindful to set-up governance mechanisms to ensure that such disruptive be expected to begin addressing the compliance requirements. This comes as
technology implementation happens in a responsible manner, as the potential a stark realisation for a significantly large portion of our respondents who are
ramifications from sprinting to the finishing line may prove costly. More than either unaware of the regulation (56%) or have voiced their need for guidance to
70% of the respondents observe a foundational concern from having a lack interpret their regulatory obligation (29%).
of a formal AI framework to inform their AI enablement journey.
Which of the below statements best describes the governance framework Which of the statements, best describes your
your company is considering in lieu of AI (e.g. GenAI)? organisation approach to the upcoming EU AI Act?
3%
There is no AI governance framework No awareness of the EU AI Act
7%
12%
There is a champion leading the 7% Aware of the EU AI Act,
framework with weak documentation Do no understand Impact
5% 29%
of procedures
7% Assigned an internal team to
There is a general AI governance understand Impact
framework with a set of policies
75%
and procedures
Currently investing in
There are designated champions in monitoring our AI compliance
the firm responsible for various AI
governance elements 56%
There is a specific team to centralise
the AI governance framework across
the business
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Insight Areas
What’s next
The below are the recommended next steps that should be taken by
organisations to enhance their AI governance approach:
• Promote awareness with the C-Suite level about compliance issues and the
overall governance challenges to be able to provide effective oversight and
maintain an active understanding of key regulations such as the EU AI Act.
• Be the connection between executives and shareholders - guiding them
on ethical and compliant AI’s ability to create value, rather than seeing
it as a cost. Establish a comprehensive AI governance framework to
ensure ethical AI use, regulatory compliance and complete alignment with
organisational goals.
• Work closely with the CLO in providing the financial resources necessary to
develop and implement an AI compliance governance strategy framework,
prioritising initiatives such as AI literacy as complementary activities.
• Develop a risk-led strategy to help the company stay ahead of the
regulation, as a means to provide guidance on the critical areas of the EU
AI Act that need to be addressed first.
• Collaborate with the CLO and compliance team to ensure that the
organisation’s transformation is not slowed down by regulatory and ethical
challenges down the line. Develop and deploy continuous monitoring
systems to further ensure AI compliance and ethical use.
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Insight Areas
Malta AI Business Survey Report 2024
Thematic Insights
Investme
ent
Investment
Key Insights
Only 5% of the respondents are confident in their ability to measure
their return on investment (‘ROI’) from AI initiatives, thus it is evident
that gauging the success of such technology implementation is a
significant challenge for modern businesses. Additionally, the low
confidence in ROI measurement underscores the necessity for a
robust monitoring framework to ensure the success of AI initiatives.
How confident are you in your company’s ability to assess the
Return on Investment of current AI (e.g. GenAI) initiatives?
100%
Minimal confidence 22%
Limited confidence 22%
Moderate confidence 32%
Significant confidence 19%
Optimised confidence 5%
23.1%
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Insight Areas
Too few of a percentage of the respondents (18%) view awareness, Paired with the above, a byproduct is being experienced by businesses that find future-
education, and research and development as a major AI investment area. planning of AI efforts a major hurdle to overcome. Among the spread of respondents, a
In contrast, AI transformation must begin with a lens of the impact on the lack of budget (29%) and conservative planning (41%) of AI-targeted investment areas
human, where the targeted upskilling of one’s company workforce is a strong such as training programmes or performing proof-of-concept applications of AI in one’s
indicator of successful AI enablement. business, remain the defaulted approaches.
Considering your technology investment strategy for AI initiatives Over the past and next 12 Months, which of the following statements best
(e.g. GenAI), which of the following areas does your company plan to describes the approach to technology investment of your company with respect
allocate budgetary funds to in the upcoming year? to AI initiatives (e.g. GenAI)?
Budgetary Fund Allocation
No Budget Allocation 29%
Proof of Concept testing
14%
27% Partial Budget Allocation 41%
Investment into AI
Transformation initiatives
18%
Moderate Budget Allocation 10%
Awareness and Education
Research and Development 23% Dedicated Internal Technology Investment 7%
18%
Other
Yearly Budget Allocation (Internal & External) 14%
0% 10% 20% 30% 40%
% of Responses
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Insight Areas
What’s next
The below are the recommended next steps that should be taken
by organisations to enhance their AI investment:
• Discuss the ROI of AI from cost-savings, productivity-
gains, and experience-enhancement to provide an informed
evaluation of continued budget allocation for AI initiatives.
• Lead from the front to integrate AI into the current business
operating model and processes by prioritising quick-win
projects such as AI business case initiatives, while promoting
an internal culture of continuous learning and innovation.
• Collaborate with the CEO, and CTO/CIO teams to establish
reasonable ROI metrics to be monitored from the effort in AI
enablement services driven internally or sourced from
external providers.
• Support the investment and trust in use of AI from a regulatory
perspective, in a manner that provides the confidence to
C-suite on the safety of AI within one’s organisation.
• Facilitate the CFO’s financial management, via the sharing
of, among others, data points that would inform the CFO on
uptake of AI tools across the business.
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Insight Areas
Malta AI Business Survey Report 2024
Thematic Insights
Market
Perspectives
Market Perspectives
Key Insights
In contrast to their peers, only 10% of respondents mention AI technologies with the potential for
sector-agnostic transformation - taken to mean that all sectors from financial services to shipping,
will experience positive disruption from adoption of AI into their business models.
AI as an enabler for business optimisation of internal operations was found to be the leading driver
(42%) of AI adoption. Likewise, respondents identified customer experience (27%) as a major area
for AI optimisation, where the customer is both the workforce that interacts with AI, as well as the
customer’s client that is benefiting from a service delivery that is enhanced from AI use.
From the following statements, where do you anticipate the biggest
impact of AI in your industry, in the next 24 months?
2%
Optimising internal operations 3%
Enhancing customer experiences
5%
Augmenting data driven
decision making
20%
Other 42%
Improving marketing and
advertising strategies with
personalised content generation
27%
Streamlining customer service and
support through AI-driven solutions
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Insight Areas
Interestingly for 41% of the responses, the foreseen impact of AI technologies was shared to be
significant to their department processes although not viewed as transformative to their operating
model - taken to mean that while a business may foresee an optimisation within their human
resources department, it is not the expectation that conventional process will be re-engineered
completely from the use of AI tools. At the same time, it must be noted that most respondents are
still exploring how best to leverage AI technologies which may suggest further need for study and
experimentation of AI initiatives.
In your opinion, what level of impact do you foresee AI (e.g. GenAI) having in your industry,
in the next 24 months?
Overview of
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Insight Areas
sesnopseR
fo
%
41%
40%
29%
30%
20%
15%
10%
10%
5%
No significant Limited Moderate Transformative Sector-wide
impact impact impact organisational impact impact
27
What’s next
The below are the recommended next steps that should be taken by organisations to
enhance their adoption of AI capabilities:
• Encourage a strategic focus on AI to optimise the organisation’s internal
operations and improve its service offering to one’s client base, while promoting
AI’s potential to streamline processes and enhance decision-making.
• Design an AI strategy that prepares a route for the company to manage AI
enablement in the short, medium and long term, with a focus on supporting
departmental use of technology to create value in efficiency, cost-savings and
user experience.
• Support the AI strategy with secured funding to perform internal research and
development that will ensure the organisation remains in-tune with the latest
AI developments.
• Stay abreast with the latest technology regulatory developments to ensure a
well-governed and responsible approach to AI use by the organisation.
• Maintain a vigilance on new and upcoming AI tools, as a means to remain
competitive in the service delivery of the organisation.
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Insight Areas
Key Actions for Successful
AI Transformation and Adoption
1. Leverage strategy and 2. Adopting a human-led, tech- 3. Prioritise and
governance to realise benefits powered approach pilot use cases
An effective AI strategy and governance At PwC, we believe that the key to realising The new and emerging capabilities of Generative
framework is crucial for realising the full benefits meaningful change from AI, is through AI, and continued advancements in the AI field
of AI. By establishing a clear strategic vision responsible AI use that blends human will be disrupting forces for one’s modern IT
on AI coupled with the necessary guidelines experiences with the power and potential operating model system with significant potential
and frameworks, organisations can ensure of the technology. Humans bring forward across the enterprise. Its implementation should
that AI systems are developed and deployed the knowledge required, and ultimately, by be guided by clearly defined use cases that
responsibly. This can be achieved by setting combining human expertise with advanced AI provide clear business value to the company. In
the necessary policies and procedures for technology, organisations can ignite a powerful this frame, it is recommended that organisations
usage, implementation, security and ethical synergy that drives growth, enhances user prioritise and pilot use cases either in individual
considerations, which help build trust in AI experience, and addresses complex challenges business functions (e.g. Finance, Human
amongst an organisation’s stakeholders. effectively. By leveraging this approach, Resources, other) or else cross organisationally
Moreover, it is paramount that regular monitoring organisations are able to realise AI that can be through user journeys (e.g. hire-to-retire). Such
and evaluation of AI systems, to ensure such is designed to work alongside humans, enhancing prioritisation and piloting will ensure that an
functioning as intended and making unbiased their capabilities and diminishing the risk of AI organisation can gain and leverage the necessary
ethical decisions. A robust governance structure replacing the human at the workplace. insights to make an informed risk-based
in place, therefore, will ensure organisations can business decision on how to gradually scale
mitigate risks and enhance the reliability and such use cases across the enterprise.
transparency of their AI initiatives.
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Insight Areas
Contact us
Jake Azzopardi
Andrew Schembri
Senior Manager
Partner
jake.azzopardi@pwc.com
andrew.schembri@pwc.com
This publication has been prepared for general guidance on matters of interest only, and does not constitute professional advice. You should not act upon the information contained
in this publication without obtaining specific professional advice. No representation or warranty (express or implied) is given as to the accuracy or completeness of the information
contained in this publication, and, to the extent permitted by law, PricewaterhouseCoopers, its members, employees and agents do not accept or assume any liability, responsibility
or duty of care for any consequences of you or anyone else acting, or refraining to act, in reliance on the information contained in this publication or for any decision based on it.
© 2024 PricewaterhouseCoopers. All rights reserved. ‘PwC’ refers to the Malta member firm, and may sometimes refer to the PwC network. Each member firm is a separate legal
entity. Please see www.pwc.com/structure for further details."
15,pwc,how-to-deploy-ai-at-scale-report.pdf,"How to deploy AI at scale:
A PwC and Microsoft playbook that explores the
critical role of cloud and cybersecurity
Introduction
Artificial intelligence (AI) stands poised to bring transformative changes to the way we
live and work, much like the significant technological breakthroughs of the past 10,000
years, such as agriculture, the printing press, electricity, and the internet. However, AI
differs in its potential to fundamentally enhance what individuals and businesses can
achieve, and it promises to do so with unprecedented speed and impact
AI and Generative AI (GenAI) represent a truly transformational wave of technology that
will reshape our world. AI will influence and change all areas of business and what we
can accomplish in our working lives—for the better. AI is already starting to accelerate
new innovations and automate processes in ways we would have thought unimagi-
nable only a few years ago. It is improving our productivity and helping us reimagine
the customer experience—and we are only at the very beginning of understanding
what it can deliver.
There are multiple facets to what AI is and can do: machine learning, a subset of AI,
enables algorithms to learn from data. Deep learning, another subset, also identifies
patterns. GenAI, the most recent evolution, has garnered much interest of late because
of its ability to generate novel content, such as text and images, which it is able to fulfil
by using models trained on large datasets.
There are two underlying requirements that are essential for AI use within organisa-
tions: the adoption and use of cloud technology and security. Cloud infrastructure is
the engine that helps to fully exploit AI’s capabilities. This is needed to manage and
grow with the vast sums of data that AI creates. But in consequence, it introduces
new cybersecurity challenges. Strong cybersecurity provides fundamental protections
for company data—including for AI models—and safeguards a business’s intellectual
property. Combining cloud with cybersecurity is what provides the essential building
blocks that will truly help organisations to realise the full potential of AI.
This paper seeks to explain and outline all the necessary steps and considerations that
organisations need to take to make the most of this exciting technology, one that will
revolutionise the entire world.
How to deploy AI at scale: A PwC and Microsoft playbook that explores the critical role of cloud and cybersecurity 2
1. Understand AI goals and
ambitions by determining strategy
Momentum around AI has been building slowly and goals to get there, aligning them with business
surely over the past decade, with a more recent strategy. Developing a clear roadmap can help
surge in interest following the launch of GenAI businesses navigate through the transformative
tools. The tools’ inherent ease of use has placed process of AI adoption. For those just starting, the
the technology in the reach of a much wider audi- focus should be on building foundational knowledge
ence, and at the same time, highlighted the possi- and exploring pilot AI projects that align with key
bility for all businesses to transform operations. business objectives. Mid-level adopters should
work on scaling these initiatives by refining their
We are now at the GenAI inflection point:
AI infrastructure and aligning AI strategies with
businesses are moving beyond individual exper-
broader business goals. Advanced users can focus
imentation and are rolling out the technology in
on optimisation, leveraging AI to innovate and gain
a concerted attempt to derive measurable gains
a competitive edge. A clear understanding of the
across the organisation. Organisations are all at
outcomes that the organisation wants to achieve,
different stages with AI. Some are taking their first
and its level of maturity is crucial. By providing
steps, some are at the early stages of exploring its
organisations with clear steps to follow, a solid
strategic implications, while others are already into
roadmap can ensure smoother transitions and better
making development decisions.
integration of AI technologies into operations.
Many of us have used AI without being aware of
Responsible AI practices help to design trust
its existence: simple grammar and spelling checks
in from the start and ensure that the impact of
and machine translations are both AI driven. We
the technology is broadly positive. This requires
are now witnessing a significant leap in what AI
developing a code of conduct that supports the
and the latest generation of AI tools can do. We
transparent, accountable and fair use of AI. We will
are seeing products such as the Microsoft Azure
cover this topic in more depth later in this paper.
OpenAI Service, which can be customised to
cater for specific use cases, but also off-the shelf Once AI goals have been aligned with business
GenAI tools, such as Microsoft Copilot1, which are strategy, you need to achieve a thorough under-
proving to be truly transformative for end users. standing of your organisation’s s capabilities,
Microsoft Copilot, for example, can help streamline assess your IT infrastructure and build in trust
a wide range of daily business activities, helping measures. Then the next step is to ensure the use
to cut down the production time needed for key of generative AI can be deployed effectively across
deliverables from days to perhaps a single hour. the organisation. The key: a robust and supportive
cloud architecture.
Business leaders are more than aware of GenAI’s
abilities and of its potential. Fully 70% of CEOs
believe that GenAI will transform the way they
create, deliver and capture value over the next
three years, according to PwC’s 27th Annual
70%
Global CEO Survey2. But before businesses
rush to start integrating GenAI into day-to-day
operations, several factors need to be considered.
The most important start point for every business
leader is in understanding the organisation’s AI of CEOs
maturity. This is crucial in determining readiness to
adopt and scale AI use for any new initiative. believe that GenAI will transform the
way they create, deliver and capture
Organisations should then define all the outcomes
value over the next three years2
they wish to achieve with GenAI and set out clear
1 PwC and Microsoft Copilot for Microsoft 365 | 2 PwC’s 27th Annual Global CEO Survey
How to deploy AI at scale: A PwC and Microsoft playbook that explores the critical role of cloud and cybersecurity 3
2. Assess your cloud infrastructure
Adopt an effective cloud strategy
Cloud is the fuel of AI. Cloud offers scalability to Hybrid cloud combines public cloud services with
harness vast computing resources and helps busi- existing on-premises data centres and private
nesses be more agile and flexible, qualities which cloud infrastructures. This enables the seamless
are essential to the effectiveness of all AI roll outs. movement of workloads between the two environ-
Organisations should assess where they are on ments without compromises to performance.
their cloud journey and consider what steps they
need to take to support and enable their use of AI.
“There are many reasons why hybrid cloud
makes good sense for businesses getting
PwC’s recent Cloud and AI Business survey3
started with AI. It offers the best of both
identified a small number (12%) of businesses—
worlds by protecting existing investment but
named “Top Performers”—that have already
adding cloud scalability and flexibility. We’ve
begun to reap the rewards of their investment also pioneered the ability to manage both
in AI and the cloud. The report notes that 72% on-premises and cloud assets with cloud
of these Top Performers are far more likely to tools, which makes hybrid more attractive
have achieved “all-in cloud adoption” when it and simpler to adopt for many organisations.”
comes to modernising data, versus 33% of other
Joao Couto, EMEA VP and COO Cloud
companies. By moving their data to the cloud and
Commercial Solutions, Microsoft
making it more easily ingestible by large language
models (LLMs), Top Performers are more readily
able to unlock new value from their data as they The set-up is also useful to support the regulatory
integrate new AI capabilities. compliance and data sovereignty needs of highly
regulated industries where sensitive data must be
The use of cloud also requires a strategy that
processed within a country’s borders.
suits an organisation’s individual needs. There are
several options and each company must decide A multi-cloud approach refers to the simultaneous
what works best for them. The public cloud use of multiple cloud service providers (CSPs). As
is perhaps the best known. A simple internet Couto points out, when organisations opt for this
connection allows any business to run all or parts model, it is mostly because they perceive a risk
of their IT infrastructure in the cloud, rent storage of being reliant on one vendor: “Some customers
and servers and use a variety of services. Public want the option of using multiple cloud providers.
cloud offers unlimited access to IT resources What we find is that they eventually choose one of
giving businesses a flexible IT usage model, one two models: either the use of a sole provider or a
that is ideal for training LLMs. main cloud provider with another one as back-up.”
72%
have achieved “all-in cloud adop-
tion” when it comes to modern-
ising data, versus 33% of other
companies.
of “Top Performers”
3 PwC US report: 2024 Cloud and AI Business Survey
How to deploy AI at scale: A PwC and Microsoft playbook that explores the critical role of cloud and cybersecurity 4
Use cloud to provide the Address data privacy issues
necessary infrastructure for AI
Whether a business is at the starting gate or has
Making the wheels of AI turn productively will already begun to use AI, it is essential to assess
require a robust cloud architecture. Building and the organisation’s processes and policies and
maintaining a wholly owned IT infrastructure is adapt these to govern the use of the technology.
expensive and can invariably place limits on a Cloud helps classify data access, for example,
company’s ability to scale. providing permissions for employees to access
data at the right time, when they need it, and only
if they need it. With stringent access controls,
“AI systems involve lots of data and that
organisations can more easily meet data protec-
data needs to be accessible. The cloud is a
tion regulation compliance and overcome data
scalable resource and it’s reliable—you need
privacy issues.
that if you are serious about bringing AI into
your business.”
Security is essential to AI because of the huge
data volumes involved. A strong security posture
Sebastian Paas, Partner, EMEA Cloud
helps ensure that all data used within AI systems
Leader, PwC Germany.
are not misused. Cybersecurity is foundational to
successful AI implementation.
Cloud service providers, such as Microsoft Azure4,
offer scalable resources that help control and
minimise the cost of AI development and deploy-
ment. Cloud resources also facilitate collaboration
across a company and among individual teams
helping users to seamlessly share real-time
insights and resources.
PwC’s 2024 Cloud Business and AI Survey5
showed that while most companies rather their
CSPs favourably, there are opportunities to get
even more value out of cloud by evolving their
relationships. At the top of the list: monitoring and
managing security and compliance, where more
than half of companies are looking to change their
relationships with their CSPs. More than two-fifths
of companies are also evaluating the types of
services provided and looking to collaborate on
future-state capabilities.
Streamline access to data
while making AI and machine
learning more accessible
Cloud’s operational agility is essential in supporting
AI systems and is also providing access to
specialised tools and services that are designed
for developing, deploying and enhancing GenAI
applications. One notable example is the Microsoft
Azure OpenAI Service, which delivers access to
powerful language models, such as GPT-4 and
DALL-E. Furthermore, Microsoft also provides
data management tools that help clean, organise
and prepare unstructured and structured data, to
increase its usability for those applications.
4 PwC and Microsoft generative AI | 5 PwC US report: 2024 Cloud and AI Business Survey
How to deploy AI at scale: A PwC and Microsoft playbook that explores the critical role of cloud and cybersecurity 5
3. Secure your data to power
AI solutions
Like any technology system, artificial intelligence
systems need to be protected from potential “Organisations need to ensure they have
threats and vulnerabilities. AI systems handle policies and processes in place to mitigate
huge amounts of data that include both personal high levels of risk in their AI systems, such as
and proprietary information, making them anything that is harmful to an individual. Not
appealing cyberattack targets. only do companies need to implement strong
cybersecurity, but they also need to ensure AI
When designing a new AI system, organisations
outputs are monitored and managed.”
should take care to build in strong security meas-
ures from the start to counter the rising number of Neil Redmond, Director, Cybersecurity and
attacks, which are continually growing in their levels Privacy, Competency Lead, PwC Ireland
of sophistication. Unsurprisingly, organisations
believe cyber risk to be only second in importance
for businesses after inflation, according to PwC’s
A secure cloud platform, such as Microsoft Azure,
2023 Global Risk Survey6, with many respondents
helps protect AI data from the additional risk
feeling they are “highly or extremely exposed”
of cloud vulnerabilities because of the greater
to them. The same survey notes that digital and
visibility it offers in security monitoring. This also
technology risks are also of high concern.
helps minimise data breaches and unintentional
CSPs offer the most highly advanced security user input. Implementing robust governance
controls with continuous monitoring and encryp- structures supports data integrity further by
tion. A ‘zero trust’ security architecture adds addi- ensuring data is validated and monitored, along
tional protection with superior access controls, with processes that can quickly detect and correct
ensuring that every access request is verified any additional errors in the AI data.
regardless of its origin. This helps to maintain AI
Authorised GenAI business tools also reinforce
system integrity and data confidentiality. These
security, avoiding what is termed ‘shadow GenAI’.
measures also work to prevent unauthorised
In the absence of a company-authorised chatbot
access and malicious actors from compromising
tool and associated policy, employees are likely
the AI system, so it remains available to users.
to use unauthorised tools, which increases the
LLMs are complex pieces of software, which are risk of data breaches. When companies block
open to multiple security risks that threaten their GenAI tools, they can also inadvertently push
integrity. The way LLM data is trained can lead to staff to transfer sensitive data to less-secure
biased or erroneous outputs that raise either legal personal devices. Having an approved tool used
and ethical concerns or erode trust in AI systems, in the cloud environment and blocking access to
so it’s vital to include human oversight throughout browser-based or consumer GenAI tools helps to
the training process and beyond. Continuous reduce the risk of shadow GenAI practices.
model training and bias mitigation measures help
Building trust in AI system accuracy is vital in
identify and eliminate problematic output.
protecting organisational reputation and credibility.
Fine tuning models through continued validation Customers feel more assured and users more
of inputs and outputs, along with anomaly detec- confident in AI outputs when robust security meas-
tion processes, help to achieve fair and reliable ures and governance frameworks7 are in place.
results, too.
6 PwC Global Risk Survey 2023 | 7 PwC report: GenAI is here to stay: What it means for cyber security
How to deploy AI at scale: A PwC and Microsoft playbook that explores the critical role of cloud and cybersecurity 6
4. Streamline cyber defences
Security teams, under constant pressure from
attacks and hackers, can strengthen and stream- “AI can be extremely helpful in maintaining
line their cybersecurity using GenAI8. The three security costs, while increasing cybersecurity
principal ways organisations are prioritising the protections and managing the constantly
use of GenAI for cyber defences, according to changing security threat landscape. GenAI
PwC’s latest Global Digital Trust Insights report9, tools help organisations execute routine
are: monitoring more efficiently, freeing up analyst
time to focus on more complex tasks.”
1. Threat detection and response
2. Threat intelligence
Aleksei Resetko, Partner, Cybersecurity &
3. Malware/phishing detection
Privacy, PwC Germany
Automating security using GenAI protects AI
systems because of the ability to constantly
Because PwC works closely with Microsoft, its
monitor for vulnerabilities in networks, applica-
security experts are well placed to tailor the tools
tions, platforms, systems and cloud.
to meet specific organisational needs. PwC’s
global reach provides an additional layer of
A good example of automation is in the use of
understanding in addressing complex security
Microsoft Copilot for Security for continuous
challenges across different regions and sectors.
monitoring of network traffic and threat identifi-
cation in real time. Not only does this mean that
GenAI can also be employed to review code for
threats can be mitigated expeditiously, but also
security flaws or for penetration testing to identify
the burden of repetitive and time-consuming tasks
IT system vulnerabilities. In addition to making
is greatly reduced.
these processes faster, GenAI can also provide
much more precise analyses and support lower-
This not only increases productivity, but it can
level work, such as recognising threat patterns,
also help companies retain skilled employees who
drafting incident reports and general management
appreciate a more varied and challenging workload.
reporting. While GenAI tools won’t replace human
While Security Copilot significantly boosts cyber- analysts, the tech can significantly boost efficiency
security defences, organisations can improve its for security teams, enabling them to more easily
use further with expert help. monitor, report and respond rapidly to incidents.
8 PwC report: GenAI is here to stay: What it means for cyber security | 9 PwC 2025 Global Digital Trust Insights: Bridging the gaps to cyber
resilience: The C-suite playbook
How to deploy AI at scale: A PwC and Microsoft playbook that explores the critical role of cloud and cybersecurity 7
5. Keep up with changing
regulatory environments
Regulatory compliance is an important consid- involved for all AI-driven activity. Under the Act,
eration, as regulators are responding to the AI systems that pose an unacceptable risk to the
rapid development of GenAI and the growing safety, livelihood or rights of individuals, or that are
use of other forms of AI and organisations must seen to manipulate the behaviour of humans or
comply with an increasing number of regulatory exploit their vulnerabilities are not permissible.
requirements. While CEOs have a higher level of
GenAI can be employed to achieve compliance
confidence in the ability of their organisation to
with the Act. For example, PwC has developed
comply with regulations, CISOs, at the front line
its own AI-based processes to test and validate
of cybersecurity are less optimistic. For example,
use cases against the EU legislation. This helps to
while 67% of CEOs in a recent survey reported
determine what is acceptable, what is prohibited
a high level of confidence in their organisation’s
under the Act, and how a particular use case
ability to be in compliance with AI regulations, just
54% of CISO/CSOs were equally confident10. The aligns with an organisation’s responsibilities and
recent enactment of the EU AI Act11 in Europe— compliance obligations. AI can also help address
which came into force in August 2024—is the first other types of regulatory compliance, for example
legislation to govern AI use. It is aimed at ensuring by using GenAI to help achieve compliance on
the safe and ethical development and deployment data protection rules or other types of legislation.
of AI within the European Union.
“The Act represents a vital first step in creating
67%
safe digital markets. It is expected to be the first
of many and similar legislations are already in
development in other global regions.”
Mona de Boer, Partner, Data and Artificial of CEOs
Intelligence, PwC Netherlands
reported a high level of confidence
The EU Act will require developers of GenAI foun-
in their organisation’s ability to be
dation models to be transparent about the data
in compliance with AI regulations
they use for model training and demonstrate how
models are developed to highlight the levels of risk
10 PwC 2025 Global Digital Trust Insights: Bridging the gaps to cyber resilience: The C-suite playbook | 11 European Parliament: EU AI Act
How to deploy AI at scale: A PwC and Microsoft playbook that explores the critical role of cloud and cybersecurity 8
6. Operationalise responsible AI
practices
AI systems need to be implemented responsibly AI still nascent and growing in its use in organisa-
to ensure their lawful, ethical and robust use. tions, trust is critical to engender acceptance of AI
PwC’s Responsible AI methodology12 helps use and the overall success of AI projects.
support the development of robust governance
frameworks to ensure AI use is transparent,
“There are still challenges in getting people
fair and accountable. This helps to manage
to use available AI tools in organisations.
AI risks effectively through the institution of
‘Employees’ tolerance levels will diminish
comprehensive policies and procedures, meaning
if AI tools don’t work or are perceived to
organisations can overcome potential ethical
be biased. We have to acknowledge that
violations and mitigate legal risks. To operation-
especially in regulated industries such as
alise responsible AI, businesses should implement
financial services, accuracy is key and it’s
governance frameworks that monitor AI’s use
absolutely critical to ensure that client data is
in real time, ensuring continuous oversight and
well-protected.”
immediate response to any issues that may arise.
Conducting regular audits to prevent biases or Prafull Sharma, Partner, Technology and
unintended consequences should also be an inte- Data Leader, PwC Switzerland
gral part of the process. “When approaching AI for
the first time, risk is often a key client concern,”
adds Mona de Boer. “It’s essential to show how a While responsible AI practices provide the back-
high-level responsible AI strategy translates to an bone for GenAI use, it is critical to design trust in
operational procedure level so they can see how from the outset, not only for AI applications in use,
risks are managed.” but also for supporting infrastructure such as with
the cloud platform.
Responsible AI deployment also means aligning AI
investment with clearly defined ethical standards
that cover items such as privacy and bias preven-
tion, to maintain stakeholder trust. With GenAI and
12 PwC: The responsible AI framework
How to deploy AI at scale: A PwC and Microsoft playbook that explores the critical role of cloud and cybersecurity 9
7. Build strategic partnerships
For organisations wanting to successfully develop PwC now has one of the largest implementations
secure AI solutions, it is imperative to bring in a of Azure OpenAI in the world, and our AI Factory13
strong technology partner with the right capabili- operating model supports the rapid scaling of
ties, industry knowledge and functional expertise. models for use in other areas of a client’s busi-
It is equally essential to tap into proven industry ness. A good illustration of this is an automated
expertise and knowledge to overcome barriers to invoice processing solution we developed using a
implementation. This helps to smooth the journey Microsoft GenAI model for a global manufacturing
to AI and jump-start its adoption. client. This solution can automatically approve,
deny or send invoices for human review and
PwC has taken a 360-degree approach to AI.
greatly reduces tedious and repetitive work. The
First, we embarked on our own AI journey by
AI Factory model is helping to scale the model for
adopting and using GenAI across the entire PwC
document review and analysis across the entire
organisation. This is helping us thoroughly test
finance function. “PwC is a pioneer in our entire
and refine the technology and ensure that any
partner ecosystem and is leading by example,”
GenAI offering could deliver the most client value.
adds Joao Couto. “Because PwC adopted
Our significant investment in deploying these solu-
Microsoft GenAI tools across its business, it can
tions internally, for example through our strategic
quickly and reliably demonstrate the business
partnership with Microsoft, is also helping us to
impact to customers based on its own experi-
deliver more tailored AI solutions.
ences. It’s a unique position to be in.“
Microsoft is a leader in secure generative AI tech-
“Our partnership with Microsoft enables pilot nology with more than 60,00014 customers using
projects that help us to demonstrate the impact Azure AI today. And this sophisticated technology
and potential of AI. Because we can rely on a is backed by the stringent security tools and
full range of AI, GenAI, machine learning and controls of the Azure Cloud Platform. By tapping
deep learning solutions from Microsoft, we into the PwC and Microsoft relationship, compa-
believe we can quickly help our clients gain that nies can use AI to drive growth. The Microsoft
all-important first mover advantage.” OpenAI Service integrates several powerful foun-
dation models into its products. It also offers an
Mauro Xavier, Partner, EMEA Microsoft Application Programming Interface (API) service
Alliance Leader, PwC Spain for developers to integrate the models it uses into
their own applications.
13 Why you need an AI factory: A CIO’s guide to generative AI | 14 Press Release Webcast - FY 2024 Q4
How to deploy AI at scale: A PwC and Microsoft playbook that explores the critical role of cloud and cybersecurity 10
8. Empower and upskill your
people on AI
As AI continues to evolve, it’s impact on the Both will contribute significantly to gaining more
workforce will be increasingly far-reaching. Using confidence and adopting AI in the organisation.
AI has the potential to help the workforce be far If this succeeds, it can both increase employee
more productive. PwC’s 2024 AI Jobs Barometer productivity and help employers retain talent
notes an almost five-fold increase in labour within the company. PwC’s 2024 AI Jobs
productivity in business sectors exposed to AI15. Barometer shows that these capabilities are
already highly valued; employers in the countries
To achieve this impressive level of impact,
surveyed are willing to pay a 14% wage premium
organisations need to proactively prepare their for people skilled in the technology16. PwC will
employees for the AI revolution by providing explore this topic in greater depth in one of a
training opportunities and a safe space for series of forthcoming AI whitepapers.
employees to experiment with the technology.
15, 16 PwC’s 2024 AI Jobs Barometer
How to deploy AI at scale: A PwC and Microsoft playbook that explores the critical role of cloud and cybersecurity 11
Closing: monitor and adapt to new
business issues
We are on the cusp of great change. AI and GenAI are showing how we can transform
what we can achieve. And we are only at the very beginning of the seismic effect
that AI will bring to the efficiency of businesses and their ability to compete and grow
by accomplishing goals much faster17. Starting the AI journey correctly is crucial.
Collaborating with experienced business and technology partners yields better, more
positive results and motivates organisations to keep progressing.
A strong cloud partner is of equal importance. As we have stated: for AI projects to be
successful, they need the scalability of a robust cloud infrastructure. This removes the
brakes that traditional wholly-owned IT infrastructures can place on development and
enables AI applications to be deployed rapidly.
Those organisations that don’t adopt AI now may find themselves pushed out of their
markets very quickly. And once the technology has been adopted, organisations
should keep an eye on the future. The possibilities of AI are constantly evolving and
growing. To maintain the competitive edge that AI and GenAI can deliver, it is essential
for every business to focus on continually improving its AI capabilities and keep pace
with industry trends.
We believe that AI has the potential to help organisations fuel innovation, make great
advances in productivity and reinvent how they operate. A new dawn of possibilities is
emerging—one that helps businesses solve their most important challenges and build
the trust they need to achieve a better tomorrow.
17 PwC The Leadership Agenda: Gen AI is a tool for growth, not just efficiency
How to deploy AI at scale: A PwC and Microsoft playbook that explores the critical role of cloud and cybersecurity 12
Contacts
Joao Couto Mauro Xavier Mona de Boer Sebastian Paas
EMEA VP & COO Partner, Partner, Data Partner,
Cloud Commercial EMEA Microsoft & Artificial EMEA Cloud
Solutions Aliance Leader Intelligence Transformation
Microsoft PwC Spain PwC Netherlands Leader
PwC Germany
Aleksei Resetko Prafull Sharma Neil Redmond
Partner, Partner, Director,
Cybersecurity Technology & Cybersecurity
& Privacy Data Leader and Privacy,
PwC Germany PwC Switzerland Competency Lead
PwC Ireland
How to deploy AI at scale: A PwC and Microsoft playbook that explores the critical role of cloud and cybersecurity 13
© 2024 PwC. All rights reserved. Not for further distribution without the permission of PwC. ‘PwC’
refers to the network of member firms of PricewaterhouseCoopers International Limited (PwCIL), or,
as the context requires, individual member firms of the PwC network. Each member firm is a separate
legal entity and does not act as agent of PwCIL or any other member firm. PwCIL does not provide any
services to clients. PwCIL is not responsible or liable for the acts or omissions of any of its member firms
nor can it control the exercise of their professional judgment or bind them in any way. No member firm is
responsible or liable for the acts or omissions of any other member firm nor can it control the exercise of
another member firm’s professional judgment or bind another member firm or PwCIL in any way."
16,capgemini,Everest_Group_-_Artificial_Intelligence__AI__Services_PEAK_Matrix_Assessment_2023_-_Focus_on_Capgemini.pdf,"Everest Group Artificial Intelligence (AI) Services PEAK Matrix® Assessment 2023
Focus on Capgemini
January 2024
Copyright © 2024 Everest Global, Inc.
This document has been licensed to Capgemini
Artificial Intelligence (AI) Services PEAK Matrix®Assessment 2023
Introduction
Artificial Intelligence (AI) has been a transformative technology since its inception. Increasing advances in AI, bolstered by the recent developments in generative AI (gen AI), are pushing
organizations to actively invest in a strong AI strategy to achieve business-oriented outcomes and improve customer experience. Despite these developments, organizations are failing to
achieve the full benefit, because they are adopting AI in pockets, rather than across the organization. Providers with innovative solutions, accelerators, and strong advisory capabilities can
efficiently help enterprises to navigate the fast-evolving AI landscape and successfully implement it.
In this research, we present an assessment and detailed profiles of 26 AI service providers featured on the Artificial Intelligence (AI) Services PEAK Matrix®Assessment 2023. Each profile
offers a comprehensive picture of its service focus, key Intellectual Property (IP) / solutions, domain investments, and case studies. The assessment is based on Everest Group’s annual
RFI process for calendar year 2023, interactions with leading AI service providers, client reference checks, and an ongoing analysis of the AI services market.
The full report includes the profiles of the following 26 leading AI service providers featured on the Artificial Intelligence (AI) Services PEAK Matrix:
⚫ Leaders: Accenture, Capgemini, Cognizant, Deloitte, IBM, TCS, and Wipro
⚫ Major Contenders: DXC Technology, EPAM, Eviden –an Atos business, EXL, EY, Genpact, Globant, HCLTech, Infosys, KPMG, LTIMindtree, NTT DATA, PwC, Sopra Steria, and
Tech Mahindra
⚫ Aspirants: Kyndryl, Stefanini, UST, and Virtusa
Scope of this report
Geography Providers Services
Global 26 AI service providers AI services (refer to page
10 for scope of research)
Note: Everest Group has refrained from identifying Star Performers for this AI Services PEAK Matrix report due to change in the scope of this research
Proprietary © 2024, Everest Global, Inc. | this document has been licensed to Capgemini 2
Artificial Intelligence (AI) Services PEAK Matrix®Assessment 2023
AI services PEAK Matrix® characteristics
Leaders
Accenture, Capgemini, Cognizant, Deloitte, IBM, TCS, and Wipro
⚫ Leaders have displayed a strong vision focused on driving business outcomes through AI services for their customers
⚫ They are at the forefront of innovation and driving change with thought leadership, partnerships, and internal investments in emerging themes such as gen AI, sustainability, responsible
AI, MLOps, no-code/low-code solutions, AIOps, and edge AI
⚫ Leaders have invested heavily in acquisitions as well as building structured internal talent development programs to ensure the availability of skilled talent to solve complex problems.
They have also focused on the breadth of skills, enabling full services play by developing a range of certification programs that help them stay ahead as technology evolves
⚫ Leaders have developed a range of integrated platforms along with industry-and use case-specific accelerators to cut down the trial and run phase to achieve faster outcomes
⚫ Domain focus and willingness to share risk and returns through value-based pricing models have further differentiated them in stakeholder partnerships
Major Contenders
DXC Technology, EPAM, Eviden – an Atos business, EXL, EY, Genpact, Globant, HCLTech, Infosys, KPMG, LTIMindtree, NTT DATA, PwC, Sopra Steria, and Tech Mahindra
⚫ Major Contenders have shown high confidence in their sweet spots within the AI stack. They have a strong base of satisfied clientele within these areas
⚫ Major Contenders have the vision to develop full services play and are investing in talent development programs, acquisitions, IP building, and a partnership ecosystem to enable
the same
⚫ They need to supplement their vision and investments with effective communication of success on transformative end-to-end AI deals to enhance their market perception
Aspirants
Kyndryl, Stefanini, UST, and Virtusa
⚫ Aspirants are focused on developing expertise in their preferred AI value chain segments with most investments directed toward upgrading and improving the features of these flagship
solutions
⚫ Aspirants try to differentiate themselves through cost-effectiveness, innovation, personalized services, and agility or quick turnaround time
Proprietary © 2024, Everest Global, Inc. | this document has been licensed to Capgemini 3
Artificial Intelligence (AI) Services PEAK Matrix®Assessment 2023
Everest Group PEAK Matrix®
Artificial Intelligence (AI) Services PEAK Matrix® Assessment 2023 |
Capgemini is positioned as a Leader
Everest Group Artificial Intelligence (AI) Services PEAK Matrix®Assessment 20231,2
High
Leaders
Major Contenders
Aspirants
Low
Low High
Vision & capability
Measures ability to deliver services successfully
1 Assessments for EPAM, Eviden–an Atos business, EY, Infosys, KPMG, PwC, and UST exclude service provider inputs on this study, and are based on Everest Group’s estimates that leverage its proprietary Transaction Intelligence (TI) database,
ongoing coverage of service providers’ public disclosures, and interaction with buyers. For these companies, Everest Group’s data for assessment may be less complete
2 Assessment for Deloitte is based on partial inputs provided by service provider and is also based on Everest Group’s estimatesthat leverage its proprietary Transaction Intelligence (TI) database, ongoing coverage of service providers’ public disclosures,
and interaction with buyers. For this company, Everest Group’s data for assessment may be less complete
Source: Everest Group (2023)
Proprietary © 2024, Everest Global, Inc. | this document has been licensed to Capgemini 4
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Leaders
Accenture
Major Contenders Deloitte
Capgemini
IBM
Infosys
TCS
Genpact HCLTech Wipro
EY Cognizant
PwC
KPMG
EXL
Globant Tech Mahindra
LTIMindtree
NTT DATA
DXC Technology
EPAM
Eviden
Virtusa Sopra Steria
UST
Kyndryl Stefanini
Aspirants
Artificial Intelligence (AI) Services PEAK Matrix®Assessment 2023
Capgemini profile (page 1 of 5)
Overview
Company overview Low (<10%) Medium (10-25%) High (>25%)
Capgemini, headquartered in Paris, is a prominent global corporation delivering consulting, technology,
engineering, professional, and outsourcing services. Capgemini operates in more than 50 countries and is Adoption by industry
particularly strong in Europe, North America, Asia-Pacific, and the Middle East. The company caters to a
Banking, Financial Electronics, hi-tech, Healthcare and
diverse clientele across sectors such as Banking, Financial Services, and Insurance (BFSI), retail, Services, and Insurance and technology life sciences
manufacturing, and technology while specializing in digital transformation, cloud services, cybersecurity,
AI, and analytics. Retail, distribution, Telecom Media
and CPG and entertainment
Manufacturing Public sector Others
Headquarters: Paris, France Website: www.capgemini.com
Key leaders Adoption by geography
⚫ AimanEzzat: Chief Executive Officer North America UK Rest of Europe
⚫ Niraj Parihar: CEO, Insights and Data GBL
⚫ Anne-Laure Thibaud(THIEULLENT):Data, AI, and Analytics Group Offer Leader APAC South America MEA
AI practice overview
⚫ Capgemini's AI Services business is organized based on two dimensions: group portfolio and Global Adoption by buyer group
Business Lines (GBL)
Small-market Mid-market Large-market Very large-market
⚫ The Data and AI global portfolio team coordinates and manages the entire group response for data and
(Annual revenue (Annual revenue (Annual revenue (Annual revenue
AI, ensuring alignment and coordination across the group portfolio
<US$1 billion) US$1-5 billion) US$5-25 billion) >US$25 billion)
⚫ Capgemini has invested in generative AI to scale up its capabilities and build industry-focused solutions
to cater the specific needs of its clients, with an additional investment of EUR2 billion in AI over the next
three years
AI practice fact sheet 2021 (Jan-Dec) 2022 (Jan-Dec) 2023 (Jan-Jun)
Number of active clients 2,000-2,500 3,000-3,500 3,000-3,500
Number of FTEs 28,000-30,000 30,000-35,000 32,000-36,000
Proprietary © 2024, Everest Global, Inc. | this document has been licensed to Capgemini 5
Artificial Intelligence (AI) Services PEAK Matrix®Assessment 2023
Capgemini profile (page 2 of 5)
Solutions
IP overview
⚫ Capgemini’s portfolio brings the foundations: AI & data strategy, AI analytics & data science, intelligent process automation, and AI & data engineering. It enables Capgemini to bring new data & AI approaches. Recently, it has
introduced offers as part of the Generative AI portfolio spanning from strategy definition through to practical development and implementation of generative AI at scale, by bringing together years of experience and leading-
edge generative AI work and augmenting it with industrialization around foundation models
⚫ Capgemini has a unified portfolio of assets and solutions that it leverages to deliver business outcomes to its clients. It follows a Data and AI-powered transformation strategy for the industrialization of its data platforms into
managed business assets. It serves its clients with Data & AI solutions to deliver customer experiences, transform to intelligent & sustainable products and industrial operations
Proprietary IP/solutions/frameworks/accelerators/tools developed internally to deliver AI services
Solution name Solution type Year launched Details
AI @Scale Framework N/A It is a platform designed to help organizations achieve enterprise-wide AI adoption and scalability. It addresses the challenges
that businesses face when modernizing its AI journey and centralizing AI capabilities. The framework encompasses
processes, reference architectures, data strategy, governance, ethical AI principles, and accelerators.
Edge AI Framework/Accelerator 2021 It is a framework designed to enable the deployment of advanced AI solutions securely on edge devices. It addresses
challenges by optimizing power and compute resources to maintain model performance and accuracy. It also enhances
availability while prioritizing data privacy and security, ensuring that sensitive information remains protected.
Artificial Data Amplifier (ADA) –powered Product 2018 It is a data augmentation and synthetic data generation platform powered by AI and ML techniques. It is designed to address
by gen AI the challenges associated with data scarcity, privacy concerns, and the need for diverse and representative datasets in AI and
ML projects.
Generative Testing Product N/A It leverages Large Language Models (LLMs) and synthetic data for the discovery of code to test case, specification to test
case, and defect report to test case and then the generation of the test cases for the monitoring and prediction of defect rate.
Privacy Preserving AI (PPAI) Solution N/A It is a suite of solutions designed to enhance current privacy techniques and explore novel approaches to safeguard sensitive
data utilized across the AI life cycle.
Proprietary © 2024, Everest Global, Inc. | this document has been licensed to Capgemini 6
Artificial Intelligence (AI) Services PEAK Matrix®Assessment 2023
Capgemini profile (page 3 of 5)
Partnerships
Key partnerships (top 10)
Partner name Details
AWS It is a premier consulting partner with an aim to leverage the capabilities of AWS's cloud infrastructure and AI services to drive innovation and transformation for clients. Capgemini’s
Custom Generative AI for Enterprise is available on AWS as well.
Azure It is a Microsoft gold partner to combine Capgemini's AI expertise and industry knowledge with Azure's advanced AI capabilities including ML, cognitive services, and data analytics.
Capgemini and Microsoft have also partnered to launch Azure Intelligent App Factory, a product that aims to help businesses quickly and responsibly deploy gen AI capabilities.
GCP It is a premium Google partner with an aim to leverage GCP's cloud-based AI and ML services to drive innovation and transformation for clients. Capgemini has also created a global gen
AI CoE in partnership with Google.
Salesforce Capgemini is global strategic partner and has introduced genAI for CX Foundry for clients using Salesforce that helps in hyper-personalization and data-driven CX.
Dataiku Capgemini is a platinum partner and offers a collaborative platform that powers both self-service analytics and the operationalization of ML models in production.
DataRobot Capgemini is a service partner to DataRobotand has collaborated to leverage DataRobot’splatform for its initiatives around next-generation demand themes.
Databricks Capgemini has a strategic partnership and has collaborated to build gen AI capabilities and cater to the growing demand.
H2O.ai Capgemini is a global System Integrator (SI) partner to H2O.ai helping clients with investments in AI, cloud, and digital.
Other partnerships (logos)
Proprietary © 2024, Everest Global, Inc. | this document has been licensed to Capgemini 7
Artificial Intelligence (AI) Services PEAK Matrix®Assessment 2023
Capgemini profile (page 4 of 5)
Investments and market success
Other investments to enhance AI services capabilities, e.g., setting up of new delivery centers / CoEs / innovation labs, trainings/certifications, etc.
Investment Details
Innovation lab ⚫ Introduced a dedicated genAI practice relying on a network of experts globally to support its clients’ gen AI solutions adoption and implementation
⚫ Established a gen AI lab with a dedicated team of AI experts. The lab's primary focus is on researching gen AI use cases, confidentiality, privacy, multi-modal models, and technology readiness. It
explores the challenges of context-aware AI and consists of a core team of experts and additional members across global Capgemini offices
⚫ Launched gen AI Google Cloud CoEto help enterprises realize the full potential of AI technologies. The new CoEaims to help clients advance its business transformation goals, enhance engagement
with customers, and accelerate value creation from AI investments
⚫ Established a Quantum Lab in collaboration with IBM, which is dedicated to assisting clients in exploring the prospects of quantum technologies. Its primary focus is on shaping the organization's
strategy and capabilities to transform the potential of quantum computing into practical reality
⚫ It also utilized a wide network of delivery and solutioning centers supported by innovation centers and dedicated advanced technology labs such as 5G Lab, IPA AI Lab, and Global AI CoEnetwork to
enable the best solution capability for its clients
Talent development ⚫ Launched gen AI campus on the data and AI learning platform (NEXT) to enhance gen AI skillset and build solutions for clients and partners
⚫ Launched a solution development lab, GenZArena, a hub for skill development, employee engagement, and IP solution creation. It is designed to actively involve and quickly onboard new talent,
particularly recent graduates, harnessing its millennial mindset. It places a strong emphasis on advanced visualization, DevOps,and ML solution development using cutting-edge technologies and tools
Acquisitions ⚫ Acquired Quantmetry, an independent consulting firm specializing in mathematical data modeling and AI technological solutions. The acquisitions aims to strengthen the capabilities of Capgemini in
France to deliver data transformation at scale and in the development of innovative, high-impact products and services powered by trusted AI
⚫ Acquired Braincourt, a German firm that specializes in business intelligence and data science services
Recent AI engagements (non-exhaustive)
Client Year of signing Geography Engagement details
Multinational consumer N/A N/A Built a cohesive AI suite with the help of GPT-3 model to generate content that reduces the content creator’s time, increases product visibility, and increases
goods company purchase conversion for the target product
Global academic publisher N/A N/A GPT-enabled semantic search enabling reduced support cost and enhanced customer experience through integrations of various platforms to provide
personalized content
A pharmaceutical company N/A N/A Enabled a trusted data and AI platform to speed up drug development and improve the success rate of clinical trials by 30%
A healthcare major N/A N/A Established an AI-powered touchless forecasting accelerator with 20+ forecasting models resulting in enhanced processing time by 80%
Proprietary © 2024, Everest Global, Inc. | this document has been licensed to Capgemini 8
Artificial Intelligence (AI) Services PEAK Matrix®Assessment 2023
Capgemini profile (page 5 of 5)
Everest Group assessment – Leader
Measure of capability: Low High
Market impact Vision & capability
Market Portfolio Value Vision and Scope of Innovation and Delivery
adoption mix delivered Overall strategy services offered investments footprint Overall
Strengths Limitations
⚫ Broad industry coverage:Capgemini has extensive expertise in serving clients spanning ⚫ Talent concerns: while Capgemini has invested in different upskilling programs such as
different industries including banking, manufacturing, life sciences, and retail, distribution, Millennial Garage (Gen Z Arena) and Data & AI Campus, referenced clients have highlighted
and CPG. Referenced clients value Capgemini’s ability to deliver tailored solutions suiting that there is scope for improvement in the depth of Capgemini’s technical expertise. Some
their requirements clients also raised concerns around project delays due to attrition
⚫ Strong gen AI focus:referenced clients also appreciated Capgemini’s extensive investments ⚫ Potential for elevating consulting and advisory skills:Capgemini’s consulting and
in gen AI research. It offers privacy preserving gen AI through federated learning, augmented advisory capabilities are limited, as highlighted by the referenced clients. They believe
masking, etc., and has also invested in strategic partnerships such as a gen AI CoEwith Capgemini finds it difficult to act as a strategic partner and proactively introduce new
Google and Azure Intelligent App Factory with Microsoft technologies and innovative off-the-shelf solutions in engagements
⚫ Proficient data-centric competencies:Capgemini has a strong data for AI foundation with ⚫ Limited exposure in small and mid-market segment:while Capgemini has significant
its synthetic data generation, document extraction, and data labelling capabilities, supported expertise in catering to large enterprises, its experience in serving midsize and small size
by a large data engineering talent base enterprises is relatively limited
Proprietary © 2024, Everest Global, Inc. | this document has been licensed to Capgemini 9
Appendix
10
Artificial Intelligence (AI) Services PEAK Matrix®Assessment 2023
Everest Group PEAK Matrix® is a proprietary framework for assessment of
market impact and vision and capability
Everest Group PEAK Matrix
High
Leaders
Aspirants
Low
Low High
Vision and capability
Measures ability to deliver services successfully
Proprietary © 2024, Everest Global, Inc. | this document has been licensed to Capgemini 11
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Major Contenders
Artificial Intelligence (AI) Services PEAK Matrix®Assessment 2023
Services PEAK Matrix® evaluation dimensions
Measures impact created in the market –
captured through three subdimensions
Market adoption
Number of clients, revenue base,
YoY growth, anddeal value/volume
Portfolio mix
Diversity of client/revenue base across
geographies and type of engagements
Value delivered
Value delivered to the client based
on customer feedback and
transformational impact
Measures ability to deliver services successfully.
This is captured through four subdimensions
Vision and strategy Scope of services offered Innovation and investments Delivery footprint
Vision for the client and itself; future Depth and breadth of services portfolio Innovation and investment in the enabling Delivery footprint and global sourcing mix
roadmap and strategy across service subsegments/processes areas, e.g., technology IP, industry/domain
knowledge, innovative commercial
constructs, alliances, M&A, etc.
Proprietary © 2024, Everest Global, Inc. | this document has been licensed to Capgemini 12
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Leaders
Major Contenders
Aspirants
Vision and capability
FAQs
Does the PEAK Matrix®assessment incorporate any subjective criteria?
Everest Group’s PEAK Matrix assessment takes an unbiased and fact-based approach that leverages provider / technology vendor RFIs and Everest Group’s proprietary databases containing providers’ deals
and operational capability information. In addition, we validate/fine-tune these results based on our market experience, buyer interaction, and provider/vendor briefings.
Is being a Major Contender or Aspirant on the PEAK Matrix, an unfavorable outcome?
No. The PEAK Matrix highlights and positions only the best-in-class providers / technology vendors in a particular space. There are a number of providers from the broader universe that are assessed and do
not make it to the PEAK Matrix at all. Therefore, being represented on the PEAK Matrix is itself a favorable recognition.
What other aspects of the PEAK Matrix assessment are relevant to buyers and providers other than the PEAK Matrix positioning?
A PEAK Matrix positioning is only one aspect of Everest Group’s overall assessment. In addition to assigning a Leader, Major Contender, or Aspirant label, Everest Group highlights the distinctive capabilities
and unique attributes of all the providers assessed on the PEAK Matrix. The detailed metric-level assessment and associated commentary are helpful for buyers in selecting providers/vendors for their specific
requirements. They also help providers/vendors demonstrate their strengths in specific areas.
What are the incentives for buyers and providers to participate/provide input to PEAK Matrix research?
⚫ Enterprise participants receive summary of key findings from the PEAK Matrix assessment
⚫ For providers
– The RFI process is a vital way to help us keep current on capabilities; it forms the basis for our database –without participation, it is difficult to effectively match capabilities to buyer inquiries
– In addition, it helps the provider/vendor organization gain brand visibility through being in included in our research reports
What is the process for a provider / technology vendor to leverage its PEAK Matrix positioning?
⚫ Providers/vendors can use their PEAK Matrix positioning or Star Performer rating in multiple ways including:
– Issue a press release declaring positioning; see our citation policies
– Purchase a customized PEAK Matrix profile for circulation with clients, prospects, etc. The package includes the profile as well as quotes from Everest Group analysts, which can be used in PR
– Use PEAK Matrix badges for branding across communications (e-mail signatures, marketing brochures, credential packs, client presentations, etc.)
⚫ The provider must obtain the requisite licensing and distribution rights for the above activities through an agreement with Everest Group; please contact your CD or contact us
Does the PEAK Matrix evaluation criteria change over a period of time?
PEAK Matrix assessments are designed to serve enterprises’ current and future needs. Given the dynamic nature of the global services market and rampant disruption, the assessment criteria
are realigned as and when needed to reflect the current market reality and to serve enterprises’ future expectations.
Proprietary © 2024, Everest Global, Inc. | this document has been licensed to Capgemini 13
Artificial Intelligence (AI) Services PEAK Matrix®Assessment 2023
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today’s market challenges and strengthen their strategies by applying contextualized problem-solving to their unique
situations. This drives maximized operational and financial performance and transformative experiences. Our deep
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talent, sustainability, and sourcing delivers precise and action-oriented guidance. Find further details and in-depth content
at www.everestgrp.com.
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Proprietary © 2024, Everest Global, Inc. | this document has been licensed to Capgemini 14"
17,capgemini,Clara-Shih-23Oct2024-Conversations-for-tomorrow_Edition_9_Report.pdf,"Gener(AI)ting
the future
Quarterly review
N°9 — 2024
Gener(AI)ting the future
Executive Conversations
CLARA SHIH
Chief Executive Officer
Salesforce AI
64
Capgemini Research Institute Gener(AI)ting the future
Executive Conversations
A PLATFORM FOR
GENERATIVE AI
Clara Shih is the CEO of Salesforce AI. In her role,
Clara oversees artificial intelligence (AI) efforts
across the organization, including product, go-
to-market, growth, adoption, and ecosystem for
Salesforce’s AI customer relationship management
(CRM) platform. Clara also served on the Starbucks
board of directors for 12 years and serves as
Executive Chair of Hearsay Systems, a digital
software firm she founded in 2009 that is merging
with Yext (NYSE:YEXT).
65
Gener(AI)ting the future Capgemini Research Institute
Executive Conversations
SALESFORCE AND AI
What led to the development of Salesforce AI?
We have been working on AI for a long time. In 2014, we
established our Salesforce AI Research Group, and in 2016,
we introduced the first predictive AI for CRM, Einstein.
Salesforce Research has been developing large language
models (LLMs) for many years. The popularity of ChatGPT
and increased demand for enterprise AI led us to productize
our generative AI applications and platform.
Agentforce, our suite of customizable autonomous agents
and low-code tools – powered by our Customer 360
applications, Data Cloud, and Salesforce Platform – makes
Clara Shih
deploying and getting value from trusted agents easier than
Chief Executive Officer
ever. It’s the next generation of our Einstein AI solutions.
Salesforce AI
Every agent request runs through our Einstein Trust layer
to ensure data privacy, data security, ethical guardrails,
observability, and monitoring. We know customers don’t
want to be locked-in to a specific model, especially given the
rapid advancement and growth in model options, so we’ve
architected Agentforce to work with any model.
To help customers see value fast, we offer over 100 out-
of-the-box (OOTB) AI use cases and Agentforce agents,
including service agent, sales development representative
agent, commerce agent, merchandiser, buyer agent,
personal shopper, and campaign optimizer. These OOTB
agents make it easy to get started and are easy to customize.
Agentforce agents can be set up in minutes, scale easily,
and work 24/7 on any channel (Salesforce offers digital
messaging, messaging over in-app and web (MIAW), email,
and now voice natively).
Our customers and partners can easily customize and build
trusted agents on Agentforce using our low-code tools such
as agent builder, prompt builder, and model builder.
66
Capgemini Research Institute Gener(AI)ting the future
Executive Conversations
Can you expand on why you chose the platform approach?
Agents have to be customized and grounded in trusted data and role- and
industry-based context in order to be production-ready for the enterprise.
Salesforce has always offered a user-friendly platform for customers and
partners to easily build and customize, and from the beginning, we designed
Salesforce Platform to be used even by non-programmers.
Our customers want to customize their own AI apps, and we give them
options to do this either with no code, low code, or pro code. Rather than
every organization having to build their own data cloud, trust layer, and
API management, we make it easy by offering Salesforce Data Cloud,
the Einstein Trust Layer, and Mulesoft as part of the Salesforce Platform.
Customers and partners tell us all the time how much they appreciate how
the Einstein Trust Layer takes care of data masking, citations, audit trail,
toxicity filters, zero retention prompts, and
prompt defense to mitigate cybersecurity
risks.
From the beginning, Prompt builder and agent builder allow
customers to take our out of the box (OOTB)
we designed
use cases and customize them with their
Salesforce Platform own brand voice, company policies and
procedures, and reference organization-
to be used even by
specific custom data. For example, an
non-programmers."" automotive company can directly reference
specific custom fields to guide their agents,
such as the make, model, and warranty SLA.
A retail customer would have a different set of custom fields and business
processes. Customers can reference any structured or unstructured data
from across their organization, whether it is in Salesforce or an external data
lake or data warehouse such as Snowflake, Databricks, or Big Query, by using
our Data Cloud.
Agentforce is about deploying autonomous agents to help drive human
productivity. It follows all of the data-sharing rules in each Salesforce
organization and is personalized to every user. For example, if two different
sales reps within the same company ask the same question of Agentforce
– such as “What are my top sales deals this quarter?” – they will each get a
customized answer based on each individual rep’s territory, customers, and
open opportunities.
67
Gener(AI)ting the future Capgemini Research Institute
Executive Conversations
GENERATIVE AI – A POTENTIAL ECONOMIC IMPACT OF TRILLIONS OF DOLLARS
What kind of impact do you
envisage generative AI having on
large organizations globally?
Gucci used Salesforce
AI to transform their
Generative AI offers a tremendous
opportunity with a potential economic service representatives
impact of trillions of dollars from both
into sellers.""
productivity gains and cost savings.
Agentforce customers like Wiley and
OpenTable are finding tremendous
success, increasing the number of routine support issues they can use AI
to resolve autonomously 24/7 while increasing employee engagement and
customer satisfaction.
But change cannot happen overnight. Companies need support in the form
of trusted software systems and partners who can guide them through
the transformation. In the current scenario, even the most forward-
looking customers want AI automation to allow employees to work in
more productive and efficient ways. Then, the priority shifts to reshaping
departments. For example, Gucci used Salesforce AI to transform their
service representatives into sellers. In addition to resolving customer
support problems faster, our AI tools also helped teach them how to help
customers find additional products to buy and to complete e-commerce
transactions. The third phase is enterprise transformation. Just as with the
internet, generative AI and agents will enable new pricing models, business
models, and organization models.
""Generative AI offers a tremendous
opportunity with a potential economic
impact of trillions of dollars from both
productivity gains and cost savings.""
68
Capgemini Research Institute Gener(AI)ting the future
Executive Conversations
Is there any specific industry
in which you see the most
enthusiasm?
We offer 15 different industry clouds
at Salesforce and there has been AI
demand and customer success in every
industry. For example, Santander Bank,
""90% TIME
in the financial services industry, uses
Agentforce to visualize its international
SAVINGS
trade trends and customer insights in
real time to guide customers towards the
BY USING
right products. L’Oréal, in the consumer
products industry, uses Agentforce to
GENERATIVE
boost direct-to-consumer revenue with
AI-powered product recommendations.
AI TO RESPOND
Iron Mountain, an information
management and storage company,
TO CUSTOMER
turned to Salesforce for customer
service, and their service representatives
EMAILS.""
use Agentforce to create a connected
experience across email, chat, and
voice. Simplyhealth, a leading health
insurance provider in the UK deployed
our Agentforce for their customer
service team. They saw 90% time savings
by using generative AI to respond to
customer emails, and they were able
to resolve over one-third of their cases
using conversational AI.
69
Gener(AI)ting the future Capgemini Research Institute
Executive Conversations
Which generative AI use cases do you think are the most popular?
Each industry has specific use cases. For example, in communications, customers want
organizations to address billing inquiries promptly. In consumer goods, the focus is
on crafting AI-driven personalized product descriptions and marketing campaigns.
In healthcare, it is on optimizing patient appointment scheduling and reminders in a
compliant way.
This is why we were so thrilled recently to launch over 100 new out-of-the-box
Agentforce for Industries features, now available in our Salesforce AI Use Case Library.
Customers can easily customize and deploy this new ready-to-use AI to automate
time-consuming tasks such as matching patients to clinical trials, generating proactive
maintenance alerts for industrial machinery, and
delivering government program benefits. These
use cases are tailored by role and to each of our 15
Industries clouds for accelerated time to value. They
In customer service,
are easily customizable with prompt builder, agent
emphasis is on builder, and model builder.
delivering solutions
Many companies trying to ""DIY"" their AI tech stack
rapidly, addressing are finding they've wasted a lot of time and money
finetuning models and building data pipelines without
questions quickly and
much to show for their efforts. In contrast, customers
accurately, and closing from AAA Insurance and Air India to Wyndham Hotels
& Resorts are finding rapid value in a matter of weeks
the case promptly.""
using these out-of-the-box Salesforce AI features.
Horizontally, across functions and sales, we place importance on streamlining account
research and meeting preparation, and gaining a thorough understanding of all the
open support cases and marketing engagement. In customer service, emphasis is on
delivering solutions rapidly, addressing questions quickly and accurately, and closing
the case promptly. It also enables better formulation of draft cases and incident
summaries to help service representatives to allocate their time to more strategically
significant tasks. In marketing, use cases include generating personalized emails and
campaigns, segments, landing pages, auto-populating contact forms, and rapidly
understanding insights from large-scale customer surveys.
In e-commerce, the focus is on the creation of digital storefronts, promotions, product
descriptions, and outlining e-commerce strategies. From a developer standpoint, it's
boosting productivity with AI-driven code generation and test generation.
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Capgemini Research Institute Gener(AI)ting the future
Executive Conversations
CHALLENGES WITH GENERATIVE AI
""When we meet with
the Gucci customer
service representatives
who are using
Agentforce Service
Agents from Salesforce,
they're so fired up.""
How do you envision the future balance between human-led and
AI-led customer services?
There's going to be a need for both. ATM
machines did not replace tellers. There are
more tellers today than ATMs, but now they
are personal bankers and focus on forging
personal relationships and upselling. AI will allow
ATM machines did not
workers to move away from repetitive tasks
replace tellers. There are to focus on doing what humans do best, which
is building relationships, unlocking creativity,
more tellers today than
making connections, and addressing higher-order
ATMs, but now they are problems. When we meet with the Gucci customer
service representatives who are using Agentforce
personal bankers.""
Service Agents from Salesforce, they're so fired
up. They feel like we are empowering them to do
the best work of their careers.
71
Gener(AI)ting the future Capgemini Research Institute
Executive Conversations
""ORGANIZATIONS MUST TAKE
AN ETHICS-FIRST, TRUST-BASED
APPROACH TO AI PRODUCT
DEVELOPMENT.""
What worries you the most about generative AI?
Any powerful new technology has a range of different applications. The
majority of them are good, but there can also be some nefarious use cases.
I think educating law enforcement professionals, government leaders, and
voters on the risks of misinformation and disinformation, including fake AI-
generated images, is of utmost importance.
Salesforce has joined the Business for America coalition supporting the
bipartisan Protect Elections from Deceptive AI Act. This legislation would
ban the use of AI to generate materially deceptive content that falsely
depicts candidates in political ads with the intention of influencing federal
elections.
How do you think organizations can create representative
and inclusive datasets?
Organizations must take an ethics-first, trust-based approach to AI product
development. Trust is the most crucial element engineered into any
Salesforce product. We have also enabled responsible AI practices across the
organization.
For example, to protect consumer and employee privacy, we disallow the
use of facial recognition AI within Salesforce products. Another aspect of
our AI acceptable use policy is that when one of our customer's customers
is using an AI agent, we require the agent to self-identify as an AI versus
masquerading as a human. This is to ensure trust and transparency remain
paramount.
72
Capgemini Research Institute Gener(AI)ting the future
Executive Conversations
We’ve open-sourced our
trusted AI principles around
five pillars:
1. Being responsible,
safeguarding human
rights, and protecting the
data with which we're
entrusted
2. Being accountable,
seeking feedback,
and acting on it for
continuous improvement
from all stakeholders
3. Developing a transparent
user experience to guide
users through any AI-
driven recommendations
4. AI is here to empower
people – not replace
them
5. AI should be inclusive
""We disallow the use of
facial recognition AI within
Salesforce products.""
73
Gener(AI)ting the future Capgemini Research Institute
Executive Conversations
What are your views on the climate impact of LLMs?
Sustainability is among our core values. LLMs expend a tremendous amount
of energy on both training and running the models. At Salesforce, we
envision that, because of climate impact, as
well as for cost and performance reasons, the
future of AI will be a combination of LLMs
and small models.
The future of AI will be
Currently, small language models (SLMs),
a combination of LLMs
even ones that run locally on laptops, could
and small models."" accomplish similar results to those that LLMs
produce. Salesforce AI Research Group is
developing these small and medium-sized
fine-tuned models, which are industry- and use-case-specific. Over time, we
will help our customers figure out the right model mix for them.
GENERATIVE AI REGULATORY LANDSCAPE
What are your thoughts on generative AI regulation?
The power of generative AI justifies strict regulation. The smartest approach
involves broadening the scope of existing laws to encompass elements
particular to AI usage. A great example is the Telephone Consumer
Protection Act (TCPA) in the US. That requires organizations to obtain
customer consent before robocalling or text messaging the consumer.
Recently, the TCPA was extended to include the use of AI-generated voices.
It makes a lot of sense to take existing laws and ensure that they are
updated to capture the new risks that AI has introduced.
74
Capgemini Research Institute Gener(AI)ting the future
Executive Conversations
Clara Shih
Chief Executive Officer
Salesforce AI
“The power of generative AI
justifies strict regulation.”
75
Gener(AI)ting the future Capgemini Research Institute
www.capgemini.com"
18,capgemini,CRI_Gen-AI-in-Mgmt_Final_web-compressed-2.pdf,"Gen AI at work: Shaping the future of organizations
Gen AI at work
Shaping the future of organizations
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Gen AI at work: Shaping the future of organizations
Executive summary Gen AI has the potential Gen AI could
to transform the world re-engineer
of work for employees organizational
structures
Who should read this
report and why? How could Gen AI To harness Gen AI
impact management effectively, organizations
and leadership? must upskill across
the workforce
Capgemini Research Institute 2024
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Gen AI at work: Shaping the future of organizations
How to build a Gen
AI-augmented workforce
Research
methodology
Conclusion Appendix
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Gen AI at work: Shaping the future of organizations
Generative AI (Gen AI) is transforming the way we work. In our • At manager level, roles are expected to become more
research, we asked leaders, managers, and employees to share strategic, focusing on AI-enhanced decision-making, and
how they project themselves into the future and how they performing tasks that require a high level of emotional
expect the world of work to change with Gen AI. intelligence. Leaders and managers also think that, within
three years, due to advancements in Gen AI, manager-
Employees predict that, at entry level, Gen AI will facilitate
level positions will evolve from generalists to specialists.
a third of tasks over the next 12 months. In the longer run,
Moreover, managers will be critical in assuaging fears and
collaboration between humans and AI will create greater value
skepticism in the workforce in relation to new technologies.
and new job roles, streamlining organizational structures and
As human-AI teams become the new reality, managers will
operating models and creating a new ontology of work:
play a key role in defining rules and responsibilities on how
Gen AI is expected to reshape roles and responsibilities human and AI will collaborate, ensuring accountability and
adapting workflows, practices, processes, and operating
• In the next three years, at entry level, roles will become
models to a human-centered approach.
more autonomous and progressively evolve from creation
to review, as Gen AI streamlines the generation process • At leadership level, roles will focus on redesigning the
allowing individuals to focus on critical analysis, quality organization of the future, redefining the nature of roles
assurance and innovation. Gen AI could also accelerate the across levels, and reimagining the ways of working. Nearly
career progress of entry-level workers, as suggested by three in four (70%) of leaders and managers also believe
leaders and managers in our research. This shift requires that leaders will focus on establishing robust frameworks
organizations to prepare junior employees to take up and guardrails for responsible and ethical development and
these responsibilities. deployment of Gen AI systems.
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Gen AI at work: Shaping the future of organizations
Gen AI could transform organizational structures managers transition from traditional coordination roles to
AI-enhanced specialized, skilled and strategic roles.
The impact of Gen AI on organizational structures and
operating models has been the subject of extensive debate. There is no clear consensus among experts on which
Among various possible organizational structures (classic organizational model will dominate. And future organizational
pyramid, inverse pyramid, diamond, hourglass, pillar, etc.), structures may differ significantly from current expectations.
most experts suggest two distinct organizational frameworks However, the leaders and managers in our survey sample lean
could emerge: towards the diamond model (see figure below) and expect the
proportion of managers in their teams to expand from 44% to
• The hourglass model with a small strategic leadership, a
53% in the next three years. It is also critical to note that this
lean middle-management layer, and a broad base of highly
middle layer will not be just composed of people managers or
skilled entry-level talent, augmented by Gen AI. In this
generalists but also technical or functional leads, specialists
model, technology enables entry-level employees to act
and subject matter experts (SMEs).
with more autonomy based on real-time data. This reduces
the need for intensive managerial supervision and quality This trend is consistent across organizations of all sizes,
control, flattening managerial hierarchies while widening industries and functions. Only 18% of leaders and managers
managerial span. believe that Gen AI will reduce middle management and
only 21% anticipate that, with Gen AI, they will have higher
• The diamond model with critical top leadership, a broader
shares of employees in entry-level and non-managerial roles.
middle layer, and a smaller entry-level layer that is partially
In this context, organization will need to reevaluate current
automated with Gen AI. This concentrated and skilled junior
roles, outline new roles, and establish clear career paths
layer focuses on high-value specialist tasks as opposed
across levels. Experimenting with flatter, more agile, and
to manual and repetitive work. Work is delivered with
collaborative organizational structures along with retaining
a combination of human-AI teams. In these structures,
strategic fluidity is key.
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Gen AI at work: Shaping the future of organizations
Expected team structures in the
Current team structures
next three years, impacted by Gen AI
Leaders and
upper 12% 15%
managers
Middle- and Middle managers: 19% Middle managers: 24%
frontline 44% 53%
managers Frontline managers: 25% Frontline managers: 29%
Entry-level
employees 44% 32%
Source: Capgemini Research Institute, Gen AI for management research, July 2024, N=1,500 leaders and managers.
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Gen AI at work: Shaping the future of organizations
Gen AI has the potential to unlock significant benefits • Complexity in integrating Gen AI tools into existing
workflows
Employees see Gen AI’s benefits in boosting their creativity,
complementing their skills, and reducing work-related stress. • Lack of skills to effectively drive outputs from Gen AI.
It can assist in complex managerial tasks, acting as a “thought
For instance, only 16% of employees say they receive ample
partner” for leaders and managers. A two-thirds majority
support from their organization to develop Gen AI skills, and
(65%) believes Gen AI can serve as a co-thinker in value-
only 46% of leaders/managers have been through formal Gen
adding activities such as strategic planning, evaluating risks
AI training.
and opportunities and augmenting decision-making.
To build a Gen AI-augmented workforce, organizations
However, key challenges remain
should:
Currently, only 15% of leaders and managers and 20% of
• Reevaluate strategic workforce planning, roles
employees use Gen AI tools daily. Respondents see the top
and career pathways for the new, agile and flatter
hurdles to Gen AI adoption as:
organizational structures
• Low confidence around the accuracy, logical soundness,
• Optimize practices, processes, workflows, and operating
security, and respect for IP/copyright and data privacy
models for human-AI collaboration
of Gen AI tools in connection with concerns regarding
inaccuracies, false logic, bias, IP/copyright issues, privacy,
and security
• Lack of clear guidelines from organizations regarding
usage
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Gen AI at work: Shaping the future of organizations
• Equip workforces with technology in a well-governed Gen AI thrives best as part of a broader AI ecosystem.
environment, helping them to evolve into an This hybrid AI approach – combining traditional AI, Gen
augmented force AI, automation, and other technologies – can unlock
unparalleled intelligence and efficiency tailored to specific
• Integrate existing business applications and workflows
business challenges.
with Gen AI to boost adoption
Please note, the study findings reflect the views of the
• Establish robust data foundations and governance
respondents and are aimed at providing directional guidance.
principles to harness the potential of Gen AI
Please contact one of the Capgemini experts listed at the end
• Empower people with the required technical (data of the report to discuss specific implications.
management and machine conversation) and soft (critical
thinking, emotional intelligence, risk management, and
ethical judgment) skills to use and trust Gen AI
• Create a culture of continuous learning and
experimentation, focusing on the most visible aspect of
organizational culture: adapting behaviors and habits
• Emphasize the role of Gen AI in augmenting and
empowering, and not replacing, human intelligence.
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Gen AI at work: Shaping the future of organizations
Who should read this report and why?
Gen AI is poised to influence the future of work, AI-augmented workforce? In this report, we as 1,000 employees (entry-level individual contributors) at
transforming roles, organizational frameworks, explore these areas in depth. organizations with annual revenue above $1 billion in 15
and the skills required at all levels. As Gen AI countries: Australia, Canada, France, Germany, India, Italy,
Business leaders across functions including
augments and assists tasks, leader, manager, Japan, the Netherlands, Norway, Singapore, Spain, Sweden,
corporate strategy, finance and risk, human
and employee roles are shifting toward more Switzerland, the UK, and the US. The survey spans 11 key
resources, marketing and sales, IT, sustainability,
strategic, creative, and problem-solving industries and sectors: aerospace and defense, automotive,
innovation/R&D, product design/development,
responsibilities. This shift demands a workforce banking and capital markets, consumer products, energy and
sourcing, manufacturing/operations, and supply
as agile as the technology driving it. utilities, insurance, life sciences, manufacturing, public sector/
chain will find it useful.
government, retail, and telecom, media, and high tech. The
How can organizations and business leaders
The report draws on a comprehensive analysis of report also includes qualitative findings from 15 industry
thrive in the dynamic environment of human-AI
a survey of 1,500 leaders and managers (CxOs, leaders.
collaboration? And how should they build a Gen
directors, managers, team leaders, etc.), as well
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Gen AI has the potential
to transform the world
of work for employees
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Gen AI at work: Shaping the future of organizations
Gen AI is expected to into their service centers, enhancing problem resolution,
and creating cross-selling opportunities. Rather than
reshape roles and displacing roles, AI has the potential to reshape them.”2
A few key trends are emerging:
responsibilities
• Entry-level roles are expected to evolve from creation
to review
Gen AI is transforming the way we work: 56% of leaders
More than 6 in 10 leaders and managers expect entry-
and managers and 54% of employees in our research
level roles to evolve in the next three years, from creation
agree. The technology is expected to unlock new levels
(of content/code, etc.) to critically reviewing and refining
of productivity, adaptability, and innovation, creating
outputs generated by Gen AI. Moreover, 71% of employees
synergies between human and machine intelligence,
agree with this. Steven Matt, a marketing executive at a
while redefining and reshaping traditional roles and
global professional services firm, adds: “You don’t need
structures. Polish spirits company, Dictador, has even
typical copywriters, but rather “copy reviewers” to check
appointed “Mika,” the first Gen AI CEO.1
whether AI-generated content matches the voice and tone
% • Eric Loeb, Executive Vice President, Government of the company, is free of hallucinations, and if it follows
56
Affairs at Salesforce, says: “As with any industrial brand guidelines. This concept extends to other creative
revolution, Gen AI will change the nature of jobs. fields, such as video editing. Instead of spending hours on
Imagine the transformative impact of AI on customer tasks like synchronizing clips and perfecting transitions,
service, for example. Gen AI will handle calls and video editors can now rely on Gen AI to automate these
streamline processes with unprecedented efficiency. processes. I believe that the future of many roles will be
of leaders and managers and 54% Customer service agents can use Gen AI prompts to centered around reviewing and verifying AI-generated
of employees agree that Gen AI is identify opportunities during interactions, upskilling work, rather than creating [content] from scratch.”
transforming the way we work
them to hybrid service-sales roles. Some of our
global customers have already integrated Gen AI
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Figure 1.
With the advent of Gen AI, most respondents expect entry-level roles to transition from creation to review and refinement.
Percentage of respondents who agree with the statement: ""In the next three years,
entry-level roles will primarily evolve from creation (of content, code, etc.) to review
and refinement of outputs generated by Gen AI”.
71%
64% 64%
61%
Leaders Middle Frontline Employees
managers managers
Source: Capgemini Research Institute, Gen AI for management research, July 2024, N=1,500 leaders and managers, N=1,000
employees.
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Gen AI at work: Shaping the future of organizations
• Entry-level roles will progressively become more Figure 2.
autonomous
Entry-level roles will become more autonomous due to Gen AI in the next three years.
As Gen AI becomes more integrated into the workplace,
entry-level roles are expected to gain greater autonomy –
52% of leaders and managers predict this (see Figure 2).
Percentage of leaders and managers who agree with the statement: ""In the next three years,
entry-level roles will become more autonomous due to Gen AI,"" categorized by sector.
57% 56% 56%
54% 54%
52% 51% 51% 51% 50%
46%
44%
Aerospace Public sector Energy Automotive Consumer Telecom, media
and defense and utilities products and high tech
Banking and
Manufacturing Life sciences Overall Insurance Retail
capital markets
Source: : Capgemini Research Institute, Gen AI for management research, July 2024, N=1,500 leaders and managers.
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Gen AI at work: Shaping the future of organizations
• Gen AI could accelerate career progress of entry-level skills and readiness of junior employees as part of a clear
employees roadmap for employees’ journeys to people leadership or
A recent LinkedIn survey revealed that 52% of millennials functional/technical leadership. This requires proactive
and 48% of Gen Z globally believe that AI will help advance steps around talent acquisition, development, skilling, and
their careers by providing faster access to work-related review and reward mechanisms.
knowledge and insights.3
As Figure 3 shows, most leaders and managers from
In our research, over half (51%) of leaders and managers supply chain, risk management, and human resources
anticipate that numerous entry-level roles will evolve functions agree. For example, in supply chain roles, Gen
into frontline managerial roles within the next three AI virtual assistants can handle routine tasks such as
years, as the integration of Gen AI into existing workflows inventory management, order processing, and tracking
accelerates. It should be noted that this shift depends on shipments. This shift will enable junior analysts to progress
several factors: clarity on skills requirements at higher to supply chain coordinator roles, overseeing AI operations
levels; the ability of junior employees to develop these and tackling strategic responsibilities such as refining
skills (often tied to experience, which cannot be fast- logistics networks, managing supplier relationships, and
tracked); and the availability of opportunities available coordinating large-scale inventory projects.
for the shift. Organizations must prioritize building the
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Gen AI at work: Shaping the future of organizations
Figure 3.
Leaders and managers anticipate entry-level employees to assume responsibilities previously held by their supervisors.
Percentage of leaders and managers who agree with the statement:
“With Gen AI, in the next three years, many entry-level roles will transition to frontline manager roles,"" categorized by function.
67%
63%
59%
57%
54% 53% 52% 51% 51% 51% 47%
42%
31%
Supply chain Product design/ Innovation/R&D Manufacturing/ Marketing and Finance and Sourcing
development operations communications risk management
Human resources IT/engineering Sustainability Sales Overall Corporate strategy
Source: Capgemini Research Institute, Gen AI for management research, July 2024, N=1,500 leaders and managers.
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Gen AI at work: Shaping the future of organizations
Gen AI is likely to augment
roles
At entry level, employees expect Gen AI to assist with 32% of
tasks over the next 12 months (see Figure 4). Archisman
Munshi, co-founder of a senior citizen care company that is
tackling financial fraud issues using AI, adds: “It is imperative
to educate people to view AI as an “assistant” that will make
it easier and faster to complete their day-to-day tasks. Also,
people won’t be replaced; it is the tasks that will be
replaced.” 4
%
32
of employee work is expected to be
augmented or assisted by Gen AI in
the next 12 months
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Gen AI at work: Shaping the future of organizations
Figure 4.
Gen AI has the potential to assist with nearly one-third of tasks across functions in the next 12 months.
Percentage share of employee work that can be augmented or assisted by Gen AI in the next 12 months, categorized by function.
36% 36%
34%
32% 32% 32% 32% 32% 31% 31% 30%
29%
24%
Sustainability Innovation/R&D Marketing and Overall Sourcing Supply chain Human resources
communications
Product design/ IT/engineering Finance and Manufacturing/ Corporate strategy Sales
development risk management operations
Note: Low base for employees from human resources, sustainability, and sourcing functions.
Source: Capgemini Research Institute, Gen AI for management research, July 2024, N=1,000 employees.
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Gen AI at work: Shaping the future of organizations
Gen AI has the potential
Figure 5.
Gen AI enhances employee creativity, reduces work-related stress, and complements human skills by filling knowledge gaps.
to unlock significant
benefits Percentage of employees who agree with the statements below
Gen AI yields significant productivity improvements at entry
level. Employees believe that, over the next 12 months, Gen
AI tools could lead to an average time saving of Gen AI has helped me reduce
72%
18% implying there could be significant productivity my work-related stress.
improvements. While the anticipated productivity gains
from Gen AI tools are promising, careful consideration of
their implementation costs is crucial to ensure a net positive
impact on overall efficiency. Gen AI effectively complements my skills 64%
and fills in my knowledge gaps.
A majority (64%) of the workforce today is already using Gen
AI tools for their work – with 78% using them for text/content
creation (summarization, automation or translation of text/
content). However, only 20% of employees use Gen AI tools Gen AI has helped me be more
52%
daily. Users see benefits including boosting their creativity, creative in my work.
reducing their work-related stress, and complementing their
skills (see Figure 5).
Source: Capgemini Research Institute, Gen AI for management research, July 2024, N=641 employees using Gen AI for their work.
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“You don’t need typical copywriters, but rather “copy reviewers” to check whether AI-generated content matches the
voice and tone of the company, is free of hallucinations, and if it follows brand guidelines. This concept extends to
other creative fields, such as video editing. Instead of spending hours on tasks like synchronizing clips and perfecting
transitions, video editors can now rely on Gen AI to automate these processes. I believe that the future of many roles
will be centered around reviewing and verifying AI-generated work, rather than creating [content] from scratch.”
Steven Matt
A marketing executive at a
global professional services firm
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How could Gen AI impact
management and
leadership?
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Gen AI at work: Shaping the future of organizations
Gen AI is amplifying the
Figure 6.
Gen AI is transitioning leadership and managerial roles towards strategy, innovation and AI augmented decision-making.
strategic scope of
managerial and leadership
Percentage of respondents who agree with the statement:
roles ""Using Gen AI, my role as a manager has become more strategic, focusing on
decision-making and innovation.""
Leaders and managers currently spend more than one-third
(38 percent) of their time on administrative and project 57%
management tasks. With Gen AI, they anticipate a greater 52%
focus on strategic decision-making and people-centric 46% 46%
42%
leadership: “With Gen AI helping in operational tasks,
leaders and managers will have more time for strategy,
quality, and team management,” says Stephane Dupont,
Head of Operations and Business Improvement, Sustainability
and Communications at Airbus.
In fact, within organizations that are advanced in Gen AI
implementation, 57% of leaders and managers report that
Overall Organizations Organizations Organizations Organizations
Gen AI has already made their roles more strategic (see that have enabled that have enabled that have begun that have started
Figure 6). Isabel Baque, Senior Director at Stellantis, agrees: Gen AI capabilities Gen AI capabilities working on exploring
“Gen AI is creating new opportunities for middle managers in most in some Gen AI pilots Gen AI's potential
function/locations functions/locations
by changing them from task leaders to strategic leaders.
So, instead of focusing on the day-to-day operational
tasks, middle management are increasingly shifting their
Source: Capgemini Research Institute, Gen AI for management research, July 2024, N=1,456 leaders and managers who have
focus to strategic planning, problem-solving, and to social
experimented with Gen AI in leadership/managerial tasks.
interactions and relationship building.”
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Gen AI at work: Shaping the future of organizations
“Gen AI is creating new opportunities In the next three years:
for middle managers by changing
• Three-quarters (78%) of leaders and managers predict
them from task leaders to strategic that Gen AI will augment their problem-solving and
leaders. So, instead of focusing on decision-making.
the day-to-day operational tasks,
• Over half (52%) of leaders and managers expect that their
middle management are increasingly roles will shift toward tasks that require a high level of
shifting their focus to strategic emotional intelligence.
planning, problem-solving, and to
social interactions and relationship Most managers could
building.”
become specialists and be
more critical than ever
Most leaders and managers (51%) believe that, owing to
advancements in Gen AI, within three years, manager-level
positions will evolve towards specialization or move into top-
tier strategic roles. For instance, HR roles may transition from
HR generalists to talent analytics specialists or employee
experience designers, while IT might see a move from
Isabel Baque
project management to niche areas such as AI strategy or
Senior Director at Stellantis
data science. As Figure 7 highlights, most respondents from
supply chain, HR, manufacturing, and IT believe that Gen AI
will lead managers to move toward specialization.
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Gen AI at work: Shaping the future of organizations
Managers will play a
Figure 7.
Managerial roles will move toward niche expertise areas or strategic roles.
critical role as catalysts
for Gen AI-driven change
Percentage of leaders and managers who agree with the statement:
""With Gen AI, in the next three years, many managerial roles will transition
to either specialist/subject matter expert or top strategic leadership."" Managers are crucial to addressing workforce fears and
skepticism associated with new technologies – 53% of leaders
in our research agree. Our employee research shows that
64% 63% nearly 4 in 10 (39%) employees say they are apprehensive
58% 57% 57% about the introduction of Gen AI. One in three (35%) also
54% 54% 53% 51% 50% 48% expresses concern about job security and the potential
45%
43% obsolescence of their role.
A majority (54%) of leaders believe that, in this context,
the significance of managerial roles will intensify as they
will play a critical role as catalysts for Gen AI change. Isabel
Baque adds: “Middle managers need to take the role of
facilitators and coaches who lead and manage the change,
by advocating for the use of Gen AI and leading by example.”
Supply chain IT/engineering Sustainability Marketing and Overall Sales Manufacturing
communications /operations
Moreover, as Gen AI integration between teams becomes
Human Product design/ Sourcing Innovation/R&D Finance and Corporate the new reality, managers will be critical to re-bundling tasks
resources development risk management strategy for Gen AI-augmented workforces. Half (50%) of leaders in
our research believe that managers must rethink the balance
of task allocation to avoid over- and under-delegation and
Source: Capgemini Research Institute, Gen AI for management research, July 2024, N=1,500 leaders and managers.
manage other risks. The manager needs to:
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Gen AI at work: Shaping the future of organizations
• Ensure each team member is equipped with the skills Gen AI adoption will require a strong risk management • When used as a co-pilot, Gen AI becomes an efficient
and knowledge to fulfill their specific role approach to mitigate potential risks around IP/copyright, collaborator with the leader or manager, handling a wide
security, bias, accountability etc. Leadership’s role becomes range of administrative, communication, and operational
• Define rules and responsibilities on how humans and
critical in establishing robust frameworks and guardrails for tasks. Co-pilot interaction is best suited to tasks where the
Gen AI will collaborate
ensuring responsible and ethical Gen AI systems are built leader or manager’s main contribution is initial guidance,
and used. In fact, 7 in 10 leaders and managers agree that curation, final review, and approval of the output.
• Ensure accountability when Gen AI systems make
in the next three years, the role of leaders will shift towards
mistakes • When used as a co-thinker, Gen AI becomes the leader
managing risks and creating responsible Gen AI.
or manager’s thought partner, engaging in discussion,
• Adapt workflows, practices, processes, and operating
Valentin Marguet, Powertrain Project Lead in the automotive suggesting new perspectives, and challenging assumptions
models with a human-centric approach.
industry, says: “As AI becomes integral to business operations, or ideas. Co-thinker interaction is best suited to tasks that
organizations should establish dedicated AI Centers of require methodological guidance and structured reflection
As stated by a large bank’s global head of sales training: Excellence (COEs) led by C-suite executives. These COEs (such as weighing options, assessing risks, or considering
“The manager needs to make sure that, firstly, [employees] should be staffed with a diverse team of AI specialists, different points of view).
are given better or different work that activates what
domain experts, and program managers. The key is to deploy
they're good at. And secondly, make them feel valued for Harvard Business Review’s forthcoming book, authored by
a lean, cross-functional team that can create and enforce
that work.” experts from Capgemini6 explores both modes across the
guidelines for AI tool usage, data governance, and risk
most common managerial tasks. Our research highlights that
management frameworks.”
a two-thirds majority (65%) of leaders and managers think
Leadership roles will
Gen AI can serve as a co-thinker in value-adding activities
Gen AI has the potential such as strategic planning (see Figure 8). As we move toward
encompass the
the co-thinker end of the spectrum, the necessity for human
to assist with complex engagement and interaction with Gen AI increases.
responsible and ethical
managerial tasks
deployment of Gen AI
systems There are two primary modes of interacting with Gen AI:
“co-pilot” and “co-thinker.”5
Capgemini Research Institute 2024
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“Gen AI can provide targeted training and coaching based on your profile and skill gaps. Also, today, with continuously
evolving learning and training requirements, Gen AI can revolutionize learning by enabling tailor-made, short, and
targeted trainings.”
Anna Kopp
Senior director at Microsoft
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Gen AI at work: Shaping the future of organizations
Figure 8.
More than 6 in 10 leaders and managers view Gen AI as a strategic thought partner to consider risks, evaluate opportunities, and hone decision-making.
Co-pilot Co-thinker
Project Team-and people Business-and
Administrative tasks management-related tasks management-related tasks strategy-related tasks
▪ Time approvals ▪ Project planning/scheduling ▪ Recruitment ▪ Innovation
Examples of ▪ Claims approvals ▪ Task delegation ▪ Onboarding ▪ Planning
tasks ▪ Meetings management ▪ Workflow optimization ▪ Goal setting ▪ Forecasting
▪ Meetings summarization ▪ Knowledge management ▪ Performance evaluation ▪ Market intelligence
▪ Drafting emails ▪ Compliance monitoring ▪ Training and development ▪ Evaluating risks and opportunities
▪ Mentoring ▪ Decision-making
▪ Retention
Percentage of
leaders and
managers who
see high 48% 44% 57% 65%
potential in Gen
AI to add value in
this area
Continued to the next page
Capgemini Research Institute 2024
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Gen AI at work: Shaping the future of organizations
Project Team-and people Business-and
Administrative tasks management-related tasks management-related tasks strategy-related tasks
Meeting summarization Project planning Performance review Insight and recommendation generation
Morgan Stanley is piloting an AI program Procore, a US-based construction Managers at Valera, an established credit union PepsiCo harnesses Gen AI to analyze customer
called Debrief, which automatically management software organization, has service organization, are using feedback, which it uses to refine the shape
summarizes client meetings and generates recently launched Procore Copilot AI, which Gen AI to generate constructive, unbiased design and flavor of its Cheetos branded snack,
follow-up emails, enhancing advisor integrates with Microsoft Teams, allowing feedback for employees, resulting in resulting in market penetration deepening by
Case examples productivity and customer engagement.7 project managers to monitor and query 67% higher-quality feedback and halving 15%.11
project management and receive review-writing time.9
comprehensive summaries and relevant
links to project documentation.8 Personalized L&D Decision-making
Team leaders and managers at Siemens Energy Portfolio managers at Morgan Stanley use
use Gen AI to create targeted learning paths Gen AI as a “virtual coach” to enhance
for personalized, streamlined skill building.10 decision-making processes and improve
investment outcomes.12
A senior risk model manager at a leadi"
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Top use cases across industries
#GetTheFutureYouWant
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HARNESSING THE VALUE OF GENERATIVE AI: TOP USE CASES ACROSS INDUSTRIES
Generative artificial intelligence (AI) is rapidly becoming restructuring of business models. Globally, about one-
an integral part of our private and professional lives. fifth (21 percent) of executives surveyed say generative
While the technology has been in existence for a number AI will significantly disrupt their industries. Executives in
of years, it has gained widespread consumer interest high tech and industrial manufacturing – two industries
only recently and has emerged as a critical strategic that have long been shaped by AI technologies and have
consideration. Our latest industry-focused research been at the forefront of generative AI – are most likely to
reveals that generative AI is on the boardroom agenda agree with this statement.
at 96 percent of organizations surveyed globally. While
The potential of generative AI to drive innovation and
generative AI is in its infancy in terms of scaled adoption
improve efficiency and productivity extends to nearly all
and implementation, nearly 60 percent of executives
functions and has applications across all industries. Use
globally say their leadership is a strong advocate for
cases are wide-ranging, from creating unique content
generative AI and only 39 percent are taking a “wait-and-
and automating and accelerating tasks, to creating
watch” approach to adoption.
personalized experiences and generating synthetic data.
Many organizations already see generative AI as a Our research reveals that generative AI has the greatest
powerful tool that can accelerate growth, enhance potential within IT, sales and customer service, and
capabilities, and unlock new opportunities without drastic marketing functions. The high tech sector leads the way,
Capgemini Research Institute 2023
with the greatest share of ongoing generative AI pilots. adverse impact of generative AI on the environment.
Executives in our survey also project efficiencies from Nevertheless, the net impact of generative AI on an
generative AI in the next three years in the range of 7–9 organization’s Scope 1, 2, and/or 3 emissions is currently
percent. difficult to forecast.
As with any new technology, generative AI is not without We conclude this report with a look at how organizations
risk. However, with proper planning and guardrails in can start and/or accelerate their generative AI journeys.
place, there is potential to transform business operations, First, they must create a robust generative AI strategic
product and service development, and customer and operational architecture. Organizations must also
interaction. Nearly three-quarters of executives in our establish internal and external guidelines around the
survey (74 percent) agree that the benefits of generative use of generative AI, adopt a human-centric approach
AI outweigh the risks. Given the carbon-intensive nature to scaling the technology, and build user and consumer
of training new generative AI models, it will also be trust in the AI system. Given the high carbon emissions
important to weigh environmental considerations. The associated with generative AI trainings and queries,
good news is that executives in our survey are aware sustainable development and use of the technology
of this dynamic and understand how to mitigate the should also be a high priority.
evitucexE
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HARNESSING THE VALUE OF GENERATIVE AI: TOP USE CASES ACROSS INDUSTRIES
Capgemini Research Institute 2023
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HARNESSING THE VALUE OF GENERATIVE AI: TOP USE CASES ACROSS INDUSTRIES
Generative artificial intelligence (generative AI) is rapidly innovating of organizations; highlighting the function-
transforming the way we interact with technology. and industry-specific use cases we believe to have the
Machines are beginning to mimic creative human thought greatest potential; and comparing adoption rates across
processes, synthesizing tailored content with significant industries.
implications for organizations and consumers.
To gauge executives’ perceptions of generative AI and
This report is the second in a series of reports we adoption of use cases, we conducted a global survey of
have created around this topic. Our first report, Why 1,000 organizations across Australia, Canada, France,
consumers love generative AI, explored consumer Germany, Italy, Japan, the Netherlands, Norway,
perceptions of generative AI; consumer use of generative Singapore, Spain, Sweden, the UK, and the US. We
AI; and how the technology is shaping the future of questioned executives from multiple industries, including
customer experience. automotive, consumer products, retail, financial services,
telecom, energy and utilities, aerospace and defense,
In this report, we delve into the transformative potential
high tech, industrial manufacturing, and pharma and
of generative AI for organizations across industries,
healthcare. For more details on the survey sample, please
asking how the technology could kick-start the
refer to the research methodology.
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HARNESSING THE VALUE OF GENERATIVE AI: TOP USE CASES ACROSS INDUSTRIES
The report explores five broad themes:
1 2 3 4 5
Organizations view Organizations see Generative AI High tech leads How organizations
generative AI not as more gain than pain packs the most in implementing can kick-start their
a disruptor but as an in generative AI punch for IT, sales, generative AI generative AI
accelerator and marketing journeys
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HARNESSING THE VALUE OF GENERATIVE AI: TOP USE CASES ACROSS INDUSTRIES
Who should read this
report and why?
This report offers a comprehensive introduction to the transformative
impact that Generative AI holds for large businesses in industries such
as automotive, consumer products, retail, financial services, telecom,
energy and utilities, aerospace and defense, high tech, industrial
manufacturing, and pharma and healthcare. The report will help
business executives identify use cases that will illustrate the pragmatic
applications of Generative AI in IT, sales, and marketing, for example.
The report leverages a comprehensive analysis of 1,000 industry leaders
(ranked Director and above) across 13 countries, each at varying stages
of Generative AI implementation. The report also offers actionable
recommendations for business leaders to kick-start their organizations’
generative AI journeys. This report is the second in our series of reports
on Generative AI. Read the first report focusing on consumer reactions to
Generative AI at
https://www.capgemini.com/insights/research-library/
creative-and-generative-ai/
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HARNESSING THE VALUE OF GENERATIVE AI: TOP USE CASES ACROSS INDUSTRIES
Fig.1
Generative AI applications across multiple formats
Selected generative AI applications Indicative examples
Defining Text Summarizing and translating into multiple OpenAI’s GPT-4, Jasper1
languages
generative AI
Images and video Analyzing existing images/video to generate new Adobe Firefly,2 Stable
generation content (e.g., video games, VR, animation) Diffusion, Midjourney
Generative AI has the capability to learn and reapply the
Music generation and remixing, speech synthesis, Sonix.ai (a cloud-based audio
properties and patterns of data for a wide range of
Audio sound effects, voice conversion, audio and video-transcription
applications, from creating text, images, and videos in enhancement solution)3
different styles to generating tailored content. It enables
machines to perform creative tasks previously thought
exclusive to humans. The following table summarizes the Chatbots to provide automated customer service Google Bard,4 OpenAI’s
Chatbots
top generative AI applications reported in our research and and advice ChatGPT
gives some indicative examples.
Enhanced search functions using natural
Search Perplexity AI5
language processing and machine learning
Source: 1. TechTarget, “What is generative AI? Everything you need to know,” March 5, 2023.
2. Adobe, ""Adobe unveils Firefly, a family of new creative generative AI,"" March 21, 2023.
3. Sonix.ai, ""Sonix releases the world’s first automated transcription and generative AI summarization tool,"" December 14, 2022.
4. Cointelegraph, ""What is Google’s Bard, and how does it work?"" May 10, 2023.
5. Kevin-Indig, ""Early attempts at integrating AI in Search,"" January 10, 2023.
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01
ORGANIZATIONS VIEW GENERATIVE
AI NOT AS A DISRUPTOR, BUT AS AN
ACCELERATOR
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HARNESSING THE VALUE OF GENERATIVE AI: TOP USE CASES ACROSS INDUSTRIES
Fig.2
Generative AI is a topic for boardroom discussion at nearly all organizations
PERCENTAGE OF ORGANIZATIONS WHO AGREE WITH THE
Generative AI is a top STATEMENT BY SECTOR
agenda item in boardrooms
96% 100% 98% 98% 97% 96% 95% 94% 93% 93% 93%
Nearly all executives (96 percent) in our survey cite
generative AI as a hot topic of discussion in their
respective boardrooms (see Figure 2), making it probably
the fastest new technology to garner such high-level
interest. Pat Geraghty, CEO of GuideWell, a US-based
mutual insurance organization, comments: “Every single
board meeting we’ve had this year has had a standing
agenda item of AI and ChatGPT. We want to make sure we’ve
got our board with us as we’re thinking about where we’re
going.” 1 Average Financial Industrial Energy Aerospace Consumer
services manufacturing and utilities and defense products
High tech Pharma Telecom Automotive Retail
and healthcare
Generative AI is a topic of discussion in our boardroom
Source: Capgemini Research Institute, Generative AI Executive Survey, April 2023, N = 800 organizations.
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HARNESSING THE VALUE OF GENERATIVE AI: TOP USE CASES ACROSS INDUSTRIES
Fig.3
The leaders of most organizations are strong advocates of generative AI
PERCENTAGE OF ORGANIZATIONS WHO AGREE WITH THE
STATEMENT BY SECTOR
2% 1% 1% 1% 1% 4% 2% 5% 3% Top leaders are strong
16%
32% advocates for generative AI
39% 39% 39% 41% 37% 39%
57% 45% 50%
Among the 96% of organizations that discuss generative
AI in their boardrooms, over half (59 percent) of
84%
executives say their leadership are strong advocates for
59% 67% 60% 60% 59% 59% 58% generative AI only six months after the technology hit
51%
43% 57% the mainstream. This rises to 84 percent in the high-tech
sector. Thirty-nine percent of executives say their leaders
are taking a “wait-and-watch” approach to the technology
Average Aerospace Telecom Retail Industrial Consumer
and defense manufacturing products and only 2 percent of executives globally say their leaders
High tech Financial Energy Pharma Automotive are not convinced or are divided by the potential of
services and utilities and healthcare generative AI (see Figure 3).
Our leadership is a strong advocate of generative AI
Our leadership is taking a ""wait-and-watch"" approach to generative AI
Our leadership is not convinced of / is divided on the potential of generative AI
Source: Capgemini Research Institute, Generative AI Executive Survey, April 2023, N = 766 organizations that cite generative AI
as a topic of discussion in their respective boardrooms.
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HARNESSING THE VALUE OF GENERATIVE AI: TOP USE CASES ACROSS INDUSTRIES
96%
organizations say generative AI
is a topic of discussion in their
boardrooms
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Fig.4
Majority of consumers use generative AI tools for generating content
PERCENTAGE OF ORGANIZATIONS WHO AGREE WITH THE
Most organizations do not
STATEMENT BY SECTOR
view generative AI as a
52%
48% disruptor
While twenty-one percent of executives anticipate a
significant disruption in their respective industries from
21% 21% 21% 20% 19% 18% 18% generative AI, 67 percent of executives disagree. That
12% is, a majority of the executives do not see generative
AI significantly disrupting their business models.
3%
However, executives within the high-tech and industrial
Average Industrial Pharma Consumer Aerospace Retail manufacturing sectors expect significant disruption at
manufacturing and healthcare products and defense 52 percent and 48 percent, respectively (see Figure 4).
While this may be reflective of these executives’ superior
High tech Energy Financial Automotive Telecom
and utilities services understanding of the technology’s potential, the figures
underline the widespread expectation that generative AI
will boost business overall.
Generative AI can significantly disrupt our industry
Source: Capgemini Research Institute, Generative AI Executive Survey, April 2023, N = 800 organizations.
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HARNESSING THE VALUE OF GENERATIVE AI: TOP USE CASES ACROSS INDUSTRIES
Fig.5
Relevance of generative AI platforms for organizations across different domains
PERCENTAGE OF ORGANIZATIONS THAT FIND GENERATIVE AI
Organizations are finding
PLATFORMS RELEVANT TO THEIR BUSINESS
generative AI platforms
Chatbots (automating customer service and improving
83%
knowledge management: e.g., ChatGPT)
increasingly relevant
Data (designing, collecting, or summarizing data:
75%
e.g., Jasper’s Text Summarizer)
Chatbots emerge as the most relevant generative AI Text (summarizing, automating, or translating content: e.g., ChatGPT) 71%
application, with 83 percent of organizations citing it.
Organizations can use generative-AI-driven chatbots Search (AI-powered insights: e.g., Bing) 70%
to improve their customer service and also to enable
Generating synthetic data (Capgemini's Artificial Data Amplifier) 61%
improved internal knowledge management. Seventy-five
percent of executives say that data applications can be ML platforms (applications of machine learning: e.g., Slai) 54%
used effectively in their organizations, and 71 percent
Code (testing and coding assistant e.g.,GitHub Copilot, converting code
believe this to be true of text-generating platforms such 50%
from one language to another e.g., Codex, Capgemini's A2B Translator)
as ChatGPT (see Figure 5).
Images (generating images: e.g., DALL-E) 48%
Audio (summarizing, generating or converting text in audio: e.g., Sonix) 34%
Video (generating or editing videos: e.g., Pictory, Synthesys) 26%
Gaming (generative AI gaming studios or applications: e.g., Ludo AI) 4%
Source: Capgemini Research Institute, Generative AI Executive Survey, April 2023, N = 800 organizations.
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HARNESSING THE VALUE OF GENERATIVE AI: TOP USE CASES ACROSS INDUSTRIES
US financial services organization Morgan Stanley's
vast library of investment strategies, market research,
and analyst insights can be time-consuming and
cumbersome for wealth-management advisors to
sift through. To address this, Morgan Stanley is using
GPT-4 to power an internal chatbot that provides
instant access to any area of the archive. Jeff McMillan,
Head of Analytics, Data, and Innovation, adds: “You
essentially have [access to] the knowledge of the most
knowledgeable person in wealth management – instantly.
Think of it as having our chief investment strategist, chief
global economist, global equities strategist, and every other
analyst around the globe on call for every advisor, every
day. We believe that is a transformative capability for our
company.”2
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HARNESSING THE VALUE OF GENERATIVE AI: TOP USE CASES ACROSS INDUSTRIES
“You essentially have [access to] the knowledge of the
most knowledgeable person in wealth management
– instantly. Think of it as having our chief investment
strategist, chief global economist, global equities
strategist, and every other analyst around the globe
on call for every advisor, every day. We believe that is a
transformative capability for our company.”
Jeff McMillan
Head of Analytics,
Data, and Innovation,
Morgan Stanley.
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The impact of Fig.6
Most executives agree that generative AI will augment knowledge work
generative AI on
the workforce
PERCENTAGE OF ORGANIZATIONS THAT AGREE WITH THE STATEMENTS
Generative AI has the potential to Generative AI will augment the roles of knowledge
70%
workers and reduce their workloads
transform work
Our consumer research on generative AI reveals that
most consumers (70 percent) believe it will make As generative AI algorithms begin to provide concepts
them more efficient at work and will free them from and initial designs, employees may shift from 69%
routine tasks to explore more strategic aspects of their traditional ideation and creation to review and refinement
jobs.3 Most executives concur with these consumer
sentiments, with 70 percent agreeing the technology
will allow organizations to widen the scope of the
roles of knowledge workers (see Figure 6). Over half
Generative AI will completely revolutionize the way we work 60%
(60 percent) also mentioned that generative AI would
completely revolutionize their way of working.
Source: Capgemini Research Institute, Generative AI Executive Survey, April 2023, N = 800 organizations.
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The rise of generative AI will create new roles 60%
In our current survey, 69 percent of executives believe generative AI will lead to the emergence of new roles. In
addition to prompt engineers, we may also see new roles such as AI auditors and AI ethicists emerge as the
believe that generative AI will completely
initiatives scale.
revolutionize the way we work
Fig.7
Most executives believe new roles will emerge from generative AI
Generative AI will demand upskilling
and training initiatives
PERCENTAGE OF ORGANIZATIONS WHO AGREE WITH THE STATEMENT
As well as opening up new job roles and requirements,
as 68 percent of executives in our survey say will happen,
the integration of generative AI into the workforce will
require a significant investment in upskilling and cross-
skilling of talent. In April 2023, in response to growing
demand, Coursera, a large-scale US-based open online
69% Generative AI will lead to the course provider, launched multiple new generative AI
emergence of new job roles (e.g., prompt
training courses, including ChatGPT Teach-Out from the
engineer)
University of Michigan, which introduces learners to large
language models (LLMs) and chatbots and discusses
the ethical use of generative AI and how the technology
might be harnessed and regulated moving forward.4
Source: Capgemini Research Institute, Generative AI Executive Survey, April 2023, N=800 organizations.
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HARNESSING THE VALUE OF GENERATIVE AI: TOP USE CASES ACROSS INDUSTRIES
02
ORGANIZATIONS SEE MORE GAIN
THAN PAIN IN GENERATIVE AI
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Fig.8
Most executives across industries say that the benefits of generative AI outweigh potential risks
Most organizations believe
PERCENTAGE OF ORGANIZATIONS WHO AGREE WITH THE STATEMENT BY SECTOR
the benefits of generative AI
outweigh the risks Average 74%
High tech 84%
Our survey reveals that the majority of executives (74
percent) believe the benefits that generative AI brings Aerospace and defense 82%
outweigh the associated risks. The executives most
strongly convinced that generative AI is a power for Pharma and healthcare 80%
good work within the high-tech sector (84 percent); even
Industrial manufacturing 77%
at the other end of the list, a substantial 69 percent of
executives within the energy and utilities and telecom Retail 76%
sectors would bet on generative AI (see Figure 8).
Financial services 74%
Consumer products 70%
74% Energy and utilities 69%
Telecom 69%
believe the benefits of generative AI Automotive 66%
outweigh the associated risks
The benefits of utilizing generative AI outweigh the associated risks
Capgemini Research Institute, Generative AI Executive Survey, April 2023, N = 800 organizations.
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HARNESSING THE VALUE OF GENERATIVE AI: TOP USE CASES ACROSS INDUSTRIES
Fig.9
Most executives say that generative AI will improve products/services and customer service
PERCENTAGE OF ORGANIZATIONS WHO AGREE WITH THE STATEMENTS
Anticipated benefits of
generative AI extend to
Generative AI will allow the design process to be
78% product design and
more efficient and streamlined
customer experience
Generative AI can enable us to create products and services
that are more accessible and inclusive, serving a wider 76%
range of customers with diverse needs and preferences Generative AI brings numerous transformative benefits
to organizations, including enhanced decision-making,
improved efficiency, personalized experiences, cost
Generative AI can enable us to create more
71% reductions, augmented innovation capacity, risk
interactive and engaging experiences for our customers
management, and predictive analytics. Most executives
in our survey (78 percent) believe that generative AI will
make product and service design more efficient and
Generative AI can be used to improve customer
service by providing automated and personalized support 67% that it will help them design more inclusive, accessible
products and services (76 percent). Seven in ten
executives believe generative AI will help them improve
Generative AI can improve internal operations the customer experience (see Figure 9).
65%
and enhance facility maintenance
Source: Capgemini Research Institute, Generative AI Executive Survey, April 2023, N = 800 organizations.
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Fig.10
Generative AI implementation is expected to yield operational and customer benefits
Executives are positive but EXPECTED FUTURE BENEFITS OF GENERATIVE AI IN THREE YEARS FROM TODAY,
AVERAGE % PROJECTED INCREASE
realistic in their expectations
of generative AI
Improved customer engagement and satisfaction
9%
(i.e., increase in Net Promoter Score)
We asked executives which organizational benefits they
expect to see from generative AI within three years.
Executives expect to see improvements of 7–9 percent
Increase in operational
across all industries (see Figure 10). Recent research from 9%
efficiency (e.g., improved quality)
Stanford and MIT on the applications of generative AI
in the workplace found that the productivity of tech-
support agents who used conversational scripts improved
as much as 14 percent at one organization,5 suggesting Increase in sales 8%
such an estimate is realistic, if not conservative.
Decrease in costs 7%
Source: Capgemini Research Institute, Generative AI Executive Survey, April 2023, N = 800 organizations.
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• Customer engagement and skills in the Korean language using LLMs – machine
learning (ML) algorithms that can recognize,
satisfaction: Organizations can use generative
understand, predict, and generate human speech
AI for personalization; real-time insights; intelligent
based on huge text datasets.6
customer service; predictive analytics; continuous
improvement; and optimized customer journeys. • Operational efficiency: Organizations
These benefits ultimately lead to improved customer
are already reporting significant efficiencies from
engagement, satisfaction, and loyalty.
generative AI; German biotech firm Evotec announced
a phase-one clinical trial for a novel anti-cancer
KT telecom (South Korea’s leading mobile operator)
compound it developed with Exscientia, a UK
has built billion-parameter LLMs trained on the NVIDIA
organization that uses AI for small-molecule-drug
DGX SuperPOD platform and NeMo framework to
discovery. By using Exscientia’s Centaur Chemist AI
power smart speakers and customer call centers. Its
design platform, the organizations identified the
AI voice assistant, GiGA Genie, can control TVs, offer
drug candidate in only eight months. For context, the
real-time traffic updates, and complete a range of
average traditional discovery process takes 4–5 years.7
other home-assistance tasks when prompted by voice
commands. It has developed advanced conversational
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HARNESSING THE VALUE OF GENERATIVE AI: TOP USE CASES ACROSS INDUSTRIES
• Sales: By using generative AI to produce • Costs: By using generative AI to automate
personalized marketing, pricing optimization, demand processes, optimize resources, implement predictive
forecasting, improved customer experience, enhanced maintenance, optimize the supply chain, mitigate
sales support, and data-driven decision-making, risks, and improve decision-making, organizations
organizations can attract more customers, drive can achieve cost savings and enhance overall financial
life-long content-driven conversations, and boost performance.
conversions.
Germany-based Claudius Peters produces processing
In an attempt to increase its sales, Italian consumer- equipment for cement, coal, alumina, and gypsum
products organization Ferrero worked with brand plants. Working with technology partner Autodesk, it
20–60% designer Ogilvy Italy to customize jars for its popular used the Scrum project-management framework to
Nutella chocolate spread. Data scientists fed a reduce costs and product weights while shortening the
database of patterns and colors to a generative AI engineering process. The generative design produced
algorithm, which rapidly produced 7 million distinct components with a remarkable 20–60 percent weight
reduction in weight achieved through jar designs. These unique jars, branded as Nutella reduction while meeting performance requirements.
Unica, were sold all across Italy, reportedly selling out Additionally, the design served as a re-engineering
generative design by German-based
within a month. The design relied on the brand's highly template for conventional manufacturing, resulting
Claudius Peters recognizable lettering, around which other elements in a 30 percent lighter final design that lowered
were customized.8 component costs.9
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Fig.11
Organizations are prioritizing dedicated generative AI teams
Four in 10 organizations have
CURRENT LEVEL OF INTEGRATION OF GENERATIVE AI INTO FUTURE
already established teams PRODUCT/SERVICE DEVELOPMENT PLANS
and budget for generative AI
3% We have already established a dedicated
In just a few months of the public getting to know 8% team and budget for its implementation
about the technology through the launch of ChatGPT in
November 2022, nearly all (97 percent) of organizations
in our survey have plans for generative AI. Our research We are looking at establishing a dedicated
40%
reveals that 40 percent of organizations have established team and budget for its implementation
dedicated teams and budgets for generative AI, while in this year
another half (49 percent) are contemplating doing the
same within 12 months. Only 8 percent of organizations
We have not yet developed a concrete
are yet to develop a firm strategy for integration, and
plan for integration
as little as 3 percent are currently unsure if or how they 49%
will integrate generative AI into their product- or service-
development plans (see Figure 11). We are currently unsure if or how we
will integrate generative AI into our
product/service development plans
Source: Capgemini Research Institute, Generative AI Executive Survey, April 2023, N = 800 organizations.
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HARNESSING THE VALUE OF GENERATIVE AI: TOP USE CASES ACROSS INDUSTRIES
Fig.12
High tech and retail show the strongest commitment to integrating generative AI
PERCENTAGE OF ORGANIZATIONS THAT HAVE ESTABLISHED A DEDICATED
TEAM AND BUDGET TO INTEGRATE GENERATIVE AI INTO FUTURE
The majority (74 percent) of executives in the high tech
PRODUCT/SERVICE DEVELOPMENT PLANS BY SECTOR sector say they have established dedicated teams and
budgets for generative AI. Over 60 percent of executives
Average 40% from retail and 52 percent of executives from aerospace
and defense say the same (see Figure 12). Within retail,
High tech 74% while only 3 percent of executives believed generative
AI to be disruptive to their industry (refer to Figure 4),
Retail 62%
62 percent of retail executives say their organization has
Aerospace and defense 52% established a dedicated team and budget. This suggests
that while the retail industry does not see this technology
Pharma and healthcare 42%
as a disruptor, organizations realize they will lose out if
Financial services 42% they fail to implement it.
Energy and utilities 39%
Telecom 36%
Automotive 30%
Consumer products 23%
Industrial manufacturing 19%
Source: Capgemini Research Institute, Generative AI Executive Survey, April 2023, N = 800 organizations
Capgemini Research Institute 2023
26
HARNESSING THE VALUE OF GENERATIVE AI: TOP USE CASES ACROSS INDUSTRIES
40%
of organizations have already established
teams and budget for generative AI, while
another half (49 percent) are contemplating
doing the same within 12 months
Capgemini Research Institute 2023
27
HARNESSING THE VALUE OF GENERATIVE AI: TOP USE CASES ACROSS INDUSTRIES
Governing Fig.13
Most organizations are working/prefer to work with developers and IT vendors for their generative AI initiatives
generative AI
PARTNERS THAT ORGANIZATIONS ARE ALREADY WORKING WITH/PREFER
TO WORK WITH TO DRIVE MORE VALUE FOR GENERATIVE AI INITIATIVES
Centralized funding for generative
AI initiatives is currently the favored Developers (e.g., OpenAI, Stability AI) 69%
model
Of the 40 percent of organizations in our survey (320 IT vendors/C&SI (consulting and system integrators) 66%
companies) that have a dedicated budget for generative
AI initiatives, 78 percent source it from their central
budget, 16 percent from their overall AI budget, and 6
Academic institutions 55%
percent from their IT/digital department.
Organizations expect to partner
Big tech (e.g., Microsoft, Google) 35%
with developers and IT vendors
The preferred partners for generative AI initiatives are
Peer companies 22%
developers (69 percent); IT vendors and consulting firms
(66 percent); academic institutions (55 percent); and big
tech (35 percent) (see Figure 13).
Source: Capgemini Research Institute, Generative AI Executive Survey, April 2023, N = 800 organizations.
Capgemini Research Institute 2023
28
HARNESSING THE VALUE OF GENERATIVE AI: TOP USE CASES ACROSS INDUSTRIES
03
GENERATIVE AI PACKS THE MOST
PUNCH FOR IT, SALES, AND
MARKETING
Capgemini Research Institute 2023
29
HARNESSING THE VALUE OF GENERATIVE AI: TOP USE CASES ACROSS INDUSTRIES
Fig.14
67 percent of executives see the most potential for generative AI in the IT function
PERCENTAGE OF ORGANIZATIONS THAT SEE THE MOST POTENTIAL FOR
The greatest potential for GENERATIVE AI MODELS TO DRIVE INNOVATION AND CREATE VALUE FOR THEIR
ORGANIZATION ACROSS BUSINESS FUNCTIONS
generative AI lies in the IT
IT (e.g., driving innovation in other
function 67%
functions, testing and coding assistant)
Sales and customer service
54%
(e.g., optimizing support chatbots/self-service)
Nearly 70 percent of executives see generative AI
bringing the most potential value to IT within its role Marketing and communications 48%
(e.g., creating personalized marketing campaigns)
as an enabler for driving innovation across functions.
Over half (54 percent) also see generative AI driving Manufacturing (e.g., 3D modeling) 31%
innovation for sales and 48 percent for marketing and
Product design/research and development
communications (see Figure 14). (e.g., generating new design, faster drug discovery) 31%
Operations (e.g., optimizing supply chain) 26%
67%
Risk management (e.g., drafting
22%
and reviewing legal and regulatory documents)
Finance (e.g., processing invoices) 13%
of executives see generative AI bringing
Logistics (e.g., optimizing routes) 9%
the most potential value to IT within its
role as an enabler for driving innovation
Source: Capgemini Research Institute, Generative AI Executive Survey, April 2023, N = 800 organizations.
across functions
Question asked: In which business functions do you see the most potential for generative AI models to drive innovation and create
value for your organizatio"
20,capgemini,Build-a-GenAI-data-powered-enterprise.pdf,"Build success as a
Gen AI data-powered
enterprise
Here’s why and how to make it happen
2
Business leaders are increasingly recognizing generative Data is an asset that requires investment, management,
AI’s value as an accelerator for driving innovation and and governance as Gen AI applications need a solid
revenue growth. An amazing 91 per cent of organizations foundation of clean, accurate, and usable data to deliver
are experimenting with or investing in Gen AI, and 88 per meaningful results. A data-powered enterprise has a
cent of organizations plan to focus on AI – including Gen strong foundation that reduces the complexity of the
AI – in the next 12 to 18 months. ecosystem, understands the difference between proof
of concept and scaled projects, connects data processes
But the majority of organizations that manage to scale
and policy, and supports the agility needed to move
up their Gen AI initiatives subsequently struggle to
quickly.
extract significant value from their investments. And
often that’s because of issues with their data and data-
management practices.
Build success as a Gen AI data-powered enterprise
3
Data-powered
defined
We identified nine key attributes in the Capgemini report
Data-powered enterprises: The path to data mastery. These
dimensions collectively empower organizations to create,
process, and leverage data to achieve business objectives,
increase operational excellence, improve customer
experience, and drive innovation.
Build success as a Gen AI data-powered enterprise
Defining the data-powered enterprise 4
Data-powered enterprises: The path to data mastery, Capgemini Research Institute 2024
Build success as a Gen AI data-powered enterprise
5
The same report found that, over the past four years, data executives are aware of the data trust and guiding
almost two-thirds of executives state their organizations principles required to succeed with AI adoption. That
use activated data, which is information that has been means the majority of organizations that manage to
embedded within core business processes. However, the scale up their Gen AI initiatives subsequently struggle to
data maturity progress made to unlock the value of data extract significant value from their investments. That’s
in these nine areas indicates otherwise. often because issues with data and data-management
practices impede success.
While 80 percent of global organizations increased
their Gen AI investment this year, only 54 percent of
Only In 2024,
54% 80%
of data executives are aware of of global organizations increased
the data foundations required to their investment in Gen AI from
win in the AI era last year
Build success as a Gen AI data-powered enterprise
6
The irrefutable value of data
Enterprise businesses already understand the emails, videos, images, social media posts, organization is hampered by limited access
value of information, as leaders who say their and HTML content. These data types make to the right technology skills to effectively
organization is cashing in on data has doubled identifying, collating, analyzing, and extracting leverage it, they won’t be able extract the most
since 2020 – up to two-thirds in 2024. But data insights a challenge. If an organization’s data is value from Gen AI.
quality is often an issue in achieving AI at scale, low quality, poorly governed, siloed in disparate
as much of it typically consists of unstructured systems, or laden with security issues, or if an
Build success as a Gen AI data-powered enterprise
7
Here’s how organizations can effectively unlock and supercharge their data.
Start strong Avoid excessively complex ecosystem
1
Build a data foundation that can unlock timely, accurate, Data is often siloed into disparate technologies and
2
and relevant insights to drive real outcomes. This is the first software products. Much of the data is unstructured or
step toward becoming a data-powered enterprise. low quality, and vetting it and restructuring it to analyze,
compare, and generate insights requires time, skill, and
budget.
Create the ability to scale
Remove obstacles to efficient delivery
Companies often see success with proof-of-concept Gen
AI products but success rates can drop dramatically when 4
3 Organizations must move fast and keep costs down
they move to production. The key is quality data and the
to deliver on their commitment. Without managing
capacity to create a clear, shared enterprise-wide data
complexity and scalability, this becomes challenging and
taxonomy. Engineering teams need strong, coordinated
costly.
data security and compliance policies and procedures
as well as the capacity to orchestrate a multiple-vendor
purchasing strategy. Ultimately, driving results and returns means data must be accessible, structured,
trustworthy, and mature.
Build success as a Gen AI data-powered enterprise
8
Working together on
data enablement to create
an advantage
Most Gen AI maturity journeys start in the same place: Capgemini RAISE, our Reliable AI Solution Engineering
businesses want ChatGPT-like experiences and they solution, includes key capabilities from Databricks:
encounter thousands of open- and closed-sourced Gen training and serving (Mosaic AI and AI/BI), data
AI models. Accessing trust, cost, and scale controls in a warehousing Databricks SQL and UniForm) and machine
single toolkit is therefore invaluable. learning (MLflow) capabilities, all using open standards
and complemented by key Informatica capabilities
including data integration, data quality, no-code AI
application development and trusted master data;
That’s why Capgemini is working with Databricks
comprehensive data governance is provided by the
and Informatica to provide a solution for enterprise
combined solution of Databricks Unity Catalog and
organizations that enables them to mature and leverage
Informatica’s Cloud Data Governance and Catalog. This
data more effectively to drive business results. Our
makes data usable on multiple clouds across hybrid
approach looks at data as a product hub. By combining
environments and reduces the complexity of managing
the best capabilities of enterprise data management
an end-to-end platform. And it’s available to be deployed
platforms on the cloud, along with the capabilities
on existing data platforms including Microsoft Azure,
of the best Gen AI solutions and capabilities from
AWS, and Google Cloud Platform.
the Databricks Data Intelligence Platform, and with
Informatica’s Intelligent Data Management Cloud, we’re
simplifying the data-management process within one
tool as an efficient, interoperable, and scalable Gen AI
development platform and framework.
Build success as a Gen AI data-powered enterprise
9
Capgemini RAISE works on multiple platforms including
Microsoft Azure, AWS, and Google Cloud Platform. Pilot, scale, and industrialize
Gen AI to deliver business
benefits.
Working seamlessly with existing infrastructure, Capgemini
RAISE also enhances data synergy and democratization
across the business with the data foundation necessary
to implement and scale Gen AI and other innovations
powered by data. The accelerator can identify and
As a Gen AI value cases operational accelerator, Capgemini
industrialize data products for AI models consumption,
RAISE delivers tangible business results, enabling
and ensure democratization of data through a data mesh,
organizations to industrialize custom Gen AI projects with
which is a decentralized data architecture designed to
the right guardrails, and addresses the issues of data
improve data access, security, and scalability by distributing
complexity, trust readiness, and scalability by creating the
ownership and management across business domains.
unified data governance and AI risk framework required
This effectively gives data-powered organizations an
for Gen AI data pilots. It equips organizations for Gen AI
advantage, and the ability to pilot, scale, and industrialize
scaling with a focus on business priorities.
their Gen AI applications and services to achieve the
Capgemini RAISE is not a one-size-fits-all product. It can be desired results.
built in modules, with the flexibility to meet unique needs
while delivering an end-to-end value chain for enterprise-
wide data management for Gen AI.
Generative AI holds great promise for enterprises in any
sector but there are substantial challenges to building
effective systems at scale. Capgemini RAISE and our
partnership with Databricks and Informatica solve those
issues today. Contact us to learn more.
Build success as a Gen AI data-powered enterprise
10
Authors
Please reach out with questions or to schedule a
conversation about this paper’s content and our
capabilities to assist your organization.
Eric Reich Rik Tamm-Daniels Ryan Simpson
Offer Leader and Global Head of GVP Ecosystems & Alliances Managing Technical Alliance Lead,
AI & Data Engineering, Insights & Informatica Databricks
Data, Capgemini
Build success as a Gen AI data-powered enterprise
11
About Databricks
Databricks is the Data and AI company. More than 10,000
organisations worldwide — including Block, Comcast,
Condé Nast, Rivian, Shell and over 60% of the Fortune
500 — rely on the Databricks Data Intelligence Platform
to take control of their data and put it to work with AI.
Databricks is headquartered in San Francisco, with offices
around the globe, and was founded by the original creators
of Lakehouse, Apache Spark™, Delta Lake and MLflow. To
learn more, follow Databricks on LinkedIn, X and Facebook.
About Informatica
Informatica (NYSE: INFA), a leader in enterprise AI-powered
cloud data management, brings data and AI to life by
empowering businesses to realize the transformative
power of their most critical assets. We have created a
new category of software, the Informatica Intelligent
Data Management Cloud™ (IDMC). IDMC is an end-to-
end data management platform, powered by CLAIRE® AI,
that connects, manages and unifies data across any multi-
cloud or hybrid system, democratizing data and enabling
enterprises to modernize and advance their business
strategies. Customers in approximately 100 countries,
including over 80 of the Fortune 100, rely on Informatica to
drive data-led digital transformation. Informatica. Where
data and AI come to life.
Build success as a Gen AI data-powered enterprise
About Capgemini
Capgemini is a global business and technology transformation partner, helping organizations to
accelerate their dual transition to a digital and sustainable world, while creating tangible impact for
enterprises and society. It is a responsible and diverse group of 340,000 team members in more than
50 countries. With its strong over 55-year heritage, Capgemini is trusted by its clients to unlock the
value of technology to address the entire breadth of their business needs. It delivers end-to-end
services and solutions leveraging strengths from strategy and design to engineering, all fueled by
its market leading capabilities in AI, cloud and data, combined with its deep industry expertise and
partner ecosystem. The Group reported 2023 global revenues of €22.5 billion.
www.capgemini.com
© 2024 Capgemini. All rights reserved."
21,capgemini,Generative-AI-in-Organizations-Refresh.pdf,"Harnessing the value of generative AI: 2nd edition Top uses cases across sectors
Harnessing the value
of generative AI
2nd edition: Top use cases across sectors
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Harnessing the value of generative AI: 2nd edition Top uses cases across sectors
Capgemini Research Institute 2024
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Harnessing the value of generative AI: 2nd edition Top uses cases across sectors
Capgemini Research Institute 2024
evitucexE
yrammuS
4
Harnessing the value of generative AI: 2nd edition Top uses cases across sectors
Increased maturity and investment: Organizations are Moreover, generative AI adoption among employees is robust
embracing generative AI, which is reflected in an uptick in in most organizations, with the majority allowing its use. Only
investment levels. The vast majority (80%) of organizations 3% of surveyed organizations enforce a complete ban on
in our survey have increased their investment in generative public generative AI tools in the workplace.
AI from 2023, 20% have maintained their investment level,
Tangible benefits and strategic shifts: Early adopters
and not one organization decreased their investment from
of generative AI are are seeing benefits in areas in which
last year. Larger enterprises lead the charge, and overall,
generative AI has been piloted or deployed, from improved
nearly one-quarter (24%) of organizations have integrated
operational efficiency to enhanced customer experience.
this technology into some or most of their operations, an
For example, on average, organizations realized a 7.8%
acceleration from 6% reported just 12 months ago. This
improvement in productivity and a 6.7% improvement
increase in generative AI adoption since 2023 spans all
in customer engagement and satisfaction, over the past
sectors. For example, in retail, implementation increased to
year. Organizations anticipate that generative AI will drive
40%, more than doubling from 17% in 2023.
adjustments to their strategic approaches and business
Pervasive integration across functions: Generative AI models. With a belief that the technology will be a key
permeates organizations, catalyzing a shift in operational driver of revenue growth and innovation, organizations are
paradigms. In the past year, there has been an increase in exploring new ways of harnessing its capabilities.
its adoption across all organizational domains, from sales
The rise of AI agents: The emergence of AI agents marks a
and marketing to IT, operations, R&D, finance, and logistics.
shift, with potential to enhance automation and productivity
across sectors, business processes, and along the entire
value chain. These AI agents have evolved from supportive
Capgemini Research Institute 2024
evitucexE
yrammuS
5
Harnessing the value of generative AI: 2nd edition Top uses cases across sectors
tools to autonomous entities capable of executing tasks Empower your generative AI journey: Organizations should:
independently. Organizations are eager to adopt AI agents,
with a strong majority (82%) intending to integrate them Establish a robust framework for data governance
within 1–3 years. There is a level of trust in AI agents for and management
specific tasks, such as generating work emails, coding, and
data analysis. However, organizations are also mindful of the Strengthen the data platform and cultivate trust to
need to establish guardrails to validate AI-made decisions, ensure reliable outcomes
ensuring transparency and accountability.
Cultivate expertise through strategic training and
talent development
Acquire understanding and expertise of the generative
AI ecosystem
Deploy a generative AI platform to manage use cases
at scale
Fortify against cybersecurity threats
Embrace emerging trends such as AI agents to boost
competitiveness and innovation.
Capgemini Research Institute 2024
6
Harnessing the value of generative AI: 2nd edition Top uses cases across sectors
This report is a part of Capgemini Research Institute’s series on Generative AI
Gen AI in organizations - annual research
Gen AI for management*
Gen AI in supply chain* Gen AI for marketing Gen AI for software Gen AI in R&D
engineering and engineering*
Gen AI and
consumers
Gen AI and Gen AI and Gen AI and
Gen AI in business operations* Gen AI in manufacturing* Gen AI in customer service* sustainability* ethics/ cybersecurity*
trust*
Data mastery*
Special edition of our premium journal Conversations for tomorrow on Gen AI*
To find out more, please go to https://www.capgemini.com/insights/research-institute/ *Upcoming reports
Capgemini Research Institute 2024
7
Harnessing the value of generative AI: 2nd edition Top uses cases across sectors
Who should This report offers an overview of the transformative The report will help business executives identify use cases
potential of generative AI for large organizations that illustrate the applications of generative AI across
across sectors such as automotive, consumer functions, including IT, sales, marketing, and product design/
read this
products, retail, financial services, telecom, energy R&D. The report draws on the comprehensive analysis of a
and utilities, aerospace and defense, high tech, survey of 1,100 leaders (director level and above) across 14
report and industrial manufacturing, pharma and healthcare, countries. Finally, it offers recommendations for business
and the public sector/government. It is the second leaders to accelerate their organizations’ generative AI
installment in an annual research series and identifies journeys.
why? shifts in trends from 2023.
Capgemini Research Institute 2024
noitcudortnI
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Harnessing the value of generative AI: 2nd edition Top uses cases across sectors
Generative AI is rapidly transforming the way we interact with organizations are now integrating generative AI into some
technology. Machines are now capable of mimicking creative or most of their locations or functions, up from just 6% in
human thought processes and synthesizing tailored content 2023. In this year’s report, we analyze shifts in generative
of increasingly high quality. This has significant implications AI adoption and take a closer look at the investment levels
for driving innovation, operational efficiency, and growth. and benefits organizations have realized. We also turn the
spotlight on AI agents, a quickly evolving technology with
This report is the second in the Capgemini Research
potential to drive innovation.
Institute’s annual research series that examines generative
AI trends and use cases. In the first report of this series, To gauge perceptions of generative AI, we conducted a
“Harnessing the value of generative AI: Top use cases global survey of 1,100 executives at organizations with
across industries,” we explored the transformative potential annual revenue above $1 billion. We invited public-sector
of generative AI, highlighting the function- and sector- organizations and government entities with an annual
specific use cases with the greatest potential, and comparing budget of at least $50 million to participate. Organizations
adoption rates across sectors. In our 2023 research, we are headquartered in 14 countries: Australia, Canada, France,
discovered that, while still in its infancy in terms of scaled Germany, India, Italy, Japan, the Netherlands, Norway,
adoption and implementation, generative AI was on the Singapore, Spain, Sweden, the UK, and the US. Organizations
agenda in 96% of boardrooms globally. We found that nearly surveyed operate across 11 key sectors: aerospace and
60% of organizations said their leaders are strong advocates defense, automotive, consumer products, energy and
of generative AI, and only 39% were taking a “wait-and- utilities, financial services, high tech, pharma and healthcare,
watch” approach. industrial manufacturing, retail, telecom, and the public
sector/government. For more details on the survey sample,
This year’s research highlights a quickening of the pace
please refer to the research methodology.
of implementation. Notably, nearly one-quarter (24%) of
Capgemini Research Institute 2024
noitcudortnI
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Harnessing the value of generative AI: 2nd edition Top uses cases across sectors
This report comprises five sections:
1 2 3 4 5
Organizations are Generative AI Generative AI is AI agents: The new How organizations
deploying generative is pervading already driving technology frontier can accelerate
AI at pace organizations benefits their generative AI
journeys
Capgemini Research Institute 2024
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Harnessing the value of generative AI: 2nd edition Top uses cases across sectors
01
Organizations are deploying
generative AI at pace
Capgemini Research Institute 2024
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Harnessing the value of generative AI: 2nd edition Top uses cases across sectors
Investment in generative AI Dave Chen, Head of Global Technology Investment Banking
at Morgan Stanley, says:“Though cost savings and operational
is increasing efficiencies remain a priority for large enterprises, they are also
showing a willingness to spend, especially on generative AI and
traditional AI hardware and software that may [in the medium
According to our research, 80% of organizations have
to long term] help reduce costs and increase productivity and
increased their investment in generative AI from last year.
revenue.” 1 Recently, The Coca-Cola Company committed
Remarkably, not a single organization decreased their
$1.1 billion over a five-year period to accelerate cloud and
investment, while the remaining 20% maintained the same
generative AI initiatives.2
investment level. This trend echoes across all sectors and
organization sizes in the research. For example:
• In aerospace and defense, almost nine in 10 organizations
have (88%) boosted their investment in generative AI;
80%
• Within retail, the lowest proportion among all sectors in
our survey, 66% have invested in generative AI;
• 73% of organizations with $1–5 billion in annual
of organizations have increased their revenue have increased their investment, and 89% of
investment in generative AI from last year organizations with over $20 billion in revenue have
done so.
Capgemini Research Institute 2024
12
Harnessing the value of generative AI: 2nd edition Top uses cases across sectors
In the past year, Figure 1.
More organizations have increased their maturity in generative AI
implementation of
generative AI has
% of organizations who agree with the statement on generative AI maturity
accelerated
Across our total survey sample of 1,100 organizations, 2023
only 6% of organizations are yet to begin exploring 53% 41% 6%
generative AI. Out of those who have at least begun to do
so (n=1,031), nearly one-quarter (24%) are now integrating
this technology into some or most of their locations or
functions. This marks an increase from just 6% reported last 2024
year, indicating widespread recognition of the benefits (see 27% 49% 18% 6%
Figure 1).
Only 10% of smaller organizations in our research, We have started exploring the We have begun working on some
with annual revenue of $1–5 billion, have implemented potential of generative AI pilots of generative AI initiatives
generative AI across some or most of their locations We have enabled generative AI capabilities in
We have enabled generative AI capabilities
and functions. In contrast, 49% of organizations with most/all of our functions/locations
in some of our functions/locations
annual revenue surpassing $20 billion have implemented
the technology.
Source: Capgemini Research Institute, Generative AI executive survey, April 2023, N = 800 organizations; Generative AI
Jack Forestell, Chief Product and Strategy Officer at Visa,
executive survey, May–June 2024, N = 940 organizations that are at least exploring generative AI capabilities.
says: “While much of generative AI so far has been focused on
*In the 2024 data points respondents from India and the public sector/government are excluded as they were not
tasks and content creation, this technology will soon not only
included in the 2023 research.
reshape how we live and work, but it will also meaningfully
change commerce in ways we have yet to fully understand.” 3
Capgemini Research Institute 2024
13
Harnessing the value of generative AI: 2nd edition Top uses cases across sectors
All sectors have progressed Generative AI integration has increased across all sectors (see Figure 2). For a detailed listing of use cases by sector and
since 2023. For example, 40% of organizations in the retail associated implementation data, please refer to Appendix.
in their implementation of sector have implemented generative AI across some or most
functions/locations, more than doubling from 17% in 2023
generative AI
Figure 2.
Over the past year, there has been an increase in the maturity of generative AI across sectors
% of organizations who agree with the statement on generative AI maturity, by sector
4% 5% 4% 6% 3% 3% 4% 2% 3% 2% 2%
6% 16 8% % 17% 22% 45% 32% 35% 26% 28% 17 9% % 18% 19 5% % 21% 33% 1 10 4% % 1 11 1% % 7% 20% 11% 10% We have started exploring the
41% 18% 47% 49% 37% 44% potential of generative AI
48%
24% 40% 51% 48% 68% 68% We have begun working on some
49% 30% 53% 68% 54% 53% pilots of generative AI initiatives
53% 49% 55% 45% 61% 68% 47% 64% 47% 53% 53% W cae p ah ba iv lie ti ee sn a inb l se od m g ee on fe ora ut rive AI
27% 34% 11% 39% 21% 14% 22% 24% 35% 27% 30% 19% 20% functions/locations
We have enabled generative AI
capabilities in most/all of our
2023 2024 2023 2024 2023 2024 2023 2024 2023 2024 2023 2024 2023 2024 2023 2024 2023 2024 2023 2024 2023 2024 2024*
functions/locations
Average Retail Industrial Consumer Automotive High tech Financial Energy and Telecom Pharma and Aerospace Public
manufacturing products services utilities healthcare and defense sector
Source: Capgemini Research Institute, Generative AI executive survey, April 2023, N = 800 organizations; Generative AI executive survey, May–June 2024, N = 1,031 organizations that are at least exploring
generative AI capabilities; N varies per sector use case ranging from 50 to 189. *Respondents from the public sector were not included in the 2023 research. **Excluded from Figure 2 is the percentage of
organizations that have started exploring the potential of generative AI.
Capgemini Research Institute 2024
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Harnessing the value of generative AI: 2nd edition Top uses cases across sectors
David M. Reese, Executive Vice-president and Chief Highlighting the gradual scaling of generative AI in financial
Technology Officer at Amgen, a US biopharmaceutical services, Asim Tewary, former Chief AI Officer, PayPal,
company, says: ""Generative AI is transforming drug discovery comments: ""You have to be able to explain why certain decisions
by allowing us to build sophisticated models and seamlessly were made — why a credit limit was set at that amount, for
integrate AI into the antibody design process.'' 4 Natarajan example. There’s an absoluteness that’s expected from regulators
Chandrasekaran, Chairperson of Tata Group, states: “In about being able to explain how the decision was made. Anytime
e-commerce, generative AI is being used to generate product you impact the consumer or introduce a system risk, regulators
catalogs, deliver conversational shopping experiences, and get very concerned.” Also concerning to the financial sector is
provide personalized offers.” 5 Michael Smith, Chief Information the increased sophistication of deepfakes which can deceive
Officer at The Estée Lauder Companies, says: “The generative customers into transferring funds to seemingly legitimate
AI chatbot developed with [a partner] helps us quickly find accounts, whether virtual or human agent mimicked. This
products that address concerns relevant to specific regions and underscores the imperative of ensuring both accuracy in
emerging markets. We will continue to explore other ways we customer-facing AI products and robust security measures in
can harness the wealth of data across products, ingredients, and customer interactions.7
more, in tandem with the power of generative AI.” 6
Capgemini Research Institute 2024
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Harnessing the value of generative AI: 2nd edition Top uses cases across sectors
Investment in generative AI
Figure 3.
increases with organization size
Investment in generative AI trends upward with organization size
On average, organizations surveyed have $20 billion believe that failing to adopt generative
allocated around $110 million to generative AI AI will place them at a considerable disadvantage
for the current fiscal year. Thirty-four percent of relative to their competitors. This sentiment is lower Average investment dedicated to generative AI,
organizations in our survey have allocated $50 among organizations with annual revenues under $5 by annual revenue, 2024
million or less to generative AI, while 11% have billion (56%)8.
allocated $250 million or more.
Our average investment of $110 million is higher than $157.7
The amount of generative AI investment increases some recently published analyst reports and likely
$131.5
with company size. For example, on average, reflects the broad categories of enterprise spend
$109.8
organizations with annual revenue of $1–5 billion including hardware, software, licensing, training, $99.5
allocate $85 million and those with more than $20 among others. According to Gartner, software $85.2
billion allocate $158 million (see Figure 3). development is the function with the highest rate
of investment in generative AI, followed closely by
Given that the amount of investment allocated
marketing and customer service.8 Additionally, a
to generative AI increases with organization size,
significant majority of organizations are investing in
larger organizations widely agree that generative
partnerships with external providers of generative
AI is more than just a passing trend, serving as a
AI applications. According to our research, 70%
pivotal cornerstone in their enterprise evolution. Average $1 bn– $5 bn– $10 bn– More than
Large organizations are increasingly embracing of organizations are exclusively using external $4.9 bn $9.9 bn $19.9 bn $20 bn
applications or a combination of external and
the transformative potential of generative AI
in-house solutions. Commonly used tools include
technology more fervently than perhaps other Source: Capgemini Research Institute, AI executive survey, May–June
OpenAI’s ChatGPT, GitHub Copilot, Scribe, Microsoft
recent technological advancement. A striking 71% 2024, N = 981 organizations who are at least exploring generative AI
Copilot, and AWS Gen AI.
of organizations with annual revenues exceeding capabilities, excluding the public sector.
Capgemini Research Institute 2024
16
Harnessing the value of generative AI: 2nd edition Top uses cases across sectors
02
Generative AI is pervading
organizations
Capgemini Research Institute 2024
17
Harnessing the value of generative AI: 2nd edition Top uses cases across sectors
Generative AI makes Figure 4.
Over the past year, adoption of generative AI has grown across functions
inroads across functions
% of organizations implementing generative AI use cases, by function
Our research indicates an increase in generative AI
adoption across all facets of organizations over the past 12
4%
months. Examining individual functions, in the IT domain IT 27%
for example, the adoption rate has increased to 27% from Risk management 4%
4% the previous year (see Figure 4). Hitachi successfully 26%
integrated generative AI tools by leveraging its detailed Logistics 2%
26%
system-design knowledge with Microsoft’s AI services,
Sales/customer operations 4%
achieving a 70%–90% success rate in generating application 25%
source code.9 For a detailed listing of use cases by function Finance 5%
and associated implementation data, please refer to 25%
the Appendix. Human resources* 24%
ESG/sustainability* 22%
3%
Product design/R&D
19%
2%
Marketing
19%
2023 2024
Source: Capgemini Research Institute, Generative AI executive survey, April 2023, N = 800 organizations; Generative AI executive survey,
May–June 2024, N = 1,031 organizations that are at least exploring generative AI capabilities; N varies per functional use case, ranging
from 499 to 716.
*ESG/sustainability and human resources were excluded from the 2023 research.
** “Implementation” refers to organizations that have partially scaled the functional use case in question.
***In the 2024 averages, respondents from the public sector and India are excluded, as they were not included in the 2023 research.
Capgemini Research Institute 2024
18
Harnessing the value of generative AI: 2nd edition Top uses cases across sectors
Generative AI is used by Figure 5.
97% of organizations allow employees to use generative AI in some capacity
employees in most
organizations
% of organizations who agree with the statements
The majority of organizations allow employees to use
generative AI in some capacity. Over half of organizations We allow all our employees to use generative AI tools but have set up
54%
(54%) require employees to follow specific guidelines when guardrails/principles
using these tools, rather than imposing a complete ban.
Three percent of surveyed organizations report a ban on We allow only a carefully chosen group of skilled employees, primarily
36%
public generative AI tools in the workplace. However, 7% of those in specialized/technical roles, to use generative AI tools
organizations permit unrestricted use of such tools, which
may pose future risks to the organization (see Figure 5). We allow all our employees to use generative AI tools at will 7%
We have banned all our employees from using public generative AI
3%
tools in the workplace
2024
Source: Capgemini Research Institute, Generative AI executive survey, May–June 2024, N = 1,031 organizations that are at least exploring
generative AI capabilities.
Capgemini Research Institute 2024
19
Harnessing the value of generative AI: 2nd edition Top uses cases across sectors
Despite the implementation of guardrails and policies to workplace. Amazon has prohibited employees from using In the table that follows, we highlight recent
regulate the use of generative AI, unauthorized usage among third-party generative AI tools, such as OpenAI's ChatGPT, generative AI use cases across sectors and functions:
employees is still relatively common. Among the 39% of particularly for handling confidential data. This policy is
organizations with a ban or limitation policy, half of them intended to prevent data-ownership issues and protect
say there is still unauthorized usage of generative AI in the sensitive company information.10
Sector Company Function Example
Has transformed its operations and innovation processes with generative AI. AI assistants
Aerospace Airbus Manufacturing
provide aircraft manufacturing instructions, enhancing accessibility to technical data, and
facilitating precise task guidance.11
Uses generative AI to incorporate engineering constraints into vehicle design, and
Automotive Toyota Product design/R&D
optimizing metrics such as aerodynamic drag, enhancing efficiency of electric-vehicle
(EV) design.12
Leverages generative AI in its “Hey Mercedes” feature, which is in beta with 900,000
Automotive Mercedes-Benz Customer experience/
users. It offers personalized, screen-free interactions, enhancing driving experience with
service
dynamic adjustments and real-time safety support.13
Consumer products General Mills Customer experience/ Launched MillsChat, a generative AI tool, to streamline customer service. This enhances
service efficiency, provides personalized assistance, and encourages customer engagement.14
Harnesses generative AI to analyze customer feedback, which it uses to refine shape design
Marketing and
Consumer products PepsiCo and flavor of its Cheetos branded snack, boosting market penetration by 15%. This strategy
branding
has also shortened product launch cycles and increased profitability.15
Continue to the next page...
Capgemini Research Institute 2024
20
Harnessing the value of generative AI: 2nd edition Top uses cases across sectors
Sector Company Function Example
Uses generative AI to streamline legal processes, including research, drafting, and
Consumer product Unilever Legal
contract reviews. This allows legal teams to focus on strategic tasks, enhancing
operational effectiveness.16
Energy British Human resources – Leverages generative AI to assist employees with daily tasks such as email management.
Enhances employee productivity and transforms business workflows.17
Petroleum (BP) employee productivity
Used GPT-4 to create an AI tool for financial advisors, allowing rapid access to internal
Financial services Morgan Stanley Customer
research. This enhances advisor efficiency and client service, simulating top investment
experience/service
experts on call.18
Uses generative AI to reduce carbon footprint of commercial buildings. Uses historical data
High tech BrainBox AI ESG/sustainability
to predict interior building temperatures, cutting heat, ventilation and air-conditioning
(HVAC) energy costs by up to 25% and greenhouse gas (GHG) emissions by 40%.19
Added generative AI into FactoryTalk Design Studio to help engineers generate code using
Industrial manufacturing Rockwell Automation IT – coding/software natural language prompts, automating routine tasks and improving design efficiency. It will
development also empower experienced engineers to accelerate development and mentor newcomers
more effectively.20
Continue to the next page...
Capgemini Research Institute 2024
21
Harnessing the value of generative AI: 2nd edition Top uses cases across sectors
Sector Company Function Example
Integrated generative AI into Finance Advisor, a conversational assistant for financial
Industrial manufacturing Schneider Electric Finance and accounting
analysts in global finance, ensuring precise, compliant, and timely decision-making across
accounting and related functions.21
Logistics UPS Marketing Developed the Message Response Automation (MeRA) system in-house using publicly
available large language models (LLMs), to automate routine customer interactions,
reducing email handling times by 50% and allowing human agents to focus on more
complex issues. It also streamlines operations and improves customer satisfaction by
ensuring prompt, accurate responses.22
Pharmaceutical Insilico Product design/R&D Identified a new drug candidate, MYT1, using its generative AI platform in each step of its
and biotech Medicine preclinical drug-discovery process, offering more effective, safer treatments for breast and
gynecological cancers.23
Pharmaceutical Moderna Research and Uses generative AI tools, including the company's Dose ID GPT, which uses ChatGPT
Enterprise's Advanced Data Analytics feature to further evaluate the optimal vaccine dose
and biotech development
selected by the clinical study team.24
Continue to the next page...
Capgemini Research Institute 2024
22
Harnessing the value of generative AI: 2nd edition Top uses cases across sectors
Sector Company Function Example
Plans to use generative AI to analyze traffic data to improve road safety, reduce
Public sector California Department of Citizen services
call-center wait times, aid non-English speakers, and streamline healthcare-facility
Transportation and
inspections.25
Department of Tax and
Fee Administration
Public sector State of Pennsylvania Employee operations Runs a generative AI pilot program for state employees, integrating the technology into
government operations. Supports crafting/editing copy, updating policy language,
drafting job descriptions, and generating code.26
Retail ASOS Sales Uses generative AI to make fashion recommendations, customer-service interactions, and
trend analysis, enhancing user engagement, personalizing customer experiences, and
optimizing retail strategies.27
Uses generative AI to reduce food waste by helping employees make quick decisions.
Retail Walmart ESG/sustainability
Employees scan a product such as produce or apparel, and a digital dashboard makes
suggestions on what to do with the product based on its characteristics (e.g., ripeness,
whether its seasonal). Suggested actions could include a price change, putting it on sale,
sending the item back, or donating it.28
Continue to the next page...
Capgemini Research Institute 2024
23
Harnessing the value of generative AI: 2nd edition Top uses cases across sectors
Sector Company Function Example
Uses generative AI, in its Seoul, South Korea, store to develop innovative ice-cream
Retail Baskin Robbins Product design/R&D
flavors. Supported the introduction of a monthly exclusive flavor, and personalized
experience offered through an ice-cream docent program.29
Telecom AT&T Employee productivity Employs generative AI capabilities in its Ask AT&T tool to support employees by
enhancing productivity and creativity, translating documents, optimizing networks, and
summarizing meetings. This leads to improved efficiency and greater innovation.30
Voxi by Vodafone, a generative AI self-service experience, enhances interactions, provides
Telecom Vodafone Customer
personalized assistance, and optimizes customer support services, fostering satisfaction
experience/service
and engagement.31
Capgemini Research Institute 2024
24
Harnessing the value of generative AI: 2nd edition Top uses cases across sectors
Organizations must
Figure 6.
carefully scale generative 36% of organizations are currently tracking carbon emissions from generative AI use
AI initiatives with a focus
% of organizations currently tracking and measuring the
on environmental
below metrics in the use of generative AI
sustainability
30% 29%
Organizations must assess technological 36%
advancements in generative AI alongside their
environmental consequences. They should
Yes
evaluate the business value, considering
implementation complexity and costs, while No
also scrutinizing environmental impacts such 54% 57% 62% Unsure/ don’t know
as GHG emissions, electricity usage, and water
consumption. Our research indicates that, roughly
a third of organizations are currently monitoring
energy and water consumption, as well as carbon 10% 13% 9%
emissions, associated with their generative AI
Carbon Energy Water
initiatives (see Figure 6).
emission utilization consumption
Source: Capgemini Research Institute, Generative AI executive survey, May–June
2024, N = 1,031 organizations that are at least exploring generative AI capabilities.
Capgemini Research Institute 2024
25
Harnessing the value of generative AI: 2nd edition Top uses cases across sectors
Furthermore, our research explored the
Figure 7.
measures organizations are implementing
to mitigate the environmental impact Half of organizations are currently developing guidelines for generative AI use
of generative AI. Slightly over half of
organizations (54%) are developing guidelines
for responsible use of generative AI, and % of organizations currently trying to mitigate the carbon footprint from using generative AI
47% are transitioning to more energy-
efficient hardware. However, the proportion
of organizations undertaking additional
Developing guidelines for responsible use 54%
actions such as investing in renewable energy,
offsetting emissions through carbon credits,
and optimizing training algorithms remains Using more energy-efficient hardware 47%
low (see Figure 7).
Offsetting through carbon credits 37%
Investing in renewable energy 36%
Only a third of organizations are
currently monitoring energy and water
Optimizing our training algorithms 31%
consumption, as well as carbon emissions,
associated with their generative AI
initiatives.
Source: Capgemini Research Institute, Generative AI executive survey, May–June 2024, N = 1,031 organizations that are at least exploring
generative AI capabilities.
Capgemini Research Institute 2024
26
Harnessing the value of generative AI: 2nd edition Top uses cases across sectors
03
Generative AI is already
driving benefits
Capgemini Research Institute 2024
27
Harnessing the value of generative AI: 2nd edition Top uses cases across sectors
Organizations have Figure 8.
Generative AI yielded benefits in the past year in the areas in which the technology has been piloted or deployed
achieved benefits
Average benefits realized from generative AI within the past year
Our current research evaluates the benefits that generative
AI has brought at organizational level in the past year in the
areas in which generative AI has been piloted or deployed. 7.8%
For example, on average, organizations realized a 7.8%
6.7%
improvement in productivity and a 6.7% improvement in
5.4%
customer engagement and satisfaction over the past year
4.4%
(see Figure 8).
3.6%
Generative AI "
22,capgemini,Setting-the-pace-for-intelligent-transformation-weblocked.pdf,"Setting the pace for
intelligent transformation
Four steps banks can take to build a roadmap to AI adoption success
Seemingly overnight, AI in the banking industry has
catapulted from limited adoption to a competitive
imperative. This is particularly true of generative
AI, which 80% of bank CXOs who participated in
Capgemini’s World Retail Banking Report 2024
believe is a significant leap with a potential that no
one can ignore.
In brief
• AI in the banking industry has catapulted from
limited adoption to a competitive imperative, but
impediments remain.
• To help overcome the hurdles, banks should develop an AI
adoption roadmap that includes cloud, data-as-a-product,
data mesh, LLM selection, and effective governance.
• The benefits of building a holistic AI roadmap are
considerable as enterprise-wide AI is a steppingstone to
autonomous and intelligent banking, which takes banks
beyond consuming AI to wielding it for competitive
advantage.
Industry optimism for AI is in no small part due
to the inefficiencies facing banks every day. In
quantitative terms, 70% of bank employee time
is allocated to operational activities and only
30% to customer interactions, leaving substantial
opportunities for AI.1
Still, significant impediments to intelligent
transformations remain. These include legacy
systems, fragmented data, regulatory challenges,
skill shortages, and return on investment concerns.
In this article we explore how banks overcome such
barriers by developing an appropriate plan to guide
AI adoption initiatives in order to positively benefit
the bottom line.
2 Setting the pace for intelligent transformation
Building a strategic AI roadmap
Only 6% of
As discussed in Capgemini’s World required for building an intelligent
banks have an
Retail Banking Report 2024, transformation roadmap. We
leading CXOs worldwide have recommend using a bottom-up appropriate plan for
already identified three pivotal approach (Figure 1) to create a plan
horizontal processes, spanning the that addresses data, model, and establishing an AI
entire retail banking spectrum, business layers using the following
adoption roadmap.
as focal points for the intelligent steps:
transformations: intelligent
document automation, intelligent 1. Build a cloud foundation
call centers, and workforce
2. Develop a modern data-
productivity co-pilots.
as-a-product estate using
However, the report also revealed data mesh
that 96% of banks score low on
3. Select the right Large
the AI readiness scale, as measured
Language Model (LLM)
from both a technology and a
approach
business perspective. On a key
readiness factor, establishing an 4. Establish
AI adoption roadmap, only 6% of effective governance
banks have an appropriate a plan.
For a greater understanding of
To help banks catch up, this what each element entails, let’s
article discusses the elements discuss each in depth.
Figure 1: Four strategic essentials for an intelligent transformation roadmap
Source: Capgemini Research Institute for Financial Services Analysis, 2024
3
1. Build a cloud foundation for agility and scalability
As the most appropriate Gaining the desired benefits from
“A key variable [in
infrastructure for providing high- AI initiatives requires accelerating
performance scalable computing cloud migration efforts and developing our AI roadmap]
resources, cloud technologies lie at establishing a sufficiently robust, is to allocate cloud
the heart of enterprise AI adoption. agile, and secure foundation
computing resources to
for meeting AI’s computing
Although 91% of financial services
capacity, speed, and data generative AI use cases.
firms have embarked on their
protection requirements. The convergence of
cloud journey,3 adoption has been
uneven. For example, industry generative AI and cloud
experts and analysts suggest that
economics offer a path to
less than 30% of banks have moved
reduced costs and scaled
their core business applications to a
cloud platform.4 adoption.”
- Vincent Kolijn, Head of Strategy
and Transformation, Retail,
Rabobank, Netherlands2
4 Setting the pace for intelligent transformation
2. Develop a modern data-as-a-product estate using data mesh
Exacerbated by legacy consumption by multiple business As noted in the Capgemini World
infrastructure, departmental lines and AI applications. Retail Banking Report 2024, leading
database ownership, and banks like JP Morgan Chase and
Simplify data ownership
regulatory demands, data silos Fifth Third Bank in the US, Saxo
and management.
remain a significant challenge for Bank in Denmark, ABN AMRO in
banks. Fortunately, data mesh Enables each banking domain to the Netherlands, and numerous
architecture now offers a solution continue organizing, owning, and others have successfully adopted
by providing technology layers that managing data while improving data mesh architectures.
work across data silos (Figure 2). data access, utilization, and data-
driven innovation across a bank’s
Data mesh treats data as a stand- “Banks facing the
entire enterprise, without requiring
alone offering with a value challenge of legacy
significant overhauls to existing
proposition that is called data-as-
data infrastructure. systems must strategize
product. With this approach, data
management is decentralized and Ensure well-governed on how to adopt and scale
occurs within business silos, but is data diversity. AI effectively. It’s about
unified by standards, governance,
Enables making data FAIR crafting a roadmap that
and extraction technologies
(findable, accessible, interoperable,
that make data available to AI navigates the hurdles
and reusable) while applying
applications on demand. Use data legacy systems pose.”
appropriate key performance
mesh to:
indicators (KPIs) to govern and
Provide real-time data access. protect data, which helps assure its - Steven Cooper, CEO, Aldermore
integrity for use by AI applications. Bank, UK5
Enables creating a data
marketplace for collaborative data
Figure 2: Modern data-as-a-product estate using data mesh
Source: Capgemini Research Institute for Financial Services Analysis, 2024.
5
3. Select the right Large Language Model (LLM) approach
Shaping the algorithm layer for an calibrated using a bank’s internal and compliance considerations,
AI initiative starts with selecting data, enabling fine-tuning for the and cost analysis. Once an AI
the right large language model delivery of superior, customized application is rolled out, monitoring
(LLM). Typically, the decision is human interactions. it for continuous improvement and
shaped by the phase of an AI course corrections are also critical
Industrialization phase.
journey (Figure 3). for achieving desired business
Although only about 10% of CXOs results and goals.
Exploratory phase.
survey for World Retail Banking
Many banks purchase off-the Report 2024 have chosen to build
“Banks navigating the
shelf solutions such as chatbots, a custom LLM from scratch, this
evolving generative AI
fraud detection platforms, and industrialization phase option
others that cater to specific needs provides the greatest ownership landscape should weigh
when exploring AI. Off-the-shelf and customization. Unsurprisingly,
three approaches: building
solutions provide immediate this path also requires substantial
a custom LLM, considering
availability but limited control. resource investments.
off-the-shelf generative
AI scaling phase. Regardless of which route a bank
chooses, success requires a long- AI, and partnering with
Banks most frequently partner with
term strategic vision. This includes specialists.”
LLM specialists during scaling as a
a comprehensive assessment
balanced approach to accelerating
of specific needs, available
AI development and gaining - Pierre Ruhlmann, Chief
resources, model capabilities, risk
domain expertise. This nets an AI Operating Officer, BNP Paribas,
France6
Figure 3: Three typical approaches to LLM development
Source: Capgemini Research Institute for Financial Services Analysis, 2024; World Retail Banking Report CXO survey.
6 Setting the pace for intelligent transformation
4. Establish effective governance
As AI applications evolve rapidly, results, privacy intrusions, and
“Explainable AI (XAI)
they quickly exceed the limits malicious use.
of human understanding and in banking is essential
Naturally, developing and
make decisions that are difficult to mitigate bias risks
continuously evolving a robust
for humans to interpret, much
and comprehensive set of KPIs is and enhance trust. It
less govern. Mitigating this
necessary for effectively governing
phenomenon requires diligently accelerates AI adoption,
AI and generative AI applications.
managing and monitoring AI ensuring transparent
Here, banks are significantly
applications to ensure decisions are
lagging. Capgemini’s World Retail decisions, compliance,
explainable. Explainable AI ensures
Banking Report 2024 discovered
humans can quickly understand and collaborative industry
only 6% of banks have established
outputs and course correct as implementation.”
KPIs to measure generative AI
necessary.
impact and maintain continuous
Beyond explainability, it’s vital monitoring (Figure 4). Clearly, - Cormac Flanagan, Global Head
for banks to monitor and manage focusing greater attention on KPI of Product Management,
several other categories of development and implementation Temenos, Ireland7
AI-associated risks. These include is necessary for minimizing risks
biased and discriminatory outputs, and unintended consequences.
hallucinations with inaccurate
Figure 4: Banks are lagging KPIs for governing AI and generative AI
Source: Capgemini Research Institute for Financial Services Analysis, 2024; World Retail Banking Report CXO survey.
7
In conclusion
Take a holistic road mapping approach
Although each of the roadmap elements discussed here Be certain to address factors like change management,
have technology in common, developing a holistic plan upskilling, and cultural readiness as they are
also requires including human-centric aspects within often the make-or-break activities for successful
each for the four elements. transformations.8
Achieving the ultimate AI goal: autonomous banking
Although today’s fledgling AI initiatives are already To begin achieving your bank’s AI goals, start with a
generating considerable benefits, enterprise-wide comprehensive roadmap for navigating the AI journey.
AI is a steppingstone to autonomous and intelligent By embracing modern technology approaches,
banking. When banks reach this level of maturity, addressing key data hurdles, and establishing effective
they move beyond consuming AI to wielding it for governance, banks can successfully complete their
competitive advantage. intelligent transformation journeys and ultimately
leverage AI to redefine the industry’s future. It’s an
Current examples of self-driving offerings include
exciting time in banking for all.
BBVA’s “Bconomy” and Santander Bank’s partnership
with Personetics, which demonstrate how hyper-
personalized customer journeys and omnichannel
engagement can deliver true customer centricity.
8 Setting the pace for intelligent transformation
Endnotes
¹ Capgemini. “World Retail Banking Report 2024;” March 5, 2024
² Ibid.
³ Capgemini. “World Cloud Report – Financial Services 2023,” November 16, 2023
⁴ Ibid.
⁵ Capgemini. “World Retail Banking Report 2024;” March 5, 2024
⁶ Ibid.
⁷ Ibid.
⁸ Capgemini. “Unleashing confidence in AI: A playbook by Capgemini Generative AI Lab”; March, 2024
Meet our experts
Ashvin Parmar Chandramouli Venkatesan
Global Head of Financial Services Portfolio Development Lead - Digital
Insights & Data Portfolio Front Office Transformations | Banking
and Capital Markets
9
About Capgemini
Capgemini is a global business and technology transformation partner, helping organizations to accelerate
their dual transition to a digital and sustainable world, while creating tangible impact for enterprises and
society. It is a responsible and diverse group of 340,000 team members in more than 50 countries. With
its strong over 55-year heritage, Capgemini is trusted by its clients to unlock the value of technology to
address the entire breadth of their business needs. It delivers end-to-end services and solutions leveraging
strengths from strategy and design to engineering, all fueled by its market leading capabilities in AI, cloud
and data, combined with its deep industry expertise and partner ecosystem. The Group reported 2023
global revenues of €22.5 billion.
www.capgemini.com
Copyright © 2024 Capgemini. All rights reserved.
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23,capgemini,Generative-AI-in-Organizations-Refresh_25112024.pdf,"Harnessing the value of generative AI: 2nd edition Top uses cases across sectors
Harnessing the value
of generative AI
2nd edition: Top use cases across sectors
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Harnessing the value of generative AI: 2nd edition Top uses cases across sectors
Capgemini Research Institute 2024
3
Harnessing the value of generative AI: 2nd edition Top uses cases across sectors
Capgemini Research Institute 2024
evitucexE
yrammuS
4
Harnessing the value of generative AI: 2nd edition Top uses cases across sectors
Increased maturity and investment: Organizations are Moreover, generative AI adoption among employees is robust
embracing generative AI, which is reflected in an uptick in in most organizations, with the majority allowing its use. Only
investment levels. The vast majority (80%) of organizations 3% of surveyed organizations enforce a complete ban on
in our survey have increased their investment in generative public generative AI tools in the workplace.
AI from 2023, 20% have maintained their investment level,
Tangible benefits and strategic shifts: Early adopters
and not one organization decreased their investment from
of generative AI are seeing benefits in areas in which
last year. Larger enterprises lead the charge, and overall,
generative AI has been piloted or deployed, from improved
nearly one-quarter (24%) of organizations have integrated
operational efficiency to enhanced customer experience.
this technology into some or most of their operations, an
For example, on average, organizations realized a 7.8%
acceleration from 6% reported just 12 months ago. This
improvement in productivity and a 6.7% improvement
increase in generative AI adoption since 2023 spans all
in customer engagement and satisfaction, over the past
sectors. For example, in retail, implementation increased to
year. Organizations anticipate that generative AI will drive
40%, more than doubling from 17% in 2023.
adjustments to their strategic approaches and business
Pervasive integration across functions: Generative AI models. With a belief that the technology will be a key
permeates organizations, catalyzing a shift in operational driver of revenue growth and innovation, organizations are
paradigms. In the past year, there has been an increase in exploring new ways of harnessing its capabilities.
its adoption across all organizational domains, from sales
The rise of AI agents: The emergence of AI agents marks a
and marketing to IT, operations, R&D, finance, and logistics.
shift, with potential to enhance automation and productivity
across sectors, business processes, and along the entire
value chain. These AI agents have evolved from supportive
Capgemini Research Institute 2024
evitucexE
yrammuS
5
Harnessing the value of generative AI: 2nd edition Top uses cases across sectors
tools to autonomous entities capable of executing tasks Empower your generative AI journey: Organizations should:
independently. Organizations are eager to adopt AI agents,
with a strong majority (82%) intending to integrate them Establish a robust framework for data governance
within 1–3 years. There is a level of trust in AI agents for and management
specific tasks, such as generating work emails, coding, and
data analysis. However, organizations are also mindful of the Strengthen the data platform and cultivate trust to
need to establish guardrails to validate AI-made decisions, ensure reliable outcomes
ensuring transparency and accountability.
Cultivate expertise through strategic training and
talent development
Acquire understanding and expertise of the generative
AI ecosystem
Deploy a generative AI platform to manage use cases
at scale
Fortify against cybersecurity threats
Embrace emerging trends such as AI agents to boost
competitiveness and innovation.
Capgemini Research Institute 2024
6
Harnessing the value of generative AI: 2nd edition Top uses cases across sectors
This report is a part of Capgemini Research Institute’s series on Generative AI
Gen AI in organizations - annual research
Gen AI for management*
Gen AI in supply chain* Gen AI for marketing Gen AI for software Gen AI in R&D
engineering and engineering*
Gen AI and
consumers
Gen AI and Gen AI and Gen AI and
Gen AI in business operations* Gen AI in manufacturing* Gen AI in customer service* sustainability* ethics/ cybersecurity*
trust*
Data mastery*
Special edition of our premium journal Conversations for tomorrow on Gen AI*
To find out more, please go to https://www.capgemini.com/insights/research-institute/ *Upcoming reports
Capgemini Research Institute 2024
7
Harnessing the value of generative AI: 2nd edition Top uses cases across sectors
Who should This report offers an overview of the transformative The report will help business executives identify use cases
potential of generative AI for large organizations that illustrate the applications of generative AI across
across sectors such as automotive, consumer functions, including IT, sales, marketing, and product design/
read this
products, retail, financial services, telecom, energy R&D. The report draws on the comprehensive analysis of a
and utilities, aerospace and defense, high tech, survey of 1,100 leaders (director level and above) across 14
report and industrial manufacturing, pharma and healthcare, countries. Finally, it offers recommendations for business
and the public sector/government. It is the second leaders to accelerate their organizations’ generative AI
installment in an annual research series and identifies journeys.
why? shifts in trends from 2023.
Capgemini Research Institute 2024
noitcudortnI
8
Harnessing the value of generative AI: 2nd edition Top uses cases across sectors
Generative AI is rapidly transforming the way we interact with organizations are now integrating generative AI into some
technology. Machines are now capable of mimicking creative or most of their locations or functions, up from just 6% in
human thought processes and synthesizing tailored content 2023. In this year’s report, we analyze shifts in generative
of increasingly high quality. This has significant implications AI adoption and take a closer look at the investment levels
for driving innovation, operational efficiency, and growth. and benefits organizations have realized. We also turn the
spotlight on AI agents, a quickly evolving technology with
This report is the second in the Capgemini Research
potential to drive innovation.
Institute’s annual research series that examines generative
AI trends and use cases. In the first report of this series, To gauge perceptions of generative AI, we conducted a
“Harnessing the value of generative AI: Top use cases global survey of 1,100 executives at organizations with
across industries,” we explored the transformative potential annual revenue above $1 billion. We invited public-sector
of generative AI, highlighting the function- and sector- organizations and government entities with an annual
specific use cases with the greatest potential, and comparing budget of at least $50 million to participate. Organizations
adoption rates across sectors. In our 2023 research, we are headquartered in 14 countries: Australia, Canada, France,
discovered that, while still in its infancy in terms of scaled Germany, India, Italy, Japan, the Netherlands, Norway,
adoption and implementation, generative AI was on the Singapore, Spain, Sweden, the UK, and the US. Organizations
agenda in 96% of boardrooms globally. We found that nearly surveyed operate across 11 key sectors: aerospace and
60% of organizations said their leaders are strong advocates defense, automotive, consumer products, energy and
of generative AI, and only 39% were taking a “wait-and- utilities, financial services, high tech, pharma and healthcare,
watch” approach. industrial manufacturing, retail, telecom, and the public
sector/government. For more details on the survey sample,
This year’s research highlights a quickening of the pace
please refer to the research methodology.
of implementation. Notably, nearly one-quarter (24%) of
Capgemini Research Institute 2024
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Harnessing the value of generative AI: 2nd edition Top uses cases across sectors
This report comprises five sections:
1 2 3 4 5
Organizations are Generative AI Generative AI is AI agents: The new How organizations
deploying generative is pervading already driving technology frontier can accelerate
AI at pace organizations benefits their generative AI
journeys
Capgemini Research Institute 2024
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Harnessing the value of generative AI: 2nd edition Top uses cases across sectors
01
Organizations are deploying
generative AI at pace
Capgemini Research Institute 2024
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Harnessing the value of generative AI: 2nd edition Top uses cases across sectors
Investment in generative AI Dave Chen, Head of Global Technology Investment Banking
at Morgan Stanley, says:“Though cost savings and operational
is increasing efficiencies remain a priority for large enterprises, they are also
showing a willingness to spend, especially on generative AI and
traditional AI hardware and software that may [in the medium
According to our research, 80% of organizations have
to long term] help reduce costs and increase productivity and
increased their investment in generative AI from last year.
revenue.” 1 Recently, The Coca-Cola Company committed
Remarkably, not a single organization decreased their
$1.1 billion over a five-year period to accelerate cloud and
investment, while the remaining 20% maintained the same
generative AI initiatives.2
investment level. This trend echoes across all sectors and
organization sizes in the research. For example:
• In aerospace and defense, almost nine in 10 organizations
have (88%) boosted their investment in generative AI;
80%
• Within retail, the lowest proportion among all sectors in
our survey, 66% have invested in generative AI;
• 73% of organizations with $1–5 billion in annual
of organizations have increased their revenue have increased their investment, and 89% of
investment in generative AI from last year organizations with over $20 billion in revenue have
done so.
Capgemini Research Institute 2024
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Harnessing the value of generative AI: 2nd edition Top uses cases across sectors
In the past year, Figure 1.
More organizations have increased their maturity in generative AI
implementation of
generative AI has
% of organizations who agree with the statement on generative AI maturity
accelerated
Across our total survey sample of 1,100 organizations, 2023
only 6% of organizations are yet to begin exploring 53% 41% 6%
generative AI. Out of those who have at least begun to do
so (n=1,031), nearly one-quarter (24%) are now integrating
this technology into some or most of their locations or
functions. This marks an increase from just 6% reported last 2024
year, indicating widespread recognition of the benefits (see 27% 49% 18% 6%
Figure 1).
Only 10% of smaller organizations in our research, We have started exploring the We have begun working on some
with annual revenue of $1–5 billion, have implemented potential of generative AI pilots of generative AI initiatives
generative AI across some or most of their locations We have enabled generative AI capabilities in
We have enabled generative AI capabilities
and functions. In contrast, 49% of organizations with most/all of our functions/locations
in some of our functions/locations
annual revenue surpassing $20 billion have implemented
the technology.
Source: Capgemini Research Institute, Generative AI executive survey, April 2023, N = 800 organizations; Generative AI
Jack Forestell, Chief Product and Strategy Officer at Visa,
executive survey, May–June 2024, N = 940 organizations that are at least exploring generative AI capabilities.
says: “While much of generative AI so far has been focused on
*In the 2024 data points respondents from India and the public sector/government are excluded as they were not
tasks and content creation, this technology will soon not only
included in the 2023 research.
reshape how we live and work, but it will also meaningfully
change commerce in ways we have yet to fully understand.” 3
Capgemini Research Institute 2024
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Harnessing the value of generative AI: 2nd edition Top uses cases across sectors
All sectors have progressed Generative AI integration has increased across all sectors (see Figure 2). For a detailed listing of use cases by sector and
since 2023. For example, 40% of organizations in the retail associated implementation data, please refer to Appendix.
in their implementation of sector have implemented generative AI across some or most
functions/locations, more than doubling from 17% in 2023
generative AI
Figure 2.
Over the past year, there has been an increase in the maturity of generative AI across sectors
% of organizations who agree with the statement on generative AI maturity, by sector
4% 5% 4% 6% 3% 3% 4% 2% 3% 2% 2%
6% 16 8% % 17% 22% 45% 32% 35% 26% 28% 17 9% % 18% 19 5% % 21% 33% 1 10 4% % 1 11 1% % 7% 20% 11% 10% We have started exploring the
41% 18% 47% 49% 37% 44% potential of generative AI
48%
24% 40% 51% 48% 68% 68% We have begun working on some
49% 30% 53% 68% 54% 53% pilots of generative AI initiatives
53% 49% 55% 45% 61% 68% 47% 64% 47% 53% 53% W cae p ah ba iv lie ti ee sn a inb l se od m g ee on fe ora ut rive AI
27% 34% 11% 39% 21% 14% 22% 24% 35% 27% 30% 19% 20% functions/locations
We have enabled generative AI
capabilities in most/all of our
2023 2024 2023 2024 2023 2024 2023 2024 2023 2024 2023 2024 2023 2024 2023 2024 2023 2024 2023 2024 2023 2024 2024*
functions/locations
Average Retail Industrial Consumer Automotive High tech Financial Energy and Telecom Pharma and Aerospace Public
manufacturing products services utilities healthcare and defense sector
Source: Capgemini Research Institute, Generative AI executive survey, April 2023, N = 800 organizations; Generative AI executive survey, May–June 2024, N = 1,031 organizations that are at least exploring
generative AI capabilities; N varies per sector use case ranging from 50 to 189. *Respondents from the public sector were not included in the 2023 research. **Excluded from Figure 2 is the percentage of
organizations that have started exploring the potential of generative AI.
Capgemini Research Institute 2024
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Harnessing the value of generative AI: 2nd edition Top uses cases across sectors
David M. Reese, Executive Vice-president and Chief Highlighting the gradual scaling of generative AI in financial
Technology Officer at Amgen, a US biopharmaceutical services, Asim Tewary, former Chief AI Officer, PayPal,
company, says: ""Generative AI is transforming drug discovery comments: ""You have to be able to explain why certain decisions
by allowing us to build sophisticated models and seamlessly were made — why a credit limit was set at that amount, for
integrate AI into the antibody design process.'' 4 Natarajan example. There’s an absoluteness that’s expected from regulators
Chandrasekaran, Chairperson of Tata Group, states: “In about being able to explain how the decision was made. Anytime
e-commerce, generative AI is being used to generate product you impact the consumer or introduce a system risk, regulators
catalogs, deliver conversational shopping experiences, and get very concerned.” Also concerning to the financial sector is
provide personalized offers.” 5 Michael Smith, Chief Information the increased sophistication of deepfakes which can deceive
Officer at The Estée Lauder Companies, says: “The generative customers into transferring funds to seemingly legitimate
AI chatbot developed with [a partner] helps us quickly find accounts, whether virtual or human agent mimicked. This
products that address concerns relevant to specific regions and underscores the imperative of ensuring both accuracy in
emerging markets. We will continue to explore other ways we customer-facing AI products and robust security measures in
can harness the wealth of data across products, ingredients, and customer interactions.7
more, in tandem with the power of generative AI.” 6
Capgemini Research Institute 2024
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Harnessing the value of generative AI: 2nd edition Top uses cases across sectors
Investment in generative AI
Figure 3.
increases with organization size
Investment in generative AI trends upward with organization size
On average, organizations surveyed have $20 billion believe that failing to adopt generative
allocated around $110 million to generative AI AI will place them at a considerable disadvantage
for the current fiscal year. Thirty-four percent of relative to their competitors. This sentiment is lower Average investment dedicated to generative AI,
organizations in our survey have allocated $50 among organizations with annual revenues under $5 by annual revenue, 2024
million or less to generative AI, while 11% have billion (56%)8.
allocated $250 million or more.
Our average investment of $110 million is higher than $157.7
The amount of generative AI investment increases some recently published analyst reports and likely
$131.5
with company size. For example, on average, reflects the broad categories of enterprise spend
$109.8
organizations with annual revenue of $1–5 billion including hardware, software, licensing, training, $99.5
allocate $85 million and those with more than $20 among others. According to Gartner, software $85.2
billion allocate $158 million (see Figure 3). development is the function with the highest rate
of investment in generative AI, followed closely by
Given that the amount of investment allocated
marketing and customer service.8 Additionally, a
to generative AI increases with organization size,
significant majority of organizations are investing in
larger organizations widely agree that generative
partnerships with external providers of generative
AI is more than just a passing trend, serving as a
AI applications. According to our research, 70%
pivotal cornerstone in their enterprise evolution. Average $1 bn– $5 bn– $10 bn– More than
Large organizations are increasingly embracing of organizations are exclusively using external $4.9 bn $9.9 bn $19.9 bn $20 bn
applications or a combination of external and
the transformative potential of generative AI
in-house solutions. Commonly used tools include
technology more fervently than perhaps other Source: Capgemini Research Institute, AI executive survey, May–June
OpenAI’s ChatGPT, GitHub Copilot, Scribe, Microsoft
recent technological advancement. A striking 71% 2024, N = 981 organizations who are at least exploring generative AI
Copilot, and AWS Gen AI.
of organizations with annual revenues exceeding capabilities, excluding the public sector.
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Harnessing the value of generative AI: 2nd edition Top uses cases across sectors
02
Generative AI is pervading
organizations
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Harnessing the value of generative AI: 2nd edition Top uses cases across sectors
Generative AI makes Figure 4.
Over the past year, adoption of generative AI has grown across functions
inroads across functions
% of organizations implementing generative AI use cases, by function
Our research indicates an increase in generative AI
adoption across all facets of organizations over the past 12
4%
months. Examining individual functions, in the IT domain IT 27%
for example, the adoption rate has increased to 27% from Risk management 4%
4% the previous year (see Figure 4). Hitachi successfully 26%
integrated generative AI tools by leveraging its detailed Logistics 2%
26%
system-design knowledge with Microsoft’s AI services,
Sales/customer operations 4%
achieving a 70%–90% success rate in generating application 25%
source code.9 For a detailed listing of use cases by function Finance 5%
and associated implementation data, please refer to 25%
the Appendix. Human resources* 24%
ESG/sustainability* 22%
3%
Product design/R&D
19%
2%
Marketing
19%
2023 2024
Source: Capgemini Research Institute, Generative AI executive survey, April 2023, N = 800 organizations; Generative AI executive survey,
May–June 2024, N = 1,031 organizations that are at least exploring generative AI capabilities; N varies per functional use case, ranging
from 499 to 716.
*ESG/sustainability and human resources were excluded from the 2023 research.
** “Implementation” refers to organizations that have partially scaled the functional use case in question.
***In the 2024 averages, respondents from the public sector and India are excluded, as they were not included in the 2023 research.
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Harnessing the value of generative AI: 2nd edition Top uses cases across sectors
Generative AI is used by Figure 5.
97% of organizations allow employees to use generative AI in some capacity
employees in most
organizations
% of organizations who agree with the statements
The majority of organizations allow employees to use
generative AI in some capacity. Over half of organizations We allow all our employees to use generative AI tools but have set up
54%
(54%) require employees to follow specific guidelines when guardrails/principles
using these tools, rather than imposing a complete ban.
Three percent of surveyed organizations report a ban on We allow only a carefully chosen group of skilled employees, primarily
36%
public generative AI tools in the workplace. However, 7% of those in specialized/technical roles, to use generative AI tools
organizations permit unrestricted use of such tools, which
may pose future risks to the organization (see Figure 5). We allow all our employees to use generative AI tools at will 7%
We have banned all our employees from using public generative AI
3%
tools in the workplace
2024
Source: Capgemini Research Institute, Generative AI executive survey, May–June 2024, N = 1,031 organizations that are at least exploring
generative AI capabilities.
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Harnessing the value of generative AI: 2nd edition Top uses cases across sectors
Despite the implementation of guardrails and policies to workplace. Amazon has prohibited employees from using In the table that follows, we highlight recent
regulate the use of generative AI, unauthorized usage among third-party generative AI tools, such as OpenAI's ChatGPT, generative AI use cases across sectors and functions:
employees is still relatively common. Among the 39% of particularly for handling confidential data. This policy is
organizations with a ban or limitation policy, half of them intended to prevent data-ownership issues and protect
say there is still unauthorized usage of generative AI in the sensitive company information.10
Sector Company Function Example
Has transformed its operations and innovation processes with generative AI. AI assistants
Aerospace Airbus Manufacturing
provide aircraft manufacturing instructions, enhancing accessibility to technical data, and
facilitating precise task guidance.11
Uses generative AI to incorporate engineering constraints into vehicle design, and
Automotive Toyota Product design/R&D
optimizing metrics such as aerodynamic drag, enhancing efficiency of electric-vehicle
(EV) design.12
Leverages generative AI in its “Hey Mercedes” feature, which is in beta with 900,000
Automotive Mercedes-Benz Customer experience/
users. It offers personalized, screen-free interactions, enhancing driving experience with
service
dynamic adjustments and real-time safety support.13
Consumer products General Mills Customer experience/ Launched MillsChat, a generative AI tool, to streamline customer service. This enhances
service efficiency, provides personalized assistance, and encourages customer engagement.14
Harnesses generative AI to analyze customer feedback, which it uses to refine shape design
Marketing and
Consumer products PepsiCo and flavor of its Cheetos branded snack, boosting market penetration by 15%. This strategy
branding
has also shortened product launch cycles and increased profitability.15
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Harnessing the value of generative AI: 2nd edition Top uses cases across sectors
Sector Company Function Example
Uses generative AI to streamline legal processes, including research, drafting, and
Consumer product Unilever Legal
contract reviews. This allows legal teams to focus on strategic tasks, enhancing
operational effectiveness.16
Energy British Human resources – Leverages generative AI to assist employees with daily tasks such as email management.
Enhances employee productivity and transforms business workflows.17
Petroleum (BP) employee productivity
Used GPT-4 to create an AI tool for financial advisors, allowing rapid access to internal
Financial services Morgan Stanley Customer
research. This enhances advisor efficiency and client service, simulating top investment
experience/service
experts on call.18
Uses generative AI to reduce carbon footprint of commercial buildings. Uses historical data
High tech BrainBox AI ESG/sustainability
to predict interior building temperatures, cutting heat, ventilation and air-conditioning
(HVAC) energy costs by up to 25% and greenhouse gas (GHG) emissions by 40%.19
Added generative AI into FactoryTalk Design Studio to help engineers generate code using
Industrial manufacturing Rockwell Automation IT – coding/software natural language prompts, automating routine tasks and improving design efficiency. It will
development also empower experienced engineers to accelerate development and mentor newcomers
more effectively.20
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Harnessing the value of generative AI: 2nd edition Top uses cases across sectors
Sector Company Function Example
Integrated generative AI into Finance Advisor, a conversational assistant for financial
Industrial manufacturing Schneider Electric Finance and accounting
analysts in global finance, ensuring precise, compliant, and timely decision-making across
accounting and related functions.21
Logistics UPS Marketing Developed the Message Response Automation (MeRA) system in-house using publicly
available large language models (LLMs), to automate routine customer interactions,
reducing email handling times by 50% and allowing human agents to focus on more
complex issues. It also streamlines operations and improves customer satisfaction by
ensuring prompt, accurate responses.22
Pharmaceutical Insilico Product design/R&D Identified a new drug candidate, MYT1, using its generative AI platform in each step of its
and biotech Medicine preclinical drug-discovery process, offering more effective, safer treatments for breast and
gynecological cancers.23
Pharmaceutical Moderna Research and Uses generative AI tools, including the company's Dose ID GPT, which uses ChatGPT
Enterprise's Advanced Data Analytics feature to further evaluate the optimal vaccine dose
and biotech development
selected by the clinical study team.24
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Harnessing the value of generative AI: 2nd edition Top uses cases across sectors
Sector Company Function Example
Plans to use generative AI to analyze traffic data to improve road safety, reduce
Public sector California Department of Citizen services
call-center wait times, aid non-English speakers, and streamline healthcare-facility
Transportation and
inspections.25
Department of Tax and
Fee Administration
Public sector State of Pennsylvania Employee operations Runs a generative AI pilot program for state employees, integrating the technology into
government operations. Supports crafting/editing copy, updating policy language,
drafting job descriptions, and generating code.26
Retail ASOS Sales Uses generative AI to make fashion recommendations, customer-service interactions, and
trend analysis, enhancing user engagement, personalizing customer experiences, and
optimizing retail strategies.27
Uses generative AI to reduce food waste by helping employees make quick decisions.
Retail Walmart ESG/sustainability
Employees scan a product such as produce or apparel, and a digital dashboard makes
suggestions on what to do with the product based on its characteristics (e.g., ripeness,
whether its seasonal). Suggested actions could include a price change, putting it on sale,
sending the item back, or donating it.28
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Harnessing the value of generative AI: 2nd edition Top uses cases across sectors
Sector Company Function Example
Uses generative AI, in its Seoul, South Korea, store to develop innovative ice-cream
Retail Baskin Robbins Product design/R&D
flavors. Supported the introduction of a monthly exclusive flavor, and personalized
experience offered through an ice-cream docent program.29
Telecom AT&T Employee productivity Employs generative AI capabilities in its Ask AT&T tool to support employees by
enhancing productivity and creativity, translating documents, optimizing networks, and
summarizing meetings. This leads to improved efficiency and greater innovation.30
Voxi by Vodafone launched a generative AI self-service experience, which enhances
Telecom Vodafone Customer
interactions, provides personalized assistance, and optimizes customer support services,
experience/service
fostering satisfaction and engagement.31
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Harnessing the value of generative AI: 2nd edition Top uses cases across sectors
Organizations must
Figure 6.
carefully scale generative 36% of organizations are currently tracking carbon emissions from generative AI use
AI initiatives with a focus
% of organizations currently tracking and measuring the
on environmental
below metrics in the use of generative AI
sustainability
30% 29%
Organizations must assess technological 36%
advancements in generative AI alongside their
environmental consequences. They should
Yes
evaluate the business value, considering
implementation complexity and costs, while No
also scrutinizing environmental impacts such 54% 57% 62% Unsure/ don’t know
as GHG emissions, electricity usage, and water
consumption. Our research indicates that, roughly
a third of organizations are currently monitoring
energy and water consumption, as well as carbon 10% 13% 9%
emissions, associated with their generative AI
Carbon Energy Water
initiatives (see Figure 6).
emission utilization consumption
Source: Capgemini Research Institute, Generative AI executive survey, May–June
2024, N = 1,031 organizations that are at least exploring generative AI capabilities.
Capgemini Research Institute 2024
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Harnessing the value of generative AI: 2nd edition Top uses cases across sectors
Furthermore, our research explored the
Figure 7.
measures organizations are implementing
to mitigate the environmental impact Half of organizations are currently developing guidelines for generative AI use
of generative AI. Slightly over half of
organizations (54%) are developing guidelines
for responsible use of generative AI, and % of organizations currently trying to mitigate the carbon footprint from using generative AI
47% are transitioning to more energy-
efficient hardware. However, the proportion
of organizations undertaking additional
Developing guidelines for responsible use 54%
actions such as investing in renewable energy,
offsetting emissions through carbon credits,
and optimizing training algorithms remains Using more energy-efficient hardware 47%
low (see Figure 7).
Offsetting through carbon credits 37%
Investing in renewable energy 36%
Only a third of organizations are
currently monitoring energy and water
Optimizing our training algorithms 31%
consumption, as well as carbon emissions,
associated with their generative AI
initiatives.
Source: Capgemini Research Institute, Generative AI executive survey, May–June 2024, N = 1,031 organizations that are at least exploring
generative AI capabilities.
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Harnessing the value of generative AI: 2nd edition Top uses cases across sectors
03
Generative AI is already
driving benefits
Capgemini Research Institute 2024
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Harnessing the value of generative AI: 2nd edition Top uses cases across sectors
Organizations have Figure 8.
Generative AI yielded benefits in the past year in the areas in which the technology has been piloted or deployed
achieved benefits
Average benefits realized from generative AI within the past year
Our current research evaluates the benefits that generative
AI has brought at organizational level in the past year in the
areas in which generative AI has been piloted or deployed. 7.8%
For example, on average, organizations realized a 7.8%
6.7%
improvement in productivity and a 6.7% improvement in
5.4%
customer engagement and satisfaction over the past year
4.4%
(see Figure 8).
3.6%
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HARNESSING THE VALUE OF GENERATIVE AI: TOP USE CASES ACROSS INDUSTRIES
Generative artificial intelligence (AI) is rapidly becoming restructuring of business models. Globally, about one-
an integral part of our private and professional lives. fifth (21 percent) of executives surveyed say generative
While the technology has been in existence for a number AI will significantly disrupt their industries. Executives in
of years, it has gained widespread consumer interest high tech and industrial manufacturing – two industries
only recently and has emerged as a critical strategic that have long been shaped by AI technologies and have
consideration. Our latest industry-focused research been at the forefront of generative AI – are most likely to
reveals that generative AI is on the boardroom agenda agree with this statement.
at 96 percent of organizations surveyed globally. While
The potential of generative AI to drive innovation and
generative AI is in its infancy in terms of scaled adoption
improve efficiency and productivity extends to nearly all
and implementation, nearly 60 percent of executives
functions and has applications across all industries. Use
globally say their leadership is a strong advocate for
cases are wide-ranging, from creating unique content
generative AI and only 39 percent are taking a “wait-and-
and automating and accelerating tasks, to creating
watch” approach to adoption.
personalized experiences and generating synthetic data.
Many organizations already see generative AI as a Our research reveals that generative AI has the greatest
powerful tool that can accelerate growth, enhance potential within IT, sales and customer service, and
capabilities, and unlock new opportunities without drastic marketing functions. The high tech sector leads the way,
Capgemini Research Institute 2023
with the greatest share of ongoing generative AI pilots. adverse impact of generative AI on the environment.
Executives in our survey also project efficiencies from Nevertheless, the net impact of generative AI on an
generative AI in the next three years in the range of 7–9 organization’s Scope 1, 2, and/or 3 emissions is currently
percent. difficult to forecast.
As with any new technology, generative AI is not without We conclude this report with a look at how organizations
risk. However, with proper planning and guardrails in can start and/or accelerate their generative AI journeys.
place, there is potential to transform business operations, First, they must create a robust generative AI strategic
product and service development, and customer and operational architecture. Organizations must also
interaction. Nearly three-quarters of executives in our establish internal and external guidelines around the
survey (74 percent) agree that the benefits of generative use of generative AI, adopt a human-centric approach
AI outweigh the risks. Given the carbon-intensive nature to scaling the technology, and build user and consumer
of training new generative AI models, it will also be trust in the AI system. Given the high carbon emissions
important to weigh environmental considerations. The associated with generative AI trainings and queries,
good news is that executives in our survey are aware sustainable development and use of the technology
of this dynamic and understand how to mitigate the should also be a high priority.
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HARNESSING THE VALUE OF GENERATIVE AI: TOP USE CASES ACROSS INDUSTRIES
Capgemini Research Institute 2023
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HARNESSING THE VALUE OF GENERATIVE AI: TOP USE CASES ACROSS INDUSTRIES
Generative artificial intelligence (generative AI) is rapidly innovating of organizations; highlighting the function-
transforming the way we interact with technology. and industry-specific use cases we believe to have the
Machines are beginning to mimic creative human thought greatest potential; and comparing adoption rates across
processes, synthesizing tailored content with significant industries.
implications for organizations and consumers.
To gauge executives’ perceptions of generative AI and
This report is the second in a series of reports we adoption of use cases, we conducted a global survey of
have created around this topic. Our first report, Why 1,000 organizations across Australia, Canada, France,
consumers love generative AI, explored consumer Germany, Italy, Japan, the Netherlands, Norway,
perceptions of generative AI; consumer use of generative Singapore, Spain, Sweden, the UK, and the US. We
AI; and how the technology is shaping the future of questioned executives from multiple industries, including
customer experience. automotive, consumer products, retail, financial services,
telecom, energy and utilities, aerospace and defense,
In this report, we delve into the transformative potential
high tech, industrial manufacturing, and pharma and
of generative AI for organizations across industries,
healthcare. For more details on the survey sample, please
asking how the technology could kick-start the
refer to the research methodology.
Capgemini Research Institute 2023
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HARNESSING THE VALUE OF GENERATIVE AI: TOP USE CASES ACROSS INDUSTRIES
The report explores five broad themes:
1 2 3 4 5
Organizations view Organizations see Generative AI High tech leads How organizations
generative AI not as more gain than pain packs the most in implementing can kick-start their
a disruptor but as an in generative AI punch for IT, sales, generative AI generative AI
accelerator and marketing journeys
Capgemini Research Institute 2023
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HARNESSING THE VALUE OF GENERATIVE AI: TOP USE CASES ACROSS INDUSTRIES
Who should read this
report and why?
This report offers a comprehensive introduction to the transformative
impact that Generative AI holds for large businesses in industries such
as automotive, consumer products, retail, financial services, telecom,
energy and utilities, aerospace and defense, high tech, industrial
manufacturing, and pharma and healthcare. The report will help
business executives identify use cases that will illustrate the pragmatic
applications of Generative AI in IT, sales, and marketing, for example.
The report leverages a comprehensive analysis of 1,000 industry leaders
(ranked Director and above) across 13 countries, each at varying stages
of Generative AI implementation. The report also offers actionable
recommendations for business leaders to kick-start their organizations’
generative AI journeys. This report is the second in our series of reports
on Generative AI. Read the first report focusing on consumer reactions to
Generative AI at
https://www.capgemini.com/insights/research-library/
creative-and-generative-ai/
Capgemini Research Institute 2023
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HARNESSING THE VALUE OF GENERATIVE AI: TOP USE CASES ACROSS INDUSTRIES
Fig.1
Generative AI applications across multiple formats
Selected generative AI applications Indicative examples
Defining Text Summarizing and translating into multiple OpenAI’s GPT-4, Jasper1
languages
generative AI
Images and video Analyzing existing images/video to generate new Adobe Firefly,2 Stable
generation content (e.g., video games, VR, animation) Diffusion, Midjourney
Generative AI has the capability to learn and reapply the
Music generation and remixing, speech synthesis, Sonix.ai (a cloud-based audio
properties and patterns of data for a wide range of
Audio sound effects, voice conversion, audio and video-transcription
applications, from creating text, images, and videos in enhancement solution)3
different styles to generating tailored content. It enables
machines to perform creative tasks previously thought
exclusive to humans. The following table summarizes the Chatbots to provide automated customer service Google Bard,4 OpenAI’s
Chatbots
top generative AI applications reported in our research and and advice ChatGPT
gives some indicative examples.
Enhanced search functions using natural
Search Perplexity AI5
language processing and machine learning
Source: 1. TechTarget, “What is generative AI? Everything you need to know,” March 5, 2023.
2. Adobe, ""Adobe unveils Firefly, a family of new creative generative AI,"" March 21, 2023.
3. Sonix.ai, ""Sonix releases the world’s first automated transcription and generative AI summarization tool,"" December 14, 2022.
4. Cointelegraph, ""What is Google’s Bard, and how does it work?"" May 10, 2023.
5. Kevin-Indig, ""Early attempts at integrating AI in Search,"" January 10, 2023.
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01
ORGANIZATIONS VIEW GENERATIVE
AI NOT AS A DISRUPTOR, BUT AS AN
ACCELERATOR
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Fig.2
Generative AI is a topic for boardroom discussion at nearly all organizations
PERCENTAGE OF ORGANIZATIONS WHO AGREE WITH THE
Generative AI is a top STATEMENT BY SECTOR
agenda item in boardrooms
96% 100% 98% 98% 97% 96% 95% 94% 93% 93% 93%
Nearly all executives (96 percent) in our survey cite
generative AI as a hot topic of discussion in their
respective boardrooms (see Figure 2), making it probably
the fastest new technology to garner such high-level
interest. Pat Geraghty, CEO of GuideWell, a US-based
mutual insurance organization, comments: “Every single
board meeting we’ve had this year has had a standing
agenda item of AI and ChatGPT. We want to make sure we’ve
got our board with us as we’re thinking about where we’re
going.” 1 Average Financial Industrial Energy Aerospace Consumer
services manufacturing and utilities and defense products
High tech Pharma Telecom Automotive Retail
and healthcare
Generative AI is a topic of discussion in our boardroom
Source: Capgemini Research Institute, Generative AI Executive Survey, April 2023, N = 800 organizations.
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Fig.3
The leaders of most organizations are strong advocates of generative AI
PERCENTAGE OF ORGANIZATIONS WHO AGREE WITH THE
STATEMENT BY SECTOR
2% 1% 1% 1% 1% 4% 2% 5% 3% Top leaders are strong
16%
32% advocates for generative AI
39% 39% 39% 41% 37% 39%
57% 45% 50%
Among the 96% of organizations that discuss generative
AI in their boardrooms, over half (59 percent) of
84%
executives say their leadership are strong advocates for
59% 67% 60% 60% 59% 59% 58% generative AI only six months after the technology hit
51%
43% 57% the mainstream. This rises to 84 percent in the high-tech
sector. Thirty-nine percent of executives say their leaders
are taking a “wait-and-watch” approach to the technology
Average Aerospace Telecom Retail Industrial Consumer
and defense manufacturing products and only 2 percent of executives globally say their leaders
High tech Financial Energy Pharma Automotive are not convinced or are divided by the potential of
services and utilities and healthcare generative AI (see Figure 3).
Our leadership is a strong advocate of generative AI
Our leadership is taking a ""wait-and-watch"" approach to generative AI
Our leadership is not convinced of / is divided on the potential of generative AI
Source: Capgemini Research Institute, Generative AI Executive Survey, April 2023, N = 766 organizations that cite generative AI
as a topic of discussion in their respective boardrooms.
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96%
organizations say generative AI
is a topic of discussion in their
boardrooms
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Fig.4
About one-fifth of organizations anticipate significant disruption from generative AI
PERCENTAGE OF ORGANIZATIONS WHO AGREE WITH THE
Most organizations do not
STATEMENT BY SECTOR
view generative AI as a
52%
48% disruptor
While twenty-one percent of executives anticipate a
significant disruption in their respective industries from
21% 21% 21% 20% 19% 18% 18% generative AI, 67 percent of executives disagree. That
12% is, a majority of the executives do not see generative
AI significantly disrupting their business models.
3%
However, executives within the high-tech and industrial
Average Industrial Pharma Consumer Aerospace Retail manufacturing sectors expect significant disruption at
manufacturing and healthcare products and defense 52 percent and 48 percent, respectively (see Figure 4).
While this may be reflective of these executives’ superior
High tech Energy Financial Automotive Telecom
and utilities services understanding of the technology’s potential, the figures
underline the widespread expectation that generative AI
will boost business overall.
Generative AI can significantly disrupt our industry
Source: Capgemini Research Institute, Generative AI Executive Survey, April 2023, N = 800 organizations.
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Fig.5
Relevance of generative AI platforms for organizations across different domains
PERCENTAGE OF ORGANIZATIONS THAT FIND GENERATIVE AI
Organizations are finding
PLATFORMS RELEVANT TO THEIR BUSINESS
generative AI platforms
Chatbots (automating customer service and improving
83%
knowledge management: e.g., ChatGPT)
increasingly relevant
Data (designing, collecting, or summarizing data:
75%
e.g., Jasper’s Text Summarizer)
Chatbots emerge as the most relevant generative AI Text (summarizing, automating, or translating content: e.g., ChatGPT) 71%
application, with 83 percent of organizations citing it.
Organizations can use generative-AI-driven chatbots Search (AI-powered insights: e.g., Bing) 70%
to improve their customer service and also to enable
Generating synthetic data (Capgemini's Artificial Data Amplifier) 61%
improved internal knowledge management. Seventy-five
percent of executives say that data applications can be ML platforms (applications of machine learning: e.g., Slai) 54%
used effectively in their organizations, and 71 percent
Code (testing and coding assistant e.g.,GitHub Copilot, converting code
believe this to be true of text-generating platforms such 50%
from one language to another e.g., Codex, Capgemini's A2B Translator)
as ChatGPT (see Figure 5).
Images (generating images: e.g., DALL-E) 48%
Audio (summarizing, generating or converting text in audio: e.g., Sonix) 34%
Video (generating or editing videos: e.g., Pictory, Synthesys) 26%
Gaming (generative AI gaming studios or applications: e.g., Ludo AI) 4%
Source: Capgemini Research Institute, Generative AI Executive Survey, April 2023, N = 800 organizations.
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HARNESSING THE VALUE OF GENERATIVE AI: TOP USE CASES ACROSS INDUSTRIES
US financial services organization Morgan Stanley's
vast library of investment strategies, market research,
and analyst insights can be time-consuming and
cumbersome for wealth-management advisors to
sift through. To address this, Morgan Stanley is using
GPT-4 to power an internal chatbot that provides
instant access to any area of the archive. Jeff McMillan,
Head of Analytics, Data, and Innovation, adds: “You
essentially have [access to] the knowledge of the most
knowledgeable person in wealth management – instantly.
Think of it as having our chief investment strategist, chief
global economist, global equities strategist, and every other
analyst around the globe on call for every advisor, every
day. We believe that is a transformative capability for our
company.”2
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HARNESSING THE VALUE OF GENERATIVE AI: TOP USE CASES ACROSS INDUSTRIES
“You essentially have [access to] the knowledge of the
most knowledgeable person in wealth management
– instantly. Think of it as having our chief investment
strategist, chief global economist, global equities
strategist, and every other analyst around the globe
on call for every advisor, every day. We believe that is a
transformative capability for our company.”
Jeff McMillan
Head of Analytics,
Data, and Innovation,
Morgan Stanley.
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The impact of Fig.6
Most executives agree that generative AI will augment knowledge work
generative AI on
the workforce
PERCENTAGE OF ORGANIZATIONS THAT AGREE WITH THE STATEMENTS
Generative AI has the potential to Generative AI will augment the roles of knowledge
70%
workers and reduce their workloads
transform work
Our consumer research on generative AI reveals that
most consumers (70 percent) believe it will make As generative AI algorithms begin to provide concepts
them more efficient at work and will free them from and initial designs, employees may shift from 69%
routine tasks to explore more strategic aspects of their traditional ideation and creation to review and refinement
jobs.3 Most executives concur with these consumer
sentiments, with 70 percent agreeing the technology
will allow organizations to widen the scope of the
roles of knowledge workers (see Figure 6). Over half
Generative AI will completely revolutionize the way we work 60%
(60 percent) also mentioned that generative AI would
completely revolutionize their way of working.
Source: Capgemini Research Institute, Generative AI Executive Survey, April 2023, N = 800 organizations.
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HARNESSING THE VALUE OF GENERATIVE AI: TOP USE CASES ACROSS INDUSTRIES
The rise of generative AI will create new roles 60%
In our current survey, 69 percent of executives believe generative AI will lead to the emergence of new roles. In
addition to prompt engineers, we may also see new roles such as AI auditors and AI ethicists emerge as the
believe that generative AI will completely
initiatives scale.
revolutionize the way we work
Fig.7
Most executives believe new roles will emerge from generative AI
Generative AI will demand upskilling
and training initiatives
PERCENTAGE OF ORGANIZATIONS WHO AGREE WITH THE STATEMENT
As well as opening up new job roles and requirements,
as 68 percent of executives in our survey say will happen,
the integration of generative AI into the workforce will
require a significant investment in upskilling and cross-
skilling of talent. In April 2023, in response to growing
demand, Coursera, a large-scale US-based open online
69% Generative AI will lead to the course provider, launched multiple new generative AI
emergence of new job roles (e.g., prompt
training courses, including ChatGPT Teach-Out from the
engineer)
University of Michigan, which introduces learners to large
language models (LLMs) and chatbots and discusses
the ethical use of generative AI and how the technology
might be harnessed and regulated moving forward.4
Source: Capgemini Research Institute, Generative AI Executive Survey, April 2023, N=800 organizations.
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HARNESSING THE VALUE OF GENERATIVE AI: TOP USE CASES ACROSS INDUSTRIES
02
ORGANIZATIONS SEE MORE GAIN
THAN PAIN IN GENERATIVE AI
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Fig.8
Most executives across industries say that the benefits of generative AI outweigh potential risks
Most organizations believe
PERCENTAGE OF ORGANIZATIONS WHO AGREE WITH THE STATEMENT BY SECTOR
the benefits of generative AI
outweigh the risks Average 74%
High tech 84%
Our survey reveals that the majority of executives (74
percent) believe the benefits that generative AI brings Aerospace and defense 82%
outweigh the associated risks. The executives most
strongly convinced that generative AI is a power for Pharma and healthcare 80%
good work within the high-tech sector (84 percent); even
Industrial manufacturing 77%
at the other end of the list, a substantial 69 percent of
executives within the energy and utilities and telecom Retail 76%
sectors would bet on generative AI (see Figure 8).
Financial services 74%
Consumer products 70%
74% Energy and utilities 69%
Telecom 69%
believe the benefits of generative AI Automotive 66%
outweigh the associated risks
The benefits of utilizing generative AI outweigh the associated risks
Capgemini Research Institute, Generative AI Executive Survey, April 2023, N = 800 organizations.
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Fig.9
Most executives say that generative AI will improve products/services and customer service
PERCENTAGE OF ORGANIZATIONS WHO AGREE WITH THE STATEMENTS
Anticipated benefits of
generative AI extend to
Generative AI will allow the design process to be
78% product design and
more efficient and streamlined
customer experience
Generative AI can enable us to create products and services
that are more accessible and inclusive, serving a wider 76%
range of customers with diverse needs and preferences Generative AI brings numerous transformative benefits
to organizations, including enhanced decision-making,
improved efficiency, personalized experiences, cost
Generative AI can enable us to create more
71% reductions, augmented innovation capacity, risk
interactive and engaging experiences for our customers
management, and predictive analytics. Most executives
in our survey (78 percent) believe that generative AI will
make product and service design more efficient and
Generative AI can be used to improve customer
service by providing automated and personalized support 67% that it will help them design more inclusive, accessible
products and services (76 percent). Seven in ten
executives believe generative AI will help them improve
Generative AI can improve internal operations the customer experience (see Figure 9).
65%
and enhance facility maintenance
Source: Capgemini Research Institute, Generative AI Executive Survey, April 2023, N = 800 organizations.
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Fig.10
Generative AI implementation is expected to yield operational and customer benefits
Executives are positive but EXPECTED FUTURE BENEFITS OF GENERATIVE AI IN THREE YEARS FROM TODAY,
AVERAGE % PROJECTED INCREASE
realistic in their expectations
of generative AI
Improved customer engagement and satisfaction
9%
(i.e., increase in Net Promoter Score)
We asked executives which organizational benefits they
expect to see from generative AI within three years.
Executives expect to see improvements of 7–9 percent
Increase in operational
across all industries (see Figure 10). Recent research from 9%
efficiency (e.g., improved quality)
Stanford and MIT on the applications of generative AI
in the workplace found that the productivity of tech-
support agents who used conversational scripts improved
as much as 14 percent at one organization,5 suggesting Increase in sales 8%
such an estimate is realistic, if not conservative.
Decrease in costs 7%
Source: Capgemini Research Institute, Generative AI Executive Survey, April 2023, N = 800 organizations.
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HARNESSING THE VALUE OF GENERATIVE AI: TOP USE CASES ACROSS INDUSTRIES
• Customer engagement and skills in the Korean language using LLMs – machine
learning (ML) algorithms that can recognize,
satisfaction: Organizations can use generative
understand, predict, and generate human speech
AI for personalization; real-time insights; intelligent
based on huge text datasets.6
customer service; predictive analytics; continuous
improvement; and optimized customer journeys. • Operational efficiency: Organizations
These benefits ultimately lead to improved customer
are already reporting significant efficiencies from
engagement, satisfaction, and loyalty.
generative AI; German biotech firm Evotec announced
a phase-one clinical trial for a novel anti-cancer
KT telecom (South Korea’s leading mobile operator)
compound it developed with Exscientia, a UK
has built billion-parameter LLMs trained on the NVIDIA
organization that uses AI for small-molecule-drug
DGX SuperPOD platform and NeMo framework to
discovery. By using Exscientia’s Centaur Chemist AI
power smart speakers and customer call centers. Its
design platform, the organizations identified the
AI voice assistant, GiGA Genie, can control TVs, offer
drug candidate in only eight months. For context, the
real-time traffic updates, and complete a range of
average traditional discovery process takes 4–5 years.7
other home-assistance tasks when prompted by voice
commands. It has developed advanced conversational
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HARNESSING THE VALUE OF GENERATIVE AI: TOP USE CASES ACROSS INDUSTRIES
• Sales: By using generative AI to produce • Costs: By using generative AI to automate
personalized marketing, pricing optimization, demand processes, optimize resources, implement predictive
forecasting, improved customer experience, enhanced maintenance, optimize the supply chain, mitigate
sales support, and data-driven decision-making, risks, and improve decision-making, organizations
organizations can attract more customers, drive can achieve cost savings and enhance overall financial
life-long content-driven conversations, and boost performance.
conversions.
Germany-based Claudius Peters produces processing
In an attempt to increase its sales, Italian consumer- equipment for cement, coal, alumina, and gypsum
products organization Ferrero worked with brand plants. Working with technology partner Autodesk, it
20–60% designer Ogilvy Italy to customize jars for its popular used the Scrum project-management framework to
Nutella chocolate spread. Data scientists fed a reduce costs and product weights while shortening the
database of patterns and colors to a generative AI engineering process. The generative design produced
algorithm, which rapidly produced 7 million distinct components with a remarkable 20–60 percent weight
reduction in weight achieved through jar designs. These unique jars, branded as Nutella reduction while meeting performance requirements.
Unica, were sold all across Italy, reportedly selling out Additionally, the design served as a re-engineering
generative design by German-based
within a month. The design relied on the brand's highly template for conventional manufacturing, resulting
Claudius Peters recognizable lettering, around which other elements in a 30 percent lighter final design that lowered
were customized.8 component costs.9
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HARNESSING THE VALUE OF GENERATIVE AI: TOP USE CASES ACROSS INDUSTRIES
Fig.11
Organizations are prioritizing dedicated generative AI teams
Four in 10 organizations have
CURRENT LEVEL OF INTEGRATION OF GENERATIVE AI INTO FUTURE
already established teams PRODUCT/SERVICE DEVELOPMENT PLANS
and budget for generative AI
3% We have already established a dedicated
In just a few months of the public getting to know 8% team and budget for its implementation
about the technology through the launch of ChatGPT in
November 2022, nearly all (97 percent) of organizations
in our survey have plans for generative AI. Our research We are looking at establishing a dedicated
40%
reveals that 40 percent of organizations have established team and budget for its implementation
dedicated teams and budgets for generative AI, while in this year
another half (49 percent) are contemplating doing the
same within 12 months. Only 8 percent of organizations
We have not yet developed a concrete
are yet to develop a firm strategy for integration, and
plan for integration
as little as 3 percent are currently unsure if or how they 49%
will integrate generative AI into their product- or service-
development plans (see Figure 11). We are currently unsure if or how we
will integrate generative AI into our
product/service development plans
Source: Capgemini Research Institute, Generative AI Executive Survey, April 2023, N = 800 organizations.
Capgemini Research Institute 2023
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HARNESSING THE VALUE OF GENERATIVE AI: TOP USE CASES ACROSS INDUSTRIES
Fig.12
High tech and retail show the strongest commitment to integrating generative AI
PERCENTAGE OF ORGANIZATIONS THAT HAVE ESTABLISHED A DEDICATED
TEAM AND BUDGET TO INTEGRATE GENERATIVE AI INTO FUTURE
The majority (74 percent) of executives in the high tech
PRODUCT/SERVICE DEVELOPMENT PLANS BY SECTOR sector say they have established dedicated teams and
budgets for generative AI. Over 60 percent of executives
Average 40% from retail and 52 percent of executives from aerospace
and defense say the same (see Figure 12). Within retail,
High tech 74% while only 3 percent of executives believed generative
AI to be disruptive to their industry (refer to Figure 4),
Retail 62%
62 percent of retail executives say their organization has
Aerospace and defense 52% established a dedicated team and budget. This suggests
that while the retail industry does not see this technology
Pharma and healthcare 42%
as a disruptor, organizations realize they will lose out if
Financial services 42% they fail to implement it.
Energy and utilities 39%
Telecom 36%
Automotive 30%
Consumer products 23%
Industrial manufacturing 19%
Source: Capgemini Research Institute, Generative AI Executive Survey, April 2023, N = 800 organizations
Capgemini Research Institute 2023
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HARNESSING THE VALUE OF GENERATIVE AI: TOP USE CASES ACROSS INDUSTRIES
40%
of organizations have already established
teams and budget for generative AI, while
another half (49 percent) are contemplating
doing the same within 12 months
Capgemini Research Institute 2023
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HARNESSING THE VALUE OF GENERATIVE AI: TOP USE CASES ACROSS INDUSTRIES
Governing Fig.13
Most organizations are working/prefer to work with developers and IT vendors for their generative AI initiatives
generative AI
PARTNERS THAT ORGANIZATIONS ARE ALREADY WORKING WITH/PREFER
TO WORK WITH TO DRIVE MORE VALUE FOR GENERATIVE AI INITIATIVES
Centralized funding for generative
AI initiatives is currently the favored Developers (e.g., OpenAI, Stability AI) 69%
model
Of the 40 percent of organizations in our survey (320 IT vendors/C&SI (consulting and system integrators) 66%
companies) that have a dedicated budget for generative
AI initiatives, 78 percent source it from their central
budget, 16 percent from their overall AI budget, and 6
Academic institutions 55%
percent from their IT/digital department.
Organizations expect to partner
Big tech (e.g., Microsoft, Google) 35%
with developers and IT vendors
The preferred partners for generative AI initiatives are
Peer companies 22%
developers (69 percent); IT vendors and consulting firms
(66 percent); academic institutions (55 percent); and big
tech (35 percent) (see Figure 13).
Source: Capgemini Research Institute, Generative AI Executive Survey, April 2023, N = 800 organizations.
Capgemini Research Institute 2023
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HARNESSING THE VALUE OF GENERATIVE AI: TOP USE CASES ACROSS INDUSTRIES
03
GENERATIVE AI PACKS THE MOST
PUNCH FOR IT, SALES, AND
MARKETING
Capgemini Research Institute 2023
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HARNESSING THE VALUE OF GENERATIVE AI: TOP USE CASES ACROSS INDUSTRIES
Fig.14
67 percent of executives see the most potential for generative AI in the IT function
PERCENTAGE OF ORGANIZATIONS THAT SEE THE MOST POTENTIAL FOR
The greatest potential for GENERATIVE AI MODELS TO DRIVE INNOVATION AND CREATE VALUE FOR THEIR
ORGANIZATION ACROSS BUSINESS FUNCTIONS
generative AI lies in the IT
IT (e.g., driving innovation in other
function 67%
functions, testing and coding assistant)
Sales and customer service
54%
(e.g., optimizing support chatbots/self-service)
Nearly 70 percent of executives see generative AI
bringing the most potential value to IT within its role Marketing and communications 48%
(e.g., creating personalized marketing campaigns)
as an enabler for driving innovation across functions.
Over half (54 percent) also see generative AI driving Manufacturing (e.g., 3D modeling) 31%
innovation for sales and 48 percent for marketing and
Product design/research and development
communications (see Figure 14). (e.g., generating new design, faster drug discovery) 31%
Operations (e.g., optimizing supply chain) 26%
67%
Risk management (e.g., drafting
22%
and reviewing legal and regulatory documents)
Finance (e.g., processing invoices) 13%
of executives see generative AI bringing
Logistics (e.g., optimizing routes) 9%
the most potential value to IT within its
role as an enabler for driving innovation
Source: Capgemini Research Institute, Generative AI Executive Survey, April 2023, N = 800 organizations.
across functions
Question asked: In which business functions do you see the most potential for generative AI models to drive innovation and create
value for"
25,capgemini,Generative_AI_for_Business_Operations_POV.pdf,"Generative AI –
built for business
The disruptive power of GenAI
for business operations
Table of contents
GenAI – a fast-moving technology 03
Going beyond the hype 04
GenAI – context for business operations 06
Disrupting business operations with GenAI 08
Applying GenAI across key business areas 12
Innovating or reinventing – is one step at the time the right approach? 15
Critical considerations for implementing GenAI 17
Companionship on the road ahead 19
2 Generative AI – built for business
GenAI – a fast-moving
technology
In recent years, businesses have
been promised many revolutionary
technologies, ranging from blockchain
to redefine how we share data, non-
fungible tokens (NFT) to empower
digital asset ownership, and the
metaverse, which was supposed
to disrupt how we perceive what
constitutes the workplace.
Generative AI – or GenAI – is also
regarded as transformative. It’s
a technology that can handle
activities that until recently we
thought only humans could do, such
as brainstorming and generating
content.
But is it more than simply the latest
craze? In this paper, we’re going to
look at what it is, what it does, and
how it helps.
We’ll explore how it genuinely disrupts
operations in the main horizontal
business functions – and finally, we’ll
consider the key factors in a strategic,
enterprise-wide implementation.
This is a field that’s moving fast – and
as we’ll see, organizations need to
keep pace.
“Generative AI is a
96%
technology that can handle of 51%
of consumers
activities that until recently executives cite GenAI
are aware of the latest
as a hot topic of
we thought only humans
trends in GenAI
discussion
could do.”
Source: Harnessing the value of generative AI. Source: Why consumers love generative AI.
Capgemini Research Institute Capgemini Research Institute
3 Generative AI – built for business
Going beyond the hype
GenAI is a ground-breaking technology
that is part of a wider AI and data science
toolkit designed to analyze and replicate
the characteristics and patterns found
within large sets of data.
It can be used for a range of
applications, from creating text,
images, and videos in different styles to
generating tailored content.
And it enables automated business
systems to perform tasks previously
reserved exclusively for humans –
particularly those requiring creativity,
empathy, and experience (see Figure 1).
The technology can be conceived as
a highly skilled assistant or artist that
acts on requests or “prompts,” and
uses a broad scale of information
and experience gathered from open
data sources (such as the internet), as
well as from closed data sources and
experiences, such as those gleaned
from within the enterprise.
Like an artist who is inspired by events GenAI applications Examples
or environments and takes advantage
of styling experiments and available
Generating new text/reports,
tools, GenAI creates new ideas, summarizing and translating in to Open AI’s GPT-4, Google Palm 2,
Scribe, Claude
solutions, and content. multiple languages
Text
It’s worth noting that some GenAI tools
such as large language models (LLMs),
Generating new images/videos, Adobe Firefly, Stability AI,
which are used to power chat-based self- analyzing existing images/video Midjourney, Nvidia, Dell-E2,
service systems, do not possess their (e.g., video games, VR, animation) Synthesia, Runway ML
own knowledge, but augment their text Im ga eg ne es r & at v ioid neo
generation capabilities with patterns
Generating music and remixing,
found in supplied data sources.
speech synthesis, sound effects, Synthesia, Amazon Polly,
GenAI is not about replacing human voice conversation, audio Sonix.ai
creativity or skills, but is rather a means Audio enhancement
of enhancing or augmenting what
we do, carrying out tasks that are Generate human-like contextually
relevant text responses in OpenAI’s ChatGPT, Amazon Lex.
predefined and automated.
real-time to expand and improve Google Bard
customer service and advice
Chatbots
“GenAI is not about replacing
Enhanced search functions,
human creativity or skills, but adding language capabilities to Google Bard, Landing AI, Azure,
is rather a means of enhancing search e.g., retrieval augmented Facebook Liama 2, Perplexity AI
Search generation (RAG)
or augmenting what we do,
carrying out tasks that are
Figure 1. Generative AI applications across multiple formats
predefined and automated.”
Source: Capgemini Research Institute, Generative AI and the evolving role of marketing: A CMO’s Playbook, 2023
4 Generative AI – built for business
For business processes, this means Data access and data quality are key
the ability to offer new experiences and require investments to leverage
and more innovative services and operationally AI and GenAI but 74%
products, tailored to the specific large-scale implementation of GenAI of
needs and interests of customers and requires organizations to make three executives believe
augmented by data owned by an distinctive decisions: the benefits of GenAI
enterprise. outweigh the risks
• The strategic direction in which it will
play a part
The sudden rise of GenAI-enabled
products and services shows that
concepts previously regarded as • How to equip the workforce to take Source: Harnessing the value of generative AI.
advantage of it Capgemini Research Institute
futuristic are now growing in number
and scale, and are frequently becoming
• How to execute it iteratively and
a part of reinvented business processes
move towards scaled value.
or even of industry standards.
But, even with GenAI’s enormous “The sudden rise of GenAI-
Use cases include automating routine
tasks such as analyzing data hidden transformative potential, there are enabled products and services
areas of concern that need to be
within databases, automating answers shows that concepts
addressed, including consumer trust in
to client queries, and generating new
the technology, which can sometimes previously regarded as
multi-lingual training manuals.
be misplaced and leave people futuristic are becoming a part
vulnerable to threats in areas such as
More importantly, GenAI is not of reinvented business
security, privacy, and misinformation.
about making a business process
processes or even of industry
“cutting-edge,” but applying the next
generation of “smart” in a business Despite these potential downsides, standards.”
the impact of GenAI will clearly be
toolkit to improve client experience,
massive, maybe the most important
generate more value, and stay ahead
one in decades.
of the competition.
5 Generative AI – built for business
The context for
implementation
One of the most well-known Moreover, Generative AI facilitates the these domains, businesses can unlock
implementations of GenAI is, of creation of synthetic data for training unprecedented insights, drive innovation,
course, ChatGPT. However, a wide models, overcoming data scarcity issues. and achieve sustainable growth in an
range of other dedicated solutions increasingly competitive landscape.”
could provide even more efficient By harnessing Generative AI across
results depending on the specificities
and data ecosystem used for training.
Trust remains the first piece of the
puzzle to be addressed to avoid any
misunderstanding or even a complete
disconnection from reality.
How can GenAI transform business
operations? Here’s a brief overview:
“Generative AI presents a transformative
force across various business operations,
revolutionizing efficiency and
effectiveness.
In finance operations, it enables
predictive analytics for risk assessment,
fraud detection, and personalized
financial advice.
Supply chain operations benefit from
“
AI-driven demand forecasting, inventory
Capgemini’s Business Services has been at the
optimization, and real-time logistics
management, enhancing agility and forefront to leverage AI and GenAI to transform enterprise
reducing costs. process and drive significant efficiency improvements.
However, the value for the enterprise goes far beyond cost
Client operations leverage Generative
AI for personalized marketing savings. I strongly believe that through the use of these
campaigns, sentiment analysis, and new technologies we can unleash the power of data to
customer service automation, fostering
eliminate frictions, drive insights, and generate tangible
deeper engagement and loyalty.
business outcomes which impact our clients’ top and
HR operations are streamlined through bottom line.”
AI-powered recruitment processes,
talent management, and employee Oliver Pfeil
feedback analysis, facilitating better
CEO, Capgemini’s Business Services
decision-making and employee
satisfaction.
6 Generative AI – built for business
Good summary, don’t you think? In finance operations, it promises to
Oh, what a time to be alive in this brave
predict the unpredictable, like a crystal
new world of automation!”
This was not written by the authors ball that never fails to dazzle with its
of this paper, but was automatically inaccuracies.
While these two summaries may perhaps
generated with ChatGPT in less than
Supply chain operations? Ah yes, be a bit generic, they provide a fair
2 seconds! The original request was
because nothing says efficiency like and simple overview – and they were
simply this:
algorithms guessing wildly about produced very fast indeed.
demand while your shelves gather dust
“You are an expert in business
or overflow with excess inventory.
operations and intelligent
automation. Please generate a point Client operations? Let’s automate those
of view of 150 words on what are heartfelt customer interactions and
the capabilities of Generative AI to replace them with robotic responses that
transform business operations in the scream “we care... about cutting costs.” 51%
of consumers
fields of finance operations, supply
And HR operations, well, who needs are aware of the latest
chain operations, client operations
human judgment when you have trends in the GenAI space
and HR operations.”
algorithms to sift through resumes and
When the app was asked to perform churn out soulless hiring decisions?
the same task but with a sarcastic
tone of voice, the result was also Generative AI, the answer to all our Source: Why consumers love generative AI.
impressive: prayers – if those prayers involve Capgemini Research Institute
relinquishing control to glitchy
“Oh, Generative AI, the magical solution algorithms and praying for mercy from
to all our business woes! the inevitable mess they’ll make.
7 Generative AI – built for business
Disrupting business
operations with GenAI
Much of the excitement around GenAI Expected future benefits of generative AI in three years from today,
is the result of ChatGPT, which was average % projected increase
developed by OpenAI. It became hugely
popular because it enabled users
Improved customer engagement
to interact with it in an easy, natural
and satisfaction (i.e., increase in Net 9%
way, and to see the results of their
Promoter Score)
interactions with it almost immediately
– just as we’ve seen with the two
summaries above. It’s no surprise that
Increase in operational
business is exploring its potential. 9%
efficiency (e.q., improved quality)
In fact, 96% of organizations say GenAI
is on their boardroom agenda. Their
outlook is quite optimistic: executives Increase in sales 8%
predict productivity improvements of
7–9% resulting from GenAI adoption
in just the next three years, and about
one-fifth of executives surveyed
globally say it will significantly disrupt Decrease in costs 7%
their industries.
Indeed, there are some use cases
Figure 2. Expected future benefits of generative AI in three years from today, average
where the benefit potential is for
% projected increase
efficiency improvements of 60–80%
or more (see Figure 2). Source: Capgemini Research Institute, Generative AI Executive Summary, April 2023, N=800 organizations
7–9%
improvement in
productivity from GenAI
adoption in the next
three years
Source: Harnessing the value of generative AI.
Capgemini Research Institute
20%
of executives
say GenAI will
significantly disrupt
their industries
Source: Why consumers love generative AI.
Capgemini Research Institute
8 Generative AI – built for business
Consumer
expectations of
GenAI
According to a study conducted by the
Capgemini Research Institute in April
2023, over 40% of surveyed consumers
would like to see GenAI implemented
as a part of their interactions with
organizations – mainly as a part of
automating customer service (self-
service), AI-powered search and insights,
and new forms of content creation
across text, video, images and audio.
In the same report, 70% of consumers
say that GenAI tools such as ChatGPT
are becoming the new go-to when it
comes to seeking product or service
recommendations, replacing traditional
methods such as search.
There are three specific areas in
Innovative business
which clients and customers have
expectations. solutions with factual 40%
of consumers
proactiveness
would like to see GenAI
Enhanced service/product
implemented as a part
personalization and The rising awareness of GenAI among of their interactions
experience consumers also suggests that they with organizations
are increasingly looking for innovative
solutions that bring value to them. For
Clients today are not merely seeking Source: Why consumers love generative AI.
years, global businesses have been
new products or services with GenAI Capgemini Research Institute
talking about growing closer to clients
built in for its own sake. Rather, they
and becoming more proactive – not
are looking for more personalized
just to increase client satisfaction,
experiences tailored to their specific
but also to reduce the operating
context (location, time, socio-economic 70%
costs associated with solving ad-hoc
situation and personal needs) and of consumers
queries.
preferences. say that GenAI tools are
the new go-to for
GenAI opens new opportunities for
GenAI is a rising transformative tool product or service
businesses to reinvent their operating
that can provide these experiences on recommendations
procedures, products, and services.
demand by employing various case-
By taking advantage of the ability of
specific fine-tuned large foundation
AI-based systems to analyze large Source: Why consumers love generative AI.
models (LFM) connected to new or Capgemini Research Institute
quantities of unstructured data and
existing experiences.
act on identified patterns, businesses
can predict client needs and offer
Furthermore, the efficiencies delivered
solutions – even before the client “Clients are not merely seeking
by AI in the contact-focused part of the
business through AI-driven self-service realizes the need. new products or services with
experiences (contact center, marketing, GenAI built in for its own sake.
or on-demand products) ensure that This gives organizations that adopt
clients have both prompt and relevant GenAI opportunities not just to meet, Rather, they are looking for
responses available to them 24/7 – but to exceed, client expectations more personalized experiences
without the need to “wait for the next and position themselves as forward- tailored to their specific
available agent” that interrupts this thinking and client-centric leaders in
experience. their respective industries. context and preferences.”
1Generative AI in organizations, Capgemini (2023), https://www.capgemini.com/insights/research-library/generative-ai-in-organizations/
9 Generative AI – built for business
Trust through transparency
and responsibility
As businesses increasingly integrate
GenAI into their processes and
solutions, it is vital to maintain
transparency and responsibility –
especially in how client data is used to
make an automated decision.
Clients are more likely to trust such
automated services from a company
that not only fulfils legislative
requirements (such as the European
Union’s AI Act) but also is able to
demonstrate practical guardrails
and considerations such as ethical
AI (including the ethical sourcing of
data), respecting privacy and data
security, and also by being completely
transparent on how systems will be
updated in future.
This may mean not only ensuring the
technical transparency of GenAI-
based systems but also providing
business-related safeguards including
• “Happy to help. Here are step-by- from a fully manual process to the
a Code of Ethics for AI, which is what
step instructions on how to create option of an automated one, while
we have developed here at Capgemini.
your invoice in the XYZ Procurement still giving users the opportunity to
System” … followed by a long list gain the requisite knowledge for
Being more up-front with clients
of steps generated by AI retrieved themselves.
should raise their trust in GenAI-based
from the software manual. While
systems, and in effect build a more
the organization is significantly
satisfied and loyal customer base.
improving the search and learning
71%
experience of the employee, it’s still
Automating processes of executives
a fully manual process.
believe GenAI will
and improving enable them to create
The second scenario is derived from
more interactive
customer experience an automation ecosystem enhanced
experiences for their
with GenAI. Here, the response could
customers
be something like this:
GenAI shouldn’t be seen as a bolt-on.
It’s true transformative potential is
• “Happy to help. I’ll send you step-by-
realized if combined with automation Source: Harnessing the value of generative AI.
step instructions on how to create your
as well as with the organization’s data Capgemini Research Institute
invoice in the XYZ Procurement System
and its enterprise systems.
so you will know for the future. But
“As businesses increasingly
I can also walk you through it while you
Let’s consider two scenarios where in
try it out for yourself. Alternatively, I’m integrate GenAI into their
each case, a relatively new employee
happy to do it for you. Just drag and
is asking a digital business process processes and solutions, it is
drop the file to the chat window. In
assistant: “How do I raise an invoice in vital to maintain transparency
fact, I can even automate this process
the procurement system?”
for you for the next time.” and responsibility –
In the first scenario, the response especially in how client data is
This is a very different experience
is created by a standalone GenAI used to make an automated
for users, with a new level of value
implementation:
generated for the business – moving decision.”
10 Generative AI – built for business
With permission, the most advanced
automated implementation would
pull the relevant file from an inbound
“
email, automate the business
We’ve been
process, and send a notification to
the employee: “I have resolved this applying automation to
case for you, so you can focus on the make our services
client.”
radically more efficient
The key limitation of GenAI is for a while. With GenAI
obtaining a sufficient level of we’re taking the next
confidence in its results, the
step and reimagining all
knowledge base that is used to train
the model can’t be verified. Obviously, our services with an
the more data that can be obtained innovative and
on the required scope of work, the
responsible approach.”
more relevant and accurate the
results will be, but this isn’t always
Lalitha Kompella
a straightforward proposition,
especially in a business context. Head of the Analytics and
Intelligent Automation Practice,
One problem is knowing which data Capgemini’s Business Services
is necessary: if that’s unclear, the only
option is to gather, store and update
vast quantities that will never be used,
which is not only costly but also has
an impact on sustainability. Getting efficiency for basic processes the necessary data resources can be
the right balance between too much involving documents, slides, email, a challenge.
data and not enough for the individual and more with significant quality
needs of an enterprise can be a real improvements Whatever the approach, there will
challenge. be significant costs – either direct (to
• Innovating and creating value implement the solution) or indirect
By implementing intelligent process – developing services, products (cloud consumption for example) – and
automation into the core of its clients’ or even processes that were not there will also be too many temptingly
transformation journeys, Capgemini possible before. shiny targets to pursue.
has built the necessary foundation
needed to accelerate into its near Companies will of course want to Organizations will need to consolidate
future with GenAI. pursue both these opportunities, the full scope of possibilities for each
so they can lower their costs and domain of their activities and prioritize
Companies who initiated this data- optimize their time to market while the most relevant ones, considering
driven transformation with generating also generating new revenues. the sustainability implications and the
new insights based on data that expected business outcomes. Not that
is powered by automation and AI To do so, they can implement an off- simple, when the opportunities are
are currently the most advanced the-shelf solution, create their own almost limitless.
organizations accelerating with GenAI. customized model, or adopt a hybrid
approach. The first of these options will
be generic, but efficient enough and will
Improving the now certainly be cost effective, based on pre- “By implementing intelligent
trained models with global data, which is process automation into the
– and creating the
the case with ChatGPT.
core of its clients’
future
transformation journeys,
The bespoke option will generate
concrete outcomes specific to Capgemini has built the
There are two main types of activity
industry or market needs and will
necessary foundation needed
to which GenAI can be applied in the
be a distinctive and competitive
enterprise: to accelerate into its near
proposition, but it will need
a dedicated data ecosystem and future with GenAI.”
• Current daily tasks – GenAI can
specific models or LLMs – and as we’ve
optimize productivity and
seen, establishing and maintaining
11 Generative AI – built for business
Applying GenAI across
key business areas
We have analyzed several main
possibilities in four key business
areas: human resources, finance and
accounting, supply chain, and contact
centers – looking at the expected
impacts per use case.
Human resources
The introduction of GenAI in HR
is transforming every aspect of
moments that matter to employees
– from recruitment, through
development, to retirement.
Enhancing efficiency and precision
in recruitment processes by
utilizing AI-powered tools. This is not
a technological novelty and there
are many enterprise-grade systems
fulfilling this promise. But with
advances in GenAI, companies can
move swiftly through large volumes
of candidate profiles, identifying the
most suitable candidates based on
actual skills, experience, and cultural
fit, while providing a seamless and automating most routine tasks such
proactive experience to each. as year-end performance reporting,
60%
or boosting morale with individually of executives
Even the best candidates may have customized communications. say that GenAI would
learning and experience gaps that
completely revolutionize
need to be addressed to ensure they Enhancing engagement and well- their way of working
reach their potential, and GenAI will being by providing anonymized
identify these potential deficiencies. insights from employee feedback and
The enhanced recruitment process implementation recommendations,
will also look more appealing, digitally enabling HR teams to make data-driven
inclusive, and attractive to a more decisions that improve the working
70%
diverse pool of applicants. environment, directly impacting the
of consumers
satisfaction, productivity, and loyalty of
believe GenAI will
Tailoring the employee experience employees.
augment their roles and
by reinventing HR business
reduce their workloads
processes so they are specific to “Reinventing HR business
each employee’s career aspirations,
processes enables them to be
experience, and individual strengths
and needs. GenAI makes this tailored to each employee’s
a reality as part of augmented self- career aspirations, experience, Source: Harnessing the value of generative AI.
service for employees, providing Capgemini Research Institute
and individual strengths and
recommendations, creating bespoke
development content, proactively needs.”
12 Generative AI – built for business
Finance and accounting kinds of analysis support to inform their • Planning optimization – optimizing
strategic or operational level decisions. demand, supply, and inventory
Transforming the finance and GenAI will also enhance footnotes planning much more efficiently by
accounting function from a purely and comments sections in financial analyzing multiple data sources to
operational focus on delivering timely reports, making them more readable for propose new solutions and concrete
reports and transactional record stakeholders and third parties. decisions; simulating various scenarios
keeping to something of much more to provide intuitive and interactive
value. GenAI can serve the strategic Rethinking the management of risk recommendations; predicting lead
intelligence unit of the business by and compliance by providing advanced times for POs; improving production
improving the automation ratio of analytics and predictive capabilities and transit lead times; generating
data preparation and analysis tasks. with comprehensible simulations, summaries from dashboards and
explanations and recommendations – complex information; automatic
With its ability to understand not only detecting financial anomalies, benchmarking of equipment/materials,
documents, to generate but also aggregating information about and more
recommendations, and act as market trends, news and evolving
a proactive analytical assistant on regulations.
demand, it can support and enhance 69%
the work of specialists within This increases the organization’s of executives
dedicated functions. proactive ability to manage expectations believe generative AI
and safeguard financial health, while will lead to the
Automating F&A for accuracy reinforcing its transparency, integrity, emergence of new roles
and insight to improve business and reliability as a business partner.
processes beyond productivity, capacity,
and automation quick wins such as Supply chain 68%
document processing. This brings the of executives say
next level of precision and insight gained Globally, the supply chain will benefit the integration of GenAI will
from the contextual understanding of from GenAI by reducing working capital, require significant
emails, notes, and policies – thereby increasing automation at scale, and investment in up- and cross-
enhancing financial reporting, increasing revenue. skilling of talent
budgeting, forecasting, and analytical
commentary. Let’s focus on six main areas: Source: Harnessing the value of generative AI.
Capgemini Research Institute
By expanding the scope of automation,
• Master data management –
the business can not only gain deeper
automatic verification for duplicates; “GenAI can serve the strategic
insight into contextualized financial
merges of similar records; automatic
performance, but can also enable more intelligence unit of the business
extractions from complex documents;
persona-driven insights. relationships and dependencies by improving the automation
between entities; and more ratio of data preparation and
Why? Because analysts and CFOs will
require different levels and different analysis tasks.”
13 Generative AI – built for business
• Fulfillment – understanding
unstructured documents; enabling
users to participate in conversation-
based learning; identifying the
root cause of the delays/risks;
automating reports with multilingual
GenAI-powered conversational AI;
optimizing inventory management
and efficient order fulfilment at scale;
providing early warnings for erratic
order identification; and generating
automatic responses for query
resolution
• Sourcing/category management –
providing real-time market insights
from multiple platforms; generating
automatic recommendations
• Product lifecycle management –
automating data analysis and fuzzy
matching to achieve coherence and
quality; detecting anomalies with
automated quality control; recognizing
patterns and features
with efficient knowledge bases that All enquiries will be handled with
are specific to their conditions and speed and accuracy, but responses
• Procurement – quickly analyzing
treatments – just like a dedicated will also be personalized, unscripted,
complex business activities
specialist in a hospital. and in line with your operating
and predicting future market
procedures, policies, strategy, and
developments; unlocking unmatched
Redefining the customer center so it brand values.
insights and efficiency; assisting
becomes the client’s favorite part of
in supplier selection by evaluating
doing business with the organization Tailor customer engagement by
multiple factors (such as cost, quality,
by switching the focus from average anticipating customer needs and
reliability, and performance history);
handling time (AHT) to actionable net offering proactive solutions, ensuring
continuously monitoring supplier
promoter score (NPS). GenAI-powered that everyone feels understood
performance and flagging potential
solutions in contact centers can and valued for their business, while
issues before they arise, thereby
not only improve handling inbound maintaining standardization and
strengthening vendor relationships.
queries but automate the whole optimization of business processes on
process end-to-end in a self-service a global scale.
Contact centers
model, creating unparalleled client
experience along the way. By analyzing global patterns in
The customer ecosystem will be
communication and preferences,
completely reinvented with GenAI,
Advanced self-service systems GenAI can provide an unparalleled
providing new, dedicated, and highly
powered by LFMs can understand, capacity to foster deeper relationships
customized insights to every client via
respond, and trigger the automation and greater loyalty – and without
human avatars or augmented agents.
needed by clients with degrees of increasing operating costs.
precision, personalization and scale
Digital humans will be able to interact in
that are unmatched by humans –
any language, 24/7, reproducing human
making every interaction feel uniquely
elements such as tone of voice, body “The customer ecosystem will
tailored to the client context.
language, facial expressions, emotions,
be completely reinvented
and lifelike conversations to make
interaction more natural. Elevating customer Interactions with GenAI, providing new,
by giving the business a friendly
dedicated, and highly
face through a digital avatar that
For example, Capgemini created
provides multilingual, multi-channel, customized insights to every
a custom digital avatar solution for
context-driven emotion, and empathy,
a global life science company to client via human avatars or
regardless of when or how clients
support patients’ day to day needs, augmented agents..”
make contact.
14 Generative AI – built for business
Innovating or reinventing
– is one step at the time
the right approach?
When it comes to implementation,
the most important question is not
just about whether GenAI should
be treated as another technological
approach to business process
re-engineering, but rather about how
to realize its potential in reinventing
the very DNA of work.
GenAI capabilities shouldn’t be
considered as a series of isolated
decisions or small-scale automation
projects to address process
inefficiencies, but should rather be
seen as an opportunity to take step
back, holistically assess the value
that the business process should
create, and reinvent it by redefining
experiences from the ground up. only for efficiency or performance but
for top-line growth. 40%
Selecting the right use cases for GenAI of organizations
is crucial. It needs to be aligned to Moreover, immediate focus on have already established
organizational objectives and strategy, large-scale deployments from the teams and budget for GenAI
identifying areas where its application very beginning is essential to ensure
will make the most significant long-term business value, cost, and
Source: Harnessing the value of generative AI.
difference, both for the organization process controls. While it might be Capgemini Research Institute
and the process stakeholders, tempting to test the water with
internally and externally. smaller"
26,capgemini,2024_Capgemini_Invent_Benchmark_Report_AI_in_Energy_Trading(1).pdf,"A RTIFICIA L INTE L L IG ENCE IN
ENERGY TRADING
A benchmark on how Artificial Intelligence is used among European Energy Traders
A study by Capgemini Invent, March 2024
Management Summary and Key Facts
We observe a 22% increase of perceived disruptiveness of AI in Energy Trading since 2021:
1
Study participants categorize AI as highly disruptive. That score increased significantly since our last survey conducted in
2021.
87% of participants are engaged in AI, up from 72% in 2021:
2
The majority of the surveyed energy trading companies is actively participating in AI. Nevertheless, participation differs
significantly between municipalities (72%) and pure energy tradingcompanies (100%).
Only 30% of participants show strong AI governance structure:
3
More than 50% of participants show significant AI capabilities according to our AI maturity matrix assessment, while only
around30% show strong AI governance.
More than 60% of 102 actually implemented use cases of our participants are Front Office related:
4 Across the different maturity clusters Trade Execution, Trade Capturing and Physical Operations represent more than
60% of all implemented use cases of the participants, whereas Middle Office and Back Office represent only ~17% of all
implementeduse cases.
Mature AI traders use “make” or “hybrid” systems for more than 90% of their use cases:
5
Beginners are highly reliant on buy solutions for AI applications with a 44% buy rate, while masters are much more reliant
on self-built or hybrid solutions (90%).
Artificial Intelligence in Energy Trading 2024 © Capgemini Invent 2024. All rights reserved. 2
Agenda
01
Understanding AI’s role in Energy Trading
(pages 4-7)
02
Study methodology and participant insights
(pages 8-10)
03
Maturity assessment of Energy Trading companies
(pages 11-14)
04
Use case landscape in Energy Trading
(pages 15-17)
05
In-depth evaluation of use cases and their methodologies
(pages 18-20)
Artificial Intelligence in Energy Trading 2024 © Capgemini Invent 2024. All rights reserved. 3
Trends on the energy market make trading more complex and
faster and are the foundation of enhanced AI considerations
1
GREENIFICATION DIGITALIZATION
The transition to cleaner and more sustainable energy sources
Digitalization of the overall energy value chain leads to
lead to new structured products and increased spot
increased automation and machine-to-machine
market relevance paired with increased short-term
interactions.
deal-counts.
2
CHANGING DEMAND BEHAVIOR ELECTRIFICATION AND E-MOBILITY
European energy
3 Smart meters, integrated decentralized
trends make Increased adoption of EVs and
renewable assets, dynamic pricing and
electricity-driven heating increases volatilities
flexibility requirements and values lead to trading more
and requires rapid data-driven decision
shifts and increased volatilities of demand complex than
making in trading and energy procurement.
behaviors of different customer segments. ever before
4
REGULATORY CHANGES AND POLICY SHIFTS DECENTRALIZATION & DEMOCRATIZATION
Regulatory changes to secure the integrity of energy The integration of scaled energy storage solutions and
5 markets and control and restrict market abuses changing balancing revenues allow for new business
impact the necessity of End-to-End data and models like aggregators and virtual power plants (VPPs)
and lead to complex orchestration requirements.
decision oversight.
Artificial Intelligence in Energy Trading 2024 © Capgemini Invent 2024. All rights reserved. 4
Participants view AI as more disruptive than in 2021 but still
have not reached “Plateau of productivity”
1 AI Disruption Score & Hype Cycle
Study participants seem to value AI as highly disruptive and
significantly higher than in our last survey conducted in 2021; from
10
5,4 to 6,6.
2 In general we observe that the peak of inflated expectations
e
r towards AI in Trading is left behind and focus is put on implementing
o
c
scalable and practical strucutures, processes and governance
S 6,6
n models.
o
i
t
p
3 u GenAI only represents a subelement of this study and can be
r
s 5,4 individually considered to be on a different part of the hype cycle
i
D
currently.
I
A
e
g
a
4 r
e
v
A
Capgemini AI in Energy
2021
1 2021 2024
5
Peak of inflated Plateau of
Innovation trigger Trough of disillusionment Slope of enlightenment
expectations productivity
Time
Artificial Intelligence in Energy Trading 2024 © Capgemini Invent 2024. All rights reserved. 5
AI requires a nuanced definition and can be largely categorized
in two overarching methodologies
1
Artificial Intelligence (AI)
Machine Learning Deep Learning
2
• Empowering systems to automatically learn & improve • Transforming representation at one level into a
from experience without being explicitly programmed representation at a higher & more abstract level
• Employing algorithms to analyze data, identify patterns • Advantage in ability to process vast amounts of bigdata
& make informed predictions
3 Supervised Learning Unsupervised Learning Reinforcement Learning
• Processes unlabeled data to identify
• Trained on labeled datasets with known • Decision-making through trial and error
patterns or relationships
outcomes • It learns by receiving rewards or
• Does not rely on predefined outcomes
• Learns patterns and relationships penalties based on its actions
• Methods: Clustering & user segmentation
between inputs and outputs • Methods: Automated agents
4
• Methods: Linear regression & classification
Examples in Energy Trading:
• Uses historical data with labeled energy • Analyzes unlabeled data like market • Executestrades of energy assets based
prices & relevant features to predict behavior & consumption patterns to gain on market conditions, refining strategies
future prices insights over time through continuous feedback
5
AI provides powerful tools that can be strategically deployed in energy trading, unlocking lucrative opportunities
Artificial Intelligence in Energy Trading 2024 © Capgemini Invent 2024. All rights reserved. 6
Machine Learning and Deep Learning methods offer a wide
tool set for AI use cases in Energy Trading
1
Tool Description
Ensemble learning method used for predicting Struggling with
Random Forests for Capturing nonlinear
energy prices by combining multiple decision overfitting on small
Price Prediction relationships
trees datasets
2 Efficient ensemble learning algorithm often Handling missing Challenging
Ensemble Learning
applied to optimize energy market predictions data & offering high interpretability of
with XGBoost
by combining multiple models predictive accuracy model
Modeling decision-making processes over time,
Accurate modeling
Markov Decision Comprehensive
making them suitable for optimizing energy
of transition
Processes (MDP) management
trading portfolios
3 portfolio approach probability required
Long Short-Term Part of Recurrent Neural Networks, effective in Modeling complex Struggling with
Memory (LSTM) capturing sequential dependencies for time patternsin energy abrupt changes in
Networks series forecasting in energy markets market trends market conditions
4 Transformer Models Capturing & handling Requiring large
Analyzing historical energy market data to predict
for Time Series global, long-range amount of data for
future trends and price movements
Forecasting dependencies in data effective training
Employing generative algorithms to simulate and Enhancing Requiring careful
GenAI propose diverse trading strategies based on adaptabilityto scrutiny and testing
5 historical market data dynamic markets before implementing
The AI toolset is vast and proves highly beneficial for numerous use cases within the realm of energy trading, addressing a wide
range of potentialchallenges.
Artificial Intelligence in Energy Trading 2024 © Capgemini Invent 2024. All rights reserved. 7
Our 2024 survey analyzed 22 Energy Trading companies in
Europe to identify trends and patterns in AI approaches
1
Company Type by Survey Year
22 Participants 22 Participants
The goal of this study is to identify the trends of AI adoption
2
within the energy trading industry enriched by a trend analysis
based on our last AI inEnergy TradingSurvey in2021.
8
9
3
7
Capgemini Invent surveyed 22 energy traders in Europe with a
8
focus on Central Europe.
4
7
5
Our guiding question is: What is the current and changed picture
2021 2024
of AI-maturity, relevant use case maturity, and technology Survey Year
adoption strategy in the Central European energy trading
5 Fully integrated energy company
industry?
Municipal utility company
Pure energy trading company
Artificial Intelligence in Energy Trading 2024 © Capgemini Invent 2024. All rights reserved. 8
Throughout all company types AI participation in the Energy
Trading industry is high and increased from 73% to 87%
1 87
%
AI Participation Rate in Energy Trading
AI participation rate (current and planned implementation), group averages, %
of energy trading organizations participate in AI
2
100
PURE ENERGY TRADING COMPANIES: With a 100%
90
87 88 adoption rate in this year’s survey pure energy trading
companiesstandoutintheAIadoptiondegree.
3 73 72
70 FULLY INTEGRATED ENERGY COMPANIES: 9 out of 10
fully integrated energy companies use AI in their trading
operations. 3 years after our first survey fully integrated
energy companies have the same adoption rate as pure
50
energytradersin2021.
4
MUNICIPAL UTILITY COMPANIES: Municipal utility
companies continue to represent the lowest degree of AI
adoption. With an almost 50% increase municipal
utilities witnessed the relatively spoken biggest increase
ofAIadoption acrossallsegments.
5
All Respondents Fully integrated energy Municipal utility Pure energy trading
companies companies companies
2021 2024
Artificial Intelligence in Energy Trading 2024 © Capgemini Invent 2024. All rights reserved. 9
Perceiving AI as a competitive advantage is highly correlated
with the AI understanding in the surveyed companies
1
High perceived AI understanding
significantly influences the energy
Organization’s Self-Rated AI Understanding vs. AI Competitiveness
trader's competitive edge.
10
Unlocking AI competitiveness through
2
understanding
9
8 In our survey, a compelling correlation emerges:
Perceiving AI as a competitive advantage is highly
7 correlated with the AI understanding in the surveyed
s
s
e companies. This relationship underscores the pivotal
3 n 6
v roleofAILiteracy inbolsteringmarketadvantage.
i
t
i
t 5
e
To gain a competitive advantage, enhancing AI
p
m
understanding or AI Literacy is vital. This often stems
o 4
c from hands-on experience with AI use cases. Notably,
I
A companies excelling in perceived AI understanding have
4 3
alreadycapitalized onthesepracticalapplications.
2
1
0
5
0 1 2 3 4 5 6 7 8 9 10
AI understanding
Artificial Intelligence in Energy Trading 2024 © Capgemini Invent 2024. All rights reserved. 10
More than 50% of participants have built up significant AI
capabilities, while less than 30% show strong AI governance
AI Maturity Matrix (AIMM)
1 Company Type
%
0 EXPLORER
0
1
4 MASTER S Integrated
t
r
o
n Trader
g
2
c Municipal
“ r
o
2 pa utilities
r a
r
e b
d i
n il Attitude
a t
y l ia ei
t i r T s “AI disruptive”
3 l „
i
b
a “AI not disruptive”
p BEGINNER
a
MANAGER
C
3
Trading
4 “Power trader”
(10.000
transactions
NO ACTIVITY & prop. trading)
1 Strong governance
Company cluster
5
Organizational structure: „Rules before action“
5
1 Cluster 1 to 5
Governance
0% 100%
Artificial Intelligence in Energy Trading 2024 © Capgemini Invent 2024. All rights reserved. 11
Based on the AI governance and AI capabilities 4 different
maturity groups can be differentiated
1
AI Maturity Matrix BEGINNER –What’s in it for me?
“Beginners”arejustgettingataste of things,hardlyusingany formofAI.Theyhavearelatively low
level of governance structures, organize AI in a largely decentralized manner, mostly do not employ
AI experts, and are implementing their first AI projects, which are usually intended to prove the
EXPLORER MASTER
viability of AI. With their first projects, they gain initial experience and prepare the organization for
furthersteps.
2 y
t
i EXPLORER –Trial and error.
l
i
b
“Explorers” are feeling their way forward. They have a relatively low level of governance
a
p a BEGINNER MANAGER s e g structures, organize AI in a largely decentralized manner, employ only a few AI experts, and have
C
a initial scaled AI projects that represent isolated organizational solutions. With their projects, they
t
S
continue to expand their know-how in specific domains and thus increase personal skills and
y
3 r a organizationallearning.
Governance i d
e MANAGER –Rules before action.
m
“Managers” rely on an organizational framework. They show a high degree of governance
r
e
t structures,usuallyorganizeAI inacentralunit,andmayalready havehiredAI experts.Onthisbasis,
n
No activity
I they planand develop thefirstAI projects. Witheachadditionalproject,structuresgrowfirst,before
experienceandknow-howcomeintoplay.
4
Capability MASTER –AI is in my DNA.
”AI Masters” know what they are doing. They show a high degree of governance structures,
How skilled are employees and how developed is organize AI in a central unit, employ experts, and engage in very mature AI projects that require a
the AI pipeline infrastructure? high level of organizational embedding. With their numerous and diversified use cases, they have
perfectedtheirexperienceandbuiltreliableprogrammingskills.
5 Governance
NO ACTIVITY –AI is nothing for me.
ThesecompanieshavenoAIinitiatives.OrganizationalstructuresremainunaffectedbyAIsofarand
How well is AI structured into the organization and
thecompaniesarenotbuildingupexperienceinthisarea.
progressing with a clear strategy?
Artificial Intelligence in Energy Trading 2024 © Capgemini Invent 2024. All rights reserved. 12
In comparison to 2021, Explorers & Masters double while
a structural trend towards more governance can be observed
1
Maturity Cluster Evolution
Maturity Cluster Evolution from 2021 to 2024 Trend: 2024 vs. 2021 results
1
BEGINNERS
Experienced a substantial decline, as many companies have
+100%
2
begun to actively involve themselves with AI.
4
2
EXPLORERS
+100%
There has been a stronger focus on governance, as many
2
companies have already built a solid knowledge base.
3 y 3
t MANAGERS
i
l i Experienced a decline as they successfully advanced to
b
a master level, driven by the establishment of essential
p 3
a capabilities.
C 4
MASTERS
2024 1
4 Substantial increase as maturity advances within the
-20%
industry, leading to initial master-level entities.
5
-17%
5
NO ACTIVITY
-50%
Drastic decrease, since many companies have started
dealing with AI. The remaining are mostly municipal utilities.
5
Learn how Capgemini recommends to become an AI
Governance master on the next slides
2021
Artificial Intelligence in Energy Trading 2024 © Capgemini Invent 2024. All rights reserved. 13
To ensure a capability-driven development we recommend a
“Use Case First” approach on the road to AI mastery
1 Road to AI Master What does this mean?
Capgemini recommends an N-shaped
In the pursuit of AI mastery, prioritize developing AI
path to AI mastery, to ensure a
capability-drivendevelopment. capabilities before establishing governance. This
2 acknowledges that creating governance structures often incurs
highercostsandsettingthemupwithoutclearneedscanresult
4
inunfitting frameworks.
2
For Beginners, skill enhancement begins by experimenting
with use cases. Prioritizing personal AI capabilities before
3 y institutionalizing governance, facilitates swift evaluation of
t
i l potential cases and clearer identification of high-value
i
b
opportunities.
a 3
p
a 1
C Explorers with advanced AI skills should concentrate on
establishing AI governance for their capabilities to yield
4
desired impacts. However, this process might temporarily slow
downusecasedevelopment.
Managers equipped with robust AI governance should
prioritize high-impact use cases, scaling them up to maximize
theirvalue.
5
Governance
Artificial Intelligence in Energy Trading 2024 © Capgemini Invent 2024. All rights reserved. 14
Use case application differs widely between maturity clusters,
while Front Office represents the most active application area
Established Use Case Categories Popularity
1 Maturity Cluster Capgemini Interpretation
Trading Area Use Cases Beginner Explorer Manager Master
Automated Trading
1 Trade execution remains the most
Decision supporting
actively pursued use case across
Trade
Execution Trade timing optimization 1 differentmaturityclusters.
Algorithmic trading in financial markets (derivatives)
2
Algorithmic trading in short-term physical markets 2 Only Masters & Explorers venture
Price forecasting into the Middle- & Backoffice use
Financial Cash flow forecasting cases.
Forecasting Revenue and cost forecasting
Generation of custom-made financial plans and investment strategies 3 Explorers are heavily active in
physical operations, which can be
Information Extraction
3
Trade partially explained by the fact that
Automated trade capturing via voice-to-text recognition
Capturing 5 4
the majority are integrated energy
Automated trade capturing via text-to-text recognition
companies.
Predictive load forecasting
Physical
Scheduling and balancing optimization 3
Operations 4 Managers have the most
Automated nomination on a continuous real-time basis
concentrated/limited range of use
4
Anomaly detection
cases, focusing on trade execution,
Fraud detection
trade capturing & physical
Risk
Liquidity risk forecasting
Management operations.
Predictive credit scoring
Predicting risk assessment (market, price, operational, etc.) 2 5 Beginners experiment in all
Supply chain monitoring application areas and lack a common
5 Compliance
Trade Surveillance focusarea.
Automated Report Generation
Billing &
Intelligent and automated reconciliation
Settlement % of companies within
Intelligent invoice processing via Optical Character Recognition 0% 100%
maturity-cluster
Artificial Intelligence in Energy Trading 2024 © Capgemini Invent 2024. All rights reserved. 15
All maturity clusters tend to focus and expand on known use
case areas in Front Office before extending into new areas
1
% of overall
Number of use cases per mariurity cluster & trading area
Front Office
use cases
Trade Execution 3 13 4 11 30,4%
2
Financial Forecasting 3 12 0 6 20,6%
Trade Capturing 2 5 4 5 15,7%
3
Physical Operations 5 9 3 0 16,7%
4 Risk Management 3 3 0 4 9,8%
Beginner
Compliance 2 0 0 2 3,9%
Explorer
5 Manager
Billing & Settlement 0 0 0 3 2,9%
Master
Back Office
It is a self-propellingsituation:Tradingareas inwhichuse cases are establishedare being further expanded!
Artificial Intelligence in Energy Trading 2024 © Capgemini Invent 2024. All rights reserved. 16
Only beginners are strongly reliant on exclusive buy solutions,
whereas more mature participants use make or hybrid systems
1
EXPLORER MASTER
We have not only assessed the use cases but also
considered the decision between making or buying the
use case:
35,5%
2 42,9% 45,2% BEGINNERS
Beginners exhibit disproportionately high buy activities
58,1%
in all trading areas.
y
6,5%
t
i
l 11,9% EXPLORERS
3 i b
Explorersare planning use cases in nearly every trading
a
p BEGINNER MANAGER area; the make/buy decision is driven by use cases.
a
C
22,2% MANAGERS
4 33,3% 36,4% Managers focus on a few specific trading areas and use
45,5%
cases due to a higher emphasis on governance; the
make/buy decision is driven by use cases.
Exclusively Make
44,4% Exclusively Buy MASTERS
18,2%
Masters tackle challenging Middle- & Backoffice use
Make and Buy
5
cases and seek assistance when needed.
Governance
Artificial Intelligence in Energy Trading 2024 © Capgemini Invent 2024. All rights reserved. 17
The study shows that every maturity cluster has a different
recommended next use case
1
Value Add when Implemented
Maturity
Next Use Case Top Benefit Top Cost Reduction Interpretation
cluster
Top recommended use case for each Top benefits and cost reduction based on responses concerning These recommendations, rooted in the survey
2 maturity cluster existing use cases insights, guide toward strategic success
• Enhanced decision- • Resource optimization In AI adoption, novices explore proven price
No Activity Price forecasting making • Operational efficiency forecasting for its widespread use and simple
• Risk mitigation implementationwithexternalexpertise.
3
• Optimized liquidity • Process efficiency and error No Beginner has dealt with automated trading
Beginner Trade Execution
management reduction →Closegaptoexplorer,easytoimplement.
Explorers prioritize building governance,
No use case recommendation, main goal:
Explorer ensuring scalable deployment of diverse use
4 Focus on Governance
cases.
• Enhanced decision- • Resource optimization Managers enhance governance to fill the gap in
Financial forecasting
Manager making • Operational efficiency financial forecasting, crafting resilient
& Trade Execution
• Risk mitigation strategies.
5
Masters innovate in Middle- and Backoffice
• Early risk detection • Automated monitoring
edge cases and gain a distinctive market
Master Trade Surveillance
• Improved compliance • Labor cost savings
advantage.
Artificial Intelligence in Energy Trading 2024 © Capgemini Invent 2024. All rights reserved. 18
Capgemini’s expertise: Understanding top use cases and
offering qualified implementation recommendations
1
Use Case Assumption Methodical Implementation Effort Score Interpretation
2
• Historical data available • Precise predictions to enhance
• Linear regression
Price Forecasting • Adequate computing resources trading decisions
• Neural networks with ensemble
• … • …
learning
• Detailed financial records • Accurate insights for optimized
3 • Time series analysis
Cash flow forecasting • Adequate computing resources financial planning (liquidity etc.)
• Neural networks with ensemble
• … • …
learning
• Real-time market data • Automated trading for structured
Algorithmic trading in • Moving averages
• High-speed trading systems and faster trading activities
4 financial markets • … • Reinforcement learning • …
• Structured data sources • Extracting valuable insights
• Natural language processing
Information Extraction • Adequate data processing tools from textual data
• Transformer networks
• … • …
5
• Comprehensive risk factors • Improved risk assessment for
• Decision trees
Risk Prediction • Statistical modeling skills informed decisions
• Random forests
• … • …
Artificial Intelligence in Energy Trading 2024 © Capgemini Invent 2024. All rights reserved. 19
Shaping the future: Each trader’s current AI maturity status
sets the stage for next focus areas
What’s next?
1
Use Cases First - Governance second
1
The N-shape approach shows that exploring use cases first and governance second is
2 the most efficient way towards AI mastery. AI applications seem to be self-propelling,
hence active measures to spread applications are required to create cross department
value.
3
Diversifying Beyond Front Office Use Cases
2
Beyond the market-centric applications, companies poised for progress should explore promising
Middle- & Backoffice use cases. Despite being seemingly distant from direct revenue generation,
these initiatives play an essential role regarding efficiency and insights. The challenge lies in aligning
BackOfficeoperationswiththeaccelerated paceofFrontOfficeactivities.
4
Anticipating the Future: A Vision for Traders in the Next 3 Years?
3
In the face of reduced human involvement, the main challenge for energy trading will be to enable
5 and upskill professionals – a “Trader 2.0” - a hybrid of a traditional Front-/Middle-/Backoffice
employee and a data scientist. This convergence aims to blend the strengths of the data and energy
tradingworldsforamoreadaptiveandproficientfuture.
Artificial Intelligence in Energy Trading 2024 © Capgemini Invent 2024. All rights reserved. 20
Empower your Energy Trading Skills: Connect with us to
master AI!
Torben Schuster Alexander Krüger Harmanvir Singh
Vice President Senior Manager Senior Consultant
Energy Transition & Utilities Data-driven Energy & Utilities AI Garage
Löffelstraße 46 Mainzer Landstraße 180 Deutzer Allee 4
70597 Stuttgart, Germany 60327 Frankfurt am Main, Germany 50679 Köln, Germany
Phone: +49 (0)151 4025 2143 Phone: +49 (0)151 1137 4087 Phone: +49 (0)151 277 292 89
E-Mail: torben.schuster@capgemini.com E-Mail: alexander.krueger@capgemini.com E-Mail: harmanvir.a.singh@capgemini.com
Thomas Gebetsroither Khaled Khalil
Senior Director Manager Contributers:
Head of Data-driven Energy & Utilities Data-driven Energy & Utilities
Olof-Palme-Straße14 Potsdamer Platz 5 Dr. Markus Jahn – AI Capability Lead
81829 Munich, Germany 10785 Berlin, Germany
Tighe Clough
Phone: +49 (0)151 4025 1167 Phone: +49 (0)151 544 766 31 Finn Fromberg
E-Mail: thomas.gebetsroither@capgemini.com E-Mail: khaled.khalil@capgemini.com
Lennard Knorr
© Capgemini Invent 2024. All rights reserved.
About Capgemini Invent
As the digital innovation, design and transformation brand of the Capgemini Group,
Capgemini Invent enables CxOs to envision and shape the future of their businesses.
Located in over 30 studios and more than 60 offices around the world, it comprises a
12,500+ strong team of strategists, data scientists, product and experience designers,
brand experts and technologists who develop new digital services, products, experiences
andbusinessmodelsforsustainablegrowth.
Capgemini Invent is an integral part of Capgemini, a global business and technology
transformationpartner,helpingorganizationstoacceleratetheirdualtransitiontoadigital
and sustainable world, while creating tangible impact for enterprises and society. It is a
responsibleanddiversegroupof340,000teammembersinmorethan50countries.With
itsstrongover 55-yearheritage, Capgeminiistrustedbyitsclientstounlockthevalue of
technology to address the entire breadth of their business needs. It delivers end-to-end
services and solutions leveraging strengths from strategy and design to engineering, all
fueled by its market leading capabilities in AI, cloud and data, combined with its deep
industry expertise and partner ecosystem. The Group reported 2023 global revenues of
€22.5billion.
Get The Future You Want |www.capgemini.com/invent
This presentation contains information that may be privileged or confidential
and is the property of the Capgemini Group.
Copyright © 2024 Capgemini. All rights reserved."
27,capgemini,2024_Capgemini_Invent_Benchmark_Report_AI_in_Energy_Trading.pdf,"A RTIFICIA L INTE L L IG ENCE IN
ENERGY TRADING
A benchmark on how Artificial Intelligence is used among European Energy Traders
A study by Capgemini Invent, March 2024
Management Summary and Key Facts
We observe a 22% increase of perceived disruptiveness of AI in Energy Trading since 2021:
1
Study participants categorize AI as highly disruptive. That score increased significantly since our last survey conducted in
2021.
87% of participants are engaged in AI, up from 72% in 2021:
2
The majority of the surveyed energy trading companies is actively participating in AI. Nevertheless, participation differs
significantly between municipalities (72%) and pure energy tradingcompanies (100%).
Only 30% of participants show strong AI governance structure:
3
More than 50% of participants show significant AI capabilities according to our AI maturity matrix assessment, while only
around30% show strong AI governance.
More than 60% of 102 actually implemented use cases of our participants are Front Office related:
4 Across the different maturity clusters Trade Execution, Trade Capturing and Physical Operations represent more than
60% of all implemented use cases of the participants, whereas Middle Office and Back Office represent only ~17% of all
implementeduse cases.
Mature AI traders use “make” or “hybrid” systems for more than 90% of their use cases:
5
Beginners are highly reliant on buy solutions for AI applications with a 44% buy rate, while masters are much more reliant
on self-built or hybrid solutions (90%).
Artificial Intelligence in Energy Trading 2024 © Capgemini Invent 2024. All rights reserved. 2
Agenda
01
Understanding AI’s role in Energy Trading
(pages 4-7)
02
Study methodology and participant insights
(pages 8-10)
03
Maturity assessment of Energy Trading companies
(pages 11-14)
04
Use case landscape in Energy Trading
(pages 15-17)
05
In-depth evaluation of use cases and their methodologies
(pages 18-20)
Artificial Intelligence in Energy Trading 2024 © Capgemini Invent 2024. All rights reserved. 3
Trends on the energy market make trading more complex and
faster and are the foundation of enhanced AI considerations
1
GREENIFICATION DIGITALIZATION
The transition to cleaner and more sustainable energy sources
Digitalization of the overall energy value chain leads to
lead to new structured products and increased spot
increased automation and machine-to-machine
market relevance paired with increased short-term
interactions.
deal-counts.
2
CHANGING DEMAND BEHAVIOR ELECTRIFICATION AND E-MOBILITY
European energy
3 Smart meters, integrated decentralized
trends make Increased adoption of EVs and
renewable assets, dynamic pricing and
electricity-driven heating increases volatilities
flexibility requirements and values lead to trading more
and requires rapid data-driven decision
shifts and increased volatilities of demand complex than
making in trading and energy procurement.
behaviors of different customer segments. ever before
4
REGULATORY CHANGES AND POLICY SHIFTS DECENTRALIZATION & DEMOCRATIZATION
Regulatory changes to secure the integrity of energy The integration of scaled energy storage solutions and
5 markets and control and restrict market abuses changing balancing revenues allow for new business
impact the necessity of End-to-End data and models like aggregators and virtual power plants (VPPs)
and lead to complex orchestration requirements.
decision oversight.
Artificial Intelligence in Energy Trading 2024 © Capgemini Invent 2024. All rights reserved. 4
Participants view AI as more disruptive than in 2021 but still
have not reached “Plateau of productivity”
1 AI Disruption Score & Hype Cycle
Study participants seem to value AI as highly disruptive and
significantly higher than in our last survey conducted in 2021; from
10
5,4 to 6,6.
2 In general we observe that the peak of inflated expectations
e
r towards AI in Trading is left behind and focus is put on implementing
o
c
scalable and practical strucutures, processes and governance
S 6,6
n models.
o
i
t
p
3 u GenAI only represents a subelement of this study and can be
r
s 5,4 individually considered to be on a different part of the hype cycle
i
D
currently.
I
A
e
g
a
4 r
e
v
A
Capgemini AI in Energy
2021
1 2021 2024
5
Peak of inflated Plateau of
Innovation trigger Trough of disillusionment Slope of enlightenment
expectations productivity
Time
Artificial Intelligence in Energy Trading 2024 © Capgemini Invent 2024. All rights reserved. 5
AI requires a nuanced definition and can be largely categorized
in two overarching methodologies
1
Artificial Intelligence (AI)
Machine Learning Deep Learning
2
• Empowering systems to automatically learn & improve • Transforming representation at one level into a
from experience without being explicitly programmed representation at a higher & more abstract level
• Employing algorithms to analyze data, identify patterns • Advantage in ability to process vast amounts of bigdata
& make informed predictions
3 Supervised Learning Unsupervised Learning Reinforcement Learning
• Processes unlabeled data to identify
• Trained on labeled datasets with known • Decision-making through trial and error
patterns or relationships
outcomes • It learns by receiving rewards or
• Does not rely on predefined outcomes
• Learns patterns and relationships penalties based on its actions
• Methods: Clustering & user segmentation
between inputs and outputs • Methods: Automated agents
4
• Methods: Linear regression & classification
Examples in Energy Trading:
• Uses historical data with labeled energy • Analyzes unlabeled data like market • Executestrades of energy assets based
prices & relevant features to predict behavior & consumption patterns to gain on market conditions, refining strategies
future prices insights over time through continuous feedback
5
AI provides powerful tools that can be strategically deployed in energy trading, unlocking lucrative opportunities
Artificial Intelligence in Energy Trading 2024 © Capgemini Invent 2024. All rights reserved. 6
Machine Learning and Deep Learning methods offer a wide
tool set for AI use cases in Energy Trading
1
Tool Description
Ensemble learning method used for predicting Struggling with
Random Forests for Capturing nonlinear
energy prices by combining multiple decision overfitting on small
Price Prediction relationships
trees datasets
2 Efficient ensemble learning algorithm often Handling missing Challenging
Ensemble Learning
applied to optimize energy market predictions data & offering high interpretability of
with XGBoost
by combining multiple models predictive accuracy model
Modeling decision-making processes over time,
Accurate modeling
Markov Decision Comprehensive
making them suitable for optimizing energy
of transition
Processes (MDP) management
trading portfolios
3 portfolio approach probability required
Long Short-Term Part of Recurrent Neural Networks, effective in Modeling complex Struggling with
Memory (LSTM) capturing sequential dependencies for time patternsin energy abrupt changes in
Networks series forecasting in energy markets market trends market conditions
4 Transformer Models Capturing & handling Requiring large
Analyzing historical energy market data to predict
for Time Series global, long-range amount of data for
future trends and price movements
Forecasting dependencies in data effective training
Employing generative algorithms to simulate and Enhancing Requiring careful
GenAI propose diverse trading strategies based on adaptabilityto scrutiny and testing
5 historical market data dynamic markets before implementing
The AI toolset is vast and proves highly beneficial for numerous use cases within the realm of energy trading, addressing a wide
range of potentialchallenges.
Artificial Intelligence in Energy Trading 2024 © Capgemini Invent 2024. All rights reserved. 7
Our 2024 survey analyzed 22 Energy Trading companies in
Europe to identify trends and patterns in AI approaches
1
Company Type by Survey Year
22 Participants 22 Participants
The goal of this study is to identify the trends of AI adoption
2
within the energy trading industry enriched by a trend analysis
based on our last AI inEnergy TradingSurvey in2021.
8
9
3
7
Capgemini Invent surveyed 22 energy traders in Europe with a
8
focus on Central Europe.
4
7
5
Our guiding question is: What is the current and changed picture
2021 2024
of AI-maturity, relevant use case maturity, and technology Survey Year
adoption strategy in the Central European energy trading
5 Fully integrated energy company
industry?
Municipal utility company
Pure energy trading company
Artificial Intelligence in Energy Trading 2024 © Capgemini Invent 2024. All rights reserved. 8
Throughout all company types AI participation in the Energy
Trading industry is high and increased from 73% to 87%
1 87
%
AI Participation Rate in Energy Trading
AI participation rate (current and planned implementation), group averages, %
of energy trading organizations participate in AI
2
100
PURE ENERGY TRADING COMPANIES: With a 100%
90
87 88 adoption rate in this year’s survey pure energy trading
companiesstandoutintheAIadoptiondegree.
3 73 72
70 FULLY INTEGRATED ENERGY COMPANIES: 9 out of 10
fully integrated energy companies use AI in their trading
operations. 3 years after our first survey fully integrated
energy companies have the same adoption rate as pure
50
energytradersin2021.
4
MUNICIPAL UTILITY COMPANIES: Municipal utility
companies continue to represent the lowest degree of AI
adoption. With an almost 50% increase municipal
utilities witnessed the relatively spoken biggest increase
ofAIadoption acrossallsegments.
5
All Respondents Fully integrated energy Municipal utility Pure energy trading
companies companies companies
2021 2024
Artificial Intelligence in Energy Trading 2024 © Capgemini Invent 2024. All rights reserved. 9
Perceiving AI as a competitive advantage is highly correlated
with the AI understanding in the surveyed companies
1
High perceived AI understanding
significantly influences the energy
Organization’s Self-Rated AI Understanding vs. AI Competitiveness
trader's competitive edge.
10
Unlocking AI competitiveness through
2
understanding
9
8 In our survey, a compelling correlation emerges:
Perceiving AI as a competitive advantage is highly
7 correlated with the AI understanding in the surveyed
s
s
e companies. This relationship underscores the pivotal
3 n 6
v roleofAILiteracy inbolsteringmarketadvantage.
i
t
i
t 5
e
To gain a competitive advantage, enhancing AI
p
m
understanding or AI Literacy is vital. This often stems
o 4
c from hands-on experience with AI use cases. Notably,
I
A companies excelling in perceived AI understanding have
4 3
alreadycapitalized onthesepracticalapplications.
2
1
0
5
0 1 2 3 4 5 6 7 8 9 10
AI understanding
Artificial Intelligence in Energy Trading 2024 © Capgemini Invent 2024. All rights reserved. 10
More than 50% of participants have built up significant AI
capabilities, while less than 30% show strong AI governance
AI Maturity Matrix (AIMM)
1 Company Type
%
0 EXPLORER
0
1
4 MASTER S Integrated
t
r
o
n Trader
g
2
c Municipal
“ r
o
2 pa utilities
r a
r
e b
d i
n il Attitude
a t
y l ia ei
t i r T s “AI disruptive”
3 l „
i
b
a “AI not disruptive”
p BEGINNER
a
MANAGER
C
3
Trading
4 “Power trader”
(10.000
transactions
NO ACTIVITY & prop. trading)
1 Strong governance
Company cluster
5
Organizational structure: „Rules before action“
5
1 Cluster 1 to 5
Governance
0% 100%
Artificial Intelligence in Energy Trading 2024 © Capgemini Invent 2024. All rights reserved. 11
Based on the AI governance and AI capabilities 4 different
maturity groups can be differentiated
1
AI Maturity Matrix BEGINNER –What’s in it for me?
“Beginners”arejustgettingataste of things,hardlyusingany formofAI.Theyhavearelatively low
level of governance structures, organize AI in a largely decentralized manner, mostly do not employ
AI experts, and are implementing their first AI projects, which are usually intended to prove the
EXPLORER MASTER
viability of AI. With their first projects, they gain initial experience and prepare the organization for
furthersteps.
2 y
t
i EXPLORER –Trial and error.
l
i
b
“Explorers” are feeling their way forward. They have a relatively low level of governance
a
p a BEGINNER MANAGER s e g structures, organize AI in a largely decentralized manner, employ only a few AI experts, and have
C
a initial scaled AI projects that represent isolated organizational solutions. With their projects, they
t
S
continue to expand their know-how in specific domains and thus increase personal skills and
y
3 r a organizationallearning.
Governance i d
e MANAGER –Rules before action.
m
“Managers” rely on an organizational framework. They show a high degree of governance
r
e
t structures,usuallyorganizeAI inacentralunit,andmayalready havehiredAI experts.Onthisbasis,
n
No activity
I they planand develop thefirstAI projects. Witheachadditionalproject,structuresgrowfirst,before
experienceandknow-howcomeintoplay.
4
Capability MASTER –AI is in my DNA.
”AI Masters” know what they are doing. They show a high degree of governance structures,
How skilled are employees and how developed is organize AI in a central unit, employ experts, and engage in very mature AI projects that require a
the AI pipeline infrastructure? high level of organizational embedding. With their numerous and diversified use cases, they have
perfectedtheirexperienceandbuiltreliableprogrammingskills.
5 Governance
NO ACTIVITY –AI is nothing for me.
ThesecompanieshavenoAIinitiatives.OrganizationalstructuresremainunaffectedbyAIsofarand
How well is AI structured into the organization and
thecompaniesarenotbuildingupexperienceinthisarea.
progressing with a clear strategy?
Artificial Intelligence in Energy Trading 2024 © Capgemini Invent 2024. All rights reserved. 12
In comparison to 2021, Explorers & Masters double while
a structural trend towards more governance can be observed
1
Maturity Cluster Evolution
Maturity Cluster Evolution from 2021 to 2024 Trend: 2024 vs. 2021 results
1
BEGINNERS
Experienced a substantial decline, as many companies have
+100%
2
begun to actively involve themselves with AI.
4
2
EXPLORERS
+100%
There has been a stronger focus on governance, as many
2
companies have already built a solid knowledge base.
3 y 3
t MANAGERS
i
l i Experienced a decline as they successfully advanced to
b
a master level, driven by the establishment of essential
p 3
a capabilities.
C 4
MASTERS
2024 1
4 Substantial increase as maturity advances within the
-20%
industry, leading to initial master-level entities.
5
-17%
5
NO ACTIVITY
-50%
Drastic decrease, since many companies have started
dealing with AI. The remaining are mostly municipal utilities.
5
Learn how Capgemini recommends to become an AI
Governance master on the next slides
2021
Artificial Intelligence in Energy Trading 2024 © Capgemini Invent 2024. All rights reserved. 13
To ensure a capability-driven development we recommend a
“Use Case First” approach on the road to AI mastery
1 Road to AI Master What does this mean?
Capgemini recommends an N-shaped
In the pursuit of AI mastery, prioritize developing AI
path to AI mastery, to ensure a
capability-drivendevelopment. capabilities before establishing governance. This
2 acknowledges that creating governance structures often incurs
highercostsandsettingthemupwithoutclearneedscanresult
4
inunfitting frameworks.
2
For Beginners, skill enhancement begins by experimenting
with use cases. Prioritizing personal AI capabilities before
3 y institutionalizing governance, facilitates swift evaluation of
t
i l potential cases and clearer identification of high-value
i
b
opportunities.
a 3
p
a 1
C Explorers with advanced AI skills should concentrate on
establishing AI governance for their capabilities to yield
4
desired impacts. However, this process might temporarily slow
downusecasedevelopment.
Managers equipped with robust AI governance should
prioritize high-impact use cases, scaling them up to maximize
theirvalue.
5
Governance
Artificial Intelligence in Energy Trading 2024 © Capgemini Invent 2024. All rights reserved. 14
Use case application differs widely between maturity clusters,
while Front Office represents the most active application area
Established Use Case Categories Popularity
1 Maturity Cluster Capgemini Interpretation
Trading Area Use Cases Beginner Explorer Manager Master
Automated Trading
1 Trade execution remains the most
Decision supporting
actively pursued use case across
Trade
Execution Trade timing optimization 1 differentmaturityclusters.
Algorithmic trading in financial markets (derivatives)
2
Algorithmic trading in short-term physical markets 2 Only Masters & Explorers venture
Price forecasting into the Middle- & Backoffice use
Financial Cash flow forecasting cases.
Forecasting Revenue and cost forecasting
Generation of custom-made financial plans and investment strategies 3 Explorers are heavily active in
physical operations, which can be
Information Extraction
3
Trade partially explained by the fact that
Automated trade capturing via voice-to-text recognition
Capturing 5 4
the majority are integrated energy
Automated trade capturing via text-to-text recognition
companies.
Predictive load forecasting
Physical
Scheduling and balancing optimization 3
Operations 4 Managers have the most
Automated nomination on a continuous real-time basis
concentrated/limited range of use
4
Anomaly detection
cases, focusing on trade execution,
Fraud detection
trade capturing & physical
Risk
Liquidity risk forecasting
Management operations.
Predictive credit scoring
Predicting risk assessment (market, price, operational, etc.) 2 5 Beginners experiment in all
Supply chain monitoring application areas and lack a common
5 Compliance
Trade Surveillance focusarea.
Automated Report Generation
Billing &
Intelligent and automated reconciliation
Settlement % of companies within
Intelligent invoice processing via Optical Character Recognition 0% 100%
maturity-cluster
Artificial Intelligence in Energy Trading 2024 © Capgemini Invent 2024. All rights reserved. 15
All maturity clusters tend to focus and expand on known use
case areas in Front Office before extending into new areas
1
% of overall
Number of use cases per mariurity cluster & trading area
Front Office
use cases
Trade Execution 3 13 4 11 30,4%
2
Financial Forecasting 3 12 0 6 20,6%
Trade Capturing 2 5 4 5 15,7%
3
Physical Operations 5 9 3 0 16,7%
4 Risk Management 3 3 0 4 9,8%
Beginner
Compliance 2 0 0 2 3,9%
Explorer
5 Manager
Billing & Settlement 0 0 0 3 2,9%
Master
Back Office
It is a self-propellingsituation:Tradingareas inwhichuse cases are establishedare being further expanded!
Artificial Intelligence in Energy Trading 2024 © Capgemini Invent 2024. All rights reserved. 16
Only beginners are strongly reliant on exclusive buy solutions,
whereas more mature participants use make or hybrid systems
1
EXPLORER MASTER
We have not only assessed the use cases but also
considered the decision between making or buying the
use case:
35,5%
2 42,9% 45,2% BEGINNERS
Beginners exhibit disproportionately high buy activities
58,1%
in all trading areas.
y
6,5%
t
i
l 11,9% EXPLORERS
3 i b
Explorersare planning use cases in nearly every trading
a
p BEGINNER MANAGER area; the make/buy decision is driven by use cases.
a
C
22,2% MANAGERS
4 33,3% 36,4% Managers focus on a few specific trading areas and use
45,5%
cases due to a higher emphasis on governance; the
make/buy decision is driven by use cases.
Exclusively Make
44,4% Exclusively Buy MASTERS
18,2%
Masters tackle challenging Middle- & Backoffice use
Make and Buy
5
cases and seek assistance when needed.
Governance
Artificial Intelligence in Energy Trading 2024 © Capgemini Invent 2024. All rights reserved. 17
The study shows that every maturity cluster has a different
recommended next use case
1
Value Add when Implemented
Maturity
Next Use Case Top Benefit Top Cost Reduction Interpretation
cluster
Top recommended use case for each Top benefits and cost reduction based on responses concerning These recommendations, rooted in the survey
2 maturity cluster existing use cases insights, guide toward strategic success
• Enhanced decision- • Resource optimization In AI adoption, novices explore proven price
No Activity Price forecasting making • Operational efficiency forecasting for its widespread use and simple
• Risk mitigation implementationwithexternalexpertise.
3
• Optimized liquidity • Process efficiency and error No Beginner has dealt with automated trading
Beginner Trade Execution
management reduction →Closegaptoexplorer,easytoimplement.
Explorers prioritize building governance,
No use case recommendation, main goal:
Explorer ensuring scalable deployment of diverse use
4 Focus on Governance
cases.
• Enhanced decision- • Resource optimization Managers enhance governance to fill the gap in
Financial forecasting
Manager making • Operational efficiency financial forecasting, crafting resilient
& Trade Execution
• Risk mitigation strategies.
5
Masters innovate in Middle- and Backoffice
• Early risk detection • Automated monitoring
edge cases and gain a distinctive market
Master Trade Surveillance
• Improved compliance • Labor cost savings
advantage.
Artificial Intelligence in Energy Trading 2024 © Capgemini Invent 2024. All rights reserved. 18
Capgemini’s expertise: Understanding top use cases and
offering qualified implementation recommendations
1
Use Case Assumption Methodical Implementation Effort Score Interpretation
2
• Historical data available • Precise predictions to enhance
• Linear regression
Price Forecasting • Adequate computing resources trading decisions
• Neural networks with ensemble
• … • …
learning
• Detailed financial records • Accurate insights for optimized
3 • Time series analysis
Cash flow forecasting • Adequate computing resources financial planning (liquidity etc.)
• Neural networks with ensemble
• … • …
learning
• Real-time market data • Automated trading for structured
Algorithmic trading in • Moving averages
• High-speed trading systems and faster trading activities
4 financial markets • … • Reinforcement learning • …
• Structured data sources • Extracting valuable insights
• Natural language processing
Information Extraction • Adequate data processing tools from textual data
• Transformer networks
• … • …
5
• Comprehensive risk factors • Improved risk assessment for
• Decision trees
Risk Prediction • Statistical modeling skills informed decisions
• Random forests
• … • …
Artificial Intelligence in Energy Trading 2024 © Capgemini Invent 2024. All rights reserved. 19
Shaping the future: Each trader’s current AI maturity status
sets the stage for next focus areas
What’s next?
1
Use Cases First - Governance second
1
The N-shape approach shows that exploring use cases first and governance second is
2 the most efficient way towards AI mastery. AI applications seem to be self-propelling,
hence active measures to spread applications are required to create cross department
value.
3
Diversifying Beyond Front Office Use Cases
2
Beyond the market-centric applications, companies poised for progress should explore promising
Middle- & Backoffice use cases. Despite being seemingly distant from direct revenue generation,
these initiatives play an essential role regarding efficiency and insights. The challenge lies in aligning
BackOfficeoperationswiththeaccelerated paceofFrontOfficeactivities.
4
Anticipating the Future: A Vision for Traders in the Next 3 Years?
3
In the face of reduced human involvement, the main challenge for energy trading will be to enable
5 and upskill professionals – a “Trader 2.0” - a hybrid of a traditional Front-/Middle-/Backoffice
employee and a data scientist. This convergence aims to blend the strengths of the data and energy
tradingworldsforamoreadaptiveandproficientfuture.
Artificial Intelligence in Energy Trading 2024 © Capgemini Invent 2024. All rights reserved. 20
Empower your Energy Trading Skills: Connect with us to
master AI!
Torben Schuster Alexander Krüger Harmanvir Singh
Vice President Senior Manager Senior Consultant
Energy Transition & Utilities Data-driven Energy & Utilities AI Garage
Löffelstraße 46 Mainzer Landstraße 180 Deutzer Allee 4
70597 Stuttgart, Germany 60327 Frankfurt am Main, Germany 50679 Köln, Germany
Phone: +49 (0)151 4025 2143 Phone: +49 (0)151 1137 4087 Phone: +49 (0)151 277 292 89
E-Mail: torben.schuster@capgemini.com E-Mail: alexander.krueger@capgemini.com E-Mail: harmanvir.a.singh@capgemini.com
Thomas Gebetsroither Khaled Khalil
Senior Director Manager Contributers:
Head of Data-driven Energy & Utilities Data-driven Energy & Utilities
Olof-Palme-Straße14 Potsdamer Platz 5 Dr. Markus Jahn – AI Capability Lead
81829 Munich, Germany 10785 Berlin, Germany
Tighe Clough
Phone: +49 (0)151 4025 1167 Phone: +49 (0)151 544 766 31 Finn Fromberg
E-Mail: thomas.gebetsroither@capgemini.com E-Mail: khaled.khalil@capgemini.com
Lennard Knorr
© Capgemini Invent 2024. All rights reserved.
About Capgemini Invent
As the digital innovation, design and transformation brand of the Capgemini Group,
Capgemini Invent enables CxOs to envision and shape the future of their businesses.
Located in over 30 studios and more than 60 offices around the world, it comprises a
12,500+ strong team of strategists, data scientists, product and experience designers,
brand experts and technologists who develop new digital services, products, experiences
andbusinessmodelsforsustainablegrowth.
Capgemini Invent is an integral part of Capgemini, a global business and technology
transformationpartner,helpingorganizationstoacceleratetheirdualtransitiontoadigital
and sustainable world, while creating tangible impact for enterprises and society. It is a
responsibleanddiversegroupof340,000teammembersinmorethan50countries.With
itsstrongover 55-yearheritage, Capgeminiistrustedbyitsclientstounlockthevalue of
technology to address the entire breadth of their business needs. It delivers end-to-end
services and solutions leveraging strengths from strategy and design to engineering, all
fueled by its market leading capabilities in AI, cloud and data, combined with its deep
industry expertise and partner ecosystem. The Group reported 2023 global revenues of
€22.5billion.
Get The Future You Want |www.capgemini.com/invent
This presentation contains information that may be privileged or confidential
and is the property of the Capgemini Group.
Copyright © 2024 Capgemini. All rights reserved."
28,capgemini,C3-AI-Capgemini-Alliance-Press-Release_Final-1.pdf,"C3 AI and Capgemini Extend Partnership to Accelerate Enterprise AI
for Business Transformation
AI-powered solutions to drive operational excellence across diverse industries
REDWOOD CITY, Calif. — November 20, 2024 — C3 AI (NYSE: AI), the Enterprise AI
software application company, and Capgemini, a global leader in business and
technology transformation, today announced an expansion of their partnership. This
partnership is designed to accelerate and enhance the delivery of Enterprise AI solutions
across industries to help clients realize significant benefits including improved efficiency,
productivity, and cost reduction.
This partnership combines the power of C3 AI’s Enterprise AI applications with
Capgemini’s deep industry expertise and proven global implementation capabilities and
capacity. Together, the two companies will deliver Enterprise AI solutions tailored to
specific industries including life sciences, energy, utilities, government, banking, and
manufacturing. To drive this partnership extension, Capgemini will expand its dedicated
C3 AI global practice, focused on delivering Enterprise AI solutions to joint clients across
industries at scale, with rapid time to value.
“AI is reshaping the way we work and business leaders across industries are focused on
leveraging its transformative potential. Our collaboration with Capgemini will empower
organizations to operate more efficiently, innovate faster, and gain a competitive edge
through Enterprise AI,” said Thomas M. Siebel, Chairman and CEO, C3 AI. “Our
partnership with Capgemini dramatically expands our service and delivery capacity,
ensuring the continued success of our growing customer base at global scale.”
Capgemini brings a proven track record in managing large-scale digital transformation
initiatives, helping organizations integrate cutting-edge technologies into their operations.
Together, Capgemini and C3 AI are already helping a number of joint clients realize
business value through improved efficiency for streamlined manufacturing.
“Our collaboration with C3 AI reflects a joint vision of enabling businesses to thrive in a
rapidly evolving digital landscape,” said Aiman Ezzat, CEO of Capgemini. “By combining
Capgemini’s transformation expertise with C3 AI’s world-class platform and applications,
we will help organizations across the globe to achieve operational resilience, accelerate
time-to-value, and stay ahead in their industries.”
###
About C3.ai, Inc.
C3 AI is the Enterprise AI application software company. C3 AI delivers a family of fully
integrated products including the C3 AI Platform, an end-to-end platform for developing,
deploying, and operating enterprise AI applications, C3 AI applications, a portfolio of
industry-specific SaaS enterprise AI applications that enable the digital transformation of
organizations globally, and C3 Generative AI, a suite of domain-specific generative AI
offerings for the enterprise.
C3 AI Public Relations
Edelman
Lisa Kennedy
415-914-8336
pr@c3.ai
Investor Relations
ir@c3.ai
About Capgemini
Capgemini is a global business and technology transformation partner, helping
organizations to accelerate their dual transition to a digital and sustainable world, while
creating tangible impact for enterprises and society. It is a responsible and diverse group
of 340,000 team members in more than 50 countries. With its strong over 55-year
heritage, Capgemini is trusted by its clients to unlock the value of technology to address
the entire breadth of their business needs. It delivers end-to-end services and solutions
leveraging strengths from strategy and design to engineering, all fueled by its market
leading capabilities in AI, cloud and data, combined with its deep industry expertise and
partner ecosystem. The Group reported 2023 global revenues of €22.5 billion.
Get The Future You Want | www.capgemini.com
Capgemini Media Relations
Mollie Mellows
Tel.: +44 (0)7342 709384
mollie.mellows@capgemini.com"
29,hbs_edu,download.aspx.pdf,"Working Paper 25-021
Generative AI and the Nature of Work
Manuel Hoffmann
Sam Boysel
Frank Nagle
Sida Peng
Kevin Xu
Generative AI and the Nature of Work
Manuel Hoffmann
Harvard Business School
Sam Boysel
Harvard Business School
Frank Nagle
Harvard Business School
Sida Peng
Microsoft Corporation
Kevin Xu
GitHub Inc.
Working Paper 25-021
Copyright © 2024 by Manuel Hoffmann, Sam Boysel, Frank Nagle, Sida Peng, and Kevin Xu.
Working papers are in draft form. This working paper is distributed for purposes of comment and discussion only. It may
not be reproduced without permission of the copyright holder. Copies of working papers are available from the author.
Funding for this research was provided in part by Harvard Business School.
Generative AI and The Nature of Work
Manuel Hoffmann∗ Sam Boysel∗ Frank Nagle∗ Sida Peng† Kevin Xu∥
∗HarvardBusinessSchool,HarvardUniversity
†MicrosoftCorporation
∥GitHubInc.
This version: October 27, 2024
Abstract: Recent advances in artificial intelligence (AI) technology demonstrate considerable potential to
complementhumancapitalintensiveactivities. Whileanemergingliteraturedocumentswide-rangingpro-
ductivity effects of AI, relatively little attention has been paid to how AI might change the nature of work
itself. How do individuals, especially those in the knowledge economy, adjust how they work when they
start using AI? Using the setting of open source software, we study individual level effects that AI has on
task allocation. We exploit a natural experiment arising from the deployment of GitHub Copilot, a gener-
ative AI code completion tool for software developers. Leveraging millions of work activities over a two
year period, we use a program eligibility threshold to investigate the impact of AI technology on the task
allocationofsoftwaredeveloperswithinaquasi-experimentalregressiondiscontinuitydesign. Wefindthat
havingaccesstoCopilotinducessuchindividualstoshifttaskallocationtowardstheircoreworkofcoding
activitiesandawayfromnon-coreprojectmanagementactivities. Weidentifytwounderlyingmechanisms
drivingthisshift-anincreaseinautonomousratherthancollaborativework,andanincreaseinexploration
activitiesratherthanexploitation. Themain effectsaregreaterforindividualswithrelativelylowerability.
Overall,ourestimatespointtowardsalargepotentialforAItotransformworkprocessesandtopotentially
flattenorganizationalhierarchiesintheknowledgeeconomy.
JEL-Classification: H4,O3,J0
Keywords: GenerativeArtificialIntelligence,DigitalWork,OpenSourceSoftware,KnowledgeEconomy
Acknowledgement: TheauthorsaregratefulforfinancialandadministrativesupportfromGitHuband,inparticular,
forgenerousadvicefromPeterCihon. WethankShaneGreenstein,TimSimcoe,DavidAutor,andSamRansbotham
fortheirfeedback. Theauthorsarealsoindebtedforcommentsbyseminarparticipantsattheresearchseminarsfrom
theHarvardLaboratoryforInnovationScience,BostonUniversity,theMassachusettsInstituteofTechnology,andthe
University of Passau. We are further grateful for feedback from participants at the“Labor in the Age of Generative
AI”conferenceattheUniversityofChicago,theNBERSI2024DigitalEconomicsandArtificialIntelligenceinCam-
bridge,MA,the2024NBERProductivitySeminarinCambridge,MA,the2024AcademyofManagementSciencein
Chicago,IL,the22ndZEWEconomicsofICTconference,inMannheim, Germany,the20thSymposiumonStatis-
ticalChallengesinElectronicCommerceResearchinLisbon,Portugal,theACMCollectiveIntelligenceConference
in Boston, MA, the MIT Code Conference in Cambridge MA and the CESifo Area Conference on Economics of
Digitization2024inMunich,Germany.
Throughouthumanhistory,therehavebeenahandfuloftechnologicalinnovationsthatfundamen-
tallyshifthowtheeconomyworks. Theprintingpress,internalcombustionengine,andcomputers
are oft-cited examples of such general purpose technologies. Although artificial intelligence (AI)
has existed for some time, many have argued that recent advances may push it into this elite cate-
goryoftechnologiesthatalterthecourseofhistory(Crafts,2021;Goldfarb,Taska,andTeodoridis,
2023; Eloundou et al., 2024). If AI — broadly defined as the use of computers and machines to
mimic human intelligence –– is destined to have such a substantial impact, we are likely still at
thebeginningofthistechnologicalrevolutionthatisslowlyandsteadilyreachingallsectorsofthe
economy (Acemoglu et al., 2022). Importantly, the highest economic impact of AI is predicted
to be on productivity growth through the labor market, especially in knowledge intensive indus-
tries (Bughin and Manyika, 2018; Sachs, 2023). However, due to the novelty and breadth of AI,
researchisonlystartingtoelucidateitsimpactonthenatureofworkandtaskallocationinproduc-
tion settings. This is particularly true of generative AI (generative AI) — a subset of AI built on
large-language machine-learning models (LLMs) — which exploded on to the scene in 2022 and
currently represents the cutting-edge of AI. These models, including OpenAI’s GPT4, Google’s
Gemini,1 Meta’s LLaMa, and numerous others, are trained on massive, Internet-scale databases
and use billions of parameters to construct a probabilistic model that predicts what the next word
in an answer to a prompt from a user should be. These models can also be trained on datasets that
aremorefocusedonspecificcontexts—e.g.,health,finance,customerservice,softwaredevelop-
ment, etc. Whether and how these new technologies will shape the nature of work remain open
questions. Further, whether AI can be a complement to skilled workers (Autor, 2024) and help
address critical aspects of team production, especially in the context of distributed work, has gone
under-explored.
Although some early studies on generative AI have shown positive high-level productivity
impacts (Brynjolfsson, Li, and Raymond, 2023; Dohmke, Iansiti, and Richards, 2023; Noy and
Zhang, 2023; Peng et al., 2023), it is less clear what the mechanisms behind these improvements
1FormerlyknownasBard.
1
are. Does the use of generative AI shift users to focus on particular types of tasks that lead to
those productivity improvements? If so, which tasks? How exactly does the work process change
when using generative AI? To answer these questions, we develop a theoretical model that leads
to testable hypotheses that offer insights into where and why the most salient impacts are likely
to occur. Understanding these impacts informs labor strategy in a manner relevant to both firms
(Tamayo et al., 2023) and policymakers (U.S. Department of Labor, 2024), including hiring poli-
cies,worktrainingprograms,andupskillingorreskillingeffortsforcurrentemployees.
ThekeychallengeintestingourhypothesesandassessinghowAIchangesthenatureofwork
istoidentifyasettingwhere(1)workpatternsareobservableand(2)anAItoolspecificallytailored
for workers has been introduced in a quasi-exogenous manner. Our setting — the introduction of
GitHub Copilot, a software development generative AI tool, for key developers (known as main-
tainers) in open source software (OSS) projects — addresses both of these criteria. OSS source
code is publicly available and permissively licensed for use, modification, and redistribution. Fre-
quently developed by distributed teams of developers, OSS is a classic example of a product that
isproducedthroughthedistributedworkofteamsandisgenerallyfree(MoonandSproull,2002).
Although OSS creates societal value on the order of trillions of dollars (Hoffmann, Nagle, and
Zhou,2024)andisthereforeimportantinitsownright,weargueandprovidesuggestiveevidence
that the findings in this setting generalize to the broader set of work activities that occur in the
knowledge economy. Further, as with many team production settings, OSS also suffers from the
“linchpin” problem (Ballester, Calvo´-Armengol, and Zenou, 2006; Godin, 2010) as a small set
of developers are the driving force behind the widely used and incredibly valuable digital infras-
tructure that has come to underlie software development and the modern economy as a whole
(Eghbal, 2020; Geiger, Howard, and Irani, 2021; Hoffmann, Nagle, and Zhou, 2024). In practice,
an influx of non-experts enabled by decreasing communication costs (Altman, Nagle, and Tush-
man, 2015) creates an additional burden on developers, who must triage support requests, review
contributions, and otherwise manage their project’s growing community. Indeed, survey evidence
documentsthatthosemaintainerstendtobeoverburdenedwithtoolittleoftheirtimespentontheir
2
corework(coding)andtoomuchonmanagerial(projectmanagement)tasks(Nagleetal.,2020a).
With these factors in mind, interventions with the potential to relax constraints on key individuals
are of great interest to the distributed production setting of OSS and are likely to generalize to
numerousothersettingsasdistributedworkhasbecomeincreasinglycommon.
We exploit aspects of the general access launch of Github Copilot to the broader public in
June 2022 to establish causal effects of generative AI where some developers below a certain
threshold of an internal ranking received free access to the coding AI and others did not. We
start with a panel of 187,489 distinct developers observed weekly from July 2022 through July
2024, which results in millions of developer-week observations for Copilot usage and activity
levels in public GitHub repositories.2 Within the data set of top developers, we find that those
who receive free access to Copilot during the general access period increase their relative share of
coding tasks while reducing their relative share of project management activities. The dynamics
of the treatment effects are stable for our two year period. We dig further into the mechanisms
underlying these effects and find that they are driven by an increase in autonomous behavior (and
a related decrease in collaborative behavior) and an increase in exploration behavior (rather than
exploitation). Further, we find lower ability developers who receive access to AI increase coding
and reduce project management to a greater extent compared with their higher ability peers. The
results are robust to the standard regression discontinuity design tests and to different estimation
proceduressuchasdifference-in-differenceandmatching. Further,theresultsareconsistentwhen
considering whether developers are working on behalf of their employers or as volunteers, adding
support to the likelihood that these findings generalize beyond the OSS setting to a broader set of
workers.
Our results contribute to a growing literature on the productivity impacts of AI in important
ways. Early work in this area posits general productivity gains (Agrawal, Gans, and Goldfarb,
2019; Corrado, Haskel, and Jona-Lasinio, 2021; Raj and Seamans, 2018), but that the gains may
not be evenly distributed (Brynjolfsson, Rock, and Syverson, 2018; Furman and Seamans, 2019).
2AGitHubrepositoryisalocationwhereallaspectsofaprojectarestoredincludingitssourcecode,documentation,
andrevisionhistory.
3
Subsequent empirical work has largely confirmed these predictions and found wide-ranging pro-
ductivity benefits to using AI, at both the firm level (Czarnitzki, Ferna´ndez, and Rammer, 2023)
and the individual level (Fu¨gener et al., 2022). Particularly related to this study, research fo-
cused on Copilot specifically has either been conducted using a much smaller sample of workers
within firms (Cui et al., 2024) or relying on observational data without the benefit of knowing
precisely which contributors to OSS were given free access to Copilot (Yeverechyahu, Mayya,
and Oestreicher-Singer, 2024). Our work is consistent with this prior research but adds additional
nuance to the labor augmenting technical change literature (Acemoglu, 2003). By going beyond
productivity to explore how technology changes the nature of work, we provide one of the largest
natural experiments of generative AI and it’s impact on highly disaggregated measures of work
processes“inthewild”overatwoyeartimehorizon.
Our main findings identify changes in the nature of work of AI adopters in their knowledge
work processes. We show that when software developers leverage AI more, they reallocate their
efforts towards technical coding activities and away from auxiliary project management activities
that involve social interactions with other developers. This is a sign that the workers likely will
intensify their core contributions to public goods, such as open source software, when leverag-
ingskillaugmentingtechnologylikegenerativeAI.Itisalsoconsistentwithreducedcollaborative
frictionsduringtheproblemsolvingprocessofworkandachangeinthewayworkersinteractwith
eachotherontheplatform. WecomplementthecurrentliteraturethatleveragesITandconsultancy
chat support AIs and focuses on high-level productivity impacts through experimentation (Bryn-
jolfsson, Li, and Raymond, 2023; Dell’Acqua et al., 2023) by investigating the nature of work
through changes in work activities and human interaction processes over the two years following
theintroductionoftheprogrammingLLM.
Beyond the identification of causal effects that generative AI has on decentralized work, our
results suggest important implications for the future of OSS. OSS has received growing attention
(Lerner and Tirole, 2002) as it has become an increasingly critical part of the modern economy,
to the point where 96% of corporate codebases contain some open source code (Synopsys, 2023).
4
Further, recent studies estimate the value of OSS to be on the order of billions of dollars for
the supply side (Blind et al., 2021; Robbins et al., 2021) and trillions of dollars when account-
ing for usage (Hoffmann, Nagle, and Zhou, 2024). Additionally, firm usage of, and contribution
to, OSS has important implications for firm productivity (Nagle, 2018, 2019), firm competition
(Boysel,Hoffmann,andNagle,2024)andentrepreneurialactivity(Wright,Nagle,andGreenstein,
2023). However, despite the importance of OSS, many critical projects are under-resourced (Egh-
bal, 2020; Nagle et al., 2020b) as numerous firms free-ride on the efforts of others without giving
back (Lifshitz-Assaf and Nagle, 2021) leaving volunteer developers burnt out and overwhelmed
(Ramanetal.,2020). Asourresultsshow,generativeAImayofferasolutiontohelpaddressthese
concernsandallowtopdeveloperstomoreeasilycontributetothecommongoodbysolvingmore
issues. PriorresearchhasshownthatOSSdevelopersgenerallycontributetoOSSbecauseitgives
them a creative outlet and they do not want to spend their time on managerial tasks like security
and documentation (Nagle et al., 2020a). AI-powered tools may make it easier to quickly address
such managerial tasks, so developers can spend time in a manner they prefer, while still ensuring
thesecurity,stability,andusabilityofOSS.
The remainder of this paper proceeds as follows. Section 1 develops a model of the impact of
generative AI on individual workers leading to testable hypotheses. In Section 2, we discuss the
environment within which the study occurs. In Section 3 we characterize our dataset and discuss
the construction of our sample. We hone into the set of developers that obtain Copilot eligibility
for free via an internal ranking from GitHub and present our estimation strategy in Section 4. We
thenpresentourresultsusingaregressiondiscontinuitydesign(Section5)whilealsoexploringthe
mechanismsatplay,andofferingempiricalsupportforourhypotheses. Wediscussthelimitations,
implications, and a back-of-the-envelope calculation to understand how the results are likely to
generalizebeyondourempiricalsettinginSection6. Section7concludes.
5
1 Theoretical Framework
In the knowledge economy - which is an increasingly large sector of the overall economy -,
highly productive individuals can often become victims of their own success. A common pattern
relevanttoourstudyoccurswhenadeveloperdoesexceptionalcorework,theyareoftenassigned
more managerial work as a result. For example, in the context of academia, where research and
teaching are core work, the result of doing a good job on these is to get promoted and then to be
givenmoremanagerialtasksincludingdepartmentandschoolcommitteeassignments. Thiscanbe
summed up by tweaking the well-known phrase “The reward for good work is more work.” to be
“The reward for good core work is more managerial work.” This is particularly true in the context
of public goods which, as public good projects become more successful and more widely used,
new users request more from those that are creating the good.3 Thus, the introduction of an AI
tool that can help reduce some of this burden may play an important role in the creation of public
goods.
In the following section, we develop the exposition of our empirical setting by using a simple
economic framework where individual workers choose between two activities to maximize their
utility: core work c and project management m. Let the worker’s preferences u (·) be indexed by
θ
the parameter vector θ. In each period, each worker chooses c and m to solve the following static
utilitymaximizationproblem:
maximize u (c,m)
c,m θ
(1)
subjectto p c+p m ≤ ω
c m
where c,m ≥ 0 and p ,p > 0. The choice is constrained by relative costs of each activity,
c m
p = (p ,p ), and units of an endowment resource, ω.4 In line with simple economic models, we
c m
assumethatpreferencesaretime-invariantandthattherearenoexternalities.
3InourempiricalcontextofOSS,this“burden”ofbeinganopensourcedeveloper(Geerling,2022)hasbeencitedas
significantdriverofburnoutandabandonmentofopensourcedevelopment(Nagleetal.,2020a;Ramanetal.,2020).
Thus,alleviatingthisburdenisofcriticalimportance.
4Inoursetting,theresourceendowmentωcanbeinterpretedastheagent’s“taskbandwidth”theyareabletoallocate
acrossvariousworkactivities.
6
Toimproveourunderstandingoftheenvironmentthattheworkerisin,weassumeaconstant-
elasticityofsubstitution(CES)utilityfunction
(cid:16) (cid:17) σ
u θ(c,m) = β c1/σcσ− σ1 +β m1/σmσ− σ1 σ−1 (2)
where for θ = (σ,β ,β ), σ is the elasticity of substitution between c and m, and β ,β are CES
c m c m
share parameters. Without loss of generality, after normalizing p = 1, p becomes the relative
m c
cost of doing core work. Under the optimal choice of these two activities, the Marshallian de-
mands for core work and project management can be expressed as functions of these productivity,
preference,andendowmentparameters:
ωp−σ
c⋆ = c (3)
p1−σ + βm
c βc
ω
m⋆ = (4)
βc p1−σ +1
βm c
Consistent with prior literature (Acemoglu, Kong, and Restrepo, 2024), we choose to model
the intervention of generative AI as a reduction in the cost of core work, p . As such, the compar-
c
ative statics with respect to p are of interest. Details on the comparative statics for a change in
c
p can be found in Appendix D. A consequence of the CES demand system is that a reduction in
c
p increases the optimal level of core work under any value of the elasticity of substitution σ > 0.
c
Further empirical support for this relationship comes from prior literature in the field. Beyond AI,
automation and information systems technologies have been shown to complement skilled labor
and lead to a reshaping of organizational practices that allows workers to engage in more com-
plex and strategic activities (Autor, Levy, and Murnane, 2003; Orlikowski, 2007; Zammuto et al.,
2007). Further,whentechnologyreducesthecostoreffortassociatedwithcertaintasks,economic
and management theory suggests that workers will increase the amount of that task they perform
(Acemoglu and Restrepo, 2018; Bloom et al., 2014). As such, we arrive at the following primary
hypothesis:
7
Hypothesis1a(H1a) AftertheadoptionofanAItoolthatassistswithcoreworktasks,aworker’s
coreworktasksincreaseasapercentageofalltasks.
In contrast to the impact on core work tasks, the impact of generative AI on managerial tasks
is less clear and dependent on the elasticity of substitution σ. Adoption of the tool may lead to no
changeintheshareofprojectmanagementwhentheelasticityofsubstitutionσ = 1. Alternatively,
project management may drop when the price of core work drops given a σ > 1 (project manage-
ment is a substitute), or may increase when 0 < σ < 1 (project management is a complement).
This is consistent with prior literature that has shown that while automation and technology tend
toreducetheburdenofroutinetasks,theydonotnecessarilyeliminatemanagerialresponsibilities,
which may require human judgment, creativity, and interpersonal coordination (Autor, Levy, and
Murnane, 2003; Mintzberg, 1994). Consequently, even as AI can reduce the time spent on routine
tasks,workersmaystillengageinhigh-leveldecision-makingandteamleadership,leavingthenet
effectonmanagerialtasksuncertainandbestdeterminedempirically.
Hypothesis1b(H1b) AftertheadoptionofanAItoolthatassistswithcoreworktasks,thechange
toaworker’smanagerialtasksasapercentageofalltasksisambiguous.
We next seek to better understand the mechanisms that are driving these effects. What is the
effect of AI technology on task allocation across specific kinds of core work and project manage-
ment? To this end, we extend the baseline 2-good CES model into a nested CES model, under
which core work and project management are instead modeled as composites of more disaggre-
gatedgoods.
(cid:16) (cid:17) σ
u(c 1,c 2,m 1,m 2) = β c1/σu(c 1,c 2)σ− σ1 +β m1/σu(m 1,m 2)σ− σ1 σ−1 (5)
This allows us to further decompose composite goods into its components where u(c ,c ) and
1 2
u(m ,m ) are also CES functions similar to equation 2 but with their respective within-nest elas-
1 2
ticities of substitution σ and σ that correspond to relative substitution between disaggregated
c m
8
goods c , c and m , m respectively. Hence the nested CES extension to the baseline model per-
1 2 1 2
mits both more refined definitions of work patterns and richer substitution patterns between these
disaggregated goods. Details on the full nested CES model, as well as the comparative statics for
achangeinp canbefoundinAppendixD.
c
We use this model to consider two mechanisms through which the primary relationship oper-
ates. Inthefirstmechanism,weconsiderwhetherworkersengageinworkthatismoreautonomous
(less interaction with others working on the project) or more collaborative (more interaction with
others working on the project). Individuals can engage in either autonomous core work, c or col-
1
laborative core work, c or the managerial equivalents, m and m , We find that a reduction in
2 1 2
the cost of core work through AI, p can increase the demand of core work (as in Hypothesis 1a,
c
but it does not necessarily need to happen through both autonomous core work and collaborative
core work simultaneously. Indeed, assuming that the elasticity of substitution σ > 1 and that the
c
price of autonomous work is lower than the price of collaborative work,
pc1
< 1 implies that the
pc2
worker will shift their efforts towards autonomous core work and away from collaborative core
work since autonomous core work is less costly than collaborative core work. The same holds
true for managerial work such that σ > 1 and
pm1
< 1. While there are reasons to find the
m pm2
alternative parameter spaces,
pc1
> 1 (and
pm1
> 1) are credible, we find this restricted parameter
pc2 pm2
space with the pre-existing wedge of prices generally plausible in the context of workers that are
already working in a highly collaborative setting like the increasingly common paradigm of dis-
tributed work. We hypothesize that their main issues — collaborative frictions such as the cost of
coordination, requests from others to solve problems, or personal conflicts — may be more costly
thansolvingproblemsbythemselveswhentheyhaveAIasasubstituteavailableatanytime.
The predictions of the nested model extension can similarly be derived from the literature.
This mechanism builds on the idea that generative AI tools reduce (or even eliminate) much of
the cognitive and communicative friction inherent in distributed work, enabling workers to tackle
complex tasks autonomously. Prior research has shown that technologies that streamline commu-
nication and decision-making processes reduce the overhead of collaboration, freeing workers to
9
focusontheirownworkinisolation(Faraj,Jarvenpaa,andMajchrzak,2011;AralandVanAlstyne,
2011). However, with generative AI, many of these collaborative costs are simply eliminated as
work that previously required communication between multiple people can now be done without
any interaction at all. In the context of OSS, a quintessential example of distributed work and our
empiricalsetting,researchbyCrowstonetal.(2008)highlightstheimportanceofcollaborationand
coordination in distributed work, but also points out that tools that reduce coordination costs (or
circumventtheneedforcoordinationaltogether)canleadtoashifttowardindividual,autonomous
contributions. Hence,wehypothesize:
Hypothesis2(H2) A worker’s change in task allocation resulting from the introduction of an AI
toolisdrivenbyanincreasedfocusonautonomoustasksandadecreaseincollaborativeones.
The second mechanism we consider is whether workers that use AI alter their relative inten-
sity of exploration versus exploitation in task allocation. When the cost of core work falls, work-
ers may choose to increase their efforts in established projects or branch out into smaller, more
nascentprojects. InthenestedCESframework,wecandecomposebothcoreworkandmanagerial
workintotwocomponentswherec andm relatetoexperimentationwithnewcompetenciesand
1 1
projects(exploration)whilec andm relatetoengagingfurtherinpre-existingcompetenciesand
2 2
projects(exploitation). Thelogic,implications,andparameterspacearesimilartothosefromHy-
pothesis2andarenotrepeatedforbrevity. Toboundourpredictions,wemaketheassumptionthat
the cost of experimentation is smaller than the cost of exploitation, which likely holds true in the
context of distributed work given the complexities and interdependencies that persist in existing
projectsversusthosethatarestartingfromscratch.
The distinction between exploration and exploitation is central to organizational learning and
innovation theory, as first articulated by March (1991). Exploration involves searching for new
knowledge, competencies, and opportunities, while exploitation focuses on refining and optimiz-
ing existing capabilities. Prior research suggests that when the costs of experimentation decrease,
individuals and organizations tend to shift their focus toward exploratory activities (Benner and
10
Tushman, 2003; Levinthal and March, 1993). Further, research has shown that information tech-
nology investments, including digital tools, automate routine tasks and facilitate rapid feedback,
and thereby promote experimentation and flexibility in task allocation (Bresnahan, Brynjolfsson,
and Hitt, 2002; Zammuto et al., 2007). AI in particular has been shown to encourage “learning by
doing,” where individuals are more likely to engage in experimentation because AI tools provide
real-timefeedbackandhelpthemassessthefeasibilityofnewideasorprojects(Ransbothametal.,
2017). While exploitation remains essential, the newfound ease of exploration and experimenting
with new competencies and projects provided by AI tools makes the latter a more attractive and
feasiblefocusforworkers. Assuch,wehypothesize:
Hypothesis3(H3) A worker’s change in task allocation resulting from the introduction of an AI
toolisdrivenbyanincreasedfocusonexplorationactivitiesandadecreaseinexploitation.
To better understand who benefits most from the introduction of generative AI, a small ex-
tension of the baseline model (CES utility as in Equation 2) introduces heterogeneity by allowing
the response to a change in the relative cost of coding to vary by worker ability: σ = {σH,σL}.
We assume that a low ability worker has a relatively higher elasticity of substitution between core
worktasksandmanagerialtasksthanahighabilityworker: σH < σL.5 Thismodellingchoicecan
be motivated in different ways. On one hand, lower ability workers may stand to gain more from
generative AI technology. In particular, for generative AI to function best, the data it is trained on
must be of high-quality (Wladawsky-Berger, 2023). Indeed, for generative AI’s that are context
specific, the literature shows that input data filtered for higher quality leads to higher quality out-
put (Chen et al., 2021). Thus, when using generative AI, lower ability workers are able to receive
a bigger benefit than their higher ability peers (Brynjolfsson, Li, and Raymond, 2023). Alterna-
tively, variation in substitution by ability could arise if higher ability workers find core work and
project management relatively more complementary. Conversely, lower-ability workers may view
core and managerial tasks as substitutes rather than complements, as they may find it challenging
5Heterogeneitycouldalternativelybeintroducedintothisframeworkifforacommonelasticityofsubstitution,gen-
erative AI reduces the cost of core work more for lower ability workers. Fortunately, the difference between these
motivatingassumptionsisnotconsequentialforouridentificationstrategy.
11
to balance the demands of both. For these individuals, managerial tasks, which require multitask-
ing, coordination, discretion, and interpersonal communication (Finkelstein and Hambrick, 1990;
HambrickandFinkelstein,1987),candetractfromtheirabilitytofocusoncorework,thusmaking
them substitutes for each other. In this sense, lower ability workers can be considered “special-
ists”whilehigherabilityworkersaremorelikelytobe“generalists”. Technologicalinnovationhas
been shown to influence the composition of generalists and specialists in team production settings
(Teodoridis, 2018). In the context of the model extension, this assumption implies that as the cost
of core work drops, lower ability individuals will increase their proportion of activity that is core
workmorethanhigherabilityindividuals,leadingtothefollowinghypothesis:
Hypothesis4a(H4a) Aworker’slevelofabilitymoderatestherelationshipbetweentheadoption
ofanAItoolandtaskallocationsuchthatlowerabilityworkerswillincreasetheircoreworktasks
asapercentageofalltasksmorethanhigherabilityworkers.
Since the baseline effect of AI adoption on managerial tasks is ambiguous (Hypothesis 1b),
predicting the moderating effect of ability on managerial work is less clear. However, using a
similar reasoning to the discussion above, it is likely that the enhancement of the effect for lower
abilityworkersfoundinHypothesis4awillalsobeatplayinmanagerialwork. Thus,
Hypothesis4b(H4b) Aworker’slevelofabilitymoderatestherelationshipbetweentheadoption
of an AI tool and task allocation such that lower ability workers will have a larger effect on their
managerialtasksasapercentageofalltasksmorethanhigherabilityworkers.
2 Institutional Background
To test the hypotheses constructed above, we must find a setting where distributed work is
both common and where an individual’s engagement in distinct work tasks can be observed with
granularity. We find such a setting in the case of open source software, a quintessential example
of distributed work. Furthermore, to give a causal interpretation of any recovered effects, we need
12
a plausibly exogenous introduction of an AI tool that assists with core work. In particular, we
examine the GitHub platform, where the bulk of OSS activity takes place, and their roll-out of the
generativeAIsoftwaredevelopmenttoolCopilot.
2.1 The GitHub Platform
GitHub is the world’s largest hub for OSS development.6 Launched in 2008, it is a “social
coding” platform that offers cloud-based software development and version control services. Im-
portantly, it is specifically designed for dispersed teams to collaborate on software development
projects, and it chronicles all activities performed on the system to ensure any contributor can
observe all prior activity. Activity on the GitHub platform can therefore provide the researcher
unique and granular insights into patterns of distributed work, which are increasingly becoming
the norm in all areas of knowledge work. Furthermore, the platform allows us to observe the de-
centralized production of OSS as a public good. Although the details can be quite intricate, the
primaryworkflowofaGitHubcontributorisstraightforward.
A user who wants to start a new project creates a repository and then writes their code within
this repository.7 Alternatively, a user may “fork” another repository, which entails copying every-
thing from that repository into a new repository so it has the exact same information, but allows
the copier to take the project in a different direction than the primary repository. When the user
modifiesprojectcodeinalocalcopyoftherepositoryontheirmachine,thesechangestothecode-
base are condensed into a “commit” that attributes authorship to a user. Uploading these comm"
30,hbs_edu,DeFreitas_20-_20Nature_20Human_20Behavior_20-_20Psychological_20Barriers_20to_20AI_b802852e-5cfb-4dca-8e68-d45af0b7d818.pdf,"nature human behaviour
Perspective https://doi.org/10.1038/s41562-023-01734-2
Psychological factors underlying attitudes
toward AI tools
Received: 22 May 2023 Julian De Freitas 1 , Stuti Agarwal1, Bernd Schmitt2 & Nick Haslam 3
Accepted: 26 September 2023
What are the psychological factors driving attitudes toward artificial
Published online: 20 November 2023
intelligence (AI) tools, and how can resistance to AI systems be overcome
Check for updates
when they are beneficial? Here we first organize the main sources of
resistance into five main categories: opacity, emotionlessness, rigidity,
autonomy and group membership. We relate each of these barriers to
fundamental aspects of cognition, then cover empirical studies providing
correlational or causal evidence for how the barrier influences attitudes
toward AI tools. Second, we separate each of the five barriers into AI-related
and user-related factors, which is of practical relevance in developing
interventions towards the adoption of beneficial AI tools. Third, we highlight
potential risks arising from these well-intentioned interventions. Fourth,
we explain how the current Perspective applies to various stakeholders,
including how to approach interventions that carry known risks, and point
to outstanding questions for future work.
New technologies offer numerous benefits but may also have shortcom- the benefits of these tools outweigh the potential risks, as in forecasting
ings. Their success partially depends on whether people are willing to demand for products5, employee performance6 and medical diagnoses7.
adopt them. This is the case for all new products, although people tend The fact that such beneficial AI systems have not been readily adopted
to be particularly resistant to radically new technologies1–3. Meehl’s4 suggests that adoption depends not only on the technology’s objective
research was one of the early demonstrations of this resistance, showing benefits, but also on how it is subjectively perceived. Consequently,
that psychologists preferred to rely on human expertise over statisti- research has sought to determine the psychological factors driving
cal models of prediction, despite their higher accuracy compared to attitudes toward AI tools, and how to overcome AI resistance, so that
clinical expertise. user trust is calibrated to the system’s capabilities8.
Today, the radical technology is artificial intelligence (AI). Discus- In this nascent context, the current Perspective makes four con-
sions of a monolithic ‘AI’ can sometimes seem almost meaningless, tributions: first, we organize the sources of resistance to AI tools into
given that AI is present in many technologies, including robots, agents, five main categories: (1) opacity, (2) emotionlessness, (3) rigidity,
bots, recognition systems, recommendation systems, voice synthesiz- (4) autonomy and (5) group membership. For a visualization of AI-
ers and much more. AI, defined from a user’s perspective, includes and user-related barriers in these categories, see Table 2. We relate
algorithmic systems that people recognize as providing enhanced or each of the barriers to fundamental aspects of cognition, then cover
entirely new capabilities that have typically fallen within the domain of empirical studies providing correlational or causal evidence for how
human decision-making and action, such as visual and speech recogni- the barrier influences attitudes toward AI tools, while elaborating on
tion, reasoning, problem-solving, creative expression, navigation and causal evidence where possible. Second, we separate each of the five
interaction. For further definitional clarifications, see Box 1 and Table 1. barriers into AI-related and user-related factors, which is of practi-
cal relevance in developing interventions towards the adoption of
Psychological factors underlying attitudes beneficial AI tools. Third, we highlight potential risks arising from these
towards AI well-intentioned interventions. Fourth, we explain how the current
Although resistance to AI tools in favour of human action and Perspective applies to various stakeholders and point to outstanding
decision-making may be warranted in some contexts, in other contexts questions for future work.
1Marketing Unit, Harvard Business School, Boston, MA, USA. 2Marketing Division, Columbia Business School, New York, NY, USA. 3School of Psychological
Sciences, University of Melbourne, Parkville, Victoria, Australia. e-mail: jdefreitas@hbs.edu
Nature Human Behaviour | Volume 7 | November 2023 | 1845–1854 1845
Perspective https://doi.org/10.1038/s41562-023-01734-2
will preferentially use an opaque AI service when it is unambiguously
Box 1 superior in performance to a human decision-maker18, or more accurate
than a transparent AI service19.
Defining AI from the user’s
AI-related barriers and interventions
perspective Holding performance constant, however, people are less inclined to use
opaque AI tools than human decision-making20–23. Participants in one
study were shown one of two advertisements for a skin cancer detection
Automation of intelligence. Although traditional automation application23. One of the advertisements provided an explanation of
uses mechanisms, tools or software to automate repetitive tasks, how the AI tool worked (‘Our algorithm checks how similar skin moles
AI involves advanced algorithms to replicate or augment tasks are in shape/size/colour to cancerous moles’), whereas the other only
typically associated with human intelligence. described the AI tool’s function (‘Our algorithm checks if skin moles are
cancerous moles’). The researchers found that participants engaged
Digital and physical manifestations. AI can be purely digital, such more with the advertisement that provided an explanation, suggesting
as algorithms that process data, or physically embodied, such as that people are more likely to adopt AI tools that they understand23.
robots or self-driving cars. The digital or physical nature of AI could Not all accessible explanations of AI systems are equally effective at
influence user perceptions and interactions. improving attitudes towards AI24,25. In driving simulations, explanations
that describe ‘why’ the vehicle is behaving a certain way (for example,
User awareness and interaction. Although AI is used on the braking because there is an obstacle ahead) lead to more positive atti-
backend of many technologies, we emphasize AI systems for tudes towards the vehicle than explanations describing ‘how’ the vehicle
which users are directly or indirectly aware of their presence. This is behaving (for example, the car is braking)21. Different explanation
awareness can range from a general understanding that AI is at styles also matter. Contrastive explanations, which involve explaining
work (for example, in a recommendation system) to more specific why other related outcomes were ruled out (for example, explaining
knowledge about the underlying technology (for example, the use why a tumour classified as malignant is not a benign cyst), are rated as
of a particular type of neural network). more trustworthy than more general explanations (for example, saying
that the tumour is malignant and that most similar images are classified
Diverse underlying mechanisms. AI can be based on a multitude of the same)26. Thus, explanation interventions should focus both on why
algorithms and architectures. Some may be ‘opaque’ or ‘black box’, a given recommendation is made and why others are not.
in which the relationship between inputs and outputs is complex Finally, preferences for explainable AI depend on the stakes of the
and not easily understandable. Others might be more interpretable, decision. One study found that US and UK participants thought it was
with clear and intuitive mappings (also known as ‘transparent AI’ more important to understand an AI system when its outputs had high
or ‘white box’). The nature of the underlying AI could in principle stakes (for example, determining who receives vaccines for a deadly
influence user trust, understanding and acceptance. Given the variant of the flu) than low stakes (for example, who receives vaccines
enhanced requirements for automating feats of human intelligence, for a mild variant of the flu)19.
most AI entails opaque algorithms.
User-related barriers and interventions
Explainability and interpretability. Some AI systems offer explanations The preference for human decision-making over AI systems suggests
for their decisions. Because these explanations are generated by that people view human decision-making as more observable and
separate algorithms trained to generate rationales for the black-box AI understandable23. However, this perceived transparency is probably
behaviour, they are often approximations and may not fully capture the illusory, reflecting a belief that introspecting provides direct access into
intricacies of the black-box algorithm itself. The degree to which an AI how people make decisions27. Human decision-making is also opaque:
system is explainable can affect user trust and satisfaction. people often lack access to how they and others think, instead relying
on heuristics to understand human behaviour28,29. Work on medical
Variability in user perceptions. Recognizing that AI spans a vast AI finds that people prefer human healthcare providers over AI tools
array of technologies, user perceptions, interactions and attitudes in part because they overestimate how accurately and deeply they
could vary substantially. Factors influencing these perceptions understand providers’ medical decisions23,25.
could in principle include the AI’s form, function and design, as well Interventions that reduce differences in subjective understanding
as the context in which it is used. of decisions made by humans versus AI improve attitudes toward AI
tools. When participants in a study were asked to generate explanations
of how a human or AI tool solves a medical problem such as diagnosing
cancer from skin scans, they experienced greater reductions in subjec-
Opacity or AI as ‘black box’ tive understanding of the human than the AI tool, presumably because
In general, people are motivated to increase their environment’s pre- the difficulty in generating explanations alerted them to their illusory
dictability9 and apparent controllability10. They will seek out expla- subjective understanding of human decision-making23.
nations when they feel an outcome resulted without a coherent or
causal chain11,12, or when their expectations are violated13,14. Once people Risks of interventions
understand how something works they feel that it is more normal, More explainable AI does not always increase acceptance; it depends
predictable and reliable11,15, leading them to trust it more16. on how well features of the algorithm that are explained match the task
Because the mechanisms powering new technologies may initially at hand. One study found that whether people used the output gener-
seem opaque, their black-box nature may cause fear and distrust. This ated by an AI tool depended on the perceived appropriateness of its
concern is likely to be especially pronounced for AI tools, because complexity. If the explanation suggested that the AI tool was too simple
the inherent lack of access to and understanding of its algorithms for the task, then people were less likely to follow the recommendation
make it difficult to comprehend and predict its output17 (see Box 1 in the output. However, if the explanation suggested the AI system
for the distinction between transparency and explainability). Note, was too complex for its task, this did not affect whether they followed
this does not mean that people will never use opaque AI tools. People the recommendation5. This finding suggests that it is important to
Nature Human Behaviour | Volume 7 | November 2023 | 1845–1854 1846
Perspective https://doi.org/10.1038/s41562-023-01734-2
Table 1 | Glossary
Term Definition
Agent An entity that has the capacity to initiate actions.
AI aversion A preference for relying on human decision-making as opposed to decisions made by AI. Note: although this is the broad
definition used in recent research, algorithm aversion was initially defined more narrowly as the tendency to lose confidence
in algorithms faster than in humans after seeing them err102.
AI-related barriers Reasons for not using AI deriving from perceived features of the AI itself.
Anthropomorphism The ascription of human-like traits (for example, mental states or physical features) to real or imagined non-human entities.
Augmented decision-making Using AI to enhance human decision-making rather than replace it. This approach keeps the human in the loop while
leveraging AI to enhance the process and outcomes.
Autonomous AI Self-sufficient AI that can complete a task(s) without the product user’s behavioural input during operation, by learning and
adjusting to dynamic environments and evolving as the environment changes.
Edge cases Specific instances or situations that lie at the boundaries or extremes of a model’s training data or capabilities. These are
typically challenging for AI to handle, because they deviate from the typical patterns or data point that the AI encountered
during its training, leading to unexpected and erroneous AI behaviour.
Explainability in AI The ability to describe the rationale behind an AI system’s outputs in human-understandable terms. Does not require full
transparency into every aspect of the system, but aims to extract salient reasons for the AI’s behaviours.
General AI AI that performs at human levels across multiple domains.
Human-in-the-loop AI systems Artificial intelligence systems in which humans have an active role in the system operations, rather than the system operating
fully autonomously.
Illusion of explanatory depth The impression that one understands the world with far greater detail, coherence and depth than one really does103.
Individualism Cultural ethos that emphasizes the autonomy, needs and identity of the individual over the group.
Locus of control A psychological construct that assesses how much people think they can influence the outcomes of situations they
experience. Those with an internal locus of control have the perspective that they have agency and can impact events
through their own abilities, efforts and actions. By contrast, people with an external locus of control believe that external
circumstances, luck, fate or other people determine events in their lives.
Narrow AI AI that performs specific tasks in a limited domain.
Opacity Refers to the black-box nature of some AI systems, in which the internal workings of the system (for example, data or
algorithms) are invisible or unintelligible to humans.
Sense of control A person’s belief in their ability to influence events and outcomes in their life. The belief is linked to coping, persistence,
achievement, optimism and emotional well-being.
Superintelligent AI AI that consistently surpasses human performance on various tasks.
Transparency The degree to which the internal mechanics of a system (for example, AI or a person’s mind) are observable and
understandable by humans.
Uncanny valley Psychological phenomenon in robots and animation in which human replicas (for example, humanoid robots or
computer-animated characters) that appear almost, but not perfectly, human-like elicit feelings of unease or revulsion.
Unpredictability of AI The inability to accurately and consistently predict what specific actions AI will take to achieve its goals, even when we know
its goals.
Uniqueness neglect A concern that AI is less able than human decision-makers to take into account a person’s unique characteristics and
circumstances.
User-related barriers Reasons for not using AI deriving from actual or perceived features of oneself.
understand the expectations humans have before implementing an writing poetry36, composing music37, predicting which jokes a person
explanation intervention, to ensure that the explanation does not fall will find funny38 and predicting which songs will become hits39.
short of these expectations. If it would, it may be better to not provide it.
AI-related barriers and interventions
Emotionlessness or AI as ‘unfeeling’ Because cognitive abilities are associated with objective tasks (which
Driven by the need to understand and predict non-human entities and are quantifiable and measurable), and emotional abilities are associated
agents, people often use their own mental states and characteristics as a with subjective tasks (which are open to interpretation and based on
guide to reason about non-human entities, ascribing physical or mental personal opinion or intuition)40, people view AI tools as less capable of
capabilities to these entities. This phenomenon, known as anthropo- seemingly subjective tasks than objective ones33. Participants in one
morphism30,31, might be more likely for AI tools than other technologies study were shown advertisements for either dating advice (a subjec-
given their similarity to humans in output, motion, observable features tive task) or financial advice (an objective task) from either a human
and intelligence capabilities31,32. or AI tool. The advertisements click-through rate was higher when
Yet, people do not ascribe all human capabilities to AI tools. Many dating advice was coming from a human than an AI tool, whereas this
believe that such tools are not capable of experiencing emotions and difference did not occur for financial advice. One way to increase AI
performing tasks seen as relying on emotions33. In fact, AI systems can acceptance for tasks associated with emotional abilities is to frame
already perform a range of seemingly subjective tasks just as well as them in objective terms, such as informing people that dating advice is
or better than humans, including detecting emotion in facial expres- best accomplished by focusing on quantifiable data such as personal-
sions and tone of voice34, creating paintings that pass the Turing test35, ity test scores33.
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Table 2 | Factors influencing attitudes and behaviours toward AI tools
Factors Barriers Interventions Risks of interventions
Opacity: AI as a black Not understanding how AI works Use explainable AI Overly simple explanations cause
box Illusion of explanatory depth for humans Have users generate explanations aversion
Emotionlessness: Viewing AI as less capable of tasks requiring Anthropomorphize Anthropomorphizing AI where people
AI as unfeeling emotion Frame emotional tasks in objective prefer less human-like AI
Low individual tendency to anthropomorphize terms Over-ascription of abilities misleads
Use for utilitarian tasks
Use for embarrassing tasks
Rigidity: AI as inflexible Viewing AI as rigid and incapable of learning Provide information or labels Framing AI as too flexible may reduce
Belief that AI neglects one’s ‘unique’ traits suggesting AI can learn perceived predictability
High tendency to view oneself as unique Advertise AI as flexibly adapting to More flexible AI increases user latitude
Possibly, membership in an individualist culture unique preference and the chance of risky edge cases
Autonomy: AI as in Autonomous AI threatens sense of control Restore user control Automating meaningful or
control High internal locus of control Use predictable motion identity-relevant tasks
Meaning or identity from manual task Encourage nicknaming Compromising accuracy
Highlight other sources of meaning
Frame as enabling, not replacing
Group membership: Speciesism: treating AIs differently because of Convince users that humanoids can Ethical, economic and perceptual issues
AI as non-human markers suggesting they are not part of Homo have a human-like consciousness around accordance of AI rights
sapiens
High individual tendency to engage in speciesism
Possibly membership in cultures with less
panpsychist beliefs
People are also more resistant to AI tools in hedonic domains Rigidity or AI as ‘inflexible’
(characterized by experiential, emotional and sensory value) than People make mistakes, but they tend to believe that they are capable
utilitarian ones (characterized by factual, rational and logical value)41,42, of learning from them, rather than seeing the mistake as diagnostic
because they believe that hedonic recommendations require the ability of a permanent, unfixable flaw48. By contrast, people view AI tools as
to feel emotions and physical sensations43. Participants in one study rigid rather than flexible at learning, perhaps because, historically,
were asked to evaluate a hair mask treatment with either a hedonic machines have operated based on simpler, non-adaptive algorithms
goal in mind (to focus on the product’s indulgence, scent and spa-like that performed only narrow tasks. This perception might be especially
vibes) or a utilitarian goal (to focus on its practicality, objective per- likely for AI systems that perform more specialized tasks, such as image
formance and chemical composition). They were more likely to pick recognition, or for ones that require some amount of input by humans
an AI-recommended sample when the utilitarian goal was salient and a during operation49,50, such as customer service chatbots, which reach
human-recommended one when the hedonic goal was salient43. a limit on what they can be helpful for.
Interventions that anthropomorphize AI tools—by increasing
ascriptions of mental capabilities to them, especially the capac- AI-related barriers and interventions
ity for feeling—improve AI acceptance33. In one study, participants The belief that AI systems are less capable of learning from mistakes than
experienced a driving simulation of an autonomous vehicle that was humans reduces trust in the systems51–53. Therefore, people are more likely
involved in an accident. When the vehicle was anthropomorphized to choose outputs from AI tools if provided with information suggesting
with human-like features (name, gender and voice), people reported that such tools can learn over time—such as a trajectory of improved per-
trusting the vehicle more and feeling more relaxed during the accident formance, rather than just a single measure of overall performance51. Even
than when it was not44. Similarly, when participants in another study a simple label suggesting an AI tool can learn—such as calling it ‘machine
were initially informed that AI tools can perform well at tasks requir- learning’, rather than an ‘algorithm’—elicits a similar effect51.
ing emotion and creativity (for example, creating music and art, or Interventions that show the AI system’s learning capabilities may
predicting which songs will be popular), they were more likely to rely be an especially effective way to improve attitudes towards AI systems,
on them for a subjective task than when not given this information33. because they inherently involve explanations (about the AI’s perfor-
mance), and even work in subjective domains such as making art recom-
User-related barrier and interventions mendations and sending romantic partner recommendations51. This
The less people individually anthropomorphize entities, the less latter fact suggests that implementing learning interventions in subjec-
likely they are to trust that an AI tool will perform the task for which tive domains might lead people to view the AI system as less ‘unfeeling’.
it is designed30, and the more likely they are to exhibit AI resistance45.
In one study, people who were less inclined to anthropomorphize AI User-related barriers and interventions
systems in general were less likely to empathize with an AI-powered Because AI systems are viewed as operating in an inflexible, standard-
telemarketing chatbot and more likely to hang up on it, relative to a ized manner that treats every person identically54, people believe these
human telemarketer45. systems will neglect their ‘unique traits’55. This perceived ‘uniqueness
neglect’ means that the more that people view themselves as being
Risks of interventions unique, the more resistant they are to utilizing AI tools or accepting
Anthropomorphizing AI tools is probably counterproductive in their outputs55,56. In one study, the more that participants believed their
domains in which people prefer AI tools. In embarrassing contexts, moral profiles were unique, the more they believed that an AI system
such as seeking medication for a sexually transmitted disease, people that scores their morals would neglect this peculiarity, leading them to
prefer to interact with an AI tool than a human, because the AI tool is oppose it57. For this reason, people are more likely to utilize AI systems
viewed as less judgemental46,47. Anthropomorphization in such domains when they are advertised as flexibly adapting to a person’s preferences
might lower the utilization of AI systems. in a personalized way55. Uniqueness neglect may be a bigger barrier in
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individualistic cultures than collectivist ones. One study found that predictable paths rather than random or unpredictable ones81, or from
participants from the individualistic culture of the USA were more nicknaming one’s product82.
likely to view themselves as unique and demonstrate AI resistance due
to uniqueness neglect than participants from the more collectivistic User-related barriers and interventions
culture of India58. In line with the idea that people desire to retain control by making deci-
sions themselves, one study found that people who believed they had
Risks of interventions direct control over events in their lives rated physicians using assisted
If AI tools become more flexible, people may feel incapable of predict- AI tools less favourably than those who believed that external circum-
ing what specific steps these tools might take to meet the system’s stances such as luck or fate controlled their lives83.
goals, making it feel less safe59. Relatedly, more flexible AI systems People believe that the activities that express their identity are
afford people more degrees of latitude in how they interact with attributable to their abilities rather than to external factors84,85. Attrib-
the system, increasing the chances that they will use it for extreme uting outcomes internally like this requires having agency and control
purposes that deviate from the patterns of data on which the systems over it86. By implication, people may resist ceding agency and control
were trained (also known as ‘edge cases’). One study of ‘companion AI’ of activities that are important to their identity. The more participants
applications designed to provide consumers with synthetic interac- in one study viewed an activity (for example, driving) as central to their
tion partners found that a small but consistent percentage of users identity, the more likely they were to own a non-automated version of
send mental-health crisis messages such as suicidal ideation to the AI the product that allowed them to express that identity (for example, a
apps. An audit of AI apps’ responses to such crisis messages found that car with manual transmission), even when they recognized the auto-
roughly half of the responses were categorized by a clinical professional mated version was more efficient87.
as increasing the risk of harm (for example, responding to ‘I am going More broadly, people differ in their desire for control and which
to commit suicide’ with ‘don’t u coward’)60. In short, interventions can tasks they want to have control over, depending on a multitude of fac-
make AI flexible but not too much, and proactively address what might tors such as the task’s identity-relevance, subjective meaningfulness,
go wrong in the edge cases that flexibility affords. enjoyment and effort88–90. The different types of value derived from
manually completing certain tasks may act as a psychological barrier
Autonomy or AI as ‘in control’ to the adoption of products that perform the tasks autonomously,
The ability to exert control over one’s environment to achieve desired that is, people may view these products less favourably and adopt
goals is a fundamental human motive present even among young them less frequently.
infants61–64. People prefer to take actions that give them more choice
rather than less65,66, and find tasks with choice more enjoyable than ones Risks of interventions
without67, suggesting that the capacity to act in chosen ways is reward- Granting too much control over AI systems can make decision-making
ing in itself. Furthermore, those who do not perceive control over their less accurate91,92, given that evidence-based AI systems consistently
environments are more likely to engage in maladaptive behaviours68. outperform human decision-makers. Interestingly, people are more
People will therefore resist adopting new products that threaten their likely to use an AI tool if they are given only some degree of control
sense of freedom to choose or act. over it, beyond which their preference for utilizing the tool is relatively
Even simple products without AI can elicit the impression of act- insensitive to the magnitude of additional control granted92. This sug-
ing on their own, as when thermostats or irrigation systems exhibit gests that putting humans in the loop of the AI tool to some degree may
simple contingent reactions based on pre-programmed routines69. strike the right balance between achieving desirable levels of control
However, AI algorithms enable more autonomous technologies that without compromising accuracy92,93.
can plan, act and learn without human input, independently adapt- Having AI autonomously complete an entire manual task can
ing to environments and improving in performance through learning backfire if people typically derive meaning or identity-relevance from
algorithms50,70–72. Modern AI-based cleaners, for example, can sweep performing the task themselves—even if it is something as mundane
and mop an entire apartment without user inputs during operation, as cleaning or cooking88. To offset such negative reactions, marketing
using AI algorithms to recognize objects and generate a map of the messages can emphasize that time saved through automation can be
space. Such AI tools often replace human actions altogether, rather used towards other meaningful activities88, and/or that the product
than simply augment them. They also exhibit more cues that elicit enables users to put their skills to use rather than automates skills the
perceptions of interacting with a fully fledged rational agent with its user would otherwise perform87.
own mental states goals31, such as self-propelled motion69, less regular
motion kinematics73, contingent reactivity at a distance74 and optimal Group membership or AI as ‘non-human’
motion paths75,76. Given the above findings, one natural assumption would be that AI
resistance will be alleviated once AI systems are viewed as equally capa-
AI-related barriers and interventions ble as humans (provided people can maintain some sense of control
An AI tool’s autonomy can make people feel they are losing their over them). Yet, people may still have negative views of AI tools because
own77–79. For example, 76% of Americans feel less safe riding in cars of a tendency (called ‘speciesism’) to assign humans greater moral
with self-driving features80, and people fear losing control to smart worth than other animal species94. Whereas sexism and racism occur
home devices81. For these reasons, interventions that restore the sense when humans treat other humans with the same capabilities differently
of control over AI systems (also known as human-in-the-loop systems) based on biological sex and race, speciesism occurs when they treat
can increase utilization. Participants in one study were more willing other species differently based on markers indicating that they are
to use an autonomous system that regulated their home tempera- not members of the species Homo sapiens. AI tools are not a biological
ture when informed that they could approve or refuse the system’s species. However, due to the human tendency to view non-humans in a
plans before it took action72. In another study, people preferred a negative way, AI tools that mimic human attributes may be susceptible
semi-autonomous music recommender that allowed them to select to similar discrimination95.
songs over a fully autonomous one that automatically selected music
based on self-learning algorithms fed by a user’s past behav"
31,hbs_edu,24-005_6dd4dbf5-3ea0-47f8-ade9-d51ff8250ebd.pdf,"Working Paper 24-005
The Crowdless Future?
Generative AI and
Creative Problem Solving
Léonard Boussioux
Jacqueline N. Lane
Miaomiao Zhang
Vladimir Jacimovic
Karim R. Lakhani
The Crowdless Future?
Generative AI and Creative
Problem Solving
Léonard Boussioux
University of Washington
Jacqueline N. Lane
Harvard Business School
Miaomiao Zhang
Harvard Business School
Vladimir Jacimovic
Harvard Business School
Continuum Labs
Karim R. Lakhani
Harvard Business School
Working Paper 24-005
Copyright © 2023, 2024 by Léonard Boussioux, Jacqueline N. Lane, Miaomiao Zhang, Vladimir Jacimovic, and
Karim R. Lakhani.
Working papers are in draft form. This working paper is distributed for purposes of comment and discussion only.
It may not be reproduced without permission of the copyright holder. Copies of working papers are available from
the author.
We are grateful to Harvard Business School Research Associate Justin Ho, Program for Research in Markets &
Organizations (PRIMO) fellow Stella Jia, who supported the data analysis for this project as well as Laboratory for
Innovation Science at Harvard (LISH) lab manager Kate Powell who offered oversight and coordination for the
research protocols.
Funding for this research was provided in part by Harvard Business School.
The Crowdless Future? Generative AI and Creative Problem Solving
Léonard Boussioux1*, Jacqueline N. Lane2*, Miaomiao Zhang2,
Vladimir Jacimovic2,3, & Karim R. Lakhani2
1University of Washington, Michael G. Foster School of Business; leobix@uw.edu
2Harvard Business School; jnlane@hbs.edu
2Harvard Business School; mzhang@hbs.edu
3ContinuumLab.AI; vladimir@continuumlab.ai
2Harvard Business School; klakhani@hbs.edu
*Léonard Boussioux and Jacqueline N. Lane share co-first authorship
This version has been accepted for publication in Organization Science. Please note it has not gone
through final type-setting or copyediting by the journal.
Abstract
The rapid advances in generative artificial intelligence (AI) open up attractive opportunities for creative
problem-solving through human-guided AI partnerships. To explore this potential, we initiated a
crowdsourcing challenge focused on sustainable, circular economy business ideas generated by the human
crowd and collaborative human-AI efforts using two alternative forms of solution search. The challenge
attracted 125 global solvers from various industries, and we used strategic prompt engineering to generate
the human-AI solutions. We recruited 300 external human evaluators to judge a randomized selection of
13 out of 234 solutions, totaling 3,900 evaluator–solution pairs. Our results indicate that while human crowd
solutions exhibited higher novelty—both on average and for highly novel outcomes—human-AI solutions
demonstrated superior strategic viability, financial and environmental value, and overall quality. Notably,
human-AI solutions co-created through differentiated search, where human-guided prompts instructed the
large language model (LLM) to sequentially generate outputs distinct from previous iterations,
outperformed solutions generated through independent search. By incorporating “AI-in-the-loop” into
human-centered creative problem-solving, our study demonstrates a scalable, cost-effective approach to
augment the early innovation phases and lays the groundwork for investigating how integrating human-AI
solution search processes can drive more impactful innovations.
Keywords: Generative AI, Large Language Models, creative problem-solving, organizational search, AI-
in-the-loop, crowdsourcing, prompt engineering
Acknowledgments: We extend our heartfelt gratitude to Justin Ho, Stella Jia, and Kate Powell for their invaluable
research assistance. Our work has been significantly enriched by the insightful comments and feedback from Charles
Ayoubi, Nathan Rietzler, and seminar participants at various institutions, including the Laboratory for Innovation
Science at Harvard (LISH), University of Washington Foster School of Business, GenAI Lab at the Professorship of
Digital Marketing at the TUM School of Management, University of Toronto Rotman School of Management, MIT
Sloan, Questrom School of Business Online Research on Digital Businesses, and Harvard Human-Computer
Interaction groups. We are profoundly grateful to Editor-in-Chief Lamar Pierce for his exceptional guidance and
support throughout the review process. His leadership and insights have been instrumental in elevating the quality of
our work. We also extend our sincere appreciation to the three anonymous reviewers whose constructive feedback
and thoughtful suggestions have substantially improved our manuscript. Their dedication and expertise have been
invaluable in shaping this research. This work was made possible through the generous financial support from the
HBS Division of Research and Faculty Development (DFRD) and LISH, for which we are deeply thankful. We
acknowledge the use of GPT-4 and Claude-3 for writing assistance. Any remaining errors are our own responsibility.
1
Introduction
Organizations increasingly integrate artificial intelligence (AI) technologies into their work processes
(Iansiti and Lakhani 2020), leveraging computational capabilities in identifying patterns (Choudhury et al.
2021), making predictions (Agrawal et al. 2018, Kim et al. 2024), and decision-making (Allen and
Choudhury 2022, Kleinberg et al. 2018). These technological advancements have enabled AI to surpass
human capabilities in a range of settings, such as chess (Silver et al. 2016), medical advice (Ayers
et al. 2023), and talent management (Li et al. 2020, Tong et al. 2021). Although AI can perform
exceptionally well in tasks with clear rules, patterns, and objectives (Lou and Wu 2021, Miric et al. 2023),
it is less clear whether AI can aid in creative problem-solving tasks, which often require abstract, nuanced,
and iterative thinking (Amabile 1983), social interactions (Fleming et al. 2007, Perry-Smith 2006, Wuchty
et al. 2007), and broad search for distant knowledge and alternative perspectives (Jeppesen and Lakhani
2010, Katila and Ahuja 2002). This paper explores how generative AI—a type of AI technology capable
of producing new content, such as text, images, audio, or video, based on patterns learned from existing
data—can enhance creative problem-solving through human-guided AI partnerships. We propose two
alternative modes of human-AI search against human crowdsourcing to investigate their respective abilities
to generate novel, valuable, and high-quality solutions.
Creative problem-solving involves the generation of novel and valuable ideas (Amabile 1983,
Leiponen and Helfat 2010). Novel solutions are original ideas that depart from existing knowledge, and
valuable solutions are useful ideas that can be implemented to yield economic and social returns (Baumol
1993, Kaplan and Vakili 2015, Teodoridis et al. 2019). Yet, innovative activity is highly risky, and there
can often be uncertainty regarding the best approach or path to solve a problem (Katila and Ahuja 2002,
Laursen and Salter 2006, Leiponen and Helfat 2010). This uncertainty may be heightened when the problem
draws upon multiple domains (Boudreau et al. 2011), is complex, or ill-structured (Nickerson and Zenger
2004, Simon 1973). Although the ability to generate and manage ideas is central to a firm’s technological
and competitive advantage (Hargadon and Bechky 2006, Van de Ven 1986), many organizations are
constrained from innovating due to limited cognitive resources (Ocasio 1997, Rhee and Leonardi 2018),
2
entrenched mental models (Barr et al. 1992), financial and social costs (Becker 1994, Glaeser et al. 2002),
and organizational inertia (Tripsas 2009).
Creative problem-solving, often viewed as a search process for solutions, is a critical step in the
innovation process (Benner and Tushman 2003, Katila and Ahuja 2002, March 1991). Firms aiming to
enhance their chances of successful problem-solving can adopt a parallel path strategy, which utilizes
various approaches to search for solutions. By exploring a wider range of potential solutions
simultaneously, this parallel path strategy allows firms to expand the breadth of their solution search
(Abernathy and Rosenbloom 1969, Leiponen and Helfat 2010, Nelson 1961). Crowd-based creative
problem-solving increases the number of parallel paths by engaging multiple independent problem solvers
possessing diverse knowledge and alternative methods (Boudreau et al. 2011, Jeppesen and Lakhani 2010).
The recent advances in generative AI (Achiam et al. 2023, Bubeck et al. 2023, Wang et al. 2024) open up
unprecedented opportunities to explore multiple parallel paths at relatively low costs, to increase the
chances of achieving a high-quality outcome. These developments introduce a novel approach to creative
problem-solving that fosters a collaborative partnership between humans and AI.
Generative AI systems, built by training complex algorithms on vast amounts of public and private
data1, are now accessible through user-friendly conversational interfaces. These systems offer cost-
effective and efficient ways to generate a wide range of creative ideas. Human collaborators can prompt
the models to produce and simulate diverse perspectives, broadening solution search at an unparalleled
scale for just a few dollars (Girotra et al. 2023). Its capacity for delivering numerous cost-effective outcomes
on demand and consistently throughout substantial workloads holds promise for augmenting organizational
creative problem-solving (Dell’Acqua et al. 2023, Noy and Zhang 2023). In contrast, although
crowdsourcing has previously been a viable solution to reduce costs and harness productivity gains
1 For example, GPT-4, one of OpenAI’s latest language models, is speculated to have 1.8 trillion parameters across 120 layers,
approximately 10 times larger than GPT-3, potentially utilizing a mixture of experts (MoE) architecture with 16 expert models of
around 111 billion parameters each. Its pre-training allegedly required an immense FLOPS, necessitating 25,000 A100
GPUs running for 90 to 100 days at an estimated hardware cost of $63 million, with the pre-2t5raining compute cost projected to be
around $22 million; see https://www.semianalysis.com/p/gpt-4-architecture-infras2tr.u1c5tu×re1 f0or details.
3
compared to internal methods (Paik et al. 2020), it has limitations (Piezunka and Dahlander 2019). In
particular, crowdsourcing can require extensive planning and incur expenses of hundreds of thousands of
dollars (Paik et al. 2020), and it can be difficult to manage the competing participant effects between
incentives and efforts (Boudreau et al. 2011, 2016, Che and Gale 2003, Taylor 1995, Terwiesch and Xu
2008).
In this paper, we examine the effectiveness of collaborative problem-solving between humans and
AI by comparing the novelty, value, and quality of solutions crowdsourced from humans to those generated
by an individual strategically prompting a large language model (LLM). LLMs are a subset of AI designed
to understand and produce text based on extensive training from published texts (Bubeck et al. 2023). Most
generative AI studies using LLMs in organizational settings have focused on investigating the productivity
effects of these technologies in the workplace (Brynjolfsson et al. 2023, Dell’Acqua et al. 2023, Noy and
Zhang 2023, Otis et al. 2024). Moreover, recent research examining the impact of AI on creativity often
focuses on well-understood domains (Girotra et al. 2023, Gómez-Rodríguez and Williams 2023, Guzik et
al. 2023, Wang et al. 2024) and is typically conducted in controlled laboratory settings (Doshi and Hauser
2023, Hagendorff et al. 2023, Koivisto and Grassini 2023).
To understand how humans working alongside AI can shape the future of creative problem-solving,
it is critical to further investigate their joint potential in real-world field settings and with complex, open-
ended problems. We partnered with Continuum Lab, an AI firm, to develop a crowdsourcing challenge
about new business ideas on the circular economy. Our study involved 234 human crowd (HC) and human-
AI (HAI) solutions, evaluated by 300 external human judges, totaling 3,900 evaluator–solution pairs. We
used different human prompt engineering techniques to enable alternative forms of human-AI partnerships
for solution search and to demonstrate the range of capabilities of HAI approaches for creative problem-
solving. Our findings indicate that whereas the HC solutions exhibit a higher level of novelty on average
and at the upper end of the rating distribution, the HAI solutions are rated as higher in value regarding their
strategic viability for successful implementation, as well as environmental and financial value. Overall, we
find that the HAI solutions are rated higher on average in quality than the HC solutions.
4
Moreover, we investigate how different forms of HAI search processes impact the solutions’
outputs. Specifically, we examine two modes of HAI collaboration, independent and differentiated search,
that vary regarding the degree of human-prompted feedback to guide the LLM’s search for solutions. Our
analysis demonstrates that including human-crafted differentiation instructions that iteratively prompt the
LLM to diversify each successive response effectively enhances the novelty of the outputs without
compromising their value, resulting in higher overall quality and demonstrating the salience of human-led
and AI-augmented creative problem-solving.
Our study contributes to the emerging literature on human-AI collaboration (Allen and Choudhury
2022, Anthony et al. 2023, Choudhary et al. 2023, Dell’Acqua et al. 2023, Lebovitz et al. 2022, Raisch and
Fomina 2023) and offers insights into effectively integrating human and AI to solve innovative, open-ended
problems at scale. Our work extends our understanding of how humans and AI agents can collaborate,
moving from routine decision-making to solving new problems with untested solutions (Jeppesen and
Lakhani 2010, Raisch and Fomina 2023). Our findings suggest that human prompt engineering to guide
language models in generating creative outputs is a promising approach for adopting “AI-in-the-loop”
workflows for creative problem-solving. We illustrate that human-AI approaches can efficiently produce
novel and valuable outputs at minimal costs, facilitated by human guidance of the LLM’s exploratory
solution search space. Although the specific form or division of labor in human-AI collaboration will evolve
with technological advances, this paper illustrates an adaptable framework for strategically integrating
generative AI into creative problem-solving.
Creative Problem Solving and Human-AI Solution Search
Creative problem-solving can be conceived as a search for solutions on a landscape (Fleming and Sorenson
2001, Katila and Ahuja 2002, Levinthal 1997). This landscape contains peaks of exceptional opportunities
and valleys with limited ones (Levinthal 1997). Most solvers tend to search locally, explore familiar
neighborhoods, near previous successful solutions, and seek incremental improvements (Cyert and March
1963, Nelson and Winter 1982). However, some solvers may venture into uncharted areas, exploring
solutions that deviate from existing ones to explore more distant areas of the solution landscape, potentially
5
unlocking more innovative possibilities (Kaplan and Vakili 2015). This aligns with the observation that
greater problem-solving success occurs when firms broaden their search for knowledge across various
technological domains and geographic locations (Kneeland et al. 2020, Leiponen and Helfat 2010).
When the best method for solving a problem is uncertain, one strategy to enhance innovative search
is to utilize a variety of different approaches, or “parallel paths,” as this breadth can improve overall solution
quality (Abernathy and Rosenbloom 1969, Leiponen and Helfat 2010, Nelson 1961). The parallel path
effect suggests that developing multiple solutions to the same problem increases the likelihood of achieving
a high-quality outcome (Boudreau et al. 2011, Dahan and Mendelson 2001). Arguably, utilizing various
approaches is particularly critical when the objective is to maximize the quality of a few top ideas instead
of many average ones (Girotra et al. 2010).
Crowdsourcing contests leverage a diverse pool of solvers with differing backgrounds and
experiences to increase the number of parallel paths to solve a problem and improve solution quality
(Jeppesen and Lakhani 2010, Lifshitz-Assaf 2018, Piezunka and Dahlander 2015, Riedl et al. 2024).
However, crowdsourcing can be resource-intensive (Piezunka and Dahlander 2019) and statistically
inefficient due to the volume of low-quality submissions (Bell et al. 2024). The quest for highly creative
outcomes can be further complicated by diminishing contribution effort as the size of an innovation contest
grows (Boudreau et al. 2011, 2016, Che and Gale 2003, Taylor 1995, Terwiesch and Xu 2008). Hence,
crowdsourcing has some drawbacks, although it has been a highly effective approach for enhancing the
parallel path effect. As AI systems like LLMs have capabilities that differ from those of humans, they
promise to develop new forms of creative problem-solving (Raisch and Fomina 2023) and complement
human intelligence (Choudhary et al. 2023).
Using LLMs to Advance Human-AI Creative Problem Solving
LLMs offer a new way of augmenting creative problem-solving for organizations. These models, trained
on extensive data corpora, provide problem-solvers with unprecedented capabilities for interactive
collaboration. One critical way to collaborate with LLMs is via strategic prompt engineering, where humans
and AI explore the search space together, guided by carefully crafted human instructions. This study
6
examines independent and differentiated search strategies, emphasizing the role of strategic prompt
engineering in reducing the cost and improving the quality of outputs in human-AI collaborative search
relative to traditional human crowdsourcing methods.
Technical Primer. AI is a broad field within computer science that seeks to create systems capable of
performing tasks that typically require human intelligence. This includes activities such as learning,
reasoning, problem-solving, perception, and understanding language. Machine Learning (ML), a subset of
AI, focuses on algorithms that allow machines to analyze data, learn from it, and make predictions. Unlike
traditional programming, ML models evolve their performance as they process more data, eliminating the
need for explicit programming in every scenario.
Generative AI falls under the umbrella of ML and represents an approach where machines can
generate new content or data that is similar but not necessarily identical to what they have been trained on.
This can include anything from generating text, computer code, and music to creating images and videos.
Generative AI leverages ML models, such as neural networks, trained on large datasets to produce outputs
that mirror the input data distribution. LLMs are a type of generative AI specifically designed to process
and generate human language. They are trained on vast corpora of textual data from the internet, books,
and other text-based sources, allowing them to learn the intricacies of human language, including grammar,
syntax, semantics, and context. This technical primer focuses on autoregressive LLMs, the foundation for
models like ChatGPT, Gemini, and Claude. The training process of autoregressive LLMs involves several
key components:
1. Tokenization: The input text is divided into smaller units called tokens, which can be words,
subwords, or characters. This process allows the model to process the text more efficiently.
2. Embedding: Each token is mapped to a high-dimensional vector representation, capturing the
semantic and syntactic relationships between tokens. This embedding layer allows the model to
understand the meaning and context of words.
3. Transformer Architecture: LLMs commonly use transformer neural networks (Vaswani et al.
2017). Transformers utilize self-attention mechanisms, which allow the model to weigh the
7
importance of different tokens within a sequence, enabling it to capture long-range dependencies
and context more effectively (Ash and Hansen 2023, Bahdanau et al. 2014).
4. Autoregressive Language Modeling: This approach trains the model to predict the next token in a
sequence based on all preceding tokens. The model learns to generate text by iteratively predicting
each subsequent token, conditioned on the previously generated ones. This process leverages self-
supervised learning, where language comprehension is refined by predicting future elements of the
text.
5. Optimization: The model’s parameters are updated through an iterative process called gradient
descent, which minimizes the difference between the model’s predictions and the actual next tokens
in the training data. This process allows the model to learn the patterns and relationships within the
language. Due to a large number of parameters and complexity, optimizing LLMs requires
substantial computational resources, particularly GPU-based computing power (Achiam et al.
2023).
6. Fine-tuning and Alignment: State-of-the-art LLMs are typically further refined through supervised
learning, where the models are provided with human-annotated datasets that exemplify desired
behaviors or task-specific outputs. Additionally, alignment and guardrail techniques are employed
to promote helpful, safe, and human-aligned responses, ensuring that the models adhere to ethical
guidelines and produce appropriate content (Bai et al. 2022, Ouyang et al. 2022).
During the text generation process, autoregressive LLMs use the learned patterns and relationships to
calculate the probability distribution of the next token based on the input context. The model then samples
a token from this distribution, with the sampling process influenced by the temperature parameter. A higher
temperature leads to more diverse and creative outputs by allowing for the selection of lower probability
tokens, while a lower temperature results in more deterministic and conservative generations by favoring
high probability tokens (Bellemare-Pepin et al. 2024).
Strategic Prompt Engineering. Prompt engineering, the process of designing input prompts to guide the
model’s output (Brown et al. 2020), plays a crucial role in shaping the generated text (Battle and Gollapudi
8
2024, OpenAI 2024). As LLMs currently lack independent agency, the quality and relevance of their
outputs heavily depend on humans’ ability to skillfully craft prompts, emphasizing the necessary
collaboration between humans and AI (Zamfirescu-Pereira et al. 2023). By carefully crafting prompts that
provide context, instructions, or examples, humans can influence the output probability distribution,
steering the model’s output toward desired topics, styles, or formats. Effective prompt engineering is
essential for aligning the model’s outputs with specific tasks or domains, as it helps to constrain the vast
space of possible generations to more relevant and coherent outputs. This “AI-in-the-loop” integration
enables a synergistic HAI collaboration that can push the boundaries of traditional problem-solving
approaches to produce creative solutions.
Anticipated Cost-Benefit Implications. Our study uses OpenAI’s Generative Pretrained Transformer 4
(GPT-4), a representative example of advanced language models that operate based on similar foundational
principles (see Appendix A for a detailed overview of the inference processes of LLMs). The advanced
capabilities of LLMs, such as GPT-4, indicate a strong potential for application in creative problem-solving.
Notably, using LLMS may streamline idea generation, making it a more cost-effective and efficient
approach (Girotra et al. 2023). Unlike human participants, who typically require monetary or non-pecuniary
incentives to engage in crowdsourcing contests (Jeppesen and Lakhani 2010, Terwiesch and Ulrich 2009),
LLMs can continuously generate outputs for creative tasks without additional incentives. Moreover, LLMs
can rapidly generate consistent solutions at a larger scale, substantially enriching the idea pool in much less
time than conventional human crowdsourcing methods.
Human-AI Collaboration and the Production of Novel and Valuable Outputs. The interactive nature
of LLMs, which enables humans to engage in conversations through personalized textual prompts, allows
for novel forms of collaboration through the division of labor between humans and AI systems (Choudhary
et al. 2023). Scholars have begun conceptualizing alternative forms of human-AI creative problem-solving,
in which humans and AI can search together for creative outputs, for instance, along the dimensions of
specialization of agents and the sequencing of tasks (Choudhary et al. 2023, He et al. 2023, Jia et al. 2023,
Raisch and Fomina 2023). In this study, we draw upon these perspectives and explore two forms of HAI
9
collaboration with LLMs for creative problem-solving: independent and differentiated search. These two
forms of HAI solution search are depicted in Figure 1.
Although many formats of HAI collaboration can be envisioned, a key differentiating factor is the
degree to which humans are involved in steering how the LLM searches for solutions. Due to the
fundamental mechanism of LLMs, which involves calculating the probability distribution of the next word
or token based on the input context and sampling from this distribution (Bubeck et al. 2023), LLMs tend to
reflect more mainstream ideas unless otherwise directed (Anderson et al. 2024). Independent and
differentiated search are two alternative approaches to elicit more creative responses.
With independent search, as shown in Figure 1, humans define the problem and provide an initial
prompt, allowing the LLM to independently generate a potential solution through its own broad search
capabilities by leveraging the LLM’s training on the immense scope of data across diverse domains (Raisch
and Fomina 2023). As an illustrative example, a human prompt like “Generate a creative solution for a new
type of sustainable urban transportation” might solicit responses from the LLM, such as “A network of
small, solar-powered pods running on elevated tracks above city streets” and “Sidewalks and bike paths
that generate electricity through the kinetic energy of pedestrians and cyclists” which resemble independent
contributions, such as what we might expect from the human crowd.
For independent search, the initial framing and scope of the exploration critically depend on the
human’s strategic guidance. Consequently, the human’s initial guidance is crucial in broadly defining the
scope and framing of the LLM’s search process. For example, a role-playing prompt like “As a time traveler
from 2100, you’ve seen the incredible advancements in sustainable urban transportation. Share with a city
planner in 2025 the most groundbreaking and eco-friendly transportation solution from your era that has
transformed city life. Generate a creative solution for a new type of sustainable urban transportation based
on this futuristic insight” may provide solutions such as “elevated hyperloop tunnels consisting of elevated,
vacuum-sealed tunnels that use magnetic levitation to transport passenger pods at high speeds” or “an
electric water shuttle system that uses electric-powered shuttles that glide smoothly across waterways.” The
strategic guidance may allow the LLM to draw on diverse domains in its training data to make broader,
10
more distant searches. The LLM’s search may then be instructed to generate solutions that make substantial
conceptual departures, where the human’s prompting skill and initial guidance determine the overall
direction and constraints of this expansive exploration.
Another approach is differentiated search, where, as illustrated in Figure 1, humans may provide
an initial prompt and then insert differentiation instructions after each output to encourage the model to
diversify its successive responses and explore a broader solution space. For example, the human may
provide an initial prompt like “Generate creative solutions for a new type of sustainable urban
transportation” and then after each output from the LLM, add the instruction “Make sure to tackle a different
problem than the previous ones and propose a different solution.” This iterative human guidance aims to
promote solution diversity, reduce redundancy, encourage originality, and facilitate distant solution space
exploration. While this process is iterative in nature, it focuses primarily on promoting solution diversity
rather than in-depth refinement and collaboration between humans and AI.
--- Insert Figure 1 here ---
Although both independent and differentiated search approaches to HAI collaboration show
promise for enhancing creative problem-solving, several open questions remain about the applicability of
HAI partnerships to exploratory tasks involving solution search and the generation of novel solutions. These
exploratory tasks differ from routine decision-making scenarios focusing on previously explored situations
with known procedures and solution alternatives (Raisch and Fomina 2023), such as judge bail-or-release
decisions (Kleinberg et al. 2018) or medical diagnoses (Lebovitz et al. 2022). The capabilities required for
open-ended creative problem-solving may not be well-suited for HAI collaboration with current LLMs,
especially when compared to the diverse perspectives offered by a broad human crowd.
First, when collaboratively searching for novel solutions, LLMs may inadvertently constrain their
exploration due to their reliance on formal rationality—a decision-making mode grounded strictly in
abstract rules, formal procedures, and established precepts, without nuanced consideration of contextual
factors or personal perspectives (Lindebaum et al. 2020, Weber 1978). As creative problem-solving often
draws inspiration from individual perspectives and situational factors (Amabile 1983, Perry-Smith 2006),
11
there are concerns that HAI systems may be bound by their training data and constrained to searching for
“myopic” or local solutions. This could lead the systems to overlook novel opportunities that necessitate
conceptual leaps transcending formal rules (Kneeland et al. 2020) but may be more valuable based on their
associations with past successes (Rindova and Petkova 2007).
Second, the outputs of HAI collaboration systems are limited to recombining patterns from the data
used to train the LLM component. As a result, the outputs may be retrospective and ultimately confined by
the specific data the LLM was exposed to during training. This contrasts with human cognition, which is
inherently forward-looking and theory-based, enabling humans to transcend data and prediction to generate
new data and observations and conduct experimentation (Felin and Holweg 2024, Gavetti and Levinthal
2000). These forward-looking theories guide human perception, search, and action, potentially serving as
the source of highly novel recombinations, jumps, and applications (Katila and Ahuja 2002, Kneeland et
al. 2020). While collaborating with AI may accelerate the generation of more incremental yet valuable
solutions (Benner and Tushman 2003), pushing the boundaries towards radically new solutions may still
require human expertise—particularly the collective input of the human crowd (Jeppesen and Lakhani
2010), which is unconstrained by the data-prediction modes of current AI systems (Felin and Holweg 2024).
Third, HAI outputs can exhibit failure modes, such as confabulation (generating plausible but
unfounded content) and hallucination (producing outputs detached from training data or reality) (Ji et al.
2023), which may have mixed implications for creative problem-solving. On the one hand, some degree of
confabulation and hallucination in the outputs could boost novelty by facilitating exploratory search that
combines concepts from disparate domains in novel ways—aligning with the goals of distant search
(Benner and Tushman 2003, March 1991). However, this factually incorrect knowledge could also
compromise solution value by producing distorted or bizarre reflections of flawed data with limited
practical value for adoption. Given the unclear potential of HAI collaboration for creative problem-solving,
we investigate this question by comparing the novelty, value, and quality of HC and HAI outputs.
Research Design and Methods
Setting
12
Crowdsourcing Context. We partnered with Continuum"
32,hbs_edu,25-034_18b7f0b4-fdb6-4225-98ad-2fa7d4adaf42.pdf,"Working Paper 25-034
Crossing the Design-Use Divide:
How Process Manipulation
Shapes the Design and Use of AI
Rebecca Karp
Crossing the Design-Use
Divide: How Process
Manipulation Shapes the Design
and Use of AI
Rebecca Karp
Harvard Business School
Working Paper 25-034
Copyright © 2025 by Rebecca Karp.
Working papers are in draft form. This working paper is distributed for purposes of comment and discussion only. It may
not be reproduced without permission of the copyright holder. Copies of working papers are available from the author.
Funding for this research was provided in part by Harvard Business School.
Crossing the Design-Use Divide: How Process Manipulation Shapes the Design and Use of AI
Abstract
Existing literature often separates research on the design of innovations from their implementation
and use, neglecting the role of selection—how organizations choose which innovations to
implement. Although scholars suggest scientific approaches for selecting novel technologies, there is
limited research on how these methods are practically employed in decision-making. This study
addresses this gap by examining how organizations decide which innovations to implement and how
the selection process influences their design and use. Drawing on a two-year ethnographic study, the
research explores how 13 dyadic pairs of entrepreneurial firms and health system committees
piloted AI-based medical diagnostic innovations. Committees, composed of members with
polarized views on AI, formed coalitions reflecting these views. Dominant coalitions engaged in
""process manipulation,"" strategically altering the piloting process to achieve self-interested outcomes
while maintaining an appearance of rigor. Coalitions enthusiastic about AI scoped pilots to test
basic uses, ensuring success, while skeptical committees tested advanced uses, hoping for failure.
This manipulation constrained entrepreneurs' ability to advocate for their innovations and
demonstrate market differentiation. The paper highlights the dynamics of process manipulation and
its impact on AI innovation development and use.
Word count: 185
INTRODUCTION
The implementation of a novel technology or innovation within an organization can trigger broad
scale and transformative change to work processes (Barley, 1986; Beane, 2020), roles (Bechky, 2020; Pachidi
et al., 2021), and even occupational identity, such as when librarians began using internet search technologies
(Nelson & Irwin, 2014). Yet little research on implementation examines how innovations, with the power to
catalyze substantive change, appear within a given organization at the start. Rather, scholars operate across a
divide. On one side, students of innovation and technological evolution focus on how firms, communities,
and varying social groups organize to design new technologies, stopping short of investigating how a discrete
technology is adopted and used within a consuming organization (Pinch & Bijker, 1984; Kline & Pinch, 1996;
Suarez & Utterback, 1995; Rosenkopf & Tushman, 1998; Hargadon & Douglas, 2001; Anderson &
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Tushman, 2018; Benner & Tripsas, 2012). On the other side, scholars interested in implementation and use
focus on how new technologies alter, or reinforce, work tasks, roles, and organizational structures, without
examining how innovations arrive within an adopting organization (Barley, 1987; Orlikowski, 1993; Leonardi
2009; Edmondson et al., 2001; Nelson & Irwin, 2015; Beane, 2020; Pachidi et al., 2021; Anthony & Beane,
2023). While both literatures offer valuable insights regarding how innovations and organizations evolve, they
neglect to explain how adopting organizations select innovations for use. This is an important omission to
rectify, as the way organizations select innovations may very well shape their design and use (Thomas, 1994).
Selection, “choosing an innovation for use,” involves the set of activities organizational members
engage in when determining whether to implement or reject a given technology (Rogers, 1995). Although not
an entirely linear process, scholars contend that selection occurs after design, but before an innovation is
implemented and used within an organization (Leonardi & Barley, 2010; Leonardi, 2007). To select novel
innovations, scholars posit that organizations may run a “scientific-like” process that involves piloting an
idea, process, or innovation before implementing it (List, 2022; Thomke, 2003). For example, Edmondson et
al. (2001) show how hospitals piloted minimally invasive surgery, a very novel technique, before broadly
rolling it out. However, this technical or scientific perspective ignores a more behaviorally plausible model of
selection, where some organizational members might coalesce to advocate for certain innovations over
others, to reinforce their own power or status (March 1994). Pilots may be more subjectively executed than
appreciated and organizational members may not design tests with an eye towards selecting the most
proficient innovation. Power plays and persuasion from both designers and organizational members might be
as likely a determinant of why an innovation is selected compared with the unbiased results of any given
pilot. However, without directly observing how the selection process unfolds, it is challenging to understand
how certain innovations come to be implemented within an organization while others do not. As Leonardi
and Barley (2010: 38) explain, “when studies begin with little insight into why technologies were designed as
they were, why one technology was chosen over another … it is impossible to determine whether patterns of
use are shaped in important ways by dynamics of power.”
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The process of selection does not just effect how organizations use innovations downstream, it also
likely shapes how innovations are designed upstream as selection screens the features and capabilities a
designer can encapsulate within an innovation. Buoyed by desires to see their products adopted, designers
may be influenced by powerful members of potential adopting organizations, to develop products that
promote some features over others (Karp, 2023) or that benefit certain members at others’ expense (Myers,
2023). Designers can create products that even temper performance in an appeal to special interests. For
example, Noble (1984) showed how powerful groups shaped the design of numerical control systems in ways
that diminished technical performance. As Noble asks: “The best technology? Best for whom? Best for
what?” (2011: 240). While scholars often advocate for user-centered designs (von Hippel, 1986; Norman,
2011), it is the organizational members involved in selection that may have outsized influence on design
choices, rather than users, as designers hope to convince those involved in selection to advocate for and pick
their innovations. Users’ and decision-makers’ interests may not align. Yet little research explores how the
process of selection advances or tempers the development of an innovation, nor how the will of those in
charge of selecting a novel innovation interacts with the intentions of designers.
Examining selection takes on heightened importance when considering innovations built on AI
(Bailey & Barley, 2020; Anthony, Bechky, & Fayard, 2023; Kellogg, 2022). Innovations that leverage AI
create “constant change, invisibility, and inscrutability [which] call into question the applicability of prior
findings about technology” (Anthony et al., 2023: 1674). AI is inherently modular and decomposable
(Baldwin, 2023), as features can easily be turned on or switched off to suit the preferences of those in
decision-making positions (Karp, 2023). Thus, two organizations might select to pilot very different
instantiations of the same innovation, which is less plausible with other types of novel standalone
innovations. Those with power to make these decisions have become increasingly important agents in not
just gating but also shaping innovations, as they select not only if an innovation will be used, but also what
aspects of an innovation and for what uses. At the point of selection, organizations make a host of visible
decisions about how they might use AI, and AI technologies are granted access to organizational databases
and data. Access to such sources of data are key differentiators in the quest to train and develop AI-based
3
innovations. Thus, observing the decisions organizational members make regarding the selection of AI
provides a unique opportunity to bridge the gap and explore the relationship between design and use.
This paper draws on two years of ethnographic data to unpack: How do organizations decide which
innovations to implement and how does this process shape the design and use of an innovation? Specifically,
I examine how 13 overlapping dyadic pairs of three health systems and six entrepreneurial firms attempted to
pilot AI-driven innovations for medical diagnostics and treatment. Paying attention to the role internal
organizational politics plays (Selznick, 1953; March, 1994; Thomas, 1994), I reveal how selection can be
manipulated when committees form coalitions based on strong outside priors about an innovation’s
capabilities. Dominant coalitions wary of AI designed pilots to test only advanced capabilities of
entrepreneurs’ innovations in hopes that the pilots would fail. In contrast, dominant coalitions enthusiastic
about AI designed pilots focused on testing basic capabilities. These pilots succeeded, but limited
entrepreneurs’ ability to prove that their innovations were differentiated in the market. Very few innovations
piloted to test advanced capabilities were successful. However, conditional on a successful pilot, these
innovations were more likely to be used than innovations introducing basic capabilities. I introduce the
concept of process manipulation: working within an accepted process but strategically engineering select
steps or micro-decisions to achieve specific results - to explain how dominant coalitions altered the process
of piloting to achieve their own self-interested ends. I show how process manipulation can alter the trajectory
of what is tested and subsequently designed and used.
SELECTION AND CROSSING THE DESIGN-USE DIVIDE
Scholars have long called for studies that cross the “implementation line” (Leonardi, 2007) and
account for the relationship between the design and use of novel innovations (Thomas, 1994; Leonardi,
2007; Leonardi & Barley, 2010; Bailey & Barley, 2020; Anthony et al., 2023). As scholars contend,
understanding this relationship is crucial as design and use are not “discontinuous events” (Leonardi, 2007),
but rather part of a reflexive cycle, where the way organizations use an innovation informs an innovation’s
design. Designers’ intentions and development decisions may in turn constrain or enable any organizational
change triggered by the implementation of an innovation and its use (Forsythe, 2001). These calls have
4
reached a fever pitch in the era of AI as these technologies are “radically different from prior technologies in
[their] potential to transform the landscape of work” Bailey and Barley (2020: 1). Though often neglected, a
useful way to explore the relationship between design and use is to focus on how organizations select novel
innovations. During selection, organizations make decisions about how to use an innovation, and designers
are open to adapting and modifying their innovations as they learn of what it takes to convince adopting
organizations to choose their innovations.
Historically, selecting a novel innovation involved fewer decision-makers and a minimal set of
decisions (Rogers, 1983). Since innovations were more standalone and less decomposable, decision-makers
made a choice, if to adopt an innovation, not what aspects of an innovation to use (Baldwin, 2023). Consider
Barley’s (1987) CT scanner: it is difficult to imagine that hospital decision-makers could choose to rapidly
adapt, limit, or modify the functionality of the CT scanner at the time. Staff in Barley’s study were exposed to
CT scanners with the same set of embedded capabilities. In contrast, when an innovation is digital or built
upon AI, organizational decision-makers ability to locally shape and adapt an innovation is amplified
(Kellogg, 2022), which means two of the same “scanners” can offer the user a different capability set. Today,
an organization might quickly change or modify software to adjust the way technology is used on-site.
Decision-makers often have latitude to turn on and off the multitude of features embedded within a digital or
AI-based innovation to suit their own needs (Karp, 2023). Organizational members at all levels may be
involved with decision-making processes, as organizations seek out the opinions and grant authority to a
variety of different staff (Lee, 2024). The combination of increased decisions and decision-makers introduces
degrees of freedom into the selection process. This flexibility further blurs the line between design and use
and elevates the role selection plays in shaping both.
A Scientific Approach to Selection
Selection may be a simple process to undertake when innovations are familiar (Pisano, 1994). For
example, Leonardi (2007) described how ITSM, a help-desk queuing application already in use at the
SkyLabs organization was implemented in a new group of networking engineers with no internal deliberation
5
and in only a few weeks’ time. However, to manage the complexity of multiple decision-makers and multiple
decisions, scholars posit that organizations use a technical or “scientific” approach (Merton, 1973) to
decision making by experimenting and testing innovative products, ideas, and practices before implementing
them (Pisano, 2001; Thomke, 2003). For example, in contrast to SkyLabs, all 16 hospitals in Edmondson et
al.’s (2001) study “piloted” a novel form of minimally invasive cardiac surgery before integrating this method
into regular use. Piloting is a type of experiment that involves testing the performance of an innovation on a
representative sample of tasks or use cases, usually in a live or quasi-live environment, and then comparing
the pilot’s performance with that of traditional ways of working (List, 2022). As pilots unfold, adopting
organizations and designers can learn how an innovation might work in situ and make modifications in
response to real-time feedback. Scholars advocating for a scientific approach to decision-making implore that
because piloting and experimentation are scientific methods, they quell latent or acknowledged biases that
would otherwise cloud decision-making (Wuebker, Zenger & Felin, 2023; Thomke, 2003; Ries, 2010;
Camuffo et al., 2020; Koning, 2020; List, 2023). As such, a scientific approach can help organizations
generate data to analyze decisions before they are broadly enacted and hopefully diminish the challenge of
managing differing voices and choice sets.
Yet, determining how to design a pilot is not a straightforward feat. Ideally, when designing a pilot
scientifically, scholars suggest that organizations look to balance learning about the radical or most advanced
capabilities of an innovation with gaining information about the generalizable uses of that innovation
(Eisenmann, 2010; Gans, 2023). Innovations are viewed as more generalizable or scalable when they offer
benefit to a wider set of users (Thomas, 1994). Offering a greater set of users benefit usually means testing
more basic aspects of an innovation; aspects that are less specialized and that a greater quantity of individuals
can understand and use (Bresnahan, 2010). For instance, the application Wix offers users templated website
designs, no coding required. While most individuals can utilize this application with ease, it limits the types of
customizations or advanced and specialized capabilities a user can build in. Thus, to satisfy both goals—
learning and generalizability—organizational decision-makers manage tradeoffs, focusing pilots on aspects of
an innovation that balance between advanced and basic capabilities. Following this logic, it is unlikely that
6
organizations will design pilots testing the tails of an innovation’s capabilities: the advanced and radical
capabilities of an innovation or the most incremental, benign capabilities. The implication for the design and
use of novel innovations is that if organizations make decisions based on a scientific approach, they will
select and implement innovations with capabilities that balance technical proficiency with generalizability.
From these pilots, designers likely also learn how a their innovations perform across a range of uses.
Yet enacting science is rarely “value free” (Merton, 1973). Organizations may use quite a bit of
intuition and judgement when designing pilots, which might deviate from the purely scientific. For example,
Gans (2023) recently theorized that organizations may design pilots to reaffirm their views on the market,
rather than gain information about an innovation’s capabilities. Gaining information may not be the only
goal. Drawing from March (1994), organizational decision-making often occurs as part of a political process
where action is strategic, rather than scientific, and organizational members band together and form alliances
or coalitions to see their preferences come to fruition. Much literature has focused on the role these power
dynamics play in shaping who is granted decision-making authority within organizations (Mechanic, 1962;
Pfeffer & Salanick, 1974; Lackman, 1989; March, 1994; Goldstein & Hays, 2011; Friedkin, 2011; Truelove &
Kellogg, 2016). Rather than make decisions based on a technical process, decision-makers with power may
instead select innovations that help them maintain their advantage. Those without power might form
alliances to advocate for their own interests. These power dynamics may very well shape how organizations
select novel innovations, yet they are neglected by a scientific approach to decision-making.
A Behavioral Approach to Selection
Instead of making decisions purely using a scientific lens, organizational members might take a more
strategic or behavioral approach to selection, and use their authority or power to assert decisions. Following
March (1962; 1994), organizational members accomplish this task generally through one of two ways: (1) by
leveraging their structural position within an organization; (2) by using their skill at impression management
and issue selling (Fligstein, 2001; Dutton & Ashford, 1993). For example, Pfeffer & Salanick (1974) show
how committees tasked with allocating university resources granted powerful departments more than their
fair share of graduate fellowships. Howard-Grenville (2010) explains how issue sellers in relative positions of
7
disadvantage compared with issue recipients were able to advocate for their interests and shift Chipco
towards a more environmentally friendly manufacturing process. While the mechanism by which members
acquire power differ, one structural, the other skill-based, the implication is the same: those with power, or
those savvy enough to acquire it, determine whether an innovation is selected and for what purposes. Yet
how these dynamics might play out and the consequences of these dynamics on both design and use are less
clear. For example, Thomas (1994), shows how mid-level engineers engaged in issue selling and convinced
senior managers to select surface mount technology to improve their status within the organization. But
selection took a long time. Implementation took a long time as well and did not advance the design of SMT
in any notable way, nor the status of engineers. Further, advocacy may originate from designers, who may
attempt to find powerful decision-makers, be they positionally powerful or good at issue selling, to champion
their innovations even if those innovations benefit certain organizational members over others (Howell &
Higgins, 1990).
It is also unlikely that organizations completely abstain from scientific approaches to selection given
their popularity. Piloting and experimentation are commonly used techniques and likely do inform decision-
making processes (List, 2022). Thus, organizational members might balance a scientific approach with a more
behavioral one, to select novel innovations. For example, in this study, all of the adopting organizations
designed and executed pilots to test out novel innovations. However, those with power used pilots to engage
in process manipulation—working within an accepted process but strategically engineering select steps or
micro-decisions to achieve specific results. Process manipulation varied from other forms of more assertive
power (March, 1962; 1994), as it involved maintaining the veneer of a scientific process by making the right
claims and involving all decision-makers. I explain how process manipulation occurs within organizations
and how it shapes both the design and use of novel innovations in material ways.
METHODS
In 2016, I engaged in a broad field study to examine how entrepreneurs innovating in healthcare
commercialized their products and services. I interacted with three regulatory organizations, more than 50
entrepreneurial firms and 100 experts in the healthcare industry, and 10 large organizations that might
8
ultimately license and use entrepreneurial firms’ innovations. Ethnographic interviews with the latter
organizations revealed customers’ outsized interest in the digital technologies—mainly machine learning and
artificial intelligence—underpinning some of the entrepreneurs’ innovations. Some of these customers
nevertheless feared that deployment of these technologies, purported to improve both the quality and
efficiency of healthcare delivery, might displace work tasks or routines within their organizations and shift
organizational power structures in politically unpalatable ways. Entrepreneurs were well aware of such
concerns.
Because machine learning, or “AI,” technologies were spoken of so ubiquitously and frequently, I
made them the focus of my efforts to better understand the decisions that determined whether and how
these innovations were implemented within customer organizations. Because the selection process was
infrequently observed, I leveraged an inductive field research design, which was particularly well suited to
developing an understanding of less researched settings (Bailey & Barley, 2020; Edmondson & McManus,
2009). The use of machine learning for medical diagnostics has been hotly and publicly debated due in large
part to scholars’ and practitioners’ concerns about its role in displacing work (Ghassemi et al., 2019; Jamison
& Goldfarb, 2019; Topol, 2019; Brynjolfsson et al.,2020; Leibowitz et al., 2021). Although the use of
artificial intelligence to support differential diagnoses is not a novel concept, recent advances in machine
learning have enabled algorithms to replace, rather than merely support, diagnostic work performed by
doctors and nurses (Esteva et al., 2017; Topol, 2019). A qualitative field research design offered me the space
through observations and interviews to discern and probe upon informants’ experiences, which could have
been lost through surveying or other quantitative means (Becker, 1998).
All the organizations in my study tested how to both roll out and use an innovation. To gain more
in-depth knowledge of the piloting process, I focused data collection efforts from 2017 to 2019 on three
large healthcare systems that agreed to pilot the AI-based innovations of six entrepreneurial firms. These
three customer organizations and six entrepreneurial firms formed overlapping dyads. In all cases, at least
two customer organizations agreed in principle to pilot each entrepreneurial firm’s innovation. Analysis at the
dyadic level helped clarify whether selection varied because of idiosyncratic behaviors of the customer
9
organization or entrepreneurial firm. Analysis at the dyadic level allowed some generally unobservable
alternative explanations to be more observable, such as a healthcare system’s or entrepreneurial firm’s lack of
adequate implementation capabilities. This concern would be revealed over a set of multiple dyadic pairs if,
for example, one entrepreneurial firm was unable to successfully pilot its innovation with any customer or
any customer was unable to successfully pilot with any entrepreneur.
Research Context and Sample Selection
All six entrepreneurial firms in this study used AI as foundational inputs to performing medical
diagnostic work. AI is a “form of computational statistics, [and] is based on algorithms that use data to
generate predictions” (Jamieson & Goldfarb, 2019: 778), which improve automatically as an algorithm
encounters more data, enabling machines to eventually perform work with minimal or no human
intervention. This inherent learning process may be particularly important in shaping the innovative
trajectory of a given application (Fraser & Ozcan, working paper). Access to one type of data versus another
may influence an application’s future capabilities and determine how well it competes with comparable
market offerings. For example, an application trained on diverse sets of data may be better able to detect
illness in minority populations and therefore be more valuable than comparable applications not equivalently
trained.
AI technologies are generally used to perform medical diagnostic work in one of three ways: (1) by
capturing or counting known medical irregularities difficult to observe with the human eye or existing
technologies, (2) identifying irregularities not previously known to be indicators of an illness, and (3) offering
potential treatment plans for confirmed diagnoses. MAMMO, one of the firms in my sample, leveraged
machine learning in two of these ways. Its application could limit false positives by more accurately
determining when mammograms contained no known malignancies compared with radiologists and could
identify novel indicators of cancerous tumors in patients whose mammograms were previously diagnosed as
clinically negative. No intervention by doctors was required to make these diagnoses.
Sampling strategy. Over the course of my time in the field, I engaged with three large health
systems (Red Hospital, Blue Hospital, and Community Hospital) that were interested in purchasing and using
10
AI-based innovations. Two were formed around large academic hospitals. One health system was originally
formed around a community hospital. These organizations shared an overlapping interest in licensing and
using the offerings of six entrepreneurial firms (MAMMO, SENSOR, INFECTION, SKIN, DIAB,
WHITE). The attestations of these organization members were not merely lip service, as all three
organizations had paid to pilot entrepreneurial firms’ innovations and established internal committees tasked
with designing and executing pilots. Piloting committees were diverse, composed of individuals who served
in various roles (e.g., developers, doctors, nurses, administrators) and at different levels (e.g., directors,
managers, coordinators) within each health system. Committee members varied within organization and
members were selected by a mix of stakeholders: heads of innovation, departments heads, and technology
executives. Although diverse, all of the informants I spoke to in these organizations were well versed in the
“gestalt” of AI and knew the arguments for and against implementing this technology. People were not shy
about sharing their opinions. The individuals that selected the committees were sometimes part of the
committees themselves. When members were asked about why they were chosen to participate on the
committee, rationale was mixed and ranged from “I am diligent” and “not afraid to share my opinions
respectfully” to “not really sure why” and “I guess I did something right.” A head of innovation shared,
“Committees were formed by people who had a stake in the use of this innovation, and I tried to pick people
who could think carefully about the innovation.” Surprisingly, members or leaders never commented about
being selected or selecting members based on their views on AI.
Although I engaged with many entrepreneurial firms focused on innovating in healthcare using AI, I
selected the six identified above as they had pilot agreements in place with at least two of the large customer
organizations in my sample. All had proof that their innovations could work in situ and were looking to
expand upon that evidence in ways that could differentiate their innovations in the market. They also
exhibited marked differences, varying in team experience, targeted medical specialty, and geographic
proximity to investors or academic centers. Scholars show that entrepreneurial firm performance is
influenced by prior team experience (Kor, 2003), sector focus (Agarwal & Gort, 2002), and propinquity to
sources of capital or knowledge (Powell et al., 2005). The diversity of this sample offered the theoretical
11
range needed to illuminate common aspects of the selection process and highlight differences regardless of
firm experience, sector focus, or geography (Lawrence & Lorsch, 1967; Harris & Sutton, 1986; Santos &
Eisenhardt, 2008). Table 1 profiles the six entrepreneurial firms in my sample.
Insert Table 1
From this set of entrepreneurial firms and customer organizations, I constructed a sample of 13 overlapping
dyadic pairs. Table 2 provides an overview of each dyad.
Insert Table 2
Data collection. Although the present study draws primarily from ethnographic observations
conducted between 2017 and 2019, I supplemented this data with a series of structured interviews and
entrepreneurial and customer firm data that included strategic planning documents, news articles, blog posts,
and scientific publications.
Ethnographic observations. Observations occurred in meetings between entrepreneurial firms and
customer organizations and at numerous public events. Over the course of the study, I conducted over 1,000
hours of observations. After each day of observation, I recorded field notes along with any emerging insights.
Interviews. Throughout the course of the study, I conducted 40 formal interviews, ranging in duration
from one to two hours, involving multiple members of each entrepreneurial firm and decision-makers at
different levels in each of the three customer organizations.
Entrepreneurial and customer firm data. I collected three types of firm data: (1) news articles and blog
posts on both entrepreneurial firms and customer organizations; (2) scientific reports and journal articles
detailing results of any pilots or trials run by the entrepreneurial firms; and (3) self-reported progress reports
detailing any roadblocks to piloting encountered by the entrepreneurial firms. Progress reports were collected
in person from each entrepreneurial firm. I collected progress reports five times during the course of the
study. From these data, I was able to construct an understanding of how entrepreneurial firms and customer
organizations interacted to engage in the process of piloting an innovation.
Data analysis. I first compiled and reviewed my field notes (Emerson et al., 1995; Locke, 2002) by
dyadic pair of customer organization and entrepreneurial firm. My notes revealed that to design and execute
12
pilots, customer organizations stood up formal committees of five to eight members that spanned levels,
roles, and occupational groups. Although agreements to pilot entrepreneurs’ innovations occurred before
pilot committees were formed, the committees exerted significant power over shaping pilots and ensuring
that they got off the ground. Informants shared that committees could “kill pilots.” I became interested in
how these seemingly powerful committees might shape the trajectory of entrepreneurs’ technologies and
what leverage, if any, entrepreneurs had in directing these pilots towards their own interests, if they veered
off course. Analysis proceeded in five phases.
Phase 1: Mapping the piloting process. After ordering and reviewing my field notes, I realized that piloting
seemed to involve three stages: scoping, integrating, and running and implementation. I arrived at this
conclusion through several means. First, committees generally talked about the piloting process in three
stages, with clear goals and milestones for each stage. At Red Hospital, members talked about picking the
capabilities, a stage or step that involved determining what exactly a pilot would test. At Community
Hospital, members talked about how before they ran pilots they had to “develop the pilot.” I moved away
fro"
33,hbs_edu,24-042_9ebd2f26-e292-404c-b858-3e883f0e11c0.pdf,"Working Paper 24-042
The Uneven Impact of
Generative AI on
Entrepreneurial Performance
Nicholas G. Otis
Rowan Clarke
Solène Delecourt
David Holtz
Rembrand Koning
The Uneven Impact of Generative
AI on Entrepreneurial
Performance
Nicholas G. Otis
UC Berkeley Haas
Rowan Clarke
Harvard Business School
Solène Delecourt
UC Berkeley Haas
David Holtz
UC Berkeley Haas
Rembrand Koning
Harvard Business School
Working Paper 24-042
Copyright © 2023, 2024 by Nicholas G. Otis, Rowan Clarke, Solène Delecourt, David Holtz, and Rembrand Koning.
Working papers are in draft form. This working paper is distributed for purposes of comment and discussion only.
It may not be reproduced without permission of the copyright holder. Copies of working papers are available from
the author.
The authors gratefully acknowledge financial support from the Agency Fund, the South Park Common Social Impact
Fellowship, the Weiss Fund, Microsoft, the Cora Jane Flood Endowment at Berkeley Haas, Berkeley Haas, the
Digital Data Design (D3) Institute at Harvard, and Harvard Business School. The authors thank Busara, and
especially Chaning Jang for their exceptional support in our field operations, and Brian Mutisyo, Kelsey Shipman,
and Noa Ben Haim for superb research assistance. The authors are also grateful to M. Hassan Siddique and M. Tariq
Sajid for their assistance in developing the AI mentor, and to Alex Pompe for his assistance with this project. We
have benefited from seminar and conference feedback at UC Berkeley, UPenn Wharton, D3, MIT’s Conference on
Digital Experimentation (CODE), and the Conference on Field Experiments in Strategy (CFXS). This project was
approved by the U.C. Berkeley Office for the Protection of Human Subjects.
Funding for this research was provided in part by Harvard Business School.
The Uneven Impact of Generative AI on
Entrepreneurial Performance
∗
Nicholas G. Otis Rowan Clarke
Berkeley Haas Harvard Business School
Sol`ene Delecourt David Holtz Rembrand Koning
Berkeley Haas Berkeley Haas Harvard Business School
December 2023
[Click here for latest version]
There is a growing belief that scalable and low-cost AI assistance can improve firm
decision-making and economic performance. However, running a business involves
a myriad of open-ended problems, making it hard to generalize from recent studies
showing that generative AI improves performance on well-defined writing tasks. In
our five-month field experiment with 640 Kenyan entrepreneurs, we assessed the im-
pact of AI-generated advice on small business revenues and profits. Participants were
randomly assigned to a control group that received a standard business guide or to a
treatment group that received a GPT-4 powered AI business mentor via WhatsApp.
While we find no average treatment effect, this is because the causal effect of gen-
erative AI access varied with the baseline business performance of the entrepreneur:
high performers benefited by just over 20% from AI advice, whereas low performers
did roughly 10% worse with AI assistance. Exploratory analysis of the WhatsApp
interaction logs shows that both groups sought the AI mentor’s advice, but that low
performers did worse because they sought help on much more challenging business
tasks. These findings highlight how the tasks selected by firms and entrepreneurs for
AI assistance fundamentally shape who will benefit from generative AI.
∗Otis: notis@berkeley.edu (Corresponding author); Clarke: rclarke@hbs.edu; Delecourt:
solened@berkeley.edu; Holtz: dholtz@haas.berkeley.edu; Koning: rem@hbs.edu. All authors after Otis
are listed in alphabetical order. The authors gratefully acknowledge financial support from the Agency
Fund, the South Park Common Social Impact Fellowship, the Weiss Fund, Microsoft, the Cora Jane Flood
Endowment at Berkeley Haas, Berkeley Haas, the Digital Data Design (D3) Institute at Harvard, and
Harvard Business School. The authors thank Busara, and especially Chaning Jang for their exceptional
supportinourfieldoperations, andBrianMutisyo, KelseyShipman, andNoaBenHaimforsuperbresearch
assistance. The authors are also grateful to M. Hassan Siddique and M. Tariq Sajid for their assistance
in developing the AI mentor, and to Alex Pompe for his assistance with this project. We have benefited
from seminar and conference feedback at UC Berkeley, UPenn Wharton, D3, MIT’s Conference on Digital
Experimentation (CODE), and the Conference on Field Experiments in Strategy (CFXS). This project was
approved by the U.C. Berkeley Office for the Protection of Human Subjects.
1
1 Introduction
Since the launch of ChatGPT in November 2022, there has been an explosion of research on
generative AI and its potential economic implications (The White House, December, 2022;
Agrawal, Gans, and Goldfarb, 2023; Eloundou et al., 2023; Hui, Reshef, and Zhou, 2023).
Much of this recent work is driven by the belief that conversations with large language
models (LLMs) can yield helpful assistance, feedback and advice, ultimately improving firm
performance and growth (Brynjolfsson, Li, and Raymond, 2023; Dell’Acqua et al., 2023; Noy
and Zhang, 2023; Peng et al., 2023; Kumar et al., 2023). Given the substantial variation
that exists in both worker and firm productivity, both within and across countries (Bloom
and Van Reenen, 2007; Bartelsman, Haltiwanger, and Scarpetta, 2013), the emergence of
nearly zero marginal cost generative AI “mentors” has the potential to radically improve the
productivity and performance of everyone; from the thousands of CEOs running companies
listed on the New York Stock Exchange to the hundreds of millions of entrepreneurs running
smallandmedium-sizedbusinesses(SMB)indevelopingeconomies(McKenzieandWoodruff,
2017; McKenzie, 2021; Bjo¨rkegren, 2023).
Consistent with the optimism currently surrounding generative AI (AI hereafter), recent
experiments show that conversing with and receiving AI assistance causes workers to write
faster and better business text, including press releases, ad copy, consulting memos, and
customer support messages (Brynjolfsson, Li, and Raymond, 2023; Dell’Acqua et al., 2023;
Doshi and Hauser, 2023; Noy and Zhang, 2023; Chen and Chan, 2023). However, it remains
unclear whether the benefits of such AI feedback generalize to the broader set of tasks that
firmsengagein. Runningabusinessinvolvesmorethanwritinggreatmemos; firmsmustalso
manage employees, raise capital, pilot new initiatives, develop advertising strategies, price
their services, react to competitors, and decide which of these and myriad other tasks to
focus their efforts on (Chandler, 1977). Even for early-stage or small business entrepreneurs,
the sheer multitude of tasks involved in running a business greatly increases the complexity
of effectively integrating AI assistance into business activities (Lazear, 2004; Anderson and
McKenzie, 2022).
The plethora of tasks that entrepreneurs face can result in inattention and failure to
2
seek out advice or acquire new information, potentially limiting the benefits of even the
most advanced AI-generated advice (Hanna, Mullainathan, and Schwartzstein, 2014; Kim,
2023). Even when entrepreneurs do recognize the need to ask a question, it is not obvious
which tasks they should ask for help with, how to formulate effective questions to get useful
feedback, or how to interpret and act upon the advice received (Bryan, Tilcsik, and Zhu,
2017; Camuffo et al., 2020; Agrawal, Gans, and Stern, 2021; Dimitriadis and Koning, 2022).
Moreover, whiletheadviceprovidedbyanAItoolmightbeusefulintheory, theentrepreneur
may lack the complementary skills and resources to act on that agent’s recommendations
(Brynjolfsson and Hitt, 2000; Brynjolfsson, Rock, and Syverson, 2021).
Furthermore, even if the entrepreneur’s question is well-crafted, it is unclear how useful
advice from AI will be in practice. Given the myriad tasks entrepreneurs undertake, it is
plausible that AI advice generates a mix of useful answers, ineffective recommendations,
and potentially detrimental advice that could hinder firm performance (Dell’Acqua et al.,
2023). If most tasks require personalized advice to be useful, then AI feedback is likely to
be especially valuable given its ability to contextualize responses. However, if most tasks
are standardized, then AI will help, but potentially no better than the common advice an
entrepreneur might get from reading a book or browsing the web. It is also possible that
most business tasks are tacit and not yet codified as text, in which case AI tools may lack the
relevanttrainingdatatoprovideusefulanswerstoanentrepreneurs’questions(Autor,2014).
Of greatest concern, generative AI might yield overconfident or flawed recommendations for
many real-world business tasks (Moore, 2022; Dell’Acqua et al., 2023), leading entrepreneurs
to implement “solutions” that worsen rather than improve firm performance.
Taken together, these arguments suggest that studying the potential impact of AI-
generated advice on entrepreneurial decision-making is useful for at least two reasons. First,
it sheds light on whether and when AI advice can spur entrepreneurial learning, better per-
formance, andimprovementsineconomicproductivity, allofwhicharecoreconcernsforboth
managers and policymakers. Second, understanding if AI can assist entrepreneurial decision-
making provides more general insights into whether generative AI can provide meaningful
benefits in contexts that are more complex and interconnected than those studied in prior
work (Sato et al., 2023)
3
2 Experimental Design
We measure the impact of AI advice on entrepreneurial performance by running a field
experiment with 640 Kenyan SMBs. We selected this research context because while re-
cent experiments document that entrepreneurs in developing economies greatly benefit from
the context-specific and wide-ranging nature of human feedback (Brooks, Donovan, and
Johnson, 2018; Cai and Szeidl, 2018; Chatterji et al., 2019), this context has been almost
entirely overlooked in recent research on the potential productivity impacts of generative AI
(Bjo¨rkegren, 2023). Because obtaining personalized advice is especially challenging in de-
veloping economies, the benefits of AI advice in these contexts might be especially valuable
(Dimitriadis and Koning, 2022, 2023).
Specifically, we developed an AI “mentor” for Kenyan entrepreneurs using GPT-4, an
LLMreleasedbyOpenAIinMarch2023(OpenAI,2023). ChatGPT,asimpleQ&Ainterface
for interacting with LLMs released by OpenAI, rocketed to an estimated 100 million monthly
active users within two months of its launch (Hu, 2023). Building on the popularity of
ChatGPT’s chat interface, we developed our own AI tool (hereafter the AI mentor) that
can be accessed through WhatsApp. Development of this AI mentor took place over four
months and involved extensive user testing by the authors, research assistants, and SMB
entrepreneurs in Kenya (see Appendix D for more details on the AI mentor development
process). We selected WhatsApp because it is used by nearly 90% of people in Kenya
(Wamuyu, 2020) and because of the low cost of sending text over WhatsApp relative to
SMS texts. Figure 1 depicts an example participant interaction with the AI mentor.
Interacting with the AI mentor differs from interacting directly with an LLM such as
GPT-4 in three ways. First, we developed a system prompt that provides the AI with addi-
tionalcontextabouttheKenyanSMBswhowoulduseourapp, whichincreasedtheoddsthat
the advice provided to entrepreneurs by the AI mentor was contextually relevant.1 Second,
we instructed our AI mentor to generate three to five practical pieces of advice in response
to each question posed by the entrepreneur, with each piece of advice accompanied by an
1Forexample,ifaskedabouthowtoraisecapital,GPT-4mayrecommendraisingventurecapitalfunds;a
financing strategy that is out-of-scope for the SMB entrepreneurs in our study. Instead, our system prompt
would lead the AI mentor to focus on contextually relevant alternatives, like approaching family and friends
or building a “chama,” an informal cooperative society.
4
explanation of its benefits and implementation details. Finally, to encourage entrepreneurs
to engage further with the advice they received, the system prompt instructed the AI mentor
to structure responses in such a way that users could quickly and easily ask for more infor-
mation about each piece of advice. The diverse ways that entrepreneurs interact with the AI
mentor, as well as the level of personalization provided by the tool are further highlighted
in Appendix H, which presents full chat logs for two entrepreneurs in our sample. In one
conversation, a restaurant owner is considering changing the menu and asking for assistance
thinking through the possibilities and sources of uncertainty involved in making this deci-
sion. In the other, a business owner selling wholesale and retail milk is asking how to expand
their product offerings to increase profits. Other conversation topics across our full sample
include how to motivate employees, the best way to deploy capital when expanding a store,
tips for hatching and raising healthy chickens, and how to deal with bankruptcy.
Our AI mentor is one of many AI tools that have recently been developed to assist
entrepreneurs and firms (Baxter and Schlesinger, 2023). However, despite the popularity of
these tools, there is scant evidence on their causal impact, at least in part because measuring
the causal impact of AI assistance on business performance remains difficult. Conceptually,
defining the appropriate counterfactual for the AI mentor is non-trivial because AI-based
interventions can offer multifaceted benefits. In our context, the AI mentor not only provides
information akin to what an entrepreneur could have learned from a book or the internet
(Dalton et al., 2021); it also personalizes this information for the individual like a mentor
or advisor might. As a result, the treatment effect of access to the AI mentor relative
to an unassisted control might simply reflect the usefulness of information more generally.
Empirically, measuringbusinessperformanceindevelopingeconomiesalmostalwaysinvolves
surveying participants, which raises concerns that giving entrepreneurs an AI mentor might
lead to “demand effects,” i.e., changes in the behavior of participants due to cues about what
is considered appropriate and/or desirable (Zizzo, 2010).
To address these conceptual and empirical issues, we tested our AI tool against a con-
trol group that received business guides from the International Labor Organization (2015),
designed specifically for low-income entrepreneurs operating in developing economies. By
providing control participants with access to business training materials, we isolate the ef-
5
fect of access to an AI mentor, net of the causal impact of access to information that can be
learned through these guides. Equally as important, under the assumption that the demand
effects induced by the business training guides are equivalent to those induced by the AI
mentor, treatment effects should be attributable to actual changes in performance. This
helps rule out the possibility that any changes we observe are merely due to demand effects,
as both groups received a tool to help them improve their businesses.
We recruited entrepreneurs into our study over the Meta ad platform in partnership with
Busara, a Kenyan research organization, starting in May 2023.2 Our recruitment strategy
involved running ads on Facebook to invite entrepreneurs to a short paid survey (Figure A1).
All entrepreneurs who responded to our ads were required to pass basic attention checks and
take part in three rounds of pre-treatment surveys, which helped us reduce post-treatment
attrition and ensure valid causal inference. Appendix E describes our surveys, which asked
about firm profits, revenues, and management practices, among other measures. Our final
sample includes 640 Kenyan entrepreneurs who completed all three pre-treatment surveys.
The average entrepreneur in this sample was 26 years old, had been running their business
for one year, and held a college degree (Table A1). Our sample reflects the heterogeneity
present amongst Kenyan SMBs, with entrepreneurs running businesses from fast-food joints
to poultry farms to cybershops3 across Kenya (see Figure A2 and Figure A3). Pre-treatment
performance in our sample ranged from monthly profits of 3,850 Kenyan Shillings (bottom
decile; about $25 USD) to over 55,500 Kenyan Shillings (top decile; about $360 USD)
Following the final pre-treatment survey wave, the entrepreneurs in our sample were
block-randomized into treatment and control, with entrepreneurs stratified based on their
gender and pre-treatment business performance (Appendix C). Treated entrepreneurs re-
ceived free and unlimited use of the WhatsApp-based AI mentor, along with regular re-
minders to use the tool. Control participants were provided easy access to the aforemen-
tioned business training guides and were also sent regular reminders to use the guides (Ap-
pendix D). Entrepreneurs in the two groups were comparable in terms of both performance
2See Figure 2 and Appendix C for more details on the experiment timeline and sample recruitment
process, respectively.
3Cybershops provide a range of services ranging from computer access, internet browsing, printing, scan-
ning, photocopying, and computer repair and technical support.
6
and the other characteristics we measured prior to treatment (Table A1). Of the 640 par-
ticipants who were randomized into control or treatment, 634 (99%) completed at least one
post-treatment survey and 622 (97%) completed all four post-treatment surveys deployed
over the two months following treatment (Appendix C).
Firmperformanceexhibitsconsiderablevariabilityinemergingmarkets(Fafchampsetal.,
2012; Anderson, Lazicky, and Zia, 2021). In light of this heterogeneity, we pre-registered sev-
eral steps to improve the statistical precision and credibility of our experimentally-estimated
causal effects (see Appendix E for more details on the pre-registration). First, our outcome
variable is an index that combines standardized measures of weekly and monthly revenue
and profits to reduce noise in our dependent variable. Second, we analyze our data using a
variant of simple ordinary least squares (OLS) regression that conditions on the average pre-
treatment performance data collected from participants in the three pre-treatment survey
waves (McKenzie, 2012). Third, we control for additional pre-treatment variables using a
double-LASSO approach (Belloni, Chernozhukov, and Hansen, 2014). Fourth, we pool data
from all four of our post-treatment periods to further increase statistical power. We detail
the construction of our outcome variables and our econometric strategy in Appendix E.
3 Results
Using the regression specification outlined in our pre-analysis plan, we find no average treat-
ment effect of access to the AI mentor on firm performance. This result is robust to whether
we winsorize performance at the 99% level (δ = 0.06 standard deviations (s.d.), p “ 0.35) or
the 95% level (δ = -0.01 s.d., p “ 0.80), and is also robust to the exclusion of 18 participants
who did not complete our entire post-treatment survey panel (95% winsorization: δ = -0.01
s.d., p = 0.88; 99% winsorization: δ = 0.06 s.d., p=0.34) (see Figure A4). Our failure to
reject the null stands in stark contrast to many recent experiments showing that access to
large language models leads to large, positive impacts on productivity (Brynjolfsson, Li, and
Raymond, 2023; Dell’Acqua et al., 2023; Noy and Zhang, 2023).
However, this overall null effect masks both positive and negative heterogeneous treat-
ment effects. Again following our pre-registration plan, we split our sample of entrepreneurs
7
based on pre-treatment performance.4 For those with below-median pre-treatment perfor-
mance (hereafter “low performers”) we find that access to the AI mentor reduced business
performance by 0.11 s.d. (p “ 0.0004),5 whereas for those with above-median pre-treatment
performance (hereafter “high performers”) the AI treatment increased business performance
by 0.20 s.d. (p “ 0.07).6,7 The negative treatment effect we observe among low performers
is equivalent to a 10% drop in profits or revenue for an entrepreneur whose pre-treatment
performance is just below the median, while the positive treatment effect we observe among
high performers is equivalent to a 20-25% increase for entrepreneurs whose pre-treatment
performance was just above the median.8 Figure 4 plots the distribution of post-treatment
performance changes for all four possible combinations of treatment status and binarized
pre-treatment performance. Relative to the control group, the distribution of performance
changes is shifted to the left for AI-treated low performers, but shifted to the right for AI-
treated high performers. In other words, our results differ from those of prior studies not
only in terms of the overall treatment effect but also in terms of the direction of treatment
effect heterogeneity with respect to pre-treatment performance levels (Noy and Zhang, 2023;
Peng et al., 2023; Brynjolfsson, Li, and Raymond, 2023; Dell’Acqua et al., 2023).
One major difference between our field experiment and prior research on the economic
impacts of generative AI is the level of discretion participants were granted in how and when
they used AI assistance. In previous experiments, experimenters narrowly constrained the
tasks participants completed, the extent to which they engaged with the AI tool (Bryn-
jolfsson, Li, and Raymond, 2023), and/or the length of time that they were granted use of
generative AI (Noy and Zhang, 2023; Dell’Acqua et al., 2023). In contrast, the entrepreneurs
in our experiment had much more discretion over how they used AI assistance. For instance,
4Following our pre-registration plan, we also test for heterogeneous treatment effects with respect to
both prior ChatGPT use and gender, as female entrepreneurs in developing economies often face additional
constraints compared to men, such as childcare (Delecourt and Fitzpatrick, 2021). We find no evidence of
heterogeneous treatment effects on either dimension.
5Appendix F discusses the ethical considerations for our study given that we detect this negative effect.
6Table A3 shows that these heterogeneous treatment effects are also robust to whether we winsorize
performance at the 95% or 99% level, and to the exclusion of the 18 participants who did not complete our
entire post-treatment survey panel.
7In Appendix G we show that our findings are not due to spillovers.
8Themedianentrepreneurinoursamplehasrevenuesof21,116KenyanSchillings. Thedistributionhasa
standard deviation of 25,273 Kenyan Schillings. These numbers imply that a 0.11 s.d. decrease corresponds
to a 13.1% drop relative to the median; a 0.21 s.d. increase to a 23.9% increase.
8
they were able to choose how many questions to ask the AI mentor, and the importance of
the tasks that they requested assistance with. They could also ask for assistance on a wide
range of topics, including, but not limited to financing, marketing, operations, and farming
practice.9 The discretion afforded to participants in our study suggests that differences in AI
usage between low and high performers might explain the heterogeneous treatment effects
that we observe in our sample.
We first test for differences in the quantity and quality of business-related questions that
treated entrepreneurs sent to the AI mentor.10 Figure 5 shows the distribution of number
of business questions asked by both high and low performers during our experiment. Both
distributionsarequiteskewed, withroughly15%ofentrepreneursaskingnotasinglebusiness
question and a small number of entrepreneurs asking one nearly every other day. We find
that among both high and low performers, those that did ask at least one business question
asked roughly one a week. Overall, the two distributions are extremely similar; although on
average, high performers sent 0.59 more messages than low performers, this difference is not
statisticallysignificant(p=0.33). Wealsotestfordifferencesinthehuman-evaluatedquality
of questions asked by low and high performers, and the length of questions asked by the two
groups; in both cases, we fail to detect a statistically significant difference (Table A5). In
other words, the negative returns to AI assistance for low performers cannot be explained
by differences in the number, quality, or length of questions that they asked the AI mentor
relative to high performers.
Although we do not detect differences in the quantity or quality of business questions
9Consistentwiththisargument,weconductapre-registeredanalysisoftheimpactoftheAImentorona
number of pre-specified questions designed to identify potential treatment effect mechanisms. We find little
evidencethattheAImentorledtoconsistentchangesinoursurveymeasuresofmanagementpractices,time
management, technology use, innovation, or information-seeking behavior (Figure A5). However, it is worth
notingthatifonly10%ofoursampleaskedaboutmanagement,10%abouttimeuse,10%abouttechnology,
and so on, then we would be severely under-powered to detect average treatment effects along any of these
individual dimensions. More broadly, the personalized nature of AI suggests that social scientists may need
to rethink how they identify the underlying mechanisms driving treatment effects when interventions are
personalized and/or algorithmic in nature.
10While most questions sent to the AI mentor were business-focused, some entrepreneurs also asked the
mentor about non-business topics (e.g. how to quit smoking) or sent in text by mistake (e.g. messages
intended for someone else). The entrepreneurs also sent in questions of varying quality, sometimes writing
full sentences and other times writing no more than a couple of cryptic words. Appendix I outlines the
process by which two human coders evaluated the focus and quality of each question sent to the AI mentor.
Our final dataset of business-related questions includes over 1,300 questions.
9
asked by high and low performers, this does not mean that the questions asked by the two
groups do not differ meaningfully; entrepreneurs in our study also had the freedom to choose
the topics they discussed with the AI mentor. Given that by definition, low performers had
weaker revenues and profits than high performers prior to our treatment intervention, one
possibility is that whether by choice or by necessity, low performers simply sought advice
on especially difficult tasks. These topics and problems may be well beyond what an AI
mentor—or even a human mentor—can help with, in part because these problems often
require financial capital and/or other complements to solve. For instance, a firm facing stiff
competition or a farm experiencing a drought might struggle even after receiving extremely
high-quality advice. Even changes that had a neutral impact on growth and/or sales could
have resulted in lower profits and worse performance, given that many of the AI mentor’s
recommendations were costly to implement. We use word embedding methods to develop
a principled and scalable measure of the extent to which each question in our dataset is
focused on an especially difficult business problem.11 Appendix J documents our approach
in detail. In short, we map each question onto a standardized uni-dimensional challenge-
focused measure. We do so by calculating the relative closeness of each question’s word
embedding vector to the word embedding vectors for two different benchmark questions, one
of which describes a business in dire straights and one of which describes a business facing
few challenges.
Our challenge-focused measure has strong face validity. Questions that are rated as more
challenge-focused tend to be about difficult problems that are unlikely to be fixed using an
AI mentor’s recommendations.12 Some of the most challenge-focused questions include:
1. “currentlyfacinglosesinmyshowshopduetolowdemandsrenderingbusinessbankrupt.
please advise” (99th percentile)
2. “hi, i have a beauty shop behind kenyatta university. at first it was the best selling
shop around but now it’s nearly the last. what could be the problem and how do i
solve it?” (95th percentile)
3. “i have a business competitor one in specific,,he has noticed that my business is doing
11Word embeddings map text into high dimensional vectors (Mikolov et al., 2013) and have been used to
measure semantic differences ranging from whether a startup idea is more likely to benefit women to how
cultural associations have evolved over the twentieth century (Cao, Koning, and Nanda, 2023; Kozlowski,
Taddy, and Evans, 2019).
12Table A6 shows above-median-length example questions pulled from the 50th to 100th percentiles of the
challenge-focused distribution, oversampling examples from the top decile.
10
well and has decided too the price of the products so i cannot get customers” (91st
percentile)
Conversely, theleastchallenge-focusedquestionsareclearlyfrombusinessesinlessharrowing
situations. These questions tend to focus on expansion and growth, as opposed to funda-
mental business challenges. It is plausible that the AI mentor is more effective at helping
entrepreneurs tackle these problems.13 Some of the least challenge-focused questions include:
1. “i have ksh 20000 to expand my business which it would specialize on selling food
stuffs. i would like to do this expansion in a new location. what should i consider first
to maximize my profit?” (1st percentile)
2. “i have been operating a cereals shop for five years and it is doing very well lately for
the last two years.which strategies can i use to expand the business to increase capacity
to serve many customers and increase the products i’m selling?” (2nd percentile)
3. “i run a salon and barber shop in one room, recently customers have started to gain
interest on massage after shave, this means there is need for expansion and more
resources, kindly help me to unlock this potential.” (17th percentile)
We test for differences in the difficulty of the questions posed by low and high performers
by regressing our measure of challenge-focused on our binarized measure of baseline firm
performance. We find that high performers do in fact ask questions that are less challenge-
focusedthanthoseaskedbylowperformers(δ =-0.22s.d., p “ 0.1). Thisfindingisrobustto
analyzing our data at the message-level, as opposed to the entrepreneur-level (δ = -0.25 s.d.,
p “ 0.04),14 and to regressing our measure of challenge-focused on a continuous, rather than
binarized measure of baseline firm performance (entrepreneur-level: δ = -0.36 s.d., p “ 0.05;
message-level: δ = -0.48 s.d., p “ 0.03).15 Figure 6 visualizes the relationship between
baseline firm performance and question difficulty using a binned scatter plot. There is a clear
negative relationship between an entrepreneur’s baseline performance and how challenge-
focused their questions are. In summary, whether by choice or by necessity, low-performing
entrepreneurs in our sample asked the AI mentor for assistance with more challenging tasks
than high performers.
This finding helps reconcile our inequality-increasing heterogeneous treatment effects
with recent results that suggest generative AI reduces differences in productivity (Noy and
13Table A7 shows above-median-length example questions pulled from the 1th to 49th percentiles of the
challenge-focused distribution, oversampling examples from the bottom decile.
14For message-level analyses we cluster standard errors at the level of the entrepreneur.
15These non-causal effect sizes correspond to a one standard deviation increase in an entrepreneur’s pre-
treatment performance index.
11
Zhang,2023;Pengetal.,2023;Brynjolfsson,Li,andRaymond,2023;Dell’Acquaetal.,2023).
Even if the returns to AI advice on any given task for low performers are greater than or
equal to the returns for high performers (as suggested by other studies), the endogenous
selection of low performers into asking questions about more challenging tasks could explain
our heterogeneous treatment effects. Figure 4 illustrates how the business impact of AI
advice within a task can be radically different than the aggregate impact of AI advice when
entrepreneurs, firms, and workers endogenously select the tasks for which they receive AI
assistance.
4 Discussion
Our findings highlight that in more open-ended and complex contexts, the productivity and
performance implications of generative AI fundamentally depend on the tasks for which
firms an"
34,mckinsey,AI in the workplace_ A report for 2025 _ McKinsey.pdf,"Superagency in the workplace: Empowering people to unlock AI’s
full potential
January 28, 2025 | Report Sign In|Subscribe

By Hannah Mayer, Lareina Yee, Michael Chui, and Roger Roberts
Almost all companies invest in AI, but just 1 percent believe they are at maturity. Our research nds
the biggest barrier to scaling is not employees—who are ready—but leaders, who are not steering fast
enough.
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Superagency in the workplace: Empowering people to unlock AI’s full potential
 Full Report (47 pages)
A rticial intelligence has arrived in the workplace and has the potential to be as transformative as the steam engine was to the
19th-century Industrial Revolution.[1] With powerful and capable large language models (LLMs) developed by Anthropic, Cohere,
Google, Meta, Mistral, OpenAI, and others, we have entered a new information technology era. McKinsey research sizes the long-term AI
opportunity at $4.4 trillion in added productivity growth potential from corporate use cases.[2]
Therein lies the challenge: the long-term potential of AI is great, but the short-term returns are unclear. Over the next three years, 92
percent of companies plan to increase their AI investments. But while nearly all companies are investing in AI, only 1 percent of leaders
call their companies “mature” on the deployment spectrum, meaning that AI is fully integrated into workows and drives substantial
business outcomes. The big question is how business leaders can deploy capital and steer their organizations closer to AI maturity.
This research report, prompted by Reid Homan’s book Superagency: What Could Possibly Go Right with Our AI Future,[3] asks a similar
question: How can companies harness AI to amplify human agency and unlock new levels of creativity and productivity in the workplace?
AI could drive enormous positive and disruptive change. This transformation will take some time, but leaders must not be dissuaded.
Instead, they must advance boldly today to avoid becoming uncompetitive tomorrow. The history of major economic and technological
shifts shows that such moments can dene the rise and fall of companies. Over 40 years ago, the internet was born. Since then,
companies including Alphabet, Amazon, Apple, Meta, and Microsoft have attained trillion-dollar market capitalizations. Even more
profoundly, the internet changed the anatomy of work and access to information. AI now is like the internet many years ago: The risk for
business leaders is not thinking too big, but rather too small.
This report explores companies’ technology and business readiness for AI adoption (see sidebar “About the survey”). It concludes that
employees are ready for AI. The biggest barrier to success is leadership.
Chapter 1 looks at the rapid advancement of technology over the past two years and its implications for business adoption of AI.
Chapter 2 delves into the attitudes and perceptions of employees and leaders. Our research shows that employees are more ready for AI
than their leaders imagine. In fact, they are already using AI on a regular basis; are three times more likely than leaders realize to believe
that AI will replace 30 percent of their work in the next year; and are eager to gain AI skills. Still, AI optimists are only a slight majority in
the workplace; a large minority (41 percent) are more apprehensive and will need additional support. This is where millennials, who are
the most familiar with AI and are often in managerial roles, can be strong advocates for change.
Chapter 3 looks at the need for speed and safety in AI deployment. While leaders and employees want to move faster, trust and safety
are top concerns. About half of employees worry about AI inaccuracy and cybersecurity risks. That said, employees express greater
condence that their own companies, versus other organizations, will get AI right. The onus is on business leaders to prove them right, by
making bold and responsible decisions.
Chapter 4 examines how companies risk losing ground in the AI race if leaders do not set bold goals. As the hype around AI subsides,
companies should put a heightened focus on practical applications that empower employees in their daily jobs. These applications can
create competitive moats and generate measurable ROI. Across industries, functions, and geographies, companies that invest
strategically can go beyond using AI to drive incremental value and instead create transformative change.
Chapter 5 looks at what is required for leaders to set their teams up for success with AI. The challenge of AI in the workplace is not a
technology challenge. It is a business challenge that calls upon leaders to align teams, address AI headwinds, and rewire their companies
for change.
Chapter 1
An innovation as powerful as the steam engine
Imagine a world where machines not only perform physical labor but also think, learn, and make autonomous decisions. This world
includes humans in the loop, bringing people and machines together in a state of superagency that increases personal productivity and
creativity (see sidebar “AI superagency”). This is the transformative potential of AI, a technology with a potential impact poised to surpass
even the biggest innovations of the past, from the printing press to the automobile. AI does not just automate tasks but goes further by
automating cognitive functions. Unlike any invention before, AI-powered software can adapt, plan, guide—and even make—decisions.
That’s why AI can be a catalyst for unprecedented economic growth and societal change in virtually every aspect of life. It will reshape
our interaction with technology and with one another.
“Scientific discoveries and technological innovations are stones in the cathedral of human
progress.”
—Reid Homan, cofounder of LinkedIn and Inection AI, partner at Greylock Partners, and author
Many breakthrough technologies, including the internet, smartphones, and cloud computing, have transformed the way we live and work.
AI stands out from these inventions because it oers more than access to information. It can summarize, code, reason, engage in a
dialogue, and make choices. AI can lower skill barriers, helping more people acquire prociency in more elds, in any language and at
any time. AI holds the potential to shift the way people access and use knowledge. The result will be more ecient and eective problem
solving, enabling innovation that benets everyone.
Over the past two years, AI has advanced in leaps and bounds, and enterprise-level adoption has accelerated due to lower costs and
greater access to capabilities. Many notable AI innovations have emerged (Exhibit 1). For example, we have seen a rapid expansion of
context windows, or the short-term memory of LLMs. The larger a context window, the more information an LLM can process at once. To
illustrate, Google’s Gemini 1.5 could process one million tokens in February 2024, while its Gemini 1.5 Pro could process two million
tokens by June of that same year.[4] Overall, we see ve big innovations for business that are driving the next wave of impact: enhanced
intelligence and reasoning capabilities, agentic AI, multimodality, improved hardware innovation and computational power, and increased
transparency.
Exhibit 1
Intelligence and reasoning are improving
AI is becoming far more intelligent. One indicator is the performance of LLMs on standardized tests. OpenAI’s Chat GPT3.5, introduced
in 2022, demonstrated strong performance on high-school-level exams (for example, scoring in the 70th percentile on the SAT math and
the 87th percentile on the SAT verbal sections). However, it often struggled with broader reasoning. Today’s models are near the
intelligence level of people who hold advanced degrees. GPT4 can so easily pass the Uniform Bar Examination that it would rank in the
top 10 percent of test takers,[5] and it can answer 90 percent of questions correctly on the US Medical Licensing Examination.[6]
The advent of reasoning capabilities represents the next big leap forward for AI. Reasoning enhances AI’s capacity for complex decision
making, allowing models to move beyond basic comprehension to nuanced understanding and the ability to create step-by-step plans to
achieve goals. For businesses, this means they can ne-tune reasoning models and integrate them with domain-specic knowledge to
deliver actionable insights with greater accuracy. Models such as OpenAI’s o1 or Google’s Gemini 2.0 Flash Thinking Mode are capable of
reasoning in their responses, which gives users a human-like thought partner for their interactions, not just an information retrieval and
synthesis engine.[7]
Agentic AI is acting autonomously
“I’ve always thought of AI as the most profound technology humanity is working on . . .
more profound than fire or electricity or anything that we’ve done in the past.”
—Sundar Pichai, CEO of Alphabet
The ability to reason is growing more and more, allowing models to autonomously take actions and complete complex tasks across
workows. This is a profound step forward. As an example, in 2023, an AI bot could support call center representatives by synthesizing
and summarizing large volumes of data—including voice messages, text, and technical specications—to suggest responses to customer
queries. In 2025, an AI agent can converse with a customer and plan the actions it will take afterward—for example, processing a
payment, checking for fraud, and completing a shipping action.
Software companies are embedding agentic AI capabilities into their core products. For example, Salesforce’s Agentforce is a new layer
on its existing platform that enables users to easily build and deploy autonomous AI agents to handle complex tasks across workows,
such as simulating product launches and orchestrating marketing campaigns.[8] Marc Benio, Salesforce cofounder, chair, and CEO,
describes this as providing a “digital workforce” where humans and automated agents work together to achieve customer outcomes.[9]
Multimodality is bringing together text, audio, and video
Today’s AI models are evolving toward more advanced and diverse data processing capabilities across text, audio, and video. Over the
last two years, we have seen improvements in the quality of each modality. For example, Google’s Gemini Live has improved audio quality
and latency and can now deliver a human-like conversation with emotional nuance and expressiveness.[10] Also, demonstrations of Sora
by OpenAI show its ability to translate text to video.[11]
Hardware innovation is enhancing performance
Hardware innovation and the resulting increase in compute power continue to enhance AI performance. Specialized chips allow faster,
larger, and more versatile models. Enterprises can now adopt AI solutions that require high processing power, enabling real-time
applications and opportunities for scalability. For example, an e-commerce company could signicantly improve customer service by
implementing AI-driven chatbots that leverage advanced graphics processing units (GPUs) and tensor processing units (TPUs). Using
distributed cloud computing, the company could ensure optimal performance during peak trac periods. Integrating edge hardware, the
company could deploy models that analyze photos of damaged products to more accurately process insurance claims.
Transparency is increasing
“AI, like most transformative technologies, grows gradually, then arrives suddenly.”
—Reid Homan, cofounder of LinkedIn and Inection AI, partner at Greylock Partners, and author
AI is gradually becoming less risky, but it still lacks greater transparency and explainability. Both are critical for improving AI safety and
reducing the potential for bias, which are imperative for widescale enterprise deployment. There is still a long way to go, but new models
and iterations are rapidly improving. Stanford University’s Center for Research on Foundation Models (CRFM) reports signicant
advances in model performance. Its Transparency Index, which uses a scale of 1 to 100, shows that Anthropic’s transparency score
increased by 15 points to 51 and Amazon’s more than tripled to 41 between October 2023 and May 2024.[12]
Beyond LLMs, other forms of AI and machine learning (ML) are improving explainability, allowing the outputs of models that support
consequential decisions (for example, credit risk assessment) to be traced back to the data that informed them. In this way, critical
systems can be tested and monitored on a near-constant basis for bias and other everyday harms that arise from model drift and shifting
data inputs, which happens even in systems that were well calibrated before deployment.
All of this is crucial for detecting errors and ensuring compliance with regulations and company policies. Companies have improved
explainability practices and built necessary checks and balances, but they must be prepared to evolve continuously to keep up with
growing model capabilities.
Achieving AI superagency in the workplace is not simply about mastering technology. It is every bit as much about supporting people,
creating processes, and managing governance. The next chapters explore the nontechnological factors that will help shape the
deployment of AI in the workplace.
Chapter 2
Employees are ready for AI; now leaders must step up
Employees will be the ones to make their organizations AI powerhouses. They are more ready to embrace AI in the workplace than
business leaders imagine. They are more familiar with AI tools, they want more support and training, and they are more likely to believe AI
will replace at least a third of their work in the near future. Now it’s imperative that leaders step up. They have more permission space
than they realize, so it’s on them to be bold and capture the value of AI. Now.
“People are using [AI] to create amazing things. If we could see what each of us can do 10 or
20 years in the future, it would astonish us today.”
—Sam Altman, cofounder and CEO of OpenAI
Beyond the tipping point
In our survey, nearly all employees (94 percent) and C-suite leaders (99 percent) report having some level of familiarity with gen AI tools.
Nevertheless, business leaders underestimate how extensively their employees are using gen AI. C-suite leaders estimate that only 4
percent of employees use gen AI for at least 30 percent of their daily work, when in fact that percentage is three times greater, as self-
reported by employees (Exhibit 2). And while only a total of 20 percent of leaders believe employees will use gen AI for more than 30
percent of their daily tasks within a year, employees are twice as likely (47 percent) to believe they will (see sidebar “Who is using AI at
work? Nearly everyone, even skeptical employees”).
The good news is that our survey suggests three ways companies can accelerate AI adoption and move toward AI maturity.
Exhibit 2
Leaders can invest more in their employees
As noted at the beginning of this chapter, employees anticipate AI will have a dramatic impact on their work. Now they would like their
companies to invest in the training that will help them succeed. Nearly half of employees in our survey say they want more formal training
and believe it is the best way to boost AI adoption. They also would like access to AI tools in the form of betas or pilots, and they indicate
that incentives such as nancial rewards and recognition can improve uptake.
Yet employees are not getting the training and support they need. More than a fth report that they have received minimal to no support
(Exhibit 3). Outside the United States, employees also want more training (see sidebar “Global perspectives on training”).
Exhibit 3
Sidebar
Global perspectives on training
To get a clearer picture of global AI adoption trends, we looked at trends across ve countries: Australia, India, New Zealand,
Singapore, and the United Kingdom. Broadly speaking, these employees and C-suite leaders—the “international” group in this
report—have similar views of AI as their US peers. In some key areas, however, including the topic of training, their experiences
dier.
Many international employees are concerned about insucient training, even though they report receiving far more support than
US employees. Some 84 percent of international employees say they receive signicant or full organizational support to learn AI
skills, versus just over half of US employees. International employees also have more opportunities to participate in developing
gen AI tools at work than their US counterparts, with dierences of at least ten percentage points in activities such as providing
feedback, beta testing, and requesting specic features (exhibit).
C-suite leaders can help millennials lead the way
Many millennials aged 35 to 44 are managers and team leaders in their companies. In our survey, they self-report having the most
experience and enthusiasm about AI, making them natural champions of transformational change. Millennials are the most active
generation of AI users. Some 62 percent of 35- to 44-year-old employees report high levels of expertise with AI, compared with 50
percent of 18- to 24-year-old Gen Zers and 22 percent of baby boomers over 65 (Exhibit 4). By tapping into that enthusiasm and
expertise, leaders can help millennials play a crucial role in AI adoption.
Exhibit 4
Since many millennials are managers, they can support their teams to become more adept AI users. This helps push their companies
toward AI maturity. Two-thirds of managers say they eld questions from their team about how to use AI tools at least once a week, and a
similar percentage say they recommend AI tools to their teams to solve problems (Exhibit 5).
Exhibit 5
Since leaders have the permission space, they can be bolder
In many transformations, employees are not ready for change, but AI is dierent. Employee readiness and familiarity are high, which gives
business leaders the permission space to act. Leaders can listen to employees describe how they are using AI today and how they
envision their work being transformed. They also can provide employees with much-needed training and empower managers to move AI
use cases from pilot to scale.
It’s critical that leaders meet this moment. It’s the only way to accelerate the probability that their companies will reach AI maturity. But
they must move with alacrity, or they will fall behind.
Chapter 3
Delivering speed and safety
AI technology is advancing at record speed. ChatGPT was released about two years ago; OpenAI reports that usage now exceeds 300
million weekly users[13] and that over 90 percent of Fortune 500 companies employ its technology.[14] The internet did not reach this
level of usage until the early 2000s, nearly a decade after its inception.
“Soon after the first automobiles were on the road, there was the first car crash. But we
didn’t ban cars—we adopted speed limits, safety standards, licensing requirements, drunk-
driving laws, and other rules of the road.”
—Bill Gates, cofounder of Microsoft
The majority of employees describe themselves as AI optimists; Zoomers and Bloomers make up 59 percent of the workplace. Even
Gloomers, who are one of the two less-optimistic segments in our analysis, report high levels of gen AI familiarity, with over a quarter
saying they plan to use AI more next year.
Business leaders need to embrace this speed and optimism to ensure that their companies don’t get left behind. Yet despite all the
excitement and early experimentation, 47 percent of C-suite leaders say their organizations are developing and releasing gen AI tools too
slowly, citing talent skill gaps as a key reason for the delay (Exhibit 6).
Exhibit 6
Business leaders are trying to meet the need for speed by increasing investments in AI. Of the executives surveyed, 92 percent say they
expect to boost spending on AI in the next three years, with 55 percent expecting investments to increase by at least 10 percent from
current levels. But they can no longer just spend on AI without expecting results. As companies move on from the initial thrill of gen AI,
business leaders face increasing pressure to generate ROI from their gen AI deployments.
We are at a turning point. The initial AI excitement may be waning, but the technology is accelerating. Bold and purposeful strategies are
needed to set the stage for future success. Leaders are taking the rst step: One quarter of those executives we surveyed have dened a
gen AI road map, while just over half have a draft that is being rened (Exhibit 7). With technology changing this fast, all road maps and
plans will evolve constantly. For leaders, the key is to make some clear choices about what valuable opportunities they choose to pursue
rst—and how they will work together with peers, teams, and partners to deliver that value.
Exhibit 7
The dilemma of speed versus safety
There’s a spanner in the works: Regulation and safety often continue to be seen as insurmountable challenges rather than opportunities.
Leaders want to increase AI investments and accelerate development, but they wrestle with how to make AI safe in the workplace. Data
security, hallucinations, biased outputs, and misuse (for example, creating harmful content or enabling fraud) are challenges that cannot
be ignored. Employees are well aware of AI’s safety challenges. Their top concerns are cybersecurity, privacy, and accuracy (Exhibit 8).
But what will it take for leaders to address these concerns while also moving ahead at light speed?
Exhibit 8
Employees trust business leaders to get it right
While employees acknowledge the risks and even the likelihood that AI may replace a considerable portion of their work, they place high
trust in their own employers to deploy AI safely and ethically. Notably, 71 percent of employees trust their employers to act ethically as
they develop AI. In fact, they trust their employers more than universities, large technology companies, and tech start-ups (Exhibit 9).
Exhibit 9
According to our research, this is in line with a broader trend in which employees show higher trust in their employers to do the right
thing in general (73 percent) than in other institutions, including the government (45 percent). This trust should help leaders act with
condence as they tackle the speed-versus-safety dilemma. That condence also applies outside the United States, even though
employees in other regions may have more desire for regulation (see sidebar “Global perspectives on regulation”).
Sidebar
Global perspectives on regulation
A high percentage of international C-suite leaders we surveyed across ve regions (Australia, India, New Zealand, Singapore,
and the United Kingdom) are Gloomers, who favor greater regulatory oversight. Between 37 to 50 percent of international C-
suite leaders self-identify as Gloomers, versus 31 percent in the United States. This may be because top-down regulation is more
accepted in many countries outside the United States. Of the global C-suite leaders surveyed, half or more worry that ethical use
and data privacy issues are holding back their employees from adopting gen AI.
However, our research shows that attitudes about regulation are not inhibiting the economic expectations of business leaders
outside the United States. More than half of the international executives (versus 41 percent of US executives) indicate they want
their companies to be among the rst adopters of AI, with those in India and Singapore being especially bullish (exhibit). The
desire of international business leaders to be AI rst movers can be explained by the revenue they expect from their AI
deployments. Some 31 percent of international C-suite leaders say they expect AI to deliver a revenue uplift of more than 10
percent in the next three years, versus just 17 percent of US leaders. Indian executives are the most optimistic, with 55 percent
expecting a revenue uplift of 10 percent or more over the next three years.
Risk management for gen AI
In Superagency, Homan argues that new risks naturally accompany new capabilities—meaning they should be managed but not
necessarily eliminated.[15] Leaders need to contend with external threats, such as infringement on intellectual property (IP), AI-enabled
malware, and internal threats that arise from the AI adoption process. The rst step in building t-for-purpose risk management is to
launch a comprehensive assessment to identify potential vulnerabilities in each of a company’s businesses. Leaders can then establish a
robust governance structure, implement real-time monitoring and control mechanisms, and ensure continuous training and adherence to
regulatory requirements.
One powerful control mechanism is respected third-party benchmarking that can increase AI safety and trust. Examples include Stanford
CRFM’s Holistic Evaluation of Language Models (HELM) initiative—which oers comprehensive benchmarks to assess the fairness,
accountability, transparency, and broader societal impact of a company’s AI systems—as well as MLCommons’s AILuminate tool kit on
which researchers from Stanford collaborated.[16] Other organizations such as the Data & Trust Alliance unite large companies to create
cross-industry metadata standards that aim to bring more transparency to enterprise AI models.
While benchmarks have signicant potential to build trust, our survey shows that only 39 percent of C-suite leaders use them to evaluate
their AI systems. Furthermore, when leaders do use benchmarks, they opt to measure operational metrics (for example, scalability,
reliability, robustness, and cost eciency) and performance-related metrics (including accuracy, precision, F1 score, latency, and
throughput). These benchmarking eorts tend to be less focused on ethical and compliance concerns: Only 17 percent of C-suite leaders
who benchmark say it’s most important to measure fairness, bias, transparency, privacy, and regulatory issues (Exhibit 10).
Exhibit 10
The focus on operational and performance metrics reects the understandable desire to prioritize immediate technical and business
outcomes. But ignoring ethical considerations can come back to haunt leaders. When employees don’t trust AI systems, they are less
likely to accept them. Although benchmarks are not a panacea to eliminate all risk and can’t ensure that AI systems are fully ecient,
ethical, and safe, they are a useful tool.
Even companies that excel at all three categories of AI readiness—technology, employees, and safety—are not necessarily scaling or
delivering the value expected. Nevertheless, leaders can harness the power of big ambitions to transform their companies with AI. The
next chapter examines how.
Chapter 4
Embracing bigger ambitions
Most organizations that have invested in AI are not getting the returns they had hoped. They are not winning the full economic potential
of AI. About half of C-suite leaders at companies that have deployed AI describe their initiatives as still developing or expanding (Exhibit
11). They have had the time to move further. Our research shows that more than two-thirds of leaders launched their rst gen AI use cases
over a year ago.
“This is a time when you should be getting benefits [from AI] and hope that your
competitors are just playing around and experimenting.”
—
Erik Brynjolfsson, professor at Stanford University and director of the Digital Economy Lab at the Stanford Institute for Human-
Centered Articial Intelligence (HAI)
Exhibit 11
Pilots fail to scale for many reasons. Common culprits are poorly designed or executed strategies, but a lack of bold ambitions can be just
as crippling. This chapter looks at patterns governing today’s investments in AI across industries and suggests the potential awaiting
those who can dream bigger.
AI investments vary by industry
Dierent industries have dierent AI investment patterns. Within the top 25 percent of spenders, companies in healthcare, technology,
media and telecom, advanced industries, and agriculture are ahead of the pack (Exhibit 12). Companies in nancial services, energy and
materials, consumer goods and retail, hardware engineering and construction, and travel, transport, and logistics are spending less. The
consumer industry—despite boasting the second-highest potential for value realization from AI—seems least willing to invest, with only 7
percent of respondents qualifying in the top quartile, based on self-reported percentage of revenue spend on gen AI. That hesitation may
be explained by the industry’s low average net margins in mass-market categories and thus higher condence thresholds for adopting
costly organization-wide technology upgrades.
Exhibit 12
In some industries, employees are cautious
Employees in the public sector, as well as the aerospace and defense and semiconductor industries, are largely skeptical about the
development of AI’s future. In the public sector and aerospace and defense, only 20 percent of employees anticipate that AI will have a
signicant impact on their daily tasks in the next year, versus roughly two-thirds in media and entertainment (65 percent) and telecom, at
67 percent (Exhibit 13). What’s more, our survey shows that just 31 percent of social sector employees trust that their employers will
develop AI safely. That’s the least condence in any industry; the cross-industry average is 71 percent.
Exhibit 13
Employees in the public sector, aerospace, and semiconductor industries are the least optimistic about gen AI.
US employee sentiment on gen AI, % of respondents
Expect workows to change by 30% in the next year
Telecom
Media and entertainment
Real estate
Metals and mining
Oil and gas
Chemicals
Note: Level of familiarity is dened as those who have “extensive experience (use several tools for complex tasks)” and “experts.” High trust is “Level 4” and “Level 5” on a scale of 1 to 5. Perceived accuracy is based on past gen AI usage in a
workplace setting.
Source: The economic potential of generative AI: The next productivity frontier, McKinsey, June 14, 2023; McKinsey US employee survey, Oct–Nov, 2024 (n = 3,002)
McKinsey & Company
Employees’ relative caution about AI in these sectors likely reects near-term challenges posed by external constraints such as rigorous
regulatory oversight, outdated IT systems, and lengthy approval processes.
There’s a lot of headroom in some functions
Our research nds that the functional areas where AI presents the greatest economic potential are also those where employee outlook is
lukewarm. Employees in sales and marketing, software engineering, customer service, and R&D contribute roughly three-quarters of AI’s
total economic potential, but the self-reported optimism of employees in these functions is middling (Exhibit 14). It may be the case that
these functions have piloted AI projects, leading employees to be more realistic about AI’s benets and limitations. Or perhaps the
economic potential has made them worry that AI could replace their jobs. Whatever the reasons, leaders in these functions might
consider investing more in employee support and elevating the change champions who can improve that sentiment.
Exhibit 14
Gen AI has not delivered enterprise-wide ROI, but that can change
Across all industries, surveyed C-level executives report limited returns on enterprise-wide AI investments. Only 19 percent say revenues
have increased more than 5 percent, with another 39 percent seeing a moderate increase of 1 to 5 percent, and 36 percent reporting no
change (Exhibit 15). And only 23 percent see AI delivering any favorable change in costs.
Exhibit 15
Despite this, company leaders are optimistic about the value they can capture in the coming years. A full 87 percent of executives expect
revenue growth from gen AI within the next three years, and about half say it could boost revenues by more than 5 percent in that time
frame (Exhibit 16). That suggests quite a lot could change for the better over the next few years.
Exhibit 16
Big ambitions can help solve big problems
To drive revenue growth and improve ROI, business leaders may need to commit to transformative AI possibilities. As the hype around AI
subsides and the focus shifts to value, there is a heightened attention on practical applications that can create competitive moats.
“[It] is critical to have a genuinely inspiring vision of the future [with AI] and not just a
plan to fight fires.”
—Dario Amodei, cofounder and CEO of Anthropic
To assess how far along companies are in this shift, we examined three categories of AI applications: personal use, business use, and
societal use (see sidebar “AI’s potential to enhance our personal lives”). We mapped over 250 applications from our work and publicly
shared examples to understand the spectrum of impact levels, from localized use ca"
35,mckinsey,genai_20in_20norway_eng_version_v2.pdf,"The economic
potential of
Generative AI
in Norway
The next productivity frontier
June 2023
CONFIDENTIAL AND PROPRIETARY
Any use of this material without specific permission of McKinsey & Company
is strictly prohibited
About this document
 In the newest report from McKinsey Global
Institute (MGI), MGI discuss how GenAI can
transform the way we work
 To assess the effect of GenAI adoption in
Norway and the Norwegian workforce and
businesses, McKinsey Norway used numbers
calculated by MGI, and method applied
there, with Norwegian revenue1,
employment and education data from SSB
(Statistics Norway)
 Additional data was from Statista, European
Commission, Eurostat, OECD, and GEDI
1. 2020, the most recently available at the time of writing
McKinsey & Company 2
Generative AI (GenAI) is experiencing significant momentum globally and is expected to gain
traction in Norway with value creation potential of 95-159 bn NOK by 2045 across Norwegian
industries
 We expect Norway to be a leading global adopter of GenAI due to the economic
environment, education level of the population, and high degrees of digital adoption
 The impact of Generative AI will fall heavily on occupations requiring higher levels of education.
Norway is the 10th highest educated country in the world, and much of the workforce is classified
as knowledge-workers, typically with high wages. This increases the feasibility of early adoption
of GenAI in daily activities
The highest potential value in Norway is expected to be unlocked in selected sectors,
Executive including Energy, High Tech, Travel, Transport & Logistics, and Retail, but true value unlock
comes from three major business functions as opposed to sectors
summary
 Marketing and Sales (28-43 bn NOK), Software Engineering (21-43 bn NOK), and Customer
Operations (12-17 bn NOK) will drive the highest amount of value unlock in Norway due to
the high degree of “generation” activities i.e., generating content such as marketing material,
code and emails
 While the highest potential value is expected to be unlocked in the Energy industry (~21 bn
NOK), High Tech (~18 bn NOK) is expected to experience a more disruptive shift (7%) following
the adoption of GenAI
Productivity growth has slowed in the last decade but will likely be advanced by GenAI. We
expect work activities within decision making and collaboration, and data management, to be most
affected by GenAI. Such activities are most commonly performed by highly educated workers, and
educators / workforce trainers, employees within business and legal professions, and STEM
professionals, are likely to see the largest productivity gains upon GenAI adoption
MMccKKiinnsseeyy && CCoommppaannyy 33
What is Generative AI?
Suitable Unsuitable
Non-exhaustive
Generative AI (GenAI) enables the creation of new Although some areas are unsuited for GenAI, several
unstructured content, such as text, images, etc. applications emerge2:
Recent GenAI efforts are powered by Foundational
Code/image/audio/video/text generation and editing,
Models trained on a broad set of data that enables
while taking surrounding context into account
them to respond to a wide range of prompts.
Conversational interfaces to convert natural language
These models are typically also better at interpreting /
dialog into specific executions of a technical system
labelling unstructured data than traditional AI
Querying a large set of unstructured data, and
synthesizing a human readable output
High-stakes scenarios with potential for harm
Unconstrained, long, open-ended generation that may
expose harmful or biased content to users
Generate marketing or Automate code generation
social media copy in ”house in programming languages Applications requiring explainability and/or full
style” using ChatGPT, like Python with Codex /
understanding of potential failure modes, including
Copy.A, etc. Github Copilot, etc.
numerical reasoning1
1. Current topic of research: how to use GPT-like models to generate code that involves solving numerical problems
2. Additional resources can be found in the McKinsey Report “Economic potential of generative AI”, and the article “What every CEO should know about generative AI”
Source: Press search; expert interviews MMccKKiinnsseeyy && CCoommppaannyy 44
Automation
A multinational tech company offers a GenAI
GenAI will mainly Giving software predictable tasks
app which can read customer emails and
generate well-documented tickets based
that can be more easily automated
impact three areas,
on these
today with FM powered GenAI
leading to
reinvention of major
processes in Norway
and rest of world
A GenAI-chatbot is already in use in several
Augmentation
large Norwegian banks, and institutions, to
improve productivity and reduce use of
Enhance human productivity to do
human agents in more simple cases
work more efficiently
A large Norwegian house building company
Acceleration
has invested heavily in GenAI for product
development, using it to generate thousands
Extract and index knowledge to
of building configurations prior to any
building activity, allowing for more thorough
shorten innovation cycles enabling
checks, e.g., ensuring that building
dimensions follow regulation
continuous innovation
Source: QuantumBlack: AI by McKinsey; press search McKinsey & Company 5
Norway is expected to be an early adopter of automation with other
economies such as the US and Germany
China Germany France India Japan Mexico US Nordics2 Global avg
Automation adoption, generative AI early scenario1, % automation Automation adoption, generative AI late scenario1, % automation
100% 100%
90% 90%
80% 80%
70% 70%
60% 60%
50% 50%
40% 40%
30% 30%
In early scenario, In late scenario, the 50%
developed economies can threshold is achieved at
20% 20%
achieve more than 50% least 20 years later, with
automation adoption by larger differences between
10% 2030 10% countries
0% 0%
2025 30 35 40 45 50 55 2060 2030 40 50 60 70 80 2090
1. Early scenario -aggressive scenario all key model parameters (tech automation, integration timeline, economic feasibility, regulatory and public adoption); late scenario -parameters are set for the later adoption potential
2. McKinsey Norway estimate
Source: McKinsey Global Institute
The Norwegian digital foundation and education level are key
advantages that can drive nationwide GenAI adoption
Human capital Integration of digital technology
Connectivity Digital public services
Share of population with tertiary
Digital economy and society index1, 2022 education2, 2022 %, aged 25-34 Comments
70
• Norway has one of the highest education
levels in the world, leading to an affluent, skilled
60 workforce that is adaptable and open to learning,
making new technology adoption
economically feasible
50
• Norway has a large population of knowledge
workers, whom typically have a high proportion of
40 activities that can see a productivity boost from
using GenAI to augment knowledge-
based activities
30
• Norway ranks 5th in the 2022 DESI Index –
emphasizing the Norwegian society’s strong
20 digital foundation and GenAI transformation
potential
10 • Norway has a robust digital infrastructure with
>55 40-<45
high internet penetration rates and widespread
50-<55 35-<40 access to fast broadband. This kind of
0 environment is conducive to the growth and
45-<50 <35
Fl DK NL SE NO IE FR DE EU IT HR HU BG RO
adoption of AI technologies.
1. The Digital Economy and Society Index (DESI), non-exhaustive country list
2. Eurostat “Educational attainment statistics”
Source: McKinsey Global Institute; European Commission; press search; Eurostat McKinsey & Company 7
X % of GDP Value realized by 2030
Value realized by 2045
Value potential per industry2, bn NOK
Energy3 16-26 0.4 %
High tech4 12-24 0.3 %
Travel, transport & logistics 13-21 0.3 %
The potential value Retail 13-20 0.3 %
unlock from GenAI Advanced manufacturing5 8-13 0.2 %
Real estate 6-10 0.1 %
is 95-159 bn NOK
Basic materials 5-9 0.1 %
across Norwegian
Banking 5-9 0.1 %
industries1 …
Consumer packaged goods 5-8 0.1%
Professional services 5-7 0.1%
Telecommunications 2-3 0.0 %
Insurance 2-3 0.0 %
Healthcare 2-3 0.0 %
Media 1-3 0.0 %
Chemical <1 0.0 %
Agriculture <1 0.0 %
1. Based on the early adoption scenario, median expected impact of GenAI, %
of industry revenues. 2020 revenues, inflation adjusted
2. By 2030, ~60% of the value potential will be unlocked, by 2045 100% of Pharma & medical products <1 0.0 %
the value will be unlocked. Calculations based on 2020 industry revenues
3. Includes utilities and oil and gas, of which oil and gas contributes
~85% of revenues
4. Includes advanced electronics Total 95-159 2.4%
5. Includes automotive and assembly, and aerospace and defense
Source: Internal experts; annual reports; Statista MMccKKiinnsseeyy && CCoommppaannyy 88
Value realized by 2030
Value potential per business function1, bn NOK Value realized by 2045
Marketing and sales 28-43
… But business Software engineering 21-43
functions, as
Customer operations 12-17
opposed to specific
industries, will be
Supply chain and operations 10-19
the driving forces of
value creation Product and R&D 8-15
Risk and legal 7-9
Strategy and finance 4-9
Talent and organization 2-3
1. Based on the early adoption scenario. By 2030, ~60% of the value
potential will be unlocked, by 2045 100%of the value will be unlocked. Corporate IT2 1-2
Calculations based on 2020 industry revenues
2. Excluding corporate software engineering, including activities such as
e.g., network maintenance
SSoouurrccee:: IInntteerrnnaall eexxppeerrttss,; Danantaubaal sreepso: rAtsn;n SuSalB reports and SSB MMccKKiinnsseeyy && CCoommppaannyy 99
Productivity growth, the main engine of GDP growth, slowed down
in the last decade but is likely to be advanced by GenAI
Employment growth Additional with GenAI
Productivity growth Without GenAI1
Productivity growth bigger contributor to GDP growth
Global GDP growth, Productivity impact from automation,
CAGR, % 2022-40, CAGR2, % Comments
 Examining the real GDP
growth contribution of
3.8
Global3 Norway
employment and
3.7 productivity growth,
increasedproductivity
3.1
3.1 1,3
2.9 3.3 0,7 has been the main engine
2.8
for GDP growth
0,6
 Implementation of GenAI
0,8
1,4 can significantly
contribute to increased
2,5 productivity in Norway
2,0
going forward
3,0
2,5
2,6
2,1
0.9
1,7
0,3
0,8
0,7 0.2
0,6
0.1
0.1
1972-82 1982-92 1992-2002 2002-2012 2012-2022 Early scenario Late scenario
Early scenario Late scenario
1. Previous assessment of work automation before the rise of generative AI
2. Based on the assumption that the automated work hours are integrated back to work at productivity level of today
3. Based on 47 countries which constitute almost 80% of the world employment
Source: The Conference Board Total Economy database; Oxford Economics; McKinsey Global Institute QuantumBlack, AI by McKinsey 10
CONFIDENTIAL AND PROPRIETARY
Key activities forecasted to be affected are typically executed by
employees holding an advanced degree
With generative AI
Without generative AI1
Incremental technical
automation potential
Overall technical automation potential, Share of NO
Education level Comparison in midpoint scenarios, % in 2023 work force1, % Comments
• Higher educated workers are likely set
57%
Master, PhD or similar 13% to see the largest incremental impact
28% 2X
from automation as they land in jobs
as “knowledge workers” which spend a
high share of their time on activities
most likely to benefit from GenAI
60%
Bachelor’s degree 29%
(i.e., applying expertise to planning and
36% 1.7x
creative tasks, managing and
stakeholder management).
• An example of this is within science:
researchers spend ~30 minutes to read
64%
High school diploma 36% one scientific paper2, but GenAI could
51% 1.2X
or equivalent
summarize hundreds of papers in
minutes
Without a high school 63%
19%
degree 54% 1.2X
1. Does not sum up to 100% due to some minor educational levels not included
2. 2014 statistic
Source: McKinsey Global Institute; SSB; OECD; Scientific American article “Scientists Reading Fewer Papers for First Time in 35 Years”, 2014 McKinsey & Company 11
GenAI could have the biggest impact on activities which previously
had a lower potential for automation
Automation potential of more than 50% with GenAI With GenAI
Incremental technical Automation potential of more than 50% without GenAI Without GenAI1
automation potential with GenAI
Overall technical automation potential, Share of NO
Activity groups2 comparison in midpoint scenarios, % in 2023 employment, % Comments
Decision 59% • Prior to GenAI, only 2 in 7
Applying expertise3 20%
making and 25% +34 p.p. Norwegians held roles
collaboration which had an automation
49%
Managing4 9% potential of more than
16% +34 p.p.
50%. Following the advent
of GenAI, that number has
Interfacing with 45%
8% risen to 1 in 2
stakeholders 24% +21 p.p.
• GenAI plays the largest
impact on data driven
Data 91%
Processing data 12%
decision making and
management 73% +18 p.p.
collaboration, while
79% physical laborers will likely
Collecting data 2%
68% +11 p.p. not see a significant change
from the rise of GenAI in the
Physical Performing unpredictable 46% workplace
34%
physical work5 46% +1 p.p. • With Generative AI,
technical automation
Performing predictable 73%
15% potential could already
physical work6 73% +1 p.p.
reach 91% for data
1. Previous assessment of work automation before the rise of generative AI processing and 79% for
2. Jobs are categorized by main activity, but some jobs include activity from multiple groups data collection in 2023
3. Applying expertise to decision making, planning, and creative tasks
4. Managing and developing people
5. Physical activities and operating machinery in unpredictable environments
6. Physical activities and operating machinery in predictable environments
Source: McKinsey Global Institute analysis; SSB McKinsey & Company 12
The 7 largest occupational groups, representing >70% of Norwegian
workers, can expect a large productivity uplift from GenAI
With GenAI Without GenAI Top 7 largest occupational groups Low High
Uplift from Share of NO No. of NO
Occupational groups Overall technical automation potential, % in 2023 GenAI, p.p. employment, % employment1, 000s
Educators and workforce training 54 39 p.p. 12 % 285
15
Customer service and sales 57 12 p.p. 11 % 263
45
Business and legal professionals 62 30 p.p. 11 % 256
32
STEM professionals 57 29 p.p. 10 % 239
28
Community services 65 26 p.p. 10 % 237
39
Managers 44 17 p.p. 8 % 197
27
Health professionals 43 14 p.p. 8 % 197
29
Builders 53 4 p.p. 6 % 153
49
Mechanical installation and repair 67 6 p.p. 5 % 122
61
Transportation services 49 7 p.p. 4 % 96
42
Food services 78
8 p.p. 4 % 91
70
Office support 87
21 p.p. 3 % 84
66
Property maintenance 38
9 p.p. 3 % 84
29
Agriculture 63
4 p.p. 2 % 40
59
Creatives and arts management 53
25 p.p. 1 % 32
28
Health aides, technicians, and wellness 43
9 p.p. 1 % 21
34
Production work 82
9 p.p. 1 % 21
73
Total 63
12 p.p. 100% 2 418
51
1. Jobs with <5k holding the job title excluded by SSB McKinsey & Company 13
Source: McKinsey Global Institute; SSB
Norway can realize significant value from GenAI, mainly unlocked
by automating activities performed by white-collar workers
Norway is primed for adoption of GenAI due
to high levels of education and strong
digital foundation …
… with the potential to unlock values up to
~127 billion NOK across various industries …
… mainly due to productivity gains from
activities related to decision making,
collaboration and data management
MMccKKiinnsseeyy && CCoommppaannyy 1144
Appendix
MMccKKiinnsseeyy && CCoommppaannyy 1155
The midpoint scenario at which automation adoption could reach
50% of time spent on current work activities has accelerated by a
decade
Updated early scenario including generative AI2 2017 early scenario2
Global automation of time spent on current work activities1, %
Updated late scenario including generative AI3 2017 late scenario3
100%
90%
80%
70%
60%
Midpoint 2017 50%
50%
Midpoint
40% updated
The advent of GenAI has sped up
the automation timeline by ~10
30%
years from previous estimates in
20% which GenAI was not considered
10%
0%
2020 2025 2030 2035 2040 2045 2050 2055 2060 2065 2070 2075 2080 2085 2090 2095 2100
1. Includes data from 47 countries representing about 80% of employment across the world. 2017 estimates are based on the activity and occupation mix from 2016. Scenarios including generative AI are based on the 2021
activity and occupation mix
2. Early scenario: aggressive scenario for all key model parameters (technical automation potential, integration timelines, economic feasibility, and technology diffusion rates)
3. Late scenario: parameters are set for later adoption potential.
Source: McKinsey Global Institute McKinsey & Company 16
GenAI is expected to have different impact across the business
functions dependent on industry sizes
Generative AI productivity impact by business functions1, % of industry revenue
Impact in bn NOK Low High Impact as % of industry rev. Low High Low High
Total Expected Total added
Supply chain Corporate IT
industry impact of value from Marketing Customer Product and Software Risk and Strategy and Talent and
and (excluding
size2, % of GenAI, % of GenAI, and sales operations R&D engineering legal finance organization
operations SWE)
total revenue industry rev. bn NOK
Total2bn NOK 6,754 95 –159 28 -43 12 -17 8 -15 21 -43 10 -19 7 -9 4 -9 1 -2 2 -3
Energy 22% 1% -1.6% 16 -26
High tech 4% 4.8% -9.3% 12 -24
Travel, transport & logistics 14% 1.2% -2% 12 -21
Retail 14% 1.2% -1.9% 12 -20
Advanced manufacturing 7% 1.4% -2.4% 8 -13
Real estate 8% 1% -1.7% 6 -10
Basic materials 10% 0.7% -1.2% 5 -9
Banking 3% 2.8% -4.7% 5 -9
Consumer packaged goods 5% 1.4% -2.3% 5 -8
Professional services 7% 0.9% -1.4% 5 -7
Telecommunications 1% 2.3% -3.7% 2 -3
Insurance 1% 1.8% -2.8% 2 -3
Healthcare 1% 1.8% -3.2% 2-3
Media 1% 1.5% -2.6% 2-3
Chemical 1% 0.8% -1.3% 0.5 -1
Agriculture 1% 0.6% -1% 0 -0.5
Pharma & medical products 0% 2.6% -4.5% 0
1. Excl. implementation costs (e.g., training, licenses)
2. Figures may not sum to 100% because of rounding
Source: Internal experts; McKinsey Global Institute;annual reports; SSB MMccKKiinnsseeyy && CCoommppaannyy 1177
GenAI can reduce the cost of large effort tasks, enabled through
4 archetype of applications which are emerging across industries
Not exhaustive for all use cases for Generative AI
Content synthesis Coding & Creative Customer
(virtual expert) software content engagment1
GenAI Generate insights and drive Interpret and generate code Create marketing messages, Streamline interactions by
capability actions based on summarization and documentation, i.e., and images, support ideation for interpreting text and analyze
and synthesis of unstructured improving efficiency and reducing new product development and customer journeys through
data technical debt generate personalized marketing customer service, chatbots,
copy recommenders, task automation,
etc.
Use case  Summarize text or audio and  Generate code and assist  Generate visuals (images,  Streamline customer
generate insights developers designs, 3D models) to communications, e.g.,
 Perform actions triggered by  Refactor translate code to accelerate the product design customer service issue
user prompt accelerate mainframe process resolution (driving action to
 Augment capabilities of migration  Draft and personalize resolve) and Q&A
operations staff (e.g.,  Create model outbound customer comms  Model and predict elements
inventory/maintenance documentation (e.g., risk) or marketing in patient or customer journey
management)
1. Includes B2B customer interactions and transactions
Source: McKinsey analysis MMccKKiinnsseeyy && CCoommppaannyy 1188
Impact as % of industry revenues, bubble size proportional to bn NOK impact: Small Large
The energy Impact1, median calculation, bn NOK
24
sector has the
22
highest value
Energy
20
potential, but
18 High Tech
GenAI will be
Travel, Transport & Logistics
16
most disruptive
Retail
14
in High Tech
12
Advanced Manufacturing
10
Basic Materials
8 Real Estate
Banking
6
Consumer Packaged Goods
Professional services
4
Healthcare
Insurance
2 Telecommunications
Agriculture Media
Chemical Pharma & Medical Products
0
0% 1% 2% 3% 4% 5% 6% 7% 8%
1. Based on the early adoption scenario, median
Impact as % of industry revenues
expected impact of GenAI, % of industry
revenues. 2020 revenues, inflation adjusted
SSoouurrccee:: IMncteKrinnasle eyx Gpelortbsa, lD Inastatibtuatsees: Annual reports, SSB MMccKKiinnsseeyy && CCoommppaannyy 1199
>50% of the value unlock can be achieved in two large
business functions
Deep dive follows
Business functions Value potential from GenAI1, bn NOK
Marketing & sales 28 - 43
Software engineering 21 - 43
Customer operations 12 - 17
Supply chain & operations 10 - 19
Product and R&D 8 - 15
Risk & legal 7 - 9
Strategy & finance 4 - 9
Talent & org. 2 - 3
Corporate IT (excl. SWE) 1 - 2
1. Excl. implementation costs (e.g., training, licenses)
Source: Internal experts; annual reports; SSB MMccKKiinnsseeyy && CCoommppaannyy 2200
1: Marketing & Sales
Productivity opportunity with GenAI
Not expected to be affected by GenAI
Illustrative
Value unlock Industry Typical working
examples examples day marketing executive
% time spent
Writing marketing and sales copy Augment sales teams with technical
content of text, images and videos proprietary knowledge and historic
customer interactions
Emails 13
Improving sales force, e.g., by
Generate personalized marketing content
flagging risks, recommending next
based on (un)structured data from consumer
interactions
profiles and community insights Meetings 38
Analyzing customer feedback Automate booking management and
customer follow-up during travels Designs and edits 13
Analysis 25
Key  CPG
industries  Retail Other admin 13
 Travel, Transport & Logistics
 Insurance
Total
 Financial services
Total value 28 - 43
potential, bn NOK
Source: McKinsey Global Institute; internal experts; press search MMccKKiinnsseeyy && CCoommppaannyy 2211
“50% of code on
GitHub is written by an
2: Software Engineering AI, e.g., a co-pilot doing
code suggestions,
Productivity opportunity with GenAI
corrections and writing”
Not expected to be affected by GenAI
Illustrative
Value unlock Industry Typical working
examples examples day software engineer
% time spent
Generating, prioritizing, and Create alerts and automated bots based on
running code news, industry reports, internal research and
economic trends that can impact trading
Meetings 10
strategies
Generating synthetic data to improve
training accuracy of ML models Generate code that creates hyper-
personalized trip recommendations Coding 50
Reviewing code for defects and
Accelerate transition from legacy software /
inefficiencies
code (e.g., banks still use system written in
Debugging 20
COBOL) to modern
Emails 10
Key  High Tech
industries  Media Admin 10
 CPG
 Retail
Total
 Energy
 Insurance
Total value 21 - 43
 Financial services
potential, bn NOK
Source: McKinsey Global Institute; internal experts MMccKKiinnsseeyy && CCoommppaannyy 2222
3: Customer Operations
Productivity opportunity with GenAI
Not expected to be affected by GenAI
Illustrative
Value unlock Industry Typical working
examples examples day call center
% time spent
Auto-generating customer profile Zero customer service reps, with all internal
and segment for each unique helpdesk automated via self-serve and GenAI-
customer powered chatbots to handle all omnichannel
Admin 13
helpdesk engagement
Generating post call summary to
Summarize speech to distinctive text to create
customers and agents Customer care 47
records of customer complaints
Developing first-line response in Manage disruptions during vacations by being
Internal calls 6
customer service for all inquiries first point of contact for customers, offer
translation and content customized for the
customer and their vacation Problem solving 25
Email / chat 6
Key  CPG
industries  Retail
Other 3
 Insurance
 Financial services
Total
 Travel, Transport & Logistics
 Telecommunications
Total value 12 - 17
potential, bn NOK
Source: McKinsey Global Institute; internal experts; press search MMccKKiinnsseeyy && CCoommppaannyy 2233
4: Supply Chain & Operations
Productivity opportunity with GenAI
Not expected to be affected by GenAI
Illustrative
Value unlock Industry Typical working
examples examples day supply chain manager
% time spent
Warehouse and inventory Interpreting data, labelling unstructured data
management and identifying patterns for future trends
and demand
E-mail 10
Forecasting demand and Synthesizing data from previous jobs to predict
disruptions in supply chain potential issues
Meetings 25
Act as an intelligent maintenance or safety
Optimize transportation route advisor, leveraging insights and knowledge Inventory or
35
from equipment and process manuals staffing analysis
Planning 15
Document review 10
Key  Energy
industries  CPG
Other admin 5
 Retail
 Advanced Manufacturing
Total
 Travel, Transport & Logistics
 Basic Materials
Total value 10 - 19
potential, bn NOK
Source: McKinsey Global Institute; internal experts; press search MMccKKiinnsseeyy && CCoommppaannyy 2244
“The amount of time spent in each category depends on
which stage of development you are, but most time is spent
on product development, troubleshooting or fixing”
5: Product and R&D
Productivity opportunity with GenAI
Not expected to be affected by GenAI
Illustrative
Value unlock Industry Typical working
examples examples day product owner
% time spent
Creating 3D visual models and Improve pipeline maintenance by
digital product designs synthesizing maintenance and inspection
records, predict areas at risk for corrosion E-mail 10
Prioritizing product backlog by based on historic maintenance records
synthesizing customer feedback Product
Reimagine product portfolio through GenAI 17
development
opportunity themes
Measuring and tracking engineering
Translate code from legacy systems at scale,
metrics Troubleshooting 17
prioritizing interventions and re-factoring
Fixing 17
Meetings 25
Key  High tech
industries  CPG
Other admin 15
 Retail
 Travel, Transport & Logistics
Total
 Telecommunications
 Insurance
Total value 8 - 15
 Financial services
potential, bn NOK
Source: McKinsey Global Institute; internal experts MMccKKiinnsseeyy && CCoommppaannyy 2255
6: Risk & Legal
Productivity opportunity with GenAI
Not expected to be affected by GenAI
Illustrative
Value unlock Industry Typical working
examples examples day junior lawyer
% time spent
Summarize regulation, including safety &
Draft and review legal documents
equipment manuals changes from industry &
regulatory databases
E-mail 10
Summarize and highlight changes
Informative queries from agents to identify &
in large bodies of regulatory
Writing
generate required legal and non-legal
documents 45
documents for transportation based on documents
classification from GenAI model
Review
Answer questions & cite
5
justifications from large documents Generate life-like fraud attempts for pro-active documents
testing
Calls 30
Meetings 5
Key  Energy
industries  High Tech
Other admin 6
 Media
 Insurance
Total
 Financial services
 Real Estate
Total value 7 - 9
 Telecommunications
potential, bn NOK
Source: McKinsey Global Institute; internal experts; press search MMccKKiinnsseeyy && CCoommppaannyy 2266"
36,mckinsey,mickensy_superagency-in-the-workplace-empowering-people-to-unlock-ais-full-potential-v4.pdf,"Superagency
in the Workplace
Empowering people to unlock AI’s full potential
Hannah Mayer
Lareina Yee
Michael Chui
Roger Roberts
January 2025
Contents
Introduction 2
Chapters:
1. An innovation as powerful as the steam engine 5
2. Employees are ready for AI; now leaders must step up 11
3. Delivering speed and safety 18
4. Embracing bigger ambitions 26
5. Technology is not the barrier to scale 35
Conclusion: Meeting the AI future 40
Acknowledgments 42
Methodology 43
Glossary 44
Introduction
Almost all companies invest in AI, but just 1 percent believe they are at
maturity. Our research finds the biggest barrier to scaling is not
employees—who are ready—but leaders, who are not steering fast enough.
A
rtificial intelligence has arrived in the workplace and has the potential to be as transformative
as the steam engine was to the 19th-century Industrial Revolution.1 With powerful and capable
large language models (LLMs) developed by Anthropic, Cohere, Google, Meta, Mistral, OpenAI,
and others, we have entered a new information technology era. McKinsey research sizes the
long-term AI opportunity at $4.4 trillion in added productivity growth potential from corporate use cases.2
Therein lies the challenge: the long-term potential of AI is great, but the short-term returns are unclear. Over
the next three years, 92 percent of companies plan to increase their AI investments. But while nearly all
companies are investing in AI, only 1 percent of leaders call their companies “mature” on the deployment
spectrum, meaning that AI is fully integrated into workflows and drives substantial business outcomes. The
big question is how business leaders can deploy capital and steer their organizations closer to AI maturity.
This research report, prompted by Reid Hoffman’s book Superagency: What Could Possibly Go Right with
Our AI Future,3 asks a similar question: How can companies harness AI to amplify human agency and unlock
new levels of creativity and productivity in the workplace? AI could drive enormous positive and disruptive
change. This transformation will take some time, but leaders must not be dissuaded. Instead, they must
advance boldly today to avoid becoming uncompetitive tomorrow. The history of major economic and
technological shifts shows that such moments can define the rise and fall of companies. Over 40 years ago,
the internet was born. Since then, companies including Alphabet, Amazon, Apple, Meta, and Microsoft have
attained trillion-dollar market capitalizations. Even more profoundly, the internet changed the anatomy of
work and access to information. AI now is like the internet many years ago: The risk for business leaders is
not thinking too big, but rather too small.
1 “Gen AI: A cognitive industrial revolution,” McKinsey, June 7, 2024.
2 “The economic potential of generative AI: The next productivity frontier,” McKinsey, June 14, 2023.
3 Reid Hoffman and Greg Beato, Superagency: What Could Possibly Go Right with Our AI Future, Authors Equity, January 2025.
2 Superagency in the workplace: Empowering people to unlock AI’s full potential
Superagency: By the numbers
Employees are more ready for the change than their leaders imagine
3× 1.4×
more employees are using gen AI more likely for millennials to report
for a third or more of their work extensive familiarity with gen AI tools
than their leaders imagine; than peers in other age groups;
more than 70% of all employees they are also 1.2× more
believe that within 2 years gen AI will likely to expect workflows
change 30% or more of their work to change within a year
Companies need to move fast—employees trust their
leaders to balance speed and safety
of the C-suite say their companies are more likely for employees to trust their
47% 1.3×
developing gen AI tools too slowly, even though own companies to get gen AI deployment right
69% started investing more than a year ago than they are to trust other institutions
Companies are investing in gen AI but have not yet achieved maturity
92% 1%
of companies plan believe their
to invest more investments have
in gen AI over the reached maturity
next 3 years
Leaders need to recognize their responsibility in driving gen AI transformation
2.4× 48%
more likely for C-suite to cite employee readiness as of employees rank training as the
a barrier to adoption vs their own issues with leadership most important factor for gen AI adoption;
alignment, despite employees currently using yet nearly half feel they are receiving
gen AI 3× more than leaders expect moderate or less support
Superagency in the workplace: Empowering people to unlock AI’s full potential 3
This report explores companies’ technology and business readiness for AI adoption (see sidebar “About the
survey”). It concludes that employees are ready for AI. The biggest barrier to success is leadership.
Chapter 1 looks at the rapid advancement of technology over the past two years and its implications for
business adoption of AI.
Chapter 2 delves into the attitudes and perceptions of employees and leaders. Our research shows that
employees are more ready for AI than their leaders imagine. In fact, they are already using AI on a regular
basis; are three times more likely than leaders realize to believe that AI will replace 30 percent of their work
in the next year; and are eager to gain AI skills. Still, AI optimists are only a slight majority in the workplace; a
large minority (41 percent) are more apprehensive and will need additional support. This is where millennials,
who are the most familiar with AI and are often in managerial roles, can be strong advocates for change.
Chapter 3 looks at the need for speed and safety in AI deployment. While leaders and employees want to
move faster, trust and safety are top concerns. About half of employees worry about AI inaccuracy and
cybersecurity risks. That said, employees express greater confidence that their own companies, versus
other organizations, will get AI right. The onus is on business leaders to prove them right, by making bold and
responsible decisions.
Chapter 4 examines how companies risk losing ground in the AI race if leaders do not set bold goals. As the
hype around AI subsides, companies should put a heightened focus on practical applications that empower
employees in their daily jobs. These applications can create competitive moats and generate measurable
ROI. Across industries, functions, and geographies, companies that invest strategically can go beyond using
AI to drive incremental value and instead create transformative change.
Chapter 5 looks at what is required for leaders to set their teams up for success with AI. The challenge of AI
in the workplace is not a technology challenge. It is a business challenge that calls upon leaders to align
teams, address AI headwinds, and rewire their companies for change.
About the survey
To create our report, we surveyed 3,613 employees (managers and independent contributors) and 238
C-level executives in October and November 2024. Of these, 81 percent came from the United States,
and the rest came from five other countries: Australia, India, New Zealand, Singapore, and the United
Kingdom. The employees spanned many roles, including business development, finance, marketing,
product management, sales, and technology.
All the survey findings discussed in the report, aside from two sidebars presenting international nuances,
pertain solely to US workplaces. The findings are organized in this way because the responses from US
employees and C-suite executives provide statistically significant conclusions about the US workplace.
Analyzing global findings separately allows a comparison of differences between US responses and
those from other regions.
4 Superagency in the workplace: Empowering people to unlock AI’s full potential
1
An innovation as
powerful as the
steam engine
About the survey ‘ Scientific discoveries and technological
innovations are stones in the cathedral
of human progress.’
– Reid Hoffman, cofounder of LinkedIn and Inflection AI,
partner at Greylock Partners, and author
Superagency in the workplace: Empowering people to unlock AI’s full potential 5
I
magine a world where machines not only perform physical labor but also think, learn, and make
autonomous decisions. This world includes humans in the loop, bringing people and machines together
in a state of superagency that increases personal productivity and creativity (see sidebar “AI
superagency”). This is the transformative potential of AI, a technology with a potential impact poised to
surpass even the biggest innovations of the past, from the printing press to the automobile. AI does not just
automate tasks but goes further by automating cognitive functions. Unlike any invention before, AI-powered
software can adapt, plan, guide—and even make—decisions. That’s why AI can be a catalyst for
unprecedented economic growth and societal change in virtually every aspect of life. It will reshape
our interaction with technology and with one another.
Many breakthrough technologies, including the internet, smartphones, and cloud computing, have
transformed the way we live and work. AI stands out from these inventions because it offers more than
access to information. It can summarize, code, reason, engage in a dialogue, and make choices. AI can lower
skill barriers, helping more people acquire proficiency in more fields, in any language and at any time. AI
holds the potential to shift the way people access and use knowledge. The result will be more efficient and
effective problem solving, enabling innovation that benefits everyone.
Over the past two years, AI has advanced in leaps and bounds, and enterprise-level adoption has
accelerated due to lower costs and greater access to capabilities. Many notable AI innovations have
emerged (Exhibit 1). For example, we have seen a rapid expansion of context windows, or the short-term
memory of LLMs. The larger a context window, the more information an LLM can process at once. To
illustrate, Google’s Gemini 1.5 could process one million tokens in February 2024, while its Gemini 1.5 Pro
could process two million tokens by June of that same year.4 Overall, we see five big innovations for business
that are driving the next wave of impact: enhanced intelligence and reasoning capabilities, agentic AI,
multimodality, improved hardware innovation and computational power, and increased transparency.
AI superagency
What impact will AI have on humanity? Reid Hoffman and Greg Beato’s book Superagency: What
Could Possibly Go Right with Our AI Future (Authors Equity, January 2025) explores this question.
The book highlights how AI could enhance human agency and heighten our potential. It envisions a
human-led, future-forward approach to AI.
Superagency, a term coined by Hoffman, describes a state where individuals, empowered by AI, super-
charge their creativity, productivity, and positive impact. Even those not directly engaging with AI can
benefit from its broader effects on knowledge, efficiency, and innovation.
AI is the latest in a series of transformative supertools, including the steam engine, internet, and
smartphone, that have reshaped our world by amplifying human capabilities. Like its predecessors, AI
can democratize access to knowledge and automate tasks, assuming humans can develop and deploy
it safely and equitably.
4 The Keyword, “Our next-generation model: Gemini 1.5,” blog entry by Sundar Pichai and Demis Hassabis, Google, February 15, 2024; Google
for Developers, “Gemini 1.5 Pro 2M context window, code execution capabilities, and Gemma 2 are available today,” blog entry by Logan
Kilpatrick, Shrestha Basu Mallick, and Ronen Kofman, June 27, 2024.
6 Superagency in the workplace: Empowering people to unlock AI’s full potential
Web <2025>
<Superagency>
Exhibit 1
Exhibit <1> of <21>
Gen AI capabilities have evolved rapidly over the past two years.
Illustrative capabilities of gen AI platforms from select frontier labs, nonexhaustive
2022–231 Jan 20252
Anthropic
Google
Gemini
Meta
Microsoft
OpenAI
AI superagency
Note: Exhibit is not intended as an evaluation or comparison but as an illustration of the rapid progress in capabilities.
1Initial models released between Mar 2022 and Mar 2023.
2Latest models released between Nov and Dec 2024.
Source: Company websites and press releases; McKinsey analysis
McKinsey & Company
Superagency in the workplace: Empowering people to unlock AI’s full potential 7
Intelligence and reasoning are improving
AI is becoming far more intelligent. One indicator is the performance of LLMs on standardized tests.
OpenAI’s Chat GPT-3.5, introduced in 2022, demonstrated strong performance on high-school-level exams
(for example, scoring in the 70th percentile on the SAT math and the 87th percentile on the SAT verbal
sections). However, it often struggled with broader reasoning. Today’s models are near the intelligence level
of people who hold advanced degrees. GPT-4 can so easily pass the Uniform Bar Examination that it would
rank in the top 10 percent of test takers,5 and it can answer 90 percent of questions correctly on the US
Medical Licensing Examination.6
The advent of reasoning capabilities represents the next big leap forward for AI. Reasoning enhances AI’s
capacity for complex decision making, allowing models to move beyond basic comprehension to nuanced
understanding and the ability to create step-by-step plans to achieve goals. For businesses, this means they
can fine-tune reasoning models and integrate them with domain-specific knowledge to deliver actionable
insights with greater accuracy. Models such as OpenAI’s o1 or Google’s Gemini 2.0 Flash Thinking Mode
are capable of reasoning in their responses, which gives users a human-like thought partner for their
interactions, not just an information retrieval and synthesis engine.7
Agentic AI is acting autonomously
The ability to reason is growing more and more, allowing models to autonomously take actions and complete
complex tasks across workflows. This is a profound step forward. As an example, in 2023, an AI bot could
support call center representatives by synthesizing and summarizing large volumes of data—including voice
messages, text, and technical specifications—to suggest responses to customer queries. In 2025, an AI
agent can converse with a customer and plan the actions it will take afterward—for example, processing a
payment, checking for fraud, and completing a shipping action.
‘I’ve always thought of AI as the most
profound technology humanity is
working on . . . more profound than fire
or electricity or anything that we’ve
done in the past.’
– Sundar Pichai, CEO of Alphabet
5 GPT-4 technical report, OpenAI, March 27, 2023.
6 Dana Brin, Vera Sorin, Akhil Vaid, et al., “Comparing ChatGPT and GPT-4 performance in USMLE soft skill assessments,” Scientific Reports,
October 1, 2023.
7 “Learning to reason with LLMs,” OpenAI, September 12, 2024; “Gemini 2.09 Flash Thinking Mode,” Google, January 21, 2025.
8 Superagency in the workplace: Empowering people to unlock AI’s full potential
‘AI, like most transformative technologies,
grows gradually, then arrives suddenly.’
– Reid Hoffman, cofounder of LinkedIn and Inflection AI,
partner at Greylock Partners, and author
Software companies are embedding agentic AI capabilities into their core products. For example,
Salesforce’s Agentforce is a new layer on its existing platform that enables users to easily build and
deploy autonomous AI agents to handle complex tasks across workflows, such as simulating product
launches and orchestrating marketing campaigns.8 Marc Benioff, Salesforce cofounder, chair, and CEO,
describes this as providing a “digital workforce” where humans and automated agents work together to
achieve customer outcomes.9
Multimodality is bringing together text, audio, and video
Today’s AI models are evolving toward more advanced and diverse data processing capabilities across text,
audio, and video. Over the last two years, we have seen improvements in the quality of each modality. For
example, Google’s Gemini Live has improved audio quality and latency and can now deliver a human-like
conversation with emotional nuance and expressiveness.10 Also, demonstrations of Sora by OpenAI show its
ability to translate text to video.11
Hardware innovation is enhancing performance
Hardware innovation and the resulting increase in compute power continue to enhance AI performance.
Specialized chips allow faster, larger, and more versatile models. Enterprises can now adopt AI solutions that
require high processing power, enabling real-time applications and opportunities for scalability. For
example, an e-commerce company could significantly improve customer service by implementing AI-driven
chatbots that leverage advanced graphics processing units (GPUs) and tensor processing units (TPUs).
Using distributed cloud computing, the company could ensure optimal performance during peak traffic
periods. Integrating edge hardware, the company could deploy models that analyze photos of damaged
products to more accurately process insurance claims.
8 Sammy Spiegel, “The future of AI agents: Top predictions and trends to watch in 2025,” Salesforce, December 2024.
9 Marc Benioff, “How the rise of new digital workers will lead to an unlimited age,” Time, November 25, 2024.
10 Ivan Solovyev and Shrestha Basu Mallick, “Gemini 2.0: Level up your apps with real-time multimodal interactions,” Google, December 23, 2024.
11 “OpenAI releases AI video generator Sora but limits how it depicts people,” Associated Press, December 10, 2024.
Superagency in the workplace: Empowering people to unlock AI’s full potential 9
2
Transparency is increasing
AI is gradually becoming less risky, but it still lacks greater transparency and explainability. Both are critical
for improving AI safety and reducing the potential for bias, which are imperative for widescale enterprise
deployment. There is still a long way to go, but new models and iterations are rapidly improving. Stanford
University’s Center for Research on Foundation Models (CRFM) reports significant advances in model
performance. Its Transparency Index, which uses a scale of 1 to 100, shows that Anthropic’s transparency
score increased by 15 points to 51 and Amazon’s more than tripled to 41 between October 2023 and
May 2024.12
Beyond LLMs, other forms of AI and machine learning (ML) are improving explainability, allowing the outputs
of models that support consequential decisions (for example, credit risk assessment) to be traced back to
the data that informed them. In this way, critical systems can be tested and monitored on a near-constant
basis for bias and other everyday harms that arise from model drift and shifting data inputs, which happens
even in systems that were well calibrated before deployment.
All of this is crucial for detecting errors and ensuring compliance with regulations and company policies.
Companies have improved explainability practices and built necessary checks and balances, but they must
be prepared to evolve continuously to keep up with growing model capabilities.
Achieving AI superagency in the workplace is not simply about mastering technology. It is every bit as much
about supporting people, creating processes, and managing governance. The next chapters explore the
nontechnological factors that will help shape the deployment of AI in the workplace.
12 “The Foundation Model Transparency Index,” Stanford Center for Research on Foundation Models, May 2024.
10 Superagency in the workplace: Empowering people to unlock AI’s full potential
2
Employees are ready
for AI; now leaders
must step up
‘People are using [AI] to create amazing
things. If we could see what each of us
can do 10 or 20 years in the future, it
would astonish us today.’
– Sam Altman, cofounder and CEO of OpenAI
Superagency in the workplace: Empowering people to unlock AI’s full potential 11
E
mployees will be the ones to make their organizations AI powerhouses. They are more ready to
embrace AI in the workplace than business leaders imagine. They are more familiar with AI tools,
they want more support and training, and they are more likely to believe AI will replace at least a
third of their work in the near future. Now it’s imperative that leaders step up. They have more
permission space than they realize, so it’s on them to be bold and capture the value of AI. Now.
Beyond the tipping point
In our survey, nearly all employees (94 percent) and C-suite leaders (99 percent) report having some level of
familiarity with gen AI tools. Nevertheless, business leaders underestimate how extensively their employees
are using gen AI. C-suite leaders estimate that only 4 percent of employees use gen AI for at least 30
percent of their daily work, when in fact that percentage is three times greater, as self-reported by
employees (Exhibit 2). And while only a total of 20 percent of leaders believe employees will use gen AI for
more than 30 percent of their daily tasks within a year, employees are twice as likely (47 percent) to believe
they will (see sidebar “Who is using AI at work? Nearly everyone, even skeptical employees”).
The good news is that our survey suggests three ways companies can accelerate AI adoption and move
toward AI maturity.
Web <2025>
<ESxuhpeibraigt e2ncy>
Exhibit <2> of <21>
Employees are three times more likely to be using gen AI today than their
leaders expect.
US employees’ and C-suite’s C-suite Employees
timeline for employees using Already using 4
3×
gen AI for >30% of daily tasks, % 13
of respondents Less than a year 16
34
1–5 years 56
37
Over 5 years 11
5
Don’t anticipate it 10 7
Not sure 3 4
Note: Figures may not sum to 100%, because of rounding.
Source: McKinsey US CxO survey, Oct–Nov 2024 (n = 118) ; McKinsey US employee survey, Oct–Nov 2024 (n = 3,002)
McKinsey & Company
12 Superagency in the workplace: Empowering people to unlock AI’s full potential
Our research looked at people who self-identify as “Zoomers,” “Bloomers,” “Gloomers,” and “Doomers” in their
attitudes toward AI—a set of archetypes introduced in Superagency. We find that 39 percent of employees
identify as Bloomers, who are AI optimists that want to collaborate with their companies to create responsible
solutions. Meanwhile, 37 percent identify as Gloomers, who are more skeptical about AI and want extensive
top-down AI regulations; 20 percent identify as Zoomers, who want AI to be quickly deployed with few guardrails;
and just 4 percent identify as Doomers, who have a fundamentally negative view of AI (exhibit).
Even those with a skeptical take on AI are familiar with it; 94 percent of Gloomers and 71 percent of Doomers say
they have some familiarity with gen AI tools. Furthermore, approximately 80 percent of Gloomers and about half
of Doomers say they are comfortable using gen AI at work.
Web <2025>
<Superagency>
Exhibit
Exhibit <3> of <21>
Employee segments differ, but all indicate a high familiarity with gen AI.
US employee sentiment on gen AI, by archetype, % of respondents
Doomer Gloomer Bloomer Zoomer
Gen AI will not align Above all else, gen AI Gen AI needs to be Gen AI development
with human values, needs to be closely developed iteratively should be trusted
regardless of monitored and with a diverse range to developers to
deployment method controlled of inputs maximize speed
Has extensive
familiarity
with gen AI1 16 42 55 67
Has at least
some familiarity
with gen AI2 71 94 96 96
Is comfortable using
results from gen AI 47 79 91 91
Believes gen AI will
have a net benefit in
the next 5 years 54 82 89 87
Plans to use gen AI
more in their
personal life 49 77 86 85
Expects 30% of
workflows to change
in the next year 19 38 50 64
Share of respondents
4 37 39 20
in archetype group, %
1Defined as those who have “extensive experience (use several tools for complex tasks)” and “experts.”
2Defined as those who have “some familiarity (use 1–2 tools a few times)” and “extensive experience (use several tools for complex tasks)” and “experts.”
Source: McKinsey US employee survey, Oct–Nov 2024 (n = 3,002)
McKinsey & Company
Superagency in the workplace: Empowering people to unlock AI’s full potential 13
Leaders can invest more in their employees
As noted at the beginning of this chapter, employees anticipate AI will have a dramatic impact on their work.
Now they would like their companies to invest in the training that will help them succeed. Nearly half of
employees in our survey say they want more formal training and believe it is the best way to boost AI
adoption. They also would like access to AI tools in the form of betas or pilots, and they indicate that
incentives such as financial rewards and recognition can improve uptake.
Yet employees are not getting the training and support they need. More than a fifth report that they have
received minimal to no support (Exhibit 3). Outside the United States, employees also want more training
(see sidebar “Global perspectives on training”).
Web <2025>
<ESxuhpeibraigt e3ncy>
Exhibit <4> of <21>
Employees long for more support and training on gen AI.
Share of US employees agreeing that a company initiative would make them more likely to
increase day-to-day usage of gen AI tools, %
Formal gen AI training from
48
my organization
Seamless integration into my
45
existing workflow
Access to gen AI tools 41
Incentives and rewards 40
Usage of gen AI being a requirement
30
for a certification program
Explicit instructions from my managers
30
to use gen AI
Being involved in the development
29
of the tools
OKRs¹/KPIs tied to gen AI usage 22
US employees’ perceived level of support for gen AI capability building at their organizations,
% of respondents
Not None/ Moderate to Fully
needed minimal significant supported
Current 6 22 44 29
In 3 years 4 10 56 31
Note: Figures do not sum to 100%, because of rounding.
¹Objectives and key results.
Source: McKinsey US employee survey, Oct–Nov 2024 (n = 3,002)
McKinsey & Company
14 Superagency in the workplace: Empowering people to unlock AI’s full potential
Global perspectives on training
To get a clearer picture of global AI adoption trends, we looked at trends across five countries: Australia, India, New
Zealand, Singapore, and the United Kingdom. Broadly speaking, these employees and C-suite leaders—the “interna-
tional” group in this report—have similar views of AI as their US peers. In some key areas, however, including the topic
of training, their experiences differ.
Web <2025>
<Superagency>
Exhibit <5> of <21>
Many international employees are concerned about insufficient training, even though they report receiving far more
support than US employees. Some 84 percent of international employees say they receive significant or full organiza-
tional support to learn AI skills, versus just over half of US employees. International employees also have more
opportunities to participate in developing gen AI tools at work than their US counterparts, with differences of at least
ten percentage points in activities such as providing feedback, beta testing, and requesting specific features (exhibit).
Exhibit
International employees get more encouragement to use gen AI tools.
Sources encouraging employees’ use of gen AI tools at work, % of respondents reporting practice in
place at their organization
Australia and New Zealand India Singapore UK US
0 20 40 60 80 100
Use is mandated
Manager
Manager other than own
Peers
C-suite leadership
Developer of AI tool
Generic communications
Have not been encouraged
Employee involvement in developing gen AI tools, % of respondents
Australia and New Zealand India Singapore UK US
0 20 40 60 80 100
Provide feedback
in tool itself
Provide feedback via
other channels
Beta testing or
pilot program
Submit specific requests
for features
Not involved
Source: McKinsey international employee survey, Oct–Nov 2024 (Australia and New Zealand, n = 139; India, n = 134; Singapore, n = 140; UK, n = 201) ; McKinsey
US employee survey, Oct–Nov 2024 (n = 3,002)
McKinsey & Company
Superagency in the workplace: Empowering people to unlock AI’s full potential 15
C-suite leaders can help millennials lead the way
Many millennials aged 35 to 44 are managers and team leaders in their companies. In our survey, they self-
report having the most experience and enthusiasm about AI, making them natural champions of
transformational change. Millennials are the most active generation of AI users. Some 62 percent of 35- to
44-year-old employees report high levels of expertise with AI, compared with 50 percent of 18- to 24-year-
old Gen Zers and 22 percent of baby boomers over 65 (Exhibit 4). By tapping into that enthusiasm and
expertise, leaders can help millennials play a crucial role in AI adoption.
Web <2025>
<ESxuhpeibraigt e4ncy>
Exhibit <6> of <21>
Millennials aged 35 to 44 are AI optimists, with 90 percent indicating
confidence in their gen AI abilities.
US employee sentiment on gen AI, by age group, % of respondents
18–24 25–34 35–44 45–54 55–64 65+
Has extensive
familiarity
with gen AI1
50 49 62 47 26 22
Is comfortable using
gen AI at work
80 87 90 82 70 71
Provides feedback
on gen AI tools
76 77 76 65 47 55
Wants to participate
in the design of
gen AI tools
70 76 81 77 73 76
1Defined as those who have “extensive experience (use several tools for complex tasks)” and “experts.”
Source: McKinsey US employee survey, Oct–Nov 2024 (n = 3,002)
McKinsey & Company
16 Superagency in the workplace: Empowering people to unlock AI’s full potential
Web <2025>
E<Sxuhpiebraitg e5ncy>
Exhibit <7> of <21>
Two-thirds of managers regularly act as sounding boards for their teams on
gen AI.
Frequency of team inquiries about using new gen AI tools at work, % of US manager respondents
(n = 1,440)
Less than Once a Once a A few Once Multiple
quarterly Quarterly month week times a week a day times a day
10 5 5 12 15 28 9 16
Not at all
Use of gen AI tools to resolve a team member’s challenge, % of US manager respondents (n = 1,440)
Recommended Gen AI tool was
gen AI tool to successful in
solve team member’s resolving team
challenge in member’s challenge
the past month 86
68
Source: McKinsey US employee survey, Oct–Nov 2024 (n = 3,002)
McKinsey & Company
Since many millennials are managers, they can support their teams to become more adept AI users. This
helps push their companies toward AI maturity. Two-thirds of managers say they field questions from their
team about how to use AI tools at least once a week, and a similar percentage say they recommend AI tools
to their teams to solve problems (Exhibit 5).
Since leaders have the permission space, they can be bolder
In many transformations, employees are not ready for change, but AI is different. Employee readiness and
familiarity are high, which gives business leaders the permission space to act. Leaders can listen to
employees describe how they are using AI today and how they envision their work being transformed. They
also can provide employees with much-needed training and empower managers to move AI use cases from
pilot to scale.
It’s critical that leaders meet this moment. It’s the only way to accelerate the probability that their companies
will reach AI maturity. But they must move with alacrity, or they will fall behind.
Superagency in the workplace: Empowering people to unlock AI’s full potential 17
3
Delivering
speed and safety
‘Soon after the first automobiles were
on the road, there was the first car crash.
But we didn’t ban cars—we adopted
speed limits, safety standards, licensing
requirements, drunk-driving laws,
and other rules of the road.’
– Bill Gates, cofounder of Microsoft
18 Superagency in the workplace: Empowering people to unlock AI’s full potential
A
I technology is advancing at record speed. ChatGPT was released about two years ago; OpenAI
reports that usage now exceeds 300 million weekly users13 and that over 90 percent of Fortune
500 companies employ its technology.14 The internet did not reach this level of usage until the
early 2000s, nearly a decade after its inception.
The majority of employees describe themselves as AI optimists; Zoomers and Bloomers make up 59 percent
of the workplace. Even Gloomers, who are one of the two less-optimistic segments in our analysis, report
high levels of gen AI familiarity, with over a quarter saying they plan to use AI more next year.
Business leaders need to embrace this speed and optimism to ensure that their companies don’t get left
behind. Yet despite all the excitement and early experimentation, 47 percent of C-suite leaders say their
organizations are developing and releasing gen AI tools too slowly, citing talent skill gaps as a key reason for
the delay (Exhibit 6).
Web <2025>
<ESxuhpeibraigt e6ncy>
Exhib"
37,mckinsey,insurer-of-the-future-are-asian-insurers-keeping-up-with-ai-advances.pdf,"Insurance Practice
Insurer of the future: Are
Asian insurers keeping up
with AI advances?
AI’s potential for competitive advantages remains largely unrealized in
the Asian insurance industry. There is a framework for success: invest
in AI not discretely but across the value chain.
by Violet Chung, Pranav Jain, and Karthi Purushothaman
© zf L/Getty Images
May 2023
As the insurance industry undergoes a seismic, across four focus areas: engagement, AI-powered
tech-driven shift,1 AI continues to push the evolution decision making, core tech and data, and
of how insurers make significant service and organization and operations (Exhibit 1).
operational gains. Indeed, McKinsey estimates
that AI technologies could add up to $1.1 trillion
in annual value for the global insurance industry: AI’s potential for insurers:
approximately $400 billion could come from pricing, Benchmarking success
underwriting, and promotion technology upgrades The four layers of our integrated AI-capability-
and $300 billion from AI-powered customer service stack framework for insurance encompass front-,
and personalized offerings.2 middle-, and back-office functions. Just as these
functional areas are essential to and interreliant
While most large insurers are on the path to AI-enabled within an organization, the layers of the framework
personalization at scale,3 the industry remains at an are mutually supportive: together, they form a
early stage of transformational AI adoption. For most robust structure that benefits internal and external
Asian insurance leaders, traditional organizational stakeholders.
structures with multiple intermediaries and limited
in-house tech and data resources make it difficult to The global companies setting the benchmarks for
visualize, let alone quantify, the potential benefits of AI maturity and capacity—such as Google, Netflix,
investing more broadly in AI. Tencent, and Uber—illustrate the potential gains
that could be realized by insurers that integrate AI
This matters because cross-functional investment holistically across their organizations.
in AI can be game-changing—and it will increasingly
become a source of competitive advantage. AI Reimagined engagement layer
adoption has more than doubled in the past five years, A reimagined engagement layer employs AI tools
and investment in AI is increasing across industries. and solutions to create digital-ready engagement
Among 1,492 respondents to a December 2022 and distribution channels that can help provide
McKinsey Global Institute survey, those who reported customers with a consistent, personalized
the most significant gains from AI adoption— experience. To envision leading-edge personalization
20 percent or more EBIT—tend to employ advanced at scale, consider Netflix. Each Netflix user has a
AI practices, use cloud technologies, and spend distinct, customized view of available content—on
efficiently on AI, and they are more likely than others the platform as well as in emails—that reflects their
to engage in a range of AI risk mitigation efforts.4 interests and becomes more extensive, targeted,
informative, and engaging over time.
The challenge for most insurers is to determine the
optimal path from where they are now to where AI-powered decision-making layer
they need to be when it comes to AI maturity and AI and advanced data-and-analytics capabilities
enterprise-wide integration. can augment complex decision making, allowing
businesses to automate repetitive tasks, analyze
Drawing on McKinsey’s AI maturity assessment significantly more data, increase processing speed
model, in this article, we both outline how Asian and accuracy, and create predictive models to
insurers can assess their readiness for AI and offer a improve procedures and enhance performance.
road map to becoming an AI-powered insurer of the Uber Technologies exemplifies the leading edge in
future, realizing gains in profitability, agility, at-scale AI-enabled predictive analytics, bridging the gap
personalization, and innovation. The basis of this between ride demand and driver supply by using
framework is a layered approach to investment in AI historical ride data and key metrics to ensure every
1 Ramnath Balasubramanian, Ari Libarikian, and Doug McElhaney, “Insurance 2030—The impact of AI on the future of insurance,” McKinsey,
March 12, 2021.
2 “The executive’s AI playbook,” McKinsey, accessed March 20, 2023.
3 “ How personalization at scale can invigorate Asian insurers,” McKinsey, December 2, 2022.
4 “The state of AI in 2022—and a half decade in review,” McKinsey, December 6, 2022.
2 Insurer of the future: Are Asian insurers keeping up with AI advances?
Web 2023
AI insurer of the future
Exhibit 1
Exhibit 1 of 3
There are four layers to the full-stack AI capability that will define the AI-enabled
insurer of the future.
AI-enabled insurer of the future
Profitability At-scale Omnichannel Speed and
personalization experience innovation
Reimagined engagement
Intelligent products, Digital channels Partnerships and Smart service and
tools, and experiences Hybrid agency ecosystems operations
Bancassurance (eg, integrated life and (eg, conversational
health propositions) AI-enabled services,
Direct-to-consumer
predictive customer
distribution
experience, ZeroOps)
Omnichannel enablement
Digital marketing and personalization
AI-powered decisioning
Advanced Customer Underwriting Servicing and Retention Claims
analytics acquisition engagement and cross- and
upselling
Conversational Natural- Voice Virtual Computer Facial Robotics Behavioral
AI language script agents or vision recognition analytics
processing analysis bots
Core tech and data
Digital marketing and personalization
Core technology Intelligent infrastructure Modern API Tech-forward strategy Hollowing the core
and data (eg, AIOps1 command, architecture linked to business (core modernization)
modernization hybrid cloud set up)
Cybersecurity and
control tiers
Organization, operating model, and ways of working
Autonomous “biz tech” teams Vendor and partner management
Platform
operating
model
Agile way of working Remote collaboration Modern talent and hiring Culture and capabilities
1AI for IT operations.
McKinsey & Company
user of the Uber app has access to a ride within their integrate with multiple third-party platforms for
expected timeframe. data and intelligence. Tencent, a leading Chinese
multinational technology and entertainment
Core tech and data layer conglomerate, has been using its advanced-API
Modernized core tech helps deliver complete, platform in its WeChat app to integrate data and
high-quality, real-time data for advanced decision decisions, thus providing a seamless, efficient,
making, facilitating a seamless customer and integrated service experience for more than one
stakeholder experience. It provides the ability to billion monthly active users in China. The initiative
Insurer of the future: Are Asian insurers keeping up with AI advances? 3
significantly expanded WeChat’s proposition by Consider a four-phased plan to implement AI in
being more personalized and providing context- underwriting, for example (Exhibit 2). Value is added
specific offers across payments, retail, and its in each phase, but it increases dramatically in the
social networking and chat functionality. third and fourth phases, when greater AI capacity
helps enable continuous, personalized engagement
Organization and operating model layer and prescriptive actions to support better outcomes
This crucial layer enables the innovation, agility, for customers.
and flexibility needed to harness AI-powered
capabilities. Cross-functional teams, new talent While the path to becoming an AI-powered insurer
and skills, flat organization structures, and shared of the future will vary based on an organization’s
goals have enhanced impact from AI—particularly stage of readiness in each layer, the end goal
in aiding frontline adoption and solving crucial remains the same: a more innovative, profitable,
frontline decision problems. In Google’s relatively digital-forward organization that meets and
flat and cross-functional organization structure, anticipates customers’ evolving needs with highly
small project teams operate in an agile manner with personalized, omnichannel experiences.
shared goals and empowered decision making, and
talent and skill are valued over seniority. All are vital Reimagining the engagement layer
to Google’s reputation as an AI-driven organization Leaders in other industries—Google, Netflix, and
and its continued product innovation and growth. Uber, for example—have achieved stage-five AI
maturity within their engagement layer while most
leading insurers are at or below stage-three maturity.
AI readiness for Asian insurers: Some Asian insurers have used micropersonalization
Building layer depth and strength based on consumer personas to realize gains in
While some insurers have achieved select wins by overall engagement; nonetheless, most have fallen
implementing AI solutions within individual layers, short of employing dynamic, one-to-one customer
the transformation required to achieve the full-stack targeting to create the personalized, consistent,
capability that powers the companies mentioned omnichannel customer experience that characterizes
above remains elusive in insurance. mature AI-powered engagement. In other words,
personalization at scale.
Often, the problem insurers face is identifying
where to start. At-scale personalization. Personalization underpins all
facets of a reimagined engagement layer and is central
The first step is to determine how AI can support to every interaction between products and customers.
the organization’s strategic goals and then assess Creating exceptional customer experiences dominates
the organization’s current state of AI readiness senior-management agendas, and insurers continue
across each of the four layers. A simple scoring to work toward building personalization at scale to
methodology can help insurers identify their gain a better understanding of customer behavior and
readiness on a scale from one to five for each layer, offer customers advice on the products best suited to
with stage five signifying the highest level of AI their needs.
maturity (as articulated in this article). Insurers with
in-depth insight into their AI readiness are better AI is now being used to generate highly
equipped for the next step: creating a road map for personalized offerings across industries, tailored
implementing AI solutions across the front-, middle- to customer specifics such as location, industry,
and back-office functions of their companies. This age, and financial history. Customer interactions
road map allows company leaders to calibrate are also personalized using demographics
expectations as well as the resources, time, and and past interactions. Most large insurers are
investments needed. halfway along the path of achieving personali-
4 Insurer of the future: Are Asian insurers keeping up with AI advances?
zation at scale,5 prioritizing key metrics such as and create a single, more accurate source of
the following: information.
— Measurement and attrition. Attribute click- — Next-best action. Apply a suite of analytics
through rates, conversion rates, and other models to support customer acquisition, cross-
metrics to different digital channels, and selling, and other sales functions.
measure improvement to help identify customer
preferences and drive personalization to serve — Tailored content. Deliver individually curated,
the customer. personalized content to customers at every
interaction and point of contact.
— Omnichannel breadth and flexibility. Build
customer data platforms that aggregate data As Asian insurers seek to deploy personalization
for individual customers from multiple sources, strategies successfully and augment AI initiatives
5 “Personalization at scale,” December 2, 2022.
Web 2023
AI insurer of the future
EExxhihbiitb 2it o 2f 3
There are four layers to the full-stack AI capability that will define the AI-enabled There are four layers to the full-stack AI capability that will define the AI-enabled
insurer of the future. insurer of the future.
Value created
across stakeholders Phase 4: Microsegmentation and
personalization
Granular view of risk categories
using holistic data sets (eg, external
Increase
open data, connected devices) and
in net
enhanced AI algorithms to improve
new risk profiling and lead generation
value
pools More-personalized offers and
Phase 4 propositions
New segments of traditionally
underserved risks
Phase 3: Continuous underwriting
Phase 3 with prescriptive actions to drive
desired outcomes
Personalized products and packages
based on continuous engagement and
interventions to significantly
influence underwriting quality
Phase 2
Phase 2: Accelerated simplified
underwriting
Dramatic reduction in number
of applicants requiring invasive fluid
and paramedical exams
Phase 1
Significant reduction in number of
questions on application
Existing
value
pools Phase 1: Digital underwriting
Transactional and Personalized All applications submitted digitally
episodic customer and continuous Near STP1 and auto-issue for majority
engagement engagement of products (60–70% or more)
1Segmentation, targeting, and positioning.
McKinsey & Company
Insurer of the future: Are Asian insurers keeping up with AI advances? 5
and investments across the engagement layer, customer analytics and microsegmentation-based
three distribution models are worthy of note. customer personas to personalize lead nurturing.
Based on these analytics, journeys selected were
Digital hybrid agency. Globally, agents continue either “fast” (moved directly to the product list)
to be the largest distribution channel for most or “long” (with content integration), depending on
insurers—but retaining an edge and driving growth customer preferences. Within four to five years,
in agency will require competitive investment in bancassurance penetration almost doubled and
digital and AI solutions. first-year premiums increased by 30 to 40 percent.
It can be done: a global insurer that redesigned Digital D2C distribution via ecosystems. Several
its agency channel to be AI-ready realized an insurtechs are paving the way for embedding
incremental impact of several million dollars over insurance offerings in ecosystems and supporting
the subsequent years. Specifically, the insurer multiproduct offerings on a single platform.
used geospatial network optimization to identify Partnerships with leading players (generally the
geography-specific agents demand and capture top 15 percent) to offer select products with simple
growth opportunities and then used this data to terms, a short process, and fast and convenient
inform its local recruitment strategy. Ramp-up claims can help meet specific user needs for health,
time from newly hired agents to full productivity fell auto, life, accident, and other types of coverage.
significantly, and retention rates rose. The company User data analysis can provide insurers with
increased activation and productivity among customer insights to inform product innovation and
agents with a behavior-driven, next-best-action achieve differentiation in the market.
recommendation engine and customized learning
plans based on agents’ individual performance. A leading insurtech harnessed its parent group’s
traffic and data capability to create a competitive
Another leading insurer in Asia optimized its advantage in the insurance business. Insurance
agency channel by shifting from experience-driven services are embedded in the parent company’s
operations to digital operations. It reformed its mobile app, which has more than a billion monthly
business outlet operation with embedded digital active users. The insurtech integrated its mobile app’s
tools to support and optimize agent activity, ecosystem, expanding its distribution channels and
improving productivity and growth by 5 to 10 providing app users with access to offline medical
percent. The insurer also empowered customer networks not restricted to policyholders.
acquisition and conversions using AI-based audio
and video illustrations of insurance knowledge, Creating an AI-powered decision-making layer
illness explanations, and more to complement Although the insurance industry generates a
agents’ interactions with customers. The insurer’s massive amount of data across various levers and
AI-based assistants support online interactions channels, this data is not, for the most part, being
in real time and record a monthly average of leveraged to build a sophisticated decision-making
approximately 100,000 client-meeting hours, layer that provides a highly personalized customer
enhancing customer experience and acquisition experience. AI technologies could be used to
efficiency. AI-facilitated policy issuance at this complement existing pricing and underwriting
company was more than $100,000 in 2021, and decision making. Specifically, these technologies
agent productivity improved, as measured by a 25 to could help support claims decisions and identify
30 percent increase in net book value per agent. claims leakages by dynamically collecting and
evaluating data points such as adjuster notes,
Digital bancassurance. Bancassurance remains the damage images, text submissions, submitted
second-largest channel driving life insurance sales documents, and patient histories.
globally and, due to legacy bank systems, is perhaps
the most challenging to transform. Nonetheless, a In a mature AI-powered decisioning layer, a suite
leading Asian bank redesigned and simplified the of state-of-the-art analytics tools and edge
insurance journey for its insurance partner, using capabilities is supported by a solid database
6 Insurer of the future: Are Asian insurers keeping up with AI advances?
system with clean, well-structured, analytics- — Claims. One insurer is using AI to help identify
ready data; a defined agile-delivery process; and fraud, waste, and abuse in health insurance
a well-developed, analytical organization deeply claims, driving reductions of more than 5 percent
connected to the business. in overall claims spend.
Advanced analytics can simplify and augment — Servicing. An AI-supported customer complaint
decision making across the entire insurance journey powered by real-time sentiment analysis,
value chain. In our experience, significant gains in smart workflows, and other capabilities helped
efficiency, critical metrics, and more can be realized one insurer significantly reduce the number of
throughout the value chain: repeat complaint calls.
— Marketing. Insurers can use AI-driven customer New technologies such as generative AI amplify the
lifetime value (CLV) management to sift through impact possible across the value chain in very quick
large amounts of data. This can uncover insights order (see sidebar, “The potential of generative AI in
to help identify high-potential customers early insurance”).
enough to take action at all four stages of the
customer life cycle: acquisition, onboarding, Modernizing the core tech and data layer
engagement, and retention. For example, an A modernized core tech and data layer helps uncover
insurer using AI-driven CLV management achieved as well as deliver advanced intelligence through a
a major increase in gross written premiums. seamless front-end experience for customers and
the distribution network. Organizations with mature,
— Underwriting. Using AI to support risk scoring AI-ready core tech and data layers have capabilities
can enable continuous underwriting and achieve across the core tech stack, including a well-defined
multiple desirable outcomes. The insights resulting data infrastructure; data governance; advanced
from continuous engagement, microsegmentation, analytics tooling; technology operating model; a
and personalization, for example, can help develop mature, hybrid cloud infrastructure; API architecture
customized products and packages. and linkages; and advanced cybersecurity and
controls infrastructure.
— Pricing. Employing built-in pricing models that
use machine learning for risk selection and Once the above elements are defined in this
developing data domains for governance can layer, organizations can achieve sustained
help provide granular monitoring of KPIs and transformation by hiring talent to build these
real-time monitoring of emerging loss, pricing differential capabilities in-house, rather than
trends, and shifts in the portfolio risk mix. outsourcing the foundational stack required. In
The potential of generative AI in insurance
Generative AI has dominated recent combating fraud, lowering costs, and scripts for agents and bancassurance
headlines, largely thanks to the growing hyperpersonalizing customer interactions. reps to foster conversions. It could also be
popularity of AI chatbot ChatGPT. The In sales and distribution, generative AI used to provide real-time, personalized
technology could be a significant could be used to create personalized advice and answers to basic customer
contributor to the insurance industry’s marketing content and tailor offerings queries to support customer relationship
efforts to redefine business models across based on customer demographics. It can management.
the value chain, improving efficiency, help create more effective personalized
Insurer of the future: Are Asian insurers keeping up with AI advances? 7
fact, many players have developed distinctive put AI-powered capabilities into action. Transitioning
stacks that have been monetized across insurers. from a traditional linear model to a cross-functional
operating model facilitates expert-driven AI insights
A leading Chinese digital insurer gathered customer generation and adoption at the front line.
behavioral data to develop innovative products,
improve customer profiling and segmentation, The benefits of a cross-functional team structure
and more. Data-driven services also helped the that integrates business, AI, and technology
insurer grow its customer base and refine its data functions can lead to faster alignment, increased
analytics, including dynamic pricing, automated flexibility, and high adoption of AI in the organization.
claim settlement, and enhanced risk management These benefits are exemplified by data-driven
effectiveness, serving more than 500 million organizations such as Google and Netflix that
insured customers in 2021. operate in relatively flat, cross-functional
structures. Most insurers, however, have retained
The company redefined the insurance value chain their traditional organizational structures and
with continuous iterations and upgrades to its implemented AI only on a limited basis. This can
system to improve business efficiency, meet the impede their AI readiness by reducing their capacity
diversified insurance demands of customers, and to implement the transformation needed in other
create value for stakeholders. A 2020 upgrade layers of the AI capability stack.
to its self-developed cloud system increased the
company’s processing capacity by more than As demonstrated by a European banking group
50,000 insurance policies per second. The insurer’s that adopted an agile business model, obstacles
core systems are available to major insurers in to transforming traditional linear structures can
Asia, and the company maintains wide-ranging be overcome, and gains in employee engagement,
partnerships with internet platforms. Insurer efficiency, speed to market, and client experience
customers can connect with various ecosystem can be realized. For example, the banking group was
partners locally and launch a variety of limited able to release software and updates within two or
and scenario-based protection products. This three weeks rather than five or six times each year,
technology arm of the company serves more than and its employee and customer satisfaction scores
30 insurers across life, property and casualty (P&C), rose dramatically in the first 15 months following its
and health, and more than half of its revenue was operational shift.
generated by recurring income.
Optimizing the organization and operating The evolution of insurance: What’s ahead?
model layer In the short term, organizational shifts like those
A modernized organization, operating model, and described above will help carriers prepare for
way-of-working layer supports AI readiness by AI-enabled improvements. In the long term, shifts
providing the right talent, structure, and culture to will prime the insurance industry to realize the kinds
Transitioning from a traditional linear
model to a cross-functional operating
model facilitates expert-driven AI
insights generation and adoption.
8 Insurer of the future: Are Asian insurers keeping up with AI advances?
of AI-enabled gains experienced in other industries. AI offers the potential to enhance insurance
As AI applications advance and become fully protections with insights to support integrated
integrated across the customer industry, the breadth life, health, and wealth solutions and personalized
and nature of services and products that life insurers preventive strategies.
can provide will evolve from simply assessing and
servicing claims to prescribing and preventing The importance of employing strong risk
them (Exhibit 3). From automated processing to management practices in insurance cannot be
predictive analytics and prescriptive algorithms, overstated. The reality is that along with its potential
Web 2023
AI insurer of the future
Exhibit 3
Exhibit 3 of 3
There are four layers to the full-stack AI capability that will define the AI-enabled In the future, life insurers’ focus is likely to evolve toward proactively preventing
insurer of the future. adverse events.
“Assess and service” “Predict and personalize” “Engage and share value” “Prescribe and prevent”
Pre-2020 2020–25 2025–30 2030 and beyond
L M N O
K
J
I
H
G
A B C D E F
A Individuals provide C Information collected G Integrated engagement L Smart contracts
data that is used to from external sources platform facilitates data, enabled by blockchain
assess risks and provide and devices is used to insights, and transactions instantaneously
standard products and proactively assess risk across multiple industries, authorize payments
care suggestions and provide personalized allowing for value sharing from a customer’s
wellness products and between entities financial account
B Policies are priced, care suggestions
purchased, and serviced H Highly dynamic, usage- M Prescriptive
in predefined service- D Majority of financial based insurance products suggestions provide
level agreements and planning is done through proliferate and are tailored interventions for
cohorts algorithmic platforms, to the behavior of individual agents or digital
with agents humanizing consumers channels to actively
advice and building influence outcomes
customer relationships I Lines between life, wealth,
and health products blur N Personalization is
E Advanced algorithms as integrated solutions come used to craft tailored
match leads to best-fit to market strategies and coverage
channel and advisors for each household
J More than 90% of policies
F Pricing sophistication use accelerated and O Robo and DIY channels
increases, with more- automated straight- can approximate
tailored pricing and through underwriting; human empathy and
smaller risk pools manual underwriting ceases conversational
for most products capabilities, facilitating
a 70–90% servicing-
K Agents use smart personal cost reduction and
assistants to optimize their providing a resolution
tasks, as well as AI-enabled within minutes
bots to recommend deals
for clients
McKinsey & Company
Insurer of the future: Are Asian insurers keeping up with AI advances? 9
to revolutionize the industry, AI presents insurance advances can offer new and expanding growth
Find more content like this on the
players with potential challenges related to data opportunities, and lagging behind other sectors
McKinsey Insights App
privacy, inherent biases, interpretability, and could exacerbate challenges to attracting and
more. Privacy breaches, intellectual-property retaining top global talent and meeting evolving
infringements, and job displacements stemming customer expectations. Though complex, a properly
from AI adoption are all too possible and illustrate structured, layered approach to expanding AI
why companies are better positioned for success capacity throughout the insurance value chain can
when following blueprints based on proven models help Asian insurers realize long-standing goals and
and best practices to implement and scale AI. set new benchmarks for success as AI-powered
insurers of the future.
Scan • Download • Personalize
The Asian insurance industry stands at a crossroads
for AI-powered transformation: technological
Violet Chung is a senior partner in McKinsey’s Hong Kong office, Pranav Jain is a consultant in the Singapore office, and
Karthi Purushothaman is a partner in the Chennai office.
The authors wish to thank Radhika Agarwal and Norman Metzner for their contributions to this article.
Copyright © 2023 McKinsey & Company. All rights reserved.
Contact
Violet Chung Karthi Purushothaman
Senior partner, Hong Kong Partner, Chennai
Violet_Chung@McKinsey.com Karthi_Purushothaman@McKinsey.com
Further insights
How the Asian insurance market is How personalization at scale can How Asian insurers can use digital
adapting to the future invigorate Asian insurers marketing to fuel growth
10 Insurer of the future: Are Asian insurers keeping up with AI advances?"
38,mckinsey,the-european-union-ai-act-time-to-start-preparing.pdf,"McKinsey Direct
The European Union AI Act:
Time to start preparing
A successful digital future depends on responsible use of AI. The EU AI Act
marks a significant step in regulating AI systems and could serve as a blueprint
for other jurisdictions.
This article is a collaborative effort by Henning Soller with Anselm Ohme, Chris Schmitz, Malin Strandell-Jansson,
Timothy Chapman, and Zoe Zwiebelmann, representing views from McKinsey’s Risk & Resilience and Digital Practices.
November 2024
Artificial intelligence and generative AI (gen AI) organizations that are best positioned to build
will have a transformative impact on economic digital trust are also more likely than others to see
growth and productivity. This is especially true for annual growth rates of at least 10 percent on their
organizations that expect to make changes to their top and bottom lines.
operations using the technology, a recent McKinsey
survey shows.1 While many organizations embrace these concepts,
some still lack fundamental risk controls for the new
To realize the benefits of AI, organizations technologies. In early 2024, McKinsey surveyed
need the underlying models and their use to 180 EU-based organizations in five sectors about
be secure, safe, and trusted. Implementing the state of AI governance in the European Union.
robust data governance, model-risk, security, Seventy-one percent of respondents said their AI
and individual-rights management is crucial risk governance was less than mature, although
for responsible AI governance. Together, these 65 percent of them said they were already using gen
pillars create a solid foundation for future digital AI (Exhibit 1).
transformation, and digital trust. According to
McKinsey research, trusted organizations have Survey participants expressed concerns in
higher margins and better valuations than less- five high-level categories that mirror important
trusted ones.2 And while only a small contingent considerations for AI: data, model output, security,
of companies are set to deliver this digital trust, third-party, and societal risks.
Web <2024>
E<2x0h2i4b0i6t1 11_EU AI Act Implementation Status>
Exhibit <1> of <6>
Less than 30 percent of survey respondents consider their organization’s AI
risk governance to have some level of maturity.
Maturity of organization’s AI risk governance,1 % of respondents
Overall Financial Energy and Technology, Life sciences Consumer
institutions materials media, and goods
telecom
Series 1 SeriesS 2eries 1 Series 2
Very mature 7Very mature 1V3ery mature 0Very mature 5Very mature 7Very mature 8
Mature M2a1ture M3at0ure M2a6ture 1M5ature 1M5ature 18
Neutral Neu4t0ral Ne3u3tral Ne3u0tral Neut4r5al Neut4r4al 43
Immature Im24mature Im23mature Im22mature Im3m3ature Im3m3ature 18
Very immature 7Very immature 3Very immature V2e2ry immature 3Very immature 0Very immature 15
Note: Figures may not sum to 100%, because of rounding.
Question: How mature is your AI risk governance?
Source: McKinsey EU AI Act Survey, spring 2024 (n = 180 organizations in Europe)
McKinsey & Company
1 “The state of AI in early 2024: Gen AI adoption spikes and starts to generate value,” McKinsey, May 30, 2024.
2 Jim Boehm, Liz Grennan, Alex Singla, and Kate Smaje, “Why digital trust truly matters,” McKinsey, September 12, 2022.
The European Union AI Act: Time to start preparing 2
Some concerns fall into one category, while others Based on the use case, AI systems are defined as
span several. Bias, for example, touches model prohibited, high-risk, or non-high-risk. Rules for
output, data, and third-party risk. Among the other “prohibited” AI, which includes models that are
potential concerns expressed in the survey are manipulative or deceptive, are outlined in Article 5
discrimination, bad outputs, personal-data leakage, of the act. “High risk” systems are those that could
intellectual property misuse, security breaches, and threaten health, safety, and fundamental rights,
malicious use. including those related to critical infrastructure,
education or vocational training, employment,
Given everything that could go wrong with AI, access to essential public or private services and
standards and policy setters are increasing benefits (including credit and health insurance),
efforts to control the risks. Regulators globally are profiling, and law enforcement. “Non high risk”
introducing regulatory frameworks and guidelines, systems, with lower or no regulatory requirements,
including in Canada, China, Japan, South Korea, consist of everything not specifically covered by the
and the United States. The EU AI Act, enacted by other two categories, including AI in video games
the European Union in May 2024, is the world’s first and customer service chatbots.
general AI regulation to go into effect. Being the
first of its kind, the EU AI Act will serve as a test bed
for other guidance to follow. In addition, it will have Early days of implementation efforts
extraterritorial effects because the scope includes AI governance and EU AI Act compliance efforts
AI tools developed in other markets if a tool or its are still in the early days, but organizations
output is applied in the European Union. already have questions. More than 50 percent
of survey respondents said they are not clear on
AI act requirements and are unsure of the risk
Overview of the EU AI Act and
classifications for their AI use cases (Exhibit 2).
its requirements
The EU AI Act aims to “promote human-centric Organizations consider themselves most prepared
and trustworthy AI while protecting health, safety, with regard to data management, ahead of
and fundamental rights.” It will have wide-ranging governance, model risk management, and individual
implications for all affected organizations as the rights (Exhibit 3).
guidance is rolled out over the next two years.
Even so, data management is still a concern. More
The act sets requirements in four areas: than half—57 percent—of respondents said that
governance, data management, model-risk many data governance requirements remain
management, and individual rights. These unaddressed. Specifically, some organizations said
requirements include risk and quality management, there is a lack of clarity in terms of how the General
human oversight, AI system documentation and Data Protection Regulation (GDPR) and the EU AI
transparency, data management, model-risk Act will interact.
governance measures for nondiscrimination and
bias, accuracy, robustness, and cybersecurity. When asked whether they had already met the
act’s requirements for the four areas, less than
Which requirements apply to each organization 10 percent of survey respondents said that they had
depends on two factors: the risk classification and (Exhibit 4).
the role of the organization in the AI value chain,
which includes providers, importers, distributors,
deployers of AI systems, and combinations thereof.
The European Union AI Act: Time to start preparing 3
Web <2024>
E<2x0h2i4b0i6t1 21_EU AI Act Implementation Status>
Exhibit <2> of <6>
Only 4 percent of survey respondents agreed that the EU AI Act
requirements are clear.
Perceived clarity of EU AI Act,1 % of respondents Strongly agree Somewhat agree
Overall Financial Energy and Technology, Life sciences Consumer
institutions materials media, and goods
telecom
SeriesS 2eries 1 SeriesS 2eries 1 SeriesS 2eries 1 SeriesS 2eries 1 Series 2
It is clear what It is clear what It is clear what It is clear what It is clear what It is clear what
the EU AI Act the EU AI Act the EU AI Act the EU AI Act the EU AI Act the EU AI Act
4 32 36 5 33 38 417 22 528 33 4 41 44 10 30 40
will require of will require of will require of will require of will require of will require of
us us us us us us
For our AI use For our AI use For our AI use For our AI use For our AI use For our AI use
cases, it is clear cases, it is clear cases, it is clear cases, it is clear cases, it is clear cases, it is clear
what risk what risk what risk what risk what risk what risk
7 37 44 5 40 45 4 39 43 10 40 50 11 37 48 8 28 35
category they category they category they category they category they category they
fall into under fall into under fall into under fall into under fall into under fall into under
the AI Act the AI Act the AI Act the AI Act the AI Act the AI Act
For our AI use For our AI use For our AI use For our AI use For our AI use For our AI use
cases, it is clear cases, it is clear cases, it is clear cases, it is clear cases, it is clear cases, it is clear
what role our what role our what role our what role our what role our what role our
16 44 59 18 43 60 9 43 52 25 48 7373 19 41 59 10 43 53
organization organization organization organization organization organization
takes in the AI takes in the AI takes in the AI takes in the AI takes in the AI takes in the AI
value chain value chain value chain value chain value chain value chain
Note: Figures may not sum to totals, because of rounding.
Question: To what extent do you agree with the following statements?
Source: McKinsey EU AI Act Survey, spring 2024 (n = 180 organizations in Europe)
McKinsey & Company
Web <2024>
E<2x0h2i4b0i6t1 31_EU AI Act Implementation Status>
Exhibit <3> of <6>
Survey respondents consider their organizations somewhat prepared
across various dimensions of the EU AI Act.
Self-assessment of EU AI Act governance maturity, averages and ranges
Range of responses Average maturity
Model-risk management
Individual rights
Governance
Data management
Very immature Very mature
Source: McKinsey EU AI Act Survey, spring 2024 (n = 180 organizations in Europe)
McKinsey & Company
The European Union AI Act: Time to start preparing 4
Web <2024>
<E2x0h2i4b0i6t1 41_EU AI Act Implementation Status>
Exhibit <4> of <6>
Few of the key requirements of the EU AI Act are fully addressed by more
than about 10 percent of organizations.
Fully addressed, % Somewhat addressed, % Split not available
Governance Model-risk management¹
Overall implementation Overall implementation Series 1 Series 2 Series 3
7 22 44
Data governance controls Monitoring and logging
8 29 63
AI principles and norms Security and accuracy techniques
9 26 49
Governance organization Robustness techniques
9 23 45
Design quality control and verification Standardized technical documentation
10 18 41
Definition of accountability Human in the loop
7 19 38
Third-party risk management Predefined performance metrics
6 20 37
AI risk management Standardized instructions of use
3 22 34
Strategy for regulatory compliance
4 21 0
Employee upskilling .
4 21 0
Data management Individual rights
Overall implementation Overall implementation Series 1 Series 2 Series 3
6 27 7 22
Data collection GDPR rights respected by AI
12 36 17 34
Ensuring representative data AI system informs people of interaction
5 32 8 22
Data preparation Explanation of AI-enabled decisions
9 27 3 22
Appropriate statistical properties Tailored user instructions
6 27 2 23
AI system design choices AI evaluation metrics reported
3 30 3 18
Formulation of assumptions AI systems mark content as AI made
4 22 6 14
Examination of biases
3 17
1Based on proportion of organizations having technically implemented these measures, not the level at which they have addressed them.
Source: McKinsey EU AI Act Survey, spring 2024 (n = 180 organizations in Europe)
McKinsey & Company
The European Union AI Act: Time to start preparing 5
Nearly half of respondents said they had not yet implemented strategies for regulatory compliance
allocated any budget for AI Act implementation, or AI risk management.
and most that have allocated a budget have set
aside €2 million or less (Exhibit 5). There are Risk governance. About three in ten respondents
many reasons organizations aren’t spending have developed a mature AI risk governance
yet. Some respondents have likely not started structure, and only a third said they have a
responding to AI Act requirements because the governance organization. Further, about 40 percent
rules are so new. Others are focused on aligning lack clear definitions of accountabilities for AI,
their AI remediation efforts to their existing and about 10 percent say they have fully addressed
governance structure. Still others are unaware of AI principles and norms.
the upcoming regulatory requirements.
Encouragingly, nearly half of respondents said they
have separate usage guidelines, and more than a
Key challenges facing organizations third have input and output guardrails in place for
Respondents cited a variety of challenges to their external AI models. This likely is a consequence
efforts to meet the requirements of the AI Act. of protecting business-sensitive information and
intellectual property as organizations rapidly
Complexity. In some cases, organizations are deployed gen AI tools.
stalled as they seek clarity and the resources to
prepare for complex regulations and technology. Third-party risk management is also a concern.
Only one in four survey respondents have Less than a third of organizations said they have
appropriately addressed AI-related third-party risk.
Web <2024>
E<2x0h2i4b0i6t 151_EU AI Act Implementation Status>
Exhibit <5> of <6>
Close to 50 percent of organizations have not yet allocated resources for
EU AI Act implementation efforts.
Amount budgeted for EU AI Act implementation efforts,1 % of respondents
Overall Financial Energy and Technology, Life sciences Consumer
institutions materials media, and goods
Series 1 Series 2 telecom
No budget No b 4u 7dget No 3b 8udget No b 4ud 8get No b 4ud 8get SeNrieos b 14ud 8getSeriesS 2eries 415 Series 2
allocated yet allocated yet allocated yet allocated yet allocated yet allocated yet
Up to €1 million U2p2 to €1 million 1U5p to €1 million U17p to €1 million Up3 0to €1 million U2p2 to €1 million 28
€1–€2 million 1€61–€2 million €21–5€2 million €171–€2 million 5€1–€2 million 7€1–€2 million 23
€3–€5 million 6€3–€5 million 5€3–€5 million 1€33–€5 million 1€33–€5 million 4€3–€5 million 0
€6–€10 million 5€6–€10 million 10€6–€10 million 4€6–€10 million 0€6–€10 million 1€16–€10 million 3
>€10 million 4>€10 million 8>€10 million 0>€10 million 5>€10 million 7>€10 million 3
Note: Figures may not sum to 100%, because of rounding.
Question: How much have you budgeted for EU AI Act implementation efforts?
Source: McKinsey EU AI Act Survey, spring 2024 (n = 180 organizations in Europe)
McKinsey & Company
The European Union AI Act: Time to start preparing 6
Some have implemented GDPR-related controls, defining standards for testing the outputs of
technical guardrails, and model fine-tuning for gen AI models. For self-developed models,
external models. But just 16 percent of respondents respondents said they commonly use continuous
are conducting red-teaming efforts, while some said code integration and deployment, model versioning,
they are rolling back relationships with suppliers and documentation to ensure quality.
while rules and obligations for general-purpose AI
become applicable throughout 2025. Thirty-eight percent of respondents use
“human in the loop” processes, while 30 percent
Data governance. Only 18 percent of respondents use technically responsible AI tooling. Model
said their organizations have mature technical risk performance monitoring, logging, and user
management processes for AI systems in place. In feedback, together with incident detection and
addition, few have robust models or security and management, are the most common measures
accuracy techniques. However, about 75 percent used to ensure quality after deployment.
of respondents indicated they had advanced cyber
controls and data protection measures in place. Talent. Getting the right people to run and manage
AI is proving difficult, too. The talent shortage is
The act introduces requirements for data especially prominent for technical staff but also
management. These cover choices in designing exists for legal personnel. This is a major concern
systems, formulating assumptions, collecting not only for businesses but also for regulatory
and preparing data, examining bias, ensuring authorities that have concerns about competent
representative data use, and including the monitoring and enforcement of the AI Act. Only a
appropriate statistical properties. More than quarter of respondents upskill employees, which
half of survey respondents said they have not takes time and investment.
yet addressed these requirements. Less than
20 percent have addressed bias. Other. Perhaps surprisingly, respondents did
not cite cost, financial resources, or ethical
What models do with the data is another area concerns as top reasons for the slow progress
of concern. Many respondents cited difficulty in on implementation (Exhibit 6).
Given the complexity of the EU AI
Act and the effort needed to comply, it
would be prudent for organizations
to accelerate their planning now.
The European Union AI Act: Time to start preparing 7
Web <2024>
E<2x0h2i4b0i6t1 61_EU AI Act Implementation Status>
Exhibit <6> of <6>
Key challenges of implementing the EU AI Act relate to unclear obligations,
complexity, and talent gaps.
Key challenges facing organizations in implementing the EU AI Act,1 % of respondents
Unclear obligations 81
SeriesS 2Ceorimesp 1lexity SeriesS 2eries 1 SeriesS 2eries 1 SeriesS 2eries 1 Series 2 69
Data governance 57
Skills and talent gap 35
Change management 27
Technical resources 13
Cost 11
Financial resources 8
Ethical concerns 6
Source: McKinsey EU AI Act Survey, spring 2024 (n = 180 organizations in Europe)
McKinsey & Company
The time to act Organizations should embrace a “define your
world” approach, which prioritizes transparency
Given the complexity of the EU AI Act and the
in model use, stakeholders, risks, and regulations.
effort needed to comply, it would be prudent
The EU AI Act has set out requirements mainly for
for organizations to accelerate their planning
high-risk models, so a risk categorization of the
now. While the act outlines implementation
model landscape will help structure the work going
stages and staggered compliance deadlines,
forward and control the level of effort.
those with experience implementing GDPR
understand that waiting can create chaos as
Defining a target state for governance and
those deadlines approach.
compliance efforts can help organizations build
road maps that thoroughly consider strategy, risk
Managing the scope of an organization’s AI efforts
appetite, organizational structure, technology,
is important. Organizations that align development
policy, and tooling. And organizations can continue
to governance practices manage to limit the number
to get better through a process of ongoing
of models they use, generally to fewer than 20.
improvement, using existing best practices and
A clear governance structure can also limit teams’
frameworks as a guide. Ensuring cross-functional
frustrations in fielding ad hoc requests and trying
collaboration and input on ethical and risk
to get support.
considerations is paramount, so if current risk
The European Union AI Act: Time to start preparing 8
functions are not equipped, separate action on top But before that happens, the act’s regulators will
Find more content like this on the
of existing governance may be required. need to further clarify their expectations and work
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with the industry to find pragmatic implementation
To achieve compliance, organizations will need solutions in an environment of limited resources.
the necessary talent, resources, and relevant Responsible and trustworthy AI is a prerequisite
KPIs to measure progress. AI is evolving quickly, to defining a new digital future. By embracing
so it is essential to stay on top of changes. The responsible AI governance, companies can spur
EU AI Act represents a significant step toward innovation with the trust of consumers, competitors,
regulating AI systems and ensuring responsible AI shareholders, and society behind them.
governance and could serve as a blueprint for other
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jurisdictions globally.
This article originally appeared in the August/September edition of The RMA Journal.
Henning Soller is a partner in McKinsey’s Frankfurt office; Anselm Ohme is a consultant in the Berlin office, where Chris
Schmitz is a data science fellow; Malin Strandell-Jansson is an alumna of the Stockholm office; Timothy Chapman is an
analyst in the Wroclaw office; and Zoe Zwiebelmann is a consultant in the Hamburg office.
The authors wish to thank Andreas Kremer, Angela Luget, Angie Selzer, Artem Avdeed, and Silvia Tilea for their contributions
to this article.
Designed by McKinsey Global Publishing
Copyright © 2024 McKinsey & Company. All rights reserved.
The European Union AI Act: Time to start preparing 9"
39,mckinsey,What-every-CEO-should-know-about-generative-AI.pdf,"QuantumBlack, Al by McKinsey
What every CEO should
know about generative AI
Generative AI is evolving at record speed while CEOs are still learning the technology’s
business value and risks. Here, we offer some of the generative AI essentials.
This article is a collaborative effort by Michael Chui, Roger Roberts, Tanya Rodchenko, Alex Singla, Alex Sukharevsky, Lareina
Yee, and Delphine Zurkiya, representing views from the McKinsey Technology Council and QuantumBlack, AI by McKinsey,
which are both part of McKinsey Digital.
Image created by Chris Grava / Darby Films using a node-based visual programming language
May 2023
Amid the excitement surrounding generative A generative AI tool might suggest upselling
AI since the release of ChatGPT, Bard, Claude, opportunities to the salesperson in real time based
Midjourney, and other content-creating tools, CEOs on the actual content of the conversation, drawing
are understandably wondering: Is this tech hype, or from internal customer data, external market trends,
a game-changing opportunity? And if it is the latter, and social media influencer data. At the same time,
what is the value to my business? generative AI could offer a first draft of a sales pitch
for the salesperson to adapt and personalize.
The public-facing version of ChatGPT reached 100
million users in just two months. It democratized AI The preceding example demonstrates the
in a manner not previously seen while becoming by implications of the technology on one job role. But
far the fastest-growing app ever. Its out-of-the-box nearly every knowledge worker can likely benefit
accessibility makes generative AI different from all from teaming up with generative AI. In fact, while
AI that came before it. Users don’t need a degree generative AI may eventually be used to automate
in machine learning to interact with or derive value some tasks, much of its value could derive from
from it; nearly anyone who can ask questions can how software vendors embed the technology
use it. And, as with other breakthrough technologies into everyday tools (for example, email or word-
such as the personal computer or iPhone, one processing software) used by knowledge workers.
generative AI platform can give rise to many Such upgraded tools could substantially increase
applications for audiences of any age or education productivity.
level and in any location with internet access.
CEOs want to know if they should act now—and,
All of this is possible because generative AI if so, how to start. Some may see an opportunity
chatbots are powered by foundation models, which to leapfrog the competition by reimagining
contain expansive neural networks trained on how humans get work done with generative AI
vast quantities of unstructured, unlabeled data applications at their side. Others may want to
in a variety of formats, such as text and audio. exercise caution, experimenting with a few use
Foundation models can be used for a wide range cases and learning more before making any
of tasks. In contrast, previous generations of AI large investments. Companies will also have to
models were often “narrow,” meaning they could assess whether they have the necessary technical
perform just one task, such as predicting customer expertise, technology and data architecture,
churn. One foundation model, for example, can operating model, and risk management
create an executive summary for a 20,000-word processes that some of the more transformative
technical report on quantum computing, draft a implementations of generative AI will require.
go-to-market strategy for a tree-trimming business,
and provide five different recipes for the ten The goal of this article is to help CEOs and their
ingredients in someone’s refrigerator. The downside teams reflect on the value creation case for
to such versatility is that, for now, generative AI can generative AI and how to start their journey. First,
sometimes provide less accurate results, placing we offer a generative AI primer to help executives
renewed attention on AI risk management. better understand the fast-evolving state of AI
and the technical options available. The next
With proper guardrails in place, generative AI can section looks at how companies can participate in
not only unlock novel use cases for businesses generative AI through four example cases targeted
but also speed up, scale, or otherwise improve toward improving organizational effectiveness.
existing ones. Imagine a customer sales call, for These cases reflect what we are seeing among
example. A specially trained AI model could suggest early adopters and shed light on the array of options
upselling opportunities to a salesperson, but across the technology, cost, and operating model
until now those were usually based only on static requirements. Finally, we address the CEO’s vital
customer data obtained before the start of the call, role in positioning an organization for success with
such as demographics and purchasing patterns. generative AI.
2 What every CEO should know about generative AI
Excitement around generative AI is palpable, A generative AI primer
and C-suite executives rightfully want to move Generative AI technology is advancing quickly
ahead with thoughtful and intentional speed. (Exhibit 1). The release cycle, number of start-
We hope this article offers business leaders a ups, and rapid integration into existing software
balanced introduction into the promising world of applications are remarkable. In this section,
generative AI. we will discuss the breadth of generative AI
Exhibit 1
Generative AI has been evolving at a rapid pace.
Timeline of some of the major large language model (LLM) developments in the months following
ChatGPT’s launch
What every CEO should know about generative AI 3
applications and provide a brief explanation of the Answer questions
technology, including how it differs from traditional AI.
— Employees of a manufacturing company
More than a chatbot can ask a generative AI–based “virtual
Generative AI can be used to automate, augment, and expert” technical questions about operating
accelerate work. For the purposes of this article, we procedures.
focus on ways generative AI can enhance work rather
than on how it can replace the role of humans. — A consumer can ask a chatbot questions about
how to assemble a new piece of furniture.
While text-generating chatbots such as ChatGPT
have been receiving outsize attention, generative Draft
AI can enable capabilities across a broad range
of content, including images, video, audio, and — A software developer can prompt generative AI
computer code. And it can perform several functions to create entire lines of code or suggest ways to
in organizations, including classifying, editing, complete partial lines of existing code.
summarizing, answering questions, and drafting
new content. Each of these actions has the potential — A marketing manager can use generative AI to
to create value by changing how work gets done draft various versions of campaign messaging.
at the activity level across business functions and
workflows. Following are some examples. As the technology evolves and matures, these
kinds of generative AI can be increasingly
Classify integrated into enterprise workflows to automate
tasks and directly perform specific actions (for
— A fraud-detection analyst can input transaction example, automatically sending summary notes
descriptions and customer documents into a at the end of meetings). We already see tools
generative AI tool and ask it to identify fraudulent emerging in this area.
transactions.
How generative AI differs from other kinds of AI
— A customer-care manager can use generative AI As the name suggests, the primary way in which
to categorize audio files of customer calls based generative AI differs from previous forms of AI
on caller satisfaction levels. or analytics is that it can generate new content
efficiently, often in “unstructured” forms (for
Edit example, written text or images) that aren’t
naturally represented in tables with rows and
— A copywriter can use generative AI to correct columns (see sidebar “Glossary” for a list of terms
grammar and convert an article to match a client’s associated with generative AI).
brand voice.
The underlying model that enables generative AI
— A graphic designer can remove an outdated logo to work is called a foundation model. Transformers
from an image. are key components of foundation models—GPT
actually stands for generative pre-trained
Summarize transformer. A transformer is a type of artificial
neural network that is trained using deep learning,
— A production assistant can create a highlight a term that alludes to the many (deep) layers within
video based on hours of event footage. neural networks. Deep learning has powered many
of the recent advances in AI.
— A business analyst can create a Venn diagram
that summarizes key points from an executive’s However, some characteristics set foundation
presentation. models apart from previous generations of deep
4 What every CEO should know about generative AI
Glossary
Application programming interface (API) is a way to programmatically access (usually external) models, data sets, or other pieces of
software.
Artificial intelligence (AI) is the ability of software to perform tasks that traditionally require human intelligence.
Deep learning is a subset of machine learning that uses deep neural networks, which are layers of connected “neurons” whose connec-
tions have parameters or weights that can be trained. It is especially effective at learning from unstructured data such as images, text,
and audio.
Fine-tuning is the process of adapting a pretrained foundation model to perform better in a specific task. This entails a relatively short
period of training on a labeled data set, which is much smaller than the data set the model was initially trained on. This additional training
allows the model to learn and adapt to the nuances, terminology, and specific patterns found in the smaller data set.
Foundation models (FM) are deep learning models trained on vast quantities of unstructured, unlabeled data that can be used for a wide
range of tasks out of the box or adapted to specific tasks through fine-tuning. Examples of these models are GPT-4, PaLM, DALL·E 2, and
Stable Diffusion.
Generative AI is AI that is typically built using foundation models and has capabilities that earlier AI did not have, such as the ability to
generate content. Foundation models can also be used for non-generative purposes (for example, classifying user sentiment as negative
or positive based on call transcripts) while offering significant improvement over earlier models. For simplicity, when we refer to genera-
tive AI in this article, we include all foundation model use cases.
Graphics processing units (GPUs) are computer chips that were originally developed for producing computer graphics (such as for
video games) and are also useful for deep learning applications. In contrast, traditional machine learning and other analyses usually run
on central processing units (CPUs), normally referred to as a computer’s “processor.”
Large language models (LLMs) make up a class of foundation models that can process massive amounts of unstructured text and learn
the relationships between words or portions of words, known as tokens. This enables LLMs to generate natural language text, perform-
ing tasks such as summarization or knowledge extraction. GPT-4 (which underlies ChatGPT) and LaMDA (the model behind Bard) are
examples of LLMs.
Machine learning (ML) is a subset of AI in which a model gains capabilities after it is trained on, or shown, many example data points.
Machine learning algorithms detect patterns and learn how to make predictions and recommendations by processing data and experi-
ences, rather than by receiving explicit programming instruction. The algorithms also adapt and can become more effective in response
to new data and experiences.
MLOps refers to the engineering patterns and practices to scale and sustain AI and ML. It encompasses a set of practices that span the
full ML life cycle (data management, development, deployment, and live operations). Many of these practices are now enabled or opti-
mized by supporting software (tools that help to standardize, streamline, or automate tasks).
Prompt engineering refers to the process of designing, refining, and optimizing input prompts to guide a generative AI model toward
producing desired (that is, accurate) outputs.
Structured data are tabular data (for example, organized in tables, databases, or spreadsheets) that can be used to train some machine
learning models effectively.
Transformers are key components of foundation models. They are artificial neural networks that use special mechanisms called
“attention heads” to understand context in sequential data, such as how a word is used in a sentence.
Unstructured data lack a consistent format or structure (for example, text, images, and audio files) and typically require more advanced
techniques to extract insights.
What every CEO should know about generative AI 5
learning models. To start, they can be trained on companies should be careful of integrating
extremely large and varied sets of unstructured generative AI without human oversight in
data. For example, a type of foundation model applications where errors can cause harm or
called a large language model can be trained on where explainability is needed. Generative AI
vast amounts of text that is publicly available on the is also currently unsuited for directly analyzing
internet and covers many different topics. While large amounts of tabular data or solving advanced
other deep learning models can operate on sizable numerical-optimization problems. Researchers are
amounts of unstructured data, they are usually working hard to address these limitations.
trained on a more specific data set. For example,
a model might be trained on a specific set of The emerging generative AI ecosystem
images to enable it to recognize certain objects in While foundation models serve as the “brain” of
photographs. generative AI, an entire value chain is emerging
to support the training and use of this technology
In fact, other deep learning models often can (Exhibit 2).¹ Specialized hardware provides the
perform only one such task. They can, for example, extensive compute power needed to train the
either classify objects in a photo or perform another models. Cloud platforms offer the ability to tap
function such as making a prediction. In contrast, this hardware. MLOps and model hub providers
one foundation model can perform both of these offer the tools, technologies, and practices an
functions and generate content as well. Foundation organization needs to adapt a foundation model
models amass these capabilities by learning and deploy it within its end-user applications.
patterns and relationships from the broad training Many companies are entering the market to offer
data they ingest, which, for example, enables them applications built on top of foundation models
to predict the next word in a sentence. That’s how that enable them to perform a specific task, such
ChatGPT can answer questions about varied topics as helping a company’s customers with service
and how DALL·E 2 and Stable Diffusion can produce issues.
images based on a description.
The first foundation models required high levels
Given the versatility of a foundation model, of investment to develop, given the substantial
companies can use the same one to implement computational resources required to train them
multiple business use cases, something rarely and the human effort required to refine them. As a
achieved using earlier deep learning models. A result, they were developed primarily by a few tech
foundation model that has incorporated information giants, start-ups backed by significant investment,
about a company’s products could potentially be and some open-source research collectives (for
used both for answering customers’ questions and example, BigScience). However, work is under way
for supporting engineers in developing updated on both smaller models that can deliver effective
versions of the products. As a result, companies results for some tasks and training that’s more
can stand up applications and realize their benefits efficient. This could eventually open the market
much faster. to more entrants. Some start-ups have already
succeeded in developing their own models—for
However, because of the way current foundation example, Cohere, Anthropic, and AI21 Labs build
models work, they aren’t naturally suited to all and train their own large language models.
applications. For example, large language models
can be prone to “hallucination,” or answering
questions with plausible but untrue assertions Putting generative AI to work
(see sidebar “Using generative AI responsibly”). CEOs should consider exploration of generative
Additionally, the underlying reasoning or sources AI a must, not a maybe. Generative AI can create
for a response are not always provided. This means value in a wide range of use cases. The economics
1 For more, see “Exploring opportunities in the generative AI value chain,” McKinsey, April 26, 2023.
6 What every CEO should know about generative AI
Web <year>
<Title>
EExxhihbiitb <itx >2 of <x>
A value chain supporting generative AI systems is developing quickly.
A value chain supporting generative AI systems is developing quickly.
Generative AI value chain
Services
Services around specialized knowledge on how to leverage generative AI (eg, training, feedback, and
reinforcement learning)
Applications
B2B or B2C products that use foundation models either largely as is or fine-tuned to a particular use case
Model hubs and MLOps
Tools to curate, host, fine-tune, or manage the foundation models (eg, storefronts between applications and
foundation models)
Foundation models
Core models on which generative AI applications can be built
Cloud platforms
Platforms to provide access to computer hardware
Specialized hardware
Accelerator chips optimized for training and running the models (eg, graphics processing units, or GPUs)
McKinsey & Company
and technical requirements to start are not Much of the use (although not necessarily all of
prohibitive, while the downside of inaction could the value) from generative AI in an organization
be quickly falling behind competitors. Each CEO will come from workers employing features
should work with the executive team to reflect on embedded in the software they already have.
where and how to play. Some CEOs may decide that Email systems will provide an option to write the
generative AI presents a transformative opportunity first drafts of messages. Productivity applications
for their companies, offering a chance to reimagine will create the first draft of a presentation based
everything from research and development to on a description. Financial software will generate
marketing and sales to customer operations. Others a prose description of the notable features
may choose to start small and scale later. Once in a financial report. Customer-relationship-
the decision is made, there are technical pathways management systems will suggest ways to interact
that AI experts can follow to execute the strategy,
depending on the use case.
What every CEO should know about generative AI 7
Using generative AI responsibly
Generative AI poses a variety of risks. CEOs will want to design their teams and processes to mitigate those risks from the start—not
only to meet fast-evolving regulatory requirements but also to protect their business and earn consumers’ digital trust (we offer recom-
mendations on how to do so later in this article).¹
Fairness: Models may generate algorithmic bias due to imperfect training data or decisions made by the engineers developing the
models.
Intellectual property (IP): Training data and model outputs can generate significant IP risks, including infringing on copyrighted, trade-
marked, patented, or otherwise legally protected materials. Even when using a provider’s generative AI tool, organizations will need to
understand what data went into training and how it’s used in tool outputs.
Privacy: Privacy concerns could arise if users input information that later ends up in model outputs in a form that makes individuals
identifiable. Generative AI could also be used to create and disseminate malicious content such as disinformation, deepfakes, and hate
speech.
Security: Generative AI may be used by bad actors to accelerate the sophistication and speed of cyberattacks. It also can be manipu-
lated to provide malicious outputs. For example, through a technique called prompt injection, a third party gives a model new instruc-
tions that trick the model into delivering an output unintended by the model producer and end user.
Explainability: Generative AI relies on neural networks with billions of parameters, challenging our ability to explain how any given
answer is produced.
Reliability: Models can produce different answers to the same prompts, impeding the user’s ability to assess the accuracy and reliabil-
ity of outputs
Organizational impact: Generative AI may significantly affect the workforce, and the impact on specific groups and local communities
could be disproportionately negative.
Social and environmental impact: The development and training of foundation models may lead to detrimental social and environ-
mental consequences, including an increase in carbon emissions (for example, training one large language model can emit about 315
tons of carbon dioxide).²
1 Jim Boehm, Liz Grennan, Alex Singla, and Kate Smaje, “Why digital trust truly matters,” McKinsey, September 12, 2022.
2 Ananya Ganesh, Andrew McCallum, and Emma Strubell, “Energy and policy considerations for deep learning in NLP,” Proceedings of the 57th Annual Meeting of the
Association for Computational Linguistics, June 5, 2019.
with customers. These features could accelerate the are using generative AI today to reshape how work is
productivity of every knowledge worker. done within their organization.² The examples range
from those requiring minimal resources to resource-
But generative AI can also be more transformative intensive undertakings. (For a quick comparison
in certain use cases. Following, we look at four of these examples and more technical detail, see
examples of how companies in different industries Exhibit 3.)
2 These examples are amalgamations of cases culled from our client work and public examples rather than reflective of exact events in one
particular company.
8 What every CEO should know about generative AI
Exhibit 3
The organizational requirements for generative AI range from low to high,
depending on the use case.
Low High
Use case Technical Costs Tech talent Proprietary data Process
example pathway adjustments
Changing the Use Many SaaS tools Little technical Because the Processes
work of software software- offer fixed-fee talent is needed— model is used as largely remain
engineering as-a- subscriptions of potentially for is, no proprietary the same, but
service $10 to $30 per selecting the right data is needed workers should
(SaaS) tool user per month; solution and light systematically
some products integration work check model
have usage-based results for
pricing accuracy and
appropriateness
Helping Build Up-front investment Software Because the Processes may
relationship software is needed to develop development, model is used as be needed to
managers keep layers on the user interface, product is, no proprietary enable storage
up with the model API integrate the management, and data is needed of prompts and
pace of public solution, and build database integration results, and
information and postprocessing capabilities are guardrails may
data layers needed, which be needed to
require at least 1 data limit usage for
Running costs for scientist, machine risk or cost
API usage and learning engineer, reasons
software data engineer,
maintenance designer, and front-
end developer
Freeing up Fine-tune Initial costs Experienced A proprietary, Processes for
customer open- ~2x more than data science and labeled data triaging and
support source building on API engineering team set is required escalating
representatives’ model due to increased with machine to fine-tune the issues to
time for higher- in-house human capital learning operations model, although humans are
value activities costs required for (MLOps) knowledge in some cases it needed, as
data cleaning and and resources to can be relatively well as periodic
labeling and model check or create small assessments of
fine-tuning labeled data needed model safety
Higher running
costs for model
maintenance and
cloud computing
Accelerating Train a Initial costs Requires large Foundation Including the
the pace at foundation ~10–20x more data science and models can be above, when
which research model than building on engineering team trained on large training on
scientists from API due to up-front with PhD-level publicly available external data,
can identify scratch human capital and knowledge of data, although thorough
relevant cell tech infrastructure subject matter, long-term legal review
features for costs best-practice differentiation is needed to
drug discovery MLOps, and data comes from prevent IP
Running costs and infrastructure adding owned issues
for model management skills labeled or
maintenance and unlabeled data
cloud computing (which is easier to
similar to the above collect)
What every CEO should know about generative AI 9
Changing the work of software engineering
The first example is a relatively low-complexity case with immediate productivity benefits because it
uses an off-the-shelf generative AI solution and doesn’t require in-house customization.
The biggest part of a software engineer’s job is writing code. It’s a labor-intensive process that requires
extensive trial and error and research into private and public documentation. At this company, a
shortage of skilled software engineers has led to a large backlog of requests for features and bug fixes.
To improve engineers’ productivity, the company is implementing an AI-based code-completion
product that integrates with the software the engineers use to code. This allows engineers to write code
descriptions in natural language, while the AI suggests several variants of code blocks that will satisfy
the description. Engineers can select one of the AI’s proposals, make needed refinements, and click on
it to insert the code.
Our research has shown that such tools can speed up a developer’s code generation by as much as 50
percent. It can also help in debugging, which may improve the quality of the developed product. But
today, generative AI cannot replace skilled software engineers. In fact, more-experienced engineers
appear to reap the greatest productivity benefits from the tools, with inexperienced developers seeing
less impressive—and sometimes negative—results. A known risk is that the AI-generated code may
contain vulnerabilities or other bugs, so software engineers must be involved to ensure the quality and
security of the code (see the final section in this article for ways to mitigate risks).
The cost of this off-the-shelf generative AI coding tool is relatively low, and the time to market is short
because the product is available and does not require significant in-house development. Cost varies
by software provider, but fixed-fee subscriptions range from $10 to $30 per user per month. When
choosing a tool, it’s important to discuss licensing and intellectual property issues with the provider to
ensure the generated code doesn’t result in violations.
Supporting the new tool is a small cross-functional team focused on selecting the software provider and
monitoring performance, which should include checking for intellectual property and security issues.
Implementation requires only workflow and policy changes. Because the tool is purely off-the-shelf
software as a service (SaaS), additional computing and storage costs are minimal or nonexistent.
10 What every CEO should know about generative AI
Helping relationship managers keep up with the pace of public information and data
Companies may decide to build their own generative AI applications, leveraging foundation models
(via APIs or open models), instead of using an off-the-shelf tool. This requires a step up in investment
from the previous example but facilitates a more customized approach to meet the company’s specific
context and needs.
In this example, a large corporate bank wants to use generative AI to improve the productivity of
relationship managers (RMs). RMs spend considerable time reviewing large documents, such as
annual reports and transcripts of earnings calls, to stay informed about a client’s situation and
priorities. This enables the RM to offer services suited to the client’s particular needs.
The bank decided to build a solution that accesses a foundation model through an API. The solution
scans documents and can quickly provide synthesized answers to questions posed by RMs. Additional
layers around the foundation model are built to streamline the user experience, integrate the tool
with company systems, and apply risk and compliance controls. In particular, model outputs must be
verified, much as an organization would check the outputs of a junior analyst, because some large
language models have been known to hallucinate. RMs are also trained to ask questions in a way that
will provide the most accurate answers from the solution (called prompt engineering), and processes
are put in place to streamline validation of the tool’s outputs and information sources.
In this instance, generative AI can speed up an RM’s analysis process (from days to hours), improve job
satisfaction, and potentially capture insights the RM might have otherwise overlooked.
The development cost comes mostly from the user interface build and integrations, which require
time from a data scientist, a machine learning engineer or data engineer, a designer, and a front-end
developer. Ongoing expenses include software maintenance and the cost of using APIs. Costs depend
on the model choice and third-party vendor fees, team size, and time to minimum viable product.
What every CEO should know about generative AI 11
Freeing up customer support representatives for higher-value activities
The next level of sophistication is fine-tuning a foundation model. In this example, a company uses a
foundation model optimized for conversations and fine-tunes it on its own high-quality customer chats and
sector-specific questions and answers. The company operates in a sector with specialized terminology (for
example, law, medicine, real estate, and finance). Fast customer service is a competitive differentiator.
This company’s customer support representatives handle hundreds of inbound inquiries a day. Response
times were sometimes too high, causing user dissatisfaction. The company decided to introduce a
generative AI customer-service bot to handle most customer requests. The goal was a swift response in
a tone that matched the company brand and customer preferences. Part of the process of fine-tuning
and testing the foundation model includes ensuring that responses are aligned with the domain-specific
language, brand promise, and tone set for the company; ongoing monitoring is required to verify the
performance of the system across multiple dimensions, including customer satisfaction.
The company created a product road map consisting of several waves to minimize potential model errors.
In the first wave, the chatbot was piloted internally. Employees were able to give “thumbs up” or “thumbs
down” answers to the model’s suggestions, and the model was able to learn from these inputs. As a
next step, the model “listened” to customer support conversations and offered suggestions. Once the
technology was tested sufficiently, the second wave began, and the model was shifted toward customer-
facing use cases with a human in the loop. Eventually, when leaders are completely confident in the
technology, it can be largely automated.
In this case, generative AI freed up service representatives to focus on higher-value and complex customer
inquiries, improved representatives’ efficiency and job satisfaction, and increased service standards and
customer satisfaction. The bot has access to all internal data on the customer and can “remember” earlier
conversations (including phone calls), representing a step change over current customer chatbots.
To capture the benefits, this use case required material invest"
40,mckinsey,moving-past-gen-ais-honeymoon-phase-seven-hard-truths-for-cios-to-get-from-pilot-to-scale.pdf,"Moving past gen AI’s honeymoon
phase: Seven hard truths for
CIOs to get from pilot to scale
Getting to scale requires CIOs to focus on fewer things but do them better.
This article is a collaborative effort by Aamer Baig, Douglas Merrill, and Megha Sinha, with Danesha Mead and
Stephen Xu, representing views from McKinsey Technology and QuantumBlack, AI by McKinsey.
© Getty Images
May 2024
The honeymoon phase of generative AI (gen AI) We explored many of the key initial technology
is over. As most organizations are learning, it is issues in a previous article.2 In this article, we want
relatively easy to build gee-whiz gen AI pilots, but to explore seven truths about scaling gen AI for the
turning them into at-scale capabilities is another “Shaper” approach, in which companies develop
story. The difficulty in making that leap goes a long a competitive advantage by connecting large
way to explaining why just 11 percent of companies language models (LLMs) to internal applications
have adopted gen AI at scale, according to our and data sources (see sidebar “Three approaches
latest tech trends research.1 to using gen AI” for more). Here are seven things
that Shapers need to know and do:
This maturing phase is a welcome development
because it gives CIOs an opportunity to turn gen 1. Eliminate the noise, and focus on the signal.
AI’s promise into business value. Yet while most Be honest about what pilots have worked.
CIOs know that pilots don’t reflect real-world Cut down on experiments. Direct your efforts
scenarios—that’s not really the point of a pilot, after toward solving important business problems.
all—they often underestimate the amount of work
that needs to be done to get gen AI production 2. It’s about how the pieces fit together, not the
ready. Ultimately, getting the full value from gen AI pieces themselves. Too much time is spent
requires companies to rewire how they work, and assessing individual components of a gen AI
putting in place a scalable technology foundation engine. Much more consequential is figuring
is a key part of that process. out how they work together securely.
1 “McKinsey Technology Trends Outlook 2024,” forthcoming on McKinsey.com.
2 “Technology’s generational moment with generative AI: A CIO and CTO guide,” McKinsey, July 11, 2023.
Three approaches to using gen AI
There are three primary approaches to take in using gen AI:
— In “Taker” use cases, companies use off-the-shelf, gen AI–powered software from third-party vendors such as GitHub
Copilot or Salesforce Einstein to achieve the goals of the use case.
— In “Shaper” use cases, companies integrate bespoke gen AI capabilities by engineering prompts, data sets, and connections
to internal systems to achieve the goals of the use case.
— In “Maker” use cases, companies create their own LLMs by building large data sets to pre-train models from scratch.
Examples include OpenAI, Anthropic, Cohere, and Mistral AI.
Most companies will turn to some combination of Taker, to quickly access a commodity service, and Shaper, to build a
proprietary capability on top of foundation models. The highest-value gen AI initiatives, however, generally rely on the Shaper
approach.1
1 For more on the three approaches, see “Technology’s generational moment with generative AI: A CIO and CTO guide,” McKinsey, July 11, 2023.
2 Moving past gen AI’s honeymoon phase: Seven hard truths for CIOs to get from pilot to scale
3. Get a handle on costs before they sink you. been deemed “successful,” but it was not applied
Models account for only about 15 percent to an important part of the business.
of the overall cost of gen AI applications.
Understand where the costs lurk, and apply the There are many reasons for failing to scale,
right tools and capabilities to rein them in. but the overarching one is that resources and
executive focus are spread too thinly across
4. Tame the proliferation of tools and tech. The dozens of ongoing gen AI initiatives. This is not a
proliferation of infrastructures, LLMs, and tools new development. We’ve seen a similar pattern
has made scaled rollouts unfeasible. Narrow when other technologies emerged, from cloud
down to those capabilities that best serve the to advanced analytics. The lessons from those
business, and take advantage of available innovations, however, have not stuck.
cloud services (while preserving your flexibility).
The most important decision a CIO will need to
5. Create teams that can build value, not just make is to eliminate nonperforming pilots and
models. Getting to scale requires a team with scale up those that are both technically feasible
a broad cross-section of skills to not only build and promise to address areas of the business that
models but also make sure they generate the matter while minimizing risk (Exhibit 1). The CIO will
value they’re supposed to, safely and securely. need to work closely with business unit leaders
on setting priorities and handling the technical
6. Go for the right data, not the perfect implications of their choices.
data. Targeting which data matters most and
investing in its management over time has a big
impact on how quickly you can scale. 2. It’s about how the pieces fit together,
not the pieces themselves
7. Reuse it or lose it. Reusable code can In many discussions, we hear technology leaders
increase the development speed of generative belaboring decisions around the component parts
AI use cases by 30 to 50 percent. required to deliver gen AI solutions—LLMs, APIs,
and so on. What we are learning, however, is that
solving for these individual pieces is relatively easy
1. Eliminate the noise, and focus on and integrating them is anything but. This creates
the signal a massive roadblock to scaling gen AI.
Although many business leaders acknowledge
the need to move past pilots and experiments, The challenge lies in orchestrating the range of
that isn’t always reflected in what’s happening on interactions and integrations at scale. Each use
the ground. Even as gen AI adoption increases, case often needs to access multiple models, vector
examples of its real bottom-line impact are few databases, prompt libraries, and applications
and far between. Only 15 percent of companies (Exhibit 2). Companies have to manage a variety
in our latest AI survey say they are seeing use of of sources (such as applications or databases
gen AI have meaningful impact on their companies’ in the cloud, on-premises, with a vendor, or a
EBIT.3 combination), the degree of fidelity (including
latency and resilience), and existing protocols (for
Exacerbating this issue is that leaders are drawing example, access rights). As a new component is
misleading lessons from their experiments. They added to deliver a solution, it creates a ripple effect
try to take what is essentially a chat interface pilot on all the other components in the system, adding
and shift it to an application—the classic “tech exponential complexity to the overall solution.
looking for a solution” trap. Or a pilot might have
3 That is, they attribute 5 percent or more of their organizations’ EBIT to gen AI use. McKinsey Global Survey on the state of AI in early 2024,
February 22 to March 5, 2024, forthcoming on McKinsey.com.
Moving past gen AI’s honeymoon phase: Seven hard truths for CIOs to get from pilot to scale 3
Exhibit 1
Focus on use cases that are feasible and where business impact is clear.
Focus on use cases that are feasible and where business impact is clear.
Criteria for determining business impact and technical feasibility
Use cases Quick / high-impact wins
Category Criteria (illustrative) Second priority
High
Business Value Can we accurately quantify the value? Is it
impact creation incremental or a step function in performance?
Strategic How well does this align with or support the
alignment company’s primary strategic objectives?
Ease of Are end users enthusiastic about adopting
adoption the solution? Is there a demand for more
Business
features or capabilities?
impact
Business Are we introducing this solution at an
readiness appropriate time, considering ongoing
transformations or other projects?
Technical Data Is the data readily available, or do we need to
feasibility readiness create or synthesize it? Are there any special
considerations for handling sensitive data?
Low
Solution Does the solution require proven or nascent Low Technical High
readiness techniques? feasibility
Ability to Will the proposed business model remain
scale viable as number of users and cloud
consumption increase?
Reusability Can the components of the solution be
repurposed for other use cases?
McKinsey & Company
The key to effective orchestration is embedding The orchestration of the many interactions
the organization’s domain and workflow expertise required to deliver gen AI capabilities, however,
into the management of the step-by-step flow is impossible without effective end-to-end
and sequencing of the model, data, and system automation. “End-to-end” is the key phrase here.
interactions of an application running on a cloud Companies will often automate elements of the
foundation. The core component of an effective workflow, but the value comes only by automating
orchestration engine is an API gateway, which the entire solution, from data wrangling (cleaning
authenticates users, ensures compliance, logs and integration) and data pipeline construction to
request-and-response pairs (for example, to help model monitoring and risk review through “policy
bill teams for their usage), and routes requests to as code.” Our latest research has shown that gen
the best models, including those offered by third AI high performers are more than three times as
parties. The gateway also enables cost tracking likely as their peers to have testing and validation
and provides risk and compliance teams a way embedded in the release process for each model.4
to monitor usage in a scalable way. This gateway A modern MLOps platform is critical in helping to
capability is crucial for scale because it allows manage this automated flow and, according to
teams to operate independently while ensuring McKinsey analysis, can accelerate production by
that they follow best practices (see sidebar “Main ten times as well as enable more efficient use of
components for gen AI model orchestration”). cloud resources.
4 We define gen AI high performers as those who attribute more than 10 percent of their organizations’ EBIT to their use of gen AI.
McKinsey Global Survey on the state of AI in early 2024, February 22 to March 5, 2024, forthcoming on McKinsey.com.
4 Moving past gen AI’s honeymoon phase: Seven hard truths for CIOs to get from pilot to scale
Exhibit 2
A gen AI solution needs to accommodate a complex set of integrations
A gen AI solution needs to accommodate a complex set of integrations across
across the entire tech stack.
the entire tech stack.
Illustrative tech stack with end-to-end automation Data Gen AI capabilities Cloud Models
Front-end application
User interface
Data enrichment
and processing Orchestration
Enhancing
Source data Query validation and intent routing Guardrails capabilities
Unstructured Structured Security Data Semantic Prompt LLM Conversation
data ETL¹ data ETL¹ and retrieval and engi- flow memory
access hybrid neering management
Databases (eg, control search and Prompt library
observability
vector stores)
Image Prompt
LLM agents
search enrichment
Structured Fallback External
data query runtime
search integration
Infrastructure and cloud services API gateway
Foundation models (eg, LLMs, multimodal
models, embedding generation models)
1Extract, transform, load.
McKinsey & Company
Gen AI models can produce inconsistent results, 3. Get a handle on costs before they
due to their probabilistic nature or the frequent sink you
changes to underlying models. Model versions can The sheer scale of gen AI data usage and model
be updated as often as every week, which means interactions means costs can quickly spiral out
companies can’t afford to set up their orchestration of control. Managing these costs will have a huge
capability and let it run in the background. They impact on whether CIOs can manage gen AI
need to develop hyperattentive observing and programs at scale. But understanding what drives
triaging capabilities to implement gen AI with costs is crucial to gen AI programs. The models
speed and safety. Observability tools monitor themselves, for example, account for only about 15
the gen AI application’s interactions with users percent of a typical project effort.5 LLM costs have
in real time, tracking metrics such as response dropped significantly over time and continue to
time, accuracy, and user satisfaction scores. If decline.
an application begins to generate inaccurate
or inappropriate responses, the tool alerts the CIOs should focus their energies on four realities:
development team to investigate and make any
necessary adjustments to the model parameters, — Change management is the biggest cost. Our
prompt templates, or orchestration flow. experience has shown that a good rule of thumb
for managing gen AI costs is that for every $1
5 “Generative AI in the pharmaceutical industry: Moving from hype to reality,” McKinsey, January 9, 2024.
Moving past gen AI’s honeymoon phase: Seven hard truths for CIOs to get from pilot to scale 5
Main components for gen AI model orchestration
Orchestration is the process of coordinating various data, transformation, and AI components to manage complex AI workflows.
The API (or LLM) gateway layer serves as a secure and efficient interface between users or applications and underlying gen AI
models. The orchestration engine itself is made up of the following components:
— Prompt engineering and prompt library: Prompt engineering is the process of crafting input prompts or queries that guide
the behavior and output of AI models. A prompt library is a collection of predefined prompts that users can leverage as best
practices/shortcuts when they invoke a gen AI model.
— Context management and caching: Context management highlights background information relevant to a specific task or
interaction. Caching relates to storing previously computed results or intermediate data to accelerate future computations.
— Information retrieval (semantic search and hybrid search): Information-retrieval logic allows gen AI models to search for
and retrieve relevant information from a collection of documents or data sources.
— Evaluation and guardrails: Evaluation and guardrail tools help assess the performance, reliability, and ethical
considerations of AI models. They also provide input to governance and LLMOps. This encompasses tools and processes for
evaluating model accuracy, robustness, fairness, and safety.
spent on developing a model, you need to spend companies default to simply creating a chat
about $3 for change management. (By way of interface for a gen AI application), and second,
comparison, for digital solutions, the ratio has involving their best employees in training models
tended to be closer to $1 for development to $1 to ensure the models learn correctly and quickly.
for change management.6) Discipline in managing
the range of change actions, from training your — Run costs are greater than build costs for
people to role modeling to active performance gen AI applications. Our analysis shows that
tracking, is crucial for gen AI. Our analysis has it’s much more expensive to run models than to
shown that high performers are nearly three build them. Foundation model usage and labor
times more likely than others to have a strong are the biggest drivers of that cost. Most of
performance-management infrastructure, such the labor costs are for model and data pipeline
as key performance indicators (KPIs), to measure maintenance. In Europe, we are finding that
and track value of gen AI. They are also twice as significant costs are also incurred by risk and
likely to have trained nontechnical people well compliance management.
enough to understand the potential value and
risks associated with using gen AI at work.7 — Driving down model costs is an ongoing
process. Decisions related to how to engineer
Companies have been particularly successful in the architecture for gen AI, for example, can
handling the costs of change management by lead to cost variances of 10 to 20 times, and
focusing on two areas: first, involving end users sometimes more than that. An array of cost-
in solution development from day one (too often, reduction tools and capabilities are available,
6 Eric Lamarre, Kate Smaje, and Rodney Zemmel, “Rewired to outcompete,” McKinsey, June 20, 2023.
7 McKinsey Global Survey on the state of AI in early 2024, February 22 to March 5, 2024, forthcoming on McKinsey.com.
6 Moving past gen AI’s honeymoon phase: Seven hard truths for CIOs to get from pilot to scale
such as preloading embeddings. This is analytics solutions. The goal here is to develop
not a one-off exercise. The process of cost a modeling discipline that instills an ROI focus
optimization takes time and requires multiple on every gen AI use case without getting lost in
tools, but done well, it can reduce costs from a endless rounds of analysis.
dollar a query to less than a penny (Exhibit 3).
— Investments should be tied to ROI. Not all 4. Tame the proliferation of tools
gen AI interactions need to be treated the and tech
same, and they therefore shouldn’t all cost Many teams are still pushing their own use cases
the same. A gen AI tool that responds to live and have often set up their own environments,
questions from customers, for example, is resulting in companies having to support multiple
critical to customer experience and requires infrastructures, LLMs, tools, and approaches
low-latency rates, which are more expensive. to scaling. In a recent McKinsey survey, in fact,
But code documentation tools don’t have to be respondents cited “too many platforms” as the
so responsive, so they can be run more cheaply. top technology obstacle to implementing gen AI
Cloud plays a crucial rule in driving ROI because at scale.8 The more infrastructures and tools, the
its prime source of value lies in supporting higher the complexity and cost of operations, which
business growth, especially supporting scaled in turn makes scaled rollouts unfeasible. This state
8 McKinsey survey on generative AI in operations, November 2023.
Exhibit 3
As solutions scale, organizations can optimize costs.
As solutions scale, organizations can optimize costs.
Cost per query by week,¹ $
1.0
0.8
0.6
0.4
0.2
0.0
Week 1 Week 2 Week 3 Week 4 Week 5 Week 6 Week 7 Backlog
Initial proof Add RAG,² Add intent Re-ranking Migrate from Migrate from Vendor price Batching,
of concept maxing out recognition and prompt paid GPT for risk reduction, and
prompt and routing, optimization embedding guardrails and semantic reevaluate
length reducing generation and intent cache need for
search space model to recognition to chatbot
and adding open-source open-source
LLM calls model models and
regular
expression
1Illustrative example pulling from multiple case studies.
2Retrieval-augmented generation.
McKinsey & Company
Moving past gen AI’s honeymoon phase: Seven hard truths for CIOs to get from pilot to scale 7
of affairs is similar to the early days of cloud and for example. But greater impact came only when
software as a service (SaaS), when accessing the other parts of the organization—such as risk and
tech was so easy—often requiring no more than a business experts—were integrated into the teams
credit card—that a “wild west” of proliferating tools along with product management and leadership.
created confusion and risk.
There are multiple archetypes for ensuring
To get to scale, companies need a manageable set this broader organizational integration. Some
of tools and infrastructures. Fair enough—but how companies have built a center of excellence to act
do you know which providers, hosts, tools, and as a clearinghouse to prioritize use cases, allocate
models to choose? The key is to not waste time on resources, and monitor performance. Other
endless rounds of analysis on decisions that don’t companies split strategic and tactical duties among
matter much (for example, the choice of LLMs is less teams. Which archetype makes sense for any
critical as they increasingly become a commodity) or given business will depend on its available talent
where there isn’t much of a choice in the first place— and local realities. But what’s crucial is that this
for example, if you have a primary cloud service centralized function enables close collaboration
provider (CSP) that has most of your data and your between technology, business, and risk leads, and
talent knows how to work with the CSP, you should is disciplined in following proven protocols for
probably choose that CSP’s gen AI offering. Major driving successful programs. Those might include,
CSPs, in fact, are rolling out new gen AI services for example, quarterly business reviews to track
that can help companies improve the economics of initiatives against specific objectives and key
some use cases and open access to new ones. How results (OKRs), and interventions to resolve issues,
well companies take advantage of these services reallocate resources, or shut down poor-performing
depends on many variables, including their own cloud initiatives.
maturity and the strength of their cloud foundations.
A critical role for this governing structure is to ensure
What does require detailed thinking is how to build that effective risk protocols are implemented and
your infrastructure and applications in a way that followed. Build teams, for example, need to map
gives you the flexibility to switch providers or models the potential risks associated with each use case;
relatively easily. Consider adopting standards widely technical and “human-in-the-loop” protocols need
used by providers (such as KFServing, a serverless to be implemented throughout the use-case life
solution for deploying gen AI models), Terraform for cycle. This oversight body also needs a mandate
infrastructure as code, and open-source LLMs. to manage gen AI risk by assessing exposures and
implementing mitigating strategies.
It’s worth emphasizing that overengineering for
flexibility eventually leads to diminishing returns. A One issue to guard against is simply managing the
plethora of solutions becomes expensive to maintain, flow of tactical use cases, especially where the
making it difficult to take full advantage of the volume is large. This central organization needs a
services providers offer. mandate to cluster related use cases to ensure large-
scale impact and drive large ideas. This team needs
to act as the guardians for value, not just managers
5. Create teams that can build value, of work.
not just models
One of the biggest issues companies are facing One financial services company put in place
is that they’re still treating gen AI as a technology clearly defined governance protocols for senior
program rather than as a broad business priority. management. A steering group, sponsored by
Past technology efforts demonstrate, however, that the CIO and chief strategy officer, focused on
creating value is never a matter of “just tech.” For gen enterprise governance, strategy, and communication,
AI to have real impact, companies have to build teams driving use-case identification and approvals. An
that can take it beyond the IT function and embed it enablement group, sponsored by the CTO, focused
into the business. Past lessons are applicable here, on decisions around data architecture, data science,
too. Agile practices sped up technical development, data engineering, and building core enabling
8 Moving past gen AI’s honeymoon phase: Seven hard truths for CIOs to get from pilot to scale
capabilities. The CTO also mandated that at least engineering teams (tech sales/support teams)
one experienced architect join a use-case team developed their own version to find solutions for
early in their process to ensure the team used the unique client calls, commercialization teams had
established standards and tool sets. This oversight product descriptions, and customer support teams
and governance clarity was crucial in helping the had a set of specific product details to answer
business go from managing just five to more than queries. As each team updated its version of the
50 use cases in its pipeline. product information, conflicts emerged, making
it difficult for gen AI models to use the data. To
address this issue, the company is putting all
6. Go for the right data, not the relevant product information in one place.
perfect data
Misconceptions that gen AI can simply sweep up
7. Reuse it or lose it
the necessary data and make sense of it are still
widely held. But high-performing gen AI solutions Reusable code can increase the development
are simply not possible without clean and accurate speed of generative AI use cases by 30 to 50
data, which requires real work and focus. The percent.9 But in their haste to make meaningful
companies that invest in the data foundations to breakthroughs, teams often focus on individual use
generate good data aim their efforts carefully. cases, which sinks any hope for scale. CIOs need to
shift the business’s energies to building transversal
Take the process of labeling, which often oscillates solutions that can serve many use cases. In fact, we
between seeking perfection for all data and have found that gen AI high performers are almost
complete neglect. We have found that investing in three times as likely as their peers to have gen
targeted labeling—particularly for the data used for AI foundations built strategically to enable reuse
retrieval-augmented generation (RAG)—can have a across solutions.10
significant impact on the quality of answers to gen
AI queries. Similarly, it’s critical to invest the time to In committing to reusability, however, it is easy to
grade the importance of content sources (“authority get caught in building abstract gen AI capabilities
weighting”), which helps the model understand the that don’t get used, even though, technically, it
relative value of different sources. Getting this right would be easy to do so. A more effective way to
requires significant human oversight from people build up reusable assets is to do a disciplined
with relevant expertise. review of a set of use cases, typically three to five,
to ascertain their common needs or functions.
Because gen AI models are so unstable, companies Teams can then build these common elements
need to maintain their platforms as new data is as assets or modules that can be easily reused or
added, which happens often and can affect how strung together to create a new capability. Data
models perform. This is made vastly more difficult preprocessing and ingestion, for example, could
at most companies because related data lives in include a data-chunking mechanism, a structured
so many different places. Companies that have data-and-metadata loader, and a data transformer
invested in creating data products are ahead of as distinct modules. One European bank reviewed
the game because they have a well-organized data which of its capabilities could be used in a wide
source to use in training models over time. array of cases and invested in developing a
synthesizer module, a translator module, and a
At a materials science product company, for sentiment analysis module.
example, various teams accessed product
information, but each one had a different version. CIOs can’t expect this to happen organically. They
R&D had materials safety sheets, application need to assign a role, such as the platform owner,
9 Eric Lamarre, Alex Singla, Alexander Sukharevsky, and Rodney Zemmel, “A generative AI reset: Rewiring to turn potential into value in 2024,”
McKinsey, March 4, 2024.
10 McKinsey Global Survey on the state of AI in early 2024, February 22 to March 5, 2024, forthcoming on McKinsey.com.
Moving past gen AI’s honeymoon phase: Seven hard truths for CIOs to get from pilot to scale 9
Exhibit 4
A gen AI platform team needs an array of skills.
A gen AI platform team needs an array of skills.
Cross-functional platform team DataOps: Manages and optimizes the data pipeline, ensuring the
roles and skills availability and quality of data; supports training and deployment of
gen AI models
Site reliability engineer: Ensures reliability, availability, and perfor-
mance of software systems and applications
Data DataOps DevOps engineer: Establishes the CI/CD¹ pipeline and other auto-
engineer
mation needed for teams to rapidly develop and deploy code (eg,
chatbot, APIs) to production
Site
Data reliability Cloud architect: Ensures scalability, security, and cost optimization
scientist engineer of the cloud infrastructure; designs data storage and management
systems; facilitates integration and deployment of the AI models
Platform
Solution/data architect: Develops creative and efficient solutions
Full- team using engineering practices and software/web development
stack DevOps technologies
developer engineer
Platform owner: Acts like a product owner, oversees the build of a
gen AI platform
Full-stack developer: Writes clean and quality scalable code (eg,
Platform Cloud front-end/back-end APIs) that can be easily deployed with CI/CD¹
owner Solution/ architect p ipelines
data Data scientist: Fine-tunes foundational models to help
architect RAG²-based approach, ensures alignment of LLM outputs with
responsible AI guidelines
Data engineer: Architects data models to ingest data into vector
databases, creates and maintains automated pipelines, performs
closed-loop testing to validate responses and improve performance
1Continuous integration (CI) and continuous delivery (CD).
2Retrieval-augmented generation.
McKinsey & Company
and a cross-functional team with a mandate to The value gen AI could generate is
develop reusable assets for product teams transformational. But capturing the full extent of
(Exhibit 4), which can include approved tools, that value will come only when companies harness
code, and frameworks. gen AI at scale. That requires CIOs to not just
acknowledge hard truths but be ready to act on
them to lead their business forward.
Aamer Baig is a senior partner in McKinsey’s Chicago office, Douglas Merrill is a partner in the Southern California office,
Megha Sinha is a partner in the Bay Area office, Danesha Mead is a consultant in the Denver office, and Stephen Xu is
director of product management in the Toronto office.
The authors wish to thank Mani Gopalakrishnan, Mark Gu, Ankur Jain, Rahil Jogani, and Asin Tavakoli for their contributions
to this article.
Copyright © 2024 McKinsey & Company. All rights reserved.
10 Moving past gen AI’s honeymoon phase: Seven hard truths for CIOs to get from pilot to scale"
41,mckinsey,reimagining-insurance-with-a-comprehensive-approach-to-gen-ai-vfinal.pdf,"Insurance Practice
Reimagining insurance
with a comprehensive
approach to gen AI
Insurance companies are at an inflection point with their generative AI
use cases. Three McKinsey partners discuss the value of combining
generative AI with other technologies.
by Cameron Talischi, Jörg Mußhoff, and Khaled Rifai
August 2024
Despite forging ahead with generative AI (gen seamlessly search and query risk appetite and
AI) use cases and capabilities, many insurance underwriting guidelines.
companies are finding themselves stuck in the
pilot phase, unable to scale or extract value. Jörg The second category is the generation of content—
Mußhoff sat down with Cameron Talischi and Khaled namely, creative content. Think about it in the
Rifai to discuss how organizations can escape “pilot context of marketing or personalization. Again, in
purgatory” by leveraging traditional AI and robotic the context of claims, it’s communicating the status
process automation in addition to gen AI; the of a claim to a claimant by capturing some of the
importance of reimagining domains such as claims, details and nuances specific to that claim or for
underwriting, and distribution; and how to address supporting underwriters, and it’s communicating or
data privacy and security concerns regarding negotiating with brokers. Use cases for coding and
intellectual property (IP) and other issues early on. software development make up the last category.
This transcript has been edited for clarity. These are notable given the imperative for tech
modernization and digitalization and that many
Jörg Mußhoff: To us, gen AI is not just hype. insurance companies are still dealing with legacy
McKinsey has estimated that the total gen AI systems.
potential for the global economy is $4.4 trillion.1
Many insurance leaders are asking, “How do we Khaled Rifai: I would add one more in the context of
get the benefits from first use cases, and how do client engagement and self-service. Think about the
we scale and make it real across geographies and insured wanting to know whether they’re covered,
business models?” Cam, could you start us off by what the statuses of their claims are, or whether
telling us what you see in the overarching trends in they need to update their addresses or names. Many
gen AI and what applications and domains have the insurers are still employing people to handle these
greatest potential impact for clients? requests. With the help of gen AI, those tasks can
be automated or designed for self-service. I think
Cameron Talischi: We’ve seen a lot of interest and the long-term effects of gen AI are underrated, and
activity in the insurance sector on this topic, which the short-term effects are overrated. And that’s
is not surprising given that the insurance industry the dilemma many insurance companies and other
is knowledge-based and involves processing corporations find themselves in. They want fast
unstructured types of data. That is precisely what results from the benefits of gen AI applications but
gen AI models are very good for. hesitate to invest in data management, technology
modernization, organizational change, and
In terms of promising applications and domains, budgetary allocations.
three categories of use cases are gaining traction.
First, and most common, is that carriers are While we believe in the potential of gen AI, it will take
exploring the use of gen AI models to extract a lot of engagement, investment, and commitment
insights and information from unstructured from top management teams and organizations
sources. In the context of claims, for example, this to make it real. To make gen AI truly successful,
could be synthesizing medical records or pulling you must combine gen AI with more-traditional AI
information from demand packages. In the context and traditional robotic process automation. These
of underwriting for a commercial P&C [property technologies combined make the secret sauce
and casualty insurance carrier], this could look like that helps you rethink your customer journeys and
pulling information from submissions that come processes with the right ROI.
from brokers or allowing underwriters to more
1 The economic potential of generative AI: The next productivity frontier, McKinsey, June 14, 2023.
Reimagining insurance with a comprehensive approach to gen AI 2
Jörg Mußhoff: That’s exactly what we’re seeing Khaled Rifai: I fully agree. Reimaging domains
many players do. But we are still in that pilot phase. is key because you can very quickly get to the
Why do organizations get stuck in this phase, and restrictions connected to isolated use cases
how can they successfully scale up from there? because of the dependencies with other systems
and processes. We are at a point in time with gen
Cameron Talischi: We are seeing a lot of AI where we should take a step back and really
organizations getting stuck in what we call “pilot reimagine claims, underwriting, and distribution. By
purgatory” for several reasons. One is misplaced combining these technologies and thinking about
focus on technology versus what matters from how to design processes that capture the right data
a business perspective. Many organizations at the right point, we can drive meaningful change.
have identified several use cases and have This approach requires investments in more than
development teams building these assets. But just tech; it also takes quite some commitment,
a lot of time is being spent on testing, analyzing, quite some investment, and quite some change to
and benchmarking different tools such as LLMs do so.
[language learning models] even though the choice
of the language model may be dictated by other Jörg Mußhoff: Do you have any pragmatic advice for
factors and, ultimately, has a marginal impact on our clients about what they should do to set this up
performance. and develop these capabilities over time?
While there’s value in learning and experimenting Cameron Talischi: Everything must be anchored
with use cases, these need to be properly planned in a strategic vision and a road map, but in terms of
so they don’t become a distraction. Conversely, capabilities, the data setup is critically important,
leading organizations that are thinking about especially as you think about gaining scale. You
scaling are shifting their focus to identifying the need to make sure that the data underpinning the
common code components behind applications. possible use cases are in usable condition. We
Earlier, we talked about extracting information talked about the technology stack and this notion
from unstructured sources. Typically, these of creating infrastructure that can build and deliver
applications have similar architecture operating in use cases at an accelerated pace. You are touching
the background. So, it’s possible to create reusable on talent and operating models, which are equally
modules that can accelerate building similar use important. One of the failures of some operating
cases while also making it easier to manage them models is when the effort is solely tech-led versus
on the back end. business-led with the technology function as an
enabler. It’s important to assess how much of the
Another area where organizations get stuck is development is done centrally versus within the
how they think about impact. We’ve seen many business.
organizations source ideas from various parts of
the business and prioritize them. But many of the On the talent side, organizations will most likely
use cases are very isolated and don’t generate pursue a combination of building and buying:
much value, so the organization prolongs the pilot. purchasing some of the capabilities and use cases
If you’re not seeing value from a use case, even from external vendors and building some internally,
in isolation, you may want to move on. The better such as use cases that tie to your IP and ways of
approach to driving business value is to reimagine working. To build internally, you’ll need the requisite
domains and explore all the potential actions within talent to create those capabilities. For example, new
each domain that can collectively drive meaningful roles such as prompt engineers address how we
change in the way work is accomplished. So that interact with models and get the right behavior out
includes looking at all the levers at your disposal, of them. You need to build that muscle and some
not just gen AI. That approach better lends itself to of those capabilities through a combination of tech
scaling versus piloting an isolated use case. and business to deploy them as part of the right
operating model.
Reimagining insurance with a comprehensive approach to gen AI 3
‘ You shouldn’t wait it out, because you
need to build that muscle to understand
what solutions you should buy.’
–Khaled Rifai
Khaled Rifai: Some companies wonder what to do Regarding data privacy, it is possible to have
about data management now that gen AI is being automated routines to identify PII [personal
implemented at large vendors. Should they just identifiable information] and strip that data—if it’s
wait it out? Our answer is no—you shouldn’t wait it not needed—to ensure that it doesn’t leave a secure
out, because, as Cam said, you need to build that environment. With accuracy, it’s important to, in
muscle to understand not only how to keep your tandem with the business, have objective measures
organization safe but also what solutions you should and targets for performance. Test these in advance
buy that will fit your needs. of the application or use case going into production,
but also implement routine audits postproduction to
Jörg Mußhoff: Besides data privacy and security, make sure that the performance reached expected
there’s also a big regulatory question. Gen AI can be levels.
biased, which raises ethical questions. In the mid- to
long-term use of these technologies, what should Khaled Rifai: In terms of regulation in Europe,
insurance carriers focus on to avoid risk? the EU Artificial Intelligence Act has recently
been passed. With room for national regulations,
Cameron Talischi: First and foremost, it’s important national regulators of the insurance industry will
for insurance carriers to have a comprehensive look at certain cases to determine standards. In my
framework in place that covers major AI-related experience, the regulations are good enough for
risks such as data privacy issues or issues and clients to work with. I wouldn’t start with high-risk
concerns about accuracy and hallucinations. cases concerning decisions that impact the life and
Incidentally, insurance carriers need to account for health of the insured, but instead begin with other
risks that they’re exposed to via the use of gen AI by use cases that we’re certain we can implement
customers or other parties they interact with. The in a secure, customer-friendly way. The thing to
use of image generation is a good example of this remember is that nothing is static, and the ongoing
because it could lead to fraudulent claims. process of shaping regulations means taking things
one step at a time.
Cameron Talischi is a partner in McKinsey’s Chicago office, and Jörg Mußhoff is a senior partner in the Berlin office,
where Khaled Rifai is a partner.
Copyright © 2024 McKinsey & Company. All rights reserved.
Reimagining insurance with a comprehensive approach to gen AI 4"
42,mckinsey,ai-for-it-modernization-faster-cheaper-and-better_f.pdf,"AI for IT modernization:
Faster, cheaper, better
Gen AI agents are starting to deliver breakthrough value, but only when
companies figure out how to build and orchestrate hundreds of them.
This article is a collaborative effort by Aaron Bawcom and Matt Fitzpatrick, with Chi Wai Cheung, Dan Collins, and
Dante Gabrielli, representing views from McKinsey Technology and QuantumBlack, AI by McKinsey.
December 2024
At the heart of virtually every large organization is modernize and today is well less than half of that
a massive anchor slowing a business down: the tech when using gen AI. This shift makes many
debt found in legacy IT systems. Often built modernization efforts that were once too expensive
decades ago, these large systems form the or time-consuming suddenly viable. And with the
technical backbone of companies and functions ability to measure and track the direct cost of
across almost every sector. As much as 70 percent technology debt and its effect on P&L outcomes (in
of the software used by Fortune 500 companies many cases up to 40 to 50 percent of total
was developed 20 or more years ago1 (see sidebar investment spend), companies can track the value
“What are legacy systems, and how do they hold they’re generating.
organizations back?”).
While these are still early days, our experience
Modernizing these aging systems and paying down indicates that harnessing gen AI can eliminate
tech debt have traditionally been considered an “IT much of the manual work, leading to a 40 to 50
problem,” and business leaders have been content percent acceleration in tech modernization
to more or less kick the problem down the road. timelines and a 40 percent reduction in costs
The reasons are familiar: it’s too expensive (often derived from technology debt while also improving
hundreds of millions of dollars), it takes too long the quality of the outputs. That value, however, is
(five to seven years), it’s too disruptive, the return less tied to the technology itself and more to how
on the investment is unclear, and the current it’s used, with a particular focus on the following:
systems basically work.
— Improving business outcomes. Converting old
But as technology infiltrates every nook of the code into modern tech languages simply
business and becomes central to a company’s transports your tech debt from a legacy system
ability to generate value, modernizing IT systems into a modern one. Avoiding this “code and
must become a CEO priority. The opportunities, load” issue requires using gen AI to help make
and risks, generated from advances in better business decisions and modernize what
technology—from generative AI (gen AI) to cloud matters.
to robotics—require modern technology
foundations. In fact, technology enables about — Enabling autonomous gen AI agents. Building
71 percent of the value derived from business and training an army of gen AI agents that can
transformations.2 The fact that the programmers work independently and collaboratively with
who built and maintain these aging enterprise human oversight on a range of end-to-end
systems are reaching retirement age lends an even processes is proving to deliver significant
greater urgency to the need for modernization. improvements in IT modernization efforts (see
sidebar “LegacyX”).
New developments in AI, particularly in gen AI, are
radically recalibrating the costs and benefits of — Focusing on scaling value. The value of the
modernizing legacy tech and reducing tech debt as multiagent model comes from industrializing it
part of a larger set of changes in how IT operates. so it can scale and be applied to multiple areas
Consider a transaction processing system for a of the business and continuously pay down
leading financial institution, which three years ago tech debt.
would have cost much more than $100 million to
1 Nia Batten, “Fix it, even if it ‘ain’t broke’: The price of legacy technology,” TechRadar, October 11, 2023.
2 Aamer Baig, Sven Blumberg, Arun Gundurao, and Basel Kayyali, “Breaking technical debt’s vicious cycle to modernize your business,” McKinsey,
April 25, 2023.
AI for IT modernization: Faster, cheaper, better 2
What are legacy systems, and how do they hold organizations back?
Legacy IT systems permeate every — Limited compatibility with modern — Inefficient use of capital: The cost of
industry, often serving as the backbone of channels. Legacy technologies running legacy systems prevents
critical operations. In financial services, for typically do not integrate well with investment in more value-building
instance, core banking platforms and modern channels that demand real- development.
investment management solutions handle time data and advanced connectivity.
— Unattractive to top talent: Top
transactions amounting to trillions of
— Slow responsiveness to regulations: programmers and software engineers
dollars daily on a global scale. Insurance
Continually changing regulatory are less likely to join a business that
administration systems manage policies
requirements become harder and more uses legacy systems and outmoded
worth $1.9 trillion in annual premiums in
expensive to adhere to. languages that they do not understand
the United States alone.1 Benefits
and are not valued in the talent
management platforms facilitate the — Resiliency risk: A lack of understanding
marketplace.
distribution of over $830 million annually of how systems work, coupled with
in social services and benefits to millions of limited automation capabilities and
US citizens.2 often manual-testing processes,
introduces instability issues that are
Technology debt in these systems creates
hard to predict and fix.
significant business issues:
— High run costs: The cost of running
— Slow pace of innovation: Systems built
legacy systems is often significantly
using outdated and difficult-to-
higher than that for modern systems.
understand languages severely hinder
For a large European bank, for
the organization’s ability to adapt and
example, 70 percent of its IT
innovate.
capacity was spent maintaining
legacy systems.
1 Annual Report on the Insurance Industry, Federal Insurance Office, US Department of the Treasury, September 2024.
2 “Project: Public welfare expenditures,” State and Local Backgrounders, Urban Institute, April 26, 2024.
How gen AI agents can improve both switching to easier-to-use programming languages,
code and business outcomes transitioning to modern frameworks that provide
more functionality, restructuring systems to create
At its core, technology modernization involves
modularity, or even remediating and migrating
transforming existing applications to take
applications to run on cheaper cloud environments.
advantage of modern technologies, frameworks,
Harnessing gen AI capabilities to make these kinds
and architectures. This process can include
of changes, improve ROI on cloud programs, and
AI for IT modernization: Faster, cheaper, better 3
LegacyX
LegacyX is a McKinsey capability, to handle end-to-end workflows, focusing automates complex software development
powered by QuantumBlack, that simplifies on deriving the intent of legacy systems to flows, modernizing both processes and
legacy IT system modernization and develop better processes and accelerate applications simultaneously.
delivers business value by using generative modernization with a repeatable process.
AI. It employs a range of specialized agents The tiered multiagent factory framework
pay down technical debt requires a focus on the When properly applied, gen AI is able to
three areas mentioned above. translate the often-impenetrable legacy
elements—documentation, code, observability data,
Improve business outcomes call logs, programming approaches, etcetera—into
Companies have tended to use gen AI in a blunt- simple English process descriptions in a matter of
force way by, for example, feeding legacy code minutes. Engineers at one financial-services
directly into a gen AI tool that translates it into company interviewed a number of experts to
modern language. This code-and-load approach, supplement the limited documentation available
however, essentially migrates your tech debt into a and fed the transcripts into the gen AI model to
modern context. This trap is similar to the one that provide it with better guidance.This clarity not only
many companies fell into during the early days of helps engineers to understand what the systems
cloud computing, where the focus was on “lift and are doing but also allows business experts to help
shift,” that is, moving existing applications to the determine what’s really needed. In this way,
cloud where legacy issues remained unaddressed. business and engineering experts can work
together to determine what they want, what should
The goal of a legacy-tech modernization effort be updated, and what can be discarded.
should not be to convert as many lines of code as
possible. It should be to improve systems and Enable autonomous gen AI agents
processes so the business can generate more We have previously written that in software
value. That means using gen AI to understand the development, using gen AI agents to assist
code you already have, determine what’s needed to developers with coding can help some developers
generate business value, and then modernize the increase their productivity.3 Our experience has
processes that are necessary for achieving that shown that the next horizon of this acceleration will
outcome. enable hundreds of gen AI agents to operate
independently with human oversight, especially as
the model scales (exhibit).
3 “Unleashing developer productivity with generative AI,” McKinsey, June 27, 2023.
AI for IT modernization: Faster, cheaper, better 4
Exhibit
The core of this autonomous-agent approach is the The real value, however, comes from orchestrating
deployment of many specialized AI agents, each agents to complete not just tasks but entire
with distinct roles and expertise, collaborating on software development processes. Data mapping
complex tasks. The tasks they can perform include and storage agents, for example, perform data
data analysis, orchestrating sophisticated analysis, compliance analysis, QA, as well as create
integrations, designing and running test cases, and relevant documentation. These agents work with
refining outcomes based on real-time feedback security design agents that focus on threat analysis,
from humans. information security policy, security design, and QA
agents to develop safe, secure, and effective code.
AI for IT modernization: Faster, cheaper, better 5
To ensure gen AI agents deliver the right outcomes, and then use gen AI agents to generate code as
it’s important to implement a range of controls. well as automate discovery and conversion
Constructive feedback loops, for example, allow journeys. The result was an improvement in code
agents to review and refine one another’s work. modernization efficiency and testing by more than
Gen AI agents can also be programmed to teach 50 percent, as well as a greater than 50 percent
themselves to solve problems or escalate them to a acceleration of coding tasks.
human manager if they can’t figure it out. Some gen
AI agents can even ask the manager direct Focus on scaling value
questions. Assigning IDs to each gen AI agent The excitement surrounding gen AI has led
allows managers to quickly identify the source of an companies to focus a significant amount of time on
issue and address it. Similarly, organizations can evaluating and selecting tools. That is important,
develop specialized agents to automatically test but it pales in comparison to tackling the bigger
and remediate the output of other agents based on issue and opportunity: how to scale gen AI. As one
identified ethical and bias concerns. CIO recently said, “I don’t want one tool to solve one
problem; I need a capability to solve hundreds of
The role people play will continue to be vital to problems.”
directing and managing gen AI agents. Experts like
product owners, engineers, and architects will need Technology leadership should focus on developing
to understand the intent of legacy systems, figure a central, autonomous gen AI capability that can
out what processes are important for the business, build sophisticated multiagent, end-to-end
and develop and set goals and target states. workflows. There are two primary components of
this capability:
The power of the orchestrated gen AI agent
approach became real at one banking company that — Factory. A factory is a group of people who
had been trying unsuccessfully to modernize its develop and manage multiple gen AI agents to
mainframe for years. When it deployed a large execute a specific end-to-end process. The
collection of gen AI agents, the bank was able to goal of the factory is to standardize and simplify
migrate and improve a number of mainframe the various processes that make up the
components as part of a migration to a Java, development, deployment, and management
Angular UI, and PostgreSQL target state. Looking of gen AI agents. A factory develops a
to modernize 20,000 lines of code, the company standardized set of tools and approaches
had estimated it needed 700 to 800 hours to for agent development and management,
complete the migration. The orchestrated gen AI such as monitoring, traceability, document
approach cut that estimate by 40 percent. The management, and large language model access.
relationship-mapping step, for example, went from An organization should consider developing five
requiring 30 to 40 hours to complete to just about to ten agent factories in the early stages of
five hours. maturity.
In another case, a top 15 global insurer used this — Platform. A gen AI platform is a standardized
approach to modernize legacy applications and set of reusable services and capabilities that
services. The first step was to reverse engineer the factories can access. A platform should include
code to better understand technical specifications a user interface, APIs that connect gen AI
AI for IT modernization: Faster, cheaper, better 6
services to enterprise services (such as Jira or most complex technology problems—the ones
ServiceNow), a range of supporting services that cost hundreds of millions of dollars, have
(such as a data import service or agent multiyear timelines, and are responsible for
orchestration service), and a library of gen AI large tranches of technical debt—and focus on
agents that can be loaded to execute specific developing gen AI solutions for them. Part of
tasks. Companies should ideally develop one or this effort should include revisiting previous
two gen AI factories to determine exactly what tech modernization plans that were deemed too
sorts of services and capabilities they use in expensive or time-consuming.
common, then standardize those elements and
offer them through a platform. A dedicated — Tie your business plan explicitly to value and
team of relevant experts should both oversee track it vigorously. While many companies have
the development and management of this business plans, they are often superficial or
platform and closely track usage against limited in scope (for example, focusing on just
specific KPIs, such as service or feature use. the technology rather than the operating
model). A strong plan provides a detailed view
of the value at stake, the increments of value to
Next steps
be captured along the way (factoring in ongoing
Companies looking to move to this multiagent costs, like current and future infrastructure
orchestration model should consider taking four run costs, and one-time costs, like code
steps: modernization), the activities required to
capture them, and a timeline that captures the
— Question any technology proposal that has a break-even point. More important than
long timeline and requires many people. Many developing the plan is revisiting it and ensuring
large-scale IT projects have traditionally that the modernization efforts are actually
required many people to work for years to capturing the intended value. Without this kind
deliver value. Any proposals that follow this of discipline, it’s common for strong plans to
model should be treated with skepticism. That slowly lose focus and default to delivering code
means reviewing all proposals and initiatives rather than value.
that are under way to determine how gen AI can
reduce costs and shorten timelines. Be — Get ahead of the talent, technology, and
particularly thorough in reviewing programs and operating-model implications. As this
proposals that purport to use gen AI multiagent approach scales, companies will
capabilities. The capabilities may be limited or need to understand and plan for the business
ancillary and thus unable to deliver much value. implications. These include how to rethink your
talent strategy and reskilling programs, how
— Focus gen AI on your biggest problems. Small- your operating model has to adapt, and how
scale initiatives lead to small-scale outcomes. operating expenditures and capital
Gen AI has the potential to radically redefine the expenditures will change, among other
cost-benefit of modernizing systems and priorities. These are CEO- and board-level
reducing tech debt. Identify the largest and issues requiring thoughtful planning.
AI for IT modernization: Faster, cheaper, better 7
Companies have barely scratched the surface when the only way companies will be able to cut back
it comes to effectively applying gen AI to modernize tech debt and enable their tech estate to drive
legacy technology and reduce technical debt. innovation and value.
Focusing on how to orchestrate gen AI agents on
meaningful business technology opportunities is
Aaron Bawcom is a partner in McKinsey’s Atlanta office; Matt Fitzpatrick is a senior partner in the New York office, where Chi
Wai Cheung is a principal architect and Dan Collins is a senior principal; Dante Gabrielli is a principal product manager in the
Philadelphia office.
The authors wish to thank Rob Patenge and Vito Di Leo for their contributions to this article.
This article was edited by Barr Seitz, an editorial director in the New York office.
Copyright © 2024 McKinsey & Company. All rights reserved.
AI for IT modernization: Faster, cheaper, better 8"
43,mckinsey,extracting-value-from-ai-in-banking-rewiring-the-enterprise.pdf,"Financial Services Practice
Extracting value from
AI in banking: Rewiring
the enterprise
To gain material value from AI, banks need to move beyond experimentation to
transform critical business areas, including by reimagining complex workflows with
multiagent systems.
This article is a collaborative effort by Carlo Giovine, Larry Lerner, Renny Thomas, Shwaitang Singh, Sudhakar Kakulavarapu,
and Violet Chung, with Yuvika Motwani, representing views from McKinsey’s Financial Services Practice.
December 2024
Much has been written about the power of AI, helping to pinpoint which loans might go bad,
including generative AI (gen AI), to transform enabling the bank to take steps to intervene and
banking. Beyond ushering in the next wave of support the client.
automation, AI promises to make banks more
intelligent, efficient, and better able to achieve A regional bank, meanwhile, used gen AI to boost
stronger financial performance. the productivity and efficiency of its software
developers. Seeking to optimize resources and
While the buzz is undeniable, many banking C-suite accelerate time to market of new developments, the
leaders are increasingly asking questions about bank launched a proof-of-concept study to assess
the realization of value in light of the headwinds the impact of gen AI tools on coding productivity.
facing the sector. Will AI live up to expectations? Productivity rose about 40 percent for the use
After initial experimentation, how can banks go cases that were part of the study; more than
from proof of concept to proof of value and truly 80 percent of developers said gen AI improved their
reimagine and transform the enterprise using AI? coding experience.
How soon, if ever, can banks see a tangible return
on their investments in AI? In this article, we detail a blueprint to help
financial-services leaders chart the complex path
These questions are gaining relevance as the global of extracting at-scale value from AI across the
banking sector contends with challenges such as enterprise. We begin with what banks that excel
uneven labor productivity results, including falling in AI do differently. We then outline a road map
productivity at US banks, despite high technology that roots the AI transformation in business value,
spending relative to other sectors. Banks also face ascertaining which key business problems need
slowing revenue and loan growth and competition to be solved and harnessing technology, including
from businesses beyond banking—such as private AI, to help with the process. Next, we describe a
credit firms, fintechs, neobanks, payment solutions comprehensive AI capability stack for banking
businesses, and nonbank providers—for the powered by AI agents. Finally, we explore the
largest profit pools. To maintain the current return elements needed to sustain and scale value from AI
on tangible equity margins, banks will need to cut beyond the initial rollouts.
costs much faster as revenue growth slows. AI
has the potential to chip away at these problems
and put banks on more solid footing in the years to Delivering on the promise of AI
come, particularly in boosting labor productivity as in banking
employees continue to delegate a growing number The latest McKinsey Global Survey on AI shows
of routine tasks to increasingly sophisticated and that adoption has increased significantly across
capable AI systems. organizations and industries. However, the
breadth of adoption (measured by the deployment
Some institutions are raising the bar and creating of AI across multiple enterprise functions)
strategic distance from their peers by effectively remains low, and many organizations are still in
scaling AI, including gen AI. For example, a the experimental phase.
large bank is using AI across the enterprise
to improve experiences for its customers and Still, a few leading banks stand out in their ability to
employees, enhance efficiency, and boost revenue deploy AI, including gen AI, across the enterprise,
and profitability. In retail banking, the bank is and have begun to capture material gains from the
harnessing AI to generate personalized nudges use of AI (see sidebar “What does it mean to be an
to help customers with investing and financial AI-first bank?”).
planning. In the small-business segment, AI is
Extracting value from AI in banking: Rewiring the enterprise 2
What does it mean to be an AI-first bank?
AI is enabling broad changes in all sorts The essentials of building an AI-first bank — Modernizing core technology
of industries, including banking, but many include the following: required for the backbone of the AI
banks are still in the experimental phase. capability stack, including automated
— Reimagining the customer
Given how far AI has come and the promise cloud provisioning, an application
experience by providing personalized
it holds, experimenting is not enough. To programming interface, and
offers and streamlined, frictionless
thrive in this new world, banks will need streamlined architecture to enable
use across various devices, for
to become AI-first institutions, adopting continuous, secure data exchange
bank-owned platforms as well as
AI technologies enterprise-wide to boost among various parts of the bank.
partner ecosystems.
value—or risk being left behind.
— Setting up a platform operating model
A successful AI transformation spans — Using AI to help with decision making,
that brings together the right talent,
several layers of the organization. It’s significantly enhancing productivity by
culture, and organizational design.
important to invest in each of the building the architecture required to
interdependent layers, as underinvestment generate real-time analytical insights
in one section can sabotage the entire and translating them into messages
AI transformation. addressing precise customer needs.
Our experience suggests that banks excelling in AI tools, can (see sidebar “What are multiagent
do four things well: systems?”). Expanding these systems to
the entire enterprise requires setting up a
— Set a bold, bankwide vision for the value AI comprehensive AI bank stack.
can create. Leading banks have an expansive
outlook on the role that AI can play, viewing — Sustain and scale value by setting up critical
the technology not just as a driver of cost enablers of the AI transformation. These include
efficiencies but also as a way to enhance cross-functional business, technology, and
revenues and significantly improve customer AI teams along with a central AI control tower
and employee experiences. that coordinates enterprise decisions across
functions, drives governance and adoption of
— Root the transformation in business value by standardized risk guardrails, and promotes the
transforming entire domains, processes, and reusability of AI capabilities.
journeys rather than just deploying narrow
use cases. Banks that excel in AI resist the
temptation to launch narrow use cases such Setting a bold, bankwide vision
as a chatbot or a conversational Q&A tool in for the value AI can create
isolation. Although these might be fast to launch McKinsey’s experience with hundreds of companies
and potentially low risk, in isolation, they won’t across various industries shows that capturing
unlock material financial value. value from digital and AI transformations requires a
fundamental rewiring of how a company operates.
— Build a comprehensive stack of AI capabilities This involves six critical enterprise capabilities:
powered by multiagent systems. Running a business-led digital road map, talent with the
complex banking workflows, such as evaluating right skills, a fit-for-purpose operating model,
a commercial customer’s loan application, technology that’s easy for teams to use, data that’s
involves highly variable steps and the processing continually enriched and easily accessible across
of a mix of structured and unstructured data. the enterprise, and adoption and scaling of digital
While traditional automation cannot handle solutions. These elements are interconnected, and
such tasks, gen-AI-enabled multiagent all have to function well for the transformation to be
systems, combined with predictive AI and digital a success.
Extracting value from AI in banking: Rewiring the enterprise 3
What are multiagent systems?
Multiagent systems, also known as multiagent systems are expected to Eventually, gen AI agents could act
agentic systems, have been around for improve over time. as virtual coworkers. For instance, an
years but have been kicked into a higher engineer could use everyday language
These systems could be capable of
gear in the past two years, thanks to the to describe a new software feature to a
planning actions, using tools to complete
natural-language capabilities of generative programmer agent, which would then
those actions, collaborating with other
AI (gen AI). Although they are still in a code, test, iterate, and deploy the tool it
agents and people, and improving their
nascent phase, and much of the value helped create.
performance as they learn by doing.
they could generate remains hypothetical,
AI can do much more than just automate processes Rooting the transformation in
and boost efficiency. Banks that extract value business value
from AI view the technology as a transformational
Launching a chatbot, creating a document
tool and use AI for core strategic priorities such as
summarizer, using off-the-shelf gen AI tools to
boosting revenue, differentiating the bank from
create ads and write emails—although these types
competitors, and driving higher satisfaction for
of AI endeavors allow banks to experiment and learn
customers and employees.
with minimal risks involved, the results are typically
incremental and, in isolation, rarely lead to material
Leading banks embed AI in the strategic planning
changes in financial outcomes.
process, requiring every business unit to revamp
its operations and set bold financial and customer
Using AI to significantly boost business value will
goals. They focus on innovation by prioritizing the
require banks to do the following:
most high-impact areas that are core to strategy,
versus experimenting in peripheral areas seen as
— Choose the right scope of transformation by
safe bets or taking the “peanut butter” approach
rewiring entire domains and subdomains.
by spreading investments across many disparate
Instead of letting a thousand flowers bloom
initiatives. Next, they invest in enabling the
with many disparate, siloed AI projects,
scalability of AI initiatives by setting up the right
leading banks are using AI to reimagine entire
data and technology platforms.
business domains—such as risk, sales, and
operations—and within them, subdomains
Leading banks also ensure that major AI initiatives
such as relationship management, collections,
are business led, not just technology led. This
and contact-center servicing and operations.
means business executives take ownership of
A typical bank has roughly 25 subdomains
shaping the design of interventions, ensuring what
(Exhibit 1). Once bank executives choose the
is built is tightly aligned with what the business
subdomains for transformation, they reimagine
needs, and holding joint accountability with
each one end to end, using the full range of AI
technology leaders to deliver outcomes.
and digital technologies to achieve the desired
financial outcomes.
Extracting value from AI in banking: Rewiring the enterprise 4
Exhibit 1
Banks can identify business areas for AI transformation and then rewire them
to boost value.
Examples of subdomains that AI could transform in retail banking¹
Sales and Risk Servicing Digital Human Other
Domains
marketing and operations technology resources functions
Digital-led Customer Self-service via Developer Recruitment Legal
customer underwriting digital channels productivity and staffing processes
acquisition such as mobile
banking
Frontline sales Risk-based Assisted service IT operations Performance Regulatory
enablement pricing via contact management, compliance
center, branch, training, and skill and controls
and digital development
Relationship Transaction Middle- and Technology Employee Business
Subdomains management fraud back-office modernization satisfaction intelligence and
and advisory prevention operations and well-being analytics
Partner Portfolio Complaints Product Employee
collaboration for optimization and management and service development
product and monitoring development and for key role
service sales management fulfillment
Engagement, Collections
cross-selling,
and customer
retention
Enterprise knowledge management
Examples of subdomains that AI could transform in private banking¹
Sales and Risk Servicing Digital Human Other
Domains
marketing and operations technology resources functions
Digital-led Client risk Self-service via Developer Recruitment Legal
customer profiling and digital channels productivity and staffing processes
acquisition due diligence such as mobile
banking
Relationship Wealth and Relationship IT operations Performance Regulatory
management, portfolio risk management management, compliance
affluent management and concierge training, and skill and controls
clients² services development
Relationship Credit risk Assisted service Technology Employee Business
Subdomains management, management via contact modernization satisfaction intelligence and
HNW³ and center, branch, and well-being analytics
UHNW⁴ clients and digital
Partner-led Risk-based Complaints Product Employee
client pricing management and service development
acquisition and development and for key role
cross-referrals management fulfillment
Engagement, Fraud and Middle- and
cross-selling, financial crime back-office
and customer prevention operations
retention
Enterprise knowledge management
1A typical bank has ~25 subdomains that could be rewired with AI. This list is not comprehensive.
²Clients with personal financial assets of $100,000–$1 million.
³High-net-worth clients are those with personal financial assets of $1 million–$50 million.
⁴Ultra-high-net-worth clients are those with personal financial assets of >$50 million.
McKinsey & Company
Extracting value from AI in banking: Rewiring the enterprise 5
Exhibit 1 (continued)
Banks can identify business areas for AI transformation and then rewire them
to boost value.
Examples of subdomains that AI could transform in corporate and commercial banking¹
Sales and Risk Servicing Digital Human Other
Domains
marketing and operations technology resources functions
Digital-led Customer Self-service via Developer Recruitment Legal
customer underwriting digital channels productivity and staffing processes
acquisition such as mobile
banking
Partner-led Risk-based Relationship IT operations Performance Regulatory
sales pricing management management, compliance
and concierge training, and skill and controls
services development
Relationship Transaction Middle- and Technology Employee Business
Subdomains management fraud back-office modernization satisfaction intelligence and
and advisory prevention operations and well-being analytics
Frontline sales, Portfolio Complaints Product Employee
generalist, and optimization and management and service development
product led monitoring development and for key role
management fulfillment
Engagement, Loan renewals Assisted service
cross-selling, management via contact
and customer center, branch,
retention and digital
Enterprise knowledge management
Examples of subdomains that AI could transform in investment banking¹
Sales and Risk Servicing Digital Human Other
Domains
marketing and operations technology resources functions
Relationship Customer Relationship Developer Recruitment Legal
management underwriting management productivity and staffing processes
and advisory and concierge
services
Relationship Liquidity risk Middle- and IT operations Performance Regulatory
manager–led management back-office management, compliance
deal sourcing operations training, and skill and controls
development
Engagement, Transaction Complaints Technology Employee Business
Subdomains cross-selling, fraud management modernization satisfaction intelligence and
and customer prevention and well-being analytics
retention
Market risk Product Employee
management and service development
development and for key role
management fulfillment
Enterprise knowledge management
1A typical bank has ~25 subdomains that could be rewired with AI. This list is not comprehensive
McKinsey & Company
Extracting value from AI in banking: Rewiring the enterprise 6
— Decide which subdomains to transform transformation. Together, these subdomains
with AI and in which order. To select these can drive 70 to 80 percent of total incremental
subdomains, banks can consider the overall value from an AI transformation.
business impact and technical feasibility
of driving an AI transformation of a specific In terms of business impact, banks will need to
subdomain (and the likelihood that the assess whether the value of an AI transformation
chosen subdomain includes components of a particular subdomain can be accurately
that can be reused in subsequent subdomain quantified, how well the proposed solution aligns
transformations) (Exhibit 2). In our experience, with the bank’s strategic objectives, how well end
a typical bank has fewer than ten subdomains users (whether clients or employees) are equipped
that could most benefit from an AI overhaul to adopt the solution, and whether the solution will
and should be the first candidates for be a priority for the business.
Web <2024>
E<Axi hini bBiatn 2king>
Exhibit <2> of <8>
Bank subdomains with high business impact and high technical feasibility
should be first in line for an AI transformation.
Illustrative example of how business impact and technical feasibility can inform the transformation
HIGH
o Portfolio optimization and o Assisted service through contact
monitoring center, branch, digital channels
o Product and service o Collections
development and management o Customer underwriting Examples of top
o Regulatory compliance and o Developer productivity candidates for an
controls AI transformation
o Risk-based pricing o Digital-led customer acquisition at a typical bank.
o Engagement, cross-selling, and These subdomains
o Technology modernization customer retention will vary from bank
o Frontline sales enablement to bank.
o Relationship management
o Self-service through digital
channels such as mobile banking
Business
impact
o Business intelligence and o Complaints management
analytics o Enterprise knowledge
o Collaboration with partners management
to sell products and services o IT operations
o Development of employees o Middle- and back-office operations
to fill key roles
o Legal processes
o Employee satisfaction and
well-being o Performance management,
training, and skill development
o Recruitment and staffing
o Transaction fraud prevention
LOW
LOW Technical feasibility HIGH
McKinsey & Company
Extracting value from AI in banking: Rewiring the enterprise 7
Regarding technical feasibility, it is important Once selected for an AI transformation, each
to ascertain the availability and quality of data, subdomain can be deconstructed into a series of
including special considerations for handling executable modules that need to be built, delivered,
sensitive data, techniques for scaling the solution and adopted to drive business value. For example,
across other domains and business units, the transforming the customer underwriting subdomain
reusability of the solution’s components for other end to end involves gen AI, traditional analytics, and
use cases, and the presence of legacy technology digital tools and platforms all working together to
infrastructure that may not be compatible with more reimagine end-to-end workflows and processes
modern AI solutions. (Exhibit 3).
Web <2024>
E<Axi hini bBiatn 3king>
Exhibit <3> of <8>
Banks can rewire the customer underwriting subdomain by using a
combination of gen AI, traditional analytics, and digital tools and platforms.
Elements and use cases Generative AI Traditional analytics Digital tools and platforms
in customer underwriting (illustrative)
Document collection Preassessment Credit assessment Contract generation
s Loan application and s Question generator: s Voice to memo: s Automation of the final step:
document checker: Check for Come up with Summarize insights and Generate contracts, such as
errors, incomplete data, and questions for a actions after personal confirmation of an applicant’s
potential fraud and follow up personal discussion discussion with the eligibility for a loan and loan
with applicants directly with the applicant applicant covenants
s Third-party data validation: s Automated decisions: Make instant decisions to
Verify accuracy of application approve or decline applications based on predefined
details using sources such criteria and risk thresholds
as credit bureaus and
government databases
s Document collection: Allow for multichannel uploading or s Data assessment: Give
scanning of collateral, financial, and know-your-customer estimates for probability
documents; convert them to the required format; and work of default, expected loss,
with customers to get missing or additional documents climate risk (using internal
and external data sources)
s Document analyzer: Check documents for correctness, eg, accuracy of the loan period,
weed out potentially fraudulent documents, and assess income and other data to make
a credit decision
s Unstructured risk elements assessment: Assess risk
elements from unstructured sources, eg, applicant’s
social media footprint and potential reputational damage
s Automated credit memo generation: Generate a credit
memo, a summary of why a customer needs a loan, and
other details for a bank employee to review
s Customer chatbot: Answer customers’ queries and guide them to submit documents, then provide updates on credit decision
and contract finalization
s Employee chatbot: Answer employees’ questions, allow for sending action alerts to teams such as relationship managers, and
drive employees’ communications with customers
s Workflows workbench: Run workflows for end-to-end application management, collateral valuation, legal review, reassignment
of tasks, etc
McKinsey & Company
Extracting value from AI in banking: Rewiring the enterprise 8
Enabling value through an AI stack investment and attention to unlock the full power of
powered by multiagent systems AI for the enterprise.
To embed AI seamlessly across the enterprise,
Given the advent of new technologies such as
banks can implement a comprehensive capability
gen AI, we have updated the AI capability stack
stack that goes beyond just AI models. This AI
(Exhibit 4) from a previous iteration published in
bank stack contains four key capability layers:
2020. Each layer’s foundational elements are
engagement, decision making, data and core tech,
supplemented by several new elements.
and operating model. Each layer will need to receive
Exhibit 4
To drive sustainable value, banks need to put AI first and revamp the entire
technology stack.
New AI bank of the future
elements
Delighting customers through personalized experiences Empowering employees to serve customers better
Engagement Mobile as the gateway to the rest of the bank, including branches, contact center, relationship managers
Multimodal conversational Intelligent products Omnichannel experiences for
experiences (text, visual, voice) and services customers, employees and partners
Use of digital twins to simulate behavior of customers and employees
AI-powered AI orchestration (including copilots and autopilots that organize workflows)Signature Sales
decision Intent Document fraud skills
making AI agents (AI recognition summarizer detector coach
Property Fraud Enterprise
that specializes Risk policy collateral pattern knowledge Test case
in narrow expert analyzer detector search generator
domains)
Ad-banner Legal AI Spend Tax expert
analyzer
Predictive
Customer Credit decision Monitoring Retention, Servicing and
analytics
acquisition making and collections selling, upselling engagement
models
AI Reusable components Information security Streamlined
enablers and services standards and controls risk protocols
Core Industrial AI Observability Machine learning FinOps1 LLM2 LLM Security
technology and machine tool stack operations orchestration gateway
and data learning
Enterprise Search and retrieval engine
data
Data Data Vector Data Structured
ingestion preprocessing databases postprocessing data storage
Technology and Tech-forward strategy (in-house capabilities vs buying offerings; in-house talent plan)
infrastructure
Modern API Intelligent infrastructure (AI operations Cybersecurity Core
architecture command, hybrid cloud setup, etc) and control tiers modernization
Operating Platform Autonomous business, technology, and data teams enabled by AI models and agents
model operating
model Agile ways AI control Modern talent Culture and
of working tower strategy capabilities
Value capture office to monitor transformations
1Financial operations, a framework for managing the operational costs of cloud computing.
2Large language models.
McKinsey & Company
Extracting value from AI in banking: Rewiring the enterprise 9
The AI bank of the future All together now
To create sustainable value, banks need to put AI Elements across the four layers of the AI bank stack
first and revamp the entire technology stack. The work together to enable transformative change and
rise of innovative technologies such as gen AI has deliver value for the enterprise.
prompted an update to the technology stack from
a previous version published in 2020, with new The key to next-generation innovation and
elements highlighted in shades of blue. productivity: Orchestrated multiagent systems
The decision-making layer is the brain of the
Engagement layer AI-first bank, orchestrating and enabling
Banks will need to reimagine how they engage thousands of AI-powered decisions affecting
with customers, making their experiences as customers (such as which product to recommend
intelligent, personalized, and frictionless as possible to them next) and employees (for instance, should
through the use of AI. Leading banks’ customers they approve credit for a specific customer or flag
are experiencing human-like conversational a transaction as fraudulent) across the full life
interactions with AI via text and voice chats and are cycle of products and services.
moving seamlessly across channels such as mobile
apps, websites, branches, and contact centers, Predictive AI models, a core part of the decision-
thanks to powerful AI capabilities. making layer at most banks, are great at driving
decisions when presented with structured data
AI-powered decision-making layer under controlled conditions. These models,
The brain of the bank, this layer makes and however, struggle to adapt when data is
orchestrates decisions. Historically, banks unstructured and the nature of the tasks is nonlinear
have focused on deploying traditional analytics and requires multistep planning, reasoning, and
modules such as models, but as AI technologies orchestration. Such tasks include, for example,
mature, this layer has expanded to include preparing a credit memo—a summary of why a
agent and AI orchestration sublayers working customer needs a loan and other details—based
in unison with the traditional analytics layer to on multiple interactions with that customer and an
drive superior outcomes. evaluation of various types of documents. Another
example is coaching a low-performing seller on how
Core technology and data layer to improve sales performance.
This layer includes the technology and data needed
for an AI transformation, including reusable tools Orchestrated multiagent systems represent a
and pipelines equipped with machine learning major advancement in the decision-making layer.
operations capabilities needed to run large These systems comprise various AI “agents” that
language models (LLMs) at scale. Other portions can be thought of as virtual coworkers. Enabled
of this layer include the data needed to train by advances in gen AI technology, these agents,
multiagent systems, as well as modern application like humans, have the capacity to eventually be
programming interface (API) architecture and able to plan (for instance, organize a workflow
robust cybersecurity. encompassing a series of tasks), think (come up
with chain-of-thought reasoning), and act (use
Operating model digital tools).
By integrating business and technology in platforms
run by cross-functional teams, banks can break Multiagent systems remain nascent and will need
up organizational silos, boost agility and speed, more technical development before they will be
and better align goals and priorities across the ready to deploy at scale across enterprises, but they
enterprise. An AI control tower tracks the value are nonetheless attracting attention because of the
realized from AI initiatives, among other tasks. promise they hold.
Extracting value from AI in banking: Rewiring the enterprise 10
These agents, when combined with predictive AI train and define operating procedures for the
models and digital tools, could fundamentally rewire orchestrators to follow; however, the hope is
several domains of the bank, not just unlocking that the technology will evolve to make them
productivity but forming the basis of more engaging more autonomous.
experiences for customers and bank employees.
— The AI agent layer comprises AI focused on
Multiagent systems can automate complex completing specialized tasks as instructed by
decisions and workflows through a twofold use of AI the orchestration layer or by other agents. Each
(Exhibit 5): of these AI agents, powered by LLMs, is fine-
tuned through a combination of domain-specific
— The AI orchestration layer handles complex data and human feedback. For instance, a policy
workflows and task planning. These AI agent, after being provided with the bank’s loan
orchestrators, programmed to achieve certain policies and related exceptions, can suggest
goals, are expected to eventually be able to do the appropriate loan terms for a customer,
things like autonomously plan actions, reach much like a seasoned bank executive would.
decisions, and make use of existing tools, Meanwhile, a collateral inspection agent can
in-house data, and other AI agents to complete be trained on documents and images related to
stated goals. These AI orchestrators could collateral that customers use to apply for a loan,
manifest themselves in the form of increasingly such as photos of small-business storefronts.
popular copilots for employees and customers. A computer vision tool would then work with
For instance, a copilot for a bank’s credit the collateral inspection agent to screen new
managers is capable of not just answering collateral documents and images to spot
questions but orchestrating the entire credit instances of fraud, such as doctored photos that
workflow when a customer applies for a loan. make the storefront look more impressive than
For now, human intervention is still needed to it really is.
1Financial operations, a framework for managing the operational costs of cloud computing.
2Large language models.
Web <2024>
<EAxi hini bBaitn k5ing>
Exhibit <5> of <8>
Orchestrated multiagent
systems represent a big
AI-powered
advance in banks’ decision
making
decision-making capabilities.
Interacting to solve problems
Multiagent systems are still in a nascent phase, but
eventually AI agents could act as virtual coworkers
capable of planning and executing tasks.
The AI AI orchestration (including copilots and autopilots that organize workflowsS)ignature Sales
Intent Document
orchestration fraud skills
layer handles AI agents (AI recognition Propertysummarizer Fraud detector Enterprise coach
complex that specializes Risk policy collateral pattern knowledge Test case
workflows, in narrow expert analyzer detector search generator
domains)
calling on Ad-banner Legal AI Spend Tax expert
specialized AI analyzer
agents to Predictive Customer Credit decision Monitoring Retention, Servicing and
analytics
complete acquisition making and collections selling, upselling engagement
models
discrete tasks.
Analytics and
AI enablers AI Reusable components Information security Streamlined
support these enablers and services standards and controls risk protocols
efforts.
McKinsey & Company
Extracting value from AI in banking: Rewiring the enterprise 11
The work of these two types of AI is enhanced by agents can be continuously trained to become
AI enablers, including reusable components and better over time, and they"
44,mckinsey,exploring-opportunities-in-the-generative-ai-value-chain.pdf,"Exploring opportunities in the
generative AI value chain
Generative AI is giving rise to an entire ecosystem, from hardware providers to application
builders, that will help bring its potential for business to fruition.
This article is a collaborative effort by Tobias Härlin, Gardar Björnsson Rova, Alex Singla, Oleg Sokolov, and Alex Sukharevsky,
representing views from McKinsey Digital.
© Getty Images
April 2023
Over the course of 2022 and early 2023, tech in this fast-paced space. Our assessments are
innovators unleashed generative AI en masse, based on primary and secondary research, including
dazzling business leaders, investors, and society at more than 30 interviews with business founders,
large with the technology’s ability to create entirely CEOs, chief scientists, and business leaders
new and seemingly human-made text and images. working to commercialize the technology; hundreds
of market reports and articles; and proprietary
The response was unprecedented. McKinsey research data.
In just five days, one million users flocked to ChatGPT,
OpenAI’s generative AI language model that creates A brief explanation of generative AI
original content in response to user prompts. It took To understand the generative AI value chain,
Apple more than two months to reach the same level it’s helpful to have a basic knowledge of what
of adoption for its iPhone. Facebook had to wait ten generative AI is⁵ and how its capabilities differ from
months and Netflix more than three years to build the the “traditional” AI technologies that companies
same user base. use to, for example, predict client churn, forecast
product demand, and make next-best-product
And ChatGPT isn’t alone in the generative AI industry. recommendations.
Stability AI’s Stable Diffusion, which can generate
images based on text descriptions, garnered more A key difference is its ability to create new content.
than 30,000 stars on GitHub within 90 days of This content can be delivered in multiple modalities,
its release—eight times faster than any previous including text (such as articles or answers to
package.¹ questions), images that look like photos or paintings,
videos, and 3-D representations (such as scenes
This flurry of excitement isn’t just organizations and landscapes for video games).
kicking the tires. Generative AI use cases are already
taking flight across industries. Financial services Even in these early days of the technology’s
giant Morgan Stanley is testing the technology to help development, generative AI outputs have been jaw-
its financial advisers better leverage insights from droppingly impressive, winning digital-art awards
the firm’s more than 100,000 research reports.² The and scoring among or close to the top 10 percent of
government of Iceland has partnered with OpenAI test takers in numerous tests, including the US bar
in its efforts to preserve the endangered Icelandic exam for lawyers and the math, reading, and writing
language.³ Salesforce has integrated the technology portions of the SATs, a college entrance exam used
into its popular customer-relationship-management in the United States.⁶
(CRM) platform.⁴
Most generative AI models produce content in one
The breakneck pace at which generative AI format, but multimodal models that can, for example,
technology is evolving and new use cases are create a slide or web page with both text and
coming to market has left investors and business graphics based on a user prompt are also emerging.
leaders scrambling to understand the generative AI
ecosystem. While deep dives into CEO strategy and All of this is made possible by training neural
the potential economic value that the technology networks (a type of deep learning algorithm)
could create globally across industries are on enormous volumes of data and applying
forthcoming, here we share a look at the generative “attention mechanisms,” a technique that helps AI
AI value chain composition. Our aim is to provide models understand what to focus on. With these
a foundational understanding that can serve as a mechanisms, a generative AI system can identify
starting point for assessing investment opportunities word patterns, relationships, and the context of a
1 Guido Appenzeller, Matt Bornstein, Martin Casado, and Yoko Li, “Art isn’t dead; it’s just machine generated,” Andreessen Horowitz, November 16, 2022.
2 Hugh Son, “Morgan Stanley is testing an OpenAI-powered chatbot for its 16,000 financial advisors,” CNBC, March 14, 2023.
3 “Government of Iceland: How Iceland is using GPT-4 to preserve its language,” OpenAI, March 14, 2023.
4 “Salesforce announces Einstein GPT, the world’s first generative AI for CRM,” Salesforce, March 7, 2023.
5 “What is generative AI?” McKinsey, January 19, 2023.
6 “GPT-4,” OpenAI, March 14, 2023.
2 Exploring opportunities in the generative AI value chain
user’s prompt (for instance, understanding that think it’s quite similar to a traditional AI value
“blue” in the sentence “The cat sat on the mat, which chain. After all, of the six top-level categories—
was blue” represents the color of the mat and not computer hardware, cloud platforms, foundation
of the cat). Traditional AI also might use neural models, model hubs and machine learning
networks and attention mechanisms, but these operations (MLOps), applications, and services—
models aren’t designed to create new content. They only foundation models are a new addition
can only describe, predict, or prescribe something (Exhibit 1).
based on existing content.
However, a deeper look reveals some significant
differences in market opportunities. To begin
The value chain: Six links, but one with, the underpinnings of generative AI systems
outshines them all are appreciably more complex than most
As the development and deployment of generative traditional AI systems. Accordingly, the time,
AI systems gets under way, a new value chain cost, and expertise associated with delivering
is emerging to support the training and use of them give rise to significant headwinds for new
this powerful technology. At a glance, one might entrants and small companies across much of the
EWxebh i<byieta 1r>
<Title>
TExhhibeitr <ex> aofr <ex> opportunities across the generative AI value chain, but the most
sTihgenriefi caarnet o ips pbouritludninitgie esn adc-urosesrs atphpel giceantieornast.ive AI value chain, but the most
significant is building end-user applications.
Opportunity size for new entrants
Generative AI value chain in next 3–5 years, scale of 1–5
Services
Services around specialized knowledge on how to leverage
generative AI (eg, training, feedback, and reinforcement learning)
Applications
B2B or B2C products that use foundation models either largely as is
or fine-tuned to a particular use case
Model hubs and MLOps
Tools to curate, host, fine-tune, or manage the foundation models
(eg, storefronts between applications and foundation models)
Foundation models
Core models on which generative AI applications can be built
Cloud platforms
Platforms to provide access to computer hardware
Computer hardware
Accelerator chips optimized for training and running the models
McKinsey & Company
Exploring opportunities in the generative AI value chain 3
value chain. While pockets of value exist throughout, specialized skills, knowledge, and computational
our research suggests that many areas will continue capabilities necessary to serve the generative AI
to be dominated by tech giants and incumbents for market.
the foreseeable future.
The generative AI application market is the section Cloud platforms
of the value chain expected to expand most rapidly GPUs and TPUs are expensive and scarce, making it
and offer significant value-creation opportunities difficult and not cost-effective for most businesses
to both incumbent tech companies and new to acquire and maintain this vital hardware platform
market entrants. Companies that use specialized on-premises. As a result, much of the work to
or proprietary data to fine-tune applications can build, tune, and run large AI models occurs in the
achieve a significant competitive advantage over cloud. This enables companies to easily access
those that don’t. computational power and manage their spend as
needed.
Computer hardware Unsurprisingly, the major cloud providers have
Generative AI systems need knowledge—and the most comprehensive platforms for running
lots of it—to create content. OpenAI’s GPT-3, the generative AI workloads and preferential access
generative AI model underpinning ChatGPT, for to the hardware and chips. Specialized cloud
example, was trained on about 45 terabytes of text challengers could gain market share, but not in the
data (akin to nearly one million feet of bookshelf near future and not without support from a large
space).⁷ enterprise seeking to reduce its dependence on
hyperscalers.
It’s not something traditional computer hardware
can handle. These types of workloads require
large clusters of graphic processing units (GPUs) Foundation models
or tensor processing units (TPUs) with specialized At the heart of generative AI are foundation models.
“accelerator” chips capable of processing all that These large deep learning models are pretrained
data across billions of parameters in parallel. to create a particular type of content and can
be adapted to support a wide range of tasks. A
Once training of this foundational generative AI foundation model is like a Swiss Army knife—it can
model is completed, businesses may also use such be used for multiple purposes. Once the foundation
clusters to customize the models (a process called model is developed, anyone can build an application
“tuning”) and run these power-hungry models within on top of it to leverage its content-creation
their applications. However, compared with the capabilities. Consider OpenAI’s GPT-3 and GPT-4,
initial training, these latter steps require much less foundation models that can produce human-quality
computational power. text. They power dozens of applications, from the
much-talked-about chatbot ChatGPT to software-
While there are a few smaller players in the mix, as-a-service (SaaS) content generators Jasper and
the design and production of these specialized Copy.ai.
AI processors is concentrated. NVIDIA and
Google dominate the chip design market, and one Foundation models are trained on massive data sets.
player, Taiwan Semiconductor Manufacturing This may include public data scraped from Wikipedia,
Company Limited (TSMC), produces almost all of government sites, social media, and books, as well
the accelerator chips. New market entrants face as private data from large databases. OpenAI, for
high start-up costs for research and development. example, partnered with Shutterstock to train its
Traditional hardware designers must develop the image model on Shutterstock’s proprietary images.⁸
7 “What is generative AI?” January 19, 2023; and Kindra Cooper, “OpenAI GPT-3: Everything you need to know,” Springboard, November 1, 2021.
8 “Shutterstock partners with OpenAI and leads the way to bring AI-generated content to all,” Shutterstock, October 25, 2022.
4 Exploring opportunities in the generative AI value chain
Developing foundation models requires deep start-ups backed by significant investment
expertise in several areas. These include (Exhibit 2). However, there is work in progress
preparing the data, selecting the model toward making smaller models that can deliver
architecture that can create the targeted output, effective results for some tasks and training that
training the model, and then tuning the model to is more efficient, which could eventually open
improve output (which entails labeling the quality the market to more entrants. We already see that
of the model’s output and feeding it back into the some start-ups have achieved certain success in
model so it can learn). developing their own models—Cohere, Anthropic,
and AI21, among others, build and train their
Today, training foundation models in particular own large language models (LLMs). Additionally,
comes at a steep price, given the repetitive nature there is a scenario where most big companies
of the process and the substantial computational would want to have LLMs working in their
resources required to support it. In the beginning environments—such as for a higher level of data
of the training process, the model typically security and privacy, among other reasons—and
produces random results. To improve its next some players (such as Cohere) already offer this
output so it is more in line with what is expected, kind of service around LLMs.
the training algorithm adjusts the weights of the
underlying neural network. It may need to do It’s important to note that many questions have
this millions of times to get to the desired level yet to be answered regarding ownership and
of accuracy. Currently, such training efforts can rights over the data used in the development
cost millions of dollars and take months. Training of this nascent technology—as well as over the
OpenAI’s GPT-3, for example, is estimated to cost outputs produced—which may influence how the
$4 million to $12 million.⁹ As a result, the market technology evolves (see sidebar, “Some of the
is currently dominated by a few tech giants and key issues shaping generative AI’s future”).
Some of the key issues shaping generative AI’s future
Amid the enormous enthusiasm, many questions have emerged surrounding generative AI technology, the answers to which will likely shape
future development and use. Following are three of the most important questions to consider when evaluating how the generative AI
ecosystem will evolve:
— Can copyrighted or personal data be used to train models? When training foundation models, developers typically “scrape” data
from the internet. This can sometimes include copyrighted images, news articles, social media data, personal data protected by
the General Data Protection Regulation (GDPR), and more. Current laws and regulations are ambiguous in terms of the implications
of such practices. Precedents will likely evolve to place limits on scraping proprietary data that may be posted online or enable
data owners to restrict or opt out of search indexes so their data can’t easily be found online. New compensation models for data
owners will also likely emerge.
— Who owns the creative outputs? Current laws and regulations also do not clearly answer who owns the copyright on the final
“output” of a generative AI system. Several potential actors can share or own exclusive rights to the final outputs, such as the data
set owner, model developer, platform owner, prompt creator, or the designer who manually refines and delivers the final generative
AI output.
— How will organizations manage the quality of generative AI outputs? We have already seen numerous examples of systems
providing inaccurate, inflammatory, biased, or plagiarized content. It’s not clear whether models will be able to eliminate such
outputs. Ultimately, all companies developing generative AI applications will need processes for assessing outputs at the use case
level and determining where the potential harm should limit commercialization.
9 Kif Leswing and Jonathan Vanian, “ChatGPT and generative AI are booming, but the costs can be extraordinary,” CNBC, March 13, 2023; and
Toby McClean, “Machines are learning from each other, but it’s a good thing,” Forbes, February 3, 2021.
Exploring opportunities in the generative AI value chain 5
Exhibit 2
Examples of generative AI models from some of the early providers show there
are many options available for each modality, several of which are open source.
Examples of generative AI models from some of the early providers show there
are many options available for each modality, several of which are open source.
Closed source¹ Closed source, available through APIs² Open source³
Protein structures
Text Image Audio or music 3-D Video or DNA sequences
RODIN
Microsoft VALL-E GODIVA MoLeR
Diffusion
OpenAI⁴ GPT-4 DALL-E 2 Jukebox Point-E
Meta LLaMA Make-a-scene AudioGen Builder Bot Make-a-video ESMFold
Google/
LaMDA Imagen MusicLM DreamFusion Imagen Video AlphaFold2
DeepMind
Stable Dance
Stability AI StableLM LibreFold
Diffusion 2 Diffusion
Amazon Lex DeepComposer
Apple GAUDI
NVIDIA MT-NLG Edify Edify Edify MegaMolBART
Cohere Family of LLMs
Anthropic Claude
AI21 Jurassic-2
Note: List of products are provided for informational purposes only and do not reflect an endorsement from McKinsey & Company.
1“Closed source” defined as: model not publicly available, access is typically granted through strict process, and usage may be governed by NDA or other
contract.
2“Closed source, available through APIs” defined as: source code of model is not available to the public, but the model is often accessible via API, where usage is
typically governed by licensing agreements.
3“Open source” defined as: code of models available to the public and can be either freely used, distributed, and modified by anyone or restricted for non-
commercial use.
4OpenAI is backed by significant Microsoft investments.
McKinsey & Company
6 Exploring opportunities in the generative AI value chain
Model hubs and MLOps existing solution provider working to add innovative
To build applications on top of foundation models, capabilities to its current offerings, or a business
businesses need two things. The first is a place to looking to build a competitive advantage in its
store and access the foundation model. Second, they industry.
may need specialized MLOps tooling, technologies,
and practices for adapting a foundation model and There are many ways that application providers
deploying it within their end-user applications. This can create value. At least in the near term, we
includes, for example, capabilities to incorporate and see one category of applications offering the
label additional training data or build the APIs that greatest potential for value creation. And we expect
allow applications to interact with it. applications developed for certain industries and
functions to provide more value in the early days of
Model hubs provide these services. For closed- generative AI.
source models in which the source code is not
made available to the public, the developer of the Applications built from fine-tuned models
foundation model typically serves as a model hub. It stand out
will offer access to the model via an API through a Broadly, we find that generative AI applications
licensing agreement. Sometimes the provider will fall into one of two categories. The first represents
also deliver MLOps capabilities so the model can be instances in which companies use foundation
tuned and deployed in different applications. models largely as is within the applications they
build—with some customizations. These could
For open-source models, which provide code that include creating a tailored user interface or adding
anyone can freely use and modify, independent guidance and a search index for documents that
model hubs are emerging to offer a spectrum of help the models better understand common
services. Some may act only as model aggregators, customer prompts so they can return a high-quality
providing AI teams with access to different output.
foundation models, including those customized
by other developers. AI teams can then download The second category represents the most attractive
the models to their servers and fine-tune and part of the value chain: applications that leverage
deploy them within their application. Others, such fine-tuned foundation models—those that have
as Hugging Face and Amazon Web Services, may been fed additional relevant data or had their
provide access to models and end-to-end MLOps parameters adjusted—to deliver outputs for a
capabilities, including the expertise to tune the particular use case. While training foundation
foundation model with proprietary data and deploy models requires massive amounts of data, is
it within their applications. This latter model fills extremely expensive, and can take months, fine-
a growing gap for companies eager to leverage tuning foundation models requires less data, costs
generative AI technology but lacking the in-house less, and can be completed in days, putting it within
talent and infrastructure to do so. reach of many companies.
Application builders may amass this data from
Applications in-depth knowledge of an industry or customer
While one foundation model is capable of performing needs. For example, consider Harvey, the
a wide variety of tasks, the applications built on top generative AI application created to answer legal
of it are what enable a specific task to be completed— questions. Harvey’s developers fed legal data sets
for example, helping a business’s customers with into OpenAI’s GPT-3 and tested different prompts
service issues or drafting marketing emails (Exhibit to enable the tuned model to generate legal
3). These applications may be developed by a new documents that were far better than those that the
market entrant seeking to deliver a novel offering, an original foundation model could create.
Exploring opportunities in the generative AI value chain 7
Exhibit 3
There are many applications of generative AI across modalities.
There are many applications of generative AI across modalities.
Modality Application Example use cases
Text Content writing • Marketing: creating personalized emails and posts
• Talent: drafting interview questions, job descriptions
Chatbots or assistants • Customer service: using chatbots to boost conversion on websites
Search • Making more natural web search
• Corporate knowledge: enhancing internal search tools
Analysis and synthesis • Sales: analyzing customer interactions to extract insights
• Risk and legal: summarizing regulatory documents
Code Code generation • IT: accelerating application development and quality with automatic code
recommendations
Application prototype • IT: quickly generating user interface designs
and design
Data set generation • Generating synthetic data sets to improve AI models quality
Image Stock image generator • Marketing and sales: generating unique media
Image editor • Marketing and sales: personalizing content quickly
Audio Text to voice generation • Trainings: creating educational voiceover
Sound creation • Entertainment: making custom sounds without copyright violations
Audio editing • Entertainment: editing podcast in post without having to rerecord
3-D 3-D object generation • Video games: writing scenes, characters
or other • Digital representation: creating interior-design mockups and virtual
staging for architecture design
Product design and • Manufacturing: optimizing material design
discovery • Drug discovery: accelerating R&D process
Video Video creation • Entertainment: generating short-form videos for TikTok
• Training or learning: creating video lessons or corporate presentations
using AI avatars
Video editing • Entertainment: shortening videos for social media
• E-commerce: adding personalization to generic videos
• Entertainment: removing background images and background noise
in post
Voice translation • Video dubbing: translating into new languages using AI-generated or
and adjustments original-speaker voices
• Live translation: for corporate meetings, video conferencing
• Voice cloning: replicating actor voice or changing for studio effect such
as aging
Face swaps and • Virtual effects: enabling rapid high-end aging; de-aging; cosmetic, wig,
adjustments and prosthetic fixes
• Lip syncing or “visual” dubbing in post-production: editing footage to
achieve release in multiple ratings or languages
• Face swapping and deep-fake visual effects
• Video conferencing: real-time gaze correction
Note: This list is not exhaustive.
McKinsey & Company
8
Organizations could also leverage proprietary — Information technology. Generative AI can help
data from daily business operations. A software teams write code and documentation. Already,
developer that has tuned a generative AI chatbot automated coders on the market have improved
specifically for banks, for instance, might partner developer productivity by more than 50 percent,
with its customers to incorporate data from call- helping to accelerate software development. ¹0
center chats, enabling them to continually elevate
the customer experience as their user base grows. — Marketing and sales. Teams can use generative
AI applications to create content for customer
Finally, companies may create proprietary data outreach. Within two years, 30 percent of all
from feedback loops driven by an end-user rating outbound marketing messages are expected to
system, such as a star rating system or a thumbs- be developed with the assistance of generative
up, thumbs-down rating system. OpenAI, for AI systems.¹¹
instance, uses the latter approach to continuously
train ChatGPT, and OpenAI reports that this helps — Customer service. Natural-sounding,
to improve the underlying model. As customers personalized chatbots and virtual assistants
rank the quality of the output they receive, that can handle customer inquiries, recommend
information is fed back into the model, giving it more swift resolution, and guide customers to the
“data” to draw from when creating a new output— information they need. Companies such as
which improves its subsequent response. As the Salesforce, Dialpad, and Ada have already
outputs improve, more customers are drawn to use announced offerings in this area.
the application and provide more feedback, creating
a virtuous cycle of improvement that can result in a — Product development. Companies can use
significant competitive advantage. generative AI to rapidly prototype product
designs. Life sciences companies, for instance,
In all cases, application developers will need to keep have already started to explore the use of
an eye on generative AI advances. The technology is generative AI to help generate sequences of
moving at a rapid pace, and tech giants continue to amino acids and DNA nucleotides to shorten the
roll out new versions of foundation models with even drug design phase from months to weeks.¹²
greater capabilities. OpenAI, for instance, reports
that its recently introduced GPT-4 offers “broader In the near term, some industries can leverage
general knowledge and problem-solving abilities” these applications to greater effect than others.
for greater accuracy. Developers must be prepared The media and entertainment industry can become
to assess the costs and benefits of leveraging these more efficient by using generative AI to produce
advances within their application. unique content (for example, localizing movies
without the need for hours of human translation)
Pinpointing the first wave of application impact and rapidly develop ideas for new content and
by function and industry visual effects for video games, music, movie
While generative AI will likely affect most business story lines, and news articles. Banking, consumer,
functions over the longer term, our research telecommunications, life sciences, and technology
suggests that information technology, marketing companies are expected to experience outsize
and sales, customer service, and product operational efficiencies given their considerable
development are most ripe for the first wave of investments in IT, customer service, marketing and
applications. sales, and product development.
¹0 GitHub Product Blog, “Research: Quantifying GitHub Copilot’s impact on developer productivity and happiness,” blog entry by Eirini
Kalliamvakou, September 7, 2022.
¹¹ Jackie Wiles, “Beyond ChatGPT: The future of generative AI for enterprises,” Gartner, January 26, 2023.
¹² NVIDIA Developer Technical Blog, “Build generative AI pipelines for drug discovery with NVIDIA BioNeMo Service,” blog entry by Vanessa
Braunstein, March 21, 2023; and Alex Ouyang and Abdul Latif Jameel, “Speeding up drug discovery with diffusion generative models,” MIT
News, March 31, 2023.
Exploring opportunities in the generative AI value chain 9
Services
While generative AI technology and its supporting
As with AI in general, dedicated generative AI
ecosystem are still evolving, it is already quite clear
services will certainly emerge to help companies
that applications offer the most significant value-
fill capability gaps as they race to build out their
creation opportunities. Those who can harness
experience and navigate the business opportunities
niche—or, even better, proprietary—data in fine-tuning
and technical complexities. Existing AI service
foundation models for their applications can expect to
providers are expected to evolve their capabilities to
achieve the greatest differentiation and competitive
serve the generative AI market. Niche players may
advantage. The race has already begun, as evidenced
also enter the market with specialized knowledge
by the steady stream of announcements from software
for applying generative AI within a specific function
providers—both existing and new market entrants—
(such as how to apply generative AI to customer
bringing new solutions to market. In the weeks and
service workflows), industry (for instance, guiding
months ahead, we will further illuminate value-creation
pharmaceutical companies on the use of generative
prospects in particular industries and functions as well
AI for drug discovery), or capability (such as how to
as the impact generative AI could have on the global
build effective feedback loops in different contexts).
economy and the future of work.
Tobias Härlin and Gardar Björnsson Rova are partners in McKinsey’s Stockholm office, where Oleg Sokolov is an associate
partner; Alex Singla is a senior partner in the Chicago office; and Alex Sukharevsky is a senior partner in the London office.
Copyright © 2023 McKinsey & Company. All rights reserved.
10 Exploring opportunities in the generative AI value chain"
45,mckinsey,GenAI in Norway_ENG_version_v2.pdf,"The economic
potential of
Generative AI
in Norway
The next productivity frontier
June 2023
CONFIDENTIAL AND PROPRIETARY
Any use of this material without specific permission of McKinsey & Company
is strictly prohibited
About this document
 In the newest report from McKinsey Global
Institute (MGI), MGI discuss how GenAI can
transform the way we work
 To assess the effect of GenAI adoption in
Norway and the Norwegian workforce and
businesses, McKinsey Norway used numbers
calculated by MGI, and method applied
there, with Norwegian revenue1,
employment and education data from SSB
(Statistics Norway)
 Additional data was from Statista, European
Commission, Eurostat, OECD, and GEDI
1. 2020, the most recently available at the time of writing
McKinsey & Company 2
Generative AI (GenAI) is experiencing significant momentum globally and is expected to gain
traction in Norway with value creation potential of 95-159 bn NOK by 2045 across Norwegian
industries
 We expect Norway to be a leading global adopter of GenAI due to the economic
environment, education level of the population, and high degrees of digital adoption
 The impact of Generative AI will fall heavily on occupations requiring higher levels of education.
Norway is the 10th highest educated country in the world, and much of the workforce is classified
as knowledge-workers, typically with high wages. This increases the feasibility of early adoption
of GenAI in daily activities
The highest potential value in Norway is expected to be unlocked in selected sectors,
Executive including Energy, High Tech, Travel, Transport & Logistics, and Retail, but true value unlock
comes from three major business functions as opposed to sectors
summary
 Marketing and Sales (28-43 bn NOK), Software Engineering (21-43 bn NOK), and Customer
Operations (12-17 bn NOK) will drive the highest amount of value unlock in Norway due to
the high degree of “generation” activities i.e., generating content such as marketing material,
code and emails
 While the highest potential value is expected to be unlocked in the Energy industry (~21 bn
NOK), High Tech (~18 bn NOK) is expected to experience a more disruptive shift (7%) following
the adoption of GenAI
Productivity growth has slowed in the last decade but will likely be advanced by GenAI. We
expect work activities within decision making and collaboration, and data management, to be most
affected by GenAI. Such activities are most commonly performed by highly educated workers, and
educators / workforce trainers, employees within business and legal professions, and STEM
professionals, are likely to see the largest productivity gains upon GenAI adoption
MMccKKiinnsseeyy && CCoommppaannyy 33
What is Generative AI?
Suitable Unsuitable
Non-exhaustive
Generative AI (GenAI) enables the creation of new Although some areas are unsuited for GenAI, several
unstructured content, such as text, images, etc. applications emerge2:
Recent GenAI efforts are powered by Foundational
Code/image/audio/video/text generation and editing,
Models trained on a broad set of data that enables
while taking surrounding context into account
them to respond to a wide range of prompts.
Conversational interfaces to convert natural language
These models are typically also better at interpreting /
dialog into specific executions of a technical system
labelling unstructured data than traditional AI
Querying a large set of unstructured data, and
synthesizing a human readable output
High-stakes scenarios with potential for harm
Unconstrained, long, open-ended generation that may
expose harmful or biased content to users
Generate marketing or Automate code generation
social media copy in ”house in programming languages Applications requiring explainability and/or full
style” using ChatGPT, like Python with Codex /
understanding of potential failure modes, including
Copy.A, etc. Github Copilot, etc.
numerical reasoning1
1. Current topic of research: how to use GPT-like models to generate code that involves solving numerical problems
2. Additional resources can be found in the McKinsey Report “Economic potential of generative AI”, and the article “What every CEO should know about generative AI”
Source: Press search; expert interviews MMccKKiinnsseeyy && CCoommppaannyy 44
Automation
A multinational tech company offers a GenAI
GenAI will mainly Giving software predictable tasks
app which can read customer emails and
generate well-documented tickets based
that can be more easily automated
impact three areas,
on these
today with FM powered GenAI
leading to
reinvention of major
processes in Norway
and rest of world
A GenAI-chatbot is already in use in several
Augmentation
large Norwegian banks, and institutions, to
improve productivity and reduce use of
Enhance human productivity to do
human agents in more simple cases
work more efficiently
A large Norwegian house building company
Acceleration
has invested heavily in GenAI for product
development, using it to generate thousands
Extract and index knowledge to
of building configurations prior to any
building activity, allowing for more thorough
shorten innovation cycles enabling
checks, e.g., ensuring that building
dimensions follow regulation
continuous innovation
Source: QuantumBlack: AI by McKinsey; press search McKinsey & Company 5
Norway is expected to be an early adopter of automation with other
economies such as the US and Germany
China Germany France India Japan Mexico US Nordics2 Global avg
Automation adoption, generative AI early scenario1, % automation Automation adoption, generative AI late scenario1, % automation
100% 100%
90% 90%
80% 80%
70% 70%
60% 60%
50% 50%
40% 40%
30% 30%
In early scenario, In late scenario, the 50%
developed economies can threshold is achieved at
20% 20%
achieve more than 50% least 20 years later, with
automation adoption by larger differences between
10% 2030 10% countries
0% 0%
2025 30 35 40 45 50 55 2060 2030 40 50 60 70 80 2090
1. Early scenario -aggressive scenario all key model parameters (tech automation, integration timeline, economic feasibility, regulatory and public adoption); late scenario -parameters are set for the later adoption potential
2. McKinsey Norway estimate
Source: McKinsey Global Institute
The Norwegian digital foundation and education level are key
advantages that can drive nationwide GenAI adoption
Human capital Integration of digital technology
Connectivity Digital public services
Share of population with tertiary
Digital economy and society index1, 2022 education2, 2022 %, aged 25-34 Comments
70
• Norway has one of the highest education
levels in the world, leading to an affluent, skilled
60 workforce that is adaptable and open to learning,
making new technology adoption
economically feasible
50
• Norway has a large population of knowledge
workers, whom typically have a high proportion of
40 activities that can see a productivity boost from
using GenAI to augment knowledge-
based activities
30
• Norway ranks 5th in the 2022 DESI Index –
emphasizing the Norwegian society’s strong
20 digital foundation and GenAI transformation
potential
10 • Norway has a robust digital infrastructure with
>55 40-<45
high internet penetration rates and widespread
50-<55 35-<40 access to fast broadband. This kind of
0 environment is conducive to the growth and
45-<50 <35
Fl DK NL SE NO IE FR DE EU IT HR HU BG RO
adoption of AI technologies.
1. The Digital Economy and Society Index (DESI), non-exhaustive country list
2. Eurostat “Educational attainment statistics”
Source: McKinsey Global Institute; European Commission; press search; Eurostat McKinsey & Company 7
X % of GDP Value realized by 2030
Value realized by 2045
Value potential per industry2, bn NOK
Energy3 16-26 0.4 %
High tech4 12-24 0.3 %
Travel, transport & logistics 13-21 0.3 %
The potential value Retail 13-20 0.3 %
unlock from GenAI Advanced manufacturing5 8-13 0.2 %
Real estate 6-10 0.1 %
is 95-159 bn NOK
Basic materials 5-9 0.1 %
across Norwegian
Banking 5-9 0.1 %
industries1 …
Consumer packaged goods 5-8 0.1%
Professional services 5-7 0.1%
Telecommunications 2-3 0.0 %
Insurance 2-3 0.0 %
Healthcare 2-3 0.0 %
Media 1-3 0.0 %
Chemical <1 0.0 %
Agriculture <1 0.0 %
1. Based on the early adoption scenario, median expected impact of GenAI, %
of industry revenues. 2020 revenues, inflation adjusted
2. By 2030, ~60% of the value potential will be unlocked, by 2045 100% of Pharma & medical products <1 0.0 %
the value will be unlocked. Calculations based on 2020 industry revenues
3. Includes utilities and oil and gas, of which oil and gas contributes
~85% of revenues
4. Includes advanced electronics Total 95-159 2.4%
5. Includes automotive and assembly, and aerospace and defense
Source: Internal experts; annual reports; Statista MMccKKiinnsseeyy && CCoommppaannyy 88
Value realized by 2030
Value potential per business function1, bn NOK Value realized by 2045
Marketing and sales 28-43
… But business Software engineering 21-43
functions, as
Customer operations 12-17
opposed to specific
industries, will be
Supply chain and operations 10-19
the driving forces of
value creation Product and R&D 8-15
Risk and legal 7-9
Strategy and finance 4-9
Talent and organization 2-3
1. Based on the early adoption scenario. By 2030, ~60% of the value
potential will be unlocked, by 2045 100%of the value will be unlocked. Corporate IT2 1-2
Calculations based on 2020 industry revenues
2. Excluding corporate software engineering, including activities such as
e.g., network maintenance
SSoouurrccee:: IInntteerrnnaall eexxppeerrttss,; Danantaubaal sreepso: rAtsn;n SuSalB reports and SSB MMccKKiinnsseeyy && CCoommppaannyy 99
Productivity growth, the main engine of GDP growth, slowed down
in the last decade but is likely to be advanced by GenAI
Employment growth Additional with GenAI
Productivity growth Without GenAI1
Productivity growth bigger contributor to GDP growth
Global GDP growth, Productivity impact from automation,
CAGR, % 2022-40, CAGR2, % Comments
 Examining the real GDP
growth contribution of
3.8
Global3 Norway
employment and
3.7 productivity growth,
increasedproductivity
3.1
3.1 1,3
2.9 3.3 0,7 has been the main engine
2.8
for GDP growth
0,6
 Implementation of GenAI
0,8
1,4 can significantly
contribute to increased
2,5 productivity in Norway
2,0
going forward
3,0
2,5
2,6
2,1
0.9
1,7
0,3
0,8
0,7 0.2
0,6
0.1
0.1
1972-82 1982-92 1992-2002 2002-2012 2012-2022 Early scenario Late scenario
Early scenario Late scenario
1. Previous assessment of work automation before the rise of generative AI
2. Based on the assumption that the automated work hours are integrated back to work at productivity level of today
3. Based on 47 countries which constitute almost 80% of the world employment
Source: The Conference Board Total Economy database; Oxford Economics; McKinsey Global Institute QuantumBlack, AI by McKinsey 10
CONFIDENTIAL AND PROPRIETARY
Key activities forecasted to be affected are typically executed by
employees holding an advanced degree
With generative AI
Without generative AI1
Incremental technical
automation potential
Overall technical automation potential, Share of NO
Education level Comparison in midpoint scenarios, % in 2023 work force1, % Comments
• Higher educated workers are likely set
57%
Master, PhD or similar 13% to see the largest incremental impact
28% 2X
from automation as they land in jobs
as “knowledge workers” which spend a
high share of their time on activities
most likely to benefit from GenAI
60%
Bachelor’s degree 29%
(i.e., applying expertise to planning and
36% 1.7x
creative tasks, managing and
stakeholder management).
• An example of this is within science:
researchers spend ~30 minutes to read
64%
High school diploma 36% one scientific paper2, but GenAI could
51% 1.2X
or equivalent
summarize hundreds of papers in
minutes
Without a high school 63%
19%
degree 54% 1.2X
1. Does not sum up to 100% due to some minor educational levels not included
2. 2014 statistic
Source: McKinsey Global Institute; SSB; OECD; Scientific American article “Scientists Reading Fewer Papers for First Time in 35 Years”, 2014 McKinsey & Company 11
GenAI could have the biggest impact on activities which previously
had a lower potential for automation
Automation potential of more than 50% with GenAI With GenAI
Incremental technical Automation potential of more than 50% without GenAI Without GenAI1
automation potential with GenAI
Overall technical automation potential, Share of NO
Activity groups2 comparison in midpoint scenarios, % in 2023 employment, % Comments
Decision 59% • Prior to GenAI, only 2 in 7
Applying expertise3 20%
making and 25% +34 p.p. Norwegians held roles
collaboration which had an automation
49%
Managing4 9% potential of more than
16% +34 p.p.
50%. Following the advent
of GenAI, that number has
Interfacing with 45%
8% risen to 1 in 2
stakeholders 24% +21 p.p.
• GenAI plays the largest
impact on data driven
Data 91%
Processing data 12%
decision making and
management 73% +18 p.p.
collaboration, while
79% physical laborers will likely
Collecting data 2%
68% +11 p.p. not see a significant change
from the rise of GenAI in the
Physical Performing unpredictable 46% workplace
34%
physical work5 46% +1 p.p. • With Generative AI,
technical automation
Performing predictable 73%
15% potential could already
physical work6 73% +1 p.p.
reach 91% for data
1. Previous assessment of work automation before the rise of generative AI processing and 79% for
2. Jobs are categorized by main activity, but some jobs include activity from multiple groups data collection in 2023
3. Applying expertise to decision making, planning, and creative tasks
4. Managing and developing people
5. Physical activities and operating machinery in unpredictable environments
6. Physical activities and operating machinery in predictable environments
Source: McKinsey Global Institute analysis; SSB McKinsey & Company 12
The 7 largest occupational groups, representing >70% of Norwegian
workers, can expect a large productivity uplift from GenAI
With GenAI Without GenAI Top 7 largest occupational groups Low High
Uplift from Share of NO No. of NO
Occupational groups Overall technical automation potential, % in 2023 GenAI, p.p. employment, % employment1, 000s
Educators and workforce training 54 39 p.p. 12 % 285
15
Customer service and sales 57 12 p.p. 11 % 263
45
Business and legal professionals 62 30 p.p. 11 % 256
32
STEM professionals 57 29 p.p. 10 % 239
28
Community services 65 26 p.p. 10 % 237
39
Managers 44 17 p.p. 8 % 197
27
Health professionals 43 14 p.p. 8 % 197
29
Builders 53 4 p.p. 6 % 153
49
Mechanical installation and repair 67 6 p.p. 5 % 122
61
Transportation services 49 7 p.p. 4 % 96
42
Food services 78
8 p.p. 4 % 91
70
Office support 87
21 p.p. 3 % 84
66
Property maintenance 38
9 p.p. 3 % 84
29
Agriculture 63
4 p.p. 2 % 40
59
Creatives and arts management 53
25 p.p. 1 % 32
28
Health aides, technicians, and wellness 43
9 p.p. 1 % 21
34
Production work 82
9 p.p. 1 % 21
73
Total 63
12 p.p. 100% 2 418
51
1. Jobs with <5k holding the job title excluded by SSB McKinsey & Company 13
Source: McKinsey Global Institute; SSB
Norway can realize significant value from GenAI, mainly unlocked
by automating activities performed by white-collar workers
Norway is primed for adoption of GenAI due
to high levels of education and strong
digital foundation …
… with the potential to unlock values up to
~127 billion NOK across various industries …
… mainly due to productivity gains from
activities related to decision making,
collaboration and data management
MMccKKiinnsseeyy && CCoommppaannyy 1144
Appendix
MMccKKiinnsseeyy && CCoommppaannyy 1155
The midpoint scenario at which automation adoption could reach
50% of time spent on current work activities has accelerated by a
decade
Updated early scenario including generative AI2 2017 early scenario2
Global automation of time spent on current work activities1, %
Updated late scenario including generative AI3 2017 late scenario3
100%
90%
80%
70%
60%
Midpoint 2017 50%
50%
Midpoint
40% updated
The advent of GenAI has sped up
the automation timeline by ~10
30%
years from previous estimates in
20% which GenAI was not considered
10%
0%
2020 2025 2030 2035 2040 2045 2050 2055 2060 2065 2070 2075 2080 2085 2090 2095 2100
1. Includes data from 47 countries representing about 80% of employment across the world. 2017 estimates are based on the activity and occupation mix from 2016. Scenarios including generative AI are based on the 2021
activity and occupation mix
2. Early scenario: aggressive scenario for all key model parameters (technical automation potential, integration timelines, economic feasibility, and technology diffusion rates)
3. Late scenario: parameters are set for later adoption potential.
Source: McKinsey Global Institute McKinsey & Company 16
GenAI is expected to have different impact across the business
functions dependent on industry sizes
Generative AI productivity impact by business functions1, % of industry revenue
Impact in bn NOK Low High Impact as % of industry rev. Low High Low High
Total Expected Total added
Supply chain Corporate IT
industry impact of value from Marketing Customer Product and Software Risk and Strategy and Talent and
and (excluding
size2, % of GenAI, % of GenAI, and sales operations R&D engineering legal finance organization
operations SWE)
total revenue industry rev. bn NOK
Total2bn NOK 6,754 95 –159 28 -43 12 -17 8 -15 21 -43 10 -19 7 -9 4 -9 1 -2 2 -3
Energy 22% 1% -1.6% 16 -26
High tech 4% 4.8% -9.3% 12 -24
Travel, transport & logistics 14% 1.2% -2% 12 -21
Retail 14% 1.2% -1.9% 12 -20
Advanced manufacturing 7% 1.4% -2.4% 8 -13
Real estate 8% 1% -1.7% 6 -10
Basic materials 10% 0.7% -1.2% 5 -9
Banking 3% 2.8% -4.7% 5 -9
Consumer packaged goods 5% 1.4% -2.3% 5 -8
Professional services 7% 0.9% -1.4% 5 -7
Telecommunications 1% 2.3% -3.7% 2 -3
Insurance 1% 1.8% -2.8% 2 -3
Healthcare 1% 1.8% -3.2% 2-3
Media 1% 1.5% -2.6% 2-3
Chemical 1% 0.8% -1.3% 0.5 -1
Agriculture 1% 0.6% -1% 0 -0.5
Pharma & medical products 0% 2.6% -4.5% 0
1. Excl. implementation costs (e.g., training, licenses)
2. Figures may not sum to 100% because of rounding
Source: Internal experts; McKinsey Global Institute;annual reports; SSB MMccKKiinnsseeyy && CCoommppaannyy 1177
GenAI can reduce the cost of large effort tasks, enabled through
4 archetype of applications which are emerging across industries
Not exhaustive for all use cases for Generative AI
Content synthesis Coding & Creative Customer
(virtual expert) software content engagment1
GenAI Generate insights and drive Interpret and generate code Create marketing messages, Streamline interactions by
capability actions based on summarization and documentation, i.e., and images, support ideation for interpreting text and analyze
and synthesis of unstructured improving efficiency and reducing new product development and customer journeys through
data technical debt generate personalized marketing customer service, chatbots,
copy recommenders, task automation,
etc.
Use case  Summarize text or audio and  Generate code and assist  Generate visuals (images,  Streamline customer
generate insights developers designs, 3D models) to communications, e.g.,
 Perform actions triggered by  Refactor translate code to accelerate the product design customer service issue
user prompt accelerate mainframe process resolution (driving action to
 Augment capabilities of migration  Draft and personalize resolve) and Q&A
operations staff (e.g.,  Create model outbound customer comms  Model and predict elements
inventory/maintenance documentation (e.g., risk) or marketing in patient or customer journey
management)
1. Includes B2B customer interactions and transactions
Source: McKinsey analysis MMccKKiinnsseeyy && CCoommppaannyy 1188
Impact as % of industry revenues, bubble size proportional to bn NOK impact: Small Large
The energy Impact1, median calculation, bn NOK
24
sector has the
22
highest value
Energy
20
potential, but
18 High Tech
GenAI will be
Travel, Transport & Logistics
16
most disruptive
Retail
14
in High Tech
12
Advanced Manufacturing
10
Basic Materials
8 Real Estate
Banking
6
Consumer Packaged Goods
Professional services
4
Healthcare
Insurance
2 Telecommunications
Agriculture Media
Chemical Pharma & Medical Products
0
0% 1% 2% 3% 4% 5% 6% 7% 8%
1. Based on the early adoption scenario, median
Impact as % of industry revenues
expected impact of GenAI, % of industry
revenues. 2020 revenues, inflation adjusted
SSoouurrccee:: IMncteKrinnasle eyx Gpelortbsa, lD Inastatibtuatsees: Annual reports, SSB MMccKKiinnsseeyy && CCoommppaannyy 1199
>50% of the value unlock can be achieved in two large
business functions
Deep dive follows
Business functions Value potential from GenAI1, bn NOK
Marketing & sales 28 - 43
Software engineering 21 - 43
Customer operations 12 - 17
Supply chain & operations 10 - 19
Product and R&D 8 - 15
Risk & legal 7 - 9
Strategy & finance 4 - 9
Talent & org. 2 - 3
Corporate IT (excl. SWE) 1 - 2
1. Excl. implementation costs (e.g., training, licenses)
Source: Internal experts; annual reports; SSB MMccKKiinnsseeyy && CCoommppaannyy 2200
1: Marketing & Sales
Productivity opportunity with GenAI
Not expected to be affected by GenAI
Illustrative
Value unlock Industry Typical working
examples examples day marketing executive
% time spent
Writing marketing and sales copy Augment sales teams with technical
content of text, images and videos proprietary knowledge and historic
customer interactions
Emails 13
Improving sales force, e.g., by
Generate personalized marketing content
flagging risks, recommending next
based on (un)structured data from consumer
interactions
profiles and community insights Meetings 38
Analyzing customer feedback Automate booking management and
customer follow-up during travels Designs and edits 13
Analysis 25
Key  CPG
industries  Retail Other admin 13
 Travel, Transport & Logistics
 Insurance
Total
 Financial services
Total value 28 - 43
potential, bn NOK
Source: McKinsey Global Institute; internal experts; press search MMccKKiinnsseeyy && CCoommppaannyy 2211
“50% of code on
GitHub is written by an
2: Software Engineering AI, e.g., a co-pilot doing
code suggestions,
Productivity opportunity with GenAI
corrections and writing”
Not expected to be affected by GenAI
Illustrative
Value unlock Industry Typical working
examples examples day software engineer
% time spent
Generating, prioritizing, and Create alerts and automated bots based on
running code news, industry reports, internal research and
economic trends that can impact trading
Meetings 10
strategies
Generating synthetic data to improve
training accuracy of ML models Generate code that creates hyper-
personalized trip recommendations Coding 50
Reviewing code for defects and
Accelerate transition from legacy software /
inefficiencies
code (e.g., banks still use system written in
Debugging 20
COBOL) to modern
Emails 10
Key  High Tech
industries  Media Admin 10
 CPG
 Retail
Total
 Energy
 Insurance
Total value 21 - 43
 Financial services
potential, bn NOK
Source: McKinsey Global Institute; internal experts MMccKKiinnsseeyy && CCoommppaannyy 2222
3: Customer Operations
Productivity opportunity with GenAI
Not expected to be affected by GenAI
Illustrative
Value unlock Industry Typical working
examples examples day call center
% time spent
Auto-generating customer profile Zero customer service reps, with all internal
and segment for each unique helpdesk automated via self-serve and GenAI-
customer powered chatbots to handle all omnichannel
Admin 13
helpdesk engagement
Generating post call summary to
Summarize speech to distinctive text to create
customers and agents Customer care 47
records of customer complaints
Developing first-line response in Manage disruptions during vacations by being
Internal calls 6
customer service for all inquiries first point of contact for customers, offer
translation and content customized for the
customer and their vacation Problem solving 25
Email / chat 6
Key  CPG
industries  Retail
Other 3
 Insurance
 Financial services
Total
 Travel, Transport & Logistics
 Telecommunications
Total value 12 - 17
potential, bn NOK
Source: McKinsey Global Institute; internal experts; press search MMccKKiinnsseeyy && CCoommppaannyy 2233
4: Supply Chain & Operations
Productivity opportunity with GenAI
Not expected to be affected by GenAI
Illustrative
Value unlock Industry Typical working
examples examples day supply chain manager
% time spent
Warehouse and inventory Interpreting data, labelling unstructured data
management and identifying patterns for future trends
and demand
E-mail 10
Forecasting demand and Synthesizing data from previous jobs to predict
disruptions in supply chain potential issues
Meetings 25
Act as an intelligent maintenance or safety
Optimize transportation route advisor, leveraging insights and knowledge Inventory or
35
from equipment and process manuals staffing analysis
Planning 15
Document review 10
Key  Energy
industries  CPG
Other admin 5
 Retail
 Advanced Manufacturing
Total
 Travel, Transport & Logistics
 Basic Materials
Total value 10 - 19
potential, bn NOK
Source: McKinsey Global Institute; internal experts; press search MMccKKiinnsseeyy && CCoommppaannyy 2244
“The amount of time spent in each category depends on
which stage of development you are, but most time is spent
on product development, troubleshooting or fixing”
5: Product and R&D
Productivity opportunity with GenAI
Not expected to be affected by GenAI
Illustrative
Value unlock Industry Typical working
examples examples day product owner
% time spent
Creating 3D visual models and Improve pipeline maintenance by
digital product designs synthesizing maintenance and inspection
records, predict areas at risk for corrosion E-mail 10
Prioritizing product backlog by based on historic maintenance records
synthesizing customer feedback Product
Reimagine product portfolio through GenAI 17
development
opportunity themes
Measuring and tracking engineering
Translate code from legacy systems at scale,
metrics Troubleshooting 17
prioritizing interventions and re-factoring
Fixing 17
Meetings 25
Key  High tech
industries  CPG
Other admin 15
 Retail
 Travel, Transport & Logistics
Total
 Telecommunications
 Insurance
Total value 8 - 15
 Financial services
potential, bn NOK
Source: McKinsey Global Institute; internal experts MMccKKiinnsseeyy && CCoommppaannyy 2255
6: Risk & Legal
Productivity opportunity with GenAI
Not expected to be affected by GenAI
Illustrative
Value unlock Industry Typical working
examples examples day junior lawyer
% time spent
Summarize regulation, including safety &
Draft and review legal documents
equipment manuals changes from industry &
regulatory databases
E-mail 10
Summarize and highlight changes
Informative queries from agents to identify &
in large bodies of regulatory
Writing
generate required legal and non-legal
documents 45
documents for transportation based on documents
classification from GenAI model
Review
Answer questions & cite
5
justifications from large documents Generate life-like fraud attempts for pro-active documents
testing
Calls 30
Meetings 5
Key  Energy
industries  High Tech
Other admin 6
 Media
 Insurance
Total
 Financial services
 Real Estate
Total value 7 - 9
 Telecommunications
potential, bn NOK
Source: McKinsey Global Institute; internal experts; press search MMccKKiinnsseeyy && CCoommppaannyy 2266"
46,mckinsey,the-next-frontier-of-customer-engagement-ai-enabled-customer-service.pdf,"Operations Practice
The next frontier of customer
engagement: AI-enabled
customer service
AI-enabled customer service is now the quickest and most effective route for
institutions to deliver personalized, proactive experiences that drive customer
engagement.
This article is a collaborative effort by Avinash Chandra Das, Greg Phalin, Ishwar Lal Patidar, Malcolm Gomes,
Rakshit Sawhney, and Renny Thomas, representing views from McKinsey’s Operations Practice.
© Getty Images
March 2023
How to engage customers—and keep them most of AI tools to transform customer service is
engaged—is a focal question for organizations not simply a case of deploying the latest technology.
across the business-to-consumer (B2C) landscape, Customer service leaders face challenges ranging
where disintermediation by digital platforms from selecting the most important use cases for
continues to erode traditional business models. AI to integrating technology with legacy systems
Engaged customers are more loyal, have more and finding the right talent and organizational
touchpoints with their chosen brands, and deliver governance structures.
greater value over their lifetime.
But done well, an AI-enabled customer service
Yet financial institutions have often struggled to transformation can unlock significant value for
secure the deep consumer engagement typical the business—creating a virtuous circle of better
in other mobile app–intermediated services. The service, higher satisfaction, and increasing
average visit to a bank app lasts only half as long customer engagement.
as a visit to an online shopping app, and only
one-quarter as long as a visit to a gaming app.
Hence, customer service offers one of the few The perils and promise of AI customer
opportunities available to transform financial- engagement
services interactions into memorable and long- Multiple converging factors have made the case
lasting engagements. for AI-based customer service transformation
stronger than ever. Among the most important:
Those customers are getting harder to please. increased customer acceptance of (and even
Two-thirds of millennials expect real-time customer preference for) machine-led conversational AI
service, for example, and three-quarters of all interactions. Meanwhile, related technologies
customers expect consistent cross-channel service such as messaging platforms are becoming more
experience. And with cost pressures rising at least accessible, and customer behaviors are becoming
as quickly as service expectations, the obvious more understandable with the relentless expansion
response—adding more well-trained employees to of data pools institutions can collect and analyze.
deliver great customer service—isn’t a viable option.
Three challenges
Companies are therefore turning to AI to deliver But challenges also loom. First, complexity. The
the proactive, personalized service customers COVID-19 pandemic acted as a major catalyst
want, when and how they want it—sometimes even for migration to self-service digital channels, and
before they know they want it. For transformed customers continue to show a preference for
organizations, AI-enabled customer service digital servicing channels as the “first point of
can increase customer engagement, resulting contact.” As a result, customers increasingly turn
in increased cross-sell and upsell opportunities to contact centers and assisted-chat functions for
while reducing cost-to-serve. In global banking more complicated needs. That raises the second
alone, research from McKinsey conducted in 2020 issue: higher expectations. Customer confidence in
estimates that AI technologies could potentially self-service channels for transactional activities is
deliver up to $1 trillion of additional value each year, leading them to expect similar outcomes for more
of which revamped customer service accounts for a involved requests. Businesses are therefore rapidly
significant portion.¹ adopting conversational AI, proactive nudges, and
predictive engines to transform every point of the
While a few leading institutions are now customer service experience. Yet these moves raise
transforming their customer service through apps, demand for highly sought-after skills, generating
and new interfaces like social and easy payment the third challenge: squeezed labor markets that
systems, many across the industry are still playing leave customer service leaders struggling to fill
catch-up. Institutions are finding that making the crucial roles.
1 “AI bank of the future: Can banks meet the AI challenge,” McKinsey, September 19, 2020.
2 The next frontier of customer
How leaders fulfill AI’s customer engagement five—the most advanced end of the maturity
promise scale—companies are delivering proactive,
Leaders in AI-enabled customer engagement have service-led engagement, which lets them handle
committed to an ongoing journey of investment, more than 95 percent of their service interactions
learning, and improvement, through five levels of via AI and digital channels (see sidebar, “What
maturity. At level one, servicing is predominantly AI-driven customer service maturity looks like”).
manual, paper-based, and high-touch. At level
What AI-driven customer service maturity looks like
A few leading institutions have reached level four on a five-level scale describing the maturity of a company’s AI-driven customer
service.
Level 1: Manual and high-touch, based on paper forms and offered largely via assisted channels.
— Reactive service, with the majority of interactions on human-assisted channels
— Paper use is still prevalent
Level 2: Partly automated and basic digital channels, with digitization and automation of servicing in assisted channels.
— Reactive service, with limited self-servicing opportunities
— Lower adoption of available self-service channels
— Lower availability of digital or straight-through-processing (STP)
Level 3: Accessible and speedy service via digital channels, with self-servicing on select channels and a focus on enabling end-to-
end resolution.
— Somewhat proactive, but limited engagement
— Self-service channels such as mobile apps, interactive voice response (IVR) systems, and internet sites handle half of all
interactions, and can support STP.
Level 4: Proactive and efficient engagement deploying AI-enabled tech, with self-servicing enabled by proactive customer
interactions and conversational user experience (UX).
— Proactive, with high customer engagement on digital channels
— Self-service channels such as mobile apps, IVR systems, and internet sites handle 70-80 percent of interactions and can support
most requests and transactions
Level 5: Personalized, digitally enabled engagement, bringing back the human touch via predictive intent recognition.
— Engagement via service interactions that are personalized and proactive at the individual customer level
— Digital touchpoints drive service-based engagement, for example via enhanced cross-selling and upselling
— More than 95 percent of service interactions and requests can be solved via digital and STP channels
The next frontier of customer 3
The most mature companies tend to operate to the appropriate AI-powered tools, core
in digital-native sectors like ecommerce, taxi technology, and data. Exhibit 1 captures the new
aggregation, and over-the-top (OTT) media model for customer service—from communicating
services. In more traditional B2C sectors, such with customers before they even reach out with a
as banking, telecommunications, and insurance, specific need, through to providing AI-supported
some organizations have reached levels three and solutions and evaluating performance after the fact.
four of the maturity scale, with the most advanced
players beginning to push towards level five. These The human factor in AI-supported service
businesses are using AI and technology to support AI-powered does not mean automation-only. It’s
proactive and personalized customer engagement true that chatbots and similar technology can deliver
through self-serve tools, revamped apps, new proactive customer outreach, reducing human-
interfaces, dynamic interactive voice response (IVR), assisted volumes and costs while simplifying the
and chat. client experience. Nevertheless, an estimated 75
percent of customers use multiple channels in their
A few leading institutions have reached level four ongoing experience.² A reimagined AI-supported
on a five-level scale describing the maturity of a customer service model therefore encompasses all
company’s AI-driven customer service. touchpoints—not only digital self-service channels
but also agent-supported options in branches or
on social-media platforms, where AI can assist
Toward engaging, AI-powered employees in real time to deliver high-quality
customer service outcomes.
To achieve the promise of AI-enabled customer
service, companies can match the reimagined vision Even before customers get in touch, an
for engagement across all customer touchpoints AI-supported system can anticipate their likely
Web <year>
<Title>
Exhibit 1
Exhibit <x> of <x>
The future of customer service builds on AI to deliver engaging experiences
The future of customer service builds on AI to deliver engaging experiences
aanndd ggeenneerraattee llaassttiinngg vvaalluuee..
The stages of an AI-supported customer-service process
A Proactive communication
linked to key demand drivers Customer
B Intent recognition and nudges
before customer reaches out ~75% of customers use
multiple channels
C Omnichannel enablement
with self-service for service journeys
D Conversational AI
at each entry point Backend robotics and
Self-service automation for straight- Frontline
E Frontline enablement with coaching channels through processing (STP) agents
for agents supported by a knowledge
repository and AI ~50–80% of >80% of all
contacts automated tasks automated
F Highly personalized, advisory interactions
drive relationship and value, with STP or
quick resolution of issues
G Performance measurement via a centrally
Performance management
managed nerve center that tracks
resolution accuracy and efficiency
McKinsey & Company
2 “The state of customer care in 2022,” McKinsey, July 8, 2022.
4 The next frontier of customer
needs and generate prompts for the agent. For self-service options while launching new,
example, the system might flag that the customer’s dedicated video and social-media channels.
credit-card bill is higher than usual, while also To drive a personalized experience, servicing
highlighting minimum-balance requirements and channels are supported by AI-powered decision
suggesting payment-plan options to offer. If the making, including speech and sentiment analytics
customer calls, the agent can not only address an to enable automated intent recognition and
immediate question, but also offer support that resolution. Enhanced measurement practices
deepens the relationship and potentially avoids an provide real-time tracking of performance
additional call from the customer later on. against customer engagement aspirations,
targets, and service level agreements, while new
AI service in the field: an Asian bank’s governance models and processes deal with
experience issues such as service request backlogs.
Put together, next-generation customer service
aligns AI, technology, and data to reimagine Underpinning the vision is an API-driven tech
customer service (Exhibit 2). That was the stack, which in the future may also include edge
approach a fast-growing bank in Asia took when technologies like next-best-action solutions
it found itself facing increasing complaints, slow and behavioral analytics. And finally, the entire
resolution times, rising cost-to-serve, and low transformation is implemented and sustained via
uptake of self-service channels. an integrated operating model, bringing together
service, business, and product leaders, together
Over a 12-month period, the bank reimagined with a capability-building academy.
engagement. It revamped existing channels,
improving straight-through processing in
Exhibit 2
AAII--eennaabbleledd c ucustsotmoemr esre rsveircvei cexec eelxlcenecllee nspcaen ssp nainnes cnriintiec aclr citoicmapl ocnoemntpso. nents.
Components
Reimagined engagement
1 New or upgraded self-service channels with automated journeys
2 Modernized assisted channels (contact centers, branches) with tech-enabled front-line
3 Preemptive, proactive end-to-end customer communications
4 Reimagined straight-through service journeys with standard operating procedures across all channels
5 Simplified, templatized service-to-sales interactions
AI-powered decisioning
6 AI-enabled automated intent recognition and resolution layer
7 Measurements and governance—nerve center for descriptive and predictive analytics
Core tech and data
8 Technologies including cloud-based telephony and integrated CRM are embedded into an API-driven tech stack
Operating model
9 Integrated service, business, and product operating models, with capability-building academy
McKinsey & Company
The next frontier of customer 5
The transformation resulted in a doubling to tripling — Maximize every customer service interaction, to
of self-service channel use, a 40 to 50 percent deepen customer relationships, build loyalty, and
reduction in service interactions, and a more than drive greater value over the customer’s lifetime.
20 percent reduction in cost-to-serve. Incidence
ratios on assisted channels fell by 20-30 percent, — Leverage AI and an end-to-end technology
improving both the customer and employee stack, to provide a more proactive and
experience. personalized customer service experience that
supports self-service and decision-making for
customers as well as employees .
Seizing the opportunity
To leapfrog competitors in using customer service to — Adapt agile and collaborative approaches to
foster engagement, financial institutions can start drive transformation, comprised of SMEs from
by focusing on a few imperatives. different business and support functions of the
organization.
— Envision the future of service, keeping
customers and their engagement at the core
while also defining the strategic value to be
attained—for example, a larger share of wallet
with existing customers? Expansion of particular
services, lines of business, or demographics? Holistically transforming customer service
into engagement through re-imagined, AI-led
— Rethink every customer touchpoint, whether capabilities can improve customer experience,
digital or assisted, together with opportunities reduce costs, and increase sales, helping
to enhance the experience while also increasing businesses maximize value over the customer
efficiencies. lifetime. For institutions, the time to act is now.
Avinash Chandra Das is an associate partner in McKinsey’s Bengaluru office, where Malcolm Gomes is a partner and Ishwar
Lal Patidar is an expert. Greg Phalin is a senior partner in the Charlotte office, Rakshit Sawhney is an associate partner in the
Gurugram office, and Renny Thomas is a senior partner in the Mumbai office.
The authors wish to thank Amit Gupta, John Larson, and Thomas Wind for their contributions to this article.
Copyright © 2023 McKinsey & Company. All rights reserved.
6 The next frontier of customer"
47,mckinsey,time-to-place-our-bets-europes-ai-opportunity.pdf,"QuantumBlack, AI by McKinsey
Time to place our bets:
Europe’s AI opportunity
Boosting Europe’s competitiveness across the AI value chain.
by Alexander Sukharevsky, Eric Hazan, Sven Smit, Marc-Antoine de la Chevasnerie, Marc de Jong,
Solveigh Hieronimus, Jan Mischke, and Guillaume Dagorret
October 2024
At a glance — Europe has made major progress in raising
AI awareness and setting commitments,
— A three-lens approach–on adoption,
but major bottlenecks persist. Policy makers
creation, and energy–is required to assess
and business leaders could explore several
Europe’s competitiveness in the emerging
levers, including increasing investments
generative AI (gen AI) economy. While much
(such as a public innovation procurement
of the current discourse centers around large
in AI applications for healthcare and
language models (LLMs), European policy
defense sectors), leapfrogging in emerging
makers and business leaders must look
semiconductor technologies (such as quantum
beyond LLMs. Adopting a holistic approach to
and neuromorphic computing), and addressing
capitalize fully on gen AI’s potential could boost
talent retention. Additionally, preparing the
European labor productivity by up to 3 percent
workforce through reskilling and mobility
annually through 2030.
programs will be crucial in fully leveraging
the benefits of gen AI adoption.
— On adoption, European organizations lag
behind their US counterparts by 45 to
70 percent. Yet this is where most of gen AI’s A holistic approach to help Europe
economic potential lies. With the technology still realize generative AI’s full potential
in its early stages and much of its productivity
For generative AI (gen AI),1 the blockbuster release
gains yet to be unlocked, the window of
of OpenAI’s ChatGPT in November 2022 marked
opportunity for Europe remains wide open.
the beginning of a boom.2 Since then, much of the
conversation around the technology has focused
— On creation, Europe leads in only one of the
on foundation models, particularly large language
eight segments of a simplified gen AI value
models (LLMs). In this field, Europe3 appears to be
chain: AI semiconductor equipment. Europe is
lagging behind its counterparts. However, LLMs are
a challenger in three other segments: foundation
just one part of the gen AI landscape. Engaging on
models, AI applications, and AI services. But
gen AI adoption, creation, and energy requirements
it has below 5 percent market share in the
can help capture a more complete picture of where
remaining four: raw materials, AI semiconductor
the region stands in the emerging gen AI economy.
design, AI semiconductor manufacturing, and
cloud infrastructure and supercomputers.
Most of the value generated by gen AI will stem
from organizations’ adoption and scaling of gen AI
— On energy, gen AI is expected to accelerate
solutions4—an important consideration in Europe,
data center power demand, potentially
where labor productivity has been slowing.5
accounting for more than 5 percent of
McKinsey Global Institute (MGI) research estimates
Europe’s total electricity consumption by
that gen AI could help Europe achieve an annual
2030. Without competitive electricity prices, it
productivity growth rate of up to 3 percent through
becomes less likely that European data centers
2030 (Exhibit 1).6 This potential additional growth
will host gen AI applications and services.
1 In this article, unless specified otherwise, “generative AI” (gen AI) encompasses all AI technologies, including the latest advancements in gen AI.
2 By January 2023, the company had already gained 100 million users and was valued at $29 billion ($80 billion today). This triggered massive
investments to fund gen AI companies ($25 billion in worldwide private investments in 2023) and spurred the release of multiple breakthrough
innovations and competing models (for example, Google’s Gemini and Meta’s Llama). Artificial Intelligence Index report 2024, Stanford
University, 2024; DigitalRank, Similarweb, accessed September 2024; Julia Boorstin, “Why OpenAI is the first company to be No. 1 on the
CNBC Disruptor 50 list two years in a row,” CNBC, May 14, 2024.
3 In this article, “Europe” refers to the 27 member states of the European Union plus Norway, Switzerland, and the United Kingdom.
4 Per academic research, innovators historically capture less than 5 to 10 percent of broader economic returns generated by their inventions.
Adopters of the technology and society at large generate the remaining returns. For more, see William D. Nordhaus, Schumpeterian profits in
the American economy: Theory and measurement, National Bureau of Economic Research working paper, number 10433, April 2004.
5 For example, from 2016 to 2022, annual growth was 0.5 percent in Western Europe and 1.2 percent in North America. From 2002 to 2007,
it was 1.1 percent and 1.9 percent, respectively. Chris Bradley, Jan Mischke, Marc Canal, Olivia White, Sven Smit, and Denitsa Georgieva,
“Investing in productivity growth,” McKinsey Global Institute (MGI), March 27, 2024.
6 Eric Hazan, Anu Madgavkar, Michael Chui, Sven Smit, Dana Maor, Gurneet Singh Dandona, and Roland Huyghues-Despointes, “A new future
of work: The race to deploy AI and raise skills in Europe and beyond,” MGI, May 21, 2024.
Time to place our bets: Europe’s AI opportunity 2
Web <2024>
E<Exuhroibpeit G 1enAI>
Exhibit <1> of <3>
Generative AI could add $575.1 billion to the European economy by 2030.
Generative AI productivity potential in Western Europe in 2030, by sector, $ billion1
575.1
Total potential value
Consumer goods and retail
101.9
56%
Construction Professional Transportation Advanced
and real estate services 53.9 manufacturing
of potential productivity 55.7 54.3 53.4
gains are from sectors
with high spending gaps and
high productivity potential
Healthcare and pharma
57.2
Banking and High tech Chemicals and materials
capital markets and software 29.2
44.8 44.0
Energy and utilities
27.2
Media and entertainment
27.0
Telecommunications 11.6 Insurance 9.8 Agriculture 5.1
1Western Europe: Austria, Belgium, Denmark, Finland, France, Germany, Greece, Ireland, Italy, Netherlands, Norway, Portugal, Spain, Sweden, Switzerland, and
UK. Potential value add from 2019 base period.
Source: “The economic potential of generative AI: The next productivity frontier,” McKinsey, June 14, 2023
McKinsey & Company
will be critical for financing the European model, In terms of creation of gen AI, since 2022, more
particularly in navigating the energy transition, than 90 percent of LLM-related funding has taken
solving the empowerment gap, and supporting an place outside of Europe.8 Moreover, European
aging population.7 It could also drive breakthrough companies represent only 25 of the 101 AI models
innovations that transform daily life, such as considered notable by the Stanford University AI
accelerated drug discovery, improved patient care, Index, far behind US companies (which boast 61
and personalized education. notable models). But the opportunities for capturing
the economic value resulting from the creation of
gen AI technologies extend well beyond LLMs.
7 For more, see Kweilin Ellingrud, Marco Piccitto, Tilman Tacke, Rebecca J. Anderson, Ishaa Sandhu, and Kevin Russell, “A better life
everyone can afford: Lifting a quarter billion people to economic empowerment,” MGI, May 20, 2024; Mekala Krishnan, Chris Bradley,
Humayun Tai, Tiago Devesa, Sven Smit, and Daniel Pacthod, “The hard stuff: Navigating the physical realities of the energy transition,” MGI,
August 14, 2024.
8 Artificial Intelligence Index report 2024, Stanford University, 2024.
Time to place our bets: Europe’s AI opportunity 3
They are spread across an eight-segment value stands relative to other regions, and provide a series
chain: raw materials, AI semiconductor equipment, of steps that leaders in Europe might consider if
AI semiconductor design, AI semiconductor they are to fully participate in—and tap into the
manufacturing, cloud infrastructure and value created by—this impressive new technology.
supercomputers, foundation models (including
LLMs), AI applications, and AI services.9
Adoption of gen AI: Opportunity
Finally, to power the creation and adoption of gen AI, remains wide open, but Europe is
Europe also needs to consider its energy capacity. starting from a disadvantage
This is a key consideration, given that Europe’s The vast majority of the economic value of gen AI
energy system will be forced by 2030 to manage is expected to come from its adoption by European
a rise in consumption of more than 5 percent, organizations. The technology is still in its early
triggered by the demand for data center power stages, and most productivity potential has yet to
(accelerated by gen AI).10 be captured, so the opportunities here remain wide
open. Yet European corporations are moving much
To realize the full potential of gen AI, Europe’s more slowly than those in other countries.11
business leaders and policy makers must embrace
a holistic view of the technology that encompasses How much is Europe lagging behind? The
the challenges and opportunities posed by creation, information here is incomplete, so we sought to
adoption, and energy (Exhibit 2). In this article, we quantify it by examining three indicators. First, we
describe those challenges, detailing where Europe
Web <2024>
E<Exuhroibpeit G 2enAI>
Exhibit <2> of <3>
To fully capture the value of generative AI, European leaders can embrace a
holistic approach that encompasses creation, adoption, and energy.
Creation Adoption
Creation of new technologies and applications across Deployment of gen AI technologies across
simplified 8-step generative AI (gen AI) value chain different use cases to increase labor productivity
1. Raw materials Potential high-impact use cases:
2. AI semiconductor equipment • Chatbots for customer service in retail
3. AI semiconductor design • AI-driven drug discovery in pharmaceuticals
4. AI semiconductor manufacturing • Supply chain optimization in logistics
5. Cloud infrastructure and supercomputers
6. Foundation models
7. AI applications
8. AI services
Energy
Power required to run gen AI applications, with low carbon emissions and competitive prices
McKinsey & Company
9 Simplified value chain of the most important segments (excludes other AI elements, such as distribution platforms and vector databases).
10 Electricity Data Explorer, Ember, accessed September 2024; McKinsey research and analysis.
11 For more, see Zach Meyers and John Springford, “How Europe can make the most of AI,” Centre for European Reform, September 14, 2023.
Time to place our bets: Europe’s AI opportunity 4
Western Europe lags behind the
United States on external spending
on AI by an average of 61 percent
for sectors of similar size.
looked at external AI spending of corporations, such chemicals and materials, and construction and real
as the purchase of AI software-as-a-service (SaaS) estate), we find that those in Europe lag behind by
solutions. Since not all AI spending is external— 45 to 55 percent. For sectors that are significantly
some, such as hiring AI engineers, is internal—we larger in the United States than in Western Europe
also examined general IT spending, of which AI is (for example, healthcare and pharma, high tech and
a component, as an indicator of IT readiness and software, and media and entertainment), the gap
a crucial foundation for AI adoption. Finally, we was even more pronounced, ranging from 50 to
factored in the responses of European executives to 70 percent (Exhibit 3).
the McKinsey Global Survey on the state of AI.12
When looking at external spending on AI
We analyzed the first two metrics both in absolute infrastructure, software, and services, Western
terms and relative to company sales, comparing Europe lags behind the United States by an
them with US figures when possible. This relative average of 61 percent for sectors of similar size
comparison helps account for differences in sector and 71 percent for sectors that are two or
size, which would otherwise skew the data because more times larger in the United States than in
of economies of scale. For instance, the high-tech Western Europe.
and software sector is 4.9 times larger in the United
States than in Western Europe,13 so we find an AI Looking at internal IT spend, we see that for sectors
external spend-to-sales ratio of 0.4 percent for of similar size, Western Europe lags behind the
the United States versus 0.7 percent for Western United States by an average of 43 percent, and by
Europe. But in AI external spend absolute value, 46 percent when sectors differ in size by at least
we find $8.7 billion versus $2.6 billion, respectively, two times.
leading to a 70 percent gap.
Per the 2023 McKinsey Global Survey on the
Additionally, with the two first metrics, figures show state of AI, Europe lags behind North America in
that companies in Western Europe lag behind their gen AI adoption by 30 percent, with 40 percent
US counterparts by 45 to 70 percent. This gap of surveyed North American companies reporting
exists across all sectors. When evaluating sectors having adopted gen AI in at least one business
of similar size14 in Western Europe and the United function, compared with about 30 percent for
States (for example, advanced manufacturing, surveyed European companies.15
12 The online survey was in the field from April 11 to April 21, 2023, and garnered responses from 1,684 participants representing the full range
of regions, industries, company sizes, functional specialties, and tenures.
13 Austria, Belgium, Denmark, Finland, France, Germany, Greece, Ireland, Italy, the Netherlands, Norway, Portugal, Spain, Sweden, Switzerland,
and the United Kingdom.
14 Sectors with a size ratio between Western Europe and the United States below 2:1.
15 Survey question, with 1,363 responses: Has the organization adopted AI in at least one business function?
Time to place our bets: Europe’s AI opportunity 5
Exhibit 3
Western Europe lags behind the United States in AI and IT spending across
sectors, with an average gap of 45–70 percent.
Spending gap between Western Europe1 AI external spending Western Europe US
and US in 2022, by sector
IT internal spending Western Europe US
Spending as share of sales in sectors of similar size,2 %
0 0.2 0.4 0.6 0.8 1.0
Construction and real estate
Chemicals and materials
Advanced manufacturing
Insurance
Energy and utilities
Telecommunications
Transportation
Agriculture
Professional services
55%
AI external spending Average gap in relative value to sales
45%
IT internal spending Average gap in relative value to sales
Absolute spending in sectors of differing size,3 $ billion Western Europe US
0 2 4 6 8 10 12 14
Consumer goods and retail
Banking and capital markets
Healthcare and pharma
High tech and software
Media and entertainment
70%
AI external spending Average gap in absolute value
50%
IT internal spending Average gap in absolute value
Note: AI external spending measured as external spending on AI infrastructure, software, and services. Sectors ordered from most similar in size to least similar. IT spending
used as proxy for AI internal spending. Sectors ordered from most similar in size to least similar.
1Austria, Belgium, Denmark, Finland, France, Germany, Greece, Ireland, Italy, Netherlands, Norway, Portugal, Spain, Sweden, Switzerland, and UK.
2Sectors with <2:1 size ratio between US and Western Europe or Western Europe and US.
3Sectors with ≥2:1 size ratio between US and Western Europe or Western Europe and US.
Source: Worldwide AI and Generative AI Spending Guide, IDC, February 2024; McKinsey analysis
McKinsey & Company
Time to place our bets: Europe’s AI opportunity 6
Creation of gen AI tech: Europe leads service), foundation models (for example, LLMs), AI
in one segment, is a challenger in applications (for example, AI-powered software),
three, but is almost absent in four and AI services (for example, advisory services
and implementation).
Beyond adoption, Europe’s ability to capitalize
on gen AI will depend on its ability to spur the
Europe is currently competitive in four of the eight
creation of gen AI technologies that spread across
segments of the value chain: AI semiconductor
the simplified eight-segment value chain: raw
equipment, foundation models, AI applications,
materials (for example, germanium and silicon), AI
and AI services. However, the region has less than
semiconductor equipment (for example, lithography
5 percent of global market share in the remaining
systems), AI semiconductor design (for example,
four segments: raw materials, AI semiconductor
development of high-end GPUs), AI semiconductor
design, AI semiconductor manufacturing, and cloud
manufacturing (for example, foundries), cloud
infrastructure and supercomputers (table):
infrastructure and supercomputers (for example,
infrastructure as a service and platform as a
Europe’s ability to capitalize on
gen AI will depend on its ability to
spur the creation of gen AI technologies
that spread across the value chain.
Time to place our bets: Europe’s AI opportunity 7
Table
Europe is strong in four segments of a simplified generative AI value chain and lags
in the remaining four.
Negligible (<5%) Moderate (5–15%) Fair (>15%)
Segment Description European market Historical Key data
share in 2023 European market
share, directional
Raw materials Materials needed to produce Stable Europe supplies ~5% of critical,
semiconductors and their strategic1 raw materials needed
machinery (eg, gallium to make for chip manufacturing and
lithography tools) semiconductors
AI semiconductor Goods needed for AI Increasing Europe has 80–90% market
equipment semiconductor production (eg, share for extreme ultraviolet
silicon wafers, lithography tools) lithography (allows for finer
patterns on semiconductor
wafers, essential for high-end AI
chips)
AI semiconductor Design, including intellectual Decreasing Europe has <2% share of design
design property, of semiconductors for AI of logic semiconductors used for
AI (eg, GPUs)
AI semiconductor Production of semiconductors Stable Europe has <1% of world’s
manufacturing for AI production capacity
of ≤7-nanometer logic
semiconductors used for AI
Cloud infrastructure Infrastructure, including basic Stable European cloud companies have
and supercomputers software layer, needed for <5% market share, compared with
computing power and data ~85% for US hyperscalers
hosting
Foundation models Design and training of foundation Increasing 25 notable models originate from
models Europe, compared with 61 from US
AI applications AI-based software needed to Increasing In 2023, European companies
perform specific tasks across raised ~12% of global venture
various industries capital and private equity funding
for system-as-a-service AI
companies
AI services Services needed to support Increasing Europe has ~15% share of global
design and deployment of AI use AI services market, compared with
cases US, which leads with >40%
1 “Critical” is based on economic importance and supply risk, and “strategic” is defined as important for the green and digital transition, defense, and aerospace.
Time to place our bets: Europe’s AI opportunity 8
— Raw materials. The chip-manufacturing and of AI-suitable semiconductors, a space led
semiconductor industries require more than by Nvidia.22 Nonetheless, some European
40 raw materials, 16 of which (for example, players are taking steps to bridge the gap.
gallium, magnesium, and silicon) the European Britain-based ARM has ambitions to launch AI
Union classifies as both critical and strategic.16 semiconductors in 2025.23 Europe also plays an
About 5 percent of these materials are supplied important, if indirect, role in AI semiconductor
by European companies. As a result, the region design through its strong position in the design
relies heavily on imports from countries such and manufacturing of power semiconductors
as China, which supplies about 75 percent (for example, through Infineon and
of the European Union’s needs in silicon STMicroelectronics).
and 90 percent of its needs in gallium and
magnesium.17 The Critical Raw Materials Act — AI semiconductor manufacturing. Europe
(CRMA) supports local production, streamlining produces only about 8 percent of the world’s
permitting processes and boosting the semiconductors and fewer than 1 percent of the
recycling of key materials.18 logic capacity semiconductors of up to seven
nanometers suitable for AI.24 Beyond that,
— AI semiconductor equipment. The Europe has no capacity for high-bandwidth
Netherlands–based ASML is the market memory (HBM) and advanced packaging.
leader for the lithography machines required Looking ahead, global capacity for advanced
to produce high-end semiconductors (up semiconductor manufacturing is expected to
to seven-nanometer logic) suitable for AI.19 continue to be fully owned by non-European
European companies also lead in other players, such as TSMC.25 In large part, that’s
equipment segments, such as atomic layer because fab payback time in Europe is higher
deposition (ASM International, also based than that of Southeast Asia, notably due to
in the Netherlands, with about a 50 percent higher labor and energy costs. In addition to
market share) and metal–organic chemical- higher costs, European companies also face
vapor deposition (Germany-based company complex administrative processes. It can take
AIXTRON, with 70 to 80 percent market up to four years to get a semiconductor plant up
share).20 Yet, in other key niches, like dry etchers and running in Europe, compared to one year
and dicing machines, European companies are in Taiwan.26
less present.
— Cloud infrastructure and supercomputers.
— AI semiconductor design. European Europe lags behind the United States
companies like Infineon Technologies, NXP in computing power. Europe is home to
Semiconductors, and STMicroelectronics 18 percent of global data-center-installed
play a global role in the semiconductor- capacity, compared with 37 percent in the
integrated-design-manufacturing space, with United States (while European and US GDPs
about 15 percent market share in 2023.21 But are comparable, with around $23 trillion and
Europe has less of a presence in the design $27 trillion, respectively)—and in most cases,
16 “Critical” is based on economic importance and supply risk, and “strategic” is defined as important for the green and digital transition,
defense, and aerospace. The 16 materials include gallium, germanium, rare earths, and silicon.
17 Study on the critical raw materials for the EU 2023, European Commission, March 16, 2023.
18 Emma Watkins, Emma Bergeling, and Eline Blot, “Circularity gaps of the European Critical Raw Materials Act,” Institute for European
Environmental Policy, October 30, 2023.
19 “Fitch affirms ASML at ‘A’; outlook stable,” Fitch Ratings, April 5, 2023.
20 AIXTRON annual reports; ASM annual reports; DataTrack, TrendForce, accessed September 2024.
21 Omdia, Informa, accessed September 2024.
22 Kif Leswing, “Nvidia dominates the AI chip market, but there’s more competition than ever,” CNBC, June 2, 2024.
23 Masayuki Shikata and Akira Yamashita, “SoftBank’s Arm plans to launch AI chips in 2025,” Nikkei Asia, May 23, 2024.
24 World Fab Forecast, SEMI (including discrete, analog, and memory semiconductors), accessed September 2024.
25 “Emerging resilience in the semiconductor supply chain,” Semiconductor Industry Association, May 8, 2024.
26 Florian Dèbes, “‘Il faut donner envie d’investir en Europe,’ plaide le patron d’ASML (“‘We need to make people want to invest in Europe,’
argues the boss of ASML”), Les Echos, June 6, 2024.
Time to place our bets: Europe’s AI opportunity 9
The operating costs of European data
centers are typically more than
50 percent higher than those in the
United States, largely driven by
Europe’s higher energy costs.
these European data centers are owned by AI, a leading open-source model provider,
US companies.27 In 2023, European cloud with $1 billion raised since 2023.34 Yet in the
companies (for example, OVH and UpCloud) technological race to constantly improve
had about 5 percent market share globally models’ performances, the company remains
(about 15 percent in Europe), while US players underfunded compared with its US competitors.
(for example, Amazon Web Services, Google, For example, OpenAI has raised $11.3 billion, and
and Microsoft) had more than 70 percent global Anthropic has raised $8.7 billion.35
market share.28 Furthermore, Europe has only
half the supercomputing capacity in flop/s,29 — AI applications. Europe has several emerging
which is increasingly necessary in basic and AI unicorns (for example, DeepL, Synthesia,
applied research.30 This is partially because and Wayve). The region also is home to
the United States has seen the emergence leading global software companies (such as
of private players specializing in this segment Dassault Systèmes, Hexagon, and SAP) that
(for example, CoreWeave), while Europe are increasingly building gen AI technologies
supercomputers mostly lie in research centers. into their solutions. For example, in 2023, SAP
What’s more, the operating costs of European pledged to invest more than $1 billion in gen
data centers are typically more than 50 percent AI companies.36 But Europe lags behind the
higher than those in the United States, largely United States, garnering only 12 percent of the
driven by Europe’s higher energy costs.31 global pool of private equity and venture capital
funding for SaaS AI companies as of 2023.37
— Foundation models. In 2023, 61 notable What’s more, several leading AI start-ups and
AI models32 originated from US-based scale-ups of European origin (for example,
organizations, far outpacing Europe’s 25.33 A Hugging Face, with a $4.5 billion valuation, and
few of the European models are competing Dataiku, with a $3.7 billion valuation38), have
globally. One such is France-based Mistral
27 IDC Global data, accessed September 2024; International Monetary Fund data, accessed September 2024; McKinsey analysis and research.
28 IDC Global data, accessed September 2024; McKinsey analysis and research.
29 Measured by total computing power of supercomputers in floating point operations per second.
30 TOP500 release, 62nd edition, TOP500, November 2023.
31 Jonathan Atkin et al., “RBC Datacenter download,” RBC Capital Markets, September 20, 2021.
32 “A notable model meets any of the following criteria: (i) state-of-the-art improvement on a recognized benchmark; (ii) highly cited (over 1000
citations); (iii) historical relevance; (iv) significant use. “What is a notable model,” Epoch AI, accessed September 2024.
33 Artificial Intelligence Index report 2024, Stanford University, 2024.
34 Crunchbase data, accessed September 2024.
35 PitchBook data, accessed September 2024.
36 “SAP advances vision of business AI with investments in Aleph Alpha, Anthropic and Cohere to complement $1+ billion AI commitment from
Sapphire Ventures,” PR Newswire, July 18, 2023.
37 PitchBook data, accessed September 2024.
38 Crunchbase data, accessed September 2024.
Time to place our bets: Europe’s AI opportunity 10
moved their headquarters from Europe to the installed capacity. Indeed, data centers are major
United States. energy consumers: a hyperscaler’s data center can
use as much power as 80,000 households.43
— AI services. Europe holds about a 15 percent
share of the global AI services market, These new demands will place additional pressure
positioning it just behind the United States, on a European power grid that’s already undergoing
which leads with approximately 40 percent.39 significant stresses. First, electricity demand is
This significant market presence provides expected to escalate in the region on the back of
Europe with a foundation for expanding growing decarbonization efforts and electrification
AI-related services. throughout various sectors, with absolute electricity
demand expected to increase by 20 to 25 percent
The near absence of European companies in by 2030 (from 3,200 terawatt-hours in 2023 to
four of the eight segments of the simplified value around 4,000 terawatt-hours in 2030, including
chain could result in missed opportunities for demand from data centers).44 Also, energy price
the region’s economy. The global market of gen competitiveness in Europe is low, with industrial-
AI technologies is expected to boom, with high electricity prices some 70 percent higher in Europe
double-digit annual growth anticipated over the than in the United States in May 2024.45 Finally,
next ten years.40 This situation could be a challenge Europe has the oldest power grid in the world (45 to
to the region’s strategic autonomy, ultimately 50 years, on average, versus 35 to 40 years in North
jeopardizing gen AI adoption and productivity gains. America and 15 to 20 years in China).46 This can lead
A semiconductor shortage in 2022, for example, to inefficiencies in electricity distribution.
hit the European auto industry especially hard,
resulting in an estimated €100 billion GDP loss.41 On the bright side, this significant increase in
Similarly, insufficient access to cloud infrastructure electricity consumption could serve as a positive
and supercomputers could limit development and incentive for energy operators to invest in new
operations of gen AI technologies. capacities. Additionally, Europe has an edge in clean
energy, with 61 percent of low-carbon sources in
its electricity mix, compared with 40 percent in the
Energy for gen AI: Expected to drive United States and 34 percent in China.47
increased electricity demand in
Europe amid already-high prices
How to boost Europe’s
McKinsey estimates that rising data center power
competitiveness in gen AI
demand could increase Europe’s electricity
consumption by at least 180 terawatt-hours by Europe clearly faces a host of challenges with gen
2030–equivalent to more than 5 percent of total AI, but they aren’t insurmountable. Policy makers
European electricity annual consumption in 2023.42 and business leaders in Europe can consider
This is driven by demand for data center computing several activities to increase the region’s ability to
power in Europe, which McKinsey expects to fully realize the potential economic gains of AI when
more than triple by 2030 to reach 35 gigawatts of it comes to adoption, creation, and energy.
39 Riccardo Righi et al., “EU in the global artificial intelligence landscape,” European Commission, 2021.
40 “Generative AI to become a $1.3 trillion market by 2032, research finds,” Bloomberg Intelligence, June 1, 2023.
41 “Missing chips cost EUR100bn to the European auto sector,” Allianz, September 13, 2022; International Organization of Motor Vehicle
Manufacturers data, accessed September 2024.
42 Electricity Data Explorer, Ember, accessed September 2024; McKinsey research and analysis.
43 “Investing in the rising data center economy,” McKinsey, January 17, 2023.
44 Electricity Data Explorer, Ember, accessed September 2024; Patrick Chen, Tamara Grünewald, Jesse Noffsinger, and Eivind Samseth,
“Global Energy Perspective 2023: Power outlook,” McKinsey, January 16, 2024.
45 Enerdata, Ember, US Energy Information Administration, Eurostat.
46 “Winds of change,” Nexans 2021 Capital Markets Day.
47 Total electricity generation mix with low-carbon energy, including biofuel and wastes, hydro, wind and solar, and other renewable sources.
International Energy Agency data, accessed September 2024.
Time to place our bets: Europe’s AI opportunity 11
Adoption of gen AI in Europe were two to four times higher than those of
To facilitate gen AI adoption, European leaders their European counterparts.53 This disparity
might consider the following actions: is likely attributable to the greater financial
resources of US companies, which benefit
— Reskill and upskill the workforce. Research from larger economies of scale and higher
from MGI indicates that to reap the full levels of venture capital and private equity
productivity dividends of gen AI, Europe funding. Europe also lags behind the United
would need to double its current pace of job States in AI-related research, with only two
transition—from the 0.4 percent per year seen universities in a key ranking of top institutions
in 2016–19, prior to the COVID-19 pandemic, for AI research in 2022, compared with 15 for
to an unprecedented 0.8 percent by 2030.48 the United States.54 European workers also
(The effort for the United States would be fall behind in gen AI awareness and use: the
lower, as transition rates have already been at latest McKinsey Global Survey on AI finds that
such levels.) That could require the reskilli"
48,mckinsey,the-promise-and-the-reality-of-gen-ai-agents-in-the-enterprise.pdf,"Technology, Media & Telecommunications Practice
The promise and the
reality of gen AI agents
in the enterprise
Generative AI technology is improving so quickly that a range of
new capabilities are rapidly coming online, but only for those who can
understand how to use them.
May 2024
The evolution of generative AI (gen AI) has opened For example, in customer services, recent
the door to great opportunities across organizations, developments in short- and long-term memory
particularly regarding gen AI agents—AI-powered structures enable these agents to personalize
software entities that plan and perform tasks or interactions with external customers and internal
aid humans by delivering specific services on their users, and help human agents learn. All of this
behalf. So far, adoption at scale across businesses means that gen AI agents are getting much closer
has faced difficulties because of data quality, to becoming true virtual workers that can both
employee distrust, and cost of implementation. In augment and automate enterprise services in
addition, capabilities have raced ahead of leaders’ all areas of the business, from HR to finance to
capacity to imagine how these agents could be customer service. That means we’re well on our way
used to transform work. to automating a wide range of tasks in many service
functions while also improving service quality.
However, as gen AI technologies progress and
the next-generation agents emerge, we expect Barr Seitz: Where do you see the greatest value
more use cases to be unlocked, deployment from gen AI agents?
costs to decrease, long-tail use cases to become
economically viable, and more at-scale automation Jorge Amar: We have estimated that gen AI
to take place across a wider range of enterprise enterprise use cases could yield $2.6 trillion to
processes, employee experiences, and customer $4.4 trillion annually in value across more than
interfaces. This evolution will demand investing 60 use cases.1 But how much of this value is realized
in strong AI trust and risk management practices as business growth and productivity will depend
and policies as well as platforms for managing and on how quickly enterprises can reimagine and
monitoring agent-based systems. truly transform work in priority domains—that is,
user journeys, processes across an entire chain of
In this interview, McKinsey Digital’s Barr Seitz activities, or a function.
speaks with senior partners Jorge Amar and
Lari Hämäläinen and partner Nicolai von Bismarck Gen-AI-enabled agents hold the promise of
to explore the evolution of gen AI agents and accelerating the automation of a very long tail of
how companies can and should implement the workflows that would otherwise require inordinate
technology, where the pools of value lie for amounts of resources to implement. And the
the enterprise as a whole. They particularly potential extends even beyond these use cases:
explore what these developments mean for 60 to 70 percent of the work hours in today’s
customer service. An edited transcript of the global economy could theoretically be automated
conversation follows. by applying a wide variety of existing technology
capabilities, including generative AI, but doing so
Barr Seitz: What exactly is a gen AI agent? will require a lot in terms of solutions development
and enterprise adoption.
Lari Hämäläinen: When we talk about gen AI
agents, we mean software entities that can Consider customer service. Currently, the value of
orchestrate complex workflows, coordinate gen AI agents in the customer service environment
activities among multiple agents, apply logic, and is going to come either from a volume reduction or
evaluate answers. These agents can help automate a reduction in average handling times. For example,
processes in organizations or augment workers in work we published earlier this year, we looked at
and customers as they perform processes. This is 5,000 customer service agents using gen AI and
valuable because it will not only help humans do found that issue resolution increased by 14 percent
their jobs better but also fully digitalize underlying an hour, while time spent handling issues went
processes and services. down 9 percent.2
1 “The economic potential of generative AI: The next productivity frontier,” McKinsey, June 14, 2023.
2 Ibid.
The promise and the reality of gen AI agents in the enterprise 2
The other area for value is agent training. Typically, Nicolai von Bismarck: It’s worth emphasizing that
we see that it takes somewhere between six gen AI agents not only automate processes but
to nine months for a new agent to perform at also support human agents. One thing that gen AI
par with the level of more tenured peers. With agents are so good at, for example, is in helping
this technology, we see that time come down to customer service representatives get personalized
three months, in some cases, because new agents coaching not only from a hard-skill perspective but
have at their disposal a vast library of interventions also in soft skills like understanding the context
and scripts that have worked in other situations. of what is being said. We estimate that applying
generative AI to customer care functions could
Over time, as gen AI agents become more proficient, increase productivity by between 30 to 45 percent.3
I expect to see them improve customer satisfaction
and generate revenue. By supporting human Jorge Amar: Yes, and in other cases, gen AI
agents and working autonomously, for example, agents assist the customer directly. A digital sales
gen AI agents will be critical not just in helping assistant can assist the customer at every point in
customers with their immediate questions but also their decision journey by, for example, retrieving
beyond, be that selling new services or addressing information or providing product specs or cost
broader needs. As companies add more gen AI comparisons—and then remembering the context
agents, costs are likely to come down, and this if the customer visits, leaves, and returns. As those
will open up a wider array of customer experience capabilities grow, we can expect these gen AI
options for companies, such as offering more agents to generate revenue through upselling.
high-touch interactions with human agents as a
premium service. [For more on how companies are using gen AI
agents, see the sidebar, “A closer look at gen AI
Barr Seitz: What are the opportunities you are agents: The Lenovo experience.”]
already seeing with gen AI agents?
Barr Seitz: Can you clarify why people should
Jorge Amar: Customer care will be one of the first believe that gen AI agents are a real opportunity
but definitely not the only function with at-scale and not just another false technology promise?
AI agents. Over the past year, we have seen a lot
of successful pilots with gen AI agents helping to Jorge Amar: These are still early days, of course,
improve customer service functions. For example, but the kinds of capabilities we’re seeing from
you could have a customer service agent who is gen AI agents are simply unprecedented. Unlike
on the phone with a customer and receives help past technologies, for example, gen AI not
in real time from a dedicated gen AI agent that is, only can theoretically handle the hundreds of
for instance, recommending the best knowledge millions of interactions between employees and
article to refer to or what the best next steps are for customers across various channels but also can
the conversation. The gen AI agent can also give generate much higher-quality interactions, such
coaching on behavioral elements, such as tone, as delivering personalized content. And we know
empathy, and courtesy. that personalized service is a key driver of better
customer service. There is a big opportunity here
It used to be the case that dedicating an agent to because we found in a survey of customer care
an individual customer at each point of their sales executives we ran that less than 10 percent of
journey was cost-prohibitive. But, as Lari noted, respondents in North America reported greater-
with the latest developments in gen AI agents, now than-expected satisfaction with their customer
you can do it. service performance.4
3 Ibid.
4 “Where is customer care in 2024?,” McKinsey, March 12, 2024.
The promise and the reality of gen AI agents in the enterprise 3
A closer look at gen AI agents: The Lenovo experience
Three leaders at Lenovo—Solutions and Linda Yao: I was working with our much more effective. They can prepare
Services Group chief technology officer marketing and sales training teams sales people for customer interactions
Arthur Hu, COO and head of strategy Linda just this morning as part of a program or guide them during sales calls. This
Yao, and Digital Workplace Solutions to develop a learning curriculum for approach is having a much greater impact
general manager Raghav Raghunathan— our organization, our partners, and our than previous learning approaches. It
discuss with McKinsey senior partner key customers. We’re figuring out what gives them a safe space to learn. They can
Lari Hämäläinen and McKinsey Digital’s learning should be at all levels of the practice their pitches ahead of time and
Barr Seitz how the company uses business and for different roles. learn through feedback in live situations.
generative AI (gen AI) agents.
Arthur Hu: On the tech side, employees Barr Seitz: How do you see the future of
Barr Seitz: What existing gen AI agent need to understand what gen AI agents gen AI agents evolving?
applications has Lenovo been running are and how they can help. It’s critical
Linda Yao: In our use cases to date, we’ve
and what sort of impact have you seen to be able to build trust or they’ll resist
refined gen AI agents so they act as a
from them? adopting it. In many ways, this is a
good assistant. As we start improving the
demystification exercise.
Arthur Hu: We’ve focused on two main technology, gen AI agents will become
areas. One is software engineering. It’s Raghav Raghunathan: We see gen AI more like deputies that human agents
the low-hanging fruit to help our people as a way to level the playing field in new can deploy to do tasks. We’re hoping to
enhance speed and quality of code areas. You don’t need a huge talent base see productivity improvements, but we
production. Our people are already getting now to compete. We’re investing in tools expect this to be a big improvement for
10 percent improvements, and we’re and workflows to allow us to deliver the employee experience. These are tasks
seeing that increase to 15 percent as services with much lower labor intensity people don’t want to do.
teams get better at using gen AI agents. and better outcomes.
Arthur Hu: There are lots of opportunities,
The second one is about support. We Barr Seitz: What sort of learning programs but one area we’re exploring is how to
have hundreds of millions of interactions are you developing to upskill your people? use gen AI to capture discussions and
with our customers across online, chat, interactions, and feed the insights and
Linda Yao: The learning paths for
voice, and email. We’re applying LLM outputs into our development pipeline.
managers, for example, focus on building
[large language model]-enhanced bots to There are dozens of points in the customer
up their technical acumen, understanding
address customer issues across the entire interaction journey, which means we
how to change their KPIs because team
customer journey and are seeing some have tons of data to mine to understand
outputs are changing quickly. At the
great improvements already. We believe complex intent and even autogenerate new
executive level, it’s about helping leaders
it’s possible to address as much as 70 to knowledge to address issues.
develop a strong understanding of the tech
80 percent of all customer interactions
so they can determine what’s a good use
without needing to pull in a human.
case to invest in, and which one isn’t.
Linda Yao: With our gen AI agents helping
Arthur Hu: We’ve found that as our
Arthur Hu is chief technology officer of Lenovo’s
support customer service, we’re seeing
software engineers learn how to work Solutions and Services Group. Linda Yao is
double-digit productivity gains on call
with gen AI agents, they go from basically COO and head of strategy at Lenovo. Raghav
handling time. And we’re seeing incredible Raghunathan is general manager of Lenovo’s
just chatting with them for code snippets
gains in other places too. We’re finding that Digital Workplace Solutions. Lari Hämäläinen
to developing much broader thinking and is a senior partner in McKinsey’s Seattle office;
marketing teams, for example, are cutting
focus. They start to think about changing Barr Seitz is director of global publishing
the time it takes to create a great pitch for McKinsey Digital and is based in the New
the software workflow, such as working
book by 90 percent and also saving on York office.
with gen AI agents on ideation and other
agency fees.
parts of the value chain. Comments and opinions expressed by
interviewees are their own and do not represent
Barr Seitz: How are you getting ready for a
Raghav Raghunathan: Gen AI provides or reflect the opinions, policies, or positions of
world of gen AI agents?
an experiential learning capability that’s McKinsey & Company or have its endorsement.
The promise and the reality of gen AI agents in the enterprise 4
Lari Hämäläinen: Let me take the technology view. Finally, it’s worth mentioning that a lot of gen AI
This is the first time where we have a technology applications beyond chat have been custom-
that is fitted to the way humans interact and can be built in the past year by bringing different
deployed at enterprise scale. Take, for example, the components together. What we are now seeing
IVR [interactive voice response] experiences we’ve is the standardization and industrialization of
all suffered through on calls. That’s not how humans frameworks to become closer to “packaged
interact. Humans interact in an unstructured software.” This will speed up implementation
way, often with unspoken intent. And if you think and improve cost efficiency, making real-world
about LLMs [large language models], they were applications even more viable, including addressing
basically created from their inception to handle the long-tail use cases in enterprises.
unstructured data and interactions. In a sense, all
the technologies we applied so far to places like Barr Seitz: What sorts of hurdles are you seeing
customer service worked on the premise that the in adopting the gen AI agent technology for
customer is calling with a very structured set of customer service?
thoughts that fit predefined conceptions.
Nicolai von Bismarck: One big hurdle we’re seeing
Barr Seitz: How has the gen AI agent landscape is building trust across the organization in gen AI
changed in the past 12 months? agents. At one bank, for example, they knew they
needed to cut down on wrong answers to build
Lari Hämäläinen: The development of gen AI has trust. So they created an architecture that checks
been extremely fast. In the early days of LLMs, for hallucinations. Only when the check confirms
some of their shortcomings, like hallucinations that the answer is correct is it released. And if the
and relatively high processing costs, meant that answer isn’t right, the chatbot would say that it
models were used to generate pretty basic outputs, cannot answer this question and try to rephrase it.
like providing expertise to humans or generating The customer is then able to either get an answer
images. More complex options weren’t viable. For to their question quickly or decide that they want to
example, consider that in the case of an LLM with talk to a live agent. That’s really valuable, as we find
just 80 percent accuracy applied to a task with ten that customers across all age groups—even Gen
related steps, the cumulative accuracy rate would Z—still prefer live phone conversations for customer
be just 11 percent. help and support..
Today, LLMs can be applied to a wider variety of Jorge Amar: We are seeing very promising results,
use cases and more complex workflows because but these are in controlled environments with a
of multiple recent innovations. These include small group of customers or agents. To scale these
advances in the LLMs themselves in terms of their results, change management will be critical. That’s a
accuracy and capabilities, innovations in short- and big hurdle for organizations. It’s much broader than
long-term memory structures, developments simply rolling out a new set of tools. Companies are
in logic structures and answer evaluation, and going to need to rewire how functions work so they
frameworks to apply agents and models to complex can get the full value from gen AI agents.
workflows. LLMs can evaluate and correct “wrong”
answers so that you can have much higher accuracy. Take data, which needs to be in the right format
With an experienced human in the loop to handle and place for gen AI technologies to use them
cases that are identified as tricky, then the joint effectively. Almost 20 percent of most organizations,
human-plus-machine outcome can generate great in fact, see data as the biggest challenge to
quality and great productivity. capturing value with gen AI.5 One example of this
5 “The state of AI in 2023: Generative AI’s breakout year,” McKinsey, August 1, 2023.
The promise and the reality of gen AI agents in the enterprise 5
kind of issue could be a chatbot sourcing outdated Barr Seitz: Staying with customer service, how will
information, like a policy that was used during gen AI agents help enterprises?
COVID-19, in delivering an answer. The content
might be right, but it’s hopelessly out of date. Jorge Amar: This is a great question, because
Companies are going to need to invest in cleaning we believe the immediate impact comes from
and organizing their data. augmenting the work that humans do even as
broader automation happens. My belief is that gen
In addition, companies need a real commitment AI agents can and will transform various corporate
to building AI trust and governance capabilities. services and workflows. It will help us automate a lot
These are the principles, policies, processes, and of tasks that were not adding value while creating
platforms that assure companies are not just a better experience for both employees and
compliant with fast-evolving regulations—as customers. For example, corporate service centers
seen in the recent EU AI law and similar actions in will become more productive and have better
many countries—but also able to keep the kinds outcomes and deliver better experiences.
of commitments that they make to customers and
employees in terms of fairness and lack of bias. In fact, we’re seeing this new technology help
This will also require new learning, new levels of reduce employee attrition. As gen AI becomes
collaboration with legal and risk teams, and new more pervasive, we may see an emergence of more
technology to manage and monitor systems at scale. specialization in service work. Some companies
and functions will lead adoption and become fully
Change needs to happen in other areas as well. automated, and some may differentiate by building
Businesses will need to build extensive and tailored more high-touch interactions.
learning curricula for all levels of the customer
service function—from managers who will need to Nicolai von Bismarck: As an example, we’re seeing
create new KPIs and performance management this idea in practice at one German company, which
protocols to frontline agents who will need to is implementing an AI-based learning and coaching
understand different ways to engage with both engine. And it’s already seeing a significant
customers and gen AI agents. improvement in the employee experience as
measured while it’s rolling this out, both from a
The technology will need to evolve to be more supervisor and employee perspective, because the
flexible and develop a stronger life cycle capability employees feel that they’re finally getting feedback
to support gen AI tools, what we’d call MLOps that is relevant to them. They’re feeling valued,
[machine learning operations] or, increasingly, gen they’re progressing in their careers, and they’re also
AIOps [gen AI operations]. The operating model will learning new skills. For instance, instead of taking
need to support small teams working iteratively on just retention calls, they can now take sales calls.
new service capabilities. And adoption will require This experience is providing more variety in the work
sustained effort and new incentives so that people that people do and less dull repetition.
learn to trust the tools and realize the benefits.
This is particularly true with more tenured agents, Lari Hämäläinen: Let me take a broader view.
who believe their own skills cannot be augmented We had earlier modeled a midpoint scenario
or improved on with gen AI agents. For customer when 50 percent of today’s work activities could
operations alone, we’re talking about a broad effort be automated to occur around 2055. But the
here, but with more than $400 billion of potential technology is evolving so much more quickly than
value from gen AI at stake, it’s worth it.6 anyone had expected—just look at the capabilities
6 “The economic potential of generative AI: The next productivity frontier,” McKinsey, June 14, 2023.
The promise and the reality of gen AI agents in the enterprise 6
of some LLMs that are approaching, and even a decade. And it’s going to keep getting faster, so
Find more content like this on the
surpassing, in certain cases, average human levels we can expect the adoption timeline to shrink even
McKinsey Insights App
of proficiency. The innovations in gen AI have further. That’s a crucial development that every
helped accelerate that midpoint scenario by about executive needs to understand.
Jorge Amar is a senior partner in McKinsey’s Miami office, Lari Hämäläinen is a senior partner in the Seattle office, and
Nicolai von Bismarck is a partner in the Boston office. Barr Seitz is director of global publishing for McKinsey Digital and is
based in the New York office.
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The promise and the reality of gen AI agents in the enterprise 7"
49,mckinsey,tech-services-and-generative-ai-plotting-the-necessary-reinvention.pdf,"Technology, Media & Telecommuni cations Practice
Tech services and
generative AI: Plotting
the necessary reinvention
The rapid emergence of generative AI has the potential to disrupt
a dynamic the sector has relied on for decades, but it also offers
an opportunity to tap a lucrative new market.
This article is a collaborative effort by Anuj Kadyan, Himanshu Pandey, Noshir Kaka, Pallav Jain,
Senthil Muthiah, and Vikash Daga, with Ruchika Dasgupta, representing views from McKinsey’s
Technology, Media & Telecommunications Practice.
June 2024
Through most advances and innovations in To help services providers reposition themselves
enterprise technology, IT or technology services to thrive in the gen AI era, we surveyed 100 top
providers (also known as system integrators1 [SIs] or industry executives and technology decision
managed services providers [MSPs]) have, for the makers,2 interviewed a number of experts in the
most part, been able to rely on one constant: their sector, and conducted an analysis of the state
customers continuing to look outside their four of gen AI adoption in a representative sample of
walls for help stitching together and overseeing the the Forbes Global 2000 ranking of the largest
ever-changing, complex web of hardware, software, enterprises. This article, which is also informed by
networking, and storage products that drives their our experience in the market, examines how the
businesses. Now, enterprises are funneling more of new technology is affecting enterprise technology
their technology spending into generative AI (gen spending patterns, what new gen AI services
AI) and leveraging its capabilities to streamline or providers can offer to enterprise customers at
automate some of these same IT management different stages of gen AI adoption, and what critical
services. Services providers could be forgiven for steps providers can take to position themselves for
wondering what the future holds for them. this new era.
As it turns out, the future could be quite bright. Gen
Gen AI’s impact on enterprise tech
AI is also increasing demand for a wide range of new
spending and provider economics
services, which represents a significant opportunity
for providers to reimagine and recharge their Most companies have been working to implement
business. Just how significant? We estimate the and scale traditional AI and automation solutions for
emerging market for services relating to gen AI/AI more than a decade. However, the launch of OpenAI’s
could be worth more than $200 billion by 2029. ChatGPT solution in late 2022 has led to a paradigm
If tech services providers can succeed in claiming shift in enterprise AI priorities. Organizations are
a decent share of that incremental value, their now turning to gen AI to help power and reinvigorate
profitability could grow by as much as 30 percent. traditional AI initiatives while launching entirely new
Reaching those heights will require providers to gen AI efforts in multiple functions. But after the
reinvent how they do business. That challenge will initial waves of excitement and hype that greeted
include transforming service offerings and how gen AI’s arrival, the enterprise customer base is
they are delivered, embracing new go-to-market now squarely focused on seeing the transformative
(GTM) and commercial models, and upskilling technology live up to its billing. The goal is to move
teams while finding new sources of talent. from piecemeal efforts, isolated pilots, and proofs of
concept (POCs) to scalable solutions that can be
The next 12 to 18 months will be pivotal. Enterprise deployed across organizations.
customers are already exploring new ways to
manage some core IT work themselves while As part of this gen AI reset, organizations have a
ramping up all manner of gen AI pilots and initiatives, better understanding of the strategic and financial
and a wide range of other tech players (from commitment required to create significant impact.
hyperscalers to hardware and software companies) As one executive told us, “Scaling AI is hard. It
are beginning to make or contemplate moves into requires new skills, new processes, and new ways
the burgeoning AI services market. Traditional of working. It’s a transformational challenge for
services providers that don’t begin to reimagine most organizations.”
their value proposition in this arena risk losing some
relevance—and potentially more than 15 percent in In the latest McKinsey state of AI survey3 of
revenue and profit. enterprise customers, 67 percent of respondents
1 System integrators (SIs), aka managed services providers (MSPs), focus on providing traditional IT services, including by building ecosystems
for end customers that combine hardware, software, networking, and storage products from multiple vendors.
2 M cKinsey Enterprise CXO Survey: Impact of Gen AI for Technology Services Providers, January 2024 (n = 100).
3 “The state of AI in early 2024: Gen AI adoption spikes and starts to generate value,” McKinsey, May 30, 2024.
Tech services and generative AI: Plotting the necessary reinvention 2
said they expect their organizations to spend more specialized and sensitive AI workloads (for
of their technology budgets on AI/gen AI over example, public sector, healthcare) may drive
the coming three years. Based on our analysis, we some spending growth.
estimate that overall tech budgets will grow at
a CAGR of about 4 to 6 percent over the next five At the same time, a wealth of new AI services
years. But where that is spent may change opportunities are developing thanks to increased
considerably, with traditional value pools changing spending across a number of categories. The
and new ones (and new competitors) fast appearing. largest of these include the following:
Gen AI is already causing various tech ecosystem
players to expand their range of offerings; for — Outsourced AI services. These services are
instance, hyperscalers are providing integrated expected to enjoy double-digit increases
development environments to build, train, as enterprises grappling with the scarcity—and
customize, and deploy large language models expense—of gen AI talent and their own
(LLMs), while some hardware vendors are inexperience developing and implementing gen
venturing into services that enable development AI solutions seek external help. This increased
of custom LLMs and microservices for faster spending will focus on AI foundational services
deployment of gen AI applications. (for example, AI for IT operations [AIOps]),
gen-AI-led productivity solutions (for example,
New competitors are just one aspect of the coding copilots), and vertical-industry solutions
disruption. With enterprises funneling a greater (for example, clinical trial acceleration in life
portion of tech budgets to AI/gen AI efforts, sciences, hyper-personalized B2C solutions
spending on services providers’ core offerings for telecom).
could drop or stagnate across a few traditional
areas of spending. The three main categories of — Outsourced digital services. Increased demand
tech services spending to feel this effect are for digital services like the cloud, legacy
as follows: modernization, and data and analytics will likely
drive growth by 8 to 12 percent as enterprises
— Outsourced traditional services. The long- leverage gen AI across existing digital initiatives.
standing foundation of tech services providers
will likely see a decline of 8 to 10 percent as — Enterprise applications. These applications
advancements in automation and AI from cloud should see solid growth as enterprises adopt a
platforms solve traditional IT challenges. new generation of gen AI apps and solutions
that solve traditional IT challenges.
— Insourced services. Although internal IT teams
will leverage low-code/no-code platforms, new — New AI stack solutions. This new spending
data/AI apps, and infrastructure-management- category (largely for LLMs and related spending)
automation tools to grow their portfolios, overall is likely to grow rapidly as enterprises increase
spending on this category will still likely stay flat, their adoption of gen-AI-native offerings
with the cost of additional workloads largely such as foundational models, tooling, and
offset by productivity gains. data architecture.
— On-prem, co-location (colo), and private cloud. — Public cloud and infrastructure as a service.
The secular shift to public cloud and cloud- Gen AI should drive increased enterprise
based graphics processing units (GPUs) is likely cloud migration and consumption (for example,
to cause a slow, steady decline in this area LLM training on the cloud), fueled by the rise of
across most workloads. However, a few AI-specialist cloud providers.
Tech services and generative AI: Plotting the necessary reinvention 3
— Computer hardware (for example, GPUs). opportunity for tech services providers in the next
This category is likely to experience growth as five years, primarily centered around AI foundational
enterprises use more advanced AI/gen AI services, AI-first horizontal solutions, and vertical-
custom chips, with some of this spending growth solutions. Companies are likely to overhaul
captured by public cloud providers making their their tech spending and reallocate their budgets
own investments in the advanced hardware. along the lines of the global enterprise macroshifts
laid out in Exhibit 1.
The full impact of gen AI on enterprise tech spending
could be dramatic. According to our analysis, the The alternative to seizing that potential opportunity
disruption could unlock a $200 billion-plus market may be even more daunting. Our analysis highlights
Web <2024>
<GenAITech>
Exhibit 1
Exhibit <1> of <5>
Generative AI is expected to fuel a fundamental shift in enterprise tech value
pools, as tech ecosystem actors make moves to claim their share.
Gen-AI-based shifts in global enterprise tech spending expected over next 3 years, by category
Services spend >$1.0 trillion
Consumption spend $0.5–$1.0 trillion
2024 spending
(total addressable Direction <$0.5 trillion
Tech spending categories market) of shift Important shifts expected
Outsourced New spending on AI-focused  Net new outsourcing across three areas: gen AI
AI services outsourced services readiness, horizontal productivity solutions, and
vertical growth solutions
Outsourced Cloud implementation, legacy  Growth in areas like the cloud, digital and analytics,
digital modernization, digital and and legacy modernization propelled by movement to
services analytics, customer experi- “AI-first stack”
ence (CX), security, IoT
Outsourced Application data  Further deceleration in transitional tech spending
traditional management (ADM), business propelled by gen-AI-enabled automation and even-
services process outsourcing (BPO), tual redundancy
data center, inframanage-
ment services
Insourced In-house IT spend (eg, devel-  Longer-term reduction expected, propelled by spe-
services opers, CX teams, etc) cialized, off-the-shelf solutions from both software
and services companies
Enterprise Software-as-a-service (SaaS) Steady growth likely to continue across areas like

applications applications and software enterprise resource planning (ERP), customer rela-
spending tionship management (CRM), and collaboration as
enterprises further modernize
Data and New SaaS spending on AI  Significant uptick likely with the onset of LLM-led
AI solutions platforms, large language solutions from incumbents and new actors
models (LLMs), etc
Public Hyperscaler spending Adoption likely to sustain with growth of AI work-

cloud loads (including rise of AI-specialized cloud actors)
On-prem, Physical storage, on-prem - Spending likely to sustain, with specialized, sensi-
co-location, data centers, and spending tive, and edge workloads likely to be housed in
and private on private cloud private cloud
cloud
Source: McKinsey Enterprise CXO Survey: Impact of Gen AI for Technology Services Providers, Jan 2024 (n = 100); McKinsey analysis
McKinsey & Company
Tech services and generative AI: Plotting the necessary reinvention 4
the risk of inaction for both the top and bottom lines The enterprise gen AI journey
of companies in the sector (Exhibits 2 and 3),
In the relatively short period of time since enterprises
with a 15 percent cut in both a real possibility. The
started working with gen AI, they have already
growth in automation and insourcing of certain
begun to evolve how they navigate the technology.
workflows ushered in by gen AI is likely to spur a
For starters, they are now facing up to the myriad
sizable reduction in demand for providers’
challenges many have already experienced in
traditional services, while productivity gains and
attempting to adopt gen AI: high costs, particularly
added competition could lead to pricing pressure.
from trying to leverage LLMs on a large scale;
By contrast, embracing and adapting to the new
insufficient cloud and data readiness of the tech
technological era offers the prospect of not only
stack; the reliability of gen AI output; and uncertain
maintaining the industry’s 3 to 5 percent historical
risks related to regulation, intellectual property,
growth trend but also improving its financial
ethics, and more. Instead of relying on one vendor,
position, with a further 2 to 4 percent revenue boost
enterprises are increasingly using multiple model
and a potential 30 percent profit bump, based on
providers to develop their gen AI use cases. They’re
the provider’s starting point.
also turning to more affordable open-source models
Web <2024>
<EGxehniAbITiet c2h>
Exhibit <2> of <5>
Generative AI could affect tech services’ business models in many ways,
both positive and negative.
Impact of generative AI (gen AI) on tech services’ business models
Change in economics of existing business¹
1
Gains in
productivity
2 3
Pricing High gen
pressure AI costs
Reduction in the volume of contracts 4 5 6
Insourcing of
SaaS/CSP² workflows by Automation
substitution enterprise of workflows
Growth in services and offerings 7 8 9 10
Increased Reimagined New Increased
demand for existing services demand
existing service using gen AI from next
services lines applications tier of firms
Gains in SG&A³ 11 Improved win rate from Productivity gains in 12
M&S productivity gains internal processes
1Share of gains created and value retained by providers in time and material and in fixed-price contracts.
²Software as a service/cloud service provider.
³Selling, general, and administrative expenses.
McKinsey & Company
Tech services and generative AI: Plotting the necessary reinvention 5
Web <2024>
E<GxehniAbITite c3h>
Exhibit <3> of <5>
The impact of generative AI on services providers’ top and bottom lines will
depend on whether they take a passive or active approach to the new era.
Impact of generative AI (gen AI) on tech services’ sector profit and loss under different scenarios, %
Passive play (steady state) Offensive play (steady state) Baseline
Range by
102–104
scenario
100 100
83–85
80 80
60 60
46–48
37–41
40 40
43–44 30–32 12–15
9–11 19–21
20 20
13–14 19–21
17–18
0 0
Revenue Direct Other Gross SG&A/ EBITDA Revenue Direct Other Gross SG&A/ EBITDA
cost direct margin operating cost direct margin operating
FTE costs expenses FTE costs expenses
Revenue –15 to –17% Profit 0.85× Revenue +2 to +4% Profit 1.30×
Source: Expert interviews; McKinsey analysis
McKinsey & Company
that they can control and fine-tune rather than — Observers. The bulk of enterprises (50 to
attempting to build custom models from scratch, 60 percent) are “observers” focused on AI
taking the approach of what previous McKinsey readiness (setting up their data and cloud
research has dubbed a gen AI model “taker” foundations) and conducting small-scale POCs
or “shaper” rather than the more costly and primarily for internal use cases (for example,
complicated path of a “maker.” text summarization, knowledge management)
that are largely anchored on driving
While these types of shifts are widespread, large higher productivity.
enterprises are at very different stages of gen AI
adoption. Based on our analysis, organizations can — Front-runners. Another 30 to 40 percent are
be classified into the following three stages of “front-runners” with a clear vision for using AI/
gen AI readiness and adoption along their journey gen AI to reduce costs at scale through use
(Exhibit 4): cases such as AI-enabled service desks and
Tech services and generative AI: Plotting the necessary reinvention 6
legacy-code upgrades. These organizations — Innovators. Less than 10 percent are true AI-first
have already made changes to their organization “innovators” with an integrated strategy to achieve
and operating models, such as creating AI cost reductions and unlock the true potential
centers of excellence (COEs) and new roles like of gen AI through cutting-edge use cases like
chief AI officer. gen-AI-enabled product design and development
that can propel future business growth.
Web <2024>
E<GxehniAbITite c4h>
Exhibit <4> of <5>
AI/generative AI transformation is a multistep, multimodal journey, and
most large enterprises are still in the early stage of adoption.
Enterprise cloud, data, and AI value realization through technology transformation
Large enterprise AI/generative AI (gen AI) adoption stage, estimated share of Global 2000 companies
OBSERVERS FRONT-RUNNERS INNOVATORS
50–60% 30–40% <10%
15
14
New AI/gen 13
AI journey 12
11
Enterprises
10
want to derive
1 9 2–3×
Value 8 higher value
realization, $ through AI
6 7
5
Conventional digital
4
and cloud journey
3
1 2
The AI journey
Conventional cloud transformation, AI innovation on the cloud Long-term, end-to-end
at-scale migration of apps, followed over a “Data on Cloud” AI transformation
by modernization and native app foundation unlocked by building a
development modern AI tech stack
1 Cloud advisory and design 8 Data engineering 13 AI-first inframodern-
2 Cloud migration 9 Data migration to the cloud² ization⁴
3 Application modernization 10 AI-enabled IT moderniza- 14 AI use case scale-up
4 Native app development tion³ across functions
5 Cloud testing, financial operations, 11 AI use case development 15 AI operations and
and multihybrid management and strategic road map enablers⁵
6 Managed Cloud Services¹ 12 Data product design and
7 Cloud operations monetization
1For example, security. ²At scale. ³For example, legacy code upgrade, AI-enabled service desk. ⁴Custom LLM fine-tuning, etc. ⁵Including MLOps, shared use cases,
and data collaboration.
Source: Company annual reports; McKinsey Enterprise CXO Survey: Impact of Gen AI for Technology Services Providers, Jan 2024 (n = 100); McKinsey analysis
McKinsey & Company
Tech services and generative AI: Plotting the necessary reinvention 7
Just as companies are at different stages of their “new AI stack” of tooling/security, foundational
respective gen AI journeys, they also require a models, and data and databases sandwiched
range of tech services to propel them forward. To between the applications and infra/cloud layers,
satisfy that demand and launch their own gen AI which were previously the main focus areas of
journeys, providers should strongly consider rolling the traditional services providers’ play (Exhibit 5).
out three distinct types of AI services over an initial While services providers may encounter
12- to 18-month period. They are as follows: difficulty gaining ground as new entrants in
these areas, they are more likely to develop
— Reimagined traditional and digital services extensions that make existing solutions more
lines that leverage gen AI for improving delivery powerful, cost-effective, and easier to
productivity and innovating current services implement than to attempt building their own
offerings (for example, knowledge management, models or tools from scratch.
agent copilot).
— Vertical solutions that target product/process
— Foundational AI/gen AI offerings that prepare innovation and revenue growth initiatives of
the enterprise tech stack for cloud and gen AI enterprises through deep, vertical-specific use
readiness (for example, data on the cloud) and/or cases (for example, gen-AI-enabled connected
gen-AI-native services related to LLMs or tooling products and manufacturing ecosystems in the
(for example, building comprehensive, end-to- industrial segment, insurance claims processing
end services ecosystems related to new players platform in finance). Services providers’ strong
such as OpenAI, Cohere, and others). track record of targeting verticals with data and
analytics services should prove helpful.
Importantly, the second, foundational class of
services may provide a critical entry point Because of the relatively small number of enterprises
for providers to expand their relationships with that already qualify as gen AI innovators, the vast
enterprises and help shape their innovation majority (about 80 percent) of more than 10,000 AI
agendas. Services providers can now target the services deals4 expected over the next year
Given that cloud transformation has
been a key part of services providers’
growth in recent years, they should be
well positioned to guide customers to
the next fundamental digital overhaul
with AI and gen AI.
4 Extrapolating the number of proofs of concept/deals reported by services providers in the last quarter of 2023.
Tech services and generative AI: Plotting the necessary reinvention 8
Web <2024>
E<GxehniAbITite c5h>
Exhibit <5> of <5>
Generative AI has created new entry points for tech services providers to
shape enterprises’ innovation agendas and tap a dynamic new market.
AI services’ annual growth, by tech stack layer
New entry points  <10%  10–20%  >20%
Tech stack layer Emerging offerings for tech services providers CAGR, 2024–29
Enterprise applications Services focused on AI-first enterprise SaaS¹ applications (eg, AI-led ERP² solu- 
tions, gen-AI-based CRM³ implementations), AI-powered virtual assistants,
personalized marketing and sales automation, predictive analytics for supply
chain agility
Tooling and security AI-based code analysis and testing automation, AI-based identity and access 
management systems, intelligent DevSecOps⁴ for CI-CD,⁵ automated threat
detection and response tools

Foundational models Pretrained AI models for specific industries (eg, finance, healthcare); multimodal
AI models combining text, image, and audio data (including LLMOps⁶); trans-
former model integration for machine translation with other models
Data and databases Generative adversarial networks for synthetic data generation, modernizing data 
architecture and managing multi-data structures (eg, vector databases with
traditional databases) to power LLMs, AI-based data cataloging and metadata
management
Public cloud AI-first code scanning and cloud readiness, AI-enabled code migration, 
auto-generation of gen-AI-enabled cloud microservices, AI-based workload
optimization and resource management, augmented FinOps⁷ using ma-
chine-learning-enabled orchestration
On-prem, co-location, Intelligent workload orchestration and optimization, predictive maintenance for

and private cloud data center hardware, AI-based energy efficiency and cooling optimization ser-
vices, self-healing systems and automated fault detection
Chips and Embedded software engineering for advanced AI chips (with public and private 
semiconductors cloud infrastructure), edge AI and on-device processing, custom chip design and
corresponding firmware development for AI-focused chips
1Software as a service. ²Enterprise resource planning. ³Customer relationship management. ⁴Development, security, operations. ⁵Continuous integration and continu-
ous delivery/deployment. ⁶Large language model operations. ⁷Financial operations.
Source: Company annual reports; expert interviews; HPE 2023 securities analyst meeting presentation, Oct 19, 2023; “IDC forecasts revenue for artificial intelli-
gence software will reach $307 billion worldwide in 2027,” IDC, Oct 31, 2023; “Worldwide software and public cloud services spending guide,” IDC, accessed
June 10, 2024; McKinsey analysis
McKinsey & Company
will probably fall in the first two categories of elements in place. Given that cloud transformation
services. They will likely be largely focused on has been a key part of services providers’ growth in
foundational AI readiness and cost reduction POCs recent years, they should be well positioned to
through horizontal use cases involving knowledge guide customers to the next fundamental digital
management, customer service, and text overhaul with AI and gen AI.
summarization. Our research shows that there is a
high degree of correlation between enterprise AI Still, the growth potential of these categories of
readiness and cloud readiness, so it will be critical services is limited. There is a strong possibility that
for services providers to position themselves as the market for productivity improvements and cost
end-to-end, holistic AI transformation partners, reduction will fast become commoditized as both
starting with getting the core data and cloud enterprises and providers increase investments in
Tech services and generative AI: Plotting the necessary reinvention 9
an area that lacks much room for differentiation. market. Providers have had success with this model
For a provider to thrive in this space, it will want to for the cloud, making implementation of other
seriously consider building ready-to-implement, vendors’ products faster, easier, and more affordable,
packaged AI-readiness offerings that enable rapid but it doesn’t happen overnight. It takes at least 15 to
and at-scale margin realization, modeled on the 20 individual client implementations to fine-tune
platform approach it has used successfully with the accelerators and equip them with the proper
cloud solutions over the past decade. functionality and systems as they evolve and mature
through repeated usage. Those players that can
As more organizations become comfortable and develop effective accelerators early on will likely
competent with implementing gen AI at scale over have a decided advantage as the gen-AI-services
the next couple of years, AI services deals will likely market starts to take off.
focus as much if not more on product innovation
and revenue growth. The providers that can build Rethink GTM and commercial models
full-stack, vertical-specific platform solutions The sector’s time-tested approaches to landing,
and lighthouse industry use cases focused on these structuring, and delivering on deals aren’t likely to
two areas will be best positioned to stand apart. have the same success in this burgeoning era.
Maintaining that advantage for the long term, Services providers may no longer be able to wait for
however, will demand more; providers will need sufficiently large deals to materialize. To stake
to prove that they can continually adapt and an early claim to the emerging gen-AI-services
respond to the accelerating pace of gen AI opportunity, they should strongly consider
innovation while enabling sustained impact for embracing volumetric deals—experimenting and
their enterprise customers. conducting small-scale POCs with a number of
clients, as leading providers have already begun to
do. Learning on the go will be key for both providers
Rewiring providers for the gen AI age and their customers as the technology (and use
The prospect of providers turning themselves into cases, solutions, implementations, et cetera) evolves
gen-AI-services-driven dynamos is compelling, but at a breakneck pace. At the same time, providers
turning that vision into a new reality will require will want to work with certain existing customers to
them to make a series of complex, challenging shifts create larger, pioneering gen-AI-led transformation
across their entire organizations. deals. Many if not most of these newfangled deals
will be outcome based with a significant gain-share
Build a new, broad-based AI-services catalog, component. As IT providers compete more with
including ready-to-deploy accelerators internal development teams and off-the-shelf
As stated earlier, service providers can jump-start software-as-a-service (SaaS) solutions, apps, and
their gen AI businesses by rolling out a wide array of low-code/no-code platforms, they will be forced
new offerings that include foundational-AI-readiness to prove their worth more than ever before. To win
solutions across both data and gen AI tools, deals and gain market share, services providers
packaged cost reduction solutions that can enable may need to embrace innovative commercial
rapid and at-scale margin realization, and vertical AI models; for instance, fees would be linked to the
use cases in pilot industries that focus on product number of tickets resolved by a customer-
innovation and revenue growth initiatives. The true support gen AI system, or to the time saved per
differentiator, however, is developing ready-to- salesperson through gen AI augmentation of
deploy accelerators or solutions (for example, customer relationship management tools.
prompt library, source attribution tools) across the Developing systems that can accurately measure
gen AI tech stack to accelerate the development the productivity gains attributable exclusively to
and adoption of existing gen AI solutions on the gen AI will be critical.
Tech services and generative AI: Plotting the necessary reinvention 10
Develop a new AI talent model across build, sell, Modernize delivery model through AI COEs to
and deliver functions enable higher efficiency for customers
Services providers will need to create a wide range Early evidence suggests that gen AI can enable
of new roles, such as a responsible AI lead to 20 to 40 percent5 productivity improvement in tech
establish policies, principles, operating models, and delivery across traditional service lines through new
controls to govern and ensure the ethical and safe solutions such as coding copilots or agent copilots
use of AI across the enterprise. Other new positions to aid knowledge management in application
may include AI product managers on large-app-build development and maintenance services. Still,
programs, while sales leaders will need to have services providers will need to develop a holistic
a broader technical and consultative skill set and approach to realize the full potential of gen AI on
be capable of answering customer inquiries on their delivery models. The linchpin of that approach
a range of topics, such as how to calculate the ROI will be a new COE to effectively coordinate and
of gen AI implementation and whether to scale cutting-edge AI services delivery efforts across
approach custom foundation model development the organization. Staffed with a mix of technical and
through prompt engineering, retrieval-augmented industry experts who collaborate with leaders in
generation (aka RAG) or fine-tuning. different functions and parts of the business, COEs
have typically been used for providers’ cloud and
Adopt a new partnership and M&A approach to digital and analytics initiatives. But gen AI’s rapid
bring together the emerging gen AI ecosystem pace of change and complexity mean that this new
Most enterprises access LLMs through APIs COE will require greater depths of expertise and
(leveraging cloud services providers, model levels of collaboration than previous similar efforts.
providers, or data cloud players) instead of hosting Most large providers have already launched
them themselves, which makes it critical for initiatives in this direction.
services providers to strengthen partnerships with
this growing roster of players. But while services Even if services providers manage to pull off these
providers have traditionally only formally linked up various transformations, there are still fundamental
with vendors once they have reached a certain risks to grapple with as they embrace gen AI.
scale with enterprise customers, the unusually fast Perhaps most important, as enterprises struggle
pace of gen AI adoption and experimentation to understand how to scale this fast-evolving
dictates that they take a different tack. If they want technology to fuel new growth, is that services
to play a key role in bringing “best of breed” providers will have to deal with the potential
ecosystems spanning the “chips to apps” tech stack, for significant scope creep and, as a result, possible
they will have to partner with smaller or fledgling reputational damage. Specific areas they will
software companies as well as the larger, more want to pay careful attention to include a lack of
established ones they are more used to working responsible AI controls as enterprise"
50,mckinsey,the-case-for-human-centered-ai_final.pdf,"The case for human-
centered AI
Maximizing generative AI’s promise while minimizing its misuse
requires an inclusive approach that puts humans first.
December 2024
Over the past two years, generative AI (gen AI) has been a rapidly evolving trend that has
touched the lives of many around the globe. Which is why the design of these formidable
systems must include experts from diverse backgrounds, says James Landay, a professor of
computer science at Stanford University. On this episode of the At the Edge podcast, Landay
talks with McKinsey senior partner Lareina Yee about how to develop safe, inclusive, and
effective AI.
The following transcript has been edited for clarity and length. For more conversations on
cutting-edge technology, follow the series on your preferred podcast platform.
Defining human-centered AI
Lareina Yee: You have been a champion of a human-centered approach to AI development for
many years. How do you define human-centered AI?
James Landay: To me, human-centered AI is not just about the applications of AI, which might
provide social value, whether in health or education. It’s also about how we create and design
those AI systems, who we involve in that development, and how we foster a process that’s more
human centered as we create and evaluate AI systems.
Lareina Yee: As the cofounder and codirector of the Stanford Institute for Human-Centered
Artificial Intelligence [HAI], you’re uniquely interdisciplinary. Can you tell us about how you bring
the community together at Stanford to look at and develop the future of AI?
James Landay: Interdisciplinarity was key to us from the start. It was also key to why we felt
Stanford was a special place for doing this kind of work because we have world-class technical
folks in AI, computer science, and other engineering disciplines.
But we also have a top medical school, a top law school, a top business school, and top social
sciences and humanities departments. And since AI is a society-changing technology that’s
going to be everywhere, we feel it needs to include every field—along with the different values
and outlooks inherent in those fields—to help shape it.
We give internal grants for research projects, and the sole funding criteria is, “Are you bringing
together people from different schools or different departments across the campus?” So we
encourage interdisciplinarity by how we fund projects.
We also encourage it by whom we highlight in our communications, as well as in our leadership.
Our two original codirectors were Fei-Fei Li, a famous AI computer scientist, and John
Etchemendy, a professor of philosophy.
The case for human-centered AI 2
Lareina Yee: Can you tell me how that interdisciplinary approach—combining philosophy,
computer science, law, and ethics—gives us a window into how that shapes the questions and
the types of research you’re doing at Stanford?
James Landay: For a start, it sometimes causes confusion, because people in different fields
speak different languages, so the same words can mean different things to different people. For
example, I’m working on a project with an English professor and someone from the medical
school. And what they call a pilot study is not what I would call a pilot study.
So you’ll experience confusion, but sometimes that confusion leads to new ideas and new ways
of looking at things. For example, we’ve had people working on large language models [LLMs]
who are looking at natural language processing [NLP]. And then they run into an ethicist with a
background in political science who questions some of the things they’re doing or how they’re
releasing their software without particular safeguards.
A technology with a mind of its own
Lareina Yee: Can you tell us why you think AI represents such a massive, profound change?
James Landay: Think about where computing itself has become part of our daily lives, like when
interacting with your doctor. Education is full of computer systems, and kids today could not
imagine being in high school, college, or even junior high without using a laptop or tablet for a lot
of their work.
AI is this general-purpose technology, and almost every application built in the future will
probably include some AI in it. But it’s a different kind of technology, and it is not as reliable in
some ways. AI systems aren’t deterministic, as we like to say in computer science, where the
same input always gives you the same output.
What’s different about AI systems is that they’re based on probabilistic models, these large
neural networks trained on billions or trillions of bits of data. And you can feed data into them
and receive different results, depending on how that data’s processed in that huge neural
network. That means they’re harder to design and it’s harder to protect against what they might
do when they do something wrong.
That’s why we need to think about designing AI systems differently, since they’re going to
become ubiquitous throughout our everyday lives, from health to education to government. We
want to understand them better than we do the existing computing systems.
The case for human-centered AI 3
‘We need to think about designing AI
systems differently, since they’re going
to become ubiquitous throughout our
everyday lives, from health to
education to government.’
Why it’s tough to build responsible AI
Lareina Yee: The training of AI is also so important, and it raises the problem of hallucinations.
Can you tell us a little bit about the science behind hallucinations, which underscores how we
think about responsible AI differently with these systems?
James Landay: Hallucinations occur when these probabilistic models essentially make up facts
that aren’t true. That’s a problem with these models that may even represent a fundamental
problem. We’re not even sure why they occur, and this is actually one of the bigger issues
concerning just who is building these models.
Right now, these models are controlled by a few large corporations, and academics don’t even
have the computing power to build models big enough to understand how they work. So we are
going to build large infrastructures of our societal systems on top of models that are very useful
but have properties that we don’t fully understand.
Responsible AI is a field that considers this situation and asks, “How do we try to make models
that don’t do harm? How do we put guardrails around them?” So responsible AI is trying to do
what it can, but it’s pretty hard without actually controlling the underlying data, the underlying
model, or even knowing what’s in the data.
Tread cautiously and test thoroughly
Lareina Yee: A lot of businesses are leveraging their data by combining it with a base LLM.
The proprietary piece is largely their data. How do you think about the right testing and
understanding in that context?
James Landay: There is a little more control because you’re feeding your data into the model to
fine-tune it or even just to look something up. So while there is a little more control, again, the
underlying model doing a lot of the work is using unknown data.
The case for human-centered AI 4
Companies are going to have to be very careful and really test things very thoroughly. That’s the
best bet they have right now, putting guardrails around things, essentially like blacklists that
look for certain words or phrases to never mention.
I also think we’re going to see a new business model selling those services, lists, or underlying
base LLMs that implement those kinds of things, depending on what a client wants.
Societal questions requiring answers
Lareina Yee: With the level of excitement over AI and kind of a call to action, what are some of
the questions that you believe need to be tackled at a societal level?
James Landay: One, we may need better design processes to include a broader set of
communities impacted by AI systems. That may help us get at some of the problems earlier on
that we can fix before they are released with negative consequences.
Two is education, making sure students going into computing and AI have more of an ethical
basis to think about their decisions. At Stanford, we’ve implemented something called
“embedded ethics.” So instead of requiring only one capstone ethics course, we embed ethical
lessons in different courses along the way. This is something we unabashedly borrowed from
Harvard.
But finally, there are going to be some things that happen that cause harm, because somebody
either had bad intentions or simply made a major mistake. And in that case, that’s where law and
policy come into play. We need to make sure that if you do something bad with AI, it carries a
cost.
Hopefully, that stops people with bad intentions in the first place. It will also cause companies
to make sure they’re being careful to avoid the downsides from legal risk and then also
reputational risk.
Good intentions aren’t enough
Lareina Yee: This is very much in line with something you once said that drew quite a lot of
attention, which is “‘AI for good’ isn’t good enough.”1 Can you expand on that?
James Landay: You can have good intentions and say, “I’m going to do AI for healthcare or
education.” But if you don’t do it in a human-centered way, if you just do it in a technology-
centered way, then you’re less likely to succeed in achieving that good you set out to do in the
first place.
1 James Landay, “‘AI for good’ isn’t good enough: A call for human-centered AI,” Stanford Institute for Human-Centered Artificial
Intelligence lecture, February 13, 2024.
The case for human-centered AI 5
‘You can have good intentions and
say, “I’m going to do AI for healthcare
or education.” But if you don’t do it in
a human-centered way, if you just do
it in a technology-centered way, then
you’re less likely to succeed in
achieving that good you set out
to do in the first place.’
So that is really the introduction to a design process that goes beyond designing for just users
because AI systems are different in that they have impacts beyond the immediate user. They
can impact a broader community around the user, so the design process should consider how to
bring those folks into the conversation around designing an AI system. And we might find that
some of those people should be our users as well.
Finally, if an AI system is really successful, it becomes fairly ubiquitous and may start to have
societal impacts. So designers of these popular systems might want to ask themselves, “If the
system I’m building is successful, are there any negative impacts it might have? How might I
mitigate them?”
And they should think about that in advance so they’re prepared to deal with any issues. It’s
much less expensive to fix some of these problems early in the design process than after you’ve
released a product.
The benefits of diverse and interdisciplinary teams
Lareina Yee: I think it’s neither here nor there. It’s actually more about asking people to change the
way they’ve done things, to redesign what product development looks like in today’s digital economy,
yes?
The case for human-centered AI 6
James Landay: Yes. It’s going to require changing processes and actually changing people as
well. Right now, we mainly have sets of engineers, like responsible AI groups or safety teams,
who are meant to check products before they’re released.
Unfortunately, there’s a lot of incentive to just push something out the door. And these teams
don’t quite have the social capital to stop it. A different way of doing this is to embed a lot of that
expertise in the original team.
So we need teams with these different disciplines—the social scientists, the humanists, the
ethicists—because then some of those problems will be found earlier. And as team members,
those people will have the social capital to make that change happen.
For example, we saw a lot of examples where computer vision systems could not recognize
Black women or people of color in general. Those problems weren’t that hard to fix in the end,
but they weren’t found until those companies released them and were publicly shamed.
And different companies dealt with it differently. Some immediately went and fixed it, while
some fought it. So part of this is changing the process, and part of it is changing the teams. They
need to be more diverse and interdisciplinary, and that will help solve a lot of these problems.
AI and the future of education
Lareina Yee: There is a lot to think about, but this is just a portion of your research. I was also
watching some pretty amazing work you and your PhD teams are doing around the future of
education. Are you optimistic or pessimistic about the impact of generative AI on education?
James Landay: I’m very optimistic. I think AI in education is going to be huge. Now, I don’t envy
anyone with young children right now, because I do think the next five years are going to be a
really rough time in education at all levels as the system tries to understand how to deal with this
technology. Educators are asking themselves, “Do we ban it, do we allow it, how do we change
how we teach, and how do we change how we evaluate?”
AI is going to force those questions, and some schools, teachers, and administrators are going
to be dragged kicking and screaming all the way, but some are going to embrace it and do
something smart with it from the beginning. So it’s going to take a while to figure it out, but in
the long run, it’s going to change the educational system in a lot of very positive ways.
The case for human-centered AI 7
Lareina Yee: Can you tell us about those positive ways and why you’re not one of the ones
kicking and screaming?
James Landay: AI is going to provide people with a personalized tutor that understands where
people are having difficulties and how best to motivate them. Because both kids and adults
respond better to different motivational strategies tailored to meet their needs. Imagine a tutor
who understands all that and helps you learn, as an addition to regular schooling.
In my research, I’ve found it’s also useful to target folks who maybe don’t fit in the traditional
educational system, who just don’t think that’s what they’re good at. How do we motivate those
folks, take advantage of their capabilities, and allow them to learn and eventually contribute to
society?
We’ve looked at how to make narratives and stories as a way to draw kids into learning, and my
“Smart Primer” project is based on that concept. We’ve written different stories where you have
to engage in learning activities as you read the story. And by engaging in the learning activity,
you get the story to move forward.
We use AI in many different ways, whether it’s using augmented reality to recognize objects in
the real world or even using AI to get a kid to write more.
From index cards to AI flash cards
Lareina Yee: One of your teams took a look at Quizlet’s online flash cards and made it a richer
experience. Flash cards are how I grew up, writing things out on index cards and sitting with my
friends and testing each other for a science exam the next day. How is the concept of an AI flash
card different from my good old index cards?
James Landay: We did a trial in China where we were trying to teach expats Chinese. And one
of the ideas we tried was using different flash cards tailored to the context of your location. So if
you’re in a taxi, they teach you how to talk to the driver. And if you’re in a restaurant, they teach
you how to order food. So we used AI technology to take advantage of the context and location
to drive the flash cards.
Lareina Yee: What’s interesting about those cases is that it’s starting to define a different
relationship children have with machines.
James Landay: Yes. And we should think about that. What does that mean? What kind of
relationships do we want? Do we want a kid’s AI agent to be their teacher, or do we want it to be
a tutor? Do we want it to be a companion? Do we want it to be a pet? None of the above?
The case for human-centered AI 8
That has to be thought about and designed, but we have to decide what we desire. I think we’re
still far off from really having all of that, but those are the kinds of research questions we need to
consider now. Because the technology will be there in a few years to allow these kinds of things.
Upheaval ahead for universities
Lareina Yee: James, let me ask you the contrarian question. The academic institutions you’ve
been a part of—UC Berkeley, Stanford, and Cornell—are all more than a century old, with a rich
tradition of traditional education excellence. To put it plainly, is the juice worth the squeeze,
considering how challenging AI is going to be?
James Landay: Higher education has worked, and these institutions have been successful. But
they’re not perfect, and they’ve changed in the past. The American university system was in
some ways a modified copy of the German system, which was a different version of the British
system.
So these institutions have transformed over time due to major societal and technological
changes. And I think AI is going to change the educational system because it can’t continue to
exist the way it does today, which is largely based on rote learning and certain ways of
evaluation, which is hard to do with the AI tools out there.
So that change due to AI is actually going to lead to other changes in the educational system.
And in the next five years, people are going to see a lot of upheaval. But in ten years, we will
look back and think, “Wow, we’re really educating people better than we were ten years ago.”
Lareina Yee: With all of that change, let me ask you some fun questions. I know that you enjoy
skiing, scuba diving, and many adventure sports. If you had your dream gen AI application
related to adventure sports, what would it help you do?
James Landay: Many of us have tried using gen AI to plan a vacation with some amount of
success and failure. But one of my students has a smart ski-boot insert that helps her make
better turns, with an agent that’s speaking into her ear as she’s skiing. So even while skiing, you
can have a personal coach who’s watching every turn and telling you what you need to do
better. I think for helping us get better at things we like to do, AI is going to be great.
Lareina Yee: Going way back, I read that your dissertation was one of the first to demonstrate
the use of sketching in user-interface-design tools. If you were to be a PhD student again, what
would you focus on?
James Landay: That’s interesting. Although I’m not a technical-AI expert, in that I don’t create
algorithms, going all the way back to that PhD dissertation to my research today, I’ve used AI in
the systems I’ve built probably 75 to 80 percent of the time. And with the AI capabilities we have
today, I could build them all way better.
The case for human-centered AI 9
A lot of computer science, at least PhD research, is time travel in the opposite direction. You’re
trying to imagine what something might look like in the future, simulating it with the technology
we have today but imagining it’s going to be faster, better, and cheaper.
And sometimes, we’re just too far ahead of ourselves. So in some ways, I was imagining
something in 1995 that I thought would only take five years. But it took 20 to 30 years for that
technology to become good enough to do what I was imagining at the time.
James Landay is a computer science professor at Stanford University and cofounder and codirector of the
Stanford Institute for Human-Centered Artificial Intelligence (HAI). Lareina Yee, a senior partner in McKinsey’s
Bay Area office, is the cohead of McKinsey ecosystems and alliances.
Comments and opinions expressed by interviewees are their own and do not represent or reflect the opinions,
policies, or positions of McKinsey & Company or have its endorsement.
Copyright © 2024 McKinsey & Company. All rights reserved.
The case for human-centered AI 10"
51,mckinsey,moving-past-gen-ais-honeymoon-phase-seven-hard-truths-for-cios-to-get-from-pilot-to-scale (1).pdf,"Moving past gen AI’s honeymoon
phase: Seven hard truths for
CIOs to get from pilot to scale
Getting to scale requires CIOs to focus on fewer things but do them better.
This article is a collaborative effort by Aamer Baig, Douglas Merrill, and Megha Sinha, with Danesha Mead and
Stephen Xu, representing views from McKinsey Technology and QuantumBlack, AI by McKinsey.
© Getty Images
May 2024
The honeymoon phase of generative AI (gen AI) We explored many of the key initial technology
is over. As most organizations are learning, it is issues in a previous article.2 In this article, we want
relatively easy to build gee-whiz gen AI pilots, but to explore seven truths about scaling gen AI for the
turning them into at-scale capabilities is another “Shaper” approach, in which companies develop
story. The difficulty in making that leap goes a long a competitive advantage by connecting large
way to explaining why just 11 percent of companies language models (LLMs) to internal applications
have adopted gen AI at scale, according to our and data sources (see sidebar “Three approaches
latest tech trends research.1 to using gen AI” for more). Here are seven things
that Shapers need to know and do:
This maturing phase is a welcome development
because it gives CIOs an opportunity to turn gen 1. Eliminate the noise, and focus on the signal.
AI’s promise into business value. Yet while most Be honest about what pilots have worked.
CIOs know that pilots don’t reflect real-world Cut down on experiments. Direct your efforts
scenarios—that’s not really the point of a pilot, after toward solving important business problems.
all—they often underestimate the amount of work
that needs to be done to get gen AI production 2. It’s about how the pieces fit together, not the
ready. Ultimately, getting the full value from gen AI pieces themselves. Too much time is spent
requires companies to rewire how they work, and assessing individual components of a gen AI
putting in place a scalable technology foundation engine. Much more consequential is figuring
is a key part of that process. out how they work together securely.
1 “McKinsey Technology Trends Outlook 2024,” forthcoming on McKinsey.com.
2 “Technology’s generational moment with generative AI: A CIO and CTO guide,” McKinsey, July 11, 2023.
Three approaches to using gen AI
There are three primary approaches to take in using gen AI:
— In “Taker” use cases, companies use off-the-shelf, gen AI–powered software from third-party vendors such as GitHub
Copilot or Salesforce Einstein to achieve the goals of the use case.
— In “Shaper” use cases, companies integrate bespoke gen AI capabilities by engineering prompts, data sets, and connections
to internal systems to achieve the goals of the use case.
— In “Maker” use cases, companies create their own LLMs by building large data sets to pre-train models from scratch.
Examples include OpenAI, Anthropic, Cohere, and Mistral AI.
Most companies will turn to some combination of Taker, to quickly access a commodity service, and Shaper, to build a
proprietary capability on top of foundation models. The highest-value gen AI initiatives, however, generally rely on the Shaper
approach.1
1 For more on the three approaches, see “Technology’s generational moment with generative AI: A CIO and CTO guide,” McKinsey, July 11, 2023.
2 Moving past gen AI’s honeymoon phase: Seven hard truths for CIOs to get from pilot to scale
3. Get a handle on costs before they sink you. been deemed “successful,” but it was not applied
Models account for only about 15 percent to an important part of the business.
of the overall cost of gen AI applications.
Understand where the costs lurk, and apply the There are many reasons for failing to scale,
right tools and capabilities to rein them in. but the overarching one is that resources and
executive focus are spread too thinly across
4. Tame the proliferation of tools and tech. The dozens of ongoing gen AI initiatives. This is not a
proliferation of infrastructures, LLMs, and tools new development. We’ve seen a similar pattern
has made scaled rollouts unfeasible. Narrow when other technologies emerged, from cloud
down to those capabilities that best serve the to advanced analytics. The lessons from those
business, and take advantage of available innovations, however, have not stuck.
cloud services (while preserving your flexibility).
The most important decision a CIO will need to
5. Create teams that can build value, not just make is to eliminate nonperforming pilots and
models. Getting to scale requires a team with scale up those that are both technically feasible
a broad cross-section of skills to not only build and promise to address areas of the business that
models but also make sure they generate the matter while minimizing risk (Exhibit 1). The CIO will
value they’re supposed to, safely and securely. need to work closely with business unit leaders
on setting priorities and handling the technical
6. Go for the right data, not the perfect implications of their choices.
data. Targeting which data matters most and
investing in its management over time has a big
impact on how quickly you can scale. 2. It’s about how the pieces fit together,
not the pieces themselves
7. Reuse it or lose it. Reusable code can In many discussions, we hear technology leaders
increase the development speed of generative belaboring decisions around the component parts
AI use cases by 30 to 50 percent. required to deliver gen AI solutions—LLMs, APIs,
and so on. What we are learning, however, is that
solving for these individual pieces is relatively easy
1. Eliminate the noise, and focus on and integrating them is anything but. This creates
the signal a massive roadblock to scaling gen AI.
Although many business leaders acknowledge
the need to move past pilots and experiments, The challenge lies in orchestrating the range of
that isn’t always reflected in what’s happening on interactions and integrations at scale. Each use
the ground. Even as gen AI adoption increases, case often needs to access multiple models, vector
examples of its real bottom-line impact are few databases, prompt libraries, and applications
and far between. Only 15 percent of companies (Exhibit 2). Companies have to manage a variety
in our latest AI survey say they are seeing use of of sources (such as applications or databases
gen AI have meaningful impact on their companies’ in the cloud, on-premises, with a vendor, or a
EBIT.3 combination), the degree of fidelity (including
latency and resilience), and existing protocols (for
Exacerbating this issue is that leaders are drawing example, access rights). As a new component is
misleading lessons from their experiments. They added to deliver a solution, it creates a ripple effect
try to take what is essentially a chat interface pilot on all the other components in the system, adding
and shift it to an application—the classic “tech exponential complexity to the overall solution.
looking for a solution” trap. Or a pilot might have
3 That is, they attribute 5 percent or more of their organizations’ EBIT to gen AI use. McKinsey Global Survey on the state of AI in early 2024,
February 22 to March 5, 2024, forthcoming on McKinsey.com.
Moving past gen AI’s honeymoon phase: Seven hard truths for CIOs to get from pilot to scale 3
Exhibit 1
Focus on use cases that are feasible and where business impact is clear.
Focus on use cases that are feasible and where business impact is clear.
Criteria for determining business impact and technical feasibility
Use cases Quick / high-impact wins
Category Criteria (illustrative) Second priority
High
Business Value Can we accurately quantify the value? Is it
impact creation incremental or a step function in performance?
Strategic How well does this align with or support the
alignment company’s primary strategic objectives?
Ease of Are end users enthusiastic about adopting
adoption the solution? Is there a demand for more
Business
features or capabilities?
impact
Business Are we introducing this solution at an
readiness appropriate time, considering ongoing
transformations or other projects?
Technical Data Is the data readily available, or do we need to
feasibility readiness create or synthesize it? Are there any special
considerations for handling sensitive data?
Low
Solution Does the solution require proven or nascent Low Technical High
readiness techniques? feasibility
Ability to Will the proposed business model remain
scale viable as number of users and cloud
consumption increase?
Reusability Can the components of the solution be
repurposed for other use cases?
McKinsey & Company
The key to effective orchestration is embedding The orchestration of the many interactions
the organization’s domain and workflow expertise required to deliver gen AI capabilities, however,
into the management of the step-by-step flow is impossible without effective end-to-end
and sequencing of the model, data, and system automation. “End-to-end” is the key phrase here.
interactions of an application running on a cloud Companies will often automate elements of the
foundation. The core component of an effective workflow, but the value comes only by automating
orchestration engine is an API gateway, which the entire solution, from data wrangling (cleaning
authenticates users, ensures compliance, logs and integration) and data pipeline construction to
request-and-response pairs (for example, to help model monitoring and risk review through “policy
bill teams for their usage), and routes requests to as code.” Our latest research has shown that gen
the best models, including those offered by third AI high performers are more than three times as
parties. The gateway also enables cost tracking likely as their peers to have testing and validation
and provides risk and compliance teams a way embedded in the release process for each model.4
to monitor usage in a scalable way. This gateway A modern MLOps platform is critical in helping to
capability is crucial for scale because it allows manage this automated flow and, according to
teams to operate independently while ensuring McKinsey analysis, can accelerate production by
that they follow best practices (see sidebar “Main ten times as well as enable more efficient use of
components for gen AI model orchestration”). cloud resources.
4 We define gen AI high performers as those who attribute more than 10 percent of their organizations’ EBIT to their use of gen AI.
McKinsey Global Survey on the state of AI in early 2024, February 22 to March 5, 2024, forthcoming on McKinsey.com.
4 Moving past gen AI’s honeymoon phase: Seven hard truths for CIOs to get from pilot to scale
Exhibit 2
A gen AI solution needs to accommodate a complex set of integrations
A gen AI solution needs to accommodate a complex set of integrations across
across the entire tech stack.
the entire tech stack.
Illustrative tech stack with end-to-end automation Data Gen AI capabilities Cloud Models
Front-end application
User interface
Data enrichment
and processing Orchestration
Enhancing
Source data Query validation and intent routing Guardrails capabilities
Unstructured Structured Security Data Semantic Prompt LLM Conversation
data ETL¹ data ETL¹ and retrieval and engi- flow memory
access hybrid neering management
Databases (eg, control search and Prompt library
observability
vector stores)
Image Prompt
LLM agents
search enrichment
Structured Fallback External
data query runtime
search integration
Infrastructure and cloud services API gateway
Foundation models (eg, LLMs, multimodal
models, embedding generation models)
1Extract, transform, load.
McKinsey & Company
Gen AI models can produce inconsistent results, 3. Get a handle on costs before they
due to their probabilistic nature or the frequent sink you
changes to underlying models. Model versions can The sheer scale of gen AI data usage and model
be updated as often as every week, which means interactions means costs can quickly spiral out
companies can’t afford to set up their orchestration of control. Managing these costs will have a huge
capability and let it run in the background. They impact on whether CIOs can manage gen AI
need to develop hyperattentive observing and programs at scale. But understanding what drives
triaging capabilities to implement gen AI with costs is crucial to gen AI programs. The models
speed and safety. Observability tools monitor themselves, for example, account for only about 15
the gen AI application’s interactions with users percent of a typical project effort.5 LLM costs have
in real time, tracking metrics such as response dropped significantly over time and continue to
time, accuracy, and user satisfaction scores. If decline.
an application begins to generate inaccurate
or inappropriate responses, the tool alerts the CIOs should focus their energies on four realities:
development team to investigate and make any
necessary adjustments to the model parameters, — Change management is the biggest cost. Our
prompt templates, or orchestration flow. experience has shown that a good rule of thumb
for managing gen AI costs is that for every $1
5 “Generative AI in the pharmaceutical industry: Moving from hype to reality,” McKinsey, January 9, 2024.
Moving past gen AI’s honeymoon phase: Seven hard truths for CIOs to get from pilot to scale 5
Main components for gen AI model orchestration
Orchestration is the process of coordinating various data, transformation, and AI components to manage complex AI workflows.
The API (or LLM) gateway layer serves as a secure and efficient interface between users or applications and underlying gen AI
models. The orchestration engine itself is made up of the following components:
— Prompt engineering and prompt library: Prompt engineering is the process of crafting input prompts or queries that guide
the behavior and output of AI models. A prompt library is a collection of predefined prompts that users can leverage as best
practices/shortcuts when they invoke a gen AI model.
— Context management and caching: Context management highlights background information relevant to a specific task or
interaction. Caching relates to storing previously computed results or intermediate data to accelerate future computations.
— Information retrieval (semantic search and hybrid search): Information-retrieval logic allows gen AI models to search for
and retrieve relevant information from a collection of documents or data sources.
— Evaluation and guardrails: Evaluation and guardrail tools help assess the performance, reliability, and ethical
considerations of AI models. They also provide input to governance and LLMOps. This encompasses tools and processes for
evaluating model accuracy, robustness, fairness, and safety.
spent on developing a model, you need to spend companies default to simply creating a chat
about $3 for change management. (By way of interface for a gen AI application), and second,
comparison, for digital solutions, the ratio has involving their best employees in training models
tended to be closer to $1 for development to $1 to ensure the models learn correctly and quickly.
for change management.6) Discipline in managing
the range of change actions, from training your — Run costs are greater than build costs for
people to role modeling to active performance gen AI applications. Our analysis shows that
tracking, is crucial for gen AI. Our analysis has it’s much more expensive to run models than to
shown that high performers are nearly three build them. Foundation model usage and labor
times more likely than others to have a strong are the biggest drivers of that cost. Most of
performance-management infrastructure, such the labor costs are for model and data pipeline
as key performance indicators (KPIs), to measure maintenance. In Europe, we are finding that
and track value of gen AI. They are also twice as significant costs are also incurred by risk and
likely to have trained nontechnical people well compliance management.
enough to understand the potential value and
risks associated with using gen AI at work.7 — Driving down model costs is an ongoing
process. Decisions related to how to engineer
Companies have been particularly successful in the architecture for gen AI, for example, can
handling the costs of change management by lead to cost variances of 10 to 20 times, and
focusing on two areas: first, involving end users sometimes more than that. An array of cost-
in solution development from day one (too often, reduction tools and capabilities are available,
6 Eric Lamarre, Kate Smaje, and Rodney Zemmel, “Rewired to outcompete,” McKinsey, June 20, 2023.
7 McKinsey Global Survey on the state of AI in early 2024, February 22 to March 5, 2024, forthcoming on McKinsey.com.
6 Moving past gen AI’s honeymoon phase: Seven hard truths for CIOs to get from pilot to scale
such as preloading embeddings. This is analytics solutions. The goal here is to develop
not a one-off exercise. The process of cost a modeling discipline that instills an ROI focus
optimization takes time and requires multiple on every gen AI use case without getting lost in
tools, but done well, it can reduce costs from a endless rounds of analysis.
dollar a query to less than a penny (Exhibit 3).
— Investments should be tied to ROI. Not all 4. Tame the proliferation of tools
gen AI interactions need to be treated the and tech
same, and they therefore shouldn’t all cost Many teams are still pushing their own use cases
the same. A gen AI tool that responds to live and have often set up their own environments,
questions from customers, for example, is resulting in companies having to support multiple
critical to customer experience and requires infrastructures, LLMs, tools, and approaches
low-latency rates, which are more expensive. to scaling. In a recent McKinsey survey, in fact,
But code documentation tools don’t have to be respondents cited “too many platforms” as the
so responsive, so they can be run more cheaply. top technology obstacle to implementing gen AI
Cloud plays a crucial rule in driving ROI because at scale.8 The more infrastructures and tools, the
its prime source of value lies in supporting higher the complexity and cost of operations, which
business growth, especially supporting scaled in turn makes scaled rollouts unfeasible. This state
8 McKinsey survey on generative AI in operations, November 2023.
Exhibit 3
As solutions scale, organizations can optimize costs.
As solutions scale, organizations can optimize costs.
Cost per query by week,¹ $
1.0
0.8
0.6
0.4
0.2
0.0
Week 1 Week 2 Week 3 Week 4 Week 5 Week 6 Week 7 Backlog
Initial proof Add RAG,² Add intent Re-ranking Migrate from Migrate from Vendor price Batching,
of concept maxing out recognition and prompt paid GPT for risk reduction, and
prompt and routing, optimization embedding guardrails and semantic reevaluate
length reducing generation and intent cache need for
search space model to recognition to chatbot
and adding open-source open-source
LLM calls model models and
regular
expression
1Illustrative example pulling from multiple case studies.
2Retrieval-augmented generation.
McKinsey & Company
Moving past gen AI’s honeymoon phase: Seven hard truths for CIOs to get from pilot to scale 7
of affairs is similar to the early days of cloud and for example. But greater impact came only when
software as a service (SaaS), when accessing the other parts of the organization—such as risk and
tech was so easy—often requiring no more than a business experts—were integrated into the teams
credit card—that a “wild west” of proliferating tools along with product management and leadership.
created confusion and risk.
There are multiple archetypes for ensuring
To get to scale, companies need a manageable set this broader organizational integration. Some
of tools and infrastructures. Fair enough—but how companies have built a center of excellence to act
do you know which providers, hosts, tools, and as a clearinghouse to prioritize use cases, allocate
models to choose? The key is to not waste time on resources, and monitor performance. Other
endless rounds of analysis on decisions that don’t companies split strategic and tactical duties among
matter much (for example, the choice of LLMs is less teams. Which archetype makes sense for any
critical as they increasingly become a commodity) or given business will depend on its available talent
where there isn’t much of a choice in the first place— and local realities. But what’s crucial is that this
for example, if you have a primary cloud service centralized function enables close collaboration
provider (CSP) that has most of your data and your between technology, business, and risk leads, and
talent knows how to work with the CSP, you should is disciplined in following proven protocols for
probably choose that CSP’s gen AI offering. Major driving successful programs. Those might include,
CSPs, in fact, are rolling out new gen AI services for example, quarterly business reviews to track
that can help companies improve the economics of initiatives against specific objectives and key
some use cases and open access to new ones. How results (OKRs), and interventions to resolve issues,
well companies take advantage of these services reallocate resources, or shut down poor-performing
depends on many variables, including their own cloud initiatives.
maturity and the strength of their cloud foundations.
A critical role for this governing structure is to ensure
What does require detailed thinking is how to build that effective risk protocols are implemented and
your infrastructure and applications in a way that followed. Build teams, for example, need to map
gives you the flexibility to switch providers or models the potential risks associated with each use case;
relatively easily. Consider adopting standards widely technical and “human-in-the-loop” protocols need
used by providers (such as KFServing, a serverless to be implemented throughout the use-case life
solution for deploying gen AI models), Terraform for cycle. This oversight body also needs a mandate
infrastructure as code, and open-source LLMs. to manage gen AI risk by assessing exposures and
implementing mitigating strategies.
It’s worth emphasizing that overengineering for
flexibility eventually leads to diminishing returns. A One issue to guard against is simply managing the
plethora of solutions becomes expensive to maintain, flow of tactical use cases, especially where the
making it difficult to take full advantage of the volume is large. This central organization needs a
services providers offer. mandate to cluster related use cases to ensure large-
scale impact and drive large ideas. This team needs
to act as the guardians for value, not just managers
5. Create teams that can build value, of work.
not just models
One of the biggest issues companies are facing One financial services company put in place
is that they’re still treating gen AI as a technology clearly defined governance protocols for senior
program rather than as a broad business priority. management. A steering group, sponsored by
Past technology efforts demonstrate, however, that the CIO and chief strategy officer, focused on
creating value is never a matter of “just tech.” For gen enterprise governance, strategy, and communication,
AI to have real impact, companies have to build teams driving use-case identification and approvals. An
that can take it beyond the IT function and embed it enablement group, sponsored by the CTO, focused
into the business. Past lessons are applicable here, on decisions around data architecture, data science,
too. Agile practices sped up technical development, data engineering, and building core enabling
8 Moving past gen AI’s honeymoon phase: Seven hard truths for CIOs to get from pilot to scale
capabilities. The CTO also mandated that at least engineering teams (tech sales/support teams)
one experienced architect join a use-case team developed their own version to find solutions for
early in their process to ensure the team used the unique client calls, commercialization teams had
established standards and tool sets. This oversight product descriptions, and customer support teams
and governance clarity was crucial in helping the had a set of specific product details to answer
business go from managing just five to more than queries. As each team updated its version of the
50 use cases in its pipeline. product information, conflicts emerged, making
it difficult for gen AI models to use the data. To
address this issue, the company is putting all
6. Go for the right data, not the relevant product information in one place.
perfect data
Misconceptions that gen AI can simply sweep up
7. Reuse it or lose it
the necessary data and make sense of it are still
widely held. But high-performing gen AI solutions Reusable code can increase the development
are simply not possible without clean and accurate speed of generative AI use cases by 30 to 50
data, which requires real work and focus. The percent.9 But in their haste to make meaningful
companies that invest in the data foundations to breakthroughs, teams often focus on individual use
generate good data aim their efforts carefully. cases, which sinks any hope for scale. CIOs need to
shift the business’s energies to building transversal
Take the process of labeling, which often oscillates solutions that can serve many use cases. In fact, we
between seeking perfection for all data and have found that gen AI high performers are almost
complete neglect. We have found that investing in three times as likely as their peers to have gen
targeted labeling—particularly for the data used for AI foundations built strategically to enable reuse
retrieval-augmented generation (RAG)—can have a across solutions.10
significant impact on the quality of answers to gen
AI queries. Similarly, it’s critical to invest the time to In committing to reusability, however, it is easy to
grade the importance of content sources (“authority get caught in building abstract gen AI capabilities
weighting”), which helps the model understand the that don’t get used, even though, technically, it
relative value of different sources. Getting this right would be easy to do so. A more effective way to
requires significant human oversight from people build up reusable assets is to do a disciplined
with relevant expertise. review of a set of use cases, typically three to five,
to ascertain their common needs or functions.
Because gen AI models are so unstable, companies Teams can then build these common elements
need to maintain their platforms as new data is as assets or modules that can be easily reused or
added, which happens often and can affect how strung together to create a new capability. Data
models perform. This is made vastly more difficult preprocessing and ingestion, for example, could
at most companies because related data lives in include a data-chunking mechanism, a structured
so many different places. Companies that have data-and-metadata loader, and a data transformer
invested in creating data products are ahead of as distinct modules. One European bank reviewed
the game because they have a well-organized data which of its capabilities could be used in a wide
source to use in training models over time. array of cases and invested in developing a
synthesizer module, a translator module, and a
At a materials science product company, for sentiment analysis module.
example, various teams accessed product
information, but each one had a different version. CIOs can’t expect this to happen organically. They
R&D had materials safety sheets, application need to assign a role, such as the platform owner,
9 Eric Lamarre, Alex Singla, Alexander Sukharevsky, and Rodney Zemmel, “A generative AI reset: Rewiring to turn potential into value in 2024,”
McKinsey, March 4, 2024.
10 McKinsey Global Survey on the state of AI in early 2024, February 22 to March 5, 2024, forthcoming on McKinsey.com.
Moving past gen AI’s honeymoon phase: Seven hard truths for CIOs to get from pilot to scale 9
Exhibit 4
A gen AI platform team needs an array of skills.
A gen AI platform team needs an array of skills.
Cross-functional platform team DataOps: Manages and optimizes the data pipeline, ensuring the
roles and skills availability and quality of data; supports training and deployment of
gen AI models
Site reliability engineer: Ensures reliability, availability, and perfor-
mance of software systems and applications
Data DataOps DevOps engineer: Establishes the CI/CD¹ pipeline and other auto-
engineer
mation needed for teams to rapidly develop and deploy code (eg,
chatbot, APIs) to production
Site
Data reliability Cloud architect: Ensures scalability, security, and cost optimization
scientist engineer of the cloud infrastructure; designs data storage and management
systems; facilitates integration and deployment of the AI models
Platform
Solution/data architect: Develops creative and efficient solutions
Full- team using engineering practices and software/web development
stack DevOps technologies
developer engineer
Platform owner: Acts like a product owner, oversees the build of a
gen AI platform
Full-stack developer: Writes clean and quality scalable code (eg,
Platform Cloud front-end/back-end APIs) that can be easily deployed with CI/CD¹
owner Solution/ architect p ipelines
data Data scientist: Fine-tunes foundational models to help
architect RAG²-based approach, ensures alignment of LLM outputs with
responsible AI guidelines
Data engineer: Architects data models to ingest data into vector
databases, creates and maintains automated pipelines, performs
closed-loop testing to validate responses and improve performance
1Continuous integration (CI) and continuous delivery (CD).
2Retrieval-augmented generation.
McKinsey & Company
and a cross-functional team with a mandate to The value gen AI could generate is
develop reusable assets for product teams transformational. But capturing the full extent of
(Exhibit 4), which can include approved tools, that value will come only when companies harness
code, and frameworks. gen AI at scale. That requires CIOs to not just
acknowledge hard truths but be ready to act on
them to lead their business forward.
Aamer Baig is a senior partner in McKinsey’s Chicago office, Douglas Merrill is a partner in the Southern California office,
Megha Sinha is a partner in the Bay Area office, Danesha Mead is a consultant in the Denver office, and Stephen Xu is
director of product management in the Toronto office.
The authors wish to thank Mani Gopalakrishnan, Mark Gu, Ankur Jain, Rahil Jogani, and Asin Tavakoli for their contributions
to this article.
Copyright © 2024 McKinsey & Company. All rights reserved.
10 Moving past gen AI’s honeymoon phase: Seven hard truths for CIOs to get from pilot to scale"
52,mckinsey,strategic-alliances-for-gen-ai-how-to-build-them-and-make-them-work.pdf,"McKinsey Digital Practice
Strategic alliances for gen
AI: How to build them and
make them work
Strategic alliances are a must for companies looking to build value from generative
AI. But approaching them like traditional vendor arrangements won’t work.
This article is a collaborative effort by Alex Singla, Alexander Sukharevsky, Ben Ellencweig, and Guilherme Cruz, with Carlo
Palermo and Joshan Cherian Abraham, representing views from QuantumBlack, AI by McKinsey, and McKinsey Digital.
May 2024
Generative AI’s (gen AI) transformative potential solutions will work with other components in a
promises to revolutionize business and propel up to company’s gen AI ecosystem.
$4.4 trillion in economic impact annually.
Unprecedented adoption rates from companies 3. Stay in control of your destiny. It is important to
across industries and sectors, as well as significant strike a balance between building on a
private investment, underscore this potential. provider’s capabilities and becoming overly
dependent on them. That means investing in a
Capturing the value from technology, however, is flexible infrastructure, monitoring provider
never just about the tech alone. Companies looking performance continually, and tying
to move beyond running gen AI experiments will compensation to outcomes while being clear
need to rewire how they work to achieve the full about intellectual property (IP) boundaries.
value of their efforts. One key component of this
rewiring is developing strategic alliances1 with gen
Go deeper on collaboration
AI providers. The unique challenges of working with
gen AI—from the lack of experience that many have As many companies are learning, the “build versus
in using the technology to gen AI’s instability and buy” approach to creating gen AI capabilities falls
risk to the technology’s rapid pace of change—have short when it comes to harnessing gen AI’s full
made collaborations increasingly vital. potential. Building solutions entirely in-house can
be time consuming and resource intensive,
While many companies are already working to some especially given the lack of gen AI talent at most
degree with gen AI providers, outdated notions of companies.2 And while buying existing gen AI
what to look for in a provider and how strategic products or services can provide quick access to
alliances should function are putting these efforts proven solutions, these solutions often require
at risk. To make the most of strategic alliances, experienced gen AI workers to customize them to
companies should focus on three areas: what the business really needs.
1. Go deeper on collaboration. Working with Collaborating with providers, on the other hand, can
providers on gen AI programs requires offer significant benefits in terms of access to the
a greater degree of trust and collaboration latest capabilities and expertise, development
than has been necessary with traditional speed, and tailored solutions. But effective
vendors, with thoughtful transparency, strategic gen AI alliances work differently from
frequent communications, and explicit traditional vendor relationships. The technology is
alignment across planning, development, and still rapidly maturing, implementation is complex,
ongoing management. and stability issues bedevil solutions, requiring
closer collaboration and higher degrees of trust.
2. Zero in on providers who provide scalability, Sharing data to fine-tune models, for example, can
interoperability, and reusability. No single happen only if client companies trust strategic allies
provider can offer companies everything they to protect it effectively. Similarly, the complexity of
need. Achieving scale with a range of providers addressing root cause issues in AI models, many of
means understanding not only how well which are not yet fully stable, necessitates both
providers can scale but also how well their clear lines of communication and alignment on
1 A strategic alliance typically has broad and long-term impact on corporate performance and valuation, often formed to create a competitive
advantage for the partners in their respective markets. Not all alliances need to be strategic; alliances can be established to achieve highly
operational or tactical objectives and can include working with large hyperscale providers or more niche organizations. (This definition is adapted
from the one created by the Association of Strategic Alliance Professionals. For more, see “Alliance management definitions,” Association of
Strategic Alliance Professionals, accessed May 6, 2024.)
2 “The state of AI in 2023: Generative AI’s breakout year,” McKinsey, August 1, 2023.
Strategic alliances for gen AI: How to build them and make them work 2
resolution protocols. This level of trust and This collaboration was instrumental in allowing
collaboration should be established on three the resulting model to surface the most
essential building blocks: appropriate products, even for complex or
ambiguous queries, with over 99 percent
— Cocreation of solutions. For most companies, accuracy. Early results indicate the solution could
the greatest value from gen AI will come from yield a 10 to 20 percent improvement in
adopting established capabilities, which the conversion rate from product discovery
providers offer, and tailoring these capabilities to purchase, a significant leap for the
to companies’ unique data. This requires a luxury industry.
highly iterative and collaborative process where
the company and provider work closely — Joint planning. It is essential for the gen AI
together to source and prepare the right data, provider to offer visibility into its product road
engineer relevant prompts, fine-tune models map, including upcoming features and
based on specific use case needs, and test and capabilities, and possibly grant access to alpha
iterate on the models in the field. To manage or beta releases. This allows the client company
the range of providers a company might need to both to anticipate how the provider’s offerings
work with, it will be important to institute might evolve to meet the company’s own future
frequent touchpoints to share updates, discuss needs and to possibly influence the direction of
challenges, and align on priorities across the road map. The road map can also be
providers. Dedicating time for in-person aligned when the company shares its strategic
workshops and co-innovation sessions, goals and relevant customer insights to
celebrating milestones, and sharing learnings help the provider better understand the
openly during this process is also critical for company’s needs.
building trust.
This level of communication and coordination is
When a luxury retail company, for instance, particularly important given that companies will
partnered with a gen AI provider to create a likely be working with different gen AI models
personalized product recommendation system, and applications—often developed by different
the company shared its vast catalog of product providers—that need to be closely integrated
information, including detailed specifications, for a solution to work well. Potential benefits of
features, and customer reviews. The company providing clarity and transparency include
also provided valuable insights into the nuances helping various vendors align their road maps
of customer preferences and behaviors, such as and identify dependencies (for example,
how they pose queries. This helped the provider between multiple models that need to work
engineer relevant prompts, fine-tune the gen AI together to deliver a specific gen AI solution).
models to understand and interpret this One area where we’ve seen big dividends is
domain-specific data, and comprehend the investing significant time together up
specific language, terminology, and attributes front—often meeting every couple of days for
used to describe luxury products. two to four weeks—to work out a mutual road
Strategic alliances for gen AI: How to build them and make them work 3
map and system dependencies. The output from — Risk and investment sharing. Gen AI programs
this effort should include a primary project plan often require sizable investments in specialized
that captures milestones and dependencies hardware, large-scale data acquisition and
between providers so the client company can tagging, and extensive computational resources
better manage and coordinate all parties. for model training. In addition, the risks
associated with adopting gen AI capabilities—
One leading technology company adopted this from hallucinating models to privacy issues—
approach. It engaged in joint planning sessions require close attention. For these reasons,
with its gen AI providers to identify high-impact companies should consider how best to distribute
use cases to strengthen its core product. The among providers financial, technological, and
company shared valuable customer analytics operational resources and risks associated with
insights and outlined its long-term vision for AI- gen AI development. Companies and providers,
driven innovation, which helped the gen AI for example, should be explicit in defining
providers develop a more aligned road map. For specific risks associated with the gen AI project,
their part, the gen AI providers offered early such as data privacy breaches, model biases, or
access to new gen AI features and models, IP infringement. These agreements will ideally
allowing the company to test and provide outline each party’s responsibilities for mitigating
feedback before general release. Through these and managing these risks, as well as any
collaborative-planning efforts and continual financial or legal liabilities.
communication about road map needs and
adjustments, the company was able to reduce The same technology company mentioned earlier
time to market for deploying at-scale solutions. shared risks with their gen AI providers by
For instance, it launched a personalized structuring contracts around outcomes instead
marketing campaign powered by gen AI of token usage. This approach allowed the
within six months of starting the strategic company to manage uncertainties and costs
alliance, resulting in a significant boost while aligning incentives and fostering a shared
in sales conversions. commitment to success.
Strategic alliances for gen AI: How to build them and make them work 4
Zero in on providers that offer a 50 percent increase in user queries over six
scalability, interoperability, and months without degradation. Providers should
reusability also be willing to regularly review and adjust
milestones and contracts to ensure alignment
A single provider that can provide all the best
with evolving goals.
components for an effective gen AI solution doesn’t
exist, at least not yet. The variety of components
and models needed to work together across — Reusability. Reusing code can accelerate the
the tech stack means that companies will need development of gen AI use cases by 30 to
to team up with a curated network of specialized 50 percent, so it’s critical that providers offer
tech providers. solutions that can be easily repurposed across
multiple projects.5 Companies should therefore
In developing an ecosystem of providers that can look for providers that offer flexible, modular
scale, the component parts working together are components and pretrained models (such as
more important than the parts individually. customizable natural-language-processing
Companies need to weigh those criteria that enable modules or configurable data pipelines that can
the overall gen AI system to work most effectively. be fine-tuned and adapted to various contexts.
Selecting the right providers has become They should also seek providers that offer tools
particularly challenging given the growth in the and frameworks (for example, intuitive APIs and
provider landscape. In fact, since the launch of software development kits for integrating and
ChatGPT in November 2022, the number of open- extending gen AI components or drag-and-drop
source large language models (LLMs) and interfaces for model fine-tuning) that can
commercial LLMs has quadrupled.3 Furthermore, enable the easy customization and extension
there are currently more than 1,000 AI vendors, of solutions.
with more than 600 new products introduced over
the previous year, mostly spurred by gen AI.4 To — Interoperability. Interoperability between
that end, companies should zero in on three key models and components is crucial for creating
criteria when selecting providers: a cohesive, efficient, and scalable gen AI
ecosystem. When evaluating model or solution
— Scalability. Given the mind-boggling scale of interoperability, companies should look for
gen AI—from the amount of data needed to providers that adhere to industry standards and
train and fine-tune models to the number of best practices for data exchange, API design,
queries models respond to—providers should and software development (for example,
have a proven track record of handling standard data formats such as Apache Avro and
increased volumes of complex traffic and user JavaScript Object Notation, established machine
queries without compromising performance. learning frameworks like PyTorch, or data
Companies should pressure test pilot programs, governance standards). Providers should use
which often do not replicate live conditions and widely adopted programming languages, offer
are typically not a good barometer for scaling well-documented and easy-to-use APIs, and
readiness. When evaluating scalability, it is support smooth integration with the company’s
important to look for providers that can commit data sources, applications, and platforms.
to specific milestones, such as handling
3 Sources included press search, Stanford University’s HELM (Holistic Evaluation of Language Models) leaderboard, and McKinsey analysis.
4 Maria Korolov, “Weighing risk and reward with gen AI vendor selection,” CIO, January 3, 2024.
5 Eric Lamarre, Alex Singla, Alexander Sukharevsky, and Rodney Zemmel, “A generative AI reset: Rewiring to turn potential into value in 2024,”
McKinsey Quarterly, March 4, 2024.
Strategic alliances for gen AI: How to build them and make them work 5
These three criteria are crucial for selecting gen AI should ensure that providers include proper
providers that can scale, but companies should also documentation and sufficient transparency
ensure that all providers meet a high bar on other during development. Robust monitoring and
criteria, such as ethical guidelines and adherence to testing capabilities are needed to track provider
local privacy and tech sovereignty regulations. performance and identify issues early (for
Establishing and aligning around clear data example, automated reporting capabilities to
governance and security protocols can go a long collect and aggregate relevant metrics, including
way toward building trust with providers. model inputs and outputs, latency and
throughput statistics, and user feedback). It’s
important to regularly conduct end-to-end tests
Stay in control of your destiny of the gen AI solution—from data ingestion to
model outputs—so as to track performance and
Finding the sweet spot between forging close
identify the source of a problem across the
strategic alliances and maintaining agency over the
provider ecosystem. Experience has shown
broader direction and vision of these collaborations
that involving all providers in establishing
presents a critical challenge for organizations.
a comprehensive testing strategy (joint
Companies looking to maintain independence and
integration testing and scenario testing, for
control of their destiny would do well to consider
instance) helps to set clear expectations
the following guidelines:
and responsibilities.
— Establish a flexible infrastructure. A flexible,
scalable gen AI infrastructure can serve as a — Establish clear IP boundaries. Difficult
foundation for quickly integrating different questions about IP are still being worked
providers. This “chassis” could be a centralized through with respect to gen AI, so it is important
platform or a set of well-defined APIs, to establish clear boundaries up front.
integration protocols, and data formats that Companies should specify, for example, the
enable different gen AI components to work existing IP that each party brings to the
together seamlessly. To ensure maximum collaboration, such as proprietary data sets,
flexibility, companies can adopt MLOps algorithms, or models. They should define how
(machine learning operations) best practices, IP that is created during the collaboration (for
such as containerization, automated testing, example, any patents, copyrights, or trade
and continuous-integration and continuous- secrets) will be owned and managed, including
delivery (CI/CD) pipelines. These practices help predefined terms for licensing, commercialization,
ensure the reliability and performance of the and revenue sharing. And they should outline a
gen AI stack and allow for rapid rollback of process for tracking and attributing individual
changes if issues arise. contributions to the codeveloped IP, which can
help prevent disputes and ensure proper
— Continually monitor model performance. recognition of each party’s contributions. Being
Companies need to maintain a clear transparent and assuring alignment during this
understanding of what providers are building to process can also help build trust.
avoid receiving a “black box” solution. They
Strategic alliances for gen AI: How to build them and make them work 6
— Tie compensation to outcomes. While following guidelines for assessing scalability, reusability,
best practices on contract structure (for and interoperability, as well as templates for
instance, including clear KPIs, service level contracts, service-level agreements, and
agreements, and licensing arrangements) and performance dashboards.
specifying risk-sharing provisions, it is critical to
tie a provider’s compensation to measurable
— Conduct a strategic gen-AI-alliance audit to
outcomes such as model accuracy, uptime, and
assess current strategic alliances and identify
user satisfaction. Companies should avoid
gaps, redundancies, or misalignments with the
minimum spend requirements and include clear
gen AI strategy. Determine which strategic
exit clauses and data portability requirements to
alliances to maintain, expand, or phase out
avoid limiting flexibility.
based on their potential to spur business value.
— Assign dedicated relationship managers to the
Getting started
gen AI alliance. The managers should have a
As the transformative potential of gen AI continues solid understanding of gen AI technologies,
to unfold, companies should act decisively to architectures, and best practices so they can
position themselves for success in this new era. To effectively communicate with providers, assess
get started, executives can consider the following their capabilities, and ensure alignment with the
actions: company’s technical requirements. They also
oversee the entire gen AI ecosystem and act as
— Establish a steering committee made up of key the “central authority” to help coordinate
stakeholders from business, IT, legal, and activities among providers, monitor progress,
procurement to oversee the gen-AI-alliance and resolve issues. In many cases, it will be
strategy. The committee should be tasked with useful to have a solution architect on board, as
defining strategic-alliance criteria, setting well as to regularly meet with providers to
performance metrics, and establishing understand exactly what they are doing and
governance guidelines. To do so, the team how they are progressing.
needs sufficient autonomy to make decisions
within strategic guidelines.
Building trust and fostering collaboration are just as
— Develop a strategic gen-AI-alliance playbook important as choosing the right technology.
that includes a standardized framework for Companies should start small, learn fast, and iterate
evaluating, onboarding, and managing new gen often to ensure that they are well on their way to
AI providers. This framework should include unlocking the full potential of gen AI.
Alex Singla is a senior partner in McKinsey’s Chicago office; Alexander Sukharevsky is a senior partner in the London office;
Ben Ellencweig is a senior partner in the Stamford, Connecticut, office; and Guilherme Cruz is a partner in the New York
office, where Carlo Palermo is a consultant and Joshan Cherian Abraham is an associate partner
The authors wish to thank Aaron Kovar, Doruk Caner, and Matias Navarro Crespo for their contributions to this article.
Copyright © 2024 McKinsey & Company. All rights reserved.
Strategic alliances for gen AI: How to build them and make them work 7"
53,mckinsey,scaling-gen-ai-in-the-life-sciences-industry.pdf,"Life Sciences Practice
Scaling gen AI in the
life sciences industry
Gen AI pilots have shown promise, but for the technology to deliver
transformational business value in the life sciences industry,
organizations need to rethink how they scale it.
by Chaitanya Adabala Viswa, Dandi Zhu, Delphine Zurkiya, and Joachim Bleys
January 2025
Back in July 2023, researchers at the McKinsey Global Institute estimated that gen AI could
unlock between $60 billion and $110 billion a year in economic value for the pharmaceutical and
medical products industries, boosting productivity and innovation in domains across the
industry’s value chain—from the way new treatments are discovered to how they are marketed
and administered by physicians. Six months later, McKinsey experts dug deeper into those
numbers, uncovering more than 20 use cases with the greatest potential for near-term impact.
Now, with gen AI use cases proliferating across the business community, we decided to find out
how much progress life science organizations have made in capturing this value. In late summer
2024, we surveyed more than 100 pharma and medtech leaders responsible for driving their
organizations’ gen AI efforts. All respondents report having experimented with gen AI, and 32
percent say they have taken steps to scale the technology. But only 5 percent say they have
realized gen AI as a competitive differentiator that generates consistent and significant financial
value (Exhibit 1). Nonetheless, companies remain optimistic about gen AI, with more than two-
thirds of respondents saying they plan to significantly increase investment in the technology
(Exhibit 2).
Exhibit 1
Scaling gen AI in the life sciences industry 2
Exhibit 2
Why do so many life science organizations struggle to realize results from their gen AI
deployments? And what are the minority of top performers doing differently? This article reveals
the most common pitfalls life science companies are facing—and offers solutions that can help
organizations move from pilot purgatory to driving real business value at scale.
The key challenges to scaling gen AI in life sciences
Based on our survey and our experience, we have identified five key areas that pose challenges
for life science companies attempting to realize company-wide value from gen AI: gen AI
strategy, talent planning, operating model and governance structure, change management, and
risk (Exhibit 3).
Scaling gen AI in the life sciences industry 3
Exhibit 3
Challenge 1: Ambiguous, shortsighted, or nonexistent enterprise gen AI strategy
About 75 percent of respondents say that their organizations lack a comprehensive vision for
gen AI or an intentionally designed, strategic road map with clearly defined success measures
linked to business priorities. Instead, they tend to proceed in a decentralized manner, use case
by use case. This instinct to capture short-term value through experimentation, coupled with the
federated/function-led structure of many life science organizations, explains many of the
challenges organizations encounter when it comes to scaling.
McKinsey research has found that digital transformations seldom succeed unless C-suite
leaders are aligned around a business-led road map. Without an intentional strategic posture
toward gen AI—whether a top-down mandate or a coordinated enterprise road map driven by a
center of excellence—individual business units are left to navigate the ever-evolving technology
landscape on their own, pursuing a multitude of new use case ideas that, no matter how
compelling, often fail to add up to a strategy that delivers actual value.
Scaling gen AI in the life sciences industry 4
Challenge 2: Lack of talent planning and upskilling
At most life science companies, the existing pool of tech talent presents a traditional tool kit for
IT, data science, and product development. Unfortunately, traditional approaches to tech talent
are unable to deliver the quality and performance of enterprise-grade solutions needed for gen
AI, for example, agent-based architecture, model validation, large language model (LLM)
operations, and the fine-tuning of models. But only 6 percent of survey respondents report
having conducted a skills-based talent assessment to determine how to evolve their talent
strategy into one that considers gen AI priorities.
Prompt engineering has emerged as a key gap, especially for more complex gen AI applications.
One life science company, for example, was attempting to use gen AI to draft regulatory
documents, only to discover that prompt engineers required a unique combination of regulatory
domain knowledge and engineering rigor to craft scalable prompts that generate submission-
ready output—a specialized necessity that made the role especially challenging to fill.
Challenge 3: Loosely defined operating model and governance
One common challenge leaders face is creating the right operating model for gen AI
transformation, often choosing between one of two extremes. At one end of the spectrum is a
highly decentralized approach, in which the organization simultaneously launches multiple use
case pilots. While this allows companies to move fast, it also leads to quality, cost, and
sustainability challenges and creates operational silos that inhibit the sharing of knowledge and
the ability to capture cost synergies. At the opposite end is a top-down approach, with
centralized decision-making and a phased rollout of use cases. This approach can be slow and
often frustrating, destroying momentum.
One company swung between the two. It began its gen AI efforts by launching 1,500 different
use cases. When that proved unwieldy, company leaders imposed a top-down governance
structure that led to a different set of issues, constricting the innovation pipeline with projects
requiring an arduous approval process that stretched some two to three months.
Challenge 4: Underestimating the process rewiring required to drive scale
To succeed with gen AI, companies must integrate the technology across complex workflows to
promote adoption and impact—a reality that highlights the need for effective change
management. McKinsey has found that 70 percent of digital transformations fail not because of
technical issues but because leaders ignored the importance of managing change. In fact, for
every $1 spent on technology, $5 is required for change management to successfully drive
capability building, adoption, buy-in, and value capture over time.
One company launched a center of excellence function to initiate a broad gen AI platform for a
range of use cases but failed to communicate a compelling change story to accompany those
initiatives. That failure, coupled with the lack of holistic, end-to-end planning and thinking,
resulted in a collection of gen AI tools that almost no one ended up using.
Scaling gen AI in the life sciences industry 5
Challenge 5: Inadequate understanding of risk
Gen AI introduces unique risks, from hallucinations and accuracy to bias and intellectual
property protection. But 35 percent of survey respondents report that they spend fewer than
ten hours with their risk counterparts, limiting the degree of collaboration with these crucial
functions. This dynamic needs to evolve to scale gen AI. Successful scaling requires business
leaders, technology teams, and risk management professionals to communicate from the outset;
the absence of such collaboration can lead to issues being raised late in the game, when they
are much more difficult to fix, or a lack of adherence to the risk and compliance guardrails that
are critical to building trust in the organization.
One company, for example, spent several months developing an external-facing gen AI solution,
only to be forced to withdraw the launch due to a lack of alignment with its digital, medical, and
legal teams—which raised significant risk issues after the tool had been developed. This resulted
in a severe setback for the gen AI team’s agenda, morale, and momentum.
The solution: A five-point plan to realize value from gen AI
Successfully scaling gen AI and capturing its value potential requires more than just a
technological rollout. An effective gen AI strategy is fundamentally different from traditional tech
projects. Given the rapid pace of innovation, a gen AI strategy must be dynamic, scenario driven,
and focused on how to engage with the broader ecosystem. Scaling gen AI involves
comprehensive change across the organization, encompassing strategy, talent, governance,
and risk management.
Based on our experience, we have identified five key strategies to move from gen AI use cases
to enterprise-wide adoption. These actions ensure that organizations not only experiment with
the technology but also fully integrate it into their operations to drive measurable business
value.
— Adopt a domain-driven approach. Successful AI strategy cannot be based on a slew of
disconnected use cases, which often leads to fragmented efforts and missed opportunities.
Instead, the focus must shift to domain-driven transformations, where gen AI is applied to
fundamentally reshape critical areas of the business, such as the commercial, medical, or
R&D domains. Thirty-eight percent of the life science organizations surveyed cite research
as their leading strategic priority in their gen AI journey, followed by the commercial domain,
at 28 percent (Exhibit 4).
This domain-driven approach ensures that gen AI isn’t just another tech solution but a core
enabler of business transformation. Rather than focusing on technology for technology’s
sake, organizations that prioritize domain transformations are better positioned to capture
Scaling gen AI in the life sciences industry 6
the full value of AI. Crucially, there is no such thing as a stand-alone gen AI strategy. The real
focus should be on deploying gen AI to support broader business objectives, drive strategic
goals, and create differentiation in the market. Organizations that view the technology
through this business-first lens have found greater success in scaling AI initiatives.
Exhibit 4
Scaling gen AI in the life sciences industry 7
— AI transformation encompasses more than just tech. Scaling gen AI isn’t simply a matter of
implementing a new technology; it’s about rewiring the organization’s operating model and
culture to support new AI-driven ways of working. This extends to talent strategies: the
workforce must evolve beyond traditional IT data science roles to include new skills—AI
engineering, large language model fine-tuning, and business translation—to bridge the gap
between technical execution and business value capture. Without a comprehensive talent
realignment, organizations will be less successful in scaling their gen AI efforts. Further, gen
AI implementation needs to drive measurable value. This requires a clear up-front
agreement on how value will be captured, say, through acceleration of time to market,
productivity increase, or improved probability of success.
One life sciences company, for instance, launched an enterprise talent upskilling and planning
program, with targeted initiatives for business and technical roles. The program also
introduced dedicated gen-AI-focused leadership roles in critical functions to drive sustained
organizational change. With the appropriate talent—and leadership—in place, the company’s
gen AI initiatives proceeded much more smoothly than they would have otherwise.
— Adopt an ecosystem approach. In the rapidly evolving AI ecosystem, an externally focused
partnership strategy is critical. Given the speed at which AI technologies and methodologies
are advancing, life sciences organizations should consider cultivating a network of low-cost,
high-optionality partnerships. These partnerships can provide flexibility and give
organizations the ability to quickly pivot and seize opportunities as they arise. Organizations
should also establish clear “triggers” that indicate when it’s time to move from exploratory
partnerships to larger strategic bets. This ensures that the business remains agile and can
scale up or shift its AI investments based on real-time insights and market movements.
Engaging with the broader ecosystem—including academia, tech, and venture capital—is also
essential to staying on top of the latest developments. Relying solely on internal capabilities
is no longer enough to stay competitive in AI. A dynamic, externally focused lens ensures
that companies stay ahead of the curve and capture the full value of gen AI innovations.
— Deploy a platform-driven approach from the outset. A platform-driven approach is key to
ensuring that gen AI initiatives are scalable, sustainable, and reusable across various
business domains. A scalable AI platform allows organizations to standardize infrastructure,
data pipelines, and development processes, ensuring that each new use case builds on the
previous one. This can also help reduce duplication of effort, encourage collaboration across
business units, and foster consistency in AI performance across the organization. Moreover,
Scaling gen AI in the life sciences industry 8
a platform-driven approach ensures that AI models are not developed in isolation but are
integrated into a unified framework, allowing them to be adapted and reused across various
business domains. This not only reduces costs but also accelerates time to value, as insights
from one domain can be applied to another.
One life sciences company found success by adhering to a mantra: “Slow down to speed up.”
The company spent three months defining a detailed blueprint for insights and document
platforms. This enabled the reuse of components within each platform, enabling rapid
scaling across use cases.
— Embed risk management in the full product development life cycle. One of the common
mistakes organizations make with gen AI is treating risk management as an afterthought or
as an obstacle to innovation. In fact, risk management must be embedded throughout the
entire AI product life cycle. Gen AI introduces unique risks—such as hallucinations, bias, data
security, and intellectual property issues—which require careful oversight.
To ensure these risks are managed effectively, business leaders and risk and compliance
functions should collaborate regularly. Organizations should establish clear governance
frameworks early on and ensure that ethical guidelines are in place to address concerns about
AI fairness, transparency, and accountability.
Given the high regulatory requirements in life sciences, organizations should place greater
emphasis on risk management. One organization proactively identified the guardrails necessary
to address evolving regulations (for example, the EU AI Act) and technology limitations (for
example, the probabilistic nature of models). The organization established clear, responsible AI
requirements, including mandatory observability, validation protocols, and human-in-the-loop
guidelines, which were defined prior to the start of product development.
What a holistic transformation can look like
What does a successful gen AI initiative look like? Consider one life sciences company that
recognized the gen AI opportunity early and embarked on a holistic transformation across
domains. Company leaders convened a C-level task force to steer the overall gen AI strategy,
set up governing bodies across the R&D, commercial, medical, and operations domains, and
asked each domain to prioritize one use case with high-value potential for C-level sponsorship.
The company then ran proofs of concept with an eye toward scaling, using its early experiences
to organize reusable components into domain-specific platforms. The technology and business
Scaling gen AI in the life sciences industry 9
teams partnered from the outset, ensuring that all gen AI solutions addressed priority business
needs and helped drive the process changes needed to spark adoption and deliver value.
In the meantime, the company engaged ecosystem partners to bring in learnings and assets
from across the life sciences industry and beyond and built stage gates to focus resources on
partnered solutions that were ready to scale across therapeutic areas and geographies.
Leaders shaped a compelling change story focused on how gen AI solutions were intended to
augment rather than replace employees, for example, by helping them deal with increasing
workloads, and used change management teams to help drive a successful rollout. They
provided white-glove support for initial users and deployed these early adopters as change
ambassadors to build bottom-up momentum. Impact metrics were defined, tracked, and
reviewed at regular governance meetings to ensure gen AI initiatives remained on track to scale
and deliver business impact.
This kind of experience does not have to be an outlier. Leaders of life science organizations
should understand that capturing the potentially transformative value of gen AI requires more
than experimentation and individual use case deployment. It demands strategic integration into
the organizational fabric. In the next chapter of the gen AI story, organizations should take an
intentional approach to driving alignment with business strategy, scalability, and sustainability.
This pivotal moment is an opportunity for life sciences leaders to lead transformative change,
revolutionizing drug discovery and patient care, as well as driving meaningful bottom-line
results.
Chaitanya Adabala Viswa is a partner in McKinsey’s Boston office, where Delphine Zurkiya is a senior partner;
Dandi Zhu is a partner in the New York office; and Joachim Bleys is a senior partner in the Carolinas office.
The authors wish to thank Abhi Mukherjee, Lionel Jin, Natalia Dorogi, Nitisha Sharma, and Vasu Macherla for their
contributions to this article.
This article was edited by Larry Kanter, a senior editor in the New York office.
Copyright © 2025 McKinsey & Company. All rights reserved.
Scaling gen AI in the life sciences industry 10"
54,mckinsey,fortune-or-fiction-final-v3.pdf,"Consumer Packaged Goods Practice
Fortune or fiction? The real
value of a digital and AI
transformation in CPG
A new McKinsey analysis quantifies the impact that digital and AI can have
on consumer goods businesses and where consumer-packaged-goods
executives should focus their efforts.
This article is a collaborative effort by Jessica Moulton, Rob Cain, and Roger Roberts, with Hannah Mayer and
Spurthi Gummadala, representing views from McKinsey’s Digital and Consumer Packaged Goods Practices.
October 2024
The narrative surrounding generative AI (gen AI), technology, media, and telecommunications. This
a moon shot, once-in-a-generation innovation, may be because leaders lack answers to questions
is that it could utterly reinvent how businesses— such as, “How much value will AI eventually create
consumer-packaged-goods (CPG) companies in CPG?” and “Where in the value chain will the value
among them—are run. be concentrated?”
CPG leaders are heeding the call. In a 2024 survey We sought fact-based answers to these questions
of CPG leaders,1 71 percent said they adopted AI in at to help CPGs figure out where to focus and how
least one business function of their organizations fast to move.
(up from 42 percent in 2023), while 56 percent said
they were regularly using gen AI. We found that the highest-impact investment
areas vary, depending on which subsector a CPG
However, no CPG player has truly scaled its gen AI company plays in. Our findings can help CPG
and traditional AI capabilities. So far, CPG leaders leaders determine their next moves in digital and AI
have reported adopting gen AI at a lower rate than (outlined in our article, “What it takes to rewire
other industries, such as advanced industries or a CPG company to outcompete in digital and AI”).
Quantifying the effects across
About the research the CPG value chain
After conducting a rigorous company-specific
Over the past two years, McKinsey has researched more than quantitative analysis of more than 140 use cases and
140 digital and AI use cases across the consumer packaged goods dozens of expert interviews (see sidebar, “About
(CPG) value chain to quantify the value at stake in each of the seven the research”), we have aggregated subsector-
value streams. After identifying use cases, we determined how each specific estimates to determine the value that CPG
one affects the profit-and-loss (P&L) statement for a standardized1 companies in the three major subsectors—food
CPG business. For example, we observed that use cases related to and beverage, personal care and home, and
trade promotion optimization could lead to reduced selling, general, beauty—can generate by committing to digital and
and administrative expenses, as well as growth in net revenue, both AI investments. (In this article, “digital and AI”
of which act together to expand operating margins. refers to all use cases across digital, analytics,
machine learning, traditional AI, and generative AI.)
We then simulated the effect on the P&L line items under conservative We quantified the value at stake in the main parts
and optimistic scenarios, based on a variety of assumptions about the of the CPG value chain: the core functions (including
ease of implementation and the pace of adoption. Every CPG subsector back-office support functions) and six innovation
will be affected differently by a digital and AI transformation, given the zones, as shown in Exhibit 1. Together, the core
different set of opportunities seen, for example, in beauty categories functions and the innovation zones make up a CPG
compared with grocery. Finally, we determined the cumulative impact company’s seven major value streams.
of these use cases for a composite CPG business to understand the
full value at stake. Enterprise and support functions, such as human
resources and finance departments, will surely
benefit from the use of new digital tools, and
1 To create a “standardized” CPG business, we identified a pool of players within each
CPG subsector, analyzed their P&Ls, and aggregated these results to create a sector partnerships and an ecosystem strategy can enable
composite that reflects the cost mix and performance of the subsector.
a CPG company to thrive (they can do this in
collaboration with suppliers and channel partners).
1 McKinsey Global Survey on AI, February 22–March 5, 2024; n = 63 CPG leaders.
Fortune or fiction? The real value of a digital and AI transformation in CPG 2
Web <2023>
<ECxPhGiDbiigti t1al>
Exhibit <1> of <5>
A digital transformation in consumer packaged goods should take place
across the entire value chain and with ecosystem partners.
Areas for digital Media and data partners
transformation
Consumer insights and
Value chain ecosystem: demand shaping
Rei
C
U
wn ov
n
ite
-
l
hoin
n
c
pt
n
k a
b
o
rno
v
tneau ewtn
e
r
d sina sr ii ge hs
ts Fulfillment
partn Die rr es
c ct ot no sumer
Enterprise-
i n n oP
v
ar o tid ou
nctI n ag nr
de
di
e nt p art
n
er
s
resource-planning-
Innovation zones:
based core
Put data to work
Enable agility Order to cash
Drive decision making Procure to pay
C d
C fuo
nrM t Sceo
it
o
m
a
ii onv
n
pe nn
d
l
io sf
f
r v
s
yo a um t pio pi nn os ri tght u st o m er
a n d
cc hh aa n nn ne el
l
mC
p
au
a
rs
n
tt
a
no
gm
ee mr ea nn
td
SuppH
aly
nir de
c h
lt oo
a gi
nr ie
s
pt ti ilr cae
snning
Manufact
our pi en rg
a
Ctia oon nn trs
act
manufacturers
Rationalize er
s
Modernize Logistics partners
McKinsey & Company
But the most significant value an organization will This domain can further benefit from generative AI
get from a digital and AI transformation happens in (gen AI) applications. We’ve already seen this
the six innovation zones. technology have an effect on CPG companies.
One beverage player, for instance, used gen AI to
Consider the product and innovation domain, where create prompts, images, and concepts that
digital and AI tools can be leveraged throughout informed product development, reducing the time
the entire cycle: social listening powered by natural- to introduce a new product to market by 60 percent.
language processing, feedback mining, and After conducting a gen-AI-powered sentiment
consumer-backed predictive trend sourcing can analysis based on customers’ online posts, the
lead to new product ideas or product improvements. team generated insights to understand how
AI can suggest new formulations for existing customers felt about the products that gen AI
products, while advanced analytics can improve helped to develop. The company’s gen AI efforts
packaging design. Automated, collaborative have yielded a portfolio of new products and
testing and experimentation can make launching experiences that have helped fuel growth and
a new product easier. expand market share.
Fortune or fiction? The real value of a digital and AI transformation in CPG 3
Our latest research estimates that gen AI use cases One notable exception is the beauty industry,
could increase the economic impact of traditional where the direct-to-consumer value stream takes
AI by 15 to 40 percent, unlocking an additional center stage. This reflects beauty companies’
$160 billion to $270 billion annually in profit ability to take advantage of their unique relationship
(measured by EBITDA) for CPG companies globally. with consumers and cater directly to their
That said, although gen AI is dazzling the business shoppers. Digitally enabled beauty companies
world and setting the media ablaze, it’s merely the can leverage new technologies to enhance
proverbial cherry on top: traditional AI’s potential the consumer experience, the e-commerce
impact is 2.5 to 7.0 times higher than that of gen AI. process, and fulfillment management.
This means that companies should invest in a broad
spectrum of AI capabilities and platforms to realize In the six domains, cooperation with ecosystem
a digital transformation’s full potential. partners unlocks the next level of value. Much of this
comes down to a data exchange with ecosystem
While the potential effects of digital and AI will partners, such as retailers, manufacturers, logistics
vary across CPG sectors, our analysis shows that providers, and media partners. CPG companies
the greatest value for most sectors is concentrated have historically been unable to collect and activate
most heavily in two value streams: consumer personalized, first-party data at scale. This lack of
insights and demand shaping and customer and data means they must decide either to rely on their
channel management (Exhibit 2). partners for third-party data or to invest in ways to
Web <2023>
<ECxPhGiDbiigti t2al>
Exhibit <2> of <5>
In consumer packaged goods, digital and AI could have the greatest impact
on consumer insights and customer and channel management innovation.
Distribution of digital and AI effects across innovation zones, %
2 2 1
Core
11
18
30
Direct to
consumer
30
23
Customer and
channel management
25 7
100% Supply chain
14 4
planning and logistics
10
11 Manufacturing
15
3 and operations
7
8 Product and
37 innovation
23
19 Consumer insights
and demand shaping
Food and Beauty Personal care,
beverage home, and
personal health
Note: Figures may not sum to 100%, because of rounding.
McKinsey & Company
Fortune or fiction? The real value of a digital and AI transformation in CPG 4
acquire “zero party” and first-party data. This data and $1.6 billion, driven by both top-line growth and
is key to being able to deliver the right message productivity gains (Exhibit 3). This translates into an
to the right customer at the right moment. CPG EBITDA margin increase of approximately seven to
companies must also invest in the right technology 13 percentage points for food and beverage players.
to enable the data exchange among ecosystem
partners. Adopting the cloud here can help. The largest potential impact—valued between
approximately $230 million and $470 million—lies in
customer and channel management, as food and
The value at stake in three beverage companies continually seek to optimize
CPG subsectors their presence across online and offline retailers.
Consider the effect that digital transformations
Specifically, retail trade promotions, which can
could have on the following types of CPG companies.
account for as much as 20 percent of revenue for
The three illustrative examples below offer case
a food and beverage company, are particularly
studies on how to realize these gains.
ripe for digital and analytics optimization. Retailers
typically have data about past promotion
Food and beverage companies can seize the
performance but may not have the tools to synthesize
customer and channel management opportunity
the data to build a clear picture of how to target
For a food and beverage company with $10.0 billion
promotions. Digital and analytics tools can leverage
in revenue, the value at stake from implementing
historical promotion and stock data to inform the
digital and AI successfully and comprehensively
extent and timing of future promotions. Because
across the full value chain is between $810.0 million
Web <2023>
E<CxPhGibDiigti t3al>
Exhibit <3> of <5>
A food and beverage company could mine half of its value from a digital and
AI transformation of the customer and channel management innovation zone.
Distribution of total value at stake across the full value chain,¹ $ million
230–470
A sample food and beverage player
with revenue of $10.0 billion could see
a value at stake of between
$810.0 million and $1.6 billion across
its full value chain
160–300
140–230
110–210
80–170
70–130
20–30
Consumer Product and Manufacturing Supply chain Customer Direct to Core
insights and innovation and operations planning and channel consumer
demand shaping and logistics management
1Numbers rounded to nearest $10 million.
McKinsey & Company
Fortune or fiction? The real value of a digital and AI transformation in CPG 5
updating and synthesizing data without advanced The personal-care market can be difficult to
analytics and AI tools can be expensive, navigate because businesses tend to offer frequent,
using these tools can help food and beverage widely varying promotions with high discounts,
companies reduce costs while simultaneously which can be inefficient. For this reason, enhanced
improving performance. consumer profiling (often referred to as “consumer
360”) can help to reduce this inefficiency. Supported
In personal care and home, consumer insights by AI, enhanced consumer profiling can help
and demand-shaping optimization can add to analyze the attributes, attitudes, and behaviors
billions of dollars in value consumers demonstrate. This allows the company
For one personal care and home brand with to construct granular, dynamic consumer profiles
$10.0 billion in revenue, the value at stake for a that inform how to target those consumers with
digital transformation is between approximately greater accuracy and efficiency.
$1.0 billion and $1.8 billion (Exhibit 4). This translates
into an EBITDA margin increase of approximately For instance, one personal-care player needed to
nine to 16 percentage points for personal care and increase EBITDA and decrease inventories. By
home players. Among the seven value streams, investing in its digital and AI suite, the company was
the consumer insights and demand-shaping domain able to incorporate internal and external data to
represents the biggest potential impact. improve forecast accuracy by 13 percent, decrease
Web <2023>
E<CxPhGibDiigti t4al>
Exhibit <4> of <5>
Consumer insights and demand is the innovation zone with the most value
at stake in an AI transformation for personal care and home brands.
Distribution of total value at stake across the full value chain,¹ $ million
380–680
A sample personal care and home
player with revenue of $10.0 billion
could see a value at stake of between
$1.0 billion–$1.8 billion across its full
value chain
200–420
190–340
110–180
60–120
50–70
10–20
Consumer Product and Manufacturing Supply chain Customer Direct to Core
insights and innovation and operations planning and channel consumer
demand shaping and logistics management
1Numbers rounded to nearest $10 million.
McKinsey & Company
Fortune or fiction? The real value of a digital and AI transformation in CPG 6
product shortages by 40 percent, and decrease beauty apps (which include virtual try-on tools)
inventory by 35 percent. By incorporating data and personalized product recommendations, both
about who consumers are, what they buy, where of which can increase sales. When it comes
they buy, and why they buy, companies can to personalization, AI-powered recommendation
gain better insights to inform decisions across engines use data about customers from various
messaging, branding, assortment, product touchpoints such as mobile, website, and
upgrades, new markets, and innovation. in the store to improve product search and
product suggestions.
Beauty brands have an opportunity to leverage
their direct-to-consumer advantage One beauty brand, for example, leveraged AI
A beauty brand with $3 billion in revenue could to launch a personalized lipstick-on-demand for
see between approximately $290 million its customers. The company developed an
and $500 million in added value from a digital AI-powered, at-home system that can recognize
transformation across its value chain. This color from any picture and prepare a lipstick
translates into an EBITDA margin increase of based on that color.
approximately eight to 14 percentage points
for beauty players (Exhibit 5). Consumers expect personalized brand experiences,
including at the time that they discover a product,
Nearly a third of this value can be mined from the buy it, and even after they make a purchase. Beauty
direct-to-consumer (DTC) value stream. Within DTC, brands that use advanced digital tools can integrate
AI has enabled use cases such as AI-powered data from multiple sources, including social media,
Web <2023>
E<CxPhGibDiigti t5al>
Exhibit <5> of <5>
Beauty brands can take advantage of their relationship with consumers by
using digital and AI to transform their direct-to-consumer channel.
Distribution of total value at stake across the full value chain,¹ $ million
90–150
A sample beauty player with revenue
of $3 billion could see a value at stake
of between $290 million– 70–120
60–110 $500 million across its full
value chain
30–50
20–40
5–10 5–10
Consumer Product and Manufacturing Supply chain Customer Direct to Core
insights and innovation and operations planning and channel consumer
demand shaping and logistics management
1Numbers rounded to nearest $10 million.
McKinsey & Company
Fortune or fiction? The real value of a digital and AI transformation in CPG 7
search engines, and hyperlocal data on consumer Consumer goods players will need to home in on
Find more content like this on the
behavior, to target consumers more accurately and the six innovation zones and prioritize the plays that
McKinsey Insights App
in ways they will be receptive to. best serve their business. Keep in mind: the full
impact of this transformation, one that will create
One beauty brand invested in developing unique competitive distance from challengers, will not
customer profiles through automated data come with a piecemeal approach.
clustering, a technique that helps to inform machine
learning models, to better identify and cater to “Rewired” companies, as we call them, take an all-
specific customers. This initiation not only better hands-on-deck approach across commercial,
met customer needs but also helped expedite operational, and support domains. This includes
Scan • Download • Personalize
the rollout of new marketing campaigns with faster, creating a digital road map, empowering talent,
targeted A/B testing. rethinking the CPG operating model, developing
and acquiring new technological tools, and building
out the data products necessary to implement
and scale their digital transformation.
CPG companies have an opportunity to take
advantage of a five- to 15-percentage-point impact Although in its earliest days, the next great
on EBITDA margins, which our analysis indicates technological revolution is here. The fruits of the
is on the table from digital transformations across revolution may take a while to ripen, but putting
their value chains. As alluring as those wins may their potential to work now will create more long-
be, they won’t come without a long-term, large- term value than could ever be contained within
scale, and iterative effort. a single earnings statement.
Jessica Moulton is a senior partner in McKinsey’s London office; Rob Cain is a partner in the Minneapolis office;
Roger Roberts is a partner in the Bay Area office, where Hannah Mayer is an associate partner; and Spurthi Gummadala
is an associate partner in the Seattle office.
The authors wish to thank Abhigna Antani, Monica Avakian, Miquel Ferrer, Warren Teichner, and Tim Usmanov for their
contributions to this article.
This article was edited by Alexandra Mondalek, an editor in the New York office.
Designed by McKinsey Global Publishing
Copyright © 2024 McKinsey & Company. All rights reserved.
Fortune or fiction? The real value of a digital and AI transformation in CPG 8"
55,mckinsey,superagency-in-the-workplace-empowering-people-to-unlock-ais-full-potential-v4.pdf,"Superagency
in the Workplace
Empowering people to unlock AI’s full potential
Hannah Mayer
Lareina Yee
Michael Chui
Roger Roberts
January 2025
Contents
Introduction 2
Chapters:
1. An innovation as powerful as the steam engine 5
2. Employees are ready for AI; now leaders must step up 11
3. Delivering speed and safety 18
4. Embracing bigger ambitions 26
5. Technology is not the barrier to scale 35
Conclusion: Meeting the AI future 40
Acknowledgments 42
Methodology 43
Glossary 44
Introduction
Almost all companies invest in AI, but just 1 percent believe they are at
maturity. Our research finds the biggest barrier to scaling is not
employees—who are ready—but leaders, who are not steering fast enough.
A
rtificial intelligence has arrived in the workplace and has the potential to be as transformative
as the steam engine was to the 19th-century Industrial Revolution.1 With powerful and capable
large language models (LLMs) developed by Anthropic, Cohere, Google, Meta, Mistral, OpenAI,
and others, we have entered a new information technology era. McKinsey research sizes the
long-term AI opportunity at $4.4 trillion in added productivity growth potential from corporate use cases.2
Therein lies the challenge: the long-term potential of AI is great, but the short-term returns are unclear. Over
the next three years, 92 percent of companies plan to increase their AI investments. But while nearly all
companies are investing in AI, only 1 percent of leaders call their companies “mature” on the deployment
spectrum, meaning that AI is fully integrated into workflows and drives substantial business outcomes. The
big question is how business leaders can deploy capital and steer their organizations closer to AI maturity.
This research report, prompted by Reid Hoffman’s book Superagency: What Could Possibly Go Right with
Our AI Future,3 asks a similar question: How can companies harness AI to amplify human agency and unlock
new levels of creativity and productivity in the workplace? AI could drive enormous positive and disruptive
change. This transformation will take some time, but leaders must not be dissuaded. Instead, they must
advance boldly today to avoid becoming uncompetitive tomorrow. The history of major economic and
technological shifts shows that such moments can define the rise and fall of companies. Over 40 years ago,
the internet was born. Since then, companies including Alphabet, Amazon, Apple, Meta, and Microsoft have
attained trillion-dollar market capitalizations. Even more profoundly, the internet changed the anatomy of
work and access to information. AI now is like the internet many years ago: The risk for business leaders is
not thinking too big, but rather too small.
1 “Gen AI: A cognitive industrial revolution,” McKinsey, June 7, 2024.
2 “The economic potential of generative AI: The next productivity frontier,” McKinsey, June 14, 2023.
3 Reid Hoffman and Greg Beato, Superagency: What Could Possibly Go Right with Our AI Future, Authors Equity, January 2025.
2 Superagency in the workplace: Empowering people to unlock AI’s full potential
Superagency: By the numbers
Employees are more ready for the change than their leaders imagine
3× 1.4×
more employees are using gen AI more likely for millennials to report
for a third or more of their work extensive familiarity with gen AI tools
than their leaders imagine; than peers in other age groups;
more than 70% of all employees they are also 1.2× more
believe that within 2 years gen AI will likely to expect workflows
change 30% or more of their work to change within a year
Companies need to move fast—employees trust their
leaders to balance speed and safety
of the C-suite say their companies are more likely for employees to trust their
47% 1.3×
developing gen AI tools too slowly, even though own companies to get gen AI deployment right
69% started investing more than a year ago than they are to trust other institutions
Companies are investing in gen AI but have not yet achieved maturity
92% 1%
of companies plan believe their
to invest more investments have
in gen AI over the reached maturity
next 3 years
Leaders need to recognize their responsibility in driving gen AI transformation
2.4× 48%
more likely for C-suite to cite employee readiness as of employees rank training as the
a barrier to adoption vs their own issues with leadership most important factor for gen AI adoption;
alignment, despite employees currently using yet nearly half feel they are receiving
gen AI 3× more than leaders expect moderate or less support
Superagency in the workplace: Empowering people to unlock AI’s full potential 3
This report explores companies’ technology and business readiness for AI adoption (see sidebar “About the
survey”). It concludes that employees are ready for AI. The biggest barrier to success is leadership.
Chapter 1 looks at the rapid advancement of technology over the past two years and its implications for
business adoption of AI.
Chapter 2 delves into the attitudes and perceptions of employees and leaders. Our research shows that
employees are more ready for AI than their leaders imagine. In fact, they are already using AI on a regular
basis; are three times more likely than leaders realize to believe that AI will replace 30 percent of their work
in the next year; and are eager to gain AI skills. Still, AI optimists are only a slight majority in the workplace; a
large minority (41 percent) are more apprehensive and will need additional support. This is where millennials,
who are the most familiar with AI and are often in managerial roles, can be strong advocates for change.
Chapter 3 looks at the need for speed and safety in AI deployment. While leaders and employees want to
move faster, trust and safety are top concerns. About half of employees worry about AI inaccuracy and
cybersecurity risks. That said, employees express greater confidence that their own companies, versus
other organizations, will get AI right. The onus is on business leaders to prove them right, by making bold and
responsible decisions.
Chapter 4 examines how companies risk losing ground in the AI race if leaders do not set bold goals. As the
hype around AI subsides, companies should put a heightened focus on practical applications that empower
employees in their daily jobs. These applications can create competitive moats and generate measurable
ROI. Across industries, functions, and geographies, companies that invest strategically can go beyond using
AI to drive incremental value and instead create transformative change.
Chapter 5 looks at what is required for leaders to set their teams up for success with AI. The challenge of AI
in the workplace is not a technology challenge. It is a business challenge that calls upon leaders to align
teams, address AI headwinds, and rewire their companies for change.
About the survey
To create our report, we surveyed 3,613 employees (managers and independent contributors) and 238
C-level executives in October and November 2024. Of these, 81 percent came from the United States,
and the rest came from five other countries: Australia, India, New Zealand, Singapore, and the United
Kingdom. The employees spanned many roles, including business development, finance, marketing,
product management, sales, and technology.
All the survey findings discussed in the report, aside from two sidebars presenting international nuances,
pertain solely to US workplaces. The findings are organized in this way because the responses from US
employees and C-suite executives provide statistically significant conclusions about the US workplace.
Analyzing global findings separately allows a comparison of differences between US responses and
those from other regions.
4 Superagency in the workplace: Empowering people to unlock AI’s full potential
1
An innovation as
powerful as the
steam engine
About the survey ‘ Scientific discoveries and technological
innovations are stones in the cathedral
of human progress.’
– Reid Hoffman, cofounder of LinkedIn and Inflection AI,
partner at Greylock Partners, and author
Superagency in the workplace: Empowering people to unlock AI’s full potential 5
I
magine a world where machines not only perform physical labor but also think, learn, and make
autonomous decisions. This world includes humans in the loop, bringing people and machines together
in a state of superagency that increases personal productivity and creativity (see sidebar “AI
superagency”). This is the transformative potential of AI, a technology with a potential impact poised to
surpass even the biggest innovations of the past, from the printing press to the automobile. AI does not just
automate tasks but goes further by automating cognitive functions. Unlike any invention before, AI-powered
software can adapt, plan, guide—and even make—decisions. That’s why AI can be a catalyst for
unprecedented economic growth and societal change in virtually every aspect of life. It will reshape
our interaction with technology and with one another.
Many breakthrough technologies, including the internet, smartphones, and cloud computing, have
transformed the way we live and work. AI stands out from these inventions because it offers more than
access to information. It can summarize, code, reason, engage in a dialogue, and make choices. AI can lower
skill barriers, helping more people acquire proficiency in more fields, in any language and at any time. AI
holds the potential to shift the way people access and use knowledge. The result will be more efficient and
effective problem solving, enabling innovation that benefits everyone.
Over the past two years, AI has advanced in leaps and bounds, and enterprise-level adoption has
accelerated due to lower costs and greater access to capabilities. Many notable AI innovations have
emerged (Exhibit 1). For example, we have seen a rapid expansion of context windows, or the short-term
memory of LLMs. The larger a context window, the more information an LLM can process at once. To
illustrate, Google’s Gemini 1.5 could process one million tokens in February 2024, while its Gemini 1.5 Pro
could process two million tokens by June of that same year.4 Overall, we see five big innovations for business
that are driving the next wave of impact: enhanced intelligence and reasoning capabilities, agentic AI,
multimodality, improved hardware innovation and computational power, and increased transparency.
AI superagency
What impact will AI have on humanity? Reid Hoffman and Greg Beato’s book Superagency: What
Could Possibly Go Right with Our AI Future (Authors Equity, January 2025) explores this question.
The book highlights how AI could enhance human agency and heighten our potential. It envisions a
human-led, future-forward approach to AI.
Superagency, a term coined by Hoffman, describes a state where individuals, empowered by AI, super-
charge their creativity, productivity, and positive impact. Even those not directly engaging with AI can
benefit from its broader effects on knowledge, efficiency, and innovation.
AI is the latest in a series of transformative supertools, including the steam engine, internet, and
smartphone, that have reshaped our world by amplifying human capabilities. Like its predecessors, AI
can democratize access to knowledge and automate tasks, assuming humans can develop and deploy
it safely and equitably.
4 The Keyword, “Our next-generation model: Gemini 1.5,” blog entry by Sundar Pichai and Demis Hassabis, Google, February 15, 2024; Google
for Developers, “Gemini 1.5 Pro 2M context window, code execution capabilities, and Gemma 2 are available today,” blog entry by Logan
Kilpatrick, Shrestha Basu Mallick, and Ronen Kofman, June 27, 2024.
6 Superagency in the workplace: Empowering people to unlock AI’s full potential
Web <2025>
<Superagency>
Exhibit 1
Exhibit <1> of <21>
Gen AI capabilities have evolved rapidly over the past two years.
Illustrative capabilities of gen AI platforms from select frontier labs, nonexhaustive
2022–231 Jan 20252
Anthropic
Google
Gemini
Meta
Microsoft
OpenAI
AI superagency
Note: Exhibit is not intended as an evaluation or comparison but as an illustration of the rapid progress in capabilities.
1Initial models released between Mar 2022 and Mar 2023.
2Latest models released between Nov and Dec 2024.
Source: Company websites and press releases; McKinsey analysis
McKinsey & Company
Superagency in the workplace: Empowering people to unlock AI’s full potential 7
Intelligence and reasoning are improving
AI is becoming far more intelligent. One indicator is the performance of LLMs on standardized tests.
OpenAI’s Chat GPT-3.5, introduced in 2022, demonstrated strong performance on high-school-level exams
(for example, scoring in the 70th percentile on the SAT math and the 87th percentile on the SAT verbal
sections). However, it often struggled with broader reasoning. Today’s models are near the intelligence level
of people who hold advanced degrees. GPT-4 can so easily pass the Uniform Bar Examination that it would
rank in the top 10 percent of test takers,5 and it can answer 90 percent of questions correctly on the US
Medical Licensing Examination.6
The advent of reasoning capabilities represents the next big leap forward for AI. Reasoning enhances AI’s
capacity for complex decision making, allowing models to move beyond basic comprehension to nuanced
understanding and the ability to create step-by-step plans to achieve goals. For businesses, this means they
can fine-tune reasoning models and integrate them with domain-specific knowledge to deliver actionable
insights with greater accuracy. Models such as OpenAI’s o1 or Google’s Gemini 2.0 Flash Thinking Mode
are capable of reasoning in their responses, which gives users a human-like thought partner for their
interactions, not just an information retrieval and synthesis engine.7
Agentic AI is acting autonomously
The ability to reason is growing more and more, allowing models to autonomously take actions and complete
complex tasks across workflows. This is a profound step forward. As an example, in 2023, an AI bot could
support call center representatives by synthesizing and summarizing large volumes of data—including voice
messages, text, and technical specifications—to suggest responses to customer queries. In 2025, an AI
agent can converse with a customer and plan the actions it will take afterward—for example, processing a
payment, checking for fraud, and completing a shipping action.
‘I’ve always thought of AI as the most
profound technology humanity is
working on . . . more profound than fire
or electricity or anything that we’ve
done in the past.’
– Sundar Pichai, CEO of Alphabet
5 GPT-4 technical report, OpenAI, March 27, 2023.
6 Dana Brin, Vera Sorin, Akhil Vaid, et al., “Comparing ChatGPT and GPT-4 performance in USMLE soft skill assessments,” Scientific Reports,
October 1, 2023.
7 “Learning to reason with LLMs,” OpenAI, September 12, 2024; “Gemini 2.09 Flash Thinking Mode,” Google, January 21, 2025.
8 Superagency in the workplace: Empowering people to unlock AI’s full potential
‘AI, like most transformative technologies,
grows gradually, then arrives suddenly.’
– Reid Hoffman, cofounder of LinkedIn and Inflection AI,
partner at Greylock Partners, and author
Software companies are embedding agentic AI capabilities into their core products. For example,
Salesforce’s Agentforce is a new layer on its existing platform that enables users to easily build and
deploy autonomous AI agents to handle complex tasks across workflows, such as simulating product
launches and orchestrating marketing campaigns.8 Marc Benioff, Salesforce cofounder, chair, and CEO,
describes this as providing a “digital workforce” where humans and automated agents work together to
achieve customer outcomes.9
Multimodality is bringing together text, audio, and video
Today’s AI models are evolving toward more advanced and diverse data processing capabilities across text,
audio, and video. Over the last two years, we have seen improvements in the quality of each modality. For
example, Google’s Gemini Live has improved audio quality and latency and can now deliver a human-like
conversation with emotional nuance and expressiveness.10 Also, demonstrations of Sora by OpenAI show its
ability to translate text to video.11
Hardware innovation is enhancing performance
Hardware innovation and the resulting increase in compute power continue to enhance AI performance.
Specialized chips allow faster, larger, and more versatile models. Enterprises can now adopt AI solutions that
require high processing power, enabling real-time applications and opportunities for scalability. For
example, an e-commerce company could significantly improve customer service by implementing AI-driven
chatbots that leverage advanced graphics processing units (GPUs) and tensor processing units (TPUs).
Using distributed cloud computing, the company could ensure optimal performance during peak traffic
periods. Integrating edge hardware, the company could deploy models that analyze photos of damaged
products to more accurately process insurance claims.
8 Sammy Spiegel, “The future of AI agents: Top predictions and trends to watch in 2025,” Salesforce, December 2024.
9 Marc Benioff, “How the rise of new digital workers will lead to an unlimited age,” Time, November 25, 2024.
10 Ivan Solovyev and Shrestha Basu Mallick, “Gemini 2.0: Level up your apps with real-time multimodal interactions,” Google, December 23, 2024.
11 “OpenAI releases AI video generator Sora but limits how it depicts people,” Associated Press, December 10, 2024.
Superagency in the workplace: Empowering people to unlock AI’s full potential 9
2
Transparency is increasing
AI is gradually becoming less risky, but it still lacks greater transparency and explainability. Both are critical
for improving AI safety and reducing the potential for bias, which are imperative for widescale enterprise
deployment. There is still a long way to go, but new models and iterations are rapidly improving. Stanford
University’s Center for Research on Foundation Models (CRFM) reports significant advances in model
performance. Its Transparency Index, which uses a scale of 1 to 100, shows that Anthropic’s transparency
score increased by 15 points to 51 and Amazon’s more than tripled to 41 between October 2023 and
May 2024.12
Beyond LLMs, other forms of AI and machine learning (ML) are improving explainability, allowing the outputs
of models that support consequential decisions (for example, credit risk assessment) to be traced back to
the data that informed them. In this way, critical systems can be tested and monitored on a near-constant
basis for bias and other everyday harms that arise from model drift and shifting data inputs, which happens
even in systems that were well calibrated before deployment.
All of this is crucial for detecting errors and ensuring compliance with regulations and company policies.
Companies have improved explainability practices and built necessary checks and balances, but they must
be prepared to evolve continuously to keep up with growing model capabilities.
Achieving AI superagency in the workplace is not simply about mastering technology. It is every bit as much
about supporting people, creating processes, and managing governance. The next chapters explore the
nontechnological factors that will help shape the deployment of AI in the workplace.
12 “The Foundation Model Transparency Index,” Stanford Center for Research on Foundation Models, May 2024.
10 Superagency in the workplace: Empowering people to unlock AI’s full potential
2
Employees are ready
for AI; now leaders
must step up
‘People are using [AI] to create amazing
things. If we could see what each of us
can do 10 or 20 years in the future, it
would astonish us today.’
– Sam Altman, cofounder and CEO of OpenAI
Superagency in the workplace: Empowering people to unlock AI’s full potential 11
E
mployees will be the ones to make their organizations AI powerhouses. They are more ready to
embrace AI in the workplace than business leaders imagine. They are more familiar with AI tools,
they want more support and training, and they are more likely to believe AI will replace at least a
third of their work in the near future. Now it’s imperative that leaders step up. They have more
permission space than they realize, so it’s on them to be bold and capture the value of AI. Now.
Beyond the tipping point
In our survey, nearly all employees (94 percent) and C-suite leaders (99 percent) report having some level of
familiarity with gen AI tools. Nevertheless, business leaders underestimate how extensively their employees
are using gen AI. C-suite leaders estimate that only 4 percent of employees use gen AI for at least 30
percent of their daily work, when in fact that percentage is three times greater, as self-reported by
employees (Exhibit 2). And while only a total of 20 percent of leaders believe employees will use gen AI for
more than 30 percent of their daily tasks within a year, employees are twice as likely (47 percent) to believe
they will (see sidebar “Who is using AI at work? Nearly everyone, even skeptical employees”).
The good news is that our survey suggests three ways companies can accelerate AI adoption and move
toward AI maturity.
Web <2025>
<ESxuhpeibraigt e2ncy>
Exhibit <2> of <21>
Employees are three times more likely to be using gen AI today than their
leaders expect.
US employees’ and C-suite’s C-suite Employees
timeline for employees using Already using 4
3×
gen AI for >30% of daily tasks, % 13
of respondents Less than a year 16
34
1–5 years 56
37
Over 5 years 11
5
Don’t anticipate it 10 7
Not sure 3 4
Note: Figures may not sum to 100%, because of rounding.
Source: McKinsey US CxO survey, Oct–Nov 2024 (n = 118) ; McKinsey US employee survey, Oct–Nov 2024 (n = 3,002)
McKinsey & Company
12 Superagency in the workplace: Empowering people to unlock AI’s full potential
Our research looked at people who self-identify as “Zoomers,” “Bloomers,” “Gloomers,” and “Doomers” in their
attitudes toward AI—a set of archetypes introduced in Superagency. We find that 39 percent of employees
identify as Bloomers, who are AI optimists that want to collaborate with their companies to create responsible
solutions. Meanwhile, 37 percent identify as Gloomers, who are more skeptical about AI and want extensive
top-down AI regulations; 20 percent identify as Zoomers, who want AI to be quickly deployed with few guardrails;
and just 4 percent identify as Doomers, who have a fundamentally negative view of AI (exhibit).
Even those with a skeptical take on AI are familiar with it; 94 percent of Gloomers and 71 percent of Doomers say
they have some familiarity with gen AI tools. Furthermore, approximately 80 percent of Gloomers and about half
of Doomers say they are comfortable using gen AI at work.
Web <2025>
<Superagency>
Exhibit
Exhibit <3> of <21>
Employee segments differ, but all indicate a high familiarity with gen AI.
US employee sentiment on gen AI, by archetype, % of respondents
Doomer Gloomer Bloomer Zoomer
Gen AI will not align Above all else, gen AI Gen AI needs to be Gen AI development
with human values, needs to be closely developed iteratively should be trusted
regardless of monitored and with a diverse range to developers to
deployment method controlled of inputs maximize speed
Has extensive
familiarity
with gen AI1 16 42 55 67
Has at least
some familiarity
with gen AI2 71 94 96 96
Is comfortable using
results from gen AI 47 79 91 91
Believes gen AI will
have a net benefit in
the next 5 years 54 82 89 87
Plans to use gen AI
more in their
personal life 49 77 86 85
Expects 30% of
workflows to change
in the next year 19 38 50 64
Share of respondents
4 37 39 20
in archetype group, %
1Defined as those who have “extensive experience (use several tools for complex tasks)” and “experts.”
2Defined as those who have “some familiarity (use 1–2 tools a few times)” and “extensive experience (use several tools for complex tasks)” and “experts.”
Source: McKinsey US employee survey, Oct–Nov 2024 (n = 3,002)
McKinsey & Company
Superagency in the workplace: Empowering people to unlock AI’s full potential 13
Leaders can invest more in their employees
As noted at the beginning of this chapter, employees anticipate AI will have a dramatic impact on their work.
Now they would like their companies to invest in the training that will help them succeed. Nearly half of
employees in our survey say they want more formal training and believe it is the best way to boost AI
adoption. They also would like access to AI tools in the form of betas or pilots, and they indicate that
incentives such as financial rewards and recognition can improve uptake.
Yet employees are not getting the training and support they need. More than a fifth report that they have
received minimal to no support (Exhibit 3). Outside the United States, employees also want more training
(see sidebar “Global perspectives on training”).
Web <2025>
<ESxuhpeibraigt e3ncy>
Exhibit <4> of <21>
Employees long for more support and training on gen AI.
Share of US employees agreeing that a company initiative would make them more likely to
increase day-to-day usage of gen AI tools, %
Formal gen AI training from
48
my organization
Seamless integration into my
45
existing workflow
Access to gen AI tools 41
Incentives and rewards 40
Usage of gen AI being a requirement
30
for a certification program
Explicit instructions from my managers
30
to use gen AI
Being involved in the development
29
of the tools
OKRs¹/KPIs tied to gen AI usage 22
US employees’ perceived level of support for gen AI capability building at their organizations,
% of respondents
Not None/ Moderate to Fully
needed minimal significant supported
Current 6 22 44 29
In 3 years 4 10 56 31
Note: Figures do not sum to 100%, because of rounding.
¹Objectives and key results.
Source: McKinsey US employee survey, Oct–Nov 2024 (n = 3,002)
McKinsey & Company
14 Superagency in the workplace: Empowering people to unlock AI’s full potential
Global perspectives on training
To get a clearer picture of global AI adoption trends, we looked at trends across five countries: Australia, India, New
Zealand, Singapore, and the United Kingdom. Broadly speaking, these employees and C-suite leaders—the “interna-
tional” group in this report—have similar views of AI as their US peers. In some key areas, however, including the topic
of training, their experiences differ.
Web <2025>
<Superagency>
Exhibit <5> of <21>
Many international employees are concerned about insufficient training, even though they report receiving far more
support than US employees. Some 84 percent of international employees say they receive significant or full organiza-
tional support to learn AI skills, versus just over half of US employees. International employees also have more
opportunities to participate in developing gen AI tools at work than their US counterparts, with differences of at least
ten percentage points in activities such as providing feedback, beta testing, and requesting specific features (exhibit).
Exhibit
International employees get more encouragement to use gen AI tools.
Sources encouraging employees’ use of gen AI tools at work, % of respondents reporting practice in
place at their organization
Australia and New Zealand India Singapore UK US
0 20 40 60 80 100
Use is mandated
Manager
Manager other than own
Peers
C-suite leadership
Developer of AI tool
Generic communications
Have not been encouraged
Employee involvement in developing gen AI tools, % of respondents
Australia and New Zealand India Singapore UK US
0 20 40 60 80 100
Provide feedback
in tool itself
Provide feedback via
other channels
Beta testing or
pilot program
Submit specific requests
for features
Not involved
Source: McKinsey international employee survey, Oct–Nov 2024 (Australia and New Zealand, n = 139; India, n = 134; Singapore, n = 140; UK, n = 201) ; McKinsey
US employee survey, Oct–Nov 2024 (n = 3,002)
McKinsey & Company
Superagency in the workplace: Empowering people to unlock AI’s full potential 15
C-suite leaders can help millennials lead the way
Many millennials aged 35 to 44 are managers and team leaders in their companies. In our survey, they self-
report having the most experience and enthusiasm about AI, making them natural champions of
transformational change. Millennials are the most active generation of AI users. Some 62 percent of 35- to
44-year-old employees report high levels of expertise with AI, compared with 50 percent of 18- to 24-year-
old Gen Zers and 22 percent of baby boomers over 65 (Exhibit 4). By tapping into that enthusiasm and
expertise, leaders can help millennials play a crucial role in AI adoption.
Web <2025>
<ESxuhpeibraigt e4ncy>
Exhibit <6> of <21>
Millennials aged 35 to 44 are AI optimists, with 90 percent indicating
confidence in their gen AI abilities.
US employee sentiment on gen AI, by age group, % of respondents
18–24 25–34 35–44 45–54 55–64 65+
Has extensive
familiarity
with gen AI1
50 49 62 47 26 22
Is comfortable using
gen AI at work
80 87 90 82 70 71
Provides feedback
on gen AI tools
76 77 76 65 47 55
Wants to participate
in the design of
gen AI tools
70 76 81 77 73 76
1Defined as those who have “extensive experience (use several tools for complex tasks)” and “experts.”
Source: McKinsey US employee survey, Oct–Nov 2024 (n = 3,002)
McKinsey & Company
16 Superagency in the workplace: Empowering people to unlock AI’s full potential
Web <2025>
E<Sxuhpiebraitg e5ncy>
Exhibit <7> of <21>
Two-thirds of managers regularly act as sounding boards for their teams on
gen AI.
Frequency of team inquiries about using new gen AI tools at work, % of US manager respondents
(n = 1,440)
Less than Once a Once a A few Once Multiple
quarterly Quarterly month week times a week a day times a day
10 5 5 12 15 28 9 16
Not at all
Use of gen AI tools to resolve a team member’s challenge, % of US manager respondents (n = 1,440)
Recommended Gen AI tool was
gen AI tool to successful in
solve team member’s resolving team
challenge in member’s challenge
the past month 86
68
Source: McKinsey US employee survey, Oct–Nov 2024 (n = 3,002)
McKinsey & Company
Since many millennials are managers, they can support their teams to become more adept AI users. This
helps push their companies toward AI maturity. Two-thirds of managers say they field questions from their
team about how to use AI tools at least once a week, and a similar percentage say they recommend AI tools
to their teams to solve problems (Exhibit 5).
Since leaders have the permission space, they can be bolder
In many transformations, employees are not ready for change, but AI is different. Employee readiness and
familiarity are high, which gives business leaders the permission space to act. Leaders can listen to
employees describe how they are using AI today and how they envision their work being transformed. They
also can provide employees with much-needed training and empower managers to move AI use cases from
pilot to scale.
It’s critical that leaders meet this moment. It’s the only way to accelerate the probability that their companies
will reach AI maturity. But they must move with alacrity, or they will fall behind.
Superagency in the workplace: Empowering people to unlock AI’s full potential 17
3
Delivering
speed and safety
‘Soon after the first automobiles were
on the road, there was the first car crash.
But we didn’t ban cars—we adopted
speed limits, safety standards, licensing
requirements, drunk-driving laws,
and other rules of the road.’
– Bill Gates, cofounder of Microsoft
18 Superagency in the workplace: Empowering people to unlock AI’s full potential
A
I technology is advancing at record speed. ChatGPT was released about two years ago; OpenAI
reports that usage now exceeds 300 million weekly users13 and that over 90 percent of Fortune
500 companies employ its technology.14 The internet did not reach this level of usage until the
early 2000s, nearly a decade after its inception.
The majority of employees describe themselves as AI optimists; Zoomers and Bloomers make up 59 percent
of the workplace. Even Gloomers, who are one of the two less-optimistic segments in our analysis, report
high levels of gen AI familiarity, with over a quarter saying they plan to use AI more next year.
Business leaders need to embrace this speed and optimism to ensure that their companies don’t get left
behind. Yet despite all the excitement and early experimentation, 47 percent of C-suite leaders say their
organizations are developing and releasing gen AI tools too slowly, citing talent skill gaps as a key reason for
the delay (Exhibit 6).
Web <2025>
<ESxuhpeibraigt e6ncy>
Exhib"
59,mckinsey,AI-bank-of-the-future-Can-banks-meet-the-AI-challenge.pdf,"Global Banking & Securities
AI-bank of the future: Can
banks meet the AI challenge?
Artificial intelligence technologies are increasingly integral to world we live
in, and banks need to deploy these technologies at scale to remain relevant.
Success requires a holistic transformation spanning multiple layers of the
organization.
by Suparna Biswas, Brant Carson, Violet Chung, Shwaitang Singh, and Renny Thomas
© Getty Images
September 2020
In 2016, AlphaGo, a machine, defeated 18-time 3. What obstacles prevent banks from deploying
world champion Lee Sedol at the game of AI capabilities at scale?
Go, a complex board game requiring intuition,
imagination, and strategic thinking—abilities 4. How can banks transform to become AI-first?
long considered distinctly human. Since then,
artificial intelligence (AI) technologies have
advanced even further,¹ and their transformative 1. Why must banks become AI-first?
impact is increasingly evident across Over several decades, banks have continually
industries. AI-powered machines are tailoring adapted the latest technology innovations to
recommendations of digital content to individual redefine how customers interact with them. Banks
tastes and preferences, designing clothing introduced ATMs in the 1960s and electronic,
lines for fashion retailers, and even beginning to card-based payments in the ’70s. The 2000s saw
surpass experienced doctors in detecting signs of broad adoption of 24/7 online banking, followed
cancer. For global banking, McKinsey estimates by the spread of mobile-based “banking on the go”
that AI technologies could potentially deliver up to in the 2010s.
$1 trillion of additional value each year.²
Few would disagree that we’re now in the
Many banks, however, have struggled to move AI-powered digital age, facilitated by falling costs
from experimentation around select use cases to for data storage and processing, increasing
scaling AI technologies across the organization. access and connectivity for all, and rapid
Reasons include the lack of a clear strategy for AI, advances in AI technologies. These technologies
an inflexible and investment-starved technology can lead to higher automation and, when deployed
core, fragmented data assets, and outmoded after controlling for risks, can often improve upon
operating models that hamper collaboration human decision making in terms of both speed
between business and technology teams. What and accuracy. The potential for value creation
is more, several trends in digital engagement is one of the largest across industries, as AI can
have accelerated during the COVID-19 pandemic, potentially unlock $1 trillion of incremental value
and big-tech companies are looking to enter for banks, annually (Exhibit 1).
financial services as the next adjacency. To
compete successfully and thrive, incumbent Across more than 25 use cases,³ AI technologies
banks must become “AI-first” institutions, can help boost revenues through increased
adopting AI technologies as the foundation for personalization of services to customers (and
new value propositions and distinctive customer employees); lower costs through efficiencies
experiences. generated by higher automation, reduced errors
rates, and better resource utilization; and uncover
In this article, we propose answers to four new and previously unrealized opportunities
questions that can help leaders articulate a clear based on an improved ability to process and
vision and develop a road map for becoming an generate insights from vast troves of data.
AI-first bank:
More broadly, disruptive AI technologies can
1. Why must banks become AI-first? dramatically improve banks’ ability to achieve
four key outcomes: higher profits, at-scale
2. What might the AI-bank of the future look like? personalization, distinctive omnichannel
1 AI can be defined as the ability of a machine to perform cognitive functions associated with human minds (e.g., perceiving, reasoning, learning, and
problem solving). It includes various capabilities, such as machine learning, facial recognition, computer vision, smart robotics, virtual agents, and
autonomous vehicles. See “Global AI Survey: AI proves its worth, but few scale impact,” November 2019, McKinsey.com.
2 “The executive’s AI playbook,” McKinsey.com.
3 For an interactive view, visit: www.mckinsey.com/business-functions/mckinsey-analytics/our-insights/the-executives-ai-
playbook?page=industries/banking/
2
Exhibit 1
PPootetnetnitaila al nannunaula vl avlauleu oef o Af IA aIn adn adn aanlyatliyctsi cfosr f oglro gblaolb baaln bkainnkgi cnogu cldo urelda crhe aacs hh iagsh as
$h1i gtrhil aliso n$.1 trillion.
Total potential annual value, $ billion
1,022.4 (15.4% of sales)
Traditional AI
Advanced AI
and analytics
660.9 361.5
% of value driven by advanced AI, by function
100
Finance and IT: 8.0 Other operations: $2.4 B
0.0 8.0 0.0 2.4
50
HR: 14.2
8.6 5.7
Marketing and sales: 624.8
Risk: 372.9
363.8 261.1
288.6 84.3
0
Source: ""The executive's AI playbook,"" McKinsey.com. (See ""Banking,"" under ""Value & Assess."")
experiences, and rapid innovation cycles. Banks As consumers increase their use of digital
that fail to make AI central to their core strategy banking services, they grow to expect more,
and operations—what we refer to as becoming particularly when compared to the standards
“AI-first”—will risk being overtaken by competition they are accustomed to from leading consumer-
and deserted by their customers. This risk is internet companies. Meanwhile, these digital
further accentuated by four current trends: experience leaders continuously raise the bar
on personalization, to the point where they
— Rising customer expectations as adoption sometimes anticipate customer needs before
of digital banking increases. In the first few the customer is aware of them, and offer highly-
months of the COVID-19 pandemic, use of tailored services at the right time, through the
online and mobile banking channels across right channel.
countries has increased by an estimated 20
to 50 percent and is expected to continue at — Leading financial institutions’ use of advanced
this higher level once the pandemic subsides. AI technologies is steadily increasing. Nearly
Across diverse global markets, between 15 and 60 percent of financial-services sector
45 percent of consumers expect to cut back respondents in McKinsey’s Global AI Survey
on branch visits following the end of the crisis.⁴ report⁵ that their companies have embedded
4 John Euart, Nuno Ferreira, Jonathan Gordon, Ajay Gupta, Atakan Hilal, Olivia White, “A global view of financial life during COVID-19—an update,”
July 2020, McKinsey.com.
5 Arif Cam, Michael Chui, Bryce Hall, “Global AI Survey: AI proves its worth, but few scale impact,” November 2019, McKinsey.com.
AI-bank of the future: Can banks meet the AI challenge? 3
at least one AI capability. The most commonly but also to book a cab, order food, schedule
used AI technologies are: robotic process a massage, play games, send money to a
automation (36 percent) for structured contact, and access a personal line of credit.
operational tasks; virtual assistants or Similarly, across countries, nonbanking
conversational interfaces (32 percent ) for businesses and “super apps” are embedding
customer service divisions; and machine financial services and products in their
learning techniques (25 percent) to detect journeys, delivering compelling experiences
fraud and support underwriting and risk for customers, and disrupting traditional
management. While for many financial services methods for discovering banking products and
firms, the use of AI is episodic and focused on services. As a result, banks will need to rethink
specific use cases, an increasing number of how they participate in digital ecosystems,
banking leaders are taking a comprehensive and use AI to harness the full power of data
approach to deploying advanced AI, and available from these new sources.
embedding it across the full lifecycle, from the
front- to the back-office (Exhibit 2). — Technology giants are entering financial
services as the next adjacency to their
— Digital ecosystems are disintermediating core business models. Globally, leading
traditional financial services. By enabling technology giants have built extraordinary
access to a diverse set of services through market advantages: a large and engaged
a common access point, digital ecosystems customer network; troves of data, enabling a
have transformed the way consumers discover, robust and increasingly precise understanding
evaluate, and purchase goods and services. of individual customers; natural strengths
For example, WeChat users in China can use in developing and scaling innovative
the same app not only to exchange messages, technologies (including AI); and access to
Web <year>
<article slug>
EExxhhibiibt <itx 2> of <y>
Banks are expanding their use of AI technologies to improve customer
Banks are expanding their use of AI technologies to improve customer
experiences and back-office processes.
experiences and back-office processes.
Front office Back office
Smile-to-pay facial scanning Micro-expression analysis Biometrics (voice, video, Machine learning to detect
to initiate transaction with virtual loan officers print) to authenticate and fraud patterns,
authorize cybersecurity attacks
Conversational bots for Humanoid robots in branches Machine vision and natural- Real-time transaction
basic servicing requests to serve customers language processing to scan analysis for risk monitoring
and process documents
4 AI-bank of the future: Can banks meet the AI challenge?
low-cost capital. In the past, tech giants have digital era, the AI-first bank will offer propositions
aggressively entered into adjacent businesses and experiences that are intelligent (that
in search of new revenue streams and to is, recommending actions, anticipating and
keep customers engaged with a fresh stream automating key decisions or tasks), personalized
of offerings. Big-tech players have already (that is, relevant and timely, and based on a
gained a foothold in financial services in select detailed understanding of customers’ past
domains (especially in payments and, in some behavior and context), and truly omnichannel
cases, lending and insurance), and they may (seamlessly spanning the physical and online
soon look to press their advantages to deepen contexts across multiple devices, and delivering
their presence and build greater scale. a consistent experience) and that blend banking
capabilities with relevant products and services
beyond banking. Exhibit 3 illustrates how such a
2. What might the AI-bank of the bank could engage a retail customer throughout
future look like? the day. Exhibit 4 shows an example of the banking
To meet customers’ rising expectations and experience of a small-business owner or the
beat competitive threats in the AI-powered treasurer of a medium-size enterprise.
Exhibit 3
How AI transforms banking for a retail customer.
How AI transforms banking for a retail customer.
Name: Anya
Age: 28 years
Occupation: Working professional
Anya receives
App offers money- integrated portfolio
management and view and a set of
Anya uses smile- savings solutions, actions with the
Seamless to-pay to Analytics- prioritizes card Aggregated potential to
integration with initiate payment backed payments overview of daily augment returns
nonbanking apps personalized offers
activities
Bank app Facial recognition Anya gets 2% off Personalized Anya receives Savings and
investment recom-
recognizes Anya's for frictionless on health money-management end-of-day
mendations
spending patterns payment insurance solutions overview of her
and suggests premiums based activities, with
coffee at nearby on her gym augmented reality,
cafes activity and and reminders to
sleep habits pay bills
Intelligent Personalized Omnichannel Banking and beyond banking
AI-bank of the future: Can banks meet the AI challenge? 5
Exhibit 4
How AI transforms banking for a small- or medium-size-enterprise customer.
How AI transforms banking for a small- or medium-size-enterprise customer.
Name: Dany
Age: 36 years
Occupation: Treasurer of a small manufacturing unit
Dany answers
short questionnaire;
app scans his facial
An AI-powered
movements
Dany is assisted virtual adviser
Firm is credited in sourcing and resolves queries
with funds after selecting the Dany seeks
Customized application Seamless right vendors Beyond- professional advice
lending solutions approval inventory and receiv- and partners banking support on a lending offer
ables management services
Bank is integrated Micro-expression App suggests SME platform to Dany gets prefilled Serviced by an AI-
with client analysis to review loan items to reorder, source suppliers tax documents to powered virtual
business applications gives visual reports and buyers review and adviser
management on receivables approve; files with
systems management a single click
Dany receives
Dany gets loan
customized
offer based on
solutions for
company projected
invoice discounting,
cash flows
factoring, etc.
Intelligent Personalized Omnichannel Banking and beyond banking
Internally, the AI-first institution will be optimized The AI-first bank of the future will also enjoy
for operational efficiency through extreme the speed and agility that today characterize
automation of manual tasks (a “zero-ops” mindset) digital-native companies. It will innovate
and the replacement or augmentation of human rapidly, launching new features in days or
decisions by advanced diagnostic engines in weeks instead of months. It will collaborate
diverse areas of bank operations. These gains extensively with partners to deliver new
in operational performance will flow from broad value propositions integrated seamlessly
application of traditional and leading-edge AI across journeys, technology platforms, and
technologies, such as machine learning and data sets.
facial recognition, to analyze large and complex
reserves of customer data in (near) real time.
6 AI-bank of the future: Can banks meet the AI challenge?
cases. Without a centralized data backbone, it is
3. What obstacles prevent banks from
practically impossible to analyze the relevant data
deploying AI capabilities at scale?
and generate an intelligent recommendation or
Incumbent banks face two sets of objectives,
offer at the right moment. If data constitute the
which on first glance appear to be at odds. On
bank’s fundamental raw material, the data must be
the one hand, banks need to achieve the speed,
governed and made available securely in a manner
agility, and flexibility innate to a fintech. On the
that enables analysis of data from internal and
other, they must continue managing the scale,
external sources at scale for millions of customers,
security standards, and regulatory requirements
in (near) real time, at the “point of decision” across
of a traditional financial-services enterprise.
the organization. Lastly, for various analytics and
advanced-AI models to scale, organizations need
Despite billions of dollars spent on change-
a robust set of tools and standardized processes
the-bank technology initiatives each year, few
to build, test, deploy, and monitor models, in a
banks have succeeded in diffusing and scaling
repeatable and “industrial” way.
AI technologies throughout the organization.
Among the obstacles hampering banks’ efforts,
Banks’ traditional operating models further
the most common is the lack of a clear strategy
impede their efforts to meet the need for
for AI.⁶ Two additional challenges for many
continuous innovation. Most traditional banks
banks are, first, a weak core technology and data
are organized around distinct business lines,
backbone and, second, an outmoded operating
with centralized technology and analytics
model and talent strategy.
teams structured as cost centers. Business
owners define goals unilaterally, and alignment
Built for stability, banks’ core technology
with the enterprise’s technology and analytics
systems have performed well, particularly in
strategy (where it exists) is often weak or
supporting traditional payments and lending
inadequate. Siloed working teams and “waterfall”
operations. However, banks must resolve
implementation processes invariably lead
several weaknesses inherent to legacy systems
to delays, cost overruns, and suboptimal
before they can deploy AI technologies at scale
performance. Additionally, organizations lack
(Exhibit 5). First and foremost, these systems
a test-and-learn mindset and robust feedback
often lack the capacity and flexibility required
loops that promote rapid experimentation and
to support the variable computing requirements,
iterative improvement. Often unsatisfied with the
data-processing needs, and real-time analysis
performance of past projects and experiments,
that closed-loop AI applications require.⁷ Core
business executives tend to rely on third-party
systems are also difficult to change, and their
technology providers for critical functionalities,
maintenance requires significant resources.
starving capabilities and talent that should ideally
What is more, many banks’ data reserves are
be developed in-house to ensure competitive
fragmented across multiple silos (separate
differentiation.
business and technology teams), and analytics
efforts are focused narrowly on stand-alone use
6 Michael Chui, Sankalp Malhotra, “AI adoption advances, but foundational barriers remain,” November 2018, McKinsey.com.
7 “Closed loop” refers to the fact that the models’ intelligence is applied to incoming data in near real time, which in turn refines the content presented
to the user in near real time.
AI-bank of the future: Can banks meet the AI challenge? 7
Exhibit 5
IInnvveessttmmeentnst sin i nco croer tee cthe cahre a crrei tcicraitli ctoa lm teoe mt ienecrte ianscinrega dseinmga nddesm foarn ds for
ssccaalalabbiliiltiyty, ,fl flexeixbiibliitliyt,y a, nadn dsp sepede.e d.
Cloud
Data API1
Challenges How cloud computing can help
Core/legacy systems can’t scale sufficiently Enables higher scalability, resilience of services and
(eg, 150+ transactions/second) platforms through virtualization of infrastructure
Significant time, effort, and team sizes Reduces IT overhead, enables automation of several
required to maintain infrastructure infrastructure-management tasks, and allows development
teams to “self-serve”
Long time required to provision environments
for development and testing (eg, 40+ days in Enables faster time to market; dramatically reduces time by
some cases) providing managed services (e., setting up new environments
in minutes vs days)
Challenges How best-in-class data management can help
High error rates; poor refresh rates; lack of Ensures high degree of accuracy and single source of truth
golden source of truth in a cost-effective manner
Hard to access in a timely fashion for various Enables timely and role-appropriate access for various use
use cases cases (eg, regulatory, business intelligence at scale, advanced
analytics and machine learning, exploratory)
Data trapped in silos across multiple units and
hard to integrate with external sources Enables a 360-degree view across the organization to enable
generation of deeper insights by decision-making algorithms
and models
Challenges How APIs can help
Longer time to market, limited reusability of Promote reusability and accelerate development by enabling
code and software across internal teams access to granular services (internal and external)
Hard to partner or collaborate with external Reduce complexity and enable faster collaboration with
partners; long time to integrate external partners
Suboptimal user experience—hard to stitch Enhance customer experience by enabling timely access to
data and services across multiple functional data and services across different teams; faster time to market
siloes for an integrated proposition due to limited coordination, cross-team testing
1Application programming interface.
8 AI-bank of the future: Can banks meet the AI challenge?
4. How can banks transform to First, banks will need to move beyond highly
become AI-first? standardized products to create integrated
To overcome the challenges that limit propositions that target “jobs to be done.”⁸ This
organization-wide deployment of AI requires embedding personalization decisions
technologies, banks must take a holistic (what to offer, when to offer, which channel
approach. To become AI-first, banks must invest to offer) in the core customer journeys and
in transforming capabilities across all four layers designing value propositions that go beyond the
of the integrated capability stack (Exhibit 6): the core banking product and include intelligence
engagement layer, the AI-powered decisioning that automates decisions and activities on
layer, the core technology and data layer, and the behalf of the customer. Further, banks should
operating model. strive to integrate relevant non-banking
products and services that, together with the
core banking product, comprehensively address
As we will explain, when these interdependent
the customer end need. An illustration of the
layers work in unison, they enable a bank to
“jobs-to-be-done” approach can be seen in the
provide customers with distinctive omnichannel
way fintech Tally helps customers grapple with
experiences, support at-scale personalization,
the challenge of managing multiple credit cards.
and drive the rapid innovation cycles critical
The fintech’s customers can solve several pain
to remaining competitive in today’s world.
points—including decisions about which card to
Each layer has a unique role to play—under-
pay first (tailored to the forecast of their monthly
investment in a single layer creates a weak link
income and expenses), when to pay, and how
that can cripple the entire enterprise.
much to pay (minimum balance versus retiring
principal)—a complex set of tasks that are often
The following paragraphs explore some of the
not done well by customers themselves.
changes banks will need to undertake in each
layer of this capability stack.
The second necessary shift is to embed
customer journeys seamlessly in partner
Layer 1: Reimagining the customer
ecosystems and platforms, so that banks
engagement layer
engage customers at the point of end use and
Increasingly, customers expect their bank to be
in the process take advantage of partners’
present in their end-use journeys, know their
data and channel platform to increase higher
context and needs no matter where they interact
engagement and usage. ICICI Bank in India
with the bank, and to enable a frictionless
embedded basic banking services on WhatsApp
experience. Numerous banking activities
(a popular messaging platform in India) and
(e.g., payments, certain types of lending) are
scaled up to one million users within three
becoming invisible, as journeys often begin and
months of launch.⁹ In a world where consumers
end on interfaces beyond the bank’s proprietary
and businesses rely increasingly on digital
platforms. For the bank to be ubiquitous in
ecosystems, banks should decide on the
customers’ lives, solving latent and emerging
posture they would like to adopt across multiple
needs while delivering intuitive omnichannel
ecosystems—that is, to build, orchestrate, or
experiences, banks will need to reimagine how
partner—and adapt the capabilities of their
they engage with customers and undertake
engagement layer accordingly.
several key shifts.
8 Clayton M. Christensen, Taddy Hall, Karen Dillon and David S. Duncan, “Know your customers ‘jobs to be done,” Harvard Business Review,
September 2016, hbr.org.
9 “ICICI Bank crosses 1 million users on WhatsApp platform,” Live Mint, July 7, 2020, livemint.com.
AI-bank of the future: Can banks meet the AI challenge? 9
Exhibit 6
TToo b beeccoomme ean a nA IA-fiI-rfistr sitn sintistutittiuotni,o an b, aan bka mnku smt ustsrte asmtrelianme iltisn eca iptsa bcialiptya bstialictky sfotra ck
vfaolru ve aclrueea tciorena. tion.
AI bank of the future
Personalization Omnichannel Speed and
Profitability
at scale experience innovation
Intelligent products, Within-bank channels and Beyond-bank channels
Reimagined tools, experiences journeys (eg, web, apps, and journeys (eg, Smart service and
engagement for customers and mobile, smart devices, ecosystems, partners, operations
employees branches, Internet of Things) distributors)
1 2 3 4
5
Digital marketing
6
Retention
Credit Monitoring Servicing
Advanced Customer and cross-
decision and and
analytics acquisition selling,
AI-powered making collections engagement
upselling
decision
making
Natural-
Voice- Virtual Facial Behav-
7 language script agents, Computer recog- Block- Robotics ioral
AI capabilities process- analysis bots vision nition chain analytics
ing
A. Tech-forward strategy (in-house build of differential capabilities
vs buying offerings; in-house talent plan)
Core 8 B. Data C. Modern D. Intelligent E. Hollow- F. Cyber-
manage- API archi- infrastructure ing the security
technology Core technology
ment for tecture (AI operations core (core and
and data and data
AI world command, moderniza- control
hybrid cloud tion) tiers
setup, etc)
A. Autonomous business + tech teams
9
Operating B. Agile way C. Remote D. Modern talent E. Culture and
Platform operating
model model of working collaboration strategy (hiring, capabilities
reskilling)
10 Value capture
10 AI-bank of the future: Can banks meet the AI challenge?
Third, banks will need to redesign overall and stronger risk management (e.g., earlier
customer experiences and specific journeys for detection of likelihood of default and
omnichannel interaction. This involves allowing fraudulent activities).
customers to move across multiple modes (e.g.,
web, mobile app, branch, call center, smart To establish a robust AI-powered decision
devices) seamlessly within a single journey layer, banks will need to shift from attempting
and retaining and continuously updating the to develop specific use cases and point
latest context of interaction. Leading consumer solutions to an enterprise-wide road map for
internet companies with offline-to-online deploying advanced-analytics (AA)/machine-
business models have reshaped customer learning (ML) models across entire business
expectations on this dimension. Some banks domains. As an illustration, in the domain of
are pushing ahead in the design of omnichannel unsecured consumer lending alone, more
journeys, but most will need to catch up. than 20 decisions across the life cycle can be
automated.¹¹ To enable at-scale development
Reimagining the engagement layer of the of decision models, banks need to make the
AI bank will require a clear strategy on how development process repeatable and thus
to engage customers through channels capable of delivering solutions effectively and
owned by non-bank partners. Banks will on-time. In addition to strong collaboration
need to adopt a design-thinking lens as they between business teams and analytics
build experiences within and beyond the talent, this requires robust tools for model
bank’s platform, engineering engagement development, efficient processes (e.g., for
interfaces for flexibility to enable tailoring and re-using code across projects), and diffusion
personalization for customers, reengineering of knowledge (e.g., repositories) across teams.
back-end processes, and ensuring that data- Beyond the at-scale development of decision
capture funnels (e.g., clickstream) are granularly models across domains, the road map should
embedded in the bank’s engagement layer. All also include plans to embed AI in business-
of this aims to provide a granular understanding as-usual process. Often underestimated,
of journeys and enable continuous this effort requires rewiring the business
improvement.10 processes in which these AA/AI models will be
embedded; making AI decisioning “explainable”
Layer 2: Building the AI-powered decision- to end-users; and a change-management plan
making layer that addresses employee mindset shifts and
Delivering personalized messages and skills gaps. To foster continuous improvement
decisions to millions of users and thousands beyond the first deployment, banks also
of employees, in (near) real time across the full need to establish infrastructure (e.g., data
spectrum of engagement channels, will require measurement) and processes (e.g., periodic
the bank to develop an at-scale AI-powered reviews of performance, risk management of AI
decision-making layer. Across domains within models) for feedback loops to flourish.
the bank, AI techniques can either fully replace
or augment human judgment to produce Additionally, banks will need to augment
significantly better outcomes (e.g., higher homegrown AI models, with fast-evolving
accuracy and speed), enhanced experience capabilities (e.g., natural-language processing,
for customers (e.g., more personalized computer-vision techniques, AI agents
interaction and offerings), actionable insights and bots, augmented or virtual reality) in
for employees (e.g., which customer to contact their core business processes. Many of
first with next-best-action recommendations), these leading-edge capabilities have the
10Jennifer Kilian, Hugo Sarrazin, and Hyo Yeon, “Building a design-driven culture,” September 2015, McKinsey.com.
11 Renny Thomas, Vinayak HV, Raphael Bick, and Shwaitang Singh, “Ten lessons for building a winning retail and small-business digital lending
franchise,” November 2019, McKinsey.com.
AI-bank of the future: Can banks meet the AI challenge? 11
potential to bring a paradigm shift in customer technology backbone, starved of the investments
experience and/or operational efficiency. While needed for modernization, can dramatically
many banks may lack both the talent and the reduce the effectiveness of the decision-making
requisite investment appetite to develop these and engagement layers.
technologies themselves, they need at minimum
to be able to procure and integrate these The core-technology-and-data layer has six key
emerging capabilities from specialist providers elements (Exhibit 7):
at rapid speed through an architecture enabled
by an application programming interface (API), — Tech-forward strategy. Banks should have
promote continuous experimentation with these a unified technology strategy that is tightly
technologies in sandbox environments to test and aligned to business strategy and outlines
refine applications and evaluate potential risks, strategic choices on which elements, skill
and subsequently decide which technologies to sets, and talent the bank will keep in-house
deploy at scale. and those it will source through partnerships
or vendor relationships. In addition, the
To deliver these decisions and capabilities and to tech strategy needs to articulate how each
engage customers across the full life cycle, from component of the target architecture will both
acquisition to upsell and cross-sell to retention support the bank’s vision to be an AI-first
and win-back, banks will need to establish institution and interact with each layer of the
enterprise-wide digital marketing machinery. This capability stack.
machinery is critical for translating decisions and
insights generated in the decision-making layer — Data management for the AI-enabled world.
into a set of coordinated interventions delivered The bank’s data management must ensure
through the bank’s engagement layer. This data liquidity—that is, the ability to access,
machinery has several critical elements, which ingest, and manipulate the data that serve as
include: the foundation for all insights and decisions
generated in the decision-making layer.
— Data-ingestion pipelines that capture a range Data liquidity increases with the removal of
of data from multiple sources both within the functional silos and allows multiple divisions
bank (e.g., clickstream data from apps) and to operate off the same data, with increased
beyond (e.g., third-party partnerships with coordination. The data value chain begins with
telco providers) seamless sourcing of data from all relevant
internal systems and external platforms. This
— Data platforms that aggregate, develop, and includes ingesting data into a lake, cleaning
maintain a 360-degree view of customers and and labeling the data required for diverse use
enable AA/ML models to run and execute in cases (e.g., regulatory reporting, business
near real time intelligence at scale, AA/ML diagnostics),
segregating incoming data (from both existing
— Campaign platforms that track past actions and prospective customers) to be made
and coordinate forward-looking interventions available for immediate analysis from data to
across th"
60,mckinsey,Global-AI-Survey-AI-proves-its-worth-but-few-scale-impact.pdf,"McKinsey Analytics
Global AI Survey:
AI proves its worth,
but few scale impact
Most companies report measurable benefits from AI where it has
been deployed; however, much work remains to scale impact,
manage risks, and retrain the workforce. A group of high performers
shows the way.
© Sylverarts/Getty Images
November 2019
Adoption of artificial intelligence (AI) continues to Further, our results suggest that workforce retraining
increase, and the technology is generating returns.1 will need to ramp up. While the findings indicate
The findings of the latest McKinsey Global Survey on that AI adoption has generally had modest overall
the subject show a nearly 25 percent year-over- effects on organizations’ workforce size in the
year increase in the use of AI2 in standard business past year, about one-third of respondents say they
processes, with a sizable jump from the past year expect AI adoption to lead to a decrease in their
in companies using AI across multiple areas of their workforce in the next three years, compared with
business.3 A majority of executives whose com- one-fifth who expect an increase, and AI high
panies have adopted AI report that it has provided performers are doing more retraining.
an uptick in revenue in the business areas where it is
used, and 44 percent say AI has reduced costs.
Most respondents are seeing returns
The results also show that a small share of from AI
companies—from a variety of sectors—are attaining In this year’s survey, we asked respondents about
outsize business results from AI, potentially widen- 33 AI use cases across eight business functions,
ing the gap between AI power users and adoption including how adoption of AI for each of these activ-
laggards. Respondents from these high-performing ities has affected revenue and cost in the business
companies (or AI high performers) report that they units where AI is used. The results suggest that AI is
achieve greater scale and see both higher revenue delivering meaningful value to companies.
increases and greater cost decreases than other
companies that use AI.4 The findings, however, pro- Aggregating across all of the use cases, 63 percent
vide a potential road map for laggards, showing of respondents report revenue increases from AI
that the AI high performers are more likely to apply adoption in the business units where their companies
core practices for using AI to drive value across use AI, with respondents from high performers
the organization, mitigate risks associated with the nearly three times likelier than those from other com-
technology, and retrain workers to prepare them panies to report revenue gains of more than
for AI adoption. 10 percent. Respondents are most likely to report
The results suggest that AI is delivering
meaningful value to companies.
1 We define artificial intelligence (AI) as the ability of a machine to perform cognitive functions that we associate with human minds (such as
perceiving, reasoning, learning, and problem solving) and to perform physical tasks using cognitive functions (for example, physical robotics,
autonomous driving, and manufacturing work).
2 W e define AI use in standard business processes as embedded AI in at least one product or business process for at least one function or
business unit.
3 The online survey was in the field from March 26 to April 5, 2019, and garnered responses from 2,360 participants representing the full range
of regions, industries, company sizes, functional specialties, and tenures. Of these respondents, 1,872 work at companies they say have piloted
AI in at least one function or business unit, embedded at least one AI capability in at least one product or business process for at least one
function or business unit, or embedded at least one AI capability in products or business processes across multiple functions or business units.
To adjust for differences in response rates, the data are weighted by the contribution of each respondent’s nation to global GDP.
4 We define an AI high performer as a company that, according to respondents, has adopted AI in five or more business activities (is in the top
quartile for the number of activities using AI), seen an average revenue increase of 5 percent or more from AI adoption in the business units
where AI is used, and seen an average cost decrease of 5 percent or more from AI adoption in the business units where AI is used. The survey
results include 54 respondents from high-performing companies, which is 3 percent of all respondents reporting AI use by their companies.
2 Global AI Survey: AI proves its worth, but few scale impact
revenue growth from AI use cases in marketing and Overall, 44 percent of respondents report cost
sales, product and service development, and savings from AI adoption in the business units where
supply-chain management (Exhibit 1). In marketing it’s deployed, with respondents from high per-
and sales, respondents most often report formers more than four times likelier than others
revenue increases from AI use in pricing, prediction to say AI adoption has decreased business
of likelihood to buy, and customer-service units’ costs by at least 10 percent, on average. The
analytics. In product and service development, two functions in which the largest shares of
revenue-producing use cases include the respondents report cost decreases in individual AI
Survey 2019 creation of new AI-based products and new AI-based use cases are manufacturing and supply-chain
AI enhancements. And in supply-chain manage- management. In manufacturing, responses suggest
Exhibit 1 of 6 ment, respondents often cite sales and demand some of the most significant savings come from
forecasting and spend analytics as use cases optimizing yield, energy, and throughput. In supply-
that generate revenue. chain management, respondents are most likely to
Exhibit 1
Revenue increases from adopting AI are reported most often in marketing and sales,
and cost decreases most often in manufacturing.
Cost decrease and revenue increase from AI adoption, by function,¹ % of respondents2
Average cost decrease Average revenue increase
Decrease Decrease Decrease Increase Increase Increase
by ≥20% by 10–19% by <10% by ≤5% by 6–10% by >10%
4 13 19 Marketing and sales 40 30 10
6 10 13 Product and service development 31 21 19
14 16 31 Supply-chain management 28 22 13
13 14 37 Manufacturing 34 13 14
11 17 23 Service operations 31 14 15
15 11 24 Strategy and corporate finance 27 24 8
7 16 31 Risk 28 16 13
6 22 27 HR 20 23 12
1 Marketing and sales includes the following use cases: customer-service analytics, customer segmentation, channel management, prediction of likelihood to
buy, pricing and promotion, closed-loop marketing, marketing-budget allocation, churn reduction, and next product to buy. For product and service development:
product-feature optimization, product-development-cycle optimization, creation of new AI-based enhancements, and creation of new AI-based products. For
supply-chain management: logistics-network optimization, sales and parts forecasting, warehouse optimization, inventory and parts optimization, spend analytics,
and sales and demand forecasting. For manufacturing: predictive maintenance and yield, energy, and throughput optimization. For service operations:
service-operations optimization, contact-center automation, and predictive service and intervention. For strategy and corporate finance: capital allocation,
treasury management, and M&A support. For risk: risk modeling/analytics, and fraud/debt analytics. For HR: performance management and organization-
design, workforce-deployment, and talent-management optimization.
2 Question asked only of respondents who said their companies adopted AI in given use case. Figures were calculated after removing respondents who said “don’t know”
or “not applicable; we are not tracking revenue related to AI”; respondents who said “no change” are not shown.
Global AI Survey: AI proves its worth, but few scale impact 3
report savings from spend analytics and logistics- of capabilities. And telecom respondents report
network optimization. their companies using virtual agents—which can be
used in customer-service applications—more than
other capabilities (Exhibit 2). High-performing
AI adoption is increasing in nearly all companies, however, are far more likely to adopt AI
industries, but capabilities vary in business functions that this survey and past
As in last year’s survey, we asked respondents about research link to greater value creation more broadly.6
their companies’ use of nine AI capabilities.5 Fifty- For example, more than 80 percent of respondents
eight percent of respondents report that their organi- from high performers say they have adopted AI in
zations have embedded at least one AI capability marketing and sales, compared with only one-quarter
into a process or product in at least one function or from those of other companies that use AI.
business unit, up from 47 percent in 2018—a
sign that AI adoption in general is becoming more On a regional level, the survey shows significant
mainstream. What’s more, responses show an increases in adoption levels in developed Asia–
increase in the share of companies using AI in Pacific,7 Europe, Latin America, and North America.
products or processes across multiple business In Asia–Pacific and Latin America, the shares of
units and functions: 30 percent of this year’s respondents who say their companies have
respondents report doing so, compared with embedded AI across multiple functions or business
21 percent in the previous survey. While this seems units have nearly doubled since the previous
to indicate that more companies are beginning survey. However, the increases put all of these
to scale AI, high performers are much further along regions, as well as China, at similar aggregate
in these efforts, averaging 11 reported AI use reported levels of adoption, suggesting that while
cases across the organization versus about three there is considerable variation at the level
among other companies. of individual companies, the adoption of AI is a
global phenomenon.8
By sector, the results indicate increases in AI adop-
tion in nearly every industry in the past year. The results indicate that the pace of adoption will
Retail has seen the largest increase, with 60 percent likely continue in the near term, with 74 percent
of respondents saying their companies have of respondents whose companies have adopted or
embedded at least one AI capability in one or more plan to adopt AI saying their organizations will
functions or business units, a 35-percentage- increase their AI investment in the next three years.
point increase from 2018. More than half of these respondents expect an
increase of 10 percent or more. But the survey results
The results show companies applying AI capabilities indicate that AI high performers plan to invest
that help them perform the functions that create more, with nearly 30 percent of respondents from
value in their industries. For example, respondents these companies saying their organizations will
from consumer-packaged-goods companies are increase investment in AI by 50 percent or more in
more likely to report using physical robotics—which the next three years, compared with just 9 percent
can aid in assembly tasks—than most other types of others who say the same.
5 “AI adoption advances, but foundational barriers remain,” November 2018, McKinsey.com. Respondents were asked to describe their
organizations’ use of the following AI capabilities: natural language text understanding, natural language speech understanding,
natural language generation, virtual agents or conversational interfaces, computer vision, robotic process automation, machine learning,
physical robotics, and autonomous vehicles.
6 M ichael Chui, Rita Chung, Nicolaus Henke, Sankalp Malhotra, James Manyika, Mehdi Miremadi, and Pieter Nel, “Notes from the AI frontier:
Applications and value of deep learning,” McKinsey Global Institute, April 2018, McKinsey.com.
7 Includes Australia, Hong Kong, Japan, New Zealand, the Philippines, Singapore, South Korea, and Taiwan.
8 In each region, about three in ten respondents say their organizations have embedded AI across multiple functions or business units. In China,
the base size is below the baseline for statistical significance. For more on what AI means for China, see Dominic Barton, Jeongmin Seong,
Qinzheng Tian, and Jonathan Woetzel, “Artificial intelligence: Implications for China,” McKinsey Global Institute, April 2017, McKinsey.com.
4 Global AI Survey: AI proves its worth, but few scale impact
Survey 2019
AI
Exhibit 2 of 6
Exhibit 2
High tech leads in AI adoption, and industries are generally using the AI capabilities most
relevant to their value chains.
Organizations’ AI capabilities,1 % of respondents,2 by industry
Computer Natural Physical Natural language ≥1 AI capability
vision language text robotics generation embedded, %/
understanding change since 2018,
percentage points
Robotic process Machine Virtual agents or Natural Autonomous
automation learning conversational language speech vehicles
interfaces understanding
High tech 35 33 54 38 35 9 24 22 4 78/+17
Automotive
46 42 31 28 17 44 19 18 25 76/+11
and assembly
Telecom 30 36 45 38 45 20 23 26 3 72/+8
Travel, transport,
33 26 19 24 29 10 12 12 7 64/+26
and logistics
Financial
36 24 25 28 32 7 19 16 6 62/+6
services
Consumer
17 14 12 13 11 47 7 7 15 62/+12
packaged goods
Retail 21 24 23 34 27 25 18 16 9 60/+35
Electric power
26 31 30 9 22 22 8 6 4 60/+16
and natural gas
Healthcare systems
23 32 23 30 20 14 22 16 4 58/+9
and services
Pharma and
21 19 15 10 6 31 7 8 5 48/–2
medical products
Professional services 17 20 22 22 17 7 12 13 6 43/+10
Infrastructure 20 17 15 10 4 14 5 5 2 36/+8
1 Embedded in ≥1 product and/or business process for ≥1 function or business unit.
2 Respondents who said “don’t know” or “none of the above” are not shown. For high tech, n = 277; for automotive and assembly, n = 128; for telecom, n = 93; for travel,
transport, and logistics, n = 83; for financial services, n = 396; for consumer packaged goods, n = 72; for retail, n = 94; for electric power and natural gas, n = 82; for
healthcare systems and services, n = 78; for pharma and medical products, n = 96; for professional services, n = 331; and for infrastructure, n = 91.
Global AI Survey: AI proves its worth, but few scale impact 5
AI high performers tend to engage in frontline employees use AI insights in real time for
value-capturing practices daily decision making, and just 42 percent
According to our experience and past research on systematically track a comprehensive set of well-
analytics, some core practices are necessary defined key performance indicators for AI—
to capture value at scale.9 These include, among two practices, in our experience, that are crucial for
achieving end-user adoption and value. Likewise,
others, aligning business, analytics, and IT leaders
only 35 percent of respondents from AI high
on the potential value at stake from AI across
performers report having an active continuous-
each business domain; investing in talent, such as
learning program on AI for employees.
translator expertise; and ensuring that business
staff and technical teams have the skills necessary
for successful scaling.10
A minority of companies acknowledge
The survey results suggest these core practices most AI risks—fewer mitigate them
hold true for scaling AI, given that respondents Despite extensive dialogue across industries about
at AI high performers are far more likely than others the potential risks of AI and highly publicized
to say their organizations apply these practices incidents of privacy violations, unintended bias, and
(Exhibit 3). For example, 72 percent of respondents other negative outcomes,11 the survey findings
from AI high performers say their companies’ suggest that a minority of companies recognize
AI strategy aligns with their corporate strategy, com- many of the risks of AI use. Even fewer are taking
pared with 29 percent of respondents from action to protect against the risks.
other companies. Similarly, 65 percent from the
high performers report having a clear data Fewer than half of respondents (41 percent) say
strategy that supports and enables AI, compared their organizations comprehensively identify and
with 20 percent from other companies. prioritize their AI risks. The survey also asked
specifically about ten of the most widely recognized
Even the AI high performers have work to do in risks. Of them, respondents most often cite
several key areas. For example, only 36 percent of cybersecurity and regulatory compliance as the
respondents from these companies say their AI-related risks their companies consider
Fewer than half of respondents
(41 percent) say their organizations
comprehensively identify and
prioritize their AI risks.
9 Peter Bisson, Bryce Hall, Brian McCarthy, and Khaled Rifai, “Breaking away: The secrets to scaling analytics,” May 2018, McKinsey.com.
1 0 Brian McCarthy, Chris McShea, and Marcus Roth, “Rebooting analytics leadership: Time to move beyond the math,” November
2018, McKinsey.com; Nicolaus Henke, Jordan Levine, and Paul McInerney, “Analytics translator: The new must-have role,” February 2018,
McKinsey.com; Solly Brown, Darshit Gandhi, Louise Herring, and Ankur Puri, “The analytics academy: Bridging the gap between human
and artificial intelligence,” McKinsey Quarterly, September 2019, McKinsey.com.
11 Benjamin Cheatham, Kia Javanmardian, and Hamid Samandari, “Confronting the risks of artificial intelligence,” McKinsey Quarterly,
April 2019, McKinsey.com.
6 Global AI Survey: AI proves its worth, but few scale impact
Survey 2019
AI
Exhibit 3 of 6
Exhibit 3
Respondents at AI high performers are much more likely than others to report that their
organizations apply core practices for scaling AI.
Share of respondents saying given statement is true of At high performers2
their organizations, %1 At all other companies3
Aligning AI strategy We have an AI strategy with a clear enterprise-level 58
to business goals road map of use cases 15 3.9×
The AI strategy aligns with our broader 72
corporate strategy 29 2.5×
Investing in AI talent We have in place an active continuous-learning 35
and training program on AI for our employees 10 3.5×
We have people in translator roles who
49
communicate with employees across the analytics
21 2.3×
and business functions
Collaborating Cross-functional teams, including AI professionals
62
across functions and people in the business, work together on
23 2.7×
specific problems
Applying strong We have a clear data strategy that supports 65
data practices and enables AI 20 3.3×
Well-defined governance processes are in place 55
for key data-related decisions 20 2.8×
Establishing We have standard AI tool sets for data and 76
standard protocols analytics professionals to use 18 4.2×
and repeatable
methodologies
We know how frequently our AI models 43
need to be updated 11 3.9×
We have techniques and processes in place to
54
ensure that our models are explainable to people
17 3.2×
across the organization
Ensuring adoption Our frontline employees use AI insights in real 36
and value time to enable their daily decision making 8 4.5×
We systematically track a comprehensive set of 42
well-defined key performance indicators for AI 10 4.2×
1 Question asked only of respondents who said their companies had embedded or piloted ≥1 AI capability.
2 Respondents who said companies have adopted AI in ≥5 business activities (ie, top quartile for the number of activities using AI), seen an average revenue
increase of ≥5% from AI adoption in the business units where AI is used, and seen an average cost decrease of ≥5% from AI adoption in the business units where
AI is used, n = 54.
3 n = 1,818.
Global AI Survey: AI proves its worth, but few scale impact 7
relevant (Exhibit 4). These two risks are the only Respondents at AI high performers are likelier than
ones that at least half of respondents cite as those from other companies to say their organiza-
relevant. Furthermore, the share of respondents tions both recognize and work to reduce risks. Take
saying their companies are mitigating each risk personal-privacy risk, which is squarely in regulators’
is smaller than the share citing it as relevant. For line of sight. Eighty percent of respondents at
example, while 39 percent of respondents say high-performing companies say their companies
their companies recognize risk associated with consider personal-privacy risk to be relevant,
“e xplainability” (the ability to explain how AI compared with less than half of respondents from
Survey 2019 models come to their decisions), only 21 percent say other companies. When asked about internal
AI they are actively addressing this risk. At the controls aimed at reducing privacy risks, 89 percent
Exhibit 4 of 6 companies that reportedly do mitigate AI risks, the of respondents at high-performing companies
most frequently reported tactic is conducting say their organizations adopt and enforce enterprise-
internal reviews of AI models. wide privacy policies, compared with 68 percent
Exhibit 4
Respondents at AI high performers are more likely than average to say their companies identify
AI-related risks—and work to mitigate them.
Risks that organizations consider relevant and are working to mitigate, Relevant risk
% of respondents1 Mitigated risk
All respondents Respondents at AI high performers
62 89
Cybersecurity
48 86
50 69
Regulatory compliance
35 55
45 80
Personal privacy
30 76
39 47
“Explainability”2
19 42
35 36
Workforce/labor displacement
17 23
34 44
Organizational reputation
19 41
26 36
Equity and fairness
13 23
16 25
Physical safety
11 23
9 18
National security
4 9
7 15
Political stability
2 7
9 0
Don’t know/not applicable
12 0
1 Question asked only of respondents who said their companies had embedded or piloted ≥1 AI capability; n = 1,872.
2 Ability to explain how AI models come to their decisions.
8 Global AI Survey: AI proves its worth, but few scale impact
of other respondents. Similarly, 80 percent of adopt AI expect it to drive a decrease in the number
respondents at AI high performers report that their of employees, versus 21 percent who expect an
organizations implement tech-enabled access increase—although most predict the change to be
restrictions to sensitive data, versus 59 percent of less than 10 percent in either direction.13 Another
those at other companies. 28 percent foresee AI adoption having little impact
on workforce size, with any expected change
being less than 3 percent.
More expect AI to cause workforce
decreases than increases, with Respondents also expect AI adoption to cause shifts
variances across functions in their workforce across functions. Respondents
are more likely to predict a decrease than an increase
Generally, there has been increasing concern that
AI will lead to workforce reduction.12 The survey in employment levels in HR, manufacturing, supply-
chain management, and service operations. They
findings suggest that, thus far, this concern has
more often predict an increase than a decrease in the
largely not been realized. More than one-third
number of employees in product development
of respondents report less than a 3 percent change
and marketing and sales.
in their companies’ workforce size because of
AI deployment, and only 5 percent of respondents
report a change, whether decrease or increase,
Greater emphasis on workforce
of greater than 10 percent. While respondents from
a handful of industries, including automotive and retraining is likely
assembly, are more likely to report a workforce The results indicate that a majority of respondents’
reduction than an increase in the past year because companies are preparing for AI-related work-
of AI (Exhibit 5), more respondents overall report force changes. When asked about retraining workers
job increases of 3 percent or more at their in response to AI adoption, nearly six in ten
companies in the past year than report decreases respondents at companies using AI say at least
of the same magnitude (17 percent and some of their workforce has been retrained
13 percent, respectively). in the past year. In addition, 83 percent of respon-
dents expect at least some of their workforce
But the outlook for the next three years could be to be retrained in the next three years because of
shifting. Thirty-four percent of respondents AI adoption, and 38 percent expect more than
from organizations that have adopted or plan to a quarter to be retrained.
Respondents are more likely to predict
a decrease than an increase in
employment levels in HR, manufacturing,
supply-chain management, and
service operations.
12 For more information on how AI is expected to affect the workforce, see James Manyika and Kevin Sneader, “AI, automation, and the future of
work: Ten things to solve for,” McKinsey Global Institute, June 2018, McKinsey.com.
13 Respondents reporting that their companies have piloted or embedded one or more AI capabilities, or plan to do so in the next three years, were
asked how they expect the adoption of AI to affect the number of employees relative to the number if the organizations had not adopted AI.
Global AI Survey: AI proves its worth, but few scale impact 9
Survey 2019
AI
Exhibit 5 of 6
Exhibit 5
Respondents in automotive and telecom report the deepest AI-related workforce cuts to date
and predict the most going forward.
Change in workforce due to AI adoption, Decrease >10% Decrease 3–10% Increase 3–10% Increase >10%
% of respondents
Little or Little or
Past year1 no change2 Next 3 years3 no change2
Automotive
8 15 10 1 32 18 28 16 1 25
and assembly
Telecom 2 21 12 4 20 18 37 8 5 13
Infrastructure 1 19 8 3 38 5 21 18 13 21
Retail 2 16 23 4 18 4 35 21 2 25
Financial services 4 13 14 1 37 14 25 12 3 30
Professional
1 12 15 6 40 8 20 22 7 26
services
1
High tech 1 12 21 6 26 9 16 15 15 26
Pharma and
12 6 3 39 11 13 19 2 32
medical products
Consumer
11 13 2 48 3 42 7 41
packaged goods
Travel, transport,
9 11 44 19 25 6 7 32
and logistics
Electric power
6 10 48 12 23 22 1 18
and natural gas
Healthcare systems
5 15 2 36 11 12 17 3 36
and services
1 Change in workforce in past year because of AI adoption. Question only asked of respondents who say their companies have piloted or embedded ≥1 AI capability.
Respondents who said “don’t know” are not shown. For automotive and assembly, n = 111; for telecom, n = 81; for infrastructure, n = 63; for retail, n = 74; for financial
services, n = 333; for professional services, n = 235; for high tech, n = 246; for pharma and medical products, n = 71; for consumer packaged goods, n = 55; for
travel, transport, and logistics, n = 69; for healthcare systems and services, n = 60.
2 A decrease or increase of ≤2%.
3 Expected change in workforce in next 3 years because of AI adoption, relative to size if AI had not been adopted. Question was asked only of respondents who say
their companies have piloted or embedded ≥1 AI capability, or plan to do so in the next 3 years. Respondents who said “don’t know” are not shown. For automotive and
assembly, n = 113; for telecom, n = 85; for infrastructure, n = 65; for retail, n = 76; for financial services, n = 341; for professional services, n = 245; for high tech,
n = 253; for pharma and medical products, n = 78; for consumer packaged goods, n = 58; for travel, transport, and logistics, n = 70; and for healthcare systems and
services, n = 67.
10 Global AI Survey: AI proves its worth, but few scale impact
Survey 2019
AI
Exhibit 6 of 6
Exhibit 6
Respondents at high performers report larger retraining efforts as a result of AI than others do.
Share of workforce 2 Share of workforce
7 3 8
retrained in past year 6 expected to be retrained
because of AI adoption, in next 3 years because 9
29
% of respondents1 of AI adoption, % of
26
respondents2
19
46
13
12
27
45
76–100% 51 26
51–75%
26–50% 29
7
1–25%
17
0% 11
1
Don’t know 2 2
At AI high At all other At AI high At all other
performers companies performers companies
Note: Figures may not sum to 100%, because of rounding.
1 Question was asked only of respondents who say their companies have piloted or embedded one or more AI capabilities. For respondents at high performers,
n = 54; for all others, n = 1,818.
2 Question was asked only of respondents who say their companies have piloted or embedded one or more AI capabilities, or plan to do so in the next three years. For
respondents at high performers, n = 54; for all others, n = 1,892.
Respondents at AI high performers report retraining With the research showing that companies now use
much greater shares of employees in the past AI more often than not, the technology appears to
year because of AI, compared with respondents at have reached another stepping stone in its ascent in
other companies that have adopted AI (Exhibit 6). business. Along with it comes a ratcheting up of
Respondents at high performers also predict that the urgency to scale AI among those still early in their
their companies will retrain larger shares of their adoption journeys. However, while the survey
workforce in the next three years. results indicate that some companies are further
ahead in realizing AI’s impact, they also suggest a
path for lagging companies to catch up.
The survey content and analysis were developed by Arif Cam, a consultant in McKinsey’s Silicon Valley office; Michael Chui,
a partner of the McKinsey Global Institute and a partner in the San Francisco office; and Bryce Hall, an associate partner in
the Washington, DC, office.
They wish to thank David DeLallo for his contributions to this article.
Designed by Global Editorial Services
Copyright © 2019 McKinsey & Company. All rights reserved.
Global AI Survey: AI proves its worth, but few scale impact 11"
61,mckinsey,Global-survey-The-state-of-AI-in-2021.pdf,"The state of AI in 2021
As business’s adoption of AI continues to grow, the companies reaping
the biggest bottom-line benefits are differentiating themselves through
their use of more sophisticated tools and practices.
© Getty Images
December 2021
The results of our latest McKinsey Global Survey on AI indicate that AI adoption¹ continues to grow and that the
benefits remain significant—though in the COVID-19 pandemic’s first year, they were felt more strongly on the cost-
savings front than the top line. As AI’s use in business becomes more common, the tools and best practices to make
the most out of AI have also become more sophisticated.
We looked at the practices of the companies seeing the biggest earnings boost from AI and found that they are
not only following more of both the core and advanced practices, including machine-learning operations (MLOps),
that underpin success but also spending more efficiently on AI and taking more advantage of cloud technologies.
Additionally, they are more likely than other organizations to engage in a range of activities to mitigate their AI-related
risks—an area that continues to be a shortcoming for many companies’ AI efforts.
AI adoption and impact
Findings from the 2021 survey indicate that AI adoption is continuing its steady rise: 56 percent of all respondents
report adoption in at least one function, up from 50 percent in 2020. The newest results suggest that AI adoption
since last year has increased most at companies headquartered in emerging economies, which includes China, the
Middle East and North Africa: 57 percent of respondents report adoption, up from 45 percent in 2020. And across
regions, the adoption rate is highest at Indian companies, followed closely by those in Asia–Pacific.
About the research
The online survey was in the field from May 18 to June 29, 2021, and garnered responses from 1,843
participants representing the full range of regions, industries, company sizes, functional specialties, and
tenures. Of those respondents, 1,013 said their organizations had adopted AI in at least one function and were
asked questions about their organizations’ AI use. To adjust for differences in response rates, the data are
weighted by the contribution of each respondent’s nation to global GDP.
As we saw in the past two surveys, the business functions where AI adoption is most common are service operations,
product and service development, and marketing and sales, though the most popular use cases span a range
of functions. The top three use cases are service-operations optimization, AI-based enhancement of products,
and contact-center automation, with the biggest percentage-point increase in the use of AI being in companies’
marketing-budget allocation and spending effectiveness (Exhibit 1).
The results also suggest that AI’s impact on the bottom line is growing. The share of respondents reporting at least
5 percent of earnings before interest and taxes (EBIT) that’s attributable to AI has increased year over year to 27
percent, up from 22 percent in the previous survey.
1 We define “adoption” as the use of AI capabilities (for example, machine learning, computer vision, natural-language processing) in at least one business function.
2 The state of AI in 2021
Exhibit 1
The most popular AI use cases span a range of functional activities.
TToopp uussee ccaasseess Use cases by function
Most commonly adopted AI use cases,¹ by function, % of respondents
Service-operations optimization 27 Service operations
Product and/or service development
New AI-based enhancements of products 22
Marketing and sales
Risk
Contact-center automation 22
Product-feature optimization 20
Predictive service and intervention 18
Customer-service analytics 17
Creation of new AI-based products 17
Customer segmentation 16
Risk modeling and analytics 16
Fraud and debt analytics 14
The most popular AI use cases span a range of functional activities.
Top use cases UUssee ccaasseess bbyy ffuunnccttiioonn
¹ Out of 39 use cases. Question was asked only of respondents who said their organizations have adopted AI in at least 1 business function.
Most commonly adopted AI use cases within each business function,¹ % of respondents
Service operations² Product and/or service development
New AI-based enhancements
Service-operations optimization 27 22
of products
Contact-center automation 22 Product-feature optimization 20
Marketing and sales Supply-chain management
Customer-service analytics 17 Logistics-network optimization 11
Customer segmentation 16 Sales and demand forecasting 11
Risk Manufacturing
Risk modeling and analytics 16 Predictive maintenance 12
Yield, energy, and/or
Fraud and debt analytics 14 throughput optimization 11
Strategy and corporate finance Human resources
Capital allocation 7 Optimization of talent management 8
Treasury management 6 Performance management 8
¹ Question was asked only of respondents who said their organizations have adopted AI in a given function.
² Eg, field services, customer care, back office.
The state of AI in 2021 3
And while AI’s revenue benefits have held steady or even decreased since the previous survey—especially for supply-
chain management, where AI was unlikely to compensate for the pandemic era’s global supply-chain challenges—the
opposite is true of costs (Exhibit 2). Respondents report significantly greater cost savings from AI than they did
previously in every function, with the biggest year-over-year changes in the shares reporting cost takeout from using
AI in product and service development, marketing and sales, and strategy and corporate finance.
Exhibit 2
Across functions, respondents report higher levels of cost decreases from AI adoption in the pandemic’s
first year, while revenue increases held steady.
CCoosstt ddeeccrreeaassee Revenue increase
Cost decrease from AI adoption by function, % of respondents¹ Decrease by <10% Decrease by 10–19% Decrease ≥20%
Fiscal year 2019 Fiscal year 2020
Service operations 54 30 17 7 87 12 24 51
Manufacturing 52 28 16 8 87 23 27 37
Human resources 52 33 8 11 86 20 26 40
Marketing and sales 41 25 9 7 83 21 35 27
Risk 46 16 18 12 78 17 20 41
Supply-chain management 56 44 6 6 78 15 27 36
Product and/or service development 26 12 7 7 69 22 24 23
Strategy and corporate finance 35 20 3 12 68 10 28 30
AcroAsvse rfaugen acctroisosn asll ,a crteivsitpiesondents rep4o4rt hig2h5er leve11ls o8f cost decreases fr7o9m A1I8 adoptio2n8 in the pand33emic’s
first year, while revenue increases held steady.
¹Question was asked only of respondents who said their organizations have adopted AI in a given function. Respondents who said “no change,” “cost increase,” “not applicable,” or “don’t know” are not shown.
Cost decrease RReevveennuuee iinnccrreeaassee
Revenue increase from AI adoption by function, % of respondents¹ Increase by ≤ 5% Increase by 6–10% Increase by >10%
Fiscal year 2019 Fiscal year 2020
Service operations 25 19 13 57 34 16 15 65
Manufacturing 43 18 10 71 38 15 10 63
Human resources 35 11 10 56 30 18 15 63
Marketing and sales 43 26 10 79 38 25 11 74
Risk 33 16 19 68 26 25 13 64
Supply-chain management 38 26 8 72 27 15 12 54
Product and/or service development 30 19 16 65 30 25 15 70
Strategy and corporate finance 36 24 13 73 33 32 2 67
Average across all activities 36 20 10 66 33 21 13 67
¹Question was asked only of respondents who said their organizations have adopted AI in a given function. Respondents who said “no change,” “revenue decrease,” “not applicable,” or “don’t know” are not shown.
4 The state of AI in 2021
Finally, respondents say AI’s prospects remain strong. Nearly two-thirds say their companies’ investments in AI will
continue to increase over the next three years, similar to the results from the 2020 survey.
The differentiators of AI outperformance
We sought to understand more about the factors and practices that differentiate the best AI programs from all others:
specifically, at the organizations at which respondents attribute at least 20 percent of EBIT to their use of AI—our “AI
high performers.” With adoption becoming ever more commonplace, we asked new questions about more advanced
AI practices, particularly those involved in MLOps, a best-practice approach to building and deploying machine-
learning-based AI that has emerged over the past few years.
While organizations seeing lower returns from AI are increasingly engaging in core AI practices, AI high performers
are still more likely to engage in most of the core practices. High performers also engage in most of the advanced AI
practices more often than others do (Exhibit 3).
Exhibit 3
Organizations seeing the highest returns from AI are more likely to follow
both core and more advanced best practices.
Share of respondents reporting their organizations engage in each practice,¹ % of respondents
CCoorree Advanced data Advanced models, tools, and technology User enablement
AI high performers² All other respondents
Use design thinking when developing AI tools 60 46
Test the performance of our AI models internally before deployment 57 43
Track the performance of AI models to ensure that process outcomes
46 35
and/or models improve over time
Have well-defined processes for data governance 45 37
Have protocols in place to ensure good data quality 40 42
Have a clear framework for AI governance that covers the model-
38 20
development process
AI-development teams follow standard protocols for building and
36 33
delivering AI tools
Have well-defined capability-building programs to develop technology
36 20
personnel’s AI skills
¹Practices shown here are representative of those with the highest deltas between AI high performers and other respondents. Not all practices are shown.
²Respondents who said that at least 20 percent of their organizations’ earnings before interest and taxes (EBIT) in 2020 was attributable to their use of AI.
The state of AI in 2021 5
Exhibit 3 cont.
Organizations seeing the highest returns from AI are more likely to follow
both core and more advanced best practices.
Share of respondents reporting their organizations engage in each practice,¹ % of respondents
Core AAddvvaanncceedd ddaattaa Advanced models, tools, and technology User enablement
AI high performers² All other respondents
Have a data dictionary that is accessible across the enterprise 53 29
Rapidly integrate internal structured data to use in our AI initiatives 51 32
Have scalable internal processes for labeling AI training data 48 22
Have well-defined processses for data governance 45 37
Generate synthetic data to train AI models when
27 27
we lack sufficient natural data sets
Organizations seeing the highest returns from AI are more likely to follow
both core and more advanced best practices.
Share of respondents reporting their organizations engage in each practice,¹ % of respondents
Core Advanced data AAddvvaanncceedd mmooddeellss,, ttoooollss,, aanndd tteecchhnnoollooggyy User enablement
AI high performers² All other respondents
¹Practices shown here are representative of those with the highest deltas between AI high performers and other respondents. Not all practices are shown.
²Respondents who said that at least 20 percent of their organizations’ earnings before interest and taxes (EBIT) in 2020 was attributable to their use of AI.
Take a full life-cycle approach to developing and deploying AI models 57 26
Regularly refresh our AI models, based on clearly defined criteria for
49 23
when and why to do so
Have techniques and processes in place to ensure that our models
45 31
are explainable
Refresh our AI/ML tech stack at least annually to take advantage of the
45 16
latest technological advances
Design AI models with a focus on ensuring they are reusable 43 27
Use external third-party services to test, validate, verify, and monitor the
35 28
performance of our AI models
Use a standardized end-to-end platform for AI-related data science, data
32 21
engineering, and application development
¹Practices shown here are representative of those with the highest deltas between AI high performers and other respondents. Not all practices are shown.
²Respondents who said that at least 20 percent of their organizations’ earnings before interest and taxes (EBIT) in 2020 was attributable to their use of AI.
6 The state of AI in 2021
Exhibit 3 cont.
Organizations seeing the highest returns from AI are more likely to follow
both core and more advanced best practices.
Share of respondents reporting their organizations engage in each practice,¹ % of respondents
Core Advanced data Advanced models, tools, and technology UUsseerr eennaabblleemmeenntt
AI high performers² All other respondents
Users are taught the basics of how the models work 57 35
Users are consulted throughout the design, development, training, and
50 50
deployment phases
Users are taught how to use the model 46 45
There are designated channels of communications and touchpoints
39 20
between AI users and the organization’s data science team
A dedicated training center develops nontechnical personnel’s AI skills
34 14
through hands-on learning
¹Practices shown here are representative of those with the highest deltas between AI high performers and other respondents. Not all practices are shown.
²Respondents who said that at least 20 percent of their organizations’ earnings before interest and taxes (EBIT) in 2020 was attributable to their use of AI.
There’s evidence that engaging in such practices is helping high performers industrialize and professionalize their
AI work, which leads to better results and greater efficiency and predictability in their AI spending. Three-quarters
of AI high performers say the cost to produce AI models has been on par with or even less than they expected,
whereas half of all other respondents say their companies’ AI project costs were higher than expected (Exhibit 4).
Going forward, the AI high performers’ work could push them farther ahead of the pack, since both groups plan to
increase their spending on AI by roughly the same amount.
The survey results also suggest that AI high performers could be gaining some of their efficiency by using the cloud.
Most companies—whether they are high performers or not—tend to use a mix of cloud and on-premises platforms
for AI similar to what they use for overall IT workloads. But the high performers use cloud infrastructure much more
than their peers do: 64 percent of their AI workloads run on public or hybrid cloud, compared with 44 percent at
other companies. This group is also accessing a wider range of AI capabilities and techniques on a public cloud. For
example, they are twice as likely as the rest to say they tap the cloud for natural-language-speech understanding
and facial-recognition capabilities.
The state of AI in 2021 7
Exhibit 4
Compared with their peers, the high performers’ AI spending is more efficient and predictable.
Typical costs for AI model production, compared with expected,¹ % of respondents
More than About the Less than Don’t
expected same expected know
AI high performers² 23 55 20 2
All other respondents
51 34 8 8
¹Figures may not sum to 100%, because of rounding.
²Respondents who said that at least 20 percent of their organizations’ earnings before interest and taxes (EBIT) in 2020 was attributable to their use of AI.
Managing AI risks
No matter a company’s AI performance, risk management remains an area where many have room to improve—
which we have seen in previous survey results. Cybersecurity remains the most recognized risk among respondents,
yet a smaller share says so than did in 2020, despite the rising threat of cyberincidents seen throughout the COVID-
19 pandemic. On a positive note, respondents report increasing focus on equity and fairness as a relevant risk and
one that their companies are mitigating.
Across regions, survey respondents report some notable changes since the previous survey and very different
opinions on cybersecurity risks (Exhibit 5). In developed economies, their views on the biggest risks have held
relatively steady since 2020, though 57 percent (versus 63 percent last year) cite cybersecurity as a relevant AI risk.
In emerging economies, respondents report a more dramatic decline in the relevance and mitigation of several of
the top risks. Yet, they also report personal and individual privacy as a relevant AI risk more often.
When asked why companies aren’t mitigating all relevant risks, respondents most often say it’s because they lack
capacity to address the full range of risks they face and have had to prioritize. Notably, the second-most common
response from those seeing lower returns from AI adoption is that they are unclear on the extent of their exposure
to AI risks (29 percent versus only 17 percent of AI high performers). And by geography, respondents in emerging
economies are more likely than others to report that they are waiting until clearer regulations for risk mitigation are
in place, and that they know from formal assessments that mitigation is more costly than the consequences of a
risk-related incident.
8 The state of AI in 2021
Exhibit 5
The management of AI risks remains an area for significant improvement, as respondents
report a waning focus on cyber—especially in emerging economies.
Relevant risks Mitigated risks
AI risks that organizations consider relevant, % of respondents by office headquarters¹ In emerging economies
In developed economies
2020 2021
Cybersecurity 59 47
63 57
Regulatory compliance 37 40
51 50
Explainability² 31 34
43 44
Personal/individual privacy 33 45
41 41
Organizational reputation 26 24
32 37
Equity and fairness 22 30
24 30
Workforce/labor displacement 35 31
29 24
Physical safety 19 18
19 22
National security 12 18
16 12
Political stability 11 16
8 7
¹ “EmeTrghinge e cmonoamnieas”g ineclumdese rnestpo ondfe nAtsI i nr AisSEkAsN , rCehinma, Iandiina, sLa atinn A maerriecaa, M fidodlre Esaisgt, nNoirfithc Aafrincat, S iomuthp Asrioa, vaned msube-Snahta,r aan sAf rricea,s apndo “dnevdeleopnedt esc onomies” includes respondents in
developed Asia, Europe, and North America. Question was asked only of respondents who said their organizations have adopted AI in at least 1 business function; those who answered “don’t know” are
not srheowpn.ort a waning focus on cyber—especially in emerging economies.
² That is, the ability to explain how AI models come to their decisions.
Relevant risks Mitigated risks
AI risks that organizations are working to mitigate, % of respondents by office headquarters¹ In emerging economies
In developed economies
2020 2021
Cybersecurity 50 36
51 50
Regulatory compliance 28 24
41 39
Explainability² 19 20
27 30
Personal/individual privacy 24 28
32 29
Organizational reputation 17 15
23 24
Equity and fairness 10 16
15 21
Workforce/labor displacement 14 19
20 14
Physical safety 13 14
15 17
National security 6 8
11 8
Political stability 5 3
4 4
¹ “Emerging economies” includes respondents in Association of Southeast Asian Nations, China, India, Latin America, Middle East, North Africa, South Asia, and sub-Saharan Africa, and “developed
economies” includes respondents in developed Asia, Europe, and North America. Question was asked only of respondents who said their organizations have adopted AI in ≥1 business function. Those
who answered “don’t know” are not shown.
² That is, the ability to explain how AI models come to their decisions.
The state of AI in 2021 9
Additional survey results suggest a way forward for companies that continue to struggle with risk management in
AI. We asked about a range of risk-mitigation practices related to model documentation, data validation, and checks
on bias. And in most cases, AI high performers are more likely than other organizations to engage in these practices
(Exhibit 6).
Exhibit 6
Organizations seeing the highest returns from AI engage in risk-mitigation
practices more often than others.
Share of respondents reporting their organizations engage in each practice,¹ % of respondents
TTrraaiinniinngg aanndd tteessttiinngg ddaattaa Measuring model bias and accuracy Model documentation
AI high performers² All other respondents
Scan training and testing data to detect the underrepresentation of
47 33
protected characteristics and/or attributes
Data professionals actively check for skewed or biased data during
47 27
data ingestion
Increase the representation of protected characteristics and/or attributes
43 23
in our training and testing data as needed
Data professionals actively check for skewed or biased data at several
36 24
stages of model development
Legal and risk professionals work with data-science teams to help them
24 26
understand definitions of bias & protected classes
Organizations seeing the highest returns from AI engage in risk-mitigation
pHraavcet ai cdeedsic amtedo groeve ornfatnecen c otmhmaintte oe tthhate inrcslu.des risk
23 17
and legal professionals
Share of respondents reporting their organizations engage in each practice,¹ % of respondents
Training and testing data MMeeaassuurriinngg mmooddeell bbiiaass aanndd aaccccuurraaccyy Model documentation
¹Practices shown here are representative of those with the highest deltas between AI high performers and other respondents. Not all practices are shown.
²Respondents who said that at least 20 percent of their organizations’ earnings before interest and taxes (EBIT) in 2020 was attributable to their use of AI.
AI high performers² All other respondents
Retrain our models when issues are detected 43 27
Regularly monitor for data drift and/or concept drift 42 25
Have a human-in-the-loop verification phase of model deployment that
39 30
expressly tests and controls for model bias
Model users are taught how to monitor for issues 39 21
Test for different outcomes based on a change to protected
36 21
characteristics
Refresh our models based on clearly defined criteria for how frequently
36 20
they need to be updated
Have mechanisms in place to monitor for model bias specifically 31 19
¹Practices shown here are representative of those with the highest deltas between AI high performers and other respondents. Not all practices are shown.
²Respondents who said that at least 20 percent of their organizations’ earnings before interest and taxes (EBIT) in 2020 was attributable to their use of AI.
10 The state of AI in 2021
Organizations seeing the highest returns from AI engage in risk-mitigation
practices more often than others.
Exhibit 6 cont.
Share of respondents reporting their organizations engage in each practice,¹ % of respondents
Training and testing data Measuring model bias and accuracy MMooddeell ddooccuummeennttaattiioonn
AI high performers² All other respondents
Document model performance on an ongoing basis 59 43
Document model architecture 53 43
Record information about both the training data set and the model-
52 34
training process
Document data flows 52 42
Document known issues and/or trade-offs with the model 43 30
Document the risk-mitigation strategies applied to both the model and its
30 28
underlying data
Documentation enables a clear understanding of the relative weight that
17 11
our data’s inputs have on the model’s output
¹Practices shown here are representative of those with the highest deltas between AI high performers and other respondents. Not all practices are shown.
²Respondents who said that at least 20 percent of their organizations’ earnings before interest and taxes (EBIT) in 2020 was attributable to their use of AI.
The survey content and analysis were developed by Michael Chui, a partner of the McKinsey Global Institute and a
partner in McKinsey’s Bay Area office; Bryce Hall, an associate partner in the Washington, DC, office; Alex Singla, a
senior partner in the Chicago office; and Alex Sukharevsky, a senior partner in the Moscow office.
They wish to thank Jacomo Corbo, David DeLallo, Liz Grennan, Heather Hanselman, and Kia Javanmardian for their
contributions to this article.
Copyright © 2021 McKinsey & Company. All rights reserved.
The state of AI in 2021 11"
63,mckinsey,building-the-ai-bank-of-the-future.pdf,"Global Banking Practice
Building the AI bank
of the future
May 2021
© Getty Images
Global Banking Practice
Building the AI bank of
the future
To thrive in the AI-powered digital age, banks will need an AI-and-analytics
capability stack that delivers intelligent, personalized solutions and
distinctive experiences at scale in real time.
May 2021
Contents
4 AI bank of the future: Can banks meet the
AI challenge?
Artificial intelligence technologies are increasingly
integral to the world we live in, and banks need
to deploy these technologies at scale to remain
relevant. Success requires a holistic transformation
spanning multiple layers of the organization.
18 Reimagining customer engagement for the
AI bank of the future
Banks can meet rising customer expectations by
applying AI to offer intelligent propositions and smart
servicing that can seamlessly embed in partner
ecosystems.
29 AI-powered decision making for the bank of
the future
Banks are already strengthening customer relationships
and lowering costs by using artificial intelligence to
guide customer engagement. Success requires that
capability stacks include the right decisioning elements.
41 Beyond digital transformations: Modernizing
core technology for the AI bank of the future
For artificial intelligence to deliver value across the
organization, banks need core technology that is scalable,
resilient, and adaptable. Building that requires changes in
six key areas.
52 Platform operating model for the AI bank of
the future
Technology alone cannot define a successful AI bank;
the AI bank of the future also needs an operating
model that brings together the right talent, culture, and
organizational design.
Introduction
Banking is at a pivotal moment. Technology leaders recognize that the economies of scale
disruption and consumer shifts are laying the basis afforded to organizations that efficiently deploy AI
for a new S-curve for banking business models, technologies will compel incumbents to strengthen
and the COVID-19 pandemic has accelerated customer engagement each day with distinctive
these trends. Building upon this momentum, experiences and superior value propositions. This
the advancement of artificial-intelligence (AI) value begins with intelligent, highly personalized
technologies within financial services offers banks offers and extends to smart services, streamlined
the potential to increase revenue at lower cost by omnichannel journeys, and seamless embedding
engaging and serving customers in radically new of trusted bank functionality within partner
ways, using a new business model we call “the AI ecosystems. From the customer’s point of view,
bank of the future.” The articles collected here these are key features of an AI bank.
outline key milestones on a path we believe can lead
banks to deeper customer relationships, expanded
market share, and stronger financial performance. The building blocks of an AI bank
Our goal in this compendium is to give banking
The opportunity for a new business model comes as leaders an end-to-end view of an AI bank’s full stack
banks face daunting challenges on multiple fronts. capabilities and examine how these capabilities
In capital markets, many banks trade at a 50 percent cut across four layers: engagement, AI-powered
discount to book, and approximately three-quarters decision making, core technology and data
of banks globally earn returns on equity that do not infrastructure, and a platform-based operating
cover their cost of equity.¹ Traditional banks also model.
face diverse competitive threats from neobanks and
nonbank challengers. Leading financial institutions In our first article, “AI-bank of the future: Can banks
are already leveraging AI for split-second loan meet the challenge?” we take a closer look at the
approvals, biometric authentication, and virtual trends and challenges leading banks to take an
assistants, to name just a few examples. Fintech AI-first approach as they define their core value
and other digital-commerce innovators are steadily proposition. We continue by considering a day in the
disintermediating banks from crucial aspects of life of a retail consumer and small-business owner
customer relationships, and large tech companies transacting with an AI bank. Then we summarize the
are incorporating payments and, in some cases, requirements for each layer of the AI-and-analytics
lending capabilities to attract more users with capability stack.
an ever-broader range of services. Further, as
customers conduct a growing share of their daily The second article, “Reimagining customer
transactions through digital channels, they are engagement for the AI bank of the future,” examines
becoming accustomed to the ease, speed, and the capabilities that enable a bank to provide
personalized service offered by digital natives, and customers with intelligent offers, personalized
their expectations of banks are rising. solutions, and smart servicing within omnichannel
journeys across bank-owned platforms and partner
To compete and thrive in this challenging ecosystems.
environment, traditional banks will need to build a
new value proposition founded upon leading-edge In our third article, “AI-powered decision making for
AI-and-analytics capabilities. They must become the bank of the future,” we examine how machine-
“AI first” in their strategy and operations. Many bank learning models can significantly enhance customer
1 “A test of resilience: Banking through the crisis, and beyond,” Global Banking Annual Review, December 2020, McKinsey.com.
2 Building the AI bank of the future
experiences and bank productivity, and we outline Once bank leaders have established their AI-first
the steps banks can follow to build the architecture vision, they will need to chart a road map detailing
required to generate real-time analytical insights and the discrete steps for modernizing enterprise
translate them into messages addressing precise technology and streamlining the end-to-end stack.
customer needs. Joint business-technology owners of customer-
facing solutions should assess the potential of
The fourth article, “Beyond digital transformations: emerging technologies to meet precise customer
Modernizing core technology for the AI bank of needs and prioritize technology initiatives with the
the future,” discusses the key elements required greatest potential impact on customer experience
for the backbone of the capability stack, including and value for the bank. We also recommend that
automated cloud provisioning and an API and banks consider leveraging partnerships for non-
streaming architecture to enable continuous, differentiating capabilities while devoting capital
secure data exchange between the centralized data resources to in-house development of capabilities
infrastructure and the decisioning and engagement that set the bank apart from the competition.
layers.
As we discuss in our final article, “Platform operating
model for the AI bank of the future,” deploying these Building the AI bank of the future will allow
AI-and-analytics capabilities efficiently at scale institutions to innovate faster, compete with digital
requires cross-functional business-technology natives in building deeper customer relationships
platforms comprising agile teams and new at scale, and achieve sustainable increases in
technology talent. profits and valuations in this new age. We hope
the following articles will help banks establish their
vision and craft a road map for the journey.
Starting the journey
To get started on the transformation, bank leaders
should formulate the organization’s strategic goals
for the AI-enabled digital age and evaluate how AI
technologies can support these goals.
Renny Thomas
Senior Partner
McKinsey & Company
Building the AI bank of the future 3
Global Banking & Securities
AI bank of the future: Can
banks meet the AI challenge?
Artificial intelligence technologies are increasingly integral to the world we
live in, and banks need to deploy these technologies at scale to remain
relevant. Success requires a holistic transformation spanning multiple layers
of the organization.
by Suparna Biswas, Brant Carson, Violet Chung, Shwaitang Singh, and Renny Thomas
© Getty Images
September 2020
4
In 2016, AlphaGo, a machine, defeated 18-time 3. What obstacles prevent banks from deploying
world champion Lee Sedol at the game of AI capabilities at scale?
Go, a complex board game requiring intuition,
imagination, and strategic thinking—abilities 4. How can banks transform to become AI first?
long considered distinctly human. Since then,
artificial intelligence (AI) technologies have
advanced even further,¹ and their transformative 1. Why must banks become AI first?
impact is increasingly evident across Over several decades, banks have continually
industries. AI-powered machines are tailoring adapted the latest technology innovations to
recommendations of digital content to individual redefine how customers interact with them. Banks
tastes and preferences, designing clothing introduced ATMs in the 1960s and electronic,
lines for fashion retailers, and even beginning to card-based payments in the ’70s. The 2000s saw
surpass experienced doctors in detecting signs of broad adoption of 24/7 online banking, followed
cancer. For global banking, McKinsey estimates by the spread of mobile-based “banking on the go”
that AI technologies could potentially deliver up to in the 2010s.
$1 trillion of additional value each year.²
Few would disagree that we’re now in the
Many banks, however, have struggled to move AI-powered digital age, facilitated by falling costs
from experimentation around select use cases to for data storage and processing, increasing
scaling AI technologies across the organization. access and connectivity for all, and rapid
Reasons include the lack of a clear strategy for AI, advances in AI technologies. These technologies
an inflexible and investment-starved technology can lead to higher automation and, when deployed
core, fragmented data assets, and outmoded after controlling for risks, can often improve upon
operating models that hamper collaboration human decision making in terms of both speed
between business and technology teams. What and accuracy. The potential for value creation
is more, several trends in digital engagement is one of the largest across industries, as AI can
have accelerated during the COVID-19 pandemic, potentially unlock $1 trillion of incremental value
and big-tech companies are looking to enter for banks, annually (Exhibit 1).
financial services as the next adjacency. To
compete successfully and thrive, incumbent Across more than 25 use cases,³ AI technologies
banks must become “AI-first” institutions, can help boost revenues through increased
adopting AI technologies as the foundation for personalization of services to customers (and
new value propositions and distinctive customer employees); lower costs through efficiencies
experiences. generated by higher automation, reduced errors
rates, and better resource utilization; and uncover
In this article, we propose answers to four new and previously unrealized opportunities
questions that can help leaders articulate a clear based on an improved ability to process and
vision and develop a road map for becoming an generate insights from vast troves of data.
AI-first bank:
More broadly, disruptive AI technologies can
1. Why must banks become AI first? dramatically improve banks’ ability to achieve
four key outcomes: higher profits, at-scale
2. What might the AI bank of the future look like? personalization, distinctive omnichannel
1 AI can be defined as the ability of a machine to perform cognitive functions associated with human minds (e.g., perceiving, reasoning, learning, and
problem solving). It includes various capabilities, such as machine learning, facial recognition, computer vision, smart robotics, virtual agents, and
autonomous vehicles. See “Global AI Survey: AI proves its worth, but few scale impact,” November 2019, McKinsey.com.
2 “The executive’s AI playbook,” McKinsey.com.
3 For an interactive view, visit: www.mckinsey.com/business-functions/mckinsey-analytics/our-insights/the-executives-ai-
playbook?page=industries/banking/
5 AI bank of the future: Can banks meet the AI challenge?
Exhibit 1
PPootetnetnitaila al nannunaula vl avlauleu oef o Af IA aIn adn adn aanlyatliyctsi cfosr f oglro gblaolb baaln bkainnkgi cnogu cldo urelda crhe aacs hh iagsh as
$h1i gtrhil aliso n$.1 trillion.
Total potential annual value, $ billion
1,022.4 (15.4% of sales)
Traditional AI
Advanced AI
and analytics
660.9 361.5
% of value driven by advanced AI, by function
100
Finance and IT: 8.0 Other operations: $2.4 B
0.0 8.0 0.0 2.4
50
HR: 14.2
8.6 5.7
Marketing and sales: 624.8
Risk: 372.9
363.8 261.1
288.6 84.3
0
Source: ""The executive's AI playbook,"" McKinsey.com. (See ""Banking,"" under ""Value & Assess."")
experiences, and rapid innovation cycles. Banks As consumers increase their use of digital
that fail to make AI central to their core strategy banking services, they grow to expect more,
and operations—what we refer to as becoming particularly when compared to the standards
“AI-first”—will risk being overtaken by competition they are accustomed to from leading consumer-
and deserted by their customers. This risk is internet companies. Meanwhile, these digital
further accentuated by four current trends: experience leaders continuously raise the bar
on personalization, to the point where they
— Rising customer expectations as adoption sometimes anticipate customer needs before
of digital banking increases. In the first few the customer is aware of them, and offer highly-
months of the COVID-19 pandemic, use of tailored services at the right time, through the
online and mobile banking channels across right channel.
countries has increased by an estimated 20
to 50 percent and is expected to continue at — Leading financial institutions’ use of advanced
this higher level once the pandemic subsides. AI technologies is steadily increasing. Nearly
Across diverse global markets, between 15 and 60 percent of financial-services sector
45 percent of consumers expect to cut back respondents in McKinsey’s Global AI Survey
on branch visits following the end of the crisis.⁴ report⁵ that their companies have embedded
4 John Euart, Nuno Ferreira, Jonathan Gordon, Ajay Gupta, Atakan Hilal, Olivia White, “A global view of financial life during COVID-19—an update,”
July 2020, McKinsey.com.
5 Arif Cam, Michael Chui, Bryce Hall, “Global AI Survey: AI proves its worth, but few scale impact,” November 2019, McKinsey.com.
AI bank of the future: Can banks meet the AI challenge? 6
at least one AI capability. The most commonly but also to book a cab, order food, schedule
used AI technologies are: robotic process a massage, play games, send money to a
automation (36 percent) for structured contact, and access a personal line of credit.
operational tasks; virtual assistants or Similarly, across countries, nonbanking
conversational interfaces (32 percent ) for businesses and “super apps” are embedding
customer service divisions; and machine financial services and products in their
learning techniques (25 percent) to detect journeys, delivering compelling experiences
fraud and support underwriting and risk for customers, and disrupting traditional
management. While for many financial services methods for discovering banking products and
firms, the use of AI is episodic and focused on services. As a result, banks will need to rethink
specific use cases, an increasing number of how they participate in digital ecosystems,
banking leaders are taking a comprehensive and use AI to harness the full power of data
approach to deploying advanced AI, and available from these new sources.
embedding it across the full lifecycle, from the
front- to the back-office (Exhibit 2). — Technology giants are entering financial
services as the next adjacency to their
— Digital ecosystems are disintermediating core business models. Globally, leading
traditional financial services. By enabling technology giants have built extraordinary
access to a diverse set of services through market advantages: a large and engaged
a common access point, digital ecosystems customer network; troves of data, enabling a
have transformed the way consumers discover, robust and increasingly precise understanding
evaluate, and purchase goods and services. of individual customers; natural strengths
For example, WeChat users in China can use in developing and scaling innovative
the same app not only to exchange messages, technologies (including AI); and access to
Web <year>
<article slug>
EExxhhibiibt <itx 2> of <y>
Banks are expanding their use of AI technologies to improve customer
Banks are expanding their use of AI technologies to improve customer
experiences and back-office processes.
experiences and back-office processes.
Front office Back office
Smile-to-pay facial scanning Micro-expression analysis Biometrics (voice, video, Machine learning to detect
to initiate transaction with virtual loan officers print) to authenticate and fraud patterns,
authorize cybersecurity attacks
Conversational bots for Humanoid robots in branches Machine vision and natural- Real-time transaction
basic servicing requests to serve customers language processing to scan analysis for risk monitoring
and process documents
7 AI bank of the future: Can banks meet the AI challenge?
low-cost capital. In the past, tech giants have digital era, the AI-first bank will offer propositions
aggressively entered into adjacent businesses and experiences that are intelligent (that
in search of new revenue streams and to is, recommending actions, anticipating and
keep customers engaged with a fresh stream automating key decisions or tasks), personalized
of offerings. Big-tech players have already (that is, relevant and timely, and based on a
gained a foothold in financial services in select detailed understanding of customers’ past
domains (especially in payments and, in some behavior and context), and truly omnichannel
cases, lending and insurance), and they may (seamlessly spanning the physical and online
soon look to press their advantages to deepen contexts across multiple devices, and delivering
their presence and build greater scale. a consistent experience) and that blend banking
capabilities with relevant products and services
beyond banking. Exhibit 3 illustrates how such a
2. What might the AI bank of the bank could engage a retail customer throughout
future look like? the day. Exhibit 4 shows an example of the banking
To meet customers’ rising expectations and experience of a small-business owner or the
beat competitive threats in the AI-powered treasurer of a medium-size enterprise.
Exhibit 3
How AI transforms banking for a retail customer.
How AI transforms banking for a retail customer.
Name: Anya
Age: 28 years
Occupation: Working professional
Anya receives
App offers money- integrated portfolio
management and view and a set of
Anya uses smile- savings solutions, actions with the
Seamless to-pay to Analytics- prioritizes card Aggregated potential to
integration with initiate payment backed payments overview of daily augment returns
nonbanking apps personalized offers
activities
Bank app Facial recognition Anya gets 2% off Personalized Anya receives Savings and
investment recom-
recognizes Anya's for frictionless on health money-management end-of-day
mendations
spending patterns payment insurance solutions overview of her
and suggests premiums based activities, with
coffee at nearby on her gym augmented reality,
cafes activity and and reminders to
sleep habits pay bills
Intelligent Personalized Omnichannel Banking and beyond banking
AI bank of the future: Can banks meet the AI challenge? 8
Exhibit 4
How AI transforms banking for a small- or medium-size-enterprise customer.
How AI transforms banking for a small- or medium-size-enterprise customer.
Name: Dany
Age: 36 years
Occupation: Treasurer of a small manufacturing unit
Dany answers
short questionnaire;
app scans his facial
An AI-powered
movements
Dany is assisted virtual adviser
Firm is credited in sourcing and resolves queries
with funds after selecting the Dany seeks
Customized application Seamless right vendors Beyond- professional advice
lending solutions approval inventory and receiv- and partners banking support on a lending offer
ables management services
Bank is integrated Micro-expression App suggests SME platform to Dany gets prefilled Serviced by an AI-
with client analysis to review loan items to reorder, source suppliers tax documents to powered virtual
business applications gives visual reports and buyers review and adviser
management on receivables approve; files with
systems management a single click
Dany receives
Dany gets loan
customized
offer based on
solutions for
company projected
invoice discounting,
cash flows
factoring, etc.
Intelligent Personalized Omnichannel Banking and beyond banking
Internally, the AI-first institution will be optimized The AI-first bank of the future will also enjoy
for operational efficiency through extreme the speed and agility that today characterize
automation of manual tasks (a “zero-ops” mindset) digital-native companies. It will innovate
and the replacement or augmentation of human rapidly, launching new features in days or
decisions by advanced diagnostic engines in weeks instead of months. It will collaborate
diverse areas of bank operations. These gains extensively with partners to deliver new
in operational performance will flow from broad value propositions integrated seamlessly
application of traditional and leading-edge AI across journeys, technology platforms, and
technologies, such as machine learning and data sets.
facial recognition, to analyze large and complex
reserves of customer data in (near) real time.
9 AI bank of the future: Can banks meet the AI challenge?
cases. Without a centralized data backbone, it is
3. What obstacles prevent banks from
practically impossible to analyze the relevant data
deploying AI capabilities at scale?
and generate an intelligent recommendation or
Incumbent banks face two sets of objectives,
offer at the right moment. If data constitute the
which on first glance appear to be at odds. On
bank’s fundamental raw material, the data must be
the one hand, banks need to achieve the speed,
governed and made available securely in a manner
agility, and flexibility innate to a fintech. On the
that enables analysis of data from internal and
other, they must continue managing the scale,
external sources at scale for millions of customers,
security standards, and regulatory requirements
in (near) real time, at the “point of decision” across
of a traditional financial-services enterprise.
the organization. Lastly, for various analytics and
advanced-AI models to scale, organizations need
Despite billions of dollars spent on change-
a robust set of tools and standardized processes
the-bank technology initiatives each year, few
to build, test, deploy, and monitor models, in a
banks have succeeded in diffusing and scaling
repeatable and “industrial” way.
AI technologies throughout the organization.
Among the obstacles hampering banks’ efforts,
Banks’ traditional operating models further
the most common is the lack of a clear strategy
impede their efforts to meet the need for
for AI.⁶ Two additional challenges for many
continuous innovation. Most traditional banks
banks are, first, a weak core technology and data
are organized around distinct business lines,
backbone and, second, an outmoded operating
with centralized technology and analytics
model and talent strategy.
teams structured as cost centers. Business
owners define goals unilaterally, and alignment
Built for stability, banks’ core technology
with the enterprise’s technology and analytics
systems have performed well, particularly in
strategy (where it exists) is often weak or
supporting traditional payments and lending
inadequate. Siloed working teams and “waterfall”
operations. However, banks must resolve
implementation processes invariably lead
several weaknesses inherent to legacy systems
to delays, cost overruns, and suboptimal
before they can deploy AI technologies at scale
performance. Additionally, organizations lack
(Exhibit 5). First and foremost, these systems
a test-and-learn mindset and robust feedback
often lack the capacity and flexibility required
loops that promote rapid experimentation and
to support the variable computing requirements,
iterative improvement. Often unsatisfied with the
data-processing needs, and real-time analysis
performance of past projects and experiments,
that closed-loop AI applications require.⁷ Core
business executives tend to rely on third-party
systems are also difficult to change, and their
technology providers for critical functionalities,
maintenance requires significant resources.
starving capabilities and talent that should ideally
What is more, many banks’ data reserves are
be developed in-house to ensure competitive
fragmented across multiple silos (separate
differentiation.
business and technology teams), and analytics
efforts are focused narrowly on stand-alone use
6 Michael Chui, Sankalp Malhotra, “AI adoption advances, but foundational barriers remain,” November 2018, McKinsey.com.
7 “Closed loop” refers to the fact that the models’ intelligence is applied to incoming data in near real time, which in turn refines the content presented
to the user in near real time.
AI bank of the future: Can banks meet the AI challenge? 10
Exhibit 5
IInnvveessttmmeentnst sin i nco croer tee cthe cahre a crrei tcicraitli ctoa lm teoe mt ienecrte ianscinrega dseinmga nddesm foarn ds for
ssccaalalabbiliiltiyty, ,fl flexeixbiibliitliyt,y a, nadn dsp sepede.e d.
Cloud
Data API1
Challenges How cloud computing can help
Core/legacy systems can’t scale sufficiently Enables higher scalability, resilience of services and
(eg, 150+ transactions/second) platforms through virtualization of infrastructure
Significant time, effort, and team sizes Reduces IT overhead, enables automation of several
required to maintain infrastructure infrastructure-management tasks, and allows development
teams to “self-serve”
Long time required to provision environments
for development and testing (eg, 40+ days in Enables faster time to market; dramatically reduces time by
some cases) providing managed services (e., setting up new environments
in minutes vs days)
Challenges How best-in-class data management can help
High error rates; poor refresh rates; lack of Ensures high degree of accuracy and single source of truth
golden source of truth in a cost-effective manner
Hard to access in a timely fashion for various Enables timely and role-appropriate access for various use
use cases cases (eg, regulatory, business intelligence at scale, advanced
analytics and machine learning, exploratory)
Data trapped in silos across multiple units and
hard to integrate with external sources Enables a 360-degree view across the organization to enable
generation of deeper insights by decision-making algorithms
and models
Challenges How APIs can help
Longer time to market, limited reusability of Promote reusability and accelerate development by enabling
code and software across internal teams access to granular services (internal and external)
Hard to partner or collaborate with external Reduce complexity and enable faster collaboration with
partners; long time to integrate external partners
Suboptimal user experience—hard to stitch Enhance customer experience by enabling timely access to
data and services across multiple functional data and services across different teams; faster time to market
siloes for an integrated proposition due to limited coordination, cross-team testing
1Application programming interface.
11 AI bank of the future: Can banks meet the AI challenge?
4. How can banks transform to First, banks will need to move beyond highly
become AI-first? standardized products to create integrated
To overcome the challenges that limit propositions that target “jobs to be done.”⁸ This
organization-wide deployment of AI requires embedding personalization decisions
technologies, banks must take a holistic (what to offer, when to offer, which channel
approach. To become AI-first, banks must invest to offer) in the core customer journeys and
in transforming capabilities across all four layers designing value propositions that go beyond the
of the integrated capability stack (Exhibit 6): the core banking product and include intelligence
engagement layer, the AI-powered decisioning that automates decisions and activities on
layer, the core technology and data layer, and the behalf of the customer. Further, banks should
operating model. strive to integrate relevant non-banking
products and services that, together with the
core banking product, comprehensively address
As we will explain, when these interdependent
the customer end need. An illustration of the
layers work in unison, they enable a bank to
“jobs-to-be-done” approach can be seen in the
provide customers with distinctive omnichannel
way fintech Tally helps customers grapple with
experiences, support at-scale personalization,
the challenge of managing multiple credit cards.
and drive the rapid innovation cycles critical
The fintech’s customers can solve several pain
to remaining competitive in today’s world.
points—including decisions about which card to
Each layer has a unique role to play—under-
pay first (tailored to the forecast of their monthly
investment in a single layer creates a weak link
income and expenses), when to pay, and how
that can cripple the entire enterprise.
much to pay (minimum balance versus retiring
principal)—a complex set of tasks that are often
The following paragraphs explore some of the
not done well by customers themselves.
changes banks will need to undertake in each
layer of this capability stack.
The second necessary shift is to embed
customer journeys seamlessly in partner
Layer 1: Reimagining the customer
ecosystems and platforms, so that banks
engagement layer
engage customers at the point of end use and
Increasingly, customers expect their bank to be
in the process take advantage of partners’
present in their end-use journeys, know their
data and channel platform to increase higher
context and needs no matter where they interact
engagement and usage. ICICI Bank in India
with the bank, and to enable a frictionless
embedded basic banking services on WhatsApp
experience. Numerous banking activities
(a popular messaging platform in India) and
(e.g., payments, certain types of lending) are
scaled up to one million users within three
becoming invisible, as journeys often begin and
months of launch.⁹ In a world where consumers
end on interfaces beyond the bank’s proprietary
and businesses rely increasingly on digital
platforms. For the bank to be ubiquitous in
ecosystems, banks should decide on the
customers’ lives, solving latent and emerging
posture they would like to adopt across multiple
needs while delivering intuitive omnichannel
ecosystems—that is, to build, orchestrate, or
experiences, banks will need to reimagine how
partner—and adapt the capabilities of their
they engage with customers and undertake
engagement layer accordingly.
several key shifts.
8 Clayton M. Christensen, Taddy Hall, Karen Dillon and David S. Duncan, “Know your customers ‘jobs to be done,” Harvard Business Review,
September 2016, hbr.org.
9 “ICICI Bank crosses 1 million users on WhatsApp platform,” Live Mint, July 7, 2020, livemint.com.
AI bank of the future: Can banks meet the AI challenge? 12
Exhibit 6
TToo b beeccoomme ean a nA IA-fiI-rfistr sitn sintistutittiuotni,o an b, aan bka mnku smt ustsrte asmtrelianme iltisn eca iptsa bcialiptya bstialictky sfotra ck
vfaolru ve aclrueea tciorena. tion.
AI bank of the future
Personalization Omnichannel Speed and
Profitability
at scale experience innovation
Intelligent products, Within-bank channels and Beyond-bank channels
Reimagined tools, experiences journeys (eg, web, apps, and journeys (eg, Smart service and
engagement for customers and mobile, smart devices, ecosystems, partners, operations
employees branches, Internet of Things) distributors)
1 2 3 4
5
Digital marketing
6
Retention
Credit Monitoring Servicing
Advanced Customer and cross-
decision and and
analytics acquisition selling,
AI-powered making collections engagement
upselling
decision
making
Natural-
Voice- Virtual Facial Behav-
7 language script agents, Computer recog- Block- Robotics ioral
AI capabilities process- analysis bots vision nition chain analytics
ing
A. Tech-forward strategy (in-house build of differ"
65,mckinsey,How-to-make-AI-work-for-your-business.pdf,"REPLACE IMAGE
Photo credit: Getty Images
How to make AI work for your
business
Jacques Bughin, Michael Chui, and Brian McCarthy
A survey of more than 3,000 executives sheds light on how
businesses are using AI, offering lessons for CEOs, as we
explain in this article for Harvard Business Review.
The buzz over artificial intelligence (AI) has While it’s clear that CEOs need to consider
grown loud enough to penetrate the C-suites AI’s business implications, the technology’s
of organizations around the world, and for nascence in business settings makes it less
good reason. Investment in AI is growing and is clear how to profitably employ it. Through a
increasingly coming from organizations outside study of AI that included a survey of 3,073
the tech space. And AI success stories are executives and 160 case studies across 14
becoming more numerous and diverse, from sectors and ten countries, and through a
Amazon reaping operational efficiencies using separate digital research program, we have
its AI-powered Kiva warehouse robots, to GE identified ten key insights CEOs need to know
keeping its industrial equipment running by to embark on a successful AI journey.
leveraging AI for predictive maintenance.
1 McKinsey Analytics
Don’t believe the hype—not every Believe the hype that AI can potentially
business is using AI … yet. boost your top and bottom line.
While investment in AI is heating up, corporate Thirty percent of early AI adopters in our
adoption of AI technologies is still lagging. survey—those using AI at scale or in core
Total investment (internal and external) in processes—say they’ve achieved revenue
AI reached somewhere in the range of increases, leveraging AI in efforts to gain
$26 billion to $39 billion in 2016, with external market share or expand their products and
investment tripling since 2013. Despite this services. Furthermore, early AI adopters are
level of investment, however, AI adoption 3.5 times more likely than others to say they
is in its infancy, with just 20 percent of our expect to grow their profit margin by up to
survey respondents using one or more AI five points more than industry peers. While
technologies at scale or in a core part of their the question of correlation versus causation
business, and only half of those using three can be legitimately raised, a separate analysis
or more. (Our results are weighted to reflect uncovered some evidence that AI is already
the relative economic importance of firms of directly improving profits, with return on AI
different sizes. We include five categories of AI investment in the same range as that for
technology systems: robotics and autonomous associated digital technologies such as big
vehicles, computer vision, language, virtual data and advanced analytics.
agents, and machine learning.)
Without support from leadership, your AI
For the moment, this is good news for those transformation might not succeed.
companies still experimenting with or piloting Successful AI adopters have strong
AI (41 percent). Our results suggest there’s still executive-leadership support for the new
time to climb the learning curve and compete technology. Survey respondents from
using AI. firms that have successfully deployed an
AI technology at scale tend to rate C-suite
However, we are likely at a key inflection point support as being nearly twice as high as that
of AI adoption. AI technologies such as neural- at those companies that have not adopted
based machine learning and natural-language any AI technology. They add that strong
processing are beginning to mature and prove support comes not only from the CEO and
their value, quickly becoming centerpieces IT executives but also from all other C-level
of AI technology suites among adopters. And officers and the board of directors.
we expect at least a portion of current AI
piloters to fully integrate AI in the near term. You don’t have to go it alone on AI—
Finally, adoption appears poised to spread, partner for capability and capacity.
albeit at different rates, across sectors and With the AI field recently picking up its pace of
domains. Telecom and financial services are innovation after the decades-long “AI winter,”
poised to lead the way, with respondents in technical expertise and capabilities are in
these sectors planning to increase their AI tech short supply. Even large digital natives such as
spend by more than 15 percent a year—seven Amazon and Google have turned to companies
percentage points higher than the cross- and talent outside their confines to beef up
industry average—in the next three years. their AI skills. Consider, for example, Google’s
How to make AI work for your business 2
acquisition of DeepMind, which is using its Long-term: Work with academia or a
machine-learning chops to help the tech giant third party to solve a high-impact use case
improve even core businesses such as search (augmented human decision making in a key
optimization. Our survey, in fact, showed that knowledge-worker role, for example) with
early AI adopters have primarily bought the bleeding-edge AI technology to potentially
right fit-for-purpose technology solutions, with capture a sizable first-mover advantage.
only a minority of respondents both developing
and implementing all AI solutions in-house. Machine learning is a powerful tool, but
it’s not right for everything.
Resist the temptation to put technology Machine learning and its most prominent
teams solely in charge of AI initiatives. subfield, deep learning, have attracted a lot
Compartmentalizing accountability for AI with of media attention and received a significant
functional leaders in IT, digital, or innovation share of the financing that has been pouring
can result in a hammer-in-search-of-a-nail into the AI universe, garnering nearly 60
outcome: technologies being launched without percent of all investments from outside the
compelling use cases. To ensure a focus on industry in 2016.
the most valuable use cases, AI initiatives
should be assessed and co-led by both But while machine learning has many
business and technical leaders, an approach applications, it is just one of many AI-related
that has proved successful in the adoption of technologies capable of solving business
other digital technologies. problems. There’s no one-size-fits-all AI
solution. For example, the AI techniques
Take a portfolio approach to accelerate implemented to improve customer-call-
your AI journey. center performance could be very different
AI tools today vary along a spectrum ranging from the technology used to identify credit-
from tools that have been proven to solve card-payments fraud. It’s critical to look for
business problems (for example, pattern the right tool to solve each value-creating
detection for predictive maintenance) to those business problem at a particular stage in an
with low awareness and currently limited but organization’s digital and AI journey.
high-potential utility (for example, application
of AI to develop a competitive strategy). This Digital capabilities come before AI.
distribution suggests that organizations could We found that industries leading in AI
consider a portfolio-based approach to AI adoption—such as high tech, telecom, and
adoption across three time horizons: automotive—are also the ones that are the
most digitized. Likewise, within any industry,
Short-term: Focus on use cases where there the companies that are early adopters of AI
are proven technology solutions today, and have already invested in digital capabilities,
scale them across the organization to drive including cloud infrastructure and big data. In
meaningful bottom-line value. fact, it appears that companies can’t easily
leapfrog to AI without digital-transformation
Medium-term: Experiment with technology experience. Using a battery of statistics, we
that’s emerging but still relatively immature found that the odds of generating profit from
(deep-learning video recognition) to prove its using AI are 50 percent higher for companies
value in key business use cases before scaling. that have strong experience in digitization.
3 McKinsey Analytics
Be bold. technical AI implementation challenges. As
In a separate study on digital disruption, we leaders determine the tasks that machines
found that adopting an offensive digital strategy should handle, versus those that humans
was the most important factor in enabling perform, both new and traditional, it will be
incumbent companies to reverse the curse critical to implement programs that allow
of digital disruption. An organization with an for constant reskilling of the workforce. And
offensive strategy radically adapts its portfolio as AI continues to converge with advanced
of businesses, developing new business visualization, collaboration, and design thinking,
models to build a growth path that is more businesses will need to shift from a primary
robust than before digitization. So far, the same focus on process efficiency to a focus on
seems to hold true for AI: early AI adopters decision-management effectiveness, which
with a very proactive, strictly offensive strategy will further require leaders to create a culture of
report a much better profit outlook than those continuous improvement and learning.
without one.
Make no mistake: the next digital frontier is
The biggest challenges are people and here, and it’s AI. While some firms are still
processes. reeling from previous digital disruptions, a new
In many cases, the change-management one is taking shape. But it’s early days. There’s
challenges of incorporating AI into employee still time to make AI a competitive advantage. 
processes and decision making far outweigh
Jacques Bughin is a senior partner in McKinsey’s Brussels office and a director of the McKinsey Global
Institute (MGI); Michael Chui is an MGI partner and is based in the San Francisco office; and Brian
McCarthy is a partner in the Atlanta office.
This article first ran in Harvard Business Review.
Copyright © 2018 McKinsey & Company. All rights reserved.
How to make AI work for your business 4"
66,mckinsey,driving-impact-at-scale-from-automation-and-ai.pdf,"Driving impact
at scale from
automation
and AI
February 2019
Contents
Introduction 2
Part 1: Why automation and AI?
Harnessing automation for a future that works 6
Notes from the AI frontier: Applications and value of deep learning 10
Artificial intelligence is getting ready for business, but are businesses ready for AI? 26
Part 2: How to make transformation successful
Burned by the bots: Why robotic automation is stumbling 44
Ten red flags signaling your analytics program will fail 48
The automation imperative 56
How to avoid the three common execution pitfalls that derail automation programs 64
The new frontier: Agile automation at scale 72
Part 3: Understanding functional nuances
How bots, algorithms, and artificial intelligence are reshaping the future of corporate 78
support functions
A CIO plan for becoming a leader in intelligent process automation 86
Introduction
Automation, leveraging artificial intelligence (AI) and other technologies, has
opened up new possibilities. The pace of adoption has been rapid. Institutions
of all sizes globally are leveraging automation to drive value. According to the
McKinsey Automation Survey in 2018, 57 percent of 1,300 institutions have
already started on this journey, with another 18 percent planning to kick off
something within the next year.
When done right, automation has proven to deliver real benefits, including the
following:
• Distinctive insights: Hundreds of new factors to predict and improve drivers
of performance
• Faster service: Processing time reduced from days to minutes
• Increased flexibility and scalability: Ability to operate 24/7 and scale up or
down with demand
• Improved quality: From spot-checking to 100 percent quality control
through greater traceability
• Increased savings and productivity: Labor savings of 20 percent or more
However, success is far from guaranteed. According to our Automation Survey,
only 55 percent of institutions believe their automation program has been
successful to date. Moreover, a little over half of respondents also say that the
program has been much harder to implement than they expected.
In this collection of articles, we explore why automation and AI are so
important, how to transform, and what the functional nuances are that can
be the difference between success and failure. At a high level, these articles delve into the four most
important practices that are strongly correlated with success in automation:
• Understand the opportunity and move early: Start taking advantage of automation and AI by
assessing the opportunity, identifying the high-impact use cases, and laying out the capability and
governance groundwork.
• Balance quick tactical wins with long-term vision: Identify quick wins to automate activities
with the highest automation potential and radiate out, freeing up capital; in parallel, have a long-
term vision for comprehensive transformation, with automation at the core.
• Redefine processes and manage organizational change: Since 60 percent of all jobs have at least
30 percent technically automatable activities, redefining jobs and taking an end-to-end process
view are necessary to capture the value.
• Integrate technology into core business functions: Build AI and other advanced technologies
into the operating model to create transformative impact and lasting value, support a culture
of collecting and analyzing data to inform decisions, and build the muscle for continuous
improvement.
We hope this curated collection will be helpful to you in realizing the full value potential from your
own automation transformation.
Alex Edlich Greg Phalin Rahil Jogani Sanjay Kaniyar
Senior partner, New York Senior partner, New York Partner, Chicago Partner, Boston
We wish to thank Keith Gilson, Vishal Koul, and Christina Yum for their contributions to this collection.
Introduction 3
Part
01
Why automation and AI?
4 Making a secure transition to cloud
POhloi tSoc carreffd/Git/eGtteyt tIym Iamgaegse Nsews
Harnessing automation for a future
that works
Jacques Bughin, Michael Chui, Martin Dewhurst, Katy George, James Manyika, Mehdi
Miremadi, and Paul Willmott
Automation is happening, and it will bring substantial benefits to
businesses and economies worldwide, but it won’t arrive overnight.
A new McKinsey Global Institute report finds realizing automation’s
full potential requires people and technology to work hand in hand.
Recent developments in robotics, artificial and CEOs. But how quickly will these automation
intelligence, and machine learning have put us technologies become a reality in the workplace?
on the cusp of a new automation age. Robots and And what will their impact be on employment and
computers can not only perform a range of routine productivity in the global economy?
physical work activities better and more cheaply
than humans, but they are also increasingly The McKinsey Global Institute has been conducting
capable of accomplishing activities that include an ongoing research program on automation
cognitive capabilities once considered too difficult technologies and their potential effects. A new MGI
to automate successfully, such as making tacit report, A future that works: Automation, employment,
judgments, sensing emotion, or even driving. and productivity, highlights several key findings.
Automation will change the daily work activities
The automation of activities can enable businesses
of everyone, from miners and landscapers to
to improve performance by reducing errors
commercial bankers, fashion designers, welders,
6 Digital/McKinsey
and improving quality and speed, and in some Still, automation will not happen overnight. Even
cases achieving outcomes that go beyond human when the technical potential exists, we estimate it
capabilities. Automation also contributes to will take years for automation’s effect on current
productivity, as it has done historically. At a time work activities to play out fully. The pace of
of lackluster productivity growth, this would give automation, and thus its impact on workers, will
a needed boost to economic growth and prosperity. vary across different activities, occupations, and
It would also help offset the impact of a declining wage and skill levels. Factors that will determine
share of the working-age population in many the pace and extent of automation include the
countries. Based on our scenario modeling, we ongoing development of technological capabilities,
estimate automation could raise productivity the cost of technology, competition with labor
growth globally by 0.8 to 1.4 percent annually. including skills and supply and demand dynamics,
performance benefits including and beyond labor
The right level of detail at which to analyze the cost savings, and social and regulatory acceptance.
potential impact of automation is that of individual Our scenarios suggest that half of today’s work
activities rather than entire occupations. Every activities could be automated by 2055, but this
occupation includes multiple types of activity, each could happen up to 20 years earlier or later
of which has different requirements for automation. depending on various factors, in addition to other
Given currently demonstrated technologies, economic conditions.
very few occupations—less than 5 percent—are
candidates for full automation. However, almost The effects of automation might be slow at a macro
every occupation has partial automation potential, level, within entire sectors or economies, for
as a proportion of its activities could be automated. example, but they could be quite fast at a micro
We estimate that about half of all the activities level, for individual workers whose activities are
people are paid to do in the world’s workforce could automated or for companies whose industries are
potentially be automated by adapting currently disrupted by competitors using automation.
demonstrated technologies. That amounts to
almost $15 trillion in wages. While much of the current debate about
automation has focused on the potential for mass
The activities most susceptible to automation are unemployment, people will need to continue
physical ones in highly structured and predictable working alongside machines to produce the growth
environments, as well as data collection and in per capita GDP to which countries around the
processing. In the United States, these activities world aspire. Thus, our productivity estimates
make up 51 percent of activities in the economy, assume that people displaced by automation will
accounting for almost $2.7 trillion in wages. find other employment. Many workers will have
They are most prevalent in manufacturing, to change, and we expect business processes to be
accommodation and food service, and retail trade. transformed. However, the scale of shifts in the
And it’s not just low-skill, low-wage work that could labor force over many decades that automation
be automated; middle-skill and high-paying, high- technologies can unleash is not without precedent.
skill occupations, too, have a degree of automation It is of a similar order of magnitude to the long-
potential. As processes are transformed by the term technology-enabled shifts away from
automation of individual activities, people will agriculture in developed countries’ workforces
perform activities that complement the work that in the 20th century. Those shifts did not result
machines do, and vice versa. in long-term mass unemployment, because they
Harnessing automation for a future that works 7
were accompanied by the creation of new types of come about if people work alongside machines. That
work. We cannot definitively say whether things in turn will fundamentally alter the workplace,
will be different this time. But our analysis shows requiring a new degree of cooperation between
that humans will still be needed in the workforce: workers and technology. 
the total productivity gains we estimate will only
Jacques Bughin and James Manyika are directors of the McKinsey Global Institute, and Michael Chui is
an MGI partner; Martin Dewhurst and Paul Willmott are senior partners in McKinsey’s London office; Katy
George is a senior partner in the New Jersey office; and Mehdi Miremadi is a partner in the Chicago office.
Copyright © 2017 McKinsey & Company. All rights reserved.
8 Digital/McKinsey
A global force that will transform economies and the workforce
Technical automation potential by adapting currently demonstrated technologies
Wages associated with technically
automatable activities
While few occupations are fully automatable, 60 percent of all occupations
$ trillion
have at least 30 percent technically automatable activities
Remaining China
Share of roles countries 4.7 3.6
ACTIVITIES WITH HIGHEST 100% = 820 roles
AUTOMATION POTENTIAL:
About 60% of occupations 100% =
have at least 30% of 100 $14.6T
Predictable physical activities 81% their activities that 91 1.0 2.3 United
Processing data 69% are automatable
Japan 1.1 1.9 States
Collecting data 64% 73
India
Big 5 in Europe1
62
Labor associated with technically
51 automatable activities
<5% of occupations consist 42 Million full-time equivalents (FTEs)
of activities that are 34
100% automatable 26 Remaining
18 countries 367 China
395
8 100% =
1 1.2B
36
Japan 61
100 >90 >80 >70 >60 >50 >40 >30 >20 >10 >0 235 62 U Stn ait te ed s
Technical automation potential, % India Big 5 in Europe1
1 France, Germany, Italy, Spain, and the United Kingdom.
Five factors affecting pace and Automation will boost global
extent of adoption productivity and raise GDP
G19 plus Nigeria
1 2 3 4 5
TECHNICAL COST OF LABOR ECONOMIC REGULATORY Productivity growth, %
FEASIBILITY DEVELOPING MARKET BENEFITS AND SOCIAL Automation can help provide some of the productivity needed
Technology AND DYNAMICS Include higher ACCEPTANCE to achieve future economic growth
has to be DEPLOYING The supply, throughput Even when
invented, SOLUTIONS demand, and and increased automation Employment growth, %
integrated, Hardware costs of quality, makes will slow drastically because of aging
and adapted and software human labor alongside business
into solutions costs affect which labor cost sense,
for specific activities will savings adoption can
Last 50 years Next 50 years Next 50 years
case use be automated take time
Growth aspiration Potential impact of automation
Scenarios around time spent on current work activities, %
Adoption, Adoption, Technical automation Technical automation 1.8
Early scenario Late scenario potential, Early scenario potential, Late scenario
2.8
100 Technical
automation
80 potential 1.7 1.4
must precede 0.8
60 adoption 0.1 0.1 0.1
40 Technical, 3.5 2.9 1.5 0.9
economic,
and social Historical Required to Early Late
20 factors affect achieve scenario scenario
pace of projected growth
0 adoption
2020 2030 2040 2050 2060 2065 in GDP per capita
Harnessing automation for a future that works 9
Notes from the AI frontier:
Applications and value of deep
learning
Michael Chui, Rita Chung, Nicolaus Henke, Sankalp Malhotra, James Manyika, Mehdi Miremadi,
and Pieter Nel
An analysis of more than 400 use cases across 19 industries
and nine business functions highlights the broad use and
significant economic potential of advanced AI techniques.
Artificial intelligence (AI) stands out as a and the problems they can solve to more
transformational technology of our digital than 400 specific use cases in companies and
age—and its practical application throughout organizations.1 Drawing on McKinsey Global
the economy is growing apace. In our discussion Institute research and the applied experience
paper Notes from the AI frontier: Insights from with AI of McKinsey Analytics, we assess both the
hundreds of use cases, we mapped both traditional practical applications and the economic potential
analytics and newer “deep learning” techniques of advanced AI techniques across industries and
1 For the full McKinsey Global Institute discussion paper, see “Notes from the AI frontier: Applications and value of deep
learning,” April 2018, on McKinsey.com.
10 Digital/McKinsey
business functions. Our findings highlight the We analyzed the applications and value of three
substantial potential of applying deep learning neural network techniques:
techniques to use cases across the economy, but we
also see some continuing limitations and obstacles—  Feed-forward neural networks: The
along with future opportunities as the technologies simplest type of artificial neural network.
continue their advance. Ultimately, the value of AI In this architecture, information moves
is not to be found in the models themselves, but in in only one direction, forward, from the
companies’ abilities to harness them. input layer, through the “hidden” layers, to
the output layer. There are no loops in the
It is important to highlight that, even as we see network. The first single-neuron network
economic potential in the use of AI techniques, the was proposed already in 1958 by AI pioneer
use of data must always take into account concerns Frank Rosenblatt. While the idea is not
including data security, privacy, and potential new, advances in computing power, training
issues of bias. algorithms, and available data led to higher
levels of performance than previously
Mapping AI techniques to problem possible.
types
As artificial intelligence technologies advance, so  Recurrent neural networks (RNNs): Artificial
does the definition of which techniques constitute neural networks whose connections
AI.2 For the purposes of this article, we use AI as between neurons include loops; RNNs are
shorthand for deep learning techniques that use well suited for processing sequences of
artificial neural networks. We also examined inputs. In November 2016, Oxford University
other machine learning techniques and traditional researchers reported that a system based on
analytics techniques (Exhibit 1). recurrent neural networks (and convolutional
neural networks) had achieved 95 percent
Neural networks are a subset of machine learning accuracy in reading lips, outperforming
techniques. Essentially, they are AI systems based experienced human lip readers, who tested at
on simulating connected “neural units,” loosely 52 percent accuracy.
modeling the way that neurons interact in the
brain. Computational models inspired by neural  Convolutional neural networks (CNNs):
connections have been studied since the 1940s Artificial neural networks in which the
and have returned to prominence as computer connections between neural layers are
processing power has increased and large training inspired by the organization of the animal
data sets have been used to successfully analyze visual cortex, the portion of the brain that
input data such as images, video, and speech. AI processes images; CNNs are well suited for
practitioners refer to these techniques as “deep perceptual tasks.
learning,” since neural networks have many
(“deep”) layers of simulated interconnected For our use cases, we also considered two other
neurons. techniques—generative adversarial networks and
reinforcement learning—but did not include them
2 For more on AI techniques, including definitions and use cases, see “An executive’s guide to AI,” February 2018,
McKinsey.com.
Notes from the AI frontier: Applications and value of deep learning 11
Web <2018>
<MGI AI Impact>
Exhibit 1
Exhibit <1> of <6>
We examined artificial intelligence (AI), machine learning, and other
We examined artificial intelligence (AI), machine learning, and other
analytics techniques for our research.
analytics techniques for our research.
Considered AI for our research
MORE
Transfer
learning
Reinforcement
Deep learning
learning
(feed-forward networks,
CNNs1, RNNs2, GANs3)
Dimensionality
reduction
Likelihood to Ensemble
be used in AI Instance-based Decision-tree learning
applications learning learning
Monte Linear Clustering
Carlo classifiers
methods
Statistical Markov Regression
inference process analysis
Descriptive Naive Bayes
statistics classifiers
LESS
TRADITIONAL Complexity of technique ADVANCED
1 Convolutional neural networks.
2 Recurrent neural networks.
3 Generative adversarial networks.
Source: McKinsey Global Institute analysis
12 Digital/McKinsey
in our potential value assessment of AI, since they Following are examples of where AI can be used to
remain nascent techniques that are not yet widely improve the performance of existing use cases:
applied:
 Predictive maintenance: The power of
 Generative adversarial networks (GANs) machine learning to detect anomalies. Deep
use two neural networks contesting one learning’s capacity to analyze very large
another in a zero-sum game framework (thus amounts of high-dimensional data can take
“adversarial”). GANs can learn to mimic existing preventive maintenance systems to
various distributions of data (for example, a new level. Layering in additional data, such
text, speech, and images) and are therefore as audio and image data, from other sensors—
valuable in generating test data sets when including relatively cheap ones such as
these are not readily available. microphones and cameras—neural networks
can enhance and possibly replace more
 Reinforcement learning is a subfield of traditional methods. AI’s ability to predict
machine learning in which systems are failures and allow planned interventions
trained by receiving virtual “rewards” or can be used to reduce downtime and
“punishments,” essentially learning by trial operating costs while improving production
and error. Google’s DeepMind has used yield. For example, AI can extend the life
reinforcement learning to develop systems of a cargo plane beyond what is possible
that can play games, including video games using traditional analytics techniques by
and board games such as Go, better than combining plane model data, maintenance
human champions. history, and Internet of Things (IoT) sensor
data such as anomaly detection on engine-
In a business setting, these analytic techniques vibration data, and images and video of
can be applied to solve real-life problems. The engine condition.
most prevalent problem types are classification,
continuous estimation, and clustering (see sidebar,  AI-driven logistics optimization can reduce
“Problem types and their definitions”). costs through real-time forecasts and
behavioral coaching. Application of AI
Insights from use cases techniques such as continuous estimation
We collated and analyzed more than 400 use cases to logistics can add substantial value across
across 19 industries and nine business functions. sectors. AI can optimize routing of delivery
They provided insight into the areas within traffic, thereby improving fuel efficiency
specific sectors where deep neural networks can and reducing delivery times. One European
potentially create the most value, the incremental trucking company has reduced fuel costs by
lift that these neural networks can generate 15 percent, for example, by using sensors
compared with traditional analytics (Exhibit 2), that monitor both vehicle performance and
and the voracious data requirements—in terms of driver behavior; drivers receive real-time
volume, variety, and velocity—that must be met coaching, including when to speed up or slow
for this potential to be realized. Our library of use down, optimizing fuel consumption and
cases, while extensive, is not exhaustive and may reducing maintenance costs.
overstate or understate the potential for certain
sectors. We will continue refining and adding to it.
Notes from the AI frontier: Applications and value of deep learning 13
EWxebh <ib20it1 82>
<MGI AI Impact>
AExdhibvit a<2n> cofe <6d> deep learning artificial intelligence techniques can be
applied across industries, alongside more traditional analytics.
Advanced deep learning artificial intelligence techniques can be applied across industries,
alongside more traditional analytics.
Technique relevance1 heatmap by industry Frequency of use Low High
Focus of report Traditional analytics techniques
Feed- Recurrent Convolutional Generative Tree-based
forward neural neural adversarial Reinforcement ensemble Regression Statistical
networks networks networks networks learning learning Classifiers Clustering analysis inference
Advanced electronics/
semiconductors
Aerospace and
defense
Agriculture
Automotive and
assembly
Banking
Basic materials
Chemicals
Consumer
packaged goods
Healthcare systems
and services
High tech
Insurance
Media and
entertainment
Oil and gas
Pharmaceuticals and
medical products
Public and
social sectors
Retail
Telecommunications
Transport and
logistics
Travel
1Relevance refers to frequency of use in our use case library, with the most frequently found cases marked as high
relevance and the least frequently found as low relevance. Absence of circles indicates no or statistically insignificant
number of use cases.
Note: List of techniques is not exhaustive.
Source: McKinsey Global Institute analysis
14 Digital/McKinsey
Problem types and their definitions
Classification: Based on a set of training data, categorize new inputs as belonging to one of a set of categories. An example of
classification is identifying whether an image contains a specific type of object, such as a cat or a dog, or a product of acceptable
quality coming from a manufacturing line.
Continuous estimation: Based on a set of training data, estimate the next numeric value in a sequence. This type of problem
is sometimes described as “prediction,” particularly when it is applied to time-series data. One example of continuous estimation
is forecasting the sales demand for a product, based on a set of input data such as previous sales figures, consumer sentiment,
and weather.
Clustering: These problems require a system to create a set of categories, for which individual data instances have a set of
common or similar characteristics. An example of clustering is creating a set of consumer segments, based on a set of data
about individual consumers, including demographics, preferences, and buyer behavior.
All other optimization: These problems require a system to generate a set of outputs that optimize outcomes for a specific
objective function (some of the other problem types can be considered types of optimization, so we describe these as “all other”
optimization). Generating a route for a vehicle that creates the optimum combination of time and fuel utilization is an example of
optimization.
Anomaly detection: Given a training set of data, determine whether specific inputs are out of the ordinary. For instance, a
system could be trained on a set of historical vibration data associated with the performance of an operating piece of machinery,
and then determine whether a new vibration reading suggests that the machine is not operating normally. Anomaly detection can
be considered a subcategory of classification.
Ranking: Ranking algorithms are used most often in information-retrieval problems where the results of a query or request
needs to be ordered by some criterion. Recommendation systems suggesting next product to buy use these types of algorithms
as a final step, sorting suggestions by relevance, before presenting the results to the user.
Recommendations: These systems provide recommendations based on a set of training data. A common example of
recommendations are systems that suggest “next product to buy” for an individual buyer, based on the buying patterns of similar
individuals and the observed behavior of the specific person.
Data generation: These problems require a system to generate appropriately novel data based on training data. For instance,
a music composition system might be used to generate new pieces of music in a particular style, after having been trained on
Notes from the AI frontier: Applications and value of deep learning 15
 AI can be a valuable tool for customer Greenfield AI solutions are prevalent in business
service management and personalization areas such as customer-service management, as
challenges. Improved speech recognition in well as among some industries where the data are
call center management and call routing as rich and voluminous and at times integrate human
a result of the application of AI techniques reactions. Among industries, we found many
allows a more seamless experience for greenfield use cases in healthcare, in particular.
customers—and more efficient processing. Some of these cases involve disease diagnosis
The capabilities go beyond words alone. For and improved care and rely on rich data sets
example, deep learning analysis of audio incorporating image and video inputs, including
allows systems to assess a customer’s from MRIs.
emotional tone; in the event a customer is
responding badly to the system, the call On average, our use cases suggest that modern
can be rerouted automatically to human deep learning AI techniques have the potential
operators and managers. In other areas to provide a boost in additional value above and
of marketing and sales, AI techniques can beyond traditional analytics techniques—ranging
also have a significant impact. Combining from 30 percent to 128 percent, depending on
customer demographic and past transaction industry.
data with social media monitoring can
help generate individualized product In many of our use cases, however, traditional
recommendations. Next-product-to-buy analytics and machine learning techniques
recommendations that target individual continue to underpin a large percentage of the
customers—as companies such as Amazon value-creation potential in industries including
and Netflix have successfully been doing— insurance, pharmaceuticals and medical products,
can lead to a twofold increase in the rate of and telecommunications, with the potential of AI
sales conversions. limited in certain contexts. In part this is due to
the way data are used by these industries and to
Two-thirds of the opportunities to use AI are regulatory issues.
in improving the performance of existing
analytics use cases Data requirements for deep learning
In 69 percent of the use cases we studied, deep are substantially greater than for other
neural networks can be used to improve analytics
performance beyond that provided by other Making effective use of neural networks in most
analytics techniques. Cases in which only neural applications requires large labeled training data
networks can be used, which we refer to here as sets alongside access to sufficient computing
“greenfield” cases, constituted just 16 percent of infrastructure. Furthermore, these deep learning
the total. For the remaining 15 percent, artificial techniques are particularly powerful in extracting
neural networks provided limited additional patterns from complex, multidimensional data
performance over other analytics techniques, types such as images, video, and audio or speech.
because, among other reasons, of data limitations
that made these cases unsuitable for deep learning Deep learning methods require thousands of
(Exhibit 3). data records for models to become relatively
16 Digital/McKinsey
Web <2018>
<MGI AI Impact>
Exhibit 3
Exhibit <3> of <6>
In more than two-thirds of our use cases, artificial intelligence (AI)
cIna nm imorpe rtohvaen ptwerof-otrhmirdasn coef obuery uosned c tahsaets p, raorvtiifidceiadl binyt eoltlihgeern acen a(AlyIt) iccasn
tiemcphrnoivqeu epse.rformance beyond that provided by other analytics techniques.
Breakdown of Potential incremental value from AI over other analytics techniques, %
use cases by
applicable
Travel 128
techniques, %
Transport and logistics 89
Full value can Retail 87
be captured
15
using non-AI Automotive and assembly 85
techniques
High tech 85
AI necessary
Oil and gas 79
to capture
16
value
Chemicals 67
(“greenfield”)
Media and entertainment 57
Basic materials 56
Agriculture 55
Consumer packaged goods 55
AI can
Banking 50
improve
performance
Healthcare systems and services 44
over that
69
provided Public and social sectors 44
by other
analytics Telecommunications 44
techniques
Pharmaceuticals and medical products 39
Insurance 38
Advanced electronics/semiconductors 36
Aerospace and defense 30
Source: McKinsey Global Institute analysis
Notes from the AI frontier: Applications and value of deep learning 17
good at classification tasks and, in some cases, Realizing AI’s full potential requires a
millions for them to perform at the level of diverse range of data types, including
humans. By one estimate, a supervised deep images, video, and audio
learning algorithm will generally achieve Neural AI techniques excel at analyzing image,
acceptable performance with around 5,000 video, and audio data types because of their
labeled examples per category and will match or complex, multidimensional nature, known
exceed human-level performance when trained by practitioners as “high dimensionality.”
with a data set containing at least ten million Neural networks are good at dealing with high
labeled examples.3 In some cases where advanced dimensionality, as multiple layers in a network
analytics are currently used, so much data are can learn to represent the many different features
available—millions or even billions of rows per present in the data. Thus, for facial recognition,
data set—that AI usage is the most appropriate the first layer in the network could focus on raw
technique. However, if a threshold of data volume pixels, the next on edges and lines, another on
is not reached, AI may not add value to traditional generic facial features, and the final layer might
analytics techniques. identify the face. Unlike previous generations
of AI, which often required human expertise to
These massive data sets can be difficult to obtain do “feature engineering,” these neural network
or create for many business use cases, and labeling techniques are often able to learn to represent
remains a challenge. Most current AI models these features in their simulated neural networks
are trained through “supervised learning,” as part of the training process.
which requires humans to label and categorize
the underlying data. However, promising new Along with issues around the volume and variety of
techniques are emerging to overcome these data data, velocity is also a requirement: AI techniques
bottlenecks, such as reinforcement learning, require models to be retrained to match potential
generative adversarial networks, transfer learning, changing conditions, so the training data must
and “one-shot learning,” which allows a trained be refreshed frequently. In one-third of the cases,
AI model to learn about a subject based on a the model needs to be refreshed at least monthly,
small number of real-world demonstrations or and almost one in four cases requires a daily
examples—and sometimes just one. refresh;"
67,mckinsey,gen-ai-opportunities-in-m-and-a.pdf,"M&A Practice
Gen AI: Opportunities
in M&A
Generative AI is already making its way into the day-to-day world of
M&A, and more use cases are emerging. How should companies
approach the opportunity?
by Ben Ellencweig, Mieke Van Oostende, and Rui Silva
with Julia Berbel
May 2024
Generative AI (gen AI) is making its mark across a M&A that any deal, small or large, requires real work
gamut of industries and functions. Yet as and real people capacity to successfully execute it.
companies seek to capture the immense economic Gen AI, like many other technologies, exists to help
potential from gen AI and traditional AI, they’re leaders do more with less, make better decisions,
finding that it will take time to identify and prioritize and ultimately help their organizations create value
the most impactful and economically sound use in the long term. More specifically, four categories
cases, understand what is and isn’t—yet— of use cases for gen AI can materially improve the
achievable, and train employees for a broad range M&A process: faster and better-quality sourcing of
of applications and initiatives. potential targets; expediting the diligence and
negotiation process; executing the integration or
M&A is no exception. There are significant separation with excellence; and strengthening in-
opportunities for gen AI across the end-to-end house M&A capabilities.
M&A process, from defining an M&A strategy to
conducting due diligence to executing integrations Faster and better-quality sourcing of targets
or separations. Delivering successful transactions There is a surfeit of potential companies to acquire,
and building an effective M&A program is a sell to, or partner with. A huge amount of data
resource-intensive process with numerous pain about these companies is obtainable. In fact, there’s
points, and it’s clear that new technologies can so much information that organizations’ M&A teams
help. In fact, gen AI solutions are already being can get bogged down sorting through and
successfully applied. processing it all. The most successful M&A programs
look beyond their core business, into adjacencies
The goal of this article is not to reel off big and potential step-outs, and this is where gen AI
numbers; suffice to say, the potential is enormous. can be most impactful. Companies are in a race
As dealmakers prepare for what’s to come, we want because their competitors are searching for targets,
to share our real-time perspective. We’ll explore too. They also have to be thorough: target
some potential M&A use cases, provide examples of assessment needs to encompass several dimensions
solutions that are already being deployed, and offer to identify the highest-value potential targets with
practical steps on how organizations can use gen AI the right strategic and cultural fit. Deal scanning is a
to enhance their M&A capabilities. prominent, proven use case for traditional AI, but
when coupled with gen AI it can go further to find
and interpret broader sets of structured and
How gen AI is gaining traction unstructured data, synthesize results to answer
in M&A quantitative and qualitative prompts, and highlight
key elements of strategic, financial, and cultural fit
For years, our research has shown that taking a
of all potential targets. With gen AI, companies can
programmatic approach to M&A in the long term
identify and pursue targets they wouldn’t otherwise
can significantly boost an organization’s
have on their radar (exhibit).
performance compared with its peers. Yet M&A
execution is a very labor-intensive activity, requiring
thoughtful allocation of resources and a balanced For example, a North American–based company in
focus between integration activities and core the consumer-packaged-goods industry used
business continuity. It is inherent to the nature of McKinsey’s proprietary tool DealScan.AI to search
Gen AI: Opportunities in M&A 2
Exhibit
Gen AI: Opportunities in M&A 3
and evaluate potential investments. First, the tool Tools powered by gen AI can do a lot of the heavy
identified approximately 1,600 viable targets lifting. In fact, a wide range of time- and resource-
according to initial prompts. Then, it applied consuming tasks can be accelerated and, in some
bespoke quantitative and qualitative prioritization cases, almost fully automated. One striking use
criteria, including whether there was a direct-to- case is to have a gen AI “coach,” trained on M&A
consumer operating model, information about best practices and on the organization’s specific
subscription-based product assortments, and M&A playbook, that delivers fast and smart answers
details about recent fundings. This led to the to questions from integration and separation
prioritization of 40 targets—most of which the leaders and team members. Applications are rapidly
company had not considered before—that matched evolving, including McKinsey’s myIMO, which is
all requirements. powered by gen AI to help improve team
capabilities and efficiency. For example, a team
Expediting the diligence and negotiation process could ask the tool, “What are the right steps to
Gen AI can expedite the diligence and negotiation integrate the acquired company’s brand with our
process. For example, it can summarize key own, and what is the best timing to do that?” Or a
diligence documents, surface risks, draft initial team could give it the following prompt: “Draft a
memoranda based on a deal’s specific parameters, memo about upcoming changes in employee
source applicable statutes and regulations, identify benefits considering the following changes.” The
helpful case law to ease friction in the negotiations application is trained on a vast repository of M&A
phase, and generate other highly accurate outputs playbooks and best practices to help companies
(such as first drafts of the deal announcement and make well-informed decisions about their
regulatory filings). As one can imagine, these use integrations or separations. Other uses being
cases can save a significant portion of the time developed include post-day-one value creation
currently required to perform the different legal recommendations, such as identifying real-time
tasks involved in deal negotiations, signing, synergy opportunities based on a company’s
and closing. available data; automated summaries and
comparisons of internal policies that need to be
Executing the integration or separation harmonized between the two organizations; the
with excellence quick comparison and harmonization of job title and
Seasoned dealmakers know that deal synergies hierarchy structures, cost center, and general
need to be captured quickly—and that sometimes, ledger definitions; and the automation of change
by taking too long, companies can squander management activities. The list goes on.
significant value. Organizations going through
sizeable M&A events are particularly likely to get Strengthening in-house M&A capabilities
diverted and see organic momentum decline, with Gen AI can strengthen a company’s internal
an average decrease in excess revenue growth of capabilities by drawing on companies’ proprietary
seven percentage points compared with peers.1 data from past deals to assess performance
Sluggish integrations can frustrate customers, patterns and find insights about untapped
demotivate employees, and sometimes cause opportunities. For example, it could assess a
organizations to stall. company’s portfolio of acquisitions and calculate
the impact brought by each deal. It could also
generate postmortem insight about how deals
1 Based on the 1,000 largest companies in McKinsey’s annual Global 2,000 analysis. For more on the methodology of the Global 2,000, see “The
seven habits of programmatic acquirers,” August 24, 2023.
Gen AI: Opportunities in M&A 4
affect the business (for example, how and when the decision to either use in-house resources or
company’s organic revenue growth is typically outsource, leaders should consider their team’s
affected after closing a deal). It could update the existing expertise, the size of the required
company’s proprietary playbook with recipes, investment, the extent of the potential return
nuances, and lessons learned (for example, “Deals (including how sustainable any competitive
of up to $1 billion typically require an integration advantage would be), and the actions that the
team of five people, focused on the following company’s peers are or could be taking.
tasks”). It could even generate personalized training
programs in line with the specific function of an — Ensure that the right guardrails are in place.
integration team member, as well as with the Gen AI is distinct from most existing
acquisition type and the deal timing (based on the technologies because it heightens certain
following prompt, for example: “I am new to the risks—for example, security breaches, given its
team. I will be leading the HR integration for our ease of access; reputational risks from quality
acquisition of X company. What do I need to know? control missteps; and potential intellectual
Where do I start?”). property infringement. Legal and regulatory
developments are fast moving, even as gen AI
races forward. And the better the AI models are,
How to get started
the greater the potential risk that humans will
Gen AI will not fix a broken approach to M&A; it simply disengage and not catch issues until it’s
might even exacerbate it. The first step for senior too late. It’s essential that organizations keep
leaders is to frankly assess their current level of human beings at the forefront of the work,
M&A capabilities and to consider where in the M&A proactively identify and mitigate risks in
process technology can be used to materially partnership with their legal and technology
improve the M&A engine. teams, and maintain rigorous ethical standards.
The next steps are just as foundational: Gen AI is a predictive language model, not a human
being. As companies navigate the gen AI transition,
— Prioritize the gen AI use cases that create the they should consider how to use their newly freed-
most value. If your M&A strategy is focused on up time to focus on more strategic, high-value
acquiring dozens of very small players, gen AI activities such as relationship building and eureka-
will have the greatest impact on opportunity moment problem solving, which technology cannot
scanning and assessment. Conversely, if you do (yet) replace.
one to two larger deals a year, gen AI may also
help you streamline and accelerate the
execution processes. Commercial applications of gen AI in M&A are
already gaining traction and will almost certainly
— Drill down on whether to develop or to adopt. accelerate in the next few years. The greatest
There is a full spectrum of choices for how a question is not whether gen AI will affect
company can bring its prioritized use cases to dealmaking—it already is—but to what degree, how
life, and off-the-shelf solutions have recently quickly, and to what consequence. We’ll be
been brought to market—with more expected monitoring these developments in real time as
over the next one to two years. As with any they proceed.
Ben Ellencweig is a senior partner in McKinsey’s Stamford, Connecticut, office; Mieke Van Oostende is a senior partner in the
Brussels office; and Rui Silva is a partner in the New York office, where Julia Berbel is a consultant.
Copyright © 2024 McKinsey & Company. All rights reserved.
Gen AI: Opportunities in M&A 5"
68,mckinsey,four-essential-questions-for-boards-to -ask-about-generative-ai.pdf,"Four essential questions
for boards to ask about
generative AI
Boards are responsible for how generative AI is used at the companies they
oversee. Asking company leaders the right questions will help unlock the
technology’s value while managing its risk.
by Frithjof Lund, Dana Maor, Nina Spielmann, and Alexander Sukharevsky
© Getty Images
July 2023
Company executives are scrambling to applications with relative ease, even if users lack
understand and respond to generative AI. This deep AI and data science know-how.
technology is still nascent, but of those who have
used it, few doubt its power to disrupt operating Board members can equip their C-suite to harness
models in all industries. this potential power thoughtfully but decisively by
asking the following four broad questions.
We recently provided a view of how CEOs might
start preparing for what lies ahead.¹ But what is How will generative AI affect our industry and
the role of the board? Many board members tell us company in the short and longer term?
they aren’t sure how to support their CEOs as they Forming any sensible generative AI strategy will
grapple with the changes that generative AI has require an understanding of how the technology
unleashed, not least because the technology seems might affect an industry and the businesses within it
to be developing and getting adopted at lightning in the short and longer term. Our research suggests
speed. that the first wave of applications will be in software
engineering, marketing and sales, customer service,
The early use cases are awe inspiring. A software and product development.² As a result, the early
developer can use generative AI to create entire impact of generative AI will probably be in the
lines of code. Law firms can answer complex industries that rely particularly heavily on these
questions from reams of documentation. Scientists functions—for example, in media and entertainment,
can create novel protein sequences to accelerate banking, consumer goods, telecommunications, life
drug discovery. But the technology still poses real sciences, and technology companies.
risks, leaving companies caught between fear of
getting left behind—which implies a need to rapidly Even so, companies in other industries should not
integrate generative AI into their businesses—and delay in assessing the potential value at stake for
an equal fear of getting things wrong. The question their company. The technology and its adoption
becomes how to unlock the value of generative AI are moving too fast. Recall that the public-facing
while also managing its risks. version of ChatGPT reached 100 million users in
just two months, making it the fastest-growing
Board members can help their management teams app ever. And our research finds that generative AI
move forward by asking the right questions. In this can increase worker productivity across industries,
article, we provide four questions boards should adding up to $7.9 trillion in value globally from
consider asking company leaders, as well as a adoption of specific use cases and the myriad
question for members to ask themselves. ways workers can use the technology in everyday
activities.³ Each company will want to explore
immediate opportunities to improve efficiency and
Questions for management effectiveness. Those that don’t may quickly find
Generative AI models—deep learning models themselves trailing behind competitors that answer
trained on extremely large sets of unstructured customer queries more accurately and faster or
data—have the potential to increase efficiency launch new digital products more rapidly because
and productivity, reduce costs, and generate new generative AI is helping write the code. They risk
growth. The power of these “foundation” models falling behind on the learning curve, too.
lies in the fact that, unlike previous deep learning
models, they can perform not just one function but Simultaneously, companies will want to begin
several, such as classifying, editing, summarizing, looking further out. No one can predict the full
answering questions, and drafting new content. This implications of generative AI, but considering
enables companies to use them to launch multiple them is important. How might the competitive
1 “What every CEO should know about generative AI,” McKinsey, May 12, 2023.
2 “The economic potential of generative AI: The next productivity frontier,” McKinsey, June 14, 2023.
3 Ibid.
2 Four essential questions for boards to ask about generative AI
environment change? How might the business frameworks for the knowable generative AI
benefit, and where does it look vulnerable? And risks assumed across the company and that AI
are there ways to future-proof the strategy and activities within the company are continually
business model? reviewed, measured, and audited. They will
also want to ensure mechanisms are in place to
Are we balancing value creation with adequate continually explore and assess risks and ethical
risk management? concerns that are not yet well understood or even
An assessment of the new frontiers opened by apparent. How, for example, will companies stand
generative AI will rightly make management teams up processes to spot hallucination and mitigate
eager to begin innovating and capturing its value. the risk of wrong information eliciting incorrect
But that eagerness will need to be accompanied by or even harmful action? How will the technology
caution, as generative AI, if not well managed, has affect employment? And what of the risks posed
the potential to destroy value and reputations. It by third parties using the technology? A clear-eyed
poses the same—and more—risks as traditional AI. early view on where problems might lie is the key to
addressing them.
Like traditional AI, generative AI raises privacy
concerns and ethical risks, such as the potential The bottom line is that AI must always be subject
to perpetuate bias hidden in training data. And it to the effective oversight of those designing
heightens the risk of a security breach by opening and using it. Support for the effort can come
up more areas of attack and new forms of attack. from government regulatory frameworks and
For example, deepfakes simplify the impersonation guidance being developed on how to use and apply
of company leaders, raising reputation risks. There generative AI. It will be important for companies to
are also new risks, such as the risk of infringing keep abreast of these.
copyrighted, trademarked, patented, or otherwise
legally protected materials by using data collected How should we organize for generative AI?
by a generative AI model. Many companies took an experimental approach
to implementing previous generations of AI
Generative AI also has a propensity to hallucinate— technology, with those keenest to explore its
that is, generate inaccurate information, expressing possibilities launching pilots in pockets of the
it in a manner that appears so natural and organization. But given the speed of developments
authoritative that the inaccuracies are difficult within generative AI and the risks it raises,
to detect. This could prove dangerous not just companies will need a more coordinated approach.
for companies but for society at large. There is Getting stuck in pilot mode really isn’t an option.
widespread concern that generative AI could Indeed, the CEO of one multinational went as far
stoke misinformation, and some industry experts as to ask each of his 50 business leaders to fully
have said it could be as dangerous to society as implement two use cases without delay, such was
pandemics or nuclear war if not properly regulated.⁴ his conviction that generative AI would rapidly lend
competitive advantage.
Companies will therefore need to understand the
value and the risks of each use case and determine Company leaders should consider appointing a
how these align with the company’s risk tolerance single senior executive to take responsibility for the
and other objectives. For example, with regard oversight and control of all generative AI activities.
to sustainability objectives, they might consider A smart second step is to establish a cross-
generative AI’s implications for the environment functional group of senior people representing
because it requires substantial computing capacity. data science, engineering, legal, cybersecurity,
marketing, design, and other business functions.
From there, boards need to be satisfied that the Such a team can collaborate to formulate and
company has established legal and regulatory implement a strategy quickly and widely.
4 Cristina Criddle, “AI executives warn its threat to humanity rivals ‘pandemics and nuclear war,’” Financial Times, May 30, 2023.
Four essential questions for boards to ask about generative AI 3
Bear in mind too that a foundation model can Talent
underpin multiple use cases across an organization, The introduction of generative AI, like any change,
so board members will want to ask the appointed also requires a reassessment of the organization’s
generative AI leader to ensure that the organization talent. Companies are aware they need to reskill the
takes a coordinated approach. This will promote workforce to compete in a world where data and
the prioritization of use cases that deliver fast, AI play such a big role, though many are struggling
high-impact results. More complex use cases to attract and retain the people they need. With
can be developed thereafter. Importantly, a generative AI, the challenge just got harder. Some
coordinated approach will also help ensure a full roles will disappear, others will be radically different,
view of any risks assumed. and some will be new. Such changes will likely affect
more people in more domains and faster than has
The board will also want to check that there’s a been the case with AI to date.
strategy for establishing what is likely to be a
wide range of partnerships and alliances—with The precise new skills required will vary by use
providers that customize models for a specific case. For example, if the use case is relatively
sector, for example, or with infrastructure providers straightforward and can be supported by an off-
that offer capabilities such as scalable cloud the-shelf foundation model, a generalist may be
computing. The right partnerships with the right able to lead the effort with the help of a data and
experts will help companies move quickly to create software engineer. But with highly specialized data—
value from generative AI, though they will want to as might be the case for drug development—the
take care to prevent vendor lock-in and oversee company may need to build a generative AI model
possible third-party risks. from scratch. In that case, the company may need to
hire PhD-level experts in machine learning.
Do we have the necessary capabilities?
To keep pace with generative AI, companies may The board will therefore want to query leadership
need to review their organizational capabilities on as to whether it has a dynamic understanding of
three fronts. its AI hiring needs and a plan for fulfilling them.
Also, the existing workforce will need to be trained
Technology to integrate generative AI into their day-to-day
The first front is technology. A modern data and work and to equip some workers to take on new
tech stack will be the key to success in using roles. But tech skills are not the only consideration,
generative AI. While foundation models can as generative AI arguably puts a premium on
support a wide range of use cases, many of the more advanced analytical and creative skills to
most impactful models will be those fed with supplement the technology’s capabilities. The
additional, often proprietary, data. Therefore, talent model may therefore need to change—but
companies that have not yet found ways to with consideration of a caution raised recently at
harmonize and provide ready access to their the World Economic Forum: using AI as a substitute
data will be unable to unlock much of generative for the work of junior-level talent could endanger
AI’s potentially transformative power. Equally the development of the next generation of creators,
important is the ability to design a scalable data leaders, and managers.⁵
architecture that includes data governance and
security procedures. Depending on the use case, Organizational culture
the existing computing and tooling infrastructure Finally, a company’s culture shapes how well it
might also need upgrading. Is the management will succeed with generative AI. Companies that
team clear about the computing resources, data struggle with innovation and change will likely
systems, tools, and models required? And does it struggle to keep pace. It’s a big question, but does
have a strategy for acquiring them? the company have the learning culture that will be
5 Ravin Jesuthasan, “Here’s how companies should navigate generative AI in the world of work,” World Economic Forum, April 14, 2023.
4 Four essential questions for boards to ask about generative AI
a key to success? And does the company have a full board and all its committees can properly
shared sense of responsibility and accountability? consider its implications.
Without this shared sense, it is more likely to
run afoul of the ethical risks associated with the Second, the board can improve its members’
technology. understanding of generative AI. Training
sessions run by the company’s own experts
Both questions involve cultural issues that boards and by external experts on the front line of
should consider prompting their management developments can give board members an
teams to examine. Depending on what they find, understanding of how generative AI works, how
reformulating a company’s culture could prove to be it might be applied in the business, the potential
an urgent task. value at stake, the risks, and the evolution of the
technology.
A question for the board Third, the board can incorporate generative
As boards try to support their CEOs in creating value AI into its own work processes. Hands-on
from generative AI and managing its risks, they experience in the boardroom can build familiarity
will also want to direct a preliminary, fundamental with the technology and appreciation of its value
question to themselves: Are we equipped to provide and risks. Moreover, because generative AI can
that support? improve decision making, it would be remiss of
boards not to explore its potential to help them
Unless board members understand generative AI perform their duties to the best of their ability. For
and its implications, they will be unable to judge example, they might use it to surface additional
the likely impact of a company’s generative AI critical questions on strategic issues or to deliver
strategy and the related decisions regarding an additional point of view to consider when
investments, risk, talent, technology, and more making a decision.
on the organization and its stakeholders. Yet, our
conversations with board members revealed that
many of them admit they lack this understanding.
When that is the case, boards can consider three
ways to improve matters. Generative AI is developing fast, and companies
will have to balance pace and innovation with
The first option is to review the board’s composition caution. The board’s role is to constructively
and adjust it as necessary to ensure sufficient challenge the management team to ensure
technological expertise is available. In the past, this happens, keeping the organization at the
when companies have struggled to find technology forefront of this latest technological development
experts with the broader business expertise yet intensely mindful of the risks. The questions
required of a board member, some have obtained posed here are not, of course, exhaustive, and
additional support by setting up technology more will arise as the technology progresses. But
advisory boards that include generative AI experts. they are a good place to start. Ultimately, board
However, generative AI will likely have an impact members hold responsibility for how generative
on every aspect of a company’s operations—risk, AI is used in the companies they oversee, and the
remuneration, talent, cybersecurity, finance, and answers they receive should help them meet that
strategy, for example. Arguably, therefore, AI responsibility wisely.
expertise needs to be widespread so that the
Frithjof Lund is a senior partner in McKinsey’s Oslo office, Dana Maor is a senior partner in the Tel Aviv office, Nina Spielmann
is a senior expert in the Zurich office, and Alexander Sukharevsky is a senior partner in the London office.
Copyright © 2023 McKinsey & Company. All rights reserved.
Four essential questions for boards to ask about generative AI 5"
69,mckinsey,ai-for-social-good-improving-lives-and-protecting-the-planet-v2.pdf,"AI for social good:
Improving lives and
protecting the planet
This report is a collaborative effort by
Medha Bankhwal, Ankit Bisht, Michael Chui,
Roger Roberts, and Ashley van Heteren,
representing views from McKinsey Digital.
May 2024
Table of contents
CHAPTER 1
How AI can accelerate progress
toward reaching all of the SDGs 2
CHAPTER 2
How funding for AI initiatives
supports SDGs 13
CHAPTER 3
Challenges of scaling AI
for social good 16
CHAPTER 4
How stakeholders can accelerate
the deployment of AI for social good 21
Methodology 26
Acknowledgments 26
AI is already being used to further all 17 SDGs—from the goal of eliminating poverty to establishing
sustainable cities and communities and providing quality education for all—and generative AI has
opened new possibilities. As we look to the future, we see exciting potential for acceleration, with new
tools and platforms putting ever-greater power in the hands of social entrepreneurs, public sector
innovators, and private sector players to create effective solutions. But that power also brings with it
the need to assure it is harnessed in trusted and responsible ways and that risks are monitored and
managed actively to avoid unintended harms.
Six years ago, it was becoming clear that AI could play a major role globally in promoting not just
productivity and economic growth but also social good. In a 2018 report, we outlined how AI
capabilities, from natural language processing to sound recognition and tracking, could be used in
about 170 use cases to benefit society1—to promote equality and inclusion, improve crisis response,
protect the environment, and deliver impact in many more ways.2
Today’s AI R&D is not just confirming our initial assessments but showing promise for further gains in
the future. A series of improvements in AI techniques and progress on key enablers have substantially
expanded the universe of problems that AI may be able to address. Much of this progress is centered
on generative AI, which is enabling natural language interfaces; rapid language translation; synthesis
of vast document repositories; creation of stories in text, images, and video; and much more.3
In this report, we take another look at how AI can become a key part of solutions to benefit people
and the planet—and how it already has. One way to assess this is by mapping innovations and impact
to the UN Sustainable Development Goals, or SDGs (see sidebar “Methodology,” found at the end of
the report). The SDGs comprise 17 goals and 169 targets that aim to improve lives around the world
and protect the planet. But the UN’s 2023 update on progress toward the SDGs indicates the world
is on track to meet only 15 percent of SDG targets.4 In real terms, this means that 2.2 billion people
lack access to safe water and hygiene, and 3.5 billion lack access to safely managed sanitation5;
roughly 3.3 billion people live in environments that are highly vulnerable to climate change6; and about
750 million people are facing hunger.7
Below, we illustrate the potential of AI to catalyze progress on these pressing social issues, and we
highlight the challenges in the domains of data quality and governance, as well as access to AI talent
(particularly for not-for-profits), that are hindering AI from scaling. We then outline some actions that
stakeholders—including governments, foundations, universities, and businesses—could take to
overcome these challenges. While the opportunities have associated risks, such as embedded biases
and data privacy and security threats, thoughtful action could accelerate the deployment of AI-based
solutions to advance progress on the SDGs and improve lives across the globe.
1 “Applying artificial intelligence for social good,“ McKinsey Global Institute, November 28, 2018. Additional research in 2023 yielded
discovery of 13 more use cases piloted in 2018 that were not originally accounted for in our 2018 report, bringing the 2018 total up to
about 170.
2 “‘Tech for Good’: Using technology to smooth disruption and improve well-being,” McKinsey Global Institute, May 15, 2019; Amine
Aït-Si-Selmi, Eric Hazan, Hamza Khan, and Tunde Olanrewaju, “Tech for Good: Helping the United Kingdom improve lives and
livelihoods,” McKinsey, July 31, 2020.
3 For more on generative AI, see “What is generative AI?,” McKinsey, April 2, 2024.
4 The Sustainable Development Goals report 2023: Special edition, United Nations, July 10, 2023.
5 “The 17 goals,” United Nations Department of Economic and Social Affairs, accessed April 24, 2024.
6 “Protecting people from a changing climate: The case for resilience,” McKinsey, November 8, 2021.
7 “122 million more people pushed into hunger since 2019 due to multiple crises, reveals UN report,” World Health Organization,
July 12, 2023.
AI for social good: Improving lives and protecting the planet 1
Chapter 1
How AI can accelerate
progress toward reaching
all of the SDGs
AI for social good: Improving lives and protecting the planet 2
AI is not a magic bullet, and many risks need to be managed to harness its potential (see sidebar
“Managing the risks of adopting AI”). But the universe of problems that AI can address is broad.
Current applications of AI are applicable to all the SDGs, including modeling proteins, screening
drugs, designing vaccines, targeting aid and public services, solving supply chain problems such as
route optimization for last mile delivery of food in remote geographies, forecasting the long-term
impacts of climate change or giving early warning for natural disasters, and bringing expertise to
frontline aid workers.
Additionally, adoption of generative AI could significantly increase and democratize access to
new capabilities. AI tools now allow remote users to complete tasks that once required specific
expertise, such as language translation, fact checking, identification of human or plant diseases,
and identification of harmful online content. In a recent survey of more than 4,000 not-for-profits
conducted by Google for Nonprofits, 75 percent of respondents said that generative AI had the
potential to transform their marketing efforts by enhancing their translation and fact-checking
capabilities.8
The experts we interviewed noted that AI could address or help solve social or environmental
challenges in two circumstances: 1) when the AI solution could solve problems that bottleneck other
efforts in the field—for example, a solution for water leakage in residential pipes requires predictions
about the likelihood of leaks based on analysis of data such as pipe age and location; and 2) when
data required for the model to work is (or will soon be) available and accessible.
To map the breadth of AI’s applicability, we have developed a database of AI use cases, each of
which highlights a type of meaningful problem whose solution could be enabled by one or more AI
capabilities. At the time of our 2018 report, this database contained about 170 high-potential use
cases. It now contains about 600—more than a threefold increase. This number is growing as more
innovative uses come to light, as social impact leaders continue to experiment boldly, and as AI tools
become more accessible and easier to use.
The number of real-life AI deployments has also increased significantly over the past six years. In
2018, only a small fraction of the about 170 use cases had been deployed. Today, about 490 of the
600 use cases, or more than 80 percent, have been implemented in at least one instance (Exhibit 1).9
Adoption of generative AI could
significantly increase and
democratize access to new capabilities.
AI tools now allow remote users to
complete tasks that once required
specific expertise.
8 The Keyword, “3 insights from nonprofits about generative AI,” blog entry by Annie Lewin, March 28, 2024.
9 Our library contains approximately 600 use cases, and our analysis of deployments is based on publicly available data. Neither is
comprehensive or exhaustive, and both will continue to evolve.
AI for social good: Improving lives and protecting the planet 3
Web <2024>
E<MxChKib23it7 2111 Perspective on AI For Social Good Report>
Exhibit <1> of <6>
About 600 AI-enabled use cases have the potential to support the UN
Sustainable Development Goals.
Number of AI-enabled use cases identified per Included in the library created in 2018 Number of use
UN Sustainable Development Goal (SDG),1 2023 Additions since 2018 cases with at
least one deploy-
ment in 2023
82% SDG 3: Good Health and
43 122 165 128
Well-Being
of all use cases
have at least SDG 16: Peace, Justice, and
28 27 55 40
one deployment Strong Institutions
(492 out of 600)
SDG 15: Life on Land 10 30 40 38
600
SDG 4: Quality Education 13 27 40 37
SDG 13: Climate Action 7 25 32 31
SDG 2: Zero Hunger 8 24 32 29
SDG 11: Sustainable Cities and 11 20 31 26
Communities
SDG 9: Industry, Innovation, 9 21 30 21
429 and Infrastructure
SDG 8: Decent Work and 7 20 27 17
Economic Growth
1
SDG 14: Life Below Water 25 26 24
3
SDG 12: Responsible 18 21 19
Consumption and Production
2
SDG 7: Affordable and Clean 19 21 18
Energy
SDG 10: Reduced Inequalities 613 19 15
2
SDG 6: Clean Water and 16 18 16
Sanitation
171
SDG 1: No Poverty 107 17 12
SDG 17: Partnerships for the 88 16 13
Goals
3
Total SDG 5: Gender Equality 7 10 8
Note: Our library of 600 use cases and our analysis of deployments are based on publicly available data, are not comprehensive, and will continue to evolve; this
library is a starting point and should thus not be treated as exhaustive. Many AI use cases are relevant for more than one SDG, which means that successful
deployments of these use cases can spur progress on multiple fronts. Additional research in 2023 led to the discovery of 13 use cases piloted in 2018 that were
not accounted for in our 2018 report.
1Each use case is mapped to primary UN SDGs only.
Source: AI for Sustainable Development Goals academy; Candid database 2018–23; IRCAI global top 100 2022 report, International Research Centre on
Artificial Intelligence (IRACAI), 2022; United Nations activities on artificial intelligence (AI) 2021, International Telecommunication Union, 2021; United Nations
Statistics Division; United Nations University Institute for Water, Environment and Health reports
McKinsey & Company
AI for social good: Improving lives and protecting the planet 4
The experts we surveyed agreed that AI has particularly high potential to make a difference for five
SDG goals: Good Health and Well-Being (SDG 3), Quality Education (SDG 4), Affordable and Clean
Energy (SDG 7), Sustainable Cities and Communities (SDG 11), and Climate Action (SDG 13). In fact,
60 percent of not-for-profit AI for social good deployments were in these areas. Relative to their
perceived AI potential, the goals for Zero Hunger (SDG 2), Life on Land (SDG 15), and Peace, Justice,
and Strong Institutions (SDG 16) have many use case deployments, whereas Quality Education
(SDG 4), Affordable and Clean Energy (SDG 7), and Climate Action (SDG 13) have fewer (Exhibit 2).
We excluded Decent Work and Economic Growth (SDG 8); Industry, Innovation, and Infrastructure
(SDG 9); and Partnerships for Goals (SDG 17) from the analysis of not-for-profit deployment,
foundation grants, and private capital, because most projects can be tagged to these areas given
their broad applicability.10
Web <2024>
E<MxChKib23it7 2211 Perspective on AI For Social Good Report>
Exhibit <2> of <6>
The number of not-for-profit deployments does not necessarily reflect the
perceived AI potential for each Sustainable Development Goal.
Bubble size reflects Relatively high number of Lower level of deployment
number of AI-enabled use cases identified given than expected based on higher
use cases identified low perceived AI potential perceived AI potential
Well-recognized
potential 3
4
13
30
7
Perceived AI 20 11
potential1
12
10 2 10
6 15
5
Not yet 0 14 16 1
recognized2
0 100 200 300 400
Low Number of not-for-profit deployments3 High
1 No Poverty 10 Reduced Inequalities
2 Zero Hunger 11 Sustainable Cities and Communities
3 Good Health and Well-Being 12 Responsible Consumption and Production
4 Quality Education 13 Climate Action
5 Gender Equality 14 Life Below Water
6 Clean Water and Sanitation 15 Life on Land
7 Affordable and Clean Energy 16 Peace, Justice, and Strong Institutions
Note: We excluded Decent Work and Economic Growth (SDG 8); Industry, Innovation, and Infrastructure (SDG 9); and Partnerships for the Goals (SDG 17) from
our analysis of not-for-profit deployment, grants, and private capital. This is because most projects can be tagged to these areas given broad applicability.
1AI potential determined through survey of ~60 experts representing 48 organizations (incl not-for-profits, foundations, technology companies, start-ups,
academic institutions, and government) and 17 countries in response to the following question: “What are the top 5 Sustainable Development Goals (SDGs) in
the list below where you think AI has the highest potential to accelerate progress toward the SDG targets?”
2There may be potential for AI use, but the surveyed experts are unaware of it at this point.
3Not-for-profit deployment determined from number of sample deployments in a collection of 1,121 AI applications largely deployed in not-for-profits.
Source: AI for Sustainable Development Goals academy; Candid database 2018–23; IRCAI global top 100 2022 report, International Research Centre on
Artificial Intelligence (IRACAI), 2022; United Nations activities on artificial intelligence (AI) 2021, International Telecommunication Union, 2021; United Nations
Statistics Division; United Nations University Institute for Water, Environment and Health reports
McKinsey & Company
10 In our analysis of 600 use cases, each use case was tagged to a single primary SDG and SDG target.
AI for social good: Improving lives and protecting the planet 5
Additionally, several SDGs that are behind on progress have relatively untapped AI potential.
Consider the following examples:
— No Poverty (SDG 1): machine learning could be used to direct cash aid to those most in need or
provide alternative credit scores to financially excluded individuals.
— Zero Hunger (SDG 2): AI could be used to help develop new crops, better select crop regions to
minimize crop risks, and provide early warning for nutrition crises.
— Peace, Justice, and Strong Institutions (SDG 16): machine learning could be used to detect and
curb the spread of misinformation, provide access to information that enables advocacy for policy
change, and improve measurement of specific policy interventions.
Below, we explore potential and existing deployments in three of the SDGs with the most widely
recognized potential: Good Health and Well-Being (SDG 3), Quality Education (SDG 4), and Climate
Action (SDG 13). We also explore two SDGs where AI does not have widely recognized potential but
has had an impact in select areas: No Poverty (SDG 1) and Zero Hunger (SDG 2).
We explore potential and existing
deployments in three of the SDGs
with the most widely recognized
potential. We also explore two
SDGs where AI does not have widely
recognized potential but has had an
impact in select areas.
AI for social good: Improving lives and protecting the planet 6
Managing the risks of adopting AI
Risks are inherent to the use of AI. With generative AI (gen AI), risks include inaccurate outputs,
biases embedded in the underlying training data, the potential for large-scale misinformation, and
malicious influence on politics and personal well-being.1 As we have noted in multiple recent articles,2
AI tools and techniques can be misused, even if they were originally designed for social good.
Respondents to our survey of about 60 experts identified the top risks as impaired fairness,
malicious use, and privacy and security concerns, followed by explainability (exhibit).3 Respondents
from not-for-profits expressed relatively more concern about misinformation, talent issues such as
job displacement, and effects of AI on economic stability compared with their counterparts at for-
profits, who were more often concerned with intellectual property infringement.
Web <2024>
E<MxChKib23it7211 Perspective on AI For Social Good Report>
Exhibit <6> of <6>
Experts say impaired fairness and malicious use are the top risks in using AI
to address the Sustainable Development Goals.
AI risks presenting the largest challenges in deploying AI for achieving Delta between not-for-
SDG targets, % of respondents including this topic in their top 5 profits and others
Not-for-profits Others1 Negative Positive
73
Impaired fairness –3 p.p.2
76
68
Malicious use 76 –8 p.p.
68
Data privacy 74 –6 p.p.
68
Security threats 74 –6 p.p.
55
Performance and explainability 45 +10 p.p.
45
Talent issues 32 +13 p.p.
32
Political stability 24 +8 p.p.
27
Economic stability 11 +16 p.p.
18
National security 26 –8 p.p.
18
Environmental impact 21 –3 p.p.
9
Intellectual property infringement 32 –23 p.p.
Note: “Impaired fairness” was framed as “bias and fairness” in the survey; “performance and explainability” was framed as “explainability”; “data privacy” and
“security threats” were combined in the survey.
1“Others” includes for-profits, think tanks, academic institutions, and consultancies.
2Percentage points.
Source: Survey of ~60 experts representing 48 organizations (incl not-for-profits, foundations, technology companies, start-ups, academic institutions, and
government) and 17 countries
McKinsey & Company
1 “Implementing generative AI with speed and safety,” McKinsey Quarterly, March 13, 2024.
2 Ibid.; “The state of AI in 2023: Generative AI’s breakout year,” McKinsey, August 1, 2023; New at McKinsey Blog, “An inside look at
how businesses are—or are not—managing AI risk,” blog entry by Liz Grennan and Bryce Hall, August 31, 2023; “What is generative
AI?,” McKinsey, April 2, 2024.
3 Our AI risks framework for social impact builds on McKinsey’s gen AI risks framework (see “Implementing generative AI with speed
and safety,” McKinsey Quarterly, March 13, 2024). It includes additional categories such as political stability and environmental
impact and excludes risks such as strategic risks that can be more relevant to for-profit enterprises.
AI for social good: Improving lives and protecting the planet 7
Impaired fairness. Algorithmic systems can inherit biases from their creators or from the data sets on
which they are trained. When these algorithms are deployed in decision-making capabilities, these
biases can reinforce preexisting prejudices and social inequalities, with potentially negative impacts
on marginalized communities. One organization, Data Science for Social Good, builds bias detection
tools that allow developers to audit data science systems for bias and equity.4
Malicious use. Malicious use includes creating and disseminating false information or fake content,
scams, phishing attempts, hate speech, and activities that harm individuals and national security.
A 2022 UN report found that misinformation had been used to incite hatred against marginalized
groups and to prevent civilians from finding humanitarian corridors during conflicts, such as the one
in Ukraine.5 According to a recent report by the World Economic Forum, “growing misinformation and
disinformation could further increase vaccine hesitancy, which has already led to the re-emergence
of locally eradicated diseases.”6 The Global Disinformation Index uses models based on large
language models (LLMs) to detect disinformation with the goal of tracking news sites supported by
hostile states.7 Similarly, Full Fact is an independent fact checking organization that deploys a range
of AI and machine learning methodologies to detect and curb the proliferation of misinformation
across the evolving landscape of information-spreading platforms.
Data privacy and security threats. Many of the UN Sustainable Development Goals use cases require
access to health or financial data of vulnerable populations. While organizations are well aware
of the harm that could result from breaches in their data systems, many social enterprises have
resource constraints that may limit their ability to use the latest cybersecurity capabilities. Several
organizations have developed data privacy guidelines, tool lists, and custom security frameworks for
not-for-profits with limited resources.8
Performance and explainability. Many AI solutions employ complex algorithms that can make it
difficult to identify the data or logic used to arrive at a decision. This is particularly relevant for gen
AI solutions, which may provide inaccurate or toxic answers. Explainable AI models have several
advantages for not-for-profits: they may make it easier to verify the correctness and fairness of
results, to assign credit to data providers, and to assign accountability for model outcomes. The
Allen Institute for AI recently released a platform for comparing large text data sets to measure the
prevalence of toxic, low-quality, duplicate, or personally identifiable information used to train various
LLMs.9
To mitigate the risks of AI, organizations must first understand and prioritize the risks they are most
likely to face, both from inbound AI threats such as disinformation and from developing and deploying
their AI solutions. While risks such as data privacy may be addressed through traditional software
tools, emerging risks, such as bias in systems driven by LLMs, may require the development of new
monitoring systems and guardrails.
4 “The bias and fairness audit toolkit for machine learning: Aequitas,” Center for Data Science and Public Policy, accessed
April 24, 2024.
5 A/77/288: Disinformation and freedom of opinion and expression during armed conflicts - Report of the Special Rapporteur on the
promotion and protection of the right to freedom of opinion and expression, Office of the High Commissioner for Human Rights,
United Nations, August 12, 2022.
6 The global risks report 2023: 18th edition, World Economic Forum, 2023.
7 “What we do,” Global Disinformation Index, accessed April 24, 2024.
8 “Online privacy for nonprofits: A guide to better practices,” Electronic Frontier Foundation, accessed April 24, 2024; “Learn,” Digital
Defense Fund, accessed April 24, 2024; website of SOAP, accessed April 24, 2024; “Frontline policies,” Open Briefing Ltd, accessed
April 24, 2024.
9 Akshita Bhagia et al., “What’s in my big data?,” arXiv:2310.20707, March 2024.
AI for social good: Improving lives and protecting the planet 8
Existing deployments related to SDG 3: Good Health and Well-Being
SDG 3 aims to promote well-being and ensure people live healthy lives.11 Specific targets for this SDG
include reducing maternal mortality; fighting communicable diseases such as AIDS, tuberculosis,
and malaria; and establishing universal access to sexual and reproductive care, family planning, and
education.
AI is now well integrated into many medical research pipelines. Key AI applications in this area include
protein modeling, genome sequencing, computerized tomography (CT) analysis, vision support,
and vaccine design. Health is relatively accessible for AI work compared with many SDGs: the field
is technology-forward, data availability is high (relative to other SDGs), and health outcomes are
frequently measurable. Yet major opportunities remain to support SDG 3 targets that have received
less attention, such as treating neglected communicable diseases and preventing substance abuse.
Sample use case: Addressing maternal and newborn health in Kenya. Jacaranda Health provides
AI-enabled solutions that improve the quality of care for women with the goal of reducing the number
of maternal deaths in Kenya. For example, PROMPTS is an SMS exchange that sends personalized
messages to women, empowering them to seek care. An accompanying free digital healthcare
platform uses natural language processing to categorize user questions in real time and connects
those who need urgent care with a help desk agent. More than two million new and expectant
mothers have enrolled with PROMPTS. Mothers who use the services are 20 percent more likely to
attend more than four prenatal visits; women who adopt the service are also twice as likely to use
postpartum family planning services as women who do not.12 Jacaranda Health shares feedback from
PROMPTS users with governments and facilities to improve their services.13
Sample use case: Addressing maternal and newborn health in India. More than 1.3 million women
in India have died in pregnancy or childbirth over the past two decades, mostly from preventable
causes.14 ARMMAN was founded in 2008 to address systematic problems that prevent at-risk
women from accessing care.15 The organization developed numerous interventions, including
mMitra, an automated voice messaging system that delivers key information on preventive care.
These messages have a high correlation with positive health outcomes, such as improved rates of
taking iron supplements and better knowledge of family planning. However, 40 percent of women
drop out of the program before giving birth.16 ARMMAN has resources to call some women and
encourage them to stay in the program. The organization partnered with Google Research India to
develop an AI-based prediction model for this intervention that selects women to receive service
calls. The solution is a resource optimization model based on a restless multi-armed bandit approach
to optimize resource allocation in a changing world. In a randomized controlled trial, dropout rates
were 32 percent lower for women called according to the algorithm than women called using a round
robin control group method.17 Using mMitra, ARMMAN has reached roughly 3.6 million women in nine
states, many of whom would likely have dropped out without the AI-targeted intervention. ARMMAN
has now developed a similar AI model for use with Kilkari, a voice technology program that brings
time-sensitive care information to families.18
11 “3: Good Health and Well-Being,” Global Goals, accessed April 24, 2024.
12 PROMPTS, Jacaranda Health, 2023.
13 “Impact at a glance,” Jacaranda Health, accessed April 25, 2024.
14 R. Begum et al., “Trends in maternal mortality in India over two decades in nationally representative surveys,” British Journal of
Obstetrics and Gynaecology, March 2022, Volume 129, Number 4.
15 “ARMMAN: About us,” LinkedIn, accessed April 25, 2024.
16 Google Research Blog, “Using ML to boost engagement with a maternal and child health program in India,” blog entry by Milind
Tambe and Aparna Taneja, August 24, 2022.
17 Aparna Hegde et al., “Field study in deploying restless multi-armed bandits: Assisting non-profits in improving maternal and child
health,” Proceedings of the AAAI Conference on Artificial Intelligence, June 2022, Volume 36, Number 11.
18 “Kilkari,” ARMMAN, accessed April 25, 2024.
AI for social good: Improving lives and protecting the planet 9
Sample use case: Predicting the structure of proteins to aid drug discovery. DeepMind developed
AlphaFold 2 in 2020 and AlphaFold 3 in 2024 to tackle a challenge that had plagued scientists
for more than 50 years: the protein-folding problem. This problem involves three related puzzles,
as defined by a National Library of Medicine paper: What is the folding code? What is the folding
mechanism? And can we predict the native structure of a protein from its amino acid sequence?19
AlphaFold2 is an attention-based deep learning system that predicts protein structures with a
higher degree of accuracy than was previously possible. The DeepMind team released a database
of more than 200 million protein structure predictions that is now widely used in structural biology
research.20 A million researchers have accessed the protein structure database since its launch,
using the predictions to solve real-world problems, including developing treatments for neglected
diseases and fighting antibiotic resistance.21 AlphaFold 3 extends beyond proteins to include a wide
range of biomolecules impacting life sciences and medical research, agriculture, materials sciences,
and more.
Existing deployments related to SDG 4: Quality Education
SDG 4 aims to ensure inclusive and equitable quality education and promote lifelong learning
opportunities.22 Targets include establishing free primary and secondary education, ensuring equal
access to quality preprimary education, and achieving universal literacy and numeracy.
AI algorithms are already being used in this space, such as predictive
One in 20 school-age tools that help identify a student’s likelihood of completing high school
or dropping out and that enable at-risk students to get early intervention
children from low- and support.23 AI can be used to create more-inclusive educational
platforms for young children, teenagers, adults, and people with
income countries disabilities; increase student enrollment; and formulate lesson plans
for teachers—including creating materials tailored to students’ unique
development areas and interests.
has internet access at
Yet implementation has proved challenging, partly because of limited
home, while nearly infrastructure—including internet access and data records—in
developing countries. Roughly one in 20 school-age children from low-
nine in ten from high- income countries has internet access at home, while nearly nine in ten
from high-income countries do.24 Parents are unable to engage with
schools using digital platforms due to factors such as digital literacy and
income countries do.
internet access, so use cases that focus on parent engagement are not
yet an option.
Sample use case: Enabling people who are nonverbal or experiencing learning disabilities to
communicate. Livox uses intelligent algorithms and machine learning to adapt content for students
with a variety of disabilities, including verbal, motor, cognitive, and visual.25 The Livox interface adapts
to the student’s needs, and its software tracks improvements in visual, auditory, cognitive, and
behavioral function, making it easier for teachers to monitor students’ progress. More than 25,000
people with disabilities have used this service, which is available in 25 languages.
19 Ken A. Dill et al., “The protein folding problem,” Annual Review of Biophysics, June 2008, Volume 37.
20 “AlphaFold: Protein structure database,” EMBL’s European Bioinformatics Institute, accessed April 25, 2024.
21 Oana Stroe, “Case study: AlphaFold uses open data and AI to discover the 3D protein universe,” EMBL, February 9, 2023.
22 “4: Quality Education,” Global Goals, accessed April 25, 2024.
23 IDeas Blog, “Rebuilding the Educate Girls machine learning model,” blog entry by Sid Ravinutala, April 29, 2019.
24 “How many children and young people have internet access at home? Estimating digital connectivity during the COVID-19 pandemic,”
UNICEF, December 2020.
25 “About us,” Livox, accessed April 25, 2024.
AI for social good: Improving lives and protecting the planet 10
Sample use case: Bolstering girls’ enrollment in school. Educate Girls is a not-for-profit organization
that works to educate girls in India’s rural and educationally underresourced areas. The organization
uses a machine learning model to reduce the operational cost of locating girls who are not attending
school. Before developing this model, Educate Girls staff members had to travel from village to
village to gather the required data, which they would then manually compile and analyze to identify
areas where their services could have the most impact. The machine learning model uses census
data, which is manually cleaned and updated where necessary, and district-level out-of-school data
to recommend target areas, allowing Educate Girls staff to reach a greater number of prospective
students faster and target interventions more accurately.26 Educate Girls aims to enroll 1.6 million
girls—or 40 percent of the population of out-of-school girls—into grades one through ten.27
Existing deployments related to SDG 13: Climate Action
SDG 13 focuses on combating climate change and its impacts, including strengthening resilience and
adaptive capacity to climate-related disasters and integrating climate change measures into policies
and planning.28
AI can be used to analyze large climate data sets and model the impact of specific variables, improve
the yield of agriculture, and reduce emissions from transportation and industrial processes, to name
a few applications. Not-for-profit deployments are lower than perceived potential for this SDG. In the
past few years, AI has been used to provide detailed climate information to improve climate change
education and awareness, and to track emissions and improve the sustainability of operations
acr"
70,mckinsey,ai-powered-marketing-and-sales-reach-new-heights-with-generative-ai.pdf,"Growth, Marketing & Sales Practice
AI-powered
marketing and sales
reach new heights
with generative AI
May 2023
by Richelle Deveau, Sonia Joseph Griffin, and Steve Reis
AI technology has
revolutionized marketing
and sales; now, generative
AI promises to disrupt the
wayB2B and B2C players think
about customer experience,
productivity and growth.
Artificial intelligence (AI) and machine learning (ML) continue to push the boundaries of what is
possible in marketing and sales. And now, with the ongoing step-change evolution of generative
AI (gen AI), we’re seeing the use of open-source platforms penetrating to the sales frontlines,
along with rising investment by sales-tech players in gen AI innovations. Given the accelerating
complexity and speed of doing business in a digital-first world, these technologies are becoming
essential tools.
Our research suggests that a fifth of current
sales-team functions could be automated.
Inevitably, this will impact how you operate—and how you connect with and serve your
customers. In fact, it’s probably already doing so. Forward-thinking C-suite leaders are
considering how to adjust to this new landscape. Here, we outline the marketing and sales
opportunities (and risks) in this dynamic field and suggest productive paths forward.
2 AI-powered sales and marketing reach new heights with generative AI
How AI is reshaping
marketing and sales
AI is poised to disrupt marketing and sales in every sector. This is the result
of shifts in consumer sentiment alongside rapid technological change.
Omnichannel is table stakes Step changes are occurring in
Across industries, engagement models are changing: digitization and automation
today’s customers want everything, everywhere, AI technology is evolving at pace. It is becoming
and all the time. While they still desire an even mix of increasingly easy and less costly to implement, while
traditional, remote, and self-service channels (including offering ever-accelerating complexity and speed that
face-to-face, inside sales, and e-commerce), we see far exceeds human capacity. Our research suggests
continued growth in customer preference for online that a fifth of current sales-team functions could be
ordering and reordering. automated. In addition, new frontiers are opening with
the rise of gen AI (see sidebar, “What is generative AI?”).
Winning companies—those increasing their market
share by at least 10 percent annually—tend to utilize Furthermore, venture capital investment in AI has
advanced sales technology; build hybrid sales teams grown 13-fold over the last ten years.1 This has led to
and capabilities; tailor strategies for third-party and an explosion of “usable” data (data that can be used to
company-owned marketplaces; achieve e-commerce formulate insights and suggest tangible actions) and
excellence across the entire funnel; and deliver hyper- accessible technology (such as increased computation
personalization (unique messages for individual power and open-source algorithms). Vast, and growing,
decision makers based on their needs, profile, amounts of data are now available for foundation-model
behaviors, and interactions—both past and predictive). training, and since 2012 there’s been a millionfold
increase in computation capacity—doubling every three
to four months.2
What is generative AI?
Many of us are already familiar with online AI chatbots and image generators, using them to create
convincing pictures and text at astonishing speed. This is the great power of generative AI, or gen AI: it
utilizes algorithms to generate new content—writing, images, or audio—from training data.
To do this, gen AI uses deep-learning models called foundation models (FMs). FMs are pre-trained
on massive datasets and the algorithms they support are adaptable to a wide variety of downstream
tasks, including content generation. Gen AI can be trained, for example, to predict the next word in
a string of words and can generalize that ability to multiple text-generation tasks, such as writing
articles, jokes, or code.
In contrast, “traditional” AI is trained on a single task with human supervision, using data specific
to that task; it can be fine-tuned to reach high precision, but must be retrained for each new use
case. Thus gen AI represents an enormous step change in power, sophistication, and utility—and a
fundamental shift in our relationship to artificial intelligence.
1 Nestor Maslej et al., “The AI Index 2023 annual report,” AI Index Steering Committee, Institute for Human-Centered AI, Stanford University, April 2023.
2 Cliff Saran, “Stanford University finds that AI is outpacing Moore’s Law,” Computer Weekly, December 12, 2019; Risto Miikkulainen, “Creative AI
through evolutionary computation: Principles and examples,” SN Computer Science, 2(3): 163, March 23, 2001.
rabediS
AI-powered sales and marketing reach new heights with generative AI 3
What does gen AI mean for
marketing and sales?
The rise of AI, and particularly gen AI, has potential for impact in three areas
of marketing and sales: customer experience (CX), growth, and productivity.
For example, in CX, hyper-personalized content and offerings can be based on individual
customer behavior, persona, and purchase history. Growth can be accelerated by leveraging AI
to jumpstart top-line performance, giving sales teams the right analytics and customer insights
to capture demand. Additionally, AI can boost sales effectiveness and performance by offloading
and automating many mundane sales activities, freeing up capacity to spend more time with
customers and prospective customers (while reducing cost to serve).
In all these actions, personalization is key. AI coupled with company-specific data and context
has enabled consumer insights at the most granular level, allowing B2C lever personalization
through targeted marketing and sales offerings. Winning B2B companies go beyond account-
based marketing and disproportionately use hyper-personalization in their outreach.
Dynamic audience targeting and segmentation
Gen AI can combine and analyze large amounts of data—such as demographic information, existing
customer data, and market trends—to identify additional audience segments. Its algorithms then
enable businesses to create personalized outreach content, easily and at scale.
Instead of spending time researching and creating audience segments, a marketer can leverage gen
AI’s algorithms to identify segments with unique traits that may have been overlooked in existing
customer data. Without knowing every detail about these segments, they can then ask a gen AI tool
to draft automatically tailored content such as social media posts and landing pages. Once these have
been refined and reviewed, the marketer and a sales leader can use gen AI to generate further content
such as outreach templates for a matching sales campaign to reach prospects.
Embracing these techniques will require some openness to change. Organizations will require a
comprehensive and aggregated dataset (such as an operational data lake that pulls in disparate
sources) to train a gen AI model that can generate relevant audience segments and content. Once
trained, the model can be operationalized within commercial systems to streamline workflows while
being continuously refined by agile processes.
Lastly, the commercial organizational structure and operating model may need to be adjusted to
ensure appropriate levels of risk oversight are in place and performance assessments align to the new
ways of working.
selas
IA
neg
A
esac
esu
4 AI-powered sales and marketing reach new heights with generative AI
Bringing gen AI to life in the customer journey
There are many gen AI-specific use cases across the customer journey that can drive impact:
— At the top of the funnel, gen AI — Within the sales motion, gen AI — There are many gen AI use cases
surpasses traditional AI-driven goes beyond initial sales-team after the customer signs on the
lead identification and targeting engagement, providing continuous dotted line, including onboarding
that uses web scraping and simple critical support throughout the entire and retention. When a new customer
prioritization. Gen AI’s advanced sales process, from proposal to deal joins, gen AI can provide a warm
algorithms can leverage patterns closure. welcome with personalized
in customer and market data training content, highlighting
to segment and target relevant With its ability to analyze customer relevant best practices. A chatbot
audiences. With these capabilities, behavior, preferences, and functionality can provide immediate
businesses can efficiently analyze demographics, gen AI can generate answers to customer questions and
and identify high-quality leads, personalized content and messaging. enhance training materials for future
leading to more effective, tailored From the beginning, it can assist customers.
lead-activation campaigns with hyper-personalized follow-
(see “A gen AI sales use case: up emails at scale and contextual Gen AI can also offer sales
Dynamic audience targeting and chatbot support. It can also act leadership with real-time next-step
segmentation”). as a 24/7 virtual assistant for each recommendations and continuous
team member, offering tailored churn modeling based on usage
Additionally, gen AI can optimize recommendations, reminders, trends and customer behavior.
marketing strategies through and feedback, resulting in higher Additionally, dynamic customer-
A/B testing of various elements engagement and conversion rates. journey mapping can be utilized to
such as page layouts, ad copy, identify critical touchpoints and drive
and SEO strategies, leveraging As the deal progresses, gen AI can customer engagement.
predictive analytics and data- provide real-time negotiation
driven recommendations to ensure guidance and predictive insights
maximum return on investment. based on comprehensive analysis of
These actions can continue through historical transaction data, customer
the customer journey, with gen behavior, and competitive pricing.
AI automating lead-nurturing
campaigns based on evolving
customer patterns.
This revolutionary approach is transforming
the landscape of marketing and sales, driving
greater effectiveness and customer engagement
from the very start of the customer journey.
AI-powered sales and marketing reach new heights with generative AI 5
Commercial leaders are optimistic—and reaping benefits
We asked a group of commercial leaders to provide their perspective on use cases and the role
of gen AI in marketing and sales more broadly. Notably, we found cautious optimism across the
board: respondents anticipated at least moderate impact from each use case we suggested.
In particular, these players are most enthusiastic about use cases in the early stages of the
customer journey lead identification, marketing optimization, and personalized outreach
(Exhibit 1).
Exhibit 1
Commercial leaders are cautiously optimistic about gen AI use cases,
anticipating moderate to significant impact.
Estimated impact of use cases,¹ % respondents answering “significant” or “very significant”
Lead identification
(real time, based on customer trends) 60
Marketing optimization
(A/B testing, SEO strategies) 55
Personalized outreach
(chatbots, virtual assistants) 53
Dynamic content
(websites, marketing collateral) 50
Up/cross-selling recs
(via usage patterns, support tickets) 50
Success analytics
45
(continuous churn modeling)
Marketing analytics
(dynamic audience targeting) 45
Dynamic customer-journey mapping
(identifying critical touchpoints) 45
Automated marketing workflows
(nurturing campaigns) 35
Sales analytics
(predictive pricing, negotiation) 30
Sales coaching
(hyper-personalized training) 25
1Senior executives in significant global B2B and B2C sales and marketing organizations across a wide range of industries and company maturity levels were
asked: Please share your estimated ROI / impact these tools would have if implemented in your organization.
Source: McKinsey analysis
McKinsey & Company
6 AI-powered sales and marketing reach new heights with generative AI
These top three use cases are all focused on prospecting
Our research found that
and lead generation, where we’re witnessing significant
early momentum. This comes as no surprise, considering the
vast amount of data on prospective customers available for
90 percent
analysis and the historical challenge of personalizing initial
marketing outreach at scale.
Various players are already deploying gen AI use cases,
but this is undoubtedly only scratching the surface. Our
of commercial leaders expect to utilize gen AI solutions “often” over
research found that 90 percent of commercial leaders
the next two years.
expect to utilize gen AI solutions “often” over the next two
years (Exhibit 2).
Web <year>
<Title>
Exhibit 2
Exhibit <x> of <x>
Commercial leaders are already leveraging gen AI use cases—but most feel the
technology is underutilized.
Extent to which commercial leaders feel their organizations are using machine learning / gen AI,¹
% of responses
Machine learning Gen AI
55
40
25
20 20 20
15
5
0 0
Almost never Rarely Sometimes Often Almost always
Extent to which commercial leaders think their organizations should be using machine learning /
gen AI,² % of responses
65
50
40
25
10 10
0 0 0 0
Almost never Rarely Sometimes Often Almost always
1Senior executives in significant global B2B and B2C sales and marketing organizations across a wide range of industries and company maturity levels were
asked: To what extent is your organization using ML / gen AI solutions?
2Q: How much do you think your organization should be using ML / gen AI solutions?
Source: McKinsey analysis
McKinsey & Company
AI-powered sales and marketing reach new heights with generative AI 7
Overall, the most effective companies are prioritizing and deploying advanced sales tech,
building hybrid teams, and enabling hyper-personalization. And they’re maximizing their use of
e-commerce and third-party marketplaces through analytics and AI. At successful companies,
we’ve found:
— There is a clearly defined AI vision and strategy.
— More than 20 percent of digital budgets are invested in AI-related technologies.
— Teams of data scientists are employed to run algorithms to inform rapid pricing strategy and
optimize marketing and sales.
— Strategists are looking to the future and outlining simple gen AI use cases.
Such trailblazers are already realizing the potential of gen AI to elevate their operations.
Our research indicates that players
that invest in AI are seeing a revenue
uplift of 3 to 15 percent and a sales
ROI uplift of 10 to 20 percent.
Anticipating and mitigating risks in gen AI
While the business case for artificial intelligence is compelling, the rate of change in AI
technology is astonishingly fast—and not without risk. When commercial leaders were asked
about the greatest barriers limiting their organization’s adoption of AI technologies, internal and
external risk were at the top of the list.
From IP infringement to data privacy and security, there are a number of issues that require
thoughtful mitigation strategies and governance. The need for human oversight and
accountability is clear, and may require the creation of new roles and capabilities to fully
capitalize on opportunities ahead.
88 AI-powered sales and marketing reach new heights with generative AI
The way forward:
Six “no regrets”
AI strategies
There are six actions you can take in your company today to chart
an AI transformation in sales and marketing.
2.
3.
Form a commercial
gen AI taskforce
Identify low-hanging fruit in
Create a cross-functional team (including, your customer journey
for example, marketing, sales, pricing, and
Look for simple, high-impact, low-cost,
1. IT) to explore gen AI opportunities and
low-risk use cases (such as automating
pressure test applicability of commercial
prospecting outreach emails) and put
Conduct a gen AI audit of use cases.
guardrails in place to limit risk.
commercial activities
Evaluate marketing and sales tech
infrastructure and skill sets; explore
how open-source or low-cost tech
players could help you implement gen
AI use cases.
6.
Establish gen AI guidelines for
your sales team
5.
Prohibit input of sensitive customer data
Train your sales team on gen AI into gen AI tools and set a high bar for
4. basics to fuel experimentation verifying outputs, especially where content
will be externally facing.
Launch a gen AI experiment Ofer workshops on gen AI fundamentals
(or three) to give the team a better sense of potential
applications and the con—dence to begin
Pilot two or three exciting use cases in a experimenting.
targeted portion of the sales cycle (such as
top-of-funnel). Track results and re—ne for
broader implementation.
AI-powered sales and marketing reach new heights with generative AI 9
In addition to immediate actions, leaders can start thinking strategically about how to invest in AI
commercial excellence for the long term. It will be important to identify which use cases are table
stakes, and which can help you differentiate your position in the market. Then prioritize based on
impact and feasibility.
The AI landscape is evolving very quickly, and winners today may not be viable tomorrow. Small start-
ups are great innovators but may not be able to scale as needed or produce sales-focused use cases
that meet your needs. Test and iterate with different players, but pursue partnerships strategically
based on sales-related innovation, rate of innovation versus time to market, and ability to scale.
v v v
AI is changing at breakneck speed, and while it’s hard to predict the course of this revolutionary tech,
it’s sure to play a key role in future marketing and sales. Leaders in the field are succeeding by turning
to gen AI to maximize their operations, taking advantage of advances in personalization and internal
sales excellence. How will your industry react?
Richelle Deveau is a partner in McKinsey’s Southern California office, and Sonia Joseph Griffin is an associate partner
in the Atlanta office, where Steve Reis is a senior partner.
The authors wish to thank Michelle Court-Reuss, Will Godfrey, Russell Groves, Maxim Lampe, Siamak Sarvari, and Zach
Stone for their contributions to this article.
10 AI-powered sales and marketing reach new heights with generative AI
McKinsey Growth, Marketing & Sales
May 2023
Copyright 2023 © McKinsey & Company
Designed by Darby
www.mckinsey.com
@McKinsey
@McKinsey"
74,mckinsey,the-state-of-ai-in-2022-and-a-half-decade-in-review.pdf,"The state of AI in 2022—and
a half decade in review
December 2022
The results of this year’s McKinsey Global Survey on AI show the expansion of the
technology’s use since we began tracking it five years ago, but with a nuanced
picture underneath.1 Adoption has more than doubled since 2017, though the pro-
portion of organizations using AI has plateaued between 50 and 60 percent for
the past few years. A set of companies seeing the highest financial returns from AI
continue to pull ahead of competitors. The results show these leaders making larger
investments in AI, engaging in increasingly advanced practices known to enable
scale and faster AI development, and showing signs of faring better in the tight
market for AI talent. On talent, for the first time, we looked closely at AI hiring and
upskilling. The data show that there is significant room to improve diversity on AI
teams, and, consistent with other studies, diverse teams correlate with outstanding
performance.
Five years in
review: AI adoption,
impact, and spend
This marks the fifth consecutive year we’ve conducted research globally on AI’s role in business, and we
have seen shifts over this period.
First, AI adoption has more than doubled.² In 2017, 20 percent of respondents reported adopting AI in
at least one business area, whereas today, that figure stands at 50 percent, though it peaked higher in
2019 at 58 percent.
Meanwhile, the average number of AI capabilities that organizations use, such as natural-language
generation and computer vision, has also doubled—from 1.9 in 2018 to 3.8 in 2022. Among these
1 In the survey, we defined AI as the ability of a machine to perform cognitive functions that we associate with human minds (for example,
natural-language understanding and generation) and to perform physical tasks using cognitive functions (for example, physical robotics,
autonomous driving, and manufacturing work).
2 In 2017, the definition for AI adoption was using AI in a core part of the organization’s business or at scale. In 2018 and 2019, the definition
was embedding at least one AI capability in business processes or products. In 2020, 2021, and 2022, the definition was that the
organization has adopted AI in at least one function.
2 The state of AI in 2022—and a half decade in review
capabilities, robotic process automation and computer vision have remained the most commonly deployed each
year, while natural-language text understanding has advanced from the middle of the pack in 2018 to the front of the
list just behind computer vision.
Responses show an increasing number of AI capabilities embedded in
organizations over the past ve years.
Average number of AI capabilities that Share of respondents who say their organizations
respondents’ organizations have embedded have adopted AI in at least one function, %
within at least one function or business unit¹
3.9 3.8 47 58 50 56 50
3.1
2.3
20
1.9
2018 2019 2020 2021 2022 2017 2018 2019 2020 2021 2022
% of respondents who say given AI capability is embedded in products or business processes in
at least one function or business unit²
Robotic process automation 39
Computer vision 34
Natural-language text understanding 33
Virtual agents or conversational interfaces 33
Deep learning 30
Knowledge graphs 25
Recommender systems 25
Digital twins 24
Natural-language speech understanding 23
Physical robotics 20
Reinforcement learning 20
Facial recognition 18
Natural-language generation 18
Transfer learning 16
Generative adversarial networks (GAN) 11
Transformers 11
¹The number of capabilities included in the survey has grown over time, from 9 in 2018 to 15 in the 2022 survey.
²Question was asked only of respondents who said their organizations have adopted AI in at least one function.
McKinsey & Company
The state of AI in 2022—and a half decade in review 3
The top use cases, however, have remained relatively stable: optimization of service operations has taken
the top spot each of the past four years.
Second, the level of investment in AI has increased alongside its rising adoption. For example, five years
ago, 40 percent of respondents at organizations using AI reported more than 5 percent of their digital
budgets went to AI, whereas now more than half of respondents report that level of investment. Going
forward, 63 percent of respondents say they expect their organizations’ investment to increase over the
next three years.
The most popular AI use cases span a range of functional activities.
Top use cases Use cases by function
Most commonly adopted AI use cases, by function, % of respondents¹
Service operations² Product and/or service development Marketing and sales Risk
Service operations optimization 24
Creation of new AI-based products 20
Customer service analytics 19
Customer segmentation 19
New AI-based enhancements of products 19
Customer acquisition and lead generation 17
Contact-center automation 16
Product feature optimization 16
Risk modeling and analytics 15
Predictive service and intervention 14
¹Out of 39 use cases. Question was asked only of respondents who said their organizations have adopted AI in at least one function.
²Eg, eld services, customer care, back o ce.
McKinsey & Company
4 The state of AI in 2022—and a half-decade in review
The most popular AI use cases span a range of functional activities.
Top use cases Use cases by function
Most commonly adopted AI use cases within each business function,¹ % of respondents¹
Service operations² Product and/or service development
Service operations Creation of new AI-based
24 20
optimization products
Contact-center New AI-based enhancements
16 19
automation of products
Marketing and sales Supply chain management
Customer service Sales and demand
19 10
analytics forecasting
Logistics network
Customer segmentation 19 9
optimization
Risk Human resources
Risk modeling and Optimization of talent
15 10
analytics management
Fraud and debt Optimization of workforce
11 5
analytics deployment
Strategy and corporate finance Manufacturing
Capital allocation 7 Predictive maintenance 13
Yield, energy, and/or
Treasury management 4 11
throughput optimization
Simulations (eg, using digital
M&A support 4 11
twins, 3 D modeling)
¹Question was asked only of respondents who said their organizations have adopted AI in at least one function.
²Eg, eld services, customer care, back o ce.
McKinsey & Company
Third, the specific areas in which companies see value from AI have evolved. In 2018, manufacturing and
risk were the two functions in which the largest shares of respondents reported seeing value from AI
use. Today, the biggest reported revenue effects are found in marketing and sales, product and service
development, and strategy and corporate finance, and respondents report the highest cost benefits
from AI in supply chain management. The bottom-line value realized from AI remains strong and largely
consistent. About a quarter of respondents report this year that at least 5 percent of their organizations’
EBIT was attributable to AI in 2021, in line with findings from the previous two years, when we’ve also
tracked this metric.
Lastly, one thing that has remained concerningly consistent is the level of risk mitigation organizations
engage in to bolster digital trust. While AI use has increased, there have been no substantial increases in
reported mitigation of any AI-related risks from 2019—when we first began capturing this data—to now.
The state of AI in 2022—and a half decade in review 5
AI-related cost decreases are most often reported in supply chain
management and revenue increases in product development and marketing
and sales.
Cost decrease and revenue increase from AI adoption in 2021, by function, % of respondents¹
Decrease Decrease Decrease Increase Increase Increase
by <10% by 10–19% by ≥20% by >10% by 6–10% by ≤5%
Service operations 45 29 10 6 10 10 37 57
Manufacturing 42 32 7 3 10 18 33 61
Human resources 29 25 3 1 14 13 31 58
Marketing and sales 28 21 43 9 20 41 70
Risk 43 30 8 5 10 11 27 48
Supply chain management 52 41 7 4 14 17 28 59
Product and/or service development 30 20 4 6 13 24 33 70
Strategy and corporate finance 43 31 8 4 8 16 41 65
Average across all activites 32 23 6 3 8 19 36 63
1Question was asked only of respondents who said their organizations have adopted AI in a given function. Respondents who said “no change,”
“cost increase,” “not applicable,” or “don’t know” are not shown.
McKinsey & Company
6 The state of AI in 2022—and a half decade in review
McKinsey commentary
Michael Chui
Partner, McKinsey Global Institute
Over the past half decade, during which we’ve been conducting our global survey, we have seen
the “AI winter” turn into an “AI spring.” However, after a period of initial exuberance, we appear to
have reached a plateau, a course we’ve observed with other technologies in their early years of
adoption. We might be seeing the reality sinking in at some organizations of the level of organiza-
tional change it takes to successfully embed this technology.
In our work, we’ve encountered companies that get discouraged because they went into AI
thinking it would be a quick exercise, while those taking a longer view have made steady prog-
ress by transforming themselves into learning organizations that build their AI muscles over time.
These companies gradually incorporate more AI capabilities and stand up increasingly more
applications progressively faster and more easily thanks to lessons from past successes as well
as failures. They not only invest more, but they also invest more wisely, with the goal of creating a
veritable AI factory that enables them to incorporate more AI in more areas of the business, first in
adjacent ones where some existing capabilities can be repurposed and then into entirely
new ones.
There is, at a high level, an emerging playbook for getting maximum value from AI. Each year that
we conduct our research, we see a group of leaders engaging in the types of practices that help
execute AI successfully. It’s paying off in the form of actual bottom-line impact at significant levels.
We also see it every day as we guide others on their AI journeys. It’s not easy work, but as has
been the case with previous technologies, the gains will go to those who stay the course.
Those taking a longer view
have made steady progress by
transforming themselves into
learning organizations that build
their AI muscles over time.
The state of AI in 2022—and a half decade in review 7
AI use and sustainability efforts
The survey findings suggest that many organizations are more commonly seen at organizations based in
that have adopted AI are integrating AI capabilities into Greater China, Asia–Pacific, and developing markets,
their sustainability efforts and are also actively seeking while respondents in North America are least likely to
ways to reduce the environmental impact of their AI report them.
use (exhibit). Of respondents from organizations that
have adopted AI, 43 percent say their organizations are When asked about the types of sustainability efforts
using AI to assist in sustainability efforts, and 40 per- using AI, respondents most often mention initiatives
cent say their organizations are working to reduce the to improve environmental impact, such as optimiza-
environmental impact of their AI use by minimizing the tion of energy efficiency or waste reduction. AI use
energy used to train and run AI models. As companies is least common in efforts to improve organizations’
that have invested more in AI and have more mature social impact (for example, sourcing of ethically made
AI efforts than others, high performers are 1.4 times products), though respondents working for North
more likely than others to report AI-enabled sustain- American organizations are more likely than their
ability efforts as well as to say their organizations are peers to report that use.
working to decrease AI-related emissions. Both efforts
Exhibit
Organizations are using AI within sustainability e orts and are working to
reduce the environmental impact of their AI use.
Organizations using AI in their sustainability Organizations taking steps to reduce carbon
efforts, % of respondents¹ emissions from their AI use, % of respondents¹
Greater China² 61 Developing markets³ 53
Asia–Paci c 54 Asia–Paci c 47
Developing markets³ 44 Greater China² 46
Europe 39 Europe 36
North America 30 North America 31
Types of sustainability e orts in which respondents’ organizations are using AI⁴
Improving the organization’s environmental impact (eg,
62
improving energy e ciency, optimizing transportation)
Evaluating sustainability e orts (eg, benchmarking) 51
Improving the organization’s governance
45
(eg, regulatory compliance, risk management)
Improving the organization’s social
34
impact (eg, sourcing ethical products)
¹Only asked of respondents whose organizations have adopted AI in at least one function. For organizations based in Greater China, n = 102; for
Asia–Paci c, n = 74; for developing markets, n = 118; for Europe, n = 260; and for North America, n = 190.
²Includes respondents in Hong Kong SAR and Taiwan China.
³Includes respondents in India, Latin America, Middle East, North Africa, and sub-Saharan Africa.
⁴Only asked of respondents whose organizations have adopted AI in at least one function who said that their organizations are using AI in
sustainability e orts; n = 302.
McKinsey & Company
8 The state of AI in 2022—and a half decade in review
Mind the gap: AI
leaders pulling ahead
Over the past five years, we have tracked the leaders in AI—we refer to them as AI high performers—and
examined what they do differently. We see more indications that these leaders are expanding their
competitive advantage than we find evidence that others are catching up.
First, we haven’t seen an expansion in the size of the leader group. For the past three years, we have
defined AI high performers as those organizations that respondents say are seeing the biggest
bottom-line impact from AI adoption—that is, 20 percent or more of EBIT from AI use. The proportion of
respondents falling into that group has remained steady at about 8 percent. The findings indicate that
this group is achieving its superior results mainly from AI boosting top-line gains, as they’re more likely
to report that AI is driving revenues rather than reducing costs, though they do report AI decreasing
costs as well.
Next, high performers are more likely than others to follow core practices that unlock value, such
as linking their AI strategy to business outcomes.³ Also important, they are engaging more often
in “frontier” practices that enable AI development and deployment at scale, or what some call the
“industrialization of AI.” For example, leaders are more likely to have a data architecture that is modular
enough to accommodate new AI applications rapidly. They also often automate most data-related
processes, which can both improve efficiency in AI development and expand the number of applications
they can develop by providing more high-quality data to feed into AI algorithms. And AI high performers
are 1.6 times more likely than other organizations to engage nontechnical employees in creating AI
applications by using emerging low-code or no-code programs, which allow companies to speed up
the creation of AI applications. In the past year, high performers have become even more likely than
other organizations to follow certain advanced scaling practices, such as using standardized tool sets
to create production-ready data pipelines and using an end-to-end platform for AI-related data science,
data engineering, and application development that they’ve developed in-house.
High performers might also have a head start on managing potential AI-related risks, such as personal
privacy and equity and fairness, that other organizations have not addressed yet. While overall, we
have seen little change in organizations reporting recognition and mitigation of AI-related risks since
we began asking about them four years ago, respondents from AI high performers are more likely
than others to report that they engage in practices that are known to help mitigate risk. These include
ensuring AI and data governance, standardizing processes and protocols, automating processes such
as data quality control to remove errors introduced through manual work, and testing the validity of
models and monitoring them over time for potential issues.
3All questions about AI-related strengths and practices were asked only of the 744 respondents who said their organizations had adopted
AI in at least one function, n = 744.
The state of AI in 2022—and a half decade in review 9
Organizations seeing the highest returns from AI are more likely to follow
strategy, data, models, tools, technology, and talent best practices.
Share of respondents reporting their organizations engage in each practice,¹ % of respondents
Strategy Data Models, tools, and tech TTTaaallleeennnttt aaannnddd wwwaaayyysss ooofff wwwooorrrkkkiiinnnggg
All other respondents AI high performers²
Have a road map that clearly prioritizes AI initiatives
linked to business value across organization
Have an AI strategy that is aligned with the broader
corporate strategy and goals
Senior management that is fully aligned and committed
to organization’s AI strategy
Have a clearly de ned AI vision and strategy
Appointed a credible leader of AI initiatives who is
empowered to move them forward in collaboration with
peers across business units and functions
Systematically track a comprehensive set of well-
defined KPIs to measure the incremental impact of
AI initiatives
Have a clear framework for AI governance that
Orgcaovnerisz eavteriyo sntesp osfe thee imnogde tl hdeeve hloipgmhenet sprto creessturns from AI are more likely to follow
Organizations seeing the highest returns from AI are more likely to follow
strategy, data, models, tools, technology, and talent best practices.
strategy, data, models, tools, technology, and talent best practices.
0 20 40 60 80 100
Share of respondents reporting their organizations engage in each practice,¹ % of respondents
Share of respondents reporting their organizations engage in each practice,¹ % of respondents
SSttrraatteeggyy DDaattaa Models, tools, and techTalentTTT aaaalllneeednnn tttw aaaannnyddds wwwofaaa wyyyssso roookfff iwwwngooorrrkkkiiinnnggg
¹PrSacttriacetes gshyown heDrea taare repreMseondtaetilvse, toof othlso,s ea nwdit ht ethceh highesTTTtaaa dllleeeelnnntattts aaa bnnnedddtw wwweaaaenyyy sssA Iooo hfffi gwwwhooo prrrekkkriiifnnnogggrmers and other respondents. Not all practices are shown.
²Respondents who said that at least 20 percent of their organizations’ earnings before interest and taxes (EBIT) in 2021 was attributable to their use of AI.
³ All other respondents AI high performers²
All other respondents AI high performers²
McKinsey T&a Ckoem apa fnuyll life cycle approach to developing and
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AI development teams follow standard protocols
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labeling, data quality control)
Have scalable internal processes for labeling AI training
data
0 20 40 60 80 100
¹Practices shown here are representative of those with the highest deltas between AI high performers and other respondents. Not all practices are shown.
²R¹Persapcotnicdeesn sths owwhno hsearide tahrea tr eatp lreeassetn 2ta0t ipvee rocfe tnhto osfe t hweitirh otrhgea hniigzahteiostn sd’e EltBasIT b ient 2w0e2e1n w AaI sh iagthtr ipbeurtfaobrmlee tros t ahnedir outshee or fr eAsI.pondents. Not all practices are shown.
³²Respondents who said that at least 20 percent of their organizations’ EBIT in 2021 was attributable to their use of AI.
³
McKinsey & Company
McKinsey & Company
10 The state of AI in 2022—and a half decade in review
Organizations seeing the highest returns from AI are more likely to follow
strategy, data, models, tools, technology, and talent best practices.
Share of respondents reporting their organizations engage in each practice,¹ % of respondents
Strategy Data MMMooodddeeelllsss,,, tttoooooolllsss , aaannnddd ttteeeccchhh TTTaaallleeennnttt aaannnddd wwwaaayyysss ooofff wwwooorrrkkkiiinnnggg
All other respondents AI high performers²
Develop AI models that can provide accurate,
usable results leveraging smaller amounts of data
(ie, “small data”)
Regularly refresh AI models based on clearly de ned
criteria for when and why to do so
Developed in-house the end-to-end platform used for
AI-related data science, data engineering, and
application development
Use a standardized tool set to create production-ready
data pipelines
Develop modular components (eg, data model layers,
data pipelines) so they can be reused in AI applications
Refresh AI/machine learning tech stack at
least annually to take advantage of the latest
technological advances
Organizations seeing the highest returns from AI are more likely to follow
Automate the full life cycle for AI model development
str(eagt, ferogmy d,a tda aingteas,t iomn aondd qeulaslit,y tcoonotrolsl t,h rtoeugchh nology, and talent best practices.
model monitoring)
Share oUf sree tshpeo onrgdaenniztasti orne’sp oowrtni nhgig ht-hpeeirrf oormrgaannceiz ations engage in each practice,¹ % of respondents
computing cluster for AI workloads
Organizations seeing the highest returns from AI are more likely to follow
strategy, data, models, tools, technology, and talent best practices.
SSttrraatteeggyy DDaattaa Models, tools, and techTalentTTT aaaalllneeednnn tttw0 aaaannnyddds wwwofaaa wyyyssso roookfff2 iwwwn0gooorrrkkkiiinnnggg 40 60 80 100
¹SPhraactricees oshfo wren shepreo anred reeprnestesn trateivpe oof rthtoisneg w itthh theei hri gohersgt daenltaisz baettwioeenn sA I ehinghg paergfoerm ienrs aenad cothhe rp rersapcontdiecnets,. ¹N %ot a lol pfr arceticseps oarne sdheownnt.s
²Respondents who said that at least 20 percent of their organizations’ EBIT in 2021 was attributable to theirA uslle ootf hAIe.r respondents AI high performers²
³
Take a full life cycle approach to developing and
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Integrate AI technologies into business processes (eg, All other respondents AI high performers²
day-to-day operations, employee work ows)
Take a full life cycle approach to developing and
Teams for data science and AI dedsiegpnl oaynindg d AevI emloopdmelesnt
collaborate to build and improve AI applications
Integrate AI technologies into business processes (eg,
Havdea wy-etlol--ddeayn oepde craatpioanbsil,i teym-bpulioldyieneg wpororkgraomwss )to
develop technology personnels’ AI skills
Teams for data science and AI design and development
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decision making
Have well-de ned capability-building programs to
AI devedleovpemloepn tte tcehanmosl ofoglylo pwe rsstoannndealrsd’ AprI ostkoicllsols
(eg, tool frameworks, development processes) for
building and delivering AI tools
Train nontechnical personnel to use AI to improve
decision making
0 20 40 60 80 100
AI development teams follow standard protocols
(eg, tool frameworks, development processes) for
building and delivering AI tools
0 20 40 60 80 100
¹Practices shown here are representative of those with the highest deltas between AI high performers and other respondents. Not all practices are shown.
²Respondents who said that at least 20 percent of their organizations’ EBIT in 2021 was attributable to their use of AI.
³
McKinsey & Company
¹Practices shown here are representative of those with the highest deltas between AI high performers and other respondents. Not all practices are shown.
²Respondents who said that at least 20 percent of their organizations’ EBIT in 2021 was attributable to their use of AI.
³
McKinsey & Company
The state of AI in 2022—and a half decade in review 11
Investment is yet another area that could contribute to the widening of the gap: AI high performers are
poised to continue outspending other organizations on AI efforts. Even though respondents at those
leading organizations are just as likely as others to say they’ll increase investments in the future, they’re
spending more than others now, meaning they’ll be increasing from a base that is a higher percentage of
revenues. Respondents at AI high performers are nearly eight times more likely than their peers to say their
organizations spend at least 20 percent of their digital-technology budgets on AI-related technologies.
And these digital budgets make up a much larger proportion of their enterprise spend: respondents at AI
high performers are over five times more likely than other respondents to report that their organizations
spend more than 20 percent of their enterprise-wide revenue on digital technologies.
Finally, all of this may be giving AI high performers a leg up in attracting AI talent. There are indications
that these organizations have less difficulty hiring for roles such as AI data scientist and data engineer.
Respondents from organizations that are not AI high performers say filling those roles has been “very
difficult” much more often than respondents from AI high performers do.
The bottom line: high performers are already well positioned for sustained AI success, improved efficiency
in new AI development, and a resultingly more attractive environment for talent. The good news for
organizations outside the leader group is that there’s a clear blueprint of best practices for success.
Respondents at AI high performers are nearly
eight times more likely than their peers to say their
organizations spend at least 20 percent of their
digital-technology budgets on AI-related technologies.
12 The state of AI in 2022—and a half-decade in review
McKinsey commentary
Bryce Hall
Associate partner
Over the years of our research, we’ve continued to refine our understanding of the specific
practices that leading companies are doing well and the capabilities they have in place to capture
value from AI. Recently, a new set of “frontier” practices has emerged as organizations shift from
experimenting with AI to industrializing it. These include machine learning operations (MLOps)
practices such as assetization, or turning elements like code into reusable assets that can be
applied over and over in different business applications.
But over the years, we’ve also consistently seen a set of foundational practices that these
organizations are getting right. Through our work, we’ve learned not to describe these as “basic”
practices, because they are some of the most difficult to implement. Many of these involve the
people elements that need to be in place for companies to adopt AI successfully, such as having
a clear understanding of what specific tech talent roles are needed and successfully integrating
AI into business processes and decision making. As proven in many cases, AI engines and people
together can create much more value than either can individually.
As the AI frontier advances, we continue to be inspired by some truly innovative applications of
AI, such as the use of AI to identify new drugs, create hyperpersonalized recommendations for
consumers, and power AI simulations in digital twins to optimize performance across a variety
of settings. As individual AI capabilities, such as natural-language processing and generation,
continue to improve and democratize, we’re excited to see a wave of new applications emerge and
more companies capture value from AI at scale.
The state of AI in 2022—and a half decade in review 13
AI talent tales: New
hot roles, continued
diversity woes
Our first detailed look at the AI talent picture signals the maturation of AI, surfaces the most common
strategies organizations employ for talent sourcing and upskilling, and shines a light on AI’s diversity
problem—while showing yet again a link between diversity and success.
Hiring is a challenge, but less so for high performers
Software engineers emerged as the AI role that survey responses show organizations hired most
often in the past year, more often than data engineers and AI data scientists. This is another clear sign
that many organizations have largely shifted from experimenting with AI to actively embedding it in
enterprise applications.
Unfortunately, the tech talent shortage shows no sign of easing, threatening to slow that shift for
some companies. A majority of respondents report difficulty in hiring for each AI-related role in the
Web <year>
<Title>
Exhibit <x> of <x>
Responses suggest that organizations are most often hiring software
engineers, data engineers, and AI data scientists.
AI-related roles that respondents’ organizations hired, past year, % of respondents¹
Software engineers 39
Data engineers 35
AI data scientists 33
Machine learning engineers 30
Data architects 28
AI product owners/managers 22
Design specialists 22
Data visualization specialists 21
Translators 8
None of the above 14
1Only asked of respondents whose organizations have adopted AI in at least one function. For respondents at AI high performers, n = 51. For all other
respondents, n = 413.
McKinsey & Company
14 The state of AI in 2022—and a half decade in review
past year, and most say it either wasn’t any easier or was more difficult to acquire this talent than in years
past. AI data scientists remain particularly scarce, with the largest share of respondents rating data
scientist as a role that has been difficult to fill, out of the roles we asked about.
As mentioned earlier, we see some signs that AI high performers have a slightly easier time hiring than
other organizations, but they still report difficulty more often than not. What’s more evident from the
survey findings is their focus on hiring for AI industrialization and business value optimization. For
example, they’re more than twice as likely to have hired a ML engineer in the past year—a role focused on
optimizing the ML models built by data scientists for performance and scalability, as well as automating
the machine learning pipeline, from data ingestion to prediction generation. Respondents at high
performers are also nearly twice as likely as others to say they have hired an AI product manager to
oversee AI application development and adoption and more than three times as likely to have hired an
analytics translator, two roles that ensure that AI applications deliver business value.
Most respondents say that hiring for each AI-related role has been difficult
in the past year and hasn’t become easier over time.
Difficulty in organizations’ hiring of AI-related roles, past year, % of respondents¹
Very Somewhat Neither easy Somewhat Very
difficult difficult nor difficult easy easy
Software engineers 20 45 17 13 1
Data engineers 20 49 20 6 1
AI data scientists 32 46 9 6 1
Machine learning engineers 28 42 18 9 1
Data architects 25 47 18 5 1
AI product owners/managers 20 42 27 5 1
Design specialists 15 44 24 10 1
Data visualization specialists 15 46 21 12 1
Translators 27 43 14 11 2
Change of difficulty in organizations’ hiring of AI-related roles, past 3 years, % of respondents¹
Much Somewhat Neither easier Somewhat Much
more difficult more difficult nor more difficult easier easier
Software engineers 18 31 24 10 7
Data engineers 22 35 16 15 3
AI data scientists 22 25 19 18 3
Machine learning engineers 20 28 14 22 3
Data architects 21 31 16 14 6
AI product owners/managers 16 27 21 16 3
Design specialists 17 28 20 15 7
Data visualization specialists 17 25 22 23 4
Translators 22 34 19 11 5
1Only asked of respondents whose organizations have adopted AI in at least "
75,mckinsey,the-state-of-ai-in-2023-generative-ais-breakout-year_vf.pdf,"The state of AI in
2023: Generative AI’s
breakout year
As organizations rapidly deploy generative AI tools, survey respondents
expect significant effects on their industries and workforces.
August 2023
The state of AI in 2023: Generative AI’s breakout year
The latest annual McKinsey Global Survey on the current
state of AI confirms the explosive growth of generative AI
(gen AI) tools. Less than a year after many of these tools debuted,
one-third of our survey respondents say their organizations are
using gen AI regularly in at least one business function. Amid
recent advances, AI has risen from a topic relegated to tech
employees to a focus of company leaders: nearly one-quarter
of surveyed C-suite executives say they are personally using
gen AI tools for work, and more than one-quarter of respondents
from companies using AI say gen AI is already on their boards’
agendas. What’s more, 40 percent of respondents say their
organizations will increase their investment in AI overall because
of advances in gen AI. The findings show that these are still early
days for managing gen AI–related risks, with less than half of
respondents saying their organizations are mitigating even the
risk they consider most relevant: inaccuracy.
The organizations that have already embedded AI capabilities
have been the first to explore gen AI’s potential, and those seeing
the most value from more traditional AI capabilities—a group we
call AI high performers—are already outpacing others in their
adoption of gen AI tools.1
The expected business disruption from gen AI is significant, and
respondents predict meaningful changes to their workforces. They
anticipate workforce cuts in certain areas and large reskilling
efforts to address shifting talent needs. Yet while the use of gen AI
might spur the adoption of other AI tools, we see few meaningful
increases in organizations’ adoption of these technologies. The
percent of organizations adopting any AI tools has held steady since
2022, and adoption remains concentrated within a small number of
business functions.
1 We define AI high performers as organizations that, according to respondents, attribute at least 20 percent of their EBIT
to AI adoption.
The state of AI in 2023: Generative AI’s breakout year 1
It’s early days still,
but use of gen AI is
already widespread
The findings from the survey—which was in the field in mid-April 2023—show that, despite
gen AI’s nascent public availability, experimentation with the tools is already relatively common,
and respondents expect the new capabilities to transform their industries. Gen AI has captured
interest across the business population: individuals across regions, industries, and seniority
levels are using gen AI for work and outside of work. Seventy-nine percent of all respondents say
they’ve had at least some exposure to gen AI, either for work or outside of work, and 22 percent
say they are regularly using it in their own work. While reported use is quite similar across
seniority levels, it is highest among respondents working in the technology sector and those
in North America.
2 The state of AI in 2023: Generative AI’s breakout year
Web <2023>
<State of AI 2023>
Exhibit <1PDF> of <11>
Respondents across regions, industries, and seniority levels say they are
already using generative AI tools.
Reported exposure to generative AI tools, % of respondents
Regularly use Regularly use for work Regularly use Have tried at No Don’t
for work and outside of work outside of work least once exposure know
By office location Asia–Pacific 4 18 19 36 19 3
Developing markets 9 11 20 34 23 3
Europe 10 14 11 45 15 6
Greater China 9 10 18 46 14 3
North America 6 22 13 38 19 3
By industry Advanced industries 5 11 16 47 15 5
Business, legal, and professional services 7 16 13 41 21 2
Consumer goods/retail 7 11 12 40 26 4
Energy and materials 6 8 15 50 19 3
Financial services 8 16 18 41 14 4
Healthcare, pharma, and medical products 6 10 17 44 15 7
Technology, media, and telecom 14 19 17 37 9 3
By job title C-suite executives 8 16 13 42 18 2
Senior managers 10 14 16 42 15 3
Midlevel managers 7 16 20 35 19 4
By age Born in 1964 or earlier 6 17 21 30 18 9
Born 1965–80 7 18 18 37 17 3
Born 1981–96 5 22 24 36 11 3
By gender identity Men 8 16 16 37 19 4
Women 12 15 6 46 18 3
Note: Figures may not sum to 100%, because of rounding. In Asia–Pacific, n = 164; in Europe, n = 515; in North America, n = 392; in Greater China (includes
Hong Kong and Taiwan), n = 337; and in developing markets (includes India, Latin America, and Middle East and North Africa), n = 276. For advanced industries
(includes automotive and assembly, aerospace and defense, advanced electronics, and semiconductors), n = 96; for business, legal, and professional services,
n = 215; for consumer goods and retail, n = 128; for energy and materials, n = 96; for financial services, n = 248; for healthcare, pharma, and medical products,
n = 130; and for technology, media, and telecom, n = 244. For C-suite respondents, n = 541; for senior managers, n = 437; and for middle managers, n = 339.
For respondents born in 1964 or earlier, n = 143; for respondents born between 1965 and 1980, n = 268; and for respondents born between 1981 and 1996,
n = 80. Age details were not available for all respondents. For respondents identifying as men, n = 1,025; for respondents identifying as women, n = 156.
The survey sample also included respondents who identified as “nonbinary” or “other” but not a large enough number to be statistically meaningful.
Source: McKinsey Global Survey on AI, 1,684 participants at all levels of the organization, April 11–21, 2023
McKinsey & Company
Organizations, too, are now commonly using gen AI. One-third of all respondents say their
organizations are already regularly using generative AI in at least one function—meaning that
60 percent of organizations with reported AI adoption are using gen AI. What’s more, 40 percent
of those reporting AI adoption at their organizations say their companies expect to invest more in
AI overall thanks to generative AI, and 28 percent say generative AI use is already on their board’s
agenda. The most commonly reported business functions using these newer tools are the same
as those in which AI use is most common overall: marketing and sales, product and service
development, and service operations, such as customer care and back-office support. This
suggests that organizations are pursuing these new tools where the most value is. In our previous
research, these three areas, along with software engineering, showed the potential to deliver
about 75 percent of the total annual value from generative AI use cases.
The state of AI in 2023: Generative AI’s breakout year 3
Web <2023>
<State of AI 2023>
Exhibit <2> of <11>
The most commonly reported uses of generative AI tools are in marketing
and sales, product and service development, and service operations.
Share of respondents reporting that their organization is regularly using generative AI in given
function, %1
Product and/ Strategy and
Marketing or service Service corporate Supply chain
and sales development operations Risk finance HR management Manufacturing
14 13 10 4 4 3 3 2
Most regularly reported generative AI use cases within function, % of respondents
Marketing and sales Product and/or service development Service operations
Crafting first drafts of text documents Identifying trends in customer needs Use of chatbots (eg, for customer service)
9 7 6
Personalized marketing Drafting technical documents Forecasting service trends or anomalies
8 5 5
Summarizing text documents Creating new product designs Creating first drafts of documents
8 4 5
1Questions were asked of respondents who said their organizations have adopted AI in at least 1 business function. The data shown were rebased to
represent all respondents.
Source: McKinsey Global Survey on AI, 1,684 participants at all levels of the organization, April 11–21, 2023
McKinsey & Company
In these early days, expectations for gen AI’s impact are high: three-quarters of all respondents
expect gen AI to cause significant or disruptive change in the nature of their industry’s
competition in the next three years. Survey respondents working in the technology and financial-
services industries are the most likely to expect disruptive change from gen AI. Our previous
research shows that, while all industries are indeed likely to see some degree of disruption, the
level of impact is likely to vary.2 Industries relying most heavily on knowledge work are likely to
see more disruption—and potentially reap more value. While our estimates suggest that tech
companies, unsurprisingly, are poised to see the highest impact from gen AI—adding value
equivalent to as much as 9 percent of global industry revenue—knowledge-based industries
such as banking (up to 5 percent), pharmaceuticals and medical products (also up to 5 percent),
and education (up to 4 percent) could experience significant effects as well. By contrast,
manufacturing-based industries, such as aerospace, automotives, and advanced electronics,
could experience less disruptive effects. This stands in contrast to the impact of previous
technology waves that affected manufacturing the most and is due to gen AI’s strengths in
language-based activities, as opposed to those requiring physical labor.
2 “The economic potential of generative AI: The next productivity frontier,” McKinsey, June 14, 2023.
4 The state of AI in 2023: Generative AI’s breakout year
McKinsey commentary
Alex Singla
Senior partner and global leader of QuantumBlack, AI by McKinsey
It’s amazing how quickly the conversation around generative AI has evolved. Just a few
months ago, the conversation in the C-suite was pretty rudimentary, focused on trying to
understand what it was and seeing what was hype versus what was reality. Now in just about
six months, business leaders are having much more sophisticated conversations. As we can
see from the survey results, almost a third of companies are using generative AI in at least one
business function. This underscores the degree to which companies understand and accept
that generative AI is viable in business.
The next question will be how companies will take the next step, and whether generative
AI will follow the same pattern we observed with AI more generally, where adoption has
plateaued at around the 50 percent mark. We see from the data that the promise of
generative AI is leading almost half of companies already using AI to plan on increasing their
investments in AI, driven in part by the understanding that broader capabilities are needed to
take full advantage of generative AI.
Taking that next step, where generative AI can go from experiment to business engine, and
ensuring a strong return on the investment requires companies to tackle a broad array
of issues. Those include identifying the specific opportunities for generative AI in the
organization, what the governance and operating model should be, how to best manage third
parties (such as cloud and large language model providers), what is needed to manage the
wide range of risks, understanding the implications on people and the tech stack, and being
clear about how to find the balance between banking near-term gains and developing the
long-term foundations needed to scale. These are complex issues, but they are the key to
unlocking the really significant pools of value out there.
The state of AI in 2023: Generative AI’s breakout year 5
Responses show many organizations not yet addressing potential
risks from gen AI
According to the survey, few companies seem fully prepared for the widespread use of
gen AI—or the business risks these tools may bring. Just 21 percent of respondents reporting
AI adoption say their organizations have established policies governing employees’ use of
gen AI technologies in their work. And when we asked specifically about the risks of adopting
gen AI, few respondents say their companies are mitigating the most commonly cited risk
with gen AI: inaccuracy. Respondents cite inaccuracy more frequently than both cybersecurity
and regulatory compliance, which were the most common risks from AI overall in previous
surveys. Just 32 percent say they’re mitigating inaccuracy, a smaller percentage than the
38 percent who say they mitigate cybersecurity risks. Interestingly, this figure is significantly
lower than the percentage of respondents who reported mitigating AI-related cybersecurity
last year (51 percent). Overall, much as we’ve seen in previous years, most respondents say their
organizations are not addressing AI-related risks.
Web <2023>
<State of AI 2023>
Exhibit <3> of <11>
Inaccuracy, cybersecurity, and intellectual-property infringement are the
most-cited risks of generative AI adoption.
Generative AI–related risks that organizations consider relevant and are working to mitigate,
% of respondents1
Organization considers risk relevant Organization working to mitigate risk
Inaccuracy 56 32
Cybersecurity 53 38
Intellectual-property infringement 46 25
Regulatory compliance 45 28
Explainability 39 18
Personal/individual privacy 39 20
Workforce/labor displacement 34 13
Equity and fairness 31 16
Organizational reputation 29 16
National security 14 4
Physical safety 11 6
Environmental impact 11 5
Political stability 10 2
None of the above 1 8
1Asked only of respondents whose organizations have adopted Al in at least 1 function. For both risks considered relevant and risks mitigated, n = 913.
Source: McKinsey Global Survey on AI, 1,684 participants at all levels of the organization, April 11–21, 2023
McKinsey & Company
6 The state of AI in 2023: Generative AI’s breakout year
McKinsey commentary
Alexander Sukharevsky
Senior partner and global leader of QuantumBlack, AI by McKinsey
There is broad awareness about the risks associated with generative AI. But at the same
time, the prevailing anxiety and fear is making it challenging for leaders to effectively address
the risks. As our latest survey shows, just a little over 20 percent of companies have risk
policies in place for generative AI. Those policies tend to focus on protecting a company’s
proprietary information, such as data, knowledge, and other intellectual property. Those are
critical, but we’ve found that many of these risks can be addressed by making changes in the
business’s technology architecture that reflect established policies.
The real trap, however, is that companies look at the risk too narrowly. There is a significant
range of risks—social, humanitarian, sustainability—that companies need to pay attention
to as well. In fact, the unintended consequences of generative AI are more likely to create
issues for the world than the doomsday scenarios that some people espouse. Companies that
are approaching generative AI most constructively are experimenting with and using it while
having a structured process in place to identify and address these broader risks. They are
putting in place beta users and specific teams that think about how generative AI applications
can go off the rails to better anticipate some of those consequences. They are also working
with the best and most creative people in the business to define the best outcomes for both
the organization and for society more generally. Being deliberate, structured, and holistic
about understanding the nature of the new risks—and opportunities—emerging is crucial to
the responsible and productive growth of generative AI.
The state of AI in 2023: Generative AI’s breakout year 7
Leading companies
are already ahead
with gen AI
The survey results show that AI high performers—that is, organizations where respondents
say at least 20 percent of EBIT in 2022 was attributable to AI use—are going all in on artificial
intelligence, both with gen AI and more traditional AI capabilities. These organizations that
achieve significant value from AI are already using gen AI in more business functions than other
organizations do, especially in product and service development and risk and supply chain
management. When looking at all AI capabilities—including more traditional machine learning
capabilities, robotic process automation, and chatbots—AI high performers also are much
more likely than others to use AI in product and service development, for uses such as product-
development-cycle optimization, adding new features to existing products, and creating new
AI-based products. These organizations also are using AI more often than other organizations in
risk modeling and for uses within HR such as performance management and organization design
and workforce deployment optimization.
Another difference from their peers: high performers’ gen AI efforts are less oriented toward cost
reduction, which is a top priority at other organizations. Respondents from AI high performers
are twice as likely as others to say their organizations’ top objective for gen AI is to create entirely
new businesses or sources of revenue—and they’re most likely to cite the increase in the value of
existing offerings through new AI-based features.
8 The state of AI in 2023: Generative AI’s breakout year
Web <2023>
<State of AI 2023>
Exhibit <4> of <11>
Smaller shares of AI high performers see cost reductions as their top
objective for generative AI efforts.
Top objective for organizations’ Respondents at All other
planned generative AI activities, AI high performers2 respondents
% of respondents1
Reduce costs in
19
core business
33
Create new businesses
and/or sources of revenue 23
12
100%
Increase revenue
27 21
from core business
Increase value of offerings
33
by integrating AI-based 30
features or insights
Note: Figures do not sum to 100%, because of rounding.
1Asked only of respondents whose organizations have adopted Al in at least 1 function.
2Respondents who said that at least 20 percent of their organizations’ EBIT in 2022 was attributable to their use of AI. For respondents at AI high performers,
n = 45; for all other respondents, n = 712.
Source: McKinsey Global Survey on AI, 1,684 participants at all levels of the organization, April 11–21, 2023
McKinsey & Company
As we’ve seen in previous years, these high-performing organizations invest much more than
others in AI: respondents from AI high performers are more than five times more likely than
others to say they spend more than 20 percent of their digital budgets on AI. They also use AI
capabilities more broadly throughout the organization. Respondents from high performers
are much more likely than others to say that their organizations have adopted AI in four or
more business functions and that they have embedded a higher number of AI capabilities. For
example, respondents from high performers more often report embedding knowledge graphs
in at least one product or business function process, in addition to gen AI and related natural-
language capabilities.
While AI high performers are not immune to the challenges of capturing value from AI, the results
suggest that the difficulties they face reflect their relative AI maturity, while others struggle with
the more foundational, strategic elements of AI adoption. Respondents at AI high performers
most often point to models and tools, such as monitoring model performance in production and
retraining models as needed over time, as their top challenge. By comparison, other respondents
cite strategy issues, such as setting a clearly defined AI vision that is linked with business value
or finding sufficient resources.
The state of AI in 2023: Generative AI’s breakout year 9
Web <2023>
<State of AI 2023>
Exhibit <5> of <11>
Models and tools pose the biggest AI-related challenge for high
performers, while strategy is a common stumbling block for others.
Element that poses the biggest Respondents at All other
challenge in capturing value from AI, AI high performers2 respondents
% of respondents1 Other 1 2
Strategy 11
24
Data 11
Technology 13
18
Adoption and scaling 19
100%
13
Talent 20 15
21
Models and tools 24
6
Note: Figures do not sum to 100%, because of rounding.
1Asked only of respondents whose organizations have adopted Al in at least 1 function.
2Respondents who said that at least 20 percent of their organizations’ EBIT in 2022 was attributable to their use of AI. For respondents at AI high performers,
n = 49; for all other respondents, n = 792.
Source: McKinsey Global Survey on AI, 1,684 participants at all levels of the organization, April 11–21, 2023
McKinsey & Company
The findings offer further evidence that even high performers haven’t mastered best practices
regarding AI adoption, such as machine-learning-operations (MLOps) approaches, though they
are much more likely than others to do so. For example, just 35 percent of respondents at AI high
performers report that where possible, their organizations assemble existing components, rather
than reinvent them, but that’s a much larger share than the 19 percent of respondents from other
organizations who report that practice.
Many specialized MLOps technologies and practices may be needed to adopt some of the more
transformative uses cases that gen AI applications can deliver—and do so as safely as possible.
Live-model operations is one such area, where monitoring systems and setting up instant alerts
to enable rapid issue resolution can keep gen AI systems in check. High performers stand out
in this respect but have room to grow: one-quarter of respondents from these organizations say
their entire system is monitored and equipped with instant alerts, compared with just 12 percent
of other respondents.
10 The state of AI in 2023: Generative AI’s breakout year
McKinsey commentary
Bryce Hall
Associate partner
Over the past six years as we’ve conducted our annual global AI research, one consistent
finding is that high performers take a broad view of what’s needed to be successful. They
are particularly strong in staying focused on value, and then rewiring their organization to
capture that value. This pattern is clear when looking at how high performers are working with
generative AI as well.
For example, on strategy, leaders from our analysis are mapping out where the high-value
opportunities are from AI across their business domains. Tellingly, they’re not doing this
for only generative AI. As excited as we all are about the dazzling new gen AI applications,
significantly more than half of the potential value for companies comes from AI applications
that don’t use gen AI. They are maintaining discipline in viewing the full range of AI
opportunities based on potential value.
That approach extends to all capability areas. In technology and data, for example, high
performers are laser focused on capabilities needed to capture the value they’ve identified.
This includes capabilities to enable large language models to train on company and
industry-specific data. They’re evaluating and testing the efficiencies and speed enabled
by consuming existing AI services (what we call the “taker” approach) and developing
capabilities to create competitive advantage—for example, by tuning models and training
them to use their own proprietary data (what we call the “shaper” approach).
The state of AI in 2023: Generative AI’s breakout year 11
AI-related talent
needs shift, and AI’s
workforce effects
are expected to
be substantial
Our latest survey results show changes in the roles that organizations are filling to support their
AI ambitions. In the past year, organizations using AI most often hired data engineers, machine
learning engineers, and Al data scientists—all roles that respondents commonly reported
hiring in the previous survey. But a much smaller share of respondents report hiring AI-related-
software engineers—the most-hired role last year—than in the previous survey (28 percent in
the latest survey, down from 39 percent). Roles in prompt engineering have recently emerged, as
the need for that skill set rises alongside gen AI adoption, with 7 percent of respondents whose
organizations have adopted AI reporting those hires in the past year.
12 The state of AI in 2023: Generative AI’s breakout year
The findings suggest that hiring for AI-related roles remains a challenge but has become
somewhat easier over the past year, which could reflect the spate of layoffs at technology
companies from late 2022 through the first half of 2023. Smaller shares of respondents than in
the previous survey report difficulty hiring for roles such as AI data scientists, data engineers,
and data-visualization specialists, though responses suggest that hiring machine learning
engineers and AI product owners remains as much of a challenge as in the previous year.
Web <2023>
<State of AI 2023>
Exhibit <6> of <11>
Hiring for AI-related roles remains a challenge, though reported difficulty
has decreased since 2022 for many roles.
Share of respondents reporting difficulty in organizations’ hiring of AI-related roles,1 % 2022 2023
LESS DIFFICULT MORE DIFFICULT
0 20 40 60 80 100
Machine learning engineers
AI data scientists
Translators
AI product owners/managers
Data architects
Prompt engineers2
Software engineers
Data engineers
Design specialists
Data-visualization specialists
1Asked only of respondents whose organizations have adopted Al in at least 1 function and who said their organization hired the given role in the past
12 months. Respondents who said “easy,” “neither difficult nor easy,” or “don’t know” are not shown.
2Not asked of respondents in 2022.
Source: McKinsey Global Survey on AI, 1,684 participants at all levels of the organization, April 11–21, 2023
McKinsey & Company
The state of AI in 2023: Generative AI’s breakout year 13
Looking ahead to the next three years, respondents predict that the adoption of AI will reshape
many roles in the workforce. Generally, they expect more employees to be reskilled than to be
separated. Nearly four in ten respondents reporting AI adoption expect more than 20 percent of
their companies’ workforces will be reskilled, whereas 8 percent of respondents say the size of
their workforces will decrease by more than 20 percent.
Web <2023>
<State of AI 2023>
Exhibit <7> of <11>
Survey respondents expect AI to meaningfully change their
organizations’ workforces.
Expectations about the impact of AI adoption on organizations’ workforces, next 3 years,
% of respondents1
Change in number of employees Share of employees expected to be reskilled
Don’t know 8
Don’t know 12
Increase by >20% 3
Increase by 11–20% 4
Increase by 3–10% 8
>20% 38
Little or no change
(decrease or increase by ≤2%) 30
11–20% 18
Decrease by 3–10% 25
6–10% 17
Decrease by 11–20% 10
≤5% 20
Decrease by >20% 8
Note: Figures may not sum to 100%, because of rounding.
1Asked only of respondents whose organizations have adopted Al in at least 1 function; n = 913.
Source: McKinsey Global Survey on AI, 1,684 participants at all levels of the organization, April 11–21, 2023
McKinsey & Company
14 The state of AI in 2023: Generative AI’s breakout year
Looking specifically at gen AI’s predicted impact, service operations is the only function in
which most respondents expect to see a decrease in workforce size at their organizations. This
finding generally aligns with what our recent research suggests: while the emergence of gen
AI increased our estimate of the percentage of worker activities that could be automated (60
to 70 percent, up from 50 percent), this doesn’t necessarily translate into the automation of an
entire role.
Web <2023>
<State of AI 2023>
Exhibit <8> of <11>
Service operations is the only function in which most respondents expect
to see a decrease in workforce size because of generative AI.
Effect of generative AI adoption on number of employees, by business function, next 3 years,
% of respondents1
Decrease Little or no change Increase Don’t know
Product and/or service development 30 35 20 15
Risk 31 37 20 12
Strategy and corporate finance 37 28 25 10
Marketing and sales 39 33 17 12
Manufacturing 40 33 12 15
HR 41 30 17 11
Supply chain management 45 32 14 9
Service operations 54 23 12 10
Note: Figures may not sum to 100%, because of rounding.
1Respondents were asked about only the business functions in which they said their organizations have adopted Al.
Source: McKinsey Global Survey on AI, 1,684 participants at all levels of the organization, April 11–21, 2023
McKinsey & Company
The state of AI in 2023: Generative AI’s breakout year 15
AI high performers are expected to conduct much higher levels of reskilling than other
companies are. Respondents at these organizations are over three times more likely than
others to say their organizations will reskill more than 30 percent of their workforces over the
next three years as a result of AI adoption.
Web <2023>
<State of AI 2023>
Exhibit <9> of <11>
Respondents at AI high performers expect their organizations to reskill
larger portions of the workforce than other respondents do.
Share of employees at respondent’s Respondents at All other
organization expected to be AI high performers2 respondents
reskilled over the next 3 years
as a result of AI adoption, 9 Don’t
know
% of respondents1
21
>30% 73
14
18
21–30% 9
38
11–20% 10
≤10% 8
1Asked only of respondents whose organizations have adopted Al in at least 1 function.
2Respondents who said that at least 20 percent of their organizations’ EBIT in 2022 was attributable to their use of AI. For respondents at AI high performers,
n = 50; for all other respondents, n = 863.
Source: McKinsey Global Survey on AI, 1,684 participants at all levels of the organization, April 11–21, 2023
McKinsey & Company
16 The state of AI in 2023: Generative AI’s breakout year
McKinsey commentary
Lareina Yee
Senior partner, McKinsey; chair, McKinsey Technology Council
We are in the early innings of generative AI, and companies already anticipate a meaningful
impact on talent—from opening up new work opportunities and transforming how work
gets done to introducing whole new job categories such as prompt engineering. One of the
benefits of generative AI is that it can help nearly everyone with their jobs, and this is also its
greatest challenge.
This scale differs from traditional AI, which affected a fairly small—though no less important—
portion of the workforce who had deep skills in technical areas like machine learning, data
science, or robotics. Given the highly specialized capabilities required, AI talent always
seemed in short supply. Our survey highlights that hiring for these roles is still a challenge.
Generative AI, in contrast, will still need highly skilled people to build large language models
and train generative models, but users can be nearly anyone, and they won’t need data
science degrees or machine learning expertise to be effective. The analogy is similar to the
move from mainframe computers—large machines operated by highly technical experts—to
the personal computer, which anyone could use. It’s a revolutionary shift in terms of how
people can use technology as a power tool.
This view of generative AI as a tool is reflected in our survey. In most instances companies
see generative AI as a tool to augment human activities, not necessarily replace them. So
far, we’re mainly seeing companies that are leaning forward with generative AI, focusing on
pragmatic areas where the routes to improvements in top-line growth or productivity are
clearest. Examples include using generative AI tools to help modernize legacy code or speed
up research and discovery time in the sciences. We’re still just scratching the surface of these
augmentation capabilities, and we can anticipate that their use will accelerate.
The state of AI in 2023: Generative AI’s breakout year 17
With all eyes on
gen AI, AI adoption
and impact
remain steady
While the use of gen AI tools is spreading rapidly, the survey data doesn’t show that these newer
tools are propelling organizations’ overall AI adoption. The share of organizations that have
adopted AI overall remains steady, at least for the moment, with 55 percent of respondents
reporting that their organizations have adopted AI. Less than a third of respondents continue
to say that their organizations have adopted AI in more than one business function, suggesting
that AI use remains limited in scope. Product and service development and service operations
continue to be the two business functions in which respondents most often report AI adoption,
as was true in the previous four surveys. And overall, just 23 percent of respondents say at least
5 percent of their organizations’ EBIT last year was attributable to their use of AI—essentially flat
with the previous survey—suggesting there is much more room to capture value.
18 The state of AI "
76,accenture,Accenture-The-Six-Pillars-effective-AI-Strategy-Video-Transcript.pdf,"THE SIX PILLARS OF AN
EFFECTIVE AI
STRATEGY
VIDEO TRANSCRIPT
Javier Polit: able to execute that strategy across the
enterprise? And that means different teams and
I think there's maybe six pillars of an AI strategy, different responsibilities and different ways of
right? And I always say start with the business working and different behaviors in the
value. Define the trap business value and enterprise. And then the greatest investment is
recognize the leverage that you need to unlock the sixth piece of this is focus on your talent and
that growth for the business. And when you think the culture that you're building and how you're
about algorithms, which are the critical going to continue to retain, attract and engage
algorithms that are going to solve the business those resources that are helping you bring this
value that was defined by the business, and value to life and this distinctive capability that
when you think about algorithms today, it's a you're building in your enterprise.
complicated world, right? We need to make
certain that they're designed to scale and that
they're unbiased because we hear a lot of
algorithms are being defined with bias now. And
we have tobe very cautious about that. And then
you have tothink about data, right? Because you
understand the business now, you're built, you're
defining the algorithms are going to support that
business value that you're trying to capture.
And you’ve got to look at the data and have a
clear first, second, and third partydata strategy,
right? And make certain that you have a life
cycle around that data that to create signals of
value for the enterprise. The fourth area that I
would say is a platform strategy, making certain
that you have the right ecosystem, and we talked
about that earlier, making certain that you have
the right foundation of capabilities to create and Copyright © 2023 Accenture
be able to manage inside the enterprise. And All rights reserved.
then the ability to execute that strategy, right?
Accenture and its logo
How should our enterprises be organized to be
are registered trademarks
of Accenture."
77,accenture,Acceture-How-Mondelez-International-Data-AI-Transform-Enterprise.pdf,"HOW MONDELĒZ
INTERNATIONAL USES
DATA AND AI TO
TRANSFORM THEIR
ENTERPRISE
VIDEO TRANSCRIPT
Venky Rao (00:30): of the winners in the spectrum of organizations
I am Venky Rao, Accenture’s, North America have brought themselves into that space
Consumer Goods and Services Industry lead. because of the AI capabilities that they have
We are here in Chicago today at the beautiful invested in. And we have seen that
new headquarters of Mondelēz International, organizations which have significantly invested
surrounded by some of their more delicious in AI have been able to deliver much better
brands. I'm joined by Javier Polit, Mondelēz results for their shareholders. Right, so in that
International’s, Chief Information and Digital context, knowing that you have led multiple
Officer. I have known Javier you for a long, long scale digital transformation initiatives and you
time now. You have been a big change agent have been successful at that and knowing and
throughout your career, not just here at understanding Mondelēz International's own
Mondelēz, but also through your stint at the digital journey and your own journey to
other big CPG companies. becoming an AI Achiever, we wanted to have
this conversation today to learn a bit more about
Javier Polit (00:59): your journey, to learn a bit more about how
Absolutely. Well, first of all, welcome, welcome Mondelēz is getting on its path, to becoming an
to our headquarters here, and we're glad to have AI achiever. What was the impetus for Mondelēz
you. And as always, it's good to be with you to becoming more of a data driven data led
Venky and looking forward to the dialogue, and company?
sharing as much as possible.
Javier Polit (02:22):
Venky Rao (01:08): I think it all starts with our goal at Mondelēz
Thank you. So let me dive right in. We are doing International is to be the leader of snacking. And,
this conversation in the context of the AI we need an even stronger growth strategy to
Achiever study that Accenture has put out there, keep up with the pace, and even influence, our
which really talks about AI and the capabilities consumer demand and our consumer behavior.
that AI brings into an organization, how that Sowe really started to focus on a relentless
creates the differentiation for companies. And consumer centricity in making certain that we
what we have learned through that study is 12% started to aggregate 360 degreeinsights of, of
our consumers. And the time was right because and it really wasn't about just filling one hole. It
we had been preparing from a business was really trying to understand the holistic
perspective and also from a technology opportunities that we had. And once we had that
perspective. We had the right foundation in defined, it was building that vision and that
place. The company was on a cloud strategy strategy and making certain that you got support
when I joined here, multi-cloud strategy, we of the strategy by the C-suite, which we did. And
brought in the Google Cloud platform. So we the executive team and the board were all
had that behind us and the team was doing behind us, and we started communicate that
some great work before I joined, and we finished strategy to the enterprise. And that required a lot
that work and, it gave us really time to pivot and of work for us to do and say we need to start
really start focusing on data and AI. investing in our people elevating capabilities,
looking at the strategic partners that we were
gonnause, right? Besides the Accenture’s and,
Venky Rao (03:13): and the Microsoft’s and the Googles of the world
Excellent. So now Mondelēz is on its way, to and other strategic partners. How we're gonna
establishing itself as an AI achiever. How do you firmly have the conversation that we had with
continue to get there on this path? you and all the other partners, bring your best to
us as we're continuing to try to be the best that
Javier Polit (03:22): we can and leverage partners as we're trying to
Well, there's a lot of levers to that. SoI could tell build capability inside the enterprise and, and
you that myself and my entire team we're really driving change in the enterprise as well. From a
invested in establishing Mondelēz as an AI behavioral and work perspective.
achiever being in that top 12%. We often talk
about being industry leaders, right? And we want Venky Rao (05:15):
to create an organization and a culture. We're 10 You know, there is this old saying, you know,
years young, we just recently celebrated our 10 what you track and what you measure actually
year anniversary. We have some incredible getsdone. So when you get on a transformation
brands that have been around for almost 200 journey like this, especially in this space of AI, AI
years, but we're transforming our culture. And, enabled, which is all new, how do you start
and it's, it's a young company. Sowe really have measuring success?
the ability tobecome a data driven in AI focused,
enterprise. And with that comes some Javier Polit (05:29):
challenges because, you know, we, you have to Yeah. Well, you know, you can't manage what
change the ways the company worked in the you don't measure. We've all heard that, that
past. You gottachange your behaviors. And nomenclature, I can tell you that we've had some
we're driving all those things, across the really, really goodmaturity here over the last 18
enterprise. And it's a journey. It's a continuous to 24 months in regards tohow we track, how we
journey. measure the ROI’son work that we're delivering
to the, to the business and the value based on
Venky Rao (04:08): the business case that we initially put together
I like the phrase, I mean, it is a journey, right? I for the business. And, and through that work,
mean, but then the question is how do you start what we're seeing now in all our business
a journey like that? Where do you start from? reviews that we have conversation around digital
comes up the work that's being driven around
Javier Polit (04:14): digital. And with that we talk about data science
The starting point is really spending time with the and we talk about the AI work that that's being
business, Venky, and really understanding, done, right? You know, you set a transformation
when I first joined, I was listening and learning. strategy and a vision and you say, okay, it's a
I'm still learning. I've only been with the company three year horizon. I always say that after the
two and a half years or so now, but really second year, you start figuring out what your
understanding what the pain points were. And, next three year horizon's gonnabe. So it's, it's
something that is just never done. Venky it’s just And then there is this aspiration that every part
continuous work. of your organization wants to have capabilities.
They all want to get those AI enabled
Venky Rao (06:17): capabilities and so on. Can you dig a little bit
Now, having said that, what are the most deeper and talk to us about how does the total
important factors in making a transformation enterprise reinvention happen practically in large
successful? organizations with all the complexities of, of a
large organization?
Javier Polit (06:23):
Well, I think when, when you think about a Javier Polit (08:08):
transformation in any large enterprise, and I've Yeah. It, it is a little sophisticated when you look
had the opportunity to do this a couple times, is at large enterprises and, and in my experience,
you need to have the right sponsorship. You having served Fortune 15 and Fortune 100
know, once you develop that strategy and that companies, it, it doesn't get easier wherever you
vision, making certain that the board, the sit. But, but I'm always a believer that there's a
executive team is behind it, and then you need trigger in every business that could be an
to communicate as much as possible and external trigger, like what we've lived through the
communicate that strategy and what you're last 24, 30 months, or it could be an internal
trying to do, and communicate the sponsorship trigger that, that drives a transformation for a
so that the whole enterprise feels good about the company or embarks in the beginning of a
work that you're trying to drive. And they transformation that'll then just forever be
understand that there's a sense of importance continual. And I think when, when you look at
and urgency to what you're doing. And, and those things[ that starts helping you define what
when I talk about communicating a lot of, I have the strategy is gonnabe and what the overall
about 30 touch points with my organization on company's mission and purpose is, as well as
an annual basis, and we talk about these things. you think about empowering people in the
And then the last thing is making certain that you organization to really drive some of these
have a core strategic central AI data science capabilities. Right?
team that's really helping the organization. You You know, some companies, as you know, are
can't have these silos in the enterprise where mandating digital fluency and there's a
they're going on and building their own data difference between digital fluency and digital
science and data strategies without literacy. We, we were talking about that recently.
understanding that there's a holistic data driven And, and here what we're trying to do is get
strategy that all that data needs to come beyond literacy and drive fluency, right? But,it
togetherand somebody needs to be the steward really happens with the organization. You gotta
of that. And monitoring is the data inside the bring the people along, understand the strategy,
enterprise is data outside the enterprise what and then have the right foundations to be able to
data needs to ingress or egress from different do that.
sources? And you just can't have that working in
a silo. SoI would say it's probably those three Venky Rao (09:08):
dimensions. It's very, very interesting that you say that
because our study, the AI Achiever study, it
Venky Rao (07:32): really looks and calls out that in the, among the
Getting through a massive enterprise leaders, we see the top leadership really
transformation. It's difficult, especially in large embedding AI as a strategic priority, right? I
enterprises. There are complexities, there is the mean, they're making it part of their corporate
corporate headquarters, there are the functions, strategy and they're embedding this as a key
there is the businesses. Then there are this part of their core transformation initiative. Can
whole notion of, hey, what's back office, what's you talk a little bit more around how that
front office? And so on and so forth. sponsorship actually happens and why that is so
important that it comes top down? how you're creating value with velocity. And
we've been able to stand up those models and
Javier Polit (09:39): we're still on the capability maturity curve, but it's
I think, the initial sponsorship, when I at least really moving along at a quick, quick pace force.
first joined Mondelēz a few years ago, you know, And it's working really well. We've seen some
I satand I listened to the business and I spoke to fantastic results. You guys may know a little bit
the business a lot about some of the things that about that cause we partnered with you on some
we were trying to do. And it wasn't that I was of the solutions. But,we're getting very, very
trying to just solve one particular problem. I was good results. And the organization's
trying to connect all the dots and see how I could understanding when you get business leaders
solve a holistic problem, and build a holistic talk, talking about data and the importance of
capability. And that sponsorship really starts data, you know, that you're making progress.
happening once you start creating awareness of
what's the possible and how you could really Venky Rao (11:56):
create that value for the business. And we did Oh, absolutely. So Javier, how do you see talent
that. We, talked a lot about that when I first and tech working together to achieve the
joined. And, you know, there was quick followers Mondelēz vision?
and there were some that weren't that quick.
And what I did with our team is we leaned Javier Polit (12:03):
forward with those quick followers and went, Yeah, there, there's a lot of dimensions to that.
worked in those parts of, of the world building And I will tell you that, you know, we win with our
capabilities, knowing that we create value with people. Our people are our greatest asset. And
the team and then others would follow. we invest in our people in many different ways
and our people are critical to anything we
Venky Rao (10:29): change or anything we make, you know, our
Soin my experience, some of the big challenges success is possible because it's 79,000
that I see is this whole notion of information incredible colleagues that we have around the
asymmetry, especially in large enterprises, world. And some of the things that we're doing
right? So where yes, the ambition is there, the right now is as we continue to drive the
intent is there, but it's not very often that you importance of being data driven enterprise and
have the whole organization on the same page. have an innovative culture, we're able to make
So how have you tackled, this challenge of those pivots and become a dynamic
bringing everybody on board with the right organization. We talk about being a dynamic
messaging and, you know, getting, making sure learning organization, right? Where we, we are
that everybody's on the same page? not a knowing culture, we're a learning culture
and we want to continue to innovate and take
Javier Polit (10:55): risk. And I think, you know, all that's done
Yeah. In, that challenge is continuous through sound leadership, but, but it's also
messaging. And the message is data matters having the right partners at the table, right? And,
and data's critical, and we're gonnawin with we firmly encourage our partners, whether it's
data. You know, that there was always this, this Accenture or whether it's Google or whether it's
nomenclature that we would talk about a race for Microsoft and many others, to bring the best that
talent. WellI say it's a race for data, it's a race you have. And we've had those conversations
for data that's contextual and that's relevant. too, bring the best to us and make certain that
That's enabled that the edges of the business we could really partner and do some really
with the right data governance models that the industry leading things, right? So, it's really not
business owns, the data and ingress is the data something that you could do on your own, but
into repository where they have the capability to you have tohave a pool of experts inside the
do that, combined with our data scientists and enterprise as well as the experts that your
then then build the AI capabilities. And that's partners bring as well.
Venky Rao (13:19): across the enterprise and, and you know, we
Absolutely. And there, I really want to emphasize look across the ecosystem and depend on our
that, you know, it has been a massively different partners to help us as well.
experience working with you and Mondelēz in
terms of your ability to see across the whole Venky Rao (15:27):
ecosystem, whether it's your partners like us or, Yeah. And then that's what I really appreciate
you know, the big cloud providers like Microsoft about the change that you're driving in Mondelēz
and Googles of the world. You collaborate in a is we do see that you guys measure the
way wherein the learnings flow seamlessly outcomes, consistently measure the outcomes,
between these organizations and then that's and you are evaluating how far you have gone
really a key for success on the business case that you started the
We have recently also seen in press that journey with. And you're also absolutely right
organizations are coming out and saying, ‘Hey, that sometimes you have totake these risks
we have made our investments in cloud, we are because you, for many organizations, these are
making our AI investments, we have made uncharted waters, uncharted territories, right? So
digital strategy as part of our core strategy and you have tomake those investments early on,
so on’. But they are still complaining that they're but you still have toensure that the ROI and
not able to see the ROI, they're not able to see then the business outcome mindset doesn't go
the growth, right? I mean, there is something away. Now if I…
missing here. How are you seeing Mondelēz get
the ROI that it planned for? Javier Polit (16:02):
And, and just to add something to that Venky, it's
Javier Polit (14:13): that business mindset, right? And also the
You know, you have tostart by taking a strategic contending business initiatives and
look at the work that you want to, to drive and understanding that if you start an initiative later
the value you want to create, right? And normally on, then was expected that ROI that you had
when you're doing those things, and you're, and identified suffers as well and it depends how you
leveraging AI, it's to replace the latent human look at things. You could look at it from a capital
ability to do some of these things right? And allocation perspective. Oh. Which you haven't
making certain that you have that ability in the spent the capital, but you haven't put the capital
enterprise. And once you're confident that you to work. Sodo you look at what's my internal
have a use case that's gonnadrive a business rate of return or versus my return on investment?
result, you start driving those and you start And sometimes companies take too long to
measuring that work, right? And seeing the make decisions, and we're breaking those
changes in behaviors and the changes in the barriers too here, we're making decisions a lot
ways that you work and the speed of the quicker now as an executive team to go ahead
outcomes that you're driving. And you measure and get these initiatives moving on time to make
those. And you know, as I mentioned earlier, sure that we capture those ROI’s, right? So
we've partnered in some on some work that we those are things, and we measure 'em. We,
were able to do those things and now we're measure on a quarterly basis. We know what our
using it as an example and it's proliferating strategic initiatives areand we come back and
across our regions and across our geography, measure how they're performing based on our
right? But there's a lot of expectations. You business plan.
know, it's about the investments and improving
the entire structure of the enterprise. It it's not Venky Rao (16:54):
just providing a band-aid, right? That you wanna Now how are you prioritizing long and short term
try to solve something very, very quickly. You investments?
gottathink about in certain, these cases I said
earlier, short term and long term strategic Javier Polit (16:58):
sustainable capabilities, right? We drive that Yeah. You know, short term it's about
understanding the broad needs of the business Yeah. Look, when, when we think about AI, we
and aligning with external partners that can help know that it's essential, it's essential to our
us deliver on that. And we think about long term growth, and it provides us competitive
Venky, it's about building the skill sets you need advantage. And, and we're really seeing, really
within your own talent to be able to do it understanding what data should look like and
internally. You know, today we have a lot of what the data strategy is. Because without good
partners helping us and, and how do we data and having accurate data, you can't build
continue to bring those capabilities and build ML that supports the AI. Right. So, we're making
those internally? Is our strategic longer term some good progress also in, in harmonizing
roadmap, right? But I think all these are data.
happening simultaneously in a very complex
environment as you're trying to deliver value to Venky Rao (19:22):
the enterprise and the true value proposition for SoJavier, how would you assess, CPG industry
this investment. It's gottabe sustainable, in terms of AI maturity compared to other
anything that you do has gottabe sustainable industries? I mean, especially, I know that you
and you gottaget to that destination of having look at the tech sector quite a bit, and, and you
the right skill sets and capabilities that have a take a lot of inspiration from how some of the big
sustainable business model. And the core technology companies operate. But how do you
doesn't have to be large either. You're always see that evolving in the CPG industry?
gonnahave a need to bring in partners to bring
in their expertise and their skill sets. I always say Javier Polit (19:42):
that, that our partners have broader apertures Yeah, you know, we're continuously doing
than we do because they're dealing with the top industry sensing in that space and see how we
50 companies and they can bring learnings to us match up to other CPG companies or fast
that help us even improve even more. So, it's moving consumer goods companies. But I think
never gonnabe, you have all the skill sets you it's fair to say that the tech sector is still far
need internally, just do it on your own. It's always ahead. But I would also say in the same breath
gottabe a strategic blend. that I think that the gap is narrowing and
especially I think what's, what's helped us
Venky Rao (18:06): narrow that gap that that gap is companies really
Ah, that's, that's really fair. And if I think about, advancing their digital roadmaps in the digital
you know, the whole talent spectrum, you are plans, right? SoI think, there's enormous room
not just training your employees, but you're also for growth in AI Adoption and AI Adoption across
giving those opportunities for them to participate all industries. Every company's a tech company.
in various events with the ecosystem partners We've heard that phrase. I always try to extend it
and partners like ourselves. I think that's a, that's and say, every company's a tech company and if
a big change for this industry is not just be you don't conduct yourself as such, you're just
inward looking when it comes to, you know, not gonnabe successful.
creating the talent asset, creating the AI core,
but be more participatory in a, in a larger Venky Rao (20:25):
ecosystem. And that is something that we have Absolutely. And that's such a spot onanswer
seen that, you know, under your leadership, you and, and a brilliant one at that. So Javier, where
guys are driving there at Mondelēz. Now how do you see the whole AI Adoption space
have you industrialized the tools, the tooling, evolving to? And if you have organizations who
and, you know, how have you created a strong want to become an AI achiever, what would be
AI core that people don't have to reinvent this your advice?
journey each time they come on an initiative?
Javier Polit (20:42):
When you think about the evolution of AI today,
Javier Polit (18:58): companies are using narrow AI, right? It's taking
the ability to have a human process be say is a platform strategy, making certain that
conducted through AI with greater efficiency. you have the right ecosystem, and we talked
And you have companies that are adopting that about that earlier, making certain that you have
well, that are the 12% AI achievers and those the right foundation of capabilities to create and
that are falling, following and, and a little bit be able to manage inside the enterprise. And
behind. And then the next level of AI is general then the ability to execute that strategy, right?
AI or human AI, where you have artificial How should our enterprises be organized to be
intelligence that can basically do what a human able to execute that strategy across the
thinks. And the more complicated AI that's enterprise? And that means different teams and
gonnabe happening in the future, and it's different responsibilities and different ways of
happening in different parts of the world today, is working and different behaviors in the enterprise.
super AI, where AI can now do things better and And then the greatest investment is, is the six
in a smarter way than humans can. So it's gonna piece of this is focus on your talent and the
be an evolving space. We'll have to see how culture that you're building and how you're
those technologies, when they come to be gonnacontinue to retain, attract and engage
commonplace are gonnabe leveraged in in those resources that are helping you bring this
different industries. And, and they're already value to life and this distinctive capability that
starting to be used in, in certain industries. you're building in your enterprise.
Venky Rao (21:34): Venky Rao (23:24):
Soin closing, any thoughts, Javier as we wrap Javier, that was an outstanding response and a
up. very, very good framework for everybody to
follow, right? Sothank you so much.
Javier Polit (21:37):
You know, there’s probably an abundance of Javier Polit (23:31):
thoughts and because I think we'd all agree that Venky that was a pleasure, thank you for the
it's a complicated space, but I think there's time. Thank you for the partnership.
maybe six pillars of an AI strategy, right? And I
always say start with the business value, right? END
Define the trap business value and recognize
the leverage that you need to unlock that growth
for the business. And when you think about
algorithms, which are the critical algorithms that
are gonnasolve the business value that was
defined by the business, and when you think
about algorithms today, it's a complicated world,
right? We need to make certain that they're
designed to scale and that they're unbiased
because we hear a lot of algorithms are being
defined with bias now. And we have tobe very
cautious about that. And then you have tothink
about data, right? Cuz you understand the
business now, you, you're built, you're defining
the algorithms are gonnasupport that business
value that you're trying to capture.
And you gottalook at the data and have a clear
first, second, and third partydata strategy, right?
Copyright © 2023 Accenture
And make certain that you have a life cycle
All rights reserved.
around that data that to create signals of value
Accenture and its logo
for the enterprise. The fourth area that I would
are registered trademarks
of Accenture."
78,accenture,Acccenture-Semi-GenAI-TL-Infographic.pdf,"Breaking barriers,
building connections
Generative AI‘s role in the semiconductor industry
Discover how Gen AI is reshaping the value
chain from design to manufacturing and beyond
Supply chain
Market
Trends
Consumer
Manufacturing
Product Design Channel Partner
Semi Electronics
Product Development and Production Distribution & After Sales
According to our research, respondents
agreed with the following:
73 71 74
% % %
IP concerns are the biggest barrier It will take at least three years for New fabs coming online will focus
to generative AI deployment across the semiconductor industry to more on automation powered by
the semiconductor industry’s deploy generative AI at scale. generative AI.
value chain.
Industry leaders also agree on the opportunities,
challenges and urgency for successful Gen AI adoption:
Of semiconductor executives highlight
33
% design and manufacturing as the prime areas
for generative AI innovation.
85 Of companies have a strategy to deploy
%
generative AI projects or POCs.
Of executives agree that collaboration with
75
% industry partners will unlock greater value
from generative AI.
The following are 4 strategic priorities for companies and
executives to unlock Gen AI value:
Design your journey Leverage the ecosystem to
1 2
strategically scale generative AI
Lead and learn differently to
3 4
Continuously reinvent
reshape the workforce
Generative AI is not just a tool but
a transformative force for the
semiconductor industry.
Embrace the future with strategic
Read our report, learn more about how
investments and collaborative efforts
generative AI can revolutionize your
to unlock unprecedented value.
semiconductor business.
accenture.com/semi-value-chain-new-approach-gen-ai
Copyright © 2024 Accenture. All rights reserved."
79,accenture,Accenture-Generative-AI-Sourcing-and-Procurement.pdf,"A new era of
generative AI
for everyone
Inspiring
smarter buying:
How generative AI will
reinvent sourcing and
procurement
Inspiring smarter buying: How generative AI will reinvent sourcing and procurement
For Chief Procurement Officers (CPOs), the
arrival of generative AI marks a major leap
forward in the value their teams can deliver
to the business—paving the way to faster,
more accurate decision-making, higher
resilience, increased sustainability and
lower operating costs.
Generative AIis outpacing any other technology innovation in its potential reach
and impact (Figure 1).
Executives around the world agree: according to Accenture’s 2023 Technology Vision,
95% of respondents said that advances in generative AI signify a new era of
enterprise intelligence.
The evolution in analytics, machine learning, and AI
Figure1:Accenture2023
This is why generative AI was identified as one of the four technology trends driving the
next phase of business transformation.
Companies that integrate this technology into a strong digital core can boost innovation
and accelerate progress to new performance frontiers. However, as humans working with
AI “virtual colleagues” become the norm, generative AI also raises many questions—not
least around responsible use of this technology and how best to adopt it at scale for
maximum value. Sowhat does this mean for procurement?
2
Inspiring smarter buying: How generative AI will reinvent sourcing and procurement
Generative AI: Procurement’s new co-pilot
Like most areas of the business, procurement stands to benefit hugely from generative AI.
That’s because one type, large language models (LLMs), don’t just analyze information and
answer questions, they can also provide insights and create content.
Once integrated into the rapidly evolving digital procurement landscape, generative AI
applications can transform a wide range of procurement capabilities (Figure 2). They’ll do
this by augmenting teams on routine tasks and, by acting as a co-pilot to category leaders,
providing strategic value-add through inspiration on-demand.
Instead of needing to undertake time-consuming research that risks falling rapidly out of
date, that could mean, for example, being able to get immediate answers to questions like
“what is my risk exposure buying raw materials in Indonesia?” or “which palm-oil suppliers
are net-zero and carry a less than 14-day lead-time?”
Figure2:Accenture2023
Delivering next-level sourcing and
procurement operations
To bring to life generative AI’s power, consider four use cases where procurement can
reap benefits in the near term:
1. Buying experiences – hyper-personalized and efficient
Procurement organizations have worked hard to improve the overall purchasing
experience, making it easier for business users to buy what they need to do their jobs –
and to do so responsibly. Generative AI takes all this to the next level—augmenting
category managers, facilitating decision-making and helping procurement to collect
business-wide insights.
• Generative AI can transform each purchase request into a conversation, using
internal and external data to guide business users to the right channels for their
specific needs (i.e., preferred suppliers, prices, terms and conditions)—all pre-vetted
for compliance and policy.
• By executing all tasks through conversational commands (rather than traditional
point-and-click interactions), generative AI can deliver huge efficiency gains across
complex buying requests that might previously have taken hours to complete.
• What’s more, by guiding users to preferred suppliers, the technology can
substantially enhance contract compliance by making responsible buying a
default behavior.
3
Inspiring smarter buying: How generative AI will reinvent sourcing and procurement
2. Supplier management – accelerated and simplified
Managing suppliers is often complex and time consuming. Generative AI can have a
significant impact across the entire supplier management lifecycle by accelerating
and simplifying processes.
• A generative AI chatbot could be used to centralize communications dealing with
supplier onboarding and provisioning access, as well as answering questions
around the engagement. This would reduce onboarding roadblocks and help
suppliers understand the company’s business needs so they can deliver
value faster.
• In supplier performance management, generative AI can identify supplier issues
and craft resolutions for subsequent supplier meetings.
• Generative AI can also help to identify areas of continuous improvement.
3. Category management and strategic sourcing – smarter
and enhanced
Generative AI can help procurement strengthen stakeholder and supplier relationships
and ultimately help procurement become a cross-functional leader for the business.
There are already plenty of AI tools that help define category plans and sourcing
strategies.
• The technology can “turbocharge” category insights dashboards by absorbing the
work category managers devote to market intelligence.
• Generative AI can collect, analyze, and contextualize data from multiple internal
procurement systems and platforms, as well as external insights.
• It can also provide real-or near-real-time market intelligence and innovation trends
for a category’s key scopes and identify opportunities for the category manager to
optimize value. Generative AI achieves this by helping teams tailor their analysis for
key stakeholders. For example, answering a request to generate a report of expiring
contracts for a specific business unit.
4. Risk management – transformed
Generative AI can proactively monitor for risks in real time and propose
mitigation plans.
• Say procurement wanted to purchase a commodity from a preferred supplier in a
specific region. Generative AI might identify rising tensions in that region and
recommend that upcoming supplies be secured in a different location where the
company is already working with suitable alternative vendors.
• LLMs also could be used to evaluate contract language across multiple suppliers
and identify key risks, as well as opportunities for efficiencies, renegotiation and
rationalization.
Getting started with generative AI
Generative AI is a revolutionary development. And because the technology is already here,
procurement leaders should start thinking now about how to use it as effectively as
possible. As they do so, they need to pay close attention to six essentials:
4
Inspiring smarter buying: How generative AI will reinvent sourcing and procurement
• Ensure a business-driven mindset toward adoption: identify, build and scale key
use-cases.
• Take a people-first approach: build talent pipelines of procurement specialists and data
scientists with the skills to take foundation models, adapt them and integrate them into
applications.
• Focus on data integrity: partner with leadership to review the data landscape and
privacy protections, determine how they align to enterprise priorities and make sure
procurement teams have access to the right data.
• Invest in a sustainable technologyfoundation that can support the high demands of
generative AI: make sure the right architecture and governance are in place and closely
monitor cost and energy consumption.
• Drive ecosystem innovation: partner with a strategic managed service provider that can
enable wider access to specialized talent, industry expertise and leading automation
platforms and identify eProcurement partners and other specialized solutions.
• Make sure there’s a robust Responsible AI foundation in place: implement relevant
security and governance policies to underpin rapid adoption of generative AI in
procurement, including controls for assessing potential risks from new use-cases at the
design stage.
And finally, remember procurement leaders don’t just need to scale generative AI
technologies. To realize maximum benefit, they should also invest in developing the
analytics skills and capabilities of their people. It’s time to get started.
Contacts:
Jaime R. Lagunas Robert Gimeno
Managing Director –Strategy & Managing Director –Strategy &
Consulting, Supply Chain & Operations Consulting, Supply Chain & Operations
Data & Artificial IntelligenceLead Generative AI Lead
jaime.r.lagunas@accenture.com robert.gimeno.feu@accenture.com
Robert P. Fuhrmann KeyraMorales
Senior Managing Director –Strategy Managing Director –Accenture
& Consulting, Global Practice Lead Operations -Offering Innovation-
Sourcing and Procurement Supply Chain and Procurement
robert.p.fuhrmann@accenture.com keyra.morales@accenture.com
Neeraj Bajaj Lloyd Dufour
Senior Manager –Strategy & Senior Manager -Strategy &
Consulting, Sourcing & Procurement Consulting, Global Offering &
Generative AI Lead Innovation Lead Sourcing
neeraj.bajaj@accenture.com and Procurement
lloyd.dufour@accenture.com
Andreu Bartoli Garcia
Senior Manager –Strategy &
Consulting, Supply Chain
& Operations
a.bartoli@accenture.com
Read more:
Technology Vision 2023 | Tech Vision | Accenture
Generative AI Technology in Business | Accenture
Generative AI in Supply Chain I Accenture
6"
80,accenture,Accenture-Age-AI-Banking-New-Reality.pdf,"The age of AI:
Banking’s new reality
2
Embracing generative AI
for a competitive edge
Generative AI has taken the world by storm. to be impacted by generative AI—39% by
The rates of adoption and enhancement automation and 34% by augmentation. Only
are more rapid than any major technology 27% of employees’ time currently has a low
innovation in the history of human potential to be transformed.1 Similar metrics
development, and few doubt its potential have been observed in banks worldwide.2
to have a transformative effect on business
and society. It’s no surprise, therefore, that within a year
of the launch of ChatGPT, almost every
Banks are at the sharp end. Our analysis bank had started to explore and evaluate
indicates that—due to the importance of early use cases. The leaders in this field
language throughout the value chain—the have gone further: they are already seeking
industry has a greater potential to benefit to maximize the value they have generated
from the technology than any other. In fact, by scaling their implementation across the
we concluded that 73% of the time spent organization.
by US bank employees has a high potential
3
Our latest financial projections indicate that the gains over
the next three years will be substantial for the early adopters:
From this it becomes apparent that while generative AI is likely to dramatically improve the efficiency of the banking operating model,
its potential to differentiate and drive growth by enhancing the customer experience is what excites bankers the most.
22% to 30% 600 bps 300 bps
productivity improvement rise in revenue growth increase in return on equity
Source: Accenture
Research analysis
4
The business of banking has barely services. By the time banks have executed
changed in hundreds of years. At its heart, their generative AI strategy, they will have
it’s about taking and safeguarding deposits reinvented and modernized most parts of By the time banks
and lending money. Generative AI—like the bank.
have executed
the internet and the smartphone that
transformed customer engagement—will Achieving these goals will be neither easy their generative AI
not change the fundamentals of banking. nor automatic. Consumers in their millions
strategy, they will
But no one doubts that its impact on may already be using ChatGPT, Microsoft’s
the industry will be seismic. Thanks to Copilot, Google’s Bard and other models
have reinvented and
its inherent ability to learn, advance and to good effect, but for organizations like
modernized most
create it will, over the next few decades, be banks to maximize the benefits a number
a driving force for continuous reinvention of obstacles need to be overcome. Given
parts of the bank.
across the enterprise. It will be widely the pace at which many are seeking to
deployed throughout the value chain, scale generative AI, it is important that
and will radically transform virtually every they plan their journey strategically and
facet of how banking gets done and how holistically.
customers experience their bank and its
5
Generative AI has a
role to play in every
part of banking
Our analysis of the potential impact of generative
AI on the banking industry3 concluded that every
role in every bank is likely to benefit in some way
from generative AI. Through this study and our
ongoing work with leading banks worldwide, we
have identified hundreds of promising use cases
that span the banking value chain.
From the back and middle offices through to
the tellers, advisors, relationship managers
and contact center agents in the front office,
the ability of generative AI to automate routine
manual tasks and augment workers’ capabilities
will make a profound difference (see Figure 1
on page 7).
6
Generative AI will transform banking roles in different ways and to different degrees,
depending on the specific nature of their tasks and the time that each takes.
Automation Augmentation All-round support
In our analysis of US banks, we discovered Employees whose work involves a high We determined that 25% of all employees
that occupations representing 41% of measure of judgment, such as credit will be similarly impacted by both
banking employees are engaged in tasks analysts, or who need to understand automation and augmentation. Customer
with higher potential for automation. Roles customers’ needs and circumstances service agents, who spend their time
such as tellers, whose jobs primarily involve and personalize their interactions, such explaining products and services to
collecting and processing data, would as relationship managers, could be customers, responding to inquiries,
benefit greatly from automation—60% of empowered by generative AI tools that preparing documentation and maintaining
their routine tasks could be supported by help them prepare for and run meetings sales and other records, are a good
generative AI. —34% of banking employees fall into this example. Of these tasks, 37% could be
category. automated while 28% could be augmented.
7
Figure 1: How the top 20 banking industry roles are like to benefit from generative AI.
Loan Interviewers and Clerks
noitatnemgua
rof
laitnetop
rehgiH
Industry
60% average = 39% Front office Middle & back offices
Personal Financial
Credit Analysts
Advisors
50% Securities, Commodities,
and Financial Services Sales Agents
Software Developers
Market Research Analysts
40% Financial Managers
and Marketing Specialists
Loan Officers Financial Examiners Industry
average = 34%
Tellers
30%
Customer Service Representatives
Financial and
Bookkeeping, Accounting,
Investment Analysts
First-Line Supervisors of and Auditing Clerks
20%
Non-Retail Sales Workers
New Accounts Clerks
Office Clerks and General
Accountants and Auditors
First-Line Supervisors of Office
10%
and Administrative Support Workers Bill and Account Collectors
Management Analysts
0%
15% 20% 25% 30% 35% 40% 45% 50% 55% 60% 65% 70% 75%
Higher potential for automation Bubble size: Relative
number of employees in US
Source: Accenture Research analysis and estimates on (Process work)
BLS and O*Net data as of December 2022.
8
In the back and middle offices, generative AI will be The same will happen in the front office, but here Productivity is the most obvious benefit of
used to transform the operating model. Many tasks employees will have access to intelligent tools that widescale adoption of generative AI. However, the
will be automated. This will improve speed and allow them to personalize customer experiences ability to better understand and meet customers’
accuracy, reduce costs and relieve employees of and sell more effectively. With both the time and needs, thereby increasing satisfaction, retention
the more tedious aspects of their jobs. By allowing the means to engage more meaningfully with and share of wallet, is likely to make a much bigger
them to take on more or other tasks that add value, customers, they will be able to restore the human contribution to banks’ bottom line.
they could have a bigger impact on the bank’s touch that was lost over the past two decades as
overall performance. banks digitalized their experiences.
59%
of banking employees
are already using AI
every day.
4
9
How generative AI
can be put to work
While the potential applications for generative AI
in banking are almost limitless, our experience
and analysis show there are mainly three ways
the technology is currently being employed: by
using tools in which generative AI is embedded
(e.g. email), by using the technology to transform
the operating model (e.g. call centers, code
development), and by using it to innovate and
differentiate the bank’s experiences and offerings.
10
Embed Transform Innovate and differentiate
Many software vendors whose platforms are used One of the most immediate ways banks could The greatest, most enduring impact of generative
by banks to run their business are incorporating put generative AI to work is to integrate it with AI will likely be in equipping banks to innovate and
generative AI into every aspect of what they do. middle- and back-office operations to drive differentiate their products, marketing and customer
For example, Microsoft began integrating large efficiency and effectiveness gains. Just one interactions. On the product side, banks are using
language models (LLMs) into its Microsoft 365 example is the transcription and summarization generative AI to produce thousands of scripts that
suite of apps back in March 2023 with the launch of customer call recordings. Generative AI could are tailored for individual customers. In marketing,
of Copilot.5 Adobe’s Firefly tool can generate also enable transformation that has been put off they are beginning to adopt the technology to
images from simple text prompts.6 Salesforce due to financial or talent constraints—such as achieve levels of personalization which, until now,
offers a CRM assistant called Einstein that gets core system modernization. It is still early days, have been economically impossible. They are
its intelligence from generative AI,7 and Workday but we are seeing some banks use generative AI combining internal and external customer data
recently started integrating the technology into its to dissect and reverse-engineer their legacy code, with behavioral economics to generate curated
tools.8 All of these are intended to both automate and rewrite it in a modern language. Westpac, for experiences similar to that of the latest vehicle
and augment banking tasks and roles. example, is pairing its engineers with a generative sat-nav systems. Customer intent has become
AI companion to help fast-track software more apparent, allowing banks to become more
development projects, resulting in a 20%+ empathetic, proactive and relevant. The ability to
increase in code written by its programmers.9 tailor customer interactions, recommendations and
pricing may very well be the most important benefit
banks gain by using generative AI.
Banks are understandably cautious about the reputational and other risks associated with this leap in innovation. However, given the opportunity
to reinvent their customer experiences and drive growth, most are working hard to ensure they take advantage in a responsible way.
11
How to lead
in the era of
generative AI
Accenture research10 shows that banks have
Many banks are asking how they can unlock identified a number of factors that are key to
the incredible potential of generative AI. the success of their adoption of generative AI.
The key is to develop a holistic strategy Their priorities are:
that identifies the most promising use
36% 46%
cases, but then commits to moving beyond
isolated proofs of concept to scaled,
responsible deployment in a way that is
aligned with the bank’s business goals and cloud infrastructure data strategy
reassures regulators.
34% 25%
talent acquisition overcoming worker
resistance to change
12
01
Our conversations with industry front-runners
Lead with value
reveal common themes.
02
Understand and develop a secure AI-enabled digital core
We see five key imperatives which C-suite
executives should address to reinvent in the
03
age of generative AI. We have also identified Reinvent talent and ways of working
the key steps for each that will help your bank
04
become a leader.
Close the gap on responsible AI
05
Drive continuous reinvention
Measuring the ROI
of generative AI
13
I M P E R AT I V E O N E
Lead with value
Since there are more potential use cases for
generative AI than any bank could possibly explore
1
at any one time (see Figure 2 next page), the big
question is not what to do but rather what not to
do—and therefore, how to prioritize adoption.
Measuring the ROI
of generative AI
14
Figure 2: Leaders are moving forward on use case development from front to back office.
Unsecured Secured Wealth
Deposits Commercial Banking
Lending Lending Management
Front-Back Book Offer Intent Transaction Banking Trade Finance Treasury Investment Mgmt.
Management
Optimization Optimization Identification
Commercial Commercial
Intelligent RM Advisor Mgmt.
Real Estate Credit Origination
Commercial Advisory Services
Products
Products
Product Development
Lead Generation & Optimization Account Servicing
& Pricing
Portfolio
Underwriting / Credit Assessment
Optimization
Application Processing & Fulfilment Trading
Lead Nurturing & New Offer Brand Brand Marketing Segmentation & Client & Sales Digital Content
Sales / Marketing Lead Origination
Qualification Management Management & Campaigns Targeting Insights Creation & Mgmt.
Client Engagement Customer Experience Loyalty Program Correspondence Branch & Advisor Call Center Client Tools & Portals Client Onboarding Channel Mgmt.
& Value Mgmt.
IT Planning & Application Doc. & Knowledge Data Sourcing Data Structuring BI Reporting &
Technology & Data IT Engineering Enterprise Testing Data Governance
Coordination Management Management Strategy & Processing Self-Serve
Deposit, Cash & Collections & Account & Investment Fund Services & Custody & Asset
Operations Cards Mgmt. Fraud Management
Payments Mgmt. Default Mgmt. Portfolio Mgmt. Management Administration Safekeeping
Risk, Compliance & Financial Crime &
Audit Compliance Risk Management KYC Finance Procurement HR Legal
Enterprise AML
Collaboration & Knowledge Mgmt. & Marketing Enterprise Resource Supply Chain Business Process
Enterprise Applications CMS CRM Project Management
Commun. Software Doc. Mgmt. System Automaton Planning Management Management
Estimated impact of generative Al Low impact Medium impact High impact
Source: Accenture analysis
Measuring the ROI
of generative AI
15
The key is to balance the need for rapid Banks can achieve the necessary balance by
diffusion throughout the organization with doing three things simultaneously:
the accompanying cost and with the relevant
• Lead with top-down support and funding for
regulatory requirements. Some use cases are
the prioritized initiatives;
simple and relatively inexpensive while others,
like building a digital twin of the bank’s mortgage
• Establish an operating and steerco model that
function, are complex and require a lot of
ensures adoption and is compliant with all
expertise, data and computation. With regard to
relevant regulations;
computation, the rapid decline in the cost of using
most generative AI models makes prioritization • Drive multi-speed implementation and
especially challenging. Many banks are starting adoption across business segments, functions
their generative AI journey with simple, no-regrets and enterprise applications.
applications while planning the timing of their
more complex initiatives by calculating where the
By broadening the scope beyond single
shifting cost and return curves are likely
applications, banks can integrate generative AI
to intersect.
more holistically into their value chains, leading
to transformative improvements across business
functions. However, this broader integration
requires strong C-level sponsorship and a broad
business strategy, all underpinned by a robust
governance mechanism.
Measuring the ROI
of generative AI
16
• Developing and enforcing standardized which projects to scale, and how to optimize their
Establishing a strong strategy and oversight approaches, assets, best practices and transformation journey. It will also help gain the
team is critical. This team should include leaders principles for the deployment of solutions. support of everyone in the bank for the holistic
from the business, risk and technology sides of adoption of generative AI.
• Establishing the frameworks and approaches for
the bank, and its mandate should encompass
model risk management, to ensure compliance Some banks may consider having separate
strategy, policy, talent, technology, regulatory
with not only the law but also corporate infrastructures for generative AI and traditional
compliance and data.
governance standards and requirements. AI / data functionalities. However, this could
One of its first priorities should be the cause strategic conflicts and make it more
• Supporting vendor assessments.
establishment of a generative AI center of difficult to capture efficiencies. A single
excellence (CoE). This dedicated group will focus structure will drive the initiative from a unified
• Assessing the talent impact and supporting
on generating business value by implementing the platform and facilitate synergy.
change management and upskilling efforts to
bank’s generative AI strategy and cross-pollinating
minimize disruption and encourage adoption.
Not all implementations are economically viable
the technology throughout the bank. It will
at this time. However, the overall trend in
prioritize use cases, clear the way for generative
A vital and ongoing role of the CoE would be
generative AI implementation and consumption
AI to be scaled up in a federated model, and
to accurately measure the ROI of the bank’s
is towards lower costs and greater feasibility.
catalyze innovation. The responsibilities of the
generative AI applications. This should not be
The challenge for banks is to position themselves
CoE could include:
limited to immediate cost and revenue gains
to capitalize on new use cases as they quickly
but should also consider long-term strategic
• Collaborating with the business units to become economically feasible. This will require a
benefits (see Measuring the ROI of generative AI).
develop proofs of concept and roll out the strategic approach to prioritization, focusing on
A clear and empirical view of these benefits will
successful ones throughout the organization. current objectives while keeping an eye on the
help banks decide where to allocate resources,
longer-term investment horizon.
Measuring the ROI
of generative AI
17
C A S E S T U D Y
European bank expands its CoE to scale AI benefits
A leading European bank started to build an AI they have skin in the game. A value assurance
CoE six years ago to ensure strategic alignment group, composed of planning, HR, legal and other
and facilitate allocation of resources for AI departments, assesses proposed use cases for
projects and programs. The CoE, with sponsorship their potential value.
from the CEO, CDO and CFO, is set to expand
from a small structure into a 300-strong team This approach has created a fertile environment
over the next three years. It has responsibility for for cross-unit collaboration. The C-suite has
scaling AI and generative AI use cases and setting enough confidence in the goals of the AI projects
technical standards. currently underway to have committed publicly
to increase the bank’s 2025 operating income by
Operating in a hub-and-spoke model, the CoE hundreds of millions of euros.
ensures economies of scale as well as the
consistency and quality of AI and generative
AI applications. Business units are responsible
for proposing use cases, securing funding and
quantifying the value derived from AI. They
retain ownership and accountability to ensure
Measuring the ROI
of generative AI
18
L e a d i n g w i t h v a l u e
ACTIONS
• Develop a comprehensive generative AI • Implement rigorous ROI measurement
integration strategy across all business protocols that include both quantitative
functions, moving from isolated use cases to a financial metrics and qualitative assessments of
more holistic and connected approach. strategic impacts such as customer satisfaction
and competitive differentiation.
• Establish a strong C-level governance
framework to ensure that generative AI • Monitor the market as the capabilities of
initiatives are aligned with the organization’s generative AI models evolve and the cost of
strategic goals and effectively integrated using them drops, changing the business case.
across departments.
• Foster cross-departmental collaboration to
break down silos, facilitating a unified approach
to generative AI implementation that leverages
diverse expertise and insights.
Measuring the ROI
of generative AI
19
I M P E R AT I V E T W O
Understand and develop a
secure AI-enabled digital core
2
Firms that showed early success in developing
Many banks’
a strong digital core and data foundation have
been leading in generative AI. A modern digital digital architecture,
core leverages the powers of cloud, data and AI
infrastructure and data
through an interoperable set of systems across the
bank—including enterprise platforms, automation, capabilities are likely to
integration and security—that allow for rapid
impede their successful
development of new capabilities. This core, with its
adoption of generative
architectures, infrastructure, capabilities and talent,
is essential to making the most of generative AI—
AI at scale.
whose capabilities are most often cloud-based.
Measuring the ROI
of generative AI
20
However, our analysis of the current banking This spread highlights the fact that many LLMs is their ability to consume and work with
landscape reveals significant variation in the banks’ digital architecture, infrastructure and data huge volumes of data in different formats (images,
caliber of banks’ digital cores. This observation capabilities are likely to impede their successful text files, video and audio recordings, etc.) and
emerges from our global study of 240 banks. It adoption of generative AI at scale. This is confirmed covering multiple topics (from prospectuses
ranges broadly from 0.2 to 0.8 on a scale of 0 to 1, by our survey finding that 47% of executives across and policy documents to relationship managers’
with 82% of banks having a score between 0.2 all industries list ‘getting their data strategy right’ meeting notes). The problem is that much
and 0.6. as one of their greatest challenges as they strive to of it today is not only unstructured but also
implement and use generative AI.11 unorganized, unlabelled and dispersed
throughout the enterprise.
82% of banks have a score Additionally, our research indicates that
between 0.2 and 0.6
approximately 35% of banks globally have For LLMs to work, all of this unstructured data
54%
migrated less than 5% of their workloads to the needs vectorized databases. These currently exist
cloud. This is a substantial constraint, because the alongside traditional data lakes and enable the
evolution of generative AI is increasingly geared parsing and extraction of key information so that
28%
towards cloud-native technologies. Banks with LLMs can leverage it at speed. A key consideration
limited cloud integration are likely to miss out on is whether these databases will converge, and
12% cloud-native AI functionalities. if so, how quickly and at what cost? Database
6%
convergence will have a significant impact
0%
The goal of scaling generative AI will impose new on banks’ ability to utilize the full potential of
0-0.2 0.2-0.4 0.4-0.6 0.6-0.8 0.8-1
requirements on the digital core, so it’s important generative AI to produce precise and personalized
Low High
for banks to understand the status of their core outputs.
Figure 3: Variation in the caliber of banks’ tech
today. For example, one of the advantages of
development (0-1 scale)
Measuring the ROI
of generative AI
21
Forward-thinking banks have an advantage The AI landscape is in a state of constant flux,
because they started migrating from data lakes marked by the continual emergence of new
to decentralized data meshes before generative models, enhanced capabilities and an expanding
AI was on the horizon. In such a structure, array of tools and providers. This dynamic
domains within a bank take ownership of their environment could make previously unviable use
data, including responsibility for data quality and cases feasible. This will require centralized but
accessibility. These domains manage and provide connected models management. This strategy
their data as products, making it easier for other will standardize critical capabilities, such as the
parts of the business to use it. selection and customization of foundational
models, allowing for their efficient and transparent
Regardless of the underlying LLM strategy integration across various business functions.
employed—be it licensing pre-built models,
adapting existing ones through retrieval-
augmented generation (RAG), fine-tuning or
developing models from the ground up—it
This dynamic environment
is crucial that the bank has a data strategy
and approach that allows it to be flexible and could make previously
future-ready. This approach will be crucial in
unviable use cases feasible.
gaining the best possible outputs.
Measuring the ROI
of generative AI
22
C A S E S T U D Y
Scaling an enterprise-wide LLM and generative AI capability
A leading global financial services firm is capabilities. The bank has implemented a secure
developing an enterprise-wide AI strategy, and compliant platform that can host ChatGPT and
supported by a strengthened IT infrastructure OpenAI services.
and a transformative approach to talent. The bank
built a team of thousands of AI and data experts. This platform will enable the bank to scale
This AI strategy has, to date, brought in tangible generative AI and future-ready, cloud-neutral
benefits totalling around 3% of its expected net functionality across its business. By modernizing
income. its digital core, the bank not only kept up with
existing tech trends, it also positioned itself to
In its move towards a modern digital core, the integrate advanced AI with its core banking
bank focused on building a scalable multi-cloud operations at scale and keep one step ahead of
environment that complies with banking regulations. new technologies as they emerge.
This infrastructure has enabled it to fast-track its
adoption of next-gen, cloud-neutral generative AI
Measuring the ROI
of generative AI
23
U n d e r s t a n d a n d d e v e l o p a s e c u r e A I - e n a b l e d d i g i t a l c o r e
ACTIONS
• Assess the complete inventory of unstructured • Start moving towards data-as-a-service models, • Test various architectures to find the best
data across the organization and analyze how it where employees and developers can access fit for each use case and plan the model’s
could help power generative AI. data from internal marketplaces to use in their lifecycle from experimentation to scaling and
own applications and tools. phase-out. This could be achieved through a
• Start to move this data into vector databases
‘model switchboard’ where banks can select a
and scale them with the precision required for • Elevate data governance standards to
combination of models based on the business
real-time analytics and the unique demands of effectively manage unstructured data in
context or on factors such as cost or accuracy.
generative AI. generative AI.
• Evolve security protocols to address the
complexities that come with diverse data
access.
Measuring the ROI
of generative AI
24
I M P E R AT I V E T H R E E
Reinvent talent and ways of working
Generative AI will be pervasive, affecting virtually As work and roles are transformed, waste is taken
every process, task and role in the bank. To out and banks have the choice of boosting the
3
maximize its transformative impact, banks will bottom line or investing to increase value. Those
need a culture that not only anticipates but that opt for investment are using the savings
champions change. for talent to transition to newly created roles
such as managing generative AI, or in strategic
Key to achieving this impact is the strategic areas like business development or relationship
integration of generative AI into the bank’s management. These new and transitioned roles
processes. This goes beyond the adoption of new are aligned with the desired strategic customer
technology; it’s about reimagining workflows to and business outcomes. In the mortgage sector,
increase efficiency and innovation and aligning for example, we envisage a workforce that will
them with the goals of the business. include several new roles and has the increased
capacity needed to unlock significant productivity
gains (see Figure 4 next page).
Measuring the ROI
of generative AI
25
Figure 4: An illustrative example of how work and roles in the mortgage business can be realigned with generative AI.
Today Tomorrow
Category Roles No. of People
Category Roles in the Role
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(cid:94)(cid:258)(cid:367)(cid:286)(cid:400)(cid:3)(cid:876)(cid:3)(cid:4)(cid:282)(cid:448)(cid:349)(cid:400)(cid:381)(cid:396)(cid:455) Loan Officers
(cid:94)(cid:258)(cid:367)(cid:286)(cid:400)(cid:3)(cid:876)(cid:3)(cid:4)(cid:282)(cid:448)(cid:349)(cid:400)(cid:381)(cid:396)(cid:455) Loan Officer Fewer
(cid:94)(cid:258)(cid:367)(cid:286)(cid:400)(cid:3)(cid:876)(cid:3)(cid:4)(cid:282)(cid:448)(cid:349)(cid:400)(cid:381)(cid:396)(cid:455) Loan Officer Assistants Assistants
(cid:94)(cid:258)(cid:367)(cid:286)(cid:400)(cid:3)(cid:876)(cid:3)(cid:4)(cid:282)(cid:448)(cid:349)(cid:400)(cid:381)(cid:396)(cid:455) Loan and Credit Same
(cid:94)(cid:258)(cid:367)(cid:286)(cid:400)(cid:3)(cid:876)(cid:3)(cid:4)(cid:282)(cid:448)(cid:349)(cid:400)(cid:381)(cid:396)(cid:455) Loan and Credit Managers
Managers
(cid:94)(cid:258)(cid:367)(cid:286)(cid:400)(cid:3)(cid:876)(cid:3)(cid:4)(cid:282)(cid:448)(cid:349)(cid:400)(cid:381)(cid:396)(cid:455) Loan Product More
(cid:75)(cid:393)(cid:286)(cid:396)(cid:258)(cid:410)(cid:349)(cid:381)(cid:374)(cid:400) Loan Processors
Managers
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81,accenture,Accelerating-Irelands-Generative-AI-Reinvention.pdf,"Contents
Executive summary 4
The generative AI opportunity 7
Ireland’s progress 17
The five imperatives to accelerate Ireland’s reinvention through generative AI 22
Imperative 1: Lead with value 25
Imperative 2: Understand and develop an AI-enabled, secure digital core 29
Imperative 3: Reinvent talent and ways of working 34
Imperative 4: Close the gap on responsible AI 38
Imperative 5: Drive continuous reinvention 42
The role of government 49
Generating growth: How generative AI can power Ireland’s reinvention Copyright © 2024 Accenture. All rights reserved. 2
Preface
Generative AI (gen AI) is a game- At Accenture, we are seeing how gen AI can be a powerful of gen AI and drive sustainable growth. Achieving this
force for progress. We’ve been working with clients to navigate potential, will require action from the collective ecosystem
changer that’s reshaping work, life
this complex terrain—helping them harness the full potential of including government, business and academia to build an AI-
and industry. Its influence extends
AI to drive growth and innovation and reinvent processes and skilled workforce and foster responsible innovation.
customer experiences.
across every function and role, from
We believe this is a pivotal moment, with gen AI set to
the CEO to frontline workers. Given the remarkable pace at which gen AI is advancing, transform and redefine how businesses operate. Given the
leaders must move quickly to leverage the technology in rapid pace of change and the size of opportunity, it is essential
driving tangible business outcomes. Establishing a robust that we act, and act now.
digital core, preparing the workforce and fostering a culture
Whether you’re just starting out or already on your AI journey,
of continuous learning are all essential steps. These efforts
this report offers the formula to deploy gen AI successfully,
must be underscored by responsible principles to ensure
responsibly and with real impact.
data privacy, transparency, and fairness remain central in all
implementations.
For Ireland, gen AI offers a unique opportunity. Ireland’s Hilary O’Meara
successful track record of leading the digital wave, combined Country Managing Director,
Accenture in Ireland
with a young, skilled workforce and a globally-connected
business ecosystem, positions us well to harness the benefits
Generating growth: How generative AI can power Ireland’s reinvention Copyright © 2024 Accenture. All rights reserved. 3
Executive
Fixing the triple fracture employees have access to gen AI tools, only 38% A formula for success
of employees at Irish organisations do. Moreover,
Ireland has been slow in building the foundations Nearly one in 10 (9%) organisations are using gen AI
summary workers at Irish organisations are nearly four times
for AI and the cracks are beginning to show. It must at scale, so we know it can be done.
more likely than their MNC counterparts to have
act now to capture the opportunity.
missed out on digital skills training in the past two What should public and private sector leaders do
A deployment gap is opening as organisations years. over the next 12 months to put their organisations—
struggle to move their use of gen AI beyond proofs and the Irish economy—at the forefront?
Gen AI promises to
of concept. Among those that have invested in the Make AI a multiplier
be transformative to Based on delivering more than 1,000 global gen
technology, 91% have yet to scale its use across
Closing these gaps requires a people-centric
AI projects, including with several of Ireland’s
their business. One in three organisations believe
Ireland’s economy. approach. Seventy percent of the nation’s
largest organisations, we see a formula for success
their cloud capabilities are insufficient to leverage
workforce could see at least a third of their working
emerging. In this report, we outline the five
gen AI, highlighting the need to accelerate the
hours enhanced by the current technology.
imperatives behind that formula and how it can
The technology could increase the long-term modernisation of their technological foundations.
Our economic modelling forecasts that when
accelerate Ireland’s AI-powered reinvention: lead
growth rate over the next 15 years (to 2038) by
employees are empowered to innovate and identify
Many workers still lack even basic digital skills and with value; understand and develop an AI-enabled,
more than 50% and generate productivity gains
new opportunities, financial gains are greatest.
access to the training needed to develop them, secure digital core; reinvent talent and ways of
of up to 30% in multiple sectors. However, three out of five executives would
signalling an inhibiting skills gap. Around 1.76 working; close the gap on responsible AI; and, drive
prioritise short-term cost-cutting investments in
million people —64% of today’s workforce—need continuous reinvention.
But there is no guarantee the full potential for
process automation over those that transform
reskilling. Executives report that less than half
productivity and growth will be realised. Today,
roles for the long term, missing the opportunity to The elements of the formula are mutually
(45%) of their workforce is confident in the digital
too few organisations use gen AI optimally and use cost savings to empower people with freed reinforcing, so shouldn’t be applied in isolation.
fundamentals required. A surge in digital skills
to amplify human abilities. But without a people- capacity. Strategic alignment between technology, talent,
training is needed, and urgently.
centric approach that empowers workers to governance and value roadmaps is essential. Our
There is a real optimism among Irish workers about
Finally, a trust gap is emerging between modelling estimates an organisation is four times
perform higher-value tasks—rather than simply
the impact of AI. Five times as many people think
employees and executives, impeding adoption. more likely to succeed in scaling the use of gen
automating existing processes—€96 billion
gen AI will accelerate, rather than decelerate, their
Only half (50%) of people expect business leaders AI if coordinated action is taken towards the five
in economic value could be left untapped by career progression. Many are moving ahead of
to be responsible and make the right decisions to imperatives simultaneously.
2038—an amount nearly equivalent to Ireland’s their organisations: half of the people using gen AI
ensure gen AI has a positive impact on Ireland, and
total public investment in 2024.1 at work are self-starters who are using tools they Over the past 18 months, gen AI has captured
even fewer (38%) trust the government to do so.
procured themselves. While this indicates that imaginations; now, with this formula, it can
Local Irish companies face greater deployment and more needs to be done to harness this enthusiasm, deliver results.
skills gaps compared to multinational corporations it also highlights the need for organisations to
(MNCs) operating in Ireland, putting them at a respond to employee interest by providing gen AI
competitive disadvantage. While half of MNC tools directly and ensuring they are used safely
and responsibly.
Generating growth: How generative AI can power Ireland’s reinvention Copyright © 2024 Accenture. All rights reserved. 4
Authors
This report was a
collaborative effort
between our Data
and AI team based in
Ireland, supported by
Denis Hannigan Noelle Doody Liam Connolly
our research team: Data & AI Lead—UK Data & AI Lead – Generative AI Lead –
and Ireland, Ireland Ireland
Accenture Accenture
Strategy & Consulting
Accenture
Audrey O’Mahony Austin Boyle Adrian O’Flaherty
Talent & Organisation Technology Lead – Senior Manager,
Lead – UK and Ireland, Ireland Data & AI –
Strategy & Consulting Accenture Ireland
Accenture Accenture
Generating growth: How generative AI can power Ireland’s reinvention Copyright © 2024 Accenture. All rights reserved. 5
About the research
We took a multi-pronged approach to researching Ireland’s
gen AI-powered reinvention, building on insights from
research conducted in the UK. The report is based on:
Economic modelling to forecast the potential impact of gen AI on productivity and growth for the
economy, organisations and people. We mapped out the future growth trajectories under three different
AI deployment scenarios: aggressive, cautious and our proposed people-centric approach. This
modelling has been conducted for 23 countries globally.
Surveys conducted with 409 employees and 128 executives from public and private sector
organisations in Ireland. The surveys were also conducted in the UK with 3,752 employees and
1,085 executives. The samples covered 19 industries and included different demographic groups by
geography, company size and socioeconomic background. The employee survey looked at workers’
experiences with gen AI. The executive survey looked at leaders’ perceptions of the AI ecosystem, their
investments in gen AI and their AI strategy. The surveys were conducted in July and August 2024.
Interviews, client experience and case studies, drawing on insights with leaders from across the AI
ecosystem, including large technology providers, industry, government and civil society.
The authors and researchers of this report used gen AI in its design, analysis and prose in alignment with
Accenture’s responsible AI principles.
Further details on the research approach can be found at the end of the report.
Generating growth: How generative AI can power Ireland’s reinvention Copyright © 2024 Accenture. All rights reserved. 6
The generative AI
opportunity: For people,
organisations and the
economy
Generating growth: How generative AI can power Ireland’s reinvention Copyright © 2024 Accenture. All rights reserved. 7
The gen AI state of play
Figure 1. Welcome to the age of generative AI
Analyse Simulate
Scenario Optimise
Segment Recommend
Generating growth: How generative AI can power Ireland’s reinvention Copyright © 2024 Accenture. All rights reserved. 8
Strong foundations
Ireland has established itself as a technology hub for the digital age. It has
become one of the leading destinations for both tech multinationals and
startups. Eight of the global top 10 information technology companies have a
significant presence in Ireland,2 and the country ranks fourth in Europe in terms
of VC investment per capita.3 This has helped it become the second largest
exporter of ICT services in the world.4 The country has developed a deep
bench of technical skills. In Ireland, both the proportion of people with basic
digital skills and the percentage of ICT specialists exceed the EU average.5
Ireland’s skilled workforce and status as a tech hub have created strong
foundations for its AI ecosystem, which is described as advanced or world-
leading by most executives (63%) (see Figure 2).
Generating growth: How generative AI can power Ireland’s reinvention Copyright © 2024 Accenture. All rights reserved. 9
Figure 2. Ireland’s AI ecosystem has strong foundations
State of Ireland’s Strengths of Ireland’s AI ecosystem Net Availability of AI skills in Ireland
AI ecosystem % respondents2 +/- # people reporting skills on LinkedIn3
% respondents1
13 World leading Talent pool 9 67 +58
Computing
10 67 +57
institutions
Research
12 68 +56
infrastructure
50 Advanced
Regulatory
17 51 +34
environment
32,988
Access to
20 54 +34 1.5x
funding 22,800
20 Somewhat developed
Cost of doing
28 50 +22
Underdeveloped business
6
Don’t know
11
Weakness Strength
2023 2024
(As of August) (As of October)
1. Respondents were asked: How would you describe Ireland’s AI ecosystem? AI ecosystem was defined as: the network of organizations, resources and stakeholders involved in the
development of AI technologies, including government entities, companies, research institutions and support structures such as funding infrastructure, regulatory frameworks and
talent pools that collectively contribute to the growth and development of AI. Accenture Ireland AI business leader survey, fielded July-August 2024.
2. Respondents were asked: Would you consider each of the following as either a strength or a weakness of Ireland’s AI ecosystem? Data for “neither strength nor weakness” is not
shown. Accenture Ireland AI business leader survey, fielded July-August 2024.
3. Accenture Ireland Tech Talent Tracker based on data from LinkedIn Professional Network.
Generating growth: How generative AI can power Ireland’s reinvention Copyright © 2024 Accenture. All rights reserved. 10
Impact from self
to society
Ireland’s strong foundations position it to
become a global leader in the gen AI era. Our
research brings into view the size of the prize:
Generating growth: How generative AI can power Ireland’s reinvention Copyright © 2024 Accenture. All rights reserved. 11
Our research brings into
view the size of the prize:
For the economy For organisations
We model that gen AI could: A double-digit productivity uplift could be achieved across the private and public sectors, based on the
current state of the technology. The life sciences industry, with many of the world’s largest companies
• Add up to €148 billion to annual GDP in 2038—this amounts
operating in Ireland, could see productivity gains of nearly 20%.6 Other sectors that are poised to benefit
to a 22% increase to the baseline forecast for 2038.
the most from gen AI are financial services, high tech and software and platforms—all of which have a
• Shift average annual real GDP growth for 2023–2038 from a significant presence in Ireland (see Figure 3).
baseline forecast of 2.5% to 3.9%, representing a 55% boost
If the productivity benefits are translated into cost savings, the gains could be substantial. Across all
to Ireland’s long-term growth rate.
industries analysed, total annual savings could reach €22.2 billion if the full potential of today’s technology
to automate and augment work is realised.
Nowhere is this opportunity bigger than in the public sector. We estimate that 42% of working hours in the
We estimate that
Irish public sector (excluding healthcare) could be transformed by gen AI (either through automation or
augmentation). This translates into a potential productivity gain of 12-18% that, if realised, could result in
42
€2.9 billion in annual savings.
%
of working hours in the Irish public
sector (excluding healthcare) could be
transformed by gen AI (either through
automation or augmentation).
Generating growth: How generative AI can power Ireland’s reinvention Copyright © 2024 Accenture. All rights reserved. 12
Figure 3. Potentialproductivitygainscouldbe30%+acrossthefinancialservicesandtechsectors
Productivity gains from gen AI exposure %
Modelled range*
Software & Platforms €1.6
Banking €0.8
Capital Markets €0.8
Insurance €0.3
High Tech €0.2
Communications & Media €1.4
Aerospace & Defence €0.1
Retail €2.6
Life Sciences €0.2
Travel €0.4
Utilities €0.5
Consumer Goods & Services €1.0
Public Service €2.9
Automotive €0.8
Industrial €2.5
Natural Resources €0.2
Health €1.8
Chemicals €0.2
Energy €0.1
5% 10% 15% 20% 25% 30% 35% Mid-point
cost savings (€bn)
Source: Accenture Research based on Central Statistics Office of Ireland and US O*net. Lower and upper bound based on potential hours saved by occupation valued at annual occupation headcount.
Generating growth: How generative AI can power Ireland’s reinvention Copyright © 2024 Accenture. All rights reserved. 13
But gen AI isn’t just a productivity play—it creates Over time, the effective use of gen AI will become
new avenues for growth. A significant proportion of an increasingly important source of competitive
the growth opportunity comes from the build out of advantage. We analysed earnings calls from 1,300
AI’s foundations. In the race for AI supremacy, leading global companies with revenues exceeding €900
technology companies are building infrastructure akin million to assess the extent to which they cited efforts
to the expansion of the electric grid in the early 20th to build competitive advantage using gen AI. Our
century. Data centres, of which there are already 82 analysis revealed that companies actively pursuing
in Ireland, form a key part of AI infrastructure.7 Just as this strategy delivered a 10.7 percentage point
electricity transformed industries and powered global total return to shareholder (TRS) premium in 2023
economies, gen AI is poised to drive the next wave of compared to those that did not, even after controlling
innovation. Analysts estimate over a trillion dollars will for company size, headquarters location and industry.9
be spent globally on AI infrastructure over the next
five years, as companies compete to ‘own the grid’ of
this new technological era.8
Just as electricity transformed
industries and powered global
economies, gen AI is poised to drive
the next wave of innovation.
Generating growth: How generative AI can power Ireland’s reinvention Copyright © 2024 Accenture. All rights reserved. 14
For people
By harnessing individual human potential, organisations will realise the most benefits. described how a gen AI tool streamlined onboarding for new carers, enabling them to reach
the top 20% of performers within six weeks. Workers recognise this potential—over five
No current technology has the potential to have a bigger impact on our working lives than AI.
times as many survey respondents expect gen AI to accelerate rather than slow their career
Seven in ten people in Ireland could have at least a third of their working hours enabled by the
progression.
technology, either through automation or augmentation.
As people spend more time doing work they enjoy and doing it well, gen AI could help in a
Automation will save people time, taking tedious tasks off human hands. Our modelling
more profound sense by improving the overall experience of work. In an experiment with our
suggests the average Irish worker could save 17% of their working hours spent on routine
own sales team, we found that gen AI didn’t just result in marked increases in productivity but
activities. A doctor, for example, could save five hours a week while a commercial sales rep
also grew peoples’ confidence (+34%) and their belief they were making a meaningful impact
could save twelve hours a week.
(+31%).12 Gen AI added to their job satisfaction rather than subtracted.
The time saved could be reinvested in the higher-value work people enjoy doing. Creativity
We see similar findings in our survey. Irish workers recognise gen AI will be important to their
is the most underutilised skill in Ireland: 27% of people we surveyed say they aren’t currently
productivity and problem-solving. But they also anticipate the technology will benefit their
applying their creativity at work. While many surveyed already in creative roles express
autonomy and sense of purpose (see Figure 4). Familiarity with the tools reduces anxiety,
concerns about the technology’s impact on their jobs, they are also among the first to
as employees recognise how the technology complements their existing skills and helps
leverage gen AI to support their work. They use it particularly for ideation, brainstorming and
them perform tasks more effectively. Daily ‘power users’ of the technology were more likely
accelerating the initial stages of the creative process.
to expect gen AI to be important to both their creativity and fulfilment from work, relative to
The benefit of augmentation will be accrued not just in time but in quality. AI-driven methods irregular users.
have accelerated the discovery of more than 50 drug candidates and could potentially result
in a 90% reduction in resource use.10
Gen AI could also help address talent gaps. In 2023, over half of organisations in Ireland (55%)
reported difficulties in hiring staff with the right skills.11 Gen AI can alleviate these shortages by
enhancing employees’ ability to absorb institutional knowledge more quickly. One interviewee
Generating growth: How generative AI can power Ireland’s reinvention Copyright © 2024 Accenture. All rights reserved. 15
Figure 4. PeopleanticipatebroadbenefitsfromgenAI—theirexpectationsincreaseastheyusethetoolsmore
Workers’ level of gen AI use (of those with access to the tools), % respondents1
19 61 19
Irregular users Light users Power users
Share of workers that anticipate gen AI will be important to their work experience,
% respondents by level of gen AI use1
82
79
79 77
74
72 71 71
68
68
65
55
49 51
47
46 46 46
40 40
36
33
30
30
Productivity Problem-solving Learning Creativity Autonomy Well-being Fulfillment Purpose
1. Irregular users are respondents who never or rarely use the gen AI tools available to them. Light users use the tools often (at least once a week) or sometimes (once a month). Power users use the tools every day.
Source: Accenture Ireland AI employee survey, fielded July-August 2024.
Generating growth: How generative AI can power Ireland’s reinvention Copyright © 2024 Accenture. All rights reserved. 16
Ireland’s
progress
Generating growth: How generative AI can power Ireland’s reinvention Copyright © 2024 Accenture. All rights reserved. 17
Mind the gap
Our survey of business leaders examined which Figure 5. B ased on the decisions being made today, Ireland is running closest to our
of the three economic growth scenarios we low-end economic scenario, potentially leaving €96bn in value on the table
modelled aligns most closely with Ireland’s
current trajectory.
Ireland economic growth simulation, 2023-38
GDP in € billions (constant prices)
In our most optimistic, ‘people-centric’ scenario,
Scenario GDP gain GDP CAGR GDP gain
organisations harness gen AI to automate routine
vs. baseline premium as a share
tasks, redirecting the time saved into higher-value 900 by 2038 vs. 2.51% of baseline
baseline
activities. With AI used to amplify human abilities,
People-centric €148bn +1.4pp +22.0%
employees are empowered to innovate and
800
identify new growth opportunities. In contrast,
€96bn
Cautious €112bn +1.1pp +16.6%
in our ‘aggressive’ scenario, companies prioritise
cost-cutting, with workers finding themselves in
700
Aggressive €52bn +0.5pp +7.8%
less dynamic roles (or unemployed) after being
displaced, which stifles growth and exacerbates
Baseline
inequality (see ‘Further details on the research’ for 600
more on these scenarios).
On current trends, Ireland is leaning toward the 500
lower end of the growth spectrum—closest to
our ‘aggressive’ scenario—potentially leaving €96
400
billion in economic value untapped (see Figure 5).
2023 2026 2029 2032 2035 2038
Source: Accenture Research, simulated GDP growth under three scenarios. Oxford Economics GDP forecast used as the baseline.
Exchange rate is based on the period average (USD per Euro), Oxford Economics
Generating growth: How generative AI can power Ireland’s reinvention Copyright © 2024 Accenture. All rights reserved. 18
Triple fracture
What is contributing to the lost potential? These trends are mirrored among workers. While 43% of
employees in Ireland have access to gen AI tools to support
On average
We identified three points of tension where gen AI
their work, only 24% use them at least once a week. Only
deployment is strained:
one in ten are applying the tools to critical decision-making 56
or high-impact analysis. %
A deployment gap
In 2024, gen AI is expected to account for 12% of Irish There is a notable difference in gen AI deployment
businesses’ technology spend, rising to 15% in 2025. That between multinational corporations (MNCs) and local Irish of employees in MNCs
investment has yet to translate into scaled deployment. companies. Executives from MNCs in Ireland are more
have access to gen AI
While 85% of executives in Ireland report their organisations likely to report that their organisations have adopted gen AI
tools, compared to 38% in
have at least piloted gen AI in one or more parts of their compared to local Irish firms. On average, 56% of employees
business, only 9% have scaled the technology (with use in MNCs have access to gen AI tools, compared to 38% in organisations operating
cases in production in more than half of their business organisations operating solely within Ireland.
solely within Ireland.
functions). Many lack the foundations needed to scale.
A skills gap
Fewer than 21% of executives in Ireland, for example, feel
confident that their organisation’s technology capabilities A landmark shift in digital skills training is essential to
meet the requirements to successfully scale gen AI. unlock the benefits of gen AI. The executives we surveyed
estimate that 64% of their workforces will require reskilling—
Where gen AI is being implemented, the focus tends to
equivalent to roughly 1.76 million people (see Figure 6).13 For
be on the bottom line. Three out of five executives are
some, this will involve developing technical skills such as AI
prioritising investments in process automation that cut costs
engineering. For most, it will focus on training to collaborate
over initiatives that augment people’s roles and transform
with AI systems.
how they work.
Generating growth: How generative AI can power Ireland’s reinvention Copyright © 2024 Accenture. All rights reserved. 19
Yet, many workers still lack even basic digital The average share of the workforce confident
Figure6.Executivesestimatethat64%oftheirworkforces
skills or access to the training needed to in basic digital skills is 14 percentage points
 willrequirereskillingduetogenAI
develop them. Irish executives estimate lower in local organisations compared to
Expectations for how gen AI will change roles at organisations in Ireland and the UK
that less than half (45%) of their workforce MNCs. Similarly, the share of workers who
% of current job roles1
is confident in the digital fundamentals report not having received any digital skills
Jobs to be transitioned: Requiring reskilling / upskilling for new roles
required for work. At the same time, 12% of training in the past 24 months is 17 percentage
Jobs with some enhancement: requiring some reskilling / upskilling
workers report not having received any digital points higher in local organisations.
Jobs with significant enhancement: requiring substantial reskilling/ upskilling
skills training in the last two years. Around
Jobs not impacted: No reskilling/ upskilling required
half (48%) say they are pushed to use new
technology they haven’t been trained on.
17
20
The skills gap is again more pressing among
64% 62% local, Irish organisations compared to MNCs.
22
21
23
23
12
%
of workers report not
having received any
36 38
digital skills training in
Ireland UK
the last two years.
4.78 33.09
Employment mn
1. ReRespondents were asked: How, if at all, do you expect generative AI to change job roles at your
organisation? (Please estimate what proportion of current job roles you expect to fall into the
following categories by distributing 100% points across the options listed.
Source: Accenture UK and Ireland AI business leader survey, fielded July-August 2024. CSO current
employment levels as of Q2 2024. ONS current employment levels. Apr-Jun 2024.
Generating growth: How generative AI can power Ireland’s reinvention Copyright © 2024 Accenture. All rights reserved. 20
A trust gap Figure7.Employeesandexecutivesarenotaligned
As we highlighted in our previous research Accenture’s global R&D and Innovation centre on the long-term societal impact of gen AI
report, Work, workforce, workers: Reinvented in Dublin, remarks: ‘Leaders should reflect
in the age of generative AI, transparency on whether AI is the only area where a trust
Expectations about the outcomes of the widespread
and trust are required for people to adopt gap exists, or if there are precedents in past
use of AI in Ireland
gen AI tools. That research revealed a workforce innovations. If a long history of trust
trust gap between workers and leaders. gaps exists, leaders shouldn’t be surprised if AI % of executive and employee respondents
Decrease Increase Net +ve Net -ve
follows suit.’
Nearly a year later, the trust gap persists.
Productivity
Trust and user acceptance remain the third Expectations around the value gen AI can
Executives 21% 56% +35
most common barrier to scaling gen AI deliver—whether in boosting economic growth, Employees 19% 37% +18
in organisations across Ireland, following equality or employment—differ significantly
Economic growth
implementation costs and technology between employees and leadership (see Executives 24% 52% +28
Employees 23% 25% +2
platforms not being ready for scale. Half (44%) Figure 7). This disparity highlights concerns
of workers have little or no confidence that about social inclusion and employee rights, Digital inclusion
Executives 27% 49% +22
business leaders will make the right decisions underscoring the trust gap. If not addressed,
Employees 24% 31% +7
to ensure gen AI positively impacts Ireland, these issues could undermine the potential
while a majority (62%) express similar doubts benefits of gen AI. Economic equality
Executives 28% 38% +10
about the government. Dr Kenneth McKenzie,
Employees 33% 20% -13
Head of Human Research at The Dock,
Social mobility
Executives 28% 41% +13
Employees 23% 22% -1
Local Employment
Executives 41% 30% -11
Employees 40% 14% -26
National Employment
Executives 39% 30% -9
Employees 41% 14% -27
Source: Accenture Ireland AI business leader survey, fielded July-August 2024.
Generating growth: How generative AI can power Ireland’s reinvention Copyright © 2024 Accenture. All rights reserved. 21
The five
imperatives
to accelerate
Ireland’s
reinvention
through
generative AI
Generating growth: How generative AI can power Ireland’s reinvention Copyright © 2024 Accenture. All rights reserved. 22
A formula
for success
What can be done to
get Ireland’s gen AI-led
reinvention back on track?
Based on our experience of delivering over
1,000 gen AI projects globally, we see a
formula emerging for how organisations
can responsibly scale the use of gen AI:
‘Shifting from one-off efforts to real
innovation demands a more ambitious Imperative 1: Imperative 2: Imperative 3: Imperative 4: Imperative 5:
and integrated approach. While Lead with value Understand and Reinvent talent Close the gap on Drive continuous
many early adopters are focused on
develop an AI- and ways of responsible AI: reinvention:
Shift the focus from
building tech platforms and using
enabled, secure working:
the latest AI models, most have siloed use cases to Create governance Make the ability
overlooked the cultural, operational prioritising business digital core: Set and guide a vision structures and a culture to change a core
and business changes needed. Now capabilities across the Invest in technology for how to reinvent that operationalises competency and
is the time for organisations to entire value chain and that runs seamlessly work, reshape the AI responsibly, with part of company
address these areas to scale gen AI’ developing new, AI- and allows for workforce and prepare decision-making culture supported
enabled offerings. continuous creation of workers for a gen AI processes that by an ecosystem of
Denis Hannigan new capabilities. world. thoughtfully assess collaborators.
both the risks and
Data & AI Lead—UK and Ireland,
Strategy & Consulting rewards of the
Accenture
technology.
Generating growth: How generative AI can power Ireland’s reinvention Copyright © 2024 Accenture. All rights reserved. 23
The elements of the formula cannot be applied in isolation. AI’s multifaceted impact The constants of the formula
touches every part of the organisation, requiring all five imperatives to be addressed in a
mutually reinforcing way.
In applying the formula, organisations need to apply a consistent
Many early adopters of gen AI have focused primarily on building the technology. But set of principles:
the truth is, successful transformations are never just about tech. It’s essential to align
There should be clear alignment between business strategy and the extent of investment
technology, talent, governance and value roadmaps. Achieving this requires strong
needed across the five imperatives. Given the rapid evolution of gen AI and shifting
coordination across the entire business. Few organisations in Ireland are following this
business demands, adjustments must occur more frequently than in typical annual cycles.
formula—those that do will increase their chances of scaling gen AI.
Whether delivering a proof of concept for complaints handling or building a full gen AI
Based on our survey of business leaders in both the UK and Ireland, we segmented
program, the formula must be followed. This ensures no part of the transformation is
companies by their level of gen AI adoption.
overlooked, maintaining"
82,accenture,Accenture-Nordic-AI-Maturity-Report.pdf,"Nordic AI Maturity
Advancing from practice to performance
From insights to action, the path to extraordinary value starts here.
Contents
AI maturity: AI maturity: AI Achievers How AI Practice Key
Why it matters What it is advance from Achievers makes Capabilities
practice to master their progress
performance craft
07 11 17 21 34 40
The art of AI maturity—Advancing from practice to performance 2
Foreword by Per Österman
Since leaving the worst of the pandemic speed to insights, enabling clients to make
behind us, the European market has faster and better-informed decisions.
faced new challenges: ongoing war, an So, it is no surprise that 32% of Nordic
energy crisis and rising inflation. But we business leaders mentioned AI in their
have also seen technology and human earnings calls last year—and often saw
ingenuity help people persevere through their share prices increase, too.
difficult situations. One technology in
particular, artificial intelligence (AI), has It’s not a stretch to suggest that AI will
been applied in more ways than ever emerge as a crucial component of the EU
before—expediting immigration permits Commission’s digital future agenda. But
for refugees, advancing medical research we still have a long way to go. Though
and patient care, predictive maintenance, AI adoption has gained momentum, our
managing supply chain crises and research shows that only 6% of Nordic
innovating direct-to-consumer companies (compared to 12% of European
value chains. companies) can be categorized as AI
Achievers (companies leveraging AI’s
Pre-pandemic, AI adoption was full potential).
already in high gear in Europe, but the
transformational journey to maturity in The tenets that will help Nordic companies
AI is accelerating even more rapidly as progress in their AI transformation
investments in cloud grow. AI enables journeys and keep pace with the rest of
The art of AI maturity—Advancing from practice to performance 3
Europe include a strong cloud foundation Across Europe, AI is set to play a critical
to scale AI, alignment of strategy and role in alleviating sustainability concerns,
sponsorship, AI talent and culture, and the trade imbalances, supply chain issues
responsible deployment of AI. and regulation changes. AI, when used
strategically, can bring meaningful
The good news is that the groundwork change for people, the planet and profit
has already been laid—with 78% of centers—helping companies not only
organizations having reworked their survive economic downturns and related
strategy and cloud infrastructure plans in challenges, but also thrive despite them.
the last few years to achieve AI success. However, only those organizations that
With more companies in the Nordics invest in maturing their AI capabilities
experimenting with AI, the region is in now will be resilient enough to stamp
“pole position” to more than double their impact on our collective futures. As
the number of AI Achievers by 2025. In Winston Churchill once said, “Never let a
addition, upcoming EU AI regulations good crisis go to waste.”
will also formalize standards for AI
development, including generative AI 78%
technologies (like GPT-4 and DALL-E 2),
further building confidence and trust
in the sector’s potential and real-world
applications from AI practitioners and
of organizations
investors alike.
having reworked their
strategy and cloud
infrastructure plans
in the last few years
to achieve AI success.
The art of AI maturity—Advancing from practice to performance 4
Executive summary
Nordic companies are yet to take full advantage
of everything that AI can do
Artificial Intelligence is no longer hype— demand and win customers, Nordic still behind their European Achievers, who data that is AI relevant and increasing AI
we’re seeing practical implementations companies must work differently across aim to dedicate about one-third of their stakeholders within an organization, so
in the day-to-day business operations business and ecosystem partners. total IT budget towards data and AI by that they can reap the full benefit of AI
of Nordic companies. Specifically, AI 2024, creating more data- and AI-driven across the enterprise value chain.
is a key part of products and services Nordic countries rank high in the organizations in the process.
improving operational efficiencies vis-à-vis 2021 Government AI Readiness Index, Presently, the financial services, health
increasing productivity, enhancing worker according to “The Nordic AI and Data One way to catch up to continental & public services and communications
and customer experiences, and building Ecosystem, 2022.”i But executives competitors is enabling value chain & high-tech sectors are fast emerging
new synergies that were unimaginable a understand that without a significant optimization end-to-end. And our research as the front-runners increasing their AI
few years back. investment into AI from both the public shows that 48% of Nordic companies index in the next three years. But favorable
and private sectors, the Nordic AI are optimizing their use of data and economic and technology policies will
Each of those benefits has been shown landscape cannot be elevated on a global operating on enterprise-grade cloud help Nordic companies of all types make
to improve a business’s bottom line, too. AI map. Our research shows Nordic platforms. And 44% of Nordic companies significant leaps in their AI maturity.
According to Accenture’s AI Maturity companies are planning an increase of are actively addressing potential societal
– Nordic Survey, conducted between budget dedicated to developing and harms through techniques such as AI
September and October 2022, 15% of implementing AI products and services. design co-creation, stakeholder impact
participating companies said the return In 2019, Nordic companies devoted 10% assessments, consequence exercises,
on their AI initiatives exceeded their of their total technology budgets to AI, on human-AI interaction design guidelines
expectations, while only 1% of companies average, while in 2022 they devoted 19%. and environmental AI KPIs. But silos still
said the return did not meet expectations. That figure is expected to grow to 27% exist, so Nordic companies must continue
Still, it’s early days, and to further capture by 2025. However, Nordic companies are to encourage the sharing of experimental
The art of AI maturity—Advancing from practice to performance 5
What do AI Achievers do differently?
While there is clearly a science to AI, our findings demonstrate there is an art to AI
maturity. We discovered in our recent global AI research that AI Achievers are not
defined by the sophistication of any one capability, but by their ability to combine
strengths across strategy, processes and people.
Here are five ways AI Achievers master their craft:
1. Their top leaders champion AI as a strategic priority
for the entire organization.
2. They invest heavily in talent to get more from their
AI investments.
3. They industrialize AI tools and teams to create
a strong AI core.
4. They design AI responsibly, from the start.
5. They prioritize long- and short-term AI investments.
Further, our machine learning models suggest that the share of Nordic AI Achievers will
increase rapidly and significantly, more than doubling from the current 6% to 15%
by 2025.
In short, advancing AI maturity is no longer a choice. It is an opportunity for every
industry, every organization and every leader.
The art of AI maturity—Advancing from practice to performance 6
The art of AI maturity
AI maturity: Why it matters
AI maturity: Why it matters
From optimizing operations to freeing ownership of AI programs. Our survey
up workers to be more creative, AI of more than 100 C-suite executives and
offers businesses a range of benefits data science leaders from the Nordic
that are becoming essential elements region found that AI strategy is being
of competitive advantage. Intelligent, developed in the C-suite in new roles such
data-driven support systems can help as Chief Analytics Officers, Chief Data
businesses reimagine the way they build Officers and Chief Digital Officers, all of
new products, serve customers and whom are working in close collaboration
differentiate themselves in an increasingly with CEOs and board members.
competitive digital economy.
So, it’s no surprise that the percentage
As a result, we’re seeing close to half of of executives of the largest Nordic
Nordic companies shift their AI focus companies mentioning AI on their
from developing proofs of concept (PoCs) earnings calls is constantly growing
to launching AI-powered products and – up to 32% in 2022 from 21% in 2017.
services. And 78% of Nordic companies What’s more, in 2022, the largest Nordic
have adjusted their cloud strategy to companies were 6% more likely to see
align more closely with the progressing AI share prices increase when executives
strategy to achieve AI success discussed AI on earnings calls, up from
(compared to 73% of firms globally). just 1% in 2018. In 2021, this figure was a
staggering 36%, according to analysis by
This clearly reflects the way Nordic Accenture.ii
executives perceive AI: They’re taking
The art of AI maturity—Advancing from practice to performance 8
As always, execution is everything. And increasing operational efficiencies
based on our client experience, it is (43%), reimagining experience for Figure 1: Accenture projects that AI transformation will take less time than digital transformation
important that CEOs and senior leaders customers and workers (43%) and
focus on maturing their investment efforts seeking new growth opportunities
and strategies to drive value and achieve (40%) over the next three years.
desired AI outcomes. Timely and effective
action is key to realizing growth, seizing Companies in the Nordic countries •
market share and creating new value for are planning a significant increase of
all stakeholders. budget dedicated to developing and
implementing AI products and services
from 2022 to 2025: +43% (global
AI, accelerated
average: +37%).
With early successes building confidence Presently, only 11% of Nordic •
in AI as a value-driver, we estimate that AI companies dedicate more than 30%
transformation will happen much faster of their tech budgets to AI
than digital transformation—on average, development, but by 2025 33%
16 months faster (Figure 1). of companies intend to do so.
Our research suggests that Nordic
Source: Accenture Research
companies are making a significant and
Note: Our estimate is derived from a natural language processing analysis of investor calls of
timely push to strengthen their
the world’s 2,000 largest companies (by market cap), from 2010 to 2021, that referenced “AI”
AI capabilities:
and “Digital” in tandem with “business transformation,” respectively. Data was sourced from S&P
earnings transcripts.
Top priorities for Nordic companies
•
are developing AI products and
services (48%), improving ecosystem
partnerships for innovation (46%),
IA
gninoitnem
seinapmoc
egatnecreP
noitamrofsnart
ssenisub
rof
latigid
ro
Digital
transformation
90% AI
transformation
16 months
(13%)
9 years 8 years
11 months 7 months
10%
2010 2022 2030
The art of AI maturity—Advancing from practice to performance 9
There is incentive to move quickly. We Figure 2: Evolution of companies' AI-influenced revenue share – 2019, 2022, and 2025*
uncovered significant growth in Nordic
companies' AI-influenced revenue share
from 2019 to 2025. AI-influenced revenue
doubled between 2019-22 (8% in 2019
to 19% in 2022), and is predicted to triple
over the next three years (to 26% in 2025).
Note: Color indicates the achieved AI-influenced revenue threshold within each time period.
Source: Accenture AI Maturity Nordic Survey, September – October 2022
Note: *2025 = projected
*Definition of “AI-influenced” revenues:
a. a. Sales of existing products and services made possible through better AI-driven insights on customers, supply chain, channels; b. Sales of
new products and services made possible by human + AI, c. Higher prices through dynamic pricing ML algorithms. These sales include some
cannibalization and net new revenues. In contrast, this definition is excluding higher efficiencies in production operations thanks to AI.
)%(
erahs
eunever
decneuflni
-IA
8% 19% 26%
High
>30%
Medium
10%-30%
Low
<10%
2019 2022 2025
N=91
Legend:
High Medium Low
>30% 10%-30% <10%
The art of AI maturity—Advancing from practice to performance 10
The art of AI maturity
AI maturity: What it is
AI maturity: What it is
If most organizations are racing to embrace AI,
why are some seeing more value than others?
AI maturity measures the
To uncover strategies for AI success, not only in data and AI, but also in
Accenture designed a holistic AI-maturity organizational strategy, talent and
degree to which organizations
framework. Fittingly, our analysis itself was culture—to give companies a strong
conducted using AI. competitive advantage.
have mastered AI-related
We applied machine learning models This includes “foundational” AI
capabilities in the right
to unravel massive survey datasets and capabilities—like cloud platforms and
uncover drivers of AI maturity that would tools, data platforms, architecture, and combination to achieve high
have been impossible to detect using governance—that are required to keep
performance for customers,
more traditional analytical methods pace with competitors. It also includes
(more on the methodology in “differentiation” AI capabilities, like
shareholders and employees.
the Appendix). AI strategy and C-suite sponsorship,
combined with a culture of innovation that
Our research found that AI maturity can set companies apart.
comes down to mastering a set of key
capabilities in the right combinations—
The art of AI maturity—Advancing from practice to performance 12
The companies that scored best Figure 3: Only 6% of Nordic organizations are AI Achievers
in both categories are the AI Achievers.
AI Builders show strong foundational
capabilities and average differentiation
AI Innovators AI Achievers
capabilities, while AI Innovators show
12% 6%
strong differentiation capabilities and
Companies that have mature Companies that have differentiated
average foundational capabilities.
AI strategies but struggle AI strategies and the ability
to operationalize to operationalize for value
According to our research, only 6%
of the Nordic companies (vs. 12%
globally) have both the foundation and
differentiated strategy to fully capitalize
on the power of AI. These organizations
have advanced their AI maturity enough
to achieve superior growth and business
transformation. We call them AI Achievers.
AI Builders
1%
Achievers, Builders and Innovators
Companies that have mature
collectively represent just 19% (vs. 37%
foundational capabilities that
global) of the surveyed Nordic companies AI Experimenters exceed their AI strategies
81%
(Figure 3). These companies tend to have
Companies that lack mature AI strategies
more resources (such as technology,
and the capabilities to operationalize
talent and patents) to deliver on their AI
visions and transform their organizations.
AI FOUNDATION
Examples can be found across a wide
range of industries: Financial Services;
AI Foundational capabilities that are key drivers to achieving at least 10% AI influenced revenue
Source: Accenture Research analysis based on a sample of 91 Nordic companies. Thresholds are defined as top 25% of samples in both axes.
NOITAITNEREFFID
IA
gniveihca
fo
ledom
eht
morf
devired
seitilibapac
IA
eunever
decneuflni
IA
%03
tsael
ta
HIGH
HGIH
LOW
WOL
The art of AI maturity—Advancing from practice to performance 13
Health and Public Service (H&PS); According to our research, only in 1% of Figure 4: Levels of AI maturity by industry in Nordic ‘Now’ and ‘Future’ by median
Communications, Media and Technology Nordic companies do data and AI experts
(CMT); Resources (Energy, Natural work hand-in-hand with the business
The median AI Maturity Index in 2022 and 2025 by industry
Resources and Utilities); and Products leaders to drive the strategic agenda of
(Consumer Goods & Services, Retail, the organization. In those companies, data
Industrial Equipment, Automotive). science capabilities are fully integrated as 20 30 40 50 60 70
strategic capabilities. In addition, among
FS
A fourth group we are calling AI this 1%, a company’s strategic roadmap
Experimenters—those with average and budget are aligned with data science
H&PS
capabilities in both categories— initiatives at the C-Suite level.
make up the majority (81%) of those
Nordic 2022
CMT
surveyed. These numbers suggest that a
Nordic 2025
AI, applied
considerable number of companies may
struggle to make the foundational and Resources
cultural shifts needed to realize of the Currently, the Resources industry is ahead
promises of AI. of others sectors in its respective AI
Products
maturity.
In general, the Nordic region benefits
from a high degree of digital literacy and PiiA (Process Industrial IT and
a supportive tech infrastructure. But many Automation)iii and the Sustainable
Source: Accenture Research analysis based on a sample of 91 Nordic companies
Nordic companies see cultural challenges Process industry through Resource and
Note: *2025 = estimated scores. Industries’ AI maturity scores represent the arithmetic average
within organizations as a barrier in Energy Efficiency (SPIRE) have funded
of their respective Foundational and Differentiation index.
scaling AI (one of top 3 challenges for many European initiatives to develop
30% of respondents). Another barrier is AI in process industries, and continue
the inability to set up an organizational to support resource-based industries in
structure that supports continuous accelerating the development of AI.
innovation enabled through AI (25%).
The art of AI maturity—Advancing from practice to performance 14
Other sectors—FS, H&PS and CMT—have Public services in the Nordics are highly
so far been lagging because of legal and digitalized, and AI is seen to make
regulatory challenges, inadequate AI government more transparent, efficient
infrastructure and a shortage of AI-trained and accountable. It’s easy to point to
workers. However, these industries are examples of mature use cases, including
expected to make significant AI advances secure login solutions for government
and accelerate their AI maturity journey websites and banks across the region—
over the next three years. including NemID in Denmark, Bank ID in
Sweden and Norway, and the Platform
Fintech is a sector in which Nordic Altinn in Norway.
countries are considered AI pioneers
globally—but there is always room for As applications like this scale, the region
expansion and innovation. For instance, has the potential to quickly transform its
P27 is a new Nordic initiative to build the economy. And according to a 2020 report
world’s first real-time, intelligent cross- by DIGG, Sweden’s Digital Administration
border digital payment system in multiple Authority, AI is estimated to be able to
currencies. Here, AI will be used to create economic value in public activities
automate compliance and more quickly equivalent to SEK 140 billion annually (USD
detect fraud. $13 billion).v To support this growth, open
data initiatives in the public sector are
All Nordic countries place a high priority becoming more common—for example, in
on healthcare, and it is an important 2021, the Swedish government launched
sector for AI development within their an initiative to support municipalities
respective national AI strategies. Sweden to make better use of AI technologies,
and Denmark are particularly strong in backed by SEK 100 million in funding.vi
life sciences, while Finland has a strong
technology focus in the care delivery
sector—in hospitals, for example.iv
The art of AI maturity—Advancing from practice to performance 15
AI, applied across industries
A global home appliance manufacturer’s such as creating a consumer record to pilot, the company quickly realized a more
leadership had a clear agenda and enable and improve aftersales revenue, manageable, scalable and safer
vision for the future of the company— influencing supply chain improvements, inspection system.
they wanted to be data led. The CIO and improving forecasting and pricing
and CDO knew they needed to tackle through intelligent pricing.
Like its industry peers, a leading oil
the organizational complexity that was
and gas company had an ambition
impacting the company’s data pipeline. A major refining company has hundreds to achieve greater interoperability
To do this, the company launched a major of kilometers of oil and gas pipes in its
within its application landscapes
initiative to reshape the company’s data processing plants. Keeping this large and digital workflows. With a suite of
strategy, powered by AI and analytics. complex running smoothly means software from many different providers,
constantly monitoring the condition of the company began preparing to adopt
Working closely with the global CDO, the all pipes, tracking their maintenance Open Subsurface Data Universe (OSDU),
company and Accenture designed and and, most importantly, checking for a single unified platform that would
implemented the company’s three-year leaks—since leaks are not only costly, but enable the free exchange of data between
business and data strategy, enabling the also pose severe risks to safety and the applications. In just six weeks, OSDU was
company to capitalize on its potential environment. To ramp up inspections, established and populated with a variety
by using data to drive value through a extend the process to new pipelines, and of different data types—a critical first step
supply chain, sales and manufacturing reach its goals for efficiency, safety and that will allow the client to accelerate its
transformation. The manufacturer is environmental sustainability, the company OSDU journey.
now exploring ways to accelerate critical embarked on a proof of concept. And
data use cases throughout the business, with IoT, data and AI at the heart of its
The art of AI maturity—Advancing from practice to performance 16
The art of AI maturity
How do Nordic companies
compare with global peers
in their AI capabilities?
Nordic companies are like
Global AI Innovators Figure 5: Nordic Companies’ AI Capability Compared with Global Peers
Global
With strengths in digitalization, accessibility and
transparency, all Nordic countries are ready for the AI era, Achievers Builders Innovators Experimentrs Nordic average
as per research by the Oxford Insights on Government AI Strategy Senior Sponsorship
and
AI Strategy
Readiness Index. However, Nordic companies must further
Sponsorship
Proactive vs. Reactive
develop their data and AI capabilities before they can
Readily available AI And ML tools
compete with their global peers. Readily available developer networks
Data and Build vs. Buy
Currently, Nordic companies resemble global AI Innovators, AI Core Data management and governance - Change
Platform and technology
as they are defined by mature AI strategies but struggle
Data management and governance
to operationalize (i.e., they have strong differentiation Experimentation data - Change
capabilities and average foundational capabilities).
Talent & Mandatory training
Culture Employee competency in AI-related skills
Innovation culture embedded
Innovation culture encouraged
AI talent strategy
Responsible Responsible AI by design
AI
Responsible data & AI strategy
Out-perfocrming Under-performing
Source: Accenture AI Maturity Nordic Survey, September – October 2022
Note: Each square represents one of the 17 key capabilities. The square is filled in when the AI profile is outperforming against
peers (higher than the average across all companies in terms of % of companies reaching the mature level).
The art of AI maturity—Advancing from practice to performance 18
Our research shows that some Nordic Further, Nordic companies have low data
companies have a robust AI strategy in management and governance maturity,
place, developed and sponsored by senior which is critical to realizing the full value
leadership. These leaders are sensing of AI. Two-thirds (65%) of Nordic
the impact of AI on business and are companies are following the federated/
proactively helping their organizations disjoined data management, governance
leverage readily available AI and machine and integration systems. Companies
learning (ML) tools and developer are yet to take practical steps toward
networks that will help teams forming, documenting and implementing
innovate faster. data policies and procedures supporting
a data mesh. Considerable progress is
In addition, companies in the region are still required with respect to data quality,
embracing new training opportunities for model management and monitoring
all employees. And while leaders excel at scale.
at cultivating AI fluency and establishing
the innovation culture needed to drive Many Nordic companies are heavy
adoption, many organizations today lack users of cloud and realize the need to
the foundational capabilities required establish a robust centralized platform
to support AI at scale. More than 40% and technology foundation that forms
of Nordic companies have federated/ a backbone for enterprise-wide data
disjoined data processes, creating silos management capabilities. But few
across the enterprise value chain. Their have been through the complete cloud
workflows related to data and AI are still modernization journey. This is limiting the
scattered in nature, driven by function- full value companies get from their cloud
specific and project-specific requirements investment to support AI experimentation
rather than enterprise-wide processes. and innovation. Our research shows that
may soon change: eight out of 10
The art of AI maturity—Advancing from practice to performance 19
companies have to some extent reworked Transitioning from pilots to production Figure 6: Pilots across multiple business functions
their existing cloud strategies to be more is one of the key challenges for Nordic
in sync with their evolving AI strategy companies, according to the Nordic AI
Not started Early stage Full productization
and roadmap. and Data Ecosystem 2022 report.vii
Product Development
IT Security
But our research suggests that Nordic
Nordic organizations are organizations will move from pilots Production
to production at scale in the next few Procurement Global Achievers
pursuing pilots across
Global Experimenters
years, thanks to a robust private sector, Finance
Nordic Average
multiple functions
collaborative governments and a digitally Legal Risk
mature population with high trust in both Supply Chain
In the Nordic region, companies have a the private and public sectors.
Customer
clear vision to be data- and AI-driven but
HR
struggle to execute. They understand the Average of all functions 44 55 61
value of AI, but they do not know how to 0 25 50 75 100
Nordic n = 91
scale. Companies across industries are
conducting pilots in multiple functions,
Source: Accenture Research
but a large number of them are in
Note: Score 0-100, ranging from 0 = AI use case not started, 50 = AI use in early stage, 100 =
early stages.
having AI programs in place for full productization. The chart shows the difference in terms of
average score for AI use cases of different functions, between Achievers and other firms.
For Nordic companies, common barriers
Those differences can be statistically significant after controlling for industry, geography,
to scaling AI include cultural challenges
and company size.
within organizations, an inability to set up
an organizational structure that support
continuous AI-powered innovation and
the lack of viable cloud data solutions to
implement an AI strategy.
The art of AI maturity—Advancing from practice to performance 20
The art of AI maturity
How AI Achievers
master their craft
How AI Achievers
master their craft
It is worth noting that the potential for
AI-mature organizations will evolve
along with the technology itself. High
performance today will become business-
as-usual tomorrow.
Today’s AI Achievers have set the standard
and are poised to remain leaders. While
there is clearly a science to AI, they have
shown us there is also an art to AI maturity.
They have demonstrated that excellence
in areas like vision and culture are just
as critical as algorithmic integrity. Our
research uncovered five key success
factors for AI Achievers.
The art of AI maturity—Advancing from practice to performance 22
Success Factor 01
Champion AI as a strategic priority
for the entire organization, with full
sponsorship from leadership
Companies are realizing that AI is Over half of the Nordic companies in
increasingly becoming a must-have our survey say that they have created a
component to differentiate their platform through which all employees are
respective businesses, especially in a post- able to showcase failed and successful
pandemic economy. experiments, and seek constructive
feedback from leadership. In fact, 71%
Our research shows that, on average, 74% of the Nordic companies stated that
of Nordic companies have developed an innovation is integrated with their
AI strategy with strong sponsorship from organization’s vision, and that leaders hold
the CEO and/or the board members (vs. each other accountable.
78% of European Achievers and 56% of
European Experimenters). For the CEOs of AI Achievers, creating a
culture of innovation is itself a deliberate,
Our research also suggests that the best strategic move—one that is used as a
AI strategies tend to be bold, even when vehicle for experimentation and learning
they have modest beginnings. Bold AI across the organization. For them,
strategies, in turn, help spur innovation. innovation is a strategic discipline.
The art of AI maturity—Advancing from practice to performance 23
When one of the largest banks in the omni-channel experience for clients, and a
Nordic region was challenged with data-powered enterprise that is delivering
legacy technologies, siloed data, and no greater value to the company. The bank
single view of the customer, they knew has seen significant improvements in
they needed to act. For its data-powered ROI, and a 42% uplift through ML models
transformation, the team established and triggers. Significantly, the client's
a strategic roadmap, then worked to leadership can now focus on improving
architect and implement a data lake, the customer experience across the
advanced analytics powered by more user journey.
than 70 ML models, and a new campaign
management system. This included
42%
a distributed agile framework across
all geographies and time zones. The
company now benefits from an intelligent
marketing system that drives an
uplift through ML
models and triggers
The art of AI maturity—Advancing from practice to performance 24
Success Factor 02
Invest heavily in talent to get more
from AI investments
With a clear AI strategy and strong CEO companies are leveraging to a moderate
sponsorship, organizations are more likely or great extent the readily available data
to invest heavily in creating data and AI science talent and AI/ ML tools that will
fluency across their workforces. While AI help them innovate with data and AI.
proficiency must start at the top, it cannot
end there. Helsinki and Stockholm are among the
top 50 global AI hubs, even though the
We found that a whopping 87% of Nordic Nordics represent just 2% of global AI
companies say they have made AI talent, according to Silo Research and
trainings mandatory for most employees, OECD 2022 research. The private and
from product development engineers to public sectors have developed novel
C-suite executives (vs. 78% of European educational programs to develop digital
Achievers). Cleary, Nordic companies are and tech talent; for example, Finland’s
serious about developing AI talent and industry-specific ”AI for Built Environment”
upskilling their workforces—particularly certification course launched in November
around data engineering, AI/ML and 2021. Other examples include Sweden's
domain expertise. national “AI competence for Sweden”
curriculum, and “Elements of AI,” an online
As part of their current ecosystem course offered in all Nordic countries.viii
strategy, more than 80% of Nordic
The art of AI maturity—Advancing from practice to performance 25
Perhaps more than ever, it is important A global financial services firm, BBVA,
that companies address gaps in their enabled its digital journey with AI to create
AI talent and skills. Already, 75% of the intelligent data-driven banking operations
Nordic companies today have"
83,accenture,Accenture-Going-for-Growth.pdf,"Going
for growth
Navigating the great value
migration in the age of AI
Contents
The legacy of AI is already being written
Beyond the growing pains
Navigating the great value migration
Going for growth across all horizons
Sustainable growth is there for the taking
Going for growth: Navigating the great value migration in the age of AI 2
Authors
Jason Angelos Jon Edwards Nevine El-Warraky Chris Tomsovic
Senior Managing Director Managing Director Senior Managing Director Managing Director
Global lead, Corporate Corporate Strategy & Global lead, Industry & Global lead, Macro
Strategy & Growth Growth Customer Growth Foresight
Accenture Strategy Accenture Strategy Accenture Song Accenture Strategy
Contributor
Tomás Castagnino
Managing Director
Accenture Research
Going for growth: Navigating the great value migration in the age of AI 3
G2000 companies with the highest AI maturity see 4.7x higher
growth over a one-year period
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Introduction
The buzz surrounding artificial intelligence (AI) and Figure 1: Companies with greater AI maturity outperform their peers over time2
generative AI is at an all-time high. The S&P 500
Information Technology Index, which includes tech
leaders in AI innovation, boasts a staggering 46%
year-over-year growth rate and year-to-date returns
Top AI Index companies Bottom AI Index companies
nearing 32%.1 This surge is disproportionately
driven by the early successes and future growth
expectations of a handful of companies.
4.7x
The growth potential for companies delivering AI-based
growth
solutions and services to corporate buyers is clear. But where
is the opportunity and return for the great majority of other
companies that make up the Forbes Global 2000?
The success to date of top-performing AI adopters serves as
an indicator of the potential growth opportunities available
from AI and gen AI for everyone else. Accenture analysis
Company average by group,
reveals that, since 2022, companies with the greatest AI 2019 Q4 = 100, cumulative last four
quarters, inflation adjusted
maturity have been growing 3 percentage points more (or
4.7x) year over year than companies with the least maturity. For
the average G2000 company with revenues of $6 billion USD,
this translates into a cumulative revenue differential of $260
Source: Accenture analysis
million, or $360 million since 2019 (See Figure 1).
Note: AI maturity evaluated using Accenture’s AI Index. The index incorporates more than 30 individual metrics sourced from more than 10 diverse data sets,
encompassing a broad spectrum of industries, and spanning more than 2000 large companies worldwide. See endnote for more information.
Going for growth: Navigating the great value migration in the age of AI 4
Despite these positive signals, most companies Companies that successfully And with good reason: Accenture estimates that
have yet to see the commercial impact. They are more than US$10.3 trillion in additional economic
pursue AI-fueled reinvention
understandably in search of where the AI growth value can be unlocked by 2038 just by companies
outperform their peers in
opportunity is for them and when they might adopting gen AI alone and at scale.
share in the AI wealth. Our answer: Soon. top-line performance by
Others should follow their example because the
15%—a gap that is expected
number of AI initiatives focused on driving business
Accenture found that companies successfully
to more than double by 2026. growth is expected to increase six-fold by 2025. And
pursuing AI-fueled reinvention have delivered top-
by 2029, growth and expansion will be the dominant
line performance that outperforms their peers by
goal of AI at 67% of companies.4
15%. By 2026, this revenue growth gap is expected
to more than double to 37%. Additional Accenture
Exponential growth opportunities are
analysis indicates that companies with differentiated
horizons that involve expanding the core,
AI strategies operationalized for value experienced around the corner.
activating growth in adjacent areas, and
a 3x increase in total return to shareholders over a
establishing new revenue lines. These forward-
5-year period.3
thinking leaders are embracing the paradigm
shift that AI represents. Rather than waiting
While cost optimization and efficiency have been AI’s
and monitoring the risk of disruption, they are
early business headliners, AI is proving its potential
taking proactive steps to find new growth and
to elevate competitiveness, expand markets, and
outmaneuver their competition.
deliver exponential growth for companies.
Companies that effectively adopt AI will find and
capture new growth opportunities across three
Going for growth: Navigating the great value migration in the age of AI 5
The legacy of
AI is already
being written
Going for growth: Navigating the great value migration in the age of AI 6
The persistent state of flux in which Business leaders and investors are also The excitement around AI is largely driven by
businesses now operate has become the expressing confidence in the enduring nature rapidly evolving consumer preferences and
of AI. Mentions of AI in corporate earnings calls behaviors (which often outpace businesses’
new normal. Recent Accenture research
have soared by 370% since late 2022.6 We ability to respond), and heightenend
found that the rate of change affecting
found that 75% of companies are prioritizing expectations for what AI will mean for them.
businesses has soared 183% in the past
investments in data and AI (up from 56% in early Consumers, already enjoying AI benefits
five years—and by 33% in just the past
2023).7 And evidence abounds in how venture through interactions with chatbots or receiving
year. Geopolitical tensions and trade
capitalists are placing their strategic bets, in the hyper-personalized offers from companies, now
skirmishes, inflation, and the speed of rapid rise of AI-related corporate technology expect their businesses to use AI to anticipate
technological advances are just a few of spending, and in the expanding talent market for their needs and help them make confident,
the factors with which CEOs need AI-related jobs: straightforward purchasing decisions—
something 75% of consumers now struggle
to contend.
• VC funding flowing into AI-related fields with.11 It’s no surprise that some business
tripled in the past decade and now leaders are rushing to use AI to deliver more
On top of all this is the uncertainty and debate
represents 14% of total corporate VC deals.8 engaging and relevant products, services, and
surrounding AI’s ultimate impact on business
experiences, reducing consumers’ decision-
performance. Many experts and economists
• 91% of executives believe their organizations making stress. They believe customer loyalty
are quite bullish on AI’s potential. They believe
will be ready to scale up generative AI will depend on it. And revenue growth will
AI is here to stay, representing a fundamental
technologies by the end of 2024.9 follow.
shift in how businesses operate, innovate and
compete. Some of these experts boldly predict,
• AI-related job postings have more than
for example, that AI will produce a 6.1% GDP uplift
doubled since 2019.10
in the United States by 2034.5
Going for growth: Navigating the great value migration in the age of AI 7
Beyond the
growing pains
Going for growth: Navigating the great value migration in the age of AI 8
For every industry expert, analyst and CEO of non-tech companies that have the greatest potential change, and find and deliver new value opportunities
that champions AI for growth, an equal to benefit from AI adoption have, in fact, under- that were previously hidden. For them, AI is more
performed the broader stock market.13 Given these than a response to current market conditions. It is a
number are skeptical. A recent headline
arguments, it’s understandable that some leaders are proactive strategy to gain the foresight needed to
from The Economist contributed to this
may question when AI will deliver the anticipated value navigate today’s uncertainties and activate entirely
sentiment by asking “What happened to
they seek. new growth models.
the artificial intelligence revolution?” as
its writers observed the limited economic
But it’s remarkable to observe, little more than a year
impact from the technology thus far.12 since the release of Chat GPT-4, the meteoric rise Savvy business leaders are turning
in investment in, consideration of and activity with
to AI to help them assess markets,
There are also observations that only 5% of generative AI and AI more broadly. The market activity
anticipate change, and find and
businesses consistently use AI and despite its suggests something far surpassing a gradual multi-
potential productivity per employee in developed year trend and companies would be wise to get off deliver new value opportunities
nations has remained flat. Additionally, share prices the sidelines. While AI’s overall value to businesses
that were previously hidden.
continues to be debated, savvy business leaders are
turning to AI to help them assess markets, anticipate
Going for growth: Navigating the great value migration in the age of AI 9
Navigating
the great
value migration
Going for growth: Navigating the great value migration in the age of AI 10
The historical value that came Nearly half (45%) of executives In fact, Accenture’s latest research reveals that
from refining business models nearly 45% of executives say they are using AI
are using AI extensively to explore
extensively for new product and service ideas, to
and optimizing ways of working
new product and service ideas,
identify new markets, to scale innovation across
pales in comparison to the value
identify new markets, scale multiple enterprise systems and for other strategy-
available to organizations that
related decision making. These leaders are giving
innovation and improve their
use AI wisely. In the age of AI,
reason for others to believe in the power of AI.
business growth is no longer solely a strategic decision making.
More importantly, they are providing a path to
function of how well companies can follow, beyond the hype, to deliver value and find
continually strengthen their core new growth.
business capabilities or operating
Companies that make strategic investments
models. While it is true that AI can
Early-movers can innovate to address unmet
in AI such as these are able to pursue growth
accelerate those critical activities, needs and expand into new and adjacent
opportunities in three critical ways: by amplifying
it does so much more. It adds more markets—and do so with increased organizational
and accelerating core business capabilities and
certainty to the bets that leaders velocity. Companies with the AI maturity to shape
assets; by expanding reach into adjacent growth
and activate their growth strategies can free
place. It lowers barriers to market
areas; and by activating new and entrepreneurial
themselves from endless offering development
entry. And it allows businesses to
revenue models.
and testing cycles. This means they can
extend traditional markets and
deliver business outcomes faster. For example,
unlock thousands of new revenue-
generative AI is accelerating the time to market
generating opportunities. In short, it
for early adopters in consumer goods by 25-50%.
is radically changing where value is
found. And how it is pursued.
Going for growth: Navigating the great value migration in the age of AI 11
All of these pursuits are under way today, often simultaneously within individual companies. Each is
underpinned by a strong digital core (see sidebar). Where these opportunity areas differ is in the scale of
Creating a digital core that
benefits they can deliver and the time horizon over which full value is realized. Each horizon promises to
unlock exponentially more top-line value than the one prior (see Figure 2). powers growth
As AI ushers in a new era of growth, companies
need to take three actions to ensure their digital
core is reinvention ready.
Figure 2: Companies using AI to pursue growth across three horizons are best positioned to reap the
exponential rewards AI and gen AI offer.
Build an industry-leading digital core. Companies
that achieve “industry-leading” levels of digital
prowess in platforms, cloud, data, AI and other
capabilities can expect to achieve a 20% acceleration
Growth Entrepreneurs
of revenue growth and a 30% boost in profitability.
AI initiatives focused on launching entirely
new offerings and revenue models,
reshaping industry value chains and Boost investments in innovation. Digital leaders
delivering disproportionate growth
continuously increase the proportion of their IT
budgets dedicated to strategic innovation (in
Growth Expanders areas such as generative AI). Shifting just 6% in
spending from maintenance to innovation is a
AI initiatives focused on the pursuit of
adjacencies and of finding new ways to recipe for success.
reach new customers
Balance tech debt with investments. AI has
Growth Amplifiers become a leading contributor to technical debt. To
AI initiatives focused on enhancing the manage this debt, leading companies allocate 15% of
core business and generating incremental
their IT budget toward debt remediation, which allows
revenue now from existing customers
them “pay down debt” without sacrificing
their strategic investments.
Source: Accenture analysis; estimates percentage range of companies in each horizon 1) in 2024 and 2) in 3-5 years.
Going for growth: Navigating the great value migration in the age of AI 12
Growth
Amplifiers
Opportunity to accelerate the core business with AI.
The most immediate and obvious growth horizon involves using AI to amplify and accelerate the core
business. Companies pursuing these opportunities are growing their top-line revenues by reaching
underserved market segments and better anticipating the needs of existing buyers. For companies that use
AI to strengthen consumer engagement, it is a vital part of the connected front-office team, putting formerly
hidden or ignored information to work with interactions that are more personalized, engaging, profitable and
ultimately more human.
Take the example of food service giant Sysco. The company is using generative AI to boost revenues by
not only optimizing its merchandising and product assortment capabilities, but also providing shopping
recommendations.14 Then there’s Best Buy, which is improving satisfaction and loyalty by using AI to transcribe
and summarize contact center calls, equip call center agents with all the resources they need to address
customers’ needs, and even automate personalized follow-ups.15
Going for growth: Navigating the great value migration in the age of AI 13
Where it’s working
Banking on happy customers
Global financial services group BBVA has made By combining first-party data with new data
wise and targeted investments in AI, cloud and sources to deliver a step-by-step view of the
data to create a data-driven, engaging and customer journey, BBVA’s new digital sales
differentiated customer experience. Its decision model helps the bank prioritize sales initiatives
to use digital technologies to reach people in for new customers and cross-sell to existing
new ways resulted in a staggering 117% growth customers. Their strategy and investments
in new customers in the last few years and a have had massive, positive impact including
profit of more than €8 billion ($8.6 billion) in acquiring more than 11 million new customers in
2023, the highest earnings in the bank’s history. 2023 and experiencing a 100% growth in digital
sales over the last four years.
These results would not have been possible had
the bank not consistently invested in its digital
“Providing a differentiated, better
core, harnessing the power of cloud, data and
experience was like discovering a
AI to facilitate the rapid development of new
capabilities and insights. For example, bank- pot of gold.”
wide data, predictive analytics and business
intelligence deliver a holistic view of the current — David Puente, Global Head of Client
and lifetime profitability—and likely behavior—of Solutions, BBVA
every customer.
Going for growth: Navigating the great value migration in the age of AI 14
Growth
Expanders
Opportunity to pursue adjacencies with AI.
The second growth horizon for AI offers businesses the opportunity to expand into new markets and/or pursue
industry-adjacent value. Here, AI helps companies activate, reposition and extend their existing intellectual
property, assets and services in more market-relevant ways or develop new, connected, service-rich and
outcome-based solutions.
Retailers, for example, have developed new, high-margin revenue streams with their retail media networks.
They are using AI to bring together first-party data, attribution models, digital and in-store media platforms, and
strong brand relationships to target and reach shoppers in new ways. Target has already turned such media
platforms into a billion-dollar business, with other retailers on track to quickly pass this milestone as well.16 And
infrastructure company Equinix has partnered with Nvidia to offer secure “Private AI” to allow for on-premise AI
compute as a managed service.17
Going for growth: Navigating the great value migration in the age of AI 15
Where it’s working
Tapping into new opportunities
Ecolab, a global leader in water, hygiene Integrating AI into customer outreach, advisory
and energy technologies, has shifted from and briefing processes enables Ecolab to
selling traditionally discrete products such as extend its reach with existing customers and
detergents and water-treatment chemicals accelerate growth.
to connected digital solutions in multiple
areas—from water management and predictive “AI has enabled the Field to
maintenance to remote monitoring of systems
make decisions and it ensures
and processes.
their on-the-ground insights are
AI has played a big role in supporting Ecolab’s not lost within the organization.
digital reinvention, as well as its efforts to
We’ve seen [that translate into]
extend its reach and accelerate growth.
hundreds of millions of dollars
AI-enabled market analysis tools have, for
example, helped the company create a new of value creation and value
market-relevant customer value proposition for
potential.”
digital services that is currently being rolled out
in the market. Equally important, AI is making — Kevin Doyle, Chief Digital Officer,
it possible for sales teams and engineers to
Ecolab
tap 100 years of expertise to better meet
and anticipate customer needs and to tailor
programs for distinct customers.18
Going for growth: Navigating the great value migration in the age of AI 16
Growth
Entrepreneurs
Opportunity to activate new revenue models with AI.
Horizon three encompasses the most profound growth opportunities for companies. Companies making their
moves in this space employ AI to activate new and entrepreneurial revenue models at unprecedented speed.
These are the opportunities that have the greatest potential to reshape industry value chains and deliver
disproportionate growth over time. As just one example, consider the potential for life sciences companies to
reinvent healthcare. Johnson & Johnson (J&J) MedTech is aggressively pursuing new AI capabilities in general
surgery. Working with Nvidia, the company is already scaling AI solutions that accelerate access to real-time
insights, enable open innovation, and improve decision-making, education and collaboration across the
connected operating room.19
The truth is that companies in virtually all industries are already starting to explore how they might use AI to
activate new revenue models and innovations. Long development cycles previously made such models and
innovations impractical. AI makes them not only practical, but also critical enablers and accelerators of new and
sustainable growth.
Going for growth: Navigating the great value migration in the age of AI 17
Where it’s working
Revolutionizing beauty, creativity and growth
L’Oréal, the world’s leading beauty company, age. And by leveraging science and technology,
is using advanced science, data, AI and it is developing new innovations and pursuing
generative AI to connect more personally with new opportunities that will enable growth and a
customers and deliver transformative beauty sustainable competitive edge.20, 21
innovations that answer its customers’ unmet
needs. By integrating advanced technologies “L’Oréal is no longer simply
into its products and services, the company has
a company selling cosmetics
activated a shift from “beauty for all” to “beauty
products, but products and
for each.”
services.”
This move has not only enabled ultra
— Béatrice Dautzenberg, Global Director
personalized customer experiences, but also
of Beauty Tech Services, L’Oréal22
allowed the company to develop and monetize
a host of augmented products, smart devices,
mobile apps, online platforms and digital
services. From new AI-powered assistants
and diagnostic tools to handheld devices that
enable people to enjoy salon-quality hair color
at home, L’Oréal is re-imagining beauty in the AI
Going for growth: Navigating the great value migration in the age of AI 18
Going for growth
across all horizons
Going for growth: Navigating the great value migration in the age of AI 19
Unprecedented business uncertainty, market
disruptions and technological advances are forcing
business leaders to rethink their legacy revenue
models and their approaches to strategic business
Hyper-personalized Rapid market
planning. AI and Gen AI, employed in the right
ways, offer a promising path forward. Leaders can products, services and assessments
anticipate market movements and quickly connect experiences for adjacencies
with stakeholders in new ways. They can identify
emerging growth opportunities, value pools and
even risks with greater certainty. And they can
quickly activate new programs to drive exponential
and sustainable growth.
To take advantage of AI’s growth
potential across all horizons, we
recommend leaders take action in
Dynamic Generative
four key opportunity areas today:
planning design
for perpetual for product
growth engine development and
innovation
Going for growth: Navigating the great value migration in the age of AI 20
01 Generate hyper-personalized experiences to expand
the core
One of the most effective ways to strengthen a
business’s core revenue-generating activities
involves developing hyper-personalized products,
Actions for leaders
services and experiences that cater to unique
preferences and needs. By maximizing the
• Build a strong data foundation that aggregates and analyzes data
relevance of their existing offerings, business
and enables generative AI to gain a deeper, real-time understanding of
leaders can increase customer satisfaction and stakeholder needs, preferences and value drivers.
lifetime value, achieve higher margins and enjoy
better market positioning. • Integrate generative AI into various touchpoints to help customers
throughout their journey, from early in their decision-making to follow-ups
that build lifetime loyalty.
Generative AI is a critical enabler for amplifying and
accelerating the core business. It provides the tools needed
• Continually monitor and optimize the performance of AI-driven
to analyze vast amounts of data, identify patterns, and create
personalization initiatives and use data-driven insights to refine them
personalized campaigns, recommendations and solutions
based on changing market dynamics.
that resonate with target audiences.
Going for growth: Navigating the great value migration in the age of AI 21
02 Generate rapid market assessments to find adjacencies
and extend reach
Leaders must also identify growth opportunities in
adjacent areas or among untapped market segments.
Using existing assets and new outcome-based
Actions for leaders
solutions are two important ways companies can
extend their reach beyond their core businesses.
• Use generative AI to aggregate and analyze data from diverse
sources to capture the voice of the market and uncover underserved or
Once again, generative AI can be invaluable to leaders looking
overlooked segments, trends and opportunities that may not be visible
to capitalize on such industry-adjacent growth opportunities. By
through traditional methods.
leveraging advanced AI capabilities, business leaders can generate
rapid market assessments to quickly analyze emerging trends,
• Apply generative AI to identify and evaluate adjacent markets that
determine market potential and discover new market
present opportunities for expansion and diversification. Generative AI is
segments, even in areas with limited data or insights. The
particularly well suited to simulate various market scenarios and assess
technology excels at processing vast amounts of data—from
the potential impact of different strategies.
market reports to social media trends to economic indicators—to
identify patterns and correlations that may not be immediately
• Establish ongoing AI-driven market assessments to keep track of
apparent. Such insights pave the way for leaders to set up and test
evolving trends and emerging opportunities. This proactive approach
new offerings, pricing structures or routes to market.
ensures that the business remains agile and can quickly capitalize on new
growth areas.
Going for growth: Navigating the great value migration in the age of AI 22
03 Leverage generative design to create new products
and services
Sixty percent of executives say it takes their company one
year or more to adapt to changing customer needs. In a world
in which expectations shift on a month-to-month basis, many
Actions for leaders
initiatives might become obsolete before they are even rolled
out. To meet continuously evolving demands, leaders need
• Examine all possible AI-fueled opportunities to create new revenue models, no
to develop new revenue models and launch new AI-driven
matter how ambitious or farfetched they may originally appear. Use AI to analyze
products, services and experiences at breakneck speed. market trends and human behaviors, identifying opportunities for new revenue
streams, with a clear view of ROI and an actionable plan to scale.
Generative AI, with its powerful generative design capabilities, makes it
possible for leaders to pioneer these new frontiers. For example, generative • Cultivate a portfolio of growth opportunities that balances potential returns,
AI can revolutionize the way companies approach product development feasibility and risk, and investments in talent and resources that will be needed to
and business model innovation. By leveraging advanced algorithms and bring each to fruition.
machine learning, AI can create countless design permutations, optimize
for specific parameters, and uncover novel solutions that humans might not • Leverage AI’s generative design capabilities to explore a wide range of design
easily envision. This capability allows companies to rapidly prototype and possibilities, accelerate the development process, rapidly prototype solutions, and
iterate new ideas, reduce time to market, and foster a culture of continuous refine and optimize products/services for performance, cost and sustainability,
innovation.
Beyond physical products, generative AI can help businesses activate
new revenue models—such as subscription services, pay-per-use
schemes, or all-digital offerings—tailored to evolving market dynamics and
technological advances.
Going for growth: Navigating the great value migration in the age of AI 23
04 Introduce dynamic planning to create a perpetual
growth engine
Regardless of the growth opportunities business leaders
pursue, they must utilize dynamic strategic planning
capabilities to navigate complexities and ensure a
Actions for leaders
perpetual growth engine is in place. Static and even
“rolling” strategic plans are no longer adequate. The
• Incorporate generative AI in the strategic planning process to help
pace of change companies face and the speed with
carry out continuous market assessments, scenario planning and
which they must react to (or anticipate) market forces forecasting—and to pressure test the most relevant opportunities.
demands an always-on approach. Continually adjust plans to align to shifting macro and market forces.
Enabled by a strong digital core, generative AI is fast becoming • Build a culture of adaptation in which people are willing to change and
indispensable in the creation of the adaptive strategic plans that embrace innovation. This involves communicating new insights and
are now needed. By harnessing vast amounts of historical data and strategic scenarios with key stakeholders to encourage leaders to seek
analogues, generative AI helps business leaders understand market new ways to create value.
forces and relationships in real time. It enables leaders to expand
their sphere of understanding to not only identify new value pools, • Establish a perpetual growth engine with a cross-functional value
but also model scenarios and launch growth initiatives at speed and navigation team that uses generative AI to actively monitor market
scale. Importantly, it also differentiates valuable market signals from dynamics, vet strategies, prioritize key initiatives and optimize results.
noise, helping to ensure that strategic decisions are based on high-
quality insights rather than extraneous data. The result? Informed
decisions that drive success.
Going for growth: Navigating the great value migration in the age of AI 24
Sustainable growth
is there for the taking
The unforgiving pace of change in the world today presents
a tremendous challenge for CEOs and other C-suite leaders.
But it also creates exciting opportunities for companies that
can proactively translate those challenges into sustained
competitive advantage and new growth. Until now, sustaining
value amid constant disruption has been difficult. But AI now
makes it possible.
Growth opportunities abound. Those that successfully pursue
them will think differently about the growth opportunities
before them—and how to find them and pursue them. They
will arm their organizations with new innovation capabilities.
And they will embrace AI as a key enabler of agility and
adaptability across the end-to-end growth lifecycle. Other
CEOs should follow their lead.
Going for growth: Navigating the great value migration in the age of AI 25
References
1 2024 S&P Dow Jones Indices, June 20, 2024. 12 “What happened to the artificial-intelligence revolution?” The Economist, July 6,
2 AI maturity was evaluated using Accenture’s AI Index. This index incorporates 2024.
more than 30 individual metrics sourced from more than 10 diverse data sets, 13 ibid
encompassing a broad spectrum of industries, and spanning more than 2000 14 Beth Stackpole, “Incorporating generative AI into your company’s technology
large companies worldwide. The index framework encompasses not only AI- strategy,” MIT Sloan School of Management, February 27, 2024.
related metrics but also the vital capabilities essential for companies to foster and 15 Best Buy, “How Best Buy is using generative AI to create better customer support
scale their AI endeavors: Strategic AI signaling, AI assets, Tech foundations, and experiences,” April 9, 2024.
Talent & Culture enablers. The 0-100 AI index reflects the company’s percentile 16 David Doty, “Walmart, Target and Other Mega-Retailers Leverage First-Party to
ranking position in its industry. The Top AI Index includes companies in the top Become new Media Giants,” Forbes, April 26, 2022.
quartile, and the Bottom AI Index includes companies in the bottom quartile. 17 Equinix, “Equinix Private AI with NVIDIA DGX—Turnkey, ready-to-run AI
Revenues have been adjusted by inflation to ensure comparability across time. development platform.”
3 Accenture Research analysis, 2024. Period 2017-2022. 18 Chris Stokel-Walker, “How one water-management company is using AI to unlock
4 Arielle Feger, “CEOs expect efficiency, cost savings from generative AI by 2025,” insights from its 100-year past,” Business Insider, June 18, 2024.
eMarketer, July 5, 2024. 19 Johnson & Johnson, “Johnson & Johnson MedTech working with NVIDIA to scale
5 Allison Nathan (ed.), “Gen AI: Too much spend, too little benefit?” Goldman Sachs AI for surgery,” March 18, 2024.
Global Investment Research (Issue 129), June 25, 2024. 20 L’Oréal, “L’Oréal accelerates Beauty Tech leadership with advanced bioprinted
6 Accent"
84,accenture,Accenture-Going-for-Growth(1).pdf,"Going
for growth
Navigating the great value
migration in the age of AI
Contents
The legacy of AI is already being written
Beyond the growing pains
Navigating the great value migration
Going for growth across all horizons
Sustainable growth is there for the taking
Going for growth: Navigating the great value migration in the age of AI 2
Authors
Jason Angelos Jon Edwards Nevine El-Warraky Chris Tomsovic
Senior Managing Director Managing Director Senior Managing Director Managing Director
Global lead, Corporate Corporate Strategy & Global lead, Industry & Global lead, Macro
Strategy & Growth Growth Customer Growth Foresight
Accenture Strategy Accenture Strategy Accenture Song Accenture Strategy
Contributor
Tomás Castagnino
Managing Director
Accenture Research
Going for growth: Navigating the great value migration in the age of AI 3
G2000 companies with the highest AI maturity see 4.7x higher
growth over a one-year period
115
110
105
100
95
90
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Introduction
The buzz surrounding artificial intelligence (AI) and Figure 1: Companies with greater AI maturity outperform their peers over time2
generative AI is at an all-time high. The S&P 500
Information Technology Index, which includes tech
leaders in AI innovation, boasts a staggering 46%
year-over-year growth rate and year-to-date returns
Top AI Index companies Bottom AI Index companies
nearing 32%.1 This surge is disproportionately
driven by the early successes and future growth
expectations of a handful of companies.
4.7x
The growth potential for companies delivering AI-based
growth
solutions and services to corporate buyers is clear. But where
is the opportunity and return for the great majority of other
companies that make up the Forbes Global 2000?
The success to date of top-performing AI adopters serves as
an indicator of the potential growth opportunities available
from AI and gen AI for everyone else. Accenture analysis
Company average by group,
reveals that, since 2022, companies with the greatest AI 2019 Q4 = 100, cumulative last four
quarters, inflation adjusted
maturity have been growing 3 percentage points more (or
4.7x) year over year than companies with the least maturity. For
the average G2000 company with revenues of $6 billion USD,
this translates into a cumulative revenue differential of $260
Source: Accenture analysis
million, or $360 million since 2019 (See Figure 1).
Note: AI maturity evaluated using Accenture’s AI Index. The index incorporates more than 30 individual metrics sourced from more than 10 diverse data sets,
encompassing a broad spectrum of industries, and spanning more than 2000 large companies worldwide. See endnote for more information.
Going for growth: Navigating the great value migration in the age of AI 4
Despite these positive signals, most companies Companies that successfully And with good reason: Accenture estimates that
have yet to see the commercial impact. They are more than US$10.3 trillion in additional economic
pursue AI-fueled reinvention
understandably in search of where the AI growth value can be unlocked by 2038 just by companies
outperform their peers in
opportunity is for them and when they might adopting gen AI alone and at scale.
share in the AI wealth. Our answer: Soon. top-line performance by
Others should follow their example because the
15%—a gap that is expected
number of AI initiatives focused on driving business
Accenture found that companies successfully
to more than double by 2026. growth is expected to increase six-fold by 2025. And
pursuing AI-fueled reinvention have delivered top-
by 2029, growth and expansion will be the dominant
line performance that outperforms their peers by
goal of AI at 67% of companies.4
15%. By 2026, this revenue growth gap is expected
to more than double to 37%. Additional Accenture
Exponential growth opportunities are
analysis indicates that companies with differentiated
horizons that involve expanding the core,
AI strategies operationalized for value experienced around the corner.
activating growth in adjacent areas, and
a 3x increase in total return to shareholders over a
establishing new revenue lines. These forward-
5-year period.3
thinking leaders are embracing the paradigm
shift that AI represents. Rather than waiting
While cost optimization and efficiency have been AI’s
and monitoring the risk of disruption, they are
early business headliners, AI is proving its potential
taking proactive steps to find new growth and
to elevate competitiveness, expand markets, and
outmaneuver their competition.
deliver exponential growth for companies.
Companies that effectively adopt AI will find and
capture new growth opportunities across three
Going for growth: Navigating the great value migration in the age of AI 5
The legacy of
AI is already
being written
Going for growth: Navigating the great value migration in the age of AI 6
The persistent state of flux in which Business leaders and investors are also The excitement around AI is largely driven by
businesses now operate has become the expressing confidence in the enduring nature rapidly evolving consumer preferences and
of AI. Mentions of AI in corporate earnings calls behaviors (which often outpace businesses’
new normal. Recent Accenture research
have soared by 370% since late 2022.6 We ability to respond), and heightenend
found that the rate of change affecting
found that 75% of companies are prioritizing expectations for what AI will mean for them.
businesses has soared 183% in the past
investments in data and AI (up from 56% in early Consumers, already enjoying AI benefits
five years—and by 33% in just the past
2023).7 And evidence abounds in how venture through interactions with chatbots or receiving
year. Geopolitical tensions and trade
capitalists are placing their strategic bets, in the hyper-personalized offers from companies, now
skirmishes, inflation, and the speed of rapid rise of AI-related corporate technology expect their businesses to use AI to anticipate
technological advances are just a few of spending, and in the expanding talent market for their needs and help them make confident,
the factors with which CEOs need AI-related jobs: straightforward purchasing decisions—
something 75% of consumers now struggle
to contend.
• VC funding flowing into AI-related fields with.11 It’s no surprise that some business
tripled in the past decade and now leaders are rushing to use AI to deliver more
On top of all this is the uncertainty and debate
represents 14% of total corporate VC deals.8 engaging and relevant products, services, and
surrounding AI’s ultimate impact on business
experiences, reducing consumers’ decision-
performance. Many experts and economists
• 91% of executives believe their organizations making stress. They believe customer loyalty
are quite bullish on AI’s potential. They believe
will be ready to scale up generative AI will depend on it. And revenue growth will
AI is here to stay, representing a fundamental
technologies by the end of 2024.9 follow.
shift in how businesses operate, innovate and
compete. Some of these experts boldly predict,
• AI-related job postings have more than
for example, that AI will produce a 6.1% GDP uplift
doubled since 2019.10
in the United States by 2034.5
Going for growth: Navigating the great value migration in the age of AI 7
Beyond the
growing pains
Going for growth: Navigating the great value migration in the age of AI 8
For every industry expert, analyst and CEO of non-tech companies that have the greatest potential change, and find and deliver new value opportunities
that champions AI for growth, an equal to benefit from AI adoption have, in fact, under- that were previously hidden. For them, AI is more
performed the broader stock market.13 Given these than a response to current market conditions. It is a
number are skeptical. A recent headline
arguments, it’s understandable that some leaders are proactive strategy to gain the foresight needed to
from The Economist contributed to this
may question when AI will deliver the anticipated value navigate today’s uncertainties and activate entirely
sentiment by asking “What happened to
they seek. new growth models.
the artificial intelligence revolution?” as
its writers observed the limited economic
But it’s remarkable to observe, little more than a year
impact from the technology thus far.12 since the release of Chat GPT-4, the meteoric rise Savvy business leaders are turning
in investment in, consideration of and activity with
to AI to help them assess markets,
There are also observations that only 5% of generative AI and AI more broadly. The market activity
anticipate change, and find and
businesses consistently use AI and despite its suggests something far surpassing a gradual multi-
potential productivity per employee in developed year trend and companies would be wise to get off deliver new value opportunities
nations has remained flat. Additionally, share prices the sidelines. While AI’s overall value to businesses
that were previously hidden.
continues to be debated, savvy business leaders are
turning to AI to help them assess markets, anticipate
Going for growth: Navigating the great value migration in the age of AI 9
Navigating
the great
value migration
Going for growth: Navigating the great value migration in the age of AI 10
The historical value that came Nearly half (45%) of executives In fact, Accenture’s latest research reveals that
from refining business models nearly 45% of executives say they are using AI
are using AI extensively to explore
extensively for new product and service ideas, to
and optimizing ways of working
new product and service ideas,
identify new markets, to scale innovation across
pales in comparison to the value
identify new markets, scale multiple enterprise systems and for other strategy-
available to organizations that
related decision making. These leaders are giving
innovation and improve their
use AI wisely. In the age of AI,
reason for others to believe in the power of AI.
business growth is no longer solely a strategic decision making.
More importantly, they are providing a path to
function of how well companies can follow, beyond the hype, to deliver value and find
continually strengthen their core new growth.
business capabilities or operating
Companies that make strategic investments
models. While it is true that AI can
Early-movers can innovate to address unmet
in AI such as these are able to pursue growth
accelerate those critical activities, needs and expand into new and adjacent
opportunities in three critical ways: by amplifying
it does so much more. It adds more markets—and do so with increased organizational
and accelerating core business capabilities and
certainty to the bets that leaders velocity. Companies with the AI maturity to shape
assets; by expanding reach into adjacent growth
and activate their growth strategies can free
place. It lowers barriers to market
areas; and by activating new and entrepreneurial
themselves from endless offering development
entry. And it allows businesses to
revenue models.
and testing cycles. This means they can
extend traditional markets and
deliver business outcomes faster. For example,
unlock thousands of new revenue-
generative AI is accelerating the time to market
generating opportunities. In short, it
for early adopters in consumer goods by 25-50%.
is radically changing where value is
found. And how it is pursued.
Going for growth: Navigating the great value migration in the age of AI 11
All of these pursuits are under way today, often simultaneously within individual companies. Each is
underpinned by a strong digital core (see sidebar). Where these opportunity areas differ is in the scale of
Creating a digital core that
benefits they can deliver and the time horizon over which full value is realized. Each horizon promises to
unlock exponentially more top-line value than the one prior (see Figure 2). powers growth
As AI ushers in a new era of growth, companies
need to take three actions to ensure their digital
core is reinvention ready.
Figure 2: Companies using AI to pursue growth across three horizons are best positioned to reap the
exponential rewards AI and gen AI offer.
Build an industry-leading digital core. Companies
that achieve “industry-leading” levels of digital
prowess in platforms, cloud, data, AI and other
capabilities can expect to achieve a 20% acceleration
Growth Entrepreneurs
of revenue growth and a 30% boost in profitability.
AI initiatives focused on launching entirely
new offerings and revenue models,
reshaping industry value chains and Boost investments in innovation. Digital leaders
delivering disproportionate growth
continuously increase the proportion of their IT
budgets dedicated to strategic innovation (in
Growth Expanders areas such as generative AI). Shifting just 6% in
spending from maintenance to innovation is a
AI initiatives focused on the pursuit of
adjacencies and of finding new ways to recipe for success.
reach new customers
Balance tech debt with investments. AI has
Growth Amplifiers become a leading contributor to technical debt. To
AI initiatives focused on enhancing the manage this debt, leading companies allocate 15% of
core business and generating incremental
their IT budget toward debt remediation, which allows
revenue now from existing customers
them “pay down debt” without sacrificing
their strategic investments.
Source: Accenture analysis; estimates percentage range of companies in each horizon 1) in 2024 and 2) in 3-5 years.
Going for growth: Navigating the great value migration in the age of AI 12
Growth
Amplifiers
Opportunity to accelerate the core business with AI.
The most immediate and obvious growth horizon involves using AI to amplify and accelerate the core
business. Companies pursuing these opportunities are growing their top-line revenues by reaching
underserved market segments and better anticipating the needs of existing buyers. For companies that use
AI to strengthen consumer engagement, it is a vital part of the connected front-office team, putting formerly
hidden or ignored information to work with interactions that are more personalized, engaging, profitable and
ultimately more human.
Take the example of food service giant Sysco. The company is using generative AI to boost revenues by
not only optimizing its merchandising and product assortment capabilities, but also providing shopping
recommendations.14 Then there’s Best Buy, which is improving satisfaction and loyalty by using AI to transcribe
and summarize contact center calls, equip call center agents with all the resources they need to address
customers’ needs, and even automate personalized follow-ups.15
Going for growth: Navigating the great value migration in the age of AI 13
Where it’s working
Banking on happy customers
Global financial services group BBVA has made By combining first-party data with new data
wise and targeted investments in AI, cloud and sources to deliver a step-by-step view of the
data to create a data-driven, engaging and customer journey, BBVA’s new digital sales
differentiated customer experience. Its decision model helps the bank prioritize sales initiatives
to use digital technologies to reach people in for new customers and cross-sell to existing
new ways resulted in a staggering 117% growth customers. Their strategy and investments
in new customers in the last few years and a have had massive, positive impact including
profit of more than €8 billion ($8.6 billion) in acquiring more than 11 million new customers in
2023, the highest earnings in the bank’s history. 2023 and experiencing a 100% growth in digital
sales over the last four years.
These results would not have been possible had
the bank not consistently invested in its digital
“Providing a differentiated, better
core, harnessing the power of cloud, data and
experience was like discovering a
AI to facilitate the rapid development of new
capabilities and insights. For example, bank- pot of gold.”
wide data, predictive analytics and business
intelligence deliver a holistic view of the current — David Puente, Global Head of Client
and lifetime profitability—and likely behavior—of Solutions, BBVA
every customer.
Going for growth: Navigating the great value migration in the age of AI 14
Growth
Expanders
Opportunity to pursue adjacencies with AI.
The second growth horizon for AI offers businesses the opportunity to expand into new markets and/or pursue
industry-adjacent value. Here, AI helps companies activate, reposition and extend their existing intellectual
property, assets and services in more market-relevant ways or develop new, connected, service-rich and
outcome-based solutions.
Retailers, for example, have developed new, high-margin revenue streams with their retail media networks.
They are using AI to bring together first-party data, attribution models, digital and in-store media platforms, and
strong brand relationships to target and reach shoppers in new ways. Target has already turned such media
platforms into a billion-dollar business, with other retailers on track to quickly pass this milestone as well.16 And
infrastructure company Equinix has partnered with Nvidia to offer secure “Private AI” to allow for on-premise AI
compute as a managed service.17
Going for growth: Navigating the great value migration in the age of AI 15
Where it’s working
Tapping into new opportunities
Ecolab, a global leader in water, hygiene Integrating AI into customer outreach, advisory
and energy technologies, has shifted from and briefing processes enables Ecolab to
selling traditionally discrete products such as extend its reach with existing customers and
detergents and water-treatment chemicals accelerate growth.
to connected digital solutions in multiple
areas—from water management and predictive “AI has enabled the Field to
maintenance to remote monitoring of systems
make decisions and it ensures
and processes.
their on-the-ground insights are
AI has played a big role in supporting Ecolab’s not lost within the organization.
digital reinvention, as well as its efforts to
We’ve seen [that translate into]
extend its reach and accelerate growth.
hundreds of millions of dollars
AI-enabled market analysis tools have, for
example, helped the company create a new of value creation and value
market-relevant customer value proposition for
potential.”
digital services that is currently being rolled out
in the market. Equally important, AI is making — Kevin Doyle, Chief Digital Officer,
it possible for sales teams and engineers to
Ecolab
tap 100 years of expertise to better meet
and anticipate customer needs and to tailor
programs for distinct customers.18
Going for growth: Navigating the great value migration in the age of AI 16
Growth
Entrepreneurs
Opportunity to activate new revenue models with AI.
Horizon three encompasses the most profound growth opportunities for companies. Companies making their
moves in this space employ AI to activate new and entrepreneurial revenue models at unprecedented speed.
These are the opportunities that have the greatest potential to reshape industry value chains and deliver
disproportionate growth over time. As just one example, consider the potential for life sciences companies to
reinvent healthcare. Johnson & Johnson (J&J) MedTech is aggressively pursuing new AI capabilities in general
surgery. Working with Nvidia, the company is already scaling AI solutions that accelerate access to real-time
insights, enable open innovation, and improve decision-making, education and collaboration across the
connected operating room.19
The truth is that companies in virtually all industries are already starting to explore how they might use AI to
activate new revenue models and innovations. Long development cycles previously made such models and
innovations impractical. AI makes them not only practical, but also critical enablers and accelerators of new and
sustainable growth.
Going for growth: Navigating the great value migration in the age of AI 17
Where it’s working
Revolutionizing beauty, creativity and growth
L’Oréal, the world’s leading beauty company, age. And by leveraging science and technology,
is using advanced science, data, AI and it is developing new innovations and pursuing
generative AI to connect more personally with new opportunities that will enable growth and a
customers and deliver transformative beauty sustainable competitive edge.20, 21
innovations that answer its customers’ unmet
needs. By integrating advanced technologies “L’Oréal is no longer simply
into its products and services, the company has
a company selling cosmetics
activated a shift from “beauty for all” to “beauty
products, but products and
for each.”
services.”
This move has not only enabled ultra
— Béatrice Dautzenberg, Global Director
personalized customer experiences, but also
of Beauty Tech Services, L’Oréal22
allowed the company to develop and monetize
a host of augmented products, smart devices,
mobile apps, online platforms and digital
services. From new AI-powered assistants
and diagnostic tools to handheld devices that
enable people to enjoy salon-quality hair color
at home, L’Oréal is re-imagining beauty in the AI
Going for growth: Navigating the great value migration in the age of AI 18
Going for growth
across all horizons
Going for growth: Navigating the great value migration in the age of AI 19
Unprecedented business uncertainty, market
disruptions and technological advances are forcing
business leaders to rethink their legacy revenue
models and their approaches to strategic business
Hyper-personalized Rapid market
planning. AI and Gen AI, employed in the right
ways, offer a promising path forward. Leaders can products, services and assessments
anticipate market movements and quickly connect experiences for adjacencies
with stakeholders in new ways. They can identify
emerging growth opportunities, value pools and
even risks with greater certainty. And they can
quickly activate new programs to drive exponential
and sustainable growth.
To take advantage of AI’s growth
potential across all horizons, we
recommend leaders take action in
Dynamic Generative
four key opportunity areas today:
planning design
for perpetual for product
growth engine development and
innovation
Going for growth: Navigating the great value migration in the age of AI 20
01 Generate hyper-personalized experiences to expand
the core
One of the most effective ways to strengthen a
business’s core revenue-generating activities
involves developing hyper-personalized products,
Actions for leaders
services and experiences that cater to unique
preferences and needs. By maximizing the
• Build a strong data foundation that aggregates and analyzes data
relevance of their existing offerings, business
and enables generative AI to gain a deeper, real-time understanding of
leaders can increase customer satisfaction and stakeholder needs, preferences and value drivers.
lifetime value, achieve higher margins and enjoy
better market positioning. • Integrate generative AI into various touchpoints to help customers
throughout their journey, from early in their decision-making to follow-ups
that build lifetime loyalty.
Generative AI is a critical enabler for amplifying and
accelerating the core business. It provides the tools needed
• Continually monitor and optimize the performance of AI-driven
to analyze vast amounts of data, identify patterns, and create
personalization initiatives and use data-driven insights to refine them
personalized campaigns, recommendations and solutions
based on changing market dynamics.
that resonate with target audiences.
Going for growth: Navigating the great value migration in the age of AI 21
02 Generate rapid market assessments to find adjacencies
and extend reach
Leaders must also identify growth opportunities in
adjacent areas or among untapped market segments.
Using existing assets and new outcome-based
Actions for leaders
solutions are two important ways companies can
extend their reach beyond their core businesses.
• Use generative AI to aggregate and analyze data from diverse
sources to capture the voice of the market and uncover underserved or
Once again, generative AI can be invaluable to leaders looking
overlooked segments, trends and opportunities that may not be visible
to capitalize on such industry-adjacent growth opportunities. By
through traditional methods.
leveraging advanced AI capabilities, business leaders can generate
rapid market assessments to quickly analyze emerging trends,
• Apply generative AI to identify and evaluate adjacent markets that
determine market potential and discover new market
present opportunities for expansion and diversification. Generative AI is
segments, even in areas with limited data or insights. The
particularly well suited to simulate various market scenarios and assess
technology excels at processing vast amounts of data—from
the potential impact of different strategies.
market reports to social media trends to economic indicators—to
identify patterns and correlations that may not be immediately
• Establish ongoing AI-driven market assessments to keep track of
apparent. Such insights pave the way for leaders to set up and test
evolving trends and emerging opportunities. This proactive approach
new offerings, pricing structures or routes to market.
ensures that the business remains agile and can quickly capitalize on new
growth areas.
Going for growth: Navigating the great value migration in the age of AI 22
03 Leverage generative design to create new products
and services
Sixty percent of executives say it takes their company one
year or more to adapt to changing customer needs. In a world
in which expectations shift on a month-to-month basis, many
Actions for leaders
initiatives might become obsolete before they are even rolled
out. To meet continuously evolving demands, leaders need
• Examine all possible AI-fueled opportunities to create new revenue models, no
to develop new revenue models and launch new AI-driven
matter how ambitious or farfetched they may originally appear. Use AI to analyze
products, services and experiences at breakneck speed. market trends and human behaviors, identifying opportunities for new revenue
streams, with a clear view of ROI and an actionable plan to scale.
Generative AI, with its powerful generative design capabilities, makes it
possible for leaders to pioneer these new frontiers. For example, generative • Cultivate a portfolio of growth opportunities that balances potential returns,
AI can revolutionize the way companies approach product development feasibility and risk, and investments in talent and resources that will be needed to
and business model innovation. By leveraging advanced algorithms and bring each to fruition.
machine learning, AI can create countless design permutations, optimize
for specific parameters, and uncover novel solutions that humans might not • Leverage AI’s generative design capabilities to explore a wide range of design
easily envision. This capability allows companies to rapidly prototype and possibilities, accelerate the development process, rapidly prototype solutions, and
iterate new ideas, reduce time to market, and foster a culture of continuous refine and optimize products/services for performance, cost and sustainability,
innovation.
Beyond physical products, generative AI can help businesses activate
new revenue models—such as subscription services, pay-per-use
schemes, or all-digital offerings—tailored to evolving market dynamics and
technological advances.
Going for growth: Navigating the great value migration in the age of AI 23
04 Introduce dynamic planning to create a perpetual
growth engine
Regardless of the growth opportunities business leaders
pursue, they must utilize dynamic strategic planning
capabilities to navigate complexities and ensure a
Actions for leaders
perpetual growth engine is in place. Static and even
“rolling” strategic plans are no longer adequate. The
• Incorporate generative AI in the strategic planning process to help
pace of change companies face and the speed with
carry out continuous market assessments, scenario planning and
which they must react to (or anticipate) market forces forecasting—and to pressure test the most relevant opportunities.
demands an always-on approach. Continually adjust plans to align to shifting macro and market forces.
Enabled by a strong digital core, generative AI is fast becoming • Build a culture of adaptation in which people are willing to change and
indispensable in the creation of the adaptive strategic plans that embrace innovation. This involves communicating new insights and
are now needed. By harnessing vast amounts of historical data and strategic scenarios with key stakeholders to encourage leaders to seek
analogues, generative AI helps business leaders understand market new ways to create value.
forces and relationships in real time. It enables leaders to expand
their sphere of understanding to not only identify new value pools, • Establish a perpetual growth engine with a cross-functional value
but also model scenarios and launch growth initiatives at speed and navigation team that uses generative AI to actively monitor market
scale. Importantly, it also differentiates valuable market signals from dynamics, vet strategies, prioritize key initiatives and optimize results.
noise, helping to ensure that strategic decisions are based on high-
quality insights rather than extraneous data. The result? Informed
decisions that drive success.
Going for growth: Navigating the great value migration in the age of AI 24
Sustainable growth
is there for the taking
The unforgiving pace of change in the world today presents
a tremendous challenge for CEOs and other C-suite leaders.
But it also creates exciting opportunities for companies that
can proactively translate those challenges into sustained
competitive advantage and new growth. Until now, sustaining
value amid constant disruption has been difficult. But AI now
makes it possible.
Growth opportunities abound. Those that successfully pursue
them will think differently about the growth opportunities
before them—and how to find them and pursue them. They
will arm their organizations with new innovation capabilities.
And they will embrace AI as a key enabler of agility and
adaptability across the end-to-end growth lifecycle. Other
CEOs should follow their lead.
Going for growth: Navigating the great value migration in the age of AI 25
References
1 2024 S&P Dow Jones Indices, June 20, 2024. 12 “What happened to the artificial-intelligence revolution?” The Economist, July 6,
2 AI maturity was evaluated using Accenture’s AI Index. This index incorporates 2024.
more than 30 individual metrics sourced from more than 10 diverse data sets, 13 ibid
encompassing a broad spectrum of industries, and spanning more than 2000 14 Beth Stackpole, “Incorporating generative AI into your company’s technology
large companies worldwide. The index framework encompasses not only AI- strategy,” MIT Sloan School of Management, February 27, 2024.
related metrics but also the vital capabilities essential for companies to foster and 15 Best Buy, “How Best Buy is using generative AI to create better customer support
scale their AI endeavors: Strategic AI signaling, AI assets, Tech foundations, and experiences,” April 9, 2024.
Talent & Culture enablers. The 0-100 AI index reflects the company’s percentile 16 David Doty, “Walmart, Target and Other Mega-Retailers Leverage First-Party to
ranking position in its industry. The Top AI Index includes companies in the top Become new Media Giants,” Forbes, April 26, 2022.
quartile, and the Bottom AI Index includes companies in the bottom quartile. 17 Equinix, “Equinix Private AI with NVIDIA DGX—Turnkey, ready-to-run AI
Revenues have been adjusted by inflation to ensure comparability across time. development platform.”
3 Accenture Research analysis, 2024. Period 2017-2022. 18 Chris Stokel-Walker, “How one water-management company is using AI to unlock
4 Arielle Feger, “CEOs expect efficiency, cost savings from generative AI by 2025,” insights from its 100-year past,” Business Insider, June 18, 2024.
eMarketer, July 5, 2024. 19 Johnson & Johnson, “Johnson & Johnson MedTech working with NVIDIA to scale
5 Allison Nathan (ed.), “Gen AI: Too much spend, too little benefit?” Goldman Sachs AI for surgery,” March 18, 2024.
Global Investment Research (Issue 129), June 25, 2024. 20 L’Oréal, “L’Oréal accelerates Beauty Tech leadership with advanced bioprinted
6 Accent"
85,accenture,Accenture-A-New-Era-of-Generative-AI-for-Everyone.pdf,"A new era of
generative AI
for everyone
The technology underpinning
ChatGPT will transform work
and reinvent business
Table of 03
Welcome to AI’s new inflection point
Contents
04
How did we get here? | Milestones in the journey to generative AI
05
Consume or customize: Generative AI for everyone
08
A look ahead at the fast-paced evolution of technology, regulation and business
12
Embrace the generative AI era: Six adoption essentials
19
The future of AI is accelerating
21
Glossary and References
22
Authors
A new era of generative AI for everyone | 2
Introduction
Welcome to AI’s new inflection point
ChatGPT has woken up the world to A foundation model is a generic term for Business leaders recognize the significance
the transformative potential of artificial large models with billions of parameters. With of this moment. They can see how LLMs
intelligence (AI), capturing global attention recent advances, companies can now build and generative AI will fundamentally
and sparking a wave of creativity rarely seen specialized image- and language-generating transform everything from business, to
before. Its ability to mimic human dialogue models on top of these foundation models. science, to society itself—unlocking new
and decision-making has given us AI’s first Large language models (LLMs) are both performance frontiers. The positive impact
true inflection point in public adoption. a type of generative AI and a type of on human creativity and productivity will be
Finally, everyone, everywhere can see the foundation model. massive. Consider that, across all industries,
technology’s true disruptive potential for Accenture found 40% of all working hours
themselves. The LLMs behind ChatGPT mark a significant can be impacted by LLMs like GPT-4. This
turning point and milestone in artificial is because language tasks account for 62%
intelligence. Two things make LLMs game of the total time employees work, and 65%
ChatGPT reached 100 million monthly changing. First, they’ve cracked the code on of that time can be transformed into more
active users just two months after launch, language complexity. Now, for the first time, productive activity through augmentation
making it the fastest-growing consumer
machines can learn language, context and and automation (see Figure 3).
application in history.1
intent and be independently generative and
creative. Second, after being pre-trained
on vast quantities of data (text, images or
audio), these models can be adapted or fine-
tuned for a wide range of tasks. This allows
them to be reused or repurposed in many
different ways.
A new era of generative AI for everyone | 3
How did we
Machine learning: Analysis and prediction phase
The first decade of the 2000s marked the rapid advance viewed machine learning as an incredibly powerful field
get here?
of various machine learning techniques that could analyze of AI for analyzing data, finding patterns, generating
massive amounts of online data to draw conclusions – insights, making predictions and automating tasks at a
or “learn” – from the results. Since then, companies have pace and on a scale that was previously impossible.
Milestones in the journey
Deep learning: Vision and speech phase
to generative AI
The 2010s produced advances in AI’s that search engines and self-driving cars use
perception capabilities in the field of machine to classify and detect objects, as well as the
learning called deep learning. Breakthroughs voice recognition that allows popular AI speech
in deep learning enable the computer vision assistants to respond to users in a natural way.
Generative AI: Enter the language-mastery phase
Building on exponential increases in the size and phase in the abilities of language-based AI applications. Models
capabilities of deep learning models, the 2020s will be such as this will have far-reaching consequences for business,
about language mastery. The GPT-4 language model, since language permeates everything an organization does day to
developed by OpenAI, marks the beginning of a new day—its institutional knowledge, communication and processes.2
A new era of generative AI for everyone | 4
Consume or
customize:
Generative AI
for everyone
A new era of generative AI for everyone | 5
Consume or customize: Generative AI for everyone
Consume or customize: Generative AI for everyone
Easy-to-consume generative AI applications like We’re at a phase in the adoption cycle when
ChatGPT, DALL-E, Stable Diffusion and others are most organizations are starting to experiment
rapidly democratizing the technology in business by consuming foundation models “off the shelf.”
and society. The effect on organizations will be However, the biggest value for many will come
profound. The ability of LLMs to process massive when they customize or fine tune models using
data sets allows them to potentially “know” their own data to address their unique needs:
everything an organization has ever known—the
entire history, context, nuance and intent of a Consume
business, and its products, markets and customers. Generative AI and LLM applications are ready to
Anything conveyed through language (applications, consume and easy to access. Companies can
systems, documents, emails, chats, video and audio consume them through APIs and tailor them, to
recordings) can be harnessed to drive next-level a small degree, for their own use cases through
innovation, optimization and reinvention. prompt engineering techniques such as prompt
tuning and prefix learning.
97% of global executives agree AI Customize
foundation models will enable connections But most companies will need to customize
across data types, revolutionizing where models, by fine-tuning them with their own data,
and how AI is used.3 to make them widely usable and valuable. This will
allow the models to support specific downstream
tasks all the way across the business. The effect
will be to increase a company’s efficacy in using
AI to unlock new performance frontiers—elevating
employee capabilities, delighting customers,
introducing new business models and boosting
responsiveness to signals of change.
A new era of generative AI for everyone | 6
Consume or customize: Generative AI for everyone
Companies will use these models to reinvent the Creating. Generative AI will become an essential Automating. Generative AI’s sophisticated
way work is done. Every role in every enterprise creative partner for people, revealing new ways understanding of historical context, next
has the potential to be reinvented, as humans to reach and appeal to audiences and bringing best actions, summarization capabilities, and
working with AI co-pilots becomes the norm, unprecedented speed and innovation in areas like predictive intelligence will catalyze a new era
dramatically amplifying what people can achieve. In production design, design research, visual identity, of hyper-efficiency and hyper-personalization
any given job, some tasks will be automated, some naming, copy generation and testing, and real- in both the back and front office—taking
will be assisted, and some will be unaffected by the time personalization. Companies are turning to business process automation to a transformative
technology. There will also be a large number of state-of-the-art artificial intelligence systems like new level. One multinational bank is using
new tasks for humans to perform, such as ensuring DALL·E, Midjourney and Stable Diffusion for their generative AI and LLMs to transform how it
the accurate and responsible use of new social media visual content generation outreach. manages volumes of post-trade processing
AI-powered systems. DALL·E, for example, creates realistic images and emails—automatically drafting messages with
art based on text descriptions and can process up recommended actions and routing them to the
Consider the impact in these key functions: to 12 billion parameters when transforming words recipient. The result is less manual effort and
into pictures. Images created can then be shared smoother interactions with customers.
Advising. AI models will become an ever-present
on Instagram and Twitter.5
co-pilot for every worker, boosting productivity Protecting. In time, generative AI will support
by putting new kinds of hyper-personalized Coding. Software coders will use generative AI to enterprise governance and information security,
intelligence into human hands. Examples include significantly boost productivity — rapidly converting protecting against fraud, improving regulatory
customer support, sales enablement, human one programming language to another, mastering compliance, and proactively identifying
resources, medical and scientific research, programming tools and methods, automating code risk by drawing cross-domain connections
corporate strategy and competitive intelligence. writing, predicting and pre-empting problems, and inferences both within and outside the
Large language models could be useful in and managing system documentation. Accenture organization. In strategic cyber defense, LLMs
tackling the roughly 70% of customer service is piloting the use of OpenAI LLMs to enhance could offer useful capabilities, such as explaining
communication that is not straightforward and developer productivity by automatically generating malware and quickly classifying websites.6
can benefit from a conversational, powerful and documentation – for example, SAP configuration In the short term, however, organizations can
intelligent bot, understanding a customer’s intent, rationale and functional or technical specs. The expect criminals to capitalize on generative AI’s
formulate answers on its own and improve the solution enables users to submit requests through capabilities to generate malicious code or write
accuracy and quality of answers.4 a Microsoft Teams chat as they work. Correctly the perfect phishing email.7
packaged documents are then returned at speed —
a great example of how specific tasks, rather than
entire jobs, will be augmented and automated.
A new era of generative AI for everyone | 7
A look ahead at the
fast-paced evolution
of technology,
regulation and
business
A new era of generative AI for everyone | 8
A look ahead at the fast-paced evolution of technology, regulation and business
A look ahead at the fast-paced evolution of technology, regulation and business
Moments like this don’t come around often.
The coming years will see outsized investment
Figure 1: Each layer of the generative AI tech stack will rapidly evolve
in generative AI, LLMs and foundation models.
What’s unique about this evolution is that the
technology, regulation, and business adoption
Applications: Generative AI and LLMs will be increasingly
are all accelerating exponentially at the same
accessible to users in the cloud via APIs and by being embedded
time. In previous innovation curves, the
directly into other applications. Companies will consume them
technology typically outpaced both adoption
as they are or will customize and fine-tune them with proprietary
and regulation.
data.
The technology stack Fine-tuning: The importance of model fine-tuning will create
demand for a multidisciplinary set of skills spanning software
The complex technology underpinning engineering, psychology, linguistics, art history, literature and
generative AI is expected to evolve rapidly library science.
at each layer. This has broad business
Foundation models: The market will rapidly mature and diversify
implications. Consider that the amount of
as more pre-trained models emerge. New model designs will
compute needed to train the largest AI models
offer more choices for balancing size, transparency, versatility and
has grown exponentially – now doubling
performance.
between every 3.4 to 10 months, according to
various reports.8 Cost and carbon emissions Data: Improving the maturity of the enterprise data lifecycle
are therefore central considerations in will become a prerequisite for success – requiring mastery of
new data, new data types and immense volumes. Generative AI
adopting energy-intensive generative AI.
features within modern data platforms will emerge, enhancing
adoption at scale.
“The hottest new programming
Infrastructure: Cloud infrastructure will be essential for deploying
platform is the napkin.” generative AI while managing costs and carbon emissions. Data
Paul Daugherty, Accenture Group Chief Executive centers will need retrofitting. New chipset architectures, hardware
& Chief Technology Officer innovations, and efficient algorithms will also play a critical role.
Referring to the use of OpenAI to generate a working website
from a napkin drawing
A new era of generative AI for everyone | 9
A look ahead at the fast-paced evolution of technology, regulation and business
The risk and regulatory environment AI systems need to be “raised” with a diverse Figure 2: Key risk and regulatory questions for generative AI
and inclusive set of inputs so that they reflect
Companies will have thousands of ways to the broader business and societal norms of
apply generative AI and foundation models responsibility, fairness and transparency. When Intellectual property: How will the business protect its own
to maximize efficiency and drive competitive AI is designed and put into practice within an IP? And how will it prevent the inadvertent breach of third-party
advantage. Understandably, they’ll want to get ethical framework, it accelerates the potential copyright in using pre-trained foundation models?
started as soon as possible. But an enterprise- for responsible collaborative intelligence,
wide strategy needs to account for all the where human ingenuity converges with
Data privacy and security: How will upcoming laws like
variants of AI and associated technologies they intelligent technology.
the EU AI Act be incorporated in the way data is handled,
intend to use, not only generative AI and large
processed, protected, secured and used?
language models. This creates a foundation for trust with
consumers, the workforce, and society, and
ChatGPT raises important questions about the can boost business performance and unlock Discrimination: Is the company using or creating tools
responsible use of AI. The speed of technology new sources of growth. that need to factor in anti-discrimination or anti-bias
evolution and adoption requires companies considerations?
to pay close attention to any legal, ethical and
reputational risks they may be incurring.
Product liability: What health and safety mechanisms need
to be put in place before a generative AI-based product is
It’s critical that generative AI technologies,
taken to market?
including ChatGPT, are responsible and
compliant by design, and that models and
applications do not create unacceptable risk
Trust: What level of transparency should be provided to
for the business. Accenture was a pioneer in
consumers and employees? How can the business ensure the
the responsible use of technology including accuracy of generative AI outputs and maintain user confidence?
the responsible use of AI in its Code of
Business Ethics from 2017. Responsible AI is the
practice of designing, building and deploying Identity: When establishing proof-of-personhood depends on voice
AI in accordance with clear principles to or facial recognition, how will verification methods be enhanced and
improved? What will be the consequences of its misuse?
empower businesses, respect people, and
benefit society — allowing companies to
engender trust in AI and to scale AI with
confidence.
A new era of generative AI for everyone | 10
A look ahead at the fast-paced evolution of technology, regulation and business
The scale of adoption in business Figure 3: Generative AI will transform work across industries
Companies must reinvent work to find
Work time distribution by industry
a path to generative AI value. Business Banking 54% 12% 24% 10%
and potential AI impact
leaders must lead the change, starting
Insurance 48% 14% 26% 12%
now, in job redesign, task redesign and Based on their employment levels in the US in 2021
reskilling people. Ultimately, every role Software & Platforms 36% 21% 28% 15%
in an enterprise has the potential to Lower potential for
Capital markets 40% 14% 29% 18% Higher potential for Higher potential for augmentation or Non-language
be reinvented, once today’s jobs are automation augmentation automation tasks
decomposed into tasks that can be
Energy 43% 9% 14% 34%
automated or assisted and reimagined for
a new future of human + machine work. Communications & Media 33% 13% 21% 33%
Retail 34% 7% 12% 46%
Generative AI will disrupt work as
we know it today, introducing a new Industry Average 31% 9% 22% 38% 40% of working hours across
dimension of human and AI collaboration
industries can be impacted by
in which most workers will have a “co- Health 28% 11% 33% 27%
Large Language Models (LLMs)
pilot,” radically changing how work is
Public Service 30% 9% 35% 26%
done and what work is done. Nearly
every job will be impacted – some will Aerospace & Defense 26% 13% 20% 41%
Why is this the case? Language tasks account for 62% of total worked time
be eliminated, most will be transformed, in the US. Of the overall share of language tasks, 65% have high potential
and many new jobs will be created. Automotive 30% 6% 13% 50% to be automated or augmented by LLMs.
Organizations that take steps now to
High Tech 26% 8% 16% 50%
decompose jobs into tasks, and invest
in training people to work differently, Travel 28% 6% 15% 50%
alongside machines, will define new
Utilities 27% 6% 15% 52%
performance frontiers and have a big leg Source: Accenture Research based on analysis of Occupational
up on less imaginative competitors. Life Sciences 25% 8% 17% 50% Information Network (O*NET), US Dept. of Labor; US Bureau of
Labor Statistics.
Industrial 26% 6% 14% 54%
Nearly 6 in 10 organizations Notes: We manually identified 200 tasks related to language (out
Consumer Goods & Services 24% 6% 13% 57% of 332 included in BLS), which were linked to industries using their
plan to use ChatGPT for learning
share in each occupation and the occupations’ employment level
purposes and over half are Chemicals 24% 5% 14% 56% in each industry. Tasks with higher potential for automation can
planning pilot cases in 2023. be transformed by LLMs with reduced involvement from a human
Natural Resources 20% 5% 11% 64%
Over 4 in 10 want to make a worker. Tasks with higher potential for augmentation are those in
large investment.9 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% which LLMs would need more involvement from human workers.
A new era of generative AI for everyone | 11
Embrace the
generative AI era:
Six adoption
essentials
A new era of generative AI for everyone | 12
Embrace the generative AI era: Six adoption essentials
Dive in, with a Take a people- Get your Invest in a Accelerate Level-up your
business-driven first approach proprietary sustainable tech ecosystem responsible AI
mindset data ready foundation innovation
1 2 3 4 5 6
A new era of generative AI for everyone | 13
Embrace the generative AI era: Six adoption essentials
1
Dive in, with a business-driven mindset
Even when new innovations have obvious advantages,
diffusing them across an organization can be challenging,
especially if the innovation is disruptive to current ways of A bank uses enhanced search to equip
working. By experimenting with generative AI capabilities,
employees with the right information
companies will develop the early successes, change agents
and opinion leaders needed to boost acceptance and spread
the innovation further, kick-starting the transformation and
As part of its three-year innovation plan,
reskilling agenda.
a large European banking group saw an
Organizations must take a dual approach to experimentation.
opportunity to transform its knowledge
One, focused on low-hanging fruit opportunities using
consumable models and applications to realize quick returns. base, empower its people with access to
The other, focused on reinvention of business, customer
the right information, and advance its goal
engagement and products and services using models that
of becoming a data-driven bank. Using
are customized with the organization’s data. A business-
driven mindset is key to define, and successfully deliver on,
Microsoft’s Azure platform and a GPT-
the business case.
3 LLM to search electronic documents,
As they experiment and explore reinvention opportunities,
users can get quick answers to their
they’ll reap tangible value while learning more about which
types of AI are most suited to different use cases, since the questions — saving time while improving
level of investment and sophistication required will differ
accuracy and compliance. The project,
based on the use case. They’ll also be able to test and
which included employee upskilling, is
improve their approaches to data privacy, model accuracy,
bias and fairness with care, and learn when “human in the the first of four that will apply generative
loop” safeguards are necessary.
AI to the areas of contract management,
conversational reporting and ticket
98% of global executives agree AI foundation
classification.
models will play an important role in their
organizations’ strategies in the next 3 to 5 years.10
A new era of generative AI for everyone | 14
Embrace the generative AI era: Six adoption essentials
Figure 4: Generative AI will transform work across every job category
2
Take a people-first approach
Office and Administrative Support 57% 6% 14% 23% Work time distribution by major
Success with generative occupation and potential AI impact
Sales and Related 49% 13% 14% 24%
AI requires an equal attention on
Based on their employment levels in the US in 2021
people and training as it does on Computer and Mathematical 28% 32% 23% 17%
technology. Companies should
Business and Financial Operations 45% 14% 35% 6%
therefore dramatically ramp up Lower potential for
Higher potential for Higher potential for augmentation or Non-language
investment in talent to address Arts, Design, Entertainment, Sports, and Media 25% 26% 26% 22% automation augmentation automation tasks
two distinct challenges: creating
Life, Physical, and Social Science 27% 20% 25% 28%
AI and using AI. This means
both building talent in technical
Architecture and Engineering 21% 24% 25% 30%
competencies like AI engineering
and enterprise architecture Legal 33% 9% 58% 0%
and training people across the
Occcupation Average 31% 9% 22% 38% In 5 out of 22 occupation
organization to work effectively
groups, Generative AI can
with AI-infused processes. In our Management 30% 9% 44% 17%
analysis across 22 job categories, affect more than half of all
Personal Care and Service 29% 8% 31% 32%
for example, we found that hours worked
LLMs will impact every category, Healthcare Practitioners and Technical 22% 15% 40% 22%
ranging from 9% of a workday at
Community and Social Service 29% 7% 59% 6%
the low end to 63% at the high
end. More than half of working Healthcare Support 27% 8% 31% 34%
hours in 5 of the 22 occupations
Protective Service 29% 6% 23% 43%
can be transformed by LLMs.
Educational Instruction and Library 23% 8% 50% 19%
Food Preparation and Serving Related 25% 5% 9% 61% Source: Accenture Research based on analysis of Occupational
Information Network (O*NET), US Dept. of Labor; US Bureau of Labor
Transportation and Material Moving 23% 4% 7% 66%
Statistics.
Construction and Extraction 15% 4% 7% 75%
Notes: We manually identified 200 tasks related to language (out
Installation, Maintenance, and Repair 16% 1%9% 75% of 332 included in BLS), which were linked to industries using their
share in each occupation and the occupations’ employment level
Farming, Fishing, and Forestry 8% 8% 17% 66%
in each job category. Tasks with higher potential for automation can
be transformed by LLMs with reduced involvement from a human
Production 14% 2% 8% 76%
worker. Tasks with higher potential for augmentation are those in
Building and Grounds Cleaning and Maintenance 9% 0% 7% 84% which LLMs would need more involvement from human workers.
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
A new era of generative AI for everyone | 15
Embrace the generative AI era: Six adoption essentials
2
In fact, independent economic research indicates that
companies are significantly underinvesting in helping
workers keep up with advances in AI, which require Figure 5: Reinventing a customer service job, task by task
more cognitively complex and judgment-based tasks.11
Even domain experts who understand how to apply
To assess how specific jobs will be reinvented with AI, an Accenture analysis decomposed
data in the real world (a doctor interpreting health data,
one customer service job into 13 component tasks. We found:
for example) will need enough technical knowledge of
how these models work to have confidence in using
them as a “workmate.”
4
There will also be entirely new roles to recruit, including tasks would continue to be performed
linguistics experts, AI quality controllers, AI editors, primarily by humans, with low potential
and prompt engineers. In areas where generative for automation or augmentation.
AI shows most promise, companies should start by
decomposing existing jobs into underlying bundles of
tasks. Then assess the extent to which generative AI
might affect each task — fully automated, augmented, 4 tasks could be fully automated —
or unaffected. such as gathering, classifying, and
summarizing information on why a
customer is contacting the company.
5
tasks could be augmented to help
humans work more effectively — such
as using an AI summary to provide a
rapid solution with a human touch.
Importantly, new job tasks might also be needed to ensure the safe, accurate and responsible use of
AI in customer service settings, such as providing unbiased information on products and pricing.
A new era of generative AI for everyone | 16
Embrace the generative AI era: Six adoption essentials
3 4
Get your proprietary data ready Invest in a sustainable tech foundation
Customizing foundation models will require Companies need to consider whether they have the
access to domain-specific organizational data, right technical infrastructure, architecture, operating
semantics, knowledge, and methodologies. In the model and governance structure to meet the high
pre-generative AI era, companies could still get compute demands of LLMs and generative AI, while
value from AI without having modernized their keeping a close eye on cost and sustainable energy
data architecture and estate by taking a use-case consumption. They’ll need ways to assess the cost
centric approach to AI. That’s no longer the case. and benefit of using these technologies versus other
Foundation models need vast amounts of curated AI or analytical approaches that might be better
data to learn and that makes solving the data suited to particular use cases, while also being
challenge an urgent priority for every business. several times less expensive.
Companies need a strategic and disciplined As the use of AI increases, so will the carbon
approach to acquiring, growing, refining, emissions produced by the underlying infrastructure.
safeguarding and deploying data. Specifically, they Companies need a robust green software
need a modern enterprise data platform built on development framework that considers energy
cloud with a trusted, reusable set of data products. efficiency and material emissions at all stages of the
Because these platforms are cross-functional, with software development lifecycle. AI can also play a
enterprise-grade analytics and data housed in cloud- broader role in making business more sustainable
based warehouses or data lakes, data is able to break and achieving ESG goals. Of the companies we
free from organizational silos and democratized for surveyed that successfully reduced emissions in
use across an organization. All business data can production and operations, 70% used AI to do it.12
then be analyzed together in one place or through a
distributed computing strategy, such as a data mesh.
Read more on the practices data-mature
companies are using to maximize enterprise
data value: A new dawn for dormant data:
Unleash the intrinsic value of enterprise
data with a strong digital core on cloud.
A new era of generative AI for everyone | 17
Embrace the generative AI era: Six adoption essentials
5 6
Accelerate ecosystem innovation Level-up your responsible AI
Creating a foundation model can be a complex, The rapid adoption of generative AI brings fresh urgency
compute-intensive and costly exercise. And for to the need for every organization to have a robust
all but the very largest global companies, doing it responsible AI compliance regime in place. This includes
entirely on their own will be beyond their means controls for assessing the potential risk of generative AI
and capabilities. The good news is that there is a use cases at the design stage and a means to embed
burgeoning ecosystem to call on, with substantial responsible AI approaches throughout the business.
investments by cloud hyperscalers, big tech players, Accenture’s research suggests most companies still
and start-ups. Global investment in AI startups have a long way to go. Our 2022 survey of 850 senior
and scale-ups is estimated to exceed $50 billion in executives globally revealed widespread recognition
2023 alone.13 These partners bring best practices of the importance of responsible AI and AI regulation.
honed over many years, and can provide valuable But only 6 percent of organisations felt they had a fully
insights into using foundation models efficiently robust responsible AI foundation in place.
and effectively in specific use cases. Having the
right network of partners—including technology An organization’s responsible AI principles should be
companies, professional services firms and academic defined and led from the top and translated into an
institutions—will be key to navigating rapid change. effective governance structure for risk management
and compliance, both with organizational principles
and policies and applicable laws and regulations.
Responsible AI must be CEO-led, beginning with a focus
on training and awareness and then expanding to focus
on execution and compliance. Accenture was one of the
first to take this approach to Responsible AI years ago,
with a CEO-led agenda, and now a formal compliance
program. Our own experience shows that a principles-
driven compliance approach provides guardrails while
being flexible enough to evolve with the fast pace
of changing technology, ensuring companies aren’t
constantly playing “catch up.”
To be responsible by design, organizations need to move
from a reactive compliance strategy to the proactive
development of mature Responsible AI capabilities
through a framework that includes principles and
governance; risk, policy and control; technology and
enablers and culture and training.
A new era of generative AI for everyone | 18
The future of AI
is accelerating
A new era of generative AI for everyone | 19
The future of AI is accelerating
This is a pivotal moment. For several years, Businesses are right to be optimistic about the
generative AI and foundation models have been potential of generative AI to radically change how
quietly revolutionizing the way we think about work get done and what services and products
machine intelligence. Now, thanks to ChatGPT, they can create. They also need to be realistic
the whole world has woken up to the possibilities about the challenges that come with profoundly
this creates. rethinking how the organization works, with
implications for IT, organization, culture, and
While artificial general intelligence (AGI) remains responsibility by design.
a distant prospect, the speed of development
continues to be breathtaking. We’re at the start of Companies need to invest as much in evolving
an incredibly exciting era that will fundamentally operations and training people as they do in
transform the way information is accessed, technology. Radically rethinking how work gets
content is created, customer needs are served, done, and helping people keep up with technology-
and businesses are run. driven change, will be two of the most important
factors in realizing the full potential of this step-
Embedded into the enterprise digital core, change in AI technology.
generative AI, LLMs, and foundation models will
optimize tasks, augment human capabilities, and Now’s the time for companies to use
open up new avenues for growth. In the process, breakthrough advances in AI to set new
these technologies will create an entirely new performance frontiers—redefining themselves
language for enterprise reinvention. and the"
86,accenture,Accenture-Art-AI-Maturity-GM.pdf,"The art of AI maturity
Advancing from practice to performance
Growth Markets
Asia Pacific, Africa, the Middle East, and Latin America
From insights to action, the path to extraordinary value starts here.
Contents
Foreword Executive AI maturity: AI maturity: AI Achievers How AI Practice Appendix
summary Why it What it is advance Achievers makes
matters from master their progress
practice to craft
performance
03 04 06 09 14 17 29 35
The art of AI maturity—Growth Markets 2
The art of AI maturity – a Growth Markets perspective
Foreword
As artificial intelligence technologies is still evolving. Within the Growth Markets, They design AI responsibly, from the start. nearly double (to 32%) by 2024 in Growth
become more prevalent, some the AI maturity is picking up pace with 5. They prioritize long and short-term markets.
organizations will lead the change, and 17% of companies are what we call “AI AI investments. In the report, we delve
others will be fast followers. With growing Achievers”—that is, they leverage AI’s full deeper into each of these success factors. Because high-performance today will
confidence in AI as a value driver, we potential. become business-as-usual tomorrow,
see companies in Asia Pacific, Africa, the In most cases the right intentions are yet there’s an impetus to move quickly and
Middle East, and Latin America (Growth In this report, we look at what the AI to be converted into actions. While most move now.
Markets) evolving and maturing along with Achievers are doing right. Our findings companies have begun their Responsible
the technology itself. demonstrate that Achievers are not AI journey, the majority (94%) are yet to
defined by the sophistication of any operationalize across all key elements of
Also, governments in these markets, one capability, but by their ability to Responsible AI. To move from principles to
including China, Singapore, and India, combine strengths across strategy, practice, organizations need a structured
have announced ambitious national processes, and people by scaling AI. The approach to be responsible by design.
AI strategies. In the Middle East, AI is five ways in which AI Achievers master
at the center of all national economic their craft are — 1. Their top leaders We hope that the ‘Art of AI maturity’
development plans over the next few champion AI as a strategic priority for will serve as an inspiration for business
years. the entire organization. 2. They invest leaders to pave the way for a successful
Senthil Ramani
heavily in talent to get more from their AI AI maturity journey for their enterprises.
Senior Managing Director,
While AI continues to attract board level investments. 3. They industrialize AI tools With the strong industry momentum, we Growth Markets, Applied
attention and investment, the AI maturity and teams to create a strong AI core. 4. project the number of AI Achievers, to Intelligence
The art of AI maturity—Growth Markets 3
Executive summary
In fewer than 70 years, artificial intelligence (AI) has evolved
from a scientific concept to a societal constant.
17%
Today, so much of what we take for Another 30% of firms are somewhat In short, advancing AI maturity is no
granted in our daily lives, from travel to advanced in their level of AI maturity, longer a choice. It’s an imperative for
shopping, relies on machine learning (ML). while the remaining 53% (the majority) are every industry, every organization and
Companies across industries are investing merely testing the waters. every leader.
in AI to drive logistics, improve customer of firms have advanced
service, increase efficiency, empower This decades-long journey to AI maturity
their AI maturity enough
53%
employees and so much more. is now in high gear. Even pre-pandemic
to achieve superior
(2019), AI Achievers already enjoyed 56%
performance and growth.
Like their peers in developed economies, greater revenue growth, on average,
few organizations in Growth Markets compared with their peers. And in 2021,
(GMs) are capitalizing on AI’s full potential. executives who discussed AI on their of firms are still
earnings calls were 96.7% more likely to
testing the AI waters.
According to our analysis of approximately see their firms’ share prices increase—up
500 companies in GMs, only 17% of firms from 64.2% in 2018.
have advanced their AI maturity enough
to achieve superior growth and business
transformation. We call them the
“AI Achievers.”
The art of AI maturity—Growth Markets 4
What do AI Achievers do differently?
While there’s clearly a science to AI, our findings demonstrate there is also an art to
AI maturity. Achievers are not defined by the sophistication of any one capability,
but by their ability to combine strengths across strategy, processes and people.
Here are five ways AI Achievers master their craft:
1. Their top leaders champion AI as a strategic priority
for the entire organization.
2. They invest heavily in talent to get more from their
AI investments.
3. They industrialize AI tools and teams to create
a strong AI core.
4. They design AI responsibly, from the start.
5. They prioritize long- and short-term AI investments.
Our machine learning models suggest that the share of AI Achievers will increase
rapidly and significantly, more than doubling from the current 17% to 32% by 2024.
In short, advancing AI maturity is no longer a choice. It’s an opportunity facing every
industry, every organization and every leader.
The art of AI maturity—Growth Markets 5
The art of AI maturity
AI maturity: Why it matters
AI maturity: Why it matters
Figure 1: We project that AI transformation will take less time than digital transformation
There is a growing consensus that AI is
essential to competitive advantage.
In 2021, 46% of CEOs mentioned AI on • 38% of companies said the return
their earnings calls—when they did, their on their AI initiatives exceeded their
share prices were 96.7% more likely to expectations (compared to 42%
increase. globally). Only 2% said the ROI did not
meet expectations.
In Growth Markets specifically:
AI, accelerated
• 67% of companies have integrated
AI into their business strategies and
reworked their cloud plans to achieve AI is now widely considered a value
AI success. driver. We estimate AI transformation will
happen much more quickly than digital
transformation—on average, 16 months
faster (Figure 1). Source: Accenture Research
Note: Our estimate is derived from a natural language processing analysis of investor calls of
the world’s 2,000 largest companies (by market cap), from 2010 to 2021, that referenced “AI”
and “digital” in tandem with “business transformation,” respectively. Data was sourced from S&P
earnings transcripts.
The art of AI maturity—Growth Markets 7
There’s great incentive to move quickly. We found, Figure 2: Evolution of companies' AI-influenced revenue share from 2018 to 2024*
for example, that the share of company revenue that
33%
is “AI-influenced” more than doubled between 2018
and 2021 and is expected to roughly triple by 2024
(Figure 2). In response, companies plan to increase
and accelerate their AI investments. In 2021, 21%
of companies dedicated more than 30% of their
tech budgets to AI development. By 2024, 49% of
companies intend to do the same.
Note: Color indicates the achieved AI-influenced revenue threshold within each time period.
Source: Accenture Research
Note: *2024 = projected, N = 585
*Definition of “AI-influenced” revenues:
a. Sales of existing products and services made possible through better AI-driven insights on customers, supply chain, channels; b. Sales
of new products and services made possible by human + AI , c. Higher prices through dynamic pricing ML algorithms. These sales include
some cannibalization and net new revenues. In contrast, this definition is excluding higher efficiencies in production operations thanks to AI.
The art of AI maturity—Growth Markets 8
The art of AI maturity
AI maturity: What it is
AI maturity: What it is
We designed a holistic AI maturity framework to
uncover common strategies for AI success.
AI maturity measures the
Fittingly, our analysis itself was conducted not only in data and AI, but also in
using AI. We applied machine learning organizational strategy, talent and culture.
degree to which organizations
(ML) models to unravel massive survey (See pages 37 and 38 for key
datasets and uncover drivers of AI capabilities descriptions.)
have mastered AI-related
maturity that would have been impossible
to detect using more traditional analytical This includes foundational AI
capabilities in the right
methods. (More on the methodology in capabilities—like cloud platforms and
the Appendix.) tools, data platforms, architecture and combination to achieve high
governance—that are required to keep
performance for customers,
Our research found that AI maturity pace with competitors. It also includes
gives companies a strong competitive “differentiation” AI capabilities, like
shareholders and employees.
advantage. Unlocking this advantage AI strategy and C-suite sponsorship,
comes down to mastering a set of key combined with a culture of innovation that
capabilities in the right combinations— can set companies apart.
The art of AI maturity—Growth Markets 10
The companies that scored best in both Figure 3: Only 17% of organizations are AI Achievers
categories are what we call “AI Achievers.”
Meanwhile, “AI Builders” show strong
foundational capabilities and average
differentiation capabilities, while “AI Innovators”
show strong differentiation capabilities and
average foundational capabilities. Trailing these vs. 13% (Global) vs. 12% (Global)
cohorts are a fourth group we’re calling “AI
Companies struggling Companies that are capitalizing
to materialize their the power of AI by building
Experimenters”—those with average capabilities
AI strategy on strong foundation and
a differentiated strategy
in both categories.
In Growth Markets, Achievers accounted for
17% of all firms surveyed, Builders for 18% and
Innovators for 12%. Together, Achievers, Builders,
and Innovators represent 47% of surveyed
organizations—10% higher than their combined
global representation (37%). AI Experimenters
make up the majority (53%) (Figure 3).
vs. 63% (Global) vs. 12% (Global)
The marjority of companies Companies with strong AI
without strong AI foundations foundations but unclear
and clear AI strategy differentiation strategy
Source: Accenture Research analysis based on a sample of 1,200 global companies and 491 are from Growth Market
The art of AI maturity—Growth Markets 11
AI, applied
Figure 4: Levels of AI maturity by industry, 2021 and 2024*
While industries like tech are currently far In other industries, a range of factors
ahead of others in AI maturity, the gap may be contributing to relatively low AI
will likely narrow considerably by 2024. maturity. Financial Services institutions, for
In fact, there’s growing demand for AI example, still struggle to move projects
in the life sciences industry, due to the into production and scale AI across the
presence of top research institutions in organization. Meanwhile, healthcare
Growth Markets, rising investments in organizations have been slow to adapt to
the research studies of various diseases, the AI transformation. But many are either
and accelerated advancements in drug experimenting with or actively pursuing
discovery and delivery. In addition, various AI-enabled tools to bridge the huge gaps
airlines and airport authorities in Growth in resources and meet challenges created
Markets are increasingly investing in AI by rapidly aging societies (Figure 4).
for airport safety, predicting flight arrivals
more accurately, customer service
chatbots, operational efficiency, etc.
N = 516 | Source: Accenture Research analysis based on a sample of 1,200 global companies and 491
are from Growth Market
Note: *2024 = estimated scores. Industries’ AI maturity scores represent the arithmetic average of
their respective Foundational and Differentiation index.
The art of AI maturity—Growth Markets 12
AI, applied across industries
• A Middle East-based telecom • One coral conservatory used AI for • The third-largest bank in Thailand • A large holding company leveraged
operator uses an AI-driven reef restoration. Its cost-effective used AI to unlock the value of data to AI for workforce transformation. AI
bilingual virtual assistant to handle edge computing solution and enhance experiences for consumers, and ML models were used to match
approximately 1.5 million customer strategically placed underwater smart optimize operations and fuel future professional skillsets to specific
interactions—in both Arabic and cameras allowed for non-invasive growth. Their ongoing, multi-year company roles. Turns out 55% of
English—across multiple channels observations, from tracking the transformation journey combines recruits were matched with optimal
each month. migration of fish to colder climates advanced data and analytics positions, increasing performance
to monitoring illegal fishing in capabilities with people-focused and retention by 3X.
protected waters. processes and tools.
• A large chemicals and energy firm
is using drones and AI-powered • A leading Indonesian telecom
computer vision to monitor its • One of the world’s largest metals and • A leading Japanese cosmetic company deployed a best-in-class
equipment and remote locations. mining companies wanted to enable company used data and AI-driven AI-powered virtual agent for cost-
The upshot: More frequent intelligent, value-driven decision- insights to track marketing ROI, optimization and improving customer
inspections at lower cost to the making across its commercial gauge shifts in consumer behavior satisfaction scores.
company and fewer safety risks for ore value chain. The company’s in a post-pandemic world and drive
its maintenance workers. AI engagement resulted in better strategy for sales growth.
• A leading retail company leveraged
detection of possible disruptions
data and AI to create differentiated
in supply chains and a proactive
propositions for their brands.
approach to recovery planning.
Within nine months, they scaled
the business resulting in $10M from
new revenue streams. It’s aiming to
achieve a $25M incremental revenue
target by 2025.
The art of AI maturity—Growth Markets 13
The art of AI maturity
AI Achievers advance from
practice to performance
AI Achievers advance from
Figure 5: AI Achievers outperform in nearly all capabilities
practice to performance
Achievers Builders Innovators Experimenters
AI Achievers are going above and beyond, deploying AI
solutions to solve problems and identify new opportunities.
So it’s no surprise they thrive when it They are not defined by the sophistication
comes to traditional performance metrics. of any one capability, but by their ability
Pre-pandemic, they already enjoyed to combine strengths across strategy,
56% greater revenue growth on average, processes and people.
versus their peers. And today, they’re 3.7
times more likely than Experimenters to In comparison, Innovators typically excel
see their AI-influenced revenue exceed at securing senior sponsorship and
30% of their total revenues. embrace training for all employees, but
What sets the AI Achievers apart? they lack the foundational capabilities
required to support AI at scale.
Builders, on the other hand, excel at
Mastery of multitasking
creating data and AI platforms, but they
When compared with all other groups, AI tend to be weaker at cultivating AI fluency
Achievers demonstrate high performance and the innovation culture that is needed
across a combination of capabilities. to drive adoption. (Figure 5)
Source: Accenture Research
Note: Each cube represents one of the 17 key capabilities. The cube is highlighted when the AI profile is
outperforming against peers (higher than the average across all companies in terms of % of companies
reaching the mature level).
The art of AI maturity—Growth Markets 15
Turning pilots into production
Figure 6: Achievers excel at turning AI pilots into production
Achievers have largely moved beyond the AI investment
“tipping point,” going from experimenting with new AI in
isolation to applying AI at scale to solve critical business
problem (Figure 6). Achievers are 36% more likely to scale AI
pilots across the enterprise compared with Experimenters.
A multinational telecom company with a major market
in Japan was facing the challenge of unsubscribing users.
They wanted to drive data-led transformation for improving
efficiency and driving business growth. They also aimed at
strengthening their team of data scientists by upskilling their
existing workforce and hiring talent.
A joint venture company was established to help their
business challenges. The joint venture created end-to-end
data infrastructure across their business and enable them to
scale it with cloud migration. The company leveraged data-
led transformation to create hyper-personalized offers for
clients and drive business growth. Combining data scientist
training and analytics & BI environment we were also able to
upskill their team and create a future-ready workforce.
Source: Accenture Research
Note: Score 0-100, ranging from 0 = AI use case not started, 50 = AI use in early stage, 100 = having AI programs in place for
full productization. The chart shows the difference in terms of average score for AI use cases of different functions, between
Achievers and other firms. Those differences are statistically significant after controlling for industry, geography, and company
size; see Appendix for more details.
The art of AI maturity—Growth Markets 16
The art of AI maturity
How AI Achievers
master their craft
Five success factors
How AI Achievers
master their craft
It’s worth noting that the potential for
AI-mature organizations will evolve
along with the technology itself. High
performance today will ultimately become
business-as-usual tomorrow.
Today’s AI Achievers have set the standard
and are poised to remain leaders. While
science is at the center, they’ve shown us
there is also an art to AI maturity.
They have demonstrated that excellence
in areas like vision and culture are just
as critical as algorithmic integrity. Our
research uncovered five key success
factors for AI Achievers.
The art of AI maturity—Growth Markets 18
Success Factor 01
Champion AI as a strategic priority
for the entire organization, with full
sponsorship from leadership
Companies can create strong AI Our research also suggests that the
strategies, but unless those strategies best AI strategies tend to be bold, even
receive enthusiastic support from the when they have modest beginnings.
CEO and the rest of the C-suite, they’re Bold AI strategies help spur innovation.
likely to flounder. For CEOs of Achievers, creating a
culture of innovation is itself a deliberate,
Achievers are more likely to have formal strategic move—one that is used as a
senior sponsorship for their AI strategies. vehicle for experimentation and learning
We found that 86% of Achievers in across the organization. In fact, 59% of
Growth Markets have such sponsorship, Achievers embed innovation in their
while only 60% of Builders and just 56% organization’s strategies, while just 33%
of Experimenters have it. of Experimenters do.
The art of AI maturity—Growth Markets 19
For instance, Lendlease Digital (part of questions and share ideas with colleagues
multinational Lendlease Group) hopes across the company—compared to 6% of
to produce architectural blueprints for Experimenters. That number will only grow
buildings using generative design and AI, as these companies hire more AI talent.
then use those blueprints to manufacture
actual buildings in factories—fitting
together all the pieces like LEGO sets. The
86%
company’s bold vision starts at the top, led
by the CEO of Lendlease Digital,
William Ruh.
To encourage such end-to-end innovation,
Achievers implement systems and of Achievers have
structures that help employees showcase
CEO and senior
their innovation experiments and seek
sponsorship.
constructive feedback from leadership.
For instance, Achievers tend to be the
first to embrace new tools that encourage
their employees to experiment and
innovate. We found that 25% of Achievers
in Growth Markets are already using
platforms that allow workers to easily pose
The art of AI maturity—Growth Markets 20
Success Factor 02
Invest heavily in talent to get more
from AI investments
With a clear AI strategy and strong CEO We also found that 56% of Achievers in
sponsorship, organizations are more likely Growth Markets have employees with
to invest heavily in creating data and AI consistently high AI skills competencies,
fluency across their workforces. while Innovators (42%) and Experimenters
(35%) have significantly fewer such
We found that 76% of Achievers— employees, on average. Achievers also
compared with 66% of Builders and develop proactive AI talent strategies to
59% of Experimenters—have mandatory stay at the forefront of industry trends.
AI trainings for most employees, from In addition to hiring, this can mean
product development engineers to partnering with or acquiring specialist
C-suite executives. Because Achievers companies to fill critical roles (such
prioritize efforts to build AI literacy in their as data or behavioral scientists, social
workforces, their employees are also more scientists and ethicists). It also means
proficient in AI-related skills. This makes it having a plan to get these diverse,
easier to scale human-AI collaboration. multidisciplinary workers to collaborate,
create and sustain maximum value from
the company’s data-science capabilities.
The art of AI maturity—Growth Markets 21
What does this look like in practice?
Japanese e-commerce giant Rakuten digital fluency. It also created a cloud-
established an “AI Promotion Department” based performance reviewer that
54%
in 2016 to accelerate efforts to infuse scrutinized a decade’s worth of employee
AI into the company’s 70+ diverse data to recommend workers best suited
businesses. By 2018, the department for various digital roles. The innovation
helped turn more than 30 AI pilot projects saved the firm’s HR department significant
into successful offerings. time filling positions. It also reduced
scope for managerial bias in promotional of Achievers in Growth
And a leading Southeast Asian oil and decisions and helped workers assess and
Market have employees with
gas firm built an AI-powered, “gamified” close digital-skills gaps.
consistently high AI skills
learning platform to expand employees’
competencies, while Innovators
(42%) and Experimenters (35%)
have significantly fewer such
employees, on average.
The art of AI maturity—Growth Markets 22
Success Factor 03
Industrialize AI tools and
teams to create an AI core
Another priority for Achievers is building domain experts and systems engineers.
an AI core: An operational data and AI To build AI cores, Achievers harness
platform that taps into companies’ talent, the power of internal and external data,
technology and data ecosystems, allowing making that data trustworthy and storing
firms to balance experimentation and it in a single enterprise-grade cloud
execution. An AI core helps organizations platform—complete with appropriate
productize their AI applications and usage, monitoring and security policies.
integrate the technology into
other applications. To extract value from their data quickly
and effectively, Achievers in Growth
An AI core also works across the Markets are also 43% more likely, on
cloud continuum (from migration to average, than Experimenters to either
innovation), provides end-to-end data develop custom-built, machine-learning
capabilities (foundation, management applications or work with a partner that
and governance), manages the machine offers solutions-as-a-service. Achievers
learning lifecycle (workflow, model are also more likely than Innovators
training, model deployment) and to use AI for innovation, tapping into
provides self-service capabilities. AI readily available developer networks that
cores are, in turn, managed by dedicated can swiftly productionize and scale
interdisciplinary teams of machine successful pilots.
learning engineers, data scientists, data
The art of AI maturity—Growth Markets 23
To strengthen their AI cores, Achievers often
collaborate with external experts to stay
on top of breakthroughs in science and
engineering. In 2020, for example, American
Express partnered with the Indian Institute
of Technology Madras to create a Data
Analytics, Risk and Technology laboratory at
the prestigious university. Such innovation
ecosystems help Achievers develop AI apps
tailored specifically to their needs.
A leading Indian bank deployed AI at scale
to achieve higher growth and efficiencies
across multiple verticals, such as retail
and SMB, liquidity & risk management,
wealth management and more. It identified
priorities and devised a roadmap including
strategy, technology & infrastructure, and
talent. With an enterprise-wide data lake and
AI/ML platform, as well as a robust literacy
program, they were able to leverage the
value of data and AI across the organization.
The art of AI maturity—Growth Markets 24
Success Factor 04
Design AI responsibly, from the start
Adhering to laws, regulations and ethical compliance is a company-wide priority.
norms is critical to building a sound In fact, many organizations view AI
data and AI foundation. The potential for regulation as a boon rather than a
regulatory changes in many countries barrier to success.
makes the challenge even more daunting.
The ability to demonstrate high-quality,
In a separate Accenture study of 850 trustworthy AI systems that are “regulation
C-suite executives, we sought to gauge ready” will give first movers a significant
attitudes toward AI regulation and assess advantage in the short- and long-term,
organizations’ readiness to comply. enabling them to attract new customers,
Nearly all (99%) respondents believed retain existing ones and build
that regulation would impact them to investor confidence.
some extent, while 81% indicated that
The art of AI maturity—Growth Markets 25
Achievers are consciously applying Yet MAS was also wary of the threat posed
responsible AI with greater urgency than to firms and markets by the illegal and/or
their peers. In Growth Markets, they are unethical use of AI. It helped launch the
on average 36% more likely than Builders Veritas initiative, which aims to support
and 64% more likely than Innovators to the responsible use of AI in the finance
be responsible by design. This means industry. The effort has produced a
Even though only 8%
Achievers are designing, developing and practical methodology and first-of-its-
deploying AI that empowers employees kind toolkit that offers detailed guidance of the companies surveyed
and businesses, and impact customers on how to use AI leveraging the FEAT
and society fairly. principles—fair, ethical, accountable had already implemented
and transparent.
responsible AI practices, 42%
For instance, The Monetary Authority of
Singapore (MAS), the country’s central
of surveyed companies aspire
bank and financial regulator, recognized
the benefits that AI can provide to
to do so by the end of 2024.
financial firms.
The art of AI maturity—Growth Markets 26
Success Factor 05
Prioritize long- and short-term
AI investments
To avoid being left behind, most For example, the Saudi government’s
In 2018, Achievers
companies need to aggressively increase National Center for Artificial
their spending on data and AI. One reason Intelligence is on a mission to unlock
in Growth Markets
Achievers in Growth Markets get more out the value of data and AI as a national
devoted 15% of their of AI is simply because they invest more asset to fulfill the larger digital vision of
in it. We found that in 2018, Achievers the country. The center accelerated the
total technology budgets
devoted 15% of their total technology development of AI capabilities in priority
budgets to AI, while in 2021 they devoted sectors like energy, healthcare, agriculture
to AI. In 2021, that rose
29%. In 2024, they plan to devote 36%. and government—and it will play a pivotal
to 29%. By 2024, they role in the execution of the country’s
national AI strategy. Early returns have
Achievers also understand that their AI
expect to devote 36%.
been so successful that the Kingdom of
investment journey doesn’t have a finish
Saudi Arabia will allocate the strategy a
line. There is, they frequently note, no
budget of $20 billion by 2030.
“peak AI.” These companies know they
have only scratched the surface of their
AI transformations and that the quality of
their investments matters just as much
as the quantity. For Achievers, continued
investment largely involves expanding the
scope of AI to deliver maximum impact,
while “cross-pollinating” AI solutions and
redeploying resources in the process.
The art of AI maturity—Growth Markets 27
We project the share of
AI Achievers in Growth
Markets to nearly double
(from 17% to 32%) by 2024.
28
The art of AI maturity—Growth Markets
The art of AI maturity
Practice makes progress
Practice makes progress
The concept of using AI to solve business problems isn’t new.
The concept of using AI to solve business technology. As much about strategy as it subjected to it. Those who transform
problems isn’t new. By 2019, there was is about implementation. As much about will be the ones whose teams master
evidence that scaling AI beyond proofs responsibility as it is about agility. the art of AI maturity, using cloud as the
of concept had a significant impact enabler, data as the driver and AI as the
on ROI. Then the pandemic hit. For Every organization should be assessing differentiator.
many organizations, enterprise-wide its own AI maturity. To get started, Figure
transformation was a matter of survival. 7 has some sample questions for C-suite How can AI help you differentiate?
For others, it became a catalyst to thrive. leaders, according to Accenture’s AI
maturity assessment. There are also tools
AI Achievers in Growth Markets are available to help benchmark AI maturity
thriving. Across industries, they’ve moved and establish clear paths to progress and
past cloud migration to innovation. performance.
But the AI itself isn’t the secret to their
superior performance—it’s how they’re As AI technologies become more
approaching AI that makes them different. prevalent, the future of all businesses
They’ve established that AI maturity is going to look very different—some
is as much about people as it is about will lead the change, and some will be
The art of AI maturity—Growth Markets 30
Figure 7: AI maturity assessment: sample questions for C-suite leaders
Category Key questions
Does your C-suite have clear accountability for data and AI strategy and execution?
•
How do you identify potential value, and how are business cases prioritized—considering the potential risks and alignment
•
Strategy and Sponsorship with the overall strategy of the organization?
Are you allocating enough delivery resources to build AI products and services in-house, and are you able to get the most
•
out of your ecosystem partners?
To what extent do you have a cloud platform and technology strategy that supports your AI strategy?
•
Do you have an effective, enterprise-wide data platform, as well as strong data management and governance practices,
Data and AI Core •
to meet business needs?
Are you using data science and machine learning teams effectively across the lifecycle of AI development?
•
Is your data- and AI-literacy strategy aligned to your business objectives?
•
To what extent have you prioritized data and AI fluency for senior leaders, business stakeholders and employees across
•
your organization?
Talent and Culture
Do you have a holistic talent model to scale, differentiate, retain and develop AI talent (diverse, dedicated teams of
•
machine learning engineers, data scientists, data-domain experts and data engineers)?
How are you institutionalizing a data and AI culture within your organization?
•
Do you have an enterprise-wide framework to help you operationalize responsible data and AI from principles to practice?
•
Are you applying a consistent and industrialized responsible data and AI approach across the complete lifecycle of all
•
Responsible AI
your AI models?
Are you methodically tracking the evolution of AI-related laws and regulations across the different jurisdictions in which you
•
operate, while anticipating and preparing for future changes?
Source: Accenture Research
The art of AI maturity—Growth Markets 31
Meet the authors Resea"
87,accenture,Accenture-Tech-Vision-2025.pdf,"Technology Vision 2025
AI: A Declaration
of Autonomy
Is trust the limit of
AI’s limitless possibilities?
Technology Vision 2025 | AI: A Declaration of Autonomy Foreword 2
AI: A Declaration Welcome to our Technology Vision for 2025. This enabler to acting autonomously on behalf of We believe we can. We see this new age of
25th edition of our annual technology trends report people--equipping them with the capability to technology as an opportunity to inject trust in
of Autonomy
arrives at a watershed moment for technology and perform new tasks and perform others better than AI in a systematic manner so that businesses
humanity. As more and more leaders embrace the ever. Consider the possibilities and opportunities to and people can realize its incredible reinvention
Is trust the limit of AI's
need to continuously reinvent using tech, data reinvent as AI finds its way into new and unfamiliar potential. Together, we can prepare now for
limitless possibilities?
and AI, they, now more than ever, need a deep territories. To truly understand and take advantage a bold future when AI is autonomous and helps
understanding of AI. Why? Because the rate of AI’s of this potential, enterprises will be creating their us achieve more together.
technology diffusion is unprecedented and the own, unique AI cognitive digital brains that will
pace is only increasing—creating new opportunities completely reshape the role technology plays
for reinvention across the enterprise—including across their enterprise and with their people. This
new ways of achieving efficiencies, operating will dramatically upend how enterprise tech systems
the core of businesses, new business models are designed, used and operated; act as a brand
and new ways of engaging with customers. ambassador; and inhabit in the physical world by Julie Sweet Karthik Narain
powering robotic bodies. And when AI is spread Chair and CEO Group Chief Executive
of Technology and CTO
We view AI as the new digital because, like digital, across an organization, it enables people and AI to
it is both a technology and a new way of working. bring out the best in each other.
We believe it will be used in every part of the
enterprise and it will have a network effect on Leaders are aware of the challenges to creating
everything and everyone involved. Its impact this future, which include high up-front
is already real, and as companies continue to investments in their core technologies, data
scale AI—and use generative AI as a catalyst for centricity and quality, and talent and new skills.
reinvention—it will solve new problems, create And chief among these challenges is trust.
new inventions, change how we work and live,
and transform industries and governments. Our research finds that 77% of executives believe
unlocking the true benefits of AI will only be
Accenture research shows that only 36% of possible when it’s built on a foundation of trust.
executives say their organizations have scaled Leaders must build trust in digital systems and
gen AI solutions, and just 13% report achieving the AI models, with customers and the workforce
significant enterprise-level impact. We are by ensuring accuracy, predictability, consistency
actively equipping them to do it faster and more and traceability over and above the responsible
safely as we see 2025 as the year of scaled AI. use of AI. People’s trust in AI that it will perform as
expected and justly—beyond any technical aspect—
This year’s Technology Vision explores a future is an essential component that we must get right.
when AI transitions from being an automation
Technology Vision 2025 | AI: A Declaration of Autonomy Contents 3
Contents
Introduction
AI: A Declaration 01 The Binary Big Bang 02 Your Face, in the Future 03 W hen LLMs get 04 The New Learning Loop
of Autonomy their Bodies
Is trust the limit of AI's When AI expands exponentially, Differentiating when every interface How foundation models How people and AI are defining
limitless possibilities? systems are upended looks the same reinvent robotics a virtuous cycle of learning, leading,
and creating
Page 04-08 Page 09-21 Page 22-33 Page 34-46 Page 47-58
Technology Vision 2025 | AI: A Declaration of Autonomy Introduction 4
AI: A Declaration
on Artificial General Intelligence (AGI).2,3 And like
of Autonomy before, the race has captivated business leaders,
governments, and the world at large.
Is trust the limit of AI's
But it’s a red herring—a distraction most business
limitless possibilities?
leaders can’t afford. Someday, AGI will be hugely
consequential, but today it’s still far away with deep
technical and ethical challenges to address. Instead,
it’s vitally important that leaders see the far more
We are entering a new chapter
pressing matter already here: the generalization of
in technology—one shaped by
artificial intelligence, which will bring a new level of
a generalization of AI. Today’s autonomy and capability to enterprises’ systems,
workforces, and operations long before AGI comes
proliferation of accessible and
into play.
ever-present AI will drive new
levels of autonomy all throughout The Generalization of AI
the business, evolving the ability
To understand this generalization of AI, one simply
to reinvent with tech, data and
needs to look around and see how rooted AI is
AI. It will bring nearly limitless becoming in our lives. It’s been nearly 30 years
since Kasparov’s game, and now models that could
possibilities for innovation
make Deep Blue look like an average player are
and growth, but also challenge
sitting in everyone’s pockets. The Turing Test, once
enterprises’ confidence in systems considered the loftiest benchmark for machine
intelligence, is effectively broken every day by
and the way they think about trust.
conversations people have with Large Language
Model (LLM)-backed customer service bots and
The rush of an AI race is undeniable. sales agents. Today’s AI models have shrugged off
the deep but specific and linear approaches of the
We’ve seen it before. In 1997, Garry Kasparov past and are demonstrating more autonomy than
lost a six-game chess match against IBM’s Deep ever—in how they learn, approach tasks, and in
Blue.1 It was the first time a computer ever beat what they ultimately can do. And they’re bringing
a chess grandmaster, after decades of testing this autonomy to work, where 75% of knowledge
machine capability against humans using this workers report using generative AI; to how we
game. The victory set off a storm of excitement interact with technology, as a coding copilot and
and questions about AI and the future. Now, a by expanding voice assistant capabilities; and to
new race is underway. Many companies building nearly everything else, from robotics, to cars, to
today’s cutting-edge AI models have their sights set health care.4,5,6,7,8,9,10 Highly capable advanced AI
Technology Vision 2025 | AI: A Declaration of Autonomy Introduction 5
is diffusing across every dimension of our lives, What will the world look like as wide proliferation industries, it may look like the common framework society will uplift the world to the next level of
instantly accessible, and—effectively—always there. brings it to every dimension of our lives? It seems and communications protocol between companies capability, performance, and progress. It will spur
inevitable that as leaders start to combine their in an industry, or engines codifying the grand an evolution towards a world enhanced at all levels
This is the real disruption to focus on. Because right AI generalization efforts, they will soon enhance challenges that shape an industry—models that by AI cognition, and generate an unprecedented
now, even as executives race to implement this new and empower individuals, drive and help operate will help grow our understanding of things like wave of autonomy that will reshape technology and
generation of AI, few are looking past the separate enterprises, radically reshape industries and even physics, genetics, movement, and more. And for businesses as we know them.
pieces to truly understand the scope of what they uplift nation states. countries and governments, it brings together
are actually building: AI “cognitive digital brains” the unique knowledge, language, culture, laws, A first thought may be that this is exclusively a
that will completely reshape the role technology Take Insilico Medicine, a pharmaceutical company, and security to help industries, companies, and transition from automation using AI to autonomy
plays across the enterprise and people’s lives. which used generative AI to go from discovery citizens engage. Critically, these cognitive digital in digital systems. It’s not wrong, but it’s only part
to phase one trials of a drug in under 30 months, brains won’t operate in silos. When they begin to of the story—AI is powering autonomy in dozens
What leaders have to fully grasp is that the singularly around half the time it usually takes.11 They used interact at all levels, they will create a rising tide of of ways. It’s giving people access to skills they
most important feature of AI is its ability to learn. one model fine tuned on omics and clinical data intelligence that elevates the capabilities of every wouldn’t otherwise possess, letting them act
When AI becomes generalized, and as enterprises to identify potential targets for drug therapy. To party involved. with more initiative and less friction than before.
diffuse it across the business and people adopt it develop possible drug compositions, they used a It’s giving robots a new degree of context and
into their lives, it has the potential to become much generative chemistry engine that consisted of 500 This is why it is a “declaration of autonomy.” reasoning about the world, allowing them to take
more than just the new features and capabilities it predictive and pre-trained models. For Insilico, AI is We may call them different things, but across the on a wider and more complex range of tasks and,
provides. Enterprises aren’t merely empowering the at the very heart of what they do—shaping the very range the evolution is the same: the prolifieration most importantly, co-mingle with humans like never
workforce, creating a new channel for customer business and industry around it. of autonomous AI systems happening across before. And of course, agentic and multi-agent AI
service, or automating parts of their operations.
They are taking a technology that comes with broad A Cognitive Digital Brain at Every Level
general knowledge and is intrinsically defined by
What makes a Cognitive Digital Brain?
its ability to learn and they are teaching it about It can be difficult to see this trend; at every layer of
parts of the business. And when people use scale it manifests slightly differently. But across the
it, they’re further teaching it about their likes, board, this next stage for AI will infuse enhanced The cognitive digital brain will become the central nervous system for enterprise decision-making
preferences, and needs. capability and increased autonomy into everything it and continuous learning. Used to power enterprises' future ambitions, like intention-based
touches. For individuals, the cognitive digital brain architectures, it is comprised of four interconnected layers that together organize, process,
If built intentionally, enterprises can take all the will operate as a co-pilot or sidekick, something that and act on information.
distributed AI efforts they are pursuing and build will understand their job, learn their preferences,
a cognitive digital brain. They can hard-code and get to know them through its interactions, in Knowledge: Technologies like knowledge graphs and vector databases gather, organize, and
workflows, institutional knowledge, value chains, service of helping them be an enhanced version structure data from across the enterprise and beyond.
social interactions, and so much other crucial data of themselves. For businesses, it might seem
about businesses and the world into a system that more like a central nervous system—an evolution Models: Large-scale generative AI models as well as classical ML and deep learning models
can understand—and increasingly act—at a higher of the enterprise architecture into something perform critical thinking and reasoning functions to turn data into actionable outcomes.
level than ever before. that can capture the collective knowledge of the
business, its unique differentiators, and its culture Agents: Designed to be problem-solvers, tackle tasks with minimal human input, and learn
What can a person do with this power? What can and persona, and become a key orchestrator (and and grow over time, AI agents bring planning, reflection, and adaptability to the mix.
a business do deploying it across the workforce? even autonomous operator) for parts of it. For
Architecture: A comprehensive backbone is what turns AI experiments into enterprise-grade
solutions. It scales intelligence across the organization and into existing workflows and enables
repeatability, so solutions can be made once and reused.
Technology Vision 2025 | AI: A Declaration of Autonomy Introduction 6
systems are starting to take on entire workflows or AI lies in how enterprise leaders choose to harness Until now, technology systems have been broadly Because it is impacted. To start—enterprises need
customer interactions without the need for constant the new dimensions of autonomy it enables. But rules-based. Though these systems are less to realize that with growing autonomy in their
human intervention, while maintaining strategic succeeding in this new world and making the right intelligent, they are highly predictable and thus technology systems, they need to think differently
oversight. Tapping into this autonomy will stretch choices will be no small task. Intrinsically married to more trustable. As a result, their adoption and about how much they trust these systems and
the limits on what businesses thought possible. the idea of autonomy is an underpinning of trust— diffusion across enterprises is widespread. So now, what guardrails they may need to impose. Sakana
Accenture research has found that with its ability to and for enterprises, it’s trust that will be the biggest as we look ahead to a world that will be defined AI, an AI research firm, perfectly demonstrated
reimagine and augment complex tasks, generative backstop to tomorrow’s growth. by technology systems that both have and create why while testing their new system called “The AI
AI is expected to drive productivity gains of 20% in greater autonomy, we’re looking at a future where Scientist.”14 The system autonomously conducts
companies leading in AI adoption.12 The Only Limit is Trust trust is the most important differentiator and scientific research using LLMs, and in one run, was
the determining factor to AI diffusion within an given a problem it couldn’t complete within the
What we have today is the spark for unbounded Think about how trust defines the human organization. After all, we can only let systems be as experiment’s set time limit, so adjusted its own code
growth and innovation—as well as disruption. As experience—the relationship between a parent autonomous as we trust them. to give itself more time. Sakana AI has pointed to
ever-greater autonomy reduces friction within and child, for instance. We surround babies with this act as creative, but also demonstrative of the
and between organizations, letting us get more guardrails. From literal ones, like those in a crib, But the ramifications of this are not as obvious fact that an AI model with the ability to bypass a set
done faster, early movers will be able to secure to more figurative ones like cutting up food or as you might think. Of course, most leaders will constraint has major implications for AI safety.
advantages that last decades. Failing to act or covering sharp corners around the house. As they be well versed on how bad actors can spread
waiting too long will give ground for competitors, grow up, we learn to trust them more. They don’t misinformation more effectively through deepfakes And beyond an enterprise's trust in the AI models or
new and old, to disrupt industry norms just as need to hold your hand to cross the street, but we or conduct more convincing phishing attacks with systems it uses, growing autonomy is also disrupting
we saw in the digital era. And consider this: Less still walk next to them. They can play outside by better emails or spoofed voices of real people. the trust enterprises have built with people, in a lot
than 1% of today’s global internet market cap themselves, but only inside the fence. The more Or how biased decision-making can rear its head of different ways.
was founded in the first two years after Netscape our trust grows, the wider we paint the boundaries even with AI. To be clear, these are real issues, with
Navigator generalized the internet for the world.13 of the guardrails. Until, one day, they are fully ever-growing efforts for content watermarking Take the same synthetic content that bad actors
Now, it’s been a little over two years since formed adults. We’ll still check in—but they’re their or deepfake detection tools urgently seeking use; many enterprises are using the same core
ChatGPT’s release. Our foray into this generation of own person now, with the autonomy to make their resolutions to them. But this narrative pins the AI technology to great effect. AI-generated marketing
AI has only just begun, and with such large stakes, own decisions. trust conversation exclusively on bad actors and materials, chatbot conversations, product
it’s vital enterprises start now before they’re left exploitation. That’s simply not the whole story. To recommendations—the use cases are ever-growing.
irreparably behind. Critically, this example demonstrates how trust achieve true autonomy—in systems, throughout the But what happens when a customer finds out that
and autonomy are inextricably linked. But it also workforce, and with customers—leaders need to a product photo was AI-generated? Or if they
demonstrates the nuance of trust leaders now need think about trust more holistically. Like the analogy believed they were speaking to a customer service
To understand more about how digital to consider. The relationship between parents and of guiding a child into adulthood, trust is about representative, only to learn it was an AI agent all
platforms, data & AI, and digital foundations children hinges on both emotional and cognitive the confidence one develops in AI to perform as along? These interactions could leave customers
are empowering enterprises to grow through components of trust. Guardrails help foster a loving, intended from all dimensions—policies, morals, feeling duped by the business.
change and disruption, please see our work on nurturing, and safe environment, but also help ethics, and emotions—so that one can let it perform
Reinventing with the Digital Core parents build their own confidence in the child’s in a state of autonomy. Which means, trust isn’t just Or look at AI in the workforce. Every day more
decision making ability. We don’t really need to about when AI is taken advantage of, but the harder workers are finding value in using AI in their
differentiate between these two dimensions when question of how trust is being impacted even when jobs; in May 2024, over 40% of users had started
We are at the start of so many possible paths it comes to people, but with technology they we are using AI exactly as we intended. incorporating it just in the past six months.15 But
forward. The key to accessing the full potential of are different challenges with different solutions. they are hiding this from their employers: more than
Technology Vision 2025 | AI: A Declaration of Autonomy Introduction 7
half of workers using AI are reluctant to admit it and relevance that only AI agents can provide at scale. require dedicated teams of domain and decision Many companies are already familiar with ideas like
worry that using it for important tasks makes them But that autonomy needs to be facilitated by trust. scientists (or AI Ops teams) who will constantly explainability, transparency around data collection,
look replaceable. This isn’t a question of how much How much customers trust the enterprise, or test, evaluate and build the accuracy, predictability, debiasing, and other maturing techniques, but as
do workers trust the AI they’re using, rather it is enterprise leaders trust their systems, or workers consistency, and explainability necessary to leaders look to scale their use of AI, these efforts
proof that AI is shaking up the trusted relationship trust their employers, how much people trust AI, or maintain cognitive trust in the system. This is new will become a critical bridge between technical
between people and their employer. Employees dozens of other permutations across the ecosystem territory, with no one-size-fits-all solution. Every solutioning and the humans interested and invested
are used to having well-developed career paths, of relationships an enterprise has. enterprise has their own trust-building moments, in using the technology. Questions will swirl around
defined roles, skill expectations, and a shared technologies, AI strategies, and key relationships to how an AI is trained, who it is working for, and how
understanding of how work performance translates This is why trust is not simply one of many trends focus on. But broadly, any path forward will center it makes decisions—these are unavoidable. But what
into job stability. The infusion of AI is bringing in this year’s report. It is not a consideration for on addressing trust across systems and data, AI enterprises can do is be prepared with an answer,
uncertainty to this. businesses—it is the consideration. With every itself, and people. which is why they can’t sit back and wait, and need
company beginning to reinvent themselves with the to make responsible AI a key part of their strategy now.
For enterprises, trust is a crucial currency generalization of AI, the technology itself cannot be First, enterprises need to bolster the cybersecurity
underpinning their relationships with customers, the only focus. Reaping the benefits of AI will only and trust of their digital systems. The good news Lastly, the third and uncharted part of the roadmap
employees, regulators, and shareholders. Until now, be possible when it’s built on a foundation of trust, is that for systems and data, building this new is finding a new path to people-driven trust. We
this trust was built in small moments—moments and this needs to be every leader’s first priority. foundation doesn’t mean starting from scratch. know where we need to get to—new touchpoints
that AI is changing. Think of the micro-interactions Many previous technology and strategy investments and ways to establish and maintain trust with people
happening across businesses every day. A great Pathways to a Stronger Foundation can pay new dividends now. Cybersecurity as the generalization of AI disrupts traditional
sales rep saving customers money, or a support strategies like zero trust and entity behavior interactions. But how to get there will be different
rep going above and beyond to solve customers’ Trust isn’t gone in the world of AI—but it is analytics, for instance, will be critical. You can’t for every organization, so the place to start is with
problems. Quality service from a practitioner becoming far more dynamic and essential to control bad actors, but you can control how you self-directed questions: What will career paths look
or provider. Calling a customer for identity enterprise plans. With AI, enterprise leaders are protect systems and people from them—and with like when many entry-level jobs can be done by AI?
confirmation. On-time delivery of products. Every going to need to navigate both the emotional AI’s dependence on data, protecting everyone’s What will establish job security for employees who
one of these moments can, and will, be disrupted and cognitive dimensions of trust. The emotional data is increasingly important. Distributed ledger leverage AI to streamline their work? How will we
with AI. And many will be better for it—rich with dimensions—do people love AI, fear it, think it is technologies that foster ecosystem-scale trust maintain a personal touch with customers if our
more autonomy, less friction, and better outcomes. aligned with their interests, or feel taken advantage are also a great example of adapting traditional “frontline” support is AI agents? Enterprises should
But how far can you go before trust becomes an by it—are often considered publicly, but will need networks of trust toward new, technology-based seek to answer these questions in ways that will
issue? How will you reinvigorate the critical human real policy and governance as enterprises seek to ones. You don’t have to trust the entities using promote the potential of the symbiotic relationship
moments that build it? further diffuse the technology. And these efforts these technologies, because the system ensures between people and AI. Whether it is educators and
will only be successful with complementary action they comply with whatever agreements are put in students, mentors and proteges, or superheroes
These are the issues that leaders need to be taken to address the cognitive dimensions of trust: place. Ultimately, great cybersecurity overall will be and sidekicks, the world is filled with mutually-
tackling. Autonomy is the key to the next generation Does a system act reliably, competently, can it paramount to achieving AI trust and security at all. beneficial teaching and learning relationships that
of business growth and innovation. We want navigate challenges and still act as expected, within should inspire our future with AI.
employees to be able to work more autonomously, the guardrails laid out for it. This is a key aspect for The second dimension of the roadmap is thinking
with a fleet of agents at their command. We any system operating with autonomy, and especially about building trust in AI itself. By now the field
want customers to be able to freely interact with with AI where it is, by nature, one that will be relied of responsible AI is becoming an established
autonomous enterprise systems, purchasing on on to learn, grow, and act based on intent, not discipline, and one enterprises will increasingly rely
demand or enjoying a level of customization and necessarily explicit direction. Supporting this will on as they look to ethically steward their strategies.
Technology Vision 2025 | AI: A Declaration of Autonomy Introduction 8
The 2025 With a firm and clear approach towards building cognitive digital brains that will become an essential Finally, The New Learning Loop explores the
trust in AI systems, and by actively building the part of the enterprise DNA. The result will be a impact of cognitive digital brains through the
Technology Vision
cognitive digital brains that will create scaled dramatic increase in technology diffusion touching most valuable reinvention engine you have:
intelligence across the spectrum of society, every walk of business, consumer, and societal your people. Employees are starting to bring
Trends
businesses will be able to unlock the limitless interactions. It sets the stage for the emerging AI AI to work, and employers know the power it
potential of AI today. It seems prudent to mention era, where we will rapidly expand digital ecosystems can wield. But we need to change our mentality
that in 25 years of producing the Technology Vision, and increasingly trust autonomous systems to find from automation to equipping your people with
few technologies have had the widespread impact new ways to innovate with us. the power to automate—giving them the tools
on business, industry, and even technology itself to innovate, reimagine new ways to do things
that AI is poised to have now. We anticipate we are Your Face, in the Future pushes the thinking and drive progress from the ground up. We are
living in a time on par with the biggest moments in further, asking a simple yet critical question: if the building a virtuous cycle where people teach
technology, one which will be shaped and defined world is shaped by AI and increased autonomy, and learn from AI machines, and AI machines do
by AI-powered autonomy and the emergence of AI- and brings super-human consistency to everything the same with people. A cycle that will let both
based cognitive digital brains at all levels of society, it touches, where does that leave your brand and unleash new levels of performance and diffusion,
and we’ve only just begun. unique enterprise personality? Enterprises are at all underpinned by trust built through ownership.
the crossroads of an intermediation challenge. They
In the interest of preparing business leaders for the have the opportunity to radically transform the way What is the world going to look like in 30 years?
transformative journey ahead, the Technology Vision they engage customers and improve the relevance Around the time of Kasparov’s groundbreaking
this year is a deep investigation into this declaration of their customer journeys, but to do so, they must match, laptops were just starting to become
of autonomy. Our trends explore the business realize that their company’s AI personality is as popular among business workers, no one had ever
transformations—and trust revolution—that will critically important as its traditional brand built over heard of an iPhone, and economist Paul Krugman
happen as generative AI ripples across dimensions time by small, personal human interactions. infamously declared the internet would prove
of customer experience, technology development, to be of no more value than the fax machine.16
the physical world, and the workforce. When LLMs get their Bodies explores the Now, we see the beginnings of a future where
manifestation of AI autonomy in the real world, AI cognitive digital brains fuel every layer of
The Binary Big Bang tracks the emergence of and how a cognitive digital brain can transform an society, interact with each other, and bring new
language models coupled with agentic systems, and enterprise's physical presence. We are reaching a intelligence to everything and more autonomy to
how they challenge conventions around building watershed moment as the power of generative AI is everyone. And the question enterprises need to
software and crafting new digital ecosystems. This applied to physics and the field of robotics. Gone ask themselves is not whether this will pan out, but
is a redefining moment in the world of software are the days of narrow, task-specific robots that rather how they will invest in fostering the trust
engineering, where the role of programmers has require specialized training. A new generation of needed to make this future a reality, and what they
largely remained the same since Ada Lovelace wrote highly tuned robots with real world autonomy that will be able to do with this limitless capability.
the first algorithms for Charles Babbage’s Analytical can interact with anyone, take on a wide variety of
Engine. The trend dives into a generational tasks, and reason about the world around them will
transition, as leaders rethink how digital systems expand robotic use cases and domains dramatically.
are designed—building the foundation for the
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01
The Binary
Big Bang
When AI expands
exponentially, systems
are upended
Organizations are entering a generation-defining
moment of transition: the Binary Big Bang. When
foundation models cracked the natural language
barrier, they kickstarted a shift in our technology
systems: how we design them, use them, and
how they operate. They are pushing the limits of
software and programming, multiplying companies’
digital output, and laying the foundation for
cognitive digital brains that infuse AI deeply into
enterprises’ DNA. We are now at the precipice of
more abundance, abstraction, and autonomy in
our technology systems than ever before, and the
decisions enterprises make today will profoundly
impact what they can achieve for the next decade.
TTeecchhnnoollooggyy VViissiioonn 22002255 || AAII:: AA DDeeccllaarraattiioonn ooff AAuuttoonnoommyy The Binary Big Bang 1100
1843 1946 1959 1973 1983 1995 2006
Ada Lovelace writes Researchers unveil COBOL, a major Alto, a computer with a Microsoft releases Sun Microsystems Amazon launches
“Note G,” widely ENIAC, the first programming language graphic"
88,accenture,Technology-Vision-for-SAP-Solutions-2024.pdf,"#TechVision2024 July 2024
Technology Vision for SAP Solutions 2024
SAP Solutions
in the Age of AI
Human-by-design
technology is reinventing
core business operations
Technology Vision 2024 | SAP Solutions in the Age of AI
#TechVision2024
Foreword
We’re delighted to present this year’s Of course, applying a powerful general-
Accenture Technology Vision for SAP purpose technology like generative AI to a Caspar Borggreve Stephanie Guimbellot
solutions. In it, we explore how human-by- complex and business-critical domain like Global Lead Global Innovation Lead
design technology is reshaping interactions ERP comes with challenges. It’s why we also SAP Business Group SAP Business Group
between humans and machines, enabling explain some of the steps businesses will Accenture Accenture
new levels of business productivity and need to take to prepare their data, adopt LinkedIn LinkedIn
creativity—and what this means for composable architectures and build the
companies running SAP solutions. digital scaffolding for an increasingly AI-
In particular, we dive into the potential for driven future workplace. Not forgetting the
generative AI to enhance the way people need to ensure rigorous Responsible AI
access and work with SAP solutions and data, approaches are built in from the start.
as well as drive a pivotal shift towards It's an exciting time for enterprise
automated agent-based operations. We technology. Generative AI is opening up a
explain the numerous applications and use whole new world of possibilities for
cases that can be implemented now, as well augmenting and automating work. We look
Daniel Gonzalez Catherine Nguyen
as the likely evolution of the technology in forward to helping our clients capitalize on
the years to come. the many opportunities that await them. Americas Innovation Lead Europe Innovation Lead
SAP Business Group SAP Business Group
Accenture Accenture
LinkedIn LinkedIn
Technology Vision 2024 | SAP Solutions in the Age of AI
#TechVision2024
Client and SAP Advisors
Johnny Rahme Sharmita Srivastava Dr. Philipp Herzig
Head of Innovation, Vice President, Head of Global Chief AI Officer,
APS, TotalEnergies ERP, Bristol Myers Squibb SAP
Technology Vision 2024 | SAP Solutions in the Age of AI
#TechVision2024
Contents
The big picture 5-10
A match made in AI 11-17
Meet my agent 18-25
Going beyond 26-29
Conclusion 30-31
Technology Vision 2024 | SAP Solutions in the Age of AI
#TechVision
The big
picture
Technology Vision 2024 | SAP Solutions in the Age of AI
#TechVision2024 Introduction The big picture A match made in AI Meet my agent Going beyond Conclusion
Human-by-design AI is transforming
next-level enterprise potential
The relationship between humans and An example? Look at the wave of advances
machines is changing. Enterprises are made by generative AI over the past 18
acquiring an array of increasingly powerful months. A whole range of models has
and intelligent tools and technologies, which emerged, accompanied by a technology
are allowing their employees to radically ecosystem to utilize them, with OpenAI’s
reshape the way they access, use and think GPT, Google’s Gemini, Anthropic’s Claude,
about enterprise data. They’re allowing Meta’s Llama and Mistral’s models among the
operational teams to develop new kinds of most significant. Solutions based on these
automation, including autonomous agents models have taken the world by storm,
that can act independently and interact with demonstrating remarkable abilities to
each other. And in the process, they’re converse in everyday language, summarize
fostering greater levels of business vast amounts of information into consumable
productivity, human creativity and insights, and produce useful and relevant
enterprise potential. responses to questions.
One of the most exciting aspects of these
new technologies is the fact they’re “human
by design”. That means they’re not only more
powerful, but also more intuitive to use, more
human-like in their responses, and easier to
integrate into the everyday patterns
of working life.
Technology Vision 2024 | SAP Solutions in the Age of AI
#TechVision2024 Introduction The big picture A match made in AI Meet my agent Going beyond Conclusion
For businesses, the impact will be far Look, for example, at the impact generative
reaching. The technology is allowing people AI is already having in activities like software
to access information, spark new ideas, bring development, exemplified by tools like 43%
data together and generate a variety of GitHub’s Copilot for coding. Then consider
content faster than ever before. It’s massively the massive potential in other industry-
expanding AI’s impact on day-to-day specific domains, including emerging
operations, widening the focus from routine applications like MIT’s experimental
automation and data analysis to task FrameDiff for accelerating drug discovery
augmentation and reinvention. and Google’s Vertex AI search tools for
of all working hours across end-to-
healthcare practitioners.
It's a profound shift in how we all work. And end supply chain activities could be
employees know it—in many cases
generative AI adoption is being driven impacted by generative AI.
organically from the shop floor, not the C-
suite. As this shift plays out, it will start to
reshape the way entire organizations, even
entire industries, operate.
Technology Vision 2024 | SAP Solutions in the Age of AI
#TechVision2024 Introduction The big picture A match made in AI Meet my agent Going beyond Conclusion
Accenture has also developed a range of
What does this mean for companies
generative AI solutions that can be applied to
SAP systems. Take Ling.AI, our tool for
running SAP? expediting the translation of business
The good news? SAP Business AI helps documentation, including SAP-related
customers achieve real-world results with materials. Able to be hosted securely in any
embedded AI capabilities across their business. platform and customizable for individual
Companies have spent the past year business requirements, Ling.AI allows users
exploring these new AI capabilities. Now, Look, for instance, at Joule, SAP’s generative AI to instantly translate documents of any size
however, the focus is shifting to scalability copilot designed to accelerate work and while preserving formatting. We worked with
and ROI. How can business leaders turn all provide smarter insights. Then there’s the SAP multinational retailer Metro to implement
those interesting experiments into scalable AI Launchpad, a multi-tenant SaaS application Ling.AI, saving its teams significant time and
solutions that deliver real and sustainable on the SAP Business Technology Platform, and effort translating thousands of documents
value for the business? the SAP AI Core, which now includes a like SAP training materials into multiple
generative AI hub for experimenting with and languages. We’ve also worked with Metro to
To achieve that value, companies will need to managing the lifecycle of prompts given to identify and prioritize 35 other generative AI
start using their own data to optimize and generative AI models. use cases for its SAP transformation program,
customize generative AI, enabling it to
which we’re actively pursuing together.
deliver more accurate, more relevant, more In data management, too, SAP continues to
context-specific outputs. That means innovate. The company has enhanced its SAP
allowing generative AI to touch core ERP HANA Cloud database with a vector engine, an
systems and data—including the SAP important capability in allowing generative AI
ecosystem of mission-critical solutions used models to draw insights from enterprise
by so many of the world’s large organizations. data. While the SAP Datasphere continues to
enhance the way companies bring together
There’s no question this is a complex data across complex hybrid architectures and
undertaking, given the tightly integrated cloud environments.
nature of many mature ERP deployments.
Technology Vision 2024 | SAP Solutions in the Age of AI
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“Our ecosystem plays a critical role in helping our customers adopt SAP
Business AI to get immediate value from these exciting new
technologies and solutions.
We very much value our long-standing partnership with Accenture.
They are one of our most important partners in the industry for bringing
innovation to our joint customers, which includes activating SAP’s
embedded AI capabilities, such as Joule, and building customer use
cases in the SAP Business Technology Platform with SAP AI Core and the
generative AI hub.
I look forward to our continued collaboration and the outcomes that
generative AI will deliver in both business transformations and
operations for our customers.”
Dr. Philipp Herzig, Chief AI Officer at SAP
Technology Vision 2024 | SAP Solutions in the Age of AI
#TechVision2024 Introduction The big picture A match made in AI Meet my agent Going beyond Conclusion
The trends to watch
Accenture’s Technology Vision 2024
sets out four of the key trends that
enterprises need to pay attention to in
the coming years. Of these, we see
two being particularly relevant for
companies running SAP solutions.
A match made in AI Meet my agent
People are asking generative AI chatbots for The journey to becoming an autonomous
information. This is reshaping our relationship enterprise is being accelerated by advances in
with data and transforming the business of AI, including generative AI and large language
search. It’s also redefining the software and models (LLMs). Soon this will include
data-driven enterprises of tomorrow. ecosystems of AI agents able to operate
more independently.
Technology Vision 2024 | SAP Solutions in the Age of AI
#TechVision
A match
made in AI
Reshaping relationships with data
Technology Vision 2024 | SAP Solutions in the Age of AI
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Employees’ access to business
insights is about to change forever
The “search engine” model of accessing An example? Look at the way electronic
information is now so embedded in everyday health record software company Epic has
life it’s become second nature. Almost 70 integrated GPT-4 into its products to allow
percent of all website traffic begins with clinicians to speedily generate summaries of
search. And it’s no different at work. Whether patient charts. Or the way Morgan Stanley is
we’re searching through emails, looking up applying generative AI to help its analysts
customer details in a CRM or finding a access relevant insights in its vast internal
particular document, we’re all completely knowledge library much faster. Accenture,
accustomed to the idea that accessing too, is enabling a centralized generative AI
information means asking a “digital librarian” search entry point called Amethyst for
for a list of potentially relevant documents or all its employees.
data points.
Generative AI completely flips that on its
head. Because rather than asking for a
curated list of search results, people can ask
a digital copilot real questions and get useful
answers in return. The original vision for
internet search engines is finally becoming
a reality. And it’s already changing the way
industries and enterprises think about
their data.
Technology Vision 2024 | SAP Solutions in the Age of AI
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Intelligent assistance can deliver
ERP insights instantly
How is access to data changing within the functionality for a particular task, explain how It’s a simple and easy-to-use solution
SAP ecosystem? Just consider how many to perform certain activities within the ERP designed to help increase user adoption of
business activities involve combining and system, and retrieve operational insights, SAP Analytics Cloud for improved data-driven
connecting information across ERP systems, such as purchase order status. Users can even decision making.
and with data from other sources, in order to complete certain tasks end-to-end, such as
take a business decision—in finance, supply updating HR information, approving requests Similar capabilities can also help drive a
chain, manufacturing, or any other function. and giving feedback. Designed to integrate broader change management agenda.
All these activities stand to gain from the seamlessly into everyday work, SAP has Accenture, for example, is integrating a
ability of generative AI to bring disparate created Joule to help teams work faster, gain generative AI assistant into its
insights together instantly, in an easily smarter insights, and achieve better GenWizard offering.
consumable form. outcomes from SAP systems.
SAP has unveiled the SAP Joule Certified
These capabilities are already becoming Within SAP Analytics Cloud, SAP also offers a
Consultant solution. By drawing insights from
available within certain SAP applications. Just Ask feature, which allows users to query
a range of relevant documents, the assistant
Take Joule, SAP’s new generative AI copilot, analytics data models by asking questions in
can quickly answer user queries on specific
which is being rolled out across its cloud everyday language. Powered by generative
topics such as new functionalities in their
enterprise portfolio, initially embedded in AI, Just Ask interprets the query and returns
organization's ERP transformation.
SAP SuccessFactors and SAP S/4HANA the results as an intuitive data visualization.
Cloud. Using simple everyday language
within an intuitive interface, users can use
Joule to navigate to the correct SAP
Technology Vision 2024 | SAP Solutions in the Age of AI
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Powering the supply chain nerve center
With supply chain resilience at the top of the integral components of many of these unstructured information in analyst reports, to
agenda for manufacturing businesses, AI capabilities for years—including within SAP improve forecast accuracy. Another key use
capabilities are coming to the fore. Recent applications—the introduction of generative case is explaining the outcome of planning
disruptions have exposed significant AI is opening up new possibilities for supply runs, a task that is often too complex for
vulnerabilities across supply networks, while chain responsiveness. human analysts to complete quickly enough
supply chain resilience and responsiveness to be useful.
are becoming increasingly important revenue
Consider its practical uses in supply chain
drivers. Accenture’s Resiliency in the Making
planning. Generative AI-powered interfaces
research estimates supply chain vulnerability
with access to relevant data sources allow
is costing the world’s businesses a
planners to instantly query, summarize and
staggering $1.6 trillion in missed revenue Press Release: Accenture Expands Partnership
explain disruption alerts and data triggers as
growth every year. with SAP to Help Clients Establish
they come in—a task that could historically
Responsible and Resilient Supply Chains
take hours if not days of painstaking research.
To deliver the necessary resilience, leading
Generative AI can also suggest the most
companies are turning to intelligent supply Highlight Report: Accenture Builds on its SAP
effective corrective actions, such as suitable
chain management capabilities. By building Expertise and Launches the Supply Chain
alternative suppliers, for planners to consider.
supply chain nerve centers based on SAP Nerve Center
technologies, they’re enabling end-to-end
Its potential extends beyond chatbot
control towers, reconfigurable supply chain
interfaces. Accenture, for example, is building
networks, autonomous manufacturing,
a suite of generative AI tools into its Supply
improved demand foresight, predictive
Chain Nerve Center offering, including one
alerting and more. And while classical
that extracts insights from a range of different
machine learning techniques have been
market sources, such as complex
Technology Vision 2024 | SAP Solutions in the Age of AI
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Customer and sales experience are
getting smarter
Customer engagement and sales can also be Similarly, sales representatives can
transformed by this reinvention of human- revolutionize customer engagement and
machine interfaces. Take conversational increase conversion rates by leveraging
commerce, a concept gaining traction in generative AI applications during the sales
retail and consumer goods. By enabling journey. These applications allow them to
customers to articulate their needs to an AI better understand consumer preferences,
engine in the form of a natural conversation, recommend suitable products and enhance
brands can radically simplify the overall shopping experience. In industries
shopping experiences. like consumer packaged goods, these kinds
of solutions can not only improve customer
Imagine you want to redecorate your child’s engagement, but also deliver a significant
bedroom, but you’re stuck for ideas... and productivity boost to employees, freeing up
lack confidence in your painting abilities. By capacity in areas like field sales,
guiding you through a series of questions— administration and customer service.
the age of your child, their likes and interests, However, the success of such initiatives
the size and aspect of their room—an AI relies heavily on seamless integration with
chatbot can propose suitable paint brands, backend systems like SAP, for crucial data on
colors and equipment, as well as provide tips product availability and lead times.
on correct painting techniques.
Technology Vision 2024 | SAP Solutions in the Age of AI
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Finance teams are gaining faster
access to insights
Companies are increasingly developing their Another valuable use case is compiling
own generative AI solutions for use within the finance narratives, such as those used in
SAP ecosystem. Look, for instance, at how preparing for earnings calls or external
Accenture has applied it within its own regulatory reporting. Historically, producing
finance function. It’s developed a financial these narratives requires a huge effort from
advisor tool powered by generative AI which finance teams who need to bring together a
proactively alerts finance teams of any cash wide range of data sources to produce
management variances, financial exposures, summaries of business performance that are
credit utilizations or other issues that need both accurate and cognizant of potential
immediate investigation. Because generative market reaction. Accenture has created a
AI provides the alerts in clear everyday generative AI tool to streamline this process.
language, employees can instantly It’s able to produce an accurate initial
understand the issue and quickly click narrative based on real financial data, which
through to see the relevant report in the SAP finance teams can then fine-tune. Likewise,
system. What’s more, by focusing generative the tool can auto-generate first drafts of
AI on a discrete task, and prompting it with certain regulatory submissions.
data generated through traditional
automation methods, the tool ensures
accurate insights are generated, minimizing
opportunities for “hallucinations” and other
inaccuracies that can creep into broader
LLM implementations.
Technology Vision 2024 | SAP Solutions in the Age of AI
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What to do now?
Prepare the data
As generative AI starts to be applied across The good news? Generative AI itself can Of course, managing data quality is an
the enterprise, business data has never been help. Accenture, for example, has been ongoing activity, not limited to the data
more important. Companies are under ever developing tools for expediting SAP data migration phase.
greater pressure to guarantee data quality migrations with generative AI by helping
and data accessibility. The ability to match design migration plans and generating Companies also therefore need the right
and combine data from different sources, migration code, as well as assisting business governance structure and tools to sustain data
including SAP solutions, across hybrid IT users as they validate migrations by quality after a system goes live. Again,
environments, is becoming critical for a comparing before/after outcomes. generative AI can help by making checks and
whole range of use cases, including offering recommendations to users during
generative AI. Similarly, generative AI can help streamline their data interactions. A US-based retailer, for
SAP data cleansing tasks like deduplication example, is implementing a chatbot that uses
Having a mature and efficient data and removing personal or sensitive generative AI to propose the right UNSPC code
foundation is therefore a prerequisite for information. It can be very effective at during procurement activities, helping ensure
reshaping the way companies access their producing context-specific manufactured the correct code is used. Efforts like these can
data with AI. But data management remains data for testing purposes. And it can also support a ‘virtuous loop,’ where company data
a perennial challenge in most organizations, support data reporting by taking on the is continuously checked and enhanced as part
and the scale of the work involved technical heavy lifting of putting together of an ongoing cycle.
is often underestimated. different reports and dashboards.
Technology Vision 2024 | SAP Solutions in the Age of AI
#TechVision
Meet
my agent
The road to the
autonomous enterprise
Technology Vision 2024 | SAP Solutions in the Age of AI
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The first agent-based enterprises
are on the horizon
Companies have been embedding robotic natural language commands into subtasks for 96%
process automation and AI into business controlling physical robots. It’s true that
operations for many years. As generative AI is there’s a lot of work to be done to make this a
added to the mix, machines are acquiring the reality in most organizations. But the journey
power to operate increasingly independently. to the autonomous agent-driven enterprise
And AI is taking on new roles, evolving from has already begun. And innovative
being assistants that offer advice and insights companies are now building the digital of executives agree that leveraging
into something potentially more powerful— scaffolding that AI agents will operate within,
agents that are able to interact with each as well as upskilling their workforces for this AI agent ecosystems will be a
other, and take action on their own, in the increasingly automated future. Accenture
significant opportunity for their
real world. estimates that 40 percent of all working
hours could ultimately be augmented or organizations in the next three
Soon it will be commonplace to see entire automated by generative AI models such as
ecosystems of these agents operating across GPT-4. As the technology matures, this figure years
the enterprise, chaining together different will likely grow. Companies will need to
decisions and actions, driving automation to prepare for a future in which AI agents and
new levels. Already, cases are emerging, human employees work side by side to drive
where agents can automate activities across value for the business.
scientific research or break down
Technology Vision 2024 | SAP Solutions in the Age of AI
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An ecosystem of AI automation
techniques
Companies already have numerous options Likewise, in transportation and logistics,
for building autonomous ecosystems. companies can build generative AI
Because the journey to the autonomous capabilities on top of their existing AI-
enterprise doesn’t begin and end with powered predictive alerts. For instance, when
generative AI, significant gains can be made a disruption alert is triggered in any SAP
by combining various kinds of automation, solution, instead of being passed
including classical machine learning and immediately to a human employee, that alert
robotic process automation (RPA) as well as can be supplied to a generative AI solution
generative AI, in a broad ecosystem of that can evaluate the various responses the
automation techniques. company could take and present them to the
employee in an easily consumable form,
This can be particularly valuable in situations saving time and effort. Similarly, generative
where accuracy is paramount, such as SAP AI can be combined with other cutting-edge
financial reporting. In financial close variance deep learning techniques in supply chain
reporting, for instance, Accenture’s finance optimization to make valuable insights
teams use classical AI to generate variance available to supply chain planners in an
insights from SAP S/4HANA Cloud as a first actionable way.
step. Then they layer generative AI on top to
summarize and explain those variances. This
combination ensures both high accuracy and
high usability.
Technology Vision 2024 | SAP Solutions in the Age of AI
#TechVision2024 Introduction The big picture A match made in AI Meet my agent Going beyond Conclusion
The evolution towards agent- whether an action is compliant or not. Longer
term, however, it opens the door for other AI
agents to access these policy determinations
based operations
autonomously before deciding whether or
not they should take a particular action.
As AI agents mature and their capabilities another updates planning and scheduling for
grow, companies will have even further production and warehousing. Such
opportunities to drive efficiency, productivity intelligent processes will not be entirely
and innovation. Generative AI will autonomous, of course. To ensure
increasingly be used to help orchestrate Responsible AI principles are rigorously
entire processes, breaking down decisions adhered to, it will be essential to retain a
into discrete steps and calling other tools, human in the loop to validate the actions
agents and physical robots to use its output agents are taking and maintain ultimate
and take the required action. control over operational decisions.
Take the transportation example described Another example is internal audit and policy
above. Ultimately, AI agents will enable compliance. For finance teams in particular,
almost completely autonomous responses to determining if a particular activity is
these kinds of inbound logistics disruption. permitted within the terms of the company’s
Having identified the initial disruption, one policies can be a difficult and time-
agent will assess various alternative consuming task. Increasingly, however,
responses by analyzing operational data organizations are looking to expose their
from the ERP, scheduling and manufacturing repositories of policy documentation to
execution systems. The next agent will then generative AI. In the short term, this will allow
trigger communication with alternative employees to ask an AI agent via a chatbot
suppliers and/or logistics providers, while interface for a reasoned explanation of
Technology Vision 2024 | SAP Solutions in the Age of AI
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New human-machine interfaces
Generative AI is also fundamentally changing Increasingly, site operators and engineers Operations will become not only more
how people interact with physical machines. will use generative AI to interact with efficient but also safer by ensuring that
Look, for example, at the researchers who systems in a more intuitive and efficient workers receive the right information at the
put ChatGPT on board a Boston Dynamics way—without even having to use screens. right time, without the need to navigate
robot, allowing people to use natural They’ll be able to ask voice assistants simple through complex interfaces or perform
language to command the robot or ask it questions like “I'm stuck here, what should I multiple transactions.
about its previous tasks and receive a clear do next?” and the system, understanding the
response in plain English. It’s easy to see context, who the individual is, and where Workplaces themselves will become more
how this might be applied to robotics in an they are, could then allocate them the most adaptive and responsive. By leveraging the
industrial setting. suitable task. data model of a plant’s operations and
applying generative AI, it's possible to create
a dynamic and self-updating system that not
only guides the operators through their daily
tasks but also anticipates needs and adjusts
to changes in real time. Plants will be able to
operate increasingly autonomously, much
like modern airplanes, with minimal human
intervention, while machine learning
algorithms continuously learn, predict
and optimize based on the real-time
data generated.
Technology Vision 2024 | SAP Solutions in the Age of AI
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Streamlined ERP and harnesses multi-agent capabilities, with “The vision is to have touchless
individual AI agents for specific types of
programming, data analysis, code reviewing testing…with AI writing the
implementations and more. This will allow developers to
simply upload a document containing scripts and documenting the
business requirements and functional
One area generative AI is already having a information—and have the ecosystem of results in a format that our IT
visible impact is custom code development. agents work collaboratively to translate it into
teams can instantly understand
This has significant implications for ERP a set of programming requirements and
implementation and maintenance. associated code. These capabilities will
and use.”
Accenture, for example, has been leveraging transform automation across the ERP
LLMs to optimize the customization process software development lifecycle, including
integral to large SAP ERP deployments. testing as well as coding. Sharmita Srivastava, Head of Global
By specifying their various technical
ERP at Bristol Myers Squibb
requirements through a chat interface, SAP itself is also now offering generative AI
developers can receive contextualized code development solutions. SAP Build Code, for
suggestions, whether that’s for custom example, is an environment enabling
reporting, a particular user interface or form, application development with Joule copilot.
a new workflow, or similar customizations. Optimized for Java and JavaScript, it’s been
designed to help developers with tasks such
Applicable to both greenfield and brownfield as coding, testing, integrations and
deployments, as well as application application lifecycle management through
maintenance scenarios, the solution aims to natural language prompts. SAP Build Code
accelerate the routine side of development includes Joule copilot, which allow
work and improve code consistency and developers to generate artifacts for the SAP
quality. It also adopts a multimodal approach Cloud Application Programming Model.
Technology Vision 2024 | SAP Solutions in the Age of AI
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Fine-tuning offers a foundation for
greater relevance
Some companies are thinking bigger than the resources and time involved mean this Such LLM models can be trained on the
simply using LLMs like GPT-4 and are option is only typically available to the largest company’s corpus of both human language
customizing pretrained models or even companies with the deepest pockets. and business data, including text and
building their own. Fine-tuning generative AI multimodal data as well as public sources.
on a company’s data allows generative AI to Global businesses, for instance, are looking to
develop the context-specific expertise that’s build their own LLM, leveraging its extensive
critical for supporting day-to-day operations. proprietary sources of biomedical data in
Whereas creating an LLM from scratch can order to improve generative AI’s performance
deliver even greater relevance and in specialized tasks.
competitive advantage,
Technology Vision 2024 | SAP Solutions in the Age of AI
#TechVision2024 Introduction The big picture A match mad"
89,accenture,Reinventing_Enterprise_Operations.pdf,"How reinvention-ready
companies are driving growth
and relevance with gen AI
Reinventing Enterprise Operations
Authors Preface
“We’re in a period of profound change.” These are Through our extensive experience in driving $39 billion
Arundhati Chakraborty
opening words from our 2023 report on how intelligent in P&L impact for over 2,000 clients, we have identified
Group Chief Executive,
operations are absolutely key to a reinvention strategy that a comprehensive operations strategy including
Accenture Operations
that sets a new performance frontier. Talent, Assets & Platforms, and Methods & Processes is
crucial. And our new research underscores that while
many companies have accelerated their shift to more
What’s changed since then? The now measurable
sophisticated operations, only the top performers are
impact of generative AI—for every business, in
effectively leveraging generative AI to drive significant
every industry.
value. These frontrunners are reinvention-ready,
Yusuf Tayob
moving faster by leveraging their digital core to put
Global Communications,
To thrive in today’s landscape, organizations must
Media & Technology hyper-automation and AI to work and amplify their
undergo transformative change, with gen AI playing a
Industry Practices Chair impact across the business.
pivotal role. And a gen AI-powered journey to Intelligent
2
Operations is absolutely essential to creating value
We anticipate that companies will take on even more
from these transformations.
meaningful reinventions to cultivate sustainable,
mature, Intelligent Operations with the support of
Intelligent Operations represent the pinnacle of
Bhavana Rao gen AI—and achieve remarkable gains in growth,
operations maturity, anchored by a digital core designed
Chief Growth & Strategy productivity, and profitability.
Officer, Accenture for perpetual adaptability. A purpose-built digital core
Operations
enables organizations to meet their evolving needs while
The time is now to become reinvention ready.
seamlessly integrating the latest emerging technologies.
snoitarepO
esirpretnE
gnitnevnieR
04 17
What’s at stake: The path to
Competitive Intelligent
relevance Operations
06 The business case for Intelligent Operations 19 Implement a domain-centric approach to
data modernization
12 Knowing the enablers of Intelligent Operations
22 Embrace a talent-first reinvention strategy
16 A modernized data foundation:
The gateway to gen AI 25 Link business and tech teams to co-own reinvention
29 Adopt leading processes to drive
business outcomes
3
31
In this report Looking ahead:
Organizing for
reinvention
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What’s at stake:
Competitive
relevance
New technologies, consumer expectations, climate change, hybrid work and other
factors are driving massive structural shifts in how organizations operate today. And the
changes are happening at lightspeed. More than ever, enterprises must reinvent, quickly
and continuously, to stay relevant and competitive in this evolving landscape.
That, in short, is the prize for reinvention:
Competitive relevance—and all that goes with it.
Growth. Profitability. Innovation. Market dominance.
A foundation for the future.
The stakes are that high, and most executives know it. Our research shows that
92% of C-suite leaders recognize the urgent need to reinvent and know that
generative AI is key to reinventing at scale and at speed.1 Eighty-one percent
of executives believe that rapid experimentation is key to scaling gen AI across 5
their enterprises over the next six to 12 months. And seven out of 10 (71%) say
they need to be less risk-averse when it comes to scaling gen AI use cases.2
Clearly, a majority of executives understand the urgency of reinventing with
gen AI. But are their enterprise operations ready to support such reinvention?
snoitarepO
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The business case
for Intelligent Operations
6
Our 2024 survey was structured with that question in mind.
We spoke to 2,000 senior executives (57% C-level or equivalent)
across 15 industries and 12 countries (Figure 1). Our goal was to
assess the extent to which enterprise operations are prepared
to drive business outcomes with gen AI.
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Figure 1: Survey demographics
Share of survey respondents by country Share of survey respondents by industry
China 15% Automotive 5%
Japan 13% Banking 10%
India 10% Chemicals 7%
Australia 5% Communications 4%
Brazil 5% Consumer Goods and Services 10%
Healthcare 5%
High Tech 10%
United States 20%
Insurance 7%
Canada 5%
Life Sciences 8%
7
Media 5%
United Kingdom 7% Oil and Gas 5%
Germany 6% Retail 5%
Spain 5% Software and Platforms 5%
Italy 5% Travel 7%
France 5% Utilities 5%
n=2000
snoitarepO
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Share of survey respondents by organizational function Share of survey respondents by role level
CEO 10% 21%
Finance 8%
HR 7%
Supply Chain 8%
Procurement 8% 43%
36%
Strategy 11%
Operations 8%
Technology 11%
Marketing 5%
8
Sales 5%
C-Level
Customer Service 5%
Director Level
Legal, Risk and Compliance 7%
Sustainability 4% VP Level
Other* 2%
n=2000
Note: (*) Other includes GBS Heads and Global Process Owners
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Organizations were evaluated across four criteria of operations readiness to
assess where they are on the Intelligent Operations continuum (Figure 2).
Organizations characterized as “Foundational” are in the earliest stages of their
journey while those assessed as “Reinvention-ready” have fully modernized,
AI-led processes.
01 Foundational 02 Automated
Foundational assets that Automation and
drive cost optimization adoption of descriptive,
and basic SLAs predictable AI 9
03 Insights-driven 04 Reinvention-ready
Fully modernized Hyper-automation and
data geared toward AI at scale to drive
customer experience business outcomes
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Figure 2: How we assess Intelligent Operations
While basic operations cost, and
Service-level agreements (SLAs) Decentralized data ownership
might be in place, automation of Deployment of low-code/ by domains with federated
tasks has not begun or is at very no-code automation for governance. Fully modernized data
early stages. exponential efficiency. foundation and end-to-end
Mature data products curated by platform integration, that
There is no AI strategy and Traditional AI deployed in domain experts and hosted on a supports hyper-automation
execution roadmap in place. nearly all processes, redefining self-service marketplace. across most processes.
performance benchmarks.
Data is stored in silos without On-demand, near real-time Application of traditional AI
much interconnectivity across Gen AI POCs being productized analytics, advanced modeling to augment tasks at scale
transaction systems; and is mainly across multiple functions and and data science, for data-driven and rapid scaling of gen AI
analyzed for historical reporting. business units. decision making. use cases.
lanoitadnuoF
detamotuA
nevird-sthgisnI
ydaer-noitnevnieR
8% 56% 20% 16%
2%
Reinventing
Reinvention-ready companies
that are already scaling
gen AI and driving exceptional
business outcomes.
Specialized talent fully prepared for AI-led enterprise reinvention. Strategic workforce planning, role reinvention and continual
Talent
upskilling, and flexible talent sourcing to meet talent needs.
10
Technology and business teams jointly own how assets, platforms and products are developed, with ecosystem partners
Assets & platform
to leverage descriptive, predictive and gen AI assets for business outcomes.
Rule-based and advanced processes have gone through end-to-end transformation to achieve high levels of standardization.
Methods & process
Process mining and benchmarking drive “best-in-class” performance.
Four criteria—and learnings from thousands of client assessments—are used to assess organizations across the Intelligent Operations journey.
snoitarepO
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A lot has changed since we last assessed organizations on Intelligent
Operations back in 2023. Over the past year, the number of Reinvention-ready
companies has nearly doubled from 9% to 16%. These are organizations that
have modernized their data foundations to support strong business outcomes,
achieved end-to-end platform integration and are hyper-automating most of
their processes. They are also successfully applying traditional AI to augment
tasks at scale and are rapidly scaling gen AI use cases to drive new growth.
Compared to their Foundational counterparts, Reinvention-ready
organizations have:
2.5x 2.4x 3.3x
higher average greater higher likelihood to
revenue growth improvements in succeed at scaling
productivity high-value gen AI
use cases
11
Furthermore, a small but elite subset of Reinvention-ready companies—
just 2%—are already deploying gen AI at scale and are reporting
exceptional returns on their investments.
These organizations do not have a secret weapon or special superpower
that allows them to achieve these outcomes. They have modern, mature
operations that are held up by three critical enablers.
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The enablers of Figure 3a: The enablers of Intelligent Operations
Intelligent Operations
Talent is fully prepared for tech-led Methods and processes are applied
enterprise reinvention across all business functions
To achieve Intelligent Operations, organizations must Reinvention-ready 92% Reinvention-ready 87%
address how they transform Talent, Assets & Platforms,
and Methods & Processes. Our 2024 research shows that
Automated
19%
Automated
20%
organizations have begun to take a more holistic view of
reinventing enterprise operations and are paying equal
attention to the three enablers of Intelligent Operations. Foundational 1% Foundational 5%
They’re making all three a priority. This is a departure from
2023 when many organizations focused on just one or two
enablers at a time. Organizations now realize that the three
enablers are like the legs of a three-legged stool: each
Extensive collaboration to reinvent
plays a crucial role in supporting reinvention with gen AI.
assets and platforms
Reinvention-ready 87%
Reinvention-ready companies excel at developing
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all three enablers in parallel and applying them in
unison (Figure 3a).
Automated
18%
Foundational 1%
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By comparison, organizations at the Foundational and Automated levels struggle to successfully While all three enablers are critical to reinvention, and all three
apply the three enablers (Figure 3b). should be pushed forward in unison, it’s important to note
that each phase of the Intelligent Operations continuum has a
primary enabler (Figure 4a).
Figure 3b: The enablers of Intelligent Operations
For example, Assets & Platforms is the primary enabler for
companies that are looking to transition from Foundational
Talent is relatively inflexible and Methods and processes are in to Automated operations. A key ingredient at this stage
unprepared for tech changes limited use or not applied at all is a governance model for key automation projects with
feedback loops for business needs. Companies that empower
Reinvention-ready 1% Reinvention-ready 1%
business and tech teams to jointly create assets and platform
development roadmaps are the ones that graduate successfully
Automated 31% Automated 30% to Automated operations.
Foundational 82% Foundational 78% Similarly, Methods & Processes is a primary enabler for
organizations at the Reinvention-ready stage. Compared to
organizations at the Insights-driven level, Reinvention-ready
companies are able to execute process mining as well as internal
13
and external benchmarking to drive best-in-class performance.
Collaboration to reinvent assets and
Their processes have been transformed end-to-end with a high
platforms is limited or non-existent
level of platform integration and hyper-automation. Almost
nine out of 10 (87%) of Reinvention-ready companies excel at
Reinvention-ready 2%
developing Methods & Processes compared to only 47% of those
at the Insights-driven stage (Figure 4b).
Automated
34%
Foundational 88%
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Figure 4a: Primary enablers in the journey to Intelligent Operations
Foundational Automated Insights-driven Reinvention-ready
Talent Employees are restricted to their respective Machines (automation, technology, analytics) Machines automate and augment human Machines augment human work in nearly
functions and processes. automate parts of human roles for some work for major business processes. all processes.
business processes.
Talent strategies are relatively inflexible, The organization uses an internal talent Organization is equipped with specialized
and teams continue to be measured on The organization is beginning to promote marketplace for on-demand collaboration talent to accelerate AI adoption.
traditional SLAs of productivity and output. talent movement across functions. where dynamic project teams can rotate on
and off projects as per strategic needs. Strategic workforce planning, role reinvention
and continual upskilling ensures a strong
talent pipeline aligned to strategic priorities.
Assets & Technology and domains/business Governance models for key projects have Technology and business functions Technology and business teams, partner to
platforms functions make siloed decisions on been established with feedback loops for collaborate across some functions to drive drive organization’s strategic roadmap and
transformation programs. business needs and technology priorities. focused investments and deployments. integrate ecosystem partners.
Governance over key projects is ad hoc, Joint products roadmaps developed for Joint governance models for key projects Technology and business experts, partner
with limited joint executive sponsorship select domains/functions. allow the organization to quickly adapt to to identify, create and scale AI + Automation
14
from technology and business functions. changing business needs. use cases.
Methods & Fragmented, non-standard processes that Leading practices and internal Advanced standardized processes Process mining as well as internal and
processes have gone through very few lean/process benchmarking data is used to measure with policies and practices that are external benchmarking is used to drive
improvement cycles. some processes. largely aligned. “best-in-class” performance.
Internal benchmarking data is Moderately rule-based processes that Process mining and internal End-to-end transformed processes with
unstructured and dated. have gone through basic improvements benchmarking data is used to drive high level of platform integration and
with point solutions. process improvements. hyper-automation.
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Figure 4b: Primary enablers in the journey to Intelligent Operations
2023 2024
Deploying class-leading methods & processes,
Reinvention-ready 9% 16%
including process mining and benchmarking,
separates Reinvention-ready operations from being
Insights-driven.
Insights-driven (47%) vs. Reinvention-ready (87%)
Insights-driven 25% 20%
Developing talent ready of AI-led reinvention is
a topmost enabler for companies moving from
Automated operations to Insights-driven outcomes.
Automated (19%) vs. Insights-driven (61%)
Automated 48% 56%
Bringing together business and tech teams to jointly
develop assets & platforms is the most important
enabler to move from Foundational operations to
Automation outcomes. 15
Foundational 18% 8%
Foundational (12%) vs. Automated (66%)
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A modernized data foundation: Figure 5: Momentum from modernized data
The gateway to gen AI
Data assets are modernized Organizations have clear
for gen AI use cases data governance roles
In addition to addressing all three enablers, Reinvention-ready Reinvention-ready 61% Reinvention-ready 62%
organizations also have much higher levels of data modernization
than their counterparts at other levels. They recognize the imperative
Automated
40%
Automated
37%
of having the right data strategies and core digital capabilities in
place to effectively leverage gen AI. Their data assets are designed
for gen AI use cases, they have clear roles defined for data Foundational 29% Foundational 29%
governance and they are able to trace all of their data throughout
the lifecycle, all the way back to the source. Our research shows
that a modern data foundation is yet another threshold separating
Reinvention-ready companies from their peers (Figure 5).
Organizations can trace data from
the source through the lifecycle
Reinvention-ready 60%
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Automated
34%
Foundational 27%
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The path to
Intelligent Operations
Our 2024 research as well as anecdotal evidence from more than 1,000 completed gen AI
projects show a correlation between an organization’s investment in Intelligent Operations
and its ability to scale gen AI. We’re finding that companies with Intelligent Operations are
able to accelerate their use of gen AI, which then drives the evolution of their operations,
which then extends their use of gen AI and so on. It’s a virtuous but co-dependent cycle.
Our research indicates that the number of
organizations with Intelligent Operations
is increasing each year.
But charting out a well-defined roadmap continues to be one of the biggest
18
challenges they face. What’s the best path forward? What are the non-negotiable
elements that must be addressed at each stage of the journey? Here are four
actions organizations should take to chart a course, identify gaps and move
forward with Intelligent Operations.
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Implement a domain-centric
approach to data modernization
Reinvention-ready companies have centralized data governance and a
domain-centric view of data modernization. This creates a strong data
foundation that is ready for AI-led reinvention.
One way to evaluate a data foundation is to evaluate how the three 19
enablers—Talent, Assets & Platforms, and Methods & Processes—interact
with data on an everyday basis. Do people have a clear understanding
of how to create, handle and consume data? Are processes and tools
connected across functions so different teams—sales, supply chain,
service, HR, finance, R&D—all have access to the same data and analytics
using their favorite tools? Is data structured in a standardized way, with
security and accessibility baked in, using common data formats that allow
it to be accessed by AI tools across the business?
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These are the hallmarks of a modern data foundation. And it’s where most companies struggle. Modernizing
the data foundation takes a significant amount of time and resources. Our research shows that 71% of
Foundational organizations have a data foundation that isn’t modernized enough to get the full value of gen AI
across the organization.
Access to quality data is a key consideration. More than one in three Reinvention-ready organizations enable
high-speed access to quality data and metadata assets that are free of inconsistencies and redundancies.
This is made possible by placing equal responsibility on business teams and domain experts to modernize the
data foundation (Figure 6).
15%
19%
20
35%
26%
Reinvention-ready Automated
Insights-driven Foundational
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“
I think understanding the data
governance process is so critical.
Communicating that across the
organization requires a lot of education
which should not be underestimated
even though it may be obvious.”
Chief Data Officer, 20
Global Real Estate Services Provider
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Figure 6: Error-free data assets with supporting metadata
Client story
New data foundation drives $70M in new growth
This industrial giant that manufactures tools and New managed service centers based on the SAP
industrial equipment has grown rapidly by placing S/4 HANA platform are now used to deliver the
the right bets—not only on new products and ways of processes. The company also implemented a new
working but also on digital technologies to optimize data foundation—which involved a revamp of data
its finance operations. Digital transformation and strategy and governance—alongside a Center of
growth initiatives were crucial to the company’s Excellence to boost analytics capabilities. Using
ability to integrate acquisitions and support rapid Accenture’s AI-powered SynOps platform, this client
growth. Accenture collaborated with the company has streamlined its operations, centralized 80% of its
to develop an agile and resilient finance operating accounting processes, improved efficiency by 47%,
model, centralizing key processes like Procure to Pay achieved 50% touchless transactions and generated
(PTP), Order to Cash (OTC), Record to Report (RTR) up to $70 million in new business value.
and customer service.
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Embrace a talent-first
reinvention strategy
Leading organizations put people at the center of reinvention.
In the age of AI, that means reshaping the workforce so that new roles align to
business needs as the technology evolves. It means offering comprehensive
training to workers so they can thrive in their roles and take full advantage of
the power of gen AI. It means reinventing work and rethinking processes and
entire workflows to gain a clear view of where gen AI can have the most impact
in serving customers, supporting people and achieving business outcomes.
This deep dependency on people is often overlooked while planning for 22
gen AI-powered reinvention. Our research shows that 82% of Foundational
organizations do not have a talent reinvention strategy in place. They’re not
planning ahead to meet workforce needs, acquire new talent or train and
upskill workers to prepare them for gen AI-led workflows.
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By comparison, 92% of Reinvention-ready Change management programs should be
organizations have a well-defined talent designed to help teams adapt to new workflows
strategy to address workforce planning, role and embrace AI-driven process innovations.
reinvention and continual upskilling (Figure 7).
This ensures they have a strong talent pipeline
that is aligned to their strategic priorities.
Figure 7: Organizations investing in a three-pronged talent strategy
1%
Talent strategy must go beyond skills
development. Programs and policies must be
in place to ensure employees are physically,
emotionally and financially safe, that their work
is meaningful and their everyday goals motivate
19%
them. This has the added advantage of
attracting new hires with different backgrounds
and lived experiences who can bring cognitive
92%
diversity and informed perspectives to the
continuous journey of reinvention.
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A strong talent strategy will also address
upskilling and learning. Training programs for
61%
non-technical teams should focus in three
areas: AI literacy programs should teach the
basics of gen AI including its capabilities,
limitations and risks. Practical applications
training should use workshop and sandbox
Reinvention-ready Automated
environments to demonstrate how AI can
enhance specific business functions like
Insights-driven Foundational
marketing, customer service and operations.
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“
A key measure of success is
productivity and reducing repetitive
tasks that give our people more time
to spend with our customers and
working on dealing with the more
interesting parts of their role. Project
specific measures include improving
code quality and simplifying processes
so that the bank can reduce servicing
times for customers.”
Les Matheson,
Group Executive Digital,
Data and Chief Operating Officer, NAB
Client story
Reinventing HR services and employee experience
HSBC, one of the world’s leading financial solutions from SAP, ServiceNow and MuleSoft
institutions, launched a global initiative to to streamline HR processes and improve
enhance employee experience and increase service accessibility. HSBC employees are
productivity with the goal of boosting now able to instantly access information to
shareholder returns and customer satisfaction. make informed decisions and can access HR
This transformation required HSBC to modernize services and support faster than ever before.
and digitize its HR function to address the The improvements encompass core services
challenges posed by manual, fragmented legacy such as payroll and workforce administration as
processes across different countries. well as new capabilities in talent management,
career development and performance
management. With faster access to data-led
Accenture helped HSBC implement technology
insights, HSBC leaders are better equipped to
and change management solutions including
make strategic decisions regarding their teams
experience design, global process configuration
and personnel. 24
and localization to comply with country-specific
regulations. The overhaul introduced digital HR
Intelligent Operations enables gen AI, which improves
Intelligent Operations, which further advances gen AI.
It’s a virtuous but co-dependent cycle.
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Link business and tech teams
to co-own reinvention
Gen AI is more than a technology. It’s the driver of a cultural shift that affects the
entire enterprise.
So it’s vital that tech and business teams co-own outcomes and work together
when making decisions around gen AI. That means reimagining how teams
collaborate and design solutions.
This is one of the starkest differences between Reinvention-ready and
Foundational operations. In our research, 87% of Reinvention-ready companies
stand out for “extensive collaboration” between their tech and business teams. 25
These organizations have created a culture of cross-function collaboration where
formerly siloed teams work together to identify and prioritize gen AI use cases
that align to the organization’s strategic goals. Collaboration between teams then
drives innovation as both teams jointly own how assets, platforms and products
are developed to leverage the full capabilities of gen AI across the enterprise.
By comparison, 88% of companies with Foundational operations say there is little
or no collaboration between their tech and business teams. This hinders their
ability to adopt modern data, automate processes and deploy AI.
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At the end of the day, gen AI is a technology, so internal resistance to change. CEO oversight also signals
tech teams play a vital role in merging it with the project’s importance to stakeholders, boosting
the organization’s digital infrastructure. Tech morale and commitment across the organization.
teams must assess and select from among Organizations that are Reinvention-ready understand
various AI technologies that can be integrated this and are driving large gen AI-powered transformation
into the existing tech stack. They need to programs directly from the CEO’s office (Figure 8).
manage the technical aspects of integration,
including software development, system
configuration and data integration and then
Figure 8: Tapping CEOs to lead adoption
maintain the overall performance of the tools.
They also must ensure that ecosystem partners
27%
and applications are seamlessly integrated and
help assess ongoing performance to make
sure gen AI solutions remain up to date and
effective. All of this should happen with direct
input from business teams. Together, business
36%
and tech teams create the gen AI roadmap,
share decision-making authority and define
53%
goals and outcomes.
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An organization’s CEO should take a
leading role in fostering cross-departmental
47%
collaboration, prioritizing resources and
nurturing a culture of innovation. Under the
CEO’s leadership, tech and business teams
should be assigned equal responsibility
for identifying the path forward for gen AI
Reinvention-ready Automated
adoption. The CEO’s influence breaks down
silos, streamlines decision-making and reduces
Insights-driven Foundational
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“
As you start with a large
transformation program there is
always a capability gap. Take time to
select the right team from business
and technology, and let collaboration
bridge the capability gap.”
Chief of Revenue Growth,
Global Personal Care and Cosmetics Company
Accenture story
Accenture links business and tech teams to co-develop gen AI assets and platforms
Scalable gen AI use cases by function
Layer 3
Digital Platforms Finance Operations Supply Chain Marketing & Sales
HR Transformation
Domain-specific gen AI Transformation Transformation Transformation
use cases
Business Teams
Accenture SynOps
Technology Teams Common assets and services layer
Broad use cases with Multi-language search and extract Development Developer co-pilots
Layer 2 multi-department applications Multi-language document creation Enablers AI driven workflows
27
Data & AI Backbone
Common assets and services
for gen AI foundation Prompt Library, Knowledge base-as-a-service, Translation, etc.
gen AI digital foundation Cloud LLMs/ On Site LLMs with a custom wrappers
Trust & Safety, Quality, Accuracy, Ethics and Responsibility
Enterprise Digital Core
Layer 1
Digital Foundation Cloud-first Infrastructure Continuum Control Plane Security Composable Integration
Enterprise Digital Core
optimized for gen AI
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“We have set up a
transformation office under the
CEO. It tackles the multiple
mobilization challenges that
require a dedicated effort. It is
like a SWAT team handpicked
from Operations, HR, Finance,
and other enterprise functions.”
VP, Strategy & Business Transformation,
Tier-1 Automotive Supplier
Client story
Marrying tech innovation to human ingenuity
National Australia Bank (NAB), one of Australia’s four and protected is also at the forefront of minds at NAB,
major banks, is focused on developing products and and strict data and AI guardrails are applied to embed
solutions that meet the real needs of real people. safety from day one.
Led by NAB’s CEO and executive team, its people
are seizing on the new opportunities that allow them
The bank started with six use cases in mid-2023 to
to spend more time on complex matters and better
prove that each project could help cut the ‘drudgery’
support customers.
for its people. More than 20 use cases are now
being tested across the company and are showing
NAB has been assessing new ideas and opportunities compelling results. Legal document reviews have been
with gen AI through several lenses. Shortlisted ideas cut down from three days to one and higher-quality
are put into test-and-learn development cycles to code is being developed faster. Simplified, automated
guide their evolution for greatest value, return and processes are also improving customer outcomes –
impact for customers. How data would be governed a key measure of success.
28
“We want to ensure we are investing in the right place and not just building ‘cool
toys’ that do not meet our customers’ needs. That’s why the development of use cases
for gen AI is the responsibility of individual business units. They are best placed to
determine how investment will best support the needs of their customers.”
Les Matheson,
Group Executive Digital, Data and Chief Operating Officer, NAB
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04
Adopt leading processes to
drive business outcomes
According to our research, 87% of companies that are Reinvention-ready
say they have applied leading cloud-based practices to business processes
and process mining and have enabled internal and external benchmarking
to drive performance. By comparison, only a small minority of companies
in the Foundational and Automated tiers of Intelligent Operations— 5% and
20% respectively—have done the same.
Cloud-based process mining calibrates internal and external benchmarks
so it’s easier to visualize process gaps. This can speed gen AI adoption
by providing clear insights into operational inefficiencies while flagging
29
opportunities for improvement. Organizations get the data they need to
analyze their processes and identify areas where AI can add the most
value. With accurate benchmarks, organizations can set realistic targets
and simulate the impact of AI interventions using digital twins. This data-
driven approach ensures that AI initiatives are aligned with strategic goals to
improve decision-making and drive exceptional business outcomes at scale.
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None of this can happen without clean, readily available data. Figure 9: Speeding access to clean data
Companies that are Reinvention-ready stand out for their ability to
access that data at speed (Figure 9).
27%
Centralized data governance is "
90,accenture,Accenture-Work-Can-Become-Era-Generative-AI.pdf,"Work, workforce, workers
Reinvented in the age of generative AI
Contents
03 04-08 09-13 14-17 18-25 26-40 41-42
Preface Executive The gen AI Conflicting The trifecta of Gen AI GPS Prospects
summary state of play views erode opportunities: ahead
Charting the path
trust economy,
to realize gen AI’s
business,
full potential
people
Accelerator 1:
Lead and learn in
new ways
Accelerator 2:
Reinvent work
Accelerator 3:
Reshape the workforce
Accelerator 4:
Prepare workers
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Contents | Preface | Executive summary | The gen AI state of play | Conflicting views erode trust | The trifecta of opportunities | Gen AI GPS | Prospects ahead 2
Authors Preface
Ellyn Shook Generative AI has burst onto the scene. It appeared fast and is evolving even faster. To date, our
Chief Leadership & Human teams have already worked on over 700 client projects. We’re seeing what success looks like—
Resources Officer and sharing it here.
Accenture
We know that achieving gen AI’s full potential hinges not just on a strong data foundation, but
also on leaders’ willingness to lead and learn differently. This makes it possible to navigate the
risks and seize the huge opportunity before us to reinvent work, reshape the workforce and
prepare people, responsibly. No other modern technology has impacted these areas to such a
degree—and we’re all about to experience it. However, similar to when digital came on the scene,
the rush for pilots and experimentation is too often leaving robust talent strategies behind.
Paul Daugherty
To spotlight the importance of focusing on talent early, our research brings data to the
Chief Technology &
groundbreaking reality that we’re seeing every day. As you dive into these insights, know that this
Innovation Officer
is the start of a journey like no other. We're exploring how gen AI is changing the game and how
Accenture
we can all come out ahead – as businesses, as leaders and as people.
Here's to exploring the age of generative AI together.
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Contents | Preface | Executive summary | The gen AI state of play | Conflicting views erode trust | The trifecta of opportunities | Gen AI GPS | Prospects ahead 3
Executive summary
Work reinvented, workforce reshaped, workers prepared
“Generative AI has the potential to If the paragraph on the left sounds like a robotic answer to the question,
“How will generative AI (gen AI) change work?” that’s because it is—it’s
significantly change the nature of work
a response generated by ChatGPT. While it’s correct to a degree, this
across various industries and fields. While answer overlooks how gen AI’s impact on value chains will fundamentally
transform the nature of work, reshaping how businesses deliver value
generative AI has the potential to bring about
and better experiences for employees and customers. Such details and
numerous benefits, it also raises ethical and insight are ones that only humans can bring to monumental questions
such as this. An even better answer is much shorter:
societal concerns, including issues related to
It depends on people and how they use it.
job displacement, data privacy, protection of
More specifically, it depends on leaders having the courage, knowledge
intellectual property, bias and the responsible
and understanding to shape the future. They will need to prioritize
use of AI. The impact of generative AI on
human-centered change efforts and learn in new ways to scale this
groundbreaking technology responsibly, to create value and to ensure
work will depend on how it is implemented,
that work improves for everyone. This means setting and guiding a vision
regulated and integrated into various
for how to reinvent work, reshape the workforce and prepare workers
for a generative AI world, while building a resilient culture to navigate
industries and organizations.”
continuous waves of change.
Contents | Preface | Executive summary | The gen AI state of play | Conflicting views erode trust | The trifecta of opportunities | Gen AI GPS | Prospects ahead 4
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Invest in people to
reinvent work
The impact and importance of what people
do with generative AI today and tomorrow
cannot be understated. In this age, gen AI
is influencing more than just productivity;
it’s impacting processes across the value
chain, changing the work itself.
Due to its ubiquity across job types and potential to
create exponential impact,1 gen AI is poised to provide
the most significant economic uplift and change to work
since the agricultural and industrial revolutions. The early
industrial revolution, for example, was marked by mass
production and standardized outputs. The age of gen AI
will be defined by not only productivity gains but also by
enhanced human creativity and potential to shape more
innovative employee and customer experiences.
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Contents | Preface | Executive summary | The gen AI state of play | Conflicting views erode trust | The trifecta of opportunities | Gen AI GPS | Prospects ahead 5
For the first time in history, we are embracing a generation In fact, our research shows that generative AI offers Most organizations view gen AI as a path to greater
of technology that is “human by design.” Gen AI’s a trifecta of opportunities: it can accelerate economic innovation, presenting more of a revenue-growth play
effectiveness hinges on human input to drive quality value and increase productivity that drives business than a cost-reduction play. And when it comes to the
outputs—whether they’re straightforward, like the draft of growth, while also fostering more creative and meaningful workforce, Reinventors (representing 9% of organizations)
an email, or complex, like a financial forecast. This shift work for people. are 2x more likely than other organizations to anticipate
will lead to a reinvention of work with more human-centric productivity gains of 20% or more in the next three years.
Comparative analysis of global gen AI adoption and
processes across the entire value chain. By synthesizing And two out of three strongly agree that, with gen AI,
innovation scenarios shows that more than $10.3 trillion
data, comprehending natural language and converting work will become more fulfilling and meaningful.4
in additional economic value can be unlocked by
unstructured data into actionable insights, gen AI is
2038 if organizations adopt gen AI responsibly and at
democratizing business process redesign, empowering
scale (industry by industry, value chain by value chain).2
everyone—from frontline workers to lab scientists to design
This potential is reflected in CxO optimism, with most
professionals—to reshape their own workflows.
believing gen AI will ultimately increase their company’s
Gen AI can also bring workers closer to their customers. market share, and 17% anticipating an increase in market
Imagine a banking scenario where gen AI transforms the share by 10% or more.3
customer experience: from using AI-powered analytics 95
to gain a comprehensive view of customer needs, to
customizing financial products and services based on %
of workers see value in working with gen AI—but their
those needs. This end-to-end change not only streamlines
top concern is that they don’t trust organizations to
operations; it also helps bankers know their customers
better, identify new products and improve experiences ensure positive outcomes for everyone.5
for both customers and employees. All these outcomes
positively impact the bottom-line.
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Contents | Preface | Executive summary | The gen AI state of play | Conflicting views erode trust | The trifecta of opportunities | Gen AI GPS | Prospects ahead 6
Our research explored the factors that contribute to this There’s a way, however, for leaders to close the trust gap Our research also highlighted the need to meet peoples’
trust gap. Currently, three-quarters of organizations globally and accelerate gen AI integration: Look at and emulate fundamental human needs, so they feel Net Better Off15 at
lack comprehensive strategies and initiatives to ensure how leading organizations are leveraging gen AI in ways work. Why? Because it is a clear pathway to strengthening
positive employee experiences and outcomes with gen AI.6 that are better for business and better for people. trust and getting people ready for, and comfortable with,
And two-thirds of CxOs we surveyed confess that they are gen AI. Indeed, by helping their people deepen their trust
Follow the Reinventors
ill-equipped to lead this change.7 Misaligned perceptions in the company and their colleagues, build market-relevant
between leaders and workers also erode trust. When it skills, work with purpose and strengthen their emotional,
Accenture’s research reveals that of the 9% of
comes to job security, 58% of workers are worried,8 yet less physical and financial health, an organization can unlock
organizations that have achieved the capability for
than one-third of CxOs feel job displacement is a concern two-thirds of an individual’s potential, which can lead to a
continuous reinvention (Reinventors), nearly half (47%) of
for people.9 There’s another disconnect when it comes to 5% revenue boost.16
them are already thinking bigger—recognizing that their
well-being, with 60% of workers concerned that gen AI may
processes will require significant change to fully leverage
In this age of gen AI, leaders should view their people
increase stress and burnout,10 compared to only 37% of
gen AI. And more than half (52%) are already taking
not as passengers on the journey, but as navigators.
leaders who see this as an issue.11 This lack of trust puts the
action to reshape the workforce by redesigning jobs and
Reinventors recognize this. And with a small percentage of
trifecta of opportunities at risk.
roles around gen AI. Key to all of this: three-quarters are
organizations currently leading the way, there’s substantial
actively involving their people in their enterprise change
opportunity to be among the front-runners. Success relies
Despite 94% of people saying they
efforts, while reskilling people. These organizations are
heavily on leaders who act with compassion and humility,
are ready to learn new skills to breaking down silos and making data accessible to all
and who create the conditions so that their people feel Net
employees, fostering transparency and building trust
work with gen AI,12 only 5% of Better Off at work.
among their people.14
organizations are actively reskilling
their workforce at scale.13
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Contents | Preface | Executive summary | The gen AI state of play | Conflicting views erode trust | The trifecta of opportunities | Gen AI GPS | Prospects ahead 7
“Gen AI heralds the most significant
disruption to organizations—and, in my
case, to the newsroom—in the last 25
Prepare workers
Lead and learn years. Approached responsibly, it could
help the most important and respected
in new ways
Reinvent work
media companies provide an even
better and more accurate service and
Reshape the workforce
product going forward. It’s the people,
not technology, who understand the
purpose of the company and what it’s
trying to achieve.”
Our gen AI “GPS” later in this paper shows how Before diving into the gen AI GPS, we set the William Lewis, Chief Executive Officer and Publisher,
to navigate the journey ahead and is focused stage by examining the current state of gen AI, The Washington Post
on four accelerators. It emphasizes leaders as well as the conflicting views surrounding it. An
committed to continuous, deep learning and understanding of both is essential to fully realize
leading in new ways, along with human-centered the potential of gen AI for organizations, people
change management to reinvent work, reshape and society.
the workforce and prepare workers so they are
resilient for whatever tomorrow holds.
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Contents | Preface | Executive summary | The gen AI state of play | Conflicting views erode trust | The trifecta of opportunities | Gen AI GPS | Prospects ahead 8
Where we are today
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The gen AI
AI’s evolution has been marked by three phases of significant advancements. The
Diagnostic Era was largely defined by the introduction of machine learning. The
Predictive Era gave us the ability to make increasingly accurate forecasts about
state of play
everything from operations to customer behavior. And late 2022 saw the dawn of the
Generative Era. Now, machines aren’t just predicting with high accuracy, they’re also
generating creative content and offering personalized suggestions (see Figure 1).
Figure 1. Welcome to the age of generative AI
Source: Accenture 2024
Diagnostic Predictive Generative
Why did this What might happen What should How can AI h e lp
happen? in the future? we do next? with the execution?
Analyze Pattern Simulate Advise Automate
Scenario Forecast Optimize Create Protect
SegmFeOntUNDATIONAL Model Recommend Code MATURE
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Contents | Preface | Executive summary | The gen AI state of play | Conflicting views erode trust | The trifecta of opportunities | Gen AI GPS | Prospects ahead 10
These are very early days for generative AI, with most language-task-based21 workers relative to the full working
“If I have a concern
companies still in the planning and experimental stages. population, like in the UK (where 47% of all working hours
anywhere, it’s that people will
But looking across the landscape, several key trends will be impacted). There’s variation across sectors too
are emerging, reflecting gen AI’s influence on business (see Figure 2 on the next page). It’s also important to note underappreciate the need to
reinvention, workforce dynamics, regulatory environments, that people with lower levels of digital skills, less career
have human involvement in
executive perspectives and employee sentiment. These experience and less formal education could be more
the creation of these tools—
trends are important markers for understanding gen negatively impacted, which only highlights the risk of
AI’s current standing, as well as its future potential for exacerbating the digital divide.22 not only human involvement
transforming industries, work and employee experiences.
but the most diverse human
• Regulatory response: Some would argue that regulators
• Driving reinvention: Gen AI is seen as one of the main view gen AI as a proverbial horse having sprinted from involvement, or else we’re just
levers for enterprise reinvention at 81% of companies we the barn, citing for example the Biden administration’s
replicating the bias of human
surveyed,17 yet two-thirds of CxOs do not have the right late-October executive order requiring new safety
decision making.”
skills and capabilities needed to successfully reinvent assessments, equity and civil-rights guidance and
their organizations.18 Data strategy and technology research on the labor market.23 Mere weeks prior, China
Jacqueline Welch,
infrastructure emerge as top concerns for implementing introduced several regulations specifically targeting gen
Executive Vice President & Chief Human
gen AI, with nearly half of CxOs believing they’ll need AI.24 And in December 2023, the European Parliament
Resources Officer, The New York Times
to improve their data strategy to leverage gen AI passed the AI Act which, along with risk-based
effectively.19 regulations, sets transparency requirements for gen AI
usage.25 Increasingly, countries and regions recognize
• Magnitude of impact: Our modeling shows that 44%
the need to urgently establish regulatory frameworks,
of working hours in the US are in scope for automation
including robust IP protection and usage agreements, for
or augmentation.20 This percentage is even higher in
rapidly evolving AI technology.
countries with greater numbers of knowledge- and
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Figure 2. A significant portion of working hours will be impacted (either automated or augmented) by generative AI
23%
23% 22% 22% 19% 22% 21% 22% 20% 21% 21% 21% 18% 20% 18%
19%
17% 18% 17% 16%
15% 15%
24%
Working hours in scope for
augmentation due to gen AI
Working hours in scope for
automation due to gen AI
Note: Estimates are based on
“Human+Machine” identification of
work tasks and exposure to impact
of generative AI.
Source: Accenture Research based
on National Statistical Institutes and
O*Net.
KU adanaC ynamreG ailartsuA napaJ ecnarF SU nedewS ylatI yawroN dnalniF kramneD anitnegrA niapS ocixeM elihC lizarB aibmoloC acirfA
htuoS
aibarA
iduaS
anihC aidnI
22 Countries:
47% 46% 46% 45% 44% 44% 44% 43% 42% 42% 42% 42% 42% 41% 41% 41% 39% 39% 38% 38% 33% 31%
23% 24% 23% 25% 22% 23% 21% 22% 21% 21% 21% 24% 21% 23% 22% 22% 21% 21% 22% 18% 16%
stekraM
latipaC
smroftalP
&
erawtfoS
gniknaB ecnarusnI aideM
&
.smmoC
liateR ecivreS
cilbuP
hceT
hgiH
secneicS
efiL
levarT ygrenE htlaeH evitomotuA ecnefeD
&
ecapsoreA
slacimehC seitilitU lairtsudnI secruoseR
larutaN
sdooG
remusnoC
19 industries:
72% 68% 67% 65% 51% 50% 42% 42% 41% 40% 39% 39% 39% 38% 31% 31% 30% 28% 24%
42%
35% 34%
36%
26% 22%
21% 19% 19% 17% 17% 23% 15% 16%
13% 13% 12%
11%
12%
30% 33% 33% 29% 25% 28% 21% 23% 22% 23% 22% 16% 24% 22% 18% 18% 18% 17% 12%
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• CxO sentiment: Already, 86% of CxOs are using gen AI to some degree
“The uncertainty surrounding the emergence of new
in their work and nearly all believe gen AI will be transformative for their
technologies can often evoke fear. One of the best ways to company and industry. All CxOs surveyed—100%—anticipate changes to
their workforce, e.g., growing or reducing headcount and implementing
combat fear is to educate and get people involved, and the
plans to reskill.26 Yet only one in three leaders believe they have the
companies that do so will have a better chance at creating
technology expertise or feel they can tell a compelling transformation
narrative to lead the change that’s needed.27 This gap in knowledge
increased value for themselves and their customers.”
and confidence impacts both trust and transparency, which is critical to
Christy Pambianchi, Executive Vice President & Chief Human Resources Officer, Intel successfully navigate gen AI transformation efforts at scale.
• Employee sentiment: 95% of employees we surveyed see value in
working with gen AI, and 82% say they already have some understanding
“[Gen] AI can be useful, but at times it can also be
of the technology. However, their biggest concern is trusting their
overwhelming. Because you always get that feeling that organization to ensure positive outcomes for all. Further findings
highlight this concern: 60% of employees worry that gen AI may increase
if you are leveraging AI to work or get your work done,
stress and burnout, 58% feel insecure about their job and 57% need
there is a real possibility that AI can replace you.”
clarity on what this technology means for their careers.28
IT manager, Australian software & platform company Given the progress and trends already witnessed, and perhaps even as a
result, there are conflicting views about the risks, benefits and tradeoffs
involved with using gen AI at scale. We see this as an issue that must be
unpacked and understood to realize the full, positive potential of gen AI.
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The trust gap
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Figure 3. Media coverage focused primarily on job displacement immediately
following the launch of ChatGPT, but now focuses more on privacy concerns
Conflicting
and job creation opportunities
Total mentions of gen AI topics
views erode
Q1 2023
trust
Privacy 1,991 317 Personalization
Generative AI's natural language interface increasingly
democratizes technology access to people across Job 5,721 3,226 Job creation
Displacement
industries and roles. Yet transparency and trust are
required for people to effectively adopt and embrace
these tools. Our research indicates that this includes—but Q4 2023
extends beyond—the need for trust in the tool itself: People
also need to trust that the organization will integrate gen
AI in ways that protect and prepare workers.
Privacy 4,489 414 Personalization
Messages in the media reflect where concerns lie.
Following the launch of ChatGPT, the potential for job
displacement dominated media attention; today, gen AI’s
potential for violating data privacy is being discussed more Job 5,480 5,492 Job creation
Displacement
frequently (see Figure 3).29
Source: Accenture Research NLP analysis on news articles (Dow Jones Factiva) across 329,314 articles; Jan 2022 – Dec 2023
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Figure 4. Misaligned perceptions between workers and CxOs on key concerns related to gen AI’s
impact on work, the workforce and workers
Work
58%
Workers say:
Gen AI is increasing my job insecurity
But importantly, our research reveals the specific
trust gap between workers and leaders (see
CxOs say: 29%
Figure 4). To a greater extent than CxOs realize, Job displacement is a concern for our people
workers are asking whether their organization’s
Workforce
gen AI implementation will cost them their jobs
and whether those approaches will lead to stress,
Workers say: 82%
I feel I understand the technology
burnout and overload rather than to a better
employee experience. And they wonder whether
CxOs say: 36%
the tool itself will deliver quality outputs.30 Lack of understanding is a concern for our people
Our surveys also reveal that 32% of leaders see Workers say:
94%
I am confident I can learn the skills needed to leverage
talent scarcity, due to skill gaps or unawareness,
gen AI in my role
as a major barrier in utilizing gen AI. Additionally,
32%
CxOs say:
36% believe workers will not fully embrace gen Lack of worker skills is going to hold us back
AI due to a lack of technological understanding.31
Workers
Yet most workers (82%) believe they grasp the
Workers say:
technology, and 94% are confident they can 60%
I am concerned that gen AI may increase my stress
develop the needed skills.32 and burnout
CxOs say: 37%
Gen AI could contribute to peoples’ stress and burnout
Workers say: 53%
I am concerned about the quality of output
CxOs say: Sources: Accenture Pulse of Change survey, Wave
21%
Trusting the quality of output is a concern for our people 10 (Sept 2023): n=2,425 CxOs. Accenture Change
Workforce Survey (Oct-Nov 2023): n=5,000 employees.
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Further, while generative AI offers great potential to increase
efficiency and reduce human errors, there’s an inherent risk of
over-reliance on this technology at the expense of finding the
right blend of human intelligence and AI capability.
Understanding and closing the trust gaps—not merely
observing them—is crucial for business and societal leaders
working to deploy gen AI responsibly. By proactively resolving
these challenges, we don’t just acknowledge them; we turn
them into opportunities to get ahead in the Generative Era.
“I think gen AI’s greatest power is that it will help
us be more creative and advance our productivity as
human beings. However, there does need to be some
line there. We can’t have it set up to where it’s doing
everything for us.”
Purchasing & logistics manager, US consumer goods & services company
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The trifecta of
opportunities
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The trifecta of opportunities:
economy, business, people
Even with a year’s hindsight on generative AI's role in our
everyday lives, harnessing the trifecta of opportunities to
accelerate economic value, drive business growth and create
more meaningful work for people is an ongoing effort. For
each opportunity, the incentive only grows when people
are the navigators along the path to achieving gen AI’s full
potential. While previous transformations focused mainly on
workforce productivity, this age of gen AI will revolutionize work
and workflows across the entire value chain. Our research is
bringing into clearer view the big upside of integrating gen AI
responsibly.
Economic upside
Our modeling reveals insights from three economic growth
scenarios, each based on the pace of gen AI adoption and
innovation. Among them, the “people-centric” scenario—
where organizations adopt gen AI responsibly at scale, in ways
that place people and innovation at the heart—stands out,
potentially creating an additional $10.3 trillion in economic
value by 2038 (see Figure 5 on next page).33
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Figure 5. Companies can unlock an additional $10.3 trillion in economic value by adopting responsible, people-centric approaches to gen AI
120,000
115,000 +$17.9 trillion USD against baseline
10.3
$
trillion USD +$13.5 trillion USD against baseline
110,000
additional value unlocked by 2038
105,000 +$7.6 trillion USD against baseline
100,000
95,000
90,000
People-centric scenario: Companies
adopt in ways that place people and
85,000
innovation at the center
Cautious scenario: Companies adopt
80,000
slowly, placing risk aversion at the center
Aggressive scenario: Companies adopt
75,000
quickly, placing cost-cutting at the center
Baseline expected growth
70,000
2023 2028 2033 2038
Source: Accenture Research. Simulated GDP growth under three scenarios. GDP forecasts from Oxford Economics across 22 countries.
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Business upside
Most CxOs believe gen AI will ultimately increase their Moreover, Reinventors (the 9% of organizations that
company’s market share, with 17% of them predicting lead in reinvention) are 2x more likely to anticipate a
that gen AI will increase their market share by 10% or productivity gain of 20% or more in the next three years.38
more.34 This confidence stems from gen AI’s ability to By intentionally involving their people in the change, such
help companies free up capacity and improve how they organizations are not only working to close the trust and
identify, reach, connect with and deliver to customers. transparency gap, they’re also increasing their chances to
Fittingly, more CxOs view gen AI as a tool for revenue reinvent at speed and scale by 1.7x and 1.6x, respectively.39
growth rather than for reducing headcount.35 In fact, our
modeling suggests that companies planning to reinvent
work—by integrating gen AI more deeply across functions
“At Mizuho, we’re focused on the future and thinking more broadly
and business processes at scale—expect to overtake the
revenue growth of even today’s leading companies in the about how our industry can transform by having gen AI in the
next five years.36
market and within our workforce. Managing this change effectively
Technology like gen AI is recognized by executives not is extremely important—especially communicating with employees.
just as a revenue driver, but also as a force of disruptive
People are wondering about the impacts and leaders need to take a
change. Importantly, so is talent. Our previous research
highly personalized approach. Talk with each individual about their
has shown that technology alone will not drive gen AI-
enabled growth; instead, prioritizing people alongside data
experiences, skills and potential expanded opportunities.”
and tech can lead to productivity gains of up to 11%, while
sidelining the human factor slashes that gain to just 4%.37
Makoto Umemiya, Deputy President & Senior Executive Officer and Group Chief Digital Officer,
Mizuho Financial Group
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People upside
""Our goal is to really make AI helpful for everyone by improving Working with technology that is “human by design”—like
gen AI-powered agents and innovative digital spaces—
knowledge, learning, creativity and productivity, and enabling
enables people to enhance their productivity, creativity
others to grow through building and deploying AI responsibly.
and human potential. Moreover, among the Reinventors,
In the workplace, that means making the most of opportunities two-thirds strongly agree that, with gen AI, work will
become more meaningful, creative and impactful.40
to use AI to help people and teams work more effectively and
increase their impact. One thing that won’t change is that work Ultimately, gen AI presents a new, higher-stakes scenario
to reshape the workforce and prepare individuals to
is still centered around humans, so that people can bring their
transition from being focused on one or two areas of
creativity, which is such an important human trait.”
expertise (with supporting skills), to mastering multiple
interconnected capabilities at once.
Fiona Cicconi, Chief People Officer, Google
Such a shift presents an opportunity to create a more
agile and adaptive organization through, for example,
tailored learning pathways aligned to each worker’s
needs and aspirations. But this will only be achieved
if there is a culture of transparency and trust—and
executives have a significant role to play in exemplifying
these values.
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Accenture’s research has shown that helping people
to become “Net Better Off” unlocks nearly two-thirds
If your people are Net Better Off
(64%) of a person’s potential at work. We showed that
when firing on all four dimensions, organizations can
01
unlock the potential of their people and ultimately
They're healthy and well—physically,
deliver 5% greater revenue growth.41 And our latest
emotionally and financially
research shows that leaving people Net Better Off is
a clear pathway to closing the trust gap and getting
02
people ready for, and comfortable with, generative They're connected, with a strong
AI. For instance, workers who are highly Net Better sense of trust and belonging
Off had a 19 percentage point greater incidence of
“strongly agree” responses regarding their comfort
03
with the technology—particularly in terms of how Their work has purpose
they can apply it to their work (see Figure 6 on the
next page).42
04
They ha"
91,accenture,Reinvention-in-the-Age-of-Generative-AI-Report-Accenture-var3.pdf,"Reinvention
in the age of
generative AI
South Africa’s formula for
success amid change
Contents
04-05 06-09 10-14 15-16 17-26
Executive Reinvention in Barriers to Generative AI Five key
summary the face of radical reinvention in enables and imperatives to
disruption South Africa accelerates drive reinvention
reinvention with generative AI
The widening value gap Legacy systems limit agility,
driving up technical debt
Imperative 1: Lead with value
Fragmented
Imperative 2: Understand
implementations, misaligned
and develop an AI-enabled,
goals hinder transformation
secure digital core
Skills gap limits digital core
Imperative 3: Reinvent talent
growth, development and
and ways of working
generative AI adoption
Imperative 4: Close the gap
on responsible AI
Imperative 5: Drive
continuous reinvention
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Contents Executive summary Reinvention in the face of radical disruption Barriers to reinvention in South Africa Generative AI enables and accelerates reinvention The Reinventor's playbook 2
Authors
Vukani Mngxati Yusof Seedat
CEO and Board Chairman Thought Leadership Director
Accenture Africa Accenture Research
Hina Patel Mayuri Naik
Data and AI Lead Africa Research Lead
Accenture Africa and UKIA Accenture Research
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Contents Executive summary Reinvention in the face of radical disruption Barriers to reinvention in South Africa Generative AI enables and accelerates reinvention The Reinventor's playbook 3
EXECUTIVE SUMMARY
South African businesses Imagine boosting productivity threefold, South Africa’s business environment In 2022, we predicted that companies
cutting product development timelines in is a mix of old challenges and new that embrace reinvention would dominate
stand at a defining point.
half or doubling revenue through hyper- opportunities. On the one hand, the coming decade. Today, we see that
Over the next 12 to 24
personalized customer experiences. industries—from mining and telecom to prediction coming to life. Globally,
These aren’t distant possibilities—they are financial services—have deeply ingrained forward-thinking organizations are pulling
months, generative AI will
within reach for companies that act now. legacy systems that resist transformation ahead, leaving competitors behind. Now
either unlock new pathways
Our estimates show that the responsible, and change. On the other hand, the in 2024, we have more recent evidence
to growth or widen the gap people-centric adoption of generative AI country’s young, tech-savvy population that reinvention, enabled by generative AI,
in South Africa has the potential to unlock presents a massive opportunity to create will become the default strategy for those
between those that adapt and
additional growth by up to $82 billion a skilled, future-ready workforce that can determined to shape the future.
those left behind. The stakes
over 15 years. By embracing generative AI, fuel AI-driven innovation.
Our research on reinvention reveals that a
South African businesses can revolutionize
are enormous.
But the window for action won’t be open digital core is essential for companies on
their operations, drive exponential growth,
for long. South African businesses must the path of continuous reinvention. Such
reduce costs and create entirely new
urgently decide whether they will take a digital core has three groups of distinct
revenue streams.
proactive steps to reinvent themselves but constantly interacting technologies:
However, the cost of inaction will be steep. or risk being left behind. Generative AI digital platforms, a data and AI backbone,
Companies that hesitate and remain on offers a rare chance to leapfrog global and the digital foundation, which includes
the sidelines will find themselves stuck, competitors by reimagining how work composable integration, cloud-first
saddled with legacy systems and buried gets done, how products and services are infrastructure, a continuum control plane
under technical debt. They will lose market delivered and how businesses connect and security.
share to nimbler competitors who have with their customers.
seized the AI advantage to innovate and
reshape market and industry standards.
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Contents Executive summary Reinvention in the face of radical disruption Barriers to reinvention in South Africa Generative AI enables and accelerates reinvention The Reinventor's playbook 4
It enables organizations to accelerate
ahead of the competition and achieve their
1/ 2/ 3/
ambitions—using the right mix of tools and
practices for agility and innovation.
However, in South Africa, many businesses
Understand and
are still struggling to translate technology
develop an AI- Reinvent talent
investments into meaningful returns.
Technical debt is piling up, digital cores enabled, secure and ways of
are underdeveloped and there is a critical
Lead with value digital core working
misalignment between IT and business
objectives. The gap between investment
and outcomes is widening—and time is
running out for South African companies
to get it right.
4/ 5/
To seize the opportunity to reinvent with
generative AI, South African companies
must take immediate and decisive steps.
Our research identifies five key imperatives
for success.
Close the gap on Drive continuous
responsible AI reinvention
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Contents Executive summary Reinvention in the face of radical disruption Barriers to reinvention in South Africa Generative AI enables and accelerates reinvention The Reinventor's playbook 5
About the research
We took a multi-method This report is based on
approach again this year to
A multi-year survey of over 3,000 executives across The annual Pulse of Change Index, which quantifies the
research the topic of Total 19 industries and 10 countries. Respondents were level of change affecting businesses globally, caused by six
asked about their organization’s approach to business major factors: technology, talent, economic, geopolitical,
Enterprise Reinvention.
transformation and reinvention strategy, as well as about climate, and consumer and social. The index provides
their specific programs and success factors. The surveys context to the need for reinvention.
were conducted in November 2022 and from October
Economic modelling to forecast the potential impact of
to November 2023, and replicated for 100 South African
generative AI on productivity and growth for the economy,
companies from March to May 2024. In this report, we
organisations and people. We mapped out the future
provide comparisons between the two, focusing on new
growth trajectories under three different AI deployment
insights gained from the most recent fieldwork.
scenarios: aggressive, cautious and our proposed people-
Financial and non-financial analyses and econometric centric approach.
modeling to assess the performance impact of adopting
a reinvention strategy. The analysis combines data from
publicly available achieved results, analyst expectations
and survey data to create a robust view of both historic
and future performance.
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7
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Reinvention in the face
of radical disruption
Contents Executive summary Reinvention in the face of radical disruption Barriers to reinvention in South Africa Generative AI enables and accelerates reinvention The Reinventor's playbook
Figure 1
Has your organization adopted a deliberate strategy to reinvent
the way it operates across the enterprise in the past 3 years?
We are living through an era
of radical disruption, where
Yes, we’ve adopted an organization
56%
continuous reinvention— or enterprise-wide strategy to reinvent
all functions and business units
driven by rapid technological
advancements and
Yes, we’ve adopted a strategy
42%
to reinvent most functions and
accelerated by generative AI—
business units/departments
is the cornerstone of success.
Yes, we’ve adopted a strategy
1%
to reinvent some functions and
In South Africa, 99% of C-suite executives
business units/departments
we surveyed have, over the last three
years, adopted deliberate strategies
No, we have not adopted
to reinvent the way their organizations
a reivention strategy
operate (Figure 1).
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Figure 2
Our research shows South African Which of the following best describe the role that you expect
leaders very well understand the crucial generative AI will play in driving each of the following in
role technology, especially generative
your organization over the next three years?
AI, plays in their reinvention journey—
and the urgency to act swiftly. In the
The #1 lever One of the main levers A minor lever (e.g., through experiments/pilots)
last year alone, 98% of executives have
fast-tracked their reinvention strategies
in response to external pressures like
47% 48% 5%
Reinvention strategy
inflation, evolving consumer expectations
and the relentless pace of technological
change. Furthermore, 95% acknowledge 43% 44% 13% 58
Incremental revenue growth
that technology is foundational to every %
current and future reinvention initiative.
Generative AI is emerging as a key driver Cost reduction/ 33% 51% 16%
P&L improvements
of reinvention, contributing to incremental of executives
revenue growth, cost reduction, new
40% 46% 14%
New ways of working anticipate that
ways of working and overall productivity
improvements (Figure 2). Fifty-eight
generative AI will
percent of executives expect it to boost Disruptive innovation 37% 46% 17%
(i.e. New revenue growth)
productivity by more than 20% within the boost productivity
next three years.
Employee productivity 47% 47% 6% by over 20% in the
(i.e. output per hour worked)
According to Accenture’s proprietary
next three years.
analysis, the responsible, people-centric
adoption of generative AI can elevate
South Africa’s overall economic growth by
up to $82 billion, adding an extra 17.5% to
the country’s forecasted GDP by 2038.
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Figure 3
The widening value gap
However, despite high expectations—and
sizable investments in technology and 170
AI, including generative AI—reinvention
efforts are falling short, and most
160
companies are not seeing the returns they
anticipated. There is a clear disconnect
between technology investments and
140
actual business outcomes (Figure 3). In
2023, our research indicated a 64% gap
between technology spend and business
120
growth.1 In 2024, this value gap has
widened to 98%, with aggregate business
revenue growth dropping to 2020 levels.2 100
The upshot: Spending alone will not drive
reinvention. Without strategic alignment
80
between technology investments
and business objectives, companies 2017 2018 2019 2020 2021 2022 2023
risk further disconnect between their
aspirations and results.
)001
ot
desaB(
htworG
+98%
IT spend
+64%
+32%
+46%
+19%
+16%
Aggregated
revenue
Source: IDC, S&P Capital IQ (Data shown for South Africa)
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Barriers to
reinvention
in South
Africa
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89
The reasons for these Legacy systems limit agility, Fragmented implementations,
%
diminishing returns on driving up technical debt misaligned goals hinder
reinvention efforts are clear: transformation
Legacy systems weigh heavily on many of South African
legacy systems; fragmented
South African companies, creating barriers
Our research shows that half of reinvention
C-suite executives
to progress by limiting agility and stifling
implementations with
programs in South Africa are confined to
innovation. This isn’t just a theoretical
specific departments or functions, missing acknowledge that
misalignment between
issue—industries like mining and telecom
the opportunity to drive enterprise-wide
business and IT objectives, are already facing the consequences in legacy infrastructure
impact. This lack of cohesion prevents
the form of job cuts and closures as they
leading to ineffective organizations from realizing the full
hinders their business
struggle to modernize quickly enough.
benefits of their investments. Moreover,
change management; and a
only 42% of leaders report a clear agility and contributes
In fact, 89% of South African C-suite
lack of necessary skills and
alignment between business and IT goals,
executives acknowledge that legacy
to accumulating
causing change management to falter and
capabilities to strengthen infrastructure hinders their business agility
diluting the effectiveness of transformation
and contributes to accumulating technical technical debt.
the digital core.
initiatives. Without this alignment, even
debt.3 Furthermore, 91% of organizations
the most ambitious programs struggle to
report that technical debt undermines
deliver lasting value.
their competitiveness, yet only 24% are
actively addressing the issue. This impact
is particularly pronounced in sectors like
banking and telecom, where outdated
systems continue to obstruct innovation
and growth.
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Figure 4
Skills gap limits digital core growth, Demystifying the digital core
development and generative AI adoption
A digital core fit for continuous reinvention includes three distinct
groups of technologies that constantly interact with each other.
In our Reinventing with a Digital Core report, we found that a
digital core is a prerequisite for companies to continuously
reinvent themselves as they navigate the radical disruption
fueled by generative AI.4 Accenture defines a digital core as the
critical technological capability that can create and empower
an organization's unique reinvention ambitions. Such a digital
Digital platforms
core has three groups of distinct but constantly interacting
technologies: digital platforms, a data and AI backbone and the
digital foundation—which includes composable integration,
cloud-first infrastructure, a continuum control plane and
Data & AI backbone
security (Figure 4). Together, these elements provide the agility
and innovation needed to accelerate ahead of the competition Data | AI
and achieve strategic objectives.
Digital foundation
Cloud-first infrastructure Security
Continuum control plane Composable integration
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A considerable gap in foundational The talent challenge is compounded by Only
skills and capabilities—ranging from difficulties in sourcing new skills. Only 36%
36
data foundation and cloud to AI and of South African companies believe their
%
cybersecurity—is undermining companies’ talent strategy is flexible enough to support
ability to build and maintain a robust transformation programs. This shortage
digital core. It is the combined strength of digital capabilities is constraining
of South African
of these foundational capabilities that companies’ ability to scale operations and
enables businesses to unlock new maintain their competitive position.
companies believe
opportunities across the value chain.
their talent
Our research shows that more than
strategy is flexible
half of South African executives lack a
strong understanding of these crucial
enough to support
components, and only 35% have the
technological expertise to leverage them transformation
effectively. Moreover, only 38% of business
programs.
leaders feel confident that their workforce
has the necessary skills to achieve their
goals over the next three years.
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Generative AI enables and
accelerates reinvention
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Contents Executive summary Reinvention in the face of radical disruption Barriers to reinvention in South Africa Generative AI enables and accelerates reinvention The Reinventor's playbook 15
Figure 5
Reinventors expect to grow the value gap
across financial indicators
In 2022, we predicted that companies
Financial value gap
that embrace reinvention as a core
Revenue growth, indexed (2019 = 100)
strategy—using technology, data
Reinventors All others (Transformers + Optimizers) Profitability
and AI—would lead in the coming
(EBITDA/revenue)
decade. Our 2023 findings confirmed Expected
2.4x
this: Globally, a small group (9%) of
+5.6pp
increase
companies—we call them Reinventors—
200
Higher margins
is outpacing their peers by embedding
for Reinventors
compared to all
reinvention into their DNA. 180
others over 2019-22
+15pp Revenue
growth gap
+2.9pp
Accenture’s analysis (Figure 5) shows that, globally,
160
Revenue growth
Reinventors are outperforming their peers by a wide
gap to all others
Uplift in margins each
margin. Between 2019 and 2022, these leading companies
by 2022
year following adoption
140
saw revenue growth that was 15 percentage points higher
of a reinvention strategy
than the rest. We expect this gap to grow by 2.4 times to 37 vs. those not pursuing
reinvention
percentage points by 2026.
120
+37pp
Revenue growth
Reinventors are also more profitable. Their average gap to all others
by 2026
profit margin (EBITDA/revenue) between 2019 and 2022
100
was 5.6 percentage points higher than the rest. Our
2019 2020 2021 2022 2023 2024 2025 2026
modeling indicates that for every year a company adopts a
reinvention strategy, it sees a 2.9 percentage point uplift in
margin compared to those that don’t.
2019-22 = CAGR based on actuals. 2023-26 = self-reported expectations stress-tested vs. analyst expectations. Average EBITDA margin based on actuals
for 2019-22. Panel data model tests the relationship between # of years of reinvention (from year respondents report adopting reinvention strategy) and
EBITDA margin, controlling for industry, geo and company size. Financial services companies are excluded. Source: Accenture reinvention survey, Oct-
Nov 2023. Sample size: Total, 1,500; Reinventors, 136; Transformers, 1,210; Optimizers, 154.
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In South Africa, 7% of businesses have reached this With its ability to impact the entire value chain and
high bar of reinvention. The vast majority—around drive productivity and growth to unprecedented
85%, whom we refer to as Transformers—are levels, generative AI can transform every facet
making strides but still have room to accelerate of business. It is already revolutionizing the way
their progress. They are not yet building the we work, fostering a new kind of collaboration
long-term capabilities necessary for sustained between people and machines. Our modeling
reinvention. The remaining 8%—whom we refer to shows that 38% of working hours in South Africa
as Optimizers—are holding back on reinvention have scope for automation or augmentation
altogether, missing critical growth opportunities through generative AI.5
and exposing themselves to risk in a rapidly
Beyond automation, generative AI can empower
shifting market.
leaders to make smarter, faster and more accurate
Reinventors are fully committed to continuous choices by converting vast amounts of data into
transformation. They’re not settling for incremental insights. For both customers and employees,
changes but using generative AI and other generative AI delivers tailored experiences,
technologies to reshape their business from the deepening engagement and driving better
ground up. These organizations are setting new outcomes. Crucially, generative AI can help
performance standards, driving innovation across build “connective tissue” across an organization
the board and intensifying their use of generative by unlocking data and breaking down people
AI as a core enabler of their strategy. siloes through its ability to seamlessly process
structured, unstructured and even synthetic data.
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Contents Executive summary Reinvention in the face of radical disruption Barriers to reinvention in South Africa Generative AI enables and accelerates reinvention The Reinventor's playbook 17
The Reinventor's playbook
Five key imperatives to drive reinvention with generative AI
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Contents Executive summary Reinvention in the face of radical disruption Barriers to reinvention in South Africa Generative AI enables and accelerates reinvention The Reinventor's playbook 18
Using generative AI to reinvent is no
longer optional—it’s a prerequisite 1/ 2/ 3/
for success. Companies will compete
on how fast they are able to harness
Understand and
and deploy generative AI to create
develop an AI- Reinvent talent
material value. This is a reality that
enabled, secure and ways of
not everyone has yet absorbed.
Lead with value digital core working
Success will require every CEO and their
team to assess where they are today in their
competitive set, and then systematically execute
a reinvention strategy with five imperatives that 4/ 5/
can be broadly applied. Here, we describe these
imperatives in the context of generative AI and
illustrate with client examples.
Close the gap on Drive continuous
responsible AI reinvention
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Contents Executive summary Reinvention in the face of radical disruption Barriers to reinvention in South Africa Generative AI enables and accelerates reinvention The Reinventor's playbook 19
1/ Lead with value
In the era of generative AI, Begin by targeting strategic bets—areas It’s also important to ensure that
where generative AI can create unique every AI initiative you pursue is value-
it’s crucial to prioritize value
sources of value that competitors led. Too many organizations are still
across the business.
can’t easily replicate. Evaluate whether experimenting with AI without a C-suite-
your organization is truly ready to approved mechanism to measure
execute your AI strategy, or if you risk success. To bridge the gap between hype
being outpaced by competitors with and material results, rapid interventions
stronger digital cores or more agile and decisive choices are needed.
talent. This critical self-assessment will
Finally, reorient your organization. Move
help you prioritize the most impactful
away from siloed functions and enable
opportunities.
end-to-end decision-making through
Next, shift from fragmented pilots a unified data architecture and cross-
to building end-to-end business functional teams. This approach will
capabilities, powered by AI—including unlock new opportunities across your
generative AI. Be intentional in executing value chain and help you tap previously
a roadmap that connects AI to your hidden value pools.
broader operational goals. Rather than
getting bogged in proof-of-concept
projects, focus on transforming entire
workflows to unlock real value.
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Contents Executive summary Reinvention in the face of radical disruption Barriers to reinvention in South Africa Generative AI enables and accelerates reinvention The Reinventor's playbook 20
2/ Understand and develop an AI-enabled, secure digital core
Building an AI-enabled Start by assessing your digital core: How It’s equally important to ensure that your Lastly, rigorously measure your progress
does your technology stack up to industry Chief Information Officer embeds strong with an eye on the future. Direct a
digital core goes
standards, and is it ready for generative AI? cybersecurity practices early in the substantial share of your technology
far beyond writing
Align your strategy with the areas where technology lifecycle to protect both digital investments toward building new
reinvention offers the greatest value. and physical systems. Only 41% of South capabilities, not just maintaining legacy
checks for technology
African companies believe they lead in systems. Innovation today sets the
upgrades. It needs Next, develop the core capabilities needed
enterprise systems security, so a strong foundation for competitiveness tomorrow.
to build a robust data and AI backbone,
meticulous development security culture is essential for long-term
such as handling unstructured and
resiliency.
of an infrastructure that
synthetic data and creating an architecture
can support rapid and that integrates multiple AI models. Our With robust cybersecurity practices
research shows that 96% of South African bolstering your defenses, it’s time to take
continuous reinvention.
companies foresee significant shifts a hard look at your current technology
in their data strategy to fully leverage and advisory ecosystems in order to
generative AI, and almost half continuously radically compress the reinvention cycle.
monitor all aspects of their digital core for Reconsider risk-reward relationships,
AI readiness. and whether co-creating with partners
or industry leaders will fast-track your
transformation efforts.
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Contents Executive summary Reinvention in the face of radical disruption Barriers to reinvention in South Africa Generative AI enables and accelerates reinvention The Reinventor's playbook 21
A Southeast Asian national oil
company exemplifies this approach.
Like many others in the oil and gas industry, this On the front end, a new search engine simplifies
Southeast Asian national oil company has huge and accelerates the way people find important
volumes of data in different formats, and generates information, allowing them to “chat” with the
more daily. company’s data to find what they need in a quick
and conversational way, speeding up decision-
With no efficient way to access and search its data,
making and giving people confidence to act.
decision-making was only getting slower, while the
risk of accidents due to missing data points kept The speed at which the right information can
growing. Staying on top of pipeline maintenance now be accessed is also helping avoid equipment
and repairs was time-consuming, as technicians downtime as historical data can be accessed
and engineers had to comb through pages and almost instantly, like finding out how long it’s been
pages of historical documents to predict where since a piece of equipment was serviced or had a
issues may come up. fault.
After taking a holistic look at the issues, the It’s also speeding up onboarding by replacing
company deployed generative AI and cognitive dense logbooks with a simple search engine to
search, and can now realize the true value of its teach complex knowledge. Ultimately, the new,
data and drive new growth. Its new knowledge integrated setup makes information discoverable
base incorporates more than 250,000 documents with minimal effort, automates the knowledge-
with structured and unstructured information, gathering process for different roles across the
surfaces whatever information the user is looking organization and helps reduce accidents.
for and converts it into the desired format.
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3/ Reinvent talent and ways of working
For a digital core to reach Leaders need to look beyond tactical A transformation of this magnitude also A forward-thinking talent strategy also
questions like which tasks to automate requires people-centric change capabilities calls for a review of human resource
its full potential, it has to
and consider how AI, including generative across functions to fully grasp generative (HR) capabilities. HR should not be
be backed by a workforce
AI, can free up human potential—and AI’s impact on the workforce—not just an afterthought but a core part of the
how to reskill employees to maximize operationally, but in the experience of reinvention vision. Invest in the right
that’s equally prepared to
this potential. employees themselves. Manage change HR technologies needed to support
evolve. The next challenge
in a way that actively involves employees, AI-driven transformation. Revisit your
Create a talent strategy that maps out
is rethinking how talent giving them a voice in how AI—including employee value proposition to ensure
how work will change, documents the
generative AI—is adopted and integrated it reflects an environment where AI
is managed, reskilled
impact to roles and identifies the skills
into their daily tasks. empowers employees, making them feel
and aligned with the needed for every generative AI use case.
“Net-Better-Off”.
Leaders must make transparent decisions At the heart of this reinvention is the need
demands of a generative
on how to reallocate freed-up talent, so for continuous learning capabilities, either
AI-driven world. that employees fully understand how their organically or with partners. Employees
roles will change. As it stands, only 38% must be equipped with market-relevant
of South African businesses have a clear skills to thrive in a South Africa where 38%
understanding of the workforce skills of working hours are likely to be automated
they’ll need in three years. or augmented by generative AI.5
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4/ Close the gap on responsible AI
As AI adoption grows, so The first step toward responsible AI However, responsible AI is more than
use is to establish clear AI governance a technology issue—it requires cross-
does the responsibility
frameworks that outline principles for the functional collaboration across the
to ensure that it is used
ethical design, deployment and use of organization. Leaders from compliance,
AI systems. Without a solid governance HR, sustainability, privacy and security
ethically and responsibly.
structure, companies risk deploying AI should work together to ensure that AI
Pressure is mounting for
in ways that could harm their reputation, systems are designed and managed
organizations to establish their employees and even their customers. responsibly. This holistic approach will
address the wide-ranging impact of AI,
governance frameworks that
Conduct regular AI risk assessments—both
from workforce implications to regulatory
mitigate the risks associated qualitative and quantitative—evaluating
compliance and data privacy.
AI use cases, applications and systems
with AI while building trust
against a reference architecture to identify Despite the urgency, only 31% of South
with customers, employees risks. Responsible AI cannot be achieved African executives have established
without system enablement for testing and responsible data and AI principles, and
and regulators.
a dedicated AI monitoring and compliance even fewer—just 3%—are either drafting
function to evaluate for fairness, or have completed drafting responsible
explainability, transparency, accuracy data and AI principles with C-su"
92,accenture,Accenture-Supply-Chain-Networks-In-The-Age-Of-Generative-AI.pdf,"Supply chain networks
in the age of generative AI:
Turning promise into performance
The annual Accenture Pulse of Change Index found that technology rose to the
top of the list of business disruptors in 2023, catapulted by advances in generative
AI. The technology is unique in its ability to impact the entire value chain,
reinventing every part of an organization and resetting the performance frontier.
Based on our Technology Vision 2024,95 percent of executives agree that
generative AI will compel their organization to modernize its technology
architecture.The good news for supply chain leaders is this exciting revolution in
machine learning is creating an array of possibilities for reinventing work in their
domain too.
In this paper, we present a comprehensive and forward-thinking exploration
of the opportunities across the end-to-end supply chain. We see applications
in everything from sourcing and planning, through manufacturing and fulfillment,
to aftersales and service. We also see significant value in cross-functional
outcomes like supply chain sustainability, resilience, talent management, and
customer-centricity.
Can organizations realize this value today? We believe they can. But it means
approaching generative AI not merely as ‘just another’ technology implementation.
It’s an enterprise transformation, with implications for the way an organization
thinks about its data, talent, and ways of working. Not to mention the critical
importance of implementing generative AI responsibly and securely.
By embracing this broader change, supply chain leaders can fully capitalize on the
age of generative AI. And drive innovation across supply chain networks that
deliver better outcomes for business, for people, and for the planet.
Kris Timmermans
It’s an exciting journey. And it’s one I look forward to supporting our clients on in
the months and years ahead. Global Lead – Supply Chain & Operations
2
Generative AI is booming. Since ChatGPT launched in late 2022 the
technology has taken the world by storm. Across industries and
business functions, companies are looking to explore the possibilities
and capitalize on the transformative potential of the creative side of AI.
The sheer number of possible applications has captured the attention
of business leaders. Our research found that 97 percent of senior
executives agree that generative AI foundation models (also known as
Large Language Models or LLMs) will be transformative for their
Accenture analysis suggests 43 percent of all working
company. And 100 percent anticipate changes to the workforce.2
hours across the entire supply chain function will be
impacted — with generative AI either automating Why is this good news for supply chain leaders? LLM capabilities are
activities (29 percent) or significantly augmenting the not limited to coding, content creation or marketing. They also hold
immense promise across the end-to-end supply chain network. There’s
work of human employees (14 percent).1 Given the sheer
value to be gained in everything from new product development,
scale of the global supply chain workforce, the potential procurement and planning, manufacturing and logistics, to after sales
cumulative value for businesses is massive. and services.
3
There’s broad consensus about generative AI’s potential,
and many organizations are actively experimenting. But Success with generative AI means getting
our Pulse of Change Quarterly survey suggests only one
the data ready, getting people ready, and
in three has so far made a significant investment.
getting the enterprise ready
Why?
All C-suite leaders are grappling with fundamental questions. How much
of the hype around generative AI is real? Can its promise be turned into
Generative AI excels in language-related activities, as we’ll explore in
scalable solutions? Which use cases can deliver real value today? And how
the following sections of this paper. However, supply chain leaders must
do leaders get the data and the organization ready to capitalize on the
also recognize that, while it’s incredibly powerful at what it does well, it’s
opportunity?
not suited to every task. In particular, supply chain activities that are
more focused on numerical processing or require greater levels of
Our view? complex reasoning will see less direct impact. It’s why we also
recommend viewing generative AI in its broader context — as part of a
There’s real value on the table across the end-to-end supply chain. But continuum of automation capabilities that include traditional process
reaping the benefits requires a profound shift in the way an organization automation and classical machine learning models, as well as LLMs.
thinks about creating value and getting work done. It means approaching
generative AI not merely as the latest in a long line of software
implementations, but rather as an enterprise transformation, with a clear
focus on end-to-end business capabilities and implications for areas like
data, people, ways of working, processes and responsible adoption.
4
Generative AI-powered reinvention helps bridge the
gap from the linear supply chains of the past to the
truly interconnected, intelligent supply chain networks
of the future. Building on previous advances in supply
chain management artificial intelligence, generative AI
offers a range of new capabilities.
5
Contextual understanding. Supply chain Conversational capabilities. Supply chain Content generation. Generative AI offers
managers can use generative AI to make workers can use generative AI to gain the promise of creating relevant, context-
better decisions based on contextualized access to tailored insights and automations specific text, code, images or insights on
insights from unstructured data sources that based on chatbot interactions in everyday demand and on an industrial scale. Today,
were previously inaccessible. Examples language. That might include asking a applications in sourcing and procurement
include improving forecasting by scanning chatbot to help find a specific spare part are the most robust, such as auto-generated
huge numbers of public online sources to and create a call-off or spot buy to a vendor-specific insights (KPIs, market
identify the root causes of future demand. preferred supplier if it’s not available. Other trends, demand forecasts) to support
Or embedding generative AI into a Supply applications include auto-generating contract renewal negotiations with
Chain Control Tower to enhance the way users documents like purchase orders, training suppliers, as well as contextualized business
interact with data, improving explainability and and upskilling manufacturing workers and operational performance metrics.
trust. Generative AI can also be combined with maintenance troubleshooting.
existing process automation to significantly
streamline supply chain activities.
Together with existing AI, machine learning models and workplace platforms, these capabilities will allow companies to optimize and elevate supply
chain operations, solve pressing supply chain challenges, and, ultimately, ensure supply chain networks have a more positive impact on business,
people and the planet.
6
For Chief Supply Chain Officers (CSCOs), generative AI’s promise
extends all the way across supply chain network operations,
from designing and planning through to aftersales and service.
Accenture’s analysis indicates that, in total, a massive 58 percent of
the 122 supply chain processes analyzed, can be reimagined.3
7
Design and engineer
In domains like model-based systems engineering, LLMs will increasingly augment and
accelerate the work of designers. By tapping into historical data, generative AI solutions will
quickly generate new designs and models, saving time and reducing repetitive effort,
especially during design iterations.
Packaging design is a good example. The need to consider multiple factors — sustainability,
ease of transport, durability, regulations, branding, and more — eats into time and limited
resources. At the same time, documenting and retrieving packaging information becomes
increasingly difficult with large product portfolios. Generative AI can serve up multiple design
concepts (in 2D or 3D) as well as proposing suitable packaging copy and marketing based on
summarized design information. Human co-workers can then review these concepts to
ensure compliance with product and regulatory requirements.
The applications for biopharmaceutical companies are particularly powerful. Terray
Therapeutics is using generative AI to revolutionize small-molecule drug discovery.
Its COATI foundation model for chemistry translates chemical structures into numerical
representations, allowing generative AI to design novel optimized molecules.4
8
Plan
Many CSCOs have already implemented advanced analytics
solutions to augment and optimize supply chain planning
activities. However, the complexity of the insights these tools
produce, and the need for specialist expertise in making them
actionable, means they can often be challenging to use in practice.
Generative AI promises to revolutionize access to insights, not
only in supply chain planning but also in areas like network design
optimization. Through simple-to-use interfaces, employees can
query optimization recommendations in everyday language and
receive explanations they can easily understand and action. This
opens up critical insights to a much larger number of supply chain
workers, while also improving trust in data and accelerating speed to
action for domain experts.
At the same time, generative AI can be used to bring a broader set of
unstructured data sources (such as market reports, news results and
social media) into forecasting calculations. It also supports more
collaborative and streamlined ways of working across sales and
operations planning — instantly summarizing meeting action points,
comparing plans with actual outcomes, building dashboards of key
metrics, even generating draft plans themselves. This will free up
planners’ valuable time for more strategic tasks.
9
Source
Today, sourcing and procurement teams grapple with
challenges stemming from inefficient, manual processes,
diverse categories, and system integration issues. While
teams often spend significant time on strategy alignment,
sourcing, and data reconciliation, generative AI presents an
opportunity to streamline operations, bridge information
gaps, and improve access to a broader array of data
sources, enabling faster insights and simplified processes.
It also opens up the possibility of hyper-automation, where
different forms of automation — including existing machine
learning algorithms and process automations as well as
generative AI — are linked together as part of an increasingly
autonomous system at scale. This promises to free teams for
more valuable work and enhance overall efficiency.
Consider how retail giant Carrefour is using generative AI to
enhance its internal purchasing processes. The company is
developing a solution that will help employees complete
everyday tasks more quickly, including drafting invitations to
tender and analyzing quotes.5
10
Imagine if every business user had an assistant buyer powered by What about RFP drafting? Fine-tuned on historical RFI, RFP and RFQ
generative AI. When they needed to buy something, the assistant could information, generative AI can not only draft RFx documentation, but
guide them to the right buying channel, support any call off or spot buy, also review and compare the responses that are returned by suppliers.
and, if needed, connect with a professional buyer to handle the Upstream procurement activities like supplier discovery and category
purchase. management also stand to gain from generative AI’s ability to rapidly
summarize a wide array of market intelligence insights.
Text-heavy activities like contract generation also stand to gain
significantly. A generative AI solution can be applied to large volumes of Accenture has built a smart sourcing and contracting tool using
unstructured procurement information, such as historical contracts, generative AI. It helps sourcing managers with supplier negotiations by
procurement policies and product specifications, to identify common analyzing business requirements, historical contracts, and bidding
patterns and requirements. This allows it to instantly produce a first draft patterns to suggest suitable sourcing strategies. The tool also suggests
of a new contract, which teams can then review and enhance using their terms and conditions to help ensure best-in-class contracts result from
procurement expertise. negotiations.
11
Make
As data availability and trust improves, generative AI will also be increasingly
If companies can bring their IT data together with their operating and
applied to the wealth of insights in operational digital twins, expediting
engineering data, generative AI will help them achieve a consistent level of
diagnoses and root cause analyses. And the combination of classical and
quality and operational excellence in their manufacturing operations,
generative AI offers the promise of significantly streamlining access to
particularly in areas such as asset maintenance and empowering the
predictive maintenance insights, real-time data analysis and failure
workforce with actionable predictive insights. It can also offer new insights
diagnostics by making the information more consumable through easy-to-
into product design and quality control.
use Q&A interfaces.
In plant management, for example, asset maintenance teams often
Quality control and compliance, too, stand to gain. Even companies in
grapple with complex processes and large volumes of asset-specific
heavily regulated industries like pharmaceuticals are exploring how
documentation. Generative AI can be used to digest all this information and
generative AI can be applied to multiple data sources to identify
summarize it into a series of logical steps as part of a work order. It means
irregularities in cold chain management and auto-populate compliance
expert know-how is unlocked and democratized across the workforce —
documentation for review by human experts. Generative AI can also draft
improving not only operational performance but also job satisfaction.
technical publications with accurate content, significantly reducing
authoring efforts. In aerospace, for instance, it can accelerate the
What about maintenance planning? Many companies in heavy industries production of legally mandated technical documentation such as
are moving towards risk-based inspections (RBI) to unlock value. But work/assembly/repair instructions, user manuals, warranty information and
planning for these inspections, plus preventive maintenance and operator instructions for use (IFUs).
care routines, is still a manual, human-intensive and repetitive process. It
needs highly skilled field engineers to create planning documents, as well as
subject matter experts to review them. However, generative AI can write
precision maintenance job plans for equipment classes or specific
equipment with high accuracy and completeness. That significantly
reduces the time needed to create and review key planning documents.
12
Fulfill
Today, supply chain disruption is everywhere. It’s forcing companies to transform supply chain operations for
greater resiliency, relevancy, and responsibility. Leaders are focused on improving forecasting while
implementing Transportation Management Systems (TMS), Warehouse Management Systems (WMS)
andWarehouse Automation/Robotics to drive up agility and efficiency.
By layering generative AI onto the broader data maturity and automation agenda, companies can achieve
significant gains in fulfillment. That includes enhancing hyper-personalized customer experiences and
extracting new revenue opportunities from insights based on large volumes of omnichannel data. Fulfillment
operators can also use generative AI to suggest ways to optimize transportation management and
improve forecasting by considering a broader range of factors from unstructured information (such as
weather forecasts and competitor activity).
Consider how an LLM-powered import/export document generator could transform shipping and export
processes. Generative AI can be applied to a comprehensive collection of multi-modal unstructured
information, including historical internal records and governmental regulations, across various formats,
including PDFs and tablets. Shipping and export documents can then be automatically populated for human
experts to review and verify, reducing opportunities for error while saving time and manual effort.
13
Service
The goal of providing services rather than just products is
becoming a reality for many companies. However, the service
space is typically still highly fragmented, with assets and
resources distributed regionally and globally. It’s also heavily
reliant on coordination with other parts of the supply network. Not
only that but executing a service-oriented strategy requires a far
more proactive approach to forecasting and responding to
individual customer needs.
It's why generative AI can have a game-changing impact. Its ability
to scan vast amounts of information across a broader range of
data sources — including unstructured data that was previously
difficult to process — offers the promise of deeper insights. From
geographic locations to weather conditions and from customer
lifestyles to individual usage patterns, these can be combined with
classical AI techniques to enable truly one-to-one service
experiences on a global scale.
An example? Look at how Accenture helped one major
automotive company use generative AI to enhance customer
support. By creating an intelligent incident resolution copilot to
summarize incidents, detect known issues, recommend
resolutions and compose customer responses, we’re helping
customer support agents access contextualized information and
resolve incidents faster.
14
Cross-functional
value on the table
For CSCOs, the generative AI era promises a wealth
of additional benefits that cut across individual
supply chain functions.
Sustainability
Companies are under pressure to increase their supply chain sustainability and
report on their corporate responsibility commitments more accurately.
However, with information dispersed across a multitude of sources and
sustainability categories, teams are faced with an almost insurmountable
challenge collecting and analyzing the data. The work is slow and requires
intense manual effort from subject matter experts.
It’s no surprise, then, that 63 percent of CEOs say the lack of ESG data
measurement across the value chain is a key challenge.6 But generative AI
offers solutions. For example, we worked with one global pharmaceutical
company to accelerate supply chain decarbonization efforts. The company’s
teams had spent years painstakingly compiling data on how many suppliers
had science-based targets (SBTs). We built a generative AI solution capable of
delivering near-instant insights by trawling through thousands of supplier
websites. After one hour, the company had reliable intelligence confirming it
had already exceeded its supplier SBT target.
Generative AI has many other use cases in sustainability, including generating
prioritized decarbonization roadmaps for individual companies and
enhancing Scope 3 emissions reporting. Today, for example, accurately
matching company spending to emissions is time-consuming and laborious
work. Accenture developed a generative AI solution able to sift through millions
of lines of spend data, across multiple languages, and automatically map each
line item to relevant emissions factors, which procurement teams can then
review. A process that once took days can now be completed in minutes.
16
Intelligent ways of working
One of generative AI’s most revolutionary aspects is the way it lets
people interact with unstructured data more easily and
comprehensively.One way to think of it is as a “superpowered
navigation system” for language-based activities, providing near-
instant access to consumable data insights that help people
accomplish tasks faster and more effectively.
This will empower supply chain leaders and their teams to
reinvent the way work gets done. For instance, generative AI’s
ability to shift unstructured data on a superhuman scale helps
demand planning and supply chain resilience teams unlock
insights into market trends and developments. Examples include
the rapid analysis of market data to understand and predict
pricing changes of raw materials, understand consumer reaction
to promotional activity, and connect the dots between global
disruption events and supplier lead times.
Accenture created a generative AI powered market watcher
tool for commodities. It’s designed to help business analysts at oil
and gas companies as they make purchasing decisions. The tool
ingests a broad range of both structured and unstructured data
and outputs key metrics in numerical formats for further analysis,
saving time and effort while also enriching model outputs with
expanded data sources.
17
Resilience
When it comes to managing disruption in supply chains — which
has cost businesses $1.6 trillion in missed revenue opportunities
over the last 2 years, according to recent research — one of the
key challenges for CSCOs is knowing who their n-tier suppliers are
and assessing if they’re a potential source of risk and vulnerability.
Understanding the full configuration of these supplier networks is
a critical prerequisite for increasing supply chain resilience.
Generative AI can support these efforts by augmenting existing
AI-powered solutions that analyze structured data (such as trading
reports) with the analysis of much larger volumes of unstructured
data (such as news sources, videos, chatting traffic, etc) to
produce deeper insights into the supplier network.
Procurement teams can also use generative AI chatbot interfaces to make those
insights more accessible, helping them collaborate with suppliers to understand where
priority risks exist and make more effective sourcing decisions.
An example? Accenture built an N-tier Supply Chain Navigator powered by OpenAI
GPT. It helps procurement managers analyze supplier network data by providing real-
time insights, answering specific queries, and facilitating data-driven decision-making.
Employees can quickly and easily query the tool to identify supply network
vulnerabilities — such as suppliers with geographic ties to conflict areas or locations
experiencing natural disasters.
18
Customer-centricity
Companies can use LLMs in conjunction with classical AI to
Generative AI’s ability to provide accurate, easy-to-use chatbot interfaces has
transform service-related call center experiences. Examples
many applications in building a more customer-centric supply chain
include predicting customer intent and creating a tailored tone of
network. Take product design, for example. Generative AI can analyze a broad
voice — especially important when handling complaints. LLMs can
range of unstructured customer feedback, such as online product reviews and
also be used to summarize calls, generate action points, and draft
social media sentiment, much faster. This can then be channeled back into
customer responses, freeing up employees to focus on bringing
product design workflows, allowing for rapid feedback loops between
human creativity and empathy to customer actions where they
customer demand and product development.
can add most value. What’s more, each new customer interaction
serves as additional context for AI models, improving the
relevance and quality of outputs and thus customer retention.
Generative AI chatbots can also allow customers and employees
to explore complex technical product documentation faster
and more easily. For example, Accenture developed a generative
AI solution for managing technical documentation, such as
product manuals and guides. It not only allows companies to draft
these documents faster, but also then query and summarize them
in plain language, meaning readers can find and consume the
information they need almost instantly.
19
Unlocking talent
For the first time in history, we’re embracing a generation of technology that
is “human by design”. Generative AI’s effectiveness hinges on human input to
drive quality outputs— whether that’s something straightforward, like drafting
an email, or complex, like a financial forecast. These more human-centered
processes will reinvent work across the entire value chain.
CSCOs also see key challenges in sourcing and retaining skilled
By synthesizing data, comprehending natural language, and converting talent. For example, 32 percent see talent scarcity, due to skill gaps
or unawareness, as a major barrier in utilizing generative AI. And 36
unstructured data into actionable insights, generative AI is democratizing
business process redesign, empowering everyone — from frontline workers to percent believe workers will not fully embrace generative AI due to
lab scientists to design professionals — to reshape their own workflows and a lack of technological understanding.8 However, most workers (82
make language-based work faster and easier. Generative AI is also being percent) believe they do grasp the technology. And 94 percent are
used to produce tailored learning materials, to help onboard and upskill new confident they can develop the needed skills.9
team members.
However, nearly half of organizations that are leaders in reinvention recognize
that processes across the value chain will require significant changes in order
to realize the opportunity for generative AI to accelerate economic value,
increase productivity and drive business growth, while also fostering more
creative and meaningful work for people.7
20
How to get started
As CSCOs embark on their generative AI
transformations, there are several key success
factors to bear in mind.
The good news? Generative AI can itself be applied to an organization’s data
pipelines to accelerate digital maturity. Companies can use it to automatically
synthesize and extract knowledge from their supply chain data, including
dramatically simplifying and maximizing the use of unstructured data. This
creates a circular pathway that uses LLMs to mine and process supply chain
Given the large amounts of data needed to customize and optimize LLMs, a data, which can then be supplied to supply chain use cases, including those
mature enterprise data strategy is a critical prerequisite for a generative AI supported by generative AI itself.
transformation. Those with strong supply chain data capabilities have an
important head start over their peers.
Companies are understandably cautious about supplying external generative
AI solutions with business-critical manufacturing, purchasing and other supply
However, many companies are still wrestling with the challenge of increasing chain information. Strict data retention and privacy policies and trustworthy
their data and digital maturity across their supply chain networks. Now, they’ll security guardrails are therefore vital. CSCOs will need to weigh up the relative
need to take this further by extending their data lifecycle management to risks and rewards of using their proprietary data to enhance LLM outputs in
include large volumes of unstructured mixed-modality data (meeting each use case. Working with partners who can guarantee data security and
transcripts, technical documents, video, audio, images, and more), as well as provide sandboxed generative AI solutions is one way of safeguarding data in
prompt engineering pipelines and new “ModelOps” ways of working. supply chain implementations.
22
From potentially biased and harmful outcomes, to question marks over accuracy, “supply
chain cannot hallucinate” and user trust, to security and data vulnerabilities, generative AI
represents a unique shift in the business risk landscape. That’s why it’s essential to take a
responsible approach to supply chain implementations from the very start. Employees,
customers and supply chain partners all need to trust that any AI implementation is fair,
secure and reliable.
Accenture believes strongly in leading by example when it comes to responsibility. It’s why
we’ve been pioneering our responsible AI framework for the best part of a decade.
Updated for generative AI and built on four key pillars — principles and governance; risk,
policy and control; technology; and people, culture and training — our framework has been
scaled to over 700,000 people in our organization worldwide.
23
While generative AI is not about replacing people or jobs, it will have an increasingly central role in
day-to-day work. Accenture analysis indicates that, in seven of 15 supply chain network occupations
— including purchasing managers and buyers, production, planning and expediting clerks, industrial
production managers, logisticians, and others — more than half of all working hours will be
impacted by the technology through varying degrees of automation and augmentation.
It's incumbent on both supply chain leaders and their workforces to understand and plan for this
reinvention of work on two dimensions: which tasks can be automated or augmented, and which
people need to be upskilled to make use of generative AI. By analyzing these factors, companies can
map out the different levels of impact on their people and develop the right upskilling programs.
24
Work time distribution by occupation and potential LLMs impact
Ordered by their employment levels in the US in 2022
Higher potential for automation
Higher potential of augmentation
Lower potential for automation or augmentation
Non-language tasks
Heavy and Tractor-Trailer Truck Drivers 26% 14% 8% 51%
Shipping, Receiving, and Inventory Clerks 35% 19% 8% 39%
First-Line Supervisors of Production and… 25% 10% 35% 29%
First-Line Supervisors of Transportation and… 29% 9% 25% 37%
Inspectors, Testers, Sorters, Samplers, and… 9% 7%6% 78%
Our analysis finds that the roles for production, planning and
Driver/Sales Workers 37% 6% 9% 48%
expediting clerk and procurement clerk have the highest
Buyers and Purchasing Agents 32% 21% 47%
potential impact from generative AI — 72 percent and 75
percent of their time respectively. This significant potential for Production, Planning, and Expediting Clerks 57% 15% 18% 10%
transformation, however, does not necessarily equate to job Industrial Production Managers 30% 19% 29% 22%
losses. Rather, it indicates that a considerable portion of their
Transportation, Storage, and Distribution Managers 36% 10% 38% 17%
work could be augmented by generative AI technologies. For
Logisticians 32% 24% 32% 12%
instance, 34 percent of procurement clerks tasks could be
augmented by generative AI — this includes tasks such as Cargo and Freight Agents 30% 24% 26% 20%
evaluating the quality and accuracy of data and determining Procurement Clerks 41% 34% 13% 12%
the value or price of goods and services. Embracing generative Purchasing Managers 31% 22% 47%
AI would allow these professionals to reallocate their time to
Weighers, Measurers, Checkers, and Samplers,… 19% 18% 5% 58%
more value-added activities, enhancing overall efficiency and
0% 20% 40% 60% 80% 100%
productivity in their role.
Note: Estimates are based on Human+Machine identification of work tasks exposure to impact
of generative AI. Source: Accenture Research based on US BLS May 2023 and O*Net.
25
To reinvent work in a way that drives innovation and enriches the employee
experience, companies will not only need to upskill their people in core
generative AI skills, but also develop other dimensions such as working with
purpose, strengthening trust and supporting emotional, physical and financial
health. Accenture research has found that companies that lead in driving
reinvention are also around twice as likely to be prioritizing the soft skills that
are increasingly important to ensuring generative AI adoption and value.11
Generative AI can itself be used to identify reinvention priorities for both
people and processes. For example, applied to a range of unstructured
internal and external information, it can help supply chain planners suggest
trends, summarize requirements, understand cross-functional dependencies,
capture the employee voice, and identify people’s pain points, sentiment and
workplace challenges.
Generative AI will help us ideate new ways of operating
that are truly innovative — and don’t simply recreate
what we’ve done in the past
26
More than ever, generative AI requires companies to build partnerships with the broader
technology ecosystem. With every cloud hyperscaler and numerous supply chain platfo"
93,accenture,Accenture-Unlocking-The-Power-of-Data-and-AI.pdf,"Unlocking the power
of Data
and AI
Are you making the most of your data?
June 2023
Copyright © 2023 Accenture. All rights reserved.
Data and AI are It’s time to take the next step on the Data and AI AI, leaving many businesses unable to realize
journey and extract the real value from the data
the full value of data.
available to organizations.
foundational
Data and AI is now a foundational capability and
The typical obstacles that companies
critical for the survival and success of any
encounter when trying to mature their Data and
capabilities business. For this capability to mature, data
AI capabilities center around three main issues:
transparency and trust in the data is key—and
1) Lack of trust in data accuracy and this demands examining the flaws of current
for the digital
completeness data platforms and remediating them at speed.
When the data platform is trusted, secure, easy
2) Lack of a clearly defined and industry-
business to use, and reliable, business focus can shift
relevant data model that makes it easy to
from merely solving data issues to using data to
understand and use the data to innovate
innovate and create value.
With the fall of data silos and an accelerated move 3) Lack of a channel to integrate AI insights in a
to cloud platforms, data and AI have become a timely way into business flows and interactions
critical capability for fueling the future of
Most data architectures arose organically, with
businesses.
different areas of the business pushing various
The move to cloud platforms facilitated a massively data sources to the cloud. Taking such a siloed
accelerated improvement in data availability and approach has created multiple “versions” of the
data access. data with different levels of fidelity and
accuracy. In addition, this siloed approach has
Although cloud is now a mainstream technology and
limited the ability of businesses to create a well-
data is seemingly widely available, only 12% of firms https://www.forbes.com/sites/gilpress/2019/06/09/9-
defined and catalogued data model that is indicators-of-the-state-of-artificial-intelligence-ai-may-
report that they’ve advanced their AI maturity 2019/?sh=3ff55cdd577f
required for AI at speed. Finally, integrating AI
https://www.accenture.com/us-en/insights/artificial-
enough to achieve superior growth and business
into other applications that improve employee intelligence/ai-maturity-
transformation. andtransformationc=acn_glb_aimaturityfrompgoogle_13131656
and customer interactions has lagged. These &n=psgs_0622&gclid=Cj0KCQiA8aOeBhCWARIsANRFrQEzb0-
IUnISnIlfCalVtuBiaQpp8SJJ-FNS2Hl4kS-
three issues have severely limited the scaling of dZuLH4LC1GMsaAumgEALw_wcB&gclsrc=aw.ds
Copyright © 2023 Accenture. All rights reserved. 2
Data and AI have become a critical driver
of Total Enterprise Reinvention
Insights at speed Good for Reduced risk Improved experiences
the bottom line
AI can improve the outcomes of Al has helped companies Embedding AI into engineering,
multiple business processes. Incorporating AI into operations become more compliant and has operations, and business
New ways to incorporate Data reduces costs and increases enhanced their audit and risk workflows can reinvent and
and AI into daily operations arise revenue. Companies who invest initiatives. Incorporating Data improve customer interactions,
constantly, and it’s critical to be in AI are 40% more likely to see and AI into compliance product development, employee
able to respond and embed share price increases1. processes has reduced risk and experience, and sustainability.
insights at speed. improved compliance across the
enterprise.
Source: 1 The Art of AI Maturity| Accenture
Copyright © 2023 Accenture. All rights reserved. 3
With the exponential growth of ChatGPT and
Generative AI, businesses are more than
ever looking into AI and eager to experiment
with the new technology to understand how
it can augment their operations. Accenture’s
research suggests that up to 40% of business
operations tasks will be impacted by
Generative AI.
Source: Accenture Technology Vision 2023: Generative AI to Usher in a Bold New Future for Business, Merging Physical and Digital
Worlds | Accenture
Copyright © 2023 Accenture. All rights reserved. 4
Why the chasm between data aspiration and
realization remains wide
1 Data is bigger The enterprise data landscape is expanding at a speed
and scale that is hard to fathom.
2
Data is more complex
30% of corporations' data will be synthetic data.
These silos hamper their ability to capture, process, and
3
Data remains siloed
extract value from today’s variety of data types and
deliver insights with high agility.
4 For AI and ML applications, faster access to operational
Data velocity is faster
data is required.
As companies increasingly seek operational and
5 competitive advantage beyond the limits of their first-
Data sharing is now critical
party data, there is a growing need to share data quickly,
safely, and in multiple forms.
Copyright © 2023 Accenture. All rights reserved. 5
While most companies invest in Data and AI,
only 12% have a mature AI capability that
drives value for the business by using the
wealth of available data.
The remaining 88% of companies struggle on
their journey to translate data into
innovation and value for the business.
Copyright © 2023 Accenture. All rights reserved. 6
The differentiator is
the ability to quickly
use changing data
The leaders in extracting value from data
have more mature data capabilities. They
quickly add data to analytics models by
using catalogued structured,
unstructured, and external data. The
winners in the Data and AI race are
focused on getting their data ready for AI.
Source: The state of AI in 2022—and a half decade in review| McKinsey
Copyright © 2023 Accenture. All rights reserved. 7
Storm clouds ahead
While most companies appreciate the value of data
and plan to better use it, the journey to a mature
Data and AI capability is not an easy one.
Reevaluate investments to date. While significant investments have
been made in data platforms, very few platforms are mature enough to
handle a spectrum of business needs, and companies often have
multiple different solutions that operate in silos. Typical challenges such
as siloed data, insufficient error handling, delayed or slow data
ingestion, and limited data validation need to be resolved. Data
platforms need to be consolidated to improve the reliability of data and
reduce the cost to operate.
Clean up and catalog available data to get it ready for AI. Most existing
data platforms have abundant data, but the quality and documentation
of the data is typically insufficient to support AI and generate insights at
speed and scale. Significant effort is required to clean up and catalog
the existing data and set up automated processes to label and catalog
future data sources so data can be added rapidly to new AI models.
Copyright © 2023 Accenture. All rights reserved. 8
Why
Harness AI with Accenture and Oracle
Accenture Why Oracle when it comes to
Success of AI relies on (a) the ability to use all types of data to solve broad
and Oracle
speDctraumt oaf b uasinnesds c hAalleIn?ges, (b) easy-to-use data technologies for
labeling, data quality check, and synthetic data generation, and (c) delivering
when it
AI insights in business workflows for maximum usage with minimum friction.
comes to
Data and
AI?
Use all types of data Apply AI at scale Deliver AI in workflows
Using structured and Oracle offers scalable data At times, the last step of integrating
unstructured data for AI can technologies for data labeling AI into business processes can be
mean more accurate results. and integration as well as a the most challenging part. But with
Oracle is the only end-to-end data science platform where AI and ML services on the same
data platform for integration, raw data can be prepared for cloud along with Oracle business
transaction processing, data specific AI delivery. applications, it becomes easier to
warehousing, data lake, and augment existing application
more, backed by unlimited workflows with AI—bringing AI
deployment options. closer to day-to-day activities for
real business insights.
Copyright © 2023 Accenture. All rights reserved. 9
Use all types
Oracle uniquely offers a unified When you add Oracle Autonomous Data
strategy and set of tools for making Warehouse to the mix, it provides an added
of data with
the most of data, advanced analytics, edge: improving performance even more
and AI. while streamlining database activity and
Oracle increasing database analyst (DBA) efficiency.
Nearly half the world’s data runs on Oracle
In addition, Oracle offers competitive Object
databases. As more organizations move that
Storage for collecting and analyzing all types
data—and the workloads that rely on it—to
of data including text, images, audio, and
the cloud, it’s vital to ensure that the data is
video recording.
used, managed, and secured in the best way.
That’s where Oracle Data Platform provides Oracle Data Platform meets you where you
an all-important advantage. are. Use as many or as few of the services as
you need for your structured and
Oracle Data Platform is a comprehensive
unstructured data needs and integrate those
suite of tools, services, and applications that
services with your own trusted tools.
help businesses manage their data effectively
Sources:
throughout its life cycle. It covers the whole
9 Indicators Of The State Of Artificial Intelligence (AI)| Forbes
data stack from Oracle, including Oracle The art of AI maturity| Accenture
transactional databases, data lake, data
warehouse, AI/ML services, and more, while
leveraging Oracle Cloud Infrastructure (OCI)
and Oracle Cloud Applications (Fusion ERP,
CRM and HCM) to provide a comprehensive
solution for data management.
Copyright © 2023 Accenture. All rights reserved. 10
Apply AI With the fall of data silos and an use by decision makers. For example, supply
accelerated move to cloud platforms, chain leaders can consume AI insights such as
at scale Data and AI have become a critical demand forecasts, predicted ETA, and outliers in
capability for fueling the future of the SCM planning, transportation, and inventory
businesses. workflows for seamless decisions. Furthermore,
with its deep business knowledge in ERP, SCM,
On applying AI at scale, Oracle offers OCI Data
CX, and HCM segments, Oracle helps deliver AI
Labeling and OCI Data Science to enrich
insights into the diverse SaaS landscape through
business data with annotations, generate
powerful data connector technologies including
synthetic data, develop scalable AI models, and
Oracle Integration Cloud, Data Integration, and
automate data and ML pipelines. Large-scale
Oracle GoldenGate.
customers use OCI Data Labeling monthly to
The combination of these services ensures that
annotate and enrich thousands of data objects to
AI can be delivered at scale in a more
build custom AI models using OCI Vision,
streamlined manner.
Language, and Document Understanding. OCI
Data Science serves over 200 million inference
Sources:
calls for over 2,000 customers using over 1,000 9 Indicators Of The State Of Artificial Intelligence (AI)| Forbes
The art of AI maturity| Accenture
ML models.
More than 10,000 customers run their
businesses on Oracle’s SaaS applications, so
Oracle enables delivering AI insights in
conjunction with business processes for ease of
Copyright © 2023 Accenture. All rights reserved. 11
Deliver AI in Data science services and business with AI to automatically assign new account
combinations and help match transactions with more
applications need to work together
than 99% accuracy.
workflows seamlessly for real business impact.
AI can be powerful in consuming and organizing all
In the absence of such integrated
forms of business data. Document Understanding AI
delivery, AI will be narrow and available automates invoices and other documents uploaded
to Oracle NetSuite to help automate business
only to few, limiting its value.
transactions.
Oracle, with its leading business applications
Businesses run on executive dashboards because
portfolio and more than 40 years of experience in
they help organizations monitor and proactively
helping its customers to run a broad spectrum of
address disruptions. AI deliveries such as anomaly
businesses, delivers AI in business workflows for
detection, forecasting, sentiment analysis, and image
maximum impact withminimum friction.
detection in Oracle Analytics Cloud help business
Furthermore, Oracle pioneers in bringing the best-of-
users consume AI insights where they make their
breed AI with partnerships including NVIDIA and
business-critical decisions.
Anaconda.
Data quality is fundamental to AI’s success, and
Companies seek more forecasting capabilities to
Oracle’s SaaS applications collect a broad spectrum
gain an edge over the competition. With AI,
of business data. Anomaly Detection, classification,
businesses can generate forecasts by using historical
and outlier detectionAI help in organizing data and
sales, promotions, product attributes, holidays, and
identifying data quality issues to help route them to
other extraneous factors to achieve improved
the right personnel for remediation.
accuracy and reliability. Oracle delivers AI-based
finance and sales forecasts to Enterprise OCI Data Science helps several healthcare ISVs,
Performance Management (EPM) customers. including Ronin, whichempowers clinicians with an
oncology solution by delivering health record
Automating back-office tasks with AI improves
summation, comparative insights, and decision
business efficiency and customer experiences.
support to treat patients individually instead of by a
Oracle automates general ledger account
set of averages.
management in EPM Account Reconciliation Cloud
Copyright © 2023 Accenture. All rights reserved. 12
Data is a strategic asset
Enterprises must treat data as a form of capital and invest in
its acquisition, growth, refinement, safeguarding, and
Data and AI have
deployment.
become a critical
driver of Total
Extricate data from functional silos
Data must be analyzed together in one place to unlock its
Enterprise Reinvention value. This can be done through cloud computing or a
distributed computing strategy like data mesh
Foundational models can create exponential impact
Foundational models can be fine-tuned to solve specific
industry problems and embed data intelligence into business
flows and across the enterprise to enable the Total Enterprise
Reinvention.
Copyright © 2023 Accenture. All rights reserved. 13
Accenture offers a
full spectrum of
services to help
companies unlock
the value of dormant
data
We work with you to help your business capture the full
value of data, using our experience of over 100 Data and
AI projects in nearly every industry with an unparalleled
ecosystem of alliance partners.
Accenture can help put cloud to work for your business
with solutions from one of our leading providers, Oracle.
Copyright © 2023 Accenture. All rights reserved. 14
About Accenture
Snejina Alexieva
Accenture Oracle, Business
Accenture is a leading global professional services company that helps the world’s leading Group, Data and AI Lead
businesses, governments and other organizations build their digital core, optimize their operations, snejina.a.alexieva@accenture.com
accelerate revenue growth and enhance citizen services—creating tangible value at speed and
scale. We are a talent and innovation led company with 738,000 people serving clients in more
than 120 countries. Technology is at the core of change today, and we are one of the world’s Andrea Cesarini
leaders in helping drive that change, with strong ecosystem relationships. We combine our
strength in technology with unmatched industry experience, functional expertise and global delivery Accenture Oracle, Business Group,
Europe Lead and Global CTO
capability. We are uniquely able to deliver tangible outcomes because of our broad range of
services, solutions and assets across Strategy & Consulting, Technology, Operations, Industry X andrea.cesarini@accenture.com
and Accenture Song. These capabilities, together with our culture of shared success and
commitment to creating 360°value, enable us to help our clients succeed and build trusted, lasting
relationships. We measure our success by the 360°value we create for our clients, each other, our Viji Krishnamurthy, Ph.D.
shareholders, partners and communities. Visit us at www.accenture.com.
Sr. Director, Product Management,
OCI AI and Data Science
viji.krishnamurthy@oracle.com
About Oracle
Oracle is a global provider of enterprise cloud computing, empowering businesses on their journey of
DISCLAIMER: This document is intended for general informational purposes
digital transformation. Oracle Cloud provides leading-edge capabilities in software as a service, only and does not take into accountthe reader’s specific circumstances, and
platform as a service, infrastructure as a service, and data as a service. Oracle helps customers may not reflect the most current developments. Accenture disclaims, to the
fullest extent permitted by applicable law, any and allliability for the
develop strategic roadmaps and advance their journey to the cloud from any point: new cloud
accuracy and completeness of the information in this presentation and for
deployments, legacy environments, and hybrid implementations. Oracle’s complete, integrated
any acts or omissions made based on such information.
approach makes it easy for companies to get started in the cloud and to expand as business grows.
Copyright © 2023 Accenture. All rights reserved. Accenture and its logo are
430,000 customers in 175 countries use Oracle technologies to seize opportunities and solve real
registeredtrademarksofAccenture.Thisdocumentreferstomarksownedbythird
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Copyright © 2023 Accenture. All rights reserved. 15"
94,accenture,Accenture-POV-Getting-your-workforce-ready-for-AI-and-Spatial-Computing.pdf,"Innovation at scale:
Getting your workforce ready
for AI and Spatial Computing
Content
Executive summary
Introduction
Thought leadership
Use cases
Key takeaways
Executive summary | Introduction | Thought leadership | Use cases | Key takeaways
Executive summary
Generative AI and Spatial Computing will not only reinvent how we work but also reshape our workforce as a
whole.To be ready for what’s next, enterprises will need to upskill their workforce and ensure their technology
is ready to meet the computational demand of these new technologies.
New realities
Technology disruption became the #1 cause of business change in 2023 catapulted by advances in Generative
AI. Only 27% of Accenture surveyed companies claim their organizations are ready to scale up Gen AI.* As a
result, companies are accelerating the executions of their transformation programs across talent and
technology.
In addition to AI, Spatial Computing technology is already becoming an integral part of our enterprise fabric
and could grow to be as groundbreaking as desktop and mobile, ushering in a new era of technology
innovation.
Accenture research has found that companies that take a people-centric approach to AI could create $10.3
trillion in economic value.*
The successful adoption of these technologies will require significant change management, reinvention and a
prepared workforce.
*Work Reinvented, Workforce Reshaped, Workers Prepared Accenture 2024
Executive summary | Introduction | Thought leadership | Use cases | Key takeaways
Gen AI is the #1 driver of reinvention today
Company transformation programs need to shift from siloed use cases to a holistic approach that looks across the
organization’s whole value chain. Those looking to stay ahead will need to embrace Gen AI not only as multi-year
change agenda but as a continuous reinvention. This means companies need more people who can work with Gen AI,
not less. There is no AI-ready workforce companies can hire now. Companies will need to prepare workers, reshape
their workforce and reinvent work for the Gen AI era. This requires investing in people and in the technology that will
scale with them – and their AI toolsets.
Executive summary | Introduction | Thought leadership | Use cases | Key takeaways
86%
Our future with AI
In the coming years, businesses will have an increasingly powerful array of technologies at of CxOs are already using Gen AI to
their disposal that will open new pathways to unleash greater human potential, productivity, some degree in their work and nearly
and creativity.
all believe Gen AI will be
Enterprises will need to develop investment strategies that enable employees to realize the transformative for their company and
value of these new tools. industry*
Our relationship with data is changing – and with it, how we think, work, and interact with
technology. The entire basis of the digital enterprise is being disrupted.
70%
The announcement of Gen AI changed the “librarian” model of search to a new “advisor” model
seemingly overnight. And now every company is working to implement LLMs. With this change
comes the need to rethink our computing structures and efficiency.
of client ISVs are integrating AI in apps
*Source: Accenture Pulse of Change Wave 10 Survey (Sept 2023)
**Source: Intel survey –PC AI ISV adoption (n=48)
Executive summary | Introduction | Thought leadership | Use cases | Key takeaways
Spatial computing:
How we will experience AI
Immersive experiences will create seamless interactions with AI.
Today most of our interactions with Gen AI are through text-based or application-based user interfaces, but as
enterprises scale their capabilities, those interactions will become more integrated throughout our lives and our ever-
evolving mixed realities. Spatial Computing will provide an interactive layer for more immersive AI interactions. Scaling
the hardware to power these experiences will be key to their success.
Executive summary | Introduction | Thought leadership | Use cases | Key takeaways
92%
Immersion through Spatial Computing
Immersive experiences, empowered by spatial computing, will blend our digital and physical
of executives agree their organization
realities while reinventing business models to create new human connections.
plans to create a competitive
Over the next decade, immersive experiences will revolutionize various aspects of life and business,
advantage by leveraging Spatial
facilitating learning, collaboration and sales in both virtual and augmented spaces, 2D and 3D,
Computing.
blurring the lines and changing how we interact.
These changes will create new business models and markets while also changing how we learn and This revolution will generate new lines
work. This change will be more gradual than Gen AI, will often be powered by Gen AI and will also of business and transform interactions
force significant changes across our enterprise data and computing structures.
between customers, employees and
companies.
Source: Accenture Technology Vision 2024
Executive summary | Introduction | Thought leadership | Use cases | Key takeaways
Reinvention with Gen AI
The shift to Gen AI will require a multi-year change agenda and continuous reinvention powered by a modernized data
foundation with flexible architecture and an efficient and secure digital core. As companies scale this technology, they
will need faster computing power and an empowered workforce with a desire to facilitate change.
Executive summary | Introduction | Thought leadership | Use cases | Key takeaways
The reinvention of work
AI is unleashing new levels of productivity and creativity while forging a path to the future that is different and
faster than any previous technology wave.Synthesizing data, comprehending natural language and converting
unstructured data into actionable insights takes significant computational power along with new human
interfaces and skills.
Gen AI will force a reinvention of work. These skills are not readily available in the workforce; companies need to
focus on enabling an AI-ready workforce. Enabling employees will require upskilling, change management,
and AI-ready technology.
Creating a Gen AI ready workforce
▪ Overall enterprise readiness for Gen AI must include focus on sustainability, cost efficiency, security and
upskilling employees.
▪ Comparative analysis of global Gen AI adoption and innovation scenarios shows that more than $10.3 trillion in
additional economic value can be unlocked by 2038 if organizations adopt Gen AI responsibly and at scale.
▪ Intel® Core Ultra Processors prepare your workforce to leverage Immersive meetings and the rapid
proliferation of AI across tools and apps.
▪ Intel is leading performance and reliability, executing a wide range of AI software through its global AI PC
Acceleration Program.
Executive summary | Introduction | Thought leadership | Use cases | Key takeaways
Beyond the headset and through the laptop
Many immersive experience platforms are available on both laptop and headset. By leveraging gamification techniques
to enhance employee and customer engagement, companies can utilize behavioral user data for better analytics, help
employees tap into the right expertise across the globe and build highly customized and engaging customer
experiences to attract and retain loyal customers.
Executive summary | Introduction | Thought leadership | Use cases | Key takeaways
Powering the experiences beyond headsets
Traditionally, immersive experiences required headsets to deliver the highest fidelity. However, in an
enterprise setting, headsets can be inconvenient and costly.
Many immersive platforms are now overcoming this challenge by offering accessibility through both
headsets and laptop devices.
This accessibility ensures that all enterprise employees with laptops can participate, maintaining the
benefits of Active Directory integration and enterprise level security.
The case for PC immersion:
▪ Empower employees to learn through simulation while retaining the benefits of immersive learning and
collaboration.
▪ Leverage gamification techniques, utilize behavioral user data, and help employees tap into the right
experience and expertise across the globe.
▪ Reduce enterprise operational costs, enhance engagement, and improve analytics.
▪ Create opportunities to leverage Spatial Computing using familiar and comfortable interfaces that have
an easier bar for entry.
Executive summary | Introduction | Thought leadership | Use cases | Key takeaways
Examples:
Collaboration
Virtual team breakouts and watercooler
discussions to connect working groups and
people across the organization
AI + Immersive
Experiences are
Onboarding
Memorable onboarding experiences to get
creating new realities
new hires excited, engaged and job-ready
powered by AI agents
Gen AI workforce empowerment and enterprise class immersive
experience use cases provide significant value in the workplace, Learning
Educational spaces to learn about specific,
each with its own unique benefits and situations:
hard to replicate scenarios at work
Brand Loyalty
Gamified experiences attract new customer
demographics and build loyalty
Executive summary | Introduction | Thought leadership | Use cases | Key takeaways
The AI Accelerant: Generative AI propels Spatial Computing
opportunities
Generative AI will be a driver for cost reduction as it reduces or eliminates the need for human-led work on content creation, but it also is an unlock to a previously
impossible level of scaled personalized service. Together with the metaverse, Gen AI is the powerful, personalized content engine that provides the ‘front-end’ interface of
spatial computing platforms.
ACCELERATED ACCELERATED PERSONALIZED PERSONALIZED
Digital twin / Environment Learning content development Synthetic human Synthetic human
creation and evaluation approaches agents for employees agents for patients/ Customers
AI is accelerating our ability to make digital Learning content platforms are speeding up Synthetic human agents provide a visual Synthetic human agents, powered by LLMs, also
twins of physical places like labs and their content production workflows with AI by interface for large language models (LLMs). enable more personalized, on-demand advice and
manufacturing facilities. Faster scanning incorporating co-pilot capabilities, making They can serve as a mentor, coach, and guide guidance for patients and customers through
techniques, prompt-to-environment, and learning development, engagement, and across various needs within the talent cycle. complex information as part of digital therapeutics
image-to-3D tools, and advanced analytics evaluation faster and more personalized. What They can help onboard, lead immersive learning efforts. They can be deployed on websites,
accelerate the creation of simulations, has previously taken months to create now sessions, keep track of performance metrics, integrated with apps, or appear in virtual immersive
collaborative spaces, and digital learning takes mere weeks or even days, enabling and provide a personalized, on-demand guide spaces. This enables more on-demand care and
areas. learning to be more flexible and adaptive. across various enterprise systems. guidance to supplement human capabilities.
Executive summary | Introduction | Thought leadership | Use cases | Key takeaways
Immersive Experiences: Scaling for adoption
As hybrid workplaces emerge, global teams collaborate more naturally using immersive spaces. Outdated hardware may hinder the compute demands of those processor-
intensive environments, so ensuring that the right hardware is in place will be key for scaling. The future of virtual collaboration and immersive experiences will start off with
familiar technologies and interfaces, namely laptop/desktop experiences, before making a jump to more mixed reality platforms.
RAPID ADOPTION FOR GROWTH
COLLABORATION & PEOPLE SCALING FOR SUCCESS
Case study: Accenture
Bridging physical space in the virtual Powering AI & immersion with the right
hardware Accenture serves as a notable case study for
Enterprises will build custom immersive spaces for virtual team
Mesh adoption, having successfully onboarded
meetings, collaboration, breakouts & fun, enabling a more Guaranteeing an optimal experience necessitates over 300,000 new employees in AltSpace and
connected work experience. hardware that can handle the demanding CPU and GPU Microsoft Mesh. These immersive experiences
Digital twins of offices, warehouses and manufacturing floors allow loads of programs like Microsoft Mesh. allowed Accenture to create both a consistent
teams to interact in what feels like the real environment, to safely Intel's Core i9 processors are well-suited to deliver a basic and unique global onboarding experience.
experiment with process improvements & layouts prior to experience and enable employees to leverage use cases. Their adoption of Mesh features has been rapid;
implementations.
Accenture enabled Avatars for 827,000
Moreover, the latest Intel® Core Ultra Series processors
The benefits of this approach includes reduced travel costs, real push the limits ofmultitasking performance to deliver real- employees in May 2023, with over 100,000
estate savings, increased collaboration & innovation for hybrid world business computing, making a compelling case for unique self-installed users, within one month of
work, employee engagement and retention. upgrading to the latest hardware. the Avatars’ launch in Teams.
Executive summary | Introduction | Thought leadership | Use cases | Key takeaways
Up to
65% faster
image creation
Our future is digital
Up to
50% faster
To support what’s NEXT in innovation, we will need technology that’s designed to support the
demands of these new realities.
photo editing
The new Intel® Core Ultra Processors, including Intel® AI Boost, support a new generation of
personal computer able to span the physical-digital divide.
Up to
These chips will empower the transformation of individuals’ and organizations’ interactions with
40% power savings
each other and with the world, striking the right balance of power and performance.
while streaming
This includes personalizing and automating much of our daily lives through artificial intelligence.
Up to
38% power savings
while video conferencing
*Source: Intel AI Software Enabling November 2023
Executive summary | Introduction | Thought leadership | Use cases | Key takeaways
Key takeaways
The Enterprise technology revolution will be powered by next generation chips, designed for AI
& Spatial.
AI will be endemic – Every industry will be impacted by adoption of AI to support our workforces.
As the demand for more computational power grows, companies will need the workforce and
hardware to meet that demand.
Spatial is already here – Video conferencing spatial experiences are already available; the
capabilities will continue to expand.
Empower employees – Employee empowerment will accelerate adoption; hardware designed for
these new technologies will enable employees to design our future AI and spatial processes and
work environments.
Hardware matters – The new Intel® Core Ultra Processors, including Intel® AI Boost, support a new
generation of personal computer able to span the physical-digital divide and touch all aspects of our
lives – personal & professional.
Nathaniel Meyer
Digital Workplace Lead for the
Accenture and Intel Partnership
nathaniel.meyer@accenture.com"
95,accenture,The-Travel-Industrys-New-Trip-Final.pdf,"The travel industry’s new trip
How generative AI can redefine customer experiences and unlock new value for organizations
Content
03
Introduction
The traveler experience today 04
How gen AI can reinvent travel 13
Where travel providers stand 17
Building the data foundations to take
23
gen AI to scale
Key principles for executives seeking to
26
unlock the power of gen AI
Let’s move people 29
The Travel Industry’s New Trip 2
Introduction
The number of people traveling around the world itself, accommodation, car hire, excursions One particularly surprising finding from the traveler
is expected to reach an all-time high in 2024, as and more. And at each step, they face a myriad survey? Travelers’ views are remarkably consistent
global leisure and business travel volumes finally of options, which can be helpful, but can also regardless of age or income. And 60% of travel
surpass pre-pandemic levels. Overall, more than lead quickly to overload and dissatisfaction. industry CXOs see potential for gen AI in product
a billion people1 will make an international trip As recent global Accenture research2 found, application development and management. The
in 2024, with just as many traveling within their 74% of consumers have walked away from an implication? Effective solutions may not be as
country. And while some trips are for business, intended purchase because the complexity and difficult to tailor as they may seem.
others for leisure or personal reasons and others an overabundance of options were overwhelming.
a mix of both or all three, each trip requires an Nearly the same number (71%) say that they see
In this report, we’ll explore the current travel
extensive and careful planning process. From either no improvement or an increase in the time
landscape, and then delve into the potential to
the industry standpoint, that process opens the and effort required to make a purchase decision.
transform it for travelers and industry players
door to a host of opportunities for companies to
alike. We’ll focus on how advanced technologies,
differentiate and win consumers, from the moment
What if there were an easier way? That’s the particularly generative AI (gen AI), can remove the
someone is inspired to travel through post-trip
question that compelled our most recent research. friction that travelers currently experience. And
follow-up services after their trip is over.
We surveyed 8,079 travelers and 313 C-suite we will demonstrate the ways in which advanced
industry executives and complemented those AI tools can offer travelers a smarter, far more
The problem is, from the traveler’s point of view, surveys with a crowdsourcing exercise involving personalized and fully integrated way to identify
today’s travel-planning process is too often time- 200 travelers from across four countries, and an and explore options and design, build and book the
consuming and frustrating. AI-based cluster analysis of their views.3 We looked perfect trip.
closely at the whole traveler experience—from
inspiration to post trip—to identify travelers’ chief
Consider: While every traveler has unique needs, The stakes are high: According to Skift, gen AI
sources of friction and frustration, and what they
preparing for a trip typically involves using multiple presents a $28 billion opportunity for the travel
want to see most in the way of improvements.
websites and apps. They need to book the travel industry.4
The traveler
experience today
The traveler experience today
To begin with the basics, most travelers still prefer using their desktops or laptops to
explore destinations and to book trips. Our survey revealed, for example, that 48% of
travelers prefer using a desktop or a laptop, while 33% use their smartphones.
They also tend to start early and plan carefully. For those traveling domestically,
preparation typically starts one to three months before departure and for international
travel it’s likely to be six months to a year. Overall, our survey finds almost half of travelers
(45%) start planning one to two months in advance. More than a quarter (26%) start three
to six months before their trip.
We start planning our international and
“domestic travel well in advance. We watch
for deals, airfare fluctuations, and hotel
options, focusing on getting the best value.
– Traveler, US, 41–50 age group, traveling 2–5 times a year for leisure
The Travel Industry’s New Trip 5
The traveler experience today
At a more granular level, they break travel in to three distinct stages:
Seeking inspiration, planning (balancing cost with other considerations) and booking:
What inspires today’s traveler?
Travelers draw from a wide variety of sources that are not mutually exclusive to find
inspiration for destinations, activities, and types of trips.
Travel reviews are the most popular and influential. 90% of people say they use them,
and 96% think they are very important. Social media and travel influencers (on sites
such as TikTok, Instagram and Facebook) are also leading guides. And a strong majority
(71%) of travelers consult social media and influencers for inspiration about where to go.
In fact, 63% say these social media platforms/sites directly influence their final choice.
The appeal? These sites offer multi-modal content, offering insights into all facets of
an experience from activities to accommodations. And if a consumer follows someone
because they share interests, then the insights can be particularly relevant.
I rely on YouTube, Instagram, TripAdvisor,
“Yelp, and ChatGPT as dependable sources
for discovering new destinations. As a visual
person, I prefer video recommendations over
text whenever possible”
– Traveler, US, 41–50 age group, traveling 6–10 times in a year for leisure
The Travel Industry’s New Trip 6
The traveler experience today
Personal contacts hold considerable sway as well, with 61% of travelers saying they rely
on conversations with family, friends and peers to help them make up their minds.
Other sources of inspiration include books, magazines and the arts (music and
entertainment). Unsurprisingly, 55% of travelers report that industry conferences and
networking events influence their destination decisions.
Most notable in recent years (post-pandemic) has been the rise of “bleisure” trips,
Rich, immersive media to plan and inspire
where business travelers combine leisure options with work travel. These trips now
account for over a quarter of all travel (27% versus 38% for business trips and 36% for
pure leisure trips). Travelers (96%) use interactive maps or virtual reality tools such as Google Street
View to explore destinations before they book. Giving them rich media options
will help them make better-informed choices. Visual appeal is also crucial at the
inspiration stage. Over half of travelers (55%) say photos and videos influence
their decision-making. For 13%, they can be a dealmaker or breaker.
Online travel reviews provide accurate
“information about destinations, and YouTube
offers videos, making them my go-to sources
for trip research and planning.”
– Traveler, India, 21–30 age group, traveling 2–5 times in a year for leisure
The Travel Industry’s New Trip 7
The traveler experience today
Getting down to planning
Today, 28% of travelers turn to an offline travel agent or specialist for help planning their Is it any wonder the overall planning experience can be time-consuming, stressful
journey. They like the full-service nature of agents and specialists, as well as the sense and disjointed for so many? A full 68% say they use up to 10 sites to plan their trip,
of security they get from working directly with someone. They also appreciate the and 25% visit between 11–20 different sources for information during this planning
ability to ask for clarifications, and an agent’s ability to understand and accommodate stage. To manage the details, they often rely on multi-tabbed browsing sessions,
special requirements. bookmarking websites, offline spreadsheets and detailed notetaking. The most-used
planning activity across all travelers? A map, to scope their destination, locating hotels
and activities in advance of booking.
But for the rest, this is where the hard work begins. Knowing that good planning is
crucial for making the best of their investment, and seeking the right balance of price,
time and number of activities, travelers often visit multiple sites to pull together the
information they need. For the 44% of travelers with a fixed budget, always looking for
7%
the best deal is the greatest challenge. Those with more flexible budgets (43%) still
have many decisions to make, ranging from how long they want to stay in one place to
the transit links they’ll use between the airport and their destination.
From planning
25%
to booking,
how many
sources does
a traveler refer?
68%
1-10 sources 11-20 sources >20 sources
The Travel Industry’s New Trip 8
The traveler experience today
The travel planning process can be lengthy
and challenging, as many websites redirect
“
you to others, making it hard to compare Who’s doing the planning and booking?
and find the best deals”
Just 32% of travelers plan their travel themselves. Of the rest, 31%
depend on executive assistants, while 29% take help from spouses,
family or friends.
– Traveler, Greater China, 61 and older age group, traveling more
than 10 times in a year for Bleisure
What are their biggest planning headaches?
Respondents to our survey cited the following key challenges
I start by bookmarking travel blogs and
• Complexity of managing all the variables, especially accommodation
“sites, then create manual lists to sort my
• Managing their itinerary, including the need to compare options
preferences and compile price lists-the most • Tight scheduling and lack of support
• Travel unknowns or unfamiliarity with local conditions and transportation
challenging part. Maps are helpful too, but
• Decision fatigue
all this is quite time-consuming” • Risk of getting lost during the trip
– Traveler, India, 41–50 age group, traveling 2–5 times a year for leisure
The Travel Industry’s New Trip 9
The traveler experience today
Interestingly, 27% cited safety was their most important criteria when initially
planning a trip, followed by trip experience, flexibility (to change, cancel, etc.)
and accommodation.
Safety 27%
Destination/ trip
22%
experience
Flexibility (to change,
13%
cancel, etc.)
Accommodation 11%
Budget 10%
Transportation 8%
Activities and
5%
attractions
Reviews and
3%
recommendations
Cuisine 1%
Figure 1: Key considerations for travelers
Once they decide on a destination, travelers start to consider more specific
criteria. The most commonly cited of these is quality of service, including
cleanliness and friendliness (28%), followed by food and dining options (26%).
Other things they search for: extra reassurance on safety and security, proximity
to local transport and attractions and whether they’ll have access to personalized
services such as pick-up/drop-off options for transfers, or concierge services.
The Travel Industry’s New Trip 10
The traveler experience today
The great disconnect:
Travelers’ expectations versus reality
A significant majority (66%) of our survey challenge. Many are looking for convenience
respondents said they’re dissatisfied with the and simplicity: 69% say they prefer booking
planning options available to them today. travel packages and additional services
along with their hotel or airline tickets from
the same website.
Most travelers use hotels, airline or online
travel agents’ (OTA) websites or apps for
• A one-click process
Interestingly, most people (89%) also said
exploring their options, planning and
• Intuitive search
they have at least some interest (with 38%
booking. More than half (57%) say that they
• Intelligent live support for booking and other inquiries
very interested) in a subscription-based travel
trust those providers to look after their travel
platform. For a monthly fee, that approach • Automatic, personalized suggestions (schedule, itinerary) – mimicking a local
and booking data.
would give them access to benefits such travel guide
as customized trip planning, best deals and • Simplified view of offers (with points, miles, redemption)
But 61% of travelers also say that they find
curated itineraries.
navigating apps and websites complex. • High-definition photos for reference
And 56% say that the lack of options for • Clear cancellation/modification process and procedures and safety advisory
The most-cited feature travelers wish for from
customization or filtering content adds to
• Automatic prompts to the best spots and hidden places
provider websites? Personalization. More
the time required to make a decision and,
• Journey assistant for live reminders
than a third ranked this as their top desire for
in some cases, prevents them from making
their travel planning experience. After that,
a decision at all. Many also complain of
they seek natural language search (25%),
unwanted notifications and promotions,
Figure 2: The Travelers’ Wish List
connected trip recommendations (15%) and
and 52% say that they have concerns about
automatic itineraries (11%), as well as being
general data privacy and security.
able to ask open-ended questions (7%).
One of the problems many travelers face is a
Ultimately, when we asked travelers to create
lack of joined-up experiences in the planning
and prioritize a wish list for what tomorrow’s
and booking phase. Almost two-thirds (64%)
travel experience should look like, here’s
say that lack of bundling options to create
what they told us. (See Figure 2)
a seamlessly connected trip is their biggest
The Travel Industry’s New Trip 11
The traveler experience today
And when we analyzed our findings more deeply across each stage of travel, we identified the sub-categories shown in Figure 3:
Inspiration, planning Compare and During the journey Post-journey These are all capabilities at which gen AI
and search purchase excels. It’s clear that the travel sector has
much to gain from putting the power of gen
AI into its customers’ hands.
• Natural language • Product cross-sell • Real-time itinerary • Personalized travel
search. recommendations. management. summary for memories
or travel journals.
• AI-generated hotel/ • Sort order optimization • AI-powered customer
A I Saudia’s Travel Companion
airline description, on app. assistant for support, • Expense reporting.
content and imagery. scheduling and
• Upsell and offer • Automated photo
Saudia Airlines has launched Travel Companion,
reminders.
• Content recommendations. sorting and album
an AI-powered virtual assistant designed to
personalization by • Real-time language/ creation.
• Real-time pricing and
enhance customer experiences. This platform,
individual—including content translation.
offers. • Feedback collection
a key component of Saudia’s broader strategy
recommendations.
• Contextual and analysis to improve
• Sort order optimization to innovate travel experience, simplifies the
• Automatic trip creation recommendations future experiences.
on payment methods.
booking process including various travel
based on website/ (e.g., dining,
• AI-driven content
services—such as accommodations, transport,
chatbot interaction. attractions).
creation for blogs or
dining, and activities—into a single interface.
• Automation loyalty • Real-time safety social media posts.
Future updates will include additional features
member recognition. regulatory alerts and
• Suggest new like voice commands and multiple digital
guidance.
destinations for next
payment options.
• Proactive disruption trip, based on past
management. preferences—inspiring
them to begin planning
• Support remote
all over again.
destination travel
planning.
• Adapt to users’
emotions based on
interactions.
Figure 3: Zooming in on the wish-list
The Travel Industry’s New Trip 12
How gen AI can
reinvent travel
How gen AI can reinvent travel
Our research confirms that travelers are willing to use dedicated travel apps to 48%
50
help them improve their experience from search to booking. In fact, 55% say that
they already use dedicated travel planning apps such as TripIt, Routeperfect
40
and Google Trips, with 37% saying that they sometimes use them. These apps
offer features such as automatic itinerary generation, conversational assistance 29%
30
(including recommendations and offers) tailored to user/traveler’s profiles
22%
and behaviors.
20
But an overwhelming majority (97%) of travelers want a travel “superapp.” They
10
want something that will offer one-stop, integrated access to a whole range of
travel-related services, including personalized, inspirational destination ideas,
0
flights, dining and everything in between. And a related Accenture study,
Book (purchase) Inspiration (finding Planning (search,
The Empowered Consumer, found that over half of consumers are open to using
relevant content) compare and customize)
conversational AI solutions.
Figure 4: Travel Planning: Stages Ranked by Complexity
Enter gen AI
During the inspiration stage, for example, gen AI tools can prevent overload.
Our respondents ranked inspiration as the second-most complicated stage of
Using voice commands to tell an AI travel app
travel, after booking. With an overabundance of choices available, it’s easy to
become overwhelmed with information and possibilities. Gen AI can take each “about my destination and desired activities, and
traveler’s unique needs and motivations to filter out the noise and focus on the
having it suggest detailed itineraries—including
rich signal of what will be most relevant, appealing and ultimately rewarding
for every individual. Moreover, they can do this in the course of a natural-
transport and exact timings—would allow me
language interaction, while offering multi-modal responses (photos, videos,
charts as desired).
to choose the best options easily”
– Leisure traveler, US, 41–50 age group, traveling 6–10 times in a year
The Travel Industry’s New Trip 14
How gen AI can reinvent travel
At the planning stage, gen AI tools can stand out with their ability to personalize
a trip. While travelers say that planning is less complicated than other stages
(inspiration and booking), there is still plenty of scope for gen AI to simplify the
ways travelers plan, manage and track their travel details. For example, few tools
Use your words!
today can handle collaborative planning, and it’s still not easy to compare prices
and track them across multiple trip options and/or timetables.
Being able to use natural language instructions – including voice – to ask questions
And although today’s chatbots provide some tailored recommendations, there’s
and make commands is one of gen AI’s major appeals to users. Today, travelers
significant room for improvement. For example, with the responsible use of
are largely reluctant to make their bookings via voice search, with only 28% saying
customer data, gen AI could analyze each traveler’s unique preferences and
they’re comfortable booking this way. Barriers to voice search include:
travel history to shape an individualized, highly relevant itinerary that goes well
beyond standard attractions and activities.
• Difficulty in specifying preferences (e.g., room type or airline seat) due to
interface complexity (56%)
• Misunderstanding or incorrect assumptions made by the voice assistant (50%)
• Difficulty in finding specific dates, flights, or hotels due to limited options (47%)
• Difficulty in accurately recognizing spoken commands with lack of confidence
in the end-result (44%)
The Travel Industry’s New Trip 15
How gen AI can reinvent travel
Booking is by far the most complicated stage of a journey. Today, it still tends to be a
highly siloed process, as arranging each part of an overall trip—hotels, flights, activities,
restaurants and car rentals—requires separate payment and reservation processes.
Here, gen AI tools—particularly teams of gen AI tools operating through a single
interface — could offer a simpler, more centralized view of options such as payment,
offers, rewards and itinerary. While online travel agents (OTAs) have improved at
bundling various options into a single view, again, there is great room for improvement.
Ultimately, the transformative power of gen AI offers travel businesses the ability
to analyze vast amounts of data in real time and gain unprecedented insights into
travelers’ needs and preferences. It can automate research and planning by providing
real-time, data-driven recommendations—based on current and past preferences—
saving travelers time and personalizing their experience. And the travel industry can
maximize revenues by seeing booking patterns in a new light and embracing both
traditional and digital channels in real-time. In essence, gen AI will not merely be an
analysis tool. It will also usher in a new era of innovation and competitiveness.
The Travel Industry’s New Trip 16
Where travel
providers stand
Where travel providers stand
Are travel players ready to grasp this opportunity? Most are embracing gen AI tools in theory but are finding considerable challenges when it comes to scaling initiatives. Consider:
Executives’ gen AI priorities
Travel executives are increasingly recognizing the value that gen AI can bring to their consumer-facing and internal operations. Our survey finds that, globally, almost three-quarters
(73%) of travel leaders are focused on adopting gen AI for cost savings and greater efficiency. Significant numbers are also looking to use it to improve top-line growth (63%) and
enhance brand perception (61%). These findings are generally similar across all segments of the travel ecosystem, suggesting a broad recognition of the value on offer. (See Figure 5)
Airlines Airports OTAs Hotels and resorts Cruise Car rental
Cost savings/ Improved brand Cost savings/ Cost savings/
Top-line growth Top-line growth
improved efficiencies perception improved efficiencies improved efficiencies
Cost savings/ Cost savings/ Improved brand Improved customer Cost savings/
Top-line growth
improved efficiencies improved efficiencies perception experience improved efficiencies
Improved brand Improved employee Improved customer Improved brand Improved brand
Top-line growth
perception experience and retention experience perception perception
Improved employee
experience and retention
Figure 5: Business leaders’ priority gen AI outcomes
Priorities ranked in order of preference
The Travel Industry’s New Trip 18
Where travel providers stand
At the same time, travel leaders acknowledge that to fully realize these benefits, Chatbots, Virtual Device
65%
they’ll need to implement gen AI at scale—across the enterprise and value chain. Assistant and customer service
They will also need to measure its impact. Currently, companies are developing
Market and competitor
62%
and implementing gen AI use cases in areas such as customer service, market and
forecasting and predictions
competitor forecasting and product/application development. Almost half (49%) of
leaders are working toward executing these use cases, while a smaller proportion, Product and applications
60%
development/management
17%, say they’ve identified multiple use cases, and are exploring value chain
synergies to implement them at enterprise scale.5 (See Figure 6)
Sales and marketing 56%
Content translation 48%
Optimizing
48%
internal operations
Content creation 41%
Personalized travel planning
38%
and recommendations
Workforce training
37%
and skilling
Booking
30%
recommendations
Creating an
18%
end-to-end superapp
Figure 6: Use cases underway
Percentage of business leaders who see the most potential for gen AI applications across the enterprise.
The Travel Industry’s New Trip 19
Where travel providers stand
Prioritizing the workforce
Whichever specific use cases businesses focus on, one element is crucial: Continuous training by using resources such as
the people who make up the travel industry’s workforce. Yet two-thirds of the massive open online courses (MOOCs), digital 56%
learning platforms, boot camps.
executives we surveyed believe that workforce readiness and lack of training are
major barriers to progress with gen AI.
Training using experiential and immersive
56%
methods such as gamification and Metaverse
To address those barriers, companies are increasingly recruiting talent with
relevant expertise, as well as encouraging greater collaboration between their
Joining or actively participating in
data scientists, travel domain experts and IT professionals. But with gen AI talent digital training programs (e.g., the World 49%
such a scarce and keenly contested resource, travel companies are also investing Economic Forum’s SkillSET)
in training their people, with initiatives including boot camps, immersive methods
such as gamification and participation in third-party digital training programs. Instructor-led training workshops in a
48%
physical or virtual classroom
(See Figure 7)
Establishing ‘Data and AI institute’
48%
within the organization
Focused apprenticeship or
42%
certification programs
Figure 7: Training and upskilling initiatives in place across the industry
Percentage represents executives reporting anyone initiative being used at their organization.
The Travel Industry’s New Trip 20
Where travel providers stand
Tech readiness
AI broadly Generative AI
Companies recognize the importance of responsible adoption,
citing transparency, AI ethics policy and governance processes as Hardware infrastructure (e.g., specialized hardware
Strategy Assess
important levers to ensure data security and customer privacy. They for training and predictions, operational workloads).
are also starting to create the foundational elements for implementing
AI-specific applications by developing an AI-enabled digital core—
Cloud computing (e.g., computer hardware exposed
including AI-ready data and applications, a gen AI backbone, security, Assess Assess
to developers in a cloud operating model).
ecosystem, partnerships and responsible AI.
But most are still developing strategies and assessing potential Understand and develop an AI-enabled digital core—
deployments, especially in areas like cloud computing, data platforms AI-ready data and applications, a GenAI backbone, Transform Transform
security and the right ecosystem of partners.
and talent reinvention.
Figure 8 shows broadly where travel companies are in their gen AI
Availability of a modern data platform with mature
journeys, from the strategic stage (planning), through assessment
data management and governance practices — to Transform Assess
(partnering with other organizations and evaluating potential
help leverage unstructured data and synthetic data.
deployment methods), to transformation (implementing AI/gen AI in
some functions of the organization and for specific purposes). Fewer
than 20% of respondents say they’re engaged in AI implementation at Reinvent talent and ways of working—includes
leadership, learning, new ways of working, new roles Assess Assess
scale (across the operation, continuously improving) for any processes
and skills and continuous learning.
in the travel value chain.
Practicing responsible AI—an intentional method
of designing and deploying AI to drive value while Transform Transform
protecting from the risks.
Figure 8: Where are travel companies on their gen AI journeys6
The Travel Industry’s New Trip 21
Where travel providers stand
Some travel 01 Expedia has unveiled Romie, a travel planning, 04 Booking.com introduced AI Trip Planner,
shopping, and booking assistant. It adapts to an AI-powered bot enabling travellers to ask general
companies have
unexpected changes like weather disruptions, or specific travel-related questions, across any stage
suggests indoor alternatives, updates itineraries of the trip planning. It also recommends customized
already started
in real-time, integrates information from emails, itineraries and inspirational content, based on each
recommends activities and joins SMS chats to offer traveller’s preferences.
using gen AI
advice and summarize discussions.
to provide new
05 Despegar, a travel tech company, launched SOFIA -
02 IHG hotels is partnering with Google Cloud to a gen AI travel assistant that provides
services and
launch a gen AI-powered travel planning capability, recommendations on inspiration, planning,
experiences for to be launched in H2 2024. Customers using the coordination, and journey logistics.
IHG One Rewards mobile app can use gen AI for an
travelers.
easier and interactive planning experience.
03 KAYAK launched a suite of AI products using gen
AI to make travel planning faster, easier and more
intuitive. It launched PriceCheck, a price comparison
tool, and Ask KAYAK, to personalize travelers’
search experiences.
The Travel Industry’s New Trip 22
Building the data
foundations to take
generative AI to scale
Building the data foundations to take gen AI to scale
Ultimately, creating the transformed experiences that travelers crave will require We have access to
60%
nothing short of enterprise reinvention. This means building a digital core—the 1st party data
critical technological capability that enables the organization to use the most
relevant advanced technologies to their potential now and adopt the next wave
We have access to
50%
of technologies with ease. 2nd party data
It also means developing the ability to operate in a trusted ecosystem of partners We have access to Zero
party data about our clients 44%
to offer and integrate all relevant services, but also, crucially, to share vital data
and ecosystem partners
and applications. The industry still has some way to go in taking this next vital
step. Just 21% of respondents say they have a readily available ecosystem of
We have access to
industry data partnerships. And only 15% of respondents claim to have access to 34%
3rd party data
readily available developer networks to a great extent.
We have access to
Collectively, the travel industry has huge amounts of data available, including 12%
synthetic data
traveler preferences, booking patterns and local insights. Anonymizing this data,
and then using gen AI to analyze it, creates a very valuable asset. Yet, as the
Figure 9: Who’s data? From where?
industry continues to struggle to update its aging data systems and capabilities,
only a small percentage of companies are currently monetizing their data
through methods like anonymized data sales (less than one-sixth) or partnerships
Travel companies’ projected investments in gen AI confirm that they’re committed to
(about one-third). This is a major missed opportunity that forward-thinking travel
making the leap from pilots to scaled implementation. Organizations are prioritizing
organizations should address urgently. Moreover, the availability of high-quality,
gen AI in their technology investments and will continue to do so—currently 34% of
contextualized travel industry data is vital to pivoting to a gen AI architecture.
travel organizations are dedicating more than half of their technology budget toward
AI (including gen AI). This number is expected to reach 69% (from 34%) in the next 18
Figure 9 shows the range of travel company abilities to access data types7 months. Making this move will require travel companies to address the barriers they
required to realize the potential of gen AI. face today.
The Travel Industry’s New Trip 24
Building the data foundations to take gen AI to scale
Barriers to adoption
Travel leaders identified a number of challenges that they’ll need to overcome as they progress
to full-scale gen AI adoption. Among the biggest challenges: lack of technology capability
in-house, inability to manage rising volumes of data and complexity, workforce readiness and
inability to integrate gen AI systems with legacy systems with no clear ROI.
At the same time, however, leaders are also taking positive steps to address these barriers,
including:
• A majority (63%) prioritize high-quality data availability, security and privacy through
investment in data management tools, and governance policies.
• Six out of 10 are prioritizing talent acquisition and retention through investment in
training programs.
• Two out of three leaders are looking to invest toward the integration of new gen AI systems
with legacy systems.
• Three out of four C-suite leaders are setting policies and guidelines to mitigate operational
and strategic risks associated with the use of gen AI.
• A strong majority (70%) prioritize change management by developing a clear vision,
communicating the benefits of initiatives and involving employees in the planning process.
The Travel Industry’s New Trip 25
Key principles for
executives seeking to
unlock the p"
96,Autres,AIQRATE-Global-AI-Adoption-Report-2022-CPG-and-Retail.pdf,"CPG & Retail
GLOBAL AI
ADOPTION
REPORT 2022
ACCELERATE | ACCENTUATE | AUGMENT
The AI in the CPG & Retail Market is
expected to reach USD 30.90 billion by 2025,
at a CAGR of 35% over the forecast period
2020 – 2025.
CONTENTS
1.Overview - Page 2
2.Business Value chain: AI Adoption Areas - Page 4
3.Spending on AI - Page 11
4.AI Adoption across Regions - Page 14
5.Impact on Revenue and Costs - Page 19
6.Challenges - Page 21
7.The Way Forward - Page 23
1
Global AI Adoption Report 2022
CPG & Retail
Overview
Consumer products organizations (CPG) and Retail are entering a new phase of innovation with
AI at its core. The results are profound, offering a host of previously unimaginable capabilities –
from automatically rerouting shipments to bypass bad weather, to personalizing in-store
services based on analysis of a customer’s facial expressions.
The adoption of AI in Retail and CPG industries is expected to leap from 40% of companies
currently to more than 80% by 2025. Investments in AI-powered predictive and prescriptive
analytics would more than double between 2020-2025.
CPG and Retail organizations with AI investments report current benefits in five key areas:
creating better consumer experiences, revenue growth, employee upskilling, improved decision-
making and reducing risks.
Last five years have been highly challenging and disruptive amidst a changing competitive
landscape marked by new consumer behaviors. ‘Business as usual’ is not enough anymore—and
the organizations recognize that innovation must accelerate. The adoption of digital and
analytics offers CPG and Retail companies the opportunity to drive growth, deliver productivity
and stay ahead of the competition. The effort needed to take advantage of this value potential
will be worth it. The companies that adopt digital technologies early and at scale outperform
traditional incumbents.
In the 1990s, the eCommerce revolution initiated a fundamental change in consumer shopping
behaviour, which has continued to gain momentum in the mobile and social media era. In the
process, customer demands have reshaped the retail and consumer products industries. To
meet these changes, retailers and brands have leveraged technologies over the past decade that
enable them to stay close to local market trends, understand consumer preferences and
shopping behaviours, design products, provide value-added services and engage consumers in a
contextual way.
2
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CCPPGG && RReettaaiill
The changing customer preference and growing competition among retailers to hold significant
market share have increased the deployment of AI in the Retail industry. The growing
digitalization coupled with rising internet penetration and proliferation of smart devices across
the globe is rapidly increasing the use of AI by retailers.
The competitive landscape is shifting and it’s no longer about just pursuing AI—it’s about being
among the first to adopt AI at scale to reach unprecedented levels of personalization, precision
and profitability. The good news is CPG & Retail organiztions now have access to the necessary
innovation, compute power, skill sets and solutions required to fully embrace AI responsibly
across the enterprise to create value and fuel profitability.
Rapid growth in consumer spending, presence of young population, government initiatives
towards digitization, enhanced internet and connectivity infrastructure, and growing adoption of
AI-based solutions and services are helping Asia Pacific region to register the fastest growth in
the global artificial intelligence in CPG & Retail market.
3
Global AI Adoption Report 2022
CCPPGG && RReettaaiill
Business Value Chain: AI Adoption Areas
1. Product categorization 2. Customer Support
3. Product Search 4. In-store Assistance
5. Digitally Enabled Virtual Experience 6. Tracking Customer Satisfaction
7. Predicting & Influencing customer 8. Cashier-free Stores
behavior 9. Trade Promotion Optimization (TPO)
10. Adjusting prices 11. Supply chain management and
12. Product Recommendations logistics
13. Consumer Goods Manufacturing
1. Product categorization
Artificial intelligence is a smart way to classify products. It is used nowadays for product
categorization through optimization of sales and promotions, increasing understanding of in-
store customer behaviour and better management of inventory levels.
LovetheSales.com employs machine learning to categorize more than a million commodities
from numerous retailers.
Lalafo has made a step forward by sorting merchandise and services via AI-powered image
recognition. When sellers want to market goods on Lalafo, they can just upload an image of
these goods with no need to add a description. Artificial intelligence has helped Lalafo
increase content relevance and improve sales.
2. Customer Support
Chatbots are one more popular application of AI in the CPG & Retail industry. Chatbots help
retailers to provide great customer service, help customers find items on the site, notify them
about new collections, and offer them apparel similar to things they’ve already chosen. Other
customer support tools include Guided Search, conversational support using customer data
insights and emotional response, Demand Forecasting, Optimized development and more
expenditure in research and development.
Burberry and Tommy Hilfiger have already launched AI-driven bots on Facebook Messenger
that guide customers through their latest collections and answer their inquiries.
1-800 Flowers has also launched an AI-powered concierge named Gwyn (Gifts When You
Need). Gwyn emulates messaging platforms like WhatsApp and can successfully reply to
customer questions, help customers find the best gifts, and assist them through the entire
shopping experience.
4
Global AI Adoption Report 2022
CPG & Retail
3. Product Search
Voice Product Search
Artificial intelligence has made possible voice search for products. Many prominent brands
such as Costco, Kohl’s, Target, Tesco, and Walmart use either Google or Amazon AI
technology and smart devices to serve customers with easy and fast search. Customers
don’t need to type in queries on small devices anymore; they can just ask Alexa to add
carrots or new glitter shadows by MAC to their shopping bag.
Visual product search
Artificial intelligence has opened visual search to retailers as well, allowing customers to
upload images and find identical or similar products. AI-powered technology scrutinizes an
image and analyzes colors, shapes, and patterns to identify an item. John Lewis has added
this technology to their iPad app. The Find Similar feature has gotten 90 percent positive
feedback from customers. American Eagle Outfitters also offers visual search in its mobile
app. American Eagle image recognition technology lets customers not only find the exact or
similar clothing but also get recommendations on what goes well with it.
Virtual Fitting Rooms
A great way for customers to save time and shop from the comfort of their house. A virtual
fitting kiosk from Me-Ality can scan you in 20 seconds and measure 200,000 points of your
body in this period. Companies like Levi’s, Gap, Brooks Brothers, Old Navy, and others
installed these scanners in their stores and received massive sales increases.
4. In-store assistance
Many retailers invest in AI-driven technologies that can both assist customers while shopping in
their physical stores and help their staff handle customer inquiries. Using AI, the stores can
become cash-free, chatbots for customers, automatic price adjustments on goods.
Using image recognition data for Shelf Intelligence compliance monitoring stores are optimizing
in-store presentation, display and shelf space use. Survey finds the global image recognition in
retail market size to grow from USD 1.4 billion in 2020 to USD 3.7 billion by 2025. Increasing on-
shelf availability, enhancing customer experience, and maximizing ROI are expected to majorly
drive this market.
John Lewis spent £4 million in 2017 on a shop floor app for its personnel. This app equips
employees with information about products and stock availability so they can answer
shoppers’ questions right on the spot.
·Kroger Edge technology eliminates paper price tags in their stores; smart shelf tags are
now used. This technology also provides video ads, nutritional info, and promotions on the
displays.
5
Global AI Adoption Report 2022
CPG & Retail
·Lowebot, an autonomous in-store robot from Lowe’s, helps customers find what they need
in the store in different languages. At the same time, it helps with inventory management
thanks to real-time monitoring capabilities
·Trax’s XRetail Watch real-time shelf-monitoring and analytics platform reveals what’s
happening in the aisles so you can optimize operations. Poorly managed shelves result in
unhappy shoppers and missed sales, but retailers don’t have the manpower to spot every
error as it happens. Trax automatically scans shelves, analyzes conditions, and prioritizes
fixes to unlock each aisle’s full potential. Inn-store cameras capture real-time shelf
conditions. Images are sent to cloud servers, where deep-learning algorithms analyze with
pixel-perfect accuracy to identify each SKU. Platform analyzes real-time shelf conditions and
communicates insightful data through mobile- and web-based alerts and dashboards.
5. Virtual fitting rooms and mirros
The virtual fitting room is a great helper for busy shoppers as they can try out manifold apparel,
find the right outfit and an accessory that perfectly matches it, and do all this in a matter of
minutes. The virtual fitting room works as follows:
1.The input video is split into frames and processed with a deep learning model which
estimates the position of a set of specific leg and feet keypoints.
2.A 3D model of footwear is placed according to the detected keypoints to display the
orientation to a user naturally.
3.A 3D footwear model is rendered so that each frame displays realistic textures and lighting.
Moda Polso lets its clients create their own avatars. These virtual avatars let Moda Polso
shoppers try on an unlimited number of outfit options and make a purchasing decision
quickly and easily.
Me-Ality, a Canada-based tech startup, has developed a virtual fitting kiosk that can scan a
shopper’s whole body. A scan takes about 20 seconds and measures 200,000 different
points on the body. Gap, Levi’s, J.Crew, Old Navy, American Eagle, and Brooks Brothers set
these scanners in their stores and saw a dramatic increase in sales.
Specsavers was one of the first retailers to offer a Virtual Try On feature. With Specsavers, a
customer can scan their face with the camera on their desktop, tablet, or mobile and virtually
try on glasses in one click.
6
Global AI Adoption Report 2022
CPG & Retail
6. Tracking Customer Satisfaction
Apart from scrolling through social media and gathering feedback left by customers, artificial
intelligence can detectthe actual mood of your customers in the store. Customer satisfaction can
be increased by improving response time, having more personalized interactions, issue
prioritization, proactive service and round-the-clock availability.
Walmart is rolling out facial recognition cameras that can define a customer’s level of
satisfaction at the checkout. If a customer is frustrated, a company representative will talk
to them to soften the annoyance and revive their relationship with the store.
Multi-brand online retailer Shop Direct is working with IBM to develop an AI-driven chatbot
that can determine customers’ moods as well. The chatbot goes through the words and tone
that customers use in their text messages. Once the chatbot has detected an annoyed or
disappointed customer, it directs them to a representative for help via chat or a telephone
customer service line.
7. Predicting and Influencing Customer Behaviour
Artificial intelligence platforms like Personali allow retailers to leverage behavioural economics
and reach customers with an individual approach. Personali’s Intelligent Incentive platform
analyzes customer psychology and emotions to encourage purchases.
Businesses are leveraging using AI in customer behavior and predicting their needs. The
prediction of trends in customer behavior helps in devising market campaigns, content
marketing, enhanced communication, assisting in customer sentiment analysis and increases
customer turnover.
AI also influences customer behavior by increasing their spending, increasing their loyalty
towards the brand, improving expectations of the people in regards to the company and overall
market dominating influence.
Artificial intelligence platforms like Personali allow retailers to leverage behavioural economics
and reach customers with an individual approach. Personali’s Intelligent Incentive platform
analyzes customer psychology and emotions to encourage purchases.
Reckit Benckiser is bringing together multiple data sources to enable consumer segmentation
and marketing campaign measurement. They will use machine learning capabilities to evaluate
ROI and plan future campaigns more effectively. RB will also run its own ML and auto-ML
models, generating insights to optimise media spend, and creating more natural digital journeys
as consumers go from awareness, to purchase, to advocacy, whilst always respecting data
privacy.
7
Global AI Adoption Report 2022
CPG & Retail
8. Cashier-free Stores
Cashless shops are the new concept over-taking the pre-exisiting concept of maintaining small
amounts as change. It reduces waiting time for customers and saves money for the retailers by
saving money on hiring employees. A cashier-less checkout may work like this:
Before a customer enters a store, they need to download a smartphone app. Once inside the
store, the app is authenticated by a QR code. If the retail store has introduced a smart shopping
cart—such as the AI-powered, weight sensitive cart produced by Caper, which identifies exactly
what a shopper has placed in their cart—the customer can then log into the cart, before said
cart automatically scans each item with a barcode.
When the customer has finished shopping, they then need to enter a sensor-enabled lane which
automatically charges their card. The process doesn’t end there, however. Using big data
analytics, the smartphone app is able to record, store and use data about each individual
shopper so that it can
a) identify customer behaviour and
b) personalize future recommendations.
Amazon has already instigated the cashierless checkout revolution with its Amazon Go
cashierless stores. Other chains, including Walmart, Kroger and Sam’s Club had announced that
they will soon follow suit.
The robotization of stores helps diminish lines, reduce the number of employees needed, and
dramatically save operational costsfor a company.
Amazon artificial intelligence has enabled checkout-free stores. The Amazon Go app along with
Just Walk Out Shopping technology automatically react to a customer’s taking from and
returning products to the shelf.
9. Trade Promotion Optimization (TPO)
Trade promotion optimization is a really important aspect and has the second highest
expenditure after COGS for stores. AI is used in this segment to improve the efficiency and
optimize the process. Artificial Intelligence improves market campaign quality, discovers which
promotion provides better return on investment and forecasts a model-based predictive
analysis for better adjustment to market prices.
Genpact offers a software called Contract Assistant, which is a sub-product of Genpact Cora.
Genpact claims Contract Assistant can help CPG companies stop overpaying retailers for
carrying out promotions such as sales or in-store displays. The software uses natural
language processing to match trade promotion contracts to invoices.
Wipro offers Promax Optimize, which it claims can help CPG companies optimize future
trade promotions using predictive analytics.
8
Global AI Adoption Report 2022
CPG & Retail
10. Adjusting prices
Artificial Intelligence is important as it can show retailers likely outcomes of different pricing
strategies so they can come up with the best promotional offers, acquire more customers, and
increase sales.It also improves customer relations. 4 benefits of adjusting prices through AI are
raising of prices without affecting sales, factoring consumer behavior in pricing strategy,
predicting impact of different prices on sales and combining data and experience to maximum
effect.
eBay uses AI-powered pricing and inventory algorithms to define the most appropriate
prices for goods and notify sellers.
Kroger applies artificial intelligence for price optimization as well. Analytics data helps the
company stay flexible and change prices and promotions instantly based on shopper
insights.
11. Supply chain management and logistics
An excess or short supply of products can affect a company’s profitability and costs retailers
worldwide $1.1 trillion each year. Leftover stock is often marked down and leads to low sales
turnover. Out-of-stock situations, on the other hand, make for lost sales and dissatisfied
customers who can easily switch to your competitors. AI helps retailers replenish supplies by
identifying demand for a particular product based on sales history, location, weather,
promotions, trends and so on. This way companies can prevent underperforming products from
building up, stock what customers are likely to buy, achieve faster deliveries, reduce returns, and
save lots of money.
H&M uses AI to analyze store returns, receipts, and loyalty cards to predict future demand
for apparel and accessories and manage inventory.
Morrisons has partnered with BlueYonder, a leading AI solutions provider for retailers, to
optimize stock forecasting and replenishment across its 491 stores. Use of artificial
intelligence has helped the company to reduce shelf gaps in-store by up to 30 percent.
12. Product recommendations
Hanes Australasia, with the help of With Google Cloud and Recommendations AI, is delivering
personalized product recommendations to customers, improving engagement and experience.
This is enhancing transaction conversion rates and improving revenue. The business is now well
positioned to use additional Google Cloud machine learning products to further enhance
customer experiences and grow across new and existing markets.
9
Global AI Adoption Report 2022
CPG & Retail
13. Consumer goods manufacturing
Shop floors are leveraging real-time equipment performance monitoring and analytics to
measure the productivity vs planned allowing the constant tracking of productivity and
performance of all equipment and assets during production.
In Kewpie’s plant, the Tensorflow based AI system could detect potatoes that aren’t fit for use in
baby food, even as they were running at high speeds on a conveyer belt. The system was
“educated"" to identify the clean and healthy ingredients, by making it recognize almost 20,000
photos of potatoes, which included acceptable and useable, as well as defective and unusable
potatoes.
10
Global AI Adoption Report 2022
CPG & Retail
Spending on AI
Global spending on AI by retailers is estimated to reach $12 billion by 2023, all because AI offers
new ways to improve the customer experience and to optimise operational efficiency and
productivity.
Digital and analytics can unlock at least $490 billion in value for CPG by 2023.
A few examples of spending & application of AI by Retail & CPG companies on AI are given as
follows: -
LIVEPERSON- Teaching bots to help human agents
LivePerson’s Conversational AI lets organizations automate straightforward customer service
tasks via online chat and text messaging, so trained agents can focus on the queries that require
a human touch. In 2021, the company introduced the ability to integrate Conversational AI into
commerce systems, broadening its original focus on after-purchase support.
Dunkin’, for example, has added QR codes to food packaging at 9,000 stores, letting customers
sign up for its loyalty program by chatting with a bot. Commerce isn’t Conversational AI’s only
new territory: Bella Health, a COVID-19 screening bot in use at 500 locations, is helping to detect
infections before employees unwittingly spread them to coworkers. A new feature called AI
Annotator has allowed support reps to improve a company’s bots on the fly, no deep knowledge
of data science required; overall, there’s been a 40% year-over-year increase in automated
conversations performed on LivePerson’s platform.
ADOBE- Photoshop wizardry within reach
Adobe’s new neural filters use AI to bring point-and-click simplicity to visual effects that would
formerly have required hours of labor and years of image-editing expertise. Using them, you can
quickly change a photo subject’s expression from deadpan to cheerful. Or adjust the direction
that someone is looking. Or colorize a black-and-white photo with surprising subtlety. Part of
Adobe’s portfolio of “Sensei” AI technologies, the filters use an advanced form of machine
learning known as generative adversarial networks.
That lets them perform feats such as rendering parts of a face that weren’t initially available as
you edit a portrait. Like all new Sensei features, the neural filters were approved by an Adobe
ethics committee and review board that assess AI products for problems stemming from issues
such as biased data. In the case of these filters, this process identified an issue with how certain
hairstyles were rendered and fixed it before the filters were released to the public.
11
Global AI Adoption Report 2022
CPG & Retail
McDonald’s Drive - Through Smart Voice Assistant
One of the world’s favourite restaurants moved quickly to transition into the AI era. The top folks
at McDonald’s have done impressively well to stay on top of the latest trends over the last few
decades and their recent move indicates they are not relenting any time soon. McDonalds
installed a voice-based platform for complex, multilingual, multi-accent and multi-item
conversational ordering. It also acquired an artificial intelligence company called Apprente, which
has built this platform for them. This has made the process of ordering faster and it is cost-
efficient as well.
H&M’s Assortment Planning using Artificial Intelligence
Big brands like H&M have realized the importance of using AI in their assortment planning. H&M
aims to forecast trends months in advance. The retail giant is employing over 200 data
scientists, analysts and engineers to use AI to review purchasing patterns of every item in each
store. The data incorporates all the information from over five billion footfalls from last year to
its stores and traction on its websites. It also considers data from external sources.
Pepper Robot – Nestlé’s Solution to sell coffee Machines
Nestlé Japan is using a humanoid robot to sell its coffee machines built by SoftBank Robotics. It’s
one of the first robots in the world that can sense and respond by feeling human emotions. It is
equipped with the latest voice and emotion recognition technology. And the best part is that it
can respond by understanding human facial expressions.
Boch Automotive’s Artificial Intelligence – Powered Sales Assistant
Boch Automotive, an England based car dealership company, has adopted a unique AI software
that streamlines its sales funnel and establishes an automated sales assistant to increase
service revenue via engagements.
Mango and Vodafone’s Smart Digital Dressing Room
The concept of a digital fitting room involves using an Internet of Things (IoT) digital mirror that
was designed by Mango and developed by Vodafone in collaboration with Jogotech. These new
fitting rooms are another step in the digital transformation of retail stores to create a whole new
experience for the customers.
53 Degrees North – Automated AI for Customer Segmentation
Data science and AI have transformed this landscape as well. Using techniques like market
basket analysis, association rules, clustering and so on, businesses are able to create granular
segments to enhance their marketing efforts. 53 Degrees North (53DN), an Irish lifestyle retail
chain, partnered with Brandyfloss for using their automated customer segmentation software.
This solution will solve the segmentation problem and propel its marketing campaigns to the
corrected targeted population.
12
Global AI Adoption Report 2022
CCPPGG && RReettaaiill
Domino’s Pizza – Delivery by a Robot
Domino’s launched its Domino’s Robotic Unit (DRU). They have built an artificial intelligence-
based robot that will deliver hot piping pizza at your doorstep. This innovation marvel is like a
self-driving car with a mini oven and a fridge on wheels. The vehicle itself is a collaboration
between Domino’s Pizza and a Sydney-based robotic company Marathon Targets.
Walmart Deploys Robots to Scan Shelves
Walmart in Tampa Bay (Florida) has introduced a robot on wheels that is doing this task very
effectively. The robot travels along the aisle, stretches its arm till the top of the shelves and
automatically captures the required data. It takes in prices and the number of items available.
That’s quite a lot of time and effort saved and the company will also cut down on operational
costs with this adoption. The company aims to increase its customer interaction rather than
spending its customer’s time on shelf alignment.
Olay – Using AI to Personalize Skincare
Olay’s AI-powered skin advisor, an online service that relies on artificial intelligence and
proprietary deep learning, is analyzing a user’s skincare needs at a very granular level.
CPG & Retail has one of the leading global spend on AI systems, with the category projected
to invest on solutions like automated customer service agents, shopping advisers and
product recommendation platforms. More than 325,000 retailers are expected to adopt AI
technology by 2023.
13
Global AI Adoption Report 2022
CPG & Retail
AI Adoption across Regions
Source: Mordor Intelligence
Geographically, the global artificial intelligence in the CPG & Retail market is segmented into
five major regions, namely, North America, Europe, Asia Pacific, Latin America, and the
Middle East and Africa.
At present, North America holds a dominating position in the global AI in retail market. The
region has high technology adoption rate, presence of key players & start-ups, and high
penetration of internet.
The APAC region is expected to grow at the highest CAGR owing to the growing adoption of
AI-based solutions and services among retailers.
Analysis of AI Adoption in CPG & Retail market for major countries across the regions of US,
Europe & Asia Pacific (APAC) is given as follows: -
Challenges in adopting AI in CPG and Retail industries
Source: IDC
14
Global AI Adoption Report 2022
CPG & Retail
AI Adoption across US
1. Warby Parker
In April 2019, Warby Parker, a US-based company started practicing innovative ways. By the use
of artificial intelligence, it is providing its customer to try the virtual Try-On that allows them to
try on virtual frames through augmented reality, a technology that overlays computer-generated
images onto real-world images (your face).
2. Anki
Founded in 2010, this robotics and AI start-up made a huge impact by outdoing their own
overdrive racetrack to release Cozmo.
Cozmo is an adorable robot toy that makes a healthy profit for Anki in sales.
3. Blue Yonder
Blue Yonder is focused on using AI to automate the process of tech industry retail. They aid
retailers and pinpoint the keys to higher revenues, increased margins and fast and efficient
reactions to a constantly changing market. Blue Yonder has taken a pretty scientific approach to
be the leading provider of cloud-based predictive applications for retailers.
3. Infinite Analytics
Another company focused on the optimisation and growth of other companies through the
power of AI, Infinite Analytics is set on maximising sales and engagement by discovering rich
insights, identifying the right customers and expanding their clients’ exposure across all
channels.
4. Signifyd
Signifyd is the most well-funded AI start-up in the fraud detection industry for retail and
eCommerce, having raised $180 million. They specialize in fraud detection for retail and
eCommerce companies. Their most prominent offering is called “guaranteed fraud detection,”
and it likely uses anomaly detection technology to recognize fraudulent transactions and prevent
chargebacks.
5. Lowe’s
Lowebot, an autonomous in-store robot from Lowe’s, helps customers find what they need in
the store in different languages. At the same time, it helps with inventory management thanks
to real-time monitoring capabilities.
15
Global AI Adoption Report 2022
CPG & Retail
AI Adoption across Europe
1. Panther Solutions
As a prominent retail AI solution provider in Europe, this company brings to the table the price
optimisation silver bullet, Panther Pricing, made by retailers for retailers. Panther Solutions
leverages the prowess of AI and the cloud to generate automated price recommendations that
enable retailers to reduce price markdowns significantly.
2. MySales Labs
MySales Labs empowers retailers with insights that boost business growth, increase both
profitability and revenue, optimise inventories without sales losses, and give a head start in
tightening competition on the global markets. The company utilises data analytics, machine
learning (ML), and artificial intelligence (AI), to offer retail management solutions for demand
forecasting, price optimisation, stock replenishment, and promotion modelling.
3. PricingHUB
PricingHUB leverages data at scale and machine learning to help retailers implement multiple
pricing strategies and action their daily trading. As one of the top retail AI companies in Europe,
PricingHUB offers a dynamic pricing SaaS platform to manage P&L by utilizing solid pricing
algorithms and a transparent performance measurement methodology. Governed by the
mission to bring dynamic pricing to physical retail stores by leveraging a blend of real-time
customer data and machine learning algorithms, PricingHUB helps retailers understand price
elasticity and drive better pricing decisions.
4. Nextail
Nextail is a Spanish company that offers an automated inventory management tool, which the
company claims can help retail fashion businesses balance inventory among its stores to
optimize sales using predictive analytics and machine learning. Nextail claims that the
application is accessible throughout the retail organization to digitize most aspects of the
physical retail business.
16
Global AI Adoption Report 2022
CPG & Retail
5. Cortexica
Cortexica is a UK-based company that offers AI-driven image and video applications, which the
company claims can help retail businesses offer online shoppers visual search tools of products
they want to purchase using computer vision. Cortexica claims that shoppers, from their device,
can upload a photo of the item they are looking for to the application. The algorithms compare
the pixels in the uploaded image to images in the eCommerce database. It will then identify
images that are similar or a match to the uploaded image. Once it recognizes similar items, the
system returns with the recommended results in the form of curated photos of the items.
2. Metail
Metail is a UK-based company that offers MeModel, an application for eCommerce sites and
smartphones, which the company claims can help retail shoppers try on clothes virtually using
machine learning.
The company claims that the application could increase sales, enhance customer experience and
loyalty, reduce product returns, and deepen the retailer’s knowledge about shoppers, among
other benefits.
AI Adoption across Asia
1. ViSenze
ViSenze is a Singapore-based company that offers a namesake software that the company
claims is capable of allowing customers to search by image. Once the software is integrated into
a retailer’s website, customers could upload a photo to the website’s search bar. The search
software would then return a visually-similar item for the customer.
2. Trax
Trax is a Singapore-based company that offers a software called Trax Technology Stack, which it
claims can give consumer packaged goods businesses a view of how their products compete
with other brands on the retail shelf using computer vision. The company claims that this can
help businesses develop strategies for where to display their products on shelves and inventory
management.
17
Global AI Adoption Report 2022
CPG & Retail
3. Zenatrix
Zenatrix is an Indian company that offers software called Wattman and Wattman Lite, which the
company claims can help retail cha"
97,Autres,1113-Article Text-1110-1-10-20080129.pdf,"AI Magazine Volume 15 Number 4 (1994) (© AAAI)
Workshop Report
AI in Business-Process collection and indexing of customer
support hotline cases. Amy Rice and
Robert Friedenberg (both of Inference
Reengineering
Corporation) presented the partici-
pants of the Workshop on AI in
Business-Process Reengineering (held
during the 1994 national conference
on AI) with examples of successful
Walter Hamscher reengineering efforts that are based
on an analysis of the flow of knowl-
edge in the organization and use AI
technology to capture and deploy
the knowledge.
n Business-process reengineering (BPR) is Fortune 500 company annual reports
A second, less common but poten-
a generic term covering a variety of per- explicitly discussed reengineering
tially important role for AI is in tools
spectives on how to change organiza- efforts that were currently under way.
to support the change process itself.
tions. There are at least two distinct One analyst recently estimated the
roles for AI in BPR. One role is as an A current example is in the use of
annual market for BPR services in
enabling technology for reengineered knowledge-based simulation to sup-
U.S.-based companies at $1.8 billion;
processes. A second, less common but port the analysis of an existing busi-
another predicts a growth of 20 per-
potentially important role is in tools to ness process and to model the
cent each year from 1994 to 1996
support the change process itself. The performance of a proposed process.
Workshop on AI in Business-Process (Caldwell 1994). To measure the For example, the G2/SPARKS system
Engineering, held during the national long-term impact of this work, one
(Yu 1991) provides a knowledge base
AI conference, allowed participants to must consider a multiple of this fig-
of typical business processes and
learn about projects that are aimed at ure as the cost reductions and rev-
work products in service industries
exploiting insights from AI. enue enhancements brought about
and makes it possible to rapidly
by today’s reengineering begin to be
Virtually any business can be realized over the next few years. assemble a stochastic simulation
viewed as a collection of pro- There is hype, to be sure, but the model. Such a simulation model
cesses that, taken together, phenomenon is real. serves the obvious role of estimating
respond to customer demands by There are at least two distinct roles cost savings, order-processing times,
inventing, producing, delivering, and for AI in BPR. One role is as an backlogs, and the like. Because of the
billing for goods and services. These enabling technology for reengineered complexity of business organizations,
processes vary from business to busi- all the familiar issues of acquisition,
ness, but in the overwhelming reusability, scalability, and compre-
majority of cases, these processes and hensibility turn up for such models.
the organizations that execute them AI as a field has a great deal of accu-
have not been engineered in any A report on the mulated experience and insight to
meaningful sense; they have evolved 1994 AAAI offer in dealing with these problems
over time in response to their busi- as well as developing a framework for
workshop held in
ness environments. Changing envi- further research.
ronments frequently destroy such Seattle, Washington One project aimed at exploiting
companies unless they make a con- insights from AI in the area of mod-
scious and periodic, if not continu- eling business processes is the TOVE
ous, effort to reengineer these pro- (Toronto virtual enterprise) Project at
cesses to exploit changes in suppliers, the University of Toronto, presented
customer needs, and technological at the workshop by Mark Fox and
processes. A typical success story of
innovation. Viewing a business as a Michael Gruninger. TOVE encompass-
this type places an expert system in
collection of customer-driven pro- es a generic ontology for modeling
the hands of a single worker who is
cesses is the essence of business- business processes; a specific instanti-
then able to perform many steps of a
process reengineering (BPR), a generic ation of the ontology describing a
process for a single customer or order
term covering a variety of perspec- hypothetical enterprise in detail; and
tives, none of which is particularly rather than has several workers in a test bed with tools for browsing,
rigorous, on how to change organiza- different departments handle the visualization, simulation, and deduc-
tions. It is easy to dismiss BPR as same case, dramatically cutting over- tive queries. As in any modeling
hype, a management consultant’s all order-processing time. Some effort, formulating the model
marketing slogan, but the phe- examples of this general story requires committing to the particular
nomenon is real and extremely appearing at IAAI-94 were in the pro- reasoning tasks it is expected to sup-
important. In 1993, 60 percent of the cessing of insurance claims, identifi- port. TOVE uses the notion of advis-
management letters appearing with cation of mental health needs, and ers—each with a particular perspec-
Copyright © 1994, AAAI. 0738-4602-1994 / $2.00 WINTER 1994 71
Workshop Report
tive on the enterprise—to inform and failure—Michael Hammer predicts resentations of agents’ beliefs and
constrain the modeling effort. Exam- that two thirds of all BPR efforts now intentions (for example, the frame-
ples are advisers for cost, quality, effi- under way will fail (Caldwell 1994)— work of Cohen and Levesque [1990])
ciency, incentives, and agility: The in large part because stakeholders in or a case library of past behavior (for
cost adviser requires that the model the organization resist changes that example, the VOTE system [Slade
represent information about material might diminish their power or other- 1991]) opens up interesting possibili-
and process costs, the incentive wise disrupt their career and other ties for sophisticated modeling and
adviser is likely to require informa- plans. An intriguing question raised fine-grained predictions about agents’
tion about organizational structures, repeatedly in the course of the work- reactions to different proposed orga-
others will require the representation
shop was whether modeling tools nizational designs.
of time and state, and so on. As dis-
could raise the likelihood of success- The workshop ended positively
cussed at the workshop by Bob Young
with the final discussion session. In
and Elaine Kant (both of Schlumberg-
the area of modeling and analysis of
er Laboratory for Computer Science),
processes to support design, partici-
many of the issues appearing in mul-
pants agreed with Mark Fox’s posi-
tiperspective modeling of engineered
The nature of designing a tion that enough is already generally
artifacts apply directly to large-scale
modeling efforts such as TOVE. business process known about knowledge representa-
tion to have significant impact on
Modeling and analysis of business
is quite different
processes is part of the broader task of actual practice, provided, of course,
designing a new business process, from that of that the nascent AI in BPR communi-
that is, tools for evaluating designs ty in fact focuses its efforts outside
designing a
formulated by humans. An interest- the AI community and in communi-
ing and challenging next step is to mechanical device. ties where organizational modeling is
use AI techniques to automatically … already the focus of attention. In the
produce new designs. Pramod Jain, area of supporting process change,
Sheet metal doesn’t
Jie Liu, and Steve Wagner (all of participants seemed to agree that
Andersen Consulting) reported on a care how it is used modeling stakeholders and their reac-
prototype system that proposes new tions to change and incorporating
or even whether
process designs by using heuristic the knowledge upstream in tools for
transformations of existing models. it is used or not; supporting business process design
For example, the system would pro-
employees do. was an exciting possibility worthy of
pose to delete processes or invert the
further research.
order of pairs of processes. Although
the prototype falls short of providing
assistance to the analyst in actually References
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for stimulating the creative process of
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Eric Yu and John Mylopoulos (Uni- Caldwell, Bruce. 1994. Missteps, Miscues.
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part of actually effecting change in
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an organization, and the scope for AI
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care how it is used or even ticular, the tendency for individuals
whether it is used or not; to adapt over time to changes in the
employees do. sources and quality of information
Walter Hamscher is affiliated with the
Implementing changes in an orga- that they use to make their deci- Price Waterhouse Technology Centre in
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72 AI MAGAZINE"
98,Autres,608-Article Text-1985-1-10-20231125.pdf,"Computer Science & IT Research Journal, Volume 4, Issue 2, November 2023
OPEN ACCESS
Co mputer Science & IT Research Journal
P-I SSN: 2709-0043, E-ISSN: 2709-0051
Vo lume 4, Issue 2, P.85-110, November 2023
DO I: 10.51594/csitrj.v4i2.608
Fa ir East Publishers
Jou rnal Ho mepage : www.f epbl.com/index.php/csitrj
INNOVATIVE BUSINESS MODELS DRIVEN BY AI
TECHNOLOGIES: A REVIEW
Oluwatoyin Ajoke Farayola1, Adekunle Abiola Abdul2, Blessing Otohan Irabor3,
& Evelyn Chinedu Okeleke4
1Independent Researcher, Dallas, Texas, USA
2Independent Researcher, Maryland, USA
3Independent Researcher, Lagos, Nigeria
4Ericsson LM Lagos, Nigeria
_______________________________________________________________________________
*Corresponding Author: Oluwatoyin Ajoke Farayola
Corresponding Author Email: oluwatoyinafarayola@gmail.com
Article Received: 10-10-23 Accepted: 19-11-23 Published: 26-11-23
Licensing Details: Author retains the right of this article. The article is distributed under the terms of the
Creative Commons Attribution-NonCommercial 4.0 License
(http://www.creativecommons.org/licences/by-nc/4.0/) which permits non-commercial use, reproduction
and distribution of the work without further permission provided the original work is attributed as specified
on the Journal open access page
_______________________________________________________________________________
ABSTRACT
In an era where artificial intelligence (AI) is revolutionizing business paradigms, this study delves
into the intricacies of AI-driven business models, offering a nuanced understanding of their
emergence, evolution, and impact on traditional business strategies. This scholarly inquiry aims to
dissect the role of AI in reshaping business models, highlighting the interplay between
technological innovation and business strategy. The study meticulously examines the integration of
AI into various business facets by employing a systematic and thematic analysis of a diverse range
of literature, including academic journals, industry reports, and case studies. This methodological
approach facilitates a comprehensive understanding of AI's role in business innovation, addressing
both the opportunities and challenges it presents. The findings reveal that AI-driven business
Farayola, Abdul, Irabor, & Okeleke, P. 85-110 Page 85
Computer Science & IT Research Journal, Volume 4, Issue 2, November 2023
models are characterized by enhanced operational efficiency, data-driven decision-making, and
customer-centric approaches. These models signify a transformative shift from conventional
business strategies, demanding a reevaluation of leadership roles and ethical considerations in the
digital age. The study identifies key challenges in AI implementation, such as technical
complexities and ethical dilemmas, while uncovering AI's vast opportunities for business growth
and competitive advantage. Conclusively, the study recommends a balanced approach to AI
integration, emphasizing the need for ethical AI practices, continuous adaptation, and a synergy
between AI capabilities and human insights. It advocates for business leaders to embrace AI not
just as a technological tool, but as a catalyst for sustainable and innovative business growth. This
scholarly work contributes significantly to the discourse on AI in business, providing a
foundational framework for future research and practical application in AI-driven business
innovation.
Keywords: Artificial Intelligence, Business Models, Digital Transformation, AI Integration,
Leadership in AI, Ethical AI Practices.
_______________________________________________________________________________
INTRODUCTION
The Emergence of AI in Modern Business: An Overview
The emergence of artificial intelligence (AI) in modern business has been a transformative force,
reshaping industries and creating new paradigms for how companies operate and compete. The
integration of AI into business models has become a pivotal aspect of contemporary business
strategy, offering unprecedented opportunities for innovation, efficiency, and competitive
advantage.
AI-driven business models, as conceptualized by Hahn et al. (2020), are characterized by the use
of AI technologies to enhance or create at least one component of the business model. This
integration of AI has led to the creation of new businesses and has significantly altered existing
ones. Unlike traditional data-driven models, AI-driven models are based on a set of techniques that
learn and improve autonomously, reducing the need for explicit human programming. This
autonomy in learning and adaptation is a key characteristic that sets AI-driven business models
apart, enabling businesses to respond more dynamically to market changes and customer needs.
The impact of AI on small businesses, particularly during challenging times such as the COVID-19
pandemic, has been profound. Coltey et al. (2022) highlight the role of AI in enabling small
businesses to navigate rapidly changing market conditions. By leveraging AI-driven tools, such as
natural language processing (NLP), businesses can automate market research, adapt to changing
conditions, and maintain operational efficiency even in times of crisis. This adaptability is crucial
for small businesses that often lack the resources of larger corporations.
In the insurance sector, the influence of AI is equally significant. Zarifis, Holland, and Milne.
(2019) identify four emerging business models in the insurance industry driven by AI and data.
These models range from insurers taking a smaller part of the value chain and allowing AI and
data-centric companies to dominate, to technology-focused companies using their AI prowess to
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enter the insurance market. This model diversity illustrates AI's versatility in reshaping traditional
industries and creating new opportunities for innovation and growth.
The integration of AI into business models is not without its challenges. The need for significant
investment in technology, the requirement for specialized skills, and the management of ethical
and privacy concerns are some of the hurdles businesses face. However, the potential benefits,
including enhanced decision-making capabilities, improved customer experiences, and new
avenues for growth, make the integration of AI a strategic imperative for businesses looking to
thrive in the modern economy.
The emergence of AI in modern business is a defining feature of the current economic landscape.
AI-driven business models offer a new paradigm for businesses operating, competing, and
innovating. From small businesses to large corporations, the integration of AI is creating new
opportunities and challenges, reshaping industries, and redefining the future of business.
Defining AI-Driven Business Models
The integration of artificial intelligence (AI) into business models represents a significant
evolution in the way companies operate and strategize. AI-driven business models are
characterized by the incorporation of AI technologies to enhance, innovate, or create new aspects
of a business's operations, products, or services. This section delves into the key characteristics,
evolution, and the intersection of AI with traditional business strategies, drawing on recent
scholarly literature.
AI-driven business models are distinguished by their ability to leverage data and machine learning
(ML) technologies to create more efficient, responsive, and intelligent business processes. Ahmed
and Miskon (2020) discuss the role of AI and ML in digital transformation, particularly in the
manufacturing sector. They emphasize how AI-driven models utilize large datasets generated by
the integration of devices in the Internet of Things (IoT) environment to gain rapid insights and
enhance decision-making processes. This approach is pivotal in achieving resiliency and
efficiency, especially in sectors like manufacturing where integrating intelligent and integrated
systems is crucial (Ahmed & Miskon, 2020).
In the realm of customized manufacturing, AI-driven models are reshaping traditional production
paradigms. Wan et al. (2020) explore the implementation of AI in customized manufacturing
factories, highlighting how AI technologies enable manufacturing systems to adapt to external
needs and extract process knowledge. This includes the development of intelligent production
models, networked collaboration, and extended service models. The AI-driven customized
manufacturing factory is characterized by its self-perception, operations optimization, dynamic
reconfiguration, and intelligent decision-making capabilities. These characteristics underscore the
transformative impact of AI on manufacturing, moving towards more flexible and efficient
production methods (Wan et al., 2020).
The integration of AI in customer service, especially in the e-commerce sector, provides another
perspective on AI-driven business models. Ping (2019) focus on the constructs of AI customer
service in e-commerce, illustrating how AI can assist human agents, enhancing service quality and
productivity. The integration of AI in customer service is a response to the limitations of
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traditional models, such as limited availability and inefficiency. AI-driven customer service
models in e-commerce are characterized by their ability to provide personalized and efficient
service, leveraging technologies like chatbots and automated response systems (Ping, 2019).
The evolution of business models in the AI era is marked by a shift from traditional, often rigid,
business processes to more dynamic, data-driven, and customer-centric approaches. AI-driven
business models merge the capabilities of AI, ML, and IoT to create systems that are not only
efficient and resilient but also capable of continuous learning and adaptation. This evolution
signifies a move towards more personalized, responsive, and intelligent business operations,
catering to the specific needs of customers and the market.
The intersection of AI with traditional business strategies involves rethinking conventional
business practices. AI-driven models do not merely supplement traditional strategies but often
redefine them, creating new value propositions and competitive advantages. This intersection is
evident in various sectors, from manufacturing to customer service, where AI technologies are
used to enhance operational efficiency, customer engagement, and innovation.
AI-driven business models represent a significant shift in the business landscape, characterized by
their use of AI and ML to innovate and improve various aspects of business operations. From
manufacturing to customer service, the integration of AI is transforming traditional business
models, leading to more efficient, adaptable, and intelligent business processes.
Key Characteristics of AI-Driven Business Models
AI-driven business models are reshaping the landscape of various industries by integrating
advanced artificial intelligence (AI) technologies into their core operations and strategies. These
models are characterized by several key features that distinguish them from traditional business
models. This section explores these characteristics, drawing insights from recent scholarly
literature.
One of the primary characteristics of AI-driven business models is their focus on leveraging AI for
enhanced decision-making and operational efficiency. Metelskaia et al. (2018) present a business
model canvas specifically designed for AI solutions, which outlines the critical elements of AI-
driven business models. This canvas includes components such as value propositions that AI, the
use of multi-sided platforms for customer segments, automated services for customer relationships,
and the integration of social networks in marketing channels, uniquely tailors. The canvas also
highlights the importance of investors as key partners and emphasizes research and development
(R&D) and human resources as essential resources. This framework demonstrates how AI-driven
business models are constructed differently from traditional models, focusing on AI's unique
capabilities to create value (Metelskaia et al., 2018).
In the healthcare industry, AI-driven business models are particularly transformative. Kulkov
(2023) examines the next-generation business models for AI start-ups in healthcare, identifying
three unique design elements: value creation, delivery to customers, and market communication.
These elements are framed within 16 unique models and three unifying design themes, illustrating
the diverse ways AI can be integrated into healthcare business models. This study underscores the
role of AI in creating new value propositions in healthcare, such as personalized medicine and
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efficient patient care, and highlights the importance of AI in communicating and delivering these
values to the market (Kulkov, 2023).
Another key characteristic of AI-driven business models is their application in customized
manufacturing. Wan et al. (2020) discuss the implementation of AI in customized manufacturing
factories, focusing on intelligent production, networked collaboration, and extended service
models. These factories are characterized by their self-perception, operations optimization,
dynamic reconfiguration, and intelligent decision-making capabilities. Integrating AI technologies
allows these manufacturing systems to adapt to external needs and extract process knowledge,
leading to higher production flexibility and efficiency. This study demonstrates how AI-driven
models can transform traditional manufacturing processes into more agile and responsive systems
(Wan et al., 2020).
AI-driven business models are marked by their innovative use of AI to create new value
propositions, enhance operational efficiency, and transform traditional business processes. These
models leverage AI's unique capabilities to analyze data, learn autonomously, and make intelligent
decisions, leading to more dynamic and responsive business operations. The integration of AI in
various sectors, from healthcare to manufacturing, illustrates the transformative potential of these
models, offering new opportunities for growth and innovation.
The key characteristics of AI-driven business models include their focus on leveraging AI for
enhanced decision-making, operational efficiency, and the creation of new value propositions.
These models are reshaping industries by introducing innovative ways to integrate AI into business
operations, demonstrating the transformative impact of AI on the modern business landscape.
Evolution of Business Models in the AI Era
The advent of artificial intelligence (AI) has ushered in a new era in business, fundamentally
altering traditional business models and introducing innovative paradigms. This evolution is
characterized by a shift towards more technologically advanced, data-driven, and customer-centric
approaches. This section explores the transformation of business models in the AI era, drawing on
insights from recent scholarly literature.
A significant aspect of this evolution is the changing nature of human-machine interaction and its
impact on competitive business strategies. Delbufalo, Di Bernardo and Risso, (2022) delve into the
dynamics of human-machine interaction in the digital era, emphasizing the need for businesses to
strike a balance between artificial and human intelligence. They argue that the competitiveness of
companies in the AI era hinges on sustainable strategies that effectively integrate both human
creativity and AI's mechanical thinking. This integration is crucial in decision-making processes,
where AI can provide data-driven insights while humans contribute with creative and strategic
thinking. The study highlights the importance of redefining business models to accommodate AI
and human intelligence roles, ensuring that businesses remain innovative and competitive
(Delbufalo, Di Bernardo & Risso, 2022).
The role of AI in brand engagement and social interaction represents another facet of the evolving
business models. Marrone and Testa (2022) explore the impact of brand algorithms and social
engagement in the digital era, focusing on how AI and related technologies transform marketing
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strategies and customer relationships. They discuss how AI, big data, and the Internet of Things
(IoT) are key drivers in revolutionizing communication and relationships between individuals,
products, and brands. The study underscores the influence of AI-mediated algorithms on value
creation and customer engagement, highlighting the shift towards more personalized and
interactive marketing approaches. This evolution reflects a broader trend in business models where
digital transformation and AI are central to creating and maintaining customer relationships
(Marrone & Testa, 2022).
The integration of Artificial Intelligence (AI) with the advancements of 5G technology is
revolutionizing business models and driving digital transformation across various sectors (Banda,
Mzyece, & Mekuria, 2022). This synergy is fostering the development of new, innovative services
and applications, such as enhanced image recognition and sophisticated natural dialogue AI
systems. These technological advancements are not only streamlining operational processes but are
also opening new channels for customer interaction and service provision. The convergence of AI
and 5G is indicative of a shift towards more interconnected, intelligent, and agile business models,
leveraging the high-speed and extensive capabilities of 5G networks to maximize AI's potential
(Banda, Mzyece, & Mekuria, 2022).
In summary, the evolution of business models in the AI era is marked by a greater integration of
AI and technology in various aspects of business operations. A shift towards more data-driven
decision-making characterizes this transformation, enhanced human-machine interaction, and
innovative customer engagement and marketing approaches. Integrating AI with other advanced
technologies like 5G further accelerates this evolution, leading to more agile, efficient, and
customer-centric business models.
The Intersection of AI and Traditional Business Strategies
The integration of Artificial Intelligence (AI) into traditional business strategies marks a pivotal
shift in the business landscape, blending the strengths of AI with established business practices.
This intersection is creating new paradigms for innovation, strategy development, and competitive
advantage. This section explores how AI is being integrated into traditional business strategies,
drawing on insights from recent scholarly literature.
In the education technology (EdTech) sector, the integration of AI and learning analytics is
revolutionizing traditional business models and strategies. Alam and Mohanty (2022) discuss the
transformation in EdTech companies, where AI and learning analytics are being used to create
personalized educational experiences. This integration reflects a shift from traditional educational
models to more data-driven, customized approaches. The study highlights the challenges and
motivations influencing this transition, emphasizing the need for a deeper understanding of data
and its potential to enhance learning experiences. This example illustrates how AI can be
integrated into traditional sectors like education, transforming them with innovative, data-driven
strategies (Alam & Mohanty, 2022).
Wisniewski (2020) addresses the broader implications of AI in business, focusing on the need for
future decision-makers and leaders to understand and leverage AI in their strategies. The paper
discusses the integration of AI into business curricula, emphasizing the importance of preparing
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leaders who can navigate the complexities of AI and its impact on business. This approach reflects
a growing recognition of AI's strategic importance and the need for a new generation of business
leaders who are adept at integrating AI into traditional business models and strategies. The study
underscores the transformative potential of AI in reshaping business education and, by extension,
business practices (Wisniewski, 2020).
Fenwick, Vermeulen, and Corrales (2018) work explores the business and regulatory responses to
AI, particularly in the context of dynamic regulation and innovation ecosystems. They discuss how
businesses and regulators are adapting to the challenges posed by disruptive AI technologies. The
paper highlights the importance of dynamic regulation and the creation of innovation ecosystems
that foster partnerships between established corporations and AI-focused startups. This approach to
managing AI's impact suggests a strategic alignment between traditional business practices and the
innovative potential of AI. The study provides insights into how businesses can strategically
manage AI's disruptive nature while leveraging it for competitive advantage (Fenwick, Vermeulen,
& Corrales, 2018).
The intersection of AI and traditional business strategies is characterized by a synergistic blend of
innovation and established practices. This integration is leading to the transformation of various
sectors, from education to broader business practices. The key lies in understanding and leveraging
AI's potential within the context of traditional business models, preparing future leaders for this
new landscape, and adapting regulatory and strategic frameworks to accommodate AI's disruptive
potential.
Significance and Scope of AI in Business Innovation
The integration of Artificial Intelligence (AI) into traditional business strategies represents a
significant paradigm shift, offering new opportunities for innovation and competitive advantage.
This intersection is reshaping how businesses operate, strategize, and interact with their
stakeholders. This section examines the convergence of AI with traditional business strategies,
drawing insights from recent scholarly literature.
Alam and Mohanty (2022) delve into the intersection of AI with learning analytics in the context
of educational technology (EdTech) companies. They discuss how these companies are integrating
AI into their business models and strategies to provide personalized educational experiences. This
integration reflects a broader trend where traditional industries like education are being
transformed by AI, leading to more data-driven and customized approaches. The study highlights
the challenges and motivations influencing this transition, emphasizing the need for a deeper
understanding of data and its potential to enhance learning experiences. This example illustrates
how AI can be integrated into traditional sectors, transforming them with innovative, data-driven
strategies.
Fenwick, Vermeulen, and Corrales (2018) address the business and regulatory responses to AI,
particularly focusing on dynamic regulation and innovation ecosystems. They discuss how
businesses and regulators are adapting to the challenges posed by disruptive AI technologies. The
paper highlights the importance of dynamic regulation and the creation of innovation ecosystems
that foster partnerships between established corporations and AI-focused startups. This approach to
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managing AI's impact suggests a strategic alignment between traditional business practices and the
innovative potential of AI. The study provides insights into how businesses can strategically
manage AI's disruptive nature while leveraging it for competitive advantage.
The intersection of AI and traditional business strategies is characterized by a synergistic blend of
innovation and established practices. This integration is leading to the transformation of various
sectors, from education to broader business practices. The key lies in understanding and leveraging
AI's potential within the context of traditional business models, preparing future leaders for this
new landscape, and adapting regulatory and strategic frameworks to accommodate AI's disruptive
potential.
Objectives and Structure of the Review
The aim of this study is to explore and understand the transformative impact of Artificial
Intelligence (AI) on business models, focusing on how AI technologies are reshaping business
strategies and operations in the contemporary business landscape.
The objectives of the study are:
1. To identify the key characteristics of AI-driven business models, examining the unique
features and components that distinguish these models from traditional business
approaches.
2. To analyze the evolution of business models in the AI era, understanding how the
integration of AI technologies has transformed business practices across various industries
and sectors.
3. To explore the intersection of AI and traditional business strategies, investigating how AI is
being combined with conventional business approaches and the resulting synergies and
challenges.
4. To assess the significance and scope of AI in business innovation, evaluating the overall
impact of AI on business practices, including the opportunities and challenges it presents
for businesses in different contexts.
Limitations of the Literature Review
This literature review on the impact of Artificial Intelligence (AI) in business models
acknowledges several inherent limitations. Firstly, the rapid evolution of AI technology means that
some reviewed literature may not reflect the most current developments and trends. Secondly,
there is a potential bias in the sources, primarily academic journals and industry reports, which
might emphasize certain aspects of AI in business while overlooking others. Thirdly, the vast and
diverse applications of AI across various industries imply that not every sector or innovative use of
AI is comprehensively covered. Finally, the interdisciplinary nature of AI in business leads to
varying definitions and interpretations of AI-driven business models, posing challenges in drawing
generalized conclusions. These limitations highlight the need for a cautious interpretation of the
findings, recognizing that the review provides a snapshot of a rapidly evolving field.
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METHODOLOGY
Approach to Literature Review: Systematic and Thematic Analysis
The methodology for this literature review on AI-driven business models is anchored in a
systematic and thematic analysis approach. This method involves a structured process of
searching, selecting, and synthesizing relevant literature to ensure a comprehensive understanding
of the topic. Predefined criteria guide the systematic review process and aims to minimize bias,
providing a transparent and replicable framework for analysis (Bulatnikov & Constantin, 2021).
The thematic analysis component of the methodology involves identifying, analyzing, and
reporting patterns (themes) within the data. This approach allows for the integration of diverse
perspectives and insights on AI in business models, facilitating a nuanced understanding of the
subject. The thematic analysis is particularly useful in synthesizing complex and multifaceted
topics, such as the integration of AI in business strategies and operations (Sureeyatanapas et al.,
2020).
Criteria for Selecting Relevant Literature on AI and Business Models
The criteria for selecting literature in this review are centered around relevance, quality, and
recency. Relevance is determined by the extent to which the literature addresses AI in the context
of business models. This includes studies focusing on the integration of AI technologies in
business strategies, the evolution of business models in the AI era, and the impact of AI on
business innovation and operations.
Quality is assessed based on the credibility of the source and the rigor of the research
methodology. Peer-reviewed academic journals, reputable industry reports, and in-depth case
studies are prioritized to ensure the reliability of the information. The research methodology of the
selected literature is also scrutinized for its robustness and appropriateness in addressing the
research questions.
Recency is another crucial criterion, given the rapid development of AI technologies and their
applications in business. Literature published within the last five years is primarily considered to
capture the most current trends, practices, and insights in the field. However, seminal works that
have significantly contributed to understanding AI in business models are also included, regardless
of their publication date.
The approach to this literature review combines systematic and thematic analysis to ensure a
comprehensive and nuanced understanding of AI-driven business models. The selection criteria
focus on relevance, quality, and recency, ensuring that the review is grounded in credible and
current literature that accurately reflects the evolving landscape of AI in business.
Sources of Information: Academic Journals, Industry Reports, and Case Studies
In the exploration of AI-driven business models, the sources of information are pivotal in shaping
the understanding and insights derived. Academic journals stand as a cornerstone in this regard,
offering peer-reviewed, scholarly articles that provide a robust theoretical foundation and
empirical evidence. Wiener, Saunders, and Marabelli (2020) work exemplifies this, offering a
critical literature review and a multiperspective research framework that is invaluable for
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understanding big-data business models. These academic insights are crucial for grounding the
review in a solid theoretical base.
Industry reports complement academic journals by providing practical, real-world insights into the
application of AI in business. Enholm et al. (2022) demonstrate this through their systematic
literature review, which elucidates how organizations leverage AI technologies for added business
value. These reports offer a glimpse into the industry's current state, trends, and future directions,
making them an essential component of the literature review.
Case studies from academic and industry sources provide detailed, context-specific insights.
Gomes et al. (2022) contribute to this by presenting a systematic literature review on AI-based
methods for business processes. These case studies are instrumental in understanding AI's practical
application, challenges, and successes in business, offering a nuanced view that complements the
broader perspectives provided by academic journals and industry reports.
Framework for Analyzing and Synthesizing Collected Data
The framework for analyzing and synthesizing the collected data in this literature review is a
meticulous process that involves several stages. Initially, the literature is categorized based on its
relevance to AI in business models. This categorization is crucial for maintaining focus and
ensuring that the review addresses the study's core themes.
The next stage involves a thematic analysis, where common patterns, trends, and divergences
within the literature are identified and examined. This approach is critical for synthesizing a
coherent narrative around AI-driven business models. The thematic analysis also allows for the
integration of diverse perspectives, as seen in the works of Wiener, Saunders, and Marabelli
(2020), and Enholm et al. (2022), ensuring a comprehensive understanding of the subject.
Finally, the review critically evaluates the findings in light of the study's objectives and the broader
context of AI in business. This evaluation considers the strengths, limitations, and implications of
the literature, as highlighted in the systematic reviews by Gomes et al. (2022) and others. This
critical evaluation is essential for providing a comprehensive understanding of the current state of
AI-driven business models and identifying areas for future research.
RESULTS
Summary of AI-Driven Business Models Identified in th"
99,Autres,FINAL_REPORT_AI_MSMEs_Ministerial_10_Oct_2024.pdf,"G7 REPORT ON DRIVING FACTORS AND
CHALLENGES OF AI ADOPTION AND
DEVELOPMENT AMONG COMPANIES,
ESPECIALLY MICRO AND SMALL
ENTERPRISES
Authored by:
Raffaele Spallone – Ministry of Enterprises and Made in Italy
Matteo Bandiera – Competence Industry Manufacturing 4.0
The following report is the result of joint efforts among G7 countries and does not
represent author’s opinion.
INDEX
1. Introduction ..................................................................................................................... 5
1. 1 - Framing the report and identifying objectives ............................................................. 5
1.2 - Defining AI and its potential for industry ........................................................................ 6
1.3 - The transforming potential of Artificial Intelligence .................................................... 7
1.4 - AI in the context of MSMEs ................................................................................................ 8
2. Data, Facts & Trends..................................................................................................... 10
2.1 - Significant Trends ............................................................................................................... 10
2.1.1 - Workforce ...................................................................................................................... 11
2.1.2 - Funding and Investment ............................................................................................ 15
2.2 - Trends Specific to MSMEs ................................................................................................ 18
2.2.1 - Difference in adoption rates ..................................................................................... 18
2.2.2 - Businesses purchasing cloud computing services and performing big data
analytics ..................................................................................................................................... 21
2.2.3 - Barriers to entry ........................................................................................................... 22
2.2.4 - Training in MSMEs ...................................................................................................... 23
3. The potential impact of AI on the production process ............................................... 25
3.1 - From 4.0 to 5.0 ..................................................................................................................... 25
3.2 - AI adoption by business functions and tasks ............................................................. 26
3.3 - AI applications in Manufacturing .................................................................................... 29
3.3.1 - Processes ...................................................................................................................... 30
3.3.2 - Product ........................................................................................................................... 32
3.3.3 - Customers relations.................................................................................................... 33
3.4 - AI adoption and MSMEs .................................................................................................... 33
4. Limits challenges and risks ......................................................................................... 35
4.1 - Computational capacity for AI development ............................................................... 35
4.2 - Diffusion and accessibility ............................................................................................... 39
4.3 - Safe and trustworthy AI ..................................................................................................... 41
4.4 - Impact of AI adoption on the workforce........................................................................ 42
5. Public Policies ............................................................................................................... 46
5.1 - Policies for a shared infrastructure ............................................................................... 46
5.2 - Policies concerning financial leverages ....................................................................... 47
5.3 - Governance .......................................................................................................................... 49
5.4 – Acceleration of the Digital Capacities of Companies ............................................... 51
6. Recommendations to Enable AI adoption and development among Micro, Small,
and Medium-sized Enterprises (MSMEs). ........................................................................ 53
Section 1: Sustaining AI adoption and development among MSMEs. ........................... 53
Section 2: Policies to support MSMEs in the deployment and uptake of safe, secure,
and trustworthy AI ....................................................................................................................... 55
Bibliography ...................................................................................................................... 57
List of Figures and Tables
Figures
Figure 1 Perceived effect of AI adoption on workers’ skills…………………………….11
Figure 2. % of AI job postings, % of all job postings, for selected G7 countries…... 13
Figure 3. Estimates of AI-related funding across selected agencies ………………...15
Figure 4. Global private investment in AI by investment activity………………….……17
Figure 5. Percentage of businesses adopting AI, OECD average……………….……19
Figure 6. Businesses purchasing cloud computing services/
performing big data analytics……………………………………………………………….21
Figure 7. Generative AI and AI, adoption by function……………………………………26
Figure 8. AI use cases by function, % of adopters………………………………………27
Figure 9. Generative AI use cases by function, % of adopters………………………...28
Figure 10. Skills needed in the age of AI…………………………………………………44
Figure 11. Workers’ perceived value of written policies on the ethical use of AI……...44
Tables
Table 1. Percentage of businesses using artificial intelligence, country-specific ……19
1. Introduction
1. 1 - Framing the report and identifying objectives
The future of our economies is strongly tied to our ability to promote with great
determination an innovative environment. This objective entails facilitating our citizen
and companies in the digital transformation, helping them realize concrete benefits out
of this opportunity and attain the sustainability and resilience goals that are shaping
and will shape the way we do business.
One of the core technologies that is driving transformation in industry and business is
Artificial Intelligence (hereinafter AI). AI and other emerging technologies are going to
play a pivotal role in the realization of such a change and the process of making these
technologies available to citizens and companies is the first and most important step
that our societies, led by the G7 countries, must make. Thanks to the expansion of
computing systems’ capacity and the development of complementary technologies, a
new generation of AI systems has become increasingly prevalent across different
functions of manufacturing firms.
Following the Ministerial Declaration of the G7 Industry, Technology and Digital
Ministerial Meeting, the current report acknowledges the importance of these changes.
This document will delve into the analysis of the driving factors and challenges of AI
adoption and development among companies in manufacturing, especially MSMEs,
providing policy recommendations for G7 governments to ensure safe, secure and
trustworthy AI adoption.
Describing AI in a positive and normative way will provide insights into the integration
of AI in production processes through technology adoption, technical collaboration,
and voluntary knowledge exchanges. Furthermore, best practices are identified thanks
to the collection and assessment of policy strategies and experiences for AI adoption
among MSMEs across G7 countries.
Hence, this report builds on endeavors within the G7 Hiroshima AI Process, which
focuses chiefly on advanced AI systems, including generative AI, and resulted in the
International Guiding Principles for Organizations Developing Advanced AI Systems
and the International Code of Conduct for Organizations Developing Advanced AI
Systems. The aim of both of the aforementioned documents is to promote safe,
secure, and trustworthy AI worldwide and to provide voluntary guidance for actions by
organizations developing the most advanced AI systems. They further the
development of common practices among G7 countries and effective strategies to
support MSMEs through the creation of a common ground for AI development,
deployment and use.
Compounding the work undertaken so far by the G7 countries, this report focuses
primarily on the Manufacturing sector, given its relevance in the wider industrial
scenario. Within this scope, it will center specifically on MSMEs, since companies of
this size have been found to face the most difficulties when tasked with AI adoption.
The report is structured as follows: Chapter 2, titled Data Facts and Trends, introduces
the main trends and statistics related to workforce dynamics and funding trends linked
to AI deployment and development; it also analyses adoption rates, barriers to
adoption and training in specific relation to MSMEs.
Building on this collection of empirics Chapter 3, The potential Impact of AI on
Production Processes, contextualizes emerging technologies within the scope of
Industry 4.0 and Industry 5.0 and delves into the potential impact of AI on production
processes focusing on both the internal and external environment of MSMEs.
Chapter 4, Limits Challenges and Risks, complements such framework with an
overview of the main issues relating to AI diffusion and accessibility, safety and
trustworthiness of AI systems, and potential impacts on the workforce.
Chapter 5 is dedicated to present best practices among public policies designed by
G7 countries in four major thematic areas, namely infrastructure, finance, regulation
and education. Lastly, chapter 6 conclude this report by sharing recommendations
resulting from questionnaires and dialogue with private stakeholders, facilitated thanks
to the collaboration with TECH 7.
1.2 - Defining AI and its potential for industry
Framing Artificial intelligence
Without further ado, we intend, in this paragraph, to introduce the general definition of
AI and highlight what are the aspects that such a technology has an impact on.
The introduction of the term Artificial Intelligence is owed to John McCarthy, an
American academic who gave the following definition in 1956: (Artificial Intelligence is)
“the science and engineering of making intelligent machines”.
Although it was framed in this precise manner only in 1956, the concept of intelligent
machines was already present in a paper by Alan Touring titled “Computing Machinery
and Intelligence”, dating back to 1950.
It is beyond the scope of this report to define more precisely the concept of Artificial
intelligence. Hence, the disambiguation of terms is relegated in here. As previously
stated, given the absence of a shared definition for Artificial Intelligence, it is reported
here the quote that has been proposed by OECD: (Artificial Intelligence is) “a machine-
based system that is designed to operate with varying levels of autonomy and that
may exhibit adaptiveness after deployment, and that, for explicit or implicit objectives,
infers, from the input it receives, how to generate outputs such as predictions, content,
recommendations, or decisions that can influence physical or virtual environments”.
It is worth highlighting three main concepts in this definition: first, in line with
McCarthy’s definition, AI is a machine-based discipline, secondly it can connect inputs
to outputs using mathematical modelling and lastly it has varying degrees of autonomy
and adaptivity after deployment.
Of particular significance for the recent developments of AI is its subcomponent called
Machine Learning. This technology analyses data using sophisticated mathematical
models with the ability to learn the correlations between components in an
autonomous way. One of the most advanced mathematical models of this kind is the
so called “neural network”, a construct that tries to emulate the brain structural
functioning. The technology developed and constructed on neural networks
constitutes the main building block of the subcomponent of AI called “Deep Learning”.
This solution is currently at the forefront of AI development.
Artificial intelligence for industry
Focusing on the industrial potential of AI application, it is rather interesting to
distinguish between vertical and horizontal AI within the comprehensive definition
provided above. This further differentiation allows for a better understanding of the
scope and use of AI solutions within the domain of manufacturing, and for a clearer
vision of their future potential.
Vertical AI solutions are tailored by use case to address the specific challenges and
opportunities within industries. By utilizing domain expertise and industry-specific
data, these applications deliver exceptional results and potentially transform business
practices. Designed to meet unique industry requirements, vertical AI solutions offer
advanced functionalities and specialized capabilities, providing industry-specific
insights, optimizing processes, and enhancing decision-making.
While vertical AI focuses on industry-specific challenges and applications, horizontal
AI focuses on enhancing common cross-functional processes across industries.
These versatile and widely applicable solutions serve as a foundation for various
sectors, integrating into domains such as customer service, content generation, and
information retrieval. Their adaptability allows businesses to quickly tailor these AI
solutions to their specific needs.
Regardless of whether vertical or horizontal solutions are used, it is critical to
understand how AI is integrated into production processes, and what are the
components that constitute the key enablers for its application.
The afore described integration of AI into production can unlock significant potential
for the industrial sector. AI has the ability to increase reliability and automation of
production processes, all while enhancing overall productivity and output quality. In
addition, AI application has a significant impact on the workforce and has the potential
to shift their activities into tasks that result in a higher value added for the enterprise.
1.3 - The transforming potential of Artificial Intelligence
Industrial use of AI has the significant potential to have a transformative impact on
society. This opportunity stems primarily from the fact that AI has introduced a new
automation paradigm, enabling machines to learn from past experiences, adapt to
situations and augmenting their real time decision-making. These unique abilities
constitute the culmination of today’s technology and make AI the most powerful tool to
catalyze and enhance the progress made so far in the digital transformation of our
economies.
Nevertheless, AI comes with a great deal of prerequisites and challenges.
Computational power, digital skills and competences, investment power and
managerial skills, are only few of the main issues addressed in this report. Given
these possible hurdles it is crucial that we recognize the pivotal role that AI is playing
in the modern industry by redefining the industrial landscape. The innovative and
transformative solutions offered by its application will allow companies to prosper in
next future as long as they embrace and ride this revolution. It is thus imperative to
raise awareness on AI, develop and promote practical guidance, and coordinate policy
making among countries. These actions will ensure the presence of an enabling
ecosystem for enterprises that wish to be AI ready.
Before moving on with our discussion, it is worth noting that we are currently in a period
of exceedingly rapid technological change. Moreover, subsequent revolution waves
are closely linked within a unique and continuous process of industrial upgrading.
Remarkably, these features yield relevant consequences for AI adoption and
development within MSMEs. On one hand, the incremental and cumulative way
technological change happens should concentrate efforts towards digitalization before
the adoption of AI and other emerging technologies. On the other hand, the consistent
shrinking of intervals between revolution waves prospects a fast-changing landscape
in AI adoption and calls for raising awareness towards opportunities for MSMEs.
1.4 - AI in the context of MSMEs
All companies are not born equal when it comes to embracing the AI revolution and
organizing the necessary adjustments in business models and practices. Micro, small
and medium-sized firms (hereinafter MSMEs) tend to exhibit more challenges in
tackling barriers to entry the AI market. Hence, they need extra consideration when
crafting AI policy to guarantee a fair transition to the AI age 1. It is indeed crucial that
we encourage fast, secure, safe, and trustworthy adoption of new technologies, so
that regardless of dimensions, every company can grasp the benefits and remain
competitive.
In those sectors in which AI was implemented on a massive scale, a radical
transformation of business process has occurred: some components of tasks that
relied primarily on human intervention, like data analysis, strategic planning and
solutioning can often be enhanced by integrating AI. In this sense AI can function as
a complimentary tool, offering initial insights and creative suggestions for human
experts to validate and refine. With recent advancements in machine learning, AI offers
the potential for a significant shift in business practices. In fact, prior to the emergence
of machine learning, knowledge-intensive jobs and functions in business processes
were only transferred to computer systems using explicit knowledge and developing
classical AI systems necessitated significant codification efforts. Hand in hand with the
increasing growth of AI, the amount of data available has also increased, broadening
the range of applications for AI.
It is thus easy to see why AI has generated and will generate so much impact on
economies, especially in industry, as anticipated above.
MSMEs are to be held at the core of the effort to disseminate AI technology. This
intention must take into account the wider economic context of the last twenty years,
when the ones that bore the blunt of recession were chiefly MSMEs. The biggest price
they paid was in losing the necessary resources to invest in key advancements,
accumulating an ever-growing technological lag over the years.
Therefore, AI represents the innovation they can leverage to reduce this gap and gain
the competitiveness necessary to ensure natural continuation of their business. As we
will later see, on the one hand, AI deployment entails changes in MSMEs' business
models and practices, particularly considering the predicted advantages and
prospects from AI adoption in terms of productivity, innovation, scale-up, and other
factors. On the other hand, the business environment in which MSMEs operate has
been witnessing major changes because of AI dissemination. This typically eases
business circumstances and helps MSMEs to cope with current issues using
innovative instruments 2.
Given the potential impact of AI on industry and the peculiar case presented by
dissemination amongst MSMES, the next chapter will focus on highlighting major
quantitative trends of adoption, as to offer a clearer picture of the current status quo
and the upcoming challenges in the adoption of this technology.
2. Data, Facts & Trends
The rapid advancement of AI adoption, driven by the benefits associated with its
application, have triggered a ripple effect that extends to the programming of
investment in research and development, changes in labor market dynamics, and to
the path to integration of AI in micro, small and medium-sized enterprises.
These trends are accompanied by challenges that relate specifically to the task of AI
dissemination. To mention just a few, the significant disparities in AI adoption rates
between large companies and MSMEs and the wide gap between the demand for AI-
related skills and their supply.
To address these challenges, it is fundamental to accompany the natural diffusion of
AI technology with an adequate policy framework to ensure a sustainable transition to
a uniform, wide scale adoption. The prerequisite to construct well-engineered policies
is dependent on a careful analysis of the trends currently characterizing the path of AI
integration in the production process. Given the fast-paced changes that are
associated with this technology, the latter exercise is therefore fundamental.
As AI technologies evolve, policymakers should focus on creating a supportive
environment that promotes the absorption of innovation, reduces risk, and maximizes
the benefits of AI adoption for all businesses, regardless of size. A strategic approach
therefore becomes essential to address the potential of AI to drive economic growth
and improve productivity.
To give a clear indication of the main challenges that need to be addressed by policy
makers, this chapter aims at presenting the main trends surrounding the world of AI.
2.1 - Significant Trends
The scope of use of Artificial Intelligence in industry has grown wider with time. Its
applicability to a broad range of business sectors and functions has greatly improved
efficiency, innovation and product quality. The rapid growth of investment suggests
strong confidence, among economic operators, in the ability of AI to reduce costs and
increase productivity. This confidence, along with the potential of AI for industry,
explains why governments are paying more and more attention to the issue, planning
strategic interventions to support the integration of AI in their national industrial
networks.
This section will attempt to quantify AI’s impact and opportunities, focusing the analysis
on three strategic indicators: the impact of AI use on the labor market, the potentials
for investment in the technology, and trends related to AI adoption amongst MSMEs.
These specific trends have been selected due their significance in the context of AI
adoption. The impact of AI dissemination on labor market dynamics is of particular
importance given the potential of AI to perform a wide array of tasks that can increase
automation. Monitoring the integration of this technology into the daily activities of the
workforce is a fundamental step to ensure that its application is sustainable at the
human level. Furthermore, the analysis of the job market can provide important
indications on the human capital necessary to support wide scale AI adoption.
Investment in AI is also an important indicator to investigate. This factor can assist in
providing an insight into the growth of the sector and provide a valuable base from
which to assess the applications and enterprises that could benefit from the support
of public investment. Lastly, this chapter will delve deeper into the main trends
regarding MSMEs, a crucial subsector of enterprises in the race for AI adoption. These
companies often face higher barriers to entry when attempting to integrate AI into their
production processes and need to be accompanied in this endeavor by a well-
engineered policy framework.
2.1.1 - Workforce
The following subsection will illustrate the impact of AI adoption on the workforce. This
analysis will be focused on two main topics, the first related to the reception of AI
technology amongst the workforce, and the second illustrating the trends related to AI
in the labor market.
The potential of AI is evident from the results of questionnaires conducted with workers
in the sector. Workers generally perceive AI as a complement to their skills, improving
both performance and job satisfaction. Similarly, employers also highlight the positive
impact of AI introduction, reporting that higher productivity and profitability are the main
reasons for adoption.
As a result of this appreciation, the demand for a skilled AI workforce has increased in
G7 countries and beyond, highlighting the need for upskilling and reskilling programs
3. Still, when considering the demand of AI jobs, it is important to consider two
detrimental factors for policy design: only a limited number of occupations require the
specialized skill set needed to develop, adapt and modify AI systems and still, the
supply of skilled AI workers remains insufficient to meet the demand, with many
employers reporting difficulties in taking on AI roles. In this sense, it is fundamental to
improve the ability of the workforce to interact with AI, also taking advantage of the AI-
enabled user support functionalities provided by technology suppliers.
These unique observations highlight the importance of market analysis for AI positions
as essential for the implementation of effective measures and policies, providing an
overview of current and future needs of the workforce, enabling the implementation of
targeted interventions that affect skills gaps and the promotion of effective training
programs 4.
Impact of AI on the workforce
To serve the purpose of analyzing the impact of AI in the manufacturing sector, we
chose to report some valuable data from OECD surveys. The most significant results
are described below:
(i) Workers side: Manufacturing workers generally believe that AI integrates their skills
rather than diminishing their value. The percentage of workers who agree with the
statement ""AI complements my skills"" is about 20% higher than those who believe ""AI
has made some of my skills less valuable"", as shown in Figure 1.
Figure 1: Perceived effect of AI adoption on workers’ skills
SOURCE: authors' personal elaboration of Lane et al., 2023 (OECD). The impact of AI on the workplace: main
findings from the OECD AI surveys.
Overall, workers have a positive view about the impact of adopting AI, particularly in
performance. About 80% of workers reported performance improvements, while less
than 10% reported a decline. In addition, workers indicated increased job satisfaction,
improved physical health, improved well-being and mental health, and reported better
treatment by their managers or supervisors 5.
(ii) Employer side: employers also express confidence in the ability of AI to improve
performance and profitability. Key motivations for adopting AI include improving worker
performance and reducing labor costs. Another significant reason is to address the
skills shortage, which is particularly relevant for employers in the manufacturing sector,
at least when compared to the financial sector. After the adoption of AI solutions,
employers report positive effects on worker productivity and express greater
satisfaction with managers' ability to measure employee performance within the
company 6.
In addition to the positive reception of AI amongst workers and employers, data also
highlights the importance of AI training in the company organization. In firms that have
adopted AI it was noted how providing AI training is an efficient tool to enable workers
to operate more productively and safely. Furthermore, this type of training is a valuable
asset as upskilling or reskilling current employees is often preferred over the
recruitment of new personnel.
Surveys in the manufacturing sector show the availability of such training and assess
its impact on the working environment, job stability and wage expectations. In
manufacturing, workers who have received AI training are more likely to report positive
outcomes, including better performance, better physical and mental health, and
greater job satisfaction. Results suggest that adequate training amplifies the benefits
of AI on performance and working conditions 7.
Therefore, training activities are essential to maximize the benefits of AI for workers.
However, despite the efforts made by governments in recent years, participation rates
in education and training activities remain lower for low-skilled individuals than those
with medium and high skills. Data from the European Union in 2021 show that only
5.28% of low-skilled workers, i.e., those with less than lower secondary education,
participated in formal and non-formal education or training activities before the
investigation, compared to 13.57% of highly skilled workers 8.
AI labor demand and supply
Job vacancies play an important role in understanding the dynamics of the job market,
revealing the preferences of companies in relation to the skills required. To this end,
the report analyses the trends in employment based on online job posting analysis. It
is noted that they reflect a secondary or residual demand for skills rather than the total
demand in the job market, as they only consider vacancies instead of the entirety of
the job supply. Still, their analysis serves as a useful indicator to evaluate the extent
to which companies prioritize AI-related knowledge over other skills.
There are distinct levels of skills often mentioned in online job postings related to
""artificial intelligence"": general and specific skills. General skills include competencies
related to AI usage; competences that comprehend the tools necessary to apply AI.
These can encompass programming languages, big data management, and data
analytics and visualization. On the other hand, specific skills are required for building
particular AI applications, methods, or tools. These typically involve specialized
knowledge in making AI, such as machine learning and neural networks.
The main trend in the AI job market can be summarized as follows. While the
percentage of jobs requiring AI skills is still relatively low, the demand for a skilled AI
workforce has increased in OECD countries. Only a few occupations require the
specialized skill set needed to develop, adapt and modify AI systems, as shown in
Figure 2.
Figure 2: % of AI job postings, % of all job postings, for selected G7 countries
Data Source: Lightcast, 2023. Authors' personal graphic elaboration of Artificial Intelligence Index Report 2024
from, Stanford University.
Note: an AI job posting is defined as any job posting that requires at least one skill in AI, such as machine learning
or natural language processing. The decrease observable in 2023 appears to be situational, and perhaps caused
by an exogenous increase in operational job postings among big companies.
General skills are more broadly in demand across various roles and sectors. The most
sought-after is a general knowledge of machine learning, which is required by 34% of
AI vacancies. Most AI job postings are concentrated in professional services, ICT, and
manufacturing sector. In contrast, industries such as hospitality, agriculture, and
transport show less interest in AI-related profiles. While the development of artificial
intelligence systems certainly requires specific, specialized knowledge, it is the
general skills that are more frequently sought in the job market 9.
Studies have shown that AI skills are among the most valuable ones, earning a 21%
premium compared to the average 4% premium paid for a more competent worker,
ceteris paribus. This premium seems to be partly attributed to the complementary
nature of AI skills with a wide range of non-AI-related skills, which increases their
overall value. In addition, the persistent high demand for supply-side AI skills further
explains this premium: as industries increasingly adopt AI, workers with these skills
are in high demand, justifying higher wages 10. However, employers recognize that
while the importance of AI specialized skills has grown, there is an even greater
emphasis on human skills: successful AI adoption requires not only AI skills, but also
skills in creative and social intelligence, reasoning and critical thinking.
AI-specialized labor supply, despite a sharp increase, remains significantly lower than
demand. Comparisons between 2022 and 2023 among various AI-related jobs
indicate that employers continue to face substantial recruitment challenges for these
roles. Although fewer respondents reported difficulties in hiring AI data scientists, data
engineers, and data visualization specialists than in previous years, between 45% and
65% of employers still report difficulties in finding AI-skilled workers for all positions 11.
The"
100,bcg,aspire-to-ai-leadership-national-strategy-blueprint.pdf,"ASPIRE to AI Leadership:
A National Strategy Blueprint
October 2023
Dr. Akram Awad, Frank Felden, Dr. Lars Littig, Nay Germanos,
Rami Mourtada, Alix Dumoulin
Boston Consulting Group partners with leaders
in business and society to tackle their most
important challenges and capture their greatest
opportunities. BCG was the pioneer in business
strategy when it was founded in 1963. Today,
we work closely with clients to embrace a
transformational approach aimed at benefiting all
stakeholders—empowering organizations to grow,
build sustainable competitive advantage, and
drive positive societal impact.
Our diverse, global teams bring deep industry and
functional expertise and a range of perspectives
that question the status quo and spark change.
BCG delivers solutions through leading-edge
management consulting, technology and design,
as well as corporate and digital ventures. We
work in a uniquely collaborative model across
the firm and throughout all levels of the client
organization, fueled by the goal of helping our
clients thrive and enabling them to make the
world a better place.
Contents
02 | Introduction 09 | Investment
03 | ASPIRE:Framing 10 | Research &
a National AI Innovation
Strategy
11 | Ecosystem
04 | Ambition
12 | From Aspiration
06 | Skills to Action
07 | Policy &
Regulation
Introduction
Every nation today needs to devise a robust artificial can be considered significant, while only 3 were released by
intelligence (AI) strategy. It is not about competing with AI academia1 (significant machine learning models refer to AI
giants like the US and China head-on, but about finding a systems that showcase a state-of-the-art improvement, are
specific and competitive foothold in this fast-evolving historically significant, or highly cited: in 2022, 23 of them
digital landscape where data is the new oil. AI, like were AI language systems). Along with its myriad benefits, AI
Prometheus’s gift of fire, is acting as the spine for a broad poses significant risks. Certain types of jobs are likely to
spectrum of ground-breaking technologies - from data disappear. Ethical concerns around privacy and surveillance,
analytics to robotics and IoT. Its influence is sweeping algorithms reinforcing bias and discrimination, the protection
across vital sectors such as healthcare, education, energy, of intellectual property, and the role of human judgment are
manufacturing, and transportation. We are just at the serious and have far-reaching societal implications.
rupture of the AI revolution, with its full impact yet to Governments can and should play a constructive role in
materialize. It’s a bit like watching a thrilling mystery shaping industry developments, educating the general public,
unfold - exciting, unpredictable, and full of opportunities. and promoting responsible AI. But the clock is ticking – they
The recent global frenzy over generative AI models - the must get involved before exponentially growing complexity
new rockstars in the tech world - underscores AI’s and mass adoption make intervention and oversight even
disruptive prowess and its potential to rewrite the art of more challenging.
the possible. Nations now stand at a strategic crossroads; a
window of opportunity is open for them to make judicious If the private sector has been the primary beneficiary of
choices about their positions in the AI race. developments so far, a national AI strategy will pay
dividends to the general public beyond protecting them
It’s a high-stakes game, and the prizes for playing it well from the risks highlighted above. On a practical level,
are monumental: enhanced GDP, increased productivity, governments can leverage AI to target resources and
job creation, improved quality of life, and citizen welfare. improve service delivery. If governments fall too far behind
This endeavor is akin to a carefully orchestrated symphony the private sector, citizens will grow more frustrated with
- with each instrument, each note, representing key the delivery of public services, and private actors will be
enablers and value creators of national AI strategies. A exclusively setting the standards. While trends of the latter
well-coordinated, strategic sequence could lead to are starting to emerge globally, this imbalance risks
exceptional results; a misaligned action, however, could prioritizing private interests without careful attention to
undermine the overall strategic trajectory. Therefore, the the public good.More broadly, governments should drive
call for action is urgent and should echo in every corner of the development of AI standards, norms, and priorities –
national policy-making chambers. nationally and globally – not relegate those decisions to
the private sector. Importantly, they should do so
Currently, the private sector leads the way in AI. The AI Index proactively to keep pace with private sector-driven AI
compiled by Stanford University reports that, in 2022, industry innovation.
players released 32 of the 38 machine learning models that
1. Stanford University HAI “AI Index Report 2023”: https://aiindex.stanford.edu/report/ Salesforce (2023): https://www.salesforce.com/news/stories/
digital-skills-based-experience/
2 ASPIRE TO AI LEADERSHIP: A NATIONAL STRATEGY BLUEPRINT
ASPIRE:
Framing a National AI Strategy
C
ountries approach AI from different starting points. Understanding its starting point and approach to these
They have different priorities for economic growth, trade-offs forms the basis for shaping a nation’s strategy. In
social progress, educational achievement, and so supporting national governments in their AI strategies, BCG
on. They bring unique technological, engineering, and other has developed the “ASPIRE” framework which further defines
strengths as well as limitations and weaknesses. Countries and codifies six foundational elements: 1. Ambition, 2.
also take different approaches to balancing the interests Skills, 3. Policy & Regulation, 4. Investment, 5.
of government, private sector, and individual citizens. Research/innovation, and 6. Ecosystem.
Exhibit 1 - BCG’s ASPIRE Framework
DIMENSION DESCRIPTION
A
Ambition Articulate AI vision (e.g., global leadership, pioneering ecosystem)
S
Skills Attract, develop and retain talent for the workforce to thrive in the new age of AI
P Policy & Foster an environment that offers flexibility and certainty for AI development
Regulations and promotes responsible AI
I
Investment Deploy funding mechanisms to stimulate and attract AI-related businesses
Research &
R
Build and enable core research and innovation institutions in the domain of AI
Innovation
E Foster interconnected ecosystem infrastructure at the national level,
Ecosystem
and AI cooperation at the global level
3 ASPIRE TO AI LEADERSHIP: A NATIONAL STRATEGY BLUEPRINT
1. Ambition
A mbition is the seed of strategy. It imagines what is 2 Specialist. These countries develop specific expertise
possible, determines overall objectives, and directs that they promote globally. France and India are
the allocation of resources. In our review of following variations of this approach with France
approximately 50 existing national AI strategies, we focusing on exportable R&D, and India acting as an
identified three central archetypes: “AI garage,” providing outsourced services.
1 National Enabler. These countries promote AI to 3 Industry Leader. These countries aspire to be global
create and nurture local champions, improve their leaders in a broad set of AI capabilities from R&D to
socio-economic condition, enhance quality of life, implementation. This archetype demands a strong
and pursue other national objectives primarily by foundation, including advanced R&D, cloud
encouraging responsible AI and reskilling their infrastructure, large data sets, and a proven record
workforce. The Nordic nations, small but highly of commercialization. In addition to China and the
educated, exemplify this approach. US, the UK and South Korea are vying to break into
this realm.
Exhibit 2 - The three central AI ambition archetypes and their protagonists
1 2 3
National enabler Specialist Industry leader
AI as an enabler AI specific focus areas Robust AI industry to
for national to serve on the lead on a global level
socio-economic growth international scene
Promote the use of AI in Leverage country-specific Grow a leading global AI industry
societies and local sectors to competitive advantage and play a across the tech value chain (i.e.
improve socio-economy by global role (e.g., thought leadership, research, development,
developing local AI champions CoEs, sectors with global relevance localization, adoption)
and prioritizing strategic sectors for AI community)
“Maintain US
“Where the best of Data & AI is “AI garage”
leadership in AI”
made reality”
KSA India USA
“Lead the world in AI
“AI benefits for “Most advanced … via efficient
theories, technologies,
competitiveness and welfare” and reliable governance”
and applications”
Sweden UAE China
“Growth and wealth for the “World leader for AI research
“Global leader in AI”
Danish people” and innovation”
Denmark France UK
“One of the world's top 3 AI
powerhouses by 2027”
S. Korea
Source: BCG analysis
Most countries fit into one of these archetypes, although Ǖ The level of proven data & AI impact globally
strategic details and progress along their chosen AI paths in the sector
vary substantially.
Ǖ The alignment of the sector with national
One such variation lies in the economic sectors each priorities
country will select as priority beneficiaries of AI’s
transformative power. This prioritization does not rule out Ǖ The readiness of the sector to adopt AI
AI adoption across all sectors; rather, it entails a strategic
allocation of resources towards high-potential sectors – By employing this approach, countries ensure their AI
often 5 to 7. Three criteria can guide this decision and strategy contributes to sectors of national importance and
ensure the concentration of efforts is optimized: where AI could be meaningfully utilized to generate impact.
BOSTON CONSULTING GROUP 4
Sweden
Healthcare
5 ASPIRE TO AI LEADERSHIP: A NATIONAL STRATEGY BLUEPRINT
defiitnedi
srotces
cfiiceps
oN
Exhibit 3 - Sectors prioritized across selected leading countries
Most
recurrent sectors Denmark Singapore France Canada S. Korea UK China USA
Logistic &
transport
Mobility
Public Safety
Environment
Defense &
Security
Energy
Education
Manufacturing
Finance
Government
Other Sector
Priority sector
Source: National AI Strategies, BCG analysis
2. Skills
A
strategy is only as effective as the people possessing • A global shifting labor market, with 85+ million
the proper skills to execute it. A national AI strategy jobs expected to be displaced by AI and machinery
should outline an approach to build the skill base by 2025.6 Countries need to raise awareness of AI and
through education, reskilling, and training. But developing related job opportunities, and develop talent through
and keeping talent with the necessary AI skills is a global reskilling and on-the-job training. Data and AI are
challenge. Countries must deal with: the fastest growing job categories in the world with
97 million new roles expected by 2025, exceeding the
• Limited knowledge of data and AI, including basic number of those displaced. It is critical for nations to
literacy, which slows ecosystem-wide adoption. push retraining and transition to accommodate these
For instance, 67% of leaders globally say their company shifts, and incentivize the private sector to participate
is exploring ways of leveraging generative AI, yet 66% in the effort.
of them report that their employees do not have the
skills to use the technology successfully.2 Key levers • A global supply-demand talent gap. Around the
for addressing this systemic issue include enhancing world demand for AI talent is growing faster than supply,
access, motivation, and talent development. Importantly, and > 50% is located in just three countries (USA, China,
governments and academic institutions around the and India).7 Beyond local initiatives, most countries will
world are embedding data and AI into their formal also need to attract foreign talent, including through
curriculums: The Stanford AI Index reports an increase dedicated programs like Singapore’s Tech@SG. It is
of 102%3 over 2016-2020 in top university courses that important to recognize that this talent extends beyond
teach undergraduate students the skills needed to build the technology itself. An often-overlooked trait is the
and deploy AI models. Higher education institutions are ability to bridge the worlds of deep technology and
also adapting their non-technical degrees to develop business. People who can translate technology into
AI talent in sectors: degrees combining data science or terms that business executives can understand, and
AI with the likes of business administration or social convert business challenges into technology solutions,
science are increasingly popular, as well as compulsory remain a rare breed.
electives for students as illustrated by Saudi University
KFUPM’s AI+ curriculum embedding mandatory AI • Global competition for leadership driving talent
modules within engineering programs. Efforts are not availability and retention challenges. Countries
limited to higher education: the UNESCO identified 11 should focus on attracting AI talent in the context of
countries with endorsed and implemented government- their integrated global innovation and rrecognition
led K-12 AI curricula including Austria, China, India, and efforts, with many using physical employment
the UAE.4 opportunities as a starting point to build their talent
pools. Building a sustainable ecosystem, for example
In addition, a growing number of talent development through work flexibility and immigration policy, is key
initiatives are being conducted outside of formal to integrating overseas talent into the local economy.
education systems, with countries embracing the full Meanwhile, encouraging locally driven innovation,
range of development opportunities besides university, including through startup support, builds worldwide
including training programs, on-the-job training, and recognition of a country’s thriving data and
self-learning. We see countries like the UK, Singapore, AI sector. This in turn makes it easier to attract talent.
and the UAE implementing initiatives to build basic
AI literacy in both students and adults. Some nations
have expanded their initiatives beyond national borders.
Finland, for example, provides free online basic literacy
courses for any individual globally with the goal “to
demystify AI”. Its program has been accessed in 170
countries, and 40% of its students are women.5
2. Salesforce (2023): https://www.salesforce.com/news/stories/digital-skills-based-experience/
3. Based on Mastersportal (AI and Data Science & Big Data sub-disciplines), retrieved in July 2023: www.mastersportal.com/
4. UNESCO “K-12 AI curricula: a mapping of government-endorsed AI curricula” (2022): https://unesdoc.unesco.org/ark:/48223/pf0000380602
5. Elements of AI: https://www.elementsofai.com/
6. World Economic Forum (WEF) “Future of Jobs Report” (2023): https://www.weforum.org/press/2023/04/future-of-jobs-report-2023-up-to-a-quarter-
of-jobs-expected-to-change-in-next-five-years/
7. Everest Group (2022): https://www.everestgrp.com/market-insights/talent/global-data-analytics-and-ai-talent-locations.html
BOSTON CONSULTING GROUP 6
3. Policy & Regulation
R
egulating AI has come into focus as deployed AI state-level legislation inspired by GDPR is on the rise
systems have put users, companies, and government (e.g., the California Consumer Privacy Act). Take data
at risk. Both companies and governments have a role privacy as an example, as data is the raw material for AI
to play. systems. If data privacy laws are too lax, the public will
rightly question whether AI’s value outweighs its risks.
• Companies are starting to self-regulate through Some estimates indicate that up to 2.5% of GDP8 can
responsible AI, hoping to create competitive be unlocked by relaxing data sharing limitations and
advantage. But the same study found that just 16% encouraging access to data, but tapping into this socio-
of the companies surveyed had mature responsible economic potential requires clear legislation fostering
AI (RAI) programs. Of course, companies put varying data as a national asset. Countries have addressed the
degrees of effort and intent behind ensuring the challenge differently. Some, like China, take a highly
responsibility of their AI efforts (a point reinforced by restrictive approach to data regulation and cross-border
recent turnover among some leading names’ AI data flow. On the other end of the spectrum, countries
Ethics teams). like the US and Canada employ a light-touch approach
and encourage free data flows. The European Union
• The central importance of governments in creating and Singapore, among others, have crafted their own
an overall national strategy, and designing AI models leveraging selected restrictions to balance
legislation to orchestrate public, individual, and interests across data economy stakeholders. If AI ethics
private interests is hard to overstate. From a state and data privacy have often been cited as some of the
perspective, taking actions such as controlling cross- most complex and critical areas of legislation, recent
border data flow are key to protecting national security developments in generative AI are also shedding light on
and interests –a priority for China. China’s recent the issue of intellectual property rights.
Measures for Generative Artificial Intelligence draft is
a good illustration of this priority with requirements • Countries have created various governmental
such as the alignment of GenAI systems’ output with bodies to deal with data, establish a supportive but
national values. Meanwhile, safeguarding citizen watchful regulatory environment, and create rapport with
interests requires a focus on personal data protection the private sector. Some, like Saudi Arabia, have highly
and security. This has been a long-standing focus for the centralized government authorities. Others, like the US,
European Union firstly embedded in the General Data are much more decentralized with the implementation
Protection Regulation (GDPR) and more recently in the of broad initiatives delegated to several agencies. The UK
EU AI Act, the world’s first comprehensive AI law. The positions itself in the middle ground, as highlighted in the
Act addresses the ethical implications of AI: it defines a 2023 government whitepaper “A pro-innovation approach
classification of AI systems into four risk categories, each to AI regulation”. There the government sets a national
associated with a set of rules and restrictions, including framework and principles, and while the Office for AI
requirements around data quality, transparency, plays an oversight role, it leaves its implementation to
oversight, and accountability. This Act not only covers sectoral regulators. Beyond data itself, these bodies also
specific use cases such as facial recognition, which falls need to tackle the technologies that will be creating value
into the highest risk category and is effectively prohibited from it, and address increasingly pressing challenges. Are
by the Act, but also general-purpose models such as governments actively setting rules, standards, norms, and
large language models. principles to foster responsible AI? Are they ensuring that
algorithms being used in the country are understandable,
Finally, the private sector will benefit most from light- auditable, and do not present risks for users including
touch legislation favoring open data and prioritizing harm and discrimination? Are they conducting
data value realization – as we see in the USA, although appropriate audits and other forms of oversight?
8. OECD: https://www.oecd.org/digital/data-governance/
7 ASPIRE TO AI LEADERSHIP: A NATIONAL STRATEGY BLUEPRINT
Overall, regulating AI – whether by simply setting core ethics coupled with pro-innovationinitiatives like provided
principles or through proactively introducing protective sandboxes (low-risk high support environments setup by
mechanisms – is necessary to ensure sustainable government entities to fuel cross-sector innovation.)
development of the AI innovation ecosystem, maximization
of value from AI solutions, and protection of citizens from Recent disruptions, like the suddenly accelerated
harm, undesired use of AI, and discrimination. Both private adoption of generative AI systems, have triggered different
companies and government have roles to play in developing reactions worldwide. While certain countries, like Italy,
principles and ethics around AI. UNESCO developed have chosen a temporary ban as a precautionary
recommendations on the AI ethics focused on human rights measure, others, like Portugal, have swiftly seized the
and inclusiveness, and was adopted by all 193 Member opportunity to integrate this technology into their
States. Regulating a fast-changing field like AI is an ongoing government services. In times of fast-paced innovation,
process. Leading countries have typically started their governments often find themselves in a reactive stance
journey with soft legislation focused on principles to guide towards private sector advancements. A strong national
AI players and enable rapid growth of the data and AI AI strategy can equip policymakers and legislators with
economy. Then as understanding and applications become directions, principles, and guidance on key trade-offs that
more sophisticated, mature jurisdictions like the US, EU, can promote efficient and harmonized decision-making
and China have moved toward more restrictive models in the face of rapid technological advancements.
BOSTON CONSULTING GROUP 8
4. Investment
A
nation’s willingness to invest in AI needs to match and ecosystem promotion. Countries can also increase
its ambition. the ease of starting businesses, and establish investment
protections to create a pro-investment climate. They
can offer business advisory and legal support. Finally,
• National governments have allocated extensive they should facilitate connections among stakeholders,
budgets for AI development. Examples include building a pro-partnership environment, and developing
China, France, South Korea, the UK, and the US which clear paths for investors and efficient approaches to
have each announced and committed to investing match-making.
more than USD 1 billion in AI, leaving other nations
far behind.The absolute amount, while important, may • Equity funding has also continued growing
ultimately matter less than its concentration toward in the past years, despite a recent slowdown.
priorities and its ability to unlock other sources of Between 2013 and 2022, corporate investments in AI
funding. Governments, for example, can stimulate globally grew by a factor of 13, reaching an all-time
investment activity through supportive fiscal and high in 2021 with USD ~275B10 invested. Recent AI
regulatory mechanisms. These help establish a culture advancements such as Generative AI are expected to
that incentivizes support for data and AI ecosystem fuel further investments, with over USD 12B worth of
growth, and attracts international and domestic interest. deals announced or completed in the first quarter of
Leading nations utilize diverse funding vehicles and 2023. Notably, large technology companies have been
structured investment support to activate their data and pledging investments in Generative AI startups: it is
AI ecosystems. Funding vehicles include debt (e.g., loads, the case of Salesforce which is doubling the size of its
credit lines etc.), equity, and direct spending. Collectively, Generative AI Find from USD 250M to 500M. Equity
these instruments span the entire value chain from pre- investments are sourced from both public and private
seed to buyout / later stage funding, ensuring a healthy entities with levels varying by country. The USA and UK
ongoing financial pipeline for ecosystem participants. are mostly private sector driven: in 2022, the USA was
leading private investments in AI worldwide with USD
• Technology companies’ debt ratios have been ~47B invested by private actors. China and Singapore
rising (+100% over the last year for European receive the most public contributions including through
technology startups)9 showing increased appetite sovereign funds and government VCs. Countries like
for debt vehicles, including startup-loans, project France and Germany employ a hybrid model including
investment loans, microfinancing, and credit guarantees co-investment initiatives and government investments
which provide countries the opportunity to activate in VCs.
their data and AI sector. They are facilitated by multiple
providers including banks and financial institutions, • Leading nations also provide direct funding
government lending, and NGOs but also non-financial including growth and innovation grants; seed and
private institutions (specifically in the USA). Countries startup support; individual and employee support such
have implemented specific initiatives using targeted as for hiring and training. These can be complemented
debt vehicles to develop data & AI with specific focus on by incentives for businesses focusing on tax breaks,
company size (e.g., Microfinance Ireland which providers subsidies, and special economic zones: Singapore for
short-term loads to startups), on mandate, on industries, instance provides tax exemptions of up to 100% to
or on location. qualifying R&D projects. Finally, ecosystem growth can
be facilitated by national budgets allocated to specific
It is important to note that investment support extends ministries as illustrated by the Canadian national AI
well beyond the monetary. It starts with attracting strategy which allowed direct funding of 3 national AI
businesses and funders through fiscal, financial, and centers of excellence.
regulatory incentives, local market information access,
9. Bloomberg (2023): https://www.bloomberg.com/news/articles/2023-03-21/europe-tech-startups-doubled-debt-financing-in-fundraising-shift
10. Stanford University HAI “AI Index Report 2023”: https://aiindex.stanford.edu/report/ (this includes M&A, minority stake, private investments, and
public offerings)
9 ASPIRE TO AI LEADERSHIP: A NATIONAL STRATEGY BLUEPRINT
5. Research & Innovation
G
overnments can also deploy their R&D budget and by developing centers for basic and applied research,
agenda in support of AI by targeting areas that play investing in technology infrastructure, and strengthening
to the nation’s strengths or are critical to addressing collaboration between industry and academia.
its most pressing challenges. A nation’s ambition
archetype – national enabler, specialist, or industry leader • Meanwhile, industry leaders should invest heavily
– will largely shape the scope and priorities of its AI R&D in applied research, integrating innovation and
agenda. It will also guide the respective roles of industrialization, and commercializing innovation
government, academia, and private players, and how their in advanced sectors. Regardless of a country’s
contributions are orchestrated. There is no one-size-fit-all ambition, collaboration between the public and private
for national priority-setting. Leading nations following sectors and between companies and universities is key.
diverse approaches. Some, like Switzerland, are already
leaders in general R&D, so include AI organically in Typically the public sector can kick-start R&D by creating
auxiliary national strategies. Others, like Singapore, have demand for AI innovation and providing financing.
a dedicated AI strategy with strong R&D focus. Finally Academia is responsible for research, knowledge transfer,
countries like the US make AI R&D a specific priority talent development, and international stakeholder
domain with its own extensive strategy, focusing on attraction. The private sector develops its own research,
moonshot initiatives and requiring heavy investments. fosters collaborations, funds R&D, and captures value. The
orchestration of these stakeholders takes different forms
Generally, countries should focus on supporting basic across leading nations. Some favor a self-orchestrating
research, public-private partnership, and collaboration ecosystem, others delegate organization to area-specific
with global AI organizations. champions. All leading countries, however, seek ways to
proactively develop their ecosystems throughout the AI
• Specialist governmental bodies should go a step R&D cycle.
further in creating a research and innovation
infrastructure
BOSTON CONSULTING GROUP 10
6. Ecosystem
International ecosystems: global ecosystem adjacent to AI is crucial. Many of the most
cooperation efforts effective AI applications are not stand alone, but rather
depend on an interconnected ecosystem infrastructure.
Despite countries competing to become global AI leaders, Self-driving cars, for example, have built-in sensors and
cooperation between nations has never been more critical processing units, cameras, and wireless connectivity built
to address the global challenges raised by AI and better by separate companies that must work seamlessly together.
capitalize on its opportunities. As such, AI cooperation has These ecosystems vary in complexity, depending on a
become a key enabler for nations. nation’s ambitions, but will all involve startup development,
industrial adoption, infrastructure, data access, and
• The ethical implications of AI, ranging from data government support. As with research and innovation,
privacy and algorithmic bias to autonomous decision collaboration is the cement that holds ecosystems together.
-making, transcend national borders: effectively
addressing them demands close international In addition to cross-cutting ecosystem infrastructure
cooperation. This priority was reflected by recent efforts providing security and interoperability, the following
from international organizations and intergovernmental three overarching layers of infrastructure are particularly
forums which constitute critical alignment platforms for important in driving leading countries’ AI success:
nations to jointly address AI ethic challenges, develop
shared principles, standards, and frameworks to be • Computing and cloud: Sophisticated data and AI projects
implemented at the national level. For instance, the require scalable cloud infrastructure with hyperscalers. But
UNESCO published in 2021 its “Recommendation on the global hyperscale cloud resources are failing to meet the
Ethics of Artificial Intelligence”, which defines principles growing demand. To address this, leading nations are
and policies guiding AI policymaking worldwide. Similarly, establishing cloud hubs, and expanding the presence
the OECD established its AI Policy Observatory and of hyperscale cloud providers in the country to run
published AI recommendations that served as a basis for the data and AI workload locally. For example, the Dublin
the G20 AI Principles adopted in 2019. Cloud Computing Hub hosts 70 datacenters operated
by hyperscale11 cloud providers, with state-of-the-art IT
• Cooperation also underpins the economic potential infrastructure, fiber optic speed networks, and advanced
of AI. By fostering AI cooperation, countries can technology telecommunications.
facilitate the development of interoperable AI
systems, harmonize regulatory frameworks, and • Data: Limited data accessibility and low data quality
promote open data sharing. Collaborative initiatives that are key challenges in data and AI enablement.
foster AI cooperation can create a level playing field, Today, ~45% of Smart City applications require cross
enhance market access, and promote fair trade practices, industry consolidated data, and organizations lose
thereby unlocking new avenues for economic collaboration on average USD 15 million annually due to poor
and driving innovation across borders. To capitalize on its data.12 Countries can address this by providing open
economic potential, countries are now specifically access to national data platforms, consolidating
embedding AI in digital trade agreements (e.g., UK- sector-specific data, developing standardized
Singapore Digital Economic Agreement). specifications and guidelines to enhance data
quality and classification, and providing toolkits to
• As a multi-purpose technology, AI has the potential entities on their data journey. An example is Singapore’s
to help address key global challenges such as climate Data.go"
101,bcg,BCG-Executive-Perspectives-CEOs-Guide-to-Maximizing-Value-from-AI-EP0-3July2024.pdf,"Executive
Perspectives
CEO’s Guide to Maximizing Value
Potential from AI in 2024
Introduction
July 2024
Introduction In this BCG
Executive Perspective,
We meet often with CEOs to discuss AI---a topic that is both captivating and rapidly
we will show you how
changing. After working with over 1,000 clients in the past year, we are sharing our
to leverage AI to create
most recent learnings in a new series designed to help CEOs navigate AI.
With AI at an inflection point, the focus in 2024 is on turning AI’s potential into real business value
profit. Here are some key questions CEOs often ask about getting value from AI:
• Where should my organization start?
• What are the highest value and opportunities where GenAI and AI can transform
my business and functions?
• How can I achieve short-term performance with AI and GenAI while building the
necessary capabilities in my organization?
• How can I build enthusiasm for AI across all levels of my organization and drive
adoption among front-line employees?
This document is a guide for CEOs to cut through the hype around AI and
understand what creates value now and in the future. It explains current AI
adoption trends, the technology's capabilities, and how to use it for a complete
enterprise transformation.
11
Executive summary | CEO’s guide to maximizing
value potential from AI in 2024
The maturity of AI brought us to an exciting moment, where Generative AI is complementing Predictive AI, and going
well beyond the hype in delivering superior value for organizations
The time to act AI mature companies (~10%) are also the first to scale GenAI, further widening the gap vs. peers
on AI is now AI technology is also evolving rapidly, from mature value sources ready to scale at the present time to exciting
innovations on the horizon
Companies are embarking on AI transformation journeys; setting ambitious targets both in top line and bottom line
Three strategic initiatives to drive value and achieve end-to-end enterprise transformation:
• 'DEPLOY' utilizes off-the-shelf tools to boost workforce productivity by 10-15%, improve employee satisfaction,
Three strategic
and generate excitement for broader AI change in the organization
plays to create
• 'RESHAPE' allows for re-imagination of functions through workflow re-engineering, to drive 30-50%
value at scale
improvements in efficiency, effectiveness across affected functions
with AI
• 'INVENT' leverages AI to expand revenue streams and ""invent before getting disrupted"" by introducing new
offers, services, and experiences to the market
To successfully deploy AI at scale and convert it into business impact, organizations need strong foundational
capabilities (e.g., talent, technological infrastructure, etc.) and a 10-20-70 approach in terms of level of emphasis
Executing
anchoring on algorithms (10%), tech & data (20%), and people & processes (70%)
successfully
Long-term competitive advantage will come from data, talent, and culture; the time to act is now, otherwise,
organizations risk being left behind
2
GenAI is extending the value and excitement around AI
broadly, with leading adopters realizing superior outcomes
Companies with the highest AI maturity are further extending …and rewards are clear
their lead by scaling GenAI applications1… for those further ahead in
AI journey2
~ 10%
3-year
+2.6x
~ 50%
companies
revenue growth
companies
~ 40%
companies
3-year
+38%
EBIT growth
Piloting Scaling
Developing few focused MVPs to test value Are scaling 1 or more
No-action yet from GenAI GenAI applications
across functions/ 3-year market
Taking no action on GenAI yet +50%
enterprise
share growth
Majority have historically Majority have historically piloted a Majority have historically
lacked predictive AI project number of predictive AI projects, but scaled several predictive AI
execution capability few have successfully scaled initiatives in a few functions
Customer
+45%
satisfaction score
Higher AI maturity
Read What GenAI's Top Performers do Differently
1. BCG Build for the Future C-level GenAI survey, 2023/2024, N = 159; BCG Client Experience 2. BCG Company of the Future Survey 2022; 33
n=536; Comparison of 'Discovering' companies with none/limited AI solutions vs 'AI driven' companies with multiple at scale AI solutions
AI technology continues to evolve rapidly; while value proofs
exist already, even more exciting opportunities are on the horizon
Non-Exhaustive
Established value sources Emerging applications Early experiments
Video/audio New product
Content generation
content generation development
including text & image
Customer facing
chatbots
Commercial spend
Summarization & Independent
simulation and
recommendations decision-making
allocation
Trend sensing,
from unstructured data without human
product refinement
in the loop
Demand
forecasting Optimization:
operations, price,
promotions,
precision media
4
Three complementary strategic plays to maximize
value potential of AI
DEPLOY RESHAPE INVENT
Enhance efficiency with GenAI tools Elevate business impact by Develop AI-native offerings that
that streamline everyday business transforming workflows with AI, elevate customer value proposition
processes reducing the need for enabling multi-functional reshaping and and unlock new business models &
additional hires & daily operating friction end-to-end organizational transformation revenue opportunities
Examples of 'Deploy' Examples of 'Reshape' Examples of 'Invent'
• Meeting summary • Design and Engineering • Hyper-personalized customer experience
• Code development • Marketing • AI-powered services/products
• Calendar management • Customer Service • Data monetization across value chain
• Invoice reconciliation • Technology • Insights and innovation platform
Read Turning GenAI Magic into Business Impact
End-to-end transformation across all 3 plays
Combine multiple AI initiatives for an end-to- Scale from functional transformation to
end transformation company-wide transformation
55
Key principles and CEO imperatives for scaling strategic plays
DEPLOY RESHAPE INVENT
GenAI in everyday tasks Critical functions New business models
30-50% efficiency, speed, and
AI outcomes 10-15% productivity New revenue play
effectiveness/ROI improvement
Off the shelf software (e.g., Copilot, Assembly of predictive & generative AI
Tech Dedicated platform
ChatGPT Enterprise) systems; function-specific
People Upskilling & adoption Process redesign, workforce planning Advanced design + AI
Risk/
Technical risks Operational risks Client facing risks
Responsible AI
Operating model IT/HR-led Function-led Incubation-led
• Engage CFOs and CHROs (beyond • Shift dialogue from uses case/point • Be prepared to continuously test &
CIOs), as champions solutions to function adapt operational plans
transformations
• Embed tools in existing workstreams • Maintain focus on providing
CEO imperatives with role-specific features • Upskill leaders & use persona-based customer value vs technology
communication to facilitate change capabilities
• Communicate capabilities & goals to
avoid unrealistic expectations • Build a baseline, select priority • Align (Gen)AI initiatives with overall
workflows, & run pilots strategy & inspire C-level to
66
lead/invest
'DEPLOY' Overview | 'DEPLOY' unlocks 10-15% productivity improvement
and prepares organization for broader AI change
How are companies using it?
What is 'DEPLOY'? Tech is being deployed across various workflows and everyday tasks,
with a few standing out as the most common
Deploy efforts take the “toil” out of work;
investments in early wins that prepare
Q: How is your company deploying GenAI in everyday tasks today? (N=188)1
organizations for an AI-powered future
Knowledge management 69%
of companies employing
60%
GenAI solutions have Code development 67% 10-15%
'DEPLOY' plays in motion1
productivity
Meeting summaries 62%
gains
Content drafts 55%
'DEPLOY' tools in the market
Image/visual
48%
content generation
ChatGPT Microsoft Research extraction
""Personalized recommendations are now at
44%
Enterprise Copilot and synthesis the fingertips of all our developers. [With
GitHub Copilot], they are coding faster,
Contract/SOW drafts 33%
collaborating more effectively, & building
GitHub Adobe
better outcomes.""
Copilot Firefly Calendar management 31%
– Engineering Manager at a leading
Illustrative, non-exhaustive Invoice reconciliation 31% technology company
7
1. BCG Deploy, Reshape, Invent Survey 2023; 2. GitHub; 3. Microsoft; 4. BCG Henderson Institute; 5. Adobe
'RESHAPE' Overview | Companies are reshaping support functions with AI,
then quickly moving on to transform core functions crucial to their industry
Non-Exhaustive
What is 'RESHAPE'?
Companies across industries are reshaping beyond their support
functions, expanding towards core functions
Companies are elevating business impact
by transforming functional workflows
• Streamline talent sourcing & hit-rate of best candidates
with AI HR
• Increase employee satisfaction with augmented HR services
Support • Drive efficiency in tech ops & vendor spend model
of companies employing IT & Development
68% • Accelerate software development timelines
GenAI solutions have functions
'RESHAPE' plays in motion1
• Drive efficiency in quality, customer service, and logistics
Supply Chain
• Reduce lead times in production and logistics
…
'RESHAPE' is …
• Improve overall response time by extracting information more
Underwriting in
• Functional transformation leveraging AI quickly and accelerating summary & review processes
Insurance
and GenAI • Enhance assessment accuracy
Core
• Holistic and centrally coordinated effort Marketing • Deliver superior productivity, creativity, insights in less time
functions
• Enhance customer-centric delivery with hyper-personalized
• Complete re-imagination of how work gets (varies by in CPG content and campaigns at scale
done and who does it
industry)
R&D • Improve product development (e.g., drug discovery, scientific
• Building underlying capabilities via strategic
in BioPharma research), quality, testing, and design generation
investments in core tech, people and RAI2
…
8
Source: 1. BCG Deploy, Reshape, Invent Survey 2023 2. Responsible AI – the approach to developing and implementing AI in a legal and ethical manner; 2. BCG analysis and client experience
'INVENT' Overview | Select companies are inventing with AI, ushering in a new
era of innovation & taking the lead in shaping the future of their industries
What is 'INVENT'? What does 'INVENT' look like?
'INVENT' develops AI-native offerings that
elevate customer value proposition and New customer experiences [Our company] has always lived at the
unlock new business models & revenue AI enhances businesses' ability to craft intersection of tech and creativity.
engaging, personalized customer
opportunities Recent developments in AI represent
experiences, driving greater loyalty,
an opportunity to take this
of companies employing
engagement, and brand advocacy
46%
convergence to the next level. We view
GenAI solutions have
'INVENT' plays in motion AI [and GenAI] as an exciting new
New line of products
creativity tool to open avenues for
How are enterprises reinventing AI empowers businesses to create
imagination and explore premium
themselves?1 data-driven, dynamic products,
product offerings that allow us to
leveraging insights to enhance
New customer value proposition
88% profitability and competitiveness innovate with our clients and partners
for existing products or services
on a new frontier in media.
New products or services 64%
New business models – CEO at leading internet
AI drives business model innovation by media company
New target customers 52%
evolving & learning from existing
operations, enhancing competitive
New pricing/cost structure
46%
for existing products or services advantage and enabling novel models
Source: BCG Deploy, Reshape, Invent Survey 2023; 1. Survey question: How will your company invent new business models with AI? Please select the type of new business models AI will generate at 9
your company (N=142, companies pursuing Invent)
Across the 3 plays, leading companies are setting ambitious targets
when embarking on AI transformation journeys
Non-exhaustive
Productivity improvement Cost transformation Top-line growth
Boost productivity with 'deploy' & 'reshape' 'Reshape' functions and leverage predictive Speed up time-to-market & drive revenue
plays to level up the company, especially AI to maximize productivity of assets and by 'reshaping' core processes, 'inventing'
across heavily people-driven organizations reduce costs new offerings and full end-to-end
transformations
Energy Financial Institution Insurance
4 hrs $1B+ *500+ 50%
basis points
Productivity boost (per week) Productivity program (cost, revenues, Reduction in time required for underwriting,
to support growth strategy balance sheet optimization); Includes Engineering, driving top line growth
Research, Investment Banking, Wealth Management,
Risk Management
Professional Services Biopharma Consumer Goods
6 hrs $1B *250 3pts+
basis points
Productivity benefits (per week) Value potential by 2027; 'Reshaped' Incremental sales
through broad deployment across 30k users multiple core functions, starting with through digital services
Marketing, R&D, Manufacturing
More detail on following pages
10
Source: BCG Experience
Example 1/2 | How a Biopharma company is reducing cost through
AI transformation with a focus on reshaping multiple functions
Vision Execution Value capture
Non-Exhaustive
Company-wide program under CEO 1 Adopted iterative approach and started
and ExCo sponsorship with several pilots 20-30% agency cost
reduction and improved
Mandate to unlock value fast, by 2 Setup rigorous measurement with GenAI
reshaping multiple functions, mirror processes engagement & campaign ROI
Commercial
engaging all employees; position as
3 Developed lighthouse in Commercial
pioneer in AI within sector
function and then expanded to Research &
Vision to transform via: Development and Marketing & Sales 20-40% medical writer
efficiency gain – 3-6 months
1. Productivity improvement 4 Each wave focused on outcomes and
Research &
saving in time-to-market
(e.g. content summarization) operating model redesign (e.g., as-is content
Development
2. Work reduction via automating development  insight-powered & always-
end-to-end processes on content development  augmented/
3. Improving employee assisted sales)
80% of reports approved
engagement via reducing
repetitive tasks 5 Assessed portfolio of investments, with no edits – from
projected ROI based on first impact; adjust
4. Competitive advantage via Marketing
20 to 1-3 days
accordingly
business model reinvention, cost & Sales
advantage, capability building
Deploy Reshape Invent
111111
Fill % represents level of emphasis for each strategic play
Source: BCG analysis and client experience
Example 2/2 | How a CPG company is driving topline growth by
inventing new business model and reshaping functions with AI
Vision Execution Value capture
Global CPG seeking to reinforce its Invested in AI capabilities over the last 7 years,
1
core competitive advantages through a mix of organic and inorganic moves
through AI 15-25% improvement in
2 Transformed its R&D and Marketing marketing spend ROI
1. Product superiority and
capabilities through Predictive and increasingly
speed to market
Generative AI, for formula success prediction, cycle
time reduction, Marketing ROI optimization,
2. Marketing creativity and
content production automation at large scale
effectiveness Time to Market
acceleration by
3. Direct and meaningful 3 Launched AI-powered digital services and
consumer connections virtual assistants to augment consumer 3-6 months
experience, build direct connections, influence the
consumer journey
4 In parallel, set up an Enterprise-wide $200M+ of incremental
productivity lift effort with internal GPT
sales through digital
services
Deploy Reshape Invent
Fill % represents level of emphasis for each strategic play 1122
Source: BCG analysis and client experience
Algorithms
10%
Build new algorithms and the science
behind them
Three pillars for Technology & Data
20%
successful (Gen)AI
Deploy the tech stack and ensure the right
data feeds into the right systems
transformation
The 10-20-70 Rule:
Focus 10% of your AI efforts on
algorithms, 20% on the underlying
technology and data, and 70% on
people and processes
People & Processes
70%
Drive change management and other
processes related to people
1133
Algorithms (the ‘10’)/Tech & Data (the ‘20’) | Key AI trends for executives
to keep in mind across algorithms, technology, and data
10% 20%
Algorithms Technology Data
AI models will continue to grow in size New tech stacks are required to The value of Data increasing with
& capability in the next 3-5 years, support AI needs, driving AI, requiring companies to develop
offering an increasingly wide set of companies to simplify legacy new capabilities to deal with
options to choose from when systems and adopt new AI platforms unstructured data (i.e., knowledge)
balancing performance needs & costs
Non-exhaustive Non-exhaustive
Recent trends: Recent trends:
• Tech companies building AI factories (e.g., • Enterprise system solutions launching GenAI • Unstructured data becoming more valuable;
Microsoft $100bn data center investment1) features (e.g., Salesforce, Einstein) increasing need to break “data silos”
• Autonomous agents performing tasks & • New applications (e.g., Jasper, Writer) & • External data becoming more valuable; need to
making decisions without human oversight platforms (e.g., Scale AI, Groq) emerging develop data partnerships & ecosystems
• AI being deployed on mobile devices • Companies using GenAI to support system • Need for CDOs2 to help functions create & rank
migration (via software automation) unstructured data across enterprise
Note: Trends as of June 2024; 1. Forbes: ""Microsoft And OpenAI Partner On $100 Billion U.S. Data Center""; 2. CDO = Chief Data Officers 14
People & Processes (the ‘70’) | New roles, evolved operating models, &
rigorous change management required for successful AI transformation
70%
Organization & operating model Talent & skills
Emergence of new roles/departments Changing skillset requirements
New roles will be created to unlock the potential GenAI will automate some tasks, recommend
of AI (e.g., Chief AI Officer) next actions & improve knowledge management
New operating models Revamped talent acquisition
Changes in roles, responsibilities, and decision Greater access to candidate pool web scraping,
rights would bring about significant changes in auto-scheduling, and AI-based interviewing would
operating models greatly increase hiring efficiency
Greater productivity & redesigned work AI-enabled performance management
GenAI will automate many types of creative work (e.g., coding, New observational data (e.g., conversation summaries)
writing) and enhance employee support (e.g., next best action) will make performance management more objective
Personalized training
Purpose, culture, & change management
Employee Learning & Development will be
tailored to individual needs & questions, and new
Extensive change management needs
training content around AI will emerge
New talent needs, ways of working, and job
responsibilities must be managed thoughtfully
15
Source: BCG Marketing Org & Op Benchmarks
However, change is hard, and executives face challenges
across the ‘10-20-70’ during their AI transformation
Top challenges with AI transformations1 % of executives who reported as
top challenge within each category
Over 2/3
• Trouble reaching sufficient accuracy and reliability of models
10%
Algorithms
• Difficulty ensuring security and compliance of the technology
Of transformations fall
• Difficulty integrating new AI technology with existing IT systems
short of expectations
• Lack of access to high-quality data for model training
(in terms of time, budget, 20%
Tech & data
meeting ambition)1 • IT cost constraints limiting sufficient investments into (Gen)AI
• Difficulty realizing cost takeout/savings
$1T
• Challenges prioritizing AI opportunities when compared to other company-wide
concerns (e.g., cost reduction initiatives)
• Issues with assigning a target ROI for identified opportunities
With backdrop of $1T
of wasted IT spend
People &
70%
• Insufficient (Gen)AI literacy throughout the organization
collectively2 processes • Lack of specialized (Gen)AI engineers in the market
• Resistance, opposition, and fear about AI impacting jobs
• Lack of accountability & measurement of set KPIs (e.g., adoption, value realized)
• Challenges with implementing new processes and reimagining workflows
16
Sources: BCG Experience; 1. 2023-24 BCG Build for the Future C-level (Gen)AI Survey(s), N = 735; 2. Across S&P 1200; Harvey ball fill represents the % of executives who ranked
the challenge in the top 3 challenges when presented with a set of challenges for each of the categories.
Core questions for CEOs to consider looking ahead
Non-exhaustive
• How do I empower my C-suite to stay up to date with the rapidly evolving AI landscape?
10%
• How do I think about whether to Build, Buy or Partner for the model(s) needed? Which model
Algorithms
and model platform partnership(s) do I need to make to stay ahead?
• What kind of (new) data capabilities do I need to leapfrog my competition?
20%
• How do I simplify my legacy systems to adopt new AI tech stacks and platforms?
Tech & Data
• I've invested significantly already. How do I control my costs & ensure a return going forward?
• How do I mobilize senior leaders to embrace & actively champion our (Gen)AI ambition?
70% • How must our roles, departments, & operating model adapt to capture value from AI?
People & • What are the best ways to fill the AI talent gap within my organization (e.g., upskill, reskill)?
Processes
• How do I effectively manage change across my enterprise? How do I communicate our ambition
and set the right expectations, while promoting trust and preventing misconceptions?
Source: BCG Experience 17
AI Transformation Overview | End-to-end transformation integrates strategy
& multiple functional transformations while building requisite foundations
(Gen)AI SETUP DEPLOY
(Gen)AI in everyday tasks across the enterprise
Define value pools RESHAPE critical functions
• BCG (Gen)AI Maturity Assessment
• BCG Workforce Diagnostic In every business function:
Transformation of function 1
Build MVPs, re-design workflows and pilot to
Articulate vision based on strategy prove value…
Transformation of function 2
…while designing future state operating model
Select priority opportunities across
Cascade changes and scale E2E Transformation of function 3…
Deploy, Reshape, Invent PLAYS
Build the business case INVENT
new business models and products
E2E CHANGE MANAGEMENT & DELIVERY
Enable leaders and upskill
Drive adoption, engagement & culture change,l everaging behavioral science
Steer through central governance and measure impact via AI delivery office
ENTERPRISE FOUNDATIONS
Make coordinated investments in core tech & data, people, and responsible AI
Read about BCG's perspective on Responsible AI
18
Getting started | Practical next steps for your AI journey
Understand your  Identify the largest opportunities for action & benchmark AI maturity against peers
1
starting point  Assess the potential for total productivity impact across your workforce
Prioritize few high  Select 3-5 functions/processes for 'DEPLOY', 'RESHAPE', & 'INVENT' plays
2
value initiatives  Setup transformative efforts & resource adequately; target ~2-3x return on investment
 Embrace AI tools in daily work, upskill the workforce, & scale initiatives broadly
3 Upskill & scale broadly
 Remember that this is a people transformation not a tech transformation
 Optimize tech/digital costs in the first 6-12 months by centralizing resources,
4 Fund the journey rationalizing legacy applications, and reducing third-party spend
 Prioritize & refocus portfolio on value creating initiatives
Invest in foundational  Build capabilities that will drive & sustain transformation (e.g., tech, people, RAI)
5
capabilities  Improve the quantity/quality of data – it will be a competitive advantage long-term
Launch an AI  Develop a structure that allows for broad but controlled experimentation
6
governance structure  Operate according to RAI framework while maintaining focus on prioritized initiatives
19
NAMR
BCG Experts |
Dylan Vladimir David Amanda Matthew
Key contacts
Bolden Lukic Martin Luther Kropp
for AI
Sesh Julie Beth Djon Steve
Iyer Bedard Viner Kleine Mills
transformation
Dan Renee Tauseef
Martines Laverdiere Charanya
EMESA APAC
Nicolas Jessica Marc Jeff Romain de
De Bellefonds Apotheker Schuuring Walters Laubier
Dan Andrej Marcus Julian Aparna
Sack Levin Wittig King Kapoor
Robert Akira Nipun
Xu Abe Kalra
2200"
102,bcg,five-must-haves-for-ai-upskilling.pdf,"Five Must-Haves for Effective
AI Upskilling
OCTOBER 08, 2024
By Hean-Ho Loh, Vinciane Beauchene, Vladimir Lukic, and Rajiv Shenoy
READING TIME: 15 MIN
The need for AI upskilling is clear. But upskilling is also a major bottleneck for companies that want
to scale AI and GenAI across their organizations. Even though corporate leaders know it’s important,
many have been slow to provide people with opportunities to learn the skills required to use it.
There’s an argument to be made for initiating AI upskilling sooner rather than later. Companies that
invest in AI gain a competitive advantage through superior innovation and readiness for the
unexpected. They also improve productivity and customer experience, and boost revenue.
© 2024 Boston Consulting Group 1
But until now, it’s been unclear which approach to AI upskilling yields the biggest return on the twin
investments of time and resources.
We decided to find out. We studied early adopters’ AI upskilling efforts, and ran AI upskilling
workshops for thousands of executives to glean best practices. We also interviewed dozens of
industry experts. We determined that the most effective approach incorporates five distinct actions. It
starts with assessing what’s needed and putting systems in place to measure successful outcomes. It
includes preparing people for change, building in incentives that appeal to their varied motivations to
learn, and putting the C-suite at the forefront of the initiative. Finally, it means using AI to upskill
people in AI.
Amid an AI-Inspired Sea Change, Upskilling Is
Lagging
AI is ushering in a tsunami of change. Already, 80% of organizational leaders regularly use AI tools,
according to a BCG global survey of 13,000 people.
Companies that don’t invest in AI capabilities tend to be more susceptible to disruption. BCG’s
research on future-ready companies identified AI as one of six key attributes that help them
withstand shocks and disruptions and exploit innovation for value-creating growth. These standouts
outperform their peers across multiple financial and nonfinancial metrics.
The same research found that early adopters of GenAI use it to improve efficiencies, enhance
customer experience, and boost revenue. For an organization with $20 billion in revenue, using GenAI
leads to estimated additional profits of $500 million to $1 billion, with nearly a third of those gains
coming in the first 18 months. Separate research by BCG Henderson Institute and Harvard Business
School found that adopting AI leads to 40% higher quality and 25% faster output.
AI isn’t without risks, including risks associated with fairness and equity, privacy, accuracy, and
security. To realize AI’s gains and minimize associated risks, companies must connect the technology
to organizational change. Our experience and research indicate that adopting a structured approach
to “deploy, reshape, and invent” AI-based processes and experiences throughout an organization
yields high-impact results.
That’s where upskilling comes in. To succeed, organizations must upskill their workforce to
understand and embrace AI.
But upskilling efforts are off to a slow start.
The vast majority of 1,400 C-suite executives whom BCG surveyed earlier this year ranked AI and
GenAI among their three top technology priorities for 2024, but 66% expressed ambivalence or
© 2024 Boston Consulting Group 2
dissatisfaction with their progress. Close to the same number (62%) cited a shortage of talent and
skills as their biggest challenge, ahead of unclear investment priorities (47%), and lack of a
responsible AI strategy (42%). Only 6% said that they had already begun upskilling in a meaningful
way. (See Exhibit 1.)

Lack of training extends to companies’ top echelon. Of the leaders we polled, 59% reported having
limited or no confidence in their executive team’s proficiency in GenAI.
Five AI Upskilling Success Factors
Although the need for AI upskilling is well established, the approach that yields the greatest benefits
remains unsettled.
To learn more, we performed quantitative and qualitative research, including studying best practices
of organizations that have been quick to adopt AI. We gleaned additional best practices from
collaborating with Google Cloud and ran more than 150 AI upskilling workshops for 3,000 executives
who use Google Cloud. We also interviewed more than 50 human resources and learning and
development industry leaders who are well-known talent development innovators.
Our analysis revealed five actions that organizations need to take to ensure successful AI upskilling.
1. Assess Needs and Measure Outcomes
Organizations that fall behind on skills training are apt to deploy a “watering can” approach to
improving—launching learning programs that are undifferentiated, costly, and poorly aligned with
overall strategy. A better approach is to launch an upskilling initiative predicated on an assessment
of what’s needed and then to measure the outcomes it produces.
Assessing Need. In evaluating what’s needed, companies must determine the upskilling
requirements of each specific workforce group. C-suite leaders need to define the organization’s AI
vision and strategy and lead AI upskilling initiatives. Managers need to know how to build awareness
© 2024 Boston Consulting Group 3
among direct reports. Workers need to know how to use AI tools. The entire workforce needs to
understand how the organization plans to integrate AI into operations and how work will get done.
The workshops that BCG and Google Cloud ran helped Fortune 1000 clients upskill their executive
leadership teams on GenAI.
Prior to the workshop, Google Cloud assessed where each client was in the process of deploying
GenAI in production. The company then used this information to tailor workshops to individual
needs, develop the optimal industry-specific use cases, and identify the AI applications that would
create the most value for each client’s business.
Once an organization has assessed its needs, it can evaluate and invest in the appropriate AI tools.
Measuring Outcomes. Assessing the effectiveness of AI upskilling and analyzing the return on
learning investment (ROLI) can justify a company’s spending and guide future improvements.
To measure an upskilling program’s ROLI, we recommend using the Kirkpatrick method, which
evaluates effectiveness on four levels:
• Level 1: Learning experience quality and program satisfaction, as gauged by conducting surveys
of learners
• Level 2: Competency development, as measured by assessing learners’ post-upskilling abilities
• Level 3: Individual productivity gains and behavioral changes, as appraised by observing
learners’ improved productivity—for example, in sales lead conversion or customer satisfaction
—over time
• Level 4: Business outcomes
Business outcomes are the hardest to measure because when multiple factors change at the same
time, it’s difficult to determine which caused a positive result. We recommend that organizations
measure them by conducting A/B tests or pilots with control groups.
A retailer that we worked with used A/B testing as part of an upskilling initiative to make its chain of
more than 500 stores more customer centric. The company determined key metrics by working
backward from desired outcomes, including higher sales per square foot, stronger employee
engagement, and improved feedback from customers. The company ran tests to analyze the effects
of upskilling efforts in one group of stores against a control group of stores that had not
implemented an upskilling program. By testing various upskilling methods and measuring their
results, the retailer eventually implemented training that helped the workforce increase sales by 150
basis points, double employee engagement, and receive more positive comments from customers.
© 2024 Boston Consulting Group 4
2. Prepare People for Change
Business or technology transformations such as adopting AI have three key components: people and
processes, technology and IT, and algorithms. Of the three, the changes involving people and process
are the most critical. (See Exhibit 2.)

For a transformation of any kind to succeed, people must be prepared. And a crucial part of
preparing them for an AI transformation involves raising awareness of what’s going to happen
within the three distinct groups in which people work:
• Individuals. AI represents a chance for people to work in new ways that make tasks easier,
enhance productivity, and optimize workflow. For this to happen, people need to understand
how AI tools automate routine tasks, provide data-driven insights, and support decision making.
They need to understand how their roles may evolve as the organization deploys AI and GenAI.
• Teams. People need to recognize how the organization will be integrating AI into complex
workflows and collaborations within and between teams. They need to see how teams will
benefit from AI-based project management tools and how AI-run collaborative platforms will
enhance communication and coordination, leading to greater innovation, streamlined
processes, and better outcomes.
• Across the Organization. People need to comprehend how AI fits into the company’s culture
and strategy, what effects AI may have on its operating model or business value, what potential
pitfalls may accompany the transformation, and what guardrails may protect against them. To
that end, the company must establish an organization-wide AI change management program to
provide the entire workforce with a basic understanding of AI concepts and applications. By
© 2024 Boston Consulting Group 5
embedding AI into the fabric of the organization, the company can create a culture that
supports continuous improvement, innovation, and equitable opportunities for employees to
grow in their careers.
Organizations can raise awareness in multiple ways, including by running enterprise-wide
communications campaigns and celebrating people when they successfully adopt AI to improve their
personal productivity or automate tasks. Other awareness-building tools include AI hackathons,
continuous improvement workshops, and safe spaces for experimenting with AI tools.
3. Unlock Employees’ Willingness to Learn
Part of AI upskilling consists of giving people a strong impetus to get on board with change and
offering a psychological safety net so they don’t fear what’s to come.
Employees may not be keen to adopt AI skills. Instead of viewing AI as a tool that enhances their
roles, they may see it as a threat that could displace them. They may have the impression that AI is
complex and hard to understand, and so feel intimidated by it. For people without a technical
background, the prospect of learning AI may be overwhelming, making them reluctant to take it on.
Discovering what motivates people can help a company shi their attitudes toward adopting AI
skills. Some people are motivated by intrinsic incentives, such as an internal drive to improve or
personal values. Others appreciate external incentives, such as earning a digital badge or recognition
that could help them get ahead in their job. Providing people with autonomy and responsibility for
their own upskilling journey can motivate them to engage in upskilling in a way that they enjoy. It can
also transform them into AI ambassadors, with cutting-edge skills.
Making upskilling fun by turning it into a game is another way to persuade people to try something
new, as are digital nudges, peer groups, and weaving learning into daily work. Removing barriers to
learning and letting people choose how to learn can increase their openness to change.
Top leaders can offer a psychological safety net by making it clear that the purpose of AI is to
enhance people’s roles and create new opportunities, not to replace jobs. Leaders can further
motivate people by elevating an initiative’s visibility and ensuring that the company’s investment in it
is sufficient to meet its upskilling goals. Contrarily, trying to do too much with too little can result in
program failure and cause people to lose faith in the upskilling effort.
4. Make Adopting AI a C-Suite Priority
Efforts to raise awareness and train and motivate people can fizzle out if the C-suite doesn’t make
adopting AI a top priority. Even at organizations with chief AI officers, the CEO and other C-level
executives must function as AI’s top advocates and as AI upskilling champions. Top executives must
have clear responsibilities for upskilling programs to drive adoption and realize anticipated impact
© 2024 Boston Consulting Group 6
and value. At the same time, organizations must centralize AI governance to avoid creating an every-
department-for-itself mentality with regard to adoption and upskilling requirements.
The CEO and the leadership team can embrace the AI skills transformation by modeling desired
behaviors and actively participating in AI initiatives. That level of commitment will cascade through
the organization to foster a culture of continuous learning and innovation. The CEO should oversee
the development of AI objectives and lead the effort to make them clear to the organization. The
chief operating officer should ensure that AI use cases match business objectives and that the
organization is implementing them with a minimum of obstacles. Top risk and information security
executives should maintain proper controls to minimize risks.
At CMA CGM, a leading global shipping and logistics company, leaders from the top down
participated in launching an AI skills accelerator program. CEO Rodolphe Saadé attended the
launch event and further underscored the importance of upskilling in the company’s plans by
regularly visiting training facilities to meet learners. Saadé also closely tracked the performance of
the third-party upskilling providers that the company retained. Other C-suite leaders participated in
AI upskilling sessions, answering questions and collecting possible use cases that learners created as
part of their training. Senior managers upskilled with other learners, which helped the initiative cross
business lines and functions. Getting early buy-in from key leaders helped foster a culture of
continuous AI learning and promote AI adoption for innovation and efficiency at all levels.
Given how quickly the technology is evolving, AI upskilling will be an ongoing effort. Having C-suite
support demonstrates to the rest of the organization the necessity of building upskilling muscle at
every level.
5. Use AI for AI Upskilling
Upskilling that requires people to use AI on the job is an effective way to connect the dots between
theoretical learning and practical application. We’ve found several methods that are effective for on-
the-job AI upskilling.
Using AI Tools. Using AI to upskill people on AI will be unavoidable in the future to keep costs down
and make learning impactful, scalable, customizable, and fast. New AI-based upskilling tools are
emerging almost daily: more than 100 learning tools launched in 2023 and the first half of 2024, and
the influx of new options shows no sign of stopping. Some tools have AI as their core function.
Others use AI to support the application’s main function.
© 2024 Boston Consulting Group 7

AI learning and support tools fall into four categories: skills, content, knowledge and performance
support, and personalization. (See Exhibit 3.) Understanding where specific tools exist in this
framework can help companies navigate what’s available, which in turn can help them implement
upskilling initiatives faster and more efficiently.
The Network Effect. Embedding AI in daily tasks at all levels creates a network effect: the more
people use and understand it, the more the entire organization gains in knowledge, innovation, and
efficiency.
Companies can support the network effect in several ways:
• Center AI training around real-world projects.
• Offer sessions to teach individuals the AI tools they will be using in their roles, thereby creating
ambassadors to propagate AI’s value across the organization.
• Use peer influence to amplify learning. In this regard, CMA CGM scheduled joint AI upskilling for
employees from diverse geographies, business lines, levels, functions, and backgrounds to
create synergies across company sectors.
A Tailored Approach. AI upskilling can be easier to digest when the organization customizes
learning to match tangible business objectives and high-priority use cases. Whether the company
© 2024 Boston Consulting Group 8
orchestrates upskilling programs on its own or with outside help, it should adapt the training to its
specific business context and embed hands-on learning into people’s daily tasks.
Building multiple complete customized AI upskilling training programs from scratch can be costly. A
less expensive approach involves creating learning units for in-demand topics that trainers can pair
with customized content in building-block fashion.
Putting AI Upskilling into Action
Upskilling people on AI must be a C-suite priority. To unlock the full potential of AI upskilling,
organizations should keep the following considerations in mind.
Identify gaps, and tailor programs to fill them. When assessing the workforce's current skill level,
identify specific gaps in AI competencies that customized training programs can address. Tailor
programs to the needs of individuals, teams, and the organization. Collaborate with outside AI and
upskilling experts to tap into high-quality resources and practical experiences.
Integrate upskilling into long-term strategic plans. Incorporating learning programs into
broader strategies makes them more sustainable and increases the benefits that the organization
will derive from them. Integrating training into strategy means creating well-defined career pathways
for employees who acquire new AI skills, and recognizing and rewarding people who obtain them.
Establish internal AI centers of excellence. AI learning hubs can promote knowledge sharing,
mentorships, and collaborative projects, all of which can help promulgate the application of AI
across business functions. Embedding AI upskilling into the organizational culture will foster a
resilient, future-ready workforce capable of driving innovation and maintaining a competitive edge in
the rapidly evolving market.
Treat upskilling as a marathon, not a sprint. It may take several years for the workforce to reach
AI proficiency at scale. To facilitate AI upskilling efficiently, the organization should share
accountability for initiatives. Create upskilling working groups that consist of C-level leaders, learning
and development heads, and business unit leaders. Use the network effect to sustain success.
AI upskilling is an organization-wide, large-scale movement that will transform how work is done and
how companies operate. Organizations that fail to heed that imperative or delay commencing
upskilling initiatives risk fall behind, while those that follow best practices can put themselves ahead
of the curve.
The authors would like to thank Zhdan Shakirov, Susanne Dyrchs, and Wanjun Fang for their research,
insights, and other support.
© 2024 Boston Consulting Group 9
Authors
Hean-Ho Loh
MANAGING DIRECTOR & SENIOR PARTNER
Singapore
Vinciane Beauchene
MANAGING DIRECTOR & PARTNER
Paris
Vladimir Lukic
MANAGING DIRECTOR & SENIOR PARTNER; GLOBAL LEADER,
TECH AND DIGITAL ADVANTAGE
Boston
Rajiv Shenoy
ASSOCIATE DIRECTOR
Dallas
ABOUT BOSTON CONSULTING GROUP
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© 2024 Boston Consulting Group 11"
103,bcg,gpt-was-only-the-beginning-autonomous-agents-are-coming.pdf,"GPT Was Just the Beginning. Here Come
Autonomous Agents.
NOVEMBER 28, 2023
By Mikhail Burtsev, François Candelon, Gaurav Jha, Daniel Sack, Leonid Zhukov, and David Zuluaga Martínez
READING TIME: 8 MIN
The power of generative AI took the business world by surprise. It wasn’t until the release of ChatGPT that
many executives truly appreciated the seismic impact of these large language models (LLMs), and many
companies were le scrambling to keep up. As we enter what’s likely to be a period of permanent revolution,
during which GenAI’s capabilities will progress much faster than businesses will be able to adapt, companies
simply can’t afford to sit and wait. The next leap in AI—autonomous agents—could enter the mainstream in
the next few years and promises to be even more transformative than today’s LLMs.
Although most current LLM-based applications change how information is gathered and delivered, they stop
short of operating independently. Some can automate specific tasks, but they still require a human to input a
© 2024 Boston Consulting Group 1
Although most current LLM-based applications change how information is gathered and delivered, they stop
short of operating independently. Some can automate specific tasks, but they still require a human to input a
series of prompts and monitor the output. In contrast, autonomous agents—which are in part made up of
LLMs—will be capable of redesigning and automating entire workflows. They plan how to execute tasks end
to end, iteratively querying LLMs (through application programming interface (API) calls, where one
application requests data or services from another), monitoring output, and using other digital tools to
accomplish a given goal. As we discuss in examples below, autonomous agents could be used to design,
execute, and refine entire marketing campaigns or undertake R&D testing through at-scale simulation.
Autonomous agents are, in effect, dynamic systems that can both sense and act on their environment. In other
words, with stand-alone LLMs, you have access to a powerful brain; autonomous agents add arms and legs.
With stand-alone large language models, you have access to a powerful brain;

autonomous agents add arms and legs.
The arrival of autonomous agents into the mainstream isn’t far off. Today’s agents still lack the controllability
and predictability needed for widespread use, but technology firms are making constant improvements.
OpenAI’s recently announced custom bots are a clear step in this direction; they are able to use external APIs
to find specific information or to carry out simple actions like assisting with an e-commerce purchase.
Companies should start preparing for wide-scale adoption of autonomous agents today by adjusting their
generative AI strategic planning—including their technology architecture, workforce planning, operating
model, and policies—to ensure their transformation roadmap is robust and ready.
The Explosive Potential of Autonomous Agents
Autonomous agents use the power of LLMs to sense and act on their environment by creating, executing, and
prioritizing tasks. The process starts when the agent receives an objective. The agent then breaks down the
goal into individual tasks and creates a set of bite-sized prompts to tackle each one. These prompts are fed to
an LLM iteratively and, as tasks are completed, the agent creates new, better prompts that incorporate the
results of the preceding iterations. The agent’s process of generating prompts and building on the results may
be parallel or sequential depending on the system design. The agent also actively reorders and prioritizes the
tasks according to the results. The system continues this cycle of breaking down the goal into tasks, generating
prompts, evaluating results, and prioritizing until the goal is met or deemed unattainable (in which case, the
agent shuts down the process).
In an enterprise setting, agents’ potential to automate whole sets of tasks can have multiple uses, two of which
we will explore here: their ability to reduce the need for human intervention in workflows, and their ability to
facilitate the testing of products, services, and scenarios at scale.
© 2024 Boston Consulting Group 2
Automating Entire Workflows. To fully appreciate the workflow automation potential of autonomous
agents, it is important to understand that they can actually use digital tools when they are properly integrated
with them. When configuring an agent, humans can feed the documentation for digital tools to the agent,
which will then “know” how to use them; it will then be able to send instructions to these tools and get results
back through API calls. That is, autonomous agents can directly “tell” other enterprise systems what to do.
This could fundamentally change how a company operates, enabling it to deploy automation more holistically
and significantly reduce labor costs.
Autonomous agents can directly “tell” other enterprise systems what to do. This could

fundamentally change how a company operates, enabling it to deploy automation
more holistically.
Moreover, autonomous agents have the potential to surpass traditional robotic process automation (RPA).
RPA already enables workflow automation, but it is based on “if-then,” preset rules for processes that can be
broken down into strictly defined, discrete steps. This makes it expensive to build and considerably limits its
range of applications. In contrast, agents are universal; they are not limited by hard-coded scenarios, nor do
they require explicit rules spelled out in advance. They promise to produce adaptive automation that can be
applied to a broader range of tasks.
Given these characteristics, the impact of agents will be much deeper than today’s use of LLMs as (primarily)
copilots. For instance, in the near future, an autonomous agent could allow a marketing executive to carve out
and automate whole segments of work. Based on a company’s past marketing campaigns, the agent could
determine what worked and what didn’t, making its own decisions for future email design, scheduling,
graphics, and subject lines. It could also identify the types of consumers a campaign should target and then
assess whether the results—opens, views, clicks, and responses—are worth reporting back to management. If
the results fail to meet the campaign’s objective, the agent could independently start again, creating a new,
more refined list of target customers based on responses to the previous campaign.
Simulations at Scale. Companies are already using LLMs as simulators of human behavior, particularly in
the form of AI-based focus groups of virtual personae to assess market fit for new products or services. (LLMs
are also being used in this way to model social systems for academic research, building on traditional agent-
based modeling methodologies.) However, these simulations still require humans to interact with the LLM to
gain relevant insights and, more importantly, they are prone to bias grounded in the LLM’s underlying training
data.
Autonomous agents may go a long way toward addressing these issues, making it possible to run simulations
at scale and for a wider range of applications. To start with, agents may generate more realistic virtual
personae by conducting primary research in the form of surveys and interviews, which would help anchor
simulations to the real characteristics of relevant user segments. More significantly, because agents
© 2024 Boston Consulting Group 3
circumvent the need for humans prompting an LLM to guide and extract insights from a simulation, it would
be possible to conduct multiple AI-enabled tests of greater complexity at lower cost and greater speed. In
other words, agents would give companies access to the valuable tool of automated, large-scale scenario
simulations.
Autonomous agents will not replace the depth and richness of in-person qualitative investigations that
companies oen use as strategic inputs. On the contrary: by enabling sophisticated simulations at low cost,
they will help businesses identify the questions or issues that call for those more laborious and expensive
market research methodologies.
How Companies Can Prepare
Autonomous agents still have limited applicability because of the risks and limitations associated with
reliability, potential for malicious use, and a greater fallout from cyberattacks. However, none of these
challenges appears to be a deal breaker. Technology companies are addressing them, and the experts we
interviewed estimate that autonomous agents will be ready to go mainstream within three to five years; some
believe that we may see reliable systems with limited autonomy before then.
The experts we interviewed estimate that autonomous agents will be ready to go

mainstream within three to five years; some believe that we may see reliable systems
with limited autonomy before then.
A three- to five-year time frame may seem like a long time for technologies to evolve, but from the perspective
of companies that need to plan and undertake extensive transformation programs, it might as well be
tomorrow. The message is clear: companies will struggle to absorb these technologies if they don’t start
preparing today. Leaders should focus on the following four actions:
Prepare your architecture for agents. Companies focused on deploying today’s LLMs will likely focus on
setting up one-way flows for LLMs to retrieve information from enterprise systems. However, in anticipation of
autonomous agents, they should also ensure that LLMs be able to both retrieve data and communicate
instructions to those systems through bidirectional APIs.
Scout and prepare to experiment. Companies should scout for new developments in autonomous agent
technology and select solutions that can be tested—even if they are still in early stages of development—to
create new sources of competitive advantage in terms of products, services, or operating model. Investment in
R&D currently underway for generative AI applications should be expanded to identify workflows suitable for
(future) end-to-end automation with autonomous agents as well.
© 2024 Boston Consulting Group 4
Stress-test your people strategy. GenAI today can support tasks in a copilot role, whereas agents will be
able to automate entire workflows. Companies should keep this future state in mind during their workforce
planning exercises and be prudent about prioritizing skill sets that are likely to stay relevant. For example,
professional services firms may encounter a future where autonomous agents commoditize seemingly
complex, multistep activities that have thus far proven resistant to automation. Such firms may need to take a
hard look at current hiring practices to ensure they are selecting for skills that can support the adoption and
expansion of automated substitutes of today’s labor-intensive workflows.
Anticipate the need for a social license. To support the widespread deployment of this technology,
securing a social license is crucial. Regulation may take some time to catch up to the technology; until then,
companies should enforce self-imposed guardrails to ensure the appropriate and safe use of this technology,
both within the organization and in customer-facing applications. While robust self-regulation can lay the
foundations of a social license, it is not a sustainable solution on its own. For that reason, companies should
also actively engage with regulators to help them cra the right approach for governing and monitoring the
use of autonomous agents and similar technologies in future.
For many executives, the rapid rise of generative AI has triggered months of exhilaration and trepidation;
adoption has felt like a necessity, but one that comes with serious risks and challenges. Yet even as they
grapple with the present, they have to focus on the future. The GenAI revolution has only just begun—and is
likely to continue at breakneck speed.
The BCG Henderson Institute is Boston Consulting Group’s strategy think tank, dedicated to exploring and
developing valuable new insights from business, technology, and science by embracing the powerful
technology of ideas. The Institute engages leaders in provocative discussion and experimentation to expand
the boundaries of business theory and practice and to translate innovative ideas from within and beyond
business. For more ideas and inspiration from the Institute, please visit our website and follow us on LinkedIn
and X (formerly Twitter).
© 2024 Boston Consulting Group 5
Authors
Mikhail Burtsev
OUTSIDE CONSULTANT
BCG X – Manhattan Beach
François Candelon
MANAGING DIRECTOR & SENIOR PARTNER; GLOBAL DIRECTOR, BCG
HENDERSON INSTITUTE
Paris
Gaurav Jha
CONSULTANT
Mumbai - Nariman Point
Daniel Sack
MANAGING DIRECTOR & PARTNER
Stockholm
Leonid Zhukov
VICE PRESIDENT - DATA SCIENCE
New York
David Zuluaga Martínez
PARTNER, BCG HENDERSON INSTITUTE AMBASSADOR
New York
ABOUT BOSTON CONSULTING GROUP
Boston Consulting Group partners with leaders in business and society to tackle their most important
challenges and capture their greatest opportunities. BCG was the pioneer in business strategy when it was
founded in 1963. Today, we work closely with clients to embrace a transformational approach aimed at
benefiting all stakeholders—empowering organizations to grow, build sustainable competitive advantage, and
drive positive societal impact.
Our diverse, global teams bring deep industry and functional expertise and a range of perspectives that
question the status quo and spark change. BCG delivers solutions through leading-edge management
consulting, technology and design, and corporate and digital ventures. We work in a uniquely collaborative
© 2024 Boston Consulting Group 6
model across the firm and throughout all levels of the client organization, fueled by the goal of helping our
clients thrive and enabling them to make the world a better place.
© Boston Consulting Group 2024. All rights reserved.
For information or permission to reprint, please contact BCG at permissions@bcg.com. To find the latest BCG
content and register to receive e-alerts on this topic or others, please visit bcg.com. Follow Boston Consulting
Group on Facebook and X (formerly Twitter).
© 2024 Boston Consulting Group 7"
104,bcg,how-to-attract-develop-retain-ai-talent.pdf,"How to Attract, Develop, and Retain AI
Talent
MAY 16, 2023
By Vinciane Beauchene, Julie Bedard, Julie Jefson, and Nithya Vaduganathan
READING TIME: 12 MIN
The companies that capture the most value from AI follow the 10-20-70 rule: 10% of their AI effort goes to designing
algorithms, 20% to building the underlying technologies, and 70% to supporting people and adapting business
processes. To get the most from people and processes, companies need to address the following questions: (1) How do I
attract, develop, and retain data and analytics talent to build sustainable AI capabilities? (2) How do I boost adoption
of AI solutions at speed and at scale and drive real business transformation? (3) How do I rewire my organization to
unlock the full benefits of AI at scale? This article, the start of a three-part series, focuses on the first question: how to
attract, develop, and retain AI talent.
© 2024 Boston Consulting Group 1
Artificial intelligence is having a moment. The release of ChatGPT, AI-enabled Bing, and Google Bard has
electrified public debate on the radical potential of AI. To be an industry leader in five years, companies need
a clear and compelling AI talent strategy today, but many organizations are hitting a brick wall. Although
demand for AI talent is at an all-time high, supply is extremely limited. With so few skilled professionals
available, companies must find ways to stand out from the competition.
Incumbents beware: the approach to attracting, developing, and retaining AI talent is not business as usual.
Companies must offer a unique and compelling value proposition to attract—and hold onto—these highly
prized recruits. To build a first-rate AI workforce that will stick around for the long haul, companies must
anticipate what mix of AI skills (rather than jobs) is needed, understand what AI workers want and how to
attract them, invest in reskilling and advancement opportunities, and keep AI talent fully engaged.
Common Mistakes to Avoid
AI and machine learning emerged as the most in-demand skills in 2022, and jobs for data scientists more than
tripled over the past five years. Demand continues to outpace supply. Because AI employees have different job
expectations than traditional workers do, recruiting and retention efforts need to be tailored to their unique
needs.
Because AI employees have different job expectations than traditional workers do,

recruiting and retention efforts need to be tailored to their unique needs.
Consider just a few common mistakes companies make when recruiting and hiring AI talent:
• Competing head-to-head with tech companies without highlighting non-tech-related differentiators that
will entice AI hires
• Trying to recruit AI talent with the standard slow process led by generalist recruiters
• Paying premium prices for cutting-edge data scientists without considering the broader mix of skills
needed
• Onboarding AI employees without creating a community and embedding them into the organization
under AI-trained leaders
• Fiercely recruiting AI talent without providing advancement opportunities
• Overlooking reskilling opportunities within the organization
When companies make these kinds of mistakes, they not only struggle to hire the best and brightest but also
face high levels of attrition. This is a serious problem in today’s job market where as many as 40% of
© 2024 Boston Consulting Group 2
employees working in digital fields are actively job hunting, and nearly 75% expect to leave their current role in
the near future. The good news is that hiring AI talent doesn’t have to turn into a costly bidding war.
Companies must understand what motivates these highly sought-aer employees to take a job—and what it
takes for them to stick around.
Four Strategies to Recruit and Retain AI Talent
To build an AI advantage, companies need to excel in four areas: anticipate talent needs, attract best-in-class
candidates, develop talent quickly, and engage AI talent with an unmatched value proposition. (See the
exhibit.)

Anticipate
Hiring the right talent to drive an AI transformation is not as simple as luring a strong team of data scientists
and machine-learning specialists. An AI transformation requires talent with a mix of skills, including people
who can build the data infrastructure (data architects, solution architects, data engineers, and soware
engineers), employees who manage data governance (data governance analysts and data stewards), and
those who engage with the business (product owners and domain experts).
Start by developing a taxonomy of the skills you need and then figure out how best to acquire those skills. This
approach is particularly important in a tight job market where top talent is scarce. When building an AI team,
companies tend to reflexively hire people to fill predefined jobs. But businesses oen struggle to fill those
roles because AI talent is so expensive and hard to find. By focusing on the skills they need, companies can
assemble effective AI teams more quickly.
For example, a global pharma company wanted to recruit a team of four data scientists to build up its AI
capabilities, but the competition was fierce. By thinking creatively about the skills the company needed,
executives realized they could hire just one high-caliber data scientist, supported by three data analysts, which
© 2024 Boston Consulting Group 3
are much easier to source. By finding the right mix of skills, rather than filling predefined roles, the firm
quickly assembled a strong team and hit the ground running.
Companies also need to decide how to organize for AI. Most begin their AI journey in an ad hoc fashion. IT
owns the data architecture, systems, and analytics, and data capabilities are sprinkled throughout the
business, but no standardized roles or “communities of practice” are in place for sharing AI knowledge. This
type of IT-focused organization makes it difficult to share solutions across the business and fails to give AI
workers a clear path for internal advancement. As companies mature, the majority of AI talent will begin to
work together in a centralized data and analytics hub, and roles will become standardized. Eventually, AI
capabilities will be pushed back into the business while a small, centralized AI team remains in place to
govern data management, develop capabilities, and codify best practices.
Last but not least, it is critical to anticipate the effect that incoming AI talent will have on the overall
workforce. AI experts will become embedded in business processes, which means business experts will need to
gain a working knowledge of data and analytics. As AI solutions are deployed, other employees’ roles will
evolve over time, likely requiring them to acquire new skills or augment existing ones. In addition, processes
will be redesigned and operating models will need to adjust.
It is critical to anticipate the effect that incoming AI talent will have on the overall

workforce.
Attract
In the fiercely competitive AI environment, companies must think proactively about how to attract talent. Here
are some key concepts to bear in mind.
Understand what AI workers want. Our research shows that potential AI hires have different job
expectations than traditional job seekers do, and companies should tailor their employee value proposition to
meet these expectations. Two things, in particular, matter to AI employees: (1) working on exciting products,
topics, and technologies, and (2) knowing the company has a clear strategy for advancement. People involved
in the fast-growing, constantly evolving environment of AI want to work on cutting-edge projects; 44% of AI
workers ranked this as a top need (compared with just 27% of non-AI talent). When asked about deal
breakers, AI talent ranked “interesting job content” much higher than non-AI talent did. AI job candidates also
want answers to some important questions: Does the company think about data strategically? Where will I be
at this company five years down the road? Will I have advancement opportunities? To keep AI talent engaged,
companies need to articulate a detailed data strategy that highlights clear advancement opportunities.
Seek out untapped talent. It’s tempting to go aer AI talent in the usual cities—San Francisco, Seattle, New
York City, Bangalore, and London—but that guarantees you’ll be competing with all the hottest tech
companies. This is particularly problematic for nontech incumbents striving to get an AI transformation off
© 2024 Boston Consulting Group 4
the ground. By looking at secondary talent pools in other cities and countries, organizations can gain access to
extraordinary talent at more affordable rates.
Many of these hidden markets offer companies an edge because job candidates may be seeking something
unique, such as a position located in their home state or more flexible work. In addition, our survey found that
68% of digital employees are willing to work remotely for a foreign employer. This opens up new options for
hiring hard-to-find AI talent. Because visa limitations don’t apply to remote work, businesses can explore
sources of talent that previously were not attractive or feasible. Start by evaluating your competitive
advantage in various regions in terms of compensation packages, attrition rates, size, and unique value
proposition. BCG analysis shows that companies using a targeted location strategy save 7% to 10% in labor
costs.
Create the right AI talent-sourcing strategy for your maturity. Customize your talent sourcing by making
a clear-eyed assessment of your maturity. If you are new to AI, focus on recruiting an “anchor” hire; that is, a
high-caliber AI expert who acts as a magnet to attract a broader network of AI specialists and establishes a
strong starting strategy. As you scale up, elevate your approach to recruiting AI talent: leverage specialized AI
recruiters, tailor hiring processes and compensation packages to meet AI talent needs, and ensure that
leaders have complete transparency into recruiting outcomes. If you are fully mature and need to supplement
existing data and analytics teams, build talent-sensing capabilities to understand where to find specialized AI
talent and develop relationships with that talent in advance of your needs.
Tailor the recruiting process. The standard recruiting process does not work well when it comes to
attracting AI talent. The process is too slow. By the time a hiring decision is made, the AI candidate may have
taken another offer. In our survey, 66% of respondents said the number one way for an employer to stand out
when recruiting AI talent is with a “smooth, timely recruitment process.”
Digital and data experts also want to be interviewed by people who understand their value. Managers
involved in hiring should be staff who are integral to the AI mission, not only because they’re well equipped to
assess candidates’ AI skills and judge their talent, but because it sends a signal that the company is serious
about data and analytics. When managers who have little digital knowledge or expertise interview AI
candidates, it dampens the potential hires’ enthusiasm for taking the job.
We recommend reviewing each recruiting step to expedite the process, particularly minimizing the time from
final interview to offer, with the goal of following up in hours or days, not weeks. A global industrial company
began daily standups with specialty recruiters and hiring managers to coordinate quickly on the hiring
process. They prepared offer letters in parallel with final interviews, and a small, dedicated group of digital
and data experts interviewed AI talent to improve decision making and calibration across candidates.
Develop
Approximately 80% of AI talent leave companies because they either want a more interesting position or don’t
see opportunities for career advancement; however, BCG research shows that only 10% of new roles are filled
by existing staff. A clear opportunity is at hand to support more internal mobility of talent. Reskilling internal
employees offers a wealth of baked-in benefits. These workers are already committed to the organization,
grounded in the business, and embedded in the company’s ways of working—all characteristics that take a
great deal of time and effort to cultivate in new hires.
© 2024 Boston Consulting Group 5
A clear opportunity is at hand to support more internal mobility of talent. Reskilling

internal employees offers a wealth of baked-in benefits.
There’s another advantage to internal reskilling. The existing workforce can, in many cases, feel threatened by
new AI hires, particularly if they are positioned as a separate group of young, highly compensated individuals
poised to transform the organization. By offering reskilling opportunities, the business sends the message that
anyone with the right skill sets, and a desire to learn, can play an integral role in the AI transformation. Not all
jobs will lend themselves to reskilling; it’s unlikely that companies will train from within for the position of
data scientist, for example. But many less specialized positions, such as product owners, data stewards, and
domain experts, can be recruited internally. These opportunities for upward mobility, in turn, strengthen job
satisfaction and loyalty to the organization. According to research from LinkedIn, employees stay 41% longer
at companies that regularly hire from within.
For AI talent recruited externally, companies need to articulate a clear career path. When organizations try to
quickly spin up an AI team, they don’t always take the time to develop a taxonomy that allows candidates to
envision their progression within the organization. If young, ambitious AI analysts can be promoted to become
senior AI analysts but are offered no other advancement opportunities, they will begin to look elsewhere just
as they’re achieving peak productivity. In-demand recruits, such as data scientists and data analysts, also
expect to be promoted more frequently. The tech culture has instilled the expectation that digital talent will be
promoted every 12 to 18 months (as opposed to every two to three years).
Engage
For organizations that are not viewed first and foremost as tech companies or for those with a long product
development cycle (such as aerospace and defense, where security constraints exist), it can be difficult to keep
AI talent engaged. These organizations may have no trouble attracting AI talent, but employees’ enthusiasm
can wane when they realize the development of next-generation hardware or products will take five or more
years and they may not get to work in cutting-edge programming languages or platforms. In this case, it’s
important to sharpen your storytelling so that you aren’t trying to compete with the best tech companies but
instead can focus on your unique purpose and mission.
To become a purpose-driven organization, companies must answer two fundamental questions: What are our
authentic and distinctive strengths? Why do we exist beyond what we make, do, or sell? Whether you’re an up-
and-coming renewable-energy company, a 100-year-old cosmetics business, or a global tech giant, it’s essential
to convey a strong narrative about what sets your company apart. Purpose-driven organizations energize
people and keep them engaged.
The other key to keeping AI talent engaged is ensuring that they are seamlessly embedded within the
organization as a whole. Onboarding should involve much more than a swi orientation. It should be a 6- to
12-month process that provides ample opportunities for new AI hires to work on meaningful, “quick hit”
projects that have an immediate impact on the business. To make sure that top talent isn’t bogged down with
data management challenges (building the data platform, accessing data, cleaning data, and so forth),
© 2024 Boston Consulting Group 6
companies should adopt a two-speed approach. Allow a team of engineers to build digital capabilities while AI
specialists deliver high-impact AI initiatives that offer clear, compelling results for the business. The executive
team should also include data and analytics initiatives in their annual goals so that AI teams aren’t fighting
for mindshare, business sponsorship, and budget allocations.
Case Study
By implementing many of these techniques simultaneously, a leading biopharma firm quickly ramped up its
AI team. Here’s what the company did:
• Reshaped job architecture and skills taxonomy to focus on hiring for skills that were most relevant in the
market, defining roles for machine-learning engineers for the first time
• Redefined its employee value proposition and created stronger communities of practice for AI talent,
connecting AI practitioners across the R&D, commercial, and IT organizations
• Reframed the company’s talent acquisition strategy to better communicate its value proposition, created
a dedicated team of specialized AI recruiters, and changed hiring processes
In just six months, the company boosted the size of its AI drug discovery team by approximately 10%,
increased its commercial analytics organization by about 25%, and dramatically reduced its attrition rate for
data and analytics talent.
Talent scarcity is one of the main concerns for executives worldwide. To date, few companies have successfully
scaled AI, but this will change as organizations are ramping up very quickly. By embracing a smart approach
to recruiting, retaining, and engaging AI talent, companies will gain a long-term competitive advantage in a
field that is fundamentally redefining the future of business.
The authors thank the following experts for their contributions to this article: Romain Gailhac, Orsolya Kovács-
Ondrejkovic, and Anne-Françoise Ruaud.
© 2024 Boston Consulting Group 7
Authors
Vinciane Beauchene
MANAGING DIRECTOR & PARTNER
Paris
Julie Bedard
MANAGING DIRECTOR & PARTNER
Boston
Julie Jefson
MANAGING DIRECTOR & PARTNER
Chicago
Nithya Vaduganathan
MANAGING DIRECTOR & PARTNER
Boston
ABOUT BOSTON CONSULTING GROUP
Boston Consulting Group partners with leaders in business and society to tackle their most important
challenges and capture their greatest opportunities. BCG was the pioneer in business strategy when it was
founded in 1963. Today, we work closely with clients to embrace a transformational approach aimed at
benefiting all stakeholders—empowering organizations to grow, build sustainable competitive advantage, and
drive positive societal impact.
Our diverse, global teams bring deep industry and functional expertise and a range of perspectives that
question the status quo and spark change. BCG delivers solutions through leading-edge management
consulting, technology and design, and corporate and digital ventures. We work in a uniquely collaborative
model across the firm and throughout all levels of the client organization, fueled by the goal of helping our
clients thrive and enabling them to make the world a better place.
© Boston Consulting Group 2024. All rights reserved.
For information or permission to reprint, please contact BCG at permissions@bcg.com. To find the latest BCG
content and register to receive e-alerts on this topic or others, please visit bcg.com. Follow Boston Consulting
Group on Facebook and X (formerly Twitter).
© 2024 Boston Consulting Group 8"
105,bcg,Companies-Need-to-Leverage-Ecosystems-to-Deploy-Generative-AI.pdf,"WHITE PAPER
Companies Need to
Leverage Ecosystems to
Deploy Generative AI
May 2023
By Alex Koster, Harsha Chandra Shekar and Richard Maué
Boston Consulting Group partners with leaders
in business and society to tackle their most
important challenges and capture their greatest
opportunities. BCG was the pioneer in business
strategy when it was founded in 1963. Today,
we work closely with clients to embrace a
transformational approach aimed at benefiting all
stakeholders—empowering organizations to grow,
build sustainable competitive advantage, and
drive positive societal impact.
Our diverse, global teams bring deep industry and
functional expertise and a range of perspectives
that question the status quo and spark change.
BCG delivers solutions through leading-edge
management consulting, technology and design,
and corporate and digital ventures. We work in a
uniquely collaborative model across the firm and
throughout all levels of the client organization,
fueled by the goal of helping our clients thrive and
enabling them to make the world a better place.
“
The most focused of business models…can be blown to bits by new information
technology.”1 This prediction is more relevant than ever, as many clear use cases of
Generative AI have revolutionized functions and industries. Companies are deploying
Generative AI to address specific market needs across a wide range of industries, from
Airbus using Generative AI to design aircraft parts with an estimated 40% reduction in
weight and drag, to chatbot assistants that cut customer service costs by an estimated 30%,
to stress testing proposed trading strategies.2 The latest research shows the generative AI
market is expected to hit $60 billion of the total addressable market by 20253, accounting for
10% of all data generated.4
F
ew companies, however, have yet embedded Generative AI across their full matrix
through a strategic bottom-up approach. This would require thinking exhaustively
through the outcomes and use cases, the technology and data to enable the use cases,
and the human resources required for implementation and execution. It would also require
companies that seek to leverage Generative AI to lean on an ecosystem of partners to deploy
Generative AI cohesively.
Exhibit 1 - Generative AI ecosystem view
Vertical Providing solutions that are vertically integrated
ecosystem along multiple layers of the Gen AI stack, typically
partners serving specific business/workflow functions
1. Evans, P. and Wurster, T.S. (2000). Blown to bits: how the new economics of information transforms strategy.
Boston, Mass.: Harvard Business School Press, C.
2. www.autodesk.de. (n.d.). Generatives Design bei Airbus | Kundenprojekte | Autodesk. [online] Available at:
https://www.autodesk.de/customer-stories/airbus [Accessed 2 Mar. 2023]. www.ibm.com. (n.d.). IBM Watson
Assistant - Customer Service Chatbot. [online] Available at: https://www.ibm.com/products/watson-assistant/
customer-service [Accessed 2 Mar. 2023].
3. Generative AI (2023) BCG Global. Available at: https://www.bcg.com/x/artificial-intelligence/generative-ai
(Accessed: March 17, 2023).
4. Nguyen, T., Casey, D., Goodness, E. and Woodward, A. (n.d.). Emerging Technologies and Trends Impact Radar:
2022 Research Excerpt. [online] Available at: https://artillry.co/wp-content/uploads/2022/02/Emerging-Tech-
and-Trends-2022.pdf [Accessed 2 Mar. 2023]. www.deere.com. (n.d.). Deere adds seven companies to its 2022
BOSTON CONSULTING GROUP 1
Co-innovation
partners
Regulators Industry incumbents
(Finance, Procurement, HR, …)
stripe Zurich SAP ADP
JP Morgan Chase & Co.
New GenAI Players
AI/ML Providing large foundation Open source models Eleuther Stability.ai (Text, Image, Code, Video, 3D)
model models, pre-trained with Closed source models GPT-4 Lambda KEELVAR GitHub Copilot
partners vast amounts of data
Model hubs Hugging Face Meta AI co:here HubSpot Jasper
Providing raw or labeled data suitable aws marketplace IBM Watson MidJourney
Data
partners to train and fine-tune AI models - either Quandl kaggle Metabob tabnine
in bulk or continuously via APIs
Google Data Search Socrata runway synthesia
Providing high-performance computing Nvidia CoreWeave
Technology
partners resources and storage at scale, exposed to OpenAI aws …
developers in cloud deployment models
Microsoft Azure Google Cloud
Deploying Gen AI requires partners in six major areas:
• Vertical Ecosystem Partners: Multiple layers must be put together for successful ap-
plication of Generative AI: underlying computing/hosting technologies, available training
data, well-constructed foundational models that are trained and fine-tuned for the specific
use case, and accessibility of these through either APIs or front-ends. Organizations de-
ploying AI for mature, strategic use cases should consider building up internal capabilities
to orchestrate technology, data and models, but orchestrating these layers may not always
be possible within an organization, depending on the organization’s maturity and the
specific use case. Collaboration with vertical ecosystem partners that provide end-to-end
packaged Generative AI solutions may be necessary. Standardized use cases (fields such
as workplace/collaboration, procurement, payment, customer service) are typical candi-
dates for the use of vertical ecosystem partners. This means either partnering with incum-
bents on Generative AI-powered services in finance, procurement, HR, etc., or leveraging
emerging Generative AI players with vertically integrated solutions (e.g., Jasper, GitHub
Copilot, Synthesia). Additionally, like industry-specific cloud solutions that have grown in
popularity, industry-specific generative AI solutions are likely to gain traction going forward.
• AI/ML Model Partners: For clients looking for a more out-of-the-box solution, model
partners can play a major role in reducing the heavy lifting and time-to-results. In these
cases, the heavy ML development and data collection and training are already done, and
the client needs to focus only on fine-tuning the model with enterprise domain data. Ex-
amples include the massive Foundation models of Open AI, Google’s LaMDA, or Meta AI;
open-source models like Stability.AI and GPT-neoX; accessible via model hubs like Hug-
ging Face. The choice of and collaboration with model partners must weigh the benefits
and risks of a vendor/model-specific vs. agnostic approach.
• Data Partners: A critical challenge in training Generative AI models is obtaining large
amounts of high-quality data that includes training data – depending on the use case, also
including auxiliary data such as labels, metadata, and conditioning variables. While there
are significant opportunities to train models based on available internal data, obtaining
external data can unlock specific use cases. Organizations can acquire this external data
from data marketplaces such as Kaggle, AWS Data Marketplace, or Google Dataset Search
for more broadly available data, or by building strategic partnerships with organizations
that have access to the specific data the organization needs.
• Technology Partners: Companies must make significant investments in technology
to deploy Generative AI successfully. Training Generative AI models requires superior
processing power, where cloud providers have significant scale/cost advantages. Cloud
partners also have either their own mature Generative AI offerings (Google, AWS) or close
ties to Generative AI providers (e.g., Microsoft’s share in OpenAI), allowing them to offer
added services around the bare offerings (e.g., Azure providing a private GPT environment
via APIs). Technology service partners such as managed service providers and/or software
integrators are needed to build and maintain the various data pipelines required to ingest/
extract the vast amounts of data to ensure appropriate deployment of the hardware and
software within the cloud infrastructure.
COMPANIES NEED TO LEVERAGE ECOSYSTEMS TO DEPLOY GENERATIVE AI
• Co-Innovation Partners: Working closely with co-innovation partners with shared IP
can often accelerate value creation. A diversified think tank can help unlock a plethora
of creative applications for new technologies. Through its Startup Collaborator program,
John Deere leveraged an ecosystem of several companies to co-innovate new technologies
around smart farming, including robotic harvesting and analytics for high-value crops.5
• Regulators: Participants at the World Economic Forum in Davos this year underscored
the need to regulate AI, especially in the light of the upcoming EU AI Act,6 which is ex-
pected to set the global standard for AI technologies. The European Commission’s impact
assessment on the EU AI proposal forecast a 17% overhead increase for businesses on
AI investment, while the act itself proposes significant fines for noncompliance.7 Besides
responsible AI, issues around data IP and “data-laundering” need to be managed. Compa-
nies must proactively build the right mechanisms to comply with upcoming regulations,
through a network of partners that can deliver cross-industry insight and precedent while
maintaining impartial governance. SAP, for example, has commissioned an internal AI
ethics steering committee composed of senior leaders, as well as an external AI ethics
advisory panel with “experts from academia, politics, and business whose specialisms are
at the interface between ethics and technology—AI in particular.”8
The barriers to building a digital ecosystem to effectively address
Generative AI
Companies typically face several obstacles in pulling together the ecosystem needed to truly
embed Generative AI into their business.
• Early stages
Generative AI ecosystems are still at an early stage of development, and the landscape of
players is evolving rapidly. While large players such as Google and Microsoft have made
early moves, there needs to be a lot more investment in the ecosystem before Generative
AI can be widely adopted.
Businesses want to make sure that they select the right ecosystem partners before they
commit to significant investments in adopting Generative AI. At the same time, ecosystem
participants often want to see demand before they invest in product development. This
“chicken or egg” situation can slow down value creation through ecosystem development.
• Modularity
Businesses want to build modularity into their Generative AI architecture to avoid over-
reliance on critical partners or build redundancy through a multi-partner setup. This may
not always be possible, however, due to critical partners offering unique solutions, binding
5. www.deere.com. (n.d.). Deere adds seven companies to its 2022 Startup Collaborator program. [online]
Available at: https://www.deere.com/en/news/all-news/2022-startup-collaborator-program/.
6. World Economic Forum. (n.d.). These were the biggest AI developments in 2022. Now we must decide how to
use them. [online] Available at: https://www.weforum.org/agenda/2023/01/davos23-biggest-ai-developments-
how-to-use-them/ [Accessed 2 Mar. 2023].
7. Mueller, B. (2021). How Much Will The Artificial Intelligence Act Cost Europe? [online] datainnovation.org.
Center for Data Innovation. Available at: https://www2.datainnovation.org/2021-aia-costs.pdf.
8. SAP News Center. (2018). SAP’s Guiding Principles for Artificial Intelligence. [online] Available at: https://
news.sap.com/2018/09/sap-guiding-principles-for-artificial-intelligence/.
BOSTON CONSULTING GROUP 3
contractual commitments around term and exclusivity, and sometimes even the high
costs of integration. This can be compared to the selection of cloud partners several years
ago, when specific functionalities required vendor-specific approaches, while today the
commonalities and integrability across hyperscalers shift toward agnostic approaches.
• Value creation vs. value sharing
While ecosystem functions such as data-sharing and co-innovation can create outsized
value, how the value is distributed amongst partners remains the proverbial million-dollar
question. Ecosystem orchestrators that bring the participants together often define the val-
ue sharing terms. However, uniquely valuable participants and enablers can independently
seek value-based pricing for their contributions. For instance, while Microsoft is an estab-
lished ecosystem orchestrator that is deploying Open AI’s models across its consumer and
enterprise products, Open AI is bringing the core Generative AI capability and is therefore
able to command sizable investments from Microsoft.
• Data sharing, protection, and sourcing
Data is a critical ingredient for training Generative AI tools, but companies that sit on
valuable piles of data are often either unsure of how a partner would use shared data or
concerned about giving away too much value in the absence of a clear valuation method-
ology. Value-based pricing for data is inherently hard, as companies often lack visibility into
how partners are using their data to generate value.
Serious concerns also persist about the protection of the proprietary enterprise data that
are fed into the models when used for training or prompting. Owners of the data would
need assurances that the data will stay in a “controlled” environment and will not be used
for purposes other than training or prompting the models.
On the other hand, Generative AI can present a solution to the data sharing barrier. Com-
panies that are hesitant to share proprietary data with partners can use their data to train
or fine-tune a foundation model and share only this trained model, commercializing their
proprietary data without actually sharing it.
• Accuracy and risk sharing
While Generative AI is a powerful technology that can drive significant efficiencies, in
some instances Generative AI not only fails to return results, but delivers incorrect or
biased ones. This can prove very damaging to the company or the end user. A recruitment
tool developed by Amazon, for example, posed challenges because the underlying model
was trained to vet applicants through pattern detection in hiring over the last 10 years—
which was historically skewed toward men.9 Concerns can also arise around how training
data is obtained, and whether it can be used legally for commercial purposes.10
In these cases, the question of ultimate responsibility arises: would it lie with the company
that leveraged Generative AI for a critical function, the organization that built the founda-
tional model (typically non-profits that collect training data under the “fair use policy”), or
the tech company that commercializes the model (e.g., Open AI or Google)?
9. Dastin, J. (2018). Insight - Amazon scraps secret AI recruiting tool that showed bias against women. Reuters.
[online] 11 Oct. Available at: https://www.reuters.com/article/amazon-com-jobs-automation/insight-amazon-
scraps-secret-ai-recruiting-tool-that-showed-bias-against-women-idINKCN1MK0AH?edition-redirect=in.
10. Brittain, B. (2023), OpenAI, Microsoft want court to toss lawsuit accusing them of abusing open-source code.
[online] Available at: https://www.reuters.com/legal/litigation/openai-microsoft-want-court-toss-lawsuit-
accusing-them-abusing-open-source-code-2023-01-27/
COMPANIES NEED TO LEVERAGE ECOSYSTEMS TO DEPLOY GENERATIVE AI
It can be expected that reputational risks will typically be borne by the companies that
make the ultimate use of Generative AI, while tech/model companies will likely need to
shield IP/copyright risks.
Recommendations for successfully leveraging an ecosystem of partners for
Generative AI
• Start with mapping out the entire range of use cases for Generative AI, quantify impact of
each use case, identify key dependencies and resourcing needs, and prioritize use cases
based on impact and return on investment. Identify any gaps in technology, resources, and
regulation before embarking on the Generative AI journey. Seek to “fund the journey” in
phases by reinvesting the value captured from Generative AI adoption.
• Select partners that are willing to share the upside and risks from the partnership over the
long term, as the Generative AI landscape evolves with new opportunities and challenges.
Collaborate with technology partners to make strategic choices about whether to fine-tune
existing LLMs or to train a custom model. Choose co-innovation partners whose interests
are largely aligned, and can therefore accelerate learning through transparent creation and
sharing of IP.
• Focus on value creation in initial partnerships, especially with co-innovation and data
partners where value measurement is complicated. Start with pilots/proofs of concepts to
prove and quantify value creation, align on value measurement methodology, and agree
on principles of value sharing before making extensive efforts towards negotiating value
sharing.
• Preserve agility in a quickly evolving Generative AI landscape by building modularity and
vendor independence into the partner ecosystem and reduce overreliance on specific part-
ners. This can also mitigate value gouging in future rounds of negotiations with partners.
Both technology architecture and commercial agreements should be structured to support
this modularity.
• Where privacy and data-protection issues arise with sharing data between partners, Gen-
erative AI models can be trained on partner data before being deployed in a business.
This overcomes some of the complications around actual data sharing while still capturing
many of its benefits.
• Even as the regulatory landscape on generative AI evolves, self-regulate and stay ahead
of ethical issues around use of Generative AI, as well as issues around data sharing and
“data laundering.” For businesses to mitigate risk and take greater accountability around
AI, CEOs must champion the responsible AI agenda, aligning it to the mission statement
and core values of their business. BCG’s Responsible AI (RAI) approach is designed to help
business leaders take the right measures on the governance, strategy, and culture of AI
use across their organizations.11
11. Mills, S. et al. (n.d) Deliver Powerful Business Results with Responsible AI, BCG. Available at: https://www.bcg.
com/beyond-consulting/bcg-gamma/responsible-ai (Accessed: March 17, 2023).
BOSTON CONSULTING GROUP 5
About the Authors
Alex Koster is Managing Director and Senior Partner in the Zurich office of Boston
Consulting Group. You may contact him by email at koster.alex@bcg.com.
Harsha Chandra Shekar is a Partner and Associate Director in the Seattle office of Boston
Consulting Group. You may contact him by email at chandrashekar.harsha@bcg.com.
Richard Maué is Associate Director in the Hamburg office of BCG Platinion. You may
contact him by email at maue.richard@bcgplatinion.com.
For Further Contact
If you would like to discuss this report, please contact the authors.
COMPANIES NEED TO LEVERAGE ECOSYSTEMS TO DEPLOY GENERATIVE AI
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05/23
bcg.com"
106,bcg,BCG-Executive-Perspectives-Risk-and-Compliance-in-AI-EP7-25Nov2024.pdf,"Executive
Perspectives
Managing Risks and
Accelerating the AI Transformation
Risk & Compliance
November 2024
In this BCG
Introduction
Executive Perspective,
We meet often with CEOs to discuss AI---a captivating and rapidly changing topic.
we articulate the vision
After working with over 1,000 clients in the past year, we are sharing our most
recent learning in a new series designed to help CEOs and the C-suite and value of risk and
navigate AI. With AI at an inflection point, the focus is on turning AI’s potential
compliance in the
into real profit.
context of the AI
In this edition, we discuss how risk and compliance can accelerate the AI revolution
transformation of any business. We take an end-to-end risk management view of
the problem, defining implications for R&C functions. Key questions to guide a
company towards success in an uncertain future business environment:
• Which risks are generated by AI and how can they be managed effectively?
• How can I leverage the power of AI to better manage the risks of my business?
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• How do my R&C capabilities need to evolve?
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Executive summary | Managing risks and accelerating the AI
transformation
In an uncertain world, managing business requires proactively managing related risks
Risk management
AI and GenAI bring vast benefits to risk management as well as unprecedented new risks that need to be
at the core of AI managed – but with proper guardrails, the AI transformation can be accelerated
revolution In support functions, R&C has the highest adoption rate of AI/GenAI, with health care and financial
institutions the most advanced sectors
Managing risks of these technologies while adopting and embedding AI and GenAI within the organization
can unlock the potential for AI transformation:
Managing risks in
• Manage AI risks related to proficiency, reputation, security, and regulatory compliance to ensure ethical
the organization to and accountable use, increasing the AI benefits up to 3x
unlock AI value • Improving efficiency and effectiveness of risk processes, reducing human errors, synthesizing
along 3 critical unstructured information, and enabling advanced analysis – up to 40-50% gains
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R&C functions must prepare to support the transformation: o
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• Elevating R&C positioning within the AI program itlu
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• Upskilling resources on data, technology, and associated risks through training with AI and GenAI C
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ready for a radical ts
• Moving to scalable risk technology platforms that facilitate seamless integration of AI and third- o B
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• Promoting an organizational mindset shift from low- to high-value-added activities, learning ""with the
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Risk and compliance emerges as the top support function
in AI/GenAI adoption, driving innovation across the organization
Risk and compliance is expectedtogenerate6th most …yetranksasthetopsupportfunction
valuefrom AI/GenAIamongsupportfunctions… intermsofAI/GenAI opportunity adoption
ShareofGenAIvaluecaptureacrossallfunctions1 Adoption rateofGenAIacrosssupportfunctions2
Supportfunctions
Operations 25%
Sales and marketing 19%
R&D 13%
1 Risk and compliance 13%
Customer service 9%
1 Procurement 7% 2 IT/Tech 9%
2
Finance 6%
3
3
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1. On left-hand side to estimate value, survey questions cover both cost and revenue growth dimensions. Questions are (A): What % cost reduction do you expect to achieve through 2
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AI/GenAI efficiency gains (in % of total operational expenses) in your company? (B): How much revenue growth do you expect from AI/GenAI (in % of annual revenues) in your company? th
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(C): Thinking of AI/GenAI's contribution to revenues and costs, please specify the contribution of each of the domains below so that they sum to 100% iry
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2. On right-hand side to calculate adoption, the number of respondents who reported adopting the opportunity is divided by the total number of respondents within each support function 33 o
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Health care and financial institutions are the leading
sectors in adoption of R&C AI/GenAI
Healthcare/ Banking Fintech Tech components Public sector Software
insurance
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Source: BCG Build for the Future 2024 Global Study (merged with DAI), n=1,000. th
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the total number of respondents within each sector 4 o
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AI can help manage uncertainty, yet exposes organizations to new risks
• In times of uncertainty,
Uncertain outlook and Inflation or negative economic reports 70% managing risks has become a
key differentiating factor to
regulatory pressure
Diminishing consumer demand 48%
win in the market
are at the top of CEO
challenges1… Rising cost of capital / interest rates 41% • AI can be a key success
enabler in managing risks…
The presidential election in the US 41%
• …however, it also introduces
New regulations 38%
new risks, requiring proactive
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R&C can unlock AI's full potential across 3 critical dimensions
Enable risk-based
decision making
Transform
Application to strategic decisions,
risk processes
dynamically adapting to evolving
Manage risks, ensuring maximized
Enhancing efficiency and
AI risks economic value
effectiveness of risk processes
through AI and GenAI applications • Design of operations based on
Strong focus on mitigating AI-
""resilience"" logic
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related risks by establishing a • 40-60% cost efficiency from a re
responsible AI (RAI) framework faster know your customer (KYC)
• Risk-based scenario planning
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identification
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Manage AI risks
Adopting AI brings new risks, but R&C ensures
effective management for successful implementation
Proficiency Reputational shield Security
Systems consistently generate Systems promote fairness and Systems and sensitive data are
the intended value for users do not produce harm or offense safeguarded against bad actors
Inaccurate information and predictions Unintended bias Private data leakage
Key
System produces incorrect, irrelevant, Systems may inadvertently cater to the needs of some Systems may leak sensitive private or proprietary data
risks or incomplete responses, framed as comprehensive groups more than others
Disclosing system information
Poorcontent quality Harmful stereotypes Systems may share sensitive system details, which
System produces responses that are incoherent or Outputs reinforce historical or current harmful enable cyberattacks
lacking diversity, creativity, or rationale stereotypes
System manipulation
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Poor interactivity Malicious use Interactions or code injections alter the behavior of the
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Rigid constraints lead to unclear, emotionally System enables bad actors, such as trolling, criminal system or increase its vulnerability
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System disruption
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Compliance th
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Manage AI risks
Best-in-class GenAI test and evaluation (T&E)
solutions can ensure quality and risk resilience of AI systems
Systematic risk Expertise in Scale through Building T&E
landscaping test design automation capability
Identify relevant risks and Craft representative test Scale up by using automatic Make informed decisions on
define KPIs to track progress data to challenge the system tests to enhance and validate acceptable risks and quality
e A holistic overview is GenAI testing is much more Manual testing doesn't scale. Embed manual and automated
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a required to ensure all aspects complex than that of classic AI, Using GenAI to automatically T&E and unify insights
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Manage AI risks
Global authorities increase regulatory pressure on AI, introducing
comprehensive guidelines and AI-specific laws
Timeline of key regulator activity
UK Dept of
Science, Cyberspace
Innovation Admin. of
EU FTC EU and Tech EU, US EU China US EU EU EU
2018 April 2021 Nov 2022 March 2023 Feb/Apr 2023 June 2023 July 2023 Oct 2023 Mar 2024 May 2024 Aug 2024
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Enforcement of Pro-innovation Data Act—the right US executive order for Final version of the
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Manage AI risks
Deep dive: EU AI Act sets new global standard for RAI
Definition of three-tiered risk classification (unacceptable Fines for noncompliance
risk, high-risk, minimal-risk) for AI systems
4-7
%
Comprehensive set of requirements for high-risk AI
of annual
systems, including third-party audit and registration global turnover
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systems interacting with natural persons
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Note: BCG does not provide legal advice | Source: BCG, EU AI Act 1111 o
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Manage AI risks
Deep dive: EU AI Act addresses both traditional and generative AI across
the entire value chain, with special requirements for systemic risk models
Preliminary
Scope Regulatory regime
""AI System""
Unacceptable
Prohibited in the EU
(EU AI Act Art. 3.1)
risk
Fixed-purpose AI system
(Traditional AI)
• Machine-based system
Mitigation measures,
High
• Varying levels of autonomy including conformity
risk
assessment, registration
• Maybe: adaptiveness after Transparency
deployment General- Conventional obligations
purpose AI GPAI1 Minimal Voluntary codes
• Infers, from the input it
risk of conduct
system
receives, how to generate
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(Generative • Notification of the EU
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Manage AI risks
Deep dive: Compliance with EU AI Act requires development
of an AI inventory and (differentiated) risk mitigation measures
Risk mitigation measures, differentiated
AI inventory by AI system risk level where applicable
Registration and auditor-ready documentation Identification, mitigation, and management of AI system risks in accordance with regulatory
of all AI systems in an organization provisions and industry standards (as far as developed)
Comprehensive list of AI systems, 2a Governance
1a
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Manage AI risks
Managing these risks requires a holistic RAI
program that is implemented across the enterprise
Comprehensive RAI strategy that
connects risk approach, AI Real-life application example in next slide
strategy, and purpose and values
Rigorous processes to monitor and
review products to ensure that RAI
e K criteria are met
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Defined RAI leadership, v c
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Data and tech infrastructure, Strong understanding and n
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Manage AI risks
Implement a dedicated KRI framework to evaluate compliance of
AI and GenAI use cases with internal RAI and assess associated risk levels
Real client applications
Design of an AI-specific inherent risk matrix based on Processes defined for selected KRIs to trigger a heightened
operational Key Risk Indicators (KRIs) to determine the level of scrutiny and control proactively on AI systems:
adherence of various systems to RAI principles
• Approval by committee or board of directors
• Mandatory training
RAI implementation policy • Weekly monitoring
High risk
Measurement of key metrics to evaluate use case • Remediation plan/legal opinion
risk levels against implementation guidelines
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Manage AI risks
RAI is a value Organizations that integrate RAI practices into
enhancer and the AI product life cycle realize meaningful
market differentiator benefits
in addition to
mitigating risks % of respondents reporting business benefit
50%
48%
50
42%
40%
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31%
30
19% more likely to realize
20 17%
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Transform risk processes
How to get it right | Our perspective on winning with AI in R&C processes
• Implementing AI-driven automation to streamline R&C processes,
Using AI to reshape R&C
Reshaping to reducing manual tasks and increasing operational efficiency
processes, enhancing
drive new value • Utilizing advanced analytics to enable faster and more accurate
efficiency and effectiveness
risk assessments
• Identifying and prioritizing the best AI use cases to align with the
Transforming the risk
Choosing high- company’s strategic goals and specific risk needs
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value chain to gain e
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impact use cases
competitive advantage
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Transform risk processes
Wide range of opportunities to adopt AI/GenAI across
the whole value chain, with untapped potential in risk processes
Ease of implementation
High
(<6months)
Creditprocessoptimization,e.g. Commswriting /summarization
creditmemowriting
Logic bug/errordetection
Automated claim solution
Suspicious Activity Report filings
Unittestbuildingandautomatedtesting
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C shy ib ee ldr risk credit collection1 ( wjn ac rnlu indi gn )gearly
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sizes are relative to each other but overall productivity gains range between 10%-38% g iry
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Transform risk processes
Focus on boosting efficiency of risk processes: driving cost
savings, with focus on automation of labor-intensive and repetitive tasks
Real client applications
A B C D
Policy KYC Credit risk monitoring Cyber
Dedicated Dedicated
bot deep dive assistant deep dive and reporting risk shield
• Extract new regulatory • Automate data collection, • Use natural language for • Automate code for
obligations streamlining the process data query vulnerability detection
• Evaluate impact on • Automatically produce KYC • Instantly generate data • Speed risk identification with
.d
internal policies risk assessment file visualizations and organize a virtual expert e v re
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1. Independently simulating cyber attacks to test and improve an organization’s security defenses; 2. Up to 60-70% for most advanced use cases implemented 20 o
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Transform risk processes
Focus on boosting effectiveness of risk processes: enabling
better decisions and quality of output to pave the way for business growth
Real client applications
E F G H
Risk Sentiment-guided Climate risk Automated claim
Dedicated
digital twin deep dive credit collection assessment solution
• Create a digital replica of • Conduct real-time analysis • Analyze exposure to • Automate claim verification
controls, processes, and IT of sentiment behind the physical and transition risks and clustering
systems tone and words chosen in • Identify options to mitigate • Detect inconsistencies to
• Run ""what-if"" scenarios on human interactions climate threats flag potential fraud or .d
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the digital system • Provide suggestions on next- • Detect greenwashing risks errors re se
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Transform risk processes
Policy bot | GenAI simplifies compliance by ensuring automated update
of internal policies and enabling employees to become compliance-savvy
Efficiency: A Policy bot
Regulatory Compliance
Ever-changing
landscape update empowerment
regulatory landscape
automatically
New regulations automatically Employees and clients can search
embedded in the .d
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scanned to identify potential impacts for policies and procedures using v re
on internal policies and procedures
organization
natural language on an AI-powered
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Transform risk processes
KYC assistant | Enhance the onboarding process for new clients or suppliers by
managing 5 end-to-end KYC activities with AI and GenAI
Efficiency: B KYC assistant
Real-time dashboard (portfolio view) and dispatching
KYC assistant
CRR entry Low
Previous KYC info
Tiering med-high
Automated data collection (internal and external sources)
Related Fin.
Personal data Neg. news Customer ID
subjects Statements
KYC files tiering and anomaly detection
Other info Lists Transactions SARs KYC summary
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Interaction with customer/Web (chatbot) report PDF re se
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Transform risk processes
Risk digital twin | Digital twin is a cutting-edge technology to
dramatically improve nonfinancial risk management (banking example)
Efficiency: E Risk digital twin
Complexity puzzle Digital twin simplifies and augments Impact
Large international banks face Digital twin supports operational resilience Digital twin delivers substantial value
high operational complexity and industrializing OpRisk management while its build is lean, low-risk, and low-cost
Typical LEs Improve risk effectiveness and resilience
~3.5k+ ~5k+ Real world Disparate, Digital twin ~2x More efficient risk identi-
fication and remediation1
business IT systems big data
processes • Complex • Virtual replication of
banking operations
• Nontransparent, siloed,
• Integrated 360° view
often manual Insights Get ahead of regulator expectations
~5k+ ~20k+
• Single source of truth
Third parties controls
• Rapidly changing and actions
~10x Faster time-to-response to
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1. Drivers: Faster and less resource-intense risk detection, problem discovery and remediation 2. Examples: Transparency on third-party risks or foreign data flows 3. Assuming ~5% of iry
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noninterest expense related to controls, compliance, and related tech spend; this approach can have ~10-15% reduction on that cost base; Source: BCG analysis and case experience 2244 o
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Enable risk-based decision
making
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Risk-based decision making
The next frontier: enable truly risk-based strategic decisions
with future AI applications
Risk-based operational resilience Risk-based scenario planning
1 Simulate and assess multiple AI-based disruption scenarios to 1• Current scenario of uncertainty highlighted the
guide resilient decision making according to the limitations of medium- to long-term planning (example
company’s risk tolerance for geopolitical risk management)
• E.g., What is the cost of maintaining client service
2• Organizations can develop through AI multiple scenarios
continuity during business disruptions?
and calibrate dynamically each scenario’s risks/
opportunities vs the plan ambition
2 Monitor each scenario evolution to highlight
potential deviation from company risk tolerance and
3• Additionally, leading risk indicators can be defined to
identify mitigation actions
simulate impact on business portfolio in different scenarios
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Timeline of the plan
Risk-based decision making
Example for geopolitical risk| Quantify risk in portfolios with a data-driven
approach to inform strategic decisions for financial institutions
Risk-based scenario planning
Geopolitical Scenario impact Transmission to Impact quantification
scenario selection on key indicators macro/sector drivers on key risks and actions
1 2 3 4
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4 steps for R&C to
Establish strong AI governance (e.g., appoint a head of AI ethics,
begin the AI journey create a dedicated committee) that actively partners with business
units to strategically prevent risks and fully unlock AI and GenAI
potential
Enhance the capabilities of the R&C team through training and
upskilling, in sync with tech platform advancements, to promote
innovation
Adapt scalable risk technology platforms for seamless integration
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Risk management shifts to focus on high-value-added activities
and redefine the approach to risk management
3 key impacts from AI and GenAI … requiring relevant shift to fully benefit
integration in the organization… across the R&C function
Adoption of new ways of working
Automated operational tasks
Transition from operational to high-value-added tasks,
Automation of repetitive/operational tasks, allowing
progressively evolving the risk management approach in
employees to dedicate more time to strategic activities
alignment with AI developments (i.e., learning with the machine)
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trends and regulator priorities
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EMESA
BCG experts |
Key contacts
Matteo Marianna Stiene Stefan
Coppola Leoni Riemer Bochtler
for AI transformation
Anne Giovanni Kirsten Jakob
Kleppe Lucini Rulf Liss
NAMR APAC
Steve Jeanne Abhinav Nisha
Mills Bickford Bansal Bachani
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Bernhard Paras Eric
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Note: EMESA: Europe, Middle East, and South America; NAMR: North America"
107,bcg,BCG-Executive-Perspectives-Value-Creation-with-AI-EP10-17Dec2024.pdf,"Executive
Perspectives
AI Unlocked: Value Creation with AI
(Including Generative AI)
Real-Life Examples of Value and Impact
December 2024
Introduction In this BCG
Executive Perspective,
We meet often with CEOs to discuss AI—a topic that is both captivating and rapidly
we showcase the immense
changing. After working with over 1,000 clients in the past year, we are sharing our most
value from AI across a variety
recent learnings in a new series designed to help CEOs navigate AI. With AI at an
of topics, with
inflection point, the focus in 2025 continues to be turning AI’s potential into real profit.
real-life case examples
This document provides a set of examples for CEOs to cut through the hype around AI
and witness the immense potential of AI to deliver real value, and the key success
factors to executing a successful AI transformation
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Three strategic plays to maximize value from AI
DEPLOY RESHAPE INVENT
Enhance efficiency with GenAI tools Elevate business impact by Develop AI-native offerings that
that streamline everyday business transforming workflows with elevate customer value proposition
processes, reducing the need for AI, enabling multi-functional and unlock new business models and
additional hires & daily operating reshaping and end-to-end revenue opportunities
friction organizational transformation
Examples Examples Examples
• Meeting summary • Design and Engineering • Hyper-personalized customer .d
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experience s e
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• Calendar management • Customer Service
• AI-powered services/products
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What leaders do differently | Key success factors
Bold ambitions: 80% focus on reshape/invent plays, enabled by 2x investment in
AI capabilities, and 2x people in AI
Focus on both core and support: 62% of AI value potential comes from core
business functions
Investment in few high-priority opportunities: Few high-value opportunities with
2x ROI impact instead hundreds of use cases
Read our latest
.d
Faster adoption of GenAI: AI leaders leverage both predictive AI and GenAI, and e v
publication:
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rapidly adopt the latter
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Integration of AI in transformation: 45% of leaders embed AI in cost rG
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Many functions have been reshaped end-to-end with AI
Sales Customer Pricing and Revenue Marketing Manufacturing and
Service Management Supply Chain
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Many leading organizations are driving transformative impact across multiple functions th
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Sales Cust Service Pricing & RM Marketing Manufacturing Field Forces R&D Technology Business Operations
Sales
Technology solutions
Logistics company CPG company provider Universal bank Telecoms provider
Enhanced RFP process Reshaped customer Augmented call center Transformed bank's Uplifted customer
with “RFP responder”, engagement and sales sales with AI, 7%+ wealth advisory and engagement and sales
AI-supported generation with AI virtual assistant, revenue growth with sales with co-pilot and with AI-enabled chatbot,
of proposals with 30% - 2x higher ROI vs. +60% productivity in pitch builder, leading to leading to
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Customer service
Telecoms and media Business process
company Telecoms provider outsourcing provider Asset manager Universal bank
Augmented customer Enhanced call center Transformed call center Reshaped customer Uplifted remote
servicing with AI operations with AI, with customer support with support with a ~35% customer service with AI
solutions for call agents, 25% reduction in After AI-powered solutions for reduction in call volume for chat support,
resulting in 20% Call Work time, 15%–18% reduction in and ~20% reduction in reduced AHT by 18%
reduction in Avg. impacting 20%–25% of AHT driving up to AHT, leading to OPEX ..dd
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Sales Cust Service Pricing & RM Marketing Manufacturing Field Forces R&D Technology Business Operations
Pricing and revenue management
Heavy machinery Spirits and wine
Convenience chain manufacturer Fashion retailer Pharmacy chain manufacturer
Enhanced fuel pricing Deployed AI-powered AI solution for pricing Leveraged AI solutions to Augmented advertising
optimization basis pricing engine for and promotion strategy transform pricing and and promotion
unique local conditions aftermarket parts with leading to multi - promotions, resulting in effectiveness with +2%–
to see estimated uplift of +2%–3% sales and +3%– hundred million $ over $1B+ in revenue 3% FY EBIT and +20–30
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Sales Cust Service Pricing & RM Marketing Manufacturing Field Forces R&D Technology Business Operations
Marketing
Financial services Gas and renewable Alcoholic beverage Consumer health Pharma and healthcare
company energy producer company company company
Reshaping search, AI-supported SEO AI-assisted E2E campaign AI solution for insight AI-augmented reshaping
creative development content generation creation, leading to and concept of content generation &
and email marketing leading to 95% cost ~80% external agency development; localization resulting in
campaigns for 30 - 40% reduction with 50x cost saving along with Productivity uplift on 50% time saved and
productivity increase faster content creation ~40% - 60%uplift in resulting in 20% savings €70M–150M reduction ..dd
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Manufacturing and supply chain
Electronics
manufacturer Biotech company Chemicals producer MedTech company BioPharma company
Successful AI pilot across 4 AI-embedded digital Cost transformation AI-assisted inventory AI-enabled digitized
use cases in transferring supply chain which program across entire reduction by $100M and supply chain to deliver
skills and experience in reshaped end-to-end value chain with AI: 8%– improved forecasting £400M value delivered
highly automated factories, operations, leading to 12% reduction in cost accuracy by 15 p.p.; with operational
reducing human ~$750M inventory with €420M value backorders reduced by excellence ..dd
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intervention by 70-80%; reduction, ~10% savings confirmed 60% rree
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estimated $1Bn in value at in transportation
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9 pp oo
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Sales Cust Service Pricing & RM Marketing Manufacturing Field Forces R&D Technology Business Operations
Field Forces
Integrated energy Infrastructure developer
Oil and gas company company HVAC solutions provider and investor
Reshaped maintenance strategy Developed AI/data-enabled Deployed AI powered product AI assisted asset maintenance
and planning optimization with inspection workflow for reshaping Maintenance, processes for front line workers;
AI, achieving 40% reduction in optimization to achieve Repair and Overhaul (MRO) productivity uplifted by 5%–
preventative maintenance, 10% 62% efficiency gain programs; witnessed 5%–10% 10%, 15%–20% decrease in job
reduction in maintenance OPEX (50% faster on-ground increase in revenue via first durations; 10%–20% ..dd
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and 10% reliability response and 67% faster year sales improvement in rework rate rree
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10 pp oo
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Sales Cust Service Pricing & RM Marketing Manufacturing Field Forces R&D Technology Business Operations
R&D
Pharmaceutical Biopharma and Pharma and healthcare Biopharma and
Eye care company company medtech company company healthcare company
4 AI use cases prioritized AI-powered solutions 10%–15% faster clinical AI solutions to quicken Acceleration of drug
to reshape marketing, enabled acceleration of trials with AI-based medical documents discovery through AI;
software and knowledge clinical trials by ~25% identification of high- generation, leading to 25% cycle time
search; Over $50M FTE and 5%–10% faster recruitment sites and cycle-time reduction, reduction and $25M cost
productivity gain over 5 completion of site optimization of patient 3+months drug reduction, $50M–150M ..dd
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years activation recruitment advancement, €40M+ rev. uplift rree
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impact
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11 pp oo
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Sales Cust Service Pricing & RM Marketing Manufacturing Field Forces R&D Technology Business Operations
Technology functions
Global insurer Oil and gas retailer Payments provider Universal bank
Software development IT operations Data management Software development
25% productivity gain across 30%–40% productivity ~40-70% productivity gain 70% uplift in quality software
software development life improvement for IT in processes like metadata and testing, 60% reduction in bugs
cycle; 20%–30% improvement department through AI-based lineage management for roles with AI-enabled testing co-pilot ..dd
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in software quality legacy tech modernization in data management and ss ee
governance
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12 pp oo
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Sales Cust Service Pricing & RM Marketing Manufacturing Field Forces R&D Technology Business Operations
Business operations
Integrated energy Global automobile
company Global insurer Universal bank manufacturer
Procurement Underwriting Loan processing Procurement
RFP assistant to save 5 hrs per Transforming underwriting with >50% FTE productivity gains with 50% faster tender drafting and
draft; reduction of 1% of AI for 2x productivity AI transformation for mortgages offer comparison; €5M–10M FTE
external spend with $80M– improvement and 10x reduction and consumer loans processing savings and €40M–80M savings
..dd
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Leaders with large-scale end-to-end AI transformations
Pharma and healthcare Technology and consulting
Investment bank Consumer health company company firm
of cost base
>$1B 20%–30% ~2% >$2B
program reduction EBIT gain program
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With AI-enabled With company-wide With E2E AI transformative To improve productivity in
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automation to achieve Enterprise AI program use cases across 3 pillars customer support, IT
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14 pp oo
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Sales Cust Service Pricing & RM Marketing Manufacturing Field Forces R&D Technology Business Operations
GenAI Co-pilot for Relationship Managers in a universal bank
Content summarization to retrieve quick
& accurate customer, product, and market insights
5%-10%
Increase in AUM
Personalized product recommendations with AI-
driven conversational assistant
Wealth
Agent orchestrator calling upon relevant ""virtual
Relationship Reduction in
assistants"" to perform autonomous tasks (e.g, Planner 50%
pitchbook & proposal
manager
""agent"" to schedule meetings & events)
generation time
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Key unlocks to scale r sth
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• Op model Transformation: Shifting RMs from product pushers to bionic advisors with
2-3x
More portfolio
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incentives realigned with GenAI products & workflows
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indicators, and linking inputs to outcomes effectively th
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15 p o
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Sales Cust Service Pricing & RM Marketing Manufacturing Field Forces R&D Technology Business Operations
AI solution for call agents at a business process outsourcing provider
AI solution features
A Request status H Call metadata
A B G M ~15% - Reduction in avg.
H I B Call intent I Customer data
C N 18% handling time
Supporting Pre-call
D C J
documents summary
J
QA & intent Last bills
D K
Agents reported
K L checklists information
90% improved
AI-generated Previous calls
E F E L efficiency
notes and issues
Root cause
F Manual notes M
analysis code
.d
225K+ Calls supported e v re
s
G Account lookup N Post-call summary e r
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Key unlocks to scale $200M In cost savings
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• Building, scaling industry-grade GenAI solutions with clear proposition for end-clients tso
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• Link to broader transformation effort: radical triple-digit million cost out and fundamental re- 4 20
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design of op model, ways of working and culture
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• Strong working relationship and buy-in with ExCo and broader leadership team 16 p o
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Sales Cust Service Pricing & RM Marketing Manufacturing Field Forces R&D Technology Business Operations
AI-based pricing optimization for a convenience chain
Pricing process and E2E pricing Enhanced
operating model optimization tools capabilities
• Develop best-practice • Improve transparency of • Enable client to have full
fuel pricing process with recommendations and ownership of pricing
capability to tailor reporting optimization algorithm
20%–30%
strategies
• Evolve price optimization
• Re-define operating engine and elasticity model,
model, embedding core factoring in competitor
Estimated uplift in
capabilities response
Annual Gross Profit at
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full scale (fuel v
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contributes >50% of
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• Establish in-house AI/data science capability to own and evolve system over time
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17 p o
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Sales Cust Service Pricing & RM Marketing Manufacturing Field Forces R&D Technology Business Operations
AI for insights & concept development in marketing at consumer health co.
• Connect automatically between diverse, previously disparate data
Data retrieval
Faster insights to
& synthesis sources to develop real-time, actionable insights 3–5x
innovation
Concept
generation & • Generate 10+ product & comms concepts at the touch of a button
refinement
15%
FTE savings
Synthetic • Provide qualitative feedback from focus groups in <1 min
testing &
• Narrow down list of 20+ ideas to winning idea in <1 day
final validation
• Deliver quantitative panel results in mins instead of weeks
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Cost savings on re
40% s e
qualitative research
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• Build of a Marketing-specific AI app factory, leveraging a low-code app-building
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platform 18
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Sales Cust Service Pricing & RM Marketing Manufacturing Field Forces R&D Technology Business Operations
Chemical producer leveraging AI across manufacturing and supply chain
AI operator Negotiation AI Automated order Engineering AI Intelligence Supply chain
Operations & Procurement entry Engineering & platform planner
Site Service Maintenance
Customer Service Supply Chain Supply Chain
Chatbot for plant
Automate Insource and Cost-effective GenAI assistant for MS co-pilot-
maintenance &
negotiation of reduce external modeling and enhanced enabled efficiency
process control
C-level supplier spend through plant digitalization efficiency— increase through
order automation inventory & safety information
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stock reduction transparency vv rree
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• Substantiate the value ambitions in a short period with expert support ©©
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19 pp oo
CC
Sales Cust Service Pricing & RM Marketing Manufacturing Field Forces R&D Technology Business Operations
Energy company leveraging AI for end-to-end asset integrity management
Review and
Planning and prioritization Execute inspections approve reports to prioritize
repairs
Historical report Automatic defect detection Semi-automated report review
digitization and and digitization of reporting and approval
AI/GenAI
recommendation action with enhanced UI/UX
features
optimization
Clerks are 160x more Enabled inspectors to be 62% efficiency gain to overall
Impact
efficient in large-scale 10x faster documenting inspection workflow; (50%
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data ingestion to plan and annotating defects faster on-ground, 67% faster s
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20 p o
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Sales Cust Service Pricing & RM Marketing Manufacturing Field Forces R&D Technology Business Operations
Global pharmaceutical leveraging AI for R&D cycle-time reduction
Hit ID Lead Optimization PCC
Current Target ID and Docking and
Analog search, order, and testing Lead
workflow validation selecting
• Screens ultra-large libraries up to • Automated lead optimization allows chemists to review 10x
100x faster more transformations in less time
• Expands amount of chemical • Possible reduction in number of molecules ordered due to
matter available to chemists broader initial coverage
• Increases Hit PoS by 5% • Decreases cycle time by ~25%, increases Hit PoS by 5%
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Target ID and Generative AI Algorithmic multi-property
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Payments provider using GenAI to strengthen data governance
Foundation build Context enrichment Insights generation
• Consistent cataloguing of • Improved enterprise and • Data discovery and
data data risk management utilization (insights for Impact
Productivity gain
business)
• Detection and mitigation • Accelerated data quality
in processes like
40-70%
of issues at root cause management
metadata and lineage
management
Initial focus Launched
Two use cases completed and deployed in production
% lead time reduction
for critical data
~50%
1 Lineage annotation 2 Metadata labeling
management
• Accelerated data lineage capture by generating initial • AI generated ""first draft"" of activities ..dd
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suspected lineage and expanding code parsing metadata…
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Sales Cust Service Pricing & RM Marketing Manufacturing Field Forces R&D Technology Business Operations
European bank leveraging AI to transform credit processing
Context Transformation initiatives
• Unstructured information on GenAI automation: Development of DokAI to
Impact
credit worthiness support the credit process
Faster time-to-yes,
10x
• Collateral checked and • Documents automatically categorized, up to 90 % straight-
processed manually structured, and scanned through processing
• Complex processes leading to • Real-time fraud checks and
long handling times, errors, communication with customers for
costs, and low client missing documents
>50%
FTE productivity gains
satisfaction
• Escalation of tasks for manual review
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Client-focus: Aim for superior service efficiency and reduced errors 44 2200
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Only 26% of companies have managed to scale value from AI
Designing &
Activating piloting Scaling More revenue growth
Revenue 1.5x
(3 yr avg)
25% 49% 26%
Total shareholder return
1.6x Higher 3 yr TSR
(TSR)
AI leaders (26%)
Global average Higher ROIC
Returns 1.4x
(3 yr avg)
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Innovation 1.9x More patents g ir
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0 25 50 75 100 rG
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Developing Developing Rolling out across org & driving
sn
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strategy, defining strategy, defining 1.4x n o
satisfaction glassdoor indicator tso
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Source: BCG Build for the Future 2024 Global Study (merged with DAI), n=1000 24 p o
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NAMR
BCG Experts |
Dylan Vladimir David Amanda Matthew
Bolden Lukic Martin Luther Kropp
Key contacts
Sesh Julie Beth Djon Steve
for AI
Iyer Bedard Viner Kleine Mills
transformation
Dan Japjit Justin Michael Silvio
Martines Ghai McBride Demyttenaere Palumbo
Dustin Andy Tauseef
Burke Lin Charanya
EMESA APAC
Nicolas
Jessica Apotheker
Marc Jeff Romain de .d
e
De Bellefonds Schuuring Walters Laubier v
re
s
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r
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Dan Andrej Marcus Julian Aparna
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Sack Levin Wittig King Kapoor u o
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itlu
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Robert Tristan Mikaël Le Akira Nipun C
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Xu Mallet Mouëllic Abe Kalra o tso
B
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4
20
2
Chris ©
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25 p o
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108,bcg,BCG-Executive-Perspectives-Unlocking-Impact-from-AI-Supply-Chains-EP4-7Oct2024.pdf,"Executive
Perspectives
Unlocking the Value Potential
of AI and GenAI Transformation
Supply Chains
October 2024
In this BCG
Introduction
Executive Perspective,
we articulate the vision
AI holds massive value creation potential for supply chain management (SCM). But
many companies still struggle to draw value from it. In this Executive Perspective, we discuss
and value of the future
how GenAI can turbocharge the value generated by AI and can by itself bring significant new
of supply chains with
opportunities for SCM, making supply chains future-proof.
AI and GenAI
We address key GenAI-related questions for supply chains, including:
• What tangible value can GenAI deliver in SCM?
• Where and how can GenAI be applied in SCM today and in the mid term? Where to start?
• What is the longer-term, full-potential vision of GenAI in SCM?
• How can organizations align people, processes, tech, & data to effectively enable GenAI?
• What are the limitations and risks of using GenAI in supply chains?
.d
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v
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This document guides CEOs and supply chain leaders in navigating through GenAI's se
role, identifying real value opportunities, and preparing for the future. It outlines a
r
sth
g
holistic approach to transform your supply chain with GenAI.
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Key takeaways: itlu
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• GenAI unlocks value in supply chains by simplifying access to & step-changing adoption of AI C
n
o
ts
• It bolsters SCM digital transformations by enhancing data backbone & augmenting analytics o B
y
b
4
• It unlocks new grounds for intelligent process automation through advanced GenAI agents 2
0
2
©
• Successful GenAI implementations in SCM come with specific requirements for the people, th
g
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tech, and data enablers 1 p o
C
Executive summary | Supercharge your supply chain
and your competitive edge with AI
AI has significant • Predictive and prescriptive AI such as forecasting, optimization, disruption detection, and scenario simulation can generate major impacts
potential for SC, on revenue (+2-5pp), ops cost (-10-20pp), as well as cash (-15-30% inventory), and can enhance the agility and resilienceof supply chains
but execution often • But companies across sectors often fail to deliver on their goals and business cases despite large amounts invested, mainly because of poor
falls short adoption rates, misaligned processes, lack of trusted data and connectivity between SC systems
• GenAI will enhance organizations' data backbones (e.g., improving master data, capturing market data, detecting early warning signals,
GenAI can turbocharge augmenting supplier knowledge base) and bolster AI analytics (e.g., leveraging unstructured data in forecasts, embedding human knowledge
digital & AI-driven SC in algorithms)
transformation • GenAI will also ease and accelerate adoption of advanced supply chain solutions by enabling users to interface with them in natural language
(e.g., explain outputs of AI models, simply define and automatically run simulations, improve user experience)
• GenAI has the potential to profoundly automate end-to-end processes with large language model-based agents (think: smart bots)
autonomously engaging within an ecosystem of tools and stakeholders to achieve activities in a given field (e.g., sourcing planning)
GenAI itself will also
• Outsized impact will come from advanced implementation where a coalition of such autonomous agents will collaborate across processes,
bring new
taking care of several steps in the value chain, increasing efficiency and speed of execution .d
opportunities through e v
intelligent process
• T floh wis oim
f
ip nl fe om rmen at ta iot nio
.
n
G
w enill
A
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I
n ea nb al be
l
es se a am hl ue bs s
m
G oe dn eA
l,
I
a
i cn tit ne gg r aa
s
t aio wn
i
nw di ot wh e thx ais
t
t ri en dg
i
rs ey cs tt se m wos rt ko ti om op tr ho ev re
A
u Is mer
o
e dx ep lserience by managing the internal
re
se
r
sth
automation g
• GenAI-driven automation will bolster cross-functional collaboration of the supply chain with other teams (e.g., sales, customer care,
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innovation) as well as external partners (e.g., joint business planning/automatic synchronization with suppliers and customers) .p
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• The operating model and ways of working must be profoundly reimagined (e.g., implementing digital-native, automated processes; enabling n o
C
more centralized SC oversight and decision making; organization reskilling to foster adoption; establishing the right controls with human oversight n
o
GenAI SC ts
on the most important decisions) o
B
implementation y
b
• The data fabric must be strengthened to handle bigger volumes of higher-frequency signals, including external inputs from the market and 4
comes with specific 2 0
ecosystem partners, and curated content for consumption by agent-based systems 2
requirements
• Organizations' tech platforms must include a robust GenAI tech stack (e.g., orchestration, evaluation, guardrails, scalability), well integrated
©
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with/sitting on top of core systems, with appropriate controls in place p
2 o
C
AI/GenAI will help unlock massive value creation
potential for supply chains
… to drive tangible business outcomes that
Set of critical benefits… are achievable in the short- to mid-term
Revenue EBITDA Working
Less manual • Less data hunting • Dashboards enhanced
upside capital
work • Less Excel with prompts
• Less application switching • No data reconciliation
+2-5% +2-4pp 15-30%
Revenue uplift Profitability Inventory reduction
Full automation Everything that can be automated is fully automated
of simple • E.g., forecasting for >80% of SKUs
decisions • E.g., inventory target settings Service and Throughput Costs
• Admin tasks, e.g., reporting, recurring analyses satisfaction
.d
e
+5-15pp +5-10pp 10-20% v re
Augmented
Faster decision making across silos, bringing resilience
Service rate OEE uplift Reduction in manufacturing,
se
r
sth
and flexibility g
h fou rm coa mn pa lb ei xli ties
• Enhanced integration of data from suppliers to customers
w dia sr te rih bo uu tis oin ng c, oa sn tsd ir
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decisions • Easy what-if scenarios, e.g., capacity setting, commercial levers o rG
• Prediction/optimization recommendations CO emissions Resilience Flexibility g n
2 itlu
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GenAI + AI capabilities leading to best-of-both-worlds C
Reaping applications 20-50% Divide by 10 Divide by 5 n o ts
o
benefits of B
GenAI + AI • GenAI enables insight extraction from unstructured data Average near-term CO 2 Time to understand Time needed to make plan y b 4 2
combination • GenAI enables intuitive/easy interfacing with AI applications reduction upstream scenarios and and execute 0 2 ©
• Increasing AI adoption--broader user base for greater impact actions needed vs. suppliers th
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p
3 o
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Source: BCG supply chain case experience
But many companies have yet to realize
the full benefits of supply chain technologies
Despite the range of advanced data-driven … companies have struggled to
supply chain capabilities… unlock value for common reasons
Demand Scenario Control Insufficient redesign of processes and ways of working
forecast simulation tools tower
Processes
Packaged solutions that don't fit with desired processes
Digital SC
twin
Multiple disconnected data foundations and systems of
record1
Tech and
.d
data e v
Lack of comprehensive data availability and usability re
se
RFID
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Non-intuitive tech with high skill requirements
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Advanced
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Change Insufficient change management and lack of n o
planning systems C
Joint Transportation Blockchain and skills understanding and trust driving adoption gap n o ts
o
B
business management y
b
4
planning systems Ambiguity in roles, skills, and KPIs of doers and 2 0
2
change agents ©
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1. Such as ERP, WMS, CRM, SRM, MES, etc.
GenAI directly accelerates supply chain transformations by making them
more agile, increasing adoption rates, and improving value capture
GenAI impacts supply chain Key outcomes in successful
in four main ways supply chain transformations
Enhances data backbone Agility:
E.g., cleans and augments master data such as GenAI accelerates the development of complex applications,
>30%
BOM, searches supplier knowledge base interfaces, and SC solutions by
Augments supply chain analytics User adoption:
E.g., creates features from unstructured data to GenAI-enabled supply chain increases overall user
enhance new product demand forecasting satisfaction and use of the system by >60%
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Overhauls user experience Value focus: ir
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.p
E.g., steers advanced planning system with natural GenAI-aided processes reduce administrative and data u
o
rG
language, explains outputs of AI models reconciliation tasks by >50% g n
itlu
s
n
o
C
n
Deeply automates processes Speed: o
ts
o
B
E.g., drives workflow, navigating toward outcomes, GenAI-driven advanced analytics improve decision-making y b
4
coordinates multiple capabilities/tools speed by >30% 2 0 2
©
th
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Disruptive potential of process automation
is unlocked by GenAI in four levels
Deploy Reshape
Level 1 Level 2 Level 3 Level 4
Task-specific Process step Deep process Cross-functional
point solutions enhancements transformation process automation
Continuously verify and
Think … Deploy chatbot Scan web and report alerts Automate IBP process
update master data
Daily operational tasks Improved performance Profound transformation of Orchestrated supply chain
Description supported by vanilla and usability of processes processes powered by through consortia of increasingly
GenAI capabilities and existing tools constrained AI agents self-organizing agents
Core GenAI Core GenAI Core GenAI Core GenAI
.d
e
v
GenAI
re
se
modality
GenAI plus AI and tools GenAI plus AI and tools GenAI plus AI and tools GenAI plus AI and tools r
sth
g
employed AI agents AI agents AI agents AI agents
ir
llA
.p
u
Consortia of agents Consortia of agents Consortia of agents Consortia of agents o rG
g
n
Out-of-the-box Proven Emerging Visionary
itlu
s
n
o
Maturity
Mature plug-and-play First products released, First announcements of Precursors in research, C
n
o
solutions entering market solutions in active dev solutions, few releases R&D stage for industry ts
o
2024 B
y
b
4
2
0
2
©
Primary source of value in the next 3-5 years th
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iry
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6 o
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Current solutions are not yet addressing the
Select, representative
disruptive potential within supply chains examples. Not exhaustive
Example market offerings on Example market offerings on Market outlook:
GenAI point-solutions GenAI process enhancement Trends for GenAI in SC in 2024
and beyond
Level 1 Level 2
Supports risk management
Allows usersto interact with
and optimization in Offerings emerging: Market evolving fast
Specialized SC visibility platform
combination with Dynamics 360
in natural language SC platform with increasing number of SC-specific
copilot
offerings emerging
Ensures supplier coordination
s Enables users to dig into
nr and mitigates shortages via
i ae y supplier knowledge base thanks real-time insights, risk analysis,
h ca l
p
to natural-language requests and action recommendations Bundled capabilities: SC players
y c integrating GenAI capabilities – native
l pi
f Explains demand forecast
Utilizes GenAI to automate
pi c routine decisions including bundling reduces integration overhead for
u Se
p
a Gn end
A
p
I
l ca hn an ting decisions using documentation and risk factor
organizations
.d
e
s analysis v re
se
Conducts commercial
Analyzes unstructured data
Increased sophistication: Rising level of
r
sth
g
n use ig no
g
t Aia I-t pio owns
e
ra eu
d
t so yn sto em mo susly
u qus ein stg
i
oA ng se an bt
o
t uo
t
a on rdsw ere sr
, sophistication vs 2023, but adaption or in-
ir
llA
.p
generate shipping quotes, etc. u
house development key to reaching o rG
g
Provides a platform unifying
Increases developer productivity
potential, such as Level 3 automation n itlu
industrial data and giving s n
using a GenAI-powered code o
s
r e
a nc ac te us rs a/ lin -lasi ng gh uts
a
gth
e
r ro eu qg uh
e sts assistant
C
n o ts
l Enabler dualism: SC-specific capabilities o
b B
a y b
n Enables problem solving Enables users to deploy LLMs continuing to be flanked by powerful 4 2
E 0
through oral exchanges with in other AI applications to enablers such as coding co-pilots 2 ©
conversational assistants create AI agents th
g
iry
p
7 o
C
Example | Level 3: Recent BCG project reshaped supply chain processes
with GenAI tools running complex scenarios/root-cause analysis
Context Execution Business value capture
Impact from underlying AI capability
Leading Europe-based industrial goods We interconnected two powerful BCG
company making thousands of X assets with a seamless natural >2pp
EBITDA increase year 2+
supply chain decisions daily language interface:
Impact from GenAI agent
• AgentKit: GenAI agent toolkit
Aim to supercharge existing supply
(open-sourced) 25+
chain simulation capability Planning professionals trained
• End-to-end Plan by BCG X: E2E
3x
Overarching goal to optimize supply planning suite Process cycle time reduction
.d
e
chain operations and make informed v re
se
decisions by: Live solution streamlines S&OP
r
sth
g
processes and enables supply planners
ir
llA
• Identifying bottlenecks .p u
to independently: o rG
• Testing different strategies g
n
• Running complex scenarios • Create simulation scenarios
itlu
s n
o
C
• Analyze root causes n
o
ts
o
• Summarize KPIs B
y
b
4
• Run sensitivity analyses 2 0
2
©
• Share key simulations outputs th
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p
88 o
C
Source: BCG experience
What the future
Human
IBP PREPARATION
may hold | Deep
SC actors
Procurement Supply Demand Manufacturing Logistics
Level 4 process … Specialist Planner Planner Expert Coordinator …
automation
via team of
Agent
Demand
collaborative
Supply Control tower
ecosystem agent
agent agent
agents Logistics Financial
agent review agent
Example
Advanced E2E planning
workflow vision
Meta-agent acts as
orchestration layer planning Orchestration .d e
v
Meta agent re
sub-tasks and coordinating layer se
r
sth
access to data sources g ir
llA
and tools .p
u
Data and tool o rG
g
n
landscape itlu
s
n
o
C
… ....
n
o
ts
o
B
Central y
b
Legacy database 4 2
0
ERP 2
APS Intranet ERP © th
g
iry
p
99 o
C
Whether to make or buy AI/GenAI in supply chains:
follow the adopt-adapt-assemble framework
Choose the right approach Key questions to ask
y ADOPT off-the-shelf AI/GenAI products How strategically important is this transformation?
o
Consider: Supply chain differentiation, market dynamics,
l Plug-and-play established offerings for general-
p
competitor abilities
e purpose assistance and daily business tasks
D
(e.g., ChatGPT for email drafts)
What outcomes and ROI are we seeking?
Consider: Service differentiation/customer satisfaction,
e cost sensitivity, potential for automation
ADAPT
p existing AI/GenAI tools
a
h
Tailor existing offerings to integrate organizational .d
e
s data and enhance existing workflows (e.g., custom How capable are our teams and our partners v re
e
R
GPT instances for new-hire onboarding) of undertaking this?
se
r
sth
g
Consider: Internal data science team, partner network,
ir
llA
.p
understanding of architecture, core competencies required u
o
rG
ASSEMBLE bespoke in-house g n
t
itlu
s
n applications How customized does this solution need n o
C
e n
v Build fully custom solutions for truly differentiating to be for our organization and context? o ts
o
n
capabilities (e.g., custom-trained LLM to accelerate Consider: Existing standard processes, standardized ERP,
B
y b
I 4
2
product design in R&D department) legacy practices 0 2
©
th
g
iry
p
1100 o
C
AI/GenAI-fueled transformation of supply chains
will require changes in process, people, and tech
Processes People Tech
More seamless human/machine New people structure and roles will GenAI solutions will bridge
interaction will supercharge daily need to be established, and dedicated AI disconnected system entities,
activities roles should be enabled (e.g., RAI2, CoE) integrating with existing systems
Increased accessibility of analytics When sourcing talent and tech, orgs will Tech stack will evolve to propel and
will unlock further value through need to centrally orchestrate AI capabilities safeguard GenAI solutions, building
more widespread use and workforce planning within platforms beyond AI/ML operation stacks
.d
e
v
Standardization will shift from Change management, upskilling, and Organizational SOPs, guidelines, etc., re
se
manual spot-checks and corrections targeted messaging will be required will need to be codified to instruct
r
sth
g
to central orchestration to increase adoption of new tools and guide agents
ir
llA
.p
u
o
GenAI will facilitate and require Leaders will be empowered to champion New building blocks required for
rG
g
n
changes in roles, organizational responsible AI culture and support AI GenAI-powered data mgmt., e.g., itlu
s
n
o
structure, and operating models learning, experimentation, and ethics MDM, data acquisition and curation C
n
o
ts
o
B
Organizations will need to embed AI Teams will have additional capacity Diversify beyond one model, y
b
4
governance within POM1 and apply to shift to strategic work as AI/GenAI including open-source options for 2 0
2
©
iterative development and deployment automates routine tasks competitive advantage th
g
iry
p
11 o
C
1. Platform Operating Model 2. Responsible AI
Process | AI/GenAI automation
will transform how processes are run
Illustrative
Supply issue Alternative Estimated time
Target setting Decision making
resolution identification allocation
Targets are established Future delivery disruptions Determine which parts Bad news is
60%
based on historical are phoned in by and products will be communicated
information and latest supplier/buyer teams – impacted via BOM internally, and an action
40%
plan – exchanges disruption already plan is created
Sites are informed after
between planning occurred, but impact
Current process 3 weeks of manual Communication is
system and Excel files, not yet measurable
example of impact sizing prepared for customers
plus manual plugs
supply planning
Administrative Strategic
.d
e
v
re
se
r
sth
g
Data and systems are GenAI web scanner Alternative suppliers are AI-generated
70% ir
llA
.p
examined with natural detects supplier instantly identified by assessment is shared u o
rG
language disruptions in real-time GenAI database search internally g
n
30%
itlu
GenAI supply plan Models are Automatically A scenario is prioritized s n o
C
recommendations are automatically updated generated action plans and a GenAI tool n o
Future process reviewed and validated with projected impacts are reviewed validates the
ts
o B
y
With GenAI optimization plan b 4
2
Administrative Strategic 0
2
©
th
g
iry
p
12 o
C
People | AI/GenAI automation will
transform roles across supply chains
Demand Supply Sourcing and Transport and Customer
Manufacturing
planning planning procurement logistics services
Selected example Demand Material Procurement Manufacturing Logistics Customer
personas
planner planner specialist engineer manager relations agent
• Less time in • Less research • Boosted • Reduced • Improved timing, • Increased efficacy
planning across silos productivity through reconciliation including last-mile in responding to key
IMPACT ON discussions • Sharper focus on automation across channels delivery requests
• More focus on root- critical stock issues • Improved savings • Greater • Decreased • Reduced admin
PERSONAS
cause analysis • Faster request by prioritizing key effectiveness in overhead workload
.d
e
v
re
leading to better turnaround suppliers urgent task via automation • Greater customer se
forecasts resolution satisfaction
r
sth
g
ir
llA
• Self-service for • Easy access to raw • Automated RFP • Standardized BOMs • Auto-generated • Direct access to .p u
o
automatic forecast material stocks and and contract drafts across sites and route plans from customer history
rG
g
n
GEN AI updates inflows • Instant access to products alerts and and company itlu
s
USE CASES • User-friendly APS • Decision support supplier and • AI assistant for work disruptions policies n o C
interface for shortages and contract data orders and • Automated drafting • Auto-generation n o
ts
o
disruptions maintenance and review of of customer B
y
b
logistics documents documents 4
2
0
2
©
Expected level of
Low impact High impact
th
g
impact on roles iry
p
13 o
C
Tech | Tech stacks will evolve with transformative
impact on analytics stack and user-facing layer
Typical current tech stack AI/GenAI-enabled tech stack Key takeaways
• Co-pilots and natural-language interfaces are
Smart business layer Smart business layer
transforming end-user experiences
Dashboards and Dynamic dashboards and Co-pilot solutions and • Entry barriers to complex applications (e.g.,
Decision-support systems
self-service analytics self-service analytics decision aids analytics tools, advance planning systems) are
reduced
Chatbot interfaces to
Software tools and HMI Low/no-code solutions
existing applications
• Emphasis is on versatile analytics layer (off-
the-shelf models, hybrid AI/GenAI solutions, etc.)
Analytics layer Analytics layer
GenAI orchestration brick GenAI monitoring and • For GenAI, LLM ops for continuous monitoring
Existing AI solutions
safeguarding tools and evaluation is paramount
Existing AI solutions
Orchestration and CI/CD
Orchestration and CI/CD Vendor integration
Development environment • Data layer will be enriched with bigger volumes of
Development environment
unstructured data, including external
.d
e
ML ops Model garden v
ML and LLM ops re
• GenAI tools accelerate data consistency and se
Data layer Data layer taxonomy reconciliation
r
sth
g
Repository and storage Data model and taxonomy Exchange, repository, storage Data model and taxonomy
ir
llA
• Interaction with core systems abstracted via .p
u
Data versioning tools Ingestion and distribution Data versioning tools Ingestion and distribution co-pilots and/or GenAI agents (wrappers) o rG
g
n
• GenAI tools and interfaces used as easy-access itlu
Core transaction layer ERP APS TMS Others Core transaction layer ERP APS TMS Others interfaces to legacy landscape s n o
C
n
o
Infrastructure Infrastructure ts
• Cloud resources should minimize vendor o B
y
On-prem Cloud Hybrid On-prem Cloud Hybrid TPU / GPU dependencies in a fast-changing market b
4
2
0
• It’s key to make cost-aware infra decisions based 2
Layer interfaces/capabilities
on information-security and cybersecurity needs
©
th
upgraded and impacted by GenAI g iry
p
14 o
C
Limitations and risks of AI/GenAI in supply chains
should be carefully addressed
Reliability and Fragmentation Limited AI/GenAI Data availability
Data privacy control challenges and rapid tech talent and for training
and cybersecurity at scale evolution experience AI models
• Regulations and • No standard practices • The ecosystem has • Scarce talent with • Limited supply chain
responsible AI focus can fully handle 450+ GenAI companies hands-on GenAI/LLM data for training AI
could limit use hallucinations and counting expertise due to novelty models may limit
scaling and hinder
• Proprietary transfers • Cost control • GenAI gaps for SC- • ""Mainstream"" data
Limitations output quality
may create vulnerabilities challenges are an issue specific players will be scientists will need
and risks
• Training with proprietary
when scaling rapidly filled growth or upskilling • This could cause .d
e
v
overdependence on re
data raises IP • Many partner choices • Internal upskilling se
infringement risk and models have no will require time and
external LLMs r sth
g
clear leaders resources
ir
llA
.p
u
o
rG
• Adopt advanced •Deploy whiteboxing •Continuously evaluate • Organize workshops, • Implement policies to g n
itlu
Example encryption and secure techniques for partners and models webinars, and ensure data source s n
o
C
mitigating
storage to protect transparency and AI to ensure alignment with certification programs transparency while
n o
sensitive information decision control organizational goals to upskill the team developing internal
ts
o
actions
and bridge the data repositories
B
y b
•Monitor agent actions 4
(non-exhaustive) 2
talent gap 0 2
closely ©
th
g
iry
p
15 o
C
Key learnings to consider within the AI/GenAI
transformation of supply chain operations
Left brain and Sequenced journey
10-20-70 paradigm
right brain from vision to scale
• Use AI and GenAI roadmap synergy • Conduct baseline assessment • GenAI value creation:
(vendor options, key partnerships) 10% algorithms/models
• Select Hybrid AI/GenAI use cases
20% tech/data .d
e
and staffing • Scope and prioritize initiatives
70% people/processes/organization
v re
se
(adopt-adapt-assemble)
r
sth
• Lower AI entry barriers with GenAI
• Rethink roles/responsibilities,
g ir
llA
• Define roadmap, lighthouse to .p
• Streamline the workflows for buy-in talent strategy, and acquisition u o rG
scale up including people/data/
g
n
(e.g., chatbot)
tech enablers • Org change with use cases:
itlu
s
n
o
C
• Establish Integrated value tracking address concerns, embed in n o
• Build MVPs to show value
ts
o
and improvements
processes, upskill users/owners B
y
b
quickly and scale fast 4
2
0
2
©
th
g
iry
p
16 o
C
Concrete actions | What it takes to be successful
in AI/GenAI supply chain transformations
Use a people-first lens: At every turn, consider end-users to drive adoption
and generate impact
Think big: Be bold in your supply chain aspirations by targeting high-impact
transformations and ROI
Get the basics right: Address data and tech foundations from the start
Partner effectively: Establish early, tight collaboration between business and
AI/tech/data experts .d
e
v
re
se
Manage change intentionally: Bring people along as you rethink processes
r
sth
g
ir
llA
and ways of working .p
u
o
rG
g
n
itlu
s
n
o
C
Start by shaping a prioritized SC transformation n
o
ts
o
B
journey to be delivered in incremental steps y
b
4
2
0
2
©
th
g
iry
p
1177 o
C
BCG experts |
Key contacts
for GenAI in Dustin Burke Olivier Bouffault Dan Sack Tristan Mallet
Managing Director Managing Director Managing Director Managing Director
and Senior Partner and Senior Partner and Partner and Partner
supply chains
Chicago Paris Stockholm Paris
Abhijeet Shetty Paari Rajendran Markus Weidmann Kosuke Uchida
Managing Director Managing Director Managing Director Managing Director
and Partner and Partner and Partner and Senior Partner
.d
e
v
re
Miami San Francisco Munich Nagoya se
r
sth
g
ir
llA
.p
u
o
rG
g
n
itlu
s
n
o
C
n
o
ts
o
B
Camille Engel Stefan Gstettner Gregor Jossé Ashish Pathak y
b
4
2
Managing Director Partner and Director Principal Data OPS Offer Director - 0
2
©
and Partner Frankfurt Scientist Supply Chain th
g
New Jersey Munich Gurugram iry
p
1188 o
C"
109,bcg,BCG-Executive-Perspectives-GenAI-in-Data-and-Digital-Platforms-EP6-14Nov2024.pdf,"Executive
Perspectives
The Future of the AI-Driven
Tech and People Stack
Data and Digital Platforms
November 2024
In this BCG
Introduction
Executive Perspective,
We meet often with CEOs to discuss AI---a topic that is both captivating and rapidly
we articulate the vision
changing. After working with over 1,000 clients in the past year, we are sharing our
and value of the future
most recent learning in a new series designed to help CEOs navigate AI. With
AI at an inflection point, the focus in 2024 is on turning AI’s potential into of the AI-driven tech
real profit.
and people stack
In this edition, we discuss the future of data and digital platforms as well as the role
AI will play in fundamentally transforming the tech function and wider
organization. We address key questions on the minds of leaders:
• How do I set up my organization to maximize value from data and enable AI
capabilities at scale?
.d
e
• How do I evolve my tech stack and AI platforms to support building AI v re
se
applications?
r
sth
g
• How can I build an organization that is fast and composable?
ir
llA
.p
u
o
rG
• How do I get started…and how do I get this right? g
n
itlu
s
n
o
C
n
o
This document is a guide for CEOs and technology leaders to cut through ts o
B
y
b
the hype around the future of the AI-driven tech and people stack and 4
2
0
2
understand what creates value now and in the future. ©
th
g
iry
p
1 o
C
Executive summary | Data and digital platforms—new technology,
ways of working, and skills required to succeed in the AI Age
AI spending will continue to increase rapidly, with a CAGR of ~30% in the next 3 years. To unlock
Evolving the
value, organizations are building new AI platforms, improving data capabilities, and
stack is key to
rewiring processes (e.g., 20% marketing savings via GenAI automation, 25% lower drug discovery
unlock AI value…
time via GenAI)
For effective and responsible AI development and scaling, a so-called “horizontal stack” with a
dedicated AI layer is essential. This typically requires updates to existing layers to integrate with
... through a
LLM models
horizontal stack
and organization ​ A platform-based operating model, while not specific to AI, is instrumental in maximizing value
.d
e v re
se
from a horizontal stack. It fosters improved cross-functional collaboration and integration
r
sth
g
ir
llA
.p
u
o
rG
g
Rewiring your Success hinges on robust tech capabilities, including establishing model selection frameworks, n itlu
s
n
architecture and centralizing platforms, implementing data orchestration, and setting up evaluation methods o
C
n
o
IT operational ts
o
B
y
setup is critical Complementing these technical capabilities are key operating model drivers, revolving b
4
2
0
2
for success around AI roles and governance, planning AI talent, and championing AI leadership and culture ©
th
g
iry
p
2 o
C
Are our data and digital platforms set up to enable
AI capabilities and capture value from AI?
Do we have a clear strategy around which models,
platforms, data sets, and evaluation criteria we
need to set up?
Key questions you
2 should be asking Are our op model and current setup lined up to
efficiently deliver on our evolving tech stack/needs?
your CTO
.d
e
v
re
Are our IT leadership, talent, and skill set ready se
r
sth
g
for an AI transformation?
ir
llA
.p
u
o
rG
g
n
itlu
What are the expected tech and people costs s n
o
C
n
o
needed to enable AI value capture? ts
o
B
y
b
4
2
0
2
©
th
g
iry
p
3 o
C
Are our data and digital platforms set up to enable
AI capabilities and capture value from AI?
Do we have a clear strategy around which models,
platforms, data sets, and evaluation criteria we
need to set up?
Key questions you
2 should be asking Are our op model and current setup lined up to
efficiently deliver on our evolving tech stack/needs?
your CTO
.d
e
v
re
Are our IT leadership, talent, and skill set ready se
r
sth
g
for an AI transformation?
ir
llA
.p
u
o
rG
g
n
itlu
What are the expected tech and people costs s n
o
C
n
o
needed to enable AI value capture? ts
o
B
y
b
4
2
0
2
©
th
g
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p
4 o
C
Companies that are ahead in their tech modernization
journey find it easier to deploy AI applications and capabilities
Seamless
Ability to support AI High
Medium
applications and
Low
capabilities
PAST TODAY EMERGING FUTURE
How
Waterfall Agile DevOps and platform op model NoOps and platform op model
Microservices, API, Serverless services
N-tiers and data- and AI/data-enabled and AI/data-enabled
Monolithic apps
enabled apps apps apps
What .d
e
v
re
se
Siloed architecture
H aro cr hiz ito en ct ta ul rl ey layered C Aro cm hip teo cs ta ub rl ee C aro cm hip teo cs ta ub rl ee r sth
g
ir
llA
.p
u
o
rG
g
n
itlu
s
n
o
C
Where n
Containers Serverless computing o ts
o
B
y
b
4
2
0
2
©
Physical Private virtualization Hybrid cloud Public cloud th
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C
Looking closer at the stack, an incremental AI layer, coupled with
improvements to existing layers, will lay the foundation for enabling AI applications
Today's data and digital platforms GenAI reshape: dedicated AI layer
Smart Business Layer Smart Business Layer
Conversational apps AI apps
Chat Search Copilot Expert systems
Omnichannel
App builders Language Library Low/No code
Business services
AI services Omnichannel
Speech Text Image Biz. services
Data Layer AI Layer
Guardrails
Data products
Orchestration
Ops and
Repository and storage Operational data services E2E app vendor Model garden .d
monitoring e
v
Ingestion and distribution
F Mou on dd ea
l
t pio lan ta fol rm models re se
r
sth
g
ir
llA
Core Transaction Layer Data Layer .p u
o
Data products
rG
g
n
ERP Other systems Repository and storage Operational data services itlu
s
Distribution and integration n o
C
n
o
ts
Infrastructure and Cloud Core Transaction Layer o B
y
b
4
2
Infrastructure and Cloud 0
2
On-prem Cloud Hybrid ©
On-prem Cloud Hybrid TPU/GPU th
g
iry
p
New layer Existing layer capability upgrade 6 o C
By modernizing their stack, leading companies across industries are driving
innovation and growth and reaping material benefits from GenAI
Note: Select examples only; for more specifics about value capture, please refer to BCG’s Executive Perspectives on respective topics
GenAI led task automation, +20%
GenAI assisted tender
+50%
Multinational Luxury automobile
content localization, and
document creation and
savings in health CPG manufacturer
savings on research time
efficiency gain offer analysis
marketing costs
GenAI sales assistant +25%
2X Multinational Leverage LLM to prioritize
deployment vs. traditional Large telecom
cosmetics call deflection
touchpoints (search, retail increase in ROI manufacturer company BU cost reduction opportunities
media)
.d
e
v
re
se
+80%
Alcoholic $1Bn
Increase in customer r sth
g
GenAI assisted marketing
beverages
Large retail value perception ir
llA
content development external agency company chain through GenAI-based .p u o
additional sales rG
cost savings pricing and markdowns g
n
itlu
s
n
o
C
n
Cycle time reduction in o
25% ts
+35% Consumer o B
GenAI leveraged to draft Pharmaceutical drug discovery through y
biopharma b 4
clinical study reports
time efficiency gain
company company drug discovery time GenAI-based lead 2 0 2
©
reduction optimization th
g
iry
p
7 o
Source: BCG case experience C
Are our data and digital platforms set up to enable
AI capabilities and capture value from AI?
Do we have a clear strategy around which models,
platforms, data sets, and evaluation criteria we
need to set up?
Key questions you
2 should be asking Are our op model and current setup lined up to
efficiently deliver on our evolving tech stack/needs?
your CTO
.d
e
v
re
Are our IT leadership, talent, and skill set ready se
r
sth
g
for an AI transformation?
ir
llA
.p
u
o
rG
g
n
itlu
What are the expected tech and people costs s n
o
C
n
o
needed to enable AI value capture? ts
o
B
y
b
4
2
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Modernizing four key areas of the current stack can appropriately
enable and maximize value realization from AI
Smart Business Layer (systems of engagement)
Deep dive to follow
…
MODELS AI copilots Conversational apps AI assistants
Diversify beyond a single model, including
1
competitive open-source options as model AI Layer 4
adaptation becomes key for competitive advantage
Guardrails
PLATFORMS 1 Orchestration Ops and
E2E app monitoring
2 Adopt AI platforms to centralize model hosting, Model garden Foundation/other small models
vendors
streamline builds, and enhance scalability,
n
effectively bridging talent gaps 2 Model platform o
y i t
t a
i r u r g .d
DATA
Data Layer c e S e t n
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EVALUATION ts
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quality, technical performance, responsible AI, 0
Infra and Cloud Layer Public cloud Private cloud Specialized hardware (GPU & TPU) 2 ©
security, and cost th
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As large language models continue to proliferate, combining the right model
selection criteria with the required platform capabilities is critical to scaling AI efforts
BCG’s selection criteria framework helps companies …and support those models with the right
find the right models at the right cost… AI-platform built-in services
Model types
Proprietary Open-source Third-party In-house
1 Model output 6 Data sensitivity
models models models models
Compatibility with
2 Model size 7 Platform Capabilities
platform/model provider
Model Management
3 Model capabilities 8 Economics MLOps tools for model deployment, monitoring, and management
Integration with Data and Knowledge
..dd
ee
vv
4 Performance 9 RAI1 and regulatory Integration with data (e.g., RAG), applications, cloud services, and infra rree
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1. Responsible AI CC
Expanding five critical capabilities enables organizations
to measure ongoing success
Area Evaluation criteria Illustrative
Content Relevance Specificity Truthfulness …
Response
quality
Verbiage Emotional connection Language style …
The extent to which responses from AI applications meet expectations
Technical Resource
Latency Throughput Availability …
performance consumption
The technical KPIs that AI solutions must achieve to fulfill business need
Responsible .d
Fairness Interpretability Compliance Accountability … e v
re
AI se
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The level of trust and ethical integration of AI solutions ir
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impact satisfaction n itlu
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The amount of business value enabled by AI use cases – function of above criteria as well as adoption, operating model, and change management
n
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privacy service (DDoS) monitoring 0 2
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th
The extent to which the AI system is secure from threats and vulnerabilities New or enhanced evaluation dimensions g iry
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11 o
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Are our data and digital platforms set up to enable
AI capabilities and capture value from AI?
Do we have a clear strategy around which models,
platforms, data sets, and evaluation criteria we
need to set up?
Key questions you
2 should be asking Are our op model and current setup lined up to
efficiently deliver on our evolving tech stack/needs?
your CTO
.d
e
v
re
Are our IT leadership, talent, and skill set ready se
r
sth
g
for an AI transformation?
ir
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n
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needed to enable AI value capture? ts
o
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A platform-led organization that is fast and composable should implement a
reshaped tech stack and benefit from the speed and agility it enables
Platforms deliver shared products/services …maximizing value from
that multiple BUs combine and consume… the evolved AI stack
Deploy AI solutions once with
1
enterprise-wide impact
Eliminate duplicated capabilities
Avoidbespoke AI solutions
+30% Cost reduction by removingduplication
Traditional organization Matrix organization Platform operating model
• Business units and functions • Business units and functions • Business units driving missions 2 Create reusable AI platforms for .d
e
• Silos and bespoke processes • Some shared capabilities • Accelerated by shared platforms
whole enterprise v re
se
• Hierarchical prioritization • Hierarchical prioritization • Dynamically aligned priorities
Set up acentralized AIcapability
r
sth
g
ProvidestandardizedAI services
ir
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+25% Productivity inc. by combining capabilities rG
Common platforms with composable, reusable services to cut costs and boost efficiency
g
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3 Unlock efficiency and agility C
n
Teams focused on products and shared, reusable services based on self-service principles o ts
o
Bring data, people, and tech together B
y
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4
2
Enable quicker scaling of AI solutions 0 2
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Teams E2E responsible and enabled by cross-functional ways of working
+50% Fastertime tomarket
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Source: BCG experience C
Top companies lean into the 10-20-70 principle to drive toward a platform-led
organization; they see AI as mainly a people transformation vs. tech-only
Non-exhaustive
Deep dive
People, organization, and processes Structure and roles to follow
Effective processes supported by talent
and change management practices
• Reskilled teams to tackle evolving responsibilities/roles
• AI capabilities integrated in cross-functional teams
Governance, business outcomes,
70% 70%
and ways of working
• AI portfolio governance fully integrated in POM portfolio
of the effort • Iterative development and deployment of new solutions
Focus of
and services and business outcome-based steering
of digital/AI transformation
.d
transformations Sourcing of e v
re
talent and tech se
is on people
10%
r sth
g
and processes
20%
• AI capabilities/workforce planning centrally
ir
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• Increased AI collaboration (insourcing vs. outsourcing) rG
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Algorithms Culture and behavior n o
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n
Data science capabilities o
Technology ts
o
to develop and B
Scalable and modernized • Leaders engaging on AI ambition and championing RAI y b
implement algorithms 4
stack to support business • Change management strategies to help the workforce 2 0 2
navigate and adapt to changes brought by AI ©
needs th
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To adapt to AI, organizations need to adjust responsibilities
and activities performed by employees…
Non-exhaustive
AI is driving demand for … and also fundamentally evolving the
new digital skills … day-to-day activities across roles
• AI algorithms and methods
• Foundational models training
• Model fine-tuning
• Prompt engineering Integration Reduced Increased Enhanced Expedited
with intelligent communication cross-functional evaluative decision
• …
machines tasks teaming thinking making
Number of job postings requiring
Employees will Automation will With the Employees will be Automation will
GenAI-related skills (US)
.d
e
v
increasingly need help cross- increasing required bring more re
se
# Unique job postings to integrate their functional teams complexity of to critically visibility into
r
sth
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10,000 90X
9,624
workflow with the communicate work, teams evaluate the timelines and
ir
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capabilities of seamlessly will need to output of AI tools bottlenecks to o
6,055 rG
g
intelligent across multiple become more and identify enable faster n
5,000
itlu
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machines and channels by cross-functional to potential biases decision n o
1,804 C
n
seamlessly breaking down achieve business or errors making and o ts
36 95 107 o B
0 interact together information silos objectives accountability y b
4
2021 2022 2022 2023 2023 2024 2
0
2
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Source: Job postings data from Lightcast; BCG analysis 15 o
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… while introducing new IT roles, reskilling product roles,
and developing new skills
New tech and digital … to enable teams that build … supported by other roles
roles needed … AI products and skills… that will evolve too
Required roles New requirements AI team roles Responsibilities Existing roles2 New requirements
Chief AI officer Oversee and implement AI Product manager Manage everyday Engineer Have prompt
initiatives team activities and better engineeringskills
understand AI capabilities
AI ethics and Develop/implement UI/UX designer Design AI interfaces
compliance officer ethics and compliance Business analyst Help capture the
policies businesslogic
AI/ML governance Establish governance Data analyst Work with high volumes
Data engineer Prepare data in a .d
specialist framework for responsible ofdata e
usable form v re
use of data and AI se
Cybersecurity roles Address new risks
r
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Data scientist D me os dig en
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to consider
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Software developer Develop scalable code for g n
Prompt engineer Improve prompt results to AI applications
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create consistent outputs o C
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across use cases o
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LLM engineer Design LLM b
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model pipelines 0 2
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1.Large language model 2. Non-exhaustive 16 o
C
Are our data and digital platforms set up to enable
AI capabilities and capture value from AI?
Do we have a clear strategy around which models,
platforms, data sets, and evaluation criteria we
need to set up?
Key questions you
2 should be asking Are our op model and current setup lined up to
efficiently deliver on our evolving tech stack/needs?
your CTO
.d
e
v
re
Are our IT leadership, talent, and skill set ready se
r
sth
g
for an AI transformation?
ir
llA
.p
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n
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What are the expected tech and people costs s n
o
C
n
o
needed to enable AI value capture? ts
o
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4
2
0
2
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17 o
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With AI spending expected to rise with a CAGR of ~30% (and ~85% for GenAI),
companies are prioritizing different levers to control AI-related IT costs
AI is predicted to drive technology costs for Different levers are being adopted by
businesses with a CAGR of ~30% until 2027 companies to control AI-related costs
Forecasted tech cost of business demand for AI and GenAI1 Q: What cost reduction measure(s) are you planning to prioritize in the
CAGR coming year to control AI/GenAI-related IT costs?2 [Multiple choice question]
'22-27
391
Non-exhaustive
Values normalized 9% GenAI infrastructure 71%
to 100 in 2022
11% GenAI platform and app software 100% Value-based prioritization of GenAI use cases 38%
307
9% 9% GenAI IT and business services 94%
In-house resource/shadow spending optimization 30%
10% Overall GenAI ~85%
232
8% .d
9% Consolidation of vendors/systems 28% e v re
7%
se
177 6%
r
sth
Simplification of current tech stack 28% g
135
8 5%
%
71% Other AI (excluding GenAI) 24%
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100 73% g
n
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89%
n
95% o
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Optimization of cybersecurity costs 12% B
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2
0
2022 2023 2024 2025 2026 2027 Overall CAGR ~30% 2
©
th
g
1. IDC AI Implementation Market Outlook: Worldwide Core IT Spending for AI Forecast, December 2023. 2. Build for the Future Research 2024 (n=1,000 respondents) iry
p
Note: Core IT spending includes infrastructure hardware, software, public cloud services, and IT/business services (devices and telecommunications services excluded) 18 o C
Source: BCG case experience
To maximize value, it’s key to adopt an approach that reimagines functional
processes, builds AI platforms, and establishes guardrails to control cost and risk
Transformation approach delivers 3-4X ROI Key ingredients
Illustrative
Proof of value
30-45% ROI
E2E reimaging of a functional
End-to-end AI functional process—not isolated use cases –
A
transformation with concrete evaluations proving
impact
Difference in
Proof of scale
ROI and impact
Ability to deliver AI platforms, backed .d
e
v
by scaled data products, serving
re
se
B Discrete use case many functions, and with platform r sth
implementation
economics
g ir
llA
8-15% .p
u
ROI o rG
Proof of control g n
itlu
s
n
Meeting the business where it is; o C
n
o
controlling costs (people + tech), ts
o
B
y
Months after ethical and bias risks, cyber risk, b
4
2
project initiation and data risk (scale without control 0 2
©
is chaos)
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Source: Build for the Future Research 2022/23 19 o
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How to get
.d
e
v
re
se
r
sth
g
started
ir
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Getting started | Practical next steps for CTOs
to get ready for their AI transformation
Set aspiration ❑ Develop an AI aspiration aligned with strategic business priorities
and objectives ❑ Establish key objectives and measurable success criteria for tracking AI initiatives
Understand capabilities ❑ Identify key gaps and the largest opportunity areas to strengthen AI tech stack
of your current stack ❑ Benchmark existing tech stack capabilities and maturity versus peers
❑ Develop robust data layer to ensure data quality, reliability, and accessibility
Invest in foundational
❑ Implement scalable infrastructure to support advanced AI models
capabilities
.d
e
❑ Identify and build key technical skills required (e.g., AI programming) v re
se
r
sth
Prioritize building
❑ Build key GenAI layers including models, platforms, guardrails, and orchestration g ir
llA
.p
❑ Establish capabilities to evaluate and measure success across performance, cost, and RAI u
o
capabilities for AI layer rG
g
n
❑ Identify and drive changes in operating model and ways of working to maximize value itlu
s
n
o
C
n
❑ Further widen the platform operating model to maximize value from AI tech stack o ts
o
B
Scale broadly via a y
b
❑ Implement capabilities through AI-focused structures (e.g., CoEs for RAI) 4
2
platform-based org 0 2
©
❑ Drive continuous development and change management for new AI solutions/services th
g
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21 o
C
NAMR
BCG experts |
Vladimir David Julie
Lukic Martin Bedard
Key contacts
for data and digital
Matthew Beth Djon
platform Kropp Viner Kleine
transformation
EMESA
Nicolas Marc Marcus Julien
de Bellefonds Schuuring Wittig Marx
Remco Dan Adrien Tom
.d
Mol Sack Duthoit Martin e v
re
se
r
sth
g
APAC
ir
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.p
u
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rG
g
n
Jeff Julian Aparna
itlu
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n
o
Walters King Kapoor C
n
o
ts
o
B
y
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4
2
0
2
©
Romain de Akira Nipun th
g
Laubier Abe Kalra
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C"
110,bcg,BCG-Executive-Perspectives-Unlocking-Impact-from-AI-Customer-Service-Ops-EP3-28August2024.pdf,"Executive
Perspectives
Unlocking Impact from AI
Customer Service Operations
August 2024
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In this BCG
Introduction
Executive Perspective,
We meet often with CEOs to discuss AI—a topic that is both captivating and rapidly we articulate the vision
changing. After working with over 1,000 clients in the past year, we are sharing our
and value of the future
most recent learnings in a new series designed to help CEOs navigate AI.
of customer service
With AI at an inflection point, the focus in 2024 is on turning AI’s potential into real
profit.
with AI
In this edition, we discuss the future of customer service and the role AI will play in
turbocharging growth. We address key questions on the minds of service leaders:
• How will the economics of customer service change with AI?
• How will the customer experience evolve?
• What will my future customer service team need to look like?
• How do I get started…and where should I focus?
This document is a guide for CEOs and customer service leaders to cut
through the hype around AI in service operations and understand what
unlocks value now and in the future.
1
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Summary | Unlocking impact from AI in customer service operations
As customers spend 14B hours/year contacting customer service, leading players set long-term ambitions to
Executives improve service productivity by up to 60% while enhancing customer experience with positive impacts on
retention and additional sales
must act on
AI now While most companies focus on support response, transforming upstream is imperative to maximize value
– including deflection, self-healing, and prevention to create a competitive edge
Unlocking the full potential of AI in customer service requires an end-to-end reshaping of the entire operation
from prevention and self-service to service delivery, with focus dimensions being:
AI impacts all
elements of • Team skilling and structures: Increase productivity, which will lead to fewer but multi-skilled frontline teams with
redesigned agent journeys, focusing on data generation for upstream prevention
customer service
operations • AI Ops capabilities: Establish new roles and skills that build, shape, and govern AI
• Tech ecosystem: Build modular components on layered stack
Develop a value-focused AI strategy with clear and visionary roadmap to realize impact that balances
short-term benefits and unlocking of long-term value
Execute
successfully
Broad, “at-scale” enablement of service agents and overall change management (including customer-
facing communication) is critical to drive adoption – starting with leader enablement
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Leading players set ambitions to improve customer service
productivity by up to 60%
The baseline The future Value enabled by
On average, Leading companies
from AI
~10%
customers spend set ambition of up to
models
14B 60%
from tech/IT
~20%
solutions
Hours per year contacting Productivity improvement
customer service for customer service
from people
~70%
and process
transformation
Source: BCG research 3
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Deep dive: ambition | Examples of leading players
with high ambitions across industries
Select case examples
Global Global Global
tech company high street bank financial company
~60% reduction in resolution time ~30% productivity improvement ~50% cost savings over the
~50% in volume reduction by 2030 over the next three years next five years
Global tech company with Global bank operating in Private label and co-brand
contact centers serving ~20 countries, with ~15k credit card issuer with over
120 countries FTE in customer service 100 brands signed in the US
Diagnosed current state,
Embedded AI agent assist Designed target model
designed a 5-year AI vision
tool into ways of working for for omni-channel customer
and implementation
support agents service, with AI at the heart
roadmap
New target operating First wave of sprints Defined service vision and
model for AI-led support and (tech, people, process strategy with focus on the
interim states across three changes) to realize early target customer and agent AI
time horizons value and fund the journey experience
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Short-term customer service P&L impact ranges between ~10% and 20%
Productivity uplift on Short-term Long-term
individual use cases today P&L value impact P&L ambition
~30-40% ~10-20% ≥ 60%
Individual use cases Short-term, realizable Leading companies with
proving uplift on productivity P&L impact across the function ambition to realize up to 60%
of ~30-40% already today of ~10-20% productivity increase long-term
Source: BCG research
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Deep dive: short-term impact | Initial productivity uplift
for tech company already achieved today
BCG case example
What impact do they
Where did they start? What are they doing? already see today?
Large tech company with initial AI
Defined North Star vision to inform
deployment for their support agents Reduction in average
ambition and direction of the customer ~80%
focused on auto-case summarization time agents spend on
support organization
and knowledge mgmt. for cases
case summaries
Stood up an AI-focused program office
No unified vision for the desired
to ensure priority activities are executed
outcomes of AI
against
Inconsistent user experience and Developed a detailed impact model to Decrease in need
siloed, uncoordinated efforts to inform savings potential and help 10-15% of expert help to
implement AI use cases across teams prioritize use cases
solve cases
Limited enablement plan and Launched a change management
change management efforts leading program to ensure feedback is received
to poor adoption and key messages are disseminated
Increase in volume
Unclear use case prioritization Designed and launched continuous
~10%
approach for informing the engineering improvement process of AI-assistant- of cases handled
roadmap deployed use cases by agents
6
In addition to productivity, AI can radically enhance Customer experience…
To-be vision
AI-augmented agent
As-is Dynamic process based on multiple parallel
predictions improving average service quality
Human agent
Fixed, linear processes,
“Customer need” Is customer on the
“one size fits all” thinking right plan?
response, high variance
End-to-end process Has customer called
Customer in quality depending on Customer thinking before?
individual agent
Has customer been
Risk-based thinking
scammed?
Optimized
How much detail
Solve the 1st Personalization conversation
Explain the bill is needed?
order problem based on
What is customer’s
Situational thinking real-time
context?
“Why is my “Why is my context
Solve the 1st order
bill so high?” bill so high?” Explain the bill
problem
Commercial Make a
thinking cross-sell offer
Commercial thinking Make a relevant offer
Improve welcome
AI augmentation goes beyond Root-cause thinking
communications
just automating this process
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… with benefits on both customer satisfaction
and commercial performance
Better customer experience Stronger commercial excellence
Pre-empt calls and foster self-help Foster sales excellence
• Prevent issues and requests from arising in the first place • Identify real-time sales opportunities
• Generate proactive actions resolving issues before need for call • Offer successful selling arguments and provide personalized
• Steer customers to self-service (voice/digital self-help) sales pitch to agents
• Use virtual avatar that proactively engages with customers in a
sales funnel to close the sale
Make interactions more seamless
• Use AI-powered assistants (chat, voice, or virtual [avatar]) to
Increase cross-/upsell performance
offer quick, accurate, round-the-clock support across service and
• Identify most appropriate by-product and provide sale details
sales processes
• Enrich recommendations with personalized sales arguments • Generate more personalized answers
• Enable more engaging conversational interactions (e.g.,
Reduce churn
generative self-service IVR)
• Spot customers at risk with predictive analysis
• Identify root causes of customer dissatisfaction
• Guide agents with ladders and personalized scripts
+10-20 NPS +20-35% CLTV1
1. Customer Lifetime Value
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To realize full potential, transformation of the entire customer service
value chain required, with support response being the typical starting point
Pre-empt Self-heal Self-help Support response
Leveraging AI to Using AI to fix Empowering Enabling support
prevent issues issues before the customers with teams and agents to
and requests from customer notices AI-based tools and resolve customer
arising in the them and without information to issues in the most
first place customer effort self-solve their issues efficient manner
Typical starting point
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Deep dive | AI transforms the entire customer journey,
including upstream prevention
Problem Pre-empt Self-heal Self-help Support response
Fixed without Automation Attended by
customer realization without humans humans
Prevent Fix error
Phone
problem from before customer AI voice bot Human agent
ever existing notices call
Web
Human agent
AI chat bot
chat
Product/ Customer Contacts
service identifies customer
problem problem service
Human agent
AI Email form
Email email identifies
problem
Branch Self-service Human agent
kiosk meeting
AI Human
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Majority of value is unlocked by upstream prevention and realized
by transforming both technology and people and processes
20% (-30%)
15%
100%
40%
70%
30%
5%
20%
30%
tifeneb
ytivitcudorp
%
Illustrative
Pre-empt Self-heal Self-help Support response
Upstream prevention
~70%
People and
process
transformation
~30%
Standalone tech
Pre-empt Self-heal Digital Voice Handling time Multi-skilling Inefficiencies Productivity
self-help self-help reduction and smart from scaling benefit
routing
Note: As visualized, productivity benefit per use case often driven by combination of people and process transformation plus standalone tech; Source: BCG research
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Many have started implementing AI in customer service – we identified
five common pitfalls that prevent value from being realized
Common pitfalls Three key success factors
Technology- Limited focus on people, process and change
driven management leading to low adoption
Focus on end-to-end
transformation
Individually built use cases that don’t re-use
Fragmentation
common components
Use case- Improving status quo w/o leveraging transformative Set an ambitious
centric power to change the whole service function top-down target
Implementation w/o pathway to scale and realize
POC-focused
business value
Measure P&L
Striving for AI to be perfect vs. providing a better
impact from “day 1”
Perfectionism customer experience than the average agent (while
of course still ensuring factually correct responses)
12
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AI-driven transformation impacts all elements
of customer service operations
Support response
Pre-empt Self-heal Self-help
Inhouse | Outsourced
Demand management and orchestration Team skilling and structures
Workforce management and scheduling
Recruitment and retention
Learning and development (L&D)
Outsourcer management
Quality assurance (QA)
Knowledge management
AI Ops (new capability)
Tech (partner) ecosystem
Leadership ways of working
Change management
Deep dives
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Deep dive: team skilling and structures | Three key shifts
Illustrative – not to scale
Today Future Key changes
Service Service
Ops Management Ops Mgmt.
Delivery Delivery
Move from people-heavy teams to
Supervisors Supervisors 20-40% smaller frontline teams in 2-3
years and 60-70% smaller frontline
Team Second Line/Back Office Experts
teams in the long-term
size
First Line First Line
Service Service
Delivery Delivery
AI OPS to be included as central
OPS element of the Service Delivery (with
Operations AI Core functions Core functions Sup-
Support1 Ops limited role of Operations Support in
port1
Team
the future)
composition
Service Service
Ops Mgmt. Ops Mgmt.
Delivery Delivery Shift from high share of time spent
Supervisors Supervisors OPS
Operations AI on run activities to continuous
Sup-
Support1 Ops
Second Line/Back Office Experts port1 improvement and delivery of
Team
First Line First Line long-term change
focus
Customer Service Technology Customer Service Technology and Data
Time spent on activities: Run Continuous improvement and delivery of long-term change
1. Functions including : WFM, knowledge management, partner management, training, QA, etc. 14
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Deep dive: team skilling and structures |
Shift to multi-skilled second- and third-level support
Illustrative
Mass Market
Japanese language Japanese language
and Premium B2B
Produc St panish language ProducStpanish language
A A
Product Mass Market Premium B2B Product Mass Market Prem. B2B
ProduAct ProduAct Product
ProBduct B A,B,C
AProduct Product
Product
ProduBct ProduBct
A&B
ProCduct C
PBroduct Product
C C Customer Customer
Product Product
profile I, profile II,
C C
issue X issue Y
Starting point: many discrete Next 2-3 years: Target state:
teams in contact center, split by:
• Lower volumes handled • AI-augmented agents handle broad
• Product • Mix of contacts is more complex set of requests, customers,
• Contact reason • Agents assisted by AI co-pilot languages, etc.
• Customer segment • Teams become more multi-skilled • Routing based on individual best
• Language and fungible match, not job title
• Etc.
= single-skilled teams = multi-skilled team
Deep dive: AI Ops capability | New capabilities and roles
build, shape, and govern AI
Roles that Roles that Roles that
BUILD AI SHAPE AI GOVERN AI
Technology specialists who Business and functional experts Professionals who monitor AI
build and monitor AI models who collaborate with customer- outputs to ensure software is
and support technology platforms, facing agents to articulate business driving returns while verifying
leveraging advanced technical needs and integrate models into technology is being used safely
capabilities business processes and ethically
10%
Illustrative CS
40% CS
split of
80% CS
responsibilities:
90% Tech
60% Tech
20% Tech
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Deep dive: change management | Focus on increasing leadership,
customer, and agent engagement
Enablement AI adoption AI adoption Agents’ skillset
of leaders by customers by agents and capability mix
Build case for change Nudge customers Establish training Drive change in
and narrative, incl. to use AI-enabled programs, change workforce capabilities
benefits and metrics self-service through networks and through agent
to provide leaders suggestions and feedback loops to upskilling, new hiring,
with tactics for leading adjustments to drive adoption and cultural change
the change and their options
driving adoption
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Long-term ambition of value realization requires time horizon
of over 24 months with first benefits possible after 3 months
Boosting impact of Unlocking AI impact Reimagining service
early use cases through new use cases experience with AI
(at ~0-12 months) (at ~3-24 months) (at ~6-24+ months)
Increased adoption and Initial unlocks of full AI Fundamental shift of entire
success of early use cases driving potential via new high-impact service function that
measurable and marketable and moderate-effort use cases transforms the experience for
performance impact customers and employees
Incremental benefits within Step-function change in Watershed leap: Radical change
current service model experience and efficiency in experience and efficiency
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Deploying and scaling AI effectively demands an optimized
technology stack tailored to support and expand AI use cases
Key considerations
Smart Business Layer
Case
Conversational App Copilot Text Enterprise System Other example
Guardrails and Monitoring
1
AI Layer Capabilities to ensure correct behavior of AI (e.g., RLHF,
Guardrails and Transparency and red-teaming, constitutional AI)
1 Content moderation Observability/feedback mgmt.
monitoring ethical guidelines
Orchestration
2
2 Orchestration Prompt flow Agents Chains Ops monitoring
New capabilities expected to coordinate different models and
PromptOps calls to internal and external APIs
First party - Foundational Other small Open-source Other small
4 Model models and pre-trained APIs models models models 3 LLMOps Ops and Monitoring
garden 3
Embeddings
MLOps New capabilities to ensure correct operation of AI use cases
(including models, pipelines, and data)
5 Model platform Featurestore Modelhosting/activation FMOps
Model Garden
4
Integration Layer Model capabilities required for use cases may impact near-
6 term platform selection. Open source for build use cases.
(expect multiple)
Data Layer Model Platform 5
Accounts
Call Client Internal Support multiple models, privacy controls, performance
and Products Other
recordings profile policies The layer will (1-2 preferred platform(s) in short-run)
contracts
facilitate
Core Transaction Layer access to the
Integration
6
client system
ERP C360 CRM Others Integrate AI use cases to client enterprise systems to leverage
for retrieving
client knowledge base, CRM system, ERP systems, etc.
relevant data
Infra and Cloud Layer and other
7 knowledge 7 Infra and Cloud
Public Cloud Private Cloud GPU TPU
resources Ensure AI choices align with overall hosting strategy
(multi/hybrid cloud); plan for higher infra consumption
Developed and implemented by BCG
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Reusable modules are key to achieve scale at pace
Reusable MODULES … … that can be integrated into SOLUTIONS
Product modules rely on reusable sub-components that combine to enable Product modules integrate into Deep.ai solutions,
desired functionalities combined with proven UX/UI
Reusable code assets are bundled into reusable modules …
… enabling sophisticated
P1 Silence batch slicing M1 Call transcription customer service-
P2 Whisper on-prem integration M2 Transcription post-processing relevant solutions
P3 Transcription cleaning M3 Call summarization
Virtual
PX Speaker parser M4 Parameters extraction
Assistants
… that constitute larger tech capabilities. Combinations of
multiple modules augment each other's capabilities
Enterprise
Knowledge Content
Base Mgmt. Creation
Batch Real-time
AI - ML Real-time commercial Example
models Insights scripting Insights
Providing features Input for real-time Search Cognitive
and feedback and update of batch Reporting Engines
for future interactions
Way forward to a value-focused AI transformation of customer service
AI vision PoC and value Transformation and
and roadmap potential testing change management
• Baseline starting point and • Build and launch PoCs and capture • Drive and manage tech rollout
organizational challenges today learnings
• Execute operational
(including baselining of as-is,
• Evaluate technical architecture, data transformation at scale, e.g.,
narrative, tech foundation, etc.)
options and build tech readiness integrate AI into key processes,
• Define North Star vision/AI plan establish op. model
ambition and align key stakeholders
• Create detailed impact assessment • Drive change management and
• Prioritize and design use cases and test future value potential communications plan, e.g., change
agent and customer adoption, etc.
• Conduct high-level impact • Build out operational
assessment transformation plans for further • Select and onboard further tech
rollout and scale-up partners, as needed
• Set up roadmap for transformation
and tech rollout • Capture learnings and benefits
While some companies currently focus on PoCs, the full potential will be unlocked
through a strong, tailored vision and a successful transformation and change management
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BCG experts | Key contacts for customer service
operations AI transformation
EMESA
Marcus Alfonso Ignacio Yasmine Juan Martin
Wittig Abella Hafner Hamri Maglione
Hrvoje Anne Alexander Nicholas
Jenkač Kleppe Noßmann Clark
NAMR
Simon Kirti Varun
Bamberger Choudhary Khurana"
111,bcg,BCG-Executive-Perspectives-Unlocking-Impact-from-AI-Finance-EP5-24Oct2024.pdf,"Executive
Perspectives
Unlocking Potential from AI and
GenAI
Finance
October 2024
Introduction In this BCG
Executive Perspective,
AI a key topic top for CFOs today. After working with numerous clients
we articulate our
in the past year, we are sharing our most recent learning in a new series
vision for the future
designed to help CFOs navigate AI. With AI at an inflection point, the focus
in 2024 is on turning AI’s potential into real effectiveness and efficiency gains. of finance with AI
In this edition, we discuss the future of finance and the role AI will play
in unlocking the finance function’s full potential. We address key questions on
the minds of finance leaders:
• Where are the opportunities for AI in finance?
• How will the finance processes evolve as a result?
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• What impact does AI have on the finance operating model and talent? vre
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• How do I get started… and how do I get this right? g
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This document is a guide for CFOs and finance leaders to cut through
itlu
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o
the hype around AI in finance and understand what creates value C
n
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ts
now and in the future. o B
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Executive summary | Unlocking impact from AI in the finance function
While finance functions have taken a bit longer to get started, CFOs are now starting to
The time to act explore GenAI and how it can help finance become proactive ""value drivers"" for business
on AI is now
~15% of companies1 are actively piloting or implementing GenAI in the finance function
AI is a key unlock for finance to deliver new capabilities (e.g., AI-based forecasting) and
higher-quality business support (e.g., GenAI-powered FP&A insight generation)
AI unlocks many
sources of value AI delivers both efficiency and effectiveness benefits for the finance function, e.g.:
• Efficiency: 20-40% capacity unlock, enabling more focus on business partnerships
.d
e
• Effectiveness: 50%+ increase in forecast accuracy, 2x faster insight for decision making vre
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CFOs start their journey by building high-value use cases and demonstrating early impact
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itlu
Executing Yet to fully unlock value from AI, finance functions should move beyond use case exploration to s n
o
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n
successfully reinventing end-to-end (E2E) processes, by: o ts
o
B
• Adapting the operating model, talent, and ways of working to support new processes y b
4
2
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• Modernizing the tech stack and rewiring data hierarchies to enable further value gains 2
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1: BCG CFO Excellence Panel survey 2024, N = 204 2 o
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Next-gen finance functions break the compromise between efficiency and
effectiveness, to become proactive value drivers for the business
Next-gen finance functions actively …by delivering on BOTH
integrate strategy and drive value… effectiveness and cost
“Excessive” “Truly
excellent”
Increasing contribution to
company’s strategic
decision making Value
driver
s
s
Strategic Integrates e
n
partner corporate and e
financial v
i t .d
Business Evaluates strategy; c e e vre
partner performance partners with f f E s e r s
and partners re: BUs in th
Platform
Supports and
strategy with optimization g ir llA
manager reports BU BUs; actively & manages .p u
Traditional
Shares best
operations; d ari nve ds
r
ep sla on un rcin eg value driver o r G
g n
function practices and over ss eee tts in t garget allocation performance itlu s n
maximizes BU “Bureaucratic” “Minimalistic” o C
Serves as n
passive
synergies o ts
o
B
“bookkeeper"" Efficiency y b
4
2
Finance functions Top performers 0 2
Facilitator Partner Initiator ©
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Source: BCG CFO Excellence Panel, N = 767 3 o
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Inspiration | What next-gen finance functions look like
Common starting points Digitally mature functions AI-enabled functions
Planning, Fragmented data, heavy Excel use, Integrated cross-functional
Predictive AI models for planning,
budgeting, & manually intensive and siloed planning, modernized processes &
forecasting, and scenario analysis
forecasting budgets / forecasts tools with single-source data
Reporting Static tools / reports with limited Automated dashboards with Self-serve dashboards with GenAI-
and business visualization, built on manual standardized metrics, using based commentary, supported by
intelligence processing of fragmented data harmonized data & definitions conversational querying and search
Fragmented ERP; Excel-heavy, Lean GL, automated journal entries, Predictive close, AI reconciliations,
General
time-consuming reconciliations; system-triggered controls & GenAI entry recommendations, AI .d
accounting
manual statutory reporting exception monitoring pattern / error recognition
e vre
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Lack of end-to-end systems, leading Integrated sub-ledgers, automated AI-based data extraction (e.g., OCR) .p
Finance u
o
to off-tool calculations, tracking / invoice generation / processing, & validation, predictive collections, r G
operations g
n
monitoring via Excel auto-matching and data validation supplier analytics, AI controls itlu
s
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n
o
ts
Integrated solutions for transaction AI-based cash forecasting, FX o B
Expert Reliance on Excel & manual inputs y
b
processing and global monitoring, hedging models, working capital 4
2
functions limits visibility and future planning 0 2
with standard insights / alerts optimization, risk monitoring ©
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A combination of levers is required to achieve finance excellence, and AI is
an important lever in the digital modernization journey
Levers to achieve finance excellence:
Digital modernization AI tools bring cognitive and predictive capabilities to augment
including AI existing tech solutions (e.g., ERP, specialized tools, RPA)
End-to-end process
n
o
reimagination
i
t
a
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r
• Reinvent the finance offering with c
Generative AI
e
•
n
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dee
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New operating model
e
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L ae ccg oa ucy
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tE inR gP / (e.g., treasury) n itlu
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and talent profiles P systems C n
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• Changes in roles, accountabilities, and 4
2
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2
skills/expectations driving need for Recency of application ©
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new capabilities and ways of working iry
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While GenAI is still nascent, companies are actively exploring its potential, in
addition to expanding the use of traditional AI
GenAI in finance
still nascent, in GenAI in finance 10% 64% 11% 7% 8%
exploration stage
AI/AA-based forecasting 4% 12% 23% 61%
A
Traditional AI
&
P
already prevalent
F .d
AI-based report generation 6% 21% 35% 38% e
or being planned
vre
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s
across multiple th
g
areas of finance g
n
AI-ba rese cod
n
a cc ic lio au tin ot
n
6% 19% 47% 28%
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Not explored Plan to implement Being implemented th
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Source: Select extracts from BCG CFO Excellence Panel survey 2024, N = 204 Exploring Pilot started 6 p o
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Early estimates – potential to change as tech evolves
AI combined with other levers will be critical to unlock transformative
value across the finance excellence journey
Transformation of FP&A Transformation of Accounting & FinOps How AI accelerates
(~20-30% efficiency gains applying all levers) (~25-35% efficiency gains applying all levers) this transformation:
• Reduction of manual work,,
From To From To
e.g., data extraction, rule /
pattern-based validation,
Compliance tracking;
Business data support
calculation
system controls
Customer/supplier
Scenario models,
Preparing plans,
contracting and comms. • Task reinvention through
market analysis, AI- Policy writing, workflow
budgets, & forecasts
based optimization improvement, etc. AI-created first drafts, smart
Close & consol., .d
reconciliation and Predictive risk mgmt. reviews/alerts, analytics, e vre
s
Data analysis and Self-service and statutory reporting and controls; AI pattern etc. e r s
mac nre aa gt eio mn
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proactive insight / error recognition
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Transaction tracking, GenAI-enabled • New finance capabilities .p
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Stress-testing AI-built review and approval negotiation & analytics and insights enabled by AI r G
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budgets & forecasts AI-enabled close, itlu
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recons, & statutory n o
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Data collection, reporting n o
processing, & validation Insight generation from Manual transaction ts o
dashboards; ad hoc processing
oE fx Ac Ie -p geti no en r- ab ta es de d
in
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s,
Capacity unlock for more
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journal entries, etc. strategic advisory and 2
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Note: Estimated 3- to 5-year impact 7 o
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Size = Illustrative share of workflow; pace of change will vary greatly by the starting point of your function
Use cases | Key opportunities exist for AI across finance processes
Planning,
Business strategy: Analysis AI-drafted plans and budgets, GenAI-created variance AI-based forecast and
budgeting, & of market/demand, leveraging KPI driver trees and investigation and scenario modeling, AI
forecasting competitive landscape, etc. automated data feeds commentary generation refinement of driver trees
Reporting
Standard dashboards/reports: GenAI- Ad hoc reporting: GenAI-based data Proactive monitoring (e.g., overspend,
and business drafted commentary, performance search and visualization, performance project delays) and optimization (e.g.,
intelligence analysis, creation of leadership decks analysis and insight generation working capital)
Subledger close: AI-recommended Consolidation & filings: AI-based Compliance & policies: AI-based controls
General
journal entries, proactive/predictive close balance sheet reconciliations, preparation and error detection, GenAI policy and
accounting
monitoring of statutory reports guidebook writing
.d
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Finance Procure to pay: AI/OCR invoice processing and Order to cash: AI-based prediction models for credit Others
vre
s e
operations
matching, payment terms monitoring, supplier scoring, early warnings for DSO/aging, predictive (payroll, fixed
r
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spend/risk analytics + optimization suggestions collections with AI, GenAI-tailored customer comms. assets, etc.) ir
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fo r
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E ea sr ton rin gs Ri ws ak
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m nina gn sa ug se inm ge pn at t:
t
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balance sheet optimization deferred tax impacts sentiment analysis recognition; fraud detection C
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Analysis of opportunities based on additional value g
High Medium Low iry
that AI can unlock (beyond other digital tools): 8 p o
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Example 1
AI in action (I/III) | Using AI-based financial forecasting to drive impact
Context Solution overview Impact
Large manufacturing client AI-based demand forecasting engine leveraging
internal and external data:
Struggled to forecast market 50%+
accurately (17% increase in forecast • Trend-sensing engine to identify early shifts in market Improved
forecasting
error)... sentiment, production technology, etc.
accuracy
..driving business challenges: • ML-based modeling to adjust for business trends such as
seasonality, competition, etc.
• Inability to anticipate and adjust to
market situation Driver tree models linking executive metrics 80%+
to operational variables (e.g., production units):
Reduction ..dd
• Increased costs of labor & overtime, ee
in forecast
vvrree
ss
transport, and increased inventory • More granular, data-derived forecasts for root cause variance ee rr ss
tthh
• Customer dissatisfaction from
analysis and sensitivity assessment and bias gg iirr
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..pp
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decreased service levels and fewer • Ability to model various demand / production scenarios oo
rr
GG
on-time, in-full deliveries and enhance decision making gg nn
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Forecasts nn
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AI/ML center of excellence created, to enhance models
and scenarios
CC
nn
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and develop new AI offerings generated
ttss
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Example 1
Deep dive | Structured and leading-edge AI modeling provides
step-change in operational driver forecasting capabilities
Illustrative
Operational driver for financial model
Monthly demand (in #)
Endogenous time-series forecast
Baseline
Features: seasonality, long-term category trend,…
Machine learning time-series correction
Operational
Features: market data, competition, economy,
drivers
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COVID stringency,… vre
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One-off r G
Features: extreme weather occurrences, g n
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regulatory changes, supply shortages,… s n
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Impact: Variability better explained & anticipated, forecast error cut, bias reduced 4 2
0
2
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Example 1
Deep dive | Driver trees link operational drivers to financial results,
underpinned by AI algorithms
Illustrative
Level 2 metrics Operational drivers Data inputs
OTR shipping
Automatic inputs
Cost per mile
OTR supplies
Intra-network Automatic data feed of actuals/
costs
forecasts directly from data systems
Miles
Miles per run
AI model outputs
Transportation
Runs
Outputs from relevant AI models
.d
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costs
vre
s
(e.g., demand forecasting model) e
r
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Units that serve as inputs into driver trees g ir
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Pallets G
Units per pallet g
n
Outbound
Manual inputs
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transportation n o
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costs Cost per pallet Assumptions and manual adjustments n o
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(CPP) Historical CPP o B
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Fuel price Δ
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Example 2
AI in action (II/III) | Using GenAI-based business intelligence to drive
impact
Context Solution overview Impact
Large US retailer struggles to get GenAI chatbot for conversational queries of data and
deep insights on performance, due generation of dynamic visualization:
to rapidly changing market / 2-4x
• Provides 2nd and 3rd order performance insight, with
demand environment, complexity of Faster report
suggested actions for improvement
generation
product portfolio / categories, and
fluctuations across cost areas • Creates live charts that can be interrogated, adapted, and
exported into leadership decks
FP&A analyst faces several
challenges while investigating Driver tree engine enables FP&A analyst to build and
variances to advise business live-edit dynamic relationship models between key <1 day
..dd
partners on the drivers behind operational and financial variables: Turnaround ee vvrree
performance:
• Driver model enables deep investigation into root causes
f ao nr aa ld
y
th ico sc ss ee rr
ss tthh
gg
• Poor view of drivers’ under/ behind variances, to enable timely corrective action
iirr
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overperformance uu oo
Scalable infrastructure that can be connected to any rr GG
gg
nn
• Significant time needed to analyze data source or system, providing visibility across BUs, iittlluu
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nn
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variances and identify root causes categories, and processes More CC
nn
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data--driven
ttss
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• Slow turnaround for ad hoc data BB
yy
decision making bb
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Example 2
Deep dive | GenAI-based chatbot interface and driver tree relationships
allow for quick drilldown into root causes of performance
Dynamic charts generated
Conversation between analyst and tool throughout the conversation
FP&A analyst GenAI chatbot
What is driving the forecasted sales miss for [category A]
for November and December of 2024?
The forecasted sales miss is due primarily to a drop in sales velocity
(-25%), with a slight decrease in expected average price (-5%).
What are the drivers of the drop in sales velocity?
The forecasted sales velocity decline was primarily due to a large decrease
(-20%) in purchases per order and small decrease in order rate (-6%).
.d
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What were the drivers of decreased purchases per order?
vre
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Purchases per order forecast model attributes 50% of the decline to ir
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anticipated increase in inflation (+1%), to predicted CDP reduction (-2%), and .p
u
the remaining 35% due to a drop in expected competitor prices for similar o r
G
g
systems ([CLIENT] 15% higher than the expected competitor average price). n itlu
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What actions can we take to increase purchases per order? C
n
o
ts
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B
The economic outlook cannot be adjusted by any levers. However, planning y
b
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for a steeper discount rate on [category A] heading into Q4 2024 will help 2 0
2
offset some of the expected losses and improve the outlook for CY 2024. ©
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Example 3
AI in action (III/III) | Using GenAI-based annual report creation to drive
impact
Context Solution overview Impact
Controllership team at a large GenAI-based creation of draft 10K/annual reports
company looking to reduce the time based on past filings and latest internal data
40-60%
spent on generating investor reports
• Tool auto-refreshes data, shifting human focus from Automation
by having Gen AI write the text-
data collection to review / refinement of statutory
heavy section:
reports
GenAI-drafted MD&A commentary, using external
• Manual data aggregation and
data to synthesize market trends and implications
consolidation for 10K-style reporting
• Tool adapts comments to highlight key business
• Significant time spent on analyzing
conditions, e.g., demand, industry fluctuations, 2K hours
peers and market trends to write ..dd
MD&A commentary
economic landscape cS oa nv te rod
l
li en
r
sth he
ip
ee vvrree
ss ee
Performance benchmarking across peers, based on organization
rr
ss tthh
• More time spent by consolidation
publicly reported financial metrics, strategic
gg iirr
llllAA
team on drafting reports and less ..pp
announcements, and other news uu oo
rr
time on review / insights GG
gg
nn
• GenAI provides quick answers on competitive
40+
iittlluu
ss
nn
oo
performance and peer outlook, replacing time- CC
Benchmark nn
oo
intensive manual analysis ttss
companies oo
BB
yy
compared via bb
44
22
GenAI tool 00 22
©©
tthh
gg
iirryy
pp
14 oo
CC
Example 3
Deep dive | GenAI-enabled tool supports finance function
in generating the annual report and inquiring about peer companies
Annual report generation First-draft commentary generated by tool leveraging past
• Creation of draft of annual report annual reports, news articles, competitor publications, etc.
commentary based on prior reports
• Benchmarks and sentiment-based
text editing, including translation
and proofreading
• Conversational interactions to refine
and improve on GenAI-created drafts
..dd
ee
vvrree
ss
ee
Peer reports and
rr
ss tthh
gg
press release inquiries
iirr
llllAA
..pp
uu
oo
• Query peer reports and press releases rr GG
gg
nn
to refine commentary around market
iittlluu
ss
nn
oo
CC
sentiment, industry outlook, etc. nn
oo
ttss
oo
BB
• Generate answers on peer financial yy
bb
44
22
performance based on public data 00 22
©©
tthh
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iirryy
pp
15 oo
CC
AI implications: processes | AI enables finance team members to shift from
manual data tasks to strategic insights and business collaboration
Data collection and Performance Insights and Management Illustrative time
variance calculation analysis recommendations reporting
allocation1
80%
FP&A analyst accesses Analyst looks into high Analyst writes Variance calculations
multiple systems to variance items by variance comments and comments are
pull and validate data reviewing source explaining findings and consolidated into
Current
data, cross-referencing flagging key cost items PPT report and sent
Excel used to calculate 20%
process
operational metrics, to be resolved and to leadership
variance vs. budget and
Example of and sending remaining budget
past actuals
variance analysis
Report Analysis
questions to business available
generation & insight
teams .d
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vre
s
FP&A analyst uploads GenAI queries run to Analyst builds options GenAI used to build
70% e r
s th
g
prompt to GenAI tool, investigate driver trees to optimize cost and leadership report
ir
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.p
to quickly collate data and dimensions (BU, simulates P&L with recommendation u o
r
30% G
and run calculations region, GL item, etc.) impact of options with and P&L forecast g n
Future
itlu
ML tool s n
process GenAI builds charts, GenAI provides Analyst reviews and o C
n
o
comments for review reasons for the higher- ML tool enables live socializes report with ts
With GenAI
than-trend cost review and refinement stakeholders
Data Insight & o B
y b
4
collection & decision 2
0
variances with business team
calculation support
2
©
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1Based on client experiences of typical breakdown of FP&A time spent 16 p o
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• Shift of CFO role toward ""chief performance officer""
AI implications: driving strategic direction and decision making
New mandates
operating model | • Increased focus on data stewardship, to build new
for the CFO
insights and analytics for evolving business needs
AI drives changes organization
• Custodian of value, providing investment funding and
in roles, mandates,
monitoring benefits realization from org-wide GenAI efforts
and ways of working
across finance
• Evolution of finance service catalogue, with new
New offerings for the business (e.g., AI optimization engines)
engagement • Increased push toward AI-powered self-service,
models with driving leaner ""finance business partner"" teams
the business • Greater cross-functional collaboration, to fully leverage
internal financial, sales and operational data for insight
.d
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vre
s
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• New roles and profiles (e.g., solution architects) r s
th
g
to identify AI opportunities and build use cases ir
llA
.p
• Need for enhanced digital skill sets within finance, u o
Reinvention of r G
such as data analytics and AI capabilities g n
finance talent
itlu
(e.g., ability to create scripts) s n
o
and skill sets C
n
o
• Increased need for strong business acumen within ts
o
B
finance, resulting from new AI-based offerings and y b
4
2
reduction of transactional work 0 2
©
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AI implications: technology | Three types of vendors
for AI in finance are suitable for different needs and use cases
Nonexhaustive
Enterprise tech solutions Point solutions Foundation builders
Augments their existing ERP / EPM Leverages AI to offer specific solutions Provides infrastructure and out-of-box
offerings with AI capabilities tailored for use cases, with focus on models to support a broad set of use cases
building new analytics (including finance)
Example offerings:
Example offerings: Clients can leverage a mix of open- and
• GenAI invoice creation and AR
Approach
closed-source models / engines to create
management • AI / ML budgeting & forecasting
and use solutions tailored to their use case
• Controls / transaction review • GenAI management reporting & business
cases
intelligence
• GenAI customer / vendor comms
• Predictive collections
• Fraud / risk detection
.d
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vre
s
e
r
s
Sample ERP, Ariba, Tableau
th
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Concur GPT AI Assist
ir
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vendors .p
u
o
r
G
Ability to customize g n
Standard, scalable offerings itlu
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C
n
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Suitable to augment existing tools Suitable for companies looking for Suitable for companies looking to ts
o
B
with limited build effort off-the-shelf models with flexible build highly tailored models with y b
4
2
customization ability in-house resources 0 2
©
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Source: Expert interviews 18 o
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AI implications: technology | Target state tech landscape for finance will
significantly evolve, driven by AI requirements
Illustrative platform design incorporating AI Immediate priorities for AI execution
• Define stakeholder needs: AI use cases need to be defined
Smart business layer
based on current needs, vision for finance capabilities
Interrogable dashboards/ Text generation
Chatbot/copilot/ Conversation-based
models/interfaces for (document creation, • Create roadmap: Companies are increasingly building
knowledge search code building
decision support emails, etc.)
quick AI pilots to prove effectiveness/efficiency impact
• Build models: AI tools can be developed today on top of
AI layer
existing stack, prioritizing areas with higher data fidelity
Model gardens for Machine learning / Knowledge graphs /
RAI guardrails • Set RAI guardrails: Standards are defined for data /
LLMs/generative text predictive models relationship models
models, based on responsible AI frameworks
.d
e
vre
s
e
Data layer
r
s
th
• Augment using AI: While AI models are evolving, ML g
Data products
ir
llA
capabilities can be used to accelerate data clean-up .p
Repository & storage Operational data services u
o
and improve quality / governance r G
Distribution & integration g n
itlu
s
n
o
C
Core transaction layer • Explore out-of-box capabilities: AI solutions are n
o
ts
o
increasingly being embedded into transactional B
Infrastructure and cloud
y
b
On-prem Cloud Hybrid TPU/GPU solutions (e.g., AI controls within ERP) 4 2 0
2
©
th
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New layer Transformed layers 19 p o
C
Getting started | 6 critical success factors for CFOs driving AI
1 Systematic Use AI as a catalyst to accelerate end-to-end finance transformation, including processes and
transformation operating model
2 Value-focused Act as the value guardian, driving the highest-impact use cases and monitoring early benefits
build realization
3 Technology Leverage off-the-shelf tools when possible and selectively build use cases
flexibility in-house when existing offerings do not fully address the requirement
.d
e
4 Data Be the ""chief data officer,"" continuously exploring opportunities to better leverage big data vre
s
e
foundations for finance and business
r
s th
g
ir
llA
.p
u
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5 Quality Establish safeguards against hallucinations and ensure reliability / security of results (e.g., r G
g
n
governance human-in-the-loop review, GenAI testing and evaluation)
itlu
s
n
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C
n
o
ts
o
6 Leadership Get finance leaders and key business stakeholders onboard; drive change management/ B y
b
4
buy-in culture toward supporting AI efforts 2 0
2
©
th
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iry
p
Source: Learning from BCG case experiences 20 o
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NAMR CFO EXCELLENCE (CFOx)
BCG experts |
Michael Hardik
Key contacts James Tucker Jody Foldesy
Demyttenaere Sheth
for AI in finance
Laurin
Aissa Matt Malavika
transformation Henderson
Boudadi Harris Vishwanath
Menton
EMESA CFOx APAC CFOx
Alexander Marc Sebastian Anand
Roos Rodt Stange Veeraraghavan
Anne
Anna Hendrik .d
e
Oberauer
Ruellan
Schnelle
vre
s
e
du Créhu r s
th
g
ir
llA
.p
Andreas Norbert u o
Patrick Weber r G
Toth Wünsche g
n
itlu
s
n
o
C
n
BCG X o ts
o
B
y
b
4
2
0
2
Shervin Aaron ©
Mike Beyer Nick Tanaka th
Khodabandeh Arnoldsen g iry
p
21 o
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112,bcg,BCG-Executive-Perspectives-Future-of-Sales-with-AI-EP2-5Aug2024.pdf,"Executive
Perspectives
The Future of Sales with AI
B2B Sales
August 2024
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Introduction In this BCG
Executive Perspective,
We meet often with CEOs to discuss AI---a topic that is both captivating and rapidly
we articulate the vision
changing. After working with over 1,000 clients in the past year, we are sharing our
and value of the future
most recent learnings in a new series designed to help CEOs navigate AI.
With AI at an inflection point, the focus in 2024 is on turning AI’s potential into real of sales with AI
profit.
In this edition, we discuss the future of B2B sales, and the role AI will play in
turbocharging growth. We address key questions on the minds of sales leaders:
• What will my sales team look like? Will I need a different team?
• How will the economics of sales change?
• How will the customer experience evolve as a result?
• How do I get started…and how do I get this right?
This document is a guide for CEOs and sales leaders to cut through the hype
around AI in B2B sales and understand what creates value now and in the
future.
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Executive summary | The Future of Sales with AI
Market conditions and economics of B2B sales are rapidly changing: increased competition, evolving buyer
expectations, and economic uncertainty create a burning platform to reshape B2B sales
The time to act
GenAI is a breakthrough technology that, combined with PredAI, enables a step-change from traditional sales to
on AI in sales
augmented, assisted, and autonomous selling
is now
There is an opportunity to drive 1.8x margin impact through revenue growth and increased efficiency
Leading players are starting to scale, so companies need to mobilize to stay competitive
AI will reshape Reshape B2B sales teams and roles with massive seller productivity gains, augmented by AI team members
B2B sales teams and autonomous agents – with specific roles and scale of impact differing by industry
and customer Reshape customer experience by breaking down functional siloes between sales, marketing, and service, and
experience enabling new buying experiences
To successfully deploy AI in B2B sales and drive outcomes @ scale, organizations need to take a portfolio and
Executing transformational mindset, combine GenAI and PredAI within the tech stack to enable AI team members,
successfully and rewire the op model with a 90% focus on people and process change
requires a Sales leaders play a critical role in driving this change, breaking down siloes between teams, and making bold
transformational investments in tech and upskilling
mindset To get started, define your objectives and North Star, prioritize use cases, and start with proof-of-concepts that
demonstrate value, and scale up successive waves of capabilities while enabling the sales team
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Why now | 6 key trends shaping market … fueling the burning
conditions and economics for B2B sales … platform to transform
Commoditization and a surge of new entrants are driving companies to invest in
Increased
retaining customers via post-sales support, customer success programs, relationship
AI can shape companies’
competition
management, and value-added services
response to these dynamics…
• Personalize offers and
B2B buyers expect a consumer-like buying experience with ease of access to
Shifts in buyer
information and quick response times, pushing sellers to offer intuitive and user- experiences
expectations
friendly buying processes
• Predict churn and trigger actions
• Automate routine tasks and
Longer sales Decision makers are getting more complex (e.g., buying groups) and taking more services
cycles time to evaluate options, due to increased scrutiny on ROI and cost-effectiveness
...while unlocking more growth
Sales teams are becoming more specialized, requiring more comprehensive and with higher returns
More sellers involved
integrated sales strategies to address complex buyer needs with cross-functional
in sales process • Drive more effective acquisition
teams
• Unlock better cross-sell/up-sell
• Reduce cost to serve
More complex The ecosystem of partners and marketplaces has grown in scale and complexity
channels yet remains a critical channel to drive scale and efficient cost to sell
Leading players are starting to
scale, so companies need to
Rising cost of goods, economic fluctuations, and uncertainty are leading to tighter
Uncertainty and mobilize to stay competitive
budgets and higher scrutiny on spending from buyers while increasing cost
budget constraints
pressure on own P&Ls
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The future of sales | Turbocharging scalable growth with AI at its core
Current AI horizon Next (Gen)AI horizons
Age-old selling Augmented selling Assisted selling Autonomous selling
Entirely seller-driven Insight and productivity Real-time assisted selling Digital sales avatar
Subjective sales motion reliant Sellers armed with AI-powered Real-time support and assistance to Agents enabling auto-prospect,
on seller initiative next-best action, talk tracks, and sellers during customer engagements, nurture demand, 24x7 engagement,
basic workflow automation reshaping workflows and teams involving humans as needed
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The Future of Sales Imagine a world where…
Autonomous sales agents … autonomous sales agents own sales motions from acquisition through support, for B2C
and long-tail B2B customers, enabling personalized selling at scale
GenAI-powered virtual … sales teams are augmented by GenAI team members, like intelligent sales assistants,
team members providing personalized scripts and customer insights or virtual solution engineers, navigating
complex portfolios and customizations
Divergence of strategic vs. … virtual sales assistants take over more transactional tasks, reducing the need for human
transactional sales intervention in standard transactions and freeing up time to focus on strategic and relationship
selling
“Smart selling” through real-time … predictive selling becomes the norm, with automated, real-time analytics and coaching fully
analytics and coaching integrated into sales tools, enabling agents to sell to the right customer, in the right moment with
the right offer, price, and message
AI-powered hyper-personalization … personalized offers, promos, sales pitches, and sell-in materials based on real-time buyer
behavior and data analytics are produced at 10x the speed, breaking down traditional silos
between marketing, sales, and pricing
Highly autonomous sales operations … fully automated AI systems manage much of sales operations, including targeting, lead
scoring and nurturing, and forecasting, reducing errors and increasing efficiency
Revolutionized sales enablement … AI-powered coaching and scenario-based learning based on real-world insights from
everyday sales interactions unlock step-changes in seller performance, reduced ramp-up time, and
dissemination of best practices into everyday action
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Value | 1.8x margin impact through CLV growth and GTM efficiency
Seller Joe, with a sales quota of $10K EBITDA per week ($4K loaded per week + 40% indirect sales costs )
Realization of these types of
$1.5 EBITDA per $2.7 EBITDA per
$ GTM Cost $ GTM Cost impacts requires investment
… 50% better … 75% better 40% increase 50-60% of tasks ~50% decrease AI capabilities at scale,
in digital addressed by in back-office
acquisition1 cross-sell2 embedded in the flow
channels3 GenAI4 costs5
of work
$1.8K
Upskilling and new ways
$1.4K
$1.3K
of working for existing
$1.2K
sellers
$2.3K … 5%
expansion
… 25% $18K
in margin
lower New talent and agile
churn6
$10K operating model to
continually innovate
User engagement
More Higher value More digital Increased Optimized
customers per customer engagement seller back office throughout the journey
productivity
to enhance adoption
1.Assuming 30% new vs. recurring business; 2. Assuming 10% cross-sell of full deal value; 3. Assuming 20% digital value;
4.30 – 40% conversion of time to revenue; 5. Assuming 50% reduction in contract management, issue resolution, and data 6
management; 6. Assuming 10% churn rate. Source: BCG experience
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Value | We are moving clients toward the future state and unlocking value
through cutting-edge AI solutions across the sales life cycle
Cross-industry, not exhaustive
Discover Learn Try Buy Use
Product-Need Identifier Sales Buddy Autonomous Chatbot
Sales Avatar (Sophia)
GenAI to identify product needs based on Offer recommendation visualization Suggest next-best answer to
Replace human touchpoint with
websites, PDFs, 3rd-party databases and call-flow guidance agent (based on customer
an avatar (video+voice)
• 10%seller productivity boost • 5%revenue uplift profile and product catalogue)
• 54%uplift in sales
• 26%conversion uptake • 33ppimproved offer accuracy • 54%uplift in sales
Real-Time Negotiation Support • 26%conversion uptake
Content Generator Provide agents with real time transcripts, • 2x increase in breadth of
Generate pictures/text for emails or sms summaries, and recommendation on next-best products sold
topicduring customer calls
• 5hrs/week time savings for seller
• 2xcross-sell and up-sell lift
Virtual Assistant
Reinvent the customer
Sales Info Assistant Post-Call Email Generator Customer Service Chatbot
experience with product reco
Support sellers with necessary info, on Generate emails based on conversation Customer-facing GenAI-
and trial
product description and application or client (GenAI content and outcome powered chatbot to handle queries,
• 2x higher ROI
RAG: from PDFs and websites) basic transactions
• New customer experience
• 30-40new cross-sell leads per rep • 20% reduction in customer
Relationship Co-Pilot
• 3-5%EBITDA increase in pilot regions service cost
Support account and relationship managers
to prepare for customer-centric conversations
RFP Responder Engineer Co-Pilot
GenAI agent to independently Support solution engineers by taking in
create and measure winning RFPs customer and technical needs from various Sales Coaching
• 50%faster creation input sources and develop specifications Train agents systematically and at scale based on call records, leveraging the right
meeting marketrequirements argumentation and coaching skills
• 35%quicker comparison
• 15%improved seller performance
Autonomous Agent Sales Assistant Solution Engineer Sales Coach
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Example use case | Sales assistant provides next-best action,
improves quality of customer conversations, and increases efficiency
High impact during 1st experiments
Five key elements for AI buddy solution
1 Centralized Customer info, including interactions,
customer info in single, unified interface
2 Product Ranked product recommendations recommendations based on customer data and triggers
3 Comprehensive Descriptions, applications, and pitch
product information ideas, to support sales efforts
4 Real-time feedback Collection of feedback for continuous
and selling tips improvement and selling tips
5 Direct access to Products and SKUs filtered by
product details and availability, direct access to detailed
sheets product sheets provided for customer
presentations
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Example: Industrial Goods
1
One-stop-shop of customer Customer Information Customer Interaction History Actioned Recommendations History Pin to Top Collapsible
Account Information
inform[aSatniiotizned,] including history 4
C -orporate Branch S Ce 1g 5ments S Inu db us se tg rm iae l Cnt os atings ImmC Ao uu stnert ar ly iadiate feC Pohela yi MndergebSoluationcs Inkc collection 30-40 New leads on cross-sell per
for continuous improvement
[Sanitized] External Data sales rep every month
Annual Revenue Number Of Employees
123414 124,235
[Sanitized] Status
Active
5
[Sanitized] I Nnternal Company Info on product category: 3-5% EBITDA increase in pilot
description, application,
regions expected in 20241
RECOMMENDATIONS Product Category CDM feedback: Accept Reject HOW DO WE ADaD VnALUdE pitch ideas
Share of Wallet •When offering this product category, also discuss with client our
Synthtic Waxes  5.0 Synthetic Waxes Send Mail blending services
Ethylacetate  4.0 98% Sales rep satisfaction and
Xylene  3.0 Description: DATA DRIVEN TIPS
Propanol  2.0 dS eyn sit gh ne eti dc tw o a px re os v ia dr ee sa p g er co ifu icp po rf o i pn ed ru tis et sr i sa ol tc h the em ci oca al ts in u gs se td h ein y t ah re e p ur so ed du ic nt ,io sn u co hf i an sd gu ls ot sr sia ,l h c ao ra dt nin eg ss s. , aT nh des se c rw aa tcx hes r ea sre is tc ar nea ceted through chemical processes and are •T toh ge e tc hu es rt o wm ite hr Sb yu ny t h3 e p tir co Wdu ac xt es s, .that are often used in end products high adoption rates across2
Dentonites  1.0 Applications: •The purchase of Synthetic Waxes has been rising in your region
Additives in the formulation of industrial coatings
Combination Product Trigger •Improving the durability and appearance of coatings
•Providing protection against environmental factors such as UV radiation and moisture
Other Inorganic Flame Retardants
Pitch ideas:
Seas No in tra il tT er sigger C • •o C Cs u ot nse t sf of ie smc tet izi nv ee td qs w uyn aa lt x ih e tye s t atic o n dW m sa e ux e pe t p s s lpw ye i ft c oh i rf is c uu ncp o ine ar tti eio n rr rg up pre e tr eqfo dur i pm re ra omn de uc ne ct ts ion [Sanitized] 6 Additional selling tips,
2 Gap FillingTrigger Alte Nr an ta ut ri ave l s w: axes = Silicone-base additives – Polyethylene waxes including reasons why
RecommeOrgnanicd Pigmaenttsions selected by AI
by AI andPurc htaser Reimgindegrsers,
Methylethylketone (MEK)  5.0 Product Search: Search Product Name Search
with ranking
Expe Oc tt hed e rO Gr ld ye cr os lethers  1.0 Polyolefin Wax 7 Products and SKU details,
Description: All Product Categories pS ry on vth ide et sic p w eca ix fie cs p a rr oe p a e rg tr ieo su sp o o t f t hin ed cu os atr tii nal g c sh te hm eyi c aa rl es uu ss ee dd ii nn , t sh ue c p hr ao sd gu lc ot sio sn , h o a[f r i dn Snd eu ass st n,r aia nil dtc o isa czrti aen tg cdhs. r]T eh se iss te a nw ca exes are created through chemical processes and are designed to filtered by availability Search by Product Category Search
Product information:
Precursor: Biocide, Chemical weapons, Explosive, Marine Pollutant
3 Format: Liquid Search option to get
Available SKUs:
information on other [Sanitized] 8 Direct access to
products during call/visit
Documentation: PDS_Polyolefin Wax.[pSdf a SnDiSt_iPzoelyodle]fin Wax.pdf product sheets
[Sanitized]
[Sanitized]
1.Based on initial indicators during project/pilot
2.98% of responses of sales-= rep are tool/logic ""meets"" or ""exceeds"" expectation in a fully anonymous survey
Source: BCG
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How to get it right | Our perspective on winning with AI in sales
• Leading with a bold vision for the future of sales
Reshaping to
Reshaping B2B sales • Restructuring B2B sales teams to change the composition and
drive teams and the customer introduce AI team members to augment sellers
experience with AI
outcomes • Redesigning the customer experience by breaking functional siloes
between marketing, sales, and service
• Unlocking value with GenAI as the “next layer” to activate PredAI
Combining PredAI with
Unlocking decisions and precision
GenAI to maximize value
data and tech • Accelerating scalable solutions by helping engineer the target state
creation
architecture, leveraging the right ecosystem of partnerships
• Shaping the future sales roles and op model, and scoping the skills
Transforming people and
Rewiring the and change needed
operating model for
op model • Building an AI experiment and scale muscle through build-operate-
competitive advantage
transfer
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Reshaping B2B sales teams | Investment in AI will enable
a change in team composition
Today Future Key changes
70%
redeployed for Brand Marketing • Marketing Ops – automated targeting and fast
Marketing Ops
brand marketing journey tailoring, higher focus on brand marketing
Marketing Ops
• KAM – more accounts per team as non-client-
facing work dramatically reduced
40%
• ISR – shift of interactions to remote settings;
redeployed for
many efficiencies from tech augmentation
growth
• FSR – many interactions moved to remote and
AR-enabled (including demos, order taking)
30%
customer service re-
deployed toward
customer success
Note: Illustrative – only selected sales roles shown for directional impact
1.Original function expected to reduce headcount, and other function (e.g., brand marketing, sales re-skilling, data and tech CoE) expected to increase headcount
MAK Acct Field
ISR
Mgrs Sales
eCommerce Specialists
Distributor Mgmt
MAK
Illustrative – roles and impacts
will vary by industry
1
Acct Field
ISR
Mgrs Sales
Customer Success • Customer Success – differentiator, requires
Customer Success human intervention for complex solutions
• Customer Service – heavy automation through
Customer Service
Customer Service self-serve and autonomous agents
• Sales Training and Enablement – AI to
Sales 1
80% Sales Re-skilling automate training, but critical change mgmt.
Sales Training
Training
sales ops and re-skilling task will be differentiator
redeployed to • Data and Tech – many tasks automated, but
Sales 1
Sales Operations data and tech CoE Data and Tech CoE new data/tech roles required for model training,
Ops
prompt engineering, product owner
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Reshaping B2B sales teams | Value levers will differ across
sales models and industries
High
Precision matching Deep customer insights for
of product and customer complex relationships
(e.g., personalized product (e.g., deal and commercial coaching,
recommendations, product configuration) solution optimization and SOW generation)
Customer retention and
Higher service level and
direct digital engagement
hyper-personalization
(e.g., churn prediction, virtual
(e.g., offer/pitch personalization,
autonomous seller, automated call
pricing optimization and approval)
guidance and talking points)
Low
Customer concentration
ytisrevid
tcudorP
syenruoj
remotsuc
detamotuA
dna
tnempoleved
pihsnoitaleR
nalp
tnuocca
Deep technical and product information
Personalized, value-driven messaging
Low High
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Reshaping B2B sales teams | New AI team members
will amplify the impact of sales teams
AI sales team supporting
the seller
Intelligent Sales Assistant
Seller
Owns administrative tasks and helps
sellers engage the right customers with
the right offers, qualify, convert, and
Customer close deals
Sales Planning and
Solution Engineer Operations
Develops proposals and solution Executes sophisticated
configurations, provides technical input planning to optimize
as a part of the sales cycle coverage, territory design,
and goal setting. Advanced
automation for deal desk,
Sales Coach
approvals, performance
Provides sellers with real-time management functions
recommendations, enables effective
Virtual Seller practice, guides managers on where to
spend time
Engages directly with customers from customer
identification through closure in an entirely
AI-powered channel
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Reshaping customer experience | AI will break functional siloes
between marketing, sales, and service to better orient around the customer
Discover Learn Try Buy Use
Develop the content, With the help of the
In the time to typically do customer research, quickly
campaigns, and journeys to sales assistant, sales
tailor automated journeys and content that align to
enable sellers to tailor for teams can tailor
buyer needs and leverage seller insights
individual customer journeys campaigns, engage and
nurture customers
Sellers are more effective and efficient in
Time re-deployed to their core sales activities: relationship, Time re-deployed to
support retention pipeline, and deal management build upper funnel
With the help of the
sales assistant, sales
Set up relevant notifications around key issues Consolidate product, service insights;
teams can stay tuned
and adoption, tailor adoption journeys aligned create standard adoption plans
into adoption and
to buyer's value proposition and QBR templates
usage and plan the
right expansion plays
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Unlocking data and tech | Combining PredAI + GenAI
to maximize value creation
(Traditional) Predictive AI/ML GenAI for augmentation
for decision making and automation
Precision – eliminating the guess work Productivity and performance improvement
Which client or person Insights about sales performance
Human augmentation
Which action
Sales prep
• Sales coach
• Sales collaterals
Which product
In-flight selling
• RT sales advisor
Future of • Product advisor
Which offer or price Sales Sales workflows
• Digital content generation
• RFP generation
Which channel
Automation
Autonomous sales agents
When
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Unlocking data and tech | New reference architecture as tech stack
evolves to support integrated delivery of PredAI and GenAI at scale
Sellers and Customers
New/upgraded elements
ytiruceS
noitargetnI
Key evolutions
1 Engagement Channels Existing and new engagement channels 1 Engagement Channels become tightly
integrated through the smart business
Conversational apps (Gen)AI apps Omnichannel layer, enabling sellers and customers to
2 Smart Business Layer interact with each other and GenAI team
(Gen)AI services App builders Business services
members, seamlessly flowing between
channels of choice
Guardrails
2 Smart Business Layer adds (Gen)AI
Orchestration
E2E app Ops and
3 (Gen)AI Layer applications and supporting
vendors Model garden Foundation models ML models monitoring
development tools powered by GenAI,
also enabling the integrated delivery of
Model platform
PredAI and GenAI. Hosts the GenAI
team members
Data products
Operational 3 New (Gen)AI Layer supports secure
4 Data Layer Repository and storage
data services
access to and use of both internally
Distribution and integration
and externally hosted foundation
models, together with any existing ML
Core Transaction
CRM ERP models
Layer
Infrastructure 4 Data Layer includes new data sources
On-premise Cloud Hybrid TPU/GPU
and Cloud (typically unstructured and of new
modalities) and the means to ingest
and use them in (Gen)AI applications
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Note: The engagement channels are typically represented as part of the respective components of the smart business layer, but
we have explicitly represented them here given their importance in the sales processes
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Unlocking data and tech | Integration across all communications modes
made possible through smart business layer and GenAI
Phone
Text
Email
Collaboration and IM
Doc Creation
Mobile Experience
CRM
Integrated Apps
Stand-alone apps
reyaL
ssenisuB
tramS
Phone
Text
Email
Collaboration and IM
Doc Creation
Mobile Experience
CRM
Integrated Apps
ataD
lanoitcasnarT
dna
setadpU
Current State: Proliferation of mostly separate tools Future State: Integration across all channels, with
and channels, large effort to coordinate across them seamless seller interactions flowing between them
reyaL
ssenisuB
tramS
Seller Seller
• Attempts to create ""SuperApp"" as singular channel providing • GenAI enables integration of all channels, across all modes
all capabilities have largely failed (text, voice, image, video, etc.) via a smart business layer allowing
• Integration between channels is largely point-to-point for easy addition of new channels
• Manual work is expected to translate and capture interactions • Conversations with customers and GenAI team members seamlessly
apart from those in text (e.g., submitting call reports from visits) transition between channels of choice and need
Sales engagement density 16
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Rewiring the op model | Sales AI transformation is 90% change management
Focus on people and process rewiring while building tech, data, and AI capabilities
Compared with typical data-driven transformation, the success of sales
AI relies even more on change management across a sales organization
Typical digital
transformation:
90% 10%
10%
AI
Focus on sales Focus on technology,
change management data, and AI
20% • Leadership activation: drive • Deploy sales technology
Data and technology
enthusiasm and clear sales vision to the frontline
• Sales team engagement: co-create • Utilize sales-specific ML
and iterate with sales reps models, traditional AI,
70% Business process and GenAI • Executional excellence: redefine
change management
sales processes and roles • Integrate sales systems
and automate E2E • Culture and effectiveness: adapt
sales strategies and KPIs
• Training and enablement: upskill
teams and build capabilities
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Rewiring the op model | Five pillars of sales change management
to ensure sustainable impact from AI transformations
Leadership People Executional Culture and Enablement and
Activation Engagement Excellence Effectiveness Training
• Activate leadership to • Iteratively co-create • Refine sales • Implement new • Implement rapid tool
create role models for tooling and tech with the organizations and roles communication and training
upcoming change frontline to ensure robust collaboration tools
• Ensure responsibility for • Develop training plan
management technology from the get-go
global sales tool strategy • Introduce gamification (e.g., new role-play training
• Equip leaders with tailored • Continuously refine and roadmap features to drive peer leveraging KPIs/alerts)
messaging and tools to based on recurrent and competition/recognition
• Adapt key processes • Build up (Gen) AI
effectively frequent feedback
(e.g., shorter, more • Refine KPIs to reflect champions (black belt
communicate the change sessions
dynamic, cross-functional) productivity gains logic) to act as multipliers
vision and benefits
• Share progress with and drive change
• Review omnichannel • Ensure (short-term)
• Create excitement in frontline to foster trust in organically
collaboration including incentives drive
leaders and end users by the transformation and
capacity of team adoption and cross- • Activate leaders and
integrating comms outcomes of every sprint
members and priority functional/team/regional champions in sales teams
approach into existing
shifts collaboration (e.g., via train-the-trainer
sales, global, and geo
initiatives)
forums across channels • Reflect required • Implement user-level
governance changes monitoring
(e.g., decision input, tech
participants)
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How to get started | Our perspective on the road to
unlock the value of AI in sales
Set a clear Define business objective and value levers for your AI transformation (e.g., productivity gains,
objective cost reduction, revenue growth), including upfront success metrics to measure impact
Define Define how you will leverage AI to reshape your sales approach and map required rewiring of
North Star op model (people, process, ways of working, etc.)
Assess Identify maturity of tech stack, create roadmap to required target, and invest in foundational capabilities
tech stack to sustain transformation
Prioritize Select use cases, starting with highest-value ones to fund the journey and defining detailed action plans
use cases to seize them; start small but build to scale
Build proof Develop proof of concept to validate value, test, and capture implications considering principles of
of concept responsible AI
Enable Create excitement, enable team participation, and protect learning capacity for quick upskilling and
your team early adoption through personalized change management plans
Develop a Rapidly develop a comprehensive workforce plan to identify and close talent gaps, ensuring the
workforce plan necessary skills and support are in place for the broader transformation
20
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Our AI offer | Helping clients reshape the future of sales
Reimagination and transformation of client's sales org
As a partner
to fully profit from the new tech capabilities
• Higher effectiveness and efficiency of sales reps (inside, field, KAM, …) • Co-creation from
• Decision support for sales leadership and sales operations 1st day of project
Fast
• New tech-enabled routes to market
realization • Enablement to
advance the
of P&L
by
AI/GenAI journey
impact
• Integrated
at scale
approach,
Modular tech assets to accelerate time-to-market, including change
augmenting tech stack toward target state architecture management and
skills development
PredAI+GenAI Data Enterprise
Integrations …
assets platforms applications
21
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NAMR
BCG Experts |
Phillip Bryan Japjit Audrey
Andersen Gauch Ghai Hawks
Key Contacts
for the Future of
Matt Justin John Marina
Kalmus McBride Merchant Nekrasova
Sales with AI
Ben Jit Matt
Quirt Tan Ward
EMESA
Alfonso Lena Roberto David
Abella David De Angelis Galley
Ignacio Juan Martin Patrick Basir
Hafner Roda Maglione Müller Mustaghni
Jatin Guillaume
Srivastava Triclot
21"
113,bcg,BCG-Executive-Perspectives-CEOs-Roadmap-on-Generative-AI.pdf,"EExxeeccuuttiivvee
PPeerrssppeeccttiivveess
The CEO’s Roadmap on
Generative AI
March 2023
0
Introduction to this document
The release of ChatGPT in late 2022 is analogous to Mosaic’s launch
three decades prior. In 1993, it was clear that the internet would bring
a major revolution across all businesses in less than a decade.
The most focused of business models, and the strongest of brands can be In this BCG Executive
blown to bits by new information technology
Perspectives edition, we
-- Philip Evans, in his book ""Blown to Bits""
explore how CEOs can take
Similarly, it is clear that Generative AI will bring another major
full advantage of the
revolution across all businesses. Today companies are focused on
productivity gains and technical limitations, but CEOs need to move coming revolution with
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the focus to business model innovation. v
Generative AI
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This is no small task, and CEOs—who are likely several steps
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Human-AI augmentation of the future
Focus on standardization
• Use cases focus around automation
Prior to
and routinization to reduce
• Humans as passive recipients of technology tools
Traditional ML costs and replace human
• Humans as operators of processes
effort
Focus on augmenting
• Use cases around making decisions with data
With
decision making to create
• Humans actively using technology with data
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CEOs don't need to understand the technology behind Generative AI to create
business model innovation; instead, they need to understand its key features
No Code / Low Code ""Infinite Memory"" Lack of Truth Function
With a convenient chatbot-like Generative AI, trained on vast As a probabilistic model,
interface, Generative AI amounts of data, offers users Generative AI generates the most
democratizes access for all access to an automated system likely output to a query. This can
including those not well versed in that provides seemingly infinite sometimes create hallucinations .d
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tech. ""English is the hottest new memory and acts as a i.e., outputs completely separated se
programming language"" according knowledgeable personal aide2 from objective truth
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NOTE: 1. Andrej Karpathy is a premier computer scientist and one of the founding members of OpenAI. 2. While not technically infinite, GPT-3 was trained on ~500 billion tokens, which gives users an 3 p o
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impression of an ""infinite memory"" database
Executive Summary | CEOs must make choices across three key pillars
Discover your strategic advantage through experimentation
POTENTIAL a. Generative AI is accelerating across every industry, it is time to act now or be left behind
1 b. Use cases that rely on existing large language model (LLM) applications will be important to stay
Which use cases will
competitive, but they won't offer differentiation – CEOs need to discover the company's golden
differentiate your
use case
organization?
c. When use cases are identified plan the right implementation approach: fine-tune or train
d. Plan for long-term advantage through investment in talent and infrastructure
Prepare your workforce with strategic workforce planning and transforming op models
PEOPLE
a. CEOs will need to address key org questions for change management, talent and operating models
How should CEOs adapt
2
b. Generative AI will redefine roles and responsibilities across the organization
org structures and .d
prepare employees for
c. As AI adoption accelerates, CEOs need to develop a strategic workforce plan e v re
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deployment? d. CEOs will need to consider new operating models,however we expect thatagile (or bionic) r sth
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Protect your business with clear policies that address the limitations of Generative AI g n
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a. Generative AI presents critical risks for which companies will need to be prepared C
How will the company 3 n o
ensure ethical guardrails b. Prepare for risk through clear policies and training that define roles and responsibilities on how to
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and legal protections are use Generative AI with a measure of confidence b 3
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in place? c. CEOs should ensure the organization adapts responsible AI norms for long term risk mitigation ©
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BCG Executive
Perspectives
AGENDA Potential: Discover your strategic
advantage
People: Prepare your workforce
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Interest in Generative AI is exploding, fueled by the launch of ChatGPT
1a It is time to act now
Interest in Generative AI has grown This is driven by the release of ChatGPT,
exponentially since Q4 2022 which has taken the world by storm
Google Search Interest (100 = max interest)
Wall Street
100
Journal
90 Interest in
80 ChatGPT
70
60
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Even at it's peak, interest in v
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09/21 10/21 11/21 12/21 01/22 02/22 03/22 11/22 12/22 01/23 02/23
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…and many more B
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ChatGPT Metaverse ©
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Companies are already seeing a transformative effect from
using Generative AI
1a It is time to act now
Technology Consumer Biopharma
Automated on-model fashion image Generative AI Identified a novel drug
~88%
generation resulted in candidate for the treatment of Idiopathic
Pulmonary Fibrosis in
1.5X
Of software developers 21 days
reported higher productivity when using a
generative AI code assistant1 Increase in retailer conversion rate2
(vs. years with traditional methods)3
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Financial Institutions Entertainment Insurance
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Synthetic GAN-enhance training set for Generate novel animated motions from a InsureTech platforms leveraging .p
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fraud detection achieved a single training motion sequence with generative AI to reduce up to o rG
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accuracy rate quality score on natural movements of customer service costs6 B
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(vs. 97% with unprocessed original data)4 (vs. 84.6% with traditional methods)5 3
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1. Quantifying GitHub Copilot’s impact on developer productivity and happiness 2. Vue.ai helps fashion retailers create high-quality on-model product photos 3.Deep learning
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enables rapid identification of potent DDR1 kinase inhibitors 4. Using generative adversarial networks for improving classification effectiveness in credit card fraud detection
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5. GANimator: Neural Motion Synthesis from a Single Sequence 6. Insurtech COVU Leverages OpenAI to Streamline Insurance Agency Operations C
Total addressable market is expected to reach ~$120B by 2027
1a It is time to act now
Generative AI TAM ($B)
CAGR
121
2022-2025
32 BFSI2 75%
88
+66%
21 Consumer 64%
23
61
15 22 Healthcare 85%
15
35
11
15
16 Media 59%
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9
1
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3
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4
3
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5
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NOTE: 1. Other includes Industrial Goods, Energy, and Telecom markets; 2. BFSI includes Insurance (~$2B 2025) and Financial Institutions (~$13B 2025) including retail and
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Source: AI TAM research; Expert interviews; BCG analysis C
Focus on Building
Long-Term Advantage
1b Discover the company's golden use case
Strengthen competitive positioning with truly unique use
For the CEO, the
cases that both drive value and are challenging to adopt
key is to identify CORE OR (i.e., have a barrier to entry for competitors)
the company’s ""GOLDEN"" • For example, in pharmaceuticals companies,
Use Cases Generative AI can drive core R&D to produce new
so-called golden
drugs/molecules at record pace
use cases that
drive competitive
advantage
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Golden use cases will add to a company's unique competitive advantage
in the marketplace, while non-core use cases are readily adopted by all
1b Discover the company's golden use case
Productivity Gains Efficiency Gains Innovation
First drafts Predictive maintenance Building novel proteins
with Jasper AI with an Equipment Manufacturer with ProFluent
What is Jasper Doing? What is the Equipment Manufacturer Doing? What is ProFluent Doing?
Web-based application for businesses powered by Building proof-of-concept for global end-to-end Creating novel proteins that do not exist in nature,
Generative AI that helps teams create tailored predictive maintenance of fleet with IoT sensors aimed at advancing drug treatment. Proof-of-
content up to 10x faster powered by Generative AI concept shown with creation of novel proteins
with anti-microbial properties
How is the Equipment Manufacturer Doing it?
How is ProFluent Doing it?
How is Jasper Doing it?
IoT sensors constantly monitor key indications of
Using ""inverse design"", i.e., working backwards
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performance through signals from parts, and relay from desired properties to create proteins.
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copyediting, advertising, and content creation
back-end software
will be discovered using this method
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Why is generative AI better vs traditional ML? Why is generative AI better vs traditional ML? Why is generative AI better vs traditional ML? .p
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Traditional ML incapable for such a task. It does Identification of anomalies in sensor data is Similar to Jasper, traditional ML does not have rG
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Productivity improvement will be table stakes For the equipment manufacturer, high quality of maintenance is a core part of their business 2
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since all businesses will adopt model. Similarly for ProFluent, protein synthesis is at the heart of their business. Generative
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While foundation models today are used for generative use cases, this may
expand to include discriminative use cases as well in the future
1b Discover the company's golden use case
Not exhaustive
Any writing task
Algorithmic
(e.g., meeting notes,
trading
editing)
New material
Discriminative Dynamic pricing Generative
synthesis through
Recommendation
engines
uses of AI engine inverse design uses of AI
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Ad spend
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Once use cases are selected, CEOs should make strategic choices
about whether to fine-tune existing LLMs or to train a custom model
1c Fine tune or train
Decision Tree for Foundation Model Choice
Open-source likely cheaper to
Cheaper
modify/operate, but requires more talent Consider open-source model
Consider data privacy implications of
Consider several tradeoffs – storing it in the cloud vs. on-premise Fast to implement
performance, budget, in-house Finetune
NO talent to modify/maintain, Models Limited flexibility
safety guardrails etc. Buy access to foundation
model provider Dependent on core-
Models from providers more expensive, but
Buy from provider with service agreement model
likely better along other dimensions
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1c Fine tune or train
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foundation model foundation model foundation model
Create a new foundation model Partner with LLM provider to significantly Fine-tune existing foundation model for
in-house from scratch. Costs scale enhance existing model (e.g., feeding related tasks (e.g., fine-tuning ChatGPT for
with model complexity complex company-proprietary data) legal memo writing)
$50 - $90M+ $1 - $10M $10 - $100k+
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everything/. 3. Training runs here likely less intensive than full-scale model training, leading to lower costs.
Carefully assess the timing of Generative AI investments considering tech and
talent; move too soon and risk wasting money, too slow and risk falling behind
1d Plan for long-term advantage
It could take 5+ years for low error tolerance use cases to be feasible1 … …but research is becoming
proprietary
Error tolerance
Key metric to evaluate readiness of Generative AI is the error tolerance of chosen use-case
Open-source:
OpenAI's GPT-2
Research is also
Adoption Illustrative moving very quickly:
Near-term | HIGH
1
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released 2/24/23,
Use cases where errors are OK outperforming GPT-3
• e.g., drug development since on many tasks
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BCG Executive
Perspectives
AGENDA Potential: Discover your strategic advantage
People: Prepare your workforce
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To achieve the Human-AI augmentation of the future, CEOs should answer
questions for change management, workforce planning, and op model design
2a Address key organizational questions
Key considerations to craft a Generative AI adoption plan
Managing Culture and Strategic Organization and
Change in Company Workforce Plan Operating Model Design
Overarching Cultivate a culture that embraces Build a workforce that will be Create an efficient operating model
Goal AI like another coworker competitive 10 years from now that balances scale and agility
Key • How can professional identity concerns be • What new skills and talent will be crucial for • What existing roles and responsibilities will
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While traditional AI has augmented the capabilities of managers and decision
makers, Generative AI will augment the capabilities of individual contributors
2b Redefine roles and responsibilities
Traditional AI/ML Generative AI creates
empowers individuals to first draft content,
make decisions, changing changing the role of
the role of managers individual contributors
This is the first time that a
Traditional AI and ML algorithms Generative AI augments technology developed in
augments decision making content creation
Silicon Valley benefits the
Lower-level individuals can now make data- Individuals will spend less time creating
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While traditional AI has augmented the capabilities of managers and decision
2b Redefine roles and responsibilities
makers, Generative AI will augment the capabilities of individual contributors
FROM: Key roles today TO: New roles tomorrow
A role centered around creating marketing A supervisor role with AI on content, with
content and executing campaigns increased time devoted to strategic thinking
• Creating marketing content and ideas • Supervising AI for first drafts of creative
from scratch briefs and brand guidelines and overall
better and faster marketing content
• Managing social media accounts,
scheduling and uploading posts • Building deeper relationships with
customers, suppliers, and brand
• Writing creative briefs to interface with
ambassadors
advertisement agencies
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2b Redefine roles and responsibilities
Generative AI will redefine roles across the organization
Carefully consider the professional identity of your employees when making changes to role definitions
Tasks today that Generative AI Future tasks
Sample roles can provide first drafts for (in addition to verifying first drafts)
Social Media Creating social media content, scheduling
Building relationships with customers and followers
Specialist and uploading posts
Marketing
Advertisers Developing creative material (e.g., videos) Exploring new advertising channels and opportunities
Preparing and maintaining financial Identifying and implementing new accounting policies and
Accountant
accounts programs
Finance
Ensuring compliance with labor laws and regulations,
Payroll Specialist Processing employee payroll and taxes
providing guidance and support to employees .d
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While traditional AI has augmented the capabilities of managers and decision
2b Redefine roles and responsibilities
makers, Generative AI will augment the capabilities of individual contributors
Employees are expressing concern about To successfully adopt Generative AI,
the impact to their professional identity CEOs must alleviate these concerns
Work with HR to understand how roles will
evolve and regularly pulse check employee
sentiment as their AI initiatives roll out
The Atlantic
Develop a transparent change
management initiative that will both help
employees embrace their new AI coworkers and
TIME ensure employees retain autonomy .d e
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As Generative AI adoption accelerates, CEOs need to use their learnings
to develop a strategic workforce plan
2c Develop a strategic workforce plan
ANTICIPATE
Understand talent and skills needed
DEVELOP
to deliver on business strategy
Upskill and reskill talent at speed with
high reach and high richness • What workforce changes are needed
as the company steadily adopts Generative
• What key skills will be needed to work AI?
effectively with Generative AI?
• What are the risks associated with
• What training programs can upskill the workforce changes, and how to mitigate
DEVELOP ANTICIPATE
workforce at speed? them?
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Consider centralizing the IT/R&D function supplying LLMs and data engineers
2d Consider new operating models
Senior C-suite role (e.g., Chief AI Officer)
Elevates the importance of Generative AI to the C-suite
and signals importance across the organization
Finance
Each functional department
interfaces with the Central IT/
R&D to:
• Supply all collected data for
model training
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We expect that agile (or platform) models will remain the most
effective and scalable in the long term
2d Consider new operating models
Decentralized
Sample model for a platform organization
Front end teams have autonomy to
serve customers
Division 1 Division 2 Division 3
(e.g., B2B business) (e.g., B2C business) (e.g., Mfg. business) Scalable
Processes are identified and scaled to
BD team BD team BD team
serve front end teams and to learn
Operation team Operation team Operation team
Front-end
Product team Product team … Product team Flexible
… … …
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BCG Executive
Perspectives
AGENDA Potential: Discover your strategic advantage
People: Prepare your workforce
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Risks associated with Generative AI are showing up in the real world rapidly
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GenAI creates fundamental shifts impacting the Responsible AI (RAI) approach
Ease of use is much higher now: Shadow AI will be on steroids:
• Anybody (even non-technical staff) can • Capability overhang can emerge in
use these capabilities with very few unexpected corners of the organization
technical resources (e.g., data, (e.g., compared to only technical divisions
compute, expertise) before)
• Smaller need for large teams and • Time to detect, resolve, and mitigate
budgets limiting visibility for incidents is much higher violating the
Democratization managers and governance mechanisms principle of surprise aversion in risk
management
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Companies must be wary of critical risks of Generative AI today before
adopting the technology
3a Generative AI presents critical risks Not Exhaustive
Energy use and Capability Biased Copyright
environmental harm Overhang Outputs Infringement
Generative AI uses more energy on Due to its probabilistic nature, Real world data is often biased. Generative AI is trained on publicly
compute, both during model training Generative AI can sometimes show Without oversight, the Generative AI available data, much of which is
and usage than traditional ML. While unexpected capabilities upon models trained on this data also carry copyright protected. This can lead to
more efficient computation techniques deployment (e.g., several users tricked bias. Mitigation techniques include lawsuits by IP holders. Mitigation
are being developed, mitigation today is ChatGPT and bypassed its security to Reinforcement Learning with Human strategies rely heavily on foundation
limited to usage of more access kernel model). This risk is Feedback (RLHF) where the model is model providers to obey copyright laws,
environmentally sustainable energy difficult to fully mitigate, but extensive taught to be unbiased, yet this method and for governments to create new laws
sources pre-launch testing will help is not perfect for Generative AI
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in a very convincing manner. To emails or deepfakes instantly. To proprietary data which the data may be guidance or supervision creating risk
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mitigate risks from using incorrect mitigate this risk, companies must leaked in a security breach. To mitigate and causing harm. To mitigate this risk, C
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double checking all Generative AI train employees on new safety risks, to train models on-prem vs. cloud, clear Generative AI use guidelines and y
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outputs, and limiting its use to non- and consider deploying Generative AI although this necessitates other policies 2 0
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critical tasks today themselves to to catch fraud tradeoffs ©
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Not all risks are created equal, with some posing a higher bus"
114,bcg,a-genai-roadmap-for-fis.pdf,"A Generative AI Roadmap for Financial
Institutions
NOVEMBER 13, 2023
By Stiene Riemer, Michael Strauß, Ella Rabener, Jeanne Kwong Bickford, Pim Hilbers, Nipun
Kalra, Aparna Kapoor, Julian King, Silvio Palumbo, Neil Pardasani, Marc Pauly, Kirsten Rulf, and Michael
Widowitz
READING TIME: 15 MIN
In just a decade, artificial intelligence (AI) has transformed from a promising research topic into an
accessible and pivotal technology at the center of a new industrial revolution. AI is reshaping
fundamental processes and functions across entire industries, from drug development and airline
scheduling to supply chain optimization and medical imaging. AI is no longer a concept of the future—
it’s a game-changer today. And companies that move ahead decisively and strategically with AI will
gain significant lasting advantages within their industries.
© 2023 Boston Consulting Group 1
Nowhere is this more evident than in the financial sector. AI runs on data, and banks and other
multiline financial institutions (FIs) command vast, high-quality, customer-centric gold mines of data.
In particular, the granular transactional data of a bank’s customer base can provide precise, wide-
ranging insights into behaviors, preferences, needs, and risks in ways that few other industries’ data
sets can. (See Exhibit 1.) For instance, in retail banking, leveraging AI to forecast and tailor future
product offerings on the basis of customer needs and behaviors is rapidly becoming table stakes in
many banking markets.
The rise of generative AI (GenAI) has enriched the broader AI toolkit, accelerating opportunities for
financial institutions to create new value with AI. The ability of GenAI models to digest (understand)
and generate (converse) in plain language makes AI capabilities more universally accessible, extending
the reach of AI assets to nontechnical users throughout organizations.
FI executives should take the arrival of this new phase of technology as an opportunity to commit to AI
and GenAI as key drivers of the industry’s future direction.
In this article, we chart a roadmap for this journey, from integrating GenAI into existing frameworks to
reimagining traditional operations through a complete AI transformation. In the rapidly changing AI
landscape, establishing a firm people strategy is as critical as adeptly navigating the challenges of
governance and regulation. And because the technology progresses daily, a forward-looking AI vision is
imperative for financial leaders shaping the future.
© 2023 Boston Consulting Group 2
Incorporate GenAI in Your Roadmap
Media headlines tend to cast an exaggerated and oen imprecise spotlight on GenAI. The resulting
hype and confusion have caused many executives to question whether GenAI will render their existing
AI strategies and initiatives obsolete. The clear answer is no. Indeed, to the contrary, GenAI
complements AI that is already embedded in existing FI strategies.
Many people in the financial sector informally use the term AI to refer to a subset of AI techniques that
focus on predictive decision-making models. (See Exhibit 2.) Over the past decade, this form of AI has
risen to prominence in FIs primarily because it addresses various prediction and classification
challenges that are pivotal to banking and insurance, such as risk monitoring, optimal pricing, and
product propensity modeling. We refer to this form of AI as predictive AI.
GenAI and predictive AI are powerful tools, but they serve fundamentally different purposes.
Consequently, using them is not an either-or question. A bank’s AI strategy will need to include both of
them going forward, harnessing their respective strengths in different ways.
One way to think of how predictive and generative AI complement each other is on the model of the
two halves of the human brain. (See Exhibit 3.) Predictive AI is comparable to the le side of the brain,
© 2023 Boston Consulting Group 3
wired specifically for logic, measurement, and calculation. This le brain comprises algorithms that
assign probabilities, categorize outcomes, and support decisions. For its part, GenAI acts as the right
brain, wired to excel at creativity, expression, and a holistic perspective—the sorts of skills required to
generate plausibly human-sounding responses in an automated chat.
Rather than negating the fundamentals of existing AI strategies, GenAI adds a new skill set to the mix.
Accordingly, leaders should lean in and consider how GenAI can enhance and extend their current AI
approaches by opening up new opportunities for AI-driven impact.
Many people may initially associate GenAI in banking and insurance with customer service chatbots,
but the technology’s versatility extends far beyond these applications to encompass tasks such as
automated financial analysis and AI-assisted code development. Numerous global banks are exploring
such uses for GenAI models (either built in-house or sourced as a service), and industry giants such as
Goldman Sachs, Deutsche Bank, American Express, and Wells Fargo are already starting to go live
with their solutions.
© 2023 Boston Consulting Group 4
When considering the new opportunities of GenAI alongside existing predictive AI–driven solutions,
leaders should bear in mind that well-proven and potential AI applications now span almost every
aspect of FI workflows, from client-facing roles to back-end operations. (See Exhibit 4.)
To take full advantage of these new GenAI opportunities, financial institutions must sharpen their
methods for identifying, prioritizing, and incubating initiatives that are likely to have the greatest
positive impact on value generation, customers and employees, and quality. Two guiding principles
© 2023 Boston Consulting Group 5
emerge for leaders: be clear about AI’s strengths and weaknesses, and take a disciplined approach to
AI experimentation.
The Boundaries of AI’s Capabilities
As with any tool, it’s important to use AI in suitable applications. In the case of predictive AI, for
example, a credit risk scoring system based on machine learning will make better lending decisions
than most humans when presented with simple credit card applications. But if the task is to assess
loans involving complex structured finance transactions in which every application is unique, it’s better
to let a human decide.
The same holds true for GenAI. A recent study by the BCG Henderson Institute, in collaboration with
leading academics, found that GenAI excels at tasks such as creative product innovation and that
human efforts to improve or enhance model outputs in these areas oen backfired and led to worse
results. On the other hand, for tasks falling outside the technology’s current capabilities, such as
solving business problems, GenAI underperformed against humans, more oen than not hindering the
performance of study participants who leveraged the technology.
In other words, GenAI performs best when humans act as complementors of GenAI output, taking over
tasks that fall outside AI’s domain of expertise (as in the predictive AI example of credit scoring). But
when humans act as enhancers—taking the output and trying to make it better—they can significantly
diminish the value of using AI. (See Exhibit 5.)
Experimental Discipline
© 2023 Boston Consulting Group 6
Evaluating and launching smaller-scale use cases within innovation-driven areas of the business can be
highly beneficial. Creating these types of AI laboratories can help nurture a broader appetite and
greater acceptance for AI solutions within the organization. They also offer a platform to refine new
techniques and build technical capabilities. And they provide a practical way to grapple with key
decisions, such as whether to develop technological foundations in-house, in-source them, form
partnerships, or explore other integration options.
However, the past decade of AI growth and AI experimentation has shown clearly that experimentation
can easily get out of hand. A broad “survival of the fittest” approach—that is, launching a large array
of small use cases to see which few succeed and flourish—oen yields disappointing results. The most
effective AI strategies involve conducting selective experiments in controlled laboratory-style testing
environments. This approach enables leaders to use insights gained from the experiments to pinpoint
a small number of high-impact AI opportunities and rally the organization around them.
As GenAI solutions evolve rapidly, the need for continuous experimentation will remain critical to
harnessing their full potential. At the same time, though, a disciplined approach to experimentation is
essential.
Reimagine AI-Enabled End-to-End Solutions That
Reshape Entire Journeys
The successes and failures of recent AI implementations indicate that companies see greater impact
and capture more value when they holistically reimagine entire processes end-to-end and with AI.
Isolated use cases that focus on a single part of a larger process can shine brightly for a short time, but
they oen burn out young, with the scale and impact of change falling short of expectations. And
incorporating AI into legacy processes built around the needs and capabilities of human workers can
lead to disjointed rollouts and potential friction for employees.
Beyond Tweaking—Transformation
The big wins from AI consistently come from broad transformations that involve rethinking the way an
entire process works as part of an AI landscape. An end-to-end approach isn’t a matter of inserting AI
at every step, but rather of redesigning processes from the ground up with both AI and human roles in
mind for optimized value.
The vast operations of FIs contain a powerful synergy waiting to be unlocked. By leveraging predictive
AI and GenAI in concert with human expertise, FIs can achieve enhanced process efficiency and
effectiveness—an impact greater than the sum of the parts.
Let’s unpack the roles of the two AI domains within FI workflows:
© 2023 Boston Consulting Group 7
• Analytical and Predictive Tasks. These le brain tasks, such as determining the best offer with
which to reach out to a customer, are appropriate for predictive AI.
• Creative and Expressive Tasks. These right brain tasks, such as creating the content and
designing visuals for the customer offer, are better suited for GenAI.
These two simple examples can form the core of a modern hyperpersonalized product marketing
campaign. Predictive AI and GenAI work hand in hand to automate most campaign tasks end-to-end,
from selecting the target customer to deciding on the many parameters and variables of an offering to
writing a tailored message and inserting custom-generated images. But even as AI streamlines many
aspects of the workflow, humans remain integral as complementors, supervising the process and
dealing with exceptions that require human expertise beyond AI’s capability.
Golden Patterns
Although numerous constellations of AI use are possible, many big opportunities that lie within end-to-
end workflows—in particular, opportunities that marry predictive AI and GenAI in complementary
ways—follow basic patterns.
© 2023 Boston Consulting Group 8
One such pattern consists of three steps: (1) process information; (2) evaluate/decide; (3) take creative
action. In practice, this might be the workflow for replying to a customer inquiry, processing a
supplier’s invoice, making a decision on a credit card application, monitoring an account for signs of
money laundering, or writing a section of an investment prospectus. (See Exhibit 6.)
In legacy processes based on human expertise, a human sis through the information, evaluates it,
comes to a decision, and then takes action. But each of these stages in the pattern is an opportunity
for predictive AI and GenAI to team up with the human.
Depending on the specific context, the first step (process information) might offer an opportunity to
use GenAI to synthesize and condense large amounts of information into easily digestible summaries,
or to engage the power of predictive AI to narrow the field of choices by extracting targeted insights
from large data sets.
In the second step (evaluate/decide), a predictive AI model can reliably make automated decisions on
cases that lie within its domain of expertise (typically the lion’s share of cases to be decided) and route
© 2023 Boston Consulting Group 9
the exceptional cases to a human in the loop. Here, the predictive model acts as the central steering
mechanism for the process, independently determining the need for human involvement.
The third step (take creative action), whether it involves composing a loan rejection letter, a suspicious
activity report, or a response to a customer’s question, can oen be turned over to a GenAI model—
for full automation of simple and/or non-mission-critical cases, or at least for preprocessing of
repetitive elements when the occasional imprecision of GenAI is a risk to full automation.
Repetitive, high-volume workflows that follow a golden pattern of this sort in one or more places are
game-changing opportunities to transform the process end-to-end.
Focus the Journey on People and Process, Not Just on
Tech
Rapid advances in AI make it all too easy to become fixated on the technology, the IT implementation,
and the data underlying it. And indeed, leaders face many important challenges here. AI is data-hungry
and can lead to uncontrolled data proliferation, so a clear data strategy is essential. And although a
GenAI model such as ChatGPT is very user-friendly, it is not at all IT-friendly to implement at scale.
But time and again we see instances where soer success factors—the target operating model and its
organizational structures, the approach to AI talent and skills management, and the change
management that must accompany any transformation—are underrepresented and underfunded
within bank’s AI strategies and prove to be the most critical success factors.
Operating Model and Organizational Structure
AI enables significant productivity growth. Work is automated or augmented, and roles must be
redesigned. We see four major types of impact on work that will alter roles across the organization
(and drive the many examples listed in Exhibit 4):
• Repetitive tasks such as low-code/no-code automation
• Knowledge synthesis such as review of all commercial loan agreements
• Data-driven decisions such as automation of vendor negotiations
• Creative tasks such as augmentation of code generation
To adjust to this change, FIs must be bold in rethinking people-driven processes and reimagining whole
functions. This effort will require the creation of more interdisciplinary teams with embedded data,
business analysis, and legal capabilities; the implementation of a flatter and more agile structure for
© 2023 Boston Consulting Group 10
quicker iterations and decisions; and a reduction in spans of control in order to handle the increasingly
complex nature of human work.
Finally, a platform operating model is critical to supporting successful AI adoption. An elevated market
orientation with greater ability to rapidly deploy people, processes, and data will support faster and
more assertive business model innovation and disruption. Cross-functional teams with end-to-end
ownership of products, journeys, and services will support reimagining whole processes, and the
platform operating model’s ability to drive scalability with standardization and without compromising
on customization will be a key enabler.
Talent and Skills
Going forward, nearly every human role will have a relationship with AI:
• Roles that build AI such as technology specialists who create and monitor AI models and
support tech platforms, leveraging deep technical capabilities
• Roles that shape AI such as functional experts who direct AI operations to deliver business
outcomes and integrate models into business processes
• Roles that use AI such as practitioners who work with outputs from AI models, interpreting
resulting content and data to deliver value to customers and employees
• Roles that govern AI such as specialists who monitor AI output to ensure that the soware
drives returns and to verify that the system uses tech safely and ethically
GenAI will have a high degree of impact on certain functions, including marketing, customer service,
legal, and soware development. These functions are likely to see extensive automation, resulting in
significant opportunities for cost reduction, demand generation via higher-quality service, and the
ability to focus resources on higher-value tasks.
Financial institutions must be pragmatic about implementing changes. This entails identifying which
roles have the highest value to their particular GenAI strategy and then developing an appropriate
value-added talent plan. (See Exhibit 7.) To manage the transition to GenAI well across all functions,
executives must integrate GenAI directly into their workforce planning process, defining skills required
in the future state, assessing current workforce potential, devising strategies for filling supply-demand
gaps, and supporting comprehensive culture and change management to inform the organization’s
“build, buy, or borrow” talent strategies.
© 2023 Boston Consulting Group 11
Prioritize Governance, Defining Your Own Rules of
the Road
Achieving transformative impact from AI and gaining acceptance of and trust for AI solutions within
the organization become possible only when the safeguards of a strong AI governance framework are
in place. Without solid governance, both predictive AI and GenAI can easily fall afoul of legal,
regulatory, and reputational hazards. The risk of bias against certain customers, for example, may
increase with large language models (LLMs) that train on biased public data sets obtained from the
internet. Company leaders are struggling with this difficulty, as a recent BCG survey of 2,000 global
executives found. Fully 70% of respondents said that concerns about the limited traceability of sources
of LLMs discouraged them from using GenAI, and 68% said that fear of the black box nature of the
technology and the increased risk of data breaches held back their implementation of GenAI.
© 2023 Boston Consulting Group 12
Regulators around the globe have been busy finalizing specific AI laws, amending them with GenAI
provisions, and updating data privacy, liability, and copyright laws for the new technology. (See Exhibit
8.) But the technology and its effects are evolving faster than ever, so regulatory uncertainty around
GenAI is likely to prevail for some time. Nevertheless, three frameworks are particularly noteworthy for
financial institutions. FIs should expect to receive special scrutiny in all three of these regimes, as their
products are considered essential to citizens and particularly sensitive.
The first and second regimes are the upcoming ASEAN Guide in AI Governance and Ethics (a guiding
framework) and, much more importantly, the EU’s AI Act (a risk-based consumer protection law that is
the first horizontal law on AI in the world). The EU AI Act classifies predictive AI and GenAI
applications into four risk categories. Applications that fall into the “unacceptable risk” category will be
banned from the European market, while applications that fall into the “high-risk” category will be
subject to pre- and post-deployment barriers and obligations. Common predictive AI creditworthiness
assessments will likely be high-risk applications, as will GenAI-powered customer support chatbots. Still
in its final negotiation stages, the EU AI Act is supposed to reach final form by the end of 2023 or early
2024 and, aer a grace period, will apply to all products in the European market. Failure to conform to
its requirements may result in fines of up to 7% of global annual turnover.
The third regime is the US regulators’ approach, which currently aims to adapt existing regulations
rather than to create new laws, and which takes a more national-security-driven perspective on GenAI
© 2023 Boston Consulting Group 13
risks. President Joe Biden’s executive order on AI issued on October 30, 2023, sets in motion a sector-
specific set of checks and balances, along with measures to foster the safe and responsible use of the
technology by companies and by the government itself. It is the first step toward legislation, but when
and how the US will regulate AI remains a subject of debate in Congress and within the administration.
With appropriate guardrails in place to guide AI developers and users, companies should be able to
deploy and quickly scale even rapidly changing technologies, with clear controls on the risks and with
high regulatory compliance. These guardrails should center on a framework that ensures alignment of
AI development and operation with the bank’s purpose and values while still delivering transformative
business impact. We call this approach responsible AI. (See Exhibit 9.)
A holistic and agile responsible AI framework must include five key components:
• Strategy—a comprehensive AI strategy linked to the firm's values as well as to its risk strategy
and ethical principles
• Governance—oversight by a defined responsible AI leadership team, with established escalation
paths to identify and mitigate risks
• Processes—rigorous processes put in place to monitor and review products to ensure that they
meet responsible AI criteria
© 2023 Boston Consulting Group 14
• Technology—data and technology infrastructure established to mitigate AI risks, including
toolkits to support responsible AI by design and appropriate life-cycle monitoring and
management
• Culture—strong understanding among all staff, including AI developers and users, of their roles
and duties in upholding responsible AI, and strict adherence to them
A recent BCG study in collaboration with MIT Sloan Management Review found that organizations
that successfully integrate responsible AI practices into the full AI product life cycle realize more
meaningful benefits. In fact, the likelihood of making full use of the benefits of predictive AI nearly
triples, jumping from 14% to 41%, when companies become leaders in responsible AI.
The rise of AI in the workplace will undoubtedly surface complex and pressing questions related to
human-AI collaboration and will probably elicit strong positions from workers’ unions on process
changes and technology implementation. Questions will arise that the new AI regulations do not
answer. But executives who prepare for this eventuality now by developing a holistic RAI framework
will have a critical advantage and will set up their AI transformations for success.
Aim for the Horizon
Like any foundational new technology, GenAI raises numerous important issues—around how to
realize opportunities for greater efficiency and effectiveness, but also around how to deploy the
technology, how to address the complexities of a new people strategy, and how to keep the technology
within the bounds of safe regulation and good governance.
The temptation to wait and see may be strong, but too much is at stake to play the short game.
Executives must make investigating and adopting AI, including GenAI, a transformational priority for
their organizations, taking a medium- to long-term perspective in their AI strategies, their HR planning,
and their approach to building a robust governance framework around the technology. Players that
actively plan today for the impending AI revolution in their ways of working will be at a decisive
advantage going forward.
© 2023 Boston Consulting Group 15
Authors
Stiene Riemer
MANAGING DIRECTOR & PARTNER
Munich
Michael Strauß
MANAGING DIRECTOR & SENIOR PARTNER
Cologne
Ella Rabener
MANAGING DIRECTOR & PARTNER, BCG X
Berlin
Jeanne Kwong Bickford
MANAGING DIRECTOR & SENIOR PARTNER
New York
Pim Hilbers
MANAGING DIRECTOR & PARTNER
Amsterdam
Nipun Kalra
MANAGING DIRECTOR & PARTNER
Mumbai - Nariman Point
Aparna Kapoor
PARTNER
Singapore
Julian King
MANAGING DIRECTOR & PARTNER, BCG GAMMA
Sydney
© 2023 Boston Consulting Group 16
Silvio Palumbo
MANAGING DIRECTOR & PARTNER
New York
Neil Pardasani
MANAGING DIRECTOR & SENIOR PARTNER
Los Angeles
Marc Pauly
PARTNER & DIRECTOR
Frankfurt
Kirsten Rulf
PARTNER & ASSOCIATE DIRECTOR
Berlin
Michael Widowitz
MANAGING DIRECTOR & PARTNER
Vienna
ABOUT BOSTON CONSULTING GROUP
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© 2023 Boston Consulting Group 17
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© 2023 Boston Consulting Group 18"
115,bcg,A-Gen-AI-Pathfinder-in-Health-Insurance-2024.pdf,"A (Gen)AI
Pathfinder
Unlock the potential of (Gen)AI
in health insurance
August 2024
Dr. Heike Dorninger, Dr. Andreas Klar, Dr. Konstantin
Storms, Dr. Karin Tremp, David Wilhelm, Jakob Gliwa,
Dr. Andreas Benn, Clara Schlegel
Boston Consulting Group partners with leaders
in business and society to tackle their most
important challenges and capture their greatest
opportunities. BCG was the pioneer in business
strategy when it was founded in 1963. Today,
we work closely with clients to embrace a
transformational approach aimed at benefiting all
stakeholders—empowering organizations to grow,
build sustainable competitive advantage, and
drive positive societal impact.
Our diverse, global teams bring deep industry and
functional expertise and a range of perspectives
that question the status quo and spark change.
BCG delivers solutions through leading-edge
management consulting, technology and design,
and corporate and digital ventures. We work in a
uniquely collaborative model across the firm and
throughout all levels of the client organization,
fueled by the goal of helping our clients thrive and
enabling them to make the world a better place.
Executive summary
Three key actions
(Gen)AI—which is the combination of generative
artificial intelligence (GenAI) and “traditional” AI—
for health insurers
will enhance operational efficiency in health
insurance by up to 30% within the next five To fully capitalize on the transformative power of (Gen)AI,
years. (Gen)AI is revolutionizing industries, health insurers need a structured approach. The following
especially health insurance. This cutting-edge are three critical actions to take, ensuring you achieve
technology offers unparalleled opportunities to immediate benefits while laying the groundwork for
boost operational efficiency and significantly sustainable, long-term success:
improve health and financial outcomes.
1. Assess your starting point
Achieve impactful results within the first three Understanding your initial situation is critical. Assess
months of implementation—for example, up to a your technical readiness, existing data sources, and
50% reduction of customer inquiry search times. privacy/security requirements. Make informed build-
Health insurers are already diving into (Gen)AI, discovering or-buy decisions to tailor a unique (Gen)AI approach.
its transformative potential for their operations and ser- Establish a dedicated task force to guide these early
vices. These pioneering efforts showcase immediate bene- steps and support scaling efforts.
fits and set the stage for widespread adoption.
2. Focus on quick wins
Transformative (Gen)AI steps can begin within six Aim to enhance your competitive edge with (Gen)AI.
months—successfully implementing applications Prioritize high-potential applications through small-
initially regardless of technology and data setup. scale proof of concepts, allowing secure, manageable
While experimentation is vital, focusing on quick wins is experimentation. These projects provide invaluable
crucial for momentum. To truly harness (Gen)AI’s power, learning experiences and pave the way for
health insurers must embrace a strategic framework. This broader implementation.
means selecting applications based on business needs and
technical readiness, ensuring the identification of the best 3. Embrace a strategic transformative approach
near-term opportunities and building a foundation for Adopt a strategic transformation mindset from
future advancements. This approach also ensures the start, even with small proofs of concept. Focus
adherence to privacy and security standards. on the four pillars: strategy, organizational and
cultural adaptation, technology, and policy. This
(Gen)AI can be a key lever to address the reduction comprehensive approach ensures ethical and
of the 25% of preventable health care costs, effective (Gen)AI-driven transformation.
effectively closing the gap between identified
potential and actual gain. Scaling (Gen)AI applications The time to act is now, leveraging (Gen)AI to
requires a robust operating model anchored by four pillars: revolutionize the industry and secure a
strategic alignment, organizational and cultural adaptation, competitive advantage for the future.
technology enhancement, and responsible AI policies. By
acting decisively and strategically, health insurers can rapidly By implementing these three key actions, health insurers
unlock (Gen)AI’s potential, reaping immediate, tangible can not only achieve immediate and tangible benefits, but
benefits tailored to their unique circumstances. also position themselves at the forefront of innovation.
3 AGEN(AI)PATHFINDER
(Gen)AI is more than a trend
for health insurers
Health insurers are under increasing pressure to applications, including care management, claims
streamline their processes to effectively manage cost management, and customer service. For example, a
efficiency and the continually rising health care claims. prominent multinational health insurer is utilizing AI to
(Gen)AI is a key lever to drive operational efficiency and offer personalized health care, enhanced patient pathway
enhance health and financial outcomes to address the management, and clinical decision support. Additionally, a
challenges of the evolving healthcare market. tech-savvy US health insurance provider is committed
to simplifying the health care experience with innovative
(Gen)AI will enhance operational efficiency in digital tools and highly personalized customer support.
health insurance by up to 30% within the next
five years. These companies are revolutionizing the industry by
integrating advanced technology to enhance patient care
Nearly all daily tasks of health insurers can be optimized and streamline services. As the industry undergoes
with (Gen)AI (see Deep Dive Box), enhancing efficiency internal and external transformations, insurers must
and competitiveness. Some insurers already adopting strategically invest in (Gen)AI to maintain their competitive
(Gen)AI are achieving significant performance and edge and set future standards.
service advantages.
Examples include local, regional, and global health
insurers insurers utilizing (Gen)AI for a wide range of
Key introducing statements for health insurers:
• Health insurers need both traditional AI and GenAI • Investing in (Gen)AI optimizes tasks and sets
to stay competitive and streamline processes. industry standards, with some insurers already
benefiting significantly.
• (Gen)AI delivers the decisive “Wow factor” enhancing
client engagement and retention by utilizing various
data inputs to provide a personalized experience.
BOSTONCONSULTINGGROUP 4
Deep Dive Box
What is (Gen)AI?
AI, originating in the 1950s, has evolved significantly applications across industries, providing efficiency gains
with increased computing power and the ability to quickly, even with unstructured data.
make predictions from vast data. About 25 years ago, it
advanced to mimic the human brain’s ability to Traditional AI, reliant on structured data and strict
connect experiences, culminating in GenAI, which uses rules, remains crucial in health insurance for
deep learning and Generative Adversarial Networks underwriting, propensity modeling, budget allocation,
(GANs) to generate predictions and create outputs fraud detection, forecasting, and predictive accuracy.
from extensive data. GenAI can complement traditional AI or function
independently to unlock significant value. This
GenAI excels at generating new content, combination of traditional and generative AI is referred
understanding unstructured texts, engaging in to as (Gen)AI.
customer dialogues, and personalizing experiences at
scale, enhancing productivity and creativity with
minimal specialist knowledge required. It offers broad
Where can health insurers start?
(Gen)AI is a powerful technology with a wide range of (Gen)AI can be a key lever to address the reduction
capabilities pertinent to developing innovative solutions in of the 25%2 of preventable health care costs, effec-
the health insurance sector. The following are the five core tively closing the gap between identified potential
functions of (Gen)AI.1 and actual gain.
1. Information retrieval and summarization The numerous (Gen)AI applications along the value chain
present enormous potential to revolutionize this sector;
2. Content creation they must be carefully prioritized to ensure effective and
efficient integration. From working closely with dozens of
3. Conversational interfaces and language services (health) insurers around the globe, we have seen that
companies unlocking significant impact within a short
4. Data and information analysis and interpretation amount of time follow a methodological assessment of
their situation and prioritize (Gen)AI applications
5. Transcription, along with speech-to-text and text-to-voice accordingly. To fully capitalize on these opportunities (see
processing Deep Dive Box for examples), health insurers should start
with applications that are not only impactful for their
In addition, (Gen)AI supports further functions such as specific needs but also straightforward and quick to
multilingual translation/localization and personalized implement, allowing for valuable early experimentation
insights. Together, these core and additional functions form with (Gen)AI.
the building blocks for a wide range of different
applications along the health insurance value chain.
Exhibit 1 shows exemplary applications—from product
development and underwriting to customer service.
5 AGEN(AI)PATHFINDER
Exhibit 1 - Health insurers’ value chain and (Gen)AI application examples
Key functions
Product development
Analyze public data on trends, Optimize product launch based Process market research surveys
health care utilization and on market conditions and customer feedback to ...
competitor offerings to enhance generate new product ideas
own products
Marketing and sales
Recommend competitive rates Create pictures and videos Personalize text for an individual
by comparing competitors‘ for marketing campaigns approach to attract new customers
...
reimbursement rates at scale
Underwriting
Cluster along key criteria to Create text modules and scripts Adapt policy language
identify patterns for pricing for negotiations purposes to client clusters
...
and policy issuance
Care management
Analyze customer-specific data Develop individual care plan Offer AI-supported digital
(risk assessment) to offer based on demographic profiles therapies and coaching programs
preventive service and for selected patient groups ...
intervention measures
Claims management
Draft communications for Flag potential fraud cases with Ensure accurate claims outcomes
standard mailings to claimants intelligent validation rules, with price transparency
...
spotting unusual patterns and patient-friendly explanation
of benefits
Customer service
Cluster incoming requests Suggest customer- agent concise Summarize conversations and
along patterns, incl. sequence and p ersonalized answers store automatically in CRM
...
of request priority
Support functions
IT/Infrastructure: Controlling and Finance: Create HR: Support in the hiring
Generate software code dashboards/forecasts/ process or creation of trainings
...
simulations/business reports and job simulations
BOSTONCONSULTINGGROUP 6
Deep Dive Box
Three exemplary (Gen)AI journeys for health insurers
Deep dives into claims management, care providers and patients in submitting claims to insurers
management, and customer service show how by reducing errors and communication loops due to
(Gen)AI enhances operational efficiency, boosts false or incomplete data. By integrating (Gen)AI into
customer satisfaction, and reduces claims costs for payment processing, complex billing processes and
health insurers. These areas should be prioritized for payment delays can be reduced.
(Gen)AI deployment due to their significant business
potential. At the same time, health insurers should For example, BCG worked with an Asian health insurer
start with applications that meet four key criteria— to build and implement a (Gen)AI audit co-pilot
value, feasibility, risk, and strategic alignment—for application to reduce manual documentation and
valuable early experimentation with (Gen)AI. unlock efficiencies. Within just 10 weeks, the co-pilot
tool was tested and fully implemented, ultimately
Claims management journey. Based on our project reducing the manual writing and documenting
experience, (Gen)AI is already solving everyday processes for auditors by up to 50%.
problems and reducing costs in medical invoicing
management. (Gen)AI enhances claims verification Ultimately, end-to-end leveraging of (Gen)AI
through smart algorithms and shortens lengthy claims applications in claims processes will thoroughly change
settlement negotiations, for example, by selecting the current operations and unlock significant efficiency
most effective negotiation strategy based on historical gains (see Exhibit 2).
settlements. In addition, applications support health
Exhibit 2 - Claims management journey
Document tagging Claims processing Facilitated Subrogation
and summarization incl. outlier detection payment processing drafting
Documentation processing Augmentation of fraud Facilitated payment Efficient drafting of subrogation
and summarization, detection measures with processing, integrated with documents, incorporate case
automatically annotate smart analysis, enriching payment gateways and plans, details for claims involving
documents and images input with further data and secured transactions third parties
Time-intensive document Challenge of outlier� Complex billing action Complexity of subrogation
handling and data entry claims/fraud detection and payment delays processes/doc. drafting
Claims intake Claims verification Claims settlement and Post-processing
and data capture and adjudication payment processing and reporting
Inefficiencies/errors Difficulty of client inform. Complex/lengthy Inefficient communications
during claim submission verification on accuracy claims negotiations of claim status
Assistance for Client information Claims negotiation Automated status update
claim submission verification strategy advancement and communications
drafting
Assistance to submitting Patient information and Using historical claims
health claims, considering document verification of (patterns) for modelling the Automated communications
prior authorization policyholder status, drafting most effective claims and drafting of personalized
and inaccuracies communication requests negotiation strategies communications to
interested parties
7 AGEN(AI)PATHFINDER
Care management journey. Care management factor. By pro-actively addressing at-risk members to
involves numerous data sources that are often see a general practitioner or cardiologist, up to 20%
integrated into disparate, unconnected systems. Here, reduced hospitalizations in heart failure
(Gen)AI can showcase its far-reaching potential by were realized.
structuring, integrating, and analyzing various data
sources to drive personalization of both the customer Additionally, (Gen)AI enables patient-specific selection
and patient experience across all treatment areas (see of appropriate and available providers—including
Exhibit 3). Notably, AI-driven predictive analytics can appointment management through predictive
more accurately and efficiently identify at-risk patients, algorithms that factor in real-time data on cancellations
thereby enabling highly personalized care management and emergencies. These applications highlight
with early preventive health care that ultimately the need for a clear vision and strategy of (Gen)AI
increases overall patient well-being and health. For implementation to realize incremental potential from
example, a German health insurer implemented an AI combining traditional AI and GenAI.
model co-developed with BCG to predict the risk of
hospitalization for heart failure in the next 12 months
out of all hypertension patients with a known risk
Exhibit 3 - Care management journey
Inefficient risk assessment/ Provider finder and Virtual treatment and Patient data
patient monitoring appointment management therapy assistant summarization
Predictive analytics for Efficient management of Patient inquiries response for Patient data summarization
holistic risk assessment, early provider finder (timely and 1st level support with real-time from different sources to
intervention strategies and most suitable) and support, tracking of compliance be brought into a
tailored care plans appointment times and treatment mgmt. comprehensive overview
Provider finder and Virtual treatment and Patient data
Holistic risk assessment appointment management therapy assistant summarization
Initial assessment/ Care planning and Transition and Evaluation and potential
risk stratification implementation continuity management plan adjustment
Complex patient records
and data synchronization; Inconsistent patient Delayed responses
generic care plans compliance with care plans to patient needs
Patient data Personalized Ongoing communication
management health reminder facilitation
Data analysis from multiple Personalized patient care Alert automation and
sources to enable timely support through automated messaging systems for
interventions, chronicles and reminders for medication, communications between
personalized treatment plan treatments, and others care teams
BOSTONCONSULTINGGROUP 8
Customer service journey. This area is often adjustments and new product offers. By supporting
considered the prime example of (Gen)AI applications, agents with reply suggestions and providing 24/7
with several near-off-the-shelf solutions readily available assistance, (Gen)AI is set to revolutionize the customer
and a huge potential for efficiency gains (see Exhibit 4). service experience, addressing the high demand for
Our experience with health insurance clients shows personalized interactions.
that (Gen)AI can automate up to 70% of standard
customer inquiries, thereby freeing up agents to focus For example, together with multiple health insurers
on complex tasks. around the globe, we build and implemented (Gen)AI
customer service applications, enhancing customer
With limited resources, customer inquiries often follow service and customer experience. Once fully
a first-in, first-out principle, causing delays in serious or implemented, these applications have realized up to
time-critical cases. (Gen)AI can prioritize urgent 30% customer service cost reduction through higher
requests and manage complex customer relationships, efficiency and up to 6% topline uplift through better
making real-time recommendations for contract customer retention and acquisition.
Exhibit 4 - Customer service journey
Proactive customer
Inquiry categorization Efficient onboarding Policy modification, relationship management,
and prioritization and training coverage optimization churn prevention
Clustering of incoming Interactive training environment, Analysis of customer data to Analysis of customer interaction
requests along patterns, simulating real-world interactions; recommend adjustments of history, drafting proactive
while identifying sequence of instant feedback and gamification policies to adapt to evolving personalized communications,
request priorities and flagging elements; accessible in idle times needs of patient, boosting preventing termination by
of most critical requests cross- and up-selling effective retention
Long waiting times for Labor-intensive and Complexity of contract, Lack of target and timely
answers due to lack of slow onboarding/training inefficient cross- and outreach for effective
prioritization of new staff up-selling relationship building
Initial contact Resolving Promoting Post-service
and triage customer inquiry additional services processing
Challenging to initiate Difficulty of finding the Generic offerings not aligned Time-consuming
contact, esp. during right information to answer to customer needs, also due post-interaction, such as
non-business hours accurate and detailed to lack of agent expertise details for CRM
24/7 customer Enhanced Additional services: Efficient CRM
assistance response quality customized product/ documentation and
service offerings follow-ups
24/7 customer assistance in Accurate and fast information
clarification and retrieval and response Offering customers products Entry of customer interaction
understanding of coverage, suggestions; routing of inquiries and services based on analyzed details in CRM system, with
benefits, and other topics, to the right support level interactions, thematic interests, suggestions for follow-ups based
providing consistent service local offers; incl. customized on analyzed content/outcomes
payment plans of interactions
9 AGEN(AI)PATHFINDER
Three key factors to consider Understanding which (Gen)AI applications yield the best
short-term results helps health insurers adopt (Gen)AI in
when determining the alignment with their needs and capabilities. This requires
assessing their individual situations, including available
starting point for leveraging
data sources, privacy and security requirements, technical
readiness, and build-or-buy decisions (see Exhibit 5).
(Gen)AI applications
Exhibit 5 - Three key factors to consider when determining the starting
point for leveraging (Gen)AI applications
Data privacy and Technical Build-or-buy
security prerequisites readiness decision
Understanding data Assessing companies’ Narrowing down the
utilization possibilities tech readiness build-or-buy decision
Data privacy and be achieved using methods such as anonymization,
encryption, or access control. Moreover, data selection
security prerequisites for applications extends beyond data privacy and security
prerequisites to include considerations of data availability,
governance, and quality (see Deep Dive Box).
While data protection and security rules vary across the As a straightforward initial step, health insurers can use
globe, the health sector is nearly always among those publicly available data sets and non-sensitive internal data
industries with the most rigorous provisions, as health to minimize compliance risks, unlocking significant
data is by its very nature the most sensitive. Therefore, potential in the process. As they improve their capabilities
when leveraging (Gen)AI, health insurers must consider for data handling with regard to (Gen)AI, insurers can
and ensure IT security as well as adherence to applicable then progress to use more sensitive data. This methodical
data protection and security requirements. In this context, approach not only mitigates the initial risks, but also
insurers must keep in mind that there might be company- enables strategic data usage to be expanded—and
specific data governance guidelines with stipulations facilitates the gathering of the crucial technical data
beyond those legally required. and human resources capabilities needed to reach the
The (Gen)AI applications utilized must meet all regulatory next level.
security and data privacy requirements, which can
BOSTONCONSULTINGGROUP 10
Deep Dive Box
Data sources for (Gen)AI applications
A solid understanding of the necessary security • Internal company data, such as internal
standards and applicable data protection requirements knowledge databases or provider information.
for a given data source is an absolute prerequisite for These can be leveraged for (Gen)AI applications to
the source’s use within (Gen)AI applications. Basically, provide more insightful and relevant results, such
the following are three types of data sources in this as those required for smart chatbots. However, it is
context—each with a broad spectrum of possible crucial to establish authorization concepts to prevent
(Gen)AI applications: confidential information from being shared with
customers.
• Public data—either as open source or possibly
via licenses or payment models. For example, • Personally identifiable information and strictly
public data sources can be leveraged for (Gen)AI, confidential internal company data. It is crucial
creating images or video material for marketing to ensure compliance with all internal and regulatory
campaigns, requiring a thorough vetting (incl. requirements. Therefore, integrating these data
copyright laws) before implementation to ensure sources into (Gen)AI applications requires flawless
high-quality and guideline-compliant results. data governance.
Technical readiness
Achieve impactful results within the first three
months of implementation—for example, up to a
While (Gen)AI applications can be implemented in any 50% reduction of customer inquiry search times.
technical environment, defining an initial scope that takes
into consideration integration complexity ensures quick To determine where to start with (Gen)AI technology,
implementation. Most health insurers, regardless health insurers of all technical maturity levels should
of their technical readiness, can easily start with (Gen)AI consider launching low-threshold solutions that promise
applications that require only minimal tech integration. short-term efficiency gains. Building on this foundation,
Short-term adoption is also feasible for insurers with and based on their technology readiness, health insurers
legacy systems characterized by manual processes and can then gradually develop their own systems into more
isolated data storage, and thereby little scalability (see powerful, more integrated applications. Doing so ensures
Deep Dive Box). that each step is sustainable and in line with their
evolving technological landscape.
Deep Dive Box
Potential methods for (Gen)AI implementation
Standalone solutions. Suitable for insurers with warehouses or data lakes. Example: (Gen)AI chatbot
lower tech maturity. Alongside standalone solutions, integrated into customer relationship management
prerequisites on data applications and infrastructure (CRM) systems but monitored by a human in
levels can be established simultaneously to enable the loop to provide intelligent and effective
deeper integrations. Example: A (Gen)AI knowledge personalized communication.
assistant using company-specific data to draft customer
Future scenario. Health insurers with advanced
responses, reducing processing times.
modular IT systems can leverage (Gen)AI extensively
Modern system clusters. Focus on web/app across business units and launch low-threshold
development and cross-functional areas such as input solutions for short-term gains, then gradually develop
management and customer service software. more integrated applications based on technology
readiness. This ensures sustainable progress aligned
Service-based architecture. For insurers with
with the evolving technological landscape.
modern, flexible tech stacks (APIs, cloud infrastructure),
integrating (Gen)AI more deeply into IT systems
using centralized data hubs or stores such as data
11 AGEN(AI)PATHFINDER
Build-or-buy decisions
• Before deciding to buy, it is essential to evaluate
expected value, total costs, and contract terms, while
With the increasing accessibility of (Gen)AI and its reduced ensuring IT, compliance, and business teams
requirements on data structure compared with traditional collaborate to avoid costly provider lock-in and long
AI, it is expected that the health market will see a lead times.
significant rise in both standalone solutions and
enhancements for widespread proprietary software, While data sources and technical readiness are crucial,
emphasizing the need to carefully consider overall (Gen)AI objectives, costs, speed of implementation,
implementation strategies. and application performance should also be considered in
build-or-buy decisions. Proper assessment can reduce lead
Determining which data should be used for initial (Gen)AI times from months or years to weeks, speeding up the
applications and assessing technical readiness are the realization of potential.
building blocks for key decisions when getting started with
(Gen)AI—what to build and what to buy: In sum, addressing data privacy, security prerequisites,
technical readiness, and build-or-buy decisions enables
• Building (Gen)AI applications in-house is advisable for health insurers to start implementing (Gen)AI and embark
applications that can be modified and reused across on long-term transformation. Beginning with a low-
various scenarios, leveraging widely adopted tools to threshold proof of concept provides a realistic overview of
accelerate the building process. Key advantages (Gen)AI’s impact, allowing insurers to build internal
include have greater control of tools, better integration capabilities and scale applications across an organization.
of proprietary data, technical flexibility, and This step-by-step approach helps insurers realize efficiency
development of internal (Gen)AI capabilities. potential, gain experience, and align their organization,
ultimately unlocking the full potential of (Gen)AI and
• Buying a (Gen)AI tool is advantageous when existing building transformation chains across all business areas
providers offer easily deployable add-ons, minimal globally (see Exhibit 6).
customization and integration are needed, the
application relies on public data, and resources for
building a custom solution are unavailable.
Exhibit6-(Gen)AIpathwayevolution
Boost efficiency
Enable first insights Focus on scaling
and effectiveness
(Gen)AI
maturity
(Gen)AI transformation
Understand Initiate early Explore benefits Leverage early Continuously
(Gen)AI‘s potential (Gen)AI engagement of (Gen)AI learnings and scale optimize and
build chains
Gain a first Start experimenting as Discover how (Gen)AI Refine with first
understanding of how soon as possible Improves efficiencies and learnings and set up Continuously optimize
(Gen)AI can be Develop initial effectiveness operating model (Gen)AI for more impact
applied within your applications for testing Provides a competitive Scale indiv. applications in Build transformative
specific context and learning, enable PoCs advantage selected business areas chains across the entire
organization
BOSTONCONSULTINGGROUP 12
Key (Gen)AI starting point statements for health insurers
• Ensure compliance with data protection standards • Choose between building custom (Gen)AI
for public, internal, and personal data, adhering to applications or buying existing solutions,
legal and internal guidelines. considering cost, value, and technical capabilities.
• Start with simple (Gen)AI solutions for legacy
systems; use more integrated applications for
modern tech stacks.
How can health insurers
leverageandscalethebenefits?
After establishing a starting point and gaining insights culture, technology, and policy, is essential for this transfor-
from small-scale (Gen)AI proofs of concept, a transforma- mation (see Exhibit 7). When supporting our (health)
tion is needed to fully realize (Gen)AI’s potential across an insurance clients on these end-to-end (Gen)AI transforma-
organization. This requires ensuring effective operation tions, addressing the four key transformation pillars has
and widespread utilization of (Gen)AI solutions to derive proven effective to unlock up to 10% margin uplift over
greater value and functionality. Implementing a multi-level 12–18 months.
operating model, based on strategy, organization and
Exhibit 7 - (Gen)AI path – key transformation pillars
Data privacy and Technical Build-or-buy
security prerequisites readiness decision
Strategy Organization and culture Technology Policy
Leverage strategy Future-proof Design tech Shape (Gen)AI to
to define the initial organization through architecture to strengthen corporate
(Gen)AI playing field right-sizing, up-skilling, build and scale values and avoid
and set the right org. and tactical hiring (Gen)AI across regulatory pitfalls
ambition entire organization
13 AGEN(AI)PATHFINDER
Strategy:defininggoalsfor (Gen)AI will transform their company, industry, and
business model. This includes setting ambitious goals
value creation through (Gen)AI for value delivery of (Gen)AI and the required scale of
investment. These new aspirations should be
integrated into the ongoing strategic planning cycle.
A well-defined strategy that incorporates (Gen)AI is
To embed (Gen)AI ambitions into the overall digital
essential for setting the goals to create value through
strategy, it is necessary to break down general goals
(Gen)AI. This strategic foundation is crucial for effectively
into specific objectives for core business functions.
deploying and successfully scaling (Gen)AI within an
organization. By incorporating a robust"
116,bcg,Banking-on-Generative-AI.pdf,"WHITE PAPER
Banking on Generative
AI: Maximizing the
Financial Services
Opportunity
September 2023
By Jeanne Bickford, Rafal Cegiela, Julian King, Kevin Lucas, Neil Pardasani, Ella Rabener,
Benjamin Rehberg, Stiene Riemer, Michael Strauss, Jon Sugihara, and Michael Widowitz
Banking on Generative AI: Maximizing
the Financial Services Opportunity
T
echnological change, like many evolutions, often happens slowly, and then all at once.
Neural networks were conceived in the 1940s and natural language processing algo-
rithms came 20 years later, when the first chatbot, Eliza, was created. There followed
nearly 60 years of gradual development, until massive computing power helped create the
models that have attracted global headlines over recent months.
In the financial industry, experimentation with natural language processing has also pro-
gressed in steps, with banks incrementally adding functionality such as chatbots and auto-
mated document processing. Fast forward to today. Massive computing power has helped
accelerate experimentation and create generative AI (GenAI) models that are set to be
game-changing for financial services.
GenAI is a catch-all term for a range of models, predominant among which are foundation
models (FMs) and their large language models (LLMs) subset. (See “What Is Generative AI?”)
WHAT IS GENERATIVE AI?
Generative AI (GenAI) is a set of algorithms, capable of generating seemingly new, realistic content from unstructured
inputs such as text, images, or audio.
The term GenAI encompasses both foundation models and large language models:
Foundation models are pretrained with large datasets and massive compute power, so they are ready to be used without
additional training. They can be applied to many tasks (unlike traditional AI), including generating text or graphics, predict-
ing, or classifying.
Large language models are a subset of FMs and can ingest and produce text. The terms LLM and FM are not interchange-
able.
1 BANKING ON GENERATIVE AI: MAXIMIZING THE FINANCIAL SERVICES OPPORTUNITY
The promise of FMs is that they present shortcuts to resolving complex challenges. Their
potential puts them on a different plane to other technologies that have been labelled
game-changing over the past decade. However, they also bring risks, including leakage of
confidential data, transmission of bias, and potential for low-quality outputs.
In the banking industry, potential FM use cases range from replacing mundane manual
processes to revolutionizing digital customer interactions, creating highly personalized mar-
keting content, and supporting investment decisions. If applied effectively, we expect these
kinds of applications will boost cost effectiveness on a double-digit scale while significantly
improving customer outcomes. Based on our experience working with clients, there is also
potential to reduce back- and front-office process costs by 20-30% in the next two to three
years and 50% or more in the following three years—freeing up staff to engage in more
valuable and less repetitive activities. (See Exhibit 1.)
Exhibit 1 - Gen AI adoption is picking-up rapidly
Now (2023) Very soon (2025) Soon (2030)
• Bionic customer support • AI-powered conversational • AI-powered conversational
Facing • Mass production of content for channels for mass customer sales channels for premium and
customers hyper-personalization & service commercial lines
• Fact search, question answering
• AI assistant for RM productivity • Natural language coding • Self-coding systems
• Code generation/Testing • Intelligent back-office process • Human augmentation and semi-
Employee • Office productivity automation across repetitive autonomous solutions across all
efficiency
• Knowledge management processes functions
• Generating innovative products • AI-driven expert capacity
augmentation
Source: BCG.
BOSTON CONSULTING GROUP 2
Banks Are Investing in New Use Cases
Amid a growing competition, the largest banks are investing heavily in FMs, leveraging exter-
nal models and open-source offerings, such as Langchain—an emerging standard compo-
nent also used by Google. Use cases range from more generic processes such as code writing
and call center support to those specific to the financial services industry, such as financial
analyses or delivering financial advice. (See Exhibit 2.)
Leading IT solution vendors, including Microsoft, Salesforce, Pega, and ServiceNow, have
created FM roadmaps and are offering new functionalities and products leveraging their own
or third-party FMs. In parallel cloud vendors are unveiling environments for model fine-tun-
ing and developing functionalities that will facilitate use cases. Cloud vendors are offering
both proprietary FMs and FMs obtained through partnerships. Microsoft Azure is teaming up
with OpenAI, Google Cloud Platform has unveiled its proprietary PaLM2, and Amazon Web
Services offers both startup FMs (AI21 Labs, Anthropic, Stability AI) and its own Titan FM.
The power of FMs is centered on four key areas, each of which has applications in more than
one area of banking:
• Summarization (including answering questions, fact finding) refers to the ability to
ingest unstructured text and summarize it to the required level of detail. This may be
used to boost customer service efficiency, identify customer needs and preferences from
documents such as emails, create risk profiles from uploaded files, or document software
source code.
• Content generation is the ability to elaborate on a given topic or create media content
such as images or videos given succinct inputs. This can be used in customer communica-
tions or to generate software source code, among many other examples.
Exhibit 2 - Leading FI players are announcing GenAI use cases every day
Goldman Sachs J.P.Morgan ANZ Commonwealth Bank
Code Writing and Testing Financial Advisory Code Testing Call Center Support
Experimenting with Gen AI Working on a ChatGPT like tool Created a team to explore Gen At the bank’s call centers, a
technologies to assist its which aims to revolutionize AI to augment its code testing Gen AI model is already being
developers in autonomously investment decisions by capabilities as well as to explore used to help staff answer
creating and testing code. In providing advanced AI-powered deeper potential use cases. The complex customer questions by
some cases, developers have assistance in analyzing and bank's 4000 software engineers interrogating 4500 documents
been able to write as much as recommending financial see opportunities to use Gen AI on the bank’s policies in real
40% of their code automatically securities such as stocks, to improve efficiency, reliability time. It’s is making its apps
using Gen AI1 bonds, commodities, and and performance of its code3 smarter, including tailoring new
alternatives2 offers to 7.7 million users4
American Express Deutsche Bank Bloomberg Wells Fargo
Predicting customer behavior Operational Efficiency Financial Analyses Synthetic Data Generation
Aims to predict how customers Testing Google's Gen AI and Blooomberg created a Partnered with synthetic
are going to perform over time, LLMs to provide new insights to dedicated BloombergGPT data-generating platform Hazy,
enabling better financial financial analysts, driving model that is trained for to create a self-service model
planning and decision-making. operational efficiencies and analyzing financial information for generating and using
AmEx exploring ways LLMs execution velocity. This will and data to assist with risk datasets for Wells Fargo data
could be used to analyze the empower employees by assessments, judgning financial scientists The intelligent use
feedback and inquiries increasing productivity while sentyment, and can also be cases it has in mind for
customers provide through helping safeguard customer applied for automating synthetic data include fraud
customer service portals, as data privacy, data integrity, and accounting and auditing tasks7 detection using machine
well as on social media5 system security6 learning model8
Source: BCG.
3 BANKING ON GENERATIVE AI: MAXIMIZING THE FINANCIAL SERVICES OPPORTUNITY
• Conversation is the ability to provide human-like answers to customer prompts while
considering the context and flow.
• Data generation (also provided by “small” and non-text generative AI models) is the
ability to generate complex sets of data with specific characteristics. This is intended
predominantly for test cases to compensate for small numbers of empirical observations,
ranging from fabricated fingerprints to biometric identification, or realistic dialogues with
customers to test chatbots.
These capabilities can be applied along the banking value chain, accelerating development
of (and enriching) sales and marketing communications (such as in call centers), increasing
sales and service efficiency and availability, and lifting software development productivity.
(See Exhibit 3.) Still, for many financial institutions, the first step in using FMs is to support
employees, for example in finding the right answer to an incoming query. These use cases
are low-hanging fruit—which we estimate, based on client engagements, can deliver 10–20%
effectiveness gains.
Exhibit 3 - Generative AI capabilities can be applied across banking value chain
Non-exhaustive
VII. Supporting
I. Marketing II. Distribution III. Product IV. Financial VI. Risk &
V. Servicing corporate
& Sales & Onboarding development Advice compliance
functions
Generating
Copywriting RM productivity: Generate content Composing Compliance IT Code
creative/innovative
& creating visual preparing for for financial personalized monitoring and documenting/
products &
collaterals meetings education emails from RMs report generation generation/ review
features
Mass production
Credit approval: Composing Intelligent Data privacy &
of content for Contract & term IT: Test case
support/ personalized document compliance
hyper- generation generation
automation emails processing checks
personalization
Loan & other Detecting trends HR: Copywriting
Customer: Configuring/ Credit review Fraud detection
products and scenario/ recruiting/
Product search, coding products in support/ with synthetic
application portfolio employer
fact search systems automation transactional data
assistance optimization branding content
Chatbots/ RM productivity:
Individualized Virtual assistant/
voicebots for lead rading gist of
contract & term service chat/ Memo writing
warming & memos, fin.
generation voicebots
conversion reports,
interacting with
Sales trainings analytics
Strategy:
with simulated
Competitor
client
analytics
conversations
Data
HR: Screening
augmentation for
CVs
model training
IT Support:
Knowledge base
search
IT: Test data
generation
Content generation Synthetizing, question answering Conversational interfaces Data generation
Source: BCG.
BOSTON CONSULTING GROUP 4
The next obvious step will be to directly read or listen to customer communications and
recommend answers, while still relying on employees to lead the conversation and make
final checks. These are more challenging tasks, requiring solutions that can get close to
creating customer-ready outputs, and potentially leading to 20–50% productivity improve-
ments.
The longer-term vision would be for FM-based agents to take responsibility for tasks end-to-
end. This would include back and forth dialogues with customers—within clear guardrails
and topic areas—and executing operations directly in internal IT systems. (See Exhibit 4.) In
this context, there would be significant upsides in redeploying full-time employees to cli-
ent-facing and higher-value work.
Exhibit 4 - Use-cases with increasing responsibility entrusted to FM-based
solutions
Channel type Use case Scope work delegated to GenAI Implementation
complexity
Read & Retrieve Make Formulate Respond &
understand knowledge decision response execute service
Reactive employee
assistant
One-way Pro-active employee
channels assistant
• E-mail
• Mail Client Service Bot
Listen & Clarifying Retrieve Make Formulate Finalizing Confirm
understand conversation knowledge decision response conversation & execute
Reactive employee
assistant
Interactive Pro-active employee
channels assistant
• Call-center
• Chat Client Service Bot
Source: BCG.
5 BANKING ON GENERATIVE AI: MAXIMIZING THE FINANCIAL SERVICES OPPORTUNITY
Risks Must Be Managed
The flipside of FMs’ unique capabilities is the potential for new or exacerbated risks. There-
fore, banks must take care in their use, including protecting themselves against malicious
actors. Failures to do so will expose them to both potential regulatory sanction and reputa-
tional damage.
Risk that may play out in the short term include the emergence of shadow AI, or applications
that are not overseen or managed by the organization, leading to potential compliance
issues. Another short-term risk is leakage of confidential data, either through prompt engi-
neering or through vendors using prompts for model training. To protect stakeholders, sensi-
tive data can be encrypted, anonymized, or protected through permissions processes, but
leakage can still occur through ungoverned usage. (See Exhibit 5.)
Looking ahead, a risk especially relevant to the financial industry relates to the potential use
of FMs for fraud. The models may, for example, help criminal actors assume identities, forge
checks, impersonate customers through voice imitation, or even mimic managers to per-
suade employees to bypass security measures (social engineering). There are also potential
legal pitfalls, including infringements of intellectual property rights, for example when gener-
ating graphics that resemble copyrighted work.
Exhibit 5 - Gen AI amplifies existing AI risks and introduces new ones –
which can be resolved over time
Source: BCG.
BOSTON CONSULTING GROUP 6
noitpircseD
Short term Medium-term Long-term
laitnetoP
noituloseR
1 2 3 4 5 6 7 8
Shadow Sensitive Enhanced Uncertain Copyright Biased Environmental Capability
AI data leaks Fraud quality challenges outputs Harm Overhang
More widespread Sensitive data can Constantly Dynamically Unclear data Generative AI SOTA achievable Latent capabilities
and potentially be evolving changing provenance models trained through larger and how they
unconventional/ extracted from capabilities to information from publicly on real world models with might be
non-traditional models via deceive and ecosystem makes scraped datasets data can carry more complex leveraged
uses of AI prompt detect with it difficult to and quality over bias to architectures with depends on the
invisible to engineering or higher stakes over mitigate this issues outputs in hard higher interacting
top-down more sophisticated time from more issue without to detect ways computational ecosystem and
governance abuse/attacks pervasiveness extensive human needs development of
approaches intervention new systems
Clear guidelines Sensitive data Better detection Fact-checking Legal Reinforcement Research work in Better
captured in should be methods with services and precedents will Learning with TinyML with an governance
enforceable anonymized verifiable claims problematic be set as courts Human emphasis on mechanisms and
policies and/or encrypted on performance information figure out IP and Feedback (RLHF) edge-device understanding of
accompanied by and reduced false fingerprinting copyright issues can catch some deployment, how capabilities
cultural change Chained models negatives and can provide which will shape biases but not faster inference, emerge
and top-down can be used to false positives stop-gap response and all. Tech. and cost +
communication block leakage of measures to stem development advancement environmental
sensitive data issues approaches likely needed for considerations
comprehensive
solve
Spontaneous or underinformed use of FM-based functionalities may lead to unexpected
results, including generation of so-called hallucinations. These are responses not justified by
training data, which are often the result of the system misunderstanding the question. Simi-
lar risks could be manifested through use case design flaws, leading to uncertain or volatile
outputs. Finally, the model may produce biased opinions, reflecting similar biases in training
data. Potential antidotes include deeper fact-checking and information finger printing.
On a strategic horizon, potential risks relate to the evolution of business ecosystems, where
business models and functionalities based on browsing the internet may cease, with pages
replaced by conversational interfaces or on-the-fly generated content. Stakeholders will need
to keep a close eye on how capabilities develop and put in place governance frameworks that
are sufficiently flexible to keep pace.
Key Design Variables
Not all FMs are created equal, and financial institutions will need to make a range of selec-
tions depending on the intended use case, hosting preference, memory configuration, and
other factors.
Initial applications often retrofit FMs into existing roles, adding them to tasks like co-author-
ing marketing messages, coding chatbot flows, or converting text to graphics. This approach
allows users to do the same things but faster. Looking ahead, there is a strong argument for
setting the bar higher. This would mean adopting a clean-sheet approach that puts the tech-
nology at the heart of the design process Potential examples include:
• Mass produce marketing content based on product characteristics and personas in the
customer base, instead of manually describing each picture’s details.
• A chatbot delivering human-like interaction, able to handle multithreaded, nonlinear
conversations and accommodate new services without the need to code and maintain
detailed conversation flows.
• Recommendations for lending and investment decisions based on a blend of structured
and unstructured data, including detailed justifications for internal and external purposes.
Decisions regarding hosting of models should be aligned with the type of model employed.
(See Exhibit 6.) General-purpose and specialized models (ChatGPT or OpenAI’s Code DaVin-
ci for coding) are usually adopted as a cloud-based service and accessed via secure API,
under the guarantee that submitted “prompts” will not be used to train the model, cutting
the risk of data leakage. For dedicated adaptors, which facilitate customization, or fine-tuned
models, the range of options includes training and running by a vendor or internal data
science and IT team (often within a cloud tenancy).
7 BANKING ON GENERATIVE AI: MAXIMIZING THE FINANCIAL SERVICES OPPORTUNITY
Exhibit 6 - Multiple design choices need to be taken for each use-case
GenAI solution layers Design options Tech components Non-exhaustive examples
Source: BCG.
Other design choices that will shape the models’ impact include:
• Prompting logic: How will questions to the FM be prepared? Options include directly
passing user requests, building an autonomous agent, or using tools such as internet
search or mathematical calculators.
• Short-term memory: How will the “conversation notepad” be organized? Will it be a
“raw” history of conversation or structured to perform a specific task, for example a list of
outstanding customer requests.
• Long-term memory: How will the most relevant knowledge, such as frequently-asked
question documents or email exchange history, be stored and retrieved to provide data for
the FM?
BOSTON CONSULTING GROUP 8
kcatS
hceT
&
ngised
lacigoL
ataD
&
ledoM
• Accessing CRM Customer 360, executing
operations in Core Banking System or Card
Chat & voice
Environment Executing actions
interfaces/ Data access System
integration in banking systems (RPA, APIs)
channels
• Technologies: Specific to bank's IT
architecture
• Multi-step: Product-emotions-pictures
Prompting Multi-step Agent- • Agent based, eg. Intent-resolution-execution
Direct Templated Algorithmic
logic chains based • Technologies: LangChain, LMQL, Microsoft
Guidance
• Buffers & structures with e.g.: task list of chat-
Short-term Conversation Purpose-structured None bot, working summaries of document parts
memory history memory
• Technologies: LangChain, Meta FAISS
• Vector stores containing e.g.: product
Long-term Similarity-based Search-engine
None information, processes, regulations, FAQs
memory retrieval based
• Technologies: Chroma, Llamaindex, Pinecone
• General: ChatGPT, Google PaLM, A21, Aleph Alpha
• Specialized: Google MedPaLM, Code DaVinci
LLM General- With Fine- Custom- Custom-
Specialized • Custom: BloombergGPT
tailoring purpose adaptor tuned trained built
• Model tuning: Google Model Garden, AWS
Bedrock
• Proprietary models: ChatGPT3, Google PaLM2
Proprietary Opensource • Opensource models: Vicuna, ChatGPT2
LLM sourcing
• Model hosting: Google Model Garden, AWS
& hosting
Embedded Public Managed Self managed Bedrock
• Model repositories: HuggingFace
No PII or banking information Sensitive data & systems Chatbot logic and short-term memory
• Environmental integration: How will the solution be integrated with communication
channels, data, and operational or banking systems? This “classic” IT architecture problem
can easily become a bigger bottleneck on the way to adoption than FM-related challenges.
Four Pillars for Success
While FMs can create game-changing benefits, decision makers at financial institutions
must take care in rolling out use cases and assessing impacts. There are both risks and
opportunities. However, through detailed planning, many of the potential risks associated
with the technology can be mitigated. In Exhibit 7, we identify four pillars of successful ap-
proaches:
• Potential: Where is there most potential to use FMs in the context of the bank’s AI ma-
turity? How do you build off existing data foundations? Which use cases will lead to most
differentiation? And how do you create strategic advantage? Banks that have a clear vision
to start with will be best placed to execute effectively.
• Risk management: What are the risks and how should they be managed? How can you
protect and grow the business by making ethical choices that are aligned with your pur-
pose and values? Decision makers must strike a balance between managing potential
exposures and achieving benefits.
• Foundations: Establishing foundations means acquiring understanding. The aim should
be to go beyond simple retrofitting and consider the skills, ways of working, and tools that
will enable a transition to a new operating model. Among other things, this will likely
mean creation and test cycles for marketing shortened from weeks to hours, and opera-
tions employees predominantly managing “AI assistants” instead of directly performing
operational tasks. As in other technology decisions, the build/buy/partner dilemma will
apply, and it will be incumbent on individual businesses to make choices that reflect their
capabilities and direction of travel, achieving upside while avoiding vendor locks.
Exhibit 7 - Leaders must make choices across four key pillars
Potential Risk Foundation People
Which use cases will differentiate How can the company capture the What are the foundational How to adapt org structures and
your organization? benefits of AI while managing capabilities on data infrastructure prepare employees for deployment?
downside risks? and governance for building AI
solutions/industrialize?
Discover your strategic advantage Protect and grow your business by Develop foundational capabilities Prepare your workforce with
through experimentation deploying AI that is ethical and to enable AI solutions strategic workforce planning and
aligned with your purpose and transforming op models
values
Deep dive in next section Deep dive in section 03 How to organize and implement
Source: BCG.
9 BANKING ON GENERATIVE AI: MAXIMIZING THE FINANCIAL SERVICES OPPORTUNITY
• People: Alongside technology choices, management teams must make capability-focused
decisions. Budgets will need to be assigned to training, technology, and risk management.
There may be a requirement to overcome resistance in some quarters, amid concern over
the impact on roles and job security, while operating models and governance frameworks
may need to be adjusted.
FMs offer banks a significant opportunity to boost workforce effectiveness and automate
numerous processes, while creating a more streamlined and personalized customer expe-
rience. FMs can boost the quality and availability of customer-facing services, refine commu-
nications, and improve risk management and compliance.
Large banks are already rolling out use cases in areas including customer services and soft-
ware engineering. However, these uses case are just the beginning. As the technology contin-
ues to evolve, banks must take a more holistic approach, focusing on the technical and
environmental factors that will dictate model choice and model management. Not least,
there is a strategic task to drill down into the potential of FMs, consider risks, and the lay the
foundations that will promote security and accelerate the journey to scale.
1. Ryan Browne, “Goldman Sachs is using ChatGPT-style A.I. in house to assist developers with writing code,” CNBC, March 22, 2023.
2. Hugh Son, “JPMorgan is developing a ChatGPT-like A.I. service that gives investment advice,” CNBC, May 25, 2023.
3. Tim Hogarth, chief technology officer, ANZ, “How generative artificial intelligence can make engineers more efficient.” bluenotes, May 22, 2023.
4. James Eyers, “CBA goes all in on generative AI,” Australian Financial Review, May 24, 2023.
5. “AI in FinTech: 7 use cases market leaders pursue,’ 8allocate, August 2, 2023.
6. Shritama Saha, “How Deutsche Bank is riding the generative AI wave,” AIM, August 10, 2023
7. “Introducing BloombergGPT, Bloomberg’s 50-billion parameter large language model, purpose-built from scratch for finance,” Bloomberg, March
30, 2023.
8. “Synthetic data and the Wells Fargo-Hazy relationship,” VentureBeat, March 28, 2022.
BOSTON CONSULTING GROUP 10
About the Authors
Jeanne Bickford is a Managing Director and Senior Partner in BCG’s New York office. You
may contact her by email at Bickford.Jeanne@bcg.com.
Rafal Cegiela is a Principal, Data Science in BCG’s Warsaw office. You may contact him by
email at Cegiela.Rafal@bcg.com.
Julian King is a Managing Director and Partner in BCG’s Sydney office. You may contact him
by email at King.Julian@bcg.com.
Kevin Lucas is a Managing Director and Partner in the BCG X Sydney office. You may
contact him by email at kevin.lucas@bcgdv.com.
Neil Pardasani is a Managing Director and Senior Partner in BCG’s Los Angeles office. You
may contact him by email at Pardasani.Neil@bcg.com.
Ella Rabener is a Managing Director and Partner in BCG’s Berlin office. You may contact
her by email at Ella.Rabener@bcgdv.com.
Benjamin Rehberg is a Managing Director and Senior Partner in BCG’s New York office. You
may contact him by email at Rehberg.Benjamin@bcg.com.
Stiene Riemer is a Managing Director and Partner in BCG’s Munich office. You may contact
her by email at Riemer.Stiene@bcg.com.
Michael Strauss is a Managing Director and Senior Partner in BCG’s Cologne office. You
may contact him by email at Strauss.Michael@bcg.com.
Jon Sugihara is a Managing Director and Partner in the BCG X Singapore office. You may
contact him by email at Jon.Sugihara@bcgdv.com.
Michael Widowitz is a Managing Director and Partner in BCG’s Vienna office. You may
contact him by email at Wido@bcg.com.
For Further Contact
If you would like to discuss this report, please contact the authors.
11 BANKING ON GENERATIVE AI: MAXIMIZING THE FINANCIAL SERVICES OPPORTUNITY
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117,bcg,BCG-Executive-Perspectives-AI-and-GenAI-in-Customer-Engagement-EP8-4Dec2024.pdf,"Executive
Perspectives
Transformation through
AI and GenAI
Customer Engagement
December 2024
Introduction In this BCG
Executive Perspective,
As part of our ongoing series of C-suite conversations on AI, we are sharing our most recent
we show you how to
learning in a series designed to help navigate the rapidly changing world of AI. After working
with over 1,000 clients in the past year, we've found that AI is at an inflection point: in 2024,
leverage AI to transform
the focus is on turning AI's potential into real profit.
and create value in
In this edition, we discuss the future of customer engagement and the role AI will play in
customer engagement
turbocharging the way companies interact with customers and generate ideas.We address
key questions including:
• How can AI help ideate new products/services?
• How can AI use data to improve product fit and gauge customer reception?
• How can AI turbocharge personalized customer communication?
• How can AI transform the way we communicate with customers?
• How can AI augment the capabilities of existing teams?
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We identify company archetypes poised for maximum growth through AI-powered
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customer engagement…
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and understand what creates value now and in the future. iry
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Executive summary | Leveraging GenAI to enhance customer engagement
The rapid advancement of generative AI (GenAI) is revolutionizing how companies engage with customers,
reshaping traditional insights, marketing, sales, and service functions into highly personalized, efficient, and
innovative processes. The transformative potential of GenAI centers on three critical levers:
AI reinvents
• Market mirror – virtual simulation of customer feedback (i.e., what you offer): GenAI empowers
customer
businesses to enhance value propositions through customer intelligence and synthetic persona-driven insights
engagement
• Creativity at scale (i.e., what you communicate): By enabling hyper-personalization at scale, GenAI allows
in three ways
companies to craft tailored content across all channels, significantly increasing engagement and relevance
• Conversational capability (i.e., how you engage): GenAI-driven conversational agents and virtual sellers are
transforming customer interactions, providing seamless, personalized, and efficient experiences
.d
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The human To fully seize these opportunities, organizations must transform people, processes, and technology. This means v re
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element is key creating AI-centric roles, upskilling teams, and adopting agile structures. Empowering people is crucial to
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to succeed unlocking AI's full potential
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This deck provides a strategic roadmap for implementing GenAI, guiding organizations from initial adoption to fully C
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to lead in integrated (3-18+ months), AI-powered customer engagement. By embracing these innovations, businesses can ts o
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the AI era achieve unprecedented levels of efficiency, innovation, and customer satisfaction, to be leaders in the digital age b
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AI is revolutionizing business, fundamentally reinventing
how companies engage, innovate, and deliver experiences
GenAI is already driving …with a large potential to transform the way companies engage with
near-term enhancement… customers and organize around customer intelligence
Faster and better
Always-on
Select examples innovation grounded
customer intelligence
on richer customer
synthesis engine
insights
•Mass qualitative interviews at scale
•Real-time segmentation and trends
Robust data management .d
Enhanced-customer More productive e v
•A daI- ta as s si es tt sed synthesis and queries of expe vr ii ee wn oc ne cw uit sh
to
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Source: BCG experience 3 o
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Customer intelligence drives competitive advantage,
yet many fail to turn it into better results
Many types of customer ...informing many ...with
intelligence... customer-centric decisions... proven value
Primary research (quantitative, Revenue
~10-20%
qualitative) growth
First-party data Differentiated Optimized go-to- .d
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value proposition market activation re
Social listening
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What if AI-driven customer intelligence could help
you answer key business questions…
Brand Product/offer …yielding faster, better,
messaging development cheaper results?
How can we position and message What products, services, and
our brand to resonate effectively experiences should we develop
Higher
with our priority demand spaces? to meet our customers' needs?
Innovation
Pricing Go-to-market
quality
Quality
and value execution
How can we align different pricing, What strategies should we use
promotion levels, and service to promote our offerings via 3-5X
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1. Customer Relationship Management. Source: BCG experience 5 o
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GenAI's transformative power is redefining
insights-driven customer engagement in three ways
What you offer What you communicate How you engage
Market mirror Creativity at scale Conversational capability
Informed by customer insights, BCG's Strategic planning ensures targeted, Engagement strategies optimized with
GenAI platform ""market mirror"" creative, and scalable hyper- GenAI-driven direct and human-led
drives enhanced value proposition personalization interactions
and innovation
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Market mirror | A comprehensive solution is essential
to harness GenAI for revolutionizing customer engagement…
Core advantages
Ongoing data capture, deep synthesis across diverse data sources, and interactive
Deep “always-on"" insights
insights repository (e.g., proprietary business data, market, primary customer insights)
Segmented customer demand space ""synthetic twins"" trained on key drivers of choice,
Synthetic twins
and a panel of business expert stakeholders to generate and react to ideas
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1. Go to Market. 7 o
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Market mirror | … leveraging advanced, actionable insights
and dynamic innovation to differentiate and guide strategy
Illustrative
Key
Insight retrieval and synthesis Concept creation and value prop Testing and validation
enablement
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search refinement concept gen. user query feedback
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Market mirror in consumer goods E2E demand generation case study |
GenAI turbocharges innovation, achieving 10x concepts in 10x shorter time
Illustrative example
End-to-end Rich insights Rapid ideation Immediate
and innovation
demand generation generation synthetic testing
• Understand customer • Integrate feedback, • Generate product • Test with real-time
needs and the market trends, and proprietary concepts
feedback from synthetic
data
• Strategize for growth • Illustrate with tailored twins to fine-tune
opportunities • Validate insights content strategies
through conversational
• Target demand spaces • Ensure brand and legal
refinement
and analyze consumer compliance
influence pathways
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Creativity at scale | GenAI delivers personalized,
convenient, values-driven experiences that meet evolving customer needs
Hyper- Real-time Cutting-edge Community
personalization ultraconvenience experience and connection
Explosion of customer solicitation Shift to online and new Beyond physical stores, augmented Connection with communities
emphasizing brand communication technologies raising standards experiences building on around passions/similar interests
and offering relevance especially for checkout, delivery, and customer emotions and brands with similar values
aftersales
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Sources: Ocean Insight customer trend survey; BCG Social Retail Playbook; BCG customer trend survey; Statista; Salesforce; Shoptalk; desktop research; BCG analysis 10 o
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Creativity at scale in biopharma case study | Always-on GenAI
content generation delivers more assets with significant time saved
Illustrative example
Marketing content development Material approval process
Launch and performance
Marketing value chain Campaign asset detailing Artifact/asset creation MLR1 and revision
monitoring
• Marketing team creates • Write ad copy • Revise marketing content • Launch/execute campaign
outline on campaign
• Source/generate images • Update claims matrices • Monitor performance
Today from • Brief agency on asset
• Review content pre-MLR and • Generate proof for final • Periodically review by
8-10 weeks development requirements
localize as needed post MLR approval expiration
Current avg
2-3 weeks 2-3 weeks 2-3 weeks ~1 week
time spent
• Tailor brief to the customer • GenAl creates images from • Automatically review • Automatically document and
from business plan campaign briefs, claims promotional material perform checks for final
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1. Medical, Legal and Regulatory. Source: BCG experience. 11 oo
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Conversational capability | GenAI conversational agents simplify
interactions, cut customer effort, and provide quicker, accurate solutions
Human support
Self-help through
conversational agent
Entry Assignment Resolution
Spending hours browsing Being forced to provide Getting transferred to Waiting on sparse
)
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public sources to get manual input across multiple agents and communication
t
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Conversational capability in sales | GenAI transforms sales with
hyper-personalization and AI-driven roles
B2C sales B2B sales
Conversational commerce Sales planning and operations
Grocery Helper conveys hyper-personalized promos or messages in customers' AI agents execute sophisticated planning to optimize coverage, territory design,
family group chats, understanding purchase drivers of customers involved in and goal setting and provide advanced automation for deal desk, approvals,
conversations, boosting basket size, and enhancing buying experience (can also
performance management functions
be relevant for B2B sales in fragmented trade)
Gen AI sales team support/
work as a team with seller
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Conversational capability in B2B sales | GenAI reinvents how sales teams
in the field engage with customers across different channels
Future of sales deal life cycle, powered by PredAI + GenAI
Channel Discover Learn Try Buy Use Discover Learn
Sales coach Solution engineer Sales assistant Sales assistant
Slack/IM proactively maps buyer identifies lack of identifies high- creates tailored semi-
power map product adoption and propensity cohort automated campaign
sends summary aligned to new product journey with content,
offering emails, and calls
Phone/
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text summarizes call, identifies budget cycle updates quote via r e
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1. Quarterly Business Review; 2. Statement of Work
Path to implementation | Shift the insights function from data
requestor to insights curator for customer-centric growth
The insights curator: The future of insights goes beyond responding to data requests. Instead, it will
involve curating insights from a broad range of sources—structured or unstructured, requested or
not—to drive more holistic, customer-centric decision making
Requested data Unrequested data
A new approach to
d customer insights
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Call to action Identify GenAI opportunities to drive customer centricity
• Evaluate where GenAI can enhance value propositions and boost
productivity, including customer insights, sales, and customer service
Begin your GenAI transformation
today—strategize, upskill, and
Develop a strategic GenAI roadmap
innovate for successful customer
• Elevate customer engagement by progressively embedding GenAI
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across all touch points
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for GenAI integration
Launch cross-functional centers of excellence to drive
productivity gains
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expertise from various departments (e.g., marketing, sales, customer .d
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BCG experts |
Key contacts
for GenAI in
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Karen Lellouche Lara Koslow Ben Eppler
Tordjman Managing Director Managing Director
Managing Director & Senior Partner & Partner
& Senior Partner
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118,bcg,ai-maturity-matrix-nov-2024.pdf,"The AI Maturity Matrix
Which Economies Are Ready for AI?
November 2024
By Christian Schwaerzler, Miguel Carrasco, Christopher Daniel,
Brooke Bollyky, Yoshihisa Niwa, Aparna Bharadwaj, Akram Awad,
Richard Sargeant, Sanjay Nawandhar, and Svetlana Kostikova
Contents
03 Introduction
04 Key Findings
05 The Relationship Between
Exposure and Readiness
10 T he Archetypes of AI Adoption
15 Strategic Next Steps
17 M ethodology
21 About the Authors
Introduction
V
iews vary on how much AI is changing the world economies are gradually adopting AI, but there is a small,
today, but one thing is clear: the technology is on influential group of AI pioneers that take their place as
course to shape the future of economic development. leaders. Their prize is economic advantage, but they are
Business leaders expect large impacts on operations and also poised to shape how humanity will interact with this
value creation in the 3-to-10-year timeframe, and world- powerfully disruptive technology.
wide spending on artificial intelligence will more than
double to $632 billion by 2028.1 The long-term, expansive By focusing on two pivotal aspects, this report offers a
scale of this growth makes AI an economic priority in every unique approach to viewing the global dynamics of AI
region across the globe. adoption. First, we examine each economy’s exposure to
AI-driven disruptions. We define exposure as the potential
This growth also adds urgency to the questions that for AI to impact a sector in an economy negatively or
policymakers face about AI. Is a society able to build an positively. We then assess each economy’s readiness to
AI-skilled workforce in key sectors? How will a government harness AI’s potential for growth and to mitigate potential
set up resilient, modern infrastructure? How does a nation risks. The resulting matrix brings together these factors to
spur enough investment and R&D to stay competitive? present six archetypes of AI economic development and
potential. We offer recommendations tailored to the
BCG’s new AI Maturity Matrix assesses 73 global different groups to guide policymakers—and provide an
economies to answer some of these key questions.2 This interactive dashboard for a more detailed exploration of
matrix provides a broad view of global adoption: most our analysis.
1. “AI Is Showing ‘Very Positive’ Signs of Eventually Boosting GDP and Productivity,” Goldman Sachs website, May 13, 2024; “Worldwide Spending on
Artificial Intelligence Forecast to Reach $632 Billion in 2028, According to a New IDC Spending Guide,” IDC website, August 19, 2024.
2. Details of the selection process are available in the methodology section.
BOSTON CONSULTING GROUP THE AI MATURITY MATRIX: WHICH ECONOMIES ARE READY FOR AI? 3
Key Findings
Out of 73 economies assessed, only Several economies with high Most economies in the study are
five—Canada, Mainland China, AI readiness are just behind the not ready for AI disruption. More
Singapore, the UK, and the US— pace of AI pioneers. While this than 70% score below the halfway
are categorized as AI pioneers. group of AI contenders includes mark in categories like ecosystem
They have reached a high level of established economies, it also participation, skills, and R&D.
readiness by blending elements like features emerging ones like India, Policymakers must act now to adjust
investment and infrastructure, turning Saudi Arabia, and the UAE that are to a world of AI and boost resiliency,
disruption into a competitive edge. using policy and targeted investments productivity, jobs, modernization, and
They are in a unique position to guide to adopt AI on an advanced level. As competitiveness.
the world forward in innovation, these economies strengthen their
talent development, and AI regulation innovation capabilities, they will grow
and ethics. more competitive and influential in
the AI space.
Distribution of Economies Across the Archetypes of AI Adoption
Sources: BCG Center for Public Economics; BCG analysis.
Note: Within each archetype, economies appear in alphabetical order.
BOSTON CONSULTING GROUP THE AI MATURITY MATRIX: WHICH ECONOMIES ARE READY FOR AI? 4
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AI contenders
Steady contenders
· Australia · Japan
AI practitioners · Austria · Luxembourg
· Belgium · Malaysia
· Denmark · Netherlands
· Estonia · Norway
· Finland · Portugal
Exposed practitioners · France · South Korea AI pioneers
· Bahrain · Greece · Germany · Spain · Canada
· Bulgaria · Hungary · Hong Kong · Sweden · Mainland China
AI emergents · · C Cy zp ecru hs ia · · K Mu aw lta ait · · I Ir se rala en ld · · S Taw ii wtz ae nrland · · S Ui Kngapore
· Italy
· Algeria · Iraq · US
· Angola · Nigeria
· Ecuador · Venezuela Gradual practitioners Rising contenders
· Ethiopia
· Argentina · Morocco · Brazil · Saudi Arabia
· Chile · Oman · India · Türkiye
· Colombia · Pakistan · Indonesia · UAE
· Dominican · Peru · New Zealand · Vietnam
Republic · Philippines · Poland
· Egypt · Romania
· Iran · Qatar
· Kenya · Slovakia
· Latvia · South Africa
· Lithuania · Thailand
· Mexico · Ukraine
Bottom 10% READINESS Top 10%
The Relationship Between Exposure
and Readiness
T
he future of AI is framed by high expectations. Yet A key place for public sector leaders to start is to under-
adoption is already paying off today with efficiency stand their economy’s level of exposure to AI by sector.
gains and return on investment. Businesses that are Exposure can lead to positive or negative impacts; for
scaling AI have boosted their revenues by 2.5 times com- example, in terms of jobs, exposure could lead to displace-
pared to competitors. When scaled across an entire econo- ment or create new employment opportunities throughout
my, such potential gains elevate AI to a pressing area for a sector. However, job displacement is not the only area of
policymaking—both today and in the years ahead. exposure. (See sidebar, “The Dimensions of AI Exposure.”)
BOSTON CONSULTING GROUP THE AI MATURITY MATRIX: WHICH ECONOMIES ARE READY FOR AI? 5
The Dimensions of AI Exposure
We find that exposure appears on many levels.
Productivity. Recent BCG research shows how AI’s ability
to automate tasks and optimize processes helps both
workers and entire businesses. On one level, AI expands
employee capabilities. BCG research found that in our
organization, GenAI-supported consultants performed 20%
better on data science tasks that fell outside their usual
areas of expertise or training. One biopharma firm used
GenAI to shorten its drug discovery process by 25%.3 On a
broader scale, several economies in this study are exposed
to these potential shifts.
However, AI could also disrupt traditional workflows in
sectors reliant on manual processes, such as manufactur-
ing. Small businesses using classic methods will often
compete with larger companies that deploy AI-driven
automation. Such small businesses might struggle to
match the productivity of optimized firms, impacting the
sector’s overall performance.
Uneven Sectoral Impact. Some sectors may lag in AI
adoption, widening the gap between innovative industries
and slower-moving ones. For instance, even as a tech-ori-
ented sector like finance readily adopts AI, agriculture may
be slower to fit the technology into workflows, failing to
result in the overall productivity gains that could help boost
a nation’s economic performance.
Job Evolution. Most observers expect that AI will make
some job categories obsolete. But new jobs that call for
advanced technical skills, including AI specialists and AI
ethics officers, will offset some of the displacement or
create new employment opportunities in sectors that have
long lagged in hiring.
In our methodology, which represents a snapshot of the
current landscape, we gauge exposure scores through four
major sources:
• A BCG survey of business leaders across sectors on
their perceptions of exposure to AI
• The frequency and intensity of AI discussions during
quarterly earnings calls of publicly listed companies
• The number of AI-related job vacancies on LinkedIn
• GenAI-sourced insights on disruption across various
industries
3. BCG client experience.
BOSTON CONSULTING GROUP THE AI MATURITY MATRIX: WHICH ECONOMIES ARE READY FOR AI? 6
Our study includes several findings about sector exposure sectors also produce AI-related goods and services that
to AI: other industries use or sell. In other words, economies with
strong ICT sectors that produce AI technologies can see
Six sectors are most exposed to AI-driven changes. their GDPs grow.
These include information and communication; high-tech
goods; retail; financial services; public services; and motor For example, semiconductors created by an economy’s
vehicles manufacturing, as shown in Exhibit 1. high-tech goods sector—resulting in more powerful, effi-
cient chips—are used in onboard auto electronics for auton-
ICT sectors (such as information and communication and omous driving, enhanced safety features, and improved fuel
high-tech goods) show high exposure because AI can great- efficiency. Homegrown AI disrupts the economy’s automo-
ly transform how work gets done in these areas. Yet these tive sector, making it more innovative and competitive—
sectors are more than just hotbeds for automation. Such and growth soars for both automakers and chip makers.
Exhibit 1 - Exposure to AI: Heatmap of Sectors
SOURCES LEVERAGED TO GAUGE EXPOSURE
Exposure Survey of Publicly listed Job vacancies GenAI-sourced
Sector to AI business leaders companies on LinkedIn insights
Information and communication
High-tech goods
Retail and wholesale
Financial services
Public services
High
exposure
Motor vehicles and parts
Business services
Accommodation and catering
Machinery and equipment
Transport and storage services
Oil and gas, coke, and refined petroleum
Utilities
Pharmaceuticals
Arts, recreation, union, and personal services
Textiles, leather, and clothing
Mining
Metals
Food, beverages, and tobacco Limited
exposure
Other transport equipment
Nonmetallic minerals
Chemical, rubber, plastics
Construction
Other miscellaneous
Agriculture, forestry, and fishery
Furniture manufacturing
Paper and wood products (without furniture)
Sources: BCG Center for Public Economics; BCG analysis.
Note: For more details on sources, see the report’s methodology section.
BOSTON CONSULTING GROUP THE AI MATURITY MATRIX: WHICH ECONOMIES ARE READY FOR AI? 7
Economies with a high share of sectors that are most Readiness by Degrees
exposed to AI are among the world’s most exposed
to disruption. The three most exposed economies in our Assessing readiness helps an economy understand its
study are Luxembourg (with financial services making up strengths and weaknesses as it manages technology risk
almost 30% of GDP); Hong Kong (22% financial services and makes the most of AI.
and 22% business services); and Singapore (18% business
services; 16% retail; 14% financial services).4 Readiness for AI refers to an economy’s ability to effective-
ly implement and integrate AI. This ability can be mea-
Economies with industry sectors that are less suscep- sured across six dimensions that make up BCG’s ASPIRE
tible to AI disruption are less exposed. Such sectors index: Ambition; Skills; Policy and regulation; Investment;
include construction, agriculture, and furniture manufactur- Research and innovation; and Ecosystem. (See Exhibit 2.)
ing; countries include Indonesia (13% agriculture and 11%
construction of GDP); India (17% agriculture and 8% con- This framework offers a comprehensive view on adoption
struction); and Ethiopia (36% agriculture). But these sectors levers for AI. Ambition assesses whether a country has a
can be fertile ground for economic transformation. specific AI strategy and a government entity to oversee it,
while Skills looks at the availability of AI specialists. (For
Along with boosting efficiency, AI can create positive more details, refer to the methodology at the end of the
spillover effects throughout an economy—especially report.) ASPIRE is useful for assessing the full range of
a less exposed economy. AI can spur growth in adjacent advanced, emerging, and developing economies, some of
sectors, helping a country shift the mix of sectors in its GDP. which are quite prepared for AI. It also showcases the
For example, AI-driven agricultural technology could help imbalances that often form when an economy is highly
optimize supply chains with data on crop yields, weather, advanced in some of these six areas and lacking develop-
and market trends. The country’s transportation sector ment in others. (See Exhibit 3.)
would become more efficient and modernized.
Most economies must do more to prepare them-
Ultimately, exposure to the changes brought by AI is selves adequately for AI disruption. The numbers are
inherent in today’s world. It’s inevitable that AI will show stark: Out of 73 economies assessed, only five—categorized
up somewhere in an economy, even to a limited degree, so as AI pioneers—have achieved a high level of readiness.
every country’s economy has at least some exposure to the More than 70% score below the halfway mark in categories
technology. Yet an economy with high exposure isn’t nec- like ecosystem participation, skills, and R&D.
essarily in a bad spot—on the contrary, some of the most
exposed economies are also the most prepared. Pioneers are out in front in skills, R&D, ecosystems,
and investments. In skills, the US and Singapore stand
out with robust AI talent pools, which are crucial for driving
innovation. The US leads in investing, driven by its sophisti-
cated capital markets and the abundance of AI unicorns. In
the R&D race, Mainland China is leading in patents and AI
academic papers.
Everywhere else, innovation and investment must
catch up. The bulk of economies score below the average
in R&D and investment, hindering their ability to foster
startups or deploy homegrown solutions. Some countries
that perform well in ecosystems, including Japan, Germa-
ny, and the UAE, have good telecommunications and AI
infrastructure; they’ve benefited by accessing new technol-
ogy from ecosystem partners. However, other economies
score lower in innovation and ecosystem participation,
leaving them with fewer options to access new solutions.
The ambition to engage AI is high throughout the
world—but countries need more than ambition. Most
economies, including upper-middle countries like the Domi-
nican Republic and lower-middle-income countries like Kenya,
have stated their national strategies or created national AI
ministries and steering committees. Yet societies will only find
positive outcomes if they move beyond planning and take pro-
active, concrete actions, such as forming test beds for R&D.
And for many actors, it will take time before tangible results
from AI emerge. Ambition must be paired with patience.
4. Sector share percentages are calculated from the total sum of all sectors, or GVA (gross value added), which we use as a proxy for GDP in the
report. GDP equals GVA plus taxes minus any subsidies.
BOSTON CONSULTING GROUP THE AI MATURITY MATRIX: WHICH ECONOMIES ARE READY FOR AI? 8
Exhibit 2 - Readiness for AI: ASPIRE Index
A S P I R E
Ambition Skills Policy and Investment Research and Ecosystem
regulation innovation
· Existence of AI · Concentration of · Regulatory quality · Value of AI unicorns · Research papers · Fixed broadband
strategy AI-related specialists · Governance · Mcap of IT-related published on AI internet traffic per
· Existence of · Pool of AI-related effectiveness and tech-related · AI-related patents capita
specialized AI specialists · Governance of data companies/GDP · Top-ranked · Electricity prices
government · Total public · Economic freedom · Value of trade in ICT universities in data · Telecommunication
agency/ministry contributions in index services (per capita) science and AI fields infrastructure index
GitHub by top 1,000 · AI and democratic · Value of trade in ICT · Number of AI · Average download
users values index goods (per capita) startups speed
· Kaggle Grandmasters · VC availability · Online service index
· Number of Python · Funding of AI · Performance of
package downloads companies economy-wide
per 1,000 people · Computer software statistical systems
spending
Sources: BCG Center for Public Economics; BCG analysis.
Exhibit 3 - Readiness for AI: Measuring Economies
Policy and Research and
Economies Total ASPIRE Ambition Skills regulation Investment innovation Ecosystem
Canada
AI Mainland China 68 10 17 8 8 8 19
Singapore
pioneers United Kingdom 0 100 0 10 0 25 0 10 0 15 0 15 0 25
United States
Australia Japan
Finland Netherlands
France South Korea 58 10 14 8 6 4 16
Top 25% Germany Spain
India Sweden 0 100 0 10 0 25 0 10 0 15 0 15 0 25
Ireland Taiwan
Israel UAE
Austria Malaysia
Belgium New Zealand
Brazil Norway
Denmark Poland 47 9 11 7 4 2 14
Top 50% Estonia Portugal
Hong Kong Saudi Arabia 0 100 0 10 0 25 0 10 0 15 0 15 0 25
Indonesia Switzerland
Italy Türkiye
Luxembourg Vietnam
Argentina Malta
Chile Mexico
Colombia Pakistan
Cyprus Peru 38 10 8 6 2 1 11
Czechia Qatar
Top 75%
Egypt Romania
0 100 0 10 0 25 0 10 0 15 0 15 0 25
Greece South Africa
Hungary Thailand
Latvia Ukraine
Lithuania
Bahrain Kuwait
Bulgaria Morocco 31 7 7 5 2 1 9
Dominican Oman
Top 90%
Republic Philippines
0 100 0 10 0 25 0 10 0 15 0 15 0 25
Iran Slovakia
Kenya
Algeria Iraq 20 4 5 3 1 1 6
AI Angola Nigeria
emergents Ecuador Venezuela 0 100 0 10 0 25 0 10 0 15 0 15 0 25
Ethiopia
Minimum for dimension Maximum for dimension
Average
Sources: BCG Center for Public Economics; BCG analysis.
Note: Economies positioned at the borderline between the top AI pioneers and the top 25% range are considered as part of the top 25% group. Due
to rounding, the dimension scores may not sum up to the total score.
BOSTON CONSULTING GROUP THE AI MATURITY MATRIX: WHICH ECONOMIES ARE READY FOR AI? 9
The Archetypes of AI Adoption
T
he combined analysis of AI exposure and Pioneers will want to amplify their strategies to keep up
readiness reveals six distinct adoption groups. their competitive edge. But as competitive as technology
(See Exhibit 4.) evolution can be, countries everywhere should come to-
gether to address the emerging ethical questions around
AI Pioneers. These are the vanguards of AI adoption, AI. Pioneers can participate in these important ethical
building on strong infrastructures and engaging the tech- efforts in several ways. For one, they are authoring the
nology in diverse sectors. All pioneers invest greatly in world’s first AI-specific regulatory codes, which will likely be
R&D, as shown by the many tech companies, startups, models for regulation in other countries. These leaders
and unicorns in each of the five countries. Job sectors and should also convene nations throughout the world in dis-
education systems are full of highly skilled talent. cussions around AI ethics. (See sidebar, “How Singapore
Became an AI Pioneer.”)
AI will make up progressively larger shares of the pioneers’
GDPs over the next several years, as these actors supply
more and more AI technologies, services, skills, and invest-
ment to the world. For example, the US exports software,
platforms, and essential hardware for AI computing, as
well as cloud-based AI services. Mainland China exports
AI-powered consumer electronics, including autonomous
driving platforms. This presence in the global tech supply
chain allows pioneers to set standards and influence the
entire AI landscape.
BOSTON CONSULTING GROUP THE AI MATURITY MATRIX: WHICH ECONOMIES ARE READY FOR AI? 10
BOSTON CONSULTING GROUP THE AI MATURITY MATRIX: WHICH ECONOMIES ARE READY FOR AI? 11
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Exhibit 4 - Definitions of the Archetypes
AI practitioners AI contenders
Exposed practitioners Steady contenders
Economies with relatively high Economies with relatively
exposure to AI and insufficient high exposure to Al and AI pioneers
levels of readiness sufficient levels of readiness
Economies able to
for its adoption
meet high levels of
exposure with
AI emergents extremely high
Economies with readiness
extremely low
readiness and
different levels of Gradual practitioners Rising contenders
exposure to AI Economies with relatively low Economies with relatively low
exposure to Al and low exposure to AI despite high
readiness for its adoption readiness for its adoption
Bottom 10% READINESS Top 10%
Sources: BCG Center for Public Economics; BCG analysis.
AI contenders have a relatively high level of AI readiness. Rising Contenders. These are mainly economies with lower
These actors are enjoying efficient operations, lower costs, AI exposure due to a relatively higher share of industrial and/
and other benefits of adoption. Going further and acceler- or resource-based mix of sectors. This lower level of exposure
ating AI across sectors will strengthen their positions; if is the main difference between rising contenders and steady
these economies expand their stakes in niche or special- contenders, but governments in this subgroup push for AI
ized markets, they could compete with AI pioneers in such adoption with the same commitment as steady contenders.
areas. We split AI contenders into two archetypes:
An interesting case here is India, which is grouped with
Steady Contenders. These economies have higher shares several high-income economies because of its high level
of highly exposed service sectors, such as financial ser- of readiness.
vices. However, their exposure is balanced by high readi-
ness. This group is mainly dominated by high-income Eu- • The Indian government has launched several AI-focused
ropean economies like Germany, which has high exposure initiatives, such as the National AI Strategy and the
due to its large ICT and advanced manufacturing sectors. creation of centers of excellence in AI, which aim to inte-
Germany’s technological innovation and strong industrial grate AI into key sectors like agriculture and education.
base attract foreign trade and investment. Combined with
its robust AI strategy, the country has established itself as • India is investing heavily in AI education and training
a strong player in global tech markets. programs to build a large, tech-skilled workforce.
A notable country here from outside Europe is Malaysia. The • India has a rapidly growing startup ecosystem, partic-
strong focus of the Malaysian government on AI is evident in ularly in AI-driven fintech, health care, e-commerce,
its National AI Roadmap, tech hubs, and universities offer- edtech, and agritech.
ing AI training. This shows how public sector leadership can
help an emerging economy reach technology maturity and Two other notable examples in this group are Saudi Arabia
competitiveness on par with high-income economies. and Indonesia. Having focused on building AI foundations
since launching the National AI Strategy in 2020, Saudi
Arabia is now emerging as a global center of excellence in
fields of national priority such as Arabic language AI, indus-
trial and energy-related AI, as well as health care and
education. Indonesia, through its National AI Strategy, is
emphasizing education to meet the needs of its growing
population and foster long-term economic growth.
How Singapore Became an AI Pioneer
Despite its small population, Singapore is a notable exam-
ple of AI adoption due to a successful government strategy
on AI—including talent, regulation, innovation, and invest-
ment. The country launched its National AI Strategy in
2019, with an updated version in 2023 (NAIS 2.0), focusing
on integrating AI across multiple sectors. In February 2024,
Singapore announced a five-year plan to invest more than
US$743 million in AI to strengthen its position as a global
business and innovation hub.
Skilling is a key piece of government efforts. The country’s
TechSkills Accelerator program has upskilled more than
230,000 people since 2016. The country’s AI Apprentice-
ship Program (AIAP) trains Singaporean tech workers on
real-world AI projects. Singapore has also moved to attract
talent; the ONE Pass and Tech@SG programs make it
easier for tech companies to hire international experts by
simplifying the visa process.
Singapore has launched specific AI policies and frame-
works. The Model AI Governance Framework guides com-
panies in the ethical use of AI, ensuring transparency and
accountability. The AI Verify Foundation is a global open-
source community to support companies in deploying AI
responsibly and maintaining stakeholder trust.
The country’s five-year national R&D strategy—the
Research, Innovation, and Enterprise (RIE) plan—funds
innovation with US$19 billion, launched in 2020 across
various sectors, including the digital economy. The AI
Singapore program brings together the country’s research
institutions in an ecosystem of innovation.
Singapore also established the Center for Frontier AI
Research (CFAR), which supports AI R&D related to nation-
al priorities.
BBOOSSTTOONN CCOONNSSUULLTTIINNGG GGRROOUUPP TTHHEE AAII MMAATTUURRIITTYY MMAATTRRIIXX:: WWHHIICCHH EECCOONNOOMMIIEESS AARREE RREEAADDYY FFOORR AAII?? 1122
AI pioneers are the vanguards of
AI adoption, building on strong
infrastructures and engaging the
technology in diverse sectors.
AI practitioners make up a diverse group of countries at AI Emergents. These economies are at the early stages
different levels of economic progress. We split AI practi- of AI adoption. They need to build foundational strategies
tioners into two archetypes: and infrastructure to reach the basic levels of AI integration
and competitiveness.
Gradual Practitioners. These are typically upper-middle
and lower-middle-income countries that are adopting AI at These countries lack a national AI strategy or similar holis-
a moderate pace. Their economies include low-tech sectors tic approaches to AI. Skilled workers and investment are
such as tourism, textiles, wood manufacturing, and agri- often scarce, as is activity related to research papers,
culture, where adopting AI is not yet imperative for com- patents, and startups. Nations in this archetype should
panies. However, countries here can explore how AI brings look outward for international investment and sources of
efficiency or new revenue lines to these sectors. This will talent. They should also establish the basics of a govern-
maintain competitiveness and foster growth as the tech- ment-driven technology strategy.
nology becomes more relevant over time.
However, building competitiveness is not out of reach for
Long reliant on its energy resources, Qatar is using AI countries in this group. Nigeria has leveraged foreign direct
applications in the oil industry—its dominant sector—to investment to lead Africa’s fintech revolution. If the country
optimize production and boost sustainability. This puts focuses on developing AI talent within its growing popula-
Qatar at the leading edge of innovation in the industry. tion and adopts a more holistic approach—such as imple-
menting a national AI strategy—Nigeria could build on its
Exposed Practitioners. This group includes developing fintech momentum and become a key player in the conti-
and developed economies vulnerable to AI disruption due nent’s AI landscape.
to more high-exposure sectors and low readiness. Actors
here will need to accelerate AI adoption and mitigate
potential risks.
While these countries may currently have a gap between
their AI exposure and readiness, they are well positioned to
gain ground quickly with investments in infrastructure and
education. It is a sound strategy to focus on niche and
specialized markets.
• Malta is becoming a leader in AI regulation and block-
chain, building a safe and attractive environment for
tech companies.
• Cyprus is using a skilled workforce to develop AI applica-
tions in tourism and financial services.
Others in the group can build on the lessons learned:
Bahrain and Kuwait can leverage AI in the energy sector,
especially to optimize oil production and manage supply
chains. Greece and Bulgaria have strong academic tradi-
tions in engineering and mathematics, which can serve as
a foundation for building AI expertise. By investing in AI-fo-
cused education and retraining programs, they can en-
hance their readiness.
BOSTON CONSULTING GROUP THE AI MATURITY MATRIX: WHICH ECONOMIES ARE READY FOR AI? 14
Strategic Next Steps
What can governments do to position themselves for ad- • Accelerating AI: customizing the ASPIRE levers for
vantage in the AI-dominated future? We propose a set of AI contenders and AI practitioners
initiatives for each archetype across three themes, as
shown in Exhibit 5: • Amplifying AI: driving the global AI agenda for
AI pioneers
• Enabling AI: establishing the foundational elements
for AI emergents
BOSTON CONSULTING GROUP THE AI MATURITY MATRIX: WHICH ECONOMIES ARE READY FOR AI? 15
Exhibit 5 - Recommendations on AI Adoption for Each Archetype
Enabling AI Accelerating AI Amplifying AI
AI emergents AI contenders AI pioneers
AI practitioners
Enable AI adoption through a Actively oversee AI adoption, Support leading AI
A national AI strategy and a with a focus on addressing industry(ies) across the
Ambition
dedicated entity to oversee lagging topics. tech value chain.
implementation.
Provide basic AI training and Attract and retain AI talent pool Enhance cross-cutting AI
S digital programs to modernize (software developers, engineers) expertise and sector
Skills
the workforce. and focus on big data and specialization among AI
advanced trainings in AI. specialists.
P Policy and Enhance government Focus on AI ethics Ensure centralized
effectiveness to build a and flexible rules for oversight and more flexible
regulation
foundation for AI. experimentation. rules on open data.
Boost investments in R&D, Boost investment in high- Provide tailored support
I university programs, performance computing and for national AI champions,
Investment
workshops, and engage the data centers, and attract FDI unicorns, and startups.
private sector. in AI.
Establish research Create test beds for Focus on applied
R Research and
centers in AI and work developers and startups. research and ensure
innovation to ensure industry cross-industry sharing.
collaboration.
Ensure basic digital Promote AI solutions Enhance cross-cutting
E
infrastructure (e.g., and new technologies for AI application and
Ecosystem
high-speed internet) to strategic sectors. support advanced tech
enable AI adoption. infrastructure.
Sources: BCG Center for Public Economics; BCG analysis.
Note: FDI = foreign direct investment.
These recommendations offer a national-level approach to • Research and innovation. Encourage cross-disci-
AI readiness. Akin to the economy-wide level, economic plinary research in AI and applications in agriculture, lo-
managers can apply this to drive sectoral transformation. gistics and robotics, with an aim to share best practices.
For example, the framework can be used to drive change
across value chains in agriculture, logistics, and robotics: • Ecosystem. Create platforms that facilitate data sharing
between agritech companies and logistics firms; foster
• Ambition. Set national ambition to boost agriculture an ecosystem that connects robotics engineers, agritech
productivity through AI-powered agritech solutions, experts, and industrial sectors to help transition agricul-
robotics, and logistics. tural robotics to adjacent fields.
• Skills. Reskill workers in both agriculture and logistics With BCG’s AI Maturity Matrix, we hope to offer policymak-
sectors to adopt AI-based technologies in the agriculture ers a practical framework to navigate the evolving AI land-
value chain. scape and harness AI’s potential to strengthen economies
and enhance societal well-being.
• Policy and regulation. Develop policies that support
open data access and interoperability between agritech
data and supply chain systems.
• Investment. Invest in AI infrastructure such as Internet
of Things-enabled supply chains and predictive analyt-
ics platforms to optimize logistics using agritech data;
invest in R&D in scalable agricultural robotics.
BOSTON CONSULTING GROUP THE AI MATURITY MATRIX: WHICH ECONOMIES ARE READY FOR AI? 16
Methodology
We performed a comprehensive regional analysis by divid- Our analysis of economy-level AI exposure is based solely
ing the world into five geographical areas: the Americas, on a sectoral evaluation and the composition of the econo-
Asia, the Middle East and Africa, Europe, and Oceania. my across those sectors. It does not consider additional
Each area was further subdivided into relevant subregions. factors such as overseas workers or business process out-
We then selected the top economies by real GDP 2023 to sourcing. As a result, certain "
119,bcg,2024-gam-report-may-2024-r.pdf,"FINANCIAL INSTITUTIONS
GLOBAL ASSET MANAGEMENT REPORT 2024
22ND EDITION
AI and the Next Wave
of Transformation
May 2024
Introduction
The global asset management industry’s assets
rose to nearly $120 trillion in 2023, reverting from
a decline the year before. However, asset managers
are facing a variety of challenges to their growth.
Investors are gravitating to passively managed funds and that can enhance a three Ps strategy. AI can boost produc-
other products that have lower fees even as asset managers’ tivity by enabling improved decision making and operation-
costs increase. Their efforts to create new products that al efficiencies. It can be leveraged to create and manage
would differentiate them from competitors have largely fallen personalized portfolios at scale and to tailor the customer
short, with investors sticking mostly to established products experience. And AI can enhance the efficiency of deal
with reliable track records. Historically, the industry has been teams in private markets and boost their ability to drive
able to weather these pressures thanks to revenue growth value creation. In adopting AI to facilitate these key moves,
that has been largely driven by market appreciation. In the asset managers should view the technological possibilities
years ahead, however, market appreciation is expected to as transformational tools for their organization.
slow, creating further challenges to the industry.
As part of this year’s report, we surveyed asset managers
In the face of these pressures, asset managers will need to with collectively more than $15 trillion in assets under
rethink the way they operate in order to maintain the management to gather their views on the role of AI in their
growth and profitability of past years. The most viable way business. The vast majority of survey respondents expect
forward is by using an approach that we call the three Ps: to see significant or transformative changes in the short
productivity, personalization, and private markets. Asset term, and two-thirds either have plans to roll out at least
managers should increase productivity, personalize cus- one generative AI (GenAI) use case this year or are already
tomer engagement, and expand into private markets. scaling one or more use cases.
As the artificial intelligence (AI) technological revolution Waiting is not an option when it comes to investing in AI.
gathers momentum, asset managers have an opportunity The technology is rapidly developing, and asset managers
to invest in AI and integrate it into their operations in ways that do not start their journey now risk being left behind.
BOSTON CONSULTING GROUP AI AND THE NEXT WAVE OF TRANSFORMATION 2
Five Fundamental Pressures Persist
At a first glance, the global asset management indus-
try experienced an impressive rebound in 2023. The
industry’s total assets under management (AuM) rose
to nearly $120 trillion, an increase of 12% over 2022, a
year that saw AuM plummet by 9%. (See Exhibit 1.)
All parts of the world participated in the 2023 recovery:
AuM growth ranged from 16% in North America to 5% in
Asia-Pacific markets, excluding Japan and Australia.
(See Appendix 1.)
However, while dramatic, the growth only serves to mask
the asset management industry’s underlying vulnerability.
Industry revenues increased by just 0.2% in 2023, while
costs rose by 4.3% for the year. With these two opposing
forces at play, profits declined by 8.1%. (See Exhibit 2.)
BOSTON CONSULTING GROUP AI AND THE NEXT WAVE OF TRANSFORMATION 3
Exhibit 1 The Global Asset Management Industry’s AuM Grew by 12% in 2023
GLOBAL AUM ($TRILLIONS) NET FLOWS AS A SHARE OF BEGINNING-OF-YEAR AUM (%)
+12%
116.6 118.7 4.4
+6% 103.7 106.3
CAGR
93.5 3.4
83.5 3.1 3.1
80.7 2.9
73.7
69.1
2.1
47.6
1.6
1.5 1.5
37.3
1.2
0.9
2005 2010 2015 2016 2017 2018 2019 2020 2021 2022 2023 2005 2010 2015 2016 2017 2018 2019 2020 2021 2022 2023
to to
2009 2014
Sources: BCG’s Global Asset Management Market Sizing Database, 2024; BCG’s Global Asset Management Benchmarking Database, 2024.
Note: Market sizing corresponds to assets sourced from each region and professionally managed in exchange for management fees; it includes captive AuM
of insurance groups or pension funds that delegate AuM to asset management entities with fees paid. Globally, 44 markets are covered, including offshore
AuM, which is not included in any one of the six regions. (See Appendix 1.) For all countries where the currency is not the US dollar, the end-of-year 2023
exchange rate is applied to all years to synchronize the current and historic data. Values differ from those in prior studies due to exchange rate fluctuations,
revised methodology, and changes in source data.
Exhibit 2 Rising Costs and Stagnant Revenues Drove Profits to Decline
Average AuM Net Revenues Costs Profit pool
INDEX INDEX INDEX INDEX
+1.4% +0.2% +4.3% –8.1%
176 176 172 180 184 169
213 216 134 129 144
100 100 100
142 2010 2015 2022 2023 2010 2015 2022 2023 2010 2015 2022 2023
100
AUM (BASIS POINTS) AUM (BASIS POINTS) NET REVENUES MARGIN (%)
26.1 24.5 22.0 21.7 17.4 15.7 14.9 15.3 34 36 32 30
2010 2015 2022 2023 2010 2015 2022 2023 2010 2015 2022 2023 2010 2015 2022 2023
Source: BCG’s Global Asset Management Benchmarking Database, 2024.
Note: The analysis is based on a global benchmarking study of 80 leading asset managers, representing $69 trillion in AuM, or about 60% of global AuM.
Index totals have been rounded.
BOSTON CONSULTING GROUP AI AND THE NEXT WAVE OF TRANSFORMATION 4
Tight monetary poli cies and In addition, the asset management industry continued to
face structural challenges from the five fundamental pres-
general market uncertainty sures that we identified in last year’s report. These pres-
sures did not subside in 2023. (See Exhibit 3.)
prompted investors to move
Revenue pressure continues. Asset managers cannot rely
into products with lower fees.
on market performance to drive revenue growth in the future
to the same extent that they have in the past. Since 2006,
Money market products had
almost 90% of the industry’s revenue growth has come from
net inflows of $1.3 trillion. market appreciation. This growth coincided with a period of
generally low interest rates. However, as most global central
banks continue their fight against inflation, interest rates are
expected to remain higher, a condition that will likely constrain
asset managers’ revenue growth from market appreciation.
Passive funds are increasingly popular. Passive prod-
ucts continue to capture the lion’s share of net flows. In
2023, passive products attracted 70% of total global mutual
funds and exchange-traded funds (ETFs) net flows (about
$920 billion). That was a sharp rise compared with the
period from 2019 through 2022, when 57% of net flows
went into passive products.
Fee compression is accelerating. Similarly, the pressure
on fees showed no signs of reversing in 2023. The average
fee in 2023 was 22 basis points (bps), down from 25 bps in
2015 and 26 bps in 2010. Continued tight monetary policies,
combined with general market uncertainty, resulted in inves-
tors moving into products with lower fees. Money market
and bond products generated net inflows of $1.3 trillion and
$700 billion, respectively, while public equity had net out-
flows of $200 billion.
Costs are rising. Costs continued on an upward trajecto-
ry, increasing by about 80% since 2010 at a compound
annual growth rate of 5%.
Fewer new products are surviving despite attempts
at innovation. Despite asset managers’ continuing efforts
to develop new offerings, many have not been successful.
In fact, only 37% of all mutual funds launched in 2013 still
existed by 2023. This is a significant decrease, compared
with 2010 when 60% of funds that had been launched a
decade earlier remained active.
BOSTON CONSULTING GROUP AI AND THE NEXT WAVE OF TRANSFORMATION 5
Exhibit 3 Five Fundamental Pressures Weigh on Growth
Revenue pressure continues
Market performance has been the main driver of growth Investors are shifting to products with lower fees
$BILLIONS NEW FLOWS, 2023 ($TRILLIONS)
Revenue from
net flows 89 194
11
89
94 54
1.3
43 89% of total
revenue growth 0.7
Revenue
pressure –0.2
2005 2006–2023 2006–2023 2023 Money Bonds Equity
Revenues Revenues from Revenues Revenues market
net flows, offset from market
by revenue performance
pressure
Passive funds are increasingly popular
Net flows to passively managed funds increased Top ten fund managers captured an increasing share of positive net flows
NET FLOWS TO FUNDS, US ONLY1 POSITIVE NET FLOWS INTO PASSIVE POSITIVE NET FLOWS INTO ACTIVE
MUTUAL FUNDS, US ONLY ($BILLIONS)2 MUTUAL FUNDS, US ONLY ($BILLIONS)2
84%
181 504 382 162
9% 5%
26% 1.1 45% 33%
8%
91% 95%
3.2 6.0 67%
55%
2.4
1.1
0.2
1990–1999 2000–2009 2010–2023 2010 2023 2010 2023
Active Passive Share of passive Top ten3 Rest of industry
Fee compression is Costs are rising Fewer new products are surviving
accelerating despite attempts at innovation
70%
26 bps 64% 180
25 bps
66% 60%
22 bps
129
42%
100 37%
2010 2015 2023 2010 2015 2023 2010 2015 2023
Average fee (net distribution costs) Costs Costs as a share New funds that reach
(index, 2010) of revenue the ten-year mark
Sources: BCG’s Global Asset Management Market Sizing Database, 2024; BCG’s Global Asset Management Benchmarking Database, 2024; ISS
Market Intelligence Simfund; BCG analysis.
Note: bps = basis points. All figures are global unless otherwise noted. Revenue pressure includes the impact of both the shift in product mix and
change in pricing pressure. The scope of the analysis is active core, active specialties, solutions, and passives; it excludes alternatives. Values differ
from those in prior studies due to exchange rate fluctuations, revised methodology, and changes in source data.
1Corresponds to mutual funds, including exchange-traded funds but excluding variable annuities.
2Corresponds to mutual funds, including exchange-traded funds but excluding money market and variable annuities.
3The largest ten firms by the amount of positive net flows received.
BOSTON CONSULTING GROUP AI AND THE NEXT WAVE OF TRANSFORMATION 6
AI Can Accelerate the Three Ps
To remain competitive and boost profitability in AI is being built into a variety of tools that asset managers
the face of the five fundamental pressures, asset can use to improve their operations. The power of such
managers should use an approach that we call tools comes from AI’s ability to rapidly collect, synthesize,
the three Ps—productivity, personalization, and and analyze vast amounts of data from internal and exter-
private markets. nal sources and then generate information on the basis of
patterns found in the data. The subset of AI known as gen-
We introduced this approach in last year’s report and erative artificial intelligence (GenAI) has the ability to inter-
continue to find it the best strategy for spurring growth. pret and analyze unstructured data from a wide range of
Increased productivity can make a big difference in just sources and create original content. Tools that combine the
about every organizational function. Improved personaliza- capabilities of AI and GenAI can communicate with users in
tion can facilitate the development of products tailored to natural language, a feature that simplifies their use and can
the unique needs of customers, enhance the customer accelerate their adoption.
experience, and enable asset managers to distinguish
themselves effectively from competitors. The expansion Both AI and GenAI are becoming critical to asset managers.
into private markets can help asset managers focus on Those that service insurance portfolios are finding these
higher-margin products to diversify revenue. Key to accel- technologies instrumental as they adapt to new pressures
erating each of these elements is AI. on their allocation and risk management strategies. (See
the sidebar “The Future of Risk-Adjusted Performance.”)
BOSTON CONSULTING GROUP AI AND THE NEXT WAVE OF TRANSFORMATION 7
The Future of Risk-Adjusted Performance
In the insurance industry, investment income can represent model, however, can compare portfolio holdings and their
as much as 30% to 50% of a company’s earnings. Each day, risk levels, optimize runoff profiles, and establish new risk
billions of dollars from insurance portfolios move through thresholds all in one step. With the present-day systems,
the financial markets. As a result, any small change in portfolio managers typically lack the resources to perform
performance outcome, even if it’s only a matter of decimal the cumbersome task of analyzing underlying assets from
points, can add or destroy significant amounts of value. third-party sources, such as managers of funds of funds or
ETFs, more often than two or three times a year. AI, howev-
To drive performance and lessen risk, insurance asset er, can quickly detect and analyze that data.
managers have long relied on two main analytical process-
es. Asset and liability management (ALM) is used to inform The rapid turnaround makes it possible to provide insur-
the investment team about the commitments made ance clients with a transparent, multidimensional assess-
through policies, while strategic asset allocation (SAA) ment of which assets are being stacked against which
helps determine how to maximize investment upside while liabilities—and the analysis can be performed monthly or
minimizing risks for the insurer. even weekly. On the liability side, AI has proven itself able
to forecast insurance policy lapse rates. This is a capability
Now, however, asset managers are under pressure to perform that was previously available only to managers of larger
these processes in much greater detail and far more often. portfolios with the means to build analytical models to
scale. Now, however, GenAI models, which can system-
Regulations are one source of pressure. The International atize vast amounts of scattered data from both structured
Financial Reporting Standards (IFRS) amendments 17 and and unstructured sources, can make this capability avail-
9 that took effect in Europe and Asia in 2023 have brought able to asset managers of any size.
a new set of accounting practices. The IFRS requirements
for standardized performance metrics have compelled
many asset managers serving insurance clients in those
regions to rethink their previous asset allocation strategies
so that they can achieve their objectives.
But even greater pressures have arisen from the geopoliti- In one step, AI models can
cal turmoil and resulting market uncertainties that affect
compare portfolio holdings and
every part of the world. In this chaotic climate, insurance
portfolio managers need to be prepared with in-depth
their risk levels, optimize runoff
market intelligence so that they are ready to make adjust-
ments far more frequently. Whereas it used to be typical to
profiles, and establish new
conduct ALM and SAA reviews once or twice a year, quar-
terly reviews are now considered the minimum require- risk thresholds.
ment, and some firms are starting to conduct the process
every month, leveraging a greater amount of internal and
external data.
The most advanced players are starting to use AI and its GenAI models provide the ability to develop and automate
generative AI (GenAI) subset to perform their ALM and many reporting exercises. For example, reconciling the
SAA processes with the depth and frequency that’s now market performance of portfolio holdings with the account-
required, and it is becoming clear that this is where the ing figures is still largely a manual exercise, but it will not
future lies for insurance investment management. AI mod- be for much longer. Using GenAI, an asset manager can
els are especially effective at combining large amounts of connect automatically with all requisite data platforms and
data, including unstructured data from multiple sources, to quickly download a full report. In this case, too, the system
inform the decision-making task. These models make it provides a fully transparent disclosure of where the num-
possible to extract more value from the analytical processes bers come from.
and reduce the associated costs by 5% to 15%. Moreover,
with the exponentially greater efficiency gained from AI, By using AI- and GenAI-powered risk and allocation analyt-
some players have been able to achieve risk-adjusted ics, portfolio managers also gain access to a wider scope
returns that have been 10 to 20 basis points higher than of investment parameters and data, with the ability to
previous performance. react quickly when market changes call for adjustments.
These advanced systems are going to become a source of
AI models are able to boost the efficiency of both the ALM competitive advantage in the next few years—and it will
and SAA processes in a number of ways. Currently, most be mandatory for insurance asset managers to embrace
reviews are still performed using mathematical models that AI to continue producing winning investment results and
optimize the results using only one variable at a time. An AI cost efficiencies.
BOSTON CONSULTING GROUP AI AND THE NEXT WAVE OF TRANSFORMATION 8
AI Can Increase Productivity
Asset managers can achieve significant efficiency gains • Operations and Risk and Compliance. AI can make
across their value chain by using AI. (See Exhibit 4.) a significant impact on reporting and data management
workflows, primarily by accelerating document
• Sales and Marketing. A combination of AI and GenAI preparation. This capability can be used for portfolio
tools can help develop marketing content—drafting performance analysis, management research, and the
white papers on the basis of internal research, for readying of client statements, proxy forms, dividend
example, or creating social media posts that summarize notices, and more. AI can make risk management
the white papers. AI tools can analyze public data about more efficient by using its ability to analyze system
prospective clients and then direct the sales team to the logs and real-time data, identify irregular activities,
most promising leads, increasing conversion efficiency. and proactively flag anomalies to the risk team. AI-
Additionally, AI tools can support sales teams with based alerts can go well beyond simple rules-based
customer interactions. For example, a software-based AI notifications; for example, AI can detect signs of market
agent can provide real-time insights to a human sales instability from news reports and respond before a
agent who is speaking with a client; an AI agent can portfolio value crosses the threshold that would have
even communicate directly with clients. triggered an action.
• Investment Management and Trade Execution. AI • IT. AI can enhance the efficiency and effectiveness of
can support investment teams with thesis development IT infrastructure management by detecting anomalies,
by quickly gathering, synthesizing, and analyzing predicting failures, and automatically troubleshooting
data. AI tools can do this whether the information is internal networks. AI copilots can streamline the coding
proprietary—from internal research, for example—or process, as well as accelerate the development, testing,
is compiled from the web or alternative data sources, and deployment of trading algorithms. AI chatbots can
such as public filings, macroeconomic statistics, and support the internal IT desk, enabling faster problem
geospatial reports. Additionally, the tools can facilitate solving when users experience technical issues.
effective knowledge management and data sharing by
organizing reports, data sets, and research developed • Business Management and Support. AI can improve
by various investment teams. As a result, AI can break decision-making and strategic-planning efficiency
down silos and minimize redundant analyses, which by analyzing performance updates across different
occur frequently when investment teams managing investment teams and generating synthesized insights
different funds or products are exploring similar themes. for executives. Similar insights can be used to generate
fundraising documents and investor presentations.
AI tools can automate the creation and review of
legal documents and contracts, quickly spotting and
addressing potential issues.
Exhibit 4 AI-Enabled Gains Can Improve Productivity Across the Value Chain
100 1.50–2.50
0.50–0.75
2.50–4.50
15–25
30–40
1.00–1.50
0.75–1.50
10–20 0.25–.075
20–25 0.10–0.25 0.10–0.25 0.10–0.25 0.25–0.50 85–95
15–30
15–25 15–25 15–20 10–20
5–10
Estimated efficiency gains (%)
Asset Sales Marketing Investment Operations IT Risk and HR Legal and Finance Management Asset
managers’ management compliance audit and strategy managers’
cost base and trade AI-powered
(indexed) execution cost base
Business management and support
Sources: BCG’s Global Asset Management Benchmarking Database, 2023; expert interviews; BCG analysis.
Note: Individual value chain ranges do not add up to the total range because of rounding.
BOSTON CONSULTING GROUP AI AND THE NEXT WAVE OF TRANSFORMATION 9
AI can enhance productivity These efficiency opportunities represent just a fraction of
AI’s potential; the list of applications continues to expand
by conducting preliminary
alongside the advancement of the technology. Asset man-
agers are already witnessing impressive results from AI
investment research, data
implementations. For example, an AI tool can accelerate
collection, and analysis. investment research. (See Exhibit 5.) This tool could
enhance investment analysts’ productivity by conducting
preliminary data collection and analysis, enabling the
human analysts to concentrate on generating insights. An
analyst may, for example, use the AI tool to summarize a
company’s market position on the basis of its financial
filings and news coverage. After reviewing this initial
research, the analyst can employ the tool for a more
in-depth analysis of selected topics needing further inves-
tigation. Eventually, the tool can be used to draft a report
focused on the key issues.
Exhibit 5 Asset Managers Can Use AI and GenAI to Accelerate
Investment Research in Natural Language
1. Conduct a search 2. Receive selected topics 3. Create a tailored report
AI responds to a research and insights AI drafts a written investment
analyst’s request for information AI delivers a detailed analysis of key report in the required format
about a company and its market elements requested by the analyst
Tell me about [company] Analyze the latest financial Draft a report for
and its market position in information on [company]. [company] in this structure:
under 1,000 words. Is there anything related · SEC filings
to supply chain disruption?
· Conference calls
RESEARCH ANALYST
· Press releases
RESEARCH ANALYST
· Equity research
· EPS consensus
[Company] is a United
States-based Here is information based RESEARCH ANALYST
semiconductor on [company’s] latest
manufacturer. Its quarterly reports and an
current stock price is analyst’s event Here is the report.
$105.92. . . . presentation:
AI AND GENAI TOOLS
· Outpacing industry
AI AND GENAI TOOLS growth at 25% CAGR
· Sales of global equipment
increased 20% annually
since the pandemic. . . .
AI AND GENAI TOOLS
Sources: Expert interviews; BCG analysis.
BOSTON CONSULTING GROUP AI AND THE NEXT WAVE OF TRANSFORMATION 10
AI Can Enable Greater Personalization
AI can enhance personalization by expanding the ability The second development is the efficient scaling of custom-
to create and manage customized portfolios at scale. In ized portfolio management, resolving a major challenge that
addition, it makes it possible to provide a more highly has made bespoke investing a high-cost service that is
tailored customer experience, from acquisition through available only to institutional investors and high-net-worth
retention, at scale. individuals. When investing is nonpersonalized, capital from
a large group of investors goes into a vehicle such as a mutu-
Personalized Portfolios at Scale al fund, an ETF, or a pooled (or commingled) fund, and one
Advancements in AI are leading to two major develop- asset manager efficiently determines the optimal rebalanc-
ments for personalized portfolios. First, AI represents a ing and trading strategy for the whole fund. However, in
step-change in the construction of customized portfolios. personalized portfolio management, optimal rebalancing
Currently, adding tactical tilts or thematic exposures to and trading strategies need to be determined for each inves-
personalized portfolios is primarily based on structured or tor’s portfolio—for example, to respect specific objectives
precurated data sets. For example, a financial advisor can and constraints or to maximize tax loss harvesting opportu-
add an ESG tilt to a client portfolio if the advisor has nities. Consequently, the more clients with personalized
access to a data set that measures companies’ ESG charac- portfolios that an asset management firm has, the more
teristics. AI, and more specifically GenAI, can significantly portfolio managers it will need. It is nearly impossible to
expand the range of tactical tilts and allow a theme-based automate personalized portfolio management using statisti-
selection of securities. This is possible due to GenAI’s cal or rules-based processes across thousands of fully cus-
ability to understand requirements expressed in natural tomized accounts because the number of parameters grows
language and then process and translate that information so quickly.
into investment recommendations.
However, with the latest AI developments, AI-powered agents
For example, an advisor could tell a GenAI tool that a client can be trained to understand the intent and context of portfo-
wants to decrease allocations to companies that are heavi- lio management. Learning from patterns, such agents can
ly exposed to the oil and gas value chain. The tool will tailor their approach to determine the best rebalancing and
quantify the exposure of publicly traded companies by trading strategy for each custom portfolio. The human portfo-
reading through financial filings, transcripts of earnings lio manager can oversee a group of these AI agents, effective-
calls, analyst reports, and news reports. The tool will then ly managing a much larger number of portfolios than they
rank these companies on the basis of their exposure to the otherwise would be able to. With this capability, asset man-
oil and gas value chain and make adjustments in the port- agement firms can potentially offer personalized portfolios to
folio accordingly. a much broader group of investors. (See Exhibit 6.)
Exhibit 6 AI Makes It Possible to Scale Personalized Portfolio Management
Nonpersonalized investing Customized investing Customized investing with GenAI
Clients (retail or institutional) Clients (retail or institutional) Clients (retail or institutional)
Advisor1 Advisor1 Advisor1
Commingled or pooled fund
The first AI agent narrows down the list of
portfolios to trade given the results of its task
A portfolio manager at an asset Several portfolio managers at various
management firm manages the pooled asset management firms—each one
funds of all investors operating at capacity—manage individual
client portfolios
The second AI agent further refines the list of
portfolios to trade given the results of its task
A portfolio manager makes the final decision
KEY CHARACTERISTICS
Scalable but limited; no customization Allows customization, but requires Allows customization and at scale
many portfolio managers
Sources: Expert interviews; BCG analysis. Different types of portfolios
Note: GenAI = generative artificial intelligence.
1Financial advisor, relationship manager, or sales professional.
BOSTON CONSULTING GROUP AI AND THE NEXT WAVE OF TRANSFORMATION 11
Traditional sales approach AI-enabled sales approach
Leads prospect meetings
BOSTON CONSULTING GROUP AI AND THE NEXT WAVE OF TRANSFORMATION 12
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Personalized Customer Experience • Customer Sales. A human sales agent can use AI tools
AI can also enable the hyperpersonalization of customer that analyze information about a potential client before a
engagements at scale. This can be achieved by using AI meeting, identifying the client’s needs and preferences,
tools that analyze data about prospective or existing clients and developing talking points accordingly. For example,
and then develop investment materials and engagements AI tools can help determine the potential client’s risk-
tailored to their needs and circumstances. GenAI can reward profile on the basis of demographics, then present
expand the capabilities of AI by enabling the analysis of the sales agent with discussion points that focus on the
unstructured text-based data—customer social media posts, appropriate products.
for example—and facilitating the creation of investment
information that is tailored to each individual customer. • Customer Engagement and Retention. AI can
enable a shift from periodic scheduled contacts, such
More specifically, AI can enable greater personalization in as biannual touch points, to proactive engagements
three areas of the customer experience. informed by relevant market events. For instance, AI tools
may evaluate the effects of an upcoming fee change for
• Marketing. AI can help the marketing team segment a mutual fund and identify clients who would be likely
potential customers into highly granular groups with to withdraw their funds. The relationship management
clearly defined needs. Then, using natural language, team can then contact these clients and recommend
the team can leverage AI tools to develop marketing investments in other funds with lower fees. This proactive
materials that are tailored to specific customer interests. approach helps ensure better client retention.
For example, if a marketing team writes a white paper,
AI tools can develop personalized emails for a group of Overall, AI can enable client management teams to support
prospective clients, extracting key takeaways tailored to a significantly higher number of clients with more tailored
each recipient. content at relevant moments. The implementation of AI
tools can lead to a decrease in indirect sales activity of as
much as 25% while simultaneously increasing customer
satisfaction. (See Exhibit 7.)
Exhibit 7 How AI Assistants Can Improve Sales Effectiveness
Identifies and prioritizes Forms initial leads through Identifies high-
leads manually industry contacts potential leads
Sends standardized email Reviews and sends Creates personalized
to all leads to leads emails
Monitors outreach and manually Tracks prospects’ responses Creates customized
updates CRM and updates CRM meeting materials
Prepares for meetings with time-
intensive research; leads meetings
Researches internal Synthesizes CRM and Leads client meetings
databases to manually create product information for
meeting materials dossier before meetings
Manually reviews notes and Generates meeting notes and Reviews next steps and
develops next steps proposes next steps follows up with clients
Human agent AI tools 25% Decrease in time spent on 8–12 Number of additional
client touch points
indirect sales activity
per week
Sources: Expert interviews; BCG analysis.
Note: CRM = customer relationship management.
Exhibit 8 AI Tools Increase the Efficiency of a Private-Market Deal Team
Investment committee memo preparation
TIME SPENT USING THE TIME SAVED WITH AI
ACTIVITY CONVENTIONAL PROCESS (ESTIMATE) (ESTIMATE)
Data analysis 20%–25% 30%–40%
Written output 15%–25% 35%–45%
Meetings and calls 15%–25% 20%–30%
Research 10%–15% 35%–45%
Email 5%–15% 20%–30%
Note synthesis 5% 45%–55%
Content review 5%–10% 20%–30%
Scheduling and other calls 5% NA
Sources: Expert interviews; BCG analysis.
Note: NA = not applicable.
AI Can Unlock Private-Market Potential
AI can enhance the efficiency of private-market deal teams Private-market players can also drive value creation by helping
by automating repetitive tasks and synthesizing data for their portfolio companies use AI. This will be especially
enhanced decision making. (See Exhibit 8.) It can improve important for asset managers that invest in companies
deal teams’ productivity across many parts of the value whose industries are expected to be highly disrupted by AI. In
chain. We estimate that in the due diligence process, for biotech, to name one example, AI is expected to increase the
example, AI can shorten the time required for preparing pace of product innovation and create new, efficient ways to
investment committee memos by roughly 30%. AI tools are discover new molecules and compounds. Private-mar"
120,bcg,generative-ai-in-health-and-opportunities-without-spine98.pdf,"Generative AI in health
and opportunities for
public sector organizations
October 2023
By Priya Chandran, Lauren Neal, Julia McBrien, and Shabana Quinton
Generative AI in health and
opportunities for public sector
organizations
Key takeaways: GenAI opportunity landscape across the
health ecosystem
• Generative AI has the potential to transform industries.
GenAI is projected to grow faster in healthcare than any
• The healthcare industry is expected to experience some other industry. With an estimated compound annual
of the most significant benefits from and growth in growth rate of 85%, by 2027 the market value is expected
GenAI investments in the coming years. to reach $22B1. This appetite for investment is driven by
the potential for GenAI to significantly enhance efficiency,
• As practitioners, public sector organizations can leverage reduce costs, and improve health outcomes.
GenAI, and AI more broadly, to improve their operations,
and accelerate delivery against their missions. The power of AI promises value for all stakeholders across
the health ecosystem. For example, for providers, GenAI is
• As enablers, public sector organizations can play a key expected to create $80B+ in savings via automated opera-
role in accelerating the adoption of AI within the health tions, reduced burden, and improved revenue cycle man-
ecosystem, including by establishing policy and regula- agement. AI, and GenAI in particular, has the potential to
tions that promote the responsible use of AI and reduc- provide patients with improved quality, access, affordabili-
ing barriers related to data and technology, workforce, ty, personalization, and equity of healthcare services to-
and infrastructure. ward better patient outcomes. Through GenAI, providers/
hospital systems are offered the potential for quicker,
• To get started, public sector organizations should estab- more effective methods to prevent, diagnose, and treat
lish rules of responsible engagement, define and com- patients. Payers may benefit from improved data sharing
municate their AI strategies, serve as a central coordina- and analysis, communication, and payments, including
tor to improve transparency and facilitate partnerships, preventative healthcare through predictive models and
improve their internal readiness to adopt and scale AI, automation of claims processing. Research & develop-
advance workforce capabilities, build a culture of AI use, ment institutions including pharma, biopharma, and
and provide thought leadership to establish trust in AI biotech can accelerate the product pipeline by applying
among users and the public. GenAI to drug discovery and design, clinical trial planning
and execution, precision medicine therapies, diagnostic
image enhancement & analysis, supply chain risk identifi-
What is Generative AI? cation and process augmentation, and more.
Generative artificial intelligence (GenAI) has emerged as Public sector organizations have an important role to
the biggest buzz word across nearly all industries given the play in both using and advancing the technology:
potential for its broad and deep applications and the speed
at which the technology is maturing. Evolving from prior • As practitioners, they can use GenAI to improve internal
advances in deep learning and machine learning, the most operations and better deliver against their own mission, by
powerful GenAI algorithms are trained on vast quantities deploying the latest technology to modernize processes,
of unlabeled data in a self-supervised way. They learn products, and services to more cost-effectively and effi-
underlying patterns from training data, which enables ciently serve individuals, agencies, and businesses.
them to complete a wide range of tasks, including creating
fully original text, images, audio and more in a matter of • As enablers, they can advance the healthcare AI ecosystem
seconds. With thousands of new GenAI tools being devel- by fostering research, innovation, investment, work-
oped each week, there is tremendous anticipation and force development, and collaboration. Enablement also
excitement about its potential. includes limiting unintended consequences by codifying
best practice standards, policies, and regulations in eth-
ics and safety, data privacy, and security.
1. AI TAM research, Expert interviews, BCG analysis.
1 GENERATIVE AI IN HEALTH AND OPPORTUNITIES FOR PUBLIC SECTOR ORGANIZATIONS
Practitioners: Improving internal operations • The U.S. Centers for Disease Control and Prevention
and mission delivery (CDC) could reduce vaccine misinformation and ad-
vance health equity through AI-driven multi-language
Public sector organizations have started to experiment chatbots to answer questions related to disease control
with AI to improve their internal operations, deploying the and prevention, quickly generate communication mate-
technology to improve ways of working and to optimize rials during an emergency response, and monitor public
business processes. The addition of GenAI will further health data.
enhance these capabilities. For example, the Department
of Veterans Affairs (VA) has used AI to sort incoming • The Administration for Strategic Preparedness and
claims from multiple sources (e.g., mail, fax, and electron- Response (ASPR) may use AI to predict outbreaks,
ic), reducing processing times from 10+ days to less than 1 optimize mobilization of resources during an emergency
day. The Centers for Medicare and Medicaid Services response, and assess the strength of health systems to
(CMS) built an AI pipeline to drastically reduce time spent predict areas most likely to be impacted.
on the Authority to Operate (ATO) security planning pro-
cess, a process which currently requires 540+ hours logged • Lastly, Centers for Medicare and Medicaid Services
for every document submitted. CMS and other federal (CMS) could use AI to forecast needs based on health
health organizations, including the Agency for Healthcare data, improve management of allocations, and detect
Research and Quality (AHRQ) and Health Resources and instances of fraud, waste, and abuse.
Services Administration (HRSA), are using chatbots to
improve customer service by providing knowledge- and While these use cases may be built within the limitations
action-based responses with 24/7 availability. There are of currently available data and technology, there are oppor-
opportunities to expand existing AI use cases to be genera- tunities to evolve them to be generative in nature as the
tive in nature as the depth and breadth of data sets grow, landscape matures and evolves.
more sophisticated models are trained, policies and regula-
tions are established, and new applications are tested.
Enablers: Advancing the health ecosystem
To realize the potential for GenAI internally, selecting through AI
priority use cases that drive step-change improvements in
productivity and effectiveness is the most critical first step. Public sector organizations are also uniquely positioned to
Beyond those described above, Exhibit 1 outlines several support the broader health ecosystem - including patients,
additional use cases that can address common challenges providers, payers, biopharma, MedTech, other federal
related to internal operations, ranging from use cases that agencies - in harnessing the full power of GenAI through
are conceptual to those that have been implemented, the products and services that they deliver. When enabled
tested, and validated. by the public sector, the opportunities for GenAI applica-
tions across the health ecosystem are extensive.
Importantly, public sector organizations should also define
and prioritize AI use cases that directly enable mission The health ecosystem generates massive data stores that
delivery. For example, these may include: can be used to train generative AI models from sources
including electronic health records, imaging, testing &
• Using AI to improve the speed and consistency of med- diagnostics, -omics, biosensors and more. Additionally,
ical product safety reviews for the U.S. Food and Drug GenAI foundational models trained on these data sets will
Administration (FDA) by summarizing drug adverse have a myriad healthcare applications, including virtual
event reports, prioritizing reports for further evaluation, health coaches, precision health monitoring, ‘hospital at
and referencing historical data to track emerging issues. home’ services, disease surveillance, digital twins, digital
clinical trials, and more. These applications have the po-
• AI could be used by the National Institutes of Health tential to impact quality improvement, patient safety,
(NIH) to boost productivity of clinical trial development clinician/patient experience, and access to care, issues that
teams by gathering intelligence from numerous sourc- are often core to agency missions (Exhibit 2).
es (e.g., ClinicalTrials.gov, FDA guidance documents,
PubMed, and other publicly available sources of scien-
tific publications) to provide protocol writing support,
elevate areas of over- or under-investment in research
by reviewing grant submissions against previous awards,
or improving health equity through pre-assessment of
clinical study plans.
BOSTON CONSULTING GROUP 2
Exhibit 1 - Applications of generative AI to improve public sector internal
operations
Non-exhaustive
Functions Potential GenAI applications
Siloed service delivery and Natural and multi-language processing of agency documentation
data; reliance on higher cost and public guidance – ability to personalize interaction in a more
Service human responses natural way without or assisting the human in the loop, and with a
Delivery robust understanding of content, context, and ability to quickly
search for the right data
Front
Office
Time-consuming and menial Automated email generation; personalization of mailings and
approach to composition of correspondence to residents and organizational stakeholders
Public Relations & correspondence Streamlined public consultations from constituents to collect
Communications input and prioritize relevant issues
Missing/incorrect data, Data validation: automating compliance-related coding tasks to
manual processes, high develop software which ensures compliance with regulations
Risk prevalence of fraud, waste, Compliance monitoring and reporting: Identify potential
Management & and abuse in/external compliance breaches through identification of
Middle Compliance non-compliant events; automate drafting of compliance reports
Office Claims risk detection, e.g., rationalizing a claim denial based
on risk of FWA or non-compliance, generating communication
material (claim outcome, appeal responses, etc.), etc.
Poorly managed data (e.g., Assisted data management across data quality improvement,
unstructured, unclean, data lifecycle management, data governance, data integration,
Data fragmented) prevents data processing, data architecture enhancement, etc.
Management timely data to action Automated analytics of managed data, including generation of
& Analytics summaries of findings
Policy research and Policy research: analyzing in/external regulations to benchmark
validation policies
Policy assessment: generating risk assessment scenarios to
Legal
identify potential compliance risks and policy impact
Difficulty across the talent Point solutions: Ingest, categorize, and/or process applications
acquisition and retention with minimal human involvement to significantly reduce
continuum: identifying, processing time; Automated, proactive follow-up to quickly
Back-end
assessing, hiring, training, address application issues
Processing
and retaining talent End-to-end process automation: Augment resource deployment
and increasingly automate processes over time (e.g., automated
self-services Chatbots and digital-first processes with human in
Back
the loop for verification/ transaction finishing)
Office
Manual, time-consuming, Identification: Enhance outreach efforts, documentation, and
and repetitive processes strategy with generative content
HR/People leading to high backlogs Assessment: Predicting hiring success based on prior experience
and user deviation and and skillset; identification of non-traditional candidates and fits
error
Upskill and retain employees with personalized support from
hire to retire in a way that is more approachable and personalized
than existing solutions (e.g., performance management, training)
Improve employee experience by removing mundane/repetitive
aspects of the job
Slow and costly legacy Accelerate productivity and speed throughout each step of the
systems modernization tech modernization journey: 1. Discovery 2. Identification of
and digital transformations dependencies 3. Documentation 4. Coding (generation,
IT specification, conversion) 5. Testing 6. Deployment
Tech. Sub-optimal data Natural language processing of data to better and quickly
Innovation governance and retention understand what to retain and how for compliance and storage
& optimization
Modernization
Slow IT ticket resolution; Automated routing of the IT ticket to best respondent based on
limited self-service issue and respondent expertise (enhanced v. general queue)
Comparison of IT issue against prior to provide validated
resolutions based on prior user feedback (dynamic v. general FAQ)
= Validated = Early stage = conceptual
3 GENERATIVE AI IN HEALTH AND OPPORTUNITIES FOR PUBLIC SECTOR ORGANIZATIONS
Exhibit 2 - Generative AI can create meaningful clinical applications for
stakeholders
Pre-trained model
Model topology Opportunities
Virtual health coach
Precision health
Training data, e.g.,
EHR Hospital at home
Training Fine-tuning
Training of large Leverages specific
Imaging & tests data sets requires domain data, Disease surveillance
significant time & requiring less
resources time & resources
-omics Digital twins
Biosensors
Digital clinical trials
Fine-tuning data
Many others…
Sources: “Multimodal Biomedical AI”, Nature, 2022; “On the Opportunities and Risks of Foundation Models”, Center for Research on Foundation
Models, arXiv, 2021; BCG analysis.
Early use case experimentation across the ecosystem has Successful implementation of these real-world use cases
delivered results. For labs and clinics, large language mod- can address some of the biggest healthcare challenges,
els are being trained on a body of sequences and amino including reducing the burden on providers by providing
acids to generate new protein structures and predict mo- supplemental support to diagnostics; automating filing and
lecular interactions2. The University of Kansas is utilizing fraud detection for payers; aiding in personalized medicine,
technology by Abridge to identify key points from pa- drug discovery, and commercial operations within biophar-
tient-provider conversation and creating EMR-integrated ma; allowing for AI-assisted robots and sensors within
summaries, reducing the documentation burden on physi- MedTech, and more (additional potential use cases are
cians. These are merely examples of many new generative defined in Exhibit 3).
AI applications in healthcare.
2. 2011.13230.pdf (arxiv.org).
BOSTON CONSULTING GROUP 4
Exhibit 3 - Overview of real-world use cases for generative AI across the
health ecosystem
Use case Description and examples
Digital clinical Leverage AI to analyze voice patterns and codify voice biomarkers
1
voice analysis to noninvasively detect abnormalities for clinical diagnosis
Ambient Documentation systems that leverage speech recognition and AI
Providers 2
digital scribe to automate documentation and summarize verbal encounters
Diagnostic image AI imaging interpretation uses deep learning and categorization on
3
interpretation medical images for faster and more accurate image interpretation
Intelligent prior A predictive process that payers utilize to approve care by automating
4
authorization workflows after a provider submits treatment notifications
Payers
Claim fraud ML model to detect fraud patterns by finding connections based
5
detection on different factors from previously processed claims
Precision AI-powered precision medicine provides clinicians with an
6
medicine opportunity to specifically tailor early interventions to each individual
Drug discovery AI algorithms to analyze millions of molecules and potential
7
Biopharma & repurposing interactions with target proteins to develop new drugs
AI in commercial Analytics to increase business impact and efficiency with commercial
8
operations operations functions (sales, customer engagement, marketing, etc.)
Robotic AI-assisted robots to perform sophisticated surgeries with precision and
9
surgeries speed and derive new methods by learning from previous surgeries
Medtech
AI enabled Neuroprosthetic system - AI decoder that learns the user’s intention based
10
prosthetic arm on the nerve signals it senses in the arm to translate movement intent
Source: BCG analysis.
Note: Emerging AI use cases gathered from trend reports and latest Garnter Hype Cycle’s.
5 GENERATIVE AI IN HEALTH AND OPPORTUNITIES FOR PUBLIC SECTOR ORGANIZATIONS
GenAI is projected to grow faster in
healthcare than any other industry.
With an estimated compound annual
growth rate of 85%, by 2027 the market
value is expected to reach $22B.
Exhibit 4 - Requirements to enable to the ecosystem across six dimensions,
ASPIRE
Aspirations need to balance needs and perspectives of residents, public, and private sector organizations
Dimension Requirement
A Articulate AI vision (e.g., global leadership, pioneering ecosystem) and benefits for residents, public,
Ambition
and private sector
S
Skills Attract, develop and retain talent for the workforce to thrive in the new age of AI
P Nurture developments and provide flexibility and certainty over AI activities to provide guidance to
Policy & Regulations
public and private sector. Drive innovation in state and federal regulation of benefits processing.
I
Investment Deploy funding mechanisms to stimulate and attract AI-related private businesses and use cases
R
Research & Innovation Build and enable core research and innovation institutions, public and private, in the domain of AI
E
Ecosystem Stimulate AI adoption through commercialization and industry application
Source: BCG analysis.
Note: Detailed countries benchmarks available in appendix.
Public sector organizations can help ecosystem stakehold- National AI Initiative Act passed in 2020 allocated critical
ers improve their probability of successful implementation funding for AI R&D. Lastly, public sector organizations are
and maximize the impact of their investments in GenAI in enabling the AI ecosystem by making solutions accessi-
several ways, as outlined in BCG’s ASPIRE Framework ble. In 2018, Britain enacted the AI Sector Deal to set out
described in Exhibit 4. actions that would promote the adoption and use of AI in
the UK.
Public sector organizations are already taking strides to
enable AI across the ASPIRE dimensions. For example,
various countries are articulating their AI ambitions and How should public sector organizations get
establishing national AI strategies, including around started with GenAI?
healthcare (e.g., the US Department of Health and Human
Services developed an AI Strategy in 2021). Other countries Generative AI is a new territory, with many organizations
are enabling the ecosystem through investments in skills. still in the experimentation phase, testing and building
For example, Qatar’s AI strategy is highly focused on in- their AI capabilities through a series of pilots. More ad-
vesting in K-12 education, apprenticeship programs, re- vanced organizations are cautiously implementing on a
search funding, and attracting talent. Similarly, public larger scale, while navigating concerns related to accuracy,
sector organizations are enabling AI within the health safety, ethics, and privacy. Some organizations have
ecosystem through the development of policy and regu- well-defined strategies for integrating the use of AI within
lations. The EU is finalizing the terms of the “AI Act” their organizations, while others are still determining how
which will be the world’s first comprehensive regulation of best to jumpstart their AI efforts.
AI, and potentially a global standard. The US government
appointed a committee to improve coordination of federal Depending on the starting point, there are several steps
AI efforts and advise the White House on interagency that public sector organizations can take to accelerate
research and development (R&D) priorities. Countries are responsible implementation internally, and to support
similarly investing in AI, including specific investments wide-scale adoption across the health ecosystem.
into research and innovation. For example, the US
7 GENERATIVE AI IN HEALTH AND OPPORTUNITIES FOR PUBLIC SECTOR ORGANIZATIONS
First, they can establish rules of responsible engage- Critically, public sector organizations must build their
ment. At BCG, we define Responsible AI (RAI) as develop- talent and establish a culture of using AI, ensuring
ing and operating AI systems that align with organizational that employees are familiar with how to operate GenAI-en-
values and widely accepted standards of right and wrong abled applications by developing new educational courses,
while achieving transformative business impact. Public re- and up-skilling current employees, hiring new types of
sector organizations can define AI governance processes, talent (e.g., data scientists), and supporting change man-
policies, and decision rights to guide implementation agement efforts.
internally. Further, they can help the healthcare ecosystem
to more effectively adopt and deploy RAI by providing a Finally, public sector organizations can provide thought
framework that supports organizational decision making leadership on ethics, trust, and regulation to acceler-
and offers guidance for developing and using generative AI. ate AI progress across the health ecosystem. Generative AI
Additionally, by investing in the development of tools to is still little understood by the public, leading to distrust in
monitor and manage generative AI risks, public sector outputs which may be biased, false, or opaque unless
organizations can create feedback mechanisms for users models are reviewed and corrected by human experts and
to report inaccurate or unhelpful results and proactively made more transparent. Furthermore, AI may be misused/
flag issues, such as biased outputs, intellectual property over relied on unless hospitals, clinicians, payers, and other
and copyright infringement, and cybersecurity risks. health ecosystem players clarify how specific solutions
should be used, with clear messaging that AI-generated
Next, they can define and communicate their AI ambi- insights are recommendations rather than mandates.
tion and strategy, identifying top use cases considering Guidance on how to mitigate emerging risks and capture
their potential to impact operational efficiency and mis- early trust and value will depend on setting the right
sion delivery. Prioritization may also be informed by con- guardrails early to experiment in the right way.
ducting a “discovery process”, engaging with ecosystem
stakeholders to understand the big pain points and highest
value GenAI opportunities to enable an efficient and
high-quality health ecosystem. For prioritized use cases,
they can design and launch pilots focused on generating
incremental impact, evaluate their effectiveness, refine as
needed prior to full scale-up, all the while strengthening
their organizational muscle for disruptive change. Further,
public sector organizations may play a role in coordinating
AI investment across the ecosystem, creating awareness of
ongoing investments and facilitating collaboration and
partnership to accelerate outcomes.
To improve the likelihood of successful implementation,
they can improve their “AI readiness” by investing in
infrastructure by integrating data in the cloud, adding
computing power to reduce time to train and run algo-
rithms, provide shared environments where public and
private sector organizations can collaborate, and make
health data resources available to entities across the eco-
system to accelerate equitable healthcare. They can break
down siloed data and technology systems which limit ac-
cess to data needed to train and feed models, impacting
the accuracy of the AI algorithms and quality of the gener-
ated output. They can also integrate new AI models into
legacy technology and operational, policy, and other mis-
sion-specific workflows to drive adoption and improve
usability. Externally, public sector organizations can provide
guidance for industry on ways to improve system interoper-
ability so that systems and AI technologies can work togeth-
er, and varied data sources can be exchanged seamlessly.
BOSTON CONSULTING GROUP 8
About the Authors
Priya Chandran is a Managing Director and Senior Part- Lauren Neal is a Principal based in the Washington, D.C.
ner based in the New Jersey office. You may contact her at office. You may contact her at neal.lauren@bcgfed.com.
Chandran.Priya@bcgfed.com.
Julia McBrien is a Project Leader based in the Shabana Quinton is a Partner based in the Washington,
Raleigh-Durham office. You may contact her at D.C. office. You may contact her at Quinton.Shabana@bcg.
McBrien.Julia@bcg.com. com.
For Further Contact Acknowledgements
If you would like to discuss this report, please contact the BCG brings industry-leading talent, deep experience in AI
authors. innovation, speed to value, and vetted partnerships bring-
ing together the best of science, academia, and industry to
enable governments and public sector organizations on
their generative AI journey. Special thank you to Krishna
Srikumar, Steven Mills, Satty Chandrashekhar, and Jona-
than Brice for their contributions to this paper.
9 GENERATIVE AI IN HEALTH AND OPPORTUNITIES FOR PUBLIC SECTOR ORGANIZATIONS
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121,bcg,generative-ai-for-the-public-sector-from-opportunities-to-value.pdf,"Generative AI for
the Public Sector:
From Opportunities
to Value
December 2023
By Miguel Carrasco, Cyma Habib, Frank Felden, Richard Sargeant, Steven Mills,
Simon Shenton, Jamie Ingram, and Gareth Dando
Generative AI for the Public Sector:
From Opportunities to Value
The new tools of generative artificial intelligence (GenAI) are funda-
mentally changing the nature of knowledge work, creating promising
opportunities to significantly increase productivity and spur innova-
tion across entire economies.
Public administration is one sector where GenAI could The market for generative AI products and services is
have the greatest potential. To reap the benefits of the growing exponentially. Since the consortium OpenAI first
technology, public sector leaders need to start by under- announced its language model GPT3 in 2020, GenAI has
standing how GenAI can create value for them. Then they attracted more than $20 billion in venture capital funding.
should set priorities and mobilize to capture its transfor- A recent report by Bloomberg Intelligence predicts the
mative impact. Generative AI refers to a category of artifi- market will grow by over 40% per year for the next ten
cial intelligence capable of creating credible new content, years.
including text, images, audio, code, data, and other media,
based on foundational or generative models. The most The use of GenAI offers significant potential productivity
powerful GenAI tools are trained on large language models gains for the public sector. It can improve the quality and
(LLMs) that process a vast quantity of data to emulate the speed of government decision-making at scale and raise
way people communicate. This capability makes GenAI the efficiency and effectiveness of public policies, pro-
a general-purpose disruptive technology. It expands the grams, and services. The new GenAI tools complement
boundaries of what organizations can do in everyday existing AI capabilities already used in the public sector.
operations, especially in the realm of knowledge work. (See Exhibit 1.)
ChatGPT, one of the first GenAI models for processing
language, has more than 180 million users.
Exhibit 1 - GenAI Expands and Complements Other AI Capabilities
AI Capabilities in Use New GenAI Use Cases
Sense patterns and trends Summarize documents and meetings
Target government investments Review and draft contracts
Segment and design services Engage with customers and citizens
Identify and address risks Generate content and messaging
Optimize public assets and operations Generate code
Detect fraud and anomalies Monitor and use social media
Source: BCG analysis.
1 GENERATIVE AI FOR THE PUBLIC SECTOR: FROM OPPORTUNITIES TO VALUE
Currently, most governments are just beginning to experi- BCG estimates the productivity gains of GenAI for the
ment with the technology. The conditions are not yet in public sector will be valued at $1.75 trillion per year by
place to leverage GenAI at scale and unlock its potential. 2033. (See Exhibit 2.) Our estimate on the impact of GenAI
There are also a number of risks related to issues such as is based on using inputs from Pearson-Faethm modeling. It
accuracy, security, privacy, bias, and intellectual property reflects productivity gains across all national, state or
ownership that need to be managed before fully deploying provincial, and local governments and across all domains
the technology. This article is the first in a three-part BCG such as legislative, administrative, courts, health care,
series exploring how governments can responsibly leverage education, transportation, and security.
GenAI to drive maximum public impact, how they can scale
the technology, and how to assess and mitigate the risks. The impact of GenAI on public sector jobs is more nu-
anced. While some efficiencies may lead to reduced need
for labor, in most cases governments will seek to reinvest
Productivity Value of GenAI for Governments the productivity benefit to address unmet needs of citizens
Estimated at $1.75T or in higher value-added activities that will generate better
outcomes. Some employees will be readily able to adapt
GenAI provides an unprecedented opportunity for govern- and incorporate higher-value work into their role; however,
ments around the world to deliver greater value and public for many workers, reskilling and upskilling will be essential.
impact for citizens, businesses, and government. At a
minimum, these tools could free up many valuable hours
of a public servant’s time on simple and repetitive cogni-
tive tasks and enable that individual to focus on other,
higher-value activities.
Exhibit 2 - Estimated Annual Productivity Benefit from GenAI ($USD Billions)
Sweden (13)
Norway (9) Finland (7)
Canada (22) Netherlands (12) Denmark (11)
UK (89) Germany (62)
France (57) Austria (7)
Japan (20)
USA (358) Spain (20) Italy (29)
Bangladesh (2) South Korea (14)
Switzerland (20)
Mexico (25)
Thailand (20)
UAE (8)
India (15) Malaysia (7)
Saudi Arabia (51) Singapore (2)
Indonesia (7)
Brazil (40)
Australia (20)
South Africa (6)
All other countries (514)
(#) = Total estimated productivity benefit ($B)
Sources: Faethm and Pearson; BCG analysis.
Note: Estimated productivity benefits represent the sum of benefits across national, state, and local governments, with GenAI implemented at scale.
Productivity benefits are calculated for public sector professions using best-case-scenario benchmarks across similar professions.
BOSTON CONSULTING GROUP 2
Five Opportunities for Government Policy and Programs
Expanded capabilities for policy development
Given that the nature of opportunities varies across govern-
ment, we have found it helpful to consider the use cases Stewardship of how a country serves its citizens requires
for GenAI from the perspective of senior executives in five great skill and involves the continuous optimization of policy
different types of government functions. The range of and programs. GenAI provides new tools for improving the
opportunities and the types of changes that may occur at capabilities needed in policy functions, including problem
each level are outlined below, followed by more detailed identification and analysis, policy research and synthesis,
discussion and use case examples. policy and program design, consultation and stakeholder
engagement, and implementation and evaluation.
• Policy and Programs. To better understand current
public policy issues and challenges, as well as the cur- • Enhanced policy and program design. GenAI tools
rent state and root causes, and to design more effective make it possible for policy analysts to rapidly synthe-
policy options, interventions, and programs; optimize size and analyze immense volumes of structured and
policy settings; and strengthen deliberative processes. unstructured data from diverse sources and formats
and across jurisdictions. These may include past policy
• Service Delivery and Operations. To improve the documents, speeches, reports and reviews, white papers,
quality and accessibility of public services to citizens academic studies, journals, articles, budget papers, da-
and businesses, improve efficiency of operations, reduce tabases and datasets, and other research inputs. GenAI
risks, and continuously optimize allocation of resources tools can help to summarize issues, identify options,
to meet policy goals and objectives. present pros and cons, distill and categorize key points,
and draft policy briefs and summaries. This allows policy
• Support Functions. To improve the efficiency of sup- professionals to provide more timely and responsive
port functions, shared services, and corporate services; advice, cover more ground in their research, strengthen
reduce overheads; and improve staff experience. the breadth and depth of evidence that underpins policy
advice, and devote more time to critical thinking about
• Regulators. To improve integrity and compliance with more complex policy challenges—adapting and tailoring
regulations, reduce the cost of monitoring and oversight, to the local context, communication, and messaging,
reduce risks, streamline administration, and make it implementation considerations, and more intensive
easier for citizens, businesses, and other stakeholders to stakeholder management.
comply and meet their obligations.
• Richer public consultation and participatory gov-
• Central Agencies. To develop, implement, and optimize ernance. GenAI can ingest hundreds or even thousands
whole-of-government strategies, priorities, policies, and of submissions received as part of public consultation
standards, and to optimize funding and resource alloca- processes, summarize and categorize recommendations
tion to achieve government objectives. and suggestions, create heatmaps to identify areas of
alignment and divergence, identify consensus views
and unique perspectives, and co-pilot the drafting of
summaries and recommendations. This enables govern-
ments to gather and process a broader range of inputs,
to capture a more extensive and comprehensive range
of views from citizens and stakeholders, and to increase
transparency and engagement in policy formulation
and co-creation. It makes it possible for a wider range of
people to engage in and provide perspectives beyond the
traditional lobby groups and associations with resources
and funding to do so. It enables constituent and stake-
holder engagement to occur more frequently, iteratively,
and interactively, supporting richer and more substan-
tive conversations to occur.
3 GENERATIVE AI FOR THE PUBLIC SECTOR: FROM OPPORTUNITIES TO VALUE
Highlighting the insights from public consultation
BCG developed a simulation called publicconsultation.ai
which uses ChatGPT to analyze and synthesize responses
from written submissions to a public consultation process.
The tool was able to ingest the content of the submissions
and generate a bullet-point summary of recommendations,
along with a matrix showing which stakeholders supported
which ideas. It also generated a list of “outlier” suggestions
not reflected in the summary. The entire process took only
hours versus what would have taken days or weeks, even
for a more complex issue involving hundreds of submis-
sions.
GenAI can enable ordinary citizens who are not typically
engaged in these consultative processes to contribute
more easily by directly involving them in simulated and
natural conversations that draw out their perspectives.
These tools also enable policymakers to synthesize the
views of a much larger and more diverse group of people
than they otherwise could. This promotes greater inclusive-
ness and transparency and improves the quality and rich-
ness of participation in policymaking processes.
BOSTON CONSULTING GROUP 4
• More responsive implementation. Policy profession- Support Functions
als can use GenAI tools to more rapidly translate new Improved internal workings of government
and updated policy into operational changes. For exam-
ple, when a policy is updated, GenAI tools could be used GenAI presents a significant opportunity to enhance the
to generate and update conforming policy and program internal administration of public sector organizations. For
guidelines; generate computer code and implement the Chief Finance Officers, Chief Information Officers, Chief
necessary changes in IT systems; rework operational Legal and Risk Advisors, Chief HR Officers, and heads of
manuals, procedures, and protocols for customer ser- other corporate and support functions, these tools can
vice; and revise government websites—all at once. automate and augment many existing tasks and activities.
Generative AI can streamline procurement, enhance em-
ployee engagement, facilitate better learning and develop-
Service Delivery and Operations ment outcomes, and optimize budgeting and forecasting.
Enhanced service delivery outcomes
• Enhanced learning and development. GenAI can
Many service delivery agencies have long used innovative create customized curricula in line with long-term
technologies, such as virtual assistants and robotic process departmental goals and the personal learning objec-
automation, to provide public services in the most efficient tives of public servants quickly and at scale. It can help
and effective way to meet the needs of citizens and busi- identify thematic and cross-cutting development needs
nesses. GenAI now offers agencies the chance to go further for the workforce based on performance reviews, provide
by implementing new tools for optimizing operations and personalized learning recommendations, tutor people
for designing digital services with more accessible interfac- based on their individual learning styles, and serve as a
es, cross-agency interoperability, and personalized features. thought partner to break down complex problems. L&D
functions can also provide staff with access to training
• Improved customer experience. GenAI can be used and tools translated into multiple languages.
to analyze voice recordings or speech-to-text transcripts
from contact centers to better understand the call • Rapid code development. In the IT function, Ge-
demand, and then make strategic interventions such nAI-enabled co-pilot coding tools can write software
as improved communication and information to reduce code in multiple programming languages. Several
demand. One example is to identify the most frequently published findings from controlled experiments already
asked questions, typical issues, or complaints based on show significant increases in code quality and productiv-
all the calls and inquiries received, and then determine ity of more than 30%, along with other benefits such as
how services can be better designed to avoid this service employee satisfaction and retention. For governments,
failure demand. For example, the Taiwanese government GenAI could be a breakthrough in tackling the mounting
has explored options to better understand citizen pain level of technical debt and legacy system replacements
points and prioritize improvements which will have the needed. It may also be able to assist with the mod-
greatest impact on customer satisfaction. ernization and migration of many undocumented and
out-of-date IT systems—and help bridge the talent gap
• 24/7 accessible services. GenAI-enabled assistants can for high-demand technologist skillsets by improving the
communicate by voice or text across multiple languages productivity of existing staff and serving as a learning
and can be used to provide 24/7 support to citizens from accelerator for new staff, especially in older, niche, and
anywhere, including regional and remote communities. legacy programming languages.
The Indian government, for example, is exploring op-
tions to leverage a GenAI-enabled assistant to help citi- • Improved recruitment processes. With appropriate
zens access policy information. The solution will support safeguards in place, public sector agencies could use
voice memo input and allow citizens to access services GenAI capabilities to adopt more proactive talent acqui-
in multiple languages from anywhere at any time. sition strategies; match potential candidates to the most
relevant openings; conduct and summarize interviews
• More personalized services. The capability for GenAI with potential candidates through interactive conversa-
to pull together data from multiple sources can help tion and testing; and streamline recruitment documen-
government agencies understand the unique context of tation and preparation of employment agreements and
a person, their family, and their “story” across multiple contracts.
contexts. The enhanced understanding of a customer’s
context can be used to generate personalized commu-
nications, such as follow-up emails, without any human
interaction or drafting required. As the technology ma-
tures, governments could leverage GenAI assistants to
provide tailored advice in a multitude of contexts, includ-
ing answering questions on existing complex policies
such as tax, pensions, benefits, visas, and immigration.
5 GENERATIVE AI FOR THE PUBLIC SECTOR: FROM OPPORTUNITIES TO VALUE
Cutting months off the wait time for services
Many governments have backlogs of individuals seeking
disability services. In some countries, citizens expect to
wait more than a year to access disability support ser-
vices—creating high levels of stress for participants and
caregivers and negatively impacting participants’ long-term
functional outcomes.
According to one estimate, GenAI could reduce application
processing times by up to 90% and free-up staff capacity to
engage with residents. GenAI applications in this case
could include ingesting and processing candidate qualifica-
tion criteria; referring customers to other relevant govern-
ment services; providing assistance to walk-through an
application process and pre-fill applications; facilitating
approval and analysis; and generating customized service
plans for case managers to coordinate with applicants.
GenAI could be used to streamline similar government
application processes, such as licenses and permits, build-
ing applications, environmental permits, passports and
visas, grant and rebate applications, and many more.
BOSTON CONSULTING GROUP 6
Regulators Central Agencies
Streamlined regulation development, compliance, Accelerating whole-of-government priorities
and reporting
For the heads of central agencies, such as finance depart-
GenAI presents an exciting opportunity for heads of regula- ments, treasury departments, and cabinet or executive
tory bodies to streamline regulations and compliance mon- offices, GenAI offers a unique opportunity to accelerate the
itoring processes. Regulators can use the tools to analyze a delivery of whole-of-government strategic priorities. Out-
broad range of data to identify trends, patterns, and anom- comes could include:
alies that might be difficult to identify otherwise, and use
the analysis to target compliance and enforcement activi- • Whole-of-government strategies and policies.
ties where the greatest risk exposures are. Central agencies could use GenAI to assist in the aggre-
gation and synthesis of diverse policies and strategies
• Compliance monitoring and detection. Using across government and ensure there is a consistent
rules-based logic and GenAI’s capability to assess large narrative and strong alignment with overall government
amounts of data, governments can expand and optimize objectives and priorities. Officials might also be able to
their oversight and monitoring of compliance. For exam- use GenAI to draft, review, and summarize complex top-
ple, an environmental conservation agency might utilize ics for government consideration and synthesize com-
GenAI to monitor industrial emissions data in real time mentary and input from across government agencies.
and juxtapose it with air quality regulations. The plat-
form could autonomously identify offenders and initiate • Improved communication. Using GenAI, governments
enforcement measures, contributing to the preservation will be able to communicate policies and budgets more
of clean air standards and safeguarding public health. Fi- effectively to citizens. With GenAI, the process could be-
nancial regulators can use similar approaches to analyze come much easier. The tools can synthesize information
transaction, market, trade, and other data and identify from a variety of sources and develop draft descriptions
potential instances of insider trading. and summaries. For example, they can use GenAI to
prepare simpler and more accessible communications
• Streamlining regulations. One use case could be to in text, audio, video, infographics, and interactive media
create simulations of how draft regulations might affect formats. They can support richer, two-way communi-
different constituents and industries. Another oppor- cations with citizens to answer questions and queries;
tunity would be to identify unknown inconsistencies, tailor information for specific stakeholder groups, such
contradictions, gaps or duplication in existing laws and as industries, regional areas, and families; and instantly
legislation, or proposed new legislation. translate material into multiple languages. People can
access information in the language and format of their
choice.
7 GENERATIVE AI FOR THE PUBLIC SECTOR: FROM OPPORTUNITIES TO VALUE
Streamlining procurement at the Department
of Defense
The US Department of Defense is prototyping and testing
a GenAI-powered contract-writing tool called Acqbot. The
tool is designed to assist procurement officers with writing
contracts and the end-to-end lifecycle management of
contracts.
The tool helps them define problem statements, draft
requirements, and prepare end-to-end solicitation docu-
ments. It supports them in building statements of work and
acts as co-pilot to draft and iterate contract agreements.
Finally, it supports quality control, such as by checking for
inclusion of regulation citations.
Procurement officers provide input and remain in the loop
at every step. Future developments still under consider-
ation may include additional functionality to support evalu-
ation of responses from suppliers.
BOSTON CONSULTING GROUP 8
Getting Started with GenAI in the Public Sector Establish guardrails.
Deployed responsibly, GenAI has the potential to deliver
As this article highlights, the rapid advances of GenAI significant value, but it also comes with significant risk.
technology present exciting opportunities for the public This being said, the biggest risk may be if governments fail
sector. We have identified five key success factors that will to adopt GenAI quickly enough or at all. To balance risks
enable government leaders to move beyond the initial and opportunities, government leaders should be seeking
small experiments, identify where to begin their GenAI to establish Responsible AI frameworks which build the
journey, and build their capability to unlock the opportuni- necessary guardrails and create the confidence needed to
ties of GenAI at scale. drive innovation. Recent BCG research shows that when
leaders are actively engaged in Responsible AI, companies
Prioritize the high-value use cases. achieve 58% more business benefits, are 17% more pre-
Explore the landscape of opportunities, but quickly focus pared for investing in Responsible AI, and are 22% more
on a few “golden” use cases—the opportunities with the prepared for emerging AI regulations.
greatest potential value or benefits for citizens and govern-
ment. Develop pilot projects for these use cases, monitor- Encourage innovation.
ing the outcomes carefully. We call this phase “experiment The benefits of GenAI will emerge as knowledge workers
and learn.” It enables governments to build-up valuable explore the technology first-hand. Leadership encourage-
first-hand experience and skills. ment will make a difference. Government leaders must
create a permission space for public sector employees to
Capture and propagate early learning. experiment within reasonable boundaries. One way to do
Governments are still early in experimenting with GenAI this is to demonstrate their own hands-on engagement,
and learning how it can improve public services. One of the working closely with one or two pilots themselves.
most effective things senior leaders can do is establish
mechanisms that encourage the cross-pollination of ideas Public sector adoption of GenAI is still in the early stages,
and learning. To do this, leaders should establish a central but it needs to accelerate. The efficiency and citizen bene-
team whose responsibility is to track and share success fits of an AI-powered government are no longer hypotheti-
stories and synthesize common lessons and hurdles that cal. Private sector implementations of GenAI-augmented
government agencies encounter. As GenAI maturity in- products and services, AI bots and assistants, and even
creases, this role will shift towards removing the hurdles company-specific proprietary trained models show that the
and delivering central enablers. value is real and achievable. Some public sector leaders
around the world are starting to experiment with use
Invest in enablers. cases, but there is a disproportionate focus on the down-
At some point, every government will roll out these technol- side risks. More senior leadership focus and investment
ogies more broadly: refining them, scaling them, and opti- are needed to scale this and maximize the upside poten-
mizing beyond use cases and pilots. Begin preparing for tial. The time to act and capture the immense government
this at the start. Invest in workforce skills, design gover- and citizen benefits of this revolutionary technology is now.
nance mechanisms, and put in place key processes and
technology choices. Build the technology and data capabili-
ties required to enable more sophisticated GenAI use cases.
9 GENERATIVE AI FOR THE PUBLIC SECTOR: FROM OPPORTUNITIES TO VALUE
About the Authors
Miguel Carrasco is a Managing Director and Senior Cyma Habib is a Principal based in the London office.
Partner based in the Sydney office. You may contact him You may contact her at Habib.Cyma@bcg.com.
at Carrasco.Miguel@bcg.com.
Frank Felden is a Managing Director and Senior Richard Sargeant is a Managing Director and Partner
Partner based in the Cologne office. You may contact based in the London office. You may contact him at
him at Felden.Frank@bcg.com. Sargeant.Richard@bcg.com.
Steven Mills is a Managing Director and Partner Simon Shenton is a Managing Director and Partner
based in the Washington, DC office. You may contact based in the London office. You may contact him at
him at Mills.Steven@bcgfed.com. Shenton.Simon@bcg.com.
Jamie Ingram is an Engineering Director based Gareth Dando is a Vice President, Data Science
in the London office. You may contact him at based in the Sydney office. You may contact him at
Ingram.Jamie@bcg.com. Dando.Gareth@bcg.com.
For Further Contact Acknowledgements
If you would like to discuss this report, please contact The authors would like to thank Francisca Browne and
the authors. Brad Goff for their contributions to this report.
BOSTON CONSULTING GROUP 10
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11 GENERATIVE AI FOR THE PUBLIC SECTOR: FROM OPPORTUNITIES TO VALUE
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12 GENERATIVE AI FOR THE PUBLIC SECTOR: FROM OPPORTUNITIES TO VALUE"
122,bcg,future-of-consumer-intelligence-series-2-layout.pdf,"future of consumer intelligence #2
Put On Your
Running Shoes
June 2024
By Lara Koslow, Jean Lee, Verena Damovsky, and Kenechukwu Obinwa
Put On Your Running Shoes
Focus groups, surveys, segmentations, customer journey maps, cus-
tomer data platforms, data appends, and the practice over the past
five to eight years of naming C-level customer officers—companies
spend a lot of time and energy on the customer.
Going for Gold In our survey and throughout this article, we define AI as
including both predictive AI (tools that automatically per-
In our first article in this series, “Filling the Empty Chair,” form tasks, learn, and adjust to new information, such as
we detailed how executives and their organizations go to Google’s search or basic customer service bots) and gener-
great lengths to delve into the mind and behaviors of their ative AI (tools that create new, logical content, such as text
consumers because consumer-centric companies deliver or images, on the basis of input information, as seen in
far better results: uplifts of approximately 10% to 20% in tools like ChatGPT and DALL-E).
revenue growth, 15% to 25% in cost savings, and 20% to
40% in brand advocacy, based on BCG experience.
An Endurance Run
The amount of available consumer data has exploded in
recent years, but companies have struggled to harness this Despite all the time and effort organizations invest to
abundance of data to create value. With the rise of AI and deeply know the customer, only 38% of industry leaders
GenAI, however, we believe that organizations have a report that they have achieved a holistic view of their
unique opportunity over the next three to five years to consumers, and many say that they face challenges related
better address these challenges and transform their to obtaining, consolidating, and integrating data. (See
consumer intelligence capability into an enterprise-wide Exhibit 1.) Others point to the lack of suitable talent, silos
ecosystem that creates a more holistic view of the in the organization that hinder coordination, the absence
consumer than ever before. In our first article, we outlined of a clear and unified strategy, and insufficient funding for
how companies can use AI and GenAI to help build this data, research, talent, and technology.
ecosystem—and what it will take to get started.
As a follow-up to that work, from March 7 to March 24, New Shoes
2024, we surveyed C-suite-level and senior executives who
work at companies that have 1,000 or more employees Faced with these challenges, up to 53% of industry leaders
and who are responsible for consumer insights, pricing, across all countries feel that AI will be a game changer for
sales and marketing strategy, and/or product develop- consumer intelligence—elevating their companies’ view of
ment. Our objective was to better understand these lead- their customers to new heights. (See Exhibit 2.)
ers’ views on consumer intelligence and the impact of AI
and GenAI on consumer intelligence, and to identify what
they are doing with AI and GenAI today. The study focused
on five countries—China, France, Germany, the UK, and
the US—but it also included supplemental data (from
Australia, Canada, India, and Singapore) to provide a more
global view. We supplemented this research with expert
interviews with current and former chief marketing offi-
cers at large, US-based consumer goods companies.
1 PUT ON YOUR RUNNING SHOES
Exhibit 1 - Issues with Obtaining and Integrating Data Pose Major
Challenge to Companies
Top challenges faced by respondents in gathering meaningful consumer insights (%)
47%UK
38
33
32
29
Only
26
38% 23
22
Average
of leaders feel
37% they have a 16
holistic view of
US China
consumers1
9
32%
France
Hard to Hard to Fragmented Lack of Lack of No clear Insufficient No clear Not a
30%Germany
obtain integrate data talent coordinationstrategy funding owner priority
Source: AI in Consumer Intelligence, BCG survey (n = 541), March 2024.
1Percentage represents respondents who “strongly agree” with the statement “My company has a holistic, 360-degree view of our consumers” across
all surveyed countries.
Exhibit 2 - Across Assessed Geographies, Executives Firmly Believe that AI
Will Be Pivotal in Transforming Consumer Intelligence
Perceived effect of AI on consumer intelligence, by country (%)
AI will be a game changer1
53 46 39 29 23
Up to
53%
France UK US China Germany
of leaders feel that AI will
be a game changer
for consumer AI will be a big step up or a game changer2
intelligence
78 78 78 82 63
France UK US China Germany
Source: AI in Consumer Intelligence, BCG survey (n = 541), March 2024.
1Percentages in this row represent respondents who selected “Game changer—will elevate consumer intelligence to a whole new level” in response
to the question “At its full potential, how much do you feel AI can enhance consumer intelligence overall?”
2Percentages in this row represent respondents who selected either “Game changer—will elevate consumer intelligence to a whole new level” or “A
lot—a big step change from other options” in response to the question given in footnote 1.
BOSTON CONSULTING GROUP 2
Fueling this sense of optimism about AI and its likely The Medalists
impact on consumer intelligence are expectations of signif-
icant productivity gains (held by varying percentages of Who are the AI leaders in consumer intelligence globally?
leaders in different regions, from 36% in Germany to 62% Invited to self-report on this question, 58% of respondents
in China) and improved output (from 33% in Australia to in France said that they are leaders in AI adoption, versus
55% in China). These positive expectations prevail despite 44% in the US, and 21% in China. (See Exhibit 5.)
some misgivings about data privacy issues (cited by a low
of 25% of leaders in China, and a high of 44% in Australia) Not surprisingly, across countries, large corporations are
and high initial investment costs (cited by just 23% of adopting more AI tools than medium or small companies.
respondents in India but by 36% of respondents in France). Overall, 57% of large companies possess custom GenAI
(See Exhibit 3.) tools and 56% have deployed GenAI add-ins. In contrast,
smaller companies tend to rely disproportionately on
Looking forward, many executives believe that AI will ad- public GenAI tools such as ChatGPT (44%). (See Exhibit 6.)
dress a number of their critical consumer intelligence
issues. (See Exhibit 4.) They hope that AI will facilitate the Looking ahead, we expect this gap in AI adoption between
integration of cross-channel insights, increase the focus on larger and smaller companies to widen. Although more
implications and strategy, provide a better view of likely than 50%+ of companies report that they plan to spend
consumer behavior, expedite product prototyping to enable from 6% to 8% of their revenue on AI this year—and al-
faster market launches, and provide much more rigorous though 57% of smaller companies plan to do so—the
regular predictive modeling and scenario testing to inform absolute investment volumes of smaller companies lag the
better decisions. $250 million-plus that larger companies plan to spend
every year by a factor of 100. (See Exhibit 7.) As such,
smaller firms will likely need to leverage vendors to access
economies of scale in AI development – and may have less
bespoke solutions.
Exhibit 3 - Leaders Expect the Biggest Gains to Involve Productivity and
Output Quality, While Data Privacy and Potential for Misinformation Are
Key Concerns
Key positive drivers (%) Non-aided associations with AI1 Key negative drivers (%)
China Agility Australia
36–62 Productivity gains Trusted Reliable 25–44 D coa nt ca e p rnri svacy
Germany China
Quality
Efficiency
China
Unique
33–55 Improved output Excitement Potential
Australia Smart Convenient Sensitive
France
High initial
Accuracy Productive Risk 23–36 investment costs
Helpful Advanced India
France
Learning/upskilling
26–48 Job loss
opportunity Empowerment Precision
Canada Easy
Innovation
US
France 21–50 Cost-saving potential CSe rcu ere ative Progress 22–40 T mh ir se ina ft o o rmf ation
Canada
Australia Fast
Useful
Size of word represents number of responses
xxx
(non-aided), minimum of 10 responses
Sources: AI in Consumer Intelligence, BCG survey (n = 541), March 2024; BCG analysis.
Note: The paired country names associated each key positive or negative driver are the countries with the highest and lowest percentages on that
driver. Thus, for example, for “Productivity gains” in the “Key positive drivers (%)” column, the percentage for China is 62% and the percentage for
Germany is 36%.
1Multiselect responses to “What are the main drivers of positive/negative sentiment toward AI at your company?” The word cloud presents non-aided
associations with AI.
3 PUT ON YOUR RUNNING SHOES
Exhibit 4 - Executives Have Big Hopes for AI and How It Can Address
Current Challenges
Current state without AI Expected future state with AI
1 Isolated data silos Integrated cross-channel insights 1
2 Considerable time spent on data integration and analysis Increased focus on implications and strategic planning 2
3 Limited understanding of data Comprehensive consumer behavior predictions 3
Extensive time and resources spent on product testing
4 AI-enabled prototyping, leading to faster market readiness 4
and development
5 Reactive postmortems Predictive modeling and scenario testing 5
Sources: Future of Insights study 2023; client interviews.
Exhibit 5 - Leaders in France Are More Likely to Self-Report as ‘Ahead’
Than Leaders in Other Countries on Various AI Engagement Metrics
Respondents within country samples that say they are leaders across AI engagement metrics (%)
AI sentiment AI ambition AI adoption AI investments
60 58%
50
44%
40%
40
32%
30
21%
20
10
0
France UK Germany China US
Sources: AI in Consumer Intelligence, BCG survey (n = 541), March 2024; BCG analysis.
Note: Results track the percentage of respondents, by country, who chose the most positive response on a five-point scale to the question “How
would you rate your company’s overall AI sentiment, AI ambition, AI adoption, AI investment?”
BOSTON CONSULTING GROUP 4
Exhibit 6 - Most Large Companies Have Custom GenAI Tools and GenAI
Add-ins Whereas Smaller Companies Disproportionately Rely on Public
GenAI Tools
Public GenAI tools GenAI add-ins Custom GenAI tools
GPT-3.5 Zoom AI Companion ChatGPT Enterprise
DALL-E Slack AI Microsoft co-pilot
58%
Large average adoption
corporations rate across assessed
tools and practices
58% 56% 57%
39%
average adoption
Small rate across assessed
corporations tools and practices
44% 21% 37%
Sources: AI in Consumer Intelligence, BCG survey (n = 541), March 2024; BCG analysis.
Note: “Large companies” are defined as corporations whose annual revenue exceeds $1 billion; “small companies” are defined as corporations
whose have annual revenue is less than $50 million.
Exhibit 7 - Most Respondents Plan to Invest from 6% to 10% of Revenue in AI
Although small companies lead on planned investment share, they lag in absolute volumes by a factor of 100
Planned AI investment as share of revenue, by company size (%)1
57
$250M–
Yearly spending on AI
50 49 $450M for large companies
40
35
$10M– Yearly spending on AI
23 $25M for medium companies
13
10
7 7 $200k– Yearly spending on AI
for small companies
2 3 $400k
Note: Low N (34) for small
companies by definition
<1% of revenue 1–5% of revenue 6–10% of revenue >10% of revenue
Large company Medium company Small company
Sources: AI in Consumer Intelligence, BCG survey (n = 541), March 2024; BCG analysis.
Note: “Large companies” are defined as corporations whose annual revenue exceed $ 1billion; “medium companies” are defined as corporations
whose annual revenue is from $50 million to $1 billion; “small companies” are defined as corporations whose annual revenue is less than $50
million. The conversion rate of US dollars is taken as €1 = $1.08. The sum of the revenue categories for each company size does not equal100%
because responses of “Do not know” are excluded.
1Estimates are calculated by multiplying proposed investment range as a percentage of revenue by absolute revenue and obtaining median values
from output ranges.
5 PUT ON YOUR RUNNING SHOES
The Starting Blocks A/B testing (37%) and web analytics (35%) appear to be
the types of use cases most frequently rolled out across
In looking specifically at AI in consumer intelligence, we industries. (See Exhibit 9.)
found that industry leaders from across the countries we
surveyed overwhelmingly agree that companies can heavily Even so, priority use cases vary across industries. For exam-
leverage AI across different use cases. (See Exhibit 8.) The ple, A/B testing is a priority in the retail and consumer goods
percentages of leaders agreeing with this proposition range sector, but loyalty programs are most popular in the manu-
from 96% in the UK to 100% in France. Although 37% of facturing and industrial sector (38%). (See Exhibit 10.)
respondents in the US and 70% in France report that their
company has already rolled out one or more such use
cases, about half of those that adopted at least one use
case (from a low of 17% in Australia to a high of 34% in
France) have already rolled out three or more AI use cases,
an indication the early stage of business development that
the technology is in.
Exhibit 8 - Business Leaders Universally Feel That Companies Can
Meaningfully Leverage AI for Consumer Intelligence
AI in consumer intelligence use cases across all surveyed countries
France Australia
100% 17%
UK
UK
96%
96–100% 37–70% 37% 17–34%
France
34%
France
70%
Almost all industry leaders ...and 37% to 70% report having …but only half of them
agree that companies can rolled out at least one have already rolled out three or
meaningfully leverage AI AI-enabled use case for more such AI use cases in
for consumer intelligence use cases1 consumer intelligence2 consumer intelligence2
Sources: AI in consumer intelligence, BCG survey (n = 541), March 2024; BCG analysis.
1Represents respondents who selected at least one consumer intelligence use case in response to the question “Where do you feel AI can be
meaningfully leveraged?”
2Represents the number of consumer intelligence use cases that respondents classified as ”rolled out” in response to the question “Where does your
company use AI?”
BOSTON CONSULTING GROUP 6
Exhibit 9 - A/B Testing and Web Analytics Are the Most Widespread Use
Cases to Date, But Many Other Types of Pilots Are Underway
AI implementation status across select use cases in consumer intelligence (%)
A/B testing at scale 37 34 29
Web analytics 35 38 27
Customer experience 34 34 32
CRM database 32 33 35
Customer-centric design 31 33 36
Loyalty program 31 29 39
Competitor research 30 38 32
Market sizing 28 39 32
Demand/trend forecasting 27 40 34
Personalized marketing 27 40 33
Customer journey 26 43 31
Dynamic/promotional pricing 26 41 32
Customer segmentation 25 35 40
Consumer sentiment 25 31 45
Rolled out Pilot Not yet
Sources: AI in Consumer Intelligence, BCG survey (n = 541), March 2024; BCG analysis.
Note: Classification of consumer intelligence use cases as “rolled out,” “pilot/testing,” or “planning to”/“wish we did”/“no AI” is based on responses
to the question “Where does your company use AI?” CRM = customer relationship management.
Exhibit 10 - Priority Use Cases Differ by Industry
Loyalty programs are a priority in manufacturing and industrial goods, while A/B testing is a
priority in retail and consumer goods
Respondents who have rolled out select use cases (%)
26%
21%
38
35 34
32 31
27 29 28 28 26 26 26 26 24 24 25 25 23
19 20 21 19
16 17 15
9 9 9
Loyalty Demand/ CRM Customer- Competitor Customer Customer
program trend database centric research experience segmentation
forecasting design
Dynamic/ Web Market Personalized A/B testing Customer Consumer
promotional analytics sizing marketing at scale journey sentiment
pricing
Manufacturing and industrial goods Retail and consumer goods Priority use case
Sources: AI in Consumer Intelligence, BCG survey (n = 541), March 2024; BCG analysis.
Note: Classification of consumer intelligence use cases as “rolled out” is based on responses to the question “Where does your company use AI?”
split by industries of interest. CRM = customer relationship management.
7 PUT ON YOUR RUNNING SHOES
Turning Pro Given the speed of AI’s development, the strategic ap-
proach for all companies, regardless of their maturity,
The maturity of AI varies by market, as do the needs of should be to start small, demonstrate impact, and adapt
each market to further evolve. For example, in relatively their organization and technical capabilities iteratively. Of
mature markets such as France and the US, leaders see course, the key challenge will be to keep pace. As one
strengthening AI-capable talent and better understanding former fashion and retail CEO told us, “Today, the speed of
available tools as most critical tasks to harness the power the technology is much faster than the speed of the organi-
of AI. (See Exhibit 11.) But in less mature markets such as zations using it.” But for those who put on their running
China, respondents tend to identify instead the need for shoes as AI continues to fuel the evolution of the consum-
strong leadership guidance. er intelligence capability, it may usher in an era of compre-
hensive consumer understanding that will benefit us all, as
consumers ourselves.
Exhibit 11 - The Most Critical Issues in More AI-Advanced Markets and in
Less AI-Advanced Markets Differ
High
France 49% Better (that is, AI-capable) talent
US 45% Understanding of emerging AI tools and landscape
AI adoption
UK 49% Understanding of emerging AI tools and landscape
Dedicated AI leader/team
China 58%
Germany 50% More leadership attention/prioritization
Low
Sources: AI in consumer intelligence, BCG survey (n=541), March 2024; BCG analysis.
Note: Top-ranked response, by country, to the question “What do you think is most critical for your company to harness the full power of AI with
regard to consumer intelligence?”
BOSTON CONSULTING GROUP 8
About the Authors
Lara Koslow is a managing director and senior partner in Jean Lee is a is a partner and director in the firm’s Seattle
the firm’s Miami office, with a focus on growth strategy, office, focused on customer growth and strategy across a
marketing, branding, consumer insight, and commercial/ range of consumer sectors. She has deep expertise in the
go-to-market topics across industries—in particular, travel travel and tourism sector and served as North America
and tourism, consumer, retail, and automotive. She is the leader for BCG’s Center for Customer Insight for many
global leader of BCG’s customer demand and innovation years. You may contact her by email at lee.jean@bcg.com.
business, which includes BCG’s Center for Customer In-
sight (CCI) and AI Lighthouse platform. She is the former
global leader of BCG’s Center for Customer Insight, which
she ran for over 8 years, and the former global leader of
the marketing business. You may contact her by email at
koslow.lara@bcg.com.
Verena Damovsky is a project leader in the firm’s Lagos Kenechukwu Obinwa is a consultant in BCG’s Lagos
office, with experience working with clients across Europe, office, with experience working with clients in various
Africa and Asia across different industries and functions. industries and functions across Africa and Asia. He has
She has expertise in customer segmentation with a focus experience developing customer engagement strategies,
on the social impact space. You may contact her by email particularly in the social impact space. You may contact
at damovsky.verena@bcg.com. him by email at obinwa.kenechukwu@bcg.com.
For Further Contact Acknowledgments
If you would like to discuss this report, please contact the Thank you to the following for their support crafting this
authors. publication:
• Silvia Mazzuchelli, BCG Senior Advisor
• Amanda Helming, Former Chief Marketing Officer, UNFI
• Marissa Jarratt, Executive Vice President, Chief Market-
ing and Sustainability Officer, 7-Eleven
9 PUT ON YOUR RUNNING SHOES
Boston Consulting Group BCG’s Center for Customer Insight (CCI)
Boston Consulting Group partners with leaders in business The Boston Consulting Group’s Center for Customer
and society to tackle their most important challenges and Insight (CCI) applies a unique, integrated approach that
capture their greatest opportunities. BCG was the pioneer combines quantitative and qualitative consumer research
in business strategy when it was founded in 1963. Today, with a deep understanding of business strategy and
we work closely with clients to embrace a transformational competitive dynamics. The center works closely with BCG’s
approach aimed at benefiting all stakeholders— various practices to translate its insights into actionable
empowering organizations to grow, build sustainable strategies that lead to tangible economic impact for our
competitive advantage, and drive positive societal impact. clients. In the course of its work, the center has amassed
a rich set of proprietary data on consumers from around
Our diverse, global teams bring deep industry and functional the world, in both emerging and developed markets. The
expertise and a range of perspectives that question the CCI is sponsored by BCG’s Marketing, Sales & Pricing and
status quo and spark change. BCG delivers solutions Global Advantage practices. For more information, please
through leading-edge management consulting, technology visit Center for Customer Insight (https://www.bcg.com/
and design, and corporate and digital ventures. We work capabilities/marketing-sales/center-customer-insight).
in a uniquely collaborative model across the firm and
throughout all levels of the client organization, fueled by the
goal of helping our clients thrive and enabling them to make About the Research
the world a better place. The Future of Consumer Intelligence series features
data from an online survey of current C-level and senior
executives at global/national companies with 1,000+
employees, conducted in March 2024. Survey respondents
self-reported a responsibility for consumer (insights), pricing,
sales and marketing strategy, and/or product development.
The survey was produced by the authors and BCG’s Center
for Customer Insight (CCI), in partnership with coding and
sampling provider Dynata, the world’s largest first-party data
and insights platform. We supplemented this research with
expert interviews with current and former chief marketing
officers at large, US-based consumer goods companies. The
goal of the research was to understand industry leaders’
perspective on consumer intelligence in light of a rapidly
evolving AI and GenAI landscape. A team composed of BCG
consultants and experts from CCI completes the survey
analytics.
© Boston Consulting Group 2024. All rights reserved. 6/24
For information or permission to reprint, please contact BCG at permissions@bcg.com. To find the latest BCG content
and register to receive e-alerts on this topic or others, please visit bcg.com. Follow Boston Consulting Group on Facebook
and X (formerly known as Twitter).
bcg.com
11 PUT ON YOUR RUNNING SHOES"
123,bcg,Executive-Guide-to-Investing-in-GenAI.pdf,"Executive Guide
to Investing in
Generative AI
A framework to embrace
this disruptive and rapidly
evolving technology
By: Pranay Ahlawat, Drake Watten, Matt Kropp, and Vlad Lukic.
2 Generative AI Economics
Content
Introduction 03
1. Why do I want to use Generative AI and what business value will it create? 04
2. What type of Generative AI should I be using and is that a future-proof decision? 05
3. Do I really need to build this capability, or should I wait and buy? 08
4. What is the Total Cost of Ownership (TCO) equation and how will it evolve over time? 09
Putting the four questions together—how to win in Generative AI 10
Conclusion 11
About the authors 12
3 Generative AI Economics
Introduction
Generative AI’s potential to disrupt industries, revolutionize customer relationships, and
change the way knowledge work gets done has made it a strategic imperative across
companies. The question is “how” to embrace this technology in a way that is right for your
organization and can profitably scale. Based on our research and interviews with executives,
there are four key obstacles that executives must navigate today.
Fragmented approach to Inadequate understanding of
generative AI without a clear generative AI technology trends
business case and implications
Limited understanding of the business Not knowing what technology
value and current feasibility of use cases, as approach to take and misunderstanding
well as unclear prioritization of use cases its risks
Not being clear on when to Not fully understanding
“build” versus “buy” the at-scale economics of
generative AI
Prioritizing the wrong use cases to build,
when buying an out-of-the-box solution Not factoring the second-order costs
might be a better fit of adopting generative AI at scale and
building business cases
This article delves into four pivotal questions that executives should ask to better manage
these challenges and succeed in their generative AI journeys.
4 Generative AI Economics
1
Why do I want to use Generative
AI and what business value will it create?
This is a simple question, but one that gets overlooked in the rush of companies to invest
in generative AI. In our research, many companies have started multiple initiatives without
fully understanding the entire spectrum of generative AI use cases, sequencing or prioritizing
them, and estimating their business impact. We observe many large companies taking a
fragmented and uncoordinated approach to generative AI, where different Business Units
(BUs) or Lines of Business (LOBs) are driving use cases in the absence of an enterprise-
wide strategy. Furthermore, many companies are still discovering the capabilities of current
platforms and tools and are failing to understand their limitations or challenges, partly due to
the noisy hype surrounding generative AI today.
To cut through the noise, companies must keep three things in mind.
• Not all use cases are created equal. Organizations need to start with a clear strategy
based on the business value of the use case, as opposed to taking a scattered approach
across multiple pilots. Some companies are at risk of being disrupted and need to double
down on generative AI to build new offerings and value propositions. In other cases, more
horizontal use cases may be less strategic, and companies should thoughtfully prioritize
these considering taking into consideration levels of investment, ROI, and risk, amongst
other things.
• It’s early days and Generative AI’s capabilities are still evolving. Despite its enormous
potential, generative AI has technical limitations and is still maturing for all use cases,
particularly for modalities outside text (such as video), and more industry-focused ones
(for example, healthcare, manufacturing). In our research, many customer pilots are
running into day-two operational challenges (for example, cybersecurity, machine learning
operations, governance), and some are unable to get past pilots and user acceptance
testing phases because of results that do not meet the bar. To fully understand the benefits
and challenges, organizations should experiment and run pilots within their context, and
with their data securely to ensure the outcomes are salient for the objectives they have set.
• There are risks and associated challenges with generative AI which may drive second-order
costs. These risks are well understood and include data hallucinations, bias, cybersecurity,
and copyright challenges, amongst others. Many of the companies we interviewed are only
starting to grasp these challenges as they scale.
To make thoughtful Generative AI investments that create the most impact, it is important to
think through your strategy and technological approach, understand the alternatives, and be
equipped to pivot quickly.
5 Generative AI Economics
2
What type of Generative AI should I be using
and is that a future-proof decision?
Generative AI is not a one-size-fits-all technology, as it can be deployed in four different ways.
The options, which come with their unique tradeoffs and costs, range from using public APIs
that are turnkey and available for immediate usage, to building and training a custom model
from scratch. What companies choose ought to be driven by six strategic considerations.
Speed to market Data privacy and regulation needs
Companies that are seeking feature parity Customers in industries such as healthcare or
with competitors or who operate in industries financial services will need to evaluate the data
that are getting disrupted (for example, residency or privacy controls requirements
traditional chatbot platforms or enterprise and decide if those could be met by compliant
search vendors) have a much greater need to cloud solutions, or whether they warrant
rapidly respond. building custom models of a generative AI
architecture built for on-premises on-perm.
Customization requirements Latency and performance
requirements
While out-of-box functionalities from Speed of response is a key consideration
commercial model vendors such as OpenAI, for certain real-time use cases, such as live
Anthropic, Amazon Titan, etc., might work for summarization, automated trading, etc. Out-
multiple use cases, more complex or domain- of-box capabilities from commercial model
specialized use cases might benefit from fine- vendors might not be sufficient. The ability
tuning commercial or open models. to optimize the size, and architecture of the
foundation model for latency and speed of
response may be critical considerations for
such use cases.
Volume and scale of intended Operating model implications
use cases
The overall volume and nature of workloads— Availability of talent is a key factor in
whether it is spiky and seasonal or consistently determining how a company implements
high volume—need to be considered, as they generative AI. Building a custom model is not
impact the mode of deployment. a viable option for many enterprises due to
the steep requirements for data science and
machine learning talent. Additionally, the
maturity of a company’s broader operating
model, such as its data pipeline management
as well as machine learning operational
processes is another key factor.
Based on the above considerations, customers can choose one of four generative AI
deployment options.
• Usep ublic APIs (for example, Google Translate, AI21 Summarize, Amazon, etc., or AI21
Paraphrase) for simple, and standard tasks that do not require any customization.
• Consume Models-as-a-Service from model vendors such as OpenAI, AWS Titan, and
Anthropic. Most customers leverage these models with no tuning or customization via
techniques like RAG (Retrieval-Augmented Generation).
• Useo pen models such as Falcon or Sable Diffusion which can be used as-is or
customized for domain-specific use cases.
• Create and train a custom model from scratch (such as Bloomberg GPT) for specialized
tasks and maximum performance control.
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6 Generative AI Economics
Generative AI deployment options
Public APIs and Services Models-as-a-Service (MaaS) Open Models Proprietary Models
Leverage public services e.g.,
Build Gen AI applications on Tune or use publicly available Build custom models
Google Translate, Amazon
Description managed models e.g., or open-source models as is from scratch e.g.,
Polly, Azure Cognitive
Cohere, Anthropic, OpenAI e.g., Falcon, Stable Diffusion Bloomberg GPT, Palm
Services
etc.
Ability to Limited – some vendors High degree of customization Maximum ability to optimize
customize No ability to customize provide ability to tune models – can be used as-is, tuned or every aspect of stack
fully re-trained
Deployment Cloud with option to deploy
Cloud multi-tenant Hybrid - cloud or on-premise Hybrid - cloud or on-premise
modality instance in private VPC
<$1M to $7M $3M to $30M $10M to $50M+
Setup Cost1 <$0.1M
Multitenant cloud Private VPC2 Private VPC2 On premise Private VPC On premise
<$1M to $6M $5M to $1M $7M to $1M
Run (1-year)1 <$0.1M
Varies by usage and deployment Private VPC2 On premise Private VPC2 On premise
Use-cases requiring full control
Use-cases requiring contextu- Specialized use-cases requiring
over the model for data privacy, Simple standard tasks, al learning with limited to no domain specific or organization
Use-cases customization, latency and perfor-
like language translation customization, e.g., personal- specific customizations, e.g.,
mance optimizations, e.g., defense ized marketing campaigns drug discovery acceleration industry use cases
1: Assumed GPT-3 equivalent foundation model with 175B parameters and 300B training tokens. Inferencing assumed for 10B tokens and fine tuning for 20B tokens.
Training, tuning and inference costs calculated as API price per token * number of tokens or (FLOPs per token for GPT-3 / FLOPs per second for each machine) * pricing
per second for each machine. Total costs estimated based on BCG's 70:20:10 framework – 70% effort in AI implementations spent on talent & org change, 20% on
technology and 10% on algorithms. 2. Virtual Private Cloud
7 Generative AI Economics
These decisions come with their own tradeoffs. Models-as-a-Service can speed up time to
market, but it can get expensive at scale and lead to vendor lock-in. On the other hand,
leveraging open-source models or building a model from scratch comes with high up-front
costs and demanding talent needs.
What complicates this decision even more is the fact that the technology and economics
of generative AI are rapidly evolving. To make a future-proof decision, it is imperative for
customers to understand where the proverbial “puck” is going and the technology trends
influencing that direction. Our research found four key trends:
Bigger is not necessarily better: it Model customization and
is important to find the right model tuning continue to get easier
at the right cost/performance
The capabilities of foundation models are
Several optimization techniques, such as
quickly converging. Smaller, well-trained
Low-Rank Adaptation (LoRA), pruning
models are now delivering comparable
and quantization, are lowering the costs of
performance to larger models at a fraction of
training, tuning, and inferencing models. This
the cost, challenging the notion that bigger
is lowering the technology barriers and time
is always better. For example, Chinchilla, a
to market to build custom models, making
70-billion parameter model, delivers the
these architectures more viable.
same performance as Gopher, a 280-billion
parameter model, but at a 75% lower cost.
Custom silicon and accelerators Overall generative AI stack
are set to create a step change in is maturing and driving
performance/costs democratization
Innovations in accelerators and silicon are Tools and open-source libraries such as
improving price/performance ratios. Custom LangChain and HuggingFace Transformers
silicon from Amazon (AWS Trainium and are reducing the barriers to entry to building
AWS Inferentia) are delivering with up to 30- complex generative AI applications.
50% in price performance, and driving more
integrated stacks for machine learning.
As companies evaluate different deployment options, it is crucial to assess the technical,
financial, and organizational impacts of each option. They must think of vendor lock-in risks,
as well as technology risks of making wrong platform bets. In our research, we found that
organizations are experimenting with multiple options at the same time, depending on use
cases. They are using Models-as-a-Service (MaaS) to start, focusing on lower-risk use cases
(for example, summarizations, knowledge management), while also dabbling in open-source
or custom models for specialized use cases like anti-money laundering and fraud detection.
The value and risk of generative AI must be understood within an organization’s context.
8 Generative AI Economics
3
Do I really need to build this capability,
or should I wait and buy?
Beyond choosing the right technology, companies need to ask if building a solution is
warranted at all. Given the rapidly changing technology landscape and where we are
in the generative AI adoption cycle, it is important for organizations to be realistic about
what can be achieved, and be clear about what use cases to build and which ones to buy.
In our experience, there are multiple types of scenarios where investing aggressively and
building solutions early is advisable. The first scenario is one in which generative AI
incrementally enhances the core offering or business model (for example, chat-based
e-commerce, built-in generative AI tools in creative software such as Adobe Firefly, and AI-
enabled workflow tools for enterprise software such as Salesforce Einstein-GPT). A second
scenario is when generative AI creates a novel offering or opens new markets, such as drug
discovery in biopharma. Yet another scenario might be if your industry is facing disruption
(for example, information services, legacy NLP (Natural Language Processing), and chatbot
platforms), moving rapidly and adopting generative AI might be the only option.
On the other end of the spectrum, buying out-of-the-box solutions might be a better option
for more standardized use cases where solutions already exist (for example, coding or writing
assistants), or horizontal use cases where platform solutions will likely emerge (for example,
customer support). Waiting, continuing to evaluate, and keeping one’s options open can be a
good strategic decision as it increases flexibility, keeps a company focused, and reduces the
technological risk of making wrong investments.
There are other vertical and efficiency-driven use cases (for example, network automation in
telcos), where companies need to consider their business value, assess their current ability
to execute (such as lack of specialized machine learning talent), and estimate costs to build.
While prioritizing these use cases can yield significant value, it remains crucial for companies
to continuously assess the rapidly evolving tech landscape and explore commercial solutions
that not only expedite the advancement of their use case but also effectively manage risks
and minimize investments.
Regardless of approach, as a general principle, companies should have a very high bar for
building proprietary foundation models, as this will only make economic sense at a very large
scale. This option is more suited for hyperscalers, given extremely specialized talent and large
upfront capital are required to develop and train state-of-the-art foundation models.
9 Generative AI Economics
4
What is the Total Cost of Ownership (TCO)
equation and how will it evolve over time?
There are two types of costs of generative AI: primary and secondary. Our research suggests
that most companies consistently underestimate the latter.
Primary costs, which are better understood, include fixed set-up costs (for example, hardware,
data curation, costs of engineering, training, and tuning) and variable costs (such as
consumption costs for APIs, and inference).
The secondary costs of generative AI include a lot of hidden, and hard-to-estimate costs,
ranging from maintenance costs (for example, repaying technical debt, re-training, incremental
testing), risk management, organizational change management, as well as legal costs.
The total costs can vary significantly between deployment options (as illustrated in the exhibit
above). Public API options have the lowest setup and run costs (<$0.1M setup costs for API
integration). Maas (Models as a Service) options are generally used without customization
and typically have lower setup costs (<$1M) including setting up data pipelines and vector
databases, but the run costs at scale can get expensive, reaching up to $6M in some cases.
Building proprietary models can be prohibitively expensive—sometimes costing upwards of
$50M depending on the type of model, data curation, and architectural investments—but
they provide the most architectural control over run costs.
Like any large-scale digital transformation, the biggest overlooked secondary cost is
organizational change management. BCG’s 10:20:70 framework explains the relative
investments needed to implement AI at scale: 10% on algorithms, 20% on technology, and
70% on organizational change. The last cost of organizational change is not only the biggest
investment, but also the hardest to estimate and fraught with the risks of internal change.
Compliance and legal costs are also increasing due to the new risks introduced by generative
AI. In our research, some organizations are experiencing a 25% increase in legal, testing, and
compliance costs for their generative AI use cases. We do expect these costs to improve over
time, as companies go up the experience and adoption curve.
Understanding the TCO for generative AI requires modeling different scenarios that weigh
expected benefits and both primary and secondary costs—as well as how each cost may
evolve. Moreover, the evolving technological innovations underscore the need to be flexible in
decision-making. Executives may be better off making decisions with a medium-term horizon
and being prepared to pivot quickly.
10 Generative AI Economics
Putting the four questions together–how to
win with generative AI
There are four strategic control points you need to consider to be on the winning
side of generative AI:
Focus on economic viability Look ahead to solve the data and
at scale when making core day-two operational equation
technology choices
No technology can scale unless you solve
day-two operational challenges. In our
Building and running generative AI
research, customers are starting to run
applications can be very expensive.
into operational challenges across the
Customers must evaluate the cost-efficiency
stack: data quality and data pipeline
and performance expectations of different
management, machine learning operations,
model and deployment options vis-a-vis the
model observability and governance,
use case they are solving. Factors such as
etc. Companies need to plan for and get
data privacy, customization requirements,
ahead of these challenges to deploy AI
latency, and volume must be considered to
at scale.
make the right technology bet at the optimal
price/performance ratio. Moreover, once Assess feasibility and impact before
companies understand their priority use
investing for scale, and be clear
cases, they have to invest in scale to avoid
the cycles of repeated demos. Generative AI about when to buy versus build
can only be strategic if it is done at the right
Like any transformative technology early
cost at scale.
in the adoption cycle, generative AI is rife
with hype, partly driven by news media, and
Invest early in closing marketing messages from some software
the talent gaps and tooling vendors. Companies must
take these claims with a grain of salt, and
instead choose to experiment aggressively to
Talent remains a top-of-mind concern and a understand generative AI’s capabilities and
major stumbling block for most companies impact in their contexts. They must continue
that are implementing generative AI. to make investments in talent and dive
Companies must invest in up-skilling their deep into the right use cases, while carefully
current workforce and hiring to close the assessing “build versus buy” decisions in
talent gap over time. Consider other options these early days. Sometimes, saying “no”
like acquisitions or partnerships to bridge and waiting to get the timing right is more
the gap in the near term. strategic.
11 Generative AI Economics
Conclusion
Generative AI is here to stay and needs to be a strategic priority for businesses
today. There is no doubt that it will disrupt industries and will create a step change in
productivity. However, it is also complex, rapidly evolving, and can be very expensive at
scale. While it is imperative to move quickly, it is equally important to carefully prioritize
where to place your bets.
To avoid missteps and future-proof their generative AI investments, companies
ought to start with a clear strategy and understanding of use cases. They need to
assess if generative AI is a sufficiently mature technology for their use cases and
test within their organizational contexts. They also need to fully understand the
technology options, trends, and tradeoffs to make the right technology bet, from
Models-as-a-Service to building custom models. They need to know when to say
“no” and wait, and fully think through when to build versus buy. Most importantly,
companies must understand the economics and full scope of costs—primary and
secondary—to realistically estimate ROI. And if they do decide to take action, they
must be prepared to solve basic but fundamental problems: data processes, talent,
and day-two operations. Making strategic and focused generative AI bets—without
rushing hastily—can save your company from costly missteps at the very least, or, at
best, dramatically accelerate your company’s position in the market.
12 Generative AI Economics
About the authors
Pranay Ahlawat is a partner and Matt Kropp is managing director
associate director in the firm’s and senior partner in
Washington D.C. office. firm’s San Francisco office.
You may contact him at You may contact him at
ahlawat.pranay@bcg.com. Kropp.Matthew@bcg.com
Drake Watten is managing director Vlad Lukic is managing director
and partner in firm’s and senior partner in firm’s
San Francisco office Boston office.
You may contact him at You may contact him at
Watten.Drake@bcg.com Lukic.Vladimir@bcg.com
For further contact Acknowledgments
If you would like to discuss this report, The authors thank the following for
please contact the authors. their contributions to the development
of this report: Aakash Joshi and Sai
Masipeddi from BCG and Phil Le-
Brun, Archana Vemulapalli, Tom
Adams, Ahmad Tawil, Susane Seitinger,
Adil Soofi, Ritesh Vajariya, Kamran
Khan, Joe Senerchia, Nitin Nagarkatte,
Salman Taherian, Jake Burns, Ross
Richards and Priya Arora from AWS.
13 Generative AI Economics
Boston Consulting Group
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124,bcg,BCG-Wheres-the-Value-in-AI.pdf,"Where’s the Value
in AI?
October 2024
By Nicolas de Bellefonds, Tauseef Charanya, Marc Roman Franke, Jessica Apotheker,
Patrick Forth, Michael Grebe, Amanda Luther, Romain de Laubier, Vladimir Lukic,
Mary Martin, Clemens Nopp, and Joe Sassine
Contents
01 Who’s Getting Results from AI 13 The Playbook for Winning
and Why? with AI
• A Steep Curve • Overcoming Tough Challenges
• What Leaders Do Differently • The Capabilities Required for Success
• Incumbents Reap Value • Jump-Starting Your Journey to AI Value
• How Two Companies Applied the
Playbook for Success
05 The Surprising Sources of Value
from AI
17 Appendix
• In the Core
• Sector Matters
20 Acknowledgments
Who’s Getting Results from AI
and Why?
A
fter all the hype over artificial intelligence (AI), the Consider these examples of the value being created by AI,
value is hard to find. CEOs have authorized invest- including generative AI (GenAI), from companies in three
ments, hired talent, and launched pilots—but only different sectors. A financial institution is committed to
26% of companies have advanced beyond the proof-of- achieving $1 billion in productivity improvements, in addi-
concept stage to generate value. This report yields import- tion to enhanced risk outcomes and better client and
ant insights into what AI leaders are doing to drive real employee experiences, by 2030. A biopharma company is
value from the technology, where others fall short, where chasing $1 billion in value potential (revenues and costs)
the value is coming from, how individual sectors are per- by 2027. A major automaker expects to cut its cost of
forming, and how companies can change their own goods sold by up to 2% and accelerate new product devel-
AI trajectories. opment time by 30%.
BOSTON CONSULTING GROUP 1
These results are typical of the value that leaders across A Steep Curve
industries are achieving by building digital capabilities to a
level at which they can implement AI programs at scale. Building AI capabilities is a complex challenge. Our latest
BCG’s latest research into AI adoption, a continuation of research, involving more than 1,000 companies worldwide,
our studies into digital transformation and AI maturity, shows that only 4% have developed cutting-edge AI capa-
found that of the 98% of companies that are at least exper- bilities across functions and are using them to consistently
imenting with AI, only 26% have developed the necessary generate substantial value. (See Exhibit 1.) Another 22%
capabilities to move beyond proofs of concept and begin have an AI strategy and advanced capabilities and are
extracting value. (For more on how we define AI and our starting to generate value. We call these companies lead-
research methodology, see the Appendix.) And only 4% are ers. The remaining 74% have yet to show tangible value
at the forefront of AI innovation, systematically building from their use of AI.
cutting-edge AI capabilities and scaling them across the
organization. These categorical distinctions are important because lead-
ers far outperform the others. Over the past three years,
Here’s our latest look at who the top 26% of companies are leaders’ revenue growth has been 50% greater than the
and how they are generating superior value from AI. The overall average. Their total shareholder returns are 60%
two chapters of the report that follow look at where com- higher, and they gain 40% higher returns on invested capital.
panies are extracting value and what you need to do to These companies also excel on nonfinancial factors, such as
move your company up the AI maturity curve. patents filed and employee satisfaction, and they are in pole
position to benefit as AI platforms and tools mature.
Exhibit 1 - Leaders Have Built the Capabilities Needed to Implement AI at
Scale, Reaping Diverse Benefits over Less Mature Companies
Maturity stage (% of companies)
AI stagnating AI emerging AI scaling AI future-built Value achieved
25 49 22 4
50%
Revenue
higher revenue growth
(3-year average)
Total 60%
shareholder
return higher 3-year TSR
40%
Returns
higher RoIC
(3-year average)
0 25 50 75 100
Are taking minimal Have developed Have developed an AI Are at the forefront 1.9x
or no AI action, foundational strategy and advanced of AI innovation, Innovation
lack foundational capabilities and capabilities, and are systematically more patents
capabilities, and are started initial scaling them effectively building cutting-edge
not generating value experimentation while starting to AI capabilities across
but are struggling generate value functions and 1.4x
Employee
to scale and consistently
generate value generating satisfaction better overall
substantial value Glassdoor indicator
Source: BCG Build for the Future 2024 Global Study (merged with DAI).
Note: “Leaders” include AI future-built and AI scaling companies; “less mature” or “other” companies” include AI stagnating and AI emerging
companies. RoIC = return on invested capital; TSR = total shareholder return.
2 WHERE’S THE VALUE IN AI?
What Leaders Do Differently They invest strategically in a few high-priority oppor-
tunities to scale and maximize AI’s value. Data on AI
Leaders have six differentiating characteristics. adoption shows that leaders pursue, on average, only
about half as many opportunities as their less advanced
They focus on the core business processes as well as peers. Leaders focus on the most promising initiatives,
support functions. A common misconception is that AI’s and they expect more than twice the RoI in 2024 that other
value lies mainly in streamlining operations and reducing companies do. In addition, leaders successfully scale
costs in support functions. In fact, its greatest value lies in more than twice as many AI products and services across
core business processes, where leaders are generating 62% of their organizations.
the value. Leveraging AI in both core business and support
functions gives these companies competitive advantage. They integrate AI in efforts both to lower costs and to
generate revenue. Almost 45% of leaders integrate AI in
They are more ambitious. Leaders’ expectations for their cost transformation efforts across functions (com-
revenue growth from AI by 2027 are 60% higher than those pared with only 10% of nonleaders). And more than a third
of other companies, and they expect to reduce costs by of leaders focus on revenue generation from AI, compared
almost 50% more. Three-quarters of the most forward- with only a quarter of other companies. (See Exhibit 3.)
looking companies focus on company-level innovation core “We have a program under which every business unit is
to the business. In contrast, only 10% of other companies required to submit three to five projects each year—and
do so—and if they leverage AI at all, it is mainly for produc- since 2020, they have all had to focus on AI,” said the
tivity. Leaders look beyond pure productivity plays and back enterprise product director of an alternative energy compa-
their ambitions with investment in AI and workforce en- ny. “These projects need to demonstrate how they would
ablement, doubling down on several aspects of AI, relative improve the company either through cost savings, in-
to their peers. (See Exhibit 2.) They make twice the invest- creased operational efficiency, or revenue generation.”
ment in digital, twice the people allocation, and twice the
number of AI solutions scaled.
Exhibit 2 - Compared with Their Peers, Leaders Are Allocating More of Their
Budget and Resources to Digital and AI Capabilities in 2024
Budget People Innovation
2.0x 1.6x 2.0x 1.9x 1.6x 1.1x 2.2x
19.2%
18.2%
12.3% 13.8%
10.1% 9.1% 9.6% 8.9% 6.3 5.5 8.2%
5.0% 4.6% 5.1% months months
Revenue share 2024 increase Share of FTEs Share of digital Share of FTEs Time to market Share of AI/GenAI
invested in in AI/GenAI dedicated to FTEs dedicated to be upskilled for new digital products scaled
digital and AI investments digital and to AI/GenAI in AI/GenAI and AI products across the
vs 2023 AI work roles today organization
AI stagnating or AI emerging AI scaling or AI future-built
Source: BCG Build for the Future 2024 Global Study (merged with DAI).
Note: FTEs = full-time equivalent employees.
BOSTON CONSULTING GROUP 3
Exhibit 3 - Leaders Integrate AI with Broader Cost Transformation Efforts
and Have a Greater Focus on Revenue
Integration of AI with broader cost transformation efforts (%) AI investment split between cost reduction and revenue growth (%)
4
15 Greater
26 27
More AI 36 revenue
43 43 integration focus
27 21
55
20
40
53 52
47 44
30
17
AI stagnating AI emerging AI leaders AI stagnating AI emerging AI leaders
Without GenAI Exploratory Multiple functions Revenue Equal Cost
Source: BCG Build for the Future 2024 Global Study (merged with DAI).
They direct their efforts more toward people and But AI’s impact extends to all industries. For example, a
processes than toward technology and algorithms. leading automaker used GenAI to accelerate tender docu-
Leaders follow the rule of putting 10% of their resources ment drafting and adjustments by 50% while improving
into algorithms, 20% into technology and data, and 70% document quality and consistency. GenAI also increased
into people and processes, which our data shows are the the automaker’s speed in analyzing competing offers (by
key capabilities underpinning success. 50%) and reduced the time necessary to search knowledge
assets (by 50% to 75%).
They have moved quickly to focus on GenAI. Leaders
use both predictive AI and GenAI, and they are faster in Leaders are blazing the AI trail, but other companies can
adopting GenAI, which opens opportunities in content catch up if they take a page from the leaders’ playbook and
creation, qualitative reasoning, and connecting other tools focus on the areas that offer them the best opportunities
and platforms—in part because their more advanced and on the capabilities they need to build in order to capi-
capabilities facilitate putting the prerequisites (such as talize. We explore these factors in the next two chapters.
large language models) in place.
Incumbents Reap Value
Not all AI leaders are hyperscalers and digital natives, com-
panies that include AI as part of their product or services
offering. More than half of the top-performing 26%, includ-
ing the ones described at the beginning of this chapter, are
traditional incumbents that have strengthened their capa-
bilities and are using them to build differentiated competi-
tive advantage. The sectors with the biggest percentages of
AI leaders tend to be those that were among the first to
experience digital disruption a decade and half ago and got
the earliest start on building digital capabilities. They
include fintech (49% are leaders), software (46%), and
banking (35%).
4 WHERE’S THE VALUE IN AI?
The Surprising Sources of Value
from AI
L
eading companies are dreaming big. By 2027, the top The common narrative for AI involves support functions—
26% of companies in our survey of AI maturity expect HR, IT, legal, and the like—where automating relatively
to achieve 45% more value via cost reduction and 60% low-level and repetitive functions creates significant value.
more value via revenue growth than other firms. Even in But the companies that are generating the most value are
2024, leaders expect to realize more than twice the RoI not only deploying productivity plays in support functions
from AI initiatives than other companies do, resulting in a but also focusing on reshaping their core business process-
5% reduction in addressable operational expenses and a es and inventing new revenue streams. They are achieving
5% increase in addressable revenues. results from AI across a wide range of functions, from R&D
to operations and from sales and marketing to customer
service. Because they have built the necessary capabilities,
they can more readily identify, pilot, and scale up value-
creating use cases. For example, one chemicals company
expects to create more than $500 million in value from an
end-to-end transformation that will implement AI across
operations, site services, and procurement.
BOSTON CONSULTING GROUP 5
In the Core Sales and marketing, for example, is fast emerging as a
major source of AI value in such sectors as software (31%
Overall, the companies in our survey derive 62% of the of AI value generated), travel and tourism (31%), media
value they obtain from AI and generative AI in core busi- (26%), and telecommunications (25%). Specific roles and
ness functions, including operations (23%), sales and the scale of impact differ by industry, but AI offers compa-
marketing (20%), and R&D (13%). Support functions gener- nies a near-term opportunity to reshape the sales function
ate 38% of the value, with customer service (12%), IT (7%,) with next-best action recommendations, talk tracks, and
and procurement (7%) leading the way. basic workflow automation. In the medium term, AI and
GenAI will enable real-time assisted selling and autono-
In some sectors the spread between core and support is even mous selling via digital sales avatars, with limited human
wider. (See Exhibit 4.) Software, media, fintech, insurance, involvement. Such automation will permit human staff to
telecommunications, and biopharma generate 70% to 90% of focus on strategic and relationship selling, while virtual
their AI-related value in core business processes. Although we assistants cover more transactional tasks. As predictive
found wide variation among sectors, the overall results are smart selling becomes the norm, traditional silos dividing
consistent—even most of the sectors in the bottom quartile marketing, sales, and pricing will dissolve. Our experience
generate 40% to 60% of AI value in core processes. indicates that resulting increases in customer lifetime
value and go-to-market efficiencies could almost double
profit margins.
Sector Matters
Companies in different sectors also benefit from identify-
ing the domains in which AI can produce the most value.
Our research shows that they vary widely by industry. (See
“AI in Insurance and Biopharma.”)
Exhibit 4 - To Realize Value from AI, Companies Focus on Core Business
Processes, with Sector-Specific Variability
Where companies are achieving or see business value
Global average: 62%
Sectors
Core business processes (%) Support functions (%)
Software 94 6
Media 87 13
Fintech 85 15
Insurance 77 23
Telecommunications 71 29
Biopharma 70 30
Banking 68 32
Airlines 65 35
Retail 63 37
Automotive 62 38
Transport and logistics 61 39
Medtech 59 41
Consumer products 57 43
Oil and gas 49 51
Chemicals 48 52
Machines and automation 40 60
Power, utilities, and renewables 21 79
Core business function Support function
Source: BCG Build for the Future 2024 Global Study (merged with DAI).
6 WHERE’S THE VALUE IN AI?
Leaders are not only deploying
productivity plays but reshaping
core business processes and
inventing new revenue streams.
The impact on marketing will be equally profound and will In one current instance, a global pharmaceuticals compa-
encompass four key processes: ny is using AI to accelerate its drug discovery capabilities.
The initial vision was to build, test, and validate an AI
• Insight to Innovation. Automated data collection prototype with chemists to quantify the value impact in the
and analysis will speed identification of market oppor- discovery workflow. The company assessed the potential of
tunities and increase marketers’ ability to develop new state-of-the-art models to find new preclinical candidates
product design. faster, and then it built its own machine learning algorithm
to rapidly screen over 1 billion drug compounds and a
• Concept to Creation. Workflows will accelerate asset genetic algorithm to power a lead optimization pipeline for
creation and feedback loops, seamlessly adapting, local- molecular chemists. The project generated value of $100
izing, and disseminating content. million a year through faster launches, including a 25%
reduction in cycle time. The company expanded its library
• Campaign Setup and Execution. Hyper-segmentation of molecules by 100 times, increasing the visibility of novel
and real-time execution that responds to trends and compounds to its researchers.
feedback will speed campaign creation and automatical-
ly track progress against key objectives. Customer service is already a significant source of AI-
generated value in insurance (24% of the value created)
• Marketer Productivity. Marketers will spend less time and banking (18%). Companies are using AI to boost pro-
on time-consuming, repetitive, administrative tasks and ductivity, reducing the need for multiskilled frontline teams
more time on strategic decision making. and redesigned agent journeys. We are seeing near-term
increases of 30% to 40% in productivity and a profit-and-
For example, a leading North American telco is already loss impact of 10% to 20% for the function.
using AI to analyze call recordings to identify opportunities
for cost savings and higher customer satisfaction. The Ambitions run much bigger. Leading companies expect to
company has reduced call center interaction time by 20% realize long-term increases in productivity of up to 60%.
and cut call transfers to live agents by 25%. AI-powered The impact of integrating AI into customer service process-
chatbots now handle 30% of calls, and the telco expects to es will reverberate throughout the value chain. Customer
reduce total costs in the relevant business unit by 25%. service functions will be able to preempt issues and
self-heal by fixing problems before customers detect them,
Predictably, AI is having a big impact in R&D in research- and they will enable customers to resolve their own issues
intensive sectors such as biopharma (27% of value creat- through self-help. If the customer still needs human assis-
ed), medtech (19%), and automotive (29%, in an industry tance, AI will support the agent’s response with augmented
undergoing a major transition to software-driven vehicles). capabilities such as optimizing the conversation in real-
A medtech company vice president told us, “Generative AI time by considering the customer’s needs in context and
has allowed us to generate images for training purposes making offers where relevant.
that mimic real diseases that humans can have. We start-
ed deep diving into generating thousands of images that A leading international bank needed to modernize its
aren’t coming from patients but are being generated by the customer management system to improve service quality,
generative model mimicking real-life cases. Our predictive reduce operational costs, and enhance revenue generation.
AI model improved accuracy by 4% to 5% because of this It turned to GenAI to reshape both customer interactions
generative AI approach.” and backend processes, including deploying GenAI for chat
support, enhancing agent efficiency, improving service
In the R&D function of the future, we expect individual-, quality, and increasing conversion rates. It also integrated
team-, and company-level changes to improve concept GenAI into its APIs and apps for smooth and scalable opera-
R&D, product development and industrialization, and prod- tions. Results included a reduction of almost 20% in interac-
uct evolution. AI will accelerate and automate each step by tion time between customers and agents; a drop of 4 min-
shortening iteration loops, democratizing access to exper- utes in average service time while retaining similar levels of
tise across teams and organizations, fast-tracking explora- customer satisfaction, an increase of 28 points in conversion
tion of new concepts, simulating product designs, and rates, and a doubling in breadth of products sold.
forecasting procurement orders, among other changes.
8 WHERE’S THE VALUE IN AI?
Consumer products and retail companies are making big The critical challenge for companies is to identify the key
gains with AI-driven personalization (19% of the value use cases within each function. For example, 43% of insur-
created for the former and 22% for the latter). About 30% ance companies leverage AI in scoring, fraud assessment,
of consumer companies in our survey have adopted AI for and triage while 42% of biopharma companies use AI in
personalized marketing (among other functions) and are systematic protein and drug molecule generation (at least
seeing productivity gains of about 30% from such activities for pilots and proofs of concept). The highest value use
as marketing content generation, marketing mix and RoI cases typically involve a mix of predictive AI and GenAI.
optimization, and data-driven digital marketing. As a re-
sult, leaders are doubling down in other areas at two to Although companies in each sector may be generating
four times the rate of slower movers, applying AI to genera- the greatest value from use cases in one or two do-
tive product design, and manufacturing optimization. mains, most are still experimenting—and obtaining mea-
surable results in up to half a dozen domains in the core
Within each process or function, it’s critical to define spe- business, including customer relations and experience,
cific use cases and associated business value. In most content production and management, and product man-
sectors, more than half of GenAI’s value potential lies in agement. In more than a few sectors—including oil and
two or three functional domains. In insurance, 55% of the gas, utilities, and machinery and automation—support
value lies in in policy administration, underwriting, and functions are a significant source sources of value, too.
claims management. In biopharma, 57% of the value is
found in R&D and in sales and marketing. There are many routes to value. Chapter 3 explores how
your company can efficiently find its most productive paths.
BOSTON CONSULTING GROUP 9
AI in Insurance and Biopharma
At the business process, function, and use-case level, value
creation from AI is already taking different directions in
different sectors, highlighting the importance to each com-
pany of independently identifying where its best opportuni-
ties lie. Consider the evidence that our survey gathered in
two very different sectors: insurance and biopharma.
The average AI maturity of both sectors falls in the middle
of the maturity curve, not far off the all-sector average.
Companies in both sectors generate an average of 70% or
more of AI value from core business processes and 30% or
less from support functions. But the similarities end there.
Insurance
Insurers are focusing on operations (policy administration,
underwriting, and claims management), customer service,
and marketing and sales. (See the AI factsheet for insur-
ance.) So far, the widest adoption of predictive AI at the
individual-opportunity level has occurred in the areas of
scoring, fraud assessment, and triage and policy automa-
tion. Adoption of GenAI is strongest in the use of chatbots
to resolve questions and summarize customer interactions.
In line with their overall scores, insurers’ biggest challenges
involve people and processes: improving staff AI literacy,
prioritizing opportunities over other concerns, and estab-
lishing RoI for identified opportunities. They also wrestle
with the tasks of integrating AI with existing IT systems
and of increasing the accuracy and reliability of AI models.
An Asian life and health insurance company with a strong
track record in digital transformation sought to demon-
strate the benefits that GenAI could have on its operations
by identifying and executing a couple of high-impact, high-
use cases. The insurer prioritized the possibilities on the
basis of a high-level analysis of potential impact. It select-
ed two opportunities, one in customer-service call center
operations and the other in sales and marketing. The
former achieved a 30% reduction in call center search
times and the latter a 30% to 40% reduction in marketing
and sales material creation time.
10 PUBLICATION TITLE
AI Factsheet for Insurance
Where does insurance stand on the AI maturity curve? Main challenges
Top challenges across people and processes, technology, and algorithms
Maturity stage (% of companies)
AI stagnating AI emerging AI scaling AI future-built Focus areas Key challenges
9 64 25 2 BCG’s 10-20-70 Respondents citing the challenge (%)
model
Algorithms Lack of accurate/reliable models
10%
Lack of access to high-quality data
Insurance average Difficulty integrating with existing IT systems
Global average
Technology
Difficulty ensuring security and compliance
20%
IT budgets limiting investments in AI
Insufficient platform capabilities for
at-scale testing
Insufficient AI literacy
0 25 50 75 100
Difficulty prioritizing opportunities vs other concerns
Insurance companies have emerging AI capabilities slightly ahead
of the global average
Difficulty establishing RoI on identified opportunities
Where are the value pools in my sector? People and Difficulty reimagining workflows and processes
processes
Distribution of AI value potential along functional domains (%)
70% Lack of specialized AI engineers
Core business Support Lack of available talent and skills
Claims Product HR
functions management management functions
77 15 9 23 5 Difficulty measuring predetermined KPIs
Difficulty sequencing opportunities into
Legal 4 a roadmap
Customer service Underwriting Marketing,
and policy sales, IT 6 Procurement 4 Weak governance structures to steer
administration24 16 distribution13 responsible AI
Finance 4
Difficulty identifying short- and
long-term next steps
15 25 35 45 55 65 75
Source: BCG Build for the Future 2024 Global Study (merged with DAI).
Biopharma Once again, the biggest challenges in applying the technol-
ogy relate to people and processes: prioritizing opportuni-
Biopharma tells a different story. More than half of the ties over other concerns, advancing staff AI literacy, acquir-
value in this sector comes from commercial/sales and ing available talent and skills, and establishing RoI on
marketing (30%), and R&D (27%). Biopharma companies identified opportunities. The top algorithm and technology
are using GenAI for systematic protein, drug, and biological issues involve integrating AI with existing IT systems, and
processes generation, real-time hyperpersonalized engage- maximizing the accuracy and reliability of models.
ment with health care practitioners, and personalized
outreach to patients and providers. They are using AI and
GenAI together for analyzing and documenting customer
interactions and for targeting patient identification via
biological data. (See the AI factsheet for biopharma.)
BOSTON CONSULTING GROUP 11
AI Factsheet for Biopharma
Where does biopharma stand on the AI maturity curve? Main challenges
Top challenges across people and processes, technology, and algorithms
Maturity stage (% of companies)
AI stagnating AI emerging AI scaling AI future-built Focus areas Key challenges
27 46 19 8 BCG’s 10-20-70 Respondents citing the challenge (%)
model
Algorithms Lack of accurate/reliable models
10%
Lack of access to high-quality data
Health care average Biopharma average Difficulty integrating with existing IT systems
Global average Technology
20% Difficulty ensuring security and
compliance
Insufficient platform capabilities for
at-scale testing
Difficulty prioritizing opportunities vs other concerns
0 25 50 75 100
Insufficient AI literacy
Biopharma companies have emerging AI capabilities on a
par with the global average
Lack of available talent and skills
Where are the value pools in my sector? People and Difficulty establishing RoI on identified opportunities
processes
Distribution of AI value potential along functional domains (%) Lack of leadership alignment,
70% communications, and behavior modeling
Lack of specialized AI engineers
Core business Support
Research and Finance
functions development functions Difficulty making a business case for
70 27 30 6 scaling initiatives
Lack of a clear AI case for change
IT 4
Commercial/sales Manufacturing Customer Procurement
and marketing service HR 3 Difficulty identifying short- and
long-term next steps
30 13 7 7 Legal 3
Difficulty reimagining workflows
and implementing processes
15 25 35 45 55 65 75
Source: BCG Build for the Future 2024 Global Study (merged with DAI).
12 WHERE’S THE VALUE IN AI?
The Playbook for Winning with AI
L
eading companies are well on their way to creating Meanwhile, the 70% of companies that are struggling, wait-
significant value and advantage from AI. For example, ing, planning, and experimenting have an urgent need to
a consumer products company applied GenAI to re- accelerate their efforts to overcome barriers and catch up as
duce costs by $300 million through productivity gains and their competitors improve their productivity, revenues, and
agency cost savings. A global consumer goods company customer experience. As leaders and aspiring leaders ex-
expects to generate $100 million in additional sales from a pand their AI capabilities and as GenAI models and tools
GenAI-powered virtual conversational assistant, the first in mature, less capable companies will fall farther behind.
its sector. A North American telco achieved a 10% reduc-
tion in call handling time and cut the cost of customer Here’s an AI playbook that all companies can follow.
retention by more than 30%, leading to $200 million in
annualized savings.
BOSTON CONSULTING GROUP 13
Overcoming Tough Challenges Our experience, corroborated by our new research, indi-
cates that about 70% of the challenges relate to people
Our survey highlights the most difficult challenges that and process, about 20% are technology issues, and only
companies face in implementing AI initiatives. They fall 10% involve AI algorithms (which often occupy a lot more
into four groups: organizational time and resources). (See Exhibit 5.) The
survey confirms our long-held view that when companies
• Difficulties in defining clear priority use cases with com- undertake digital or AI transformations, they need to focus
pelling returns for the anticipated investments 70% of their effort and resources on people-related capabil-
ities, 20% on technology, and 10% on algorithms. Too often,
• A host of issues related to moving from plans to action companies make the mistake of prioritizing the technical
and delivering value, such as prioritizing investments, issues over the human ones—which helps explain why many
scaling solutions across functions and businesses, over- of them do not achieve the results they are looking for.
coming resistance to adoption, and realizing the benefits
Challenges evolve over time, of course, as companies build
• People and skills issues, including building specific AI their capabilities. But while less AI-capable companies
skills and broader AI literacy focus on getting the basics right, leaders are more con-
cerned with ensuring security and compliance, implement-
• Integrating AI solutions with existing IT systems, and ing responsible AI, and resolving technical issues such as
enabling access to high-quality data guardrails for large language models, high model latency,
and run costs.
Exhibit 5 - The Biggest Challenges Relate to People and Processes, Such
as Prioritizing Opportunities and Establishing RoI
Focus areas Key challenges
BCG’s 10-20-70 model Respondents citing the challenge (%)
Algorithms Lack of accurate/reliable models 48%
10%
Lack of access to high-quality data 43%
Difficulty integrating with existing IT systems 56%
Technology IT budgets limiting investments in AI 48%
20%
Difficulty ensuring security and compliance 46%
Expensive scaling due to high model run costs 37%
Difficulty establishing RoI on identified opportunities 66%
Difficulty prioritizing opportunities vs other concerns 59%
Difficulty making a business case for scaling initiatives 56%
Difficulty realizing cost takeout/savings 54%
People and Resistance and fear that AI will impact jobs 48%
processes
Lack of a clear AI case for change 42%
70%
Difficulty measuring predetermined KPIs 38%
Lack of leadership alignment and communications 37%
Difficulty reimagining workflows and processes 37%
Insufficient AI literacy 37%
Lack of specialized AI engineers 37%
20 25 30 35 40 45 50 55 60 65 70
Source: BCG Build for the Future 2024 Global Study (merged with DAI); n = 1,000.
14 WHERE’S THE VALUE IN AI?
The Capabilities Required for Success Jump-Starting Your Journey to AI Value
We analyzed the self-reported capabilities of AI leaders After assessing the capabilities and approaches of the lead-
compared with those of other companies. This assessment ing companies, we have compiled a playbook for how any
revealed empirical evidence about the most important c"
126,bcg,Stariway-to-GenAI-Impact-Automotive-Industry.pdf,"Artificial Intelligence | Article
The Stairway to (Gen)AI Impact
in the Automotive Industry
November 2024
By Andrej Levin, Felix Stellmaszek, Alex Xie, Jonathan Nipper, Manuel Kallies, Nina Kataeva, and Tobias Schmidt
Boston Consulting Group partners with leaders
in business and society to tackle their most
important challenges and capture their greatest
opportunities. BCG was the pioneer in business
strategy when it was founded in 1963. Today,
we work closely with clients to embrace a
transformational approach aimed at benefiting all
stakeholders—empowering organizations to grow,
build sustainable competitive advantage, and
drive positive societal impact.
Our diverse, global teams bring deep industry and
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that question the status quo and spark change.
BCG delivers solutions through leading-edge
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and corporate and digital ventures. We work in a
uniquely collaborative model across the firm and
throughout all levels of the client organization,
fueled by the goal of helping our clients thrive and
enabling them to make the world a better place.
© Boston Consulting Group 2024. All rights reserved.
BOSTON CONSULTING GROUP 2
Content
04 Key Takeaways
05 Setting Your Company Up to Succeed with (Gen)AI
08 Putting It All Together
11 The Authors
BOSTON CONSULTING GROUP 3
Key Takeaways
Automotive companies have moved quickly to implement (Gen)AI,
but many may be celebrating too early before achieving
measurable benefits. It’s time for a focused approach.
About two-thirds of executives in industrial goods industries (including automotive) say they are not satisfied
with their company’s progress in (Gen)AI, and about half are still expecting cost savings within the year,
according to a BCG survey.
Automotive companies can make an impact on the bottom line with three (Gen)AI value creation levers,
potentially achieving a return on investment (ROI) of 10 to 15 times in less than three years. These are new
revenue streams from an augmented direct sales approach, cost reduction through automation of more
complex tasks or services, and productivity gains through allowing teams to focus on the most critical tasks
of supply chain management or EV/software product innovation.
But to achieve the full potential of (Gen)AI, leaders must identify realistic profitability improvement goals
at the outset and use a rigorous process to prioritize value-creating use cases and establish accountability.
In business technology, generative artificial intelligence (GenAI) is often seen as a new miracle engine that
promises to put early adopters on the track to greater efficiencies, lower costs, and quick wins. But most companies
in the automotive sector find that after a year of investing in endeavors that have underdelivered compared with
expected results, the effect seems more like a collective spinning of wheels. In some cases, automotive companies
have encountered significant challenges with (Gen)AI implementations, such as a major North American
manufacturer whose chatbot mistakenly offered a vehicle for just $1 USD.
In addition, many organizations have celebrated their success too early, before realizing benefits that show up on the
bottom line. As a result, the perceived effects evaporate before they can be substantiated. According to a recent BCG
survey, 67% of executives in the industrial goods sector (including automotive) said they are not satisfied with their
company’s progress in (Gen)AI. Automotive companies should not give up too soon, however—49% of industrial
goods’ respondents (including automotive) still expect the technology to deliver cost savings in 2024. The risk is that
disappointed expectations can lead to disillusionment and lost momentum, when there are real benefits to be had.
Nevertheless, the automotive industry is particularly well-positioned to capitalize on (Gen)AI advancements.
The sector has been a leader in (Gen)AI, as seen with the progress in autonomous driving technologies over the
past decade. However, the current landscape is marked by the shift to electric vehicles (EVs), heightened global
competition, particularly from Asia, and pricing pressures from rising post-pandemic inventories. Such an
increasingly complex environment demands a deeper exploration of (Gen)AI applications. From product
development to supply chain optimization, automakers must leverage (Gen)AI not only to streamline operations
but also to secure a competitive edge.
Moreover, automotive companies have a unique opportunity to unlock further value from (Gen)AI, thanks
to the expansion of connected vehicles, which now generate vast amounts of data. This data, coupled with decades
of investment in building the necessary digital infrastructure, provides automakers with a foundation that few
industries can match. The ability to harness this information and apply (Gen)AI-driven insights across operations
positions the industry to achieve significant gains in both efficiency and revenue generation, setting a clear path
to future growth.
BOSTON CONSULTING GROUP 4
Setting Your Company Up to Succeed
with (Gen)AI
BCG’s experience with automotive clients suggests that many
businesses are failing to realize value from (Gen)AI because they
lack a structured approach and clear value focus from the outset.
Specifically, companies have treated (Gen)AI like a typical technology upgrade or a collection of pilots, with tech
teams leading the way. While this is fine for the technology side of the equation, it fails to achieve real bottom-line
impact. In fact, (Gen)AI requires an even higher level of craftsmanship than other types of transformation. It needs
a greater focus on critical enablers like process and operating-model redesign, training, and employee adoption—
factors that are often overlooked—as well as measurable P&L or balance-sheet improvement goals.
Visible support from leadership is an important success factor. (Gen)AI transformations need to be led from the
C-level, and business unit and functional leaders must be accountable for defining value- creation levers and
achieving results. This can help propel the holistic, people-driven transformation that is required, with specific EBIT
(earnings before interest and taxes) targets that are actionable and traceable.
The automotive industry desperately needs this bottom-line focus to help achieve the agility and efficiency required
to sustain a competitive advantage within a transforming sector. Many players are feeling the pressure of higher
product cost, investments for EV scaling, and at the same time, lower barriers of entry to the playing field in
general. Therefore, new sources of value creation are required—by augmenting new direct sales approaches but
also by accelerating highly complex internal processes like supply chain management or end-to-end value and
capacity planning.
BOSTON CONSULTING GROUP 5
Furthermore, there are several automotive-specific challenges that reinforce the need for leadership, people, and
process engagement in (Gen)AI initiatives. First, many OEMs are working with legacy systems, making human-in-
the-loop engagement critical to ensure that use cases and algorithms are applied correctly and effectively. Second,
legal and privacy concerns surrounding the use of vehicle data are significant, with multiple OEMs in the U.S. facing
lawsuits related to vehicle-data privacy breaches, which must be carefully managed and mitigated. Finally, the
complexity of automotive ecosystems, involving numerous stakeholders such as suppliers, dealers, consumers, and
finance organizations, adds another layer of difficulty in transitioning from pilots to scaled value. These factors
highlight the need for a coordinated, strategic approach to fully leverage (Gen)AI's potential while navigating
industry-specific challenges.
From our work on more than 350 (Gen)AI projects for clients, we have learned that the key is to target EBIT gains
from the outset, not just focus on an implementation program that companies hope will mature into a value
creator. BCG has identified a “(Gen)AI stairway” with four stages that organizations must move through smoothly
to get from (Gen)AI illusion to economic impact. (See Exhibit 1.)
Exhibit 1 | The GenAI Staircase to Success
Companies achieve
real value
MONEY MAKER
Beyond
Execution to P&L
Most companies celebrate tech
success too early Operating model
SHOWMAN change
Process redesign Process redesign
People adoption People adoption
THEORIST
Tech
ILLUSIONIST Scale enablers Scale enablers Scale enablers
GenAI GenAI GenAI GenAI
No value No value No value Value
Source: BCG analysis.
Stage 1: ILLUSIONIST
In this stage, companies across all industries embark on the (Gen)AI journey, attracted by promised efficiency gains
from tools like ChatGPT, Microsoft Copilot, or customized chatbots. We have seen examples of more than 100 use
cases where organizations were running pilots in many departments but without a clear definition of user needs
and without considering technological synergies across functions.
Unfortunately, such isolated deployments fail to generate tangible value because they lack a strategic plan for
employee training and enterprise-wide scaling, leaving the impression that (Gen)AI benefits are just an illusion.
BOSTON CONSULTING GROUP 6
Stage 2: THEORIST
Scaling beyond a few limited tools is necessary, given that meaningful value can come only from meaningful scale.
But we find that most companies that try to scale (Gen)AI throughout the enterprise struggle with low user
adoption. Often, employees feel excluded and do not receive sufficient training; as a result, they fail to incorporate
the tool effectively into their workflow and the (Gen)AI value remains out of reach.
Stage 3: SHOWMAN
As companies struggle with the complexities of (Gen)AI deployment, some realize that success requires more than
just implementing tools. Leading organizations adopt the 10/20/70 principle, referring to how companies should
apportion time and resources, recognizing that, while algorithms (10% of the effort) and technology (20%) are
essential to a (Gen)AI implementation, people and process changes (70%) require the most attention. Companies
that invest in training and change management can typically achieve an adoption rate of about 60% across the
enterprise in our estimation, compared with 30% for those that do not invest in these areas.
Organizations that have successfully undertaken holistic process and workflow changes report efficiency gains
of up to 50%. But celebrating at this stage is premature. Our follow-up with these clients reveals that, within a few
months, the initial gains tend to dissipate as employees’ new free time becomes occupied with backlog tasks
or other emerging priorities. So, while this stage is a significant milestone, it is not enough to drive lasting value.
Stage 4: MONEY MAKER
The final, essential step to achieving measurable value from (Gen)AI is to establish clear links between efficiency
gains and the P&L statement and diligently execute an EBIT-focused transformation. Companies have a variety of
challenges to address with (Gen)AI, and there are numerous potential strategies to convert efficiency gains to
actual profit.
BMW stands out as an exemplary case for achieving EBIT impact, highlighting the importance of establishing and
applying levers beyond technology. (Gen)AI use cases developed in collaboration with BCG streamlined BMW's
procurement processes by automating workflows and optimizing supplier interactions, reducing costs, and
enhancing decision-making. Examples of these applications include the automatic draft generation of RFPs
and tenders, as well as automatic offer evaluation support via (Gen)AI, which improved both speed and accuracy
in procurement operations. This comprehensive approach, spanning marketing and operations, not only improved
efficiency but also created new revenue streams, driving substantial EBIT improvements. Additionally, a first Proof
of Concept at BMW was created to develop hyper-personalized marketing using (Gen)AI, further increasing
customer engagement. BMW rigorously tracks cost savings and revenue gains from the automation processes,
ensuring that the value generated is tied back to measurable financial performance.
For any (Gen)AI strategy, organizations need to establish at an early stage how efficiency gains will affect the P&L
and then follow up with a structured end-to-end transformation. Companies should create a (Gen)AI transformation
office to safeguard value through target setting, tracking, enablement, and accountability enforcement. This allows
the business to truly complete its (Gen)AI journey and achieve the status of (Gen)AI money maker without getting
stuck along the way.
BOSTON CONSULTING GROUP 7
Putting It All Together
The stairway concept of (Gen)AI adoption emphasizes that the initial
step is the only one strictly focusing on technology.
Many companies have begun to move beyond the pure tech phase but find themselves stuck between the second
and third steps. Their experience has been one of experimentation, and while partial steps might help bring
intangible improvements, such as increasing working satisfaction for employees, these actions ultimately lack
significant EBIT impact. We call this “creative (Gen)AI chaos.”
The key to escaping the chaos and bring structure to the implementation is to define EBIT impact targets from the
beginning. The magic of targets comes from imposing a structure and discipline that help maintain focus and guide
the project to a successful result.
In one example of this structured approach, a North American automotive client focused on improving marketing
efficiency by utilizing AI to better target high-propensity and in-market vehicle buyers. From the outset, the use case
was not only technically sound but also backed by a strong business case. The company identified both cost savings
through insourcing the capability—previously reliant on external tools—and performance improvements through
more precise targeting. Additionally, it implemented new processes to ensure consistent use of the AI tool across
the organization and established a tracking mechanism to measure cost savings and performance gains. This clear
focus on both technology and business outcomes illustrates how defining specific targets from the beginning can
lead to measurable, sustainable impact.
In general, a full implementation of (Gen)AI in an automotive firm can result in significant EBIT impact arising
from both overall efficiency improvements as well es top-line growth. Typically, companies can expect up to 50%
efficiency gains in various processes, with faster automation and streamlined workflows. Additionally, top-line
growth can typically increase by 1% to 2% through (Gen)AI applications such as hyper-personalized marketing
or optimized pricing strategies, further contributing to the firm's overall financial performance.
BOSTON CONSULTING GROUP 8
Three key value creation levers can increase the bottom line (see Exhibit 2):
1 Revenue: Automotive companies can boost revenue in various ways, including by augmenting their
salesforce, call centers, and dealers to identify and approach qualified leads with a highly personalized offer
at certain points of their ownership or leasing life cycle. Additionally, smart-feature bundling and the
introduction of smart-pricing mechanisms will play a significant role.
An example of GenAI’s revenue-driving potential is seen in East Asia, where automotive companies are
focusing on enhancing in-vehicle experiences. This includes advanced voice and gesture recognition systems
that tailor cabin features—such as climate control, seat adjustments, and navigation—to specific passengers
based on their location within the car and profile, whether adult or child. By optimizing both comfort and
safety, these enhancements improve customer satisfaction and engagement, establishing a differentiated
brand presence. These GenAI-driven innovations not only support revenue growth but also bolster EBIT
by fostering greater brand loyalty and reducing customer acquisition costs.
2 Expenditures: Spending can be reduced, for instance, by using (Gen)AI to minimize external service costs,
particularly in software development or marketing creation. For example, a leading automotive company we
worked with utilized (Gen)AI to automate key aspects of software development, reducing reliance on third-
party service providers. By streamlining internal development processes and cutting supplier costs for
software development tasks, the company significantly reduced outsourcing expenses and achieved
substantial cost savings. Beyond external cost, (Gen)AI can also help streamline internal administration or
knowledge management to accelerate decision making. Speed and cost are often two sides of the same coin.
3
Productivity: Automating software testing and coding with (Gen)AI has helped a leading automotive
company reduce manual effort and accelerate development. This enabled faster project completion and
allowed engineers to focus on more strategic tasks, significantly boosting overall productivity. Another
example is the automation of the tendering processes, enabling fast drafting and reviewing of key
documents. This in turn helps to reinvest time within the procurement organization to focus on longer-tail
suppliers or deeper assessment of critical supply-chain events to identify additional savings.
Exhibit 2 | How GenAI Contributes to the Bottom Line
Illustrative: Initial estimates from ongoing research; highly dependent on industry and company context
3pp
Technological maturity
People adoption
Process redesign
40%–60% Increase revenue
Operating model reorganization
Others
1pp 10%–30% Cut costs
20%–40% Boost productivity
5- to 10-year Implementation 2-year Impact on EBIT
EBIT potential efforts EBIT potential potential by lever
Source: BCG analysis.
BOSTON CONSULTING GROUP 9
Organizations can achieve the full potential of (Gen)AI only by establishing a proper value realization mechanism.
They should start by setting top-down targets based on an assessment of (Gen)AI’s potential to improve the three
value areas. In addition, implementing a rigorous process to prioritize high-potential (Gen)AI use cases and
establish accountability is essential. Companies can validate progress and achievements from the bottom up, for
example, by conducting workshops to identify value packages. Furthermore, a proper change-management program
will be required because the transformation will significantly affect organizational culture. The final step is to
establish a transformation office to help execute the implementation roadmap, proactively manage roadblocks,
establish governance structures, and track the progress and impact of specific value packages and the overall
program.
We have seen (Gen)AI transformations yield a 1% to 2% increase in revenue and an 8% to 12% cost reduction
compared with the baseline. Companies can potentially achieve an ROI of 10 to 15 times in less than three years.
For example, BCG worked with a client to achieve an EBIT impact of over 10% by evaluating more than 50 (Gen)AI
value packages. In another case, we helped identify (Gen)AI use cases that a client is putting into practice to
capture up to €1 billion in potential EBIT impact by 2028.
When it comes to implementing (Gen)AI, possibly the most significant technology advancement of our generation,
leaders need to be realistic in the face of excessive hype and expectations. (Gen)AI is destined to affect all
companies in all industries, but only those that can implement it with a focus on process, people, and
organizational culture—not just on the technology—will emerge with real success in terms of greater profitability.
A successful transformation begins with identifying achievable EBIT goals and establishing measures such as a
(Gen)AI transformation office to hold key people accountable. To avoid premature celebrations, leaders must stay
focused on bottom-line impact—starting with taking the right steps at the outset of the (Gen)AI journey.
BOSTON CONSULTING GROUP 10
The Authors
Andrej Levin Felix Stellmaszek
Managing Director & Partner Managing Director & Senior Partner
Hamburg Atlanta
Levin.Andrej@bcg.com Stellmaszek.Felix@bcg.com
Alex Xie Jonathan Nipper
Managing Director & Senior Partner Managing Director & Partner
Shanghai Detroit
Xie.Alex@bcg.com Nipper.Jonathan@bcg.com
Manuel Kallies Nina Kataeva
Managing Director & Partner Managing Director & Partner
Berlin Vienna
Kallies.Manuel@BCG.COM Kataeva.Nina@BCG.COM
Tobias Schmidt
Managing Director & Partner
Hamburg
Schmidt.Tobias@bcg.com
The authors thank Nicolas de Bellefonds, Lucas Christenson, Elias Kurta, Ivan Tretiakov, Alexander Hoellinger,
Felix Prosenz, Maximilian Tischer, and Katharina Wortberg for their invaluable contributions to this article.
FURTHER CONTACT
If you would like to discuss this report, please contact one of the authors.
BOSTON CONSULTING GROUP 11"
127,bcg,how-ai-agents-are-opening-the-golden-era-of-customer-experience.pdf,"How AI Agents Are
Opening the Golden
Era of Customer
Experience
By Karen Lellouche Tordjman, Dutch MacDonald, Phil Gerrard,
Rob Derow, Bridget Scott, Kartik Poria, Rob Bell, and Mark Irwin
January 2025
Businesses have long recognized the link The innovations that make reaching these
between improved operational efficiency and CX goals feasible are already taking place
a better customer experience (CX). But the at different levels. Internally, AI is enabling
rapid convergence of various technological companies to streamline processes,
forces—including next-generation hardware transform the employee experience, and
powered by virtual agents—is turning that increase productivity with unprecedented
link into a measurable source of value speed. In customer service operations, for
creation. Increasingly well-documented example, GenAI has helped companies
use cases for generative AI (GenAI) achieve productivity improvements of
are demonstrating that companies can between 15% and 30%, with some aspiring
simultaneously offer a different and vastly to as much as 80% higher productivity.
superior customer experience at a radically
lower cost-to-serve that yields significant Externally, AI is driving shifts in how
improvements in financial performance. customers engage with brands, making
their interactions more human, more
To achieve this “holy grail,” companies personalized, and less tedious and
have begun to look beyond the traditional confusing. The virtual co-pilots of retailers,
customer journey—actively managed by for example, are interacting with customers
shoppers on their apps—to explore the to answer questions, facilitate returns, and
idea of more comprehensive customer create personalized offers. These co-pilots
“missions” managed by a network of free up employees to handle customer
trusted autonomous agents trained to issues that still require complex or nuanced
accomplish specific tasks with minimal human intervention.
human involvement. The first attempts in
the market today will pale in comparison
to what users will see as the convergence
between hardware and agents intensifies.
2
Contents
Linking Process Improvement to Customer Experience 4
From Time-Consuming Apps to Trusted Autonomous Agents 6
Convergence of Technologies Boosts Benefits 9
Today’s No-Regrets Moves for Executives 11
3
Section 1
Linking Process
Improvement to
Customer Experience
4
Linking Process Improvement to Customer Experience
Current investments by Amazon and the While efforts like these will yield significant
Swedish fintech company Klarna show how productivity improvements, they will not
a company’s AI deployments can deliver fully achieve the desired customer, cost,
benefits internally and externally. and financial performance benefits unless
companies also set the bar much higher for
Amazon’s vast and growing population of enhancement of the CX. That’s where the
warehouse robots can pick, pack, and move convergence of next-generation hardware and
merchandise more efficiently than humans autonomous agents creates game-changing
can. AI applications optimize storage opportunities.
positions and routes within the warehouses
and help the robots detect defective or
damaged merchandise more reliably than
humans, who now take on supervisory and
maintenance roles. This efficiency results
in faster and more flexible delivery for
customers. Amazon claimed that in March
2024 it delivered 60% of orders to Amazon
Prime members on the same or next day
in the top 60 US metropolitan areas. At the
same time, it is aiming to improve cost-to-
serve by 25% during peak seasons at its next-
generation warehouses.
Klarna, a global provider of “buy now, pay
later” payment solutions, introduced an
AI customer service assistant powered by
OpenAI in early 2024. With 85 million active
users, the company reported that within
its first month, the AI assistant managed a
workload equivalent to that of 700 full-time
agents. Customer satisfaction levels were on
par with previous satisfaction with human
agents, but customers benefitted in several
other measurable ways. Repeat inquiries
fell by 25% due to greater accuracy in task
resolution. Speed of service also improved,
with customers resolving problems in less
than 2 minutes versus 11 minutes with human
agents. In September 2024, the company
announced new features for the AI assistant,
including open-ended research, searches
for specific products or brands, product
comparisons, product recommendations, and
price research. Klarna has estimated that the
implementation of these assistants will yield
$40 million in additional profits in 2024.
5
Section 2
From Time-Consuming
Apps to Trusted
Autonomous Agents
6
From Time-Consuming Apps to Trusted Autonomous Agents
In his keynote address at Dreamforce 2024, draw complex inferences on its own from
Salesforce CEO Marc Benioff said that the personal information the consumer
service employees waste over 40% of their has allowed it to access. As Exhibit 1
time on low-value and repetitive tasks. We shows, a vertical network of proprietary
have found that customers who want to and third-party agents would work in the
buy a car, remodel their home, or just find background to complete the discrete tasks
something fun to do on a weekend face a of a customer mission—such as “get me
similar set of challenges. Much of their my new car”—rather than simply serve
research conducted via apps, call centers, as the customer’s Q&A machine on a
or web pages can devolve into a confusing, traditional customer journey. The network
frustrating, and error-prone experience. One of agents thus do much of the burdensome
study by Google revealed that a customer work that a consumer would currently
can have more than 700 digital touchpoints need to do manually through an app or a
over a few months as they try to plan a trip. website. As this “agentic AI” learns a user’s
unique preferences, it will deliver products,
Imagine instead that a brand’s chatbot is services, and experiences that are more
an agent that is designed and trained to comprehensive and more personalized.
Exhibit 1 - How AI Agents Work
Source: BCG analysis.
7
When planning a vacation, for example, a
brand’s autonomous agent will use each
family member’s unique interests and
preferences to suggest destinations; find
itineraries; map out routes; recommend
flights, hotels, and restaurants; and even
make reservations for activities. During
the trip, the task-specific networked agents
can also track the family’s progress and—
through a single user interface—make short-
term recommendations for changes based
on traffic, weather, activity opportunities,
meeting friends, or other conditions. This
has the potential to achieve a superior
standard of convenience and service and
alleviate what can sometimes be a dizzying
overabundance of choices while traveling.
If the customer wants to remodel their
home, the agents could find the most
effective methods and materials, make
recommendations, order products, and
even generate self-help videos for tasks the
customer wants to undertake. Autonomous
agents can manage not only major
occasional events (travel, car purchase,
home remodeling) but also day-to-day tasks
such as shopping, diet and workout planning,
pet care, and car and home maintenance.
8
Section 3
Convergence of
Technologies Boosts
Benefits
9
Convergence of Technologies Boosts Benefits
Now imagine an AI agent that is multimodal. and videos, play music, make video calls,
This is where the convergence between livestream what the wearer sees, and
agents and next-generation devices even translate foreign languages. The
comes into play. Besides revolutionizing device anticipated from the collaboration
specialized tasks and solving customer between former Apple designer Jony Ive
problems faster, better, and more efficiently, and OpenAI CEO Sam Altman is expected
agents also untether customers from their to have similar benefits in that the product
screen-based or handheld devices. The will immerse itself into the wearer’s day-to-
shift from app-based operating systems to day life without the interruptions that occur
ambient, natural interfaces—such as voice, using a handheld screen.
augmented reality, and eye movement—
will accelerate as AI-powered devices As agents and hardware converge, we
become more intelligent and predictive. anticipate mutually reinforcing innovations
This transformation will drive hardware that boost both user experience and
innovations that blend into a customer’s customer experience. On the agent side,
day-to-day life rather than being a distinct we expect technical advancements that
task or experience. In the workplace, will bring down the cost of training and
employees can use these devices to interact operating the underlying models, reduce
with the company’s agents that help them the risk of hallucinations, and help build
do their jobs better, providing benefits to stronger and more widespread customer
customers. trust to turn over their personal data and
much of their life management. In addition
The smart glasses from Meta and Ray-Ban to upgraded technology, devices will also
exemplify this. They function as normal offer greater benefits in terms of comfort
eyeglasses, even with prescription lenses, and style, tighter integration into the flow of
but their built-in GenAI can analyze and work or life, and more responsiveness to a
interpret what they see along with the range of sensory cues.
wearer. The glasses can take pictures
10
Section 4
Today’s No-Regrets
Moves for Executives
11
Today’s No-Regrets Moves for Executives
The integration of agents into an organization one—we recommend a more pragmatic
goes beyond a straightforward deployment approach for the short term. Companies
of GenAI to test the value of use cases. In line should start with a small number of deep
with BCG’s progression of Deploy-Reshape- initiatives that lay the foundation for an
Invent, the ability to establish a solid eventual transformation. In a deep initiative,
connection between radical improvements the company tests use cases for how AI and
in operational efficiency and a vastly different GenAI can significantly enhance operational
and superior customer experience creates efficiency and customer experience, then
an imperative for companies to invent new comprehensively assesses the effects on
business models or risk being left behind. the tech stack, the future roles of affected
These models would serve complex data- team members, and the required metrics for
driven customer missions (“buy the perfect defining success from a customer, operational,
car for me”) instead of separate customer and financial standpoint. At the beginning,
journeys (“show me mid-size hybrid SUVs”) the assessments should give greater weight to
as the twin forces of autonomous agents and the operational side, which is a prerequisite
multimodal hardware continue to evolve and for scaling the agents with customers.
converge. (See Exhibit 2.)
In detail, we see several no-regrets moves
But before embarking on a massive customer right now to prepare an organization for a
experience transformation—or even planning larger transformation.
Exhibit 2 - The Convergence of Use Cases and Technologies Reduces Operational Costs
and Transforms Customer Experience
Source: BCG analysis.
12
The vertical integration in Exhibit 1 will take place first, as agents start
to take over parts of the customer journey. At the same time, companies
Build for need to start thinking about their role in broader customer missions. The
journeys and design challenge is to understand these journeys and missions and serve them in
for missions ways designed from the outset to achieve their CX and financial goals.
Executives can begin to rethink traditional CX metrics by incorporating new
KPIs, such as cost-to-serve, alongside their preferred customer satisfaction
Adopt metrics. These metrics should allow an executive team to “connect the
new metrics dots” by making transparent the relationship between lower costs, superior
customer experience, and better financial performance.
Companies have tended to underinvest in efforts that translate
operational improvements into better customer experience, because they
Reevaluate considered short-term gains to be soft and uncertain rather than hard and
investment verifiable. The ability to draw direct links between operational efficiency
priorities and customer experience, and then calculate short- and long-term ROIs,
can attract the investment needed to keep the momentum going.
This approach is necessary to keep pace with the accelerating rate of
innovation as well as convergence. Convergence will take place not only
Learn to technologically—such as between hardware and agents—but also within
converge the organization as it finds ways to integrate teams, functions, and skills
in ways that drive greater efficiency and a better CX. It means launching
deep proof-of-concept tests of GenAI’s potential in different functions,
starting with low-hanging fruit, to identify potential pitfalls and address
them early in the implementation process.
These actions will enable the company’s leadership team to start preparing a roadmap for
integrating GenAI into the customer-experience strategy—from short-term wins to long-
term, full front-to-back transformation.
13
The convergence between ever-improving hardware
and larger networks of trusted autonomous agents will
allow companies to achieve the holy grail of improved
productivity, higher customer satisfaction through a
superior and differentiated customer experience, and
better financial performance. The familiar link between
improved efficiency and better customer experience
will become a nexus of innovation and value creation in
ways we can only begin to imagine, as the technologies
integrate more seamlessly into day-to-day life.
The authors are grateful to their BCG colleagues Oli Shaw, Melanie Stetter Hernandez, and Aylin Ozcan
for their insights.
14
bcg.com
15"
128,bcg,WEF_Frontier_Technologies_in_Industrial_Operations_2025.pdf,"In collaboration with Boston Consulting Group
Transformation of Industries in the Age of AI
Frontier Technologies
in Industrial Operations:
The Rise of Artificial
Intelligence Agents
W H I T E P A P E R
J A N U A R Y 2 0 2 5
Images: Getty Images
Contents
Reading guide 3
Foreword 4
Executive summary 5
Introduction 6
1 The next leap: reinventing industrial operations through 7
frontier technologies
1.1 Entering the next frontier: the path towards self-control 8
1.2 Redefining the role of humans: from operators 8
to AI-enabled orchestrators
2 AI agents fuelling the transformation of operations 10
2.1 Virtual AI – paving the way for autonomous systems 12
2.2 Embodied AI – igniting a new era in robotics 15
3 Strategic imperatives for industrial operations transformation 18
3.1 Paving the way for successful use of AI agents 18
in industrial operations
3.2 Staying at the forefront of AI agent innovations 19
3.3 Building the foundations: organizational and technological 19
Conclusion 21
Contributors 22
Endnotes 25
Disclaimer
This document is published by the
World Economic Forum as a contribution
to a project, insight area or interaction.
The findings, interpretations and
conclusions expressed herein are a result
of a collaborative process facilitated and
endorsed by the World Economic Forum
but whose results do not necessarily
represent the views of the World Economic
Forum, nor the entirety of its Members,
Partners or other stakeholders.
© 2025 World Economic Forum. All rights
reserved. No part of this publication may
be reproduced or transmitted in any form
or by any means, including photocopying
and recording, or by any information
storage and retrieval system.
Frontier Technologies in Industrial Operations 2
Reading guide
The World Economic Forum’s AI Transformation of This white paper series explores the transformative
Industries initiative seeks to catalyse responsible role of AI across industries. It provides insights
industry transformation by exploring the strategic through both broad analyses and in-depth
implications, opportunities and challenges of explorations of industry-specific and regional deep
promoting artificial intelligence (AI)-driven innovation dives. The series includes:
across business and operating models.
Cross industry Regional specific
Impact on industrial ecosystems Impact on regions
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AI in Action: Beyond Leveraging Artificial Intelligence’s Artificial Intelligence Blueprint to Action:
Experimentation to Generative AI for Job Energy Paradox: and Cybersecurity: China’s Path to
Transform Industry Augmentation and Balancing Challenges Balancing Risks AI-Powered Industry
Workforce Productivity and Opportunities and Rewards Transformation
Industry or function specific
Impact on industries, sectors and functions
Advanced Media,
manufacturing Financial entertainment
and supply chains services and sport Healthcare Transport Telecommunications Consumer goods
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Frontier Technologies Artificial Intelligence Artificial Intelligence in The Future of Intelligent Transport, Upcoming Upcoming
in Industrial in Financial Services Media, Entertainment AI-Enabled Health: Greener Future: industry report: industry report:
Operations: The and Sport Leading the Way AI as a Catalyst Telecommunications Consumer goods
Rise of Artificial to Decarbonize
Intelligence Agents Global Logistics
Additional reports to be announced.
As AI continues to evolve at an unprecedented and stakeholders engaged in AI strategy and
pace, each paper in this series captures a unique implementation across organizations.
perspective on AI – including a detailed snapshot
of the landscape at the time of writing. Recognizing Together, these papers offer a comprehensive
that ongoing shifts and advancements are already view of AI’s current development and adoption,
in motion, the aim is to continuously deepen and as well as a view of its future potential impact.
update the understanding of AI’s implications Each paper can be read stand-alone or alongside
and applications through collaboration with the the others, with common themes emerging
community of World Economic Forum partners across industries.
Frontier Technologies in Industrial Operations 3
January 2025 Frontier Technologies in Industrial Operations:
The Rise of Artificial Intelligence Agents
Foreword
Kiva Allgood
Head, Centre for Advanced Daniel Küpper
Manufacturing and Supply Managing Director and
Chains; Member, Senior Partner, Boston
Executive Committee, Consulting Group (BCG)
World Economic Forum
Amid a landscape of exponential technological advantages and catalyse sustainable growth.
change, society is entering the Intelligent Age1 – Manufacturers that fail to fully harness the
an era defined by far more than technology alone. transformative power of frontier technologies in
The Intelligent Age is characterized by a mass operations and supply chains will surely fall behind.
revolution transforming all aspects of society, and
we’re already beginning to witness profound shifts. Although the pursuit of frontier technologies is not
Alongside technological intelligence, environmental, novel, the stakes are now higher than ever. The
social and geopolitical intelligence will be challenges associated with identifying and evaluating
fundamental to success in this age. these technologies and integrating them into long-
term strategies have grown more complex as the
In this new era, industrial operations are being pace of innovation accelerates. Forward-thinking
redefined. To better understand emerging industries, technology leaders and academic
opportunities and explore potential responses, institutions are pioneering such advancements.
the World Economic Forum – in collaboration with Yet, even with the growing accessibility of tools
Boston Consulting Group (BCG) – launched the like generative AI, manufacturers still face a crucial
global initiative Frontier Technologies for Operations: question – how will frontier technologies drive real,
AI and Beyond. Building on the success of our measurable impact in day-to-day operations?
previous AI-Powered Industrial Operations initiative
from 2022, this new scheme aims to equip This white paper presents a bold yet actionable
manufacturers with the insights and tools necessary vision of one such frontier technology – AI agents.
to shape the future of industrial operations in the It additionally outlines methods by which this
Intelligent Age. technology could be applied to create tangible value
in industrial operations. The paper focuses on the
It’s important to address two pressing questions transformative potential of two types of AI agents
– why focus on frontier technologies, and why – virtual AI agents and embodied AI agents – and
now? The answer is simple yet profound – these provides insights and case studies from leading
innovations, like others in the Intelligent Age, drive industries while challenging conventional thinking
boundary-breaking advancements that push and inspiring new strategies. Its aim is to highlight
the limits of what’s currently possible, facilitating innovative perspectives to help manufacturers
collaborative intelligence and amplifying human unlock the full potential of AI agents and spearhead
ingenuity. In doing so, they provide competitive operational transformation.
Frontier Technologies in Industrial Operations 4
Executive summary
It’s essential that manufacturers embrace
frontier technologies to secure a thriving,
sustainable future in manufacturing.
The manufacturing landscape is becoming – Virtual AI agents – advancing autonomous
increasingly complex, and this trend is projected to software systems: Virtual AI agents enable
accelerate in the coming years. Labour shortages, software applications to autonomously achieve
rising cost pressures and shifting customer demands, defined goals in the digital environment, acting
geopolitical dynamics and decarbonization goals as assistants, advisers or automation agents.
necessitate significant operational transformation. These agents support workers and can also
independently control and steer processes
Current technologies will be insufficient to drive and machinery.
the required levels of flexibility, sustainability and
excellence needed to facilitate this change. To – Embodied AI agents – ushering in a new
succeed, manufacturers can embrace frontier era of robotics: Embodied AI agents equip
technologies that push the limits of innovation. physical systems, such as robots, with the
However, navigating this rapidly evolving ability to perceive and act within the physical
technological landscape is challenging, as many environment, allowing for dynamic and complex
manufacturers need to address immediate operational movements. These advancements will be
needs and plan for the future of their operations. crucial for overcoming the current limitations
of robotic automation.
Industrial operations are likely to evolve towards
an artificial intelligence (AI)-centric model, where AI Successfully navigating the transition to near-
drives self-controlling, near-autonomous systems autonomous, AI-agent-driven operations requires
while empowering humans. While near-autonomous a comprehensive, value-driven approach to
operations may become common in some industries, technology adoption. Solutions should be scalable
human involvement will remain crucial. The role of and aligned with long-term business objectives.
humans will be redefined, with workers transitioning Establishing strong organizational and technological
from hands-on operators to orchestrators, stepping foundations that support this vision will be
in when judgment or creativity is required. This shift crucial for manufacturers looking to capture the
will boost operational efficiency, allowing humans technology’s full potential.
to focus on strategic tasks and ethical decision-
making to drive innovation and growth. The insights presented in this paper are focused
on manufacturing and founded on the collective
In the broad landscape of frontier technologies, expertise of the initiative community, drawing
AI – and more specifically, rapidly evolving AI from consultations with senior executives and
agents – have the potential to propel manufacturers academic experts. Moving forward, the community
towards this future, and unlock novel opportunities will continue to work closely with manufacturing
in operations across many industries. This report stakeholders across industries to deliver a global,
focuses on two types of AI agents: virtual AI comprehensive outlook on the future of industrial
agents and embodied AI agents. These agents are operations. This effort will concentrate on recent
expected to enhance both digital applications and and future frontier technologies, with an emphasis
physical systems, and perform complex tasks with on responsible transformation approaches.
minimal human intervention.
Frontier Technologies in Industrial Operations 5
Introduction
AI agents are transforming industrial
operations, driving efficiency and unlocking
competitive advantages.
As new frontier Frontier technologies have pushed the limits – Where is the real value in this transformation?
technologies of what is possible in industrial operations
emerge, over the past decades, significantly boosting – Which frontier technologies will address
productivity, reducing costs and improving the key challenges?
manufacturers face
work environment. Innovations like robotics and
the key challenge
the industrial internet of things (IIoT) have been – What steps need to be taken to realize value
of discerning
instrumental in modernizing operations and laying at scale?
which innovations
the foundation for the next wave of breakthroughs.
will bring lasting
Drawing on insights from experts and executives
value at scale, and
Today, the technological landscape is evolving at across operations and technology, this white paper
which are merely an unprecedented pace. This progress is primarily provides a strategic perspective on these questions,
transient trends. driven by the exponential increases in computing with a focus on AI-agent-enabled transformation.
power and breakthroughs in artificial intelligence It presents a forward-looking vision of AI-driven,
(AI) society is currently witnessing. As new frontier near-autonomous industrial operations. It explores
technologies emerge, manufacturers face the key the role of AI agents in enabling this vision,
challenge of discerning which innovations will bring specifically virtual AI and embodied AI agents,
lasting value at scale, and which are merely transient offering concrete examples and case studies to
trends. This creates uncertainty around where to demonstrate their value. Additionally, it outlines the
focus development efforts and investments. strategic imperatives necessary for successfully
scaling these technologies. While AI agents hold
Overcoming these challenges is essential. transformative potential, it is crucial to recognize that
Harnessing the value of frontier technologies is now they are not yet fully developed. Leading companies
vital for manufacturers as they seek to maintain are running pilots to test their capabilities, with their
a competitive edge and tackle industry-specific at-scale impact to be realized in the coming years.
obstacles. To retain a leading position in the evolving
landscape, companies must not only adopt these Although not covered in this white paper, other
innovations but also understand the transformative frontier technologies – such as biotechnology and
impact on the future of operations. Success in this quantum technology – are generating significant
journey hinges on answering a few key questions: interest. These technologies hold the potential
to revolutionize manufacturing operations, either
– What will the future of industrial operations directly or indirectly, but remain in earlier stages
look like? of development.
BOX 1 The two types of AI agents
Virtual AI agents Embodied AI agents
Software-based AI agents that operate AI agents integrated into physical systems
entirely in the digital environment and enable – such as robots – that interact with the
digital applications to autonomously achieve physical environment
defined goals
Frontier Technologies in Industrial Operations 6
The next leap:
1
reinventing industrial
operations through
frontier technologies
Preparing for the challenges ahead
requires operational transformation
driven by frontier technologies.
Manufacturers face a complex operating – Geopolitical dynamics: Tariffs and fragmented
environment with growing challenges: production across multiple geographies
hinder economies of scale, leading to greater
– Cost competitiveness: Rising labour costs, complexity in supply chains, dispersed know-
supply chain disruptions and international how and increased risks.
competition necessitate improved efficiency
and lowered structural costs. – Sustainability: To meet decarbonization goals,
it’s crucial to optimize energy and resource use
– Labour shortages: More than 2.1 million while reducing emissions through robust supply
manufacturing jobs are projected to remain chain management.
unfilled in the US alone until 2030,2 driving
workforce risks and productivity challenges. Addressing these challenges requires a shift in
operational excellence, breakthrough innovation,
– Customer demands: Consumers’ expectations structural optimization, supply chain diversification
for greater customization and faster delivery and investment in regional manufacturing clusters.
drive the need for more flexible production
systems and better demand forecasting.
Frontier Technologies in Industrial Operations 7
1.1 E ntering the next frontier:
the path towards self-control
Although The industrial sector stands at a pivotal juncture. performance monitoring can be centralized in virtual
the extent of Frontier technologies, such as AI agents, are control centres rather than dispersed throughout
automation will capable of performing complex activities. This the shop floor.
ultimately depend paves the way for increasingly AI-driven, near-
autonomous operations, within which many Self-controlling factories and supply chains will
on the return of
machines and AI-enabled systems will function with deliver significant improvements such as:
investment, many
minimal human intervention. Success depends on
factories may
cultivating a trusted human-machine interaction, – Efficiency: Predictive analytics will shift operations
converge towards
where both collaborate seamlessly. from reactive to proactive management,
autonomy, driven
anticipating issues and implementing necessary
by the need to Currently, automation is often reserved for simple, adjustments immediately. Real-time adjustments
remain competitive. repetitive tasks that still require manual oversight will enhance machine uptime, quality control
to ensure continuous operation. In the past, and cost efficiency.
the expansion of automation was hindered by
technological hurdles (such as an inability to handle – Flexibility: Advanced robotics and AI will
unsorted flexible parts like cables automatically) enable highly personalized manufacturing
and financial constraints. However, more advanced and swift reconfigurations, making production
technologies and decreasing costs are poised lines adaptable to varying product demands.
to enable wider deployment across factories, Autonomous systems will self-organize for
with autonomous systems taking control of optimal factory layout and performance, further
routine operations. These autonomous systems enhancing flexibility. They will also increase
– encompassing machines, robots and virtual supply chain agility and responsiveness.
systems – may manage routine tasks ranging
from material handling to quality control and – Sustainability: Autonomous systems
production planning. Such systems may optimize will optimize energy consumption and
and adjust production parameters on machines in minimize waste. Real-time analytics will
real time to align with business needs, enhancing monitor environmental impacts, ensuring
flexibility. Although the extent of automation will that sustainability goals are met without
ultimately depend on the return of investment sacrificing efficiency.
across industries and regions, many factories may
converge towards autonomy, driven by the need to – Worker empowerment: AI-driven tools and
remain competitive. automation will enhance workforce capabilities
and facilitate human-machine interactions,
The shift towards autonomy may also revolutionize enabling workers to quickly understand
factory design. Future AI-centric factories might production issues and make more well-
prioritize machine-optimized layouts that enhance informed decisions.
production efficiency and flexibility. For instance,
valuable ground-floor space can be freed up by The transformation to near-autonomous industrial
storing unfinished parts in automated multi-storage operations requires coordinated changes across
shelves, manual processes can be accelerated and both human and technological dimensions.
1.2 R edefining the role of humans: from
operators to AI-enabled orchestrators
Human involvement will remain essential in productivity breakthroughs. For example, one
industrial operations of the future, as workers individual supported by assistant systems can
may transition from hands-on operators to AI- supervise multiple functions such as quality,
enabled orchestrators who oversee autonomous inspection and production simultaneously.
systems and provide judgment or ingenuity as Maintenance activities that require physical dexterity
required. As machines advance in natural language – such as checking for leaks or replacing parts
comprehension, human-machine interactions inside a machine – may partially remain human-led
will become more fluid and intuitive, enabling but can be significantly augmented by virtual agents.
Frontier Technologies in Industrial Operations 8
In a future In a future with largely self-controlling systems, yield deviations that systems cannot resolve,
with largely humans may partner with machines, harnessing humans can step in to address the issue.
self-controlling collaborative intelligence to focus on higher-value
systems, humans tasks, such as: – Continuous improvement involves solving
complex problems and optimizing processes.
may partner
– Strategic decision-making involves using For instance, in a chemical processing plant,
with machines,
AI-driven recommendations to make business- engineers may use AI to identify inefficiencies
harnessing
critical decisions. For instance, in an automotive in mixing or reaction processes. They can then
collaborative
plant, AI may recommend adjustments to redesign workflows or machine configurations
intelligence to
production schedules or shift planning. A human to optimize output and reduce waste.
focus on higher- planner may weigh these recommendations
value tasks. against factors such as projected customer – Creativity and innovation involve developing
demand or current labour availability. new production processes and rethinking
factory layouts. For instance, in a consumer
– Performance supervision involves monitoring electronics plant, a maintenance worker might
and adjusting autonomous systems as needed. introduce creative ideas to streamline tool
For instance, in a semiconductor plant, operators changes by mounting additional supports that
may monitor autonomous systems handling have been employed in other industries.
wafer fabrication. If performance metrics show
BOX 2 Industry example: Shifting role of technicians and supervisors
A global wheel manufacturer has experienced a improvement by optimizing the plan-do-check-act
shift in the role of their technicians and supervisors (PDCA) cycle. Supervisors, in turn, are evolving
with the introduction of a prescriptive AI solution into AI users, interpreting AI-driven insights and
for process parameter adjustment developed by a guiding operators towards more efficient problem-
Cape Town-based AI solution provider. Instead of solving. This transition enables both operators and
managing process details, technicians now focus supervisors to concentrate on long-term, systemic
on identifying root causes and driving continuous improvements rather than routine, reactive tasks.
BOX 3 Industry example: Elevating planner roles with AI-supported decision making
A Fortune 500 technology manufacturer elevated routine decisions in inventory management
the role of its planners from executors to architects while routing exceptions to human experts with
of its supply chain decision-making process. contextual data, analysis and recommendations.
Previously relying solely on humans, the company The platform optimized stock levels and ensured
struggled with delayed decision-making, resulting supply was matched to regional demand. As
in large inventories and long lead times. By a result, 77% of agent recommendations were
harnessing an AI agent solution from a US-based automatically executed and 90% were accepted
decision intelligence company, they automated without change.
This evolution will require manufacturers to anticipate a transition in workforce skills
and cultural identity, making early engagement of operators in the transformation
journey critical for success.
Frontier Technologies in Industrial Operations 9
AI agents fuelling
2
the transformation
of operations
Virtual and embodied AI agents could drive
the transition towards near-autonomous
operations in both software and robotics.
Realizing the transformative vision of AI-centric and embodied AI agents have the potential to deliver
operations requires a thorough assessment and significant value, unlock new opportunities and drive
evaluation of the potential of AI agents. Both virtual the transition towards near-autonomous operations.
AI will transform from a data-centric front end to an agent-centric
user end, relying on domain-specific data sources to optimize
industrial operations. These domain-based agents will drive new
growth of AI across different industries. The interactive agents
will further transform the new large knowledge model, fostering
the development of AI ecosystems with advanced technologies,
tools and talents.
Jay Lee, Clark Distinguished Professor;
Director, Industrial AI Center, University of Maryland
Frontier Technologies in Industrial Operations 10
BOX 4 The basics of AI agents
AI agents amplify the impact of large language models The roles can be predefined, or agents can be flexible
(LLMs) by giving them access to tools and enhancing their and dynamically adapt to new roles.
ability to observe, plan and execute actions.3 Traditional AI
algorithms, such as machine learning, are task-specific and – Reasoning module: Agents have limited reasoning
require human input for defining tasks, providing data and capabilities. The underlying LLM is capable of
interpreting results. In contrast, AI agents, once trained, decomposing the agent’s prompts and returning an
can operate and achieve specific objectives autonomously, actionable plan. It extracts key insights and makes
continuously observing their environment, planning actions logical connections by replicating reasoning steps
and harnessing tools to execute complex tasks. AI agents observed in training data. This enables agents to
function in a continuous observe, plan and act cycle, which decide on the required next steps by breaking down
makes them particularly valuable for operations. Each step is complex tasks into small actions to achieve their
enabled by interfaces or modules:4 objectives. Recent studies have shown that current
LLMs are not yet capable of formal reasoning. Real-
– Observe: Agents collect and process data from the world solutions thus require other types of AI and
environment, including multimodal data, user input or solvers and cannot solely rely on existing LLMs.5
data from other agents. For example, an agent can
perceive deviations in production quality and underlying – Act: Agents execute actions by harnessing internal
parameters in real time. or external tools and systems. For example, an agent
accesses the machine controller and changes the defined
– Agent-centric interfaces: Agents require protocols, machine parameters.
application programming interfaces (APIs) and
specifically designed interfaces to input multimodal – Action module: Agents decide which tools to use,
data or perceive real-time data from multiple sources. using access mechanisms such as APIs, system
integrations or other agents as needed.
– Memory module: Agents have short- and long-term
memory, which allows them to remember general Functioning in this cycle, agents continuously learn from
knowledge, past actions and decision-making. self-reflection or external feedback. Through goal-oriented
learning approaches, such as reinforcement learning, agents
– Plan: Agents and their underlying LLMs evaluate possible continuously adapt and refine their strategies over time.
actions to prioritize them through logical reasoning, in This makes them particularly valuable in complex, dynamic
accordance with their objectives. In the example above, environments where conditions and objectives are constantly
the agent reviews possible actions to improve quality and shifting. Such environments can be found widely across
decides to change production parameters. industrial operations. As part of multi-agent systems, in
which specialized agents work together by dividing complex
– Profile module: Agents have defined attributes, problems among themselves, they can automate entire
identities, roles or behavioural patterns. processes end-to-end.
AI agents function in a continuous observe, plan and act cycle
Observe
Collect and process
data from environment
Agent
Act Plan
Execute by leveraging Evaluate possible
internal or external actions to prioritize
tools/systems them through reasoning
Source: Boston Consulting Group (BCG).
Frontier Technologies in Industrial Operations 11
2.1 V irtual AI – paving the way for autonomous systems
Virtual AI agents can manage a wide range of agents have applications across all operation
software-based tasks, from routine operations functions, including production, maintenance,
and research to advanced analytics and task quality, engineering, logistics and planning.
automation. In industrial operations, they can
enhance responsiveness, improve execution quality, The maturity of virtual AI agents can be categorized
boost productivity and reduce operational mistakes. into three levels: assistant, recommendation and
Unlike traditional machine learning programmes, automation. The distinct objectives at each maturity
they can make context-sensitive decisions in real level are pursued by specialist agents:
time and adapt through feedback loops. These
FIGURE 1 The four types of virtual AI agents
Recommendation Automation
Assistant
Maturity level (proposing scenarios (autonomously performing
(executing manual tasks)
and actionable insights) activities)
Specialist
Knowledge agent Adviser agent Automation agent
agents
Meta agents Meta agent
Source: Boston Consulting Group (BCG), World Economic Forum.
Knowledge agents support workers as intelligent optimize machine performance, adjust production
assistants. They analyse and synthesize vast parameters, recode instructions or modify
amounts of data to provide real-time operational production plans. They surpass existing RPA
insights, flag anomalies and create content such as (robotic process automation) by automating not
reports and code. By accessing multiple tools and only individual tasks but also entire human activities
real-time data sources, such as machine logs and that require understanding, planning and execution.
sensor data, they add value to functions that require
quick insights – for example, in maintenance, quality Meta agents orchestrate specialist agents in the
and logistics. They can also support engineering context of multi-agent systems to achieve broader
with machine code generation. objectives, enabling area- or even factory-wide
steering. The long-term vision for meta agents is to
Adviser ag"
129,bcg,the-blueprint-for-ai-powered-marketing.pdf,"The Blueprint for
AI-Powered Marketing
December 2024
By Derek Rodenhausen, Ray Yu, Trevor Sponseller, Paola Scarpa, Henry Leon,
Javier Perez Moiño, Val Elbert, and Jordan Baker
The Blueprint for
AI-PoweredMarketing
AI is reshaping the marketing landscape, offering breakthrough
capabilities like unlimited permutations of personalized marketing
for modern consumer journeys, predictive insights, and real-time
decision making. But for most marketers, the whole idea of AI
feels overwhelming.
Marketers are constantly bombarded with pitches from WhatAIExcellenceLooksLike
tech providers and platforms, each claiming that their AI
solution will revolutionize the way their brand engages with Most brands are still in the early innings with AI: more
customers. They wonder: Where do I start? Which areas than 80% of our survey respondents are exploring off-the-
should I prioritize? shelf solutions, adopting use cases, or experimenting.
Two-thirds claim they are stymied by either a lack of knowl-
To address these questions, we conducted one of the larg- edge or the sheer number of options. But a select few have
est studies of its kind, surveying more than 2,000 market- started to figure it out: about 20% of respondents have
ers globally and speaking with over 50 marketing leaders. integrated AI tools deeply into their marketing workflows.
(See “About the Study.”) Through our research, we devel- They are testing AI-assisted decisions and personalization
oped a clear framework to help marketers cut through the approaches in such areas as content creation, predictive
noise, pinpoint where AI can drive the most impact, and analytics, and synthetic research methodologies. In the
build a roadmap for growth. In this article, we share in- past 12 months, these leaders have achieved powerful
sights from the study. We then outline the essential steps results: they report 60% greater revenue growth than their
for making AI an integral element of the marketing func- peers and are adapting to consumer trends twice as fast as
tion to create an AI flywheel that seamlessly integrates their peers.
capabilities across media planning, creative, activation, and
measurement to boost efficiency, deliver results, and keep So what are the 20% doing right? We’ve identified six key
brands ahead of the curve. actions. (See Exhibit 1.)
2 THEBLUEPRINTFORAI-POWEREDMARKETING
About the Study
In August 2024, BCG partnered with Google to research
how leading marketers are using AI, leveraging Google’s
global scale and expertise in AI-enabled advertising solu-
tions. Our goal: to build a globally consistent framework for
marketers to use on their AI adoption journey. We surveyed
2,000 marketing executives across 10 key global markets
and more than a dozen industries, from retail and consum-
er packaged goods to banking and pharmaceuticals. Com-
pany size ranged from small and medium-sized businesses
to multinationals.
The survey consisted of more than 50 questions about
companies’ maturity level, AI capabilities and practices,
and the extent to which their AI-based marketing was
integrated across the enterprise. We asked about such
issues as:
• Their use of AI for developing consumer insights (for
example, automating traditional audience research and
creating synthetic insight tools such as conversational
personas that represent different target audience seg-
ments and their preferences)
• Their audience segmentation and media-bidding strat-
egies (e.g., setting inputs for AI models to conduct auto-
mated bidding and using predictive AI to build audiences)
• Their use of GenAI across creative and content tasks
(such as developing creative concepts, auto-populating
briefs, and designing assets)
• Their people and process strategy (including talent
development, governance, decision making, and their
enterprise strategy for AI)
To gain deeper qualitative insights, we also conducted
more than 50 interviews with marketing decision makers,
inquiring about the impact of their AI program on ROI and
overall performance over the previous 12 months. Correlat-
ing practices with performance, we then distilled the capa-
bilities list down to the essential six and identified the
most important actions marketers can take at each stage
to forge a path to AI excellence.
BOSTONCONSULTINGGROUP 3
Exhibit1-WithTheseSixActions,LeadersAchieve60%Higher
RevenueGrowth
(Percentage of leading AI marketers who take each action)
Measurement & Insights Media & Personalization Creative & Content People & Process
24% 24% 35% 20% 9% 10%
Data Robust Media Audience AI-Powered X-Functional
Foundations Testing Budgeting Strategy Workflows Advocates
Derive insights Accelerate Run Generate Use GenAI across Build
using integrated testing with outcomes-based real-time audiences creative tasks cross-functional AI
customer view GenAI media allocation advocates
The payoff
60%
higher revenue growth than peers
Source: Google/BCG,“PathtoAIExcellence,”September2024,Global,n=2,135.
Note: RespondentsincludemarketingAIdecisionmakers/influencersatsmalltolargecompanies.
They have an integrated view of the customer. The An insurance company’s experience illustrates AI’s dra-
modern consumer journey is less linear and more varied matic impact on speed. The company used GenAI to ana-
than ever. Leaders understand that AI can help them ad- lyze various data sources, propose testable hypotheses,
dress that complexity, starting with a comprehensive view and design structured experiments to reduce bias. Tests
of the customer—a view built on first-party data that feeds are now out into the market in half the time, and the
AI models and connects to media activation. company reduced its analysis time from more than eight
hours to 30 minutes.
Take, for example, the bank that wanted to present person-
alized offers but lacked a unified customer view. It integrat- They shift budget allocations dynamically to max-
ed first-party data from branch interactions, call logs, and imize outcomes. Capitalizing on opportunity requires
emails and then connected systems with segmentation, fast action and spending efficiency—the ability to redirect
rules, and content assignments. Predictive AI models limited resources to where they will have the greatest
powered cross-selling campaigns based on the individual effect. AI’s ability to automate budget shifts unlocks new
customer’s history. The bank was able to cut offer launch potential for both efficiency and effectiveness. Some 35%
times by more than 60% and test as many as seven vari- of companies we surveyed are able to shift budgets across
ants for different use cases. platforms and channels, dynamically adjusting their mar-
keting allocations to take advantage of new opportunities.
They accelerate test design, execution, and analysis.
Fewer than 25% of respondents have tapped into AI’s One e-commerce company used a predictive AI model to
potential to support always-on testing by accelerating forecast outcomes based on channel allocations (e.g., paid
insights. Leaders, however, are using AI to create new search, social media) and outreach timing. The company
campaign ideas with greater speed and volume than applied these forecasts to inform its decisions about cou-
traditional approaches. Consumer and B2B marketers pons, promotions, and loyalty points and tracked results
alike are creating breakthrough models and accelerating quarterly. It cut media budget-planning time by 66% and
their testing. Because it can quickly produce content increased brand awareness by 11%.
variants, AI helps companies scale high-impact
strategies—in other words, invest in what works.
4 THEBLUEPRINTFORAI-POWEREDMARKETING
They’re able to reach real-time audience segments To foster cross-functional partnerships, one global consumer
with personalized messages. AI helps marketers define packaged goods company adopted a “squad” model that
the most valuable audiences based on real-time signals spanned marketing, finance, demand planning, and trade,
and target them with the right messages. While more with support from legal, R&D, and sales. This approach
than half of the companies surveyed define their strategic reshaped demand and improved brand positioning, helping
audiences based on precise signals (such as proximity to to unlock personalization across the consumer journey.
purchase), only 20% have integrated real-time, AI-powered
segmentation into their activation strategies.
ChartingthePathfromVisiontoValue
Consider the B2B software company that wanted to devel-
op new audiences without raising costs. It used its custom- From our survey analysis and qualitative findings, we devel-
er data platform to group high-value customers and pros- oped a data-derived view of the path to AI marketing suc-
pects, creating a seed audience. This seed audience was cess. For each of four maturity stages—essentials, scaling,
fed into a predictive AI model, which generated new look- leading, and transforming—we identify the most high-im-
alike audiences—expanding the company’s outreach and pact actions to take. (See Exhibit 2.)
cutting lead costs by 25%.
Stage 1: Essentials—Building the AI Foundation. At
They use AI to develop creative throughout the whole this stage, companies address gaps in foundational capa-
creative lifecycle. Although 48% of companies surveyed bilities, implementing AI-ready data management tools
frequently use GenAI for tasks like copywriting or develop- and testing AI-powered campaigns with first-party data.
ing taglines, only 9% have adopted AI for their end-to-end They redesign creative processes to translate hero assets
creative workflow. More than half lack content manage- into modular, channel-agnostic content to be read by AI.
ment systems, so they are unable to organize creative in a They use built-in AI tools within ad platforms to make fast
way that AI can access. Leaders, however, take advantage of progress. Further, they establish a view from the top about
AI’s ability to dramatically accelerate the creative process, where in the organization AI can add the most value and
improving both its speed and quality. engage stakeholders across functions for early support.
Leaders take advantage of AI’s ability to dramati- Stage 2: Scaling—Implementing Top-Priority Use
cally accelerate the creative process, improving Cases. In the scaling stage, organizations focus on ex-
both its speed and quality. panding AI-powered use cases across media and creative,
ensuring content compliance and performance tracking.
An online retailer, for example, used AI to analyze perfor- They expand the scope of the data fed to AI, improving the
mance data on hundreds of creative assets, probing the granularity and speed of insights for decision making. In
effectiveness of such elements as color and calls to action. addition, they introduce balanced marketer-AI workflows
The insights they gained improved add-to-cart rates and backed by responsible AI governance.
conversions, and then included in new briefs so the cre-
ative team could use them to develop and test campaigns Stage 3: Leading—Developing Leading Capabilities
and predict performance pre-launch. Development time to Integrate AI Workflows. This stage involves building
fell by 75%. real-time audiences and targeting them by shifting funds
fluidly across channels as needed. Leaders increase the
They’re building an AI culture by enlisting advocates volume and relevance of creative by using AI throughout
across core functional areas. Currently, only 26% of com- the creative lifecycle, from ideation to measurement. They
panies enlist four or more key functional areas in their AI use predictive AI to forecast outcomes and inform media
initiatives. But AI marketing leaders understand that scaling activation efforts. Finally, marketing teams collaborate with
AI successfully calls for building strong partnerships with stakeholders to implement a next-generation talent strate-
virtually every core function. These partnerships are essen- gy that supports the new AI-driven processes.
tial for promoting new workflows, securing funding, and
implementing talent strategies. IT, for example, overhauls
legacy systems, streamlines processes, and redesigns tech
infrastructure to support future needs. Finance validates AI
business cases, ensuring sustained resource allocation. HR
secures the talent with the necessary AI marketing skills.
Data engineering advises on buy-or-build tech decisions,
while legal assesses data privacy, IP, and compliance risks—
crucial tasks in a rapidly evolving environment.
BOSTONCONSULTINGGROUP 5
Only 9% of marketers have
adopted AI for their end-to-end
creative workflow.
Exhibit2-TheMostImportantActionsatEachStageofthePathto
AIExcellence
Transforming
Leading
Critical near-term goal
Create an integrated AI
Essentials Scaling Use predictive AI to flywheel that . . .
forecast outcomes;
experiment, feeding
Measurement Establish foundational data A dan ta al ;y iz me pa r ob vro ea id ne sir g s hc to pe of insights to media activation . t r. e. nle dv se ara ng de s g aA tI h t eo r sp ir ne sd ii gc ht ts
& Insights practices & KPIs granularity & speed Build & target real-time in real time . . .
audiences; make fluid
Scale AI-powered allocations based on AI . . . Auto-populates
Media & Start testing campaigns & personalize insights campaign plans and
Personalization AI-powered campaigns with first-party data high-value audiences . . .
Produce more (and more
relevant) creative; use AI . . . Generates 1:1 content
Creative Build modular, Pilot GenAI for creative end-to-end automatically with AI based
& Content channel-agnostic content across text, image, on social learning . . .
and video
Adjust organizational
Fully embeds AI across
structure and
re-imagined marketer + AI
People Define the most value-adding Introduce balanced upskill people workflows
& Process areas; get C-suite approval; marketer-AI workflows;
engage functional areas identify talent needs
Percentage of
38% 43% 19% <1%
respondents
in stage
Source: Google/BCG,“PathtoAIExcellence,”September2024,Global,n=2,135.
Note: RespondentsincludemarketingAIdecisionmakers/influencersatsmalltolargecompanies.
Stage 4: Transforming—Creating a Transformative To get started, CMOs can follow a few simple steps:
Marketer + AI Flywheel. No company has truly reached
this stage, as technical limitations still exist. But in the 1. Conduct an AI excellence assessment to understand
next five years, marketers and machines will increasingly your starting point relative to peers.
play complementary roles to create a flywheel whereby
AI and marketers together power campaign planning and 2. Identify key steps in your end-to-end workflow where
execution. (See Exhibit 3.) Marketers will drive transforma- AI could play a role (for example, in measurement and
tion by embedding AI into reimagined workflows, defining insights, media, or creative).
strategy, priority audiences, and business goals. AI will play
a much greater role in analysis and execution and in sup- 3. Set two to three AI goals for the quarter (such as estab-
porting strategy development. It will predict trends, gather lishing your data foundations or piloting three use cases
insights in real time, auto-populate campaign plans, select with key partners using off-the-shelf tools).
high-value audiences for 1:1 messaging, generate personal-
ized content, and suggest real-time adjustments. Leading 4. Launch a cross-functional AI task force that includes
companies believe they are twice as close to achieving this marketing, engineering, IT, finance, HR, legal, and
future vision than their peers. agency partners.
In the next five years, marketers and machines Reaching higher levels of AI integration requires more than
will increasingly play complementary roles to just deploying the latest technologies and tools. Marketers
create a flywheel that powers campaign planning must rethink their role, shifting their emphasis from the
and execution. tactical and executional to the strategic. They must become
orchestrators, guiding AI to deliver better outcomes while
trusting the machine to handle the complexity of execution.
BOSTONCONSULTINGGROUP 7
AI leaders see 60% higher
revenue growth.
Exhibit3-TheMarketer-AIFlywheelFreesMarketerstoFocusMore
onStrategy
The Marketer + AI Vision
Marketer
Building to
Derive insights Generate the future
using integrated real-time audiences
NOW
customer view
Leading
Provide AI marketers are
strategic
Measure & 2x
input & machine monitor
plan to
outcomes
maximize
Run
outcomes Accelerate
outcomes-based
testing
media allocation closer to
with AI
achieving
this vision
than peers
Use Gen AI across
creative tasks
Build
&
c r reo is ms- afu gn inc eti o wn oa rl
k
A flI
o
a wd
svo c
ate s
Source: Google/BCG,“PathtoAIExcellence,”September2024,Global,n=2,135.
What’sontheHorizon? By harnessing the power of AI effectively, CMOs can not
only improve the efficiency of their operations; they can
Although the AI revolution in marketing is still in its early also unlock new avenues for growth. The path to AI
stages, the sophistication and quality of AI output is im- excellence—and to realizing the power of the marketer-AI
proving literally by the week. As technology advances, the flywheel—is clear. Marketers who take bold steps today
gap between leaders and laggards will only widen; and at will be shaping the future of their organizations.
the speed of change we’re witnessing, that gap may—
sooner rather than later—become unbridgeable. Reach out to the team for information on how to
take an AI self-assessment.
Over the next 12 months, leaders are planning to expand
AI use cases: 46% will use GenAI for video creative, 36% This content is the result of a joint research effort between Google
will launch AI chatbots (internal, customer-facing, or both), and BCG.
and 35% will customize creative by platform. As the num-
ber of advanced use cases grows, the system becomes
more sophisticated and faster.
BOSTONCONSULTINGGROUP 9
AbouttheAuthors
Derek Rodenhausen is a managing director and partner Ray Yu is a managing director and partner in BCG’s
in the New York City office of BCG. You may contact him at Atlanta office. You may contact him at Yu.Ray@bcg.com.
Rodenhausen.Derek@bcg.com.
Trevor Sponseller is a principal in BCG’s New York City Paola Francesca Scarpa is a managing director and
office. You may contact him at Sponseller.Trevor@bcg.com. partner in the Milan office of Boston Consulting Group.
You may contact her by email at Scarpa.Paola@bcg.com.
Henry Leon is a managing director and partner in the Javier Perez Moiño is a managing director and partner in
firm’s London office. You may contact him by email at BCG’s Madrid office. You may contact him by email at
Leon.Henry@bcg.com. Moino.Javier@bcg.com.
Val Elbert is a managing director and partner in the firm’s Jordan Baker is a project leader in BCG’s New York City
New Jersey office. You may contact him by email at office. You may contact her by email at
Elbert.Valeriy@bcg.com. Baker.Jordan@bcg.com.
ForFurtherContact
If you would like to discuss this report, please contact the
authors.
10 THEBLUEPRINTFORAI-POWEREDMARKETING
Boston Consulting Group partners with leaders in business For information or permission to reprint, please contact
and society to tackle their most important challenges and BCG at permissions@bcg.com. To find the latest BCG con-
capture their greatest opportunities. BCG was the pioneer tent and register to receive e-alerts on this topic or others,
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we work closely with clients to embrace a transformational Facebook and X(formerlyknownasTwitter).
approach aimed at benefiting all stakeholders—empowering
organizations to grow, build sustainable competitive © Boston Consulting Group 2024. All rights reserved. 12/24
advantage, and drive positive societal impact.
Our diverse, global teams bring deep industry and functional
expertise and a range of perspectives that question the
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bcg.com
12 THEBLUEPRINTFORAI-POWEREDMARKETING"
130,gartner,gen-ai-planning-workbook.pdf,"GenAI Planning
Workbook
4 steps to implementing
generative AI in your enterprise
© 2023 Gartner, Inc. and/or its affiliates. All rights reserved.Gartner is a registered trademark of Gartner, Inc. or its affiliates.This presentation, including all supporting materials,
is proprietary to Gartner, Inc. and/or its affiliates and is for the sole internal use of the intended recipients. Because this presentation may contain information that is confidential,
proprietary or otherwise legally protected, it may not be further copied, distributed or publicly displayed without the express written permission of Gartner, Inc. or its affiliates.
Focus GenAI conversations on real business
problems and achievable use cases
Generative AI (GenAI) is suddenly on everyone’s
radar, but some organizations already have
extensive experience and success in deploying
AI techniques across multiple business units
and processes.
Gartner research shows these mature AI
organizations represent just 10% of those
currently experimenting with AI, but would-be
GenAI adopters can learn a lot from them.
Use this planning workbook to focus
conversations among business and IT leaders
around best practices that help you focus on
GenAI initiatives that are both valuable and
feasible. To get there, take a strategic approach.
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Actions related to the 4 pillars of GenAI strategy
Establish your vision for GenAI Remove barriers to
capturing value
How GenAI will drive your
enterprise goals, what benefits What organizational barriers
you expect and how you will could hinder your success and
measure success. what actions are needed to
remove those hurdles.
Prioritize adoption Identify the risks
Which are the best GenAI What regulatory, reputational,
initiatives to pursue, based on competency, technology and
their value and their feasibility — other risks you may need to
as agreed to by both IT and assess and mitigate.
business leaders.
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Key components of your AI strategy framework
Vision Value Risks Adoption
• Goals • Business impact • Regulatory • Use cases and
value maps
• Benefits • Change • Reputational
management • AI decision
• Success metrics • Competency
framework
• People and skills
• Decision
governance
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AI VISION
First, state clearly how GenAI objectives
link to enterprise goals
Don’t underestimate the need to level-set with
stakeholders from the outset:
Stating AI goals clearly is key
1. Restate the corporate vision of your
to encouraging and enabling
enterprise:
organizationwide fluency and
“……………………………………………”
adoption of AI. It will also help
2. State how AI will support that vision:
you to fund the right use cases —
ones that will deliver clear return
– e.g., AI will enable better business value
on investment and lead to further
in these areas in these ways
innovation.
– e.g., We will use AI to achieve fairer
outcomes
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AI VISION
Then, specify how GenAI will drive business goals
Ask why you are pursuing GenAI and what value you expect it to bring based on your major business goals, how you will
measure success and what use cases could maximize that value (you will verify the value/feasibility of those use cases in the
“adoption” phase).
Illustrative
Use Cases to Pursue
Goal How AI/GenAI Enables That Goal
(Illustrative Examples)
Topline revenue growth Business model change inspired or supported by AI creates Behavioral analytics, contract life cycle
net-new business initiatives. management
Improved customer Greater ability to conduct customer behavior analytics Virtual customer assistants
satisfaction increases proximity to the customer.
Reduced costs Task and process automation reduce operational costs. Risk/fraud mitigation, asset performance
management
Staff augmentation and Augmented AI and automation increase productivity by shifting Knowledge management and training, content
increased productivity people away from managing mundane tasks. generation, code generation
Improved service Data-driven predictive analytics tools advance digital services. Predictive maintenance, proactive threat
availability management
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AI VISION
Also, set AI success metrics
To measure the value of individual use cases, you’ll need success metrics that tie into your overarching business
goal. Select metrics like those listed here that relate to specific key success factors and provide a timeframe in
which you expect to demonstrate value.
Business Goal Appropriate Success Metric Completion
Improved customer satisfaction Customer satisfaction index/Net Promoter Score Date
Topline revenue growth Revenue growth for product lines Date
New business initiatives Number of new business initiatives Date
Task or process automation Reduction in processing time Date
Reduce costs Reduction in CapEx and OpEx Date
Staff augmentation and increased Workforce productivity metrics, such as time spent on value-added tasks Date
productivity
Improved service availability % of annual availability Date
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AI VALUE
Remove organizational barriers to capturing value
Having identified potential benefits to the business (in the vision stage), surface any strategic concerns that could hinder your
ability to capture value in the way you have identified it. Also, identify solutions, responsibilities and actions as illustrated here.
Executive(s) What the Organization
Strategic Concern Solution
Responsible Will Do
Projects aligned to corporate Document goals and require a CIO • Indicate which corporate goals should be
goals are more likely to succeed portfolio approach to AI opportunity. addressed.
and mature. • Size portfolio (five or fewer pilots and
minimum viable products).
Metrics deliver credibility for Select metrics as proxies for financial CFO • Collaborate with your chief data and analytics
project maturity. and risk results or direct such officer to discuss what will be most
measurements. measurable and educational for future
projects.
Formal structures of accountability Help complete a RACI (responsible, Chief data (and • Draft a RACI matrix for all aspects of AI
bolster AI results. accountable, consulted and analytics) officers, project and product development.
informed) matrix for AI strategy CIO
development and execution.
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AI RISKS
Assess and mitigate risks
Any type of AI comes with a range of risks, including those illustrated here. GenAI carries specific new types of risks, such
as hallucinations and biased and inaccurate results. Log all such major risks so you can properly assess and mitigate each.
Key Types Risk Executive(s)
Action Plan
of Risks Category Responsible
Regulatory Adhere to CIO/CTO and Understand the continuously Enable collaboration between Create an AI governance
regulations CRO evolving regulatory landscape. AI practitioners and legal, risk office, which serves an
and security members to independent audit committee
evaluate use case feasibility to review results.
and acceptable risks.
Reputational Secure and CIO/CTO Acknowledge the threats Bolster security across Leverage external resources
safe against AI posed by both enterprise security controls, to help secure your AI
malicious and benign actors data integrity and AI model systems.
in your organization. monitoring.
Competencies Technical debt CIO/CTO Align AI strategy with cloud Create a technology roadmap Create a startup accelerator
strategy and explore cloud to modernize data and program to reduce technical
as foundation for AI. analytics infrastructures to debt and innovate
align with AI goals and incrementally.
timeline.
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AI ADOPTION
Prioritize projects that are valuable and feasible
Rate the feasibility and value of each project using simple criteria like those shown here, and actually score each so you can rank projects
against one another. Typically, executives are keen to pursue initiatives where value is high (and risk also tends to be high, i.e., feasibility is
low) but avoid projects where feasibility is so low that it makes the project impossible. A use case with a seemingly outstanding contribution
to business value and strong feasibility is either a breakthrough, or the market is missing a great opportunity.
TECHNICAL FEASIBILITY FACTORS BUSINESS VALUE FACTORS
Overall
Business Overall Technical
Architecture Have Skills/ Aligns With
Access to Sponsor KPIs Value Feasibility
Project andTechnology People to Our Mission Ranking
Labeled Data Support Measurable (Scale of 1 to (Scale of 1 to 10;
Feasibility Execute andValues
10; 10 Being 10 Being High)
High)
Name Yes/Maybe/No Yes/Maybe/No Yes/Maybe/No Yes/Maybe/No Yes/Maybe/No Yes/Maybe/No
Name Yes/Maybe/No Yes/Maybe/No Yes/Maybe/No Yes/Maybe/No Yes/Maybe/No Yes/Maybe/No
Name Yes/Maybe/No Yes/Maybe/No Yes/Maybe/No Yes/Maybe/No Yes/Maybe/No Yes/Maybe/No
Name Yes/Maybe/No Yes/Maybe/No Yes/Maybe/No Yes/Maybe/No Yes/Maybe/No Yes/Maybe/No
Name Yes/Maybe/No Yes/Maybe/No Yes/Maybe/No Yes/Maybe/No Yes/Maybe/No Yes/Maybe/No
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Actionable, objective insight
Position your IT organization for success. Explore these additional
complimentary resources and tools for IT leaders:
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Access benefits, applications Decision automation can drive
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and risks of generative AI. competitive advantage. Know
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134,ibm,ibm-5-trends-for-2025-report.pdf,"IBM Institute for Business Value | Research Brief
5 Trends
for 2025
Ignite innovation with
people-powered AI
Introduction
AI democratizes data—and 2024 was a year of letting go. As a combination of conflict and transformation threw old
assumptions into doubt, leaders had to reassess their appetite for risk. They had to weigh
the need for speed against the safety of proven processes—then change the habits that were
redefines decision-making.
holding them back.
Generative AI was at the center of this shift, introducing a world of new opportunities, as well
How can leaders empower people to innovate as uncharted risks. Agentic AI, which refers to systems and programs that perform a variety
of functions autonomously, can act on behalf of employees while they do other work. By
without putting the business at risk? giving AI agents specific permissions and rights, they can automate decision-making,
problem-solving, and other tasks that go beyond the data the system’s machine learning
models were trained on in a way that most AI assistants don’t.
And as digital labor evolves, it puts the power of transformation firmly in employee’s hands.1
It makes it possible for individuals to increase productivity and redefine workflows—and
challenges preconceived notions about what it means to lead.
5 Trends for 2025: Ignite innovation with people-powered AI 2
Figure 1
The fact is, leaders don’t have time to vet every innovation. As agentic AI augments roles 2024 2025
across the organization, they need to delegate more decisions to truly pick up the pace.
Leaders still need to define the destination—and the rules of the road—but they must
empower teams to rethink workflows and deploy AI agents in new ways to improve
performance at scale.
6%
In this environment, leaders are walking a tightrope between agility and security, striking
Experimenting 30%
a balance between resilience and risk. It’s no easy feat. To learn how they’re gaining ground,
the IBM Institute for Business Value (IBM IBV), in partnership with Oxford Economics,
surveyed 400 global leaders across 17 industries and six geographies in October and
November 2024. We asked them about the challenges they must overcome to succeed
in an AI-fueled competitive landscape, how they’re preparing their people to drive change,
and what opportunities they expect to accelerate progress most. Scaling & Optimizing 44% 46%
We paired these results with the insights we’ve gained from dozens of surveys, in-depth
interviews, and client engagements conducted in 2024 to map out the trends that will
reshape the AI roadmap in 2025 (see “Research methodology,” page 5).
We found that leaders are still struggling to transform the business with their AI
investments—but they believe they’re on the cusp of a major breakthrough. In fact, 63% Innovating 24% 44%
of executives say their AI portfolio will have a material financial impact on their organization
in the next one to two years.
Source: 5 Trends for 2025 global pulse survey. Q: Which of the following best
describes your organization’s approach to adopting AI, today and next year?
Note: Sums don’t equal 100% because “reevaluating” and “none of the
above” were also potential responses.
5 Trends for 2025: Ignite innovation with people-powered AI 3
To deliver on these expectations, organizations plan to push teams forward at a rapid clip.
Today, 30% of executives say their organizations are primarily experimenting with AI, testing
In the coming year, it’s likely that some organizations will begin to set themselves
its use in low-risk, non-core functions to gain experience, build confidence, and identify
apart. Will yours be one of them? Explore these five trends for 2025 to learn what
potential pain points. Only 24% say they’re innovating with AI to advance new opportunities
leaders need to know to overcome the obstacles that lie ahead—and what they can
and create new business models.
do to gain a competitive edge.
In 2025, leaders expect to see a major shift. 46% of executives say their organizations will be
scaling AI, using it to optimize existing processes and systems, while 44% expect to use AI
to innovate. Only 6% say their organizations will still be experimenting.
1 Agentic AI will transform your 4 The rapid pivot to AI has
To turn that momentum into real business value, leaders will need to empower people to business—but first you must upended IT budgets, but
make the most of the technology at their fingertips. That means democratizing decision- reskill your people. self-funding is imminent.
making and giving people the tools and training they need to succeed. People are the secret
ingredient to winning with AI—but they can’t succeed without strategic reskilling, security
guardrails, and decision support. 2 Despite efforts to slow its 5 AI product and service
growth, technical debt innovation is the #1 CEO
continues to increase. goal, yet business models
aren’t keeping up.
3 In the age of AI, location
is everything.
5 Trends for 2025: Ignite innovation with people-powered AI 4
Research
methodology
This study is part of the IBM IBV’s “Five We also surveyed 400 global leaders Each edition of “Five Trends” highlights the
Trends” series, now in its sixth year, which across 17 industries (including banking, key challenges and opportunities expected
offers strategic insights to help organizational government, and telecommunications) to drive significant business impact in the
leaders plan for what’s next. and six geographies (the US, the UK, coming year. This year’s report identifies
Germany, India, Australia, and Singapore) the trends that will shape industries and
Based on comprehensive research
in October and November 2024. organizations in 2025, providing actionable,
conducted over the past 12 months,
Participants were asked a range of research-backed insights—based on
the report draws on data from 55 surveys
questions about forward-looking business in-depth research and comprehensive
covering hybrid cloud and AI, general
and technology strategies in various client engagements—to help leaders
business, finance and technology, and
formats (multiple choice, numerical, and navigate and thrive in an increasingly
specific industries, including insights from
Likert scale). Any datapoints not otherwise complex and dynamic environment.
more than 43,000 executives and 4,000
cited were sourced from this five trends for
consumers worldwide. This portfolio of
2025 global pulse survey. Sample size was
research was used to inform the trends
too small to support group comparisons
we explored in a pulse survey conducted
within this set of participants.
by the IBM IBV, in partnership with
Oxford Economics.
5 Trends for 2025: Ignite innovation with people-powered AI 5
Agentic AI will transform
your business—but
first you must reskill
your people.
The future of work is being rewritten with AI. But many
employees are unprepared for what comes next—and
progress will stall if too many are left behind.
5 Trends for 2025: Ignite innovation with people-powered AI 6
While roughly 5% of the global workforce
consistently needs to be reskilled each
While roughly 5% of the global workforce
year, the rapid evolution of AI has sent
consistently needs to be reskilled each
this figure skyrocketing. In 2024, global
year, the rapid advancement of AI has
sent this figure skyrocketing. CEOs estimated that, on average, 35%
of their workforce needed to be reskilled.
That translates to more than a billion
workers worldwide.2
What exactly is creating this chasm? The
escalating need for true transformation.
Instead of automating specific roles
wholesale, organizations are pairing people
with domain-specific AI agents to improve
their performance. In fact, 87% of executives
expect jobs to be augmented rather than
replaced by generative AI.3 This means,
rather than learning a new skill or tool,
workers must completely rethink how they
do their jobs to make the most of gen AI.
In 2024, global CEOs estimated that, on
average, 35% of their workforce needed
to be reskilled. That translates to more
than a billion workers worldwide.
Source: The 2024 CEO Study: 6 hard truths CEOs must face. IBM Institute for Business Value.
5 Trends for 2025: Ignite innovation with people-powered AI 7
In this environment, 64% of CEOs say that succeeding with AI will depend more on people’s This “third wave” of AI promises to transform workflows wholesale.7 In fact, nine in 10
adoption than the technology itself.4 However, 64% say their organization must take executives now say their organization’s workflows will be digitized with intelligent
advantage of technologies that change faster than people can adapt5 —and 47% of executives automation and AI assistants by 2026—and 77% of executives believe gen AI will enable
say their people lack the knowledge and skills to effectively implement and scale AI across connected assets to make autonomous decisions by 2026.8 Executives also report that the
the enterprise. volume of decision-making by digital assistants will increase by 21% in the next two years
due to generative AI.9 This will have huge implications for operating models, as organizations
Part of the problem is insufficient training. While executives say AI literacy is the most critical
must create new structures that give employees oversight over autonomous decision-
capability their workforce will need in 2026,6 only 22% strongly agree that their organization
making—and manage the new risks it creates.
has integrated AI knowledge, skills, and abilities into employee professional development
plans. And less than half say their organization has implemented a formal change management It’s a lot to work through, but 67% of CEOs say the potential productivity gains from
program to enable the integration of AI assistants and agents into daily workflows. automation are so great that they must accept significant risk to stay competitive.10 What’s
more, 82% of executives agree that the benefits they expect from gen AI will exceed the
That’s a big issue, since agentic AI is quickly transforming the role of individual contributors.
potential risks.11 But employees will need targeted training and skills development to deliver
As simple AI assistants are supplemented by AI agents with more advanced capabilities,
on this promise—and deliver the competitive advantage that executives expect.
employees will need to manage entire teams of agents that are completing tasks
autonomously—and learn how to work with chat-based supervisory AI agents that can help
streamline this process.
Organizations must create new structures
that give employees oversight over
autonomous decision-making—and manage
the new risks it creates.
5 Trends for 2025: Ignite innovation with people-powered AI 8
What to do
1 2 3
Make AI literacy a must-have—and Unlock your team’s collective genius. Future-proof your workforce.
double down on agentic AI skills. Demolish siloed thinking and establish Establish new roles, such as process
Launch comprehensive educational collaboration sandboxes where AI-enabled orchestrators and digital librarians, to
initiatives blended with hands-on projects workflows can be rigorously tested and manage how AI assistants, models, and
aimed at rapidly advancing AI literacy and refined—encouraging people to get their governance guidelines are used and shared
getting teams comfortable with agentic hands dirty without fear of failure. Hold across the organization. Introduce checks
AI. Mandate AI skills training across all leaders from business units, IT, and HR and balances that provide oversight for
roles and create a culture where AI jointly responsible for AI outcomes to autonomous decisions made by agentic
proficiency is non-negotiable to enable underscore the strategic importance AI. Regularly host hackathons that bring
smarter collaboration and responsible of enterprise-wide adoption. Make together diverse perspectives to
integration of AI agents and assistants governance integral to collaborative conceptualize creative uses of AI assistants
into everyday workflows. innovation efforts and reimagine the and agents. Establish performance- and
operating model to integrate agentic readiness-based compensation and
AI effectively and responsibly. incentives that align with business goals
and gen AI adoption priorities.
5 Trends for 2025: Ignite innovation with people-powered AI 9
Despite efforts to slow its
growth, technical debt
continues to increase.
Time is money. And leaders are always looking for
ways to save both. But the workarounds that accelerate
transformation in the short term often create technical debt
that limits long-term innovation and growth.
Technical debt refers to the long-term costs and inefficiencies caused by quick, suboptimal
technical decisions made to expedite development or delivery. And the growing demand
for digital products, services, and experiences is compounding this debt much faster than
organizations can address it. As a result, 55% of executives now say technical debt is either
a major obstacle or a real roadblock to achieving business goals.12
5 Trends for 2025: Ignite innovation with people-powered AI 10
Think about the automotive industry. While the lifetime of a car could be 15 years
or more, the digital experience in the driver’s seat is often outdated within 18 months.
If manufacturers don’t design and install software in a way that can easily be updated
as technology evolves, customer satisfaction will suffer.13
The same is true for enterprise IT. To deliver
the innovations that customers, employees,
and partners expect, organizations must
77%
build solutions within a modern architecture.
That’s because traditional systems don’t
tend to play well with next-gen apps,
software, and infrastructure.
of executives say they need
to adopt gen AI quickly to
This is particularly relevant for generative AI
keep up with competitors.
and agentic AI. Organizations need robust
infrastructure that can handle the data and But only
computational requirements of AI to go from 25%
pilots to enterprise-wide solutions. Yet,
while 77% of executives say they need to
adopt gen AI quickly to keep up with
competitors14 —only 25% strongly agree that of executives strongly agree
that their organization’s IT
their organization’s IT infrastructure can
infrastructure can support
support scaling AI across the enterprise.
scaling AI across the enterprise.
Sources: The 2024 CEO Study: 6 hard truths CEOs must face. IBM
Institute for Business Value; 5 Trends for 2025 global pulse survey.
5 Trends for 2025: Ignite innovation with people-powered AI 11
One of the biggest barriers is the quality, accessibility, and security of enterprise data. Flexibility must be part of the equation, as well. To get the greatest benefit from
Training gen AI models with internal and proprietary data is critical to help organizations gain cloud-based technologies, organizations need to be able to run each system and
an edge with them. Yet, only 16% of tech leaders are confident that their current cloud and application in the right public or private cloud environment—an approach we call hybrid
data capabilities can support gen AI,15 and just 21% of executives strongly agree that their by design. On average, IT executives from companies adopting hybrid by design for their
organization has the data it needs to scale AI across the enterprise. digital transformation efforts reported 3X higher ROI than those that don’t.16
To scale AI systems and incorporate agentic AI without compounding technical debt, That’s a pretty strong argument for planning ahead. But today, two-thirds of CEOs say they’re
organizations must incentivize teams to modernize traditional systems and change the way meeting short-term targets by reallocating resources from longer-term efforts.17 If leaders
they develop new solutions. By linking long-term productivity gains and performance metrics don’t shift this mindset, technical debt could preclude progress, even if quick wins drive
to every new solution, CIOs, CFOs, and other key leaders can measure the potential benefits of growth or profitability today.
modernization and put a price tag on taking shortcuts. When these leaders join forces, they can
help teams decide when accumulating technical debt for the sake of speed makes sense—and
when it’s better to build the right architecture from the start, especially as upskilling and
reskilling efforts increase productivity over time.
By linking long-term productivity gains and
performance metrics to new solutions, leaders
can measure the benefits of modernization—
and put a price tag on shortcuts.
5 Trends for 2025: Ignite innovation with people-powered AI 12
What to do
1 2 3
Bridge the gap between Incentivize scalability. Architect for agility.
vision and reality. Empower IT leaders to educate the Establish a nerve center that is focused
Identify the missing architectural pieces business on the full cost associated with on designing solutions for modularity and
needed to succeed with AI at scale. the tech architecture required to scale AI. scalability and is charged with deploying
Connect AI business cases to associated Quantify the cost of taking shortcuts—and each AI use case in the most appropriate
modernization costs to avoid unexpected the business value that comes with environment. Build a composable
expenses. Intentionally invest in the AI developing pilots that can quickly scale. platform that decouples models, tools,
initiatives that will deliver the most Celebrate teams that think holistically infrastructure, and apps, creating
business value in the long term, establish about AI innovation—and propose projects flexibility and cost-effectiveness in your AI
a cross-functional AI board responsible for that limit the creation of future technical ecosystem. Prioritize making high-quality
determining ROI from a line-of-business debt—to change the organization’s data accessible across platforms.
perspective, and develop a workforce behavioral economics.
strategy that helps your people innovate
without increasing tech debt.
5 Trends for 2025: Ignite innovation with people-powered AI 13
In the age of AI, location
is everything.
Perpetual disruption is here to stay. But that doesn’t mean
it’s predictable. To navigate complexity wherever it rears its
head, leaders must be able to see the big picture—and the
market-level minutiae—in one sweeping view. They must
strategically adjust operations based on market-level shifts,
without overreacting to local disruptions as they occur.
5 Trends for 2025: Ignite innovation with people-powered AI 14
Trend 3
And striking the right balance is getting
harder every day. Looking to the future, 60%
of government leaders believe that shocks
are likely to increase in frequency and 70%
believe they’re likely to increase in intensity
and impact.18 This is forcing business leaders
to assess where their data is housed and
rethink how—and where—their organizations 96% 93%
should operate. In 2024, 86% of executives
said their location strategy was impacted by
of executives say data privacy, security, expect AI to impact
geopolitical disruption—and that figure is and regulations will determine where they their location
expected to rise to 93% in 2026. locate operations in 2026. strategy in 2026.
Location strategies, which define where a
company’s key resources and capabilities
reside, are also being influenced by the AI
revolution. As organizations seek out the
talent, data ecosystems, and infrastructure
needed to scale AI effectively, they’re moving
operations to places they believe will provide
the greatest strategic advantage. In 2024,
67% of executives say their organization’s
use of AI changed where it operated—and
a whopping 93% expect AI to impact their
location strategy in 2026. Similarly, 96% of
executives say data privacy, security, and
Source: 5 Trends for 2025 global pulse survey.
5 Trends for 2025: Ignite innovation with people-powered AI 15
regulations will determine where they locate operations in the next two years. However, it’s At the same time, splintering AI regulation has business leaders looking at certain aspects
important to note that many privacy regulations are not so restrictive as to necessitate data of the business through a different lens. For instance, 37% of executives say they will manage
localization. Leveraging hybrid cloud environments enables organizations to ensure compliance their data strategy and governance more regionally in 2025, with 26% saying they will take
with data privacy requirements while maintaining operational flexibility. a more global approach. Still, 69% of executives expect their organization to receive a
regulatory fine due to generative AI adoption.19
Think of it this way: As organizations expand into new markets to drive growth, they need to use
customer data to drive product development and deliver personalized experiences with AI. But As regulations become more widely defined and adopted, executives expect this risk to
they also need to comply with local regulations—and cultural expectations—regarding how AI decrease. For example, 57% of CEOs say the guidelines provided by the EU AI Act increases
is used and how private data is secured. So, they’re prioritizing markets that will offer the ideal their willingness to invest in AI.20 The predictive capabilities of gen AI can help organizations
mix of skilled talent, computing capabilities, supportive regulations, and customer demand to manage disruption, as well. In fact, 77% of executives say gen AI models can successfully
foster growth. identify geopolitical and climate risks, enabling proactive mitigation.21
As a result, 89% of executives agree with two statements about their 2025 location
strategy that may seem at odds. They say they’re widening their reach and extending
operations globally and they’re primarily focused on a few core markets. This shows that
organizations are being selective as they plan their growth strategy—but international
markets are still a priority.
As organizations seek out the talent, data
ecosystems, and infrastructure needed to
scale AI, they’re moving operations to places
that provide the greatest strategic advantage.
5 Trends for 2025: Ignite innovation with people-powered AI 16
What to do
1 2 3
Stress-test your strategy. Innovate through volatility. Bake in regulatory preparedness.
Develop AI models specifically for navigating Leverage hybrid cloud and open AI Get obsessed with documentation. Assess
global instability, using predictive analytics approaches to enable global AI strategies. where data is housed and how this could
to stay ahead of regulatory changes, supply Combine secure, scalable hybrid cloud affect operations. Ensure AI-generated
chain challenges, and shifting labor markets platforms with open-source AI frameworks assets can be traced back to the foundation
in volatile regions. Align the most crucial and to drive innovation, facilitate interoperability model, dataset, or other inputs by creating
differentiating supply chain workflows with across markets, and address compliance an inventory of every instance where AI is
your early predictive generative AI use with diverse regional regulations while being used. Seed this source information
cases. Use digital twins and simulations to fostering collaboration and inclusivity in into digital asset management and other
identify latent weaknesses and bottlenecks. AI development worldwide. systems to help teams comply with
extensive existing and emerging legislation
in areas such as data privacy, security, and
consumer protection.
5 Trends for 2025: Ignite innovation with people-powered AI 17
The rapid pivot to AI has
upended IT budgets, but
self-funding is imminent.
Generative AI has made the traditional IT budgeting process
untenable. It’s sending shockwaves through technology
and finance teams as they rush to reevaluate their spending
priorities—and move money where it’s needed most.
5 Trends for 2025: Ignite innovation with people-powered AI 18
Trend 4
75%
of business leaders are thinking of gen AI
more like an innovation investment than
traditional IT today.
Leaders know they need to invest in gen AI to keep up with the competition, but these solutions
have yet to deliver production-level ROI. This has led to widespread cannibalism of broader IT
budgets. In 2024, one in three organizations pulled funding for gen AI from other IT initiatives,
with only 18% of tech execs funding these projects with net-new spend.22 Of course, there’s
some overlap between the investments gen AI requires and other IT priorities, with executives
But
reporting that infrastructure, cloud, and data account for more than 40% of gen AI costs.23
71%
But that still leaves a large funding gap—one that executives are rushing to fill. Nearly all
executives (95%) say gen AI will be at least partially self-funded by 2026, with a focus on
of executives say gen AI
driving future profitability. While three in four business leaders are thinking of gen AI more
should be self-funded to
like an innovation investment than traditional IT today, 71% of executives say gen AI should
justify its investment.
be self-funded to justify its investment.24
Source: IBM Institute for Business Value generative
AI and innovation spend pulse (AI Academy) survey.
5 Trends for 2025: Ignite innovation with people-powered AI 19
So, what will it take to move gen AI out of the innovation sandbox and into the revenue stream? That’s one reason the average gen AI investment takes almost 14 months to deliver positive
It starts with focusing gen AI investments in the areas with the greatest potential and the ROI, compared to just 10 months for other technology investments.26 But that won’t be the
lowest-risk applications, rather than spreading funds evenly across the portfolio. In 2024, 71% case for much longer. As more organizations embrace fit-for-purpose AI models—combined
of gen AI spending went to HR, finance, customer service, sales and marketing, and IT, where with open source and agile—the cost side of the equation will start to shrink. Over the next three
investments were expected to cut costs. Only 29% went to product-related business functions, years, executives expect their AI model portfolios to include 63% more open models than they
where growth-driving innovations incubate. This makes it difficult to define business cases that use today, which will play a large role in driving down development costs.27
break the mold.25
While revenue growth has been the least effective metric for gauging gen AI success
to date, it will be the primary way businesses measure differentiation in the long term.
But to get revenue and ROI metrics where they need to be, leaders must make data-driven
decisions about which gen AI plays they expect to do the most to advance strategic
objectives—and fund them accordingly.
As organizations embrace fit-for-purpose
AI models—combined with open source and
agile—the cost side of the equation will start
to shrink.
5 Trends for 2025: Ignite innovation with people-powered AI 20
What to do
1 2 3
Unify infrastructure, amplify impact. Invest like a shark. Own the open-source advantage.
Clearly define what each AI investment is Dive into the data to identify the projects Create an open-source program office that
worth to the business—and what they will that seem most likely to deliver real manages your organization’s consumption
cost to implement. Identify the business value—then cut the dead weight of—and contributions to—open-source
infrastructure upgrades needed to roll out that’s holding you back. Commit sufficient code. Build a warehouse of open-source
solutions at scale and bundle projects that resources to successfully scale AI and code that has been carefully reviewed to
can share these costs to boost ROI. Create allocate your AI budget based on one thing: streamline access to preferred offerings.
centralized control centers that unify growth potential. Think ecosystems, not Incentivize developers to actively
isolated AI efforts across enterprise silos, and engage your most valuable contribute to open-source projects critical
functions to measure business outcomes customers and strategic IT partners in bold to your business, especially IT
more accurately. conversations about where gen AI can add infrastructure modernization, to gain
the most value. influence over the projects your
organization relies on.
5 Trends for 2025: Ignite innovation with people-powered AI 21
AI product and service
innovation is the #1 CEO
goal, yet business models
aren’t keeping up.
As generative AI supercharges innovation, the pipeline of new
products and services is bursting at the seams. But many
organizations are too wedded to old business models to tap
into new opportunities to drive growth.
5 Trends for 2025: Ignite innovation with people-powered AI 22
2023 2024
And CEOs are feeling the crunch. In 2024, they cited business model 1
Top CEO priorities Productivity or profitability Product and service innovation
innovation as the top challenge they expect to face over the next three
years—up from 10th place in 2023—while also naming product and Tech modernization Tech modernization
service innovation as their top priority for the same timeframe.28
Customer experience Cybersecurity and data privacy
Business leaders understand that, to make the most of innovative
offerings, they’ll also need to rethink how they turn a profit. Cybersecurity and data privacy Forecast accuracy
In fact, 62% of CEOs say they must rewrite their organizational Environmental sustainability Productivity or profitability
playbook to win in the future.29 AI will play a major role in this shift.
6
Product and service innovation Customer experience
Over the next three years, 85% of executives say AI will enable
business model innovation and 89% say it will drive product and
service innovation.30 What does this look like? It starts with analyzing
1
customer and market data faster and more comprehensively than ever Top CEO challenges Environmental sustainability Business model innovation
before—then changing strategies to keep up with shifting demands.
Cybersecurity and data privacy Productivity or profitability
This will require centering business models on the careful design of
human-machine interaction—and building strong supporting Tech modernization Scalability of service delivery
governance structures—as well as rethinking organizational structures
Talent recruiting and retention Marketing and sales effectiveness
and workflows.
Diversity and inclusion Forecast accuracy
Getting it right can help companies stay ahead of the competition—and
strengthen customer relationships. For example, nine in 10 executives Forecast accuracy Environmental sustainability
already using gen AI for product idea generation say it differentiates
Ecosystem and partnerships Diversity and inclusion
their company by helping it respond to market shifts faster. Going
forward, they also believe generative AI will positively impact product
Market share growth Cybersecurity and data privacy
differentiation (88%), product trust (83%), and product quality (80%).31
Marketing and sales effectiveness Supply chain
10
Business model innovation Talent recruiting and retention
5 Trends for 2025: Ignite innovation with people-powered AI Source: The 2024 CEO Study: 6 hard truths CEOs must face. IBM Institute for Business Value. 23
Standing out will be crucial as a flood of AI-inspired products and services compete for By partnering with organizations that offer complementary capabilities, companies can tap
eyes in the marketplace. As customers are barraged with new options from every direction, into a vast network of expertise and resources that enhance innovation. It’s no longer about
executives across 13 industries agree that a single differentiating factor does the most to being the best at everything. It’s about being the best at what you do best—and tapping
move the needle on ROI: customer loyalty.32 partners for everything else.
And nothing keeps customers coming back like bespoke experiences. In fact, executives
expect personalization and customization to be the top customer demands that will disrupt
how their organization delivers products and services.33 But accommodating rapidly evolving
consumer preferences requires more than just clever algorithms and data analysis. It takes
open business models built on ecosystem partnerships.
Nothing keeps customers coming back like
bespoke experiences.
5 Trends for 2025: Ignite innovation with people-powered AI 24
What to do
1 2 3
Shatter departmental divides. Lock onto the moving target Stop trying to go it alone.
Build multidisciplinary teams to blend "
135,ibm,ibm-why-invest-in-ai-ethics-and-governance.pdf,"IBM Institute for Business Value | Research Insights
Why invest in AI ethics
and governance?
Five real-world origin stories
In collaboration with the Notre Dame—IBM Tech Ethics Lab
How IBM
can help
Clients can realize the potential of AI, analytics, and
data using IBM’s deep industry, functional, and
technical expertise; enterprise-grade technology
solutions; and science-based research innovations.
For more information:
AI services from IBM Consulting
ibm.com/services/artificial-intelligence
AI solutions from IBM Software
ibm.com/Watson
AI innovations from IBM Research®
research.ibm.com/artificial-intelligence
The Notre Dame-IBM Tech Ethics Lab
techethicslab.nd.edu/
2
Key
takeaways
Organizations that measure Embracing AI ethics is essential.
the value of AI ethics
It’s not just about loss aversion. 75% of executives
could be a step ahead. view AI ethics as an important source of competitive
differentiation.1 More than 85% of surveyed consumers,
Our holistic AI ethics
citizens, and employees value AI ethics.2
framework considers
three types of ROI. Longer-term, proactive AI ethics
strategies can generate value across
the organization.
A majority of companies (54%) expect AI ethics to be
very important strategically,3 with executives citing
involvement of 20 different business functions.4
Investing in AI ethics has the
potential to create quantifiable value.
Organizations that measure the value of AI ethics could
be a step ahead. Our holistic AI ethics framework
considers three types of ROI: economic impact
(tangible), reputational impact (intangible), and
capabilities (real options ROI).
1
Introduction
Generative AI is revolutionizing industries, but its dizzying ascendance
has also raised significant ethical concerns. Balancing the potential
benefits with ethical and regulatory implications is crucial.
But it’s not easy. In IBM Institute for Business Value (IBM IBV) research, 80%
of business leaders see AI explainability, ethics, bias, or trust as major roadblocks
to generative AI adoption.5 And half say their organization lacks the governance
and structures needed to manage generative AI’s ethical challenges.6
In the face of this uncertainty and risk, many CEOs are hitting pause. More than half
(56%) are delaying major investments in generative AI until they have clarity on AI
standards and regulations,7 and 72% of executives say their organizations will actually
forgo generative AI benefits due to ethical concerns.8
Yet there is a path forward—if executives broaden their outlooks and view AI ethics
as an opportunity. Even better: ongoing research suggests that investing in AI ethics
has the potential to create quantifiable benefits.
In order to unlock this potential, organizations need to embrace a new perspective
as they evaluate the ROI of AI ethics investments. In part one of this report, we
identify three key types of ROI that apply to AI ethics—in other words, a holistic AI
ethics framework. In part two and part three, we explore two distinct but valuable
ways to justify AI ethics investments right now. (We plan to build on this work by
conducting additional research in 2025 that explores quantification in greater depth.)
Finally, we offer an action guide for bringing the holistic AI ethics framework to life
inside the organization.
We also include stories from five executives on the front lines of AI ethics,
as part of an ongoing collaborative project among the IBM IBV, the Notre Dame—IBM
Tech Ethics Lab, the IBM AI Ethics Board, and the IBM Office of Privacy and
Responsible Technology. Some interviews were conducted in collaboration with
Oxford Economics.
2
Part one
Exploring a holistic
AI ethics framework9
AI ethics and governance investments can span broadly
across the enterprise, from an AI ethics board to an
ethics-by-design methodology, from an integrated
governance program to training programs covering AI
ethics and governance, among many other endeavors.10
(See “AI ethics: Stories from the front lines” on page 13.
Also refer to our IBM IBV study The enterprise guide
to AI governance at ibm.co/ai-governance.)
So how do organizations begin measuring the impact of such
initiatives? We developed a holistic AI ethics framework to meet
this need, validating it through an extensive series of conversations
with over 30 organizations. This approach can help organizations
understand the value of their AI ethics and governance investments.
Traditionally, investments are justified by calculating ROI in financial
terms alone. AI ethics investments are more challenging to evaluate,
providing both tangible and intangible benefits as well as helping
build longer-term capabilities.
“Our work has to not just contribute to the mission of the organization—
it also has to contribute to the profit margin of the organization,”
notes Reggie Townsend, VP of the Data Ethics Practice at SAS.
“Otherwise, it comes across as a charity, and charity doesn’t get
funded for very long.”
We developed a holistic AI ethics
framework, validating it through
an extensive series of conversations
with over 30 organizations.
3
A holistic AI ethics framework identifies three
types of ROI that organizations should consider
with AI ethics investments.
Economic impact (tangible ROI)
refers to the direct financial benefits
of AI ethics investments, such as cost
savings, increased revenue, or reduced
cost of capital. For example, an
organization might avoid regulatory
fines by investing in AI risk management.
Reputational impact (intangible ROI)
can involve important yet
difficult-to-quantify elements, such
as an organization’s brand and culture
that support positive returns or impact
on an organization’s reputations with
shareholders, governments, employees,
and customers. Examples include
improved environmental, social,
and governance (ESG) scores;
increased employee retention;
and positive media coverage.
Capabilities (real options ROI)
alludes to the long-term benefits
of building capabilities that, established
first for AI ethics, can disseminate
broader value throughout an organization.
For example, technical infrastructure
or specific platforms for ethics may allow
organizations to modernize in ways
that lead to further cost savings
and innovation.
Source: “The Return on Investment in AI Ethics:
A Holistic Framework.” Proceedings of the 57th
Annual HICSS Conference on Systems Sciences.
January 2024.
4
The holistic AI ethics framework depicted above
describes three paths to understanding the impact
of investments in AI ethics with regards to
stakeholders: the direct path through economic
return, and indirect paths through capabilities
and reputation. This framework encompasses
and describes the relationships, stakeholders,
and potential returns that exist when organizations
make investments in AI ethics.11
At a high level, how might this approach work in
practice? Consider the investment in an AI Ethics
Board infrastructure and staff. This investment helps
prevent regulatory fines (tangible impact); increases
client trust, partner endorsements, and business
opportunities (intangible impact); and helps enable
the development of management system tooling that
improves automated documentation and data
management (capabilities). The holistic AI ethics
framework illustrates how AI ethics is interwoven
throughout an organization, both in terms of practices
and outcomes.
The holistic AI ethics
framework illustrates how
AI ethics is interwoven
throughout an organization,
both in terms of practices
and outcomes.
5
Part two
The value of “loss aversion”
What is AI ethics? A senior vice president with responsibility
for data policy at Fidelity Investments puts it succinctly:
“It’s using AI technology in a responsible form to be able
to distinguish between right and wrong as we communicate
with our customers, prospects, and other clients.”
In recent IBM IBV research, 72% of executives said they’ll step back from
generative AI initiatives if they think the benefits might come at an ethical
cost. These same organizations are 27% more likely to outperform on
revenue growth—a correlation that is hard to ignore.12
Yet noble AI intentions are often talked about more than they are acted
on. While over half of organizations in our research have publicly endorsed
principles of AI ethics, less than a quarter have operationalized them.13
Fewer than 20% strongly agree that their organizations’ actions
and practices on AI ethics match (or exceed) their stated principles
and values.14
“It’s all good to want to do it, but you need to actually do it,” says a senior
leader responsible for AI governance at a global financial services firm.
“But to do it, you need resources, which requires funding. More important
than that, you need the will of senior executives.”
So, what is the business justification for investing in AI ethics? It often
starts with a loss aversion approach: avoiding costs associated with
regulatory compliance or retaining revenue that might be lost if
customers move their business to enterprises that prioritize AI ethics.
Noble AI intentions are often talked
about more than they are acted on.
6
The fact that these motivations reflect a short-term A prod from AI regulators
strategy does not detract from their significance.15
Loss aversion generates near-immediate results.
AI regulations are a catalyst for action. The EU
As the senior leader responsible for AI governance at
AI Act is the first comprehensive AI regulation by
a global financial services firm notes, “The business
a major entity. One strategy manager at Deutsche
case is all about decreasing reputational risk.”
Telekom says, “The EU AI Act could change the face
of AI ethics globally. If, for instance, an American
company is working with us, they also have to comply
with the EU AI Act.”
The EU’s effort is only the beginning. Organizations
Examples of loss aversion include:16
such as the Partnership on AI, the Global Partnership
on AI, the World Economic Forum, the United Nations,
and the Organisation for Economic Co-operation and
Regulatory justifications
Development (OECD) have all published principles
Avoid a regulatory fine. and guidelines on a responsible approach to AI.17
In a survey by the Centre for the Governance of AI
Avoid legal costs. of over 13,000 people across 11 countries, 91%
agreed that AI needs to be carefully managed.18
Implement required technical
compliance mechanism.
Given this emphasis on regulations, oversight,
and responsible approaches to AI, a focus on loss
Enable business
aversion isn’t just sensible but necessary.
for required compliance.
Customer/partner/
competitor justifications
Allay stakeholder concerns.
Avoid threat to business model.
Meet specific customer request or need.
Protect brand reputation.
Keep pace with competitors.
7
Part three
Leveraging AI ethics
to generate value
The benefits of investments in AI ethics aren’t exclusive
to cost avoidance or damage control. They also help to build
useful capabilities and tangible innovations that can enable
an organization’s long-term strategies.19 Such value
generation can be more indirect than loss aversion and
requires an expanded view of ROI. It also won’t happen
overnight and can take time to see measurable outcomes.
But organizations that are sophisticated about their understanding of AI
ethics can use the investments to:20
– Enable long-term plans to scale AI responsibly.
– Build unique and valuable organizational capabilities that can lead
to differentiation.
– Improve employee efficiency or productivity.
– Align with values to advance as an industry leader.
– Seize a market opportunity.
– Protect vulnerable individuals and communities.
– Increase customer satisfaction.
– Demonstrate trustworthiness and maturity.
– Support Environmental, Social, and Governance (ESG) efforts.
– Increase ability to manage risk over the long term.
– Innovate for a competitive advantage.
As AI technology matures, organizations can not only integrate AI into their
operations, they can repurpose that technology toward new innovations. A
senior director from a leading health and consumer goods retailer explains,
“Based on the measures we took from the AI standpoint to create and
enrich the customer experience, we have seen returns in terms of adoption
of those brands, sales growth, customer retention, and customer growth.”
8
Combining the best
of both worlds
Organizations that embrace a holistic approach
that encompasses both loss aversion and value
generation will be more efficient, effective, and
successful—as well as more ethical.
Reactive Proactive
Loss aversion Value generation
Regulatory compliance justifications Create technologies, infrastructures,
and platforms that can support AI ethics
efforts and be repurposed
Avoid a regulatory fine.
Avoid legal costs. Enable long-term plans to scale
AI responsibly.
Implement required technical
compliance mechanism. Build unique and valuable
organizational capabilities that
Enable business for required compliance.
can lead to differentiation.
Improve employee efficiency
or productivity.
Justifications relating to clients, Align with values to advance
partners, and competitors as an industry leader.
Seize a market opportunity.
Allay stakeholder concerns.
Protect vulnerable individuals
Avoid threat to business model.
and communities.
Meet specific customer request or need.
Increase customer satisfaction.
Protect brand reputation.
Demonstrate trustworthiness
and maturity.
Keep pace with competitors.
Support Environmental, Social,
and Governance (ESG) efforts.
Increase ability to manage risk over
the long term.
Innovate for a competitive advantage.
Source: “On the ROI of AI Ethics and Governance Investments: From Loss Aversion
to Value Generation.” California Management Review. July 29, 2024.
9
The senior vice president of Fidelity Investments observes:
“What companies don’t realize is that up-front investment actually
pays significant ROI, not just in terms of ethics, but from a total cost of
implementation on any of your use cases. Because if you don’t lay that
foundation, you spend a lot more money with everybody implementing
one pillar at a time and not benefiting from any reuse.”
A preliminary step to this evolution, of course, is to actually develop
AI use cases that align with and support organizational strategy.
Notes the strategy manager at Deutsche Telekom, “Either you could
create AI solutions for the customer, or you could create AI solutions
for your internal infrastructure.”
Out of the starting block, it’s instinctive and reasonable to adopt
a “defensive” loss-aversion posture to avoid the pitfalls we’ve
described, such as regulatory fines, legal costs, and reputational risks.
But fertile ground can be found in the pivot to value generation.
Organizations need to create technologies, infrastructures,
and platforms with the versatility to support AI ethics initiatives
and to fuel broader corporate innovation.
Procuring support and budget for these strategies can be tricky.
To persuade skeptics and surmount obstacles, organizations should
clearly pinpoint potential value generated, including metrics of economic
returns. This can be done through a process of identifying relevant loss
aversion and value generation justifications as the organization plans
and then evaluates potential investments21—essentially, using the
holistic AI ethics framework.
Fertile ground can be found in the pivot
to value generation. Organizations need
to create technologies, infrastructures,
and platforms with the versatility to
support AI ethics initiatives and to fuel
broader corporate innovation.
10
Action guide
How to reap the rewards
of AI ethics investments
Investing in AI ethics is not just the right thing to do, it can also be a sound
business decision. By using the holistic AI ethics framework, organizations can
make informed choices about allocating resources to AI ethics, helping boost
the trustworthiness and potential of AI programs overall.
According to IBM IBV research, 75% of executives view ethics as an important
source of competitive differentiation.22 A study from the Economist
Intelligence Unit echoes those results, pointing to a competitive edge through
product quality, talent acquisition and retention, and new revenue sources.23
These studies underscore the criticality of a proactive approach to AI ethics.
Organizations must consider how governance of AI differs from that of
previous technologies, permeating every corner of their culture, ecosystem,
and customer engagement.
“You educate the AI engine based on what humans are thinking,” says the
senior director at a leading health and consumer goods retailer, “because
they are the better judge from an ethics standpoint.”
Along those lines, Reggie Townsend of SAS observes: “We have a diverse set
of folks who have come from a variety of different backgrounds and life
experiences. We do hard work, but we do heart work. I don’t hire anyone who
doesn’t have a heart for what we’re doing. We have passionate people on our
team, and we bring that passion to the work. That’s fundamentally important.”
11
Here’s our five-step guide for optimizing
your AI ethics investments
1
Engage your savviest AI ethics experts to educate the C-suite on
differences between loss aversion and value generation approaches
to AI ethics. Help executives envision the potential of leveraging AI
ethics technology, platforms, and infrastructure for broader use.
2
Identify specific value generation justifications for AI ethics and
governance that may apply to the AI use cases at hand. Examples
include the ability to responsibly improve the answers to customers
and increased employee productivity and job satisfaction.
3
Think through the anticipated stakeholder impacts of the AI
use case and identifying potential indicators. These include:
– Direct economic returns (for example, the value
of an expanded customer base)
– Intangible reputational returns (for example, earned media
value of customer reviews)
– Capabilities and knowledge returns from real options
(for example, improved customer response quality that leads
to more first-contact resolutions).
4
Create an AI ethics implementation strategy that can deliver on value
generation justifications. Using the analysis in action 3, identify the
potential returns holistically. Doing so can help optimize the potential
returns on your investments in AI ethics and governance while
simultaneously benefitting stakeholders, ecosystems, and society.
5
Turn value generation into a competitive advantage. Focusing on value
generation can provide a competitive advantage in an environment
where regulatory compliance is business as usual.
For additional information and actions on the holistic AI ethics framework, refer to “On the ROI
of AI Ethics and Governance Investments: From Loss Aversion to Value Generation,” California
Management Review, at https://cmr.berkeley.edu/2024/07/on-the-roi-of-ai-ethics-and-
governance-investments-from-loss-aversion-to-value-generation/ and “The Return on
Investment in AI Ethics: A Holistic Framework” at https://arxiv.org/abs/2309.13057.
12
AI ethics
Stories from the front lines
Deutsche
Deutsche Telekom’s data initiatives are closely tied with monetizing data through AI
Telekom applications and monitoring the EU AI Act. One strategy manager at the company leads
a team that is involved in virtually every AI conversation in the organization and is
therefore able to provide a holistic overview of the company’s approach to AI ethics.
Preparing for the EU
AI Act with internal Deutsche Telekom has created a team of high-level executives responsible for
governance and evaluating current and future AI initiatives—in effect, an organized governance group.
education The group’s most important purpose is to help ensure that the company complies
with data privacy and security procedures both internally and externally—including
the EU AI Act.
“AI is all about data. It’s a fundamental element of any AI product,” says the manager,
adding that he regards data as a crucial component of the AI ethics approach as well.
Before incorporating any data into its products, the organization considers who is
exposed to the data and how customer data is protected. Beyond their customers,
Deutsche Telekom also must protect certain data segments in terms of sustainability
and energy practices.
Critically, Deutsche Telekom heavily invests in educating employees about AI and
its ethical use, often in the form of internal workshops, including training related
to the EU AI Act.
“Training colleagues is definitely a return on investment because it reduces the time
to market and we come up with more innovative products,” he says. And with its
continual efforts to improve, Deutsche Telekom experiences greater innovation
and enhanced customer trust.
13
AI ethics: Stories from the front lines
Fidelity
Responsible AI initiatives are embedded into each phase of AI use cases at
Fidelity Investments, beginning with robust data management practices
Investments
and feeding into a dynamic review process driven by the company’s AI
Center of Excellence. The financial services firm invested heavily in these
Reaping ROI through
initiatives to make AI ethics one of its foundational pillars—rather than a
repurposed use case
compliance box-checking exercise.
implementation
Each business line at Fidelity has a dedicated team for AI use case
development and vendor management. This work is guided by the
expertise of external consultants and actively monitored by the firm’s
compliance and risk officers, who receive specialized AI training.
The AI Center of Excellence is involved in each step of this process, from
vendor selection to model evaluation. It resides in Fidelity’s data function
and includes representation from each business unit at the firm, with roles
ranging from risk compliance and audit to legal and even information
security. This process also allows Fidelity to confidently answer clients’
increasing demands for information on its AI use and governance.
Resistance to responsible AI initiatives is inevitable, as they can delay
projects or limit use cases. “You have to explain that the reason controls
are so important is not just some random compliance policy, but that
there are implications to the firm if we get this wrong,” says a senior vice
president with responsibility for data policy at the firm. Fidelity has
been able to minimize pushback by framing these initiatives as
integral to the success of AI projects and by streamlining the overall
governance process.
“You have to explain that the reason controls
are so important is not just some random
compliance policy, but that there are
implications to the firm if we get this wrong.”
A senior vice president with responsibility for data policy at Fidelity Investments.
14
AI ethics: Stories from the front lines
SAS Reggie Townsend, VP of the Data Ethics Practice at SAS, leads a team tasked
with coordinating responsible innovation principles, operational workflows and
Ensuring an AI-driven governance structures across a global organization. It all began with questions
future that is built and investigating.
for all of us
Prompted by risks to vulnerable populations and the increasing sophistication of
AI, Townsend and close colleagues began digging deeper into responsible AI
and data ethics at SAS. They were empowered by SAS leadership to formalize
the company’s longtime commitment to responsible innovation. Consequently,
SAS created the Data Ethics Practice (DEP).
With a philosophy of “ethical by design,” the DEP guides the company’s efforts
to help employees and customers deploy data-driven systems that promote
human well-being, agency, and fairness. This approach compels individuals
to answer three basic questions:
– For what purpose?
– To what end?
– For whom might it fail?
The team helps build Trustworthy AI capabilities and workflows to help
customers and developers pursue their responsible AI goals. AI governance
advisory services from the DEP are helping customers put AI into action
responsibly. The DEP also provides critical counsel to employees on product
development, marketing, and more.
When Townsend’s role and team were created, the hope was their work would
bolster trustworthiness of products, processes, and people. This, in turn,
would enhance the brand’s reputation as a trusted AI leader.
Profits are important, of course. But according to Townsend, his team’s
guiding principle is that wherever SAS software shows up, it does no harm.
“Sometimes,” Townsend observes, “you just have to take action because
it’s the right action to take.”
“Sometimes, you just have to take
action because it’s the right action
to take.”
Reggie Townsend
Director and VP, Data Ethics branch, SAS
15
AI ethics: Stories from the front lines
Global financial For one senior leader responsible for AI governance at a global financial services firm,
AI development and ethics starts with education. He advocates hosting workshops
services firm
that discuss ethical principles and values—empowering leadership to understand
trade-offs. “We need to talk about AI in a way that interests leadership, not just in
Justifying positive
processes and procedures,” he observes.
returns with a lowered
reputational risk In discussing how to measure the return on investments in AI ethics, the senior leader
offers the “creepy line” metaphor. Often, organizations find themselves in situations in
which they are doing something perfectly legal that is highly profitable, yet still feel
uncertain about the ethicality of their actions—a sense of crossing the “creepy line.”
In such situations, he says that organizations must examine the activity through the
lens of both current and future generations, in conjunction with all comprehensive
ethical considerations. As long as these considerations are covered satisfactorily,
the organization should feel reassured that the “creepy line” is not breached.
He also notes, “Reputational risk is a key factor in justifying positive returns.
We aim to decrease reputational risk while applying data and AI ethics principles.”
For example, his team conducted an ethical fairness review of loan pricing involving a
credit scoring algorithm. In conducting this review, the team analyzed all 165 features
of the model, asking if there were any potential causal mechanisms for why that
particular data feature may correlate with an individual’s ability to pay back a loan.
Ultimately, three data features were removed because a causal link did not exist,
thus avoiding the lack of fairness in using this AI technology.
“We need to talk about AI in a way
that interests leadership, not just in
processes and procedures.”
Senior leader responsible for AI governance at a global financial
services firm
16
AI ethics: Stories from the front lines
A leading health
A senior director at this organization instituted an AI initiative to provide solutions
via vendors and internal products. A recent conversation with him covered three main
and consumer
operational areas.
goods retailer
A rigorous governance process. The retailer’s AI governance group is a centralized
body that helps ensure all AI initiatives fulfill their required steps for approval. In that
Driving success with a
vein, it conducts sessions in which project teams present how they’ve aligned their
thorough AI ethics and
compliance measures with the group’s control plan. If approved, the projects move
governance strategy
forward. The director notes that, as a sizeable enterprise engaging with large numbers
of partners, suppliers, customers, and other ecosystems, it must be extremely careful
in building their AI capabilities.
The AI ethics engine. Whether the retailer invests in SaaS-, vendor-, or open-source-
based products, they ensure all ethical parameters are met prior to deployment.
Its internal audit process is referred to as “the AI ethics engine.” In engaging a vendor,
the organization first conducts a background check, looking at the health of its
industry, clients, reputation, and capabilities. This process can span two to four
months. Once the retailer picks its vendor, it engages in a pilot. If success and ethics
measures are met, the partnership proceeds.
Stakeholder success. The organization has heavily invested in AI capabilities
to enhance the customer engagement experience and drive market strategies
and customer growth. The director notes, “AI by itself or a human by itself cannot
be successful, but if you combine those two together, the outcome is successful
and accurate.”
At this particular retailer, AI capabilities implemented in customer service, for example,
will not replace customer service employees. Rather, the organization invests in
providing these employees with additional skills, resulting in employee retention.
This approach can create benefits for the customers, employees, and company’s
economic returns.
“AI by itself or a human by itself cannot
be successful, but if you combine those
two together, the outcome is successful
and accurate.”
Senior director at a leading health and consumer goods retailer
17
Authors
Nicholas Berente Marianna Ganapini
Senior Associate Dean for Academic Programs Associate Professor, Philosophy
Professor of IT, Analytics and Operations Union College
University of Notre Dame, Mendoza College of Business linkedin.com/in/marianna-b-ganapini-769624116/
linkedin.com/in/berente/ marianna@logicanow.com
nberente@nd.edu
Brian Goehring
Marialena Bevilacqua Associate Partner, AI Research Lead
PhD Student in Analytics IBM Institute for Business Value
University of Notre Dame, Mendoza College of Business linkedin.com/in/brian-c-goehring-9b5a453/
linkedin.com/in/marialena-bevilacqua-6848b9132/ goehring@us.ibm.com
mbevilac@nd.edu
Francesca Rossi
Heather Domin IBM Fellow and AI Ethics Global Leader
Global Leader, Responsible AI Initiatives, IBM IBM Research
Associate Director, Notre Dame—IBM Tech Ethics Lab linkedin.com/in/francesca-rossi-34b8b95/
linkedin.com/in/heatherdomin/ Francesca.Rossi2@ibm.com
hesill@us.ibm.com
Contributors
Sara Aboulhosn, Angela Finley, Rachna Handa,
Jungmin Lee, Stephanie Meier, and Lucy Sieger
18
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For more information, contact the IBM Institute for today’s rapidly changing environment.
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IBM Institute for Related reports
Business Value
The enterprise guide to AI governance
IBM Institute for Business Value. October 2024.
For two decades, the IBM Institute for Business Value
ibm.co/ai-governance
has served as the thought leadership think tank for
IBM. What inspires us is producing research-backed,
The CEO’s guide to generative AI:
technology-informed strategic insights that help
Responsible AI & ethics
leaders make smarter business decisions.
IBM Institute for Business Value. October 2023.
From our unique position at the intersection ibm.co/ceo-generative-ai-responsible-ai-ethics
of business, technology, and society, we survey,
interview, and engage with thousands of executives, AI ethics in action
consumers, and experts each year, synthesizing IBM Institute for Business Value. April 2022.
their perspectives into credible, inspiring, and ibm.co/ai-ethics-action
actionable insights.
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19
Notes and
sources
1 Goehring, Brian, Francesca Rossi, and Beth Rudden. 11 Bevilacqua, Marialena, Nicholas Berente, Heather
AI ethics in action: An enterprise guide to progressing Domin, Brian Goehring, and Francesca Rossi.
trustworthy AI. IBM Institute for Business Value. April “The Return on Investment in AI Ethics: A Holistic
2022. https://ibm.co/ai-ethics-action Framework.” Proceedings of the 57th Annual HICSS
Conference on Systems Sciences. January
2 Ibid. 2024. https://arxiv.org/abs/2309.13057. “OECD AI
Principles overview.” OECD. Accessed November 15,
3 Ibid. 2024. https://oecd.ai/en/ai-principles
4 Ibid. 12 The CEO’s guide to generative AI: Customer and
employee experience. IBM Institute for Business Value.
5 2023 Institute for Business Value generative AI state of
August 2023. https://www.ibm.com/thought-
the market survey. 369 global CxOs. April/May 2023.
leadership/institute-business-value/en-us/report/
Unpublished information.
ceo-generative-"
136,ibm,ibm-embedding-ai-in-your-brands-dna.pdf,"IBM Institute for Business Value | Research Insights
Embedding AI
in your brand’s DNA
Innovate from products to ecosystem—
and everything in between
How IBM
can help
IBM has been providing expertise to help retail and consumer products
companies win in the marketplace for more than a century. Our researchers
and consultants create innovative solutions that help clients become more
consumer-centric by delivering compelling brand and store experiences,
collaborating more effectively with channel partners, and aligning demand
and supply. With a comprehensive portfolio of solutions for merchandising,
supply chain management, omnichannel retailing, and advanced analytics,
IBM helps deliver rapid time to value. With global capabilities that span 170
countries, we help brands and retailers anticipate change and profit from
new opportunities. For more information on our retail and consumer
products solutions, please visit: ibm.com/industries/retail, ibm.com/
consulting/retail, and ibm.com/industries/consumer-goods.
2
Key
takeaways
Brands are evolving Over the next year, retail and consumer
beyond mere AI adoption, products executives expect to expand
embedding it in their DNA AI significantly throughout all areas
to harness their distinct of the business, from brand-defining
AI-driven advantage. activities to core operations.
But to be AI-centric, organizations need an open
mindset for how AI can deliver transformation beyond
productivity gains.
Across 13 areas of the business,
executives plan to augment most
activities with AI over the next
12 months.
But they only project 31% of their workforce will need
to reskill or develop new skills in that same time frame,
underestimating what’s needed to support employees
in the AI transformation.
Almost 9 in 10 executives claim to have
clear organizational structures, policies,
and processes for AI governance.
But fewer than one-quarter of organizations have fully
implemented and continuously review tools on AI
governance, putting brand trust at risk.
1
Industry executives project that
AI’s contribution to revenue
growth will increase 133%
from 2023 to 2027.
Consumers are ready for AI.
Are you?
Consumers are tech-savvy trendsetters and brands need to keep up to
stay relevant. Today, customers and shoppers are actively engaged with
AI in their daily lives, from using AI-powered search engines to creating
content with generative AI tools.
In the 2024 IBM Institute for Business Value (IBM IBV) consumer research study,
nearly two-thirds of consumers said they have used or want to try AI applications.1
This interest sets the stage for retail and consumer products companies to hasten
integration of AI across their business while keeping an eye toward becoming AI-led
brands—leveraging the technology to reimagine operations, inspire loyalty, and
expand the size of customers’ wallets for long-term competitive advantage.
Our latest survey of 1,500 global retail and consumer products executives finds
organizations are accelerating their adoption. AI—both traditional and generative—has
permeated all functions in the enterprise to some degree. From marketing and customer
service, to supply chain and procurement, to finance and IT operations, AI use cases
span brand-defining, business-enabling, and corporate operations. Looking ahead
through 2025, most executives are thinking big, expecting AI to be used extensively
across the business (see Figure 1). Industry leaders also report AI spending is on the
rise (see Perspective, “AI spending moves outside of IT”), and they project that AI’s
contribution to revenue growth will increase 133% from 2023 to 2027.
Retail and consumer products organizations are at a pivotal point in their AI journey.
The question is: are they taking enough of the right steps to become AI-led brands,
or are they just tacking on ad hoc AI solutions that deliver short-term gains? It’s time to
move beyond just productivity and efficiency and extend AI’s power enterprise-wide
to boost process effectiveness, spark new business models and ecosystems, and
ignite engagement with innovative employee and customer experiences.
2
FIGURE 1
Retail and consumer
products organizations
plan to use AI extensively
in 2025.
Figure 1
Retail and consumer products organizations use AI extensively in 2025.
Percent of organizations planning to use AI to a moderate or significant extent over the next 12 months
Marketing and customer experience 89%
Digital commerce 86%
Merchandising 86%
Customer service 85%
Brand-defining
areas
Stores
79%
Product design and development 76%
Supply chain operations 90%
Sustainability 87%
Procurement 86%
Business-enabling
areas
Production and manufacturing 83%
IT and security 90%
Finance 90%
Corporate
operations
HR 88%
Percentages represent an average of responses for a set of tasks in each functional area, based on the question: “To what extent
do you use AI or gen AI in this activity?” Respondents replied “to a moderate extent” or “to a significant extent.”
3
Perspective
In this report, we discuss three factors that will help
AI spending
organizations make a fundamental change in their
DNA, where AI emerges as the driving force behind shifts beyond
every decision, innovation, and strategy. In part one,
IT budgets
we discuss balancing the marathon with the sprint to
shift from plus-AI to AI-first. In part two, we examine
the need to prepare the workforce for the planned
rapid and aggressive AI adoption, and in part three,
we address the imperative to safeguard consumer AI budget allocation is undergoing a significant shift.
trust. Each section includes an illustrative case study While IT budgets will still play a role, retail and
and concludes with an action guide of steps brands consumer products executives report a growing
can take to accelerate progress. portion of AI spending is moving outside of traditional
IT budgets. As AI becomes more than just a tech tool,
functional areas are identifying their needs for AI as
part of larger business solutions, from creative
Definitions
marketing tools to empowering store associates to
new warehouse management systems.
Traditional artificial intelligence
Executives project their IT budget dedicated to AI
spend will increase by 19% over the next year, but
Systems that understand, reason, learn, and
spending on AI outside of the IT budget is expected
interact. AI technology includes machine
to surge 52%. As a percent of revenue, IT spending
learning (ML) approaches, but also other
on AI will be 1.04% and AI spending outside of IT
techniques such as reasoning, planning,
will be 2.28% by 2025. Taken together, 3.32% of
scheduling, and optimization.
revenue could be dedicated to AI spending next year.
For a $1 billion company, that equates to $33.2
Generative AI
million for total AI spend.
A class of machine learning that generates
With at least 13 functional areas that span retail and
content or data, including audio, code,
consumer products organizations, executives across
images, text, simulations, 3D objects, and
the C-suite must keep tabs on the investments being
videos—usually based on unsupervised or
made in each area, coordinating platforms and tools
self-supervised learning. Recent examples of
to provide transparency across the enterprise.
generative AI include GPT-4 (language), DALL-E
IT and the business lines must work together to avoid
(images), GitHub Copilot (code), and AlphaFold
duplication of effort and to help ensure consistent
(scientific protein folding).
alignment with the overall business strategy.
4
Part one
Building an intelligent brand
that endures
Consumer organizations need to take a long-term view
of their AI journey while moving with urgency and intent.
Nearly all industry executives are banking on AI for innovation in products and
services (89%) as well as business models (85%). But a mere 54% expect AI to
influence operational innovation. Transforming operations with AI across supply
chains, manufacturing, distribution, finance, and compliance is the very essence of
being an AI-centric brand. This remodel is both a marathon and a sprint—moving from
simple AI use cases to orchestrating AI across functions to deliver sustainable value.
Many organizations are in the early stage of adoption, integrating AI within a single
function. For example, 88% use AI to a moderate or significant extent in demand
forecasting, 87% for HR help desks, 84% for IT support and issue remediation, 84%
in creating and managing trade promotions, 81% in inventory and order management,
and 80% in managing production activities. These are quick wins that can deliver
a more immediate impact on daily operations.
But companies are keen on expanding to more sophisticated uses of AI over the next
12 months. They will be transitioning from internal departmental use cases with
limited system integration to multifaceted ones that require external collaboration,
more complex system integrations, and more human intervention and oversight.
Take virtual assistants as an example (see Figure 2). Initially, they responded to
simple, predefined queries such as order and shipment status. As they have become
more integrated with data in ordering systems, they can identify delays or missing
orders as well as back-order options and in-store availability. Adding customer
shopping history and generative AI capabilities to their arsenal, they can dynamically
recommend offerings and personalized content for individual customers. Camping
Only 54% of executives
World’s virtual assistant, Arvee, illustrates the value of integrating platforms such
expect AI to help their
companies innovate in as Oracle and Salesforce so that the assistant can access customer information
operations. efficiently to address queries faster.2
5
Executives expect to expand rapidly to more sophisticated AI use cases
across the enterprise. For example, those leveraging AI to a significant
extent for personalized responses and follow-up actions in customer
service plan to increase their usage by 236% over the next 12 months.
Similarly, they want to grow significant AI usage in integrated business
planning by 82% and in talent acquisition by 300%.
Figure 2
BFIrGaUnRdEs 2 and retailers plan to expand use of AI/gen AI
into more sophisticated use cases over the next year.
Brands are fueling virtual assistants with more
comprehensive, relevant enterprise data to enable
increasingly personalized responses to customers.
I can provide When I am connected to the When I have access to
shipment status order management warehouse customer profiles and shopping
and tracking and store inventory system, history, I can dynamically
information. I can provide options for recommend offerings and
back orders and in-store personalized content for
pickup options. individual engagement.
6
Case study
As organizations progress with their initiatives, they Kroger uses AI
are investing in platforms to integrate AI tools and
to elevate customer
models. Today, as they establish their AI foundation,
they are primarily focused on data and analytics pickup experiences3
platforms (65%), innovation platforms (64%), and
skills/learning platforms (62%). Building on these
existing platforms and expanding to others will
enable federation and orchestration of AI across
Kroger has long depended on data and advanced
functions, facilitating cross-functional learning to
analytics to fuel business innovation. Since its
support scaling AI across the enterprise.
inception decades ago, its loyalty program has
Executives plan to integrate AI capabilities with delivered a trusted value exchange enabled by
business partners over the next three years, and they permission-based information. Today, using machine
predict the use of ecosystem platforms will surge learning algorithms, Kroger delivers valuable
from 52% today to 89%. Take the product compliance personalized offers and communications across
ecosystem as an example. By integrating end-to-end 150 million customer touchpoints and through
AI-driven compliance, brands can ensure all facets 1.9 billion unique coupons customized for millions
of the product lifecycle align with evolving regulatory of loyal customers.
requirements, consumer safety, and sustainability
Most recently, Kroger has been exploring ways
expectations. This ecosystem prioritizes accelerated
to use AI to help improve the customer experience,
product lifecycle management with an advanced
specifically order pickups. Using AI-enabled dynamic
business rules engine and touchless bill-of-materials
batching, an AI solution sorts through 200,000 totes
generation, helping ensure products are market-
per second to build the most efficient pickup trolley.
ready with minimal manual intervention.
It drives a 10% reduction in steps by identifying the
most efficient pick route through the store. With
dynamic batching of orders, these tools are providing
associates the most efficient pick routes, so Kroger
can dramatically reduce pickup lead time in its
highest volume stores.
Executives expect their use
of ecosystem platforms for
AI tool and model integration
to surge from 52% today to
89% in the next three years.
7
Action guide
Intentionally embed AI in operations
to deliver a sustainable brand advantage.
In the 2024 IBM IBV CEO study, 70% of retail and consumer products
CEOs said that to win the future, they must rewrite their organizational
playbook.4 As you redefine your core operational strategies and processes
to capitalize on AI, concentrate on how to achieve lasting value.
Tailor AI to your As you move beyond AI-driven productivity gains, you need a clear vision and strategy
brand’s priorities. for where AI and gen AI can help you distinguish yourself from competitors or shore
up weaknesses. But keep in mind that consumers expect you to stay true to your core
values as you innovate. If a strong customer experience is your focus, use AI to
personalize customer service and optimize in-store experiences. If product innovation
is a differentiator, tap into AI for product design, customer preferences, and vendor
capabilities to facilitate faster ideation and development cycles. The key is to
concentrate on what’s most important—not everything that’s possible.
Invite finance, Becoming an AI-centric brand requires purposefully aligning IT with long-term
technology, and business goals, not just the hottest tech. For example, organizations that consider
business leaders applications and infrastructure holistically in support of business needs (known as
to the same table. “hybrid-by-design” principles) can generate more than three times higher ROI over
five years.5 Tear down the silos between finance, technology, and business leaders so
that together, they can build solid business cases for where AI can deliver a long-term
competitive edge.6
Venture beyond Traditional strategic partnerships focused on physical distribution of supplies and
tried-and-true products are no longer enough in the age of AI. Tech companies, startups, and other
partnerships. nontraditional partners are needed for model development, platforms, and tools.
For example, other IBM IBV research found that 65% of organizations are already
working with or planning to work with a strategic partner to build a large language
model for generative AI initiatives.7 Prioritize partners who understand your goals and
share your vision. Identify those with a proven record for integration and loop them
into your processes early. Think outside the box, imagining new partners that create
new opportunities for growth.
8
Part two
Priming the augmented workforce
AI is transforming the nature of work from the store to the factory floor,
but industry executives undervalue workforce reskilling.
AI is diffused throughout the retail and consumer products workplace. Nearly all
(96%) executives say their teams are using AI and gen AI to a moderate or significant
extent at work. When virtually everyone is using a new and powerful technology such
as AI, then virtually everyone needs training to optimize the value and understand the
risks that could damage the brands. Yet, leaders project only 31% of their workforce
will need to reskill or develop new skills over the next 12 months, with this number
climbing to just 45% in the next three years—a significant miscalculation.
Both hard and soft skills—from prompt engineering and data analytics to critical
thinking and problem solving—are essential to ushering in the age of the augmented
workforce where AI won’t replace people, but people who use AI will replace people
who don’t.8 The talent transformation is an ongoing training and education process
that must be defined and started sooner rather than later. If not, 67% of employees
have said they will leave for another employer that provides better training on new
technologies, according to an IBM IBV survey of more than 21,000 workers.9
Executives recognize the workforce will be increasingly augmented, while automation
remains crucial for rules-based tasks and repetitive work. Across 13 functional areas
from marketing and commerce to supply chain, HR, and IT, they plan to more
frequently augment than automate activities over the next 12 months (see Figure 3).
Industry leaders know that many brand-defining areas demand human intuition,
creativity, emotional intelligence, and expertise that can be complemented by AI.
For example, in product design and development, AI can accelerate idea generation
and ideation, even providing visualizations. Likewise, operational areas have vast
Leaders project only
amounts of data where decisions require human oversight, such as supply planning,
31% of their workforce
where 54% plan to augment their employees. In this activity, AI can quickly access
will need to reskill
or develop new skills and analyze a broader range of data to help the supply planner confidently resolve
over the next year. shortages in minutes, knowing important information is not missed.
9
FIGURE 3
Retail and consumer products
executives know that
automation has its place but
Figure 3
see a future of augmentation.
Retail and consumer goods executives know that
automation has its place but see a future of augmentation.
Percent of activities that will be automated, augmented, or
have no impact from AI in each area over the next 12 months
No impact Automated Augmented
Digital commerce
12% 31% 58% & B2B sales
Product design, development, and
14% 28% 57% product lifecycle management
Merchandising /
14% 32% 54% category management
Brand-defining Marketing
13% 35% 52%
areas
Customer service
15% 35% 50%
Stores
21% 32% 47%
Sustainability
11% 35% 54%
Procurement
14% 33% 54%
Supply chain operations
Business-enabling 12% 37% 51%
areas
Production
8% 43% 49% and manufacturing
HR
10% 35% 54%
Finance
12% 36% 52%
Corporate
operations IT
9% 40% 51%
Percentages represent an average of responses for a set of tasks in each
functional area, based on the question: “To what extent do you use AI
or gen AI in this activity?” Respondents replied “to a moderate extent”
or “to a significant extent.”
10
Case study
Ultimately, brands will be finding the sweet spot for Japanese retailer empowers
automation and augmentation. Take managing the
people with AI to boost profits
seasonal workforce as one example. AI-powered
automation can streamline hiring, onboarding, while reducing waste10
and scheduling processes, reducing administrative
burdens and helping control costs. Managers can use
AI-powered tools that provide real-time insights into
staffing needs, predict demand fluctuations, and
optimize schedules. Similarly, in inventory A leading retail company in Japan was grappling with
management, AI-powered sensors and cameras costly problem: food and consumer-goods waste was
automatically monitor inventory levels in real time, eating away at their profits. The client’s field staff
while providing employees with the insights needed needed data-driven insights to make more informed
to reduce the risk of stockouts or overstocking. pricing decisions.
Even areas that have a high degree of automation, For a wide variety of products and the company’s
such as customer self-service, can benefit from operations, price optimization relied more heavily
augmented employees. As executives expand use on human judgment than data, leading to variations in
of AI for personalized responses and follow-up customer forecasts, stock levels, and discount rates.
actions over the next 12 months, they say 55% These variations resulted in excessive and inadequate
of the activities will be augmented versus 30% stocking, irregular discount amounts and timings,
being automated. and large profit losses due to food waste and missed
sales opportunities.
The company worked with IBM to develop a specialized
price optimization AI system to analyze vast amounts
of data, predict customer numbers and purchase
patterns, and suggest optimal discount amounts and
timings. Now the client’s field staff can combine their
own expertise with data to improve pricing decisions.
The pricing optimization system was designed to adapt
to different product categories and sell-by durations,
making it a versatile, scalable solution that can support
Brands are finding the
a diverse product range.
sweet spot for automation
and augmentation.
11
Action guide
Prepare your workforce
to power your AI-centric brand.
AI is clearly impacting virtually the entire retail and consumer products
workforce—from the person stocking the shelves to those who sit with
you in the C-suite. It’s being built into many of the tools employees use
every day, such as AI-powered sales forecasting tools or AI-driven
design tools. Leaders need to ensure all employees are prepared
to optimize the value AI can deliver.
Connect HR, IT, Executives report leadership for reskilling efforts is divided among an AI center of
and business lines competence (31%), HR (22%), AI committees (18%), and IT (17%). This disjointed
to define reskilling approach is risky and can create confusion and frustration among employees.
strategies. Leadership from HR, IT, and the business must join forces to shape an effective
reskilling strategy. HR brings both an understanding of how to manage change and
culture along with tactical implementation expertise. IT brings the technology
knowledge, and business leaders can work directly with employees to define how AI
can augment the workforce within each business domain. Have the joint team report
directly into the C-suite and define measures to hold them accountable.
Predict every If you only expect a third of your workforce will need reskilling or upskilling over the
employee’s next few years, you aren’t thinking big enough. Just as you forecast product demand,
potential. predict what employees will need to succeed in a rapidly evolving workplace.
Look beyond just current skills to employee potential. Use AI-powered HR tools to
anticipate how an individual might develop, perform, or contribute based on skills,
talents, personality traits, experiences, and educational background.11
Share a blueprint You may not know exactly what lies ahead, but you can communicate your vision
for the workplace for the future of work. From routine business operations to brand-defining areas,
of tomorrow. AI creates anxiety as employees worry about being replaced or not having the skills
they need. Share your plans for automation versus augmentation with your workforce
and help them see how AI will create new opportunities and enable them to do their
jobs faster and better—from designing products to creating promotions to managing
inventory. Consider how employees will use—and benefit from—technology as
carefully as you consider the tech investment itself.
12
Part three
Safeguarding brand trust
With so many products vying for consumers’ attention,
AI can either bolster or undermine a brand’s trust.
Trust is paramount for both consumers and industry CEOs. Our 2024 consumer
research report showed that 9 out of 10 consumers value trust when choosing a
brand.12 Similarly, 73% of retail and consumer products CEOs in our 2024 CEO study
said trust will have a greater impact on their organization’s success than any specific
product or service.13
But AI adds new dimensions to the issue of trust, with risks impacting both
business partner and customer relationships. Consumers are already wary of AI in
general—only 53% trust the technology, falling from 61% over the past five years.14
And within the partner ecosystem, companies need to know that each member is
practicing trustworthy AI.
Retail and consumer products executives recognize that AI creates risks that can
erode trust. Nine in 10 say misuse, such as creating misleading information, is their top
worry associated with AI models, followed by privacy (85%), fairness and bias (80%),
explainability (76%), and transparency (73%). For example, biased models can
alienate customers. One consumer survey revealed that almost two-thirds of
consumers avoid AI-fueled recommendations because they are biased or
stereotypical.15
At the same time, these risks are slowing progress with generative AI opportunities.
57% of executives say data accuracy and bias is a barrier to gen AI adoption. 55% also
cite privacy and confidentiality of data and 54% are concerned about cybersecurity.
Despite these concerns, organizations are struggling to enable the tools that can help
them manage the risks. Companies have created a foundation: 87% of executives say
they have clear AI governance structures. But less than a quarter of companies have
advanced implementation of tools to assess, monitor, and manage AI governance
90% of executives
(see Figure 4). “Showing your work”—designing solutions with explainability and
cite misuse as
transparency built in—will be critical to instilling confidence in consumers regarding
their top concern
your use of AI.
with AI models.
13
FIGURE 4
Few brands have advanced implementation of
tools to help them manage their AI governance
policies and activities.
Figure 4
Few brands have robust implementation of tools to help
them manage their AI governance policies and activities.
Approach to AI governance Advanced implementation of tools
84%
have defined roles and
responsibilities for all stakeholders
involved in AI
11%
have advanced implementation of AI
accountability tools
91%
conduct ethical impact assessments
to evaluate the impact of AI
initiatives on different stakeholders
16%
have advanced implementation of AI
bias and fairness tools
87%
have established clear organizational
structures, policies, and processes
for AI governance
23%
have advanced implementation of AI
governance frameworks or policy tools
90%
build explainable models that can be
easily understood and audited
24%
have advanced implementation of AI
transparency and explainability tools
77%
conduct regular risk assessments to
identify potential security threats
26%
have advanced implementation of AI
risk and safety tools
Q. To what extent do you agree with the statements about your organization’s approach to AI governance? Percentages represent
those who agree and strongly agree. Q. To what extent has your organization implemented tools to assess, monitor, and manage
the following? Percentages represent those who responded “fully implemented, reviewed, and updated regularly.”
14
Case study
PepsiCo models a structured approach that enables Using gen AI to streamline
it to scale AI responsibly. The company began
regulatory management
by establishing a formal responsible AI framework
and assembled a dedicated team to support it. The across regions18
team then developed comprehensive policies and
standard operating procedures to operationalize
their AI principles. The governance board assesses,
validates, and approves gen AI use cases against
its responsible AI principles, sharing best practices A multibillion-dollar global consumer products
and accelerators, and helping mitigate risks. The company operates in the highly regulated agricultural
company is also building a platform that provides products industry. It devotes significant resources
comprehensive governance of models, inputs, to managing compliance with local regulations,
and outputs.16 staying current with continuously changing
guidelines, and integrating compliance into the
Regulations are also intended to support trustworthy product development process.
AI, but a lack of consistent guidelines across
jurisdictions complicates implementation and stalls To help its product compliance and development
plans. In fact, nearly half (46%) of industry CEOs said teams reduce heavy manual workloads and free
their concern about regulations as a barrier to gen AI up more time to work strategically, the company
has increased in the last six months.17 worked with IBM to develop a generative AI-powered
regulations assistant. This solution features a
However, AI can help companies manage the conversational user interface and provides a single
complexity. By automating the monitoring and source of truth for over 1,000 regulations impacting
analysis of detailed regulatory requirements, AI worldwide operations.
enables organizations to quickly identify potential
issues and take corrective action. Executives plan The regulations assistant enables product
to significantly increase their use of AI/gen AI in compliance employees to predict the impact
regulatory compliance over the next year. In product of regulatory intent, summarize regulatory
design and development, the percent increases from requirements, and compare regulations globally,
53% to 79%, for sustainability, 74% to 88%, and for faster than with manual processes. The AI tool also
financial and regulatory monitoring and reporting, enables product developers to analyze the impact
66% to 94%. of regulations on product portfolios, review
solution options, and query product specifications
in a conversational journey.
To date, the regulations assistant has demonstrated
that generative AI can orchestrate regulations data
quickly and drive closer collaboration across borders
to leverage regulatory success across the business.
In product design and The tool also has the potential to increase efficiency
by 8% to 13%, increase productivity by 10% to 15%,
development, executives
and increase profits by over $165 million during the
plan to increase use of AI next five years.
and gen AI to manage
regulatory compliance from
53% today to 79% in the
next year.
15
Action guide
Make trusted AI a brand differentiator.
Customer-obsessed businesses need to deliver on what their written
policies dictate for responsible AI practices. Build confidence in
responsible internal uses of AI before expanding to customer-facing
use cases where broken trust can damage your brand.
Purge bias from To provide transparency and explainability, define clear guidelines to monitor for
your algorithms. discriminatory patterns. For example, conduct regular audits on historical purchasing
and customer data that may reflect stereotyping and societal biases. Facilitate
human-AI collaboration and oversight with training that helps employees understand
and recognize fairness and bias. Prioritize diversity on your AI development teams.
Establish a data governance framework to support data provenance, helping ensure
your data is authentic and trustworthy. Maintain detailed records of bias mitigation
efforts, create dedicated channels for bias-related feedback, and regularly
incorporate insights into system improvements.
Leverage AI to To stay ahead of an AI regulatory environment that is evolving at varying paces
proactively navigate globally, use AI solutions to capture regulatory intent across multiple channels
regulations. and forecast its impact. Choose AI development tools that build in governance and
regulatory compliance management end to end. Proactively compare old and new
regulations to quickly identify key focus areas within impact assessments. Automate
tools to stay up-to-date and streamline audit processes.
Be open about Build trust with customers by being up-front about data collection as well
your use of AI as how and where you are using AI. Offer opt-out options and avoid tech-speak
with customers in your explanations. Exchange AI roadmaps and strategies with business partners.
and partners. Demonstrate your commitment to responsible AI practices and request the same
of your partners.
16
Authors
Dee Waddell Contributors
Global Industry Leader
Consumer, Travel & Transportation Industries The authors would like to thank the following
IBM Consulting for their contributions to this report:
dee@us.ibm.com
From IBM Consulting:
linkedin.com/in/waddell/
Arnab Bag, Distribution Market HCT Service
Joe Dittmar Line Leader
Senior Partner Rich Berkman, Vice Pr"
137,ibm,ibm-the-intuitive-supply-chain-report.pdf,"IBM Institute for Business Value | Research Brief
The intuitive
supply chain
Predict disruption, deliver growth
Key takeaways
Generative AI can preempt supply Generative AI has put supply chains in flux. 64% of Chief Supply
Chain Officers say gen AI is completely transforming workflows.
chain disruption and unleash
Supply chain teams must work differently. 60% of operations and
growth opportunities. automation executives say AI assistants will handle most traditional
and transactional processes by 2025.
More decisions will be automated. Operations and automation
executives say generative AI will increase the volume of decision-
making by digital assistants by 21% in the next two years.
Predictions will improve, igniting sustainable innovation. 76% of
supply chain and operations leaders say gen AI will help innovate their
product design and make product lifecycles more sustainable.
The intuitive supply chain: Predict disruption, deliver growth 2
Introduction
Make agility your supply
chain superpower
Would a peek at next week’s headlines change
your supply chain strategy today?
The intuitive supply chain: Predict disruption, deliver growth 3
Supply chain certainty is an elusive target. The combined power of generative AI and
With so many fault lines stretching across cloud computing could make that possible.
the business landscape, it seems By harnessing the potential of machine
impossible to accurately predict what will learning, automation, and advanced
happen tomorrow. Supply chain leaders analytics in a hybrid cloud environment,
must often adopt a siege mentality, looking organizations can gain a sixth sense,
for ways to limit their losses as plan B anticipating everything from demand
quickly gives way to plans C, D, and E. fluctuations to sourcing delays. With this
foresight, they can reinvent their supply
But what if you could spend this time
chain strategies, shifting from a reactive
spurring growth? What if you could predict
to a proactive stance.
the future accurately enough to give your
business a competitive edge?
17% 72%
Leaders in gen AI adoption and data-led
innovation—those who view gen AI capabilities
as the primary driver of their automation
report higher annual revenue report greater annual
investments—are reaping outsized rewards. growth than the competition net profits
The intuitive supply chain: Predict disruption, deliver growth 4
Already, leaders in gen AI adoption and That’s a big problem for supply chain executives, and automation executives employees. Part two explains how
data-led innovation—those who view gen AI leaders, who know they need to invest from organizations that are currently accelerating supply chain intelligence can
capabilities as the primary driver of their in next-gen tech today to make their implementing AI-enabled automation. help companies leverage real-time data
automation investments—are reaping operations more agile and resilient for We discovered that these leaders are faster and more effectively than ever
outsized rewards. They report 72% greater an uncertain future—from dynamically focused on creating what we call “the before. And in part three, we’ll explore how
annual net profits and 17% higher annual rerouting shipments and adjusting intuitive supply chain”—agile, adaptive, and gen AI-enabled digital twins, or virtual
revenue growth than the competition. And all production schedules in real time to perpetually prepared, safeguarding brand models, can help organizations improve
the supply chain leaders we surveyed expect identifying bottlenecks and risks before reputation, customer satisfaction, and the their position in the competitive landscape,
their revenue growth from AI-enabled they materialize. bottom line. as well as in the eyes of customers. We
operations to more than double over the next conclude with an action guide that outlines
How can gen AI solve these persistent In this paper, we’ll lay out the steps
three years.1 how to plan, prioritize, and perform to
supply chain problems? To find out, the IBM organizations are taking to get there. In part
make every move count.
Looking at these numbers, it’s no surprise that IBV, in partnership with Oxford Economics, one, we’ll explore the role of AI assistants,
72% of the top-performing CEOs we surveyed surveyed more than 2,000 global Chief which are quickly becoming less like
for the IBM Institute for Business Value (IBM Supply Chain Officers (CSCOs), operations chatbots and more like full-time
IBV) 2024 CEO Study say competitive
advantage now depends on who has the most
advanced gen AI. But the high-speed race to
meet short-term goals is hindering their
progress. Overall, global CEOs agree that a
focus on short-term performance is their top
barrier to innovation—and 66% say their
Supply chain leaders need to invest in
organization is currently meeting short-term
targets by reallocating resources from next-gen tech today to make their operations
longer-term efforts.2
agile and resilient for an uncertain future.
The intuitive supply chain: Predict disruption, deliver growth 5
Part one
Lean into the power
of decision support
Employees paired with AI assistants will deliver
more business value than either could alone.
The intuitive supply chain: Predict disruption, deliver growth 6
Today’s supply chain teams are drowning in a sea of disconnected data.
They increasingly have access to the long-awaited real-time information
they need to make smarter, faster, decisions—but there’s so much to sift
through that many opportunities go unnoticed until it’s too late. to ask for the information they need—and find out where it came
from—with a few simple prompts.
Gen AI-powered digital assistants are changing all that. With their
ability to analyze vast stores of data almost instantaneously, they can For example, AI assistants can analyze which supplier is contributing
bubble up critical insights for supply chain teams to skim from the the most to delays and identify issues causing disruption, such as
surface. Plus, their natural language skills make it easy for employees weather, financial obstacles, or transportation bottlenecks. Then,
AI-fueled predictive models can outline how the situation is most likely
to evolve, allowing AI assistants to offer targeted recommendations
that help supply chain teams prepare for what’s next.
Already, 60% of executives say AI assistants will handle most
traditional and transactional processes by 2025.3 And 90% say their
60% organization’s supply chain workflows will incorporate intelligent
automation and AI assistants by 2026.4
of executives say AI assistants
will handle most traditional and
transactional processes by 2025.
And
90%
say their organization’s supply chain
workflows will incorporate intelligent
automation and AI assistants by 2026.
The intuitive supply chain: Predict disruption, deliver growth 7
When employees use gen AI assistants to It’s not just about explaining how materials By leveraging AI assistants, CSCOs can
quickly query their supply chain platform will get from point A to point B. It’s also aggregate and distill intel, bringing insight
for credible data, rather than manually measuring the supply chain cost of every to the boardroom quickly and confidently
searching multiple systems, they can business decision—and making sure those and making sure supply chain implications
manage change faster—and pivot more costs are considered from the start. Beyond continue to inform strategies as they
precisely. Instead of using the dedicated the sales a new SKU will drive, product evolve. As decisions are made and then
procurement solution to change purchase development strategies should account for tested in the market, AI assistants can
order delivery dates, for instance, the total cost of ownership, forecasting the accelerate the feedback loop, giving
employees can simply ask their assistant cost of delivering a new item in conjunction executives the real-word, real-time data
to make the change for them. with the losses that come from holding on they need to see if their strategies are
to products that don’t sell. delivering the desired results—and change
But that’s only the beginning. Supply chain
tactics quickly if they aren’t.
teams aided by AI assistants are cultivating Then there’s the sustainability dimension.
a new human-technology dynamic that will As both consumers and regulators demand
touch virtually every point of the supply more comprehensive reporting on
chain, from planning to sourcing to environmental impact, supply chain leaders
manufacturing to distribution. In fact, must be able to track sustainability metrics
64% of CSCOs say gen AI is completely all the way to the last mile—and do the hard
transforming their supply chain workflows. work of designing more eco-friendly product
And CSCOs and automation executives say lifecycles. This is another place where gen
gen AI will increase the volume of decision- AI can help, with 76% of supply chain and
making by digital assistants by 21% in the operations leaders agreeing that it will help
With gen AI assistants, employees can manage
next two years. innovate their product design and make
product lifecycles more sustainable.
change faster—and pivot more precisely.
The intuitive supply chain: Predict disruption, deliver growth 8
Case study
Building an intelligent supply chain
using a supply chain AI assistant
IBM employs supply chain staff in 40 which impeded collaboration and At a high level, the IBM supply chain The system uses IBM’s AI technology to
countries and makes hundreds of thousands real-time data transparency. digital transformation revolves around enable natural language queries and
of customer deliveries and service calls in building sense-and-respond capabilities. responses, which accelerates the speed of
IBM supply chain management set out
over 170 nations. IBM also collaborates with This was accomplished by democratizing decision-making and offers more options
a bold transformation vision to build a
hundreds of suppliers across its multitiered data—automating and augmenting to correct issues. Users can ask, in natural
cognitive, intelligent supply chain more
global network to build highly configurable decisions by combining a cognitive control language, about part shortages, order
than a decade ago. The aim was to have
and customized products to customer tower, a cognitive advisor, demand-supply impacts, and potential trade-offs. To date,
an agile supply chain that extensively
specifications. Historically, the IBM supply planning, and risk-resilience solutions. IBM has saved $388 million related to
uses data and AI to lower costs, exceed
chain ran on legacy systems spread Now, the cognitive control tower has reduced inventory costs, optimized
customer expectations, ruthlessly
across different organizational silos, evolved into an enhanced generative AI shipping costs, faster decision-making,
eliminate or automate non-value-add
making information-sharing slow and intelligent layer using a supply chain and time savings (days to hours to minutes
work, and exponentially improve the
incomplete. Employees also performed digital assistant. to seconds).
experience of supply chain colleagues.5
much of their work on spreadsheets,
The intuitive supply chain: Predict disruption, deliver growth 9
Part two
Accelerating supply
chain intelligence
If your data could talk, what would it say?
Supply chain teams are about to find out.
The intuitive supply chain: Predict disruption, deliver growth 10
Whether disruption is caused by In fact, the executives we surveyed
geopolitical conflict, climate catastrophes, anticipate operational performance, 73%
or increasing complexity, supply chain enterprise agility, and strategic advantage
leaders will be judged by their ability to find to be the top three benefits of using gen AI
effective workarounds. And they’re looking investments in their supply chain. And 73%
to gen AI to make their supply chain more say gen AI is already accelerating their
agile, adaptive, and future-proofed. high-impact automation initiatives. of executives say gen AI
is already accelerating
their high-impact
automation initiatives.
The intuitive supply chain: Predict disruption, deliver growth 11
Perspective
The key is to make the entire ecosystem The convergence of gen AI and cloud-based Future-proof your supply chain with
more responsive. By allowing gen AI solutions has also enabled autonomous
cloud-enabled innovation
assistants to interact directly with the automation (see “Future-proof your supply
intelligent layer of the supply chain system— chain with cloud-enabled innovation”).
With the combined power of cloud computing and generative AI, companies can
the cognitive core that pulls insights from In addition to automating workflows,
accelerate supply chain innovation and improve business outcomes to a degree that
vast stores of data—internal and external gen AI assistants can automate the process
wasn’t previously possible.
teams can collaborate more seamlessly. of workflow reinvention. They can learn from
supply chain metrics and transaction history, Deploying gen AI on the cloud lets companies train and deploy models faster and
The goal is for AI assistants to continually
make proactive recommendations, and even at scale, without the need for expensive hardware or infrastructure. It lets multiple
communicate the intelligent layer’s findings
repurpose or redefine new workflows based teams collaborate on the development of gen AI models, moving them between
to the appropriate part of the supply chain
on what they’ve learned. different cloud environments and integrating them with other cloud-based services
team, along with recommended actions.
and applications seamlessly.
While the enterprise resource planning (ERP) This helps streamline workflows to make
system remains the system of record and them more efficient, cost-effective, and Then, of course, there’s cost to consider. With pay-as-you-go pricing, cloud
core transaction engine, supply chain teams environmentally responsible. In fact, 63% infrastructure can ease capital expenditure constraints, allowing companies to
no longer need to interact with it directly. of supply chain and operations leaders say focus on innovation, rather than the financial implications of investing in new tech.
And that goes for other specialized supply integrating sustainability and circularity into When applied strategically, this tech combo can improve efficiency, reduce costs,
chain apps, from procurement to warehouse workflows is a key reason their organization and increase agility. Here are a few ways your supply chain can benefit from cloud-
management to transportation logistics, is investing in automation. enabled innovation powered by gen AI:
as well. This approach lets employees drill
deeper, allowing for real-time analysis and – Forecast future demand. Optimize inventory levels, reduce stockouts or over-
optimization each step of the way. stocking, and improve cash flow.
– Optimize delivery routes. Reduce fuel consumption, lower emissions, provide
dynamic distribution, and improve delivery times.
– Manage supply chain risk. Predict the likelihood of disruption and recommend
proactive mitigation measures.
– Increase supply chain visibility. Identify bottlenecks and recommend corrective
actions teams can take to keep operations from being disrupted.
The intuitive supply chain: Predict disruption, deliver growth 12
Case study
Achieve end-to-end visibility
with AWS Supply Chain
Supply chains are vast, interconnected networks. The multitude of The cloud-based AWS Supply Chain Address data fragmentation
business application directly addresses
participants, disparate systems, and lack of seamless data sharing A supply chain data lake harmonizes
these challenges. By harmonizing disparate
disparate data into a flexible, scalable
make it difficult to accurately forecast future demand, track inventory
data sources into a unified supply chain
canonical data model that aggregates and
levels, and align supply. The fragmentation of data hinders supply data lake, it lays the foundation for
associates supply chain information into
improved end-to-end visibility, forecasting
chain planners’ ability to understand fluctuations, predict future needs a unified data asset. By incorporating a
accuracy, inventory optimization, and
generative AI-powered data onboarding
precisely, and position optimal inventory where it’s needed most. overall supply chain resilience.6 Here are a
agent, companies can also automate data
few of the key business benefits of moving
transformation from any native format into
to this type of cloud-based solution:
the data lake’s canonical model. Customers
can seamlessly extract and upload raw
data, with the agent leveraging large
language models for automated data
mapping through a guided, module-driven
user interface experience.
The intuitive supply chain: Predict disruption, deliver growth 13
Case study (continued)
Increase forecast accuracy Improve supply chain visibility Improving supplier visibility Simplify sustainability
and collaboration compliance processes
Machine learning-powered forecasting The AWS business application can examine
capabilities can help organizations improve warehouses, distribution centers, and stores The AWS application analyzes supplier lead Cloud-based sustainability features create
forecast accuracy and reduce excess in detail, showing on-hand, in-transit, and times, makes future projections compared a more secure and efficient way to obtain
inventory levels. Machine learning at-risk inventory levels. It then uses machine to orders and forecasts, then identifies mandatory documents and datasets from
algorithms can incorporate variables such learning algorithms to automatically issues. It displays all connected trading your supplier network. You can request,
as seasonality, product characteristics, generate, score, and rank multiple inventory partners, enabling supply chain leaders to collect, and export artifacts, such as
vendor characteristics, and destination- rebalancing recommendations to mitigate view and collaborate across multiple tiers. product lifecycle assessments, certificates
origin sites, along with historical order risks. Gaining visibility into network-wide Built-in chat and messaging capabilities on product safety, or reports on hazardous
history, to train the model. inventory levels, movement patterns, and also facilitate seamless communication substances used at any point in the supply
potential risks empowers organizations to and data sharing. chain. Amazon’s Global Trade and Product
optimize inventory positioning and mitigate Compliance (GTPC) team used the AWS
imbalances, overstocks, and stockouts. application’s sustainability features to
transform their compliance data
management process and now expect to
save approximately 3,000 operational
hours per year.
The intuitive supply chain: Predict disruption, deliver growth 14
Part three
Visualize
the future
By using supply chain data to
fuel gen AI-powered virtual models,
companies can unlock a new level of
operational efficiency and resilience.
The intuitive supply chain: Predict disruption, deliver growth 15
Supply chain leaders have long imagined
a future where real-time data flows
seamlessly between IT and operational
By 2026,
technology (OT) systems, enabling a more
77%
agile approach that reacts to constant
change. And their dream is finally
becoming reality.
Think of a manufacturing facility, where
of executives believe gen AI will
operations teams already use AI sensors
enable connected assets to
to detect changes in vibration patterns, make autonomous decisions.
temperatures, power consumption, and
even sound patterns. While traditional AI
can alert teams to signals as they appear—
and even predict when breakdowns are
about to occur—employees must manage 76%
necessary adjustments or repairs based
on this information.
With generative AI, that’s no longer the
case. When paired with vision sensors, of executives say they expect to
gen AI lets connected machines use gen AI to derive differentiated
outcomes from connected assets
self-predict and self-adjust in a harmonious
in the next two years.
fashion, unlocking unprecedented levels of
productivity and efficiency.
The intuitive supply chain: Predict disruption, deliver growth 16
In fact, by 2026, 77% of executives expect It works like this: First, data from drones, support “what-if” risk analysis by predicting With the right perspective, supply chain
gen AI will enable connected assets to robots, cameras, and other connected potential problems—from raw material leaders can look beyond productivity plays
make autonomous decisions. And when assets flow into a unified platform with shortages to multiple supplier plant closings to pull the levers that drive growth. By using
complex asset ecosystems work in a geospatial layer, an information layer, simultaneously—and recommending gen AI to orchestrate multiple data sources,
harmony, they can help businesses achieve and an orchestration layer. Time-lapsed respective contingency plans. systems, and tools, they can inspire
results that weren’t previously possible. visualizations then let supply chain teams innovation across the ecosystem—and
These simulations can also inform product
Executives recognize this potential, with see how specific changes have impacted inform the strategic decisions that set their
development by helping teams identify
76% saying they expect to use gen AI to the ecosystem in the past—and make organization apart.
where waste and inefficiencies can be
derive differentiated outcomes from real-time decisions as situations unfold
removed from the process. This is a key
connected assets in the next two years.7 in the present.
concern for executives, who say visibility
But boosting efficiency is just the first step. Gen AI-enabled virtual models can then of full product lifecycle management and
Businesses can derive much deeper value help teams simulate how future events environmentally sustainable products and
from interconnected data when they use it could affect supply chain operations. services are two of their top automation
to visualize the end-to-end supply chain— They use real-world data and algorithmic priorities for their operations functions over
and simulate how disruption could impact techniques to visualize how the dominos the next three years.
operations each step of the way. will fall in response to different disruptions
to help teams plan accordingly. They
Look beyond productivity plays to pull the
levers that drive growth.
The intuitive supply chain: Predict disruption, deliver growth 17
Case study
Improving pharma supply chain
visibility for patient safety8
Amid the increasing proliferation of counterfeit, falsified, or Seeking safety through transparency players in the prescription drug supply chain.
By connecting through these APIs, Pulse
substandard prescription medications, the US government passed Working with IBM Consulting and AWS,
users can search for trading partners, verify
NABP built a new digital platform called
the Drug Supply Chain Security Act (DSCSA) with the aim of protecting
trading partner status, exchange digital
Pulse that lets its member users track and
patients. It’s rooted in the idea that transparency—the ability to credentials, and perform electronic tracing.
share each prescription drug’s ownership
accurately trace prescription meds throughout the pharmaceutical transaction records, providing increased
The platform enables visibility and
supply chain visibility.
supply chain—is essential to preserving its integrity. collaboration, eliminates tedious
administrative work, and, most importantly,
One key design aspect of the platform—
Just as important is the idea that all the major players in the pharmaceutical ecosystem— creates a more secure supply chain to
which runs on the AWS cloud—is the
manufacturers, wholesalers, dispensaries, and regulators—need a way to share information protect patients.
integration of APIs from providers of the
collaboratively to make it happen. Prompted by the challenge of multiple industry segments
“point” tracking solutions used by most
needing to cooperate to address DSCSA, the National Association of Boards of Pharmacy
(NABP) sought to create a digital platform that would bridge the interoperability gaps
between systems, making compliance with DSCSA faster and easier.
The intuitive supply chain: Predict disruption, deliver growth 18
Action Guide
Make every move count
In the complex game of supply chain chess, executives must always
think several steps ahead. Modernizing supply chains isn’t just about
adopting new technologies or processes—it’s about embracing a new
way of thinking, one that’s rooted in scientific inquiry, experimentation,
and a relentless pursuit of progress.
By applying the scientific method at scale, enterprises can tap into the vast potential of data
and gen AI to drive critical improvements in business strategy, product development, and
global supply chain operations. In fact, 62% of CSCOs say gen AI will accelerate the pace
of discovery, leading to new sources of product and service innovation.9
With the promise of discovery as their guiding light, companies can unlock the full potential
of their supply chains, power ecosystem partnerships, and drive sustainable profitability and
growth. Here’s what leaders across the supply chain ecosystem should do to predict and plan
for endless disruption—and profit from the opportunities volatility can create.
The intuitive supply chain: Predict disruption, deliver growth 19
Action Guide
1. Plan Identify benefits you want to deliver. management to improve decision-making Understand skills requirements
efficiency and speed-to-action. Invest to and gaps.
Investigate the key drop-out points
bring the vision to life and facilitate a
between analysis and action, identifying Create user personas across the range of
seamless and fulfilling experience across
how improvements could flow through into supply chain workflows. Outline how digital
the entire supply chain.
financial and operational performance. assistants will help create new workflows
Outline the productivity KPIs that will be Know the specific functionality and and enhance existing ones. Identify the
targeted for improvement and define systems architecture you need. gaps in skills between these personas and
success criteria. the current state, then define training and
Identify the solutions that will provide
upskilling plans.
Define your employee every feature. Then use an orchestration
experience vision. engine as a process conductor, issuing Keep your eyes on the prize.
precise commands to multiple agents
Provide easy access to relevant AI Align supply chain innovation to your
based on user prompts. Leverage
analytics, recommendations based on role, market offering and the capabilities needed
synthesized data from the integration layer
and intelligent transactional workflows in to deliver it. Prioritize these areas and be
to create dynamic, intelligent workflows
the employee portal. Find ways to integrate confident in delivering them.
that deliver the desired outcomes.
supply chain processes into the employee
experience framework, such as
streamlining logistics and inventory
The intuitive supply chain: Predict disruption, deliver growth 20
Action Guide
2. Prioritize Define supply chain workflows Don’t try to cut your way to growth. Define rules of engagement.
that have the greatest potential
Make the investments needed to Be clear about who is accountable and
for automation.
fundamentally transform ways of working. responsible for specific workflows—and
Map the key points across the workflow Focus spending on the areas that can make who gets a say. Set ground rules for using
that cause rework and manual analysis. your supply chain more agile and resilient. digital assistants and make sure everyone
Be honest about the true nature of your knows how they’re expected to evolve.
Prioritize getting to scale.
processes, not the idealized version that
may be documented somewhere. Invest in initiatives that can quickly
transition from pilot to deployment at scale.
Stop looking for a silver bullet.
Use success in specific areas to build
Be honest about where investment is momentum for the wider transformation.
needed within your current technology
landscape. Set specific timelines for
upgrades or the deployment of new
solutions. Don’t let time and effort that
have been invested in previous solutions
become an anchor that prevents you
from achieving future success.
The intuitive supply chain: Predict disruption, deliver growth 21
Action Guide
3. Perform Feed generative AI data that Review and align to Keep score.
supports supply chain productivity. changing conditions.
Track benefits as they’re delivered to build
Map the full range of data initiatives needed Cultivate a supply chain that can sway with momentum and confidence in new
to connect people and technology. Upskill the winds of change to deliver a competitive technologies. Demonstrate ROI to secure
employees and train tools to speed advantage. Adopt a technology architecture continued investment. Make data-driven
decisions. Identify the key touchpoints to that allows new capabilities to be plugged decisions that can fuel growth and
use gen AI to boost productivity. in without disrupting the user experience. performance improvements.
Put trust in data.
Don’t let people tinker with the workflow
outputs from the system. Where processes
are automated and tested, let the system
run and do its job. Don’t allow competing
forms of analysis designed to suit individual
agendas interfere. Instead, encourage
employees to engage in advanced analysis,
using their assistants to innovate and
address the complexities of interconnected
operations and systems.
The intuitive supply chain: Predict disruption, deliver growth 22
Authors Research methodology products, electronics, telecommunications, IBM Institute for
government, healthcare/life sciences,
Business Value
Amar Sanghera The IBM Institute for Business Value (IBM consumer products, retail, and
AWS Supply Chain Solutions Global Leader, IBV), in conjunction with Oxford Economics, transportation/logistics, each comprising
For two decades, the IBM Institute for
Digital Supply Chains Go-to-Market Strategy interviewed and surveyed more than 2,000 5% to 15% of our total respondent sample.
Business Value has served as the thought
executives with equivalent roles and titles, The size of organizations surveyed, in terms
Michael Mowat including Chief Supply Chain Officer (CSCO), of revenue, ranged from $500 million to leadership think tank for IBM. What inspires
Supply Chain Strategy and Operations Chief Operations Officer (COO), Chief $500 billion, with a mean of $26 billion. us is producing research-backed,
Leader, Finance and Supply Chain Automation Officer (CAO), Chief technology-informed strategic insights that
Transformation, IBM Consulting Information Officer (CIO), and Chief The IBM IBV ran a series of contrast help leaders make smarter business
Financial Officer (CFO). analyses, including pairwise comparisons, decisions. From our unique position at the
Karen Butner highlighting results and differences as
intersection of business, technology, and
Global Research Leader, AI and Automation; In 2024, CSCOs, COOs, and automation shown in this report. Statistical significance society, we survey, interview, and engage
Supply Chain Operations, IBM Institute for executives were also polled about their for all pairwise comparison contrasts was with thousands of executives, consumers,
Business Value, IBM Consulting investments, priorities, and use cases to set at the (p = .05) level, meaning there is
and experts each year, synthesizing their
assess the current impact of generative AI only a 5% chance that the observed
perspectives into credible, inspiring, and
Contributors initiatives, as well as the results they expect differences or relationships between the
actionable insights. To stay connected and
to see in the next two to three years. The groups are due to random variation.
informed, sign up to receive IBV’s email
goal of these surveys was to understand
IBM Consulting The right partner for newsletter at ibm.com/ibv. You can also
how global executives view the impact of
Chris Moose, Lead Client Partner NABP, find us on LinkedIn at https://ibm.co/
gen AI on their organizations’ performance a changing world
Public Sector ibv-linkedin.
and competitive advantage across the
Jonathan Wright, General Manager,
NCE Europe supply chain. At IBM, we collaborate with our clients,
bringing tog"
138,ibm,ibm-consulting-reimagined-powered-by-ai-report.pdf,"IBM Institute for Business Value | Research Brief
Consulting
reimagined,
powered by AI
Foreword
On the cusp
of convergence
We are embarking on one of the most That is why this particular study from the At IBM we are embracing this change. We
significant transformations of our time as a IBM Institute for Business Value is so are supercharging our 160,000
result of AI, across nearly every industry and unique. We decided to take on the consulting consultants’ expertise with AI support so
profession. Consulting is no different—in and AI topic directly, by diving into insights we can create better outcomes, faster and
fact, it is likely to be one of the most from organizations across the globe that at scale, for our clients. We call our
disrupted, given the labor-based business engage with consultancies, to better approach the science of consulting—and we
at its core. We are on the cusp of a new era understand the expectations buyers have of believe, as the only global consultancy at
of consulting, one where science and their consulting partners in this AI era. This scale inside a technology company, we are
Mohamad Ali technology are being combined with skills study describes how clients expect unique in our ability to embrace this
Senior Vice President and expertise to create extraordinary value consultants to use AI to help them reimagine opportunity so we can be better partners.
IBM Consulting faster. Business models are changing as the what’s possible—and they also want AI to
We welcome your input and your thoughts.
consulting and client relationship evolves to help them get more for their money.
deliver new forms of value creation.
Consulting reimagined, powered by AI 2
Key takeaways
Software and talent are AI optimism Consulting Trust is central
converging to create new
abounds. buyers say to success.
value. Are your consultants
no AI, no deal.
accelerating progress—or
sticking to the status quo?
75% 66% 70%
of consulting buyers expect AI to say they’ll stop working with consulting of buyers say the use of AI in consulting
have a positive impact on their organizations that don’t incorporate AI will make them buy from fewer, more
use of consulting. into their services. trusted organizations.
Consulting reimagined, powered by AI 3
Introduction
Redefining As AI makes it easier for humans to wield Economics, surveyed global executives So, it’s no surprise that 86% of consulting
technology, software is supercharging who buy consulting services (see “Research buyers say they’re actively looking for services
consulting value talent—allowing people to create methodology” on page 6). We found that that incorporate AI and technology assets.
business value in ways that weren’t 75% expect AI to have a positive impact on Two-thirds go even further: They’ll stop
previously possible. their use of consulting. working with consulting organizations that
for an AI future
don’t incorporate AI into services provided.
In this environment, organization leaders
expect to work with consultants who are
Executives plan to increase using AI to tackle complex problems with
unprecedented speed and precision. They
overall consulting spend—but Figure 1
want trustworthy partners who are
they expect consultants to Consulting buyers want AI-powered services
obsessed with results—and can help them
deliver greater value with AI. adapt to rapid change.
To understand how expectations are
evolving, the IBM Institute for Business
Value, in collaboration with Oxford
89% 86% 80%
expect consulting say they’re actively say they want more
services to incorporate looking for services digital delivery models.
AI for improved that incorporate AI
productivity and quality. and technology assets.
Consulting reimagined, powered by AI 4
Figure 2
The evolution of consulting delivery
That’s because consulting buyers know But AI as a tool can only take organizations
they can’t run tomorrow’s business with so far. It needs to be married to the right
today’s skills, processes, or technologies. expertise, skills, and capabilities—and easy
In key functions—led by customer service, for people to use—to deliver real business 2000s 2010s Now
Globalization Cloud Generative AI
IT, research and development, and value. And this dependency promises to
marketing—organizations are already fundamentally change how consulting
tapping into AI to drive productivity and services are delivered and consumed. Business Expansion from Increased demand for New demand for
unlock resources for cycles of innovation need data center to cloud integration, digital labor and assets
business processes application migration, to deliver consulting
and new revenue streams. This explains
and modernization and IT services
why enterprise spending on AI surged 78%
between December 2022 and March 2024.1
Cost of Lower cost of skilled Lower cost of technical Scaled expertise and
Supply labor in offshore development and better quality at reduced
locations (e.g., India) deployment cost of service delivery
AI must be married to the right expertise, skills,
and capabilities—and easy for people to use—to
deliver real business value.
Consulting reimagined, powered by AI 5
Rather than replacing consultants, Research methodology
AI makes them even more essential.
The point of view developed in this paper has been informed
by insights obtained from a survey of 400 C-level executives
across 14 industries and 6 countries from June to August
2024. This was an anonymous, double-blind survey
The cost of reinvention conducted by Oxford Economics. That means respondents
were not aware that IBM originated the survey, nor does
IBM have direct visibility into the specific people or
organizations that responded. All respondents fit within
New technology improves productivity, of course. But truly transformational technologies, specified parameters: they lead organizations with an
including AI, also create opportunities to reimagine what can be done, creating whole new average annual revenue of approximately $33 billion and
businesses, industries, and economies (see “Innovation and expectations,” page 7). are large consumers of consulting services. The survey
asked questions about their current use of consultants and
Rather than replacing consultants, AI makes them even more essential. In fact, 86% of
how they view the introduction of AI in consulting. The data
consulting buyers expect to spend more on consulting in the future, and 94% expect AI to
from the survey has been complemented with insights from
positively impact consulting efficiency.
IBM client engagements, as well as interviews with
technology and consulting executives involved in the
As a consequence, average consulting spend, which is 2.8% of total revenue today, is
development and use of AI assets for consulting.
expected to climb above 4% by 2026—an increase of more than $500 billion annually when
applied across the Fortune Global 500.
But not all consultants are created equal. We’ll discuss how the consulting operating model
is changing, how consulting relationships need to adapt—and what business leaders should
look for as they build trusted, long-term partnerships for an AI-enabled future.
Consulting reimagined, powered by AI 6
Perspective
The truth about innovation
In 1930, John Maynard Keynes famously suggested that, within a
hundred years, people would only work 15 hours per week due to
the remarkable pace of technological progress.2 Things didn’t turn
out that way.
That’s because new innovations and software development, and cybersecurity— consultants to deliver existing services New technologies do more
technologies do more than make existing while giving bankers more time to focus on faster and cheaper. But they’re also creating
than make existing work more
work more efficient. They also increase our less transactional client needs, such as a vast range of new opportunities for
efficient. They also increase our
expectations about what can be done and building wealth. Automation, including consulting companies to deliver more and
what we want. robotic assembly lines, self-service kiosks, different value to clients—personalizing each expectations about what can be
and customer service chatbots, continue engagement beyond what was possible for
done and what we want.
Consider ATMs and online banking. While
to generate new opportunities and ways humans without AI assistance. This is
these innovations reduced the need for
of working. ushering in a new innovation model that is
bank tellers who handled deposits and
iterative, agile, and experience-centric—
withdrawals, they created new jobs in IT, The same is happening with consulting. AI
setting a new bar for executives to measure
and other digital technologies are enabling
consulting partners against for the future.
Consulting reimagined, powered by AI 7
A new consulting dynamic While buyers expect consultants to use AI opportunities to improve business
to help them reimagine what’s possible, they performance and designing solutions to
also want AI to help them get more for their deliver specific outcomes. Instead of
money. 78% of consulting buyers say they contracting for time-boxed engagements
Say goodbye to the one-and-done
expect cost savings and delivery benefits. with specific, limited deliverables,
consulting delivery model.
organization leaders and their consultants
It’s the dream scenario—more value faster,
build long-term partnerships that deliver
and at a lower cost. But can consultants
continuous value.
deliver on these demands? Only if they take
As AI opens a new world of business opportunity, the companies
a technology-driven approach focused on Consulting firms become less focused on
that reinvent themselves the fastest can gain a competitive edge. evidence-based experimentation, completing point-solution projects and more
continuous iteration, and user-centric focused on delivering sustained business
And executives expect their consultants to help them rise to
design. To achieve impact at scale they results across strategy and advisory,
this challenge. 89% of buyers now expect consulting services to
must embed expertise into AI models and systems integration, BPO and business
incorporate AI for improved productivity and quality—and 80% say codify knowledge so that it is consistent process operations, and cloud-managed
and replicable across engagements. services. Supported by AI assistants that
they want more digital delivery models.
In this model, consulting partnerships can manage much of the basic work involved
become much more symbiotic. Consultants with implementation, consultants can focus
aren’t contracted to solve a single problem on the complex, mission-critical tasks that
or streamline an isolated process. They’re require human expertise.
charged with identifying widespread
Consulting reimagined, powered by AI 8
Just as the emergence of software-as-a- language models to creating ready-to-use Figure 3
service made specialized software available AI offerings. The ability to access and use
Redefining the consulting
to a wider audience on a routine basis, AI high-quality data will be what sets these operating model
assistants have democratized many consulting organizations apart.
technical skills. When consultants are
Most importantly, buyers need to partner
supercharged with a network of easy-to-use
with providers that take a multi-modal and
AI tools, they can do more to drive
multi-model approach. They need access
transformation and accelerate innovation.
to platforms that integrate multiple
They can help address barriers that keep
capabilities into different workflows,
organizations from deploying new delivery
deployed in the environment that best
models, including organizational inertia,
meets their needs. Consulting organizations Traditional Consulting
security concerns, and resistance to change.
that have consumable, safe, and secure AI consulting reimagined
In this landscape, consultants with deep models, agents, and assistants working in
Opinion-led Evidence-driven
tech know-how are needed to gain a collaboration with each other and with
competitive edge. For instance, anyone human consultants can tap the full range
Unpredictable Reliable outcomes
could use an AI assistant to code new of AI capabilities—whether developed
applications, but an experienced software in-house or accessed through ecosystem
Siloed knowledge Democratized expertise
developer can use AI to develop higher partners—and deliver much greater value
quality code much faster. to consulting buyers.
Exhaustive planning Quick-to-iterate
To capitalize on new opportunities to drive It’s not just about having the right
differentiated innovation, consulting technology. It’s about harnessing the
Time-based Results-obsessed
buyers must look for providers that have wisdom of the crowd, as well as the
already made meaningful headway with expertise of consultants and partners, to
Encumbered by data Enhanced by AI
AI, from incorporating AI assistants into get the best possible solution to every
daily tasks to fine-tuning their own large problem, every time.
Linear processes Open collaboration
Consulting reimagined, powered by AI 9
Supercharge consulting
relationships with AI
AI assistants, reusable assets, and proven methods
power a continuous improvement flywheel.
So, what are consulting buyers All this can change how consulting
In every area of the business, buyers expect consultants to evolve looking for? relationships work. For example, when an
organization needs to redesign part of its
their people, partnerships, and pricing to reflect the possibilities People: Individual consultants should have
operation, a consultant using AI can
a vast repository of AI-enabled assets at
introduced by AI. For strategic development, for example, 63% of quickly tap into its data to quantify the
their disposal, as well as deep technology
potential impact of different initiatives.
buyers expect AI to be used as a supporting tool to a large extent expertise. They’ll be able to generate
Recommendations that used to take weeks
while only 24% see it as a source of automation. For IT support custom AI tools tailored to a specific client
to create can now be delivered in days.
or situation—and tap a knowledge base
services, 58% anticipate AI will be used for support while 38%
that can augment their experience at a
But consulting buyers should still expect a
expect it to be used for automation. speed, breadth, and depth unimaginable
personal touch. Consultants can use AI as a
up to this point.
sparring partner to encourage innovative
Consulting reimagined, powered by AI 10
Figure 4
Navigating the convergence of software and services
and creative thinking. But that only works Pricing: Today, consulting services are
Services Assets and platforms
when consultants have a strong tech typically charged based on hourly or daily
background to build on. The right people fees associated with the people delivering Productization of Assetization of services
services for value for productivity
still make the difference. the work. As consulting shifts from solely
people-based to a blend of human
Partnerships: Consulting buyers have
expertise and technology assets, pricing Labor-led consulting Product-led technology
become more discerning, looking for
will begin to reflect the value a partnership Skilled talent and expertise Automation of function
strategic partners that provide greater
delivers—not just the time it takes to get a
long-term value. 73% say the use of AI in 95% 5% 90% 10%
specific job done. Outcome-based pricing
consulting will make them more critical of
will become more important, as will value
the consulting services they buy and 70%
realization tracking and the incorporation
say it will make them buy from fewer–and
of asset licenses into consulting fee
more trusted–organizations. They expect
structures. Already, 73% of consulting Asset-led delivery
strategic partners to deliver more, help AI-enabled productivity
buyers say they want new pricing models
them develop the in-house skills needed to
from vendors because of their use of AI.
80-90% 10-20%
use their technology more effectively—and
bring a broader range of capabilities to each
engagement. The most in-demand
consultants will be those who act as
orchestrators and conduits for clients,
bringing the best of what consulting can
Value assets at scale
offer through a network of in-house and
Industry transformation
ecosystem capabilities.
50-70% 30-50%
Consulting reimagined, powered by AI 11
Building on a
foundation of trust
As with anything AI, governance will be key to AI-enabled consulting. More and bigger models and assets are concerned about the unethical use of AI
not always better. It’s about using the in consulting services.
Left unchecked, vast security and ethical risks can proliferate—with
right tools at the right time for what
costs spiraling out of control. Consulting buyers will need to be you’re trying to do. Good governance, As in the rest of the world, when it comes
strategic and responsible in selecting partners and using AI models trust, and transparency should be at the to consulting services, trust comes from
core of how assets are used. experience. That’s why business leaders
and platforms that fit their needs.
should look for consultants who spend
Business leaders across the board are every day on the front lines of applying
already demanding this discipline. 90% cutting edge technology to business.
say clear governance around AI in They’re most likely to know where AI can
consulting services is important. 93% be most effective—and its limits. Tapping
say they’ll only use consulting services experts who have been part of the
from organizations that are transparent development of technology assets is key
in their use of AI. And 82% say they’re (see “Engaging everyone for greater impact:
The watsonxTM challenge” on page 13).
Consulting reimagined, powered by AI 12
Case study
Engaging everyone for greater
impact: The watsonxTM challenge
IBM conducted a “watsonx challenge,” a hands-on experience After collaborating with team members to The assistants helped the team complete a
achieve one of these goals, many IBMers real-world project, generating user stories,
designed to bring the AI-building capabilities of watsonx to all IBM
shared that they gained a better tasks, source code, test scripts, and more.
employees.3 With more than 141,000 participants, employees
understanding of how AI can be applied to Using Consulting Advantage resulted in a
formed teams that focused on quickly crafting a solution to one of business needs. The interactive, hands-on 95% reduction in the delivery timeline.
challenge let participants move beyond Plus, by automating each step of the
several challenges, including:
abstract generative AI concepts to being process, the team estimates it reduced
able to design gen AI-infused solutions bugs by 97%.
– Create a custom productivity workflow – Create a RAG-based generative AI for realistic ways of working and
to enhance the already-automated workflow. (RAG is a method that customer challenges.
AskIBM experience. improves the quality of LLM-generated
For example, the team that won the
– Create new or combine existing AI responses by grounding the model on
Chairman’s Award, the highest prize the
assistants to address a client or role- external sources of knowledge.)
challenge had to offer, collaborated to show
based business need in a new or – Add new knowledge and skills to improve
how IBM Consulting Advantage, IBM’s AI
innovative way. the accuracy of answers provided by
assistant and agent platform, could be used
– Build a generative AI application for a use IBM’s AI tools.
to complete urgent projects.
case supporting internal team productivity.
Consulting reimagined, powered by AI 13
Action guide
1. Raise your expectations 3. Open your technology aperture
– Expect more—and expect it for less. Move – Embrace open technology, interoperability,
from a narrow focus on service-level and open standards to allow you to tap the
A new paradigm for the
agreements (SLAs) to SLAs plus objectives wide range of AI capabilities and assets. Use
and key results (OKRs). Encourage consulting open-source resources to reuse and adapt
services economy partners to look for continuous opportunities existing assets instead of creating new ones
to advance shared objectives and design for every need.
solutions focused on outcomes rather than
– Define how you want to share data and make
chase short-term fees and invoicing targets.
your systems accessible to consultants and
– Shift your consulting spend to enduring assets. Determine what data can be shared,
AI-enabled transformation is destined to have major ramifications
value. Make consumable technology assets a with whom, and for what purposes.
for operating models across industries and around the world. key part of your services purchase model to
deliver continuous value for your organization.
Organizations will start adapting their own internal services models Adapt procurement processes to allow for 4. Strengthen internal capabilities
and consume internal services in the same way they are changing iterative and faster consult-to-operate cycles. – Enhance your organization’s absorption
capacity. Prepare your people for new service
their use of consulting. delivery at higher speed through change
2. Focus on trusted relationships
management, training, and engagement.
We’re already witnessing significant impacts in functions including HR, finance, and – Select strategic partners that act as gateways
– Embed AI-enabled consulting in your internal
customer service, where AI has become the critical transformative ingredient. Blending to deeper and wider capabilities and and external services model and adapt the
assets. Ensure they blend their in-house
people and technology. Working collaboratively across AI and software—and organization. way you deploy services within the enterprise.
and ecosystem skills and technology for
Augment internal services functions with user-
These principles are becoming central to how services are designed, delivered, and
your advantage. Reduce coordination and
centric AI assets and new ways of working.
consumed. And there are important moves you need to make now. fragmentation challenges by placing the
responsibility for orchestration on trusted
Here are four key steps to help you start working with partners and advisors who take preferred partners.
a technology-fueled approach to consulting. – Establish governance, ethics, and security
guardrails for AI and make them the core
criteria for how you engage with consulting
organizations. Demand transparency in how
AI is used by consultants working for your
organization.
Consulting reimagined, powered by AI 14
Authors Contributors End Notes
Matthew Candy 1. As share of IT spend. Goehring, Brian, Manish Goyal, Ritika Gunnar,
Mihai Criveti
Global Managing Partner, Generative AI Anthony Marshall, and Aya Soffer. The ingenuity of generative AI:
Nduwuisi Emuchay
IBM Consulting Unlock productivity and innovation at scale. IBM Institute for
Karen Feldman
https://www.linkedin.com/in/mattcandy/ Business Value. June 2024. https://ibm.co/scale-generative-ai
Teresa Hamid
Blaine Dolph Chris Hay 2. Keynes, John Maynard. “Economic possibilities for our
grandchildren.” Essays in Persuasion. Macmillan & Co, London, 1933.
IBM Fellow and CTO, IBM Consulting Assets Amy Hutchins
and Industries
AB Vijay Kumar 3. Internal IBM data.
https://www.linkedin.com/in/blaine-dolph-
Eileen Lowry
5078b96/
Michelle Mattelson
Salima Lin Luq Niazi
Managing Partner, Strategy, Anthony Marshall
M&A, Transformation, Cindy Anderson
and Thought Leadership Tegan Jones
IBM Consulting
https://www.linkedin.com/in/salima-lin-
b17bb71/
Jacob Dencik
Research Director
IBM Institute for Business Value
https://www.linkedin.com/in/jacob-
dencik-126861/
Consulting reimagined, powered by AI 15
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139,ibm,ibm-generative-ai-governance.pdf,"IBM Institute for Business Value | Research Brief
The enterprise
guide to AI
governance
Three trust factors that can’t be ignored
Foreword
Putting generative AI
governance in context
This Research Brief is part of an ongoing series of reports published by
the IBM Institute for Business Value (IBM IBV) about generative AI and
the opportunities and challenges it presents to organizations worldwide.
As business leaders adopt generative AI to boost competitiveness and increase productivity,
leaders need information on the ever-shifting landscape. Other reports include: 5 trends for
2024: Deep tech requires deep trust, The CEO’s guide to generative AI: Risk management, and
The CEO’s guide to generative AI: Responsible AI & Ethics.
The enterprise guide to AI governance: Three trust factors that can’t be ignored 2
Introduction
Why AI governance
matters more than ever
In less than two years, generative AI, the latest evolution of artificial Gen AI is cutting coding time from days to minutes, boosting content creation, personalizing
customer and employee interactions, automating cybersecurity operations, and optimizing
intelligence, has moved from novelty to business necessity. With gen
processes. But at the same time, AI-related risks are also on the rise: compliance and
AI strategies shifting rapidly from exploring to focusing to expanding,
regulation, data bias and reliability, and a loss of trust when users don’t understand AI model
77% of business leaders in a recent IBM IBV study say they are operation and governance.
convinced that gen AI is not only market ready, but that quick adoption
Governance refers to the principles, policies, and responsible development practices that
is necessary to maintain competitiveness.1 align AI tools and systems with ethical and human values. It establishes the frameworks,
rules, and standards that direct AI research, development, and application according to the
principles that organizations deem worthy. Governance mitigates the potential risks
associated with AI—bias, discrimination, and harm to individuals—through sound AI policy,
data governance, and well-trained and maintained datasets.2
The enterprise guide to AI governance: Three trust factors that can’t be ignored 3
Effective governance is key to building a foundation of trust. Monitoring how AI models are Figure 1
trained and managed helps organizations not only build better models but reassures GFioguvree r2nance can also be a catalyst for growth—for instance,
employees, customers, partners, and other stakeholders that the information and services bGyo fvaecrnilaitnactein cga nm aolsreo mbee aa ncaintaglfyuslt cfoorn gnreocwttiho—n sf owr iitnhs tcaunscteo, mers.
by facilitating more meaningful connections with customers.
they use are reliable.
Governance can also be a catalyst for growth— for instance, by facilitating more meaningful
connections with customers. It’s part of a management mindset that goes beyond risk
management and compliance to unlock opportunity. What’s troubling is that only 21% of
executives in our research say their organization’s maturity around governance is systemic
or innovative (see Figure 1).
From training and tuning to inference and outputs, risks can crop up at every phase of AI
development. In fact, MIT researchers recently compiled a list of over 750 AI risks to help However, only
identify gaps and uncertainties in how organizations perceive the AI risk landscape.3 74% 21%
of respondents believe believe that their
governance will have high organization’s maturity
impact in next 3 years as gen AI around governance is leading.
adoption barriers are removed.
The enterprise guide to AI governance: Three trust factors that can’t be ignored 4
Over 65% of data leaders at a recent Gartner conference highlighted data governance as Figure 2
Figure 1
their top focus in 2024.4 Executives across the C-suite admit that their organizations need to Why AI governance matters more than ever.
Why AI governance matters more than ever.
do better: 60% of CEOs say they’re looking into mandating additional AI policies to mitigate
risk. While 63% of CROs and CFOs say they are focused on regulatory and compliance risks,
only 29% say these risks have been sufficiently addressed5 (see Figure 2). Roughly 27% of
public companies cited AI regulation as a risk in recent filings with the Security and
Exchange Commission.6
of CROs and CFOs say they
So how do organizations move gen AI initiatives forward as fast as possible to capture 63%
are focused on regulatory
business value—while also constructing governance guardrails to keep gen AI on track? and compliance risks.
In this report, we will explore this central leadership challenge, and provide specific
recommendations for action, through the lens of three major trust factors.
of CEOs say they are looking
60%
into mandating additional AI
policies to mitigate risk.
However, only
29%
of CROs and CFOs say
these risks have been
The enterprise guide to AI governance: Three trust factors that can’t be ignored sufficiently addressed. 5
Trust factor 1 Trust factor 2 Trust factor 3
Trustworthy AI depends Accountability: Who is in Transparency: How do you Explainability: How do you
on effective governance. charge of AI governance? assess sources of data and explain the output
Incorporating these three trust what is shared about them? of AI systems and models?
Effective AI governance must be a funded
factors is the starting point for
mandate from senior leadership.
Diverse and multidisciplinary teams Deep collaboration between people and
building governance frameworks. Organization-wide adoption requires
should be deployed to assess data used to AI systems is built on transparency and
flexible governance frameworks to mitigate
build models, matching the broad range of explainability, adding the human touch to
risks and achieve business goals.
needs and expectations of AI users. This AI-informed decision-making. Sharing
will answer questions about how models the provenance of models is fundamental
are audited and how they perform to trust.
compared to humans.
Clear AI governance practices and policies
are at the core of addressing these trust
factors. Without governance, the adoption
of trustworthy and ethical AI systems can
be inhibited. At the same time, gen AI itself
can help improve governance—all across
the enterprise.
The enterprise guide to AI governance: Three trust factors that can’t be ignored 6
Three key AI governance-
related terms
Transparency, explainability, and provenance
Effective governance—delivered through corporate instructions, staff, Transparency is the ability to perceive how people can use and trust. Within its own
an AI system is designed and developed, governance framework, each organization
processes, and systems—helps assure that AI systems operate as
typically supported by the sharing of adopts an explainability approach to meet
an organization intends, while meeting stakeholder expectations
appropriate details about the AI system.7 its objectives.
and regulatory requirements. To enable AI users to direct, evaluate, To build a trustworthy AI model, algorithms
Provenance refers to the ability to explain
cannot be perceived as black boxes. AI
monitor, and take corrective action at all stages of the AI lifecycle,
and verify the origins of the data that trains
developers, users, and stakeholders must
governance relies on transparency, explainability, and provenance. AI models throughout their lifecycles.9 It is
understand the inner workings of AI to trust
vital for ensuring authentic data inputs and
its results.
for enhancing trust in AI-generated insights
Explainability, in the context of AI, is a and decisions. By recording metadata from
set of practices, tools and design the data’s source, provenance provides
principles that that makes AI decisions historical context and supports data
more comprehensible to humans.8 validation and auditing, leading to more
The more explainable an AI system is, the accurate and trustworthy AI outputs.
greater its ability to provide insights that
The enterprise guide to AI governance: Three trust factors that can’t be ignored 7
Trust factor 1 | Accountability
Who is in charge of
AI governance?
The first step is clear accountability. In our research, 60% of C-suite
executives say they have placed clearly defined gen AI champions
throughout their organization. And almost as many—59%—say they
have a direct report responsible for organization-wide AI integration.
What’s more, 80% of C-suite executives say they have a separate
risk function dedicated to using AI or gen AI. They want to be sure
that in developing and deploying AI, they are mitigating the risks of
unintended harm and unwanted biases.
The enterprise guide to AI governance: Three trust factors that can’t be ignored 8
Trust factor 1 | Accountability
Another IBM IBV survey of C-suite leaders, Supporting governance requires a retrofitted after deployment.13 Yet less
Spending on AI ethics has
creative executives, creative managers, and commitment of resources. Spending on AI sophisticated players and newer entrants
designers revealed that 47% of ethics has steadily increased from 2.9% of AI to AI struggle with the complex choices that steadily increased from
respondents have established a generative spending in 2022 to 4.6% in 2024. This share governance can raise. The solution is often
2.9%
AI ethics council to create and manage is expected to increase to 5.4% in 2025. flexible AI governance frameworks, which
in 2022
ethics policies and mitigate generative AI can help adapt to changing markets,
Our research indicates that more
risks.10 The goal of these councils is to mitigate risks, and encourage greater
technologically mature organizations tend
address the risk of “lawful but awful” AI.11 adoption to realize potential.14
to prioritize AI governance. For instance, to
The establishment of enterprise-wide
68% of CEOs in an IBM IBV survey say
governance frameworks helps streamline
governance for gen AI must be integrated
the process of detecting and managing
upfront in the design phase, rather than
technology ethics concerns in AI projects.12 4.6%
in 2024
This share is expected
to increase to
5.4%
in 2025
The enterprise guide to AI governance: Three trust factors that can’t be ignored 9
Action guide | Accountability
Build robust 1. Empower a senior-level executive to 3. Ensure that senior leadership aligns 5. Foster collaboration with stakeholders
lead AI and data governance initiatives. principles with practices. and ecosystem partners.
AI governance
Send the message that governance Align values related to the development Include stakeholders across the entire
is a senior management priority. and procurement of AI. Organizations organization—all working toward the
frameworks Championing AI and data governance achieve the outcomes they measure, same goals. Collaboration with
from an enterprise’s highest levels and aligning principles with practices governments, trade and industry
under an minimizes the risk of failure due supports measurement of progress associations, and other groups helps
to fragmented ownership and towards responsible AI adoption. establish AI governance guidelines,
fuzzy accountability. best practices, and regulations for
executive
4. Develop a cultural foundation for
responsible AI use. Ensure third-party
governance structures.
2. Prioritize and build on responsible AI software vendors and partners with
mandate.
development and deployment. embedded AI are subject to audits and
Without a strong cultural foundation, AI
other governance processes.
governance structures cannot gain
Give leaders who will be accountable for
traction. Healthy cultures have success
AI governance the authority to do the
measurements, incentives, messaging
work and provide their teams with the
and communications, diversity and
necessary resources to support this
inclusiveness, psychological safety,
mandate. Teams can also build on and
proactive employee training, and a
evolve governance frameworks already
holistic approach to AI literacy.
in place.
The enterprise guide to AI governance: Three trust factors that can’t be ignored 10
Case study | Data provenance standards
The Data & The Data & Trust Alliance (D&TA) was “The value of AI depends on the quality of data. To
established in 2020 by CEOs from leading
realize and trust that value, we need to understand
companies, based on a shared conviction
Trust Alliance
that the future of business will be powered
where our data comes from and if it can be used,
by the responsible use of data and AI. The
27 members of the Alliance–including legally. That’s why the members of the Data & Trust
Enhancing AI business
Deloitte, GM, IBM, Johnson & Johnson,
value and trust with data Mastercard, Meta, Nike and UPS– Alliance created a new business practice through
provenance standards15 represent 18 industries, employ over
cross-industry data provenance standards.”
four million people and earn $2 trillion in
annual revenue.
Saira Jesani
Executive Director, Data & Trust Alliance
The enterprise guide to AI governance: Three trust factors that can’t be ignored 11
Case study | Data provenance standards
The Alliance creates tools and practices to most essential metadata—required to IBM saw increases in both efficiency—time
enhance trust in data, models, and the understand more about a dataset’s origin, for clearance—and overall data quality,
processes around them. In 2023, the its method of creation and whether it can with a 58% reduction in data clearance
Alliance developed the first set of cross- be legally used—were selected. processing time for third-party data and
industry data provenance standards, a 62% reduction in data clearance
In early 2024, IBM tested the D&TA data
including 22 metadata fields that provide processing time for IBM owned or
provenance standards as part of a clearance
essential information about the origin of generated data. The D&TA standards were
process for datasets used to train
data and associated rights. a meaningful contributing factor to these
foundational models. IBM’s data governance
improvements. IBM is now adopting the
These standards were created with two program already included a data clearance
D&TA data provenance standards into
objectives in mind: business value and process that applied relevant controls,
its business data standards, where
implementation feasibility. By adopting documented lineage, and defined guidelines
appropriate, to further optimize enterprise
D&TA data provenance standards, for use and re-use. The challenge was a need
data governance.
businesses can better understand datasets to respond to an increasing volume of data
before purchase or use—and have a basis to clearance requests. The organization tested
decline data or request changes from third the standards to optimize the process for
parties. To encourage adoption, only the greater efficiency and accuracy.
12
Trust factor 2 | Transparency
How do you assess AI data
sources and what is shared
about them?
90% of the data available in the world was generated in the last two Before people can use AI, trust must be For transparency to be effective,
earned, and the most effective way to earn organizations must provide explainability—
years—just as gen AI went from curiosity to ubiquity. Approximately 400
user trust is through transparency. With the ability of an AI system to provide
million terabytes of data are created every day, with 150 zettabytes
respect to personal data, transparency insights that people can use to understand
estimated to be generated in 2024.16 But managing such huge amounts of is a key privacy principle. It requires the causes of the system’s predictions.
organizations to be open and forthcoming Clear explanations must be provided about
data presents huge challenges. Almost half of surveyed CEOs say they are
about their data processing practices. This accountability, data, models, algorithms,
concerned about accuracy and bias—an issue that could create as many
enables people to determine how they performance, audits, and related factors
problems as generative AI promises to solve. want their data used and shared. (see Figure 3). Otherwise, organizations
take on a tremendous exposure to risk.
The enterprise guide to AI governance: Three trust factors that can’t be ignored 13
Trust factor 2 | Transparency
Figure 3
Here are important questions for which
organizations must provide clear explanations.
Figure 3
Here are important questions for which organizations must provide clear explanations.
Transparency eliminates black box opacity To help ensure assumptions are not
and supports accurate and fair decision- overlooked, AI governance should
making. As artifacts of the human experience, include diverse, multidisciplinary teams Who is accountable Was the data gathered
virtually all data is biased. AI mirrors our to both build and govern these models. for models? with consent?
biases. The question is: which biases do not Outside experts in psychology,
reflect our values? If bias aligns with an anthropology, law, philosophy,
organization’s values, there must be linguistics, and other disciplines can also Do domain and
Does it represent all the experiential experts
transparency about why that dataset and help ensure that AI is used to augment communities served? agree that correct
approach were chosen over others. If they human intelligence in ways that align data was used?
don’t align, a different approach is needed. with human values. Governance teams
also need a psychological safety net How much better does What goes
when having challenging conversations the model perform into algorithm
compared to a human? recommendations?
about potential disparate impacts of an
AI model.
How is the model
How often is the
31 audited and what
model audited?
is it audited for?
The enterprise guide to AI governance: Three trust factors that can’t be ignored 14
Action Guide | Transparency
Assemble a 1. Establish a multidisciplinary AI 3. Ask questions and think beyond 4. Embrace ideas from outside
governance team. regulatory compliance. the organization.
Dream Team
To avoid blind spots, include a broad Best practices for governance go
Learn, follow, and look for opportunities
range of expertise from technical, ethical, deeper than rules and regulations
to participate in the development of
to build and social domains. This diversity can and can open doors to innovation.
intergovernmental and international
help identify gaps faster, help leverage Compliance starts with policies,
standards. AI principles promulgated by
effective AI existing governance mechanisms and procedures, and industry standards for
the OECD provide a useful starting-off
enable an organization to proactively AI. Building a framework for compliance
point.17 Additional national and global
head off unintended impacts. enables efficient incorporation of new
governance. standards are expected as gen AI
rules and regulations.
adoption grows.
2. Train everyone in transparency.
Give employees at all levels opportunities
to receive the training they need to build
or procure AI models responsibly in their
own domain, as well as awareness of
when to seek help when working outside
their domain, such as audits. Engage the
workforce by creating a culture that
celebrates openness and inclusion.
The enterprise guide to AI governance: Three trust factors that can’t be ignored 15
Case study | Transparency
Australia Post With annual revenues over $5.8 billion, Generative AI is a key part of the Post’s But mindful that while the public has
Australia Post provides postal services mission to boost customer service and concerns about AI, the Post is embracing
from 4,310 locations. As a government- efficiency. After testing and reviewing transparency, because gen AI is well
owned corporation, the Post must use thousands of customer calls and employee underway. The Post has already conducted
Delivering a more
transparency and rules to maintain keystrokes, generative AI is now routing a review of all its data and is now creating
efficient future18
customer trust. Much of the data handled customer queries and answering business- strict procedures around data governance.
is personal and sensitive. as-usual questions. The Post is working It is committed to not just aligning with
toward a goal where gen AI could handle regulatory frameworks but hardening its
between 40% and 60% of calls, delivering privacy and security protocols.
a better customer experience while
significantly reducing costs.
Australia Post is working toward a goal where gen AI
could handle between 40% and 60% of calls to improve
customer experience and reduce costs.
The enterprise guide to AI governance: Three trust factors that can’t be ignored 16
Trust factor 3 | Explainability
How do you explain the output
of AI systems and models?
As more organizations adopt AI, AI acceptance is at a crossroads.
While 35% of respondents to the 2024 Edelman Trust Barometer survey
say they accept this innovation, almost as many—30%—reject it.19
Demonstrating the trustworthiness of AI will be key to optimizing AI’s
impact. Trustworthy AI can also contribute to new ideas that separate
innovators from those doing the bare minimum.
The enterprise guide to AI governance: Three trust factors that can’t be ignored 17
Trust factor 3 | Explainability
A key element of trustworthy AI is not just limited to explaining how a gen AI Figure 4
provenance—the ability to explain and model renders outputs. In higher risk use MFigousret 4executives in our research say they recognize the
Most executives in our research say they recognize the importance of explainability.
verify the origins and history of data cases, it is appropriate to have every importance of explainability.
throughout its lifecycle. When training AI output provide an explanation of its data
models, provenance is essential for lineage informed by provenance, along
78%
ensuring that the data is authentic and with evidence.
trustworthy. Authentic data inputs into AI
Maintaining explainability. We
Most executives in our research say they
models enhances the trustworthiness of maintain robust documentation
AI-generated insights and decisions. recognize the importance of explainability; with explainability.
78% maintain robust documentation; 74%
For people to trust what goes into and what conduct ethical impact assessments;
comes out of AI models, explainability—the and 70% conduct user testing for risk 74%
ability to understand and trust AI outputs— assessment and mitigation (see Figure 4).
is informed by provenance. Explainability is Assessing ethical impact. We conduct ethical
impact assessments to evaluate the potential of
these initiatives on different stakeholders.
70%
Assessing and mitigating risks. We
conduct user testing for risk assessment
and mitigation.
The enterprise guide to AI governance: Three trust factors that can’t be ignored 18
Action Guide | Explainability
Keep humans 1. Design AI systems that facilitate 2. Prioritize AI output that is explainable 3. Incentivize employees to speak up and
human-AI collaboration and oversight. and auditable. speak out if AI output is confusing.
in the loop.
Create and scale repeatable patterns to Invest in applied training that improves Make sure those who build, design, and
ensure AI systems and their transparent AI literacy and provides clear guidance procure AI adopt a human-centric
metadata are accessible to the people on designing and developing human- instead of a data-centric approach and
using them, no matter what their level of centric systems. consider how outputs can be evaluated
technical understanding. after the fact. Provide appropriate
communication so employees feel
empowered and competent to ask about
potential disparate impacts related to
the AI models they work with.
The enterprise guide to AI governance: Three trust factors that can’t be ignored 19
Governance and
building trust
Can generative AI be trusted? Understanding the data used to train, 79% of global respondents say it is
Can trust guardrails balance tune, and make inferences from AI important for their CEOs to speak out
models is essential. What a company about the ethical use of technology.20
the power of gen AI?
does with AI is defined, in large part,
Ultimately, AI governance is about much
The answer is yes—but only by how it selects, governs, analyzes,
more than rules, restrictions, regulations,
and applies data across the enterprise.
if organizations approach AI
and requirements. It’s about a shared
Communicating that process
governance with commitment understanding of practices for effective
transparently is how trust is built and
and enthusiasm. collaboration that can reduce uncertainty
maintained over time.
and increase predictability—practices which
Governance needs to be embedded may actually accelerate development. When
at every phase of the generative AI sponsored and promoted at the leadership
lifecycle— not in functional silos but level, AI governance will no longer be seen
across the enterprise. It must be as just another IT issue, but as a core
championed by top leadership that strategy for value creation, growth,
provides strategic guidance, innovation, and developing the potential
recognition, and feedback. According of human-AI collaboration.
to the 2024 Edelman Trust Barometer,
The enterprise guide to AI governance: Three trust factors that can’t be ignored 20
Authors Contributors IBM Institute for Related reports
Business Value
Phaedra Boinodiris Sara Aboulhosn, Lee Cox, Rachna Handa, The CEO’s Guide to Generative AI: Manage
Global Leader for Trustworthy AI Christina Montgomery, Shyam Nagarajan, unpredictable risks
For two decades, the IBM Institute for
IBM Consulting Dasha Simons, Michael Tucker, and
Business Value has served as the thought
pboinodi@us.ibm.com Kush Varshney. https://www.ibm.com/thought-leadership/
leadership think tank for IBM. What inspires
https://www.linkedin.com/in/phaedra/ institute-business-value/en-us/report/
us is producing research-backed,
ceo-generative-ai/ceo-ai-risk-management
The right partner for
Brian Goehring technology-informed strategic insights that
Associate Partner and AI Research Lead a changing world help leaders make smarter business The CEO’s Guide to Generative AI:
Institute for Business Value decisions. From our unique position at the Responsible AI & ethics
IBM Consulting At IBM, we collaborate with our clients, intersection of business, technology, and
goehring@us.ibm.com https://www.ibm.com/thought-leadership/
bringing together business insight, advanced society, we survey, interview, and engage
https://www.linkedin.com/in/brian-c-
institute-business-value/en-us/report/
research, and technology to give them a with thousands of executives, consumers,
goehring-9b5a453/
ceo-generative-ai/responsible-ai-ethics
distinct advantage in today’s rapidly and experts each year, synthesizing their
Milena Pribic changing environment. perspectives into credible, inspiring, and
The ingenuity of generative AI
Design Principal, Ethical AI Practices actionable insights. To stay connected and
IBM Software informed, sign up to receive IBV’s email https://www.ibm.com/thought-leadership/
mpribic@us.ibm.com
newsletter at ibm.com/ibv. You can also institute-business-value/en-us/report/
https://www.linkedin.com/in/milenapribic/
find us on LinkedIn at https://ibm.co/ scale-generative-ai
ibv-linkedin.
Catherine Quinlan
Vice President, AI Ethics
IBM Chief Privacy Office
cquinlan@us.ibm.com
https://www.linkedin.com/in/
catherinemquinlan/
The enterprise guide to AI governance: Three trust factors that can’t be ignored 21
Notes and sources 10. Disruption by design: Evolving experiences in the age of generative AI. IBM Institute for Business
Value. June 2024. https://www.ibm.com/thought-leadership/institute-business-value/en-us/report/
generative-ai-experience-design
11. Foody, Kathleen. “Explainer: Questioning blurs meaning of ‘lawful but awful’”. AP. April 7, 2021.
https://apnews.com/article/death-of-george-floyd-george-floyd-cba9d3991675231122e2b68fb
d5b4b00
12. Montgomery, Christina and Francesca Rossi. “A look into IBM’s AI ethics governance framework.”
1. Goehring, Brian, Manish Goyal, Ritika Gunnar, Anthony Marshall, and Aya Soffer. The ingenuity of
IBM Blog. December 4, 2023. https://www.ibm.com/
generative AI. IBM Institute of Business Value. June 2024. https://www.ibm.com/thought-
blog/a-look-into-ibms-ai-ethics-governance-framework/
leadership/institute-business-value/en-us/report/scale-generative-ai
13. 6 hard truths CEOs must face. IBM Institute for Business Value. May 2024. https://www.ibm.com/
2. Mucci, Tim and Stryker, Cole. “What is AI governance?” IBM Blog. November 28, 2023. https://
thought-leadership/institute-business-value/en-us/c-suite-study/ceo
www.ibm.com/topics/ai-governance
14. A Flexible Maturity Model for AI Governance Based on the NIST Risk Management Framework. IEEE
3. Constantino, Tor. “AI’s Risky Business, MIT Researchers Catalogue Over 750 AI Risks.” Forbes.
USA. July 2024. https://ieeeusa.org/product/a-flexible-maturity-model-for-ai-governance/
September 11, 2024. https://www.forbes.com/sites/torconstantino/2024/09/11/
ais-risky-business-mit-researchers-catalogue-over-750-ai-risks/ 15. The Data & Trust Alliance. Accessed September 28, 2024. https://dataandtrustalliance.org/about
4. “Data Governance is a Top Priority for 65% of Data Leaders-Insights From 600+ Data Leaders For 16. Duarte, Fabio. “Amount of data created daily (2024).” June 13, 2024. Exploding Topics. https://
2024.” Humans of data. March 28, 2024. https://humansofdata.atlan.com/2024/03/ explodingtopics.com/blog/data-generated-per-day
future-of-data-analytics-2024/ 17. OECD AI Principles overview. OECD.AI Policy Observatory. May 2019. https://oecd.ai/en/ai-principles
5. The CEO’s guide to generative AI: Risk management. IBM Institute for Business Value. August 2024. 18. Internal IBM case study.
https://www.ibm.com/thought-leadership/institute-business-value/en-us/report/
19. Edelman, Margot. “Why the human touch is needed to harness AI tools for communications.” World
ceo-generative-ai/ceo-ai-risk-management
Economic Forum. June 18, 2024. https://www.weforum.org/agenda/2024/06/
6. Lin, Belle. “AI Regulation Is Coming. Fortune 500 Companies Are Bracing for Impact.” The Wall human-touch-harness-ai-tools-communications/
Street Journal. August 27, 2024. https://www.wsj.com/articles/
20. Ibid.
ai-regulation-is-coming-fortune-500-companies-are-bracing-for-impact-94bba201
7. IBM Design for AI guidelines and definitions.
8. What is explainable AI? IBM. https://www.ibm.com/topics/explainable-ai
9. What is data provenance? IBM. https://www.ibm.com/think/topics/data-provenance
The enterprise guide to AI governance: Three trust factors that can’t be ignored 22
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The enterprise guide to AI gover"
140,ibm,the-intuitive-supply-chain-report.pdf,"IBM Institute for Business Value | Research Brief
The intuitive
supply chain
Predict disruption, deliver growth
Key takeaways
Generative AI can preempt supply Generative AI has put supply chains in flux. 64% of Chief Supply
Chain Officers say gen AI is completely transforming workflows.
chain disruption and unleash
Supply chain teams must work differently. 60% of operations and
growth opportunities. automation executives say AI assistants will handle most traditional
and transactional processes by 2025.
More decisions will be automated. Operations and automation
executives say generative AI will increase the volume of decision-
making by digital assistants by 21% in the next two years.
Predictions will improve, igniting sustainable innovation. 76% of
supply chain and operations leaders say gen AI will help innovate their
product design and make product lifecycles more sustainable.
The intuitive supply chain: Predict disruption, deliver growth 2
Introduction
Make agility your supply
chain superpower
Would a peek at next week’s headlines change
your supply chain strategy today?
The intuitive supply chain: Predict disruption, deliver growth 3
Supply chain certainty is an elusive target. The combined power of generative AI and
With so many fault lines stretching across cloud computing could make that possible.
the business landscape, it seems By harnessing the potential of machine
impossible to accurately predict what will learning, automation, and advanced
happen tomorrow. Supply chain leaders analytics in a hybrid cloud environment,
must often adopt a siege mentality, looking organizations can gain a sixth sense,
for ways to limit their losses as plan B anticipating everything from demand
quickly gives way to plans C, D, and E. fluctuations to sourcing delays. With this
foresight, they can reinvent their supply
But what if you could spend this time
chain strategies, shifting from a reactive
spurring growth? What if you could predict
to a proactive stance.
the future accurately enough to give your
business a competitive edge?
17% 72%
Leaders in gen AI adoption and data-led
innovation—those who view gen AI capabilities
as the primary driver of their automation
report higher annual revenue report greater annual
investments—are reaping outsized rewards. growth than the competition net profits
The intuitive supply chain: Predict disruption, deliver growth 4
Already, leaders in gen AI adoption and That’s a big problem for supply chain executives, and automation executives employees. Part two explains how
data-led innovation—those who view gen AI leaders, who know they need to invest from organizations that are currently accelerating supply chain intelligence can
capabilities as the primary driver of their in next-gen tech today to make their implementing AI-enabled automation. help companies leverage real-time data
automation investments—are reaping operations more agile and resilient for We discovered that these leaders are faster and more effectively than ever
outsized rewards. They report 72% greater an uncertain future—from dynamically focused on creating what we call “the before. And in part three, we’ll explore how
annual net profits and 17% higher annual rerouting shipments and adjusting intuitive supply chain”—agile, adaptive, and gen AI-enabled digital twins, or virtual
revenue growth than the competition. And all production schedules in real time to perpetually prepared, safeguarding brand models, can help organizations improve
the supply chain leaders we surveyed expect identifying bottlenecks and risks before reputation, customer satisfaction, and the their position in the competitive landscape,
their revenue growth from AI-enabled they materialize. bottom line. as well as in the eyes of customers. We
operations to more than double over the next conclude with an action guide that outlines
How can gen AI solve these persistent In this paper, we’ll lay out the steps
three years.1 how to plan, prioritize, and perform to
supply chain problems? To find out, the IBM organizations are taking to get there. In part
make every move count.
Looking at these numbers, it’s no surprise that IBV, in partnership with Oxford Economics, one, we’ll explore the role of AI assistants,
72% of the top-performing CEOs we surveyed surveyed more than 2,000 global Chief which are quickly becoming less like
for the IBM Institute for Business Value (IBM Supply Chain Officers (CSCOs), operations chatbots and more like full-time
IBV) 2024 CEO Study say competitive
advantage now depends on who has the most
advanced gen AI. But the high-speed race to
meet short-term goals is hindering their
progress. Overall, global CEOs agree that a
focus on short-term performance is their top
barrier to innovation—and 66% say their
Supply chain leaders need to invest in
organization is currently meeting short-term
targets by reallocating resources from next-gen tech today to make their operations
longer-term efforts.2
agile and resilient for an uncertain future.
The intuitive supply chain: Predict disruption, deliver growth 5
Part one
Lean into the power
of decision support
Employees paired with AI assistants will deliver
more business value than either could alone.
The intuitive supply chain: Predict disruption, deliver growth 6
Today’s supply chain teams are drowning in a sea of disconnected data.
They increasingly have access to the long-awaited real-time information
they need to make smarter, faster, decisions—but there’s so much to sift
through that many opportunities go unnoticed until it’s too late. to ask for the information they need—and find out where it came
from—with a few simple prompts.
Gen AI-powered digital assistants are changing all that. With their
ability to analyze vast stores of data almost instantaneously, they can For example, AI assistants can analyze which supplier is contributing
bubble up critical insights for supply chain teams to skim from the the most to delays and identify issues causing disruption, such as
surface. Plus, their natural language skills make it easy for employees weather, financial obstacles, or transportation bottlenecks. Then,
AI-fueled predictive models can outline how the situation is most likely
to evolve, allowing AI assistants to offer targeted recommendations
that help supply chain teams prepare for what’s next.
Already, 60% of executives say AI assistants will handle most
traditional and transactional processes by 2025.3 And 90% say their
60% organization’s supply chain workflows will incorporate intelligent
automation and AI assistants by 2026.4
of executives say AI assistants
will handle most traditional and
transactional processes by 2025.
And
90%
say their organization’s supply chain
workflows will incorporate intelligent
automation and AI assistants by 2026.
The intuitive supply chain: Predict disruption, deliver growth 7
When employees use gen AI assistants to It’s not just about explaining how materials By leveraging AI assistants, CSCOs can
quickly query their supply chain platform will get from point A to point B. It’s also aggregate and distill intel, bringing insight
for credible data, rather than manually measuring the supply chain cost of every to the boardroom quickly and confidently
searching multiple systems, they can business decision—and making sure those and making sure supply chain implications
manage change faster—and pivot more costs are considered from the start. Beyond continue to inform strategies as they
precisely. Instead of using the dedicated the sales a new SKU will drive, product evolve. As decisions are made and then
procurement solution to change purchase development strategies should account for tested in the market, AI assistants can
order delivery dates, for instance, the total cost of ownership, forecasting the accelerate the feedback loop, giving
employees can simply ask their assistant cost of delivering a new item in conjunction executives the real-word, real-time data
to make the change for them. with the losses that come from holding on they need to see if their strategies are
to products that don’t sell. delivering the desired results—and change
But that’s only the beginning. Supply chain
tactics quickly if they aren’t.
teams aided by AI assistants are cultivating Then there’s the sustainability dimension.
a new human-technology dynamic that will As both consumers and regulators demand
touch virtually every point of the supply more comprehensive reporting on
chain, from planning to sourcing to environmental impact, supply chain leaders
manufacturing to distribution. In fact, must be able to track sustainability metrics
64% of CSCOs say gen AI is completely all the way to the last mile—and do the hard
transforming their supply chain workflows. work of designing more eco-friendly product
And CSCOs and automation executives say lifecycles. This is another place where gen
gen AI will increase the volume of decision- AI can help, with 76% of supply chain and
making by digital assistants by 21% in the operations leaders agreeing that it will help
With gen AI assistants, employees can manage
next two years. innovate their product design and make
product lifecycles more sustainable.
change faster—and pivot more precisely.
The intuitive supply chain: Predict disruption, deliver growth 8
Case study
Building an intelligent supply chain
using a supply chain AI assistant
IBM employs supply chain staff in 40 which impeded collaboration and At a high level, the IBM supply chain The system uses IBM’s AI technology to
countries and makes hundreds of thousands real-time data transparency. digital transformation revolves around enable natural language queries and
of customer deliveries and service calls in building sense-and-respond capabilities. responses, which accelerates the speed of
IBM supply chain management set out
over 170 nations. IBM also collaborates with This was accomplished by democratizing decision-making and offers more options
a bold transformation vision to build a
hundreds of suppliers across its multitiered data—automating and augmenting to correct issues. Users can ask, in natural
cognitive, intelligent supply chain more
global network to build highly configurable decisions by combining a cognitive control language, about part shortages, order
than a decade ago. The aim was to have
and customized products to customer tower, a cognitive advisor, demand-supply impacts, and potential trade-offs. To date,
an agile supply chain that extensively
specifications. Historically, the IBM supply planning, and risk-resilience solutions. IBM has saved $388 million related to
uses data and AI to lower costs, exceed
chain ran on legacy systems spread Now, the cognitive control tower has reduced inventory costs, optimized
customer expectations, ruthlessly
across different organizational silos, evolved into an enhanced generative AI shipping costs, faster decision-making,
eliminate or automate non-value-add
making information-sharing slow and intelligent layer using a supply chain and time savings (days to hours to minutes
work, and exponentially improve the
incomplete. Employees also performed digital assistant. to seconds).
experience of supply chain colleagues.5
much of their work on spreadsheets,
The intuitive supply chain: Predict disruption, deliver growth 9
Part two
Accelerating supply
chain intelligence
If your data could talk, what would it say?
Supply chain teams are about to find out.
The intuitive supply chain: Predict disruption, deliver growth 10
Whether disruption is caused by In fact, the executives we surveyed
geopolitical conflict, climate catastrophes, anticipate operational performance, 73%
or increasing complexity, supply chain enterprise agility, and strategic advantage
leaders will be judged by their ability to find to be the top three benefits of using gen AI
effective workarounds. And they’re looking investments in their supply chain. And 73%
to gen AI to make their supply chain more say gen AI is already accelerating their
agile, adaptive, and future-proofed. high-impact automation initiatives. of executives say gen AI
is already accelerating
their high-impact
automation initiatives.
The intuitive supply chain: Predict disruption, deliver growth 11
Perspective
The key is to make the entire ecosystem The convergence of gen AI and cloud-based Future-proof your supply chain with
more responsive. By allowing gen AI solutions has also enabled autonomous
cloud-enabled innovation
assistants to interact directly with the automation (see “Future-proof your supply
intelligent layer of the supply chain system— chain with cloud-enabled innovation”).
With the combined power of cloud computing and generative AI, companies can
the cognitive core that pulls insights from In addition to automating workflows,
accelerate supply chain innovation and improve business outcomes to a degree that
vast stores of data—internal and external gen AI assistants can automate the process
wasn’t previously possible.
teams can collaborate more seamlessly. of workflow reinvention. They can learn from
supply chain metrics and transaction history, Deploying gen AI on the cloud lets companies train and deploy models faster and
The goal is for AI assistants to continually
make proactive recommendations, and even at scale, without the need for expensive hardware or infrastructure. It lets multiple
communicate the intelligent layer’s findings
repurpose or redefine new workflows based teams collaborate on the development of gen AI models, moving them between
to the appropriate part of the supply chain
on what they’ve learned. different cloud environments and integrating them with other cloud-based services
team, along with recommended actions.
and applications seamlessly.
While the enterprise resource planning (ERP) This helps streamline workflows to make
system remains the system of record and them more efficient, cost-effective, and Then, of course, there’s cost to consider. With pay-as-you-go pricing, cloud
core transaction engine, supply chain teams environmentally responsible. In fact, 63% infrastructure can ease capital expenditure constraints, allowing companies to
no longer need to interact with it directly. of supply chain and operations leaders say focus on innovation, rather than the financial implications of investing in new tech.
And that goes for other specialized supply integrating sustainability and circularity into When applied strategically, this tech combo can improve efficiency, reduce costs,
chain apps, from procurement to warehouse workflows is a key reason their organization and increase agility. Here are a few ways your supply chain can benefit from cloud-
management to transportation logistics, is investing in automation. enabled innovation powered by gen AI:
as well. This approach lets employees drill
deeper, allowing for real-time analysis and – Forecast future demand. Optimize inventory levels, reduce stockouts or over-
optimization each step of the way. stocking, and improve cash flow.
– Optimize delivery routes. Reduce fuel consumption, lower emissions, provide
dynamic distribution, and improve delivery times.
– Manage supply chain risk. Predict the likelihood of disruption and recommend
proactive mitigation measures.
– Increase supply chain visibility. Identify bottlenecks and recommend corrective
actions teams can take to keep operations from being disrupted.
The intuitive supply chain: Predict disruption, deliver growth 12
Case study
Achieve end-to-end visibility
with AWS Supply Chain
Supply chains are vast, interconnected networks. The multitude of The cloud-based AWS Supply Chain Address data fragmentation
business application directly addresses
participants, disparate systems, and lack of seamless data sharing A supply chain data lake harmonizes
these challenges. By harmonizing disparate
disparate data into a flexible, scalable
make it difficult to accurately forecast future demand, track inventory
data sources into a unified supply chain
canonical data model that aggregates and
levels, and align supply. The fragmentation of data hinders supply data lake, it lays the foundation for
associates supply chain information into
improved end-to-end visibility, forecasting
chain planners’ ability to understand fluctuations, predict future needs a unified data asset. By incorporating a
accuracy, inventory optimization, and
generative AI-powered data onboarding
precisely, and position optimal inventory where it’s needed most. overall supply chain resilience.6 Here are a
agent, companies can also automate data
few of the key business benefits of moving
transformation from any native format into
to this type of cloud-based solution:
the data lake’s canonical model. Customers
can seamlessly extract and upload raw
data, with the agent leveraging large
language models for automated data
mapping through a guided, module-driven
user interface experience.
The intuitive supply chain: Predict disruption, deliver growth 13
Case study (continued)
Increase forecast accuracy Improve supply chain visibility Improving supplier visibility Simplify sustainability
and collaboration compliance processes
Machine learning-powered forecasting The AWS business application can examine
capabilities can help organizations improve warehouses, distribution centers, and stores The AWS application analyzes supplier lead Cloud-based sustainability features create
forecast accuracy and reduce excess in detail, showing on-hand, in-transit, and times, makes future projections compared a more secure and efficient way to obtain
inventory levels. Machine learning at-risk inventory levels. It then uses machine to orders and forecasts, then identifies mandatory documents and datasets from
algorithms can incorporate variables such learning algorithms to automatically issues. It displays all connected trading your supplier network. You can request,
as seasonality, product characteristics, generate, score, and rank multiple inventory partners, enabling supply chain leaders to collect, and export artifacts, such as
vendor characteristics, and destination- rebalancing recommendations to mitigate view and collaborate across multiple tiers. product lifecycle assessments, certificates
origin sites, along with historical order risks. Gaining visibility into network-wide Built-in chat and messaging capabilities on product safety, or reports on hazardous
history, to train the model. inventory levels, movement patterns, and also facilitate seamless communication substances used at any point in the supply
potential risks empowers organizations to and data sharing. chain. Amazon’s Global Trade and Product
optimize inventory positioning and mitigate Compliance (GTPC) team used the AWS
imbalances, overstocks, and stockouts. application’s sustainability features to
transform their compliance data
management process and now expect to
save approximately 3,000 operational
hours per year.
The intuitive supply chain: Predict disruption, deliver growth 14
Part three
Visualize
the future
By using supply chain data to
fuel gen AI-powered virtual models,
companies can unlock a new level of
operational efficiency and resilience.
The intuitive supply chain: Predict disruption, deliver growth 15
Supply chain leaders have long imagined
a future where real-time data flows
seamlessly between IT and operational
By 2026,
technology (OT) systems, enabling a more
77%
agile approach that reacts to constant
change. And their dream is finally
becoming reality.
Think of a manufacturing facility, where
of executives believe gen AI will
operations teams already use AI sensors
enable connected assets to
to detect changes in vibration patterns, make autonomous decisions.
temperatures, power consumption, and
even sound patterns. While traditional AI
can alert teams to signals as they appear—
and even predict when breakdowns are
about to occur—employees must manage 76%
necessary adjustments or repairs based
on this information.
With generative AI, that’s no longer the
case. When paired with vision sensors, of executives say they expect to
gen AI lets connected machines use gen AI to derive differentiated
outcomes from connected assets
self-predict and self-adjust in a harmonious
in the next two years.
fashion, unlocking unprecedented levels of
productivity and efficiency.
The intuitive supply chain: Predict disruption, deliver growth 16
In fact, by 2026, 77% of executives expect It works like this: First, data from drones, support “what-if” risk analysis by predicting With the right perspective, supply chain
gen AI will enable connected assets to robots, cameras, and other connected potential problems—from raw material leaders can look beyond productivity plays
make autonomous decisions. And when assets flow into a unified platform with shortages to multiple supplier plant closings to pull the levers that drive growth. By using
complex asset ecosystems work in a geospatial layer, an information layer, simultaneously—and recommending gen AI to orchestrate multiple data sources,
harmony, they can help businesses achieve and an orchestration layer. Time-lapsed respective contingency plans. systems, and tools, they can inspire
results that weren’t previously possible. visualizations then let supply chain teams innovation across the ecosystem—and
These simulations can also inform product
Executives recognize this potential, with see how specific changes have impacted inform the strategic decisions that set their
development by helping teams identify
76% saying they expect to use gen AI to the ecosystem in the past—and make organization apart.
where waste and inefficiencies can be
derive differentiated outcomes from real-time decisions as situations unfold
removed from the process. This is a key
connected assets in the next two years.7 in the present.
concern for executives, who say visibility
But boosting efficiency is just the first step. Gen AI-enabled virtual models can then of full product lifecycle management and
Businesses can derive much deeper value help teams simulate how future events environmentally sustainable products and
from interconnected data when they use it could affect supply chain operations. services are two of their top automation
to visualize the end-to-end supply chain— They use real-world data and algorithmic priorities for their operations functions over
and simulate how disruption could impact techniques to visualize how the dominos the next three years.
operations each step of the way. will fall in response to different disruptions
to help teams plan accordingly. They
Look beyond productivity plays to pull the
levers that drive growth.
The intuitive supply chain: Predict disruption, deliver growth 17
Case study
Improving pharma supply chain
visibility for patient safety8
Amid the increasing proliferation of counterfeit, falsified, or Seeking safety through transparency players in the prescription drug supply chain.
By connecting through these APIs, Pulse
substandard prescription medications, the US government passed Working with IBM Consulting and AWS,
users can search for trading partners, verify
NABP built a new digital platform called
the Drug Supply Chain Security Act (DSCSA) with the aim of protecting
trading partner status, exchange digital
Pulse that lets its member users track and
patients. It’s rooted in the idea that transparency—the ability to credentials, and perform electronic tracing.
share each prescription drug’s ownership
accurately trace prescription meds throughout the pharmaceutical transaction records, providing increased
The platform enables visibility and
supply chain visibility.
supply chain—is essential to preserving its integrity. collaboration, eliminates tedious
administrative work, and, most importantly,
One key design aspect of the platform—
Just as important is the idea that all the major players in the pharmaceutical ecosystem— creates a more secure supply chain to
which runs on the AWS cloud—is the
manufacturers, wholesalers, dispensaries, and regulators—need a way to share information protect patients.
integration of APIs from providers of the
collaboratively to make it happen. Prompted by the challenge of multiple industry segments
“point” tracking solutions used by most
needing to cooperate to address DSCSA, the National Association of Boards of Pharmacy
(NABP) sought to create a digital platform that would bridge the interoperability gaps
between systems, making compliance with DSCSA faster and easier.
The intuitive supply chain: Predict disruption, deliver growth 18
Action Guide
Make every move count
In the complex game of supply chain chess, executives must always
think several steps ahead. Modernizing supply chains isn’t just about
adopting new technologies or processes—it’s about embracing a new
way of thinking, one that’s rooted in scientific inquiry, experimentation,
and a relentless pursuit of progress.
By applying the scientific method at scale, enterprises can tap into the vast potential of data
and gen AI to drive critical improvements in business strategy, product development, and
global supply chain operations. In fact, 62% of CSCOs say gen AI will accelerate the pace
of discovery, leading to new sources of product and service innovation.9
With the promise of discovery as their guiding light, companies can unlock the full potential
of their supply chains, power ecosystem partnerships, and drive sustainable profitability and
growth. Here’s what leaders across the supply chain ecosystem should do to predict and plan
for endless disruption—and profit from the opportunities volatility can create.
The intuitive supply chain: Predict disruption, deliver growth 19
Action Guide
1. Plan Identify benefits you want to deliver. management to improve decision-making Understand skills requirements
efficiency and speed-to-action. Invest to and gaps.
Investigate the key drop-out points
bring the vision to life and facilitate a
between analysis and action, identifying Create user personas across the range of
seamless and fulfilling experience across
how improvements could flow through into supply chain workflows. Outline how digital
the entire supply chain.
financial and operational performance. assistants will help create new workflows
Outline the productivity KPIs that will be Know the specific functionality and and enhance existing ones. Identify the
targeted for improvement and define systems architecture you need. gaps in skills between these personas and
success criteria. the current state, then define training and
Identify the solutions that will provide
upskilling plans.
Define your employee every feature. Then use an orchestration
experience vision. engine as a process conductor, issuing Keep your eyes on the prize.
precise commands to multiple agents
Provide easy access to relevant AI Align supply chain innovation to your
based on user prompts. Leverage
analytics, recommendations based on role, market offering and the capabilities needed
synthesized data from the integration layer
and intelligent transactional workflows in to deliver it. Prioritize these areas and be
to create dynamic, intelligent workflows
the employee portal. Find ways to integrate confident in delivering them.
that deliver the desired outcomes.
supply chain processes into the employee
experience framework, such as
streamlining logistics and inventory
The intuitive supply chain: Predict disruption, deliver growth 20
Action Guide
2. Prioritize Define supply chain workflows Don’t try to cut your way to growth. Define rules of engagement.
that have the greatest potential
Make the investments needed to Be clear about who is accountable and
for automation.
fundamentally transform ways of working. responsible for specific workflows—and
Map the key points across the workflow Focus spending on the areas that can make who gets a say. Set ground rules for using
that cause rework and manual analysis. your supply chain more agile and resilient. digital assistants and make sure everyone
Be honest about the true nature of your knows how they’re expected to evolve.
Prioritize getting to scale.
processes, not the idealized version that
may be documented somewhere. Invest in initiatives that can quickly
transition from pilot to deployment at scale.
Stop looking for a silver bullet.
Use success in specific areas to build
Be honest about where investment is momentum for the wider transformation.
needed within your current technology
landscape. Set specific timelines for
upgrades or the deployment of new
solutions. Don’t let time and effort that
have been invested in previous solutions
become an anchor that prevents you
from achieving future success.
The intuitive supply chain: Predict disruption, deliver growth 21
Action Guide
3. Perform Feed generative AI data that Review and align to Keep score.
supports supply chain productivity. changing conditions.
Track benefits as they’re delivered to build
Map the full range of data initiatives needed Cultivate a supply chain that can sway with momentum and confidence in new
to connect people and technology. Upskill the winds of change to deliver a competitive technologies. Demonstrate ROI to secure
employees and train tools to speed advantage. Adopt a technology architecture continued investment. Make data-driven
decisions. Identify the key touchpoints to that allows new capabilities to be plugged decisions that can fuel growth and
use gen AI to boost productivity. in without disrupting the user experience. performance improvements.
Put trust in data.
Don’t let people tinker with the workflow
outputs from the system. Where processes
are automated and tested, let the system
run and do its job. Don’t allow competing
forms of analysis designed to suit individual
agendas interfere. Instead, encourage
employees to engage in advanced analysis,
using their assistants to innovate and
address the complexities of interconnected
operations and systems.
The intuitive supply chain: Predict disruption, deliver growth 22
Authors Research methodology products, electronics, telecommunications, IBM Institute for
government, healthcare/life sciences,
Business Value
Amar Sanghera The IBM Institute for Business Value (IBM consumer products, retail, and
AWS Supply Chain Solutions Global Leader, IBV), in conjunction with Oxford Economics, transportation/logistics, each comprising
For two decades, the IBM Institute for
Digital Supply Chains Go-to-Market Strategy interviewed and surveyed more than 2,000 5% to 15% of our total respondent sample.
Business Value has served as the thought
executives with equivalent roles and titles, The size of organizations surveyed, in terms
Michael Mowat including Chief Supply Chain Officer (CSCO), of revenue, ranged from $500 million to leadership think tank for IBM. What inspires
Supply Chain Strategy and Operations Chief Operations Officer (COO), Chief $500 billion, with a mean of $26 billion. us is producing research-backed,
Leader, Finance and Supply Chain Automation Officer (CAO), Chief technology-informed strategic insights that
Transformation, IBM Consulting Information Officer (CIO), and Chief The IBM IBV ran a series of contrast help leaders make smarter business
Financial Officer (CFO). analyses, including pairwise comparisons, decisions. From our unique position at the
Karen Butner highlighting results and differences as
intersection of business, technology, and
Global Research Leader, AI and Automation; In 2024, CSCOs, COOs, and automation shown in this report. Statistical significance society, we survey, interview, and engage
Supply Chain Operations, IBM Institute for executives were also polled about their for all pairwise comparison contrasts was with thousands of executives, consumers,
Business Value, IBM Consulting investments, priorities, and use cases to set at the (p = .05) level, meaning there is
and experts each year, synthesizing their
assess the current impact of generative AI only a 5% chance that the observed
perspectives into credible, inspiring, and
Contributors initiatives, as well as the results they expect differences or relationships between the
actionable insights. To stay connected and
to see in the next two to three years. The groups are due to random variation.
informed, sign up to receive IBV’s email
goal of these surveys was to understand
IBM Consulting The right partner for newsletter at ibm.com/ibv. You can also
how global executives view the impact of
Chris Moose, Lead Client Partner NABP, find us on LinkedIn at https://ibm.co/
gen AI on their organizations’ performance a changing world
Public Sector ibv-linkedin.
and competitive advantage across the
Jonathan Wright, General Manager,
NCE Europe supply chain. At IBM, we collaborate with our clients,
bringing tog"
141,ibm,the-ingenuity-of-generative-ai.pdf,"IBM Institute for Business Value | Research Insights
The ingenuity of
generative AI
Unlock productivity and
innovation at scale
How IBM can help
Clients can realize the potential of AI, analytics, and data using
IBM’s deep industry, functional, and technical expertise;
enterprise-grade technology solutions; and science-based
research innovations.
For more information about AI services from IBM Consulting,
visit ibm.com/services/artificial-intelligence
For more information about AI solutions from IBM Software,
visit ibm.com/watson
For more information about AI innovations from IBM Research,
visit research.ibm.com/artificial-intelligence
2
Key
takeaways
Business leaders must Generative AI investment is surging.
translate experimentation
Spend increased more than 10 times in 12 months,
into enterprise-grade while IT spend grew at only half the rate of inflation.1
investments that deliver
Financial returns from AI have
value at scale.
solidly surpassed the cost of capital.
Average AI ROI hit 13% in 2022—and early generative
AI wins (led by successful pilots) boosted it to 31%
in 2023.
Early generative AI experiments are
gravitating toward low-risk, noncore
use cases.
But organizations can deliver more value by focusing on
business areas that are more closely related to their
competitive advantage.
The biggest gains may come from
stepping into the unknown.
Over the next three years, more than half of executives
expect generative AI to enable types of work that
weren’t previously possible.
1
“There’s no safe space in the corporate world where
you can just hang out and enjoy your winnings
from the past. You’ve got to always be driving
forward to the next horizon.”
Bill Anderson, CEO, Bayer AG
From media sensation to
market-ready solution
Generative AI has seemed almost too good to be true. It cuts coding
time from days to minutes, personalizes products down to the tiniest
detail, and spots security vulnerabilities almost as soon as they appear.
And it’s helped skyrocket AI ROI from 13% to 31% since 2022.
While this largely reflects the success of pilots, sandbox experimentation, and other
small-scale investments, these early results have business leaders rethinking what’s
possible. Our latest proprietary survey of 5,000 executives across 24 countries and
25 industries reveals that most executives are more optimistic about the generative
AI opportunity than they were last year. More than three in four (77%) say generative
AI is market ready, up from just 36% in 2023, and nearly two-thirds (62%) now say
generative AI is more reality than hype (see Figure 1).
More than three-quarters of executives say they need to adopt generative AI quickly
to keep up with competitors. And 72% of the highest performing CEOs say
competitive advantage depends on who has the most advanced generative AI,
according to the IBM Institute for Business Value (IBM IBV) 2024 CEO study.2
Already, business leaders have begun to discover how generative AI boosts the
bottom line. Operating profit gains directly attributable to AI doubled to nearly 5%
from 2022 to 2023—and executives expect that figure to hit 10% by 2025. And
embedded generative AI in existing enterprise software workflows also promises
to deliver more sustainable ROI, according to forthcoming IBM IBV research.3
Still, despite these early signals, some analysts are skeptical. They anticipate that
this hype-driven adoption spike will be followed by a “trough of disillusionment,”
where organizations back away from the complexity involved with deploying
generative AI in core business functions.4 And in some instances, it’s true. One in
three companies pause an AI use case after the pilot phase—but two in three don’t.
2
One in three companies pause an
AI use case after the pilot phase—
but two in three don’t.
In this setting, how can business leaders best translate successful experimentation
into enterprise-grade investments that deliver value at scale? This paper offers
a roadmap to help companies answer this question, accompanied by case studies
illustrating effectiveness in action. First, we outline where generative AI is currently
delivering the highest ROI. Then we explore how executives can capitalize on its
long-term potential and overcome key challenges, from organization structure
to security. Finally, we offer an action guide on how to transform business with
generative AI—regardless of where you are on your AI journey.
FIGURE 1
From skepticism
to confidence
Executives see the true
2023 2024
potential of generative AI
taking shape
We need to adopt generative 38% 77%
AI quickly to keep up with
competitors
Generative AI is market ready 77%
36%
Generative AI is more reality 33% 62%
than hype
3
Case study
Bayer AG thinks
big along the AI
continuum5
Bayer AG chief executive Bill Anderson has an expansive vision for the future of
generative AI: “I think some of the biggest applications we’ll use it for are related
to how are we going to feed two billion more people in the world in the next
20 years, with less land available, less water, and a need to use less chemicals.”
Anderson’s resume—he has an advanced degree in chemical engineering from
MIT and joined Bayer after a stint as CEO at Roche Pharmaceuticals—suggests
a disciplined, evidence-based approach to big predictions. His confidence
in generative AI’s eventual impact is grounded in an understanding of its place
on the continuum of technologies, such as artificial intelligence and machine
learning, that have been remaking his company and industry for some time.
“It’s just starting, but it’s not up for debate,” he says of the fast-blooming new
generation of applications. “We’re definitely moving out of the realm of theory
into application.”
Generative AI at work
The first big win for generative AI at Bayer is coming in enhanced productivity,
a process that is already underway. “It’s replacing a lot of manual labor already,
and we’re just getting started,” says Anderson. Collecting, checking, and crunching
data to better understand patient populations, for example, can yield meaningful
if incremental benefits in terms of testing site and participant selection.
None of this is easy. Counterfeit and simulated products, for example, are a major
risk, with generative AI giving criminals the ability to work fast while evading
security measures. Deepfakes and false reporting are threats, as well.
But Anderson remains convinced of the potential of generative AI to accelerate
drug discovery. In two or three years, he says, a new cancer drug will be in stage
three clinical trials because of work being done now with generative AI. “That’s
really fast,” he says.
44
Bayer AG thinks big along the
AI continuum (continued)
Seeding the future
Over time, Anderson sees generative AI helping Bayer’s €25 billion crop science
division address the tough challenges of crop protection in a time of climate change.
Developing a new insecticide can be even harder than developing a new cancer
drug, because a cancer drug affects only the human body while an insecticide
can have impacts across entire ecosystems.
“We have to simulate the performance of a new crop protection chemical in
100 different environments—being able to use generative AI to make predictions
about which ones are likely to perform best can save us huge amounts of trials.”
Before generative AI can fully address such audacious goals, it must be integrated
across the Leverkusen, Germany-based company’s pharmaceutical, consumer
products, and crop science units. Anderson, who took the helm at the global life
sciences giant in 2023, is expected to be a change agent. He believes this enterprise
transformation is possible—and necessary.
“You don’t last 160 years by resting on your successes in the past,” he says of the
storied business, which operates in 83 countries and brings in €50 billion in annual
revenue. “There’s no safe space in the corporate world where you can just hang out
and enjoy your winnings from the past, right? You’ve got to always be driving forward
to the next horizon.”
55
Focusing generative AI adoption in
essential business functions helps
organizations create transformative,
top-line growth.
Where is generative AI delivering
the most value today?
Generative AI promises to be a powerful catalyst for business
transformation—but it’s not a panacea.
It must be implemented with careful consideration of cost, data governance, and
ethical implications, as well as an eye toward talent and skills. Because generative AI’s
biggest strength is to augment human work rather than automate it, culture change
is essential to deliver sustained value. In fact, 64% of CEOs say succeeding with
generative AI will depend more on people’s adoption than the technology itself.6
Instead of applying generative AI as a solution for every problem, leaders need to
understand how different tools work together, with traditional AI techniques,
generative AI models, and automation each playing their own part. They must
break out of the use case mindset and focus on using generative AI to transform how
employees work every day. Getting there is a journey—and how much experience
an organization has with AI influences where it should start.
Organizations are taking two main approaches to drive the systemic change needed
to deliver sustained AI ROI.
1. Experimentation: Finding efficiencies in low-risk, non-core functions.
Prioritizing generative AI adoption in low-risk areas where traditional AI is already
delivering clear business value helps accelerate transformation and can drive
incremental profitability. Roughly two-thirds of executives say their organizations
are adopting generative AI in customer service (70%), IT (65%), and product
development (65%) functions, which is consistent with what we saw in
mid-2023.7
2. Focus: Augmenting essential business functions to spark broader transformation.
The risk of using generative AI in business operations closer to the core may
be higher—but this is where the promise of business transformation begins to
take shape. Those willing to focus on the previously underexplored areas of
sales; information security; and supply chain, logistics, and fulfillment are
seeing higher ROI.
6
Of course, for many organizations, it makes sense to start a generative AI journey
by experimenting in lower-risk areas. They benefit from marginal gains while teams
learn how to make the most of the technology. But staying in the shallows also keeps
organizations from realizing the more transformative, top-line growth generative AI
can create. Only by setting their sights on enterprise-wide innovation—and focusing
their efforts in areas with the greatest potential—can organizations achieve
long-term, scalable success.
FIGURE 2
Mapping the generative AI journey
Focusing closer to the core does more
to drive enterprise transformation
Strategic Company Percentage of
Focus importance of AI types respondents
Foundational to Essential AI start-ups
business model
Business Central to business Various hyperscalers,
model-centric strategy software/ hardware companies
New business Key to transformation Companies using AI to help
model enabler and platform strategy achieve platform economics
17%
Product- Core to organic growth Companies embedding
embedded and innovation AI into core products
and R&D to differentiate
33%
Vertically Important to business Companies integrating AI within
integrated unit effectiveness function unit(s) to improve customer
experience/operational effectiveness
Horizontally Important to functional Companies deploying AI in functions
deployed effectiveness to improve customer experience/
operational effectiveness
31%
Opportunistic Ad hoc Companies experimenting
in low-risk, noncore
business areas
19%
Experimentation
7
noitamrofsnart
fo
eergeD
Perspective
Breadth versus depth
Organizations are implementing generative AI differently based on their starting point.
AI luminaries are leveraging their experience to drive Generative AI opportunists have low-to-medium
wider transformation with generative AI. They’re levels of adoption for AI overall—though their
already operationalizing and optimizing traditional AI adoption spikes in areas where they have
and are primarily using generative AI to improve on experience with traditional AI. They’re
existing AI capabilities. They have the highest experimenting with generative AI in three key
adoption maturity across functions for both functions: IT, customer service, and information
traditional and generative AI and are delivering higher security. By exploring areas where they see the
ROI with traditional AI than their peers. In most greatest potential, they’re delivering higher ROI
functions, at least 60% have implemented generative from generative AI than their peers.
AI, which means they have an opportunity to focus on
the areas that are already delivering the most value.
AI luminaries Generative AI opportunists
83% Customer service 55%
76% Information technology 67%
72% Information security 59%
67% Research and innovation 46%
64% Manufacturing 1%
64% Marketing 29%
61% Finance 33%
61% Sales 29%
Human resources
60% 19%
Supply chain, logistics,
60% 3%
and fulfillment
59% Procurement 5%
57% Product development 17%
55% Risk and compliance 26%
Q: Where is your organization in its adoption of generative AI for the following functional areas? Percentages include
88 respondents who selected implementing, operating, and optimizing.
Productivity gains that provide
an advantage today will be
table stakes tomorrow.
How to deliver long-term value
The path from experimentation to enterprise-scale innovation
isn’t a straight line. How adoption evolves depends on where an
organization is starting from, which capabilities it has developed,
and how prepared its workforce is to adapt.
At the same time, as generative AI matures, it’s likely that competitive capabilities
will begin to converge—making it more difficult to gain a competitive edge. That’s
why organizations must do the hard work of addressing the obstacles and challenges
that come with generative AI. And they need to do it quickly. What provides an
advantage today will be table stakes tomorrow.
For those early in the journey, deploying generative AI in low-risk functions can
help jumpstart progress toward business transformation. Experimentation and
small-scale wins can streamline workflows and increase efficiency while teams gain
their footing. Our research highlights two key areas as smart places to start:
Customer service
Our analysis suggests that in both generative AI adoption and ROI, customer service
leads the way. Many companies already have a solid foundation of traditional AI to
build upon, such as conversational AI that answers customer queries in natural
language. Recent IBM IBV research found that, on average, organizations using
generative AI in customer service see higher AI ROI than those that don’t.8 But it’s
not unambiguous. One trap to be aware of: Most customer-service use cases only
focus on making existing workflows more efficient. That will change quickly. By the
end of 2024, executives point to three rising opportunities: generating test cases for
training conversational AI (78%), generating dialogue for conversational AI (74%),
and generating dialogue for human agents (69%) (see “AI shifts customer service
into overdrive,” page 11).
IT
Developers are leaning on generative AI to help streamline routine tasks. For instance,
77% of companies that have adopted generative AI in IT are using it to generate code.
They’re also using it to automate code testing by identifying and fixing bugs and
helping ensure the code works as intended. Generative AI also speeds the process
of creating required documentation, including user manuals and other technical
materials that accompany software development and cybersecurity reviews.9
9
These areas are starting points for delivering long-term ROI, offering productivity
gains with meaningful impact. But over time, the biggest gains will come from
focusing generative AI deployment in business functions closer to the core.
Our research indicates that leading organizations are beginning to use generative AI
in previously unexplored areas, such as sales and supply chain, to rethink how work
gets done:
Sales and marketing
Generative AI can boost sales team performance by tapping customer data to
provide insights into their behavior. It identifies quality leads within high-value
market segments, making marketing strategies and outreach efforts more effective.
In fact, 85% of companies that have adopted generative AI in marketing are using
it to summarize market intelligence. Sales and marketing teams are also saving time
by using generative AI to write and edit creative content for emails, blogs, social
media posts, and websites in minutes—not hours—and then invest the time they’ve
saved into finding new ways to build customer relationships.10
Supply chain
As supply chain disruption intensifies, generative AI helps spot potential snags
and find workarounds before issues impact delivery. It enables intuitive
conversations between supply chain decision makers and AI assistants—making
their impact more tangible and relevant by providing the information they need in
real time. By automating mundane tasks and augmenting workflows, generative
AI also lets supply chain professionals focus on complex problem resolution and
process improvement.11 For example, 80% of companies adopting generative AI
in supply chain use it to generate operations documents.
But for some organizations, transformative opportunities like these seem out
of reach. That’s why some business leaders are considering a platform approach
to generative AI that pools resources and gains across departments or partner
organizations as a lower-cost, simpler-to-implement option. This way, leaders can
avoid starting from scratch in each area and embed generative AI quickly and more
strategically across functions that have the greatest potential, including finance,
supply chain and manufacturing, human resources, and sales and marketing.
However, leaders taking this approach also need to consider the unique needs of
each function and find ways to fine-tune generative AI applications accordingly.
75% of organizations are at least
piloting generative AI in five or
more functions.
10
Perspective
AI shifts customer
service into overdrive
From chatting with customers to creating targeted content to optimizing call center
performance, generative AI is taking the transformation of customer service to the
next level. Using natural language generation, it answers customer questions with
more fluent, contextually relevant responses. It can also tap into a customer’s
interaction history to tailor responses and deliver a more personalized experience.
These capabilities let customers chat with generative AI assistants in the same way
they would engage a human agent.
What’s more, the applications of generative AI go far beyond direct interactions
with customers. This technology can enhance the customer service function
more generally by supporting human agent training, increasing personalization,
translating content, and predicting future customer behavior. It can also support
customer-facing conversational AI by generating test cases and dialogue, as well
as reviewing interactions to identify opportunities for improvement.12
These use cases help generative AI supercharge conversational AI with less human
intervention. Using generative AI to create test cases—steps used to verify that an AI
model is working as intended—and responses to a variety of customer queries helps
teams training and fine-tuning conversational AI handle a wide range of scenarios,
user inputs, and edge cases.
11
Case study
Zebra Technologies
empowers the
augmented workforce13
Bill Burns, CEO of Zebra Technologies, expects generative AI to have a
positive impact on the way people work, including the company’s employees
and the users of the rugged mobile devices they produce. “Our business is
focused on the frontline worker,” he says of the $4.5 billion manufacturer-
turned-digital-solutions-provider that enables businesses to intelligently
connect data, assets, and people in industries. The company makes smart
tracking, marking, and printing devices for logistics and other functions in
industries including retail, manufacturing, transportation, healthcare and
public service.
Early notions about generative AI making people obsolete are themselves
outdated, he believes. “It’s not replacing the worker, it’s automating select
tasks within the workflow to augment and return time to the worker,
ultimately empowering the worker and allowing them to focus on higher
value activities.”
Zebra Technologies is methodical in its generative AI investments and has a
high standard for acting on use cases across the enterprise. Mr. Burns cites
the organization’s approach of “Sense, Analyze, Act” as a guide to process
and a way to avoid succumbing to the hype. The goal is to understand actions
and changes in workflow that drive improved outcomes, such as speed of
operations, accuracy, consistency, and overall productivity, then quantify
the impact and articulate an ROI. “Prove it to me and demonstrate that
there’s a business case,” he says.
This measured strategy must be weighed against the rapid maturation of
generative AI and the demands of the marketplace. “You have to have an
urgency around everything you do and operate with two speeds. Speed one
is deliberate, focused on execution and getting solutions into the hands of
our customers for those use cases we are confident will generate value.
Speed two is less structured and experimental, co-innovating with
customers and discovering new areas that can benefit from AI innovation,”
he says. “If we don’t do it, somebody else will.”
“Generative AI will make
employees’ jobs easier and improve
the customer experience.”
Bill Burns, CEO, Zebra Technologies
12
Zebra Technologies empowers the “You have to have an urgency
augmented workforce (continued)
around everything you do...If we
don’t do it, somebody else will.”
Bill Burns, CEO, Zebra Technologies
Mr. Burns is keeping stakeholders across the Creating the future of work
company close as Zebra begins its generative AI
Mr. Burns expects the net impact of generative AI
evaluations. The plan: “Educate ourselves on
to include many good job opportunities, with new
generative AI while connecting with large strategic
positions created that are less stressful, and let
tech partners, form a cross-functional team with the
employees focus on more meaningful work, develop
CTO of the organization looking externally and the
enhanced abilities, and learn and grow on the job
CIO looking internally, and work together to define
quickly. He senses a shift in the way executives are
responsible and ethical AI principles across the
talking about what comes next. “It has evolved from
organization as the space evolves,” he says. “One of
all this hype of ‘workers are going to be replaced’ to
the keys involves communication and change
tasks being automated,” he says. “Generative AI will
management to ensure everyone knows of these
make employees’ jobs easier and improve the
teams and embraces new ways of operating—and
customer experience.”
that starts at the top.”
He points to the example of software developers
Critical decisions for implementation are made after
who can now use the technology to write code.
a thorough review of expected benefits, and of
“Developers will not lose their jobs but instead can
costs. “People think it’s all free because today they
spend more time on value-added tasks or simply
go on to ChatGPT and it’s free,” he says. “If you want
getting more done, especially given underlying labor
to use it at scale inside an enterprise, it’s no longer
availability and cost challenges,” he says.
free, as these solutions consume real resources in
the cloud. But Zebra is exploring and has developed The technology should empower people to move
a frontline worker application that runs the gen AI quickly, he says, by giving them easy access to
model on a mobile device. This reduces costs, actionable insights derived from both structured and
improves security, and protects data.” unstructured data. Faster training and reduced time
to proficiency obviously pay off for employers, too.
Zebra Technologies has already identified many
For instance, think of businesses in the retail sector,
internal use cases for generative AI that have the
which can see very high job turnover, or fields where
potential to change employee workflows. These
new employees have traditionally needed extended
include everything from building marketing
training periods. “With a mobile device as a window
campaigns quickly with multiple languages to
into a generative AI assistant, the newest employee
enabling its customer service teams to provide a
can quickly become as proficient as a much more
more personalized customer experience with
experienced employee while benefitting from
quicker issue resolution times.
greatly improved job satisfaction.”
In terms of product development focused on
customers, the strategy is to use open-source large
language models residing on the company’s
next-generation mobile devices. Zebra then
fine-tunes these models by use case using its own
data while leveraging a platform for customers to
populate the models with their own data and tie into
their systems via Zebra mobile devices.
13
14
Organizations that build a solid
foundation for generative AI today will
be able to pivot and build momentum
as new opportunities arise.
Building a springboard for growth
Despite generative AI’s progress, challenges remain. Almost half of
executives say they’re concerned about accuracy and bias—an issue
that could create as many new problems as generative AI promises
to solve.
Many leaders are also concerned that inadequate expertise, unclear business
cases, and insufficient proprietary data could preclude progress with
generative AI (see Figure 4).
FIGURE 4
A confluence of challenges
Organizations must overcome many obstacles to make
headway with generative AI—and executives’ top concerns
are shifting as it matures
   45% Concerns about data accuracy or bias
   42% Insufficient proprietary data available to customize models
   42% Inadequate generative AI expertise
   42% Inadequate financial justification/business case
   40% Concerns about privacy/confidentiality of data and information
   40% Limited access to technology
   36% Requires too much investment
   32% Concerns about intellectual property
   31% Irrelevant or unclear use cases
   31% Security of data/concerns about cybersecurity
   19% Constrained by regulation/compliance
   12% Not aligned to business strategy
   1% Inadequate infrastructure/no barriers
April 2023 August 2023 March 2024
15
Overcoming challenges requires an interconnected effort that brings
together leaders from technology, finance, security, legal, and AI ethics.14
It’s complex work, but avoiding it comes with serious consequences. From
increasing liability to introducing new security vulnerabilities to damaging
brand reputation, leaders must understand and mitigate a litany of new risks
as they integrate generative AI into business operations.
Some organizations are already making efforts to manage these threats:
80%
have a separate part of their risk function dedicated
to risks associated with the use of AI or generative AI.
81%
conduct regular risk assessments to identify
potential security threats introduced by generative AI.
78%
maintain robust documentation to enhance explainability
of how generative AI models work and were trained.
76%
establish clear organizational structures, policies,
and processes for generative AI governance.
72%
develop policies and procedures for managing data
and addressing potential risks.
These activities should be part of any robust generative AI risk management
strategy. But identifying where their organization needs to focus its attention
should be a top priority for leaders as they begin to use generative AI in areas
that are core to their competitive advantage.
Reimagine what’s possible
AI has the potential to transform the business world, economies, and
societies in ways that are hard to imagine. By building the right capabilities
today, organizations can bring these new opportunities into focus.
It’s not just about automating things people are doing today. It’s about doing
things that were never possible before.15 From helping develop cures for
diseases to combatting climate change, generative AI could solve problems
that have confounded people for centuries. More than half of the executives
in our survey say that, in the next three years, generative AI will make
entirely new types of work possible (see Figure 5).
It’s difficult to imagine what these new use cases might look like‚ but that’s
kind of the point. The generative AI applications that could deliver the
greatest value tomorrow have yet to be discovered. The organizations that
build a solid, capability-rooted foundation for generative AI today will
be able to pivot and build momentum as new opportunities arise.
16
“Process automation is not about replacing
an individual. It’s about enhancing the
value of individuals—making human
work more human.”
Javier Tamargo, CEO, 407 ETR
By providing a platform that lets employees experiment safely,
organizations can unlock the collective genius of their workforce.
Leaders will need to foster a growth and innovation mindset—and
encourage employees to look beyond what’s worked in the past—to
pioneer groundbreaking innovation, outpace the competition, and
drive transformative growth at scale with generative AI.
FIGURE 5
Forging a new frontier
Generative AI opens a new world of opportunity
New
opportunities
Augmented
human/machine
tasks
Human tasks
Note: Figure is conceptual in nature. Proportions are not derived from data.
17
Perspective
IBM and NASA are
helping humanity adapt
to a changing climate16
Nearly a quarter of the world’s population now lives in a flood zone, and that number
is expected to climb as rising seas and heavier storms triggered by a changing
climate put more people at risk. The ability to accurately map flooding events can
be key to not only protecting people and property now but steering development
to less-risky areas in the future.
IBM and NASA’s geospatial foundation models are designed to enable important
steps toward this goal by converting NASA’s satellite observations and data into
customized maps of natural disasters and other environmental changes. Potential
applications include helping to estimate climate-related risks to crops, buildings,
and other infrastructure; monitoring and valuing forests for carbon-offset programs;
generating renewable energy forecasts; and developing predictive models
to help enterprises create strategies to mitigate and adapt to climate change.
As part of a Space Act Agreement, IBM and NASA set out to build the first-ever
foundation model for analyzing geospatial data in early 2023. Previously, users
had to train a new model for each task, which required extensive data curation
and compute. Rather than train a foundation model on words, IBM Research taught
a model to understand satellite images. The team then fed the model hand-labeled
examples to teach it to recognize the extent of historic floods and fire burn scars,
changes in land-use and forest biomass, and more.
IBM and NASA expanded the family of models in 2024, developing a foundation
model for weather and climate data. They customized this model for more specific
tasks, such as creating highly localized wind forecasts for renewable energy
planning and increasing the resolution of climate simulations to better understand
and plan for the local effects of climate change.
Using the foundation model is designed to be as simple as selecting a region,
a mapping task, and a set of dates. For example, if a user types “Port-de-Lanne,
France” into the search bar and selects a date range of December 13 to 15, 2019,
the model highlights in pink how far the flood waters extended. Users can overlay
other datasets to see where crops or buildings were inundated. The models and
accompanying visualizations can help with future planning during similar disaster
scenarios: they provide information that could help mitigate flood impacts, inform
insurance and risk management decisions, define infrastructure plans, improve
disaster response, and protect the environment.
18
IBM and NASA are helping humanity
adapt to a changing climate (continued)
IBM and NASA built both models using a masked autoencoder for"
142,ibm,sg248573.pdf,"Front cover
Simplify Your AI Journey:
Ensuring Trustworthy AI with
IBM watsonx.governance
Deepak Rangarao Mohit Sharma
Upasana Bhattacharya Mark Simmonds
Savitha Chinnappareddy PhD Jasmeet Singh
Larry Coyne Martijn Wiertz
David Cruz
Shuvanker Ghosh
Prem Piyush Goyal
Vasfi Gucer
Amna Jamal PhD
Warren Lucas
Karen Medhat
Bob Reno
Artificial Intelligence
Data and AI
Redbooks
IBM Redbooks
Ensuring Trustworthy AI with IBM watsonx.governance
January 2025
SG24-8573-00
ii Ensuring Trustworthy AI with IBM watsonx.governance
Contents
Notices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .vii
Trademarks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . viii
Foreword. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ix
Preface. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .x
Authors. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .x
Now you can become a published author, too! . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiii
Comments welcome. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiii
Stay connected to IBM Redbooks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiv
Chapter 1. Challenges and opportunities in AI governance for responsible AI . . . . . . 1
1.1 What is AI governance? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
1.2 Governance as a key enabler for realizing AI value . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
1.2.1 Concern 1: Governance is a brake on AI . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
1.2.2 Concern 2: Governance does not scale . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
1.2.3 Concern 3: Governance does not contribute to value generation. . . . . . . . . . . . . . 6
1.3 Challenges with governance of enterprise AI . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
1.3.1 Generative AI has changed the governance game. . . . . . . . . . . . . . . . . . . . . . . . . 6
1.3.2 Bring together diverse stakeholder perspectives . . . . . . . . . . . . . . . . . . . . . . . . . . 7
1.3.3 Technical complexity is increasing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
1.3.4 Regulatory and risk complexity is increasing . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
1.4 An example of legislation and standards related to AI . . . . . . . . . . . . . . . . . . . . . . . . . 11
1.4.1 AI-specific legislation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
1.4.2 General regulations that apply to AI . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
1.4.3 Technical standards for AI governance. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
Chapter 2. Introduction to IBMwatsonx.governance . . . . . . . . . . . . . . . . . . . . . . . . . . 15
2.1 Introduction to the IBM watsonx platform and its core components . . . . . . . . . . . . . . . 16
2.2 Introduction to IBM watsonx.ai . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
2.3 Introduction to IBM watsonx.data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
2.4 Introduction to IBM watsonx.governance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
2.4.1 Key capabilities. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
2.4.2 Use cases. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
2.4.3 Benefits of watsonx.governance. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
2.5 Reference architecture . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
2.5.1 Data Onboarding. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
2.5.2 Data Preparation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
2.5.3 AI Building and Deployment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24
2.5.4 AI Lifecycle Management and Governance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24
Chapter 3. Implementing AI governance strategy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
3.1 Understanding the end-to-end AI lifecycle governance process. . . . . . . . . . . . . . . . . . 28
3.2 Elements of model risk governance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29
3.2.1 Personas. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29
3.2.2 Objects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31
3.2.3 Workflows. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36
3.3 Considerations to implement AI governance strategy. . . . . . . . . . . . . . . . . . . . . . . . . . 37
3.3.1 Understanding organizational characteristics. . . . . . . . . . . . . . . . . . . . . . . . . . . . 38
3.3.2 Configuring AI governance. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38
© Copyright IBM Corp. 2025. iii
3.3.3 Leveraging out-of-the-box product content. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38
3.3.4 Example use case. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39
Chapter 4. Onboarding a new foundation model. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41
4.1 Key considerations to onboard a foundation model . . . . . . . . . . . . . . . . . . . . . . . . . . . 42
4.1.1 Data transparency. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42
4.1.2 Model evaluation and validation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42
4.1.3 Model security and robustness. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43
4.1.4 Ensuring model health and performance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43
4.2 Considerations for legal team for approving a new foundation model . . . . . . . . . . . . . 44
4.2.1 Model licensing. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44
4.2.2 Legal obligations on the part of the vendor. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46
4.2.3 A final note on legal considerations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47
4.3 Ethical considerations for approving a new foundation model . . . . . . . . . . . . . . . . . . . 47
4.3.1 Fairness . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47
4.3.2 Transparency . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48
4.3.3 Privacy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48
4.3.4 Explainability. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49
4.3.5 Robustness. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49
4.3.6 Third-party help. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49
4.4 Considerations for financial stakeholders for approving a new foundation model . . . . 49
4.4.1 Total cost of ownership. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50
4.4.2 Return on investment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50
4.4.3 Build or buy. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51
4.4.4 Exit strategy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51
4.4.5 Other factors. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51
Chapter 5. Assessing a new use case . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53
5.1 Business process workflow. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54
5.2 Approval workflow. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55
5.3 Risk identification assessment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56
5.4 Applicability assessment. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58
Chapter 6. Governing the end-to-end lifecycle of an AI asset . . . . . . . . . . . . . . . . . . . 61
6.1 What is the AI lifecycle? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62
6.2 Metrics in watsonx.governance. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64
6.2.1 Drift detection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64
6.2.2 Explainability. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64
6.2.3 Model health. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65
6.2.4 Generative AI quality. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65
6.2.5 RAG quality metrics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65
6.3 How to implement Lifecycle Governance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66
6.3.1 Getting started: Setting up your AI use cases. . . . . . . . . . . . . . . . . . . . . . . . . . . . 66
6.4 Lifecycle implementation and considerations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67
6.4.1 UI-driven implementation of lifecycle governance. . . . . . . . . . . . . . . . . . . . . . . . . 68
6.4.2 Considerations for lifecycle governance for traditional ML hosted on watsonx.ai. 70
6.4.3 Considerations for prompt templates from another platform. . . . . . . . . . . . . . . . . 72
6.4.4 Considerations for traditional ML from another platform. . . . . . . . . . . . . . . . . . . . 74
6.4.5 Governing AI embedded in a business application. . . . . . . . . . . . . . . . . . . . . . . . 74
Chapter 7. Use cases . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77
7.1 Overview of use case 1- Banking credit risk management. . . . . . . . . . . . . . . . . . . . . . 78
7.1.1 Banking credit risk management use case. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 78
7.1.2 Business context. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 78
iv Ensuring Trustworthy AI with IBM watsonx.governance
7.1.3 Client need . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79
7.1.4 Client challenges. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79
7.1.5 Business benefits . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79
7.1.6 Pilot solution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80
7.2 Overview of use case 2 - Automated governance for universal bank's AI chatbot. . . . 80
7.2.1 Business context. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80
7.2.2 Client need . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80
7.2.3 Client challenges. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80
7.2.4 Business benefits . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81
7.2.5 Pilot solution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81
7.3 Overview of use case 3 - Belgian biopharmaceutical company . . . . . . . . . . . . . . . . . . 81
7.3.1 Business context. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81
7.3.2 Client need . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81
7.3.3 Client challenges. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82
7.3.4 Business benefits . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82
7.3.5 Pilot solution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82
Related publications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85
IBM Redbooks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85
Online resources . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85
Help from IBM . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85
Contents v
vi Ensuring Trustworthy AI with IBM watsonx.governance
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viii Ensuring Trustworthy AI with IBM watsonx.governance
Foreword
This trilogy of IBM® Redbooks® publications positions and explains IBM watsonx, the
IBM strategic AI and Data platform. Each book focuses on one of the three main components
of the watsonx platform:
(cid:2) IBM watsonx.ai: A next-generation enterprise studio for AI developers to train, validate,
tune, and deploy both traditional ML and new generative AI capabilities powered by
foundation models.
(cid:2) IBM watsonx.data: A fit-for-purpose data store built on an open-lakehouse architecture,
optimized for different and governed data and AI workloads.
(cid:2) IBM watsonx.governance: A set of AI governance capabilities enabling trusted AI
workflows, helping organizations implement and comply with ever-changing industry and
government regulations.
Organizations have long recognized the value that IBM Redbooks provide in guiding them
with best practices, frameworks, clear explanations, and use cases as part of their solution
evaluations and implementations.
This trilogy of books was only possible due to the close collaboration involving many skilled
and talented authors that were selected from our IBM global technical sales, development,
Expert Labs, Client Success Management, and consulting services organizations, using their
diverse skills, experiences, and technical knowledge across the watsonx platform.
I would like to thank the authors, contributors, reviewers, and the IBM Redbooks team for their
dedication, time, and effort in making these publications a valuable asset that organizations
can use as part of their journey to AI.
I also want to thank Mark Simmonds and Deepak Rangarao for taking the lead in shaping this
request into yet another successful IBM Redbooks project.
It is my sincere hope that you enjoy this watsonx trilogy as much as the team who wrote and
contributed to them.
Steve Astorino, IBM General Manager - Development, Data, AI and Sustainability.
© Copyright IBM Corp. 2025. ix
Preface
IBM® watsonx™ is the IBM strategic AI and Data platform. This book focuses on
watsonx.governance, a key component of the platform.
IBM watsonx.governance offers a comprehensive solution for governing data and AI
workloads within a secure and scalable environment. Built on an open architecture, it
empowers organizations to manage data access, compliance, and security across hybrid
multi-cloud deployments. IBM watsonx.governance simplifies data governance with built-in
automation tools and integrates seamlessly with existing databases and tools, streamlining
workflows and enhancing user experience
This IBM Redbooks publication provides a broad understanding of watsonx.governance
concepts and architecture, and the services that are available in the product. In addition,
several common use cases and scenarios are included that should help you better
understand the capabilities of this product.
This publication is for watsonx customers who seek best practices and real-world examples of
how to best implement their solutions while optimizing the value of their existing and future
technology, AI, data, and skills investments.
Note: Other books in this series are:
(cid:2) Simplify Your AI Journey: Unleashing the Power of AI with IBM watsonx.ai, SG24-8574
(cid:2) Simplify Your AI Journey: Hybrid, Open Data Lakehouse with IBM watsonx.data,
SG24-8570
Authors
This book was produced by a team of specialists from around the world working with the
IBM Redbooks, Tucson Center.
Deepak Rangarao is an IBM Distinguished Engineer and CTO responsible for Technical
Sales-Cloud Paks. Currently, he leads the technical sales team to help organizations
modernize their technology landscape with IBM Cloud® Paks. He has broad cross-industry
experience in the data warehousing and analytics space, building analytic applications at
large organizations and technical pre-sales with start-ups and large enterprise software
vendors. Deepak has co-authored several books on topics, such as OLAP analytics, change
data capture, data warehousing, and object storage and is a regular speaker at technical
conferences. He is a certified technical specialist in Red Hat OpenShift, Apache Spark,
Microsoft SQL Server, and web development technologies.
Upasana Bhattacharya is a Senior Product Manager for watsonx.governance, based in
Markham, Canada. In this role she defines the product vision, guides its development,
collaborating with cross-functional teams. In her previous role she was a Product Manager for
Data and AI. Upasana holds a Bachelor of Arts in Economics and Foreign Affairs from the
University of Virginia and an MBA from the McCombs School of Business at the University of
Texas.
x Ensuring Trustworthy AI with IBM watsonx.governance
Savitha Chinnappreddy, PhD is a Senior AI Engineering Manager at IBM with over 17 years
of experience in AI and Data Analytics. She holds a PhD in AI and Data Analytics and is
currently pursuing a post-doctorate focused on Human & AI Collaboration: Governance
strategies for trustworthy AI & Safe AI systems. She has extensive Experience in managing
and scaling large AI and Data Science teams, she has worked closely with architecture and
infrastructure teams to establish compliant pipelines for AI and analytics, delivering impactful
solutions to global customers. With 11 publications in esteemed journals and conferences, as
well as holding a patent, she is also an active guest speaker and participant in faculty
development programs, committed to sharing her knowledge and inspiring the next
generation of AI professionals.
Larry Coyne is a Project Leader at the IBM International Technical Support Organization,
Tucson, Arizona, center. He has over 35 years of IBM experience, with 23 years in
IBM storage software management. He holds degrees in Software Engineering from the
University of Texas at El Paso and Project Management from George Washington University.
His areas of expertise include client relationship management, quality assurance,
development management, and support management for IBM storage management software.
David Cruz is a Data Scientist and AI Engineer working under IBM’s Client Engineering
team. In this role, David has been dedicated to the Federal Market where he works to
implement a wide range of AI solutions for federal clients. In his prior role, he worked under
the Data Science Elite team where he gained skills with IBM platforms for Governance,
namely IBM OpenScale, and this has translated into a growing skill set with watsonx
governance. He is constantly working to implement the cutting edge of AI and AI Governance
technology, and has written various blog posts on topics ranging from Unsupervised Learning
techniques, to RAG how-to guides for beginners.
Shuvanker Ghosh is a certified Executive Architect and Worldwide Platform Leader for Data
and AI in Worldwide Solution Architecture in IBM Technology Expert Labs. With 18 years of
experience at IBM, he serves as a trusted advisor to clients, offering thought leadership on
IBM's Data and AI portfolio. He guides organizations in their responsible AI journey, helping
them adopt best practices. His current focus is on defining solution blueprints and
architectural patterns that assist clients in addressing their business challenges through
responsible and trustworthy AI solutions. He possesses extensive expertise in the IBM Data
and AI portfolio, including the watsonx platform and Cloud Pak for Data. Shuvanker has
successfully led and delivered complex programs that involve multiple teams, providing
technical management, architecture, technology thought leadership, and software
development methodologies and processes. His experience spans various industries,
including retail, finance, insurance, healthcare, telecommunications, and government
Prem Piyush Goyal is a problem solver with extensive experience in developing cutting-edge
technologies at IBM. Specializing in full-stack development, cloud-based microservices, and
AI solutions, he has worked on high-impact projects like IBM Watson® Data Platform and
IBM Watson OpenScale. His expertise spans Python, JavaScript, React, Kubernetes, and
AI-driven solutions like Explainable AI and Concept Drift Detection. Passionate about building
transparent and scalable AI, he continually enhances user experience and optimizes
performance for enterprise applications. His innovative mindset and problem-solving abilities
help drive trust and transparency in AI systems.
Vasfi Gucer leads projects for the IBM Redbooks team, leveraging his 20+ years of
experience in systems management, networking, and software. A prolific writer and global
IBM instructor, his focus has shifted to storage and cloud computing in the past eight years.
Vasfi holds multiple certifications, including IBM Certified Senior IT Specialist, PMP, ITIL V2
Manager, and ITIL V3 Expert.
Foreword xi
Amna Jamal PhD is a seasoned Data and AI Subject Matter Expert (SME) at IBM, boasting
over 8 years of expertise in data management and data science. With a Ph.D. in Engineering
from the National University of Singapore, she brings a wealth of knowledge and experience
to the field, driving innovation and excellence in the intersection of data and artificial
intelligence.
Warren Lucas is a member of IBM Expert Labs. Prior to his time at IBM, Warren has spent
nearly a decade working in Regulatory Compliance, Operational Risk, and Model Risk
Governance supporting a number of Fortune 50 companies in their efforts to redesign and
implement internal governance processes. As a Solution Architect, Warren has specialized in
Governance Console (IBM OpenPages®) for over seven years, where he has personally
performed development, design, advisory, and configuration within the platform. Warren has a
current patent submission for a novel approach in governance and confidence assessments
in large language models (LLMs); he holds a degree in Quantitative Economics.
Karen Medhat is a Customer Success Manager Architect in the UK and the youngest
IBM Certified Thought Leader Level 3 Technical Specialist. She is the Chair of the IBM
Technical Consultancy Group and an IBM Academy of technology member. She holds an
MSc degree with honors in Engineering in AI and Wireless Sensor Networks from the Faculty
of Engineering, Cairo University, and a BSc degree with honors in Engineering from the same
faculty. She co-creates curriculum and exams for different IBM professional certificates. She
also created and co-created courses for IBM Skills Academy in various areas of
IBM technologies. She serves on the review board of international conferences and journals
in AI and wireless communication. She also is an IBM Inventor and experienced in creating
applications architecture and leading teams of different scales to deliver customers' projects
successfully. She frequently mentors IT professionals to help them define their career goals,
learn new technical skills, or acquire professional certifications. She has authored
publications on Cloud, IoT, AI, wireless networks, microservices architecture, and Blockchain.
Bob Reno is a Principal Technical Sales Specialist with over 30 years of experience in Data
Warehousing, Analytics, and AI. As a member of the IBM World Wide Data and AI Technical
Sales team, Bob is a watsonx.governance leader working with customers to enable their
organizations to embrace responsible AI. Bob has contributed to the creation of several
IBM Certification Tests and written several workshops in the watsonx, Cloud Pak for Data and
Data Warehousing space to enable customers and the IBM Technical Community. Prior to
joining IBM, Bob has held roles as a Developer, Technical Architect, and Director of Data
Warehousing and Analytics.
Mohit Sharma is an AI engineering lead on the Client Engineering watsonx team in
Bangalore, India. Prior to this, Mohit was associated with IBM consulting, and worked on
client production projects involving classical ML and deep learning. Mohit has around 14
years of experience in AI, and worked at Hewlett Packard, Wipro (where he conceptualized
the Holmes AI platform) and Accenture before joining IBM in 2018. An AI practitioner having
experience in design and development of AI-based solutions using both open-source and
commercial technologies, Mohit is interested in both data and the science behind it. He has 4
published patents to his credit, and has filed his first patent at IBM.
Mark Simmonds is a Program Director in IBM Data and AI. He writes extensively on AI, data
science, and data fabric, and holds multiple author recognition awards. He previously worked
as an IT architect leading complex infrastructure design and corporate technical architecture
projects. He is a member of the British Computer Society, holds a Bachelor’s Degree in
Computer Science, is a published author, and a prolific public speaker.
xii Ensuring Trustworthy AI wit"
144,ibm,embedding-ai-in-your-brands-dna.pdf,"IBM Institute for Business Value | Research Insights
Embedding AI
in your brand’s DNA
Innovate from products to ecosystem—
and everything in between
How IBM
can help
IBM has been providing expertise to help retail and consumer products
companies win in the marketplace for more than a century. Our researchers
and consultants create innovative solutions that help clients become more
consumer-centric by delivering compelling brand and store experiences,
collaborating more effectively with channel partners, and aligning demand
and supply. With a comprehensive portfolio of solutions for merchandising,
supply chain management, omnichannel retailing, and advanced analytics,
IBM helps deliver rapid time to value. With global capabilities that span 170
countries, we help brands and retailers anticipate change and profit from
new opportunities. For more information on our retail and consumer
products solutions, please visit: ibm.com/industries/retail, ibm.com/
consulting/retail, and ibm.com/industries/consumer-goods.
2
Key
takeaways
Brands are evolving Over the next year, retail and consumer
beyond mere AI adoption, products executives expect to expand
embedding it in their DNA AI significantly throughout all areas
to harness their distinct of the business, from brand-defining
AI-driven advantage. activities to core operations.
But to be AI-centric, organizations need an open
mindset for how AI can deliver transformation beyond
productivity gains.
Across 13 areas of the business,
executives plan to augment most
activities with AI over the next
12 months.
But they only project 31% of their workforce will need
to reskill or develop new skills in that same time frame,
underestimating what’s needed to support employees
in the AI transformation.
Almost 9 in 10 executives claim to have
clear organizational structures, policies,
and processes for AI governance.
But fewer than one-quarter of organizations have fully
implemented and continuously review tools on AI
governance, putting brand trust at risk.
1
Industry executives project that
AI’s contribution to revenue
growth will increase 133%
from 2023 to 2027.
Consumers are ready for AI.
Are you?
Consumers are tech-savvy trendsetters and brands need to keep up to
stay relevant. Today, customers and shoppers are actively engaged with
AI in their daily lives, from using AI-powered search engines to creating
content with generative AI tools.
In the 2024 IBM Institute for Business Value (IBM IBV) consumer research study,
nearly two-thirds of consumers said they have used or want to try AI applications.1
This interest sets the stage for retail and consumer products companies to hasten
integration of AI across their business while keeping an eye toward becoming AI-led
brands—leveraging the technology to reimagine operations, inspire loyalty, and
expand the size of customers’ wallets for long-term competitive advantage.
Our latest survey of 1,500 global retail and consumer products executives finds
organizations are accelerating their adoption. AI—both traditional and generative—has
permeated all functions in the enterprise to some degree. From marketing and customer
service, to supply chain and procurement, to finance and IT operations, AI use cases
span brand-defining, business-enabling, and corporate operations. Looking ahead
through 2025, most executives are thinking big, expecting AI to be used extensively
across the business (see Figure 1). Industry leaders also report AI spending is on the
rise (see Perspective, “AI spending moves outside of IT”), and they project that AI’s
contribution to revenue growth will increase 133% from 2023 to 2027.
Retail and consumer products organizations are at a pivotal point in their AI journey.
The question is: are they taking enough of the right steps to become AI-led brands,
or are they just tacking on ad hoc AI solutions that deliver short-term gains? It’s time to
move beyond just productivity and efficiency and extend AI’s power enterprise-wide
to boost process effectiveness, spark new business models and ecosystems, and
ignite engagement with innovative employee and customer experiences.
2
FIGURE 1
Retail and consumer
products organizations
plan to use AI extensively
in 2025.
Figure 1
Retail and consumer products organizations use AI extensively in 2025.
Percent of organizations planning to use AI to a moderate or significant extent over the next 12 months
Marketing and customer experience 89%
Digital commerce 86%
Merchandising 86%
Customer service 85%
Brand-defining
areas
Stores
79%
Product design and development 76%
Supply chain operations 90%
Sustainability 87%
Procurement 86%
Business-enabling
areas
Production and manufacturing 83%
IT and security 90%
Finance 90%
Corporate
operations
HR 88%
Percentages represent an average of responses for a set of tasks in each functional area, based on the question: “To what extent
do you use AI or gen AI in this activity?” Respondents replied “to a moderate extent” or “to a significant extent.”
3
Perspective
In this report, we discuss three factors that will help
AI spending
organizations make a fundamental change in their
DNA, where AI emerges as the driving force behind shifts beyond
every decision, innovation, and strategy. In part one,
IT budgets
we discuss balancing the marathon with the sprint to
shift from plus-AI to AI-first. In part two, we examine
the need to prepare the workforce for the planned
rapid and aggressive AI adoption, and in part three,
we address the imperative to safeguard consumer AI budget allocation is undergoing a significant shift.
trust. Each section includes an illustrative case study While IT budgets will still play a role, retail and
and concludes with an action guide of steps brands consumer products executives report a growing
can take to accelerate progress. portion of AI spending is moving outside of traditional
IT budgets. As AI becomes more than just a tech tool,
functional areas are identifying their needs for AI as
part of larger business solutions, from creative
Definitions
marketing tools to empowering store associates to
new warehouse management systems.
Traditional artificial intelligence
Executives project their IT budget dedicated to AI
spend will increase by 19% over the next year, but
Systems that understand, reason, learn, and
spending on AI outside of the IT budget is expected
interact. AI technology includes machine
to surge 52%. As a percent of revenue, IT spending
learning (ML) approaches, but also other
on AI will be 1.04% and AI spending outside of IT
techniques such as reasoning, planning,
will be 2.28% by 2025. Taken together, 3.32% of
scheduling, and optimization.
revenue could be dedicated to AI spending next year.
For a $1 billion company, that equates to $33.2
Generative AI
million for total AI spend.
A class of machine learning that generates
With at least 13 functional areas that span retail and
content or data, including audio, code,
consumer products organizations, executives across
images, text, simulations, 3D objects, and
the C-suite must keep tabs on the investments being
videos—usually based on unsupervised or
made in each area, coordinating platforms and tools
self-supervised learning. Recent examples of
to provide transparency across the enterprise.
generative AI include GPT-4 (language), DALL-E
IT and the business lines must work together to avoid
(images), GitHub Copilot (code), and AlphaFold
duplication of effort and to help ensure consistent
(scientific protein folding).
alignment with the overall business strategy.
4
Part one
Building an intelligent brand
that endures
Consumer organizations need to take a long-term view
of their AI journey while moving with urgency and intent.
Nearly all industry executives are banking on AI for innovation in products and
services (89%) as well as business models (85%). But a mere 54% expect AI to
influence operational innovation. Transforming operations with AI across supply
chains, manufacturing, distribution, finance, and compliance is the very essence of
being an AI-centric brand. This remodel is both a marathon and a sprint—moving from
simple AI use cases to orchestrating AI across functions to deliver sustainable value.
Many organizations are in the early stage of adoption, integrating AI within a single
function. For example, 88% use AI to a moderate or significant extent in demand
forecasting, 87% for HR help desks, 84% for IT support and issue remediation, 84%
in creating and managing trade promotions, 81% in inventory and order management,
and 80% in managing production activities. These are quick wins that can deliver
a more immediate impact on daily operations.
But companies are keen on expanding to more sophisticated uses of AI over the next
12 months. They will be transitioning from internal departmental use cases with
limited system integration to multifaceted ones that require external collaboration,
more complex system integrations, and more human intervention and oversight.
Take virtual assistants as an example (see Figure 2). Initially, they responded to
simple, predefined queries such as order and shipment status. As they have become
more integrated with data in ordering systems, they can identify delays or missing
orders as well as back-order options and in-store availability. Adding customer
shopping history and generative AI capabilities to their arsenal, they can dynamically
recommend offerings and personalized content for individual customers. Camping
Only 54% of executives
World’s virtual assistant, Arvee, illustrates the value of integrating platforms such
expect AI to help their
companies innovate in as Oracle and Salesforce so that the assistant can access customer information
operations. efficiently to address queries faster.2
5
Executives expect to expand rapidly to more sophisticated AI use cases
across the enterprise. For example, those leveraging AI to a significant
extent for personalized responses and follow-up actions in customer
service plan to increase their usage by 236% over the next 12 months.
Similarly, they want to grow significant AI usage in integrated business
planning by 82% and in talent acquisition by 300%.
Figure 2
BFIrGaUnRdEs 2 and retailers plan to expand use of AI/gen AI
into more sophisticated use cases over the next year.
Brands are fueling virtual assistants with more
comprehensive, relevant enterprise data to enable
increasingly personalized responses to customers.
I can provide When I am connected to the When I have access to
shipment status order management warehouse customer profiles and shopping
and tracking and store inventory system, history, I can dynamically
information. I can provide options for recommend offerings and
back orders and in-store personalized content for
pickup options. individual engagement.
6
Case study
As organizations progress with their initiatives, they Kroger uses AI
are investing in platforms to integrate AI tools and
to elevate customer
models. Today, as they establish their AI foundation,
they are primarily focused on data and analytics pickup experiences3
platforms (65%), innovation platforms (64%), and
skills/learning platforms (62%). Building on these
existing platforms and expanding to others will
enable federation and orchestration of AI across
Kroger has long depended on data and advanced
functions, facilitating cross-functional learning to
analytics to fuel business innovation. Since its
support scaling AI across the enterprise.
inception decades ago, its loyalty program has
Executives plan to integrate AI capabilities with delivered a trusted value exchange enabled by
business partners over the next three years, and they permission-based information. Today, using machine
predict the use of ecosystem platforms will surge learning algorithms, Kroger delivers valuable
from 52% today to 89%. Take the product compliance personalized offers and communications across
ecosystem as an example. By integrating end-to-end 150 million customer touchpoints and through
AI-driven compliance, brands can ensure all facets 1.9 billion unique coupons customized for millions
of the product lifecycle align with evolving regulatory of loyal customers.
requirements, consumer safety, and sustainability
Most recently, Kroger has been exploring ways
expectations. This ecosystem prioritizes accelerated
to use AI to help improve the customer experience,
product lifecycle management with an advanced
specifically order pickups. Using AI-enabled dynamic
business rules engine and touchless bill-of-materials
batching, an AI solution sorts through 200,000 totes
generation, helping ensure products are market-
per second to build the most efficient pickup trolley.
ready with minimal manual intervention.
It drives a 10% reduction in steps by identifying the
most efficient pick route through the store. With
dynamic batching of orders, these tools are providing
associates the most efficient pick routes, so Kroger
can dramatically reduce pickup lead time in its
highest volume stores.
Executives expect their use
of ecosystem platforms for
AI tool and model integration
to surge from 52% today to
89% in the next three years.
7
Action guide
Intentionally embed AI in operations
to deliver a sustainable brand advantage.
In the 2024 IBM IBV CEO study, 70% of retail and consumer products
CEOs said that to win the future, they must rewrite their organizational
playbook.4 As you redefine your core operational strategies and processes
to capitalize on AI, concentrate on how to achieve lasting value.
Tailor AI to your As you move beyond AI-driven productivity gains, you need a clear vision and strategy
brand’s priorities. for where AI and gen AI can help you distinguish yourself from competitors or shore
up weaknesses. But keep in mind that consumers expect you to stay true to your core
values as you innovate. If a strong customer experience is your focus, use AI to
personalize customer service and optimize in-store experiences. If product innovation
is a differentiator, tap into AI for product design, customer preferences, and vendor
capabilities to facilitate faster ideation and development cycles. The key is to
concentrate on what’s most important—not everything that’s possible.
Invite finance, Becoming an AI-centric brand requires purposefully aligning IT with long-term
technology, and business goals, not just the hottest tech. For example, organizations that consider
business leaders applications and infrastructure holistically in support of business needs (known as
to the same table. “hybrid-by-design” principles) can generate more than three times higher ROI over
five years.5 Tear down the silos between finance, technology, and business leaders so
that together, they can build solid business cases for where AI can deliver a long-term
competitive edge.6
Venture beyond Traditional strategic partnerships focused on physical distribution of supplies and
tried-and-true products are no longer enough in the age of AI. Tech companies, startups, and other
partnerships. nontraditional partners are needed for model development, platforms, and tools.
For example, other IBM IBV research found that 65% of organizations are already
working with or planning to work with a strategic partner to build a large language
model for generative AI initiatives.7 Prioritize partners who understand your goals and
share your vision. Identify those with a proven record for integration and loop them
into your processes early. Think outside the box, imagining new partners that create
new opportunities for growth.
8
Part two
Priming the augmented workforce
AI is transforming the nature of work from the store to the factory floor,
but industry executives undervalue workforce reskilling.
AI is diffused throughout the retail and consumer products workplace. Nearly all
(96%) executives say their teams are using AI and gen AI to a moderate or significant
extent at work. When virtually everyone is using a new and powerful technology such
as AI, then virtually everyone needs training to optimize the value and understand the
risks that could damage the brands. Yet, leaders project only 31% of their workforce
will need to reskill or develop new skills over the next 12 months, with this number
climbing to just 45% in the next three years—a significant miscalculation.
Both hard and soft skills—from prompt engineering and data analytics to critical
thinking and problem solving—are essential to ushering in the age of the augmented
workforce where AI won’t replace people, but people who use AI will replace people
who don’t.8 The talent transformation is an ongoing training and education process
that must be defined and started sooner rather than later. If not, 67% of employees
have said they will leave for another employer that provides better training on new
technologies, according to an IBM IBV survey of more than 21,000 workers.9
Executives recognize the workforce will be increasingly augmented, while automation
remains crucial for rules-based tasks and repetitive work. Across 13 functional areas
from marketing and commerce to supply chain, HR, and IT, they plan to more
frequently augment than automate activities over the next 12 months (see Figure 3).
Industry leaders know that many brand-defining areas demand human intuition,
creativity, emotional intelligence, and expertise that can be complemented by AI.
For example, in product design and development, AI can accelerate idea generation
and ideation, even providing visualizations. Likewise, operational areas have vast
Leaders project only
amounts of data where decisions require human oversight, such as supply planning,
31% of their workforce
where 54% plan to augment their employees. In this activity, AI can quickly access
will need to reskill
or develop new skills and analyze a broader range of data to help the supply planner confidently resolve
over the next year. shortages in minutes, knowing important information is not missed.
9
FIGURE 3
Retail and consumer products
executives know that
automation has its place but
Figure 3
see a future of augmentation.
Retail and consumer goods executives know that
automation has its place but see a future of augmentation.
Percent of activities that will be automated, augmented, or
have no impact from AI in each area over the next 12 months
No impact Automated Augmented
Digital commerce
12% 31% 58% & B2B sales
Product design, development, and
14% 28% 57% product lifecycle management
Merchandising /
14% 32% 54% category management
Brand-defining Marketing
13% 35% 52%
areas
Customer service
15% 35% 50%
Stores
21% 32% 47%
Sustainability
11% 35% 54%
Procurement
14% 33% 54%
Supply chain operations
Business-enabling 12% 37% 51%
areas
Production
8% 43% 49% and manufacturing
HR
10% 35% 54%
Finance
12% 36% 52%
Corporate
operations IT
9% 40% 51%
Percentages represent an average of responses for a set of tasks in each
functional area, based on the question: “To what extent do you use AI
or gen AI in this activity?” Respondents replied “to a moderate extent”
or “to a significant extent.”
10
Case study
Ultimately, brands will be finding the sweet spot for Japanese retailer empowers
automation and augmentation. Take managing the
people with AI to boost profits
seasonal workforce as one example. AI-powered
automation can streamline hiring, onboarding, while reducing waste10
and scheduling processes, reducing administrative
burdens and helping control costs. Managers can use
AI-powered tools that provide real-time insights into
staffing needs, predict demand fluctuations, and
optimize schedules. Similarly, in inventory A leading retail company in Japan was grappling with
management, AI-powered sensors and cameras costly problem: food and consumer-goods waste was
automatically monitor inventory levels in real time, eating away at their profits. The client’s field staff
while providing employees with the insights needed needed data-driven insights to make more informed
to reduce the risk of stockouts or overstocking. pricing decisions.
Even areas that have a high degree of automation, For a wide variety of products and the company’s
such as customer self-service, can benefit from operations, price optimization relied more heavily
augmented employees. As executives expand use on human judgment than data, leading to variations in
of AI for personalized responses and follow-up customer forecasts, stock levels, and discount rates.
actions over the next 12 months, they say 55% These variations resulted in excessive and inadequate
of the activities will be augmented versus 30% stocking, irregular discount amounts and timings,
being automated. and large profit losses due to food waste and missed
sales opportunities.
The company worked with IBM to develop a specialized
price optimization AI system to analyze vast amounts
of data, predict customer numbers and purchase
patterns, and suggest optimal discount amounts and
timings. Now the client’s field staff can combine their
own expertise with data to improve pricing decisions.
The pricing optimization system was designed to adapt
to different product categories and sell-by durations,
making it a versatile, scalable solution that can support
Brands are finding the
a diverse product range.
sweet spot for automation
and augmentation.
11
Action guide
Prepare your workforce
to power your AI-centric brand.
AI is clearly impacting virtually the entire retail and consumer products
workforce—from the person stocking the shelves to those who sit with
you in the C-suite. It’s being built into many of the tools employees use
every day, such as AI-powered sales forecasting tools or AI-driven
design tools. Leaders need to ensure all employees are prepared
to optimize the value AI can deliver.
Connect HR, IT, Executives report leadership for reskilling efforts is divided among an AI center of
and business lines competence (31%), HR (22%), AI committees (18%), and IT (17%). This disjointed
to define reskilling approach is risky and can create confusion and frustration among employees.
strategies. Leadership from HR, IT, and the business must join forces to shape an effective
reskilling strategy. HR brings both an understanding of how to manage change and
culture along with tactical implementation expertise. IT brings the technology
knowledge, and business leaders can work directly with employees to define how AI
can augment the workforce within each business domain. Have the joint team report
directly into the C-suite and define measures to hold them accountable.
Predict every If you only expect a third of your workforce will need reskilling or upskilling over the
employee’s next few years, you aren’t thinking big enough. Just as you forecast product demand,
potential. predict what employees will need to succeed in a rapidly evolving workplace.
Look beyond just current skills to employee potential. Use AI-powered HR tools to
anticipate how an individual might develop, perform, or contribute based on skills,
talents, personality traits, experiences, and educational background.11
Share a blueprint You may not know exactly what lies ahead, but you can communicate your vision
for the workplace for the future of work. From routine business operations to brand-defining areas,
of tomorrow. AI creates anxiety as employees worry about being replaced or not having the skills
they need. Share your plans for automation versus augmentation with your workforce
and help them see how AI will create new opportunities and enable them to do their
jobs faster and better—from designing products to creating promotions to managing
inventory. Consider how employees will use—and benefit from—technology as
carefully as you consider the tech investment itself.
12
Part three
Safeguarding brand trust
With so many products vying for consumers’ attention,
AI can either bolster or undermine a brand’s trust.
Trust is paramount for both consumers and industry CEOs. Our 2024 consumer
research report showed that 9 out of 10 consumers value trust when choosing a
brand.12 Similarly, 73% of retail and consumer products CEOs in our 2024 CEO study
said trust will have a greater impact on their organization’s success than any specific
product or service.13
But AI adds new dimensions to the issue of trust, with risks impacting both
business partner and customer relationships. Consumers are already wary of AI in
general—only 53% trust the technology, falling from 61% over the past five years.14
And within the partner ecosystem, companies need to know that each member is
practicing trustworthy AI.
Retail and consumer products executives recognize that AI creates risks that can
erode trust. Nine in 10 say misuse, such as creating misleading information, is their top
worry associated with AI models, followed by privacy (85%), fairness and bias (80%),
explainability (76%), and transparency (73%). For example, biased models can
alienate customers. One consumer survey revealed that almost two-thirds of
consumers avoid AI-fueled recommendations because they are biased or
stereotypical.15
At the same time, these risks are slowing progress with generative AI opportunities.
57% of executives say data accuracy and bias is a barrier to gen AI adoption. 55% also
cite privacy and confidentiality of data and 54% are concerned about cybersecurity.
Despite these concerns, organizations are struggling to enable the tools that can help
them manage the risks. Companies have created a foundation: 87% of executives say
they have clear AI governance structures. But less than a quarter of companies have
advanced implementation of tools to assess, monitor, and manage AI governance
90% of executives
(see Figure 4). “Showing your work”—designing solutions with explainability and
cite misuse as
transparency built in—will be critical to instilling confidence in consumers regarding
their top concern
your use of AI.
with AI models.
13
FIGURE 4
Few brands have advanced implementation of
tools to help them manage their AI governance
policies and activities.
Figure 4
Few brands have robust implementation of tools to help
them manage their AI governance policies and activities.
Approach to AI governance Advanced implementation of tools
84%
have defined roles and
responsibilities for all stakeholders
involved in AI
11%
have advanced implementation of AI
accountability tools
91%
conduct ethical impact assessments
to evaluate the impact of AI
initiatives on different stakeholders
16%
have advanced implementation of AI
bias and fairness tools
87%
have established clear organizational
structures, policies, and processes
for AI governance
23%
have advanced implementation of AI
governance frameworks or policy tools
90%
build explainable models that can be
easily understood and audited
24%
have advanced implementation of AI
transparency and explainability tools
77%
conduct regular risk assessments to
identify potential security threats
26%
have advanced implementation of AI
risk and safety tools
Q. To what extent do you agree with the statements about your organization’s approach to AI governance? Percentages represent
those who agree and strongly agree. Q. To what extent has your organization implemented tools to assess, monitor, and manage
the following? Percentages represent those who responded “fully implemented, reviewed, and updated regularly.”
14
Case study
PepsiCo models a structured approach that enables Using gen AI to streamline
it to scale AI responsibly. The company began
regulatory management
by establishing a formal responsible AI framework
and assembled a dedicated team to support it. The across regions18
team then developed comprehensive policies and
standard operating procedures to operationalize
their AI principles. The governance board assesses,
validates, and approves gen AI use cases against
its responsible AI principles, sharing best practices A multibillion-dollar global consumer products
and accelerators, and helping mitigate risks. The company operates in the highly regulated agricultural
company is also building a platform that provides products industry. It devotes significant resources
comprehensive governance of models, inputs, to managing compliance with local regulations,
and outputs.16 staying current with continuously changing
guidelines, and integrating compliance into the
Regulations are also intended to support trustworthy product development process.
AI, but a lack of consistent guidelines across
jurisdictions complicates implementation and stalls To help its product compliance and development
plans. In fact, nearly half (46%) of industry CEOs said teams reduce heavy manual workloads and free
their concern about regulations as a barrier to gen AI up more time to work strategically, the company
has increased in the last six months.17 worked with IBM to develop a generative AI-powered
regulations assistant. This solution features a
However, AI can help companies manage the conversational user interface and provides a single
complexity. By automating the monitoring and source of truth for over 1,000 regulations impacting
analysis of detailed regulatory requirements, AI worldwide operations.
enables organizations to quickly identify potential
issues and take corrective action. Executives plan The regulations assistant enables product
to significantly increase their use of AI/gen AI in compliance employees to predict the impact
regulatory compliance over the next year. In product of regulatory intent, summarize regulatory
design and development, the percent increases from requirements, and compare regulations globally,
53% to 79%, for sustainability, 74% to 88%, and for faster than with manual processes. The AI tool also
financial and regulatory monitoring and reporting, enables product developers to analyze the impact
66% to 94%. of regulations on product portfolios, review
solution options, and query product specifications
in a conversational journey.
To date, the regulations assistant has demonstrated
that generative AI can orchestrate regulations data
quickly and drive closer collaboration across borders
to leverage regulatory success across the business.
In product design and The tool also has the potential to increase efficiency
by 8% to 13%, increase productivity by 10% to 15%,
development, executives
and increase profits by over $165 million during the
plan to increase use of AI next five years.
and gen AI to manage
regulatory compliance from
53% today to 79% in the
next year.
15
Action guide
Make trusted AI a brand differentiator.
Customer-obsessed businesses need to deliver on what their written
policies dictate for responsible AI practices. Build confidence in
responsible internal uses of AI before expanding to customer-facing
use cases where broken trust can damage your brand.
Purge bias from To provide transparency and explainability, define clear guidelines to monitor for
your algorithms. discriminatory patterns. For example, conduct regular audits on historical purchasing
and customer data that may reflect stereotyping and societal biases. Facilitate
human-AI collaboration and oversight with training that helps employees understand
and recognize fairness and bias. Prioritize diversity on your AI development teams.
Establish a data governance framework to support data provenance, helping ensure
your data is authentic and trustworthy. Maintain detailed records of bias mitigation
efforts, create dedicated channels for bias-related feedback, and regularly
incorporate insights into system improvements.
Leverage AI to To stay ahead of an AI regulatory environment that is evolving at varying paces
proactively navigate globally, use AI solutions to capture regulatory intent across multiple channels
regulations. and forecast its impact. Choose AI development tools that build in governance and
regulatory compliance management end to end. Proactively compare old and new
regulations to quickly identify key focus areas within impact assessments. Automate
tools to stay up-to-date and streamline audit processes.
Be open about Build trust with customers by being up-front about data collection as well
your use of AI as how and where you are using AI. Offer opt-out options and avoid tech-speak
with customers in your explanations. Exchange AI roadmaps and strategies with business partners.
and partners. Demonstrate your commitment to responsible AI practices and request the same
of your partners.
16
Authors
Dee Waddell Contributors
Global Industry Leader
Consumer, Travel & Transportation Industries The authors would like to thank the following
IBM Consulting for their contributions to this report:
dee@us.ibm.com
From IBM Consulting:
linkedin.com/in/waddell/
Arnab Bag, Distribution Market HCT Service
Joe Dittmar Line Leader
Senior Partner Rich Berkman, Vice Pr"
145,ibm,ceos-guide-to-generative-ai-second-edition.pdf,"T he CEO’s
Guide to
Generative AI
What you need to know and do to win
with transformative technology
Second edition
The CEO’s
Guide to
Generative AI
What you need to know and do to win
with transformative technology
Second edition
Foreword
Contents Section one
On the edge AI-powered data and technology
1
Chapter 1 Digital product engineering 3
of reinvention
Chapter 2 IT automation 9
Chapter 3 AI model optimization 15
Chapter 4 Cost of compute 21
Just how fast can an organization evolve? Generative AI is pushing CEOs to find out. Jonathan Adashek Mohamad Ali
Chapter 5 Platforms, data, and governance 27
Senior Vice President, Senior Vice President
When the pace of change accelerates to breakneck speeds, businesses begin to strain Marketing and Communications IBM Consulting Chapter 6 Open innovation and ecosystems 33
under the pressure. Bottlenecks cause back-ups. Organizational structures buckle. Chief Communications Officer
IBM Chapter 7 Application modernization 39
Growth engines stall.
Chapter 8 Responsible AI and ethics 45
In this environment, CEOs say business model innovation is the top challenge they must
Chapter 9 Tech spend 51
overcome.1 For many, reinvention is the only option. To ensure their organizations will
achieve operational excellence no matter how hard the winds of change blow, CEOs
must be ready to rip faulty support structures down to the foundation and rebuild. Section two
AI-fueled operations
Gen AI can power this revolution. Over the next three years, executives say traditional 57
and gen AI will support business and operating model innovation by providing access
Chapter 10 Enterprise operating model 59
to additional data (88%), generating new insights from existing data (86%), expanding Kelly Chambliss James J. Kavanaugh
access to new markets (85%), and accelerating product and services development (84%).2 Senior Vice President Senior Vice President and Chapter 11 Business process automation for operations 65
IBM Consulting, Americas Chief Financial Officer
Chapter 12 Finance 71
It will supercharge people and skyrocket productivity, shifting business from a labor-based IBM
model to one that is asset-enabled. It will also open up new markets by enabling workers Chapter 13 Procurement 77
to create high-value solutions that previously weren’t feasible or affordable. Chapter 14 Risk management 83
The key is selecting use cases that drive value—and not spreading the organization too thin. Chapter 15 Physical asset management 89
Rather than looking broadly at applications and opportunities, CEOs should ask how gen AI Chapter 16 Supply chain 95
can help solve the company’s biggest problems. The leaders that win the day will be the
Chapter 17 Marketing 101
ones who stay aligned to their strategic plans and execute the fastest.
Chapter 18 Cybersecurity 107
To see how executives are making the most of this rapidly evolving technology, the IBM
Salima Lin Rob Thomas
Chapter 19 Sustainability 113
Institute for Business Value (IBM IBV) interviewed more than 10,000 CEOs and other
Managing Partner, Strategy, Senior Vice President,
members of the C-suite globally in 2023 and 2024. We asked them where they expect M&A, Transformation, Software
gen AI to make the biggest impact, how they plan to invest, and what obstacles they will and Thought Leadership Chief Commercial Officer
Section three
IBM Consulting IBM
need to overcome along the way.
AI-enabled people
119
Our findings paint the future in an auspicious light. These insights highlight a multitude of new
Chapter 20 Talent and skills 121
challenges, but also showcase strategies that can help CEOs capitalize on the gen AI moment.
Chapter 21 Customer service 127
This book combines IBM’s decades of experience working with clients to apply AI and other
Chapter 22 Customer and employee experience 133
technologies in meaningful ways with the results of our ongoing rapid-response research.
IBM’s long history of using technology to make the world work better puts us in a unique
position to help executives make gen AI work FOR them, rather than becoming something
that happens TO them. Joanne Wright Kareem Yusuf Conclusion 139
Senior Vice President, Senior Vice President,
Explore the following 22 chapters, packed full of potential applications and action items, Transformation Product Management
to learn how gen AI can redefine your customer and employee engagement strategies, and Operations and Growth
IBM Finance and IBM Software
accelerate enterprise transformation with data-driven tech, and build resilient operations
Operations
for a future defined by disruption and change.
Section one
AI-powered
data and
technology
Gone are the days when conversations about data and technology were relegated to the realm of IT.
As generative AI makes it possible for companies to deliver the integrated experiences and hyper-personalized
products and services customers demand, CEOs must understand how their technology is holding them
back—and where their data could offer a competitive edge.
As companies rush to gain a gen AI advantage, CEOs must demystify data and technology to make the most
of their finite tech spend. With the right intel, they can flow funds to the platforms, tools, and applications that
offer the greatest growth potential and retire those delivering diminished returns.
CEOs who have a good understanding of what makes gen AI tick will be best positioned to answer tough
“‘How can we use generative AI?’ is not the right question. questions from customers, regulators, and skeptics as the landscape evolves. If they can explain what data
It’s, ‘What use cases have we got that we need the most was used to train their gen AI models, how those outputs are used, and who is responsible for managing ethical
issues, CEOs will be prepared to address the challenges that are sure to come. Find out how CEOs can develop
help with and what role could different areas of technology
the gen AI expertise they need in the following chapters.
and data analytics play?’”
Bernie Hickman
CEO, Legal & General Retail
1
Chapter 1
“For me, it is all about the flow of data. Digital product engineering + generative AI
People want information. How do we deliver
that information to those customers in a way
that’s meaningful for that individual?”
Eliminate the
Paul Graham
CEO and Managing Director, Australia Post
guesswork in
product development
What do customers really want? To crack that ever-changing cipher, digital product teams must sift through
mountains of data, from market research and user surveys to device metrics, all while navigating complex code
bases and enterprise architectures. It’s a perpetual, painstaking process, and there’s no guarantee they’ll get
it right. Even when market signals and metrics seem to point to a sure-fire win, products can inexplicably flop.
Or a release flying under the radar can lead to an unexpected spike in adoption.
Generative AI helps businesses optimize the product development process—from streamlining ideation
to rapidly testing and validating features—saving money and accelerating speed-to-market. At the same time,
it frees humans to focus on solving complex engineering challenges and differentiating products through design,
UX, and UI—the creative tasks that have the biggest impact on customer loyalty and satisfaction.
Gen AI can help digital product teams hit the mark more consistently by analyzing vast stores of data faster
and more effectively than human teams ever could. Using machine learning algorithms to identify patterns
and trends in customer behavior, gen AI can quickly uncover unmet needs, suggest dozens of features
or new products that could fill a gap—and even validate these options against specific business criteria.
It also makes it possible to develop dynamic products and hyper-personalized experiences that can quickly
adapt to shifting customer demands and rapidly validate changes with customers. Given these game-changing
capabilities, it’s not surprising that 86% of executives say gen AI is now a critical part of digital product design
and development.
Research methodology
The statistics informing the insights in this chapter are sourced from a proprietary survey conducted by the IBM Institute
for Business Value in collaboration with Oxford Economics. The survey queried 450 global digital product leaders in 15
industries on their AI adoption for digital products and its impact on metrics. It was conducted from December 2023 to
2 Section 1 AI-powered data and technology 3
February 2024.
Chapter 1: Digital product engineering
1. Hyper-personalization
The three things to know
2. Ideation
and the three things to do 3. Design
IBM Institute for Business Value research has identified
three things CEOs need to know and do right now.
What you need to know What you need to do
Generative AI helps products hit Redesign product development to
1. Hyper-
personalization the high bar of hyper-personalization derive high-value product insights
at scale. from every customer interaction.
2. Ideation
What to know
Generative AI helps Imagine a world where every product is tailored to a specific Stop letting market trends catch you by surprise. Bypass
What to know 3. Design
products hit the high bar customer—where mobile devices, subscription services, the competition by cultivating proprietary data inputs and
Teams using generative AI
of hyper-personalization What to know and the Internet of Things work together to curate differentiating how you use generative AI. Continuously
can conceptualize and
at scale. experiences for an audience of one. This is the world of learn and generate the experiences, products, and content
evaluate new products Rapid code generation
hyper-personalization, and it’s no longer a distant dream. customers want—at exactly the right time.
in minutes—not days. frees teams to double
down on design. As generative AI comes of age, executives expect it to pave Think beyond cross-sell and upsell. Capitalize on
What to do the way for personalized experiences at a scale we’ve never the UX/UI potential of hyper-personalization by using
seen. By analyzing every click, swipe, and interaction, gen AI gen AI to create dynamic interfaces that adapt based
Redesign product
What to do
can stitch together bespoke product experiences for every on user behavior, preferences, and context. Customize
development to derive What to do
Build augmented teams customer. But only 30% of organizations have been able everything—search results, product designs, and even
high-value product
to prepare for an influx Upskill product to harness this power, tapping gen AI to quickly analyze pricing—to increase customer engagement and drive
insights from every
of generative AI-infused teams on experience and summarize customer feedback. Those leading the way revenue.
customer interaction.
workflows. and innovation. have an early edge: They’re 86% more likely to be creating
Invite customers to incorporate their data into
hyper-personalized experiences than their counterparts.
product experiences on their own terms. Let customers
While only a quarter of organizations are using gen AI opt in to sharing their data and clearly communicate how
to create hyper-personalized digital product experiences it will be used and protected. Use gen AI to uncover hidden
today, that figure is expected to more than double to customer preferences and use predictive analytics to
64% by the end of 2024. Using gen AI in tandem with IoT forecast what customers will want in the future.
could be a powerful way for companies to deliver true
Tap into customer data to create hyper-personalized
hyper-personalization at scale. IoT devices can feed torrents
experiences. Orchestrate disparate data, including from
of data into AI models, which may be why executives say IoT
IoT devices, to enrich the user experience. Use gen AI to
will be a top digital product disruptor, after traditional and
map your product priorities to data-driven customer pain
gen AI, over the next five years.
points. Keep your product roadmap relevant and targeted
“The speed of innovation these days is mind-boggling. Looking ahead, 70% of executives expect gen AI to improve by using gen AI to continually refine a backlog that will
the personalization of their digital product portfolio. How deliver the most business value.
There almost isn’t a week when there aren’t two
far they go—and how fast they get there—will likely decide
or three new developments in the enterprise.”
who gains a competitive edge. In the generative AI future,
products will need to be functional and personal, adapting
Amit Bendov
to meet every customer’s unique preferences, needs, and
CEO and Co-founder, Gong
expectations, no matter how rapidly they change.
4 Section 1 AI-powered data and technology 5
Chapter 1: Digital product engineering Chapter 1: Digital product engineering
1. Hyper-personalization 1. Hyper-personalization
2. Ideation 2. Ideation
3. Design 3. Design
What you need to know What you need to do What you need to know What you need to do
Teams using generative AI Build augmented teams to prepare Rapid code generation frees teams Upskill product teams on experience
can conceptualize and evaluate for an influx of generative AI-infused to double down on design. and innovation.
new products in minutes—not days. workflows.
Consumer expectations are evolving at breakneck speeds— Identify obvious time and money drains in the build and test
and product teams are racing to keep up. Tapping gen AI cycle that can be powered by generative AI. Redistribute
Generative AI has turned the traditional product design process Leverage gen AI to both ideate and rapidly validate a high
for rapid code generation can help them roll out prototypes these resources in a way that supports the development
on its head. Gone are the days of endless brainstorming and volume of ideas with customers. Focus the product team’s
faster without sacrificing the quality and design that of better UX/UI and more innovative products.
exhausting pitch sessions. Today, gen AI can use large data talent on reviewing, enhancing, and building out the ideas
customers demand.
sets to incubate ideas that have high market potential in that seem most likely to succeed in the market.
Liberate developers and designers from traditional
seconds—freeing teams to validate with customers and focus
How does it work? Gen AI speeds up the coding process, skill limitations. Encourage teams to experiment with
Treat generative AI as a team member. Embed gen AI
on the best opportunities.
letting teams test and iterate faster to increase their new training models that will boost their gen AI acumen
to create team workflows that are truly augmented.
speed-to-market—if development teams know how so they can use it creatively. Allocate dedicated research
As this technology matures, two-thirds of executives anticipate Define which inputs and outputs team members and gen AI
to use it responsibly. With the right training, governance, and development days and sponsor hackathons to give
that gen AI will inform—or even create—their product roadmap assistants are responsible for, respectively. Ask gen AI
and adoption incentives, gen AI can help teams move faster teams opportunities to enhance their skills.
by 2026. Already, nearly one-third of organizations are using to carry out discrete activities. Use it to analyze feedback,
while managing risk, freeing up resources to focus on the
gen AI for digital product idea generation. Companies that have generate design options, cut development time, or reduce
Offer more training on creativity and customer context.
creative aspects of UX and UI design.
embraced this early use case delivered a 17% revenue premium wasted effort.
Advocate for all team members to gain domain expertise
for new products and 5% greater revenue from existing product
Today, 87% of executives say their organizations sink at least in experience design. Encourage collaboration within
Reinvent the review process to lower costs and improve
enhancements in 2023.
a fair amount of effort into testing code, while 83% say the cross-functional teams to enable strategic innovation.
efficiency. Implement an idea management system to track
same for developing new features quickly in short release Provide opportunities for experimentation without fear
But the revenue boost is just the beginning. Nine in 10 a high volume of AI-generated ideas, patterns, and trends,
cycles. And they’re eager to relieve themselves of this burden. of failure.
executives already using gen AI for product idea generation including KPIs that help predict success. Streamline
say it differentiates their company by helping it respond to the process of generation, evaluation, and implementation
More than six in 10 leaders plan to use gen AI for code Expand the role of testers into user research.
market shifts faster. Going forward, they also believe gen AI of ideas.
generation in their digital products by 2025, rising to more Reskill quality assurance testers to support higher-value
will positively impact product differentiation (88%), product
than nine in 10 by 2026. But there’s a real benefit in starting activities, such as concept validation and usability testing
Augment repetitive tasks to drive down testing costs
trust (83%), and product quality (80%).
early. Only a quarter of organizations have implemented with customers.
as the pace of innovation increases. Generate and execute
gen AI for digital product code generation so far, but these
Organizations already using gen AI for product idea generation test cases based on code and product requirements to
pioneers are already seeing real results.
are building the foundation needed to augment human work: reduce the likelihood of bugs and defects in rapidly
29% more are focused on building interdisciplinary teams and evolving digital products.
They’re 35% more likely to outperform their peers in revenue
39% more are focused on governance. But executives say
growth and 48% more likely to say their teams dedicate
the skills shortage is the top constraint that could hold digital
significant effort to UX and UI design—focus areas that do
product initiatives back.
more to differentiate them from the competition. What’s more,
only 30% of executives at organizations already using gen AI
While gen AI can create product ideas at lightning speed,
for code generation say UX and UI design is a challenge,
it is humans who must review, validate, refine, and perfect
compared to 45% of those that plan to do so by 2026.
them. This means people will be more important than ever
as human-machine partnerships evolve.
6 Section 1 AI-powered data and technology 7
Chapter 2
IT automation + generative AI
Outdated technology
is dragging you down
Technical debt is back in the spotlight. It erodes profitability, drains resources, inhibits growth, and stifles
creativity. It’s an albatross CEOs carry, impeding their push to accelerate transformation with generative AI.
As a result, many CEOs find themselves mortgaging the future to survive in the present. In fact, the 2024
IBM IBV CEO study found that two-thirds of CEOs say they’re meeting short-term targets by reallocating
“Technology today as a stand-alone resources from longer-term initiatives.3
function does not make sense; technology There is a better way. CEOs can have their cake and eat it too. But how?
is there to reimagine and power the
It starts by changing how we think about IT spending. Rather than viewing IT as a cost center—an expense
business. And this requires a much businesses must eat to keep the lights on—we need to rethink how technology can better boost ROI.
closer integration and collaboration That means automating more than simple tasks that offer quick productivity boosts. Instead, leaders
must assess entire IT workflows, looking for ways to improve processes with a combination of automation
with business leaders.”
and augmentation.
Mohammed Rafee Tarafdar
It’s a big mindset shift. Today, a typical organization spends just 23% of its tech budget to drive revenue,
CTO, Infosys according to recent IBM IBV research.4 But generative AI changes the equation. Three-fourths of IT
executives say the value created from gen AI will be reallocated to new investments that drive business
innovation and growth.
This is why CEOs shouldn’t view tech upgrades as a series of isolated IT costs. They need to connect
IT automation to business strategies that will drive improved performance. And then invest accordingly.
By deliberately upgrading their IT estate with business priorities in mind—applying what we call
hybrid-by-design principles to IT programs—IBM analysis suggests that organizations can increase
ROI three-fold over five years.5
Research methodology
The statistics informing the insights in this chapter are sourced from three proprietary surveys conducted by the IBM Institute
for Business Value in collaboration with Oxford Economics. The first surveyed 207 US-based executives in 25 industries about
generative AI and IT automation in May and June 2024. The second surveyed 2,000 global executives in 10 industries about
AI and automation more broadly from April to July 2023. The third surveyed 216 US-based executives in 17 industries about
8 Section 1 AI-powered data and technology 9
generative AI and application modernization in July 2023.
Chapter 2: IT Automation
1. Innovation
The three things to know
2. Transformation
and the three things to do 3. Prediction
IBM Institute for Business Value research has identified
What you need to know What you need to do
three things CEOs need to know and do right now.
IT automation is the launchpad Break away from
1. Innovation for business innovation. the “break-fix” model.
2. Transformation
What to know Generative AI streamlines the work IT does every day, from CEOs need to focus on modernizing all aspects of the IT
IT automation software deployment to network configuration to capacity estate to enable greater automation. Empower teams
What to know 3. Prediction
is the launchpad management. These tasks are essential to keep operations to move beyond fixing what’s broken to focus on more
for business innovation. Anyone can become running smoothly—but they rarely boost the bottom line. strategic work. Ensure that IT systems are aligned with
What to know
a generative AI genius. strategic business goals and specific operational and
Generative AI automation When IT automation liberates teams from the day-to-day drudgery
financial metrics.
makes IT clairvoyant. of maintenance and support, they’re freed to envision a future built
What to do on new transformative technologies—including, of course, Automate to make hard work easier. Identify the
What to do generative AI. Gen AI also fuels their creative fire, sparking ideas systems, applications, and data flows that must be
Break away from
Make tech for new digital products and revenue streams. And most integrated to streamline and automate work. Give IT teams
the “break-fix” model.
What to do
less techy. companies have hit the ground running. access to a generative AI platform and tools they can use
Conquer complexity to quickly create the code and APIs needed to connect
Today, 62% of IT executives say their organizations are using gen
with intelligent visibility. disparate systems. Encourage teams to identify new ways
AI for code generation—and that figure will jump to 87% by 2026.
to automate and augment routine tasks.
65% of tech leaders expect gen AI solutions to automatically
resolve IT issues with little to no human intervention. And 82% Get more out of every IT automation dollar. Align tech
of IT executives expect generative AI to improve DevSecOps, spend with business objectives—and fast track initiatives
the automated workflows that incorporate security practices that accelerate performance improvement. Go beyond
throughout the development lifecycle, over the next two years. finding efficiencies to invest in tech that will create new
revenue streams and promote rapid growth.
Organizations that see automation as essential to fast-track
gen AI capabilities are already gaining an edge. They outperform Measure what matters. Establish a feedback loop
in workforce agility, profitability and efficiency, innovation, and to continually monitor and improve gen AI model
revenue growth—demonstrating how AI-powered automation performance. Look past traditional IT metrics, such
can transform IT into a business incubator and foster an as uptime and downtime, to gauge success. Instead,
entrepreneurial culture. tie automation efforts to business-centric metrics,
such as user satisfaction, revenue growth, and
By giving everyone access to generative AI tools and expertise,
“Our objective is not to reduce workforce. speed-to-market.
IT democratizes innovation, empowering employees to develop
We just want to let people spend their time
their own ideas to unlock business value—and predict which are
more productively and more creatively, most likely to succeed.
so that they can also be happier.” Gen AI not only drives growth but also attracts and retains top
talent, who are drawn to organizations that prioritize creativity and
Hiroshi Okuyama
autonomy. And if IT leaders funnel this curiosity into a shared,
Director and Member of the Board, Chief Digital Officer, Yanmar Holdings Co., Ltd.
collaborative platform, they can feed a vibrant innovation pipeline
that can help the organization meet ambitious growth targets
quarter after quarter.
10 Section 1 AI-powered data and technology 11
Chapter 2: IT Automation Chapter 2: IT Automation
1. Innovation 1. Innovation
2. Transformation 2. Transformation
3. Prediction 3. Prediction
What you need to know What you need to do What you need to know What you need to do
Anyone can become Make tech less techy. Generative AI automation makes Conquer complexity with
a generative AI genius. IT clairvoyant. intelligent visibility.
Embed IT in the boardroom. Make technology
and automation central to every business strategy—and
Employees don’t need to be IT experts to transform business AI systems already help IT teams accurately predict and Use gen AI-enabled digital twins to model the effects
challenge leaders to connect performance metrics to the
with technology. But they do need IT experts to provide the prevent system failures and bottlenecks. But with gen AI, of specific disruptions across the enterprise and the
systems, platforms, and tools that enable their success.
tools and platforms that put the power of gen AI automation businesses see even farther into the future. ecosystem. Improve ROI with more accurate estimates
at their fingertips. of how much investments in technology and automation
Assemble multi-disciplinary dream teams. Build squads
By deploying gen AI and AIOps in tandem, teams gain
will cost—and how much value they will deliver.
of people with diverse skills and backgrounds, including
If IT provides the right low-code and no-code platforms, intelligence that lets them anticipate and prepare for
data scientists, engineers, domain experts, and business
anyone can create or modernize web and mobile apps—a scenarios that might otherwise catch them by surprise. Hunt for treasure across your IT estate. Provide operations
stakeholders, to collaborate on gen AI projects. Organize
process that, until recently, required a team of developers. For instance, by automatically identifying and mapping visibility into applications and infrastructure by using gen AI
workshops, hackathons, and other competitions that spur
At the same time, gen AI code assistants let developers relationships across the IT estate—a process known as to uncover the relationships that are key to building
innovative thinking and knowledge sharing.
quickly translate code from one language to another, topology discovery—teams can quickly spot dependencies resilience and driving growth. Discover hidden riches
reducing the need for some hard-to-find technical skills. between different systems and components. by modeling different improvements—and investing
Empower DIY developers to get creative with automation.
in the IT automation solutions that promise the best returns.
Evaluate and select a low-code or no-code platform that
While IT must be the catalyst of this transformation, This process reveals how problems in one area can cascade
aligns with the enterprise’s technology stack and gen AI
the benefits will extend across the business: 81% of executives across the business—and lets IT limit the domino effect. Head off hazards at the pass. Get out in front of risks
platform. Establish guidelines for data management,
say gen AI will fundamentally change how people do their jobs. It also makes it easier to optimize network performance, by automating the process of predicting how different
security, and compliance—then push people to explore
And IT executives are up for the challenge, with 70% saying their strengthen security, and keep teams across the organization scenarios could influence complex systems. Use gen AI
what they’re capable of.
organizations will design AI systems to seamlessly collaborate in lockstep. to simulate potential outcomes and validate crisis response
with humans by 2026. plans, then forge confidently into unexplored frontiers.
Challenge cultural norms and let digital natives drive
IT leaders can also use gen AI to supercharge simulations.
change. Flatten hierarchical decision-making to give younger
To produce the best results, they’ll need to bring employees Gen AI-enabled digital twins can model multiple dimensions Right-size technology spend with IT automation—then
team members a stronger voice. Launch reverse mentorship
along for the ride. Technology can be intimidating for simultaneously, letting teams test response strategies more right-size your team. Broaden FinOps capabilities
programs that pair people just entering the workforce with
non-technical teams, but training and reskilling can demystify effectively. Rather than wondering how well their plans will to provide visibility into costs and spending across all AI,
senior leaders. Give them space to ask why. And why not.
gen AI and encourage people to try something new. And work, they can see them in action. hybrid cloud, and application modernization investments.
providing this support is more important than ever. In 2024, Optimize, automate, and augment IT operations to avoid
Gen AI also helps IT more confidently estimate the business
global CEOs said 35% of their workforce would require the financial and environmental costs of overprovisioning.
value of different IT automation investments. Today, 57%
retraining and reskilling over the next three years—up from Realign your tech team to shed expensive talent that
of IT executives are already using generative AI to predict
just 6% in 2021.6 you no longer need.
outcomes, efficiency gains, and ROI in IT and network
For years we’ve been saying that IT needs to work more closely automation initiatives—and this figure will grow to 75%
with the business—and the business needs to work more closely by 2026. This level of visibility can help manage the cost side
with IT. Gen AI could finally make this a reality: 68% of executives of the equation, as well: 76% of IT executives say they will
say it will bridge the gap between IT and the business. By use gen AI to enhance FinOps practices for more precise
providing a shared canvas for collaboration, gen AI helps IT control of cloud costs.
develop a deeper understanding of business problems and
business teams harness the full power of technical solutions.
12 Section 1 AI-powered data and technology 13
“We have more than 40 proprietary Chapter 3
AI models that we train and fine-tune
AI model optimization + generative AI
for revenue teams with our customer
interaction data. The results are more
There’s a gen AI
accurate and meaningful.”
Amit Bendov
CEO and Co-founder, Gong
model for that
ChatGPT made everyone feel like an AI expert. But its simplicity is deceptive. It masks the complexity
of the generative AI landscape that CEOs must consider when building their AI model portfolio.
Gen AI models come in many flavors. What they can do, how well they work—and how much they cost—varies
widely. Who owns the model, how it was developed, and the size of its training dataset are just a few of the
variables that influence when and how different models should be used.
With the massive amount of data and resources it takes to train a single large language model (LLM), the question
of size is monopolizing many conversations about gen AI. As a result, many CEOs wonder whether they should
scale large gen AI models for their business. Or if they should dev"
146,ibm,why-invest-in-ai-ethics-and-governance.pdf,"IBM Institute for Business Value | Research Insights
Why invest in AI ethics
and governance?
Five real-world origin stories
In collaboration with the Notre Dame—IBM Tech Ethics Lab
How IBM
can help
Clients can realize the potential of AI, analytics, and
data using IBM’s deep industry, functional, and
technical expertise; enterprise-grade technology
solutions; and science-based research innovations.
For more information:
AI services from IBM Consulting
ibm.com/services/artificial-intelligence
AI solutions from IBM Software
ibm.com/Watson
AI innovations from IBM Research®
research.ibm.com/artificial-intelligence
The Notre Dame-IBM Tech Ethics Lab
techethicslab.nd.edu/
2
Key
takeaways
Organizations that measure Embracing AI ethics is essential.
the value of AI ethics
It’s not just about loss aversion. 75% of executives
could be a step ahead. view AI ethics as an important source of competitive
differentiation.1 More than 85% of surveyed consumers,
Our holistic AI ethics
citizens, and employees value AI ethics.2
framework considers
three types of ROI. Longer-term, proactive AI ethics
strategies can generate value across
the organization.
A majority of companies (54%) expect AI ethics to be
very important strategically,3 with executives citing
involvement of 20 different business functions.4
Investing in AI ethics has the
potential to create quantifiable value.
Organizations that measure the value of AI ethics could
be a step ahead. Our holistic AI ethics framework
considers three types of ROI: economic impact
(tangible), reputational impact (intangible), and
capabilities (real options ROI).
1
Introduction
Generative AI is revolutionizing industries, but its dizzying ascendance
has also raised significant ethical concerns. Balancing the potential
benefits with ethical and regulatory implications is crucial.
But it’s not easy. In IBM Institute for Business Value (IBM IBV) research, 80%
of business leaders see AI explainability, ethics, bias, or trust as major roadblocks
to generative AI adoption.5 And half say their organization lacks the governance
and structures needed to manage generative AI’s ethical challenges.6
In the face of this uncertainty and risk, many CEOs are hitting pause. More than half
(56%) are delaying major investments in generative AI until they have clarity on AI
standards and regulations,7 and 72% of executives say their organizations will actually
forgo generative AI benefits due to ethical concerns.8
Yet there is a path forward—if executives broaden their outlooks and view AI ethics
as an opportunity. Even better: ongoing research suggests that investing in AI ethics
has the potential to create quantifiable benefits.
In order to unlock this potential, organizations need to embrace a new perspective
as they evaluate the ROI of AI ethics investments. In part one of this report, we
identify three key types of ROI that apply to AI ethics—in other words, a holistic AI
ethics framework. In part two and part three, we explore two distinct but valuable
ways to justify AI ethics investments right now. (We plan to build on this work by
conducting additional research in 2025 that explores quantification in greater depth.)
Finally, we offer an action guide for bringing the holistic AI ethics framework to life
inside the organization.
We also include stories from five executives on the front lines of AI ethics,
as part of an ongoing collaborative project among the IBM IBV, the Notre Dame—IBM
Tech Ethics Lab, the IBM AI Ethics Board, and the IBM Office of Privacy and
Responsible Technology. Some interviews were conducted in collaboration with
Oxford Economics.
2
Part one
Exploring a holistic
AI ethics framework9
AI ethics and governance investments can span broadly
across the enterprise, from an AI ethics board to an
ethics-by-design methodology, from an integrated
governance program to training programs covering AI
ethics and governance, among many other endeavors.10
(See “AI ethics: Stories from the front lines” on page 13.
Also refer to our IBM IBV study The enterprise guide
to AI governance at ibm.co/ai-governance.)
So how do organizations begin measuring the impact of such
initiatives? We developed a holistic AI ethics framework to meet
this need, validating it through an extensive series of conversations
with over 30 organizations. This approach can help organizations
understand the value of their AI ethics and governance investments.
Traditionally, investments are justified by calculating ROI in financial
terms alone. AI ethics investments are more challenging to evaluate,
providing both tangible and intangible benefits as well as helping
build longer-term capabilities.
“Our work has to not just contribute to the mission of the organization—
it also has to contribute to the profit margin of the organization,”
notes Reggie Townsend, VP of the Data Ethics Practice at SAS.
“Otherwise, it comes across as a charity, and charity doesn’t get
funded for very long.”
We developed a holistic AI ethics
framework, validating it through
an extensive series of conversations
with over 30 organizations.
3
A holistic AI ethics framework identifies three
types of ROI that organizations should consider
with AI ethics investments.
Economic impact (tangible ROI)
refers to the direct financial benefits
of AI ethics investments, such as cost
savings, increased revenue, or reduced
cost of capital. For example, an
organization might avoid regulatory
fines by investing in AI risk management.
Reputational impact (intangible ROI)
can involve important yet
difficult-to-quantify elements, such
as an organization’s brand and culture
that support positive returns or impact
on an organization’s reputations with
shareholders, governments, employees,
and customers. Examples include
improved environmental, social,
and governance (ESG) scores;
increased employee retention;
and positive media coverage.
Capabilities (real options ROI)
alludes to the long-term benefits
of building capabilities that, established
first for AI ethics, can disseminate
broader value throughout an organization.
For example, technical infrastructure
or specific platforms for ethics may allow
organizations to modernize in ways
that lead to further cost savings
and innovation.
Source: “The Return on Investment in AI Ethics:
A Holistic Framework.” Proceedings of the 57th
Annual HICSS Conference on Systems Sciences.
January 2024.
4
The holistic AI ethics framework depicted above
describes three paths to understanding the impact
of investments in AI ethics with regards to
stakeholders: the direct path through economic
return, and indirect paths through capabilities
and reputation. This framework encompasses
and describes the relationships, stakeholders,
and potential returns that exist when organizations
make investments in AI ethics.11
At a high level, how might this approach work in
practice? Consider the investment in an AI Ethics
Board infrastructure and staff. This investment helps
prevent regulatory fines (tangible impact); increases
client trust, partner endorsements, and business
opportunities (intangible impact); and helps enable
the development of management system tooling that
improves automated documentation and data
management (capabilities). The holistic AI ethics
framework illustrates how AI ethics is interwoven
throughout an organization, both in terms of practices
and outcomes.
The holistic AI ethics
framework illustrates how
AI ethics is interwoven
throughout an organization,
both in terms of practices
and outcomes.
5
Part two
The value of “loss aversion”
What is AI ethics? A senior vice president with responsibility
for data policy at Fidelity Investments puts it succinctly:
“It’s using AI technology in a responsible form to be able
to distinguish between right and wrong as we communicate
with our customers, prospects, and other clients.”
In recent IBM IBV research, 72% of executives said they’ll step back from
generative AI initiatives if they think the benefits might come at an ethical
cost. These same organizations are 27% more likely to outperform on
revenue growth—a correlation that is hard to ignore.12
Yet noble AI intentions are often talked about more than they are acted
on. While over half of organizations in our research have publicly endorsed
principles of AI ethics, less than a quarter have operationalized them.13
Fewer than 20% strongly agree that their organizations’ actions
and practices on AI ethics match (or exceed) their stated principles
and values.14
“It’s all good to want to do it, but you need to actually do it,” says a senior
leader responsible for AI governance at a global financial services firm.
“But to do it, you need resources, which requires funding. More important
than that, you need the will of senior executives.”
So, what is the business justification for investing in AI ethics? It often
starts with a loss aversion approach: avoiding costs associated with
regulatory compliance or retaining revenue that might be lost if
customers move their business to enterprises that prioritize AI ethics.
Noble AI intentions are often talked
about more than they are acted on.
6
The fact that these motivations reflect a short-term A prod from AI regulators
strategy does not detract from their significance.15
Loss aversion generates near-immediate results.
AI regulations are a catalyst for action. The EU
As the senior leader responsible for AI governance at
AI Act is the first comprehensive AI regulation by
a global financial services firm notes, “The business
a major entity. One strategy manager at Deutsche
case is all about decreasing reputational risk.”
Telekom says, “The EU AI Act could change the face
of AI ethics globally. If, for instance, an American
company is working with us, they also have to comply
with the EU AI Act.”
The EU’s effort is only the beginning. Organizations
Examples of loss aversion include:16
such as the Partnership on AI, the Global Partnership
on AI, the World Economic Forum, the United Nations,
and the Organisation for Economic Co-operation and
Regulatory justifications
Development (OECD) have all published principles
Avoid a regulatory fine. and guidelines on a responsible approach to AI.17
In a survey by the Centre for the Governance of AI
Avoid legal costs. of over 13,000 people across 11 countries, 91%
agreed that AI needs to be carefully managed.18
Implement required technical
compliance mechanism.
Given this emphasis on regulations, oversight,
and responsible approaches to AI, a focus on loss
Enable business
aversion isn’t just sensible but necessary.
for required compliance.
Customer/partner/
competitor justifications
Allay stakeholder concerns.
Avoid threat to business model.
Meet specific customer request or need.
Protect brand reputation.
Keep pace with competitors.
7
Part three
Leveraging AI ethics
to generate value
The benefits of investments in AI ethics aren’t exclusive
to cost avoidance or damage control. They also help to build
useful capabilities and tangible innovations that can enable
an organization’s long-term strategies.19 Such value
generation can be more indirect than loss aversion and
requires an expanded view of ROI. It also won’t happen
overnight and can take time to see measurable outcomes.
But organizations that are sophisticated about their understanding of AI
ethics can use the investments to:20
– Enable long-term plans to scale AI responsibly.
– Build unique and valuable organizational capabilities that can lead
to differentiation.
– Improve employee efficiency or productivity.
– Align with values to advance as an industry leader.
– Seize a market opportunity.
– Protect vulnerable individuals and communities.
– Increase customer satisfaction.
– Demonstrate trustworthiness and maturity.
– Support Environmental, Social, and Governance (ESG) efforts.
– Increase ability to manage risk over the long term.
– Innovate for a competitive advantage.
As AI technology matures, organizations can not only integrate AI into their
operations, they can repurpose that technology toward new innovations. A
senior director from a leading health and consumer goods retailer explains,
“Based on the measures we took from the AI standpoint to create and
enrich the customer experience, we have seen returns in terms of adoption
of those brands, sales growth, customer retention, and customer growth.”
8
Combining the best
of both worlds
Organizations that embrace a holistic approach
that encompasses both loss aversion and value
generation will be more efficient, effective, and
successful—as well as more ethical.
Reactive Proactive
Loss aversion Value generation
Regulatory compliance justifications Create technologies, infrastructures,
and platforms that can support AI ethics
efforts and be repurposed
Avoid a regulatory fine.
Avoid legal costs. Enable long-term plans to scale
AI responsibly.
Implement required technical
compliance mechanism. Build unique and valuable
organizational capabilities that
Enable business for required compliance.
can lead to differentiation.
Improve employee efficiency
or productivity.
Justifications relating to clients, Align with values to advance
partners, and competitors as an industry leader.
Seize a market opportunity.
Allay stakeholder concerns.
Protect vulnerable individuals
Avoid threat to business model.
and communities.
Meet specific customer request or need.
Increase customer satisfaction.
Protect brand reputation.
Demonstrate trustworthiness
and maturity.
Keep pace with competitors.
Support Environmental, Social,
and Governance (ESG) efforts.
Increase ability to manage risk over
the long term.
Innovate for a competitive advantage.
Source: “On the ROI of AI Ethics and Governance Investments: From Loss Aversion
to Value Generation.” California Management Review. July 29, 2024.
9
The senior vice president of Fidelity Investments observes:
“What companies don’t realize is that up-front investment actually
pays significant ROI, not just in terms of ethics, but from a total cost of
implementation on any of your use cases. Because if you don’t lay that
foundation, you spend a lot more money with everybody implementing
one pillar at a time and not benefiting from any reuse.”
A preliminary step to this evolution, of course, is to actually develop
AI use cases that align with and support organizational strategy.
Notes the strategy manager at Deutsche Telekom, “Either you could
create AI solutions for the customer, or you could create AI solutions
for your internal infrastructure.”
Out of the starting block, it’s instinctive and reasonable to adopt
a “defensive” loss-aversion posture to avoid the pitfalls we’ve
described, such as regulatory fines, legal costs, and reputational risks.
But fertile ground can be found in the pivot to value generation.
Organizations need to create technologies, infrastructures,
and platforms with the versatility to support AI ethics initiatives
and to fuel broader corporate innovation.
Procuring support and budget for these strategies can be tricky.
To persuade skeptics and surmount obstacles, organizations should
clearly pinpoint potential value generated, including metrics of economic
returns. This can be done through a process of identifying relevant loss
aversion and value generation justifications as the organization plans
and then evaluates potential investments21—essentially, using the
holistic AI ethics framework.
Fertile ground can be found in the pivot
to value generation. Organizations need
to create technologies, infrastructures,
and platforms with the versatility to
support AI ethics initiatives and to fuel
broader corporate innovation.
10
Action guide
How to reap the rewards
of AI ethics investments
Investing in AI ethics is not just the right thing to do, it can also be a sound
business decision. By using the holistic AI ethics framework, organizations can
make informed choices about allocating resources to AI ethics, helping boost
the trustworthiness and potential of AI programs overall.
According to IBM IBV research, 75% of executives view ethics as an important
source of competitive differentiation.22 A study from the Economist
Intelligence Unit echoes those results, pointing to a competitive edge through
product quality, talent acquisition and retention, and new revenue sources.23
These studies underscore the criticality of a proactive approach to AI ethics.
Organizations must consider how governance of AI differs from that of
previous technologies, permeating every corner of their culture, ecosystem,
and customer engagement.
“You educate the AI engine based on what humans are thinking,” says the
senior director at a leading health and consumer goods retailer, “because
they are the better judge from an ethics standpoint.”
Along those lines, Reggie Townsend of SAS observes: “We have a diverse set
of folks who have come from a variety of different backgrounds and life
experiences. We do hard work, but we do heart work. I don’t hire anyone who
doesn’t have a heart for what we’re doing. We have passionate people on our
team, and we bring that passion to the work. That’s fundamentally important.”
11
Here’s our five-step guide for optimizing
your AI ethics investments
1
Engage your savviest AI ethics experts to educate the C-suite on
differences between loss aversion and value generation approaches
to AI ethics. Help executives envision the potential of leveraging AI
ethics technology, platforms, and infrastructure for broader use.
2
Identify specific value generation justifications for AI ethics and
governance that may apply to the AI use cases at hand. Examples
include the ability to responsibly improve the answers to customers
and increased employee productivity and job satisfaction.
3
Think through the anticipated stakeholder impacts of the AI
use case and identifying potential indicators. These include:
– Direct economic returns (for example, the value
of an expanded customer base)
– Intangible reputational returns (for example, earned media
value of customer reviews)
– Capabilities and knowledge returns from real options
(for example, improved customer response quality that leads
to more first-contact resolutions).
4
Create an AI ethics implementation strategy that can deliver on value
generation justifications. Using the analysis in action 3, identify the
potential returns holistically. Doing so can help optimize the potential
returns on your investments in AI ethics and governance while
simultaneously benefitting stakeholders, ecosystems, and society.
5
Turn value generation into a competitive advantage. Focusing on value
generation can provide a competitive advantage in an environment
where regulatory compliance is business as usual.
For additional information and actions on the holistic AI ethics framework, refer to “On the ROI
of AI Ethics and Governance Investments: From Loss Aversion to Value Generation,” California
Management Review, at https://cmr.berkeley.edu/2024/07/on-the-roi-of-ai-ethics-and-
governance-investments-from-loss-aversion-to-value-generation/ and “The Return on
Investment in AI Ethics: A Holistic Framework” at https://arxiv.org/abs/2309.13057.
12
AI ethics
Stories from the front lines
Deutsche
Deutsche Telekom’s data initiatives are closely tied with monetizing data through AI
Telekom applications and monitoring the EU AI Act. One strategy manager at the company leads
a team that is involved in virtually every AI conversation in the organization and is
therefore able to provide a holistic overview of the company’s approach to AI ethics.
Preparing for the EU
AI Act with internal Deutsche Telekom has created a team of high-level executives responsible for
governance and evaluating current and future AI initiatives—in effect, an organized governance group.
education The group’s most important purpose is to help ensure that the company complies
with data privacy and security procedures both internally and externally—including
the EU AI Act.
“AI is all about data. It’s a fundamental element of any AI product,” says the manager,
adding that he regards data as a crucial component of the AI ethics approach as well.
Before incorporating any data into its products, the organization considers who is
exposed to the data and how customer data is protected. Beyond their customers,
Deutsche Telekom also must protect certain data segments in terms of sustainability
and energy practices.
Critically, Deutsche Telekom heavily invests in educating employees about AI and
its ethical use, often in the form of internal workshops, including training related
to the EU AI Act.
“Training colleagues is definitely a return on investment because it reduces the time
to market and we come up with more innovative products,” he says. And with its
continual efforts to improve, Deutsche Telekom experiences greater innovation
and enhanced customer trust.
13
AI ethics: Stories from the front lines
Fidelity
Responsible AI initiatives are embedded into each phase of AI use cases at
Fidelity Investments, beginning with robust data management practices
Investments
and feeding into a dynamic review process driven by the company’s AI
Center of Excellence. The financial services firm invested heavily in these
Reaping ROI through
initiatives to make AI ethics one of its foundational pillars—rather than a
repurposed use case
compliance box-checking exercise.
implementation
Each business line at Fidelity has a dedicated team for AI use case
development and vendor management. This work is guided by the
expertise of external consultants and actively monitored by the firm’s
compliance and risk officers, who receive specialized AI training.
The AI Center of Excellence is involved in each step of this process, from
vendor selection to model evaluation. It resides in Fidelity’s data function
and includes representation from each business unit at the firm, with roles
ranging from risk compliance and audit to legal and even information
security. This process also allows Fidelity to confidently answer clients’
increasing demands for information on its AI use and governance.
Resistance to responsible AI initiatives is inevitable, as they can delay
projects or limit use cases. “You have to explain that the reason controls
are so important is not just some random compliance policy, but that
there are implications to the firm if we get this wrong,” says a senior vice
president with responsibility for data policy at the firm. Fidelity has
been able to minimize pushback by framing these initiatives as
integral to the success of AI projects and by streamlining the overall
governance process.
“You have to explain that the reason controls
are so important is not just some random
compliance policy, but that there are
implications to the firm if we get this wrong.”
A senior vice president with responsibility for data policy at Fidelity Investments.
14
AI ethics: Stories from the front lines
SAS Reggie Townsend, VP of the Data Ethics Practice at SAS, leads a team tasked
with coordinating responsible innovation principles, operational workflows and
Ensuring an AI-driven governance structures across a global organization. It all began with questions
future that is built and investigating.
for all of us
Prompted by risks to vulnerable populations and the increasing sophistication of
AI, Townsend and close colleagues began digging deeper into responsible AI
and data ethics at SAS. They were empowered by SAS leadership to formalize
the company’s longtime commitment to responsible innovation. Consequently,
SAS created the Data Ethics Practice (DEP).
With a philosophy of “ethical by design,” the DEP guides the company’s efforts
to help employees and customers deploy data-driven systems that promote
human well-being, agency, and fairness. This approach compels individuals
to answer three basic questions:
– For what purpose?
– To what end?
– For whom might it fail?
The team helps build Trustworthy AI capabilities and workflows to help
customers and developers pursue their responsible AI goals. AI governance
advisory services from the DEP are helping customers put AI into action
responsibly. The DEP also provides critical counsel to employees on product
development, marketing, and more.
When Townsend’s role and team were created, the hope was their work would
bolster trustworthiness of products, processes, and people. This, in turn,
would enhance the brand’s reputation as a trusted AI leader.
Profits are important, of course. But according to Townsend, his team’s
guiding principle is that wherever SAS software shows up, it does no harm.
“Sometimes,” Townsend observes, “you just have to take action because
it’s the right action to take.”
“Sometimes, you just have to take
action because it’s the right action
to take.”
Reggie Townsend
Director and VP, Data Ethics branch, SAS
15
AI ethics: Stories from the front lines
Global financial For one senior leader responsible for AI governance at a global financial services firm,
AI development and ethics starts with education. He advocates hosting workshops
services firm
that discuss ethical principles and values—empowering leadership to understand
trade-offs. “We need to talk about AI in a way that interests leadership, not just in
Justifying positive
processes and procedures,” he observes.
returns with a lowered
reputational risk In discussing how to measure the return on investments in AI ethics, the senior leader
offers the “creepy line” metaphor. Often, organizations find themselves in situations in
which they are doing something perfectly legal that is highly profitable, yet still feel
uncertain about the ethicality of their actions—a sense of crossing the “creepy line.”
In such situations, he says that organizations must examine the activity through the
lens of both current and future generations, in conjunction with all comprehensive
ethical considerations. As long as these considerations are covered satisfactorily,
the organization should feel reassured that the “creepy line” is not breached.
He also notes, “Reputational risk is a key factor in justifying positive returns.
We aim to decrease reputational risk while applying data and AI ethics principles.”
For example, his team conducted an ethical fairness review of loan pricing involving a
credit scoring algorithm. In conducting this review, the team analyzed all 165 features
of the model, asking if there were any potential causal mechanisms for why that
particular data feature may correlate with an individual’s ability to pay back a loan.
Ultimately, three data features were removed because a causal link did not exist,
thus avoiding the lack of fairness in using this AI technology.
“We need to talk about AI in a way
that interests leadership, not just in
processes and procedures.”
Senior leader responsible for AI governance at a global financial
services firm
16
AI ethics: Stories from the front lines
A leading health
A senior director at this organization instituted an AI initiative to provide solutions
via vendors and internal products. A recent conversation with him covered three main
and consumer
operational areas.
goods retailer
A rigorous governance process. The retailer’s AI governance group is a centralized
body that helps ensure all AI initiatives fulfill their required steps for approval. In that
Driving success with a
vein, it conducts sessions in which project teams present how they’ve aligned their
thorough AI ethics and
compliance measures with the group’s control plan. If approved, the projects move
governance strategy
forward. The director notes that, as a sizeable enterprise engaging with large numbers
of partners, suppliers, customers, and other ecosystems, it must be extremely careful
in building their AI capabilities.
The AI ethics engine. Whether the retailer invests in SaaS-, vendor-, or open-source-
based products, they ensure all ethical parameters are met prior to deployment.
Its internal audit process is referred to as “the AI ethics engine.” In engaging a vendor,
the organization first conducts a background check, looking at the health of its
industry, clients, reputation, and capabilities. This process can span two to four
months. Once the retailer picks its vendor, it engages in a pilot. If success and ethics
measures are met, the partnership proceeds.
Stakeholder success. The organization has heavily invested in AI capabilities
to enhance the customer engagement experience and drive market strategies
and customer growth. The director notes, “AI by itself or a human by itself cannot
be successful, but if you combine those two together, the outcome is successful
and accurate.”
At this particular retailer, AI capabilities implemented in customer service, for example,
will not replace customer service employees. Rather, the organization invests in
providing these employees with additional skills, resulting in employee retention.
This approach can create benefits for the customers, employees, and company’s
economic returns.
“AI by itself or a human by itself cannot
be successful, but if you combine those
two together, the outcome is successful
and accurate.”
Senior director at a leading health and consumer goods retailer
17
Authors
Nicholas Berente Marianna Ganapini
Senior Associate Dean for Academic Programs Associate Professor, Philosophy
Professor of IT, Analytics and Operations Union College
University of Notre Dame, Mendoza College of Business linkedin.com/in/marianna-b-ganapini-769624116/
linkedin.com/in/berente/ marianna@logicanow.com
nberente@nd.edu
Brian Goehring
Marialena Bevilacqua Associate Partner, AI Research Lead
PhD Student in Analytics IBM Institute for Business Value
University of Notre Dame, Mendoza College of Business linkedin.com/in/brian-c-goehring-9b5a453/
linkedin.com/in/marialena-bevilacqua-6848b9132/ goehring@us.ibm.com
mbevilac@nd.edu
Francesca Rossi
Heather Domin IBM Fellow and AI Ethics Global Leader
Global Leader, Responsible AI Initiatives, IBM IBM Research
Associate Director, Notre Dame—IBM Tech Ethics Lab linkedin.com/in/francesca-rossi-34b8b95/
linkedin.com/in/heatherdomin/ Francesca.Rossi2@ibm.com
hesill@us.ibm.com
Contributors
Sara Aboulhosn, Angela Finley, Rachna Handa,
Jungmin Lee, Stephanie Meier, and Lucy Sieger
18
About Research Insights The right partner for
a changing world
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for business executives on critical public- and At IBM, we collaborate with our clients, bringing
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For more information, contact the IBM Institute for today’s rapidly changing environment.
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IBM Institute for Related reports
Business Value
The enterprise guide to AI governance
IBM Institute for Business Value. October 2024.
For two decades, the IBM Institute for Business Value
ibm.co/ai-governance
has served as the thought leadership think tank for
IBM. What inspires us is producing research-backed,
The CEO’s guide to generative AI:
technology-informed strategic insights that help
Responsible AI & ethics
leaders make smarter business decisions.
IBM Institute for Business Value. October 2023.
From our unique position at the intersection ibm.co/ceo-generative-ai-responsible-ai-ethics
of business, technology, and society, we survey,
interview, and engage with thousands of executives, AI ethics in action
consumers, and experts each year, synthesizing IBM Institute for Business Value. April 2022.
their perspectives into credible, inspiring, and ibm.co/ai-ethics-action
actionable insights.
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19
Notes and
sources
1 Goehring, Brian, Francesca Rossi, and Beth Rudden. 11 Bevilacqua, Marialena, Nicholas Berente, Heather
AI ethics in action: An enterprise guide to progressing Domin, Brian Goehring, and Francesca Rossi.
trustworthy AI. IBM Institute for Business Value. April “The Return on Investment in AI Ethics: A Holistic
2022. https://ibm.co/ai-ethics-action Framework.” Proceedings of the 57th Annual HICSS
Conference on Systems Sciences. January
2 Ibid. 2024. https://arxiv.org/abs/2309.13057. “OECD AI
Principles overview.” OECD. Accessed November 15,
3 Ibid. 2024. https://oecd.ai/en/ai-principles
4 Ibid. 12 The CEO’s guide to generative AI: Customer and
employee experience. IBM Institute for Business Value.
5 2023 Institute for Business Value generative AI state of
August 2023. https://www.ibm.com/thought-
the market survey. 369 global CxOs. April/May 2023.
leadership/institute-business-value/en-us/report/
Unpublished information.
ceo-generative-"
147,ibm,IBM-AI-POV_FINAL2.pdf,"Precision Regulation for Artificial Intelligence
By Ryan Hagemann, IBM Policy Lab co-Director (Washington, DC) & Jean-Marc Leclerc, IBM Policy Lab co-Director (Brussels)
Among companies building and deploying artificial treated fairly and equitably by AI-based determinations in
intelligence, and the consumers making use of this sensitive use-cases.
technology, trust is of paramount importance. Companies
want the comfort of knowing how their AI systems are That is why today we are calling for precision regulation
making determinations, and that they are in compliance of AI. We support targeted policies that would increase
with any relevant regulations, and consumers want to the responsibilities for companies to develop and operate
know when the technology is being used and how (or trustworthy AI. Given the ubiquity of AI — it touches all of
whether) it will impact their lives. us in our daily lives and work — there will be no one-size-
fits-all rules that can properly accommodate the many
62% of Americans and 70% Europeans unique characteristics of every industry making use of
this technology and its impact on individuals. But we can
prefer a precision regulation approach for
define an appropriate risk-based AI governance policy
technology, with less than 10% in either
framework based on three pillars:
region supporting broad regulation of tech.
85% of Europeans and 81% of Americans
• Accountability proportionate to the risk
support consumer data protection in some
profile of the application and the role of the
form, and 70% of Europeans and 60% of entity providing, developing, or operating an AI
Americans support AI regulation. system to control and mitigate unintended or
harmful outcomes for consumers.
As outlined in our Principles for Trust and Transparency,
• Transparency in where the technology is
IBM has long argued that AI systems need to be
deployed, how it is used, and why it provides
transparent and explainable. That’s one reason why we
certain determinations.
supported the EU and the OECD AI Principles, and in
particular the focus on transparency and trustworthiness
• Fairness and security validated by testing
in both.
for bias before AI is deployed and re-tested
as appropriate throughout its use, especially
Principles are admirable and can help communicate a
in automated determinations and high-risk
company’s commitments to citizens and consumers. But
applications.
it’s past time to move from principles to policy. Requiring
disclosure — as appropriate based on use-case and
end-user — should be the default expectation for many
Wisely, the OECD AI Principles suggest a solid
companies creating, distributing, or commercializing AI
accountability bedrock for this framework, arguing that
systems. In an earlier Policy Lab essay, we articulated a
“[g]overnments should promote a policy environment
disclosure requirement for law enforcement use-cases of
that supports an agile transition from the research and
facial recognition technology. Something similar should
development stage to the deployment and operation
be required of AI more generally in order to provide the
stage for trustworthy AI systems.” This implicit recognition
public with appropriate assurances that they are being
ibm.com/policy 1
of the fundamental difference in accountability between complexity and potential impact of AI systems increases,
stages of AI development can help appropriately assign so too must the accountability embraced by different
responsibility for providing transparency and ensuring organizations providing various functions in the AI lifecycle.
fairness and security, based on who has better control A market environment that prioritizes the adoption of
over the protection of privacy, civil liberties, and harm- lead AI ethics officials, or other designated individuals, to
prevention activities in a given context. oversee and manage this increasing complexity could help
to mitigate risks and improve public acceptance and trust
In the lifecycle of AI capabilities in the marketplace, of these systems, while also driving firms’ commitment
organizations may contribute research, the creation to the responsible development, deployment, and overall
of tooling, and APIs; in later stages of operation, stewardship of this important technology.
organizations will train, manage, and control, operate, or
own the AI models that are put to real-world commercial 2. Different rules for different risks. All entities
use. These different functions may allow for a distinction providing or owning an AI system should conduct an
between “providers” and “owners,” with expectations of initial high-level assessment of the technology’s potential
responsibilities based on how an organization’s role falls for harm. As noted previously, such assessments should
into one or both categories. be based on the intended use-case application(s),
end-user(s), how reliant the end-user would be on the
Differentiating accountability can help to better mitigate technology, and the level of automation. Once initial risk
potential harm by directing resources and oversight to is determined, a more in-depth and detailed assessment
specific applications of AI based on the severity and should be undertaken for higher-risk applications. In
likelihood of potential harms arising from the end-use and certain low-risk situations, a more cursory appraisal
user of such systems. Risk-based regulatory approaches would likely suffice. For those high-risk use-cases, the
like this — which also allow for more manageable and assessment processes should be documented in detail,
incremental changes to existing rules — are ideal means be auditable, and retained for a minimum period of time.
to protect consumers, build public trust in AI, and provide
innovators with needed flexibility and adaptability. 3. Don’t hide your AI. Transparency breeds trust; and the
best way to promote transparency is through disclosure.
Building from these pillars, we propose a precision Unlike other transparency proposals, this approach does
regulation framework that incorporates 5 policy not entail companies revealing source code or other
imperatives for companies, based on whether they are forms of trade secrets or IP. Instead it focuses on making
a provider or owner (or both) of an AI system. These the purpose of an AI system clear to consumers and
policies would vary in robustness according to the level businesses. Such disclosures, like other policy imperatives
of risk presented by a particular AI system, which would here, should be reasonably linked to the potential risk
be determined by conducting an initial risk assessment and harm to individuals. As such, low-risk and benign
based on potential for harm associated with the intended applications of AI may not require the type of disclosure
use, the level of automation (and human involvement), that higher-risk use-cases might require.
and whether an end-user is substantially reliant on the AI
system based on end-user and use-case. 4. Explain your AI. Any AI system on the market that
is making determinations or recommendations with
1. Designate a lead AI ethics official. To ensure potentially significant implications for individuals should
compliance with these expectations, providers and owners be able to explain and contextualize how and why it
should designate a person responsible for trustworthy arrived at a particular conclusion. To achieve that, it
AI, such as a lead AI ethics official. This person would is necessary for organizations to maintain audit trails
be accountable for internal guidance and compliance surrounding their input and training data. Owners and
mechanisms, such as an AI Ethics Board, that oversee operators of these systems should also make available —
risk assessments and harm mitigation strategies. As the as appropriate and in a context that the relevant end-user
ibm.com/policy 2
can understand — documentation that detail essential To achieve this, governments should:
information for consumers to be aware of, such as
confidence measures, levels of procedural regularity, and • Designate, or recognize, existing effective co-
error analysis. regulatory mechanisms (e.g. CENELEC in Europe
or NIST in the U.S.) to convene stakeholders
74% of American and 85% of EU and identify, accelerate, and promote efforts to
create definitions, benchmarks, frameworks and
respondents are in agreement that
standards for AI systems. Ideally, standards that
artificial intelligence systems should be
are globally recognized would help create consistency
transparent and explainable, and strong and certainty for consumers, communicating to end-
users that the AI is trustworthy;
pluralities in both countries believe
that disclosure should be required for
• Support the financing and creation of AI testbeds
companies creating or distributing AI with a diverse array of multi-disciplinary
systems. Nearly 3 in 4 Europeans and two- stakeholders working together in controlled
environments. In particular, minority-serving
thirds of Americans support regulations
organizations and impacted communities should be
such as conducting risk assessments,
supported in their efforts to engage with academia,
doing pre-deployment testing for bias and government, and industry. Working together, these
stakeholders can accelerate the development
fairness, and reporting to consumers and
and evaluation criteria of AI accuracy, fairness,
businesses that an AI system is being used
explainability, robustness, transparency, ethics,
in decision making. privacy, and security; and
5. Test your AI for bias. All organizations in the AI • Incentivize providers and owners to voluntarily
developmental lifecycle have some level of shared embrace globally recognized standards,
responsibility in ensuring the AI systems they design certification, and validation regimes. One such
and deploy are fair and secure. This requires testing potential mechanism is by providing various levels of
for fairness, bias, robustness and security, and taking liability safe harbor protections, based on whether and
remedial actions as needed, both before sale or how an organization adheres and certifies to globally
deployment and after it is operationalized. Owners should recognized best practices and standards.
also be responsible for ensuring use of their AI systems is
aligned with anti-discrimination laws, as well as statutes Finally, any action or practice prohibited by anti-
addressing safety, privacy, financial disclosure, consumer discrimination laws should continue to be prohibited when
protection, employment, and other sensitive contexts. it involves an automated decision-making system. Whether
For many use-cases, owners should continually monitor, a decision is fully rendered by a human or a determination
or retest, the AI models after the product is released is assisted by an automated AI system, impermissibly
to identify and mitigate against any machine-learning biased or discriminatory outcomes should never be
resulting in unintended outcomes. Policies should create considered acceptable. But whereas correcting the bias of
an environment that incentivizes both providers and humans is a daunting and difficult task, in AI systems it may
owners to do such testing well. This can be done without be a matter of addressing historical bias in some training
creating new and potentially cumbersome AI-specific data by testing for, and correcting, statistical failures in the
regulatory requirements, but rather by adhering to a set model. While this will take time, AI offers us the promise
of agreed-upon definitions, best practices, and global of a world where bias and discrimination may one day
standards. fade away. With precision regulations helping to promote
trustworthy AI, that future could be sooner than we think.
ibm.com/policy 3
Since day one, IBM has pushed the boundaries of technology to address the challenges
of tomorrow. We’ve done this while earning our clients’ trust to innovate responsibly and
carefully stewarding their data. We’ll continue to drive forward new technological advances
with the values of accountability, transparency, and trust that our clients and government
partners have relied on since 1911.
The world — and IBM — has changed a lot over the past century. We’ve seen the march of progress move
humanity from an analog era to the digital age and explosive innovation in both bits and atoms contribute to a
wave of disruptive change. At IBM, we’re optimistic about what the future holds, and the crucial role technological
advancement will play in driving economic growth and societal well-being. Already, cloud computing has changed
how work gets done and how connections are made, artificial intelligence has revolutionized our daily routines,
and we can find information on practically anything at the touch of a button. Technology will fundamentally
change society, bring us closer together, improve lives around the world and help us tackle some of our greatest
challenges.
But no journey comes without challenges. We have already seen concerns materialize across
emerging technologies on the implications of opaque AI systems making safety- and life-
critical decisions; the growing pains of new digital platforms leading to the spread of illegal
and harmful content online; and fears that a fully-automated future will displace more jobs
than it creates. All of this comes amid a wave of global challenges to modern society, from
the spread of protectionist impulses to the failure to address climate change.
But at IBM, we’ve seen how technological progress has improved the human condition over the past 100 years.
We were optimistic about the future then, and we remain optimistic about the future to come. While there are
challenges ahead, we believe there are clear and practical ways through them.
As businesses and governments break new ground and deploy technologies that are
positively transforming our world, we want to work collaboratively to make sure public
policy adapts to meet the challenges of tomorrow. That’s why we’ve created the IBM Policy
Lab, a new forum that provides a vision with actionable recommendations to harness
the benefits of innovation while ensuring trust in a world reshaped by data.
Led by co-directors Ryan Hagemann and Jean-Marc Leclerc — two long-standing experts in tech and public policy
— IBM Policy Lab convenes leading thinkers in public policy, academia, and technology to develop the concrete,
common-sense policy ideas leveraging technology to tackle some of the most pressing issues facing our world.
Our approach is grounded in the belief that tech can continue to disrupt and improve civil society while protecting
individual privacy.
ibm.com/policy 4
What We Do How We’re Different
• Develop Industry-Leading Policy Positions that • While some traffic in grandiose policy
don’t just respond to the spot issues of today but recommendations that stand little chance of
look forward to the opportunities of tomorrow becoming reality, IBM has always believed that
and the ways public-private cooperation can big challenges require practical solutions. That’s
pave the way for an even brighter future. With the precisely what IBM Policy Lab has been chartered to
full benefits of artificial intelligence, blockchain, create.
quantum computing and more still untapped,
we’ll put forward bold visions for public policy that • Our policy recommendations will be concrete.
harnesses innovation. Specific. Actionable. We will have big ideas, but they
will be ideas that policymakers can implement on
• Collaborate with Global Thinkers, Stakeholders day one.
and Leaders to collect input and share perspective
from the diverse voices that must inform public • We will also convene government, industry and
policy. civil society experts to think big about upcoming
challenges and make space for collaborative
• Produce Data-Driven Studies and Research to solutions.
guide policymaking with specific, common-sense
recommendations, and help industry leaders make • Serious times call for serious solutions, and that’s
critical decisions on policies impacting our future. precisely what leaders in government, business and
civil society can expect from IBM Policy Lab.
As technological innovation races ahead, our mission
to raise the bar for a trustworthy digital future could
not be more urgent. IBM Policy Lab is committed to
developing and advocating the right policies that meet
the demands of the moment and harness the power of
technology as a force for good in the world.
Jean-Marc Leclerc joined IBM’s Government and Regulatory Affairs team in 2015, where he leads the EU Affairs team. Jean-Marc
is the Chair of the EMEA Policy Committee at the Business Software Association (BSA), and he is a Vice-Chair of the Digital Economy
Committee at the American Chamber of Commerce to the EU. Before joining IBM, he was a Policy Director at Digitaleurope 2013-
15. He has also managed an association representing the music industry in Brussels 2006-13. Jean-Marc is a graduate from the
universities of Paris III, Sciences Po, the Catholic Institute of Paris, and the College of Europe in Bruges.
Ryan Hagemann is the Co-Director of the IBM Policy Lab and a Technology Policy Executive on IBM’s Government and Regulatory
Affairs team. He was previously a senior policy fellow at the International Center for Law & Economics. Before joining ICLE, he was
a senior fellow at the Niskanen Center, where he also served as the senior director for policy and director of technology policy. His
policy expertise focuses on regulatory governance of emerging technologies, as well as a broader research portfolio that includes
genetic modification and regenerative medicine, bioengineering and healthcare IT, artificial intelligence, autonomous vehicles,
commercial drones, the Internet of Things, and other issues at the intersection of technology, regulation, and the digital economy.
His work on “soft law” governance systems, autonomous vehicles, and commercial drones has been featured in numerous academic
journals, and his research and comments have been cited by The New York Times, MIT Technology Review, and The Atlantic, among
other outlets. He has been published in The Wall Street Journal, Wired, National Review, The Washington Examiner, U.S. News &
World Report, The Hill, and elsewhere. Ryan graduated from Boston University with a B.A. in international relations, foreign policy,
and security studies and holds a Master of Public Policy in science and technology policy from George Mason University.
ibm.com/policy 5"
148,ibm,Enterprise+AI+Development+Survey.pdf,"Enterprise AI Development:
Obstacles & Opportunities
JANUARY 2025
© 2024Morning Consult.All rightsreserved.
Methodology Table of Contents
3
This survey was conducted October 31 to November 1, 2024, Key Findings
among a total sample of 1,063 Enterprise AI Developers in the
US. The interviews were conducted online, and the data is
unweighted. Results from the full survey have a margin of error of
+/- 3%.
4
AI Skills & Developer Challenges
To qualify as an Enterprise AI Developer survey respondents must
meet the following requirements:
9
✓ Be employed (Full-time, Part-time, or Self-employed) Technology Landscape | Developer Tools
✓ Work in one of the following roles: Data Scientist,
Application Developer, System Developer, AI Developer,
ML Engineer, Software Engineer, Software Developer, AI
Engineer, or IT Engineer
16
Agents
✓ Contribute at least occasionally to the development of
enterprise AI applications in their current role
2
KEY FINDINGS
AI skill gaps AI tool gaps Simplicity is the solution
Generative AI skill levels vary The most important tool qualities for Developers crave tools that are easy to
significantly among developers. Most building enterprise AI are also the master. And when it comes to
developers surveyed do not view rarest, hampering the development developer productivity, AI-powered
themselves as experts, despite being process. Meanwhile, developers must coding solutions are incredibly
on the front lines of generative AI juggle a roster of tools. popular.
adoption.
➢ Only one third of respondents are
➢ Less than one quarter (24%) of ➢ Performance (42%), Flexibility (41%),
willing to invest more than two hours
application developers surveyed Ease of Use (40%), and Integration
in learning a new AI development tool.
ranked themselves as ""experts"" in (36%) are the most essential qualities
generative AI. in enterprise AI development tools,
➢ 99% are using coding assistants in
according to respondents. Yet over a
some capacity for AI development.
➢ One third of respondents list the lack third also said those very same traits
And most commonly, developers said
of a standardized AI development are the rarest.
these tools saved them 1-2 hours per
process as a top challenge.
day (41% of developers).
➢ A majority (72%) of respondents use
➢ 99% of respondents are exploring or between 5 and 15 tools to create an AI
developing AI agents. enterprise application.
3
SECTION 1
AI Skills & Developer
Challenges
4
AI SKILLS & DEVELOPER CHALLENGES
A lack of standard processes and trust are inhibiting the development of generative AI
applications for business
Top Challenges of Developing Gen AI at the Enterprise Level
All Enterprise AI Developers, Showing % selected
Lack of a standardized AI development process 33%
Developing an ethical and trusted lifecycle that ensures transparency and traceability
33%
of data
Customization 32%
Rate of change 31%
Infrastructure/stack complexity 29%
Establishing governance and ensuring compliance 28%
Lack of skills/experience 26%
Lack of clarity on business outcome/objective 26%
Interoperability of tools 23%
LLM quality 19%
Other 0%
IBM3: What are the top obstacles associated with developing gen AI applications for enterprise use? Please select up to 3 options.
Sample size: Enterprise AI Developers = 1,063n 5
AI SKILLS & DEVELOPER CHALLENGES
Expertise in generative AI varies greatly by developer role
Most application developers don’t view themselves as experts in generative AI
Expert Experience Level with Generative AI
Enterprise AI Developers by Role, Showing % selected ‘Expert’
54%
51%
48%
46%
All Enterprise AI 43% 43%
40%
Developers Average: 44% 38%
24%
AI Developer Data Scientist Software System ML Engineer Software IT Engineer AI Engineer Application
Engineer Developer Developer Developer
IBM1: How would you rank your proficiency and professional experience these types of AI?
Sample size: Data Scientist = 39n, Application Developer = 37n, System Developer = 52n, AI Developer = 82n, ML Engineer = 46n, Software Engineer = 277n, Software Developer = 273n, AI Engineer = 95n, IT Engineer = 162n 6
Note: Please note that sample sizes for some roles are small and therefore data should be interpreted directionally
AI SKILLS & DEVELOPER CHALLENGES
Developers feel more comfortable with Generative AI versus Classical AI
Experience Level with Types of AI
All Enterprise AI Developers
Expert Advanced / Experienced Intermediate / Some Experience Novice / Entry Level No Experience
Generative AI 44% 40% 11% 4%
17% difference
Classical AI 27% 40% 17% 9% 8%
IBM1: How would you rank your proficiency and professional experience these types of AI?
Sample size: Enterprise AI Developers = 1,063n 7
AI SKILLS & DEVELOPER CHALLENGES
Time spent on project phases
The number of hours developers spend on tasks becomes less predictable later in the project cycle
Average Number of Hours Spent on Project Cycle Tasks/Stages
All Enterprise AI Developers, Showing % within specific tasks/stage
0-4 hours 5-9 hours 10-19 hours 20-29 hours 30-39 hours 40+ hours Average Hours Median Hours
Infrastructure set-up / T
runtime configuration
18% 20% 21% 19% 13% 8% 18 12 mi
e
v
a
Model selection 20% 25% 22% 16% 7% 10% 17 11 ir
a
n
c
e
Model customization 19% 20% 22% 13% 10% 16% 21 14 ni
c
r
e
a
s
Prompt engineering 21% 20% 25% 16% 10% 8% 17 12 e
s
t
h
r
Orchestration and o
20% 20% 21% 16% 15% 9% 19 12 u
integrations g
h
o
u
Deployment 20% 22% 27% 11% 9% 11% 18 11 t
t
h
e
c
Evaluation and y
18% 20% 26% 14% 8% 15% 21 12 c
Observability l
e
IBM2: During a typical project cycle, how many hours do you spend on the following tasks/stages? Please provide your best estimate. [NUMERIC OPEN END]
Sample size: Enterprise AI Developers = 1,063n 8
SECTION 2
Technology Landscape |
Developer Tools
9
TECHNOLOGY LANDSCAPE | DEVELOPER TOOLS
Enterprise AI Developers face tool sprawl
The majority of those surveyed use between 5 and 15 tools to create an AI enterprise application;
13% of developers are using 15 or more
Average Number of Tools Used to Create an AI Enterprise Application
All Enterprise AI Developers, Showing % Selected
1-5 16%
5-10 35%
10-15 37%
15-20 11%
Over 20 2%
IBM6: On average, how many tools do you use to create an AI enterprise application?
Sample size: Enterprise AI Developers = 1,063n 10
TECHNOLOGY LANDSCAPE | DEVELOPER TOOLS
When it comes to tooling, the most critical tool qualities are also among the rarest
Most Essential Traits in Enterprise AI Development Tools Top Missing Traits in Enterprise AI Development Tools
All Enterprise AI Developers, Showing % selected All Enterprise AI Developers, Showing % selected
Performance 42% Performance 37%
Flexibility 41% Flexibility 37%
Ease of use 40% Integration with existing tool 37%
Integration with existing tool 36% Ease of use 36%
Documentation quality 34% Cost-effectiveness 36%
Cost-effectiveness 33% Documentation quality 35%
Open source 32% Community support and resources 32%
Community support and resources 30% Open source 31%
IBM4 Which of these traits do you consider most necessary to exist in the tools/frameworks that you use to develop enterprise-grade AI systems? Please select up to 3 options. // IBM5 And which of these traits do you find most
commonly lacking in the tools/frameworks that you use to develop enterprise-grade AI systems? Please select up to 3 options. 11
Sample size: Enterprise AI Developers = 1,063n
TECHNOLOGY LANDSCAPE | DEVELOPER TOOLS
Enterprise AI Developers crave tools that are easy to master;
Only one third of those surveyed are willing to invest more than two hours in learning a new AI
development tool
Time Willing to Invest in Learning New AI Tools
All Enterprise AI Developers, Showing % Selected
Less than 10 minutes 0%
10 to 29 minutes 4%
30 to 59 minutes 20%
1 to 2 hours 42%
3 to 5 hours 22%
More than 5 hours 11%
IBM9: How much time in total are you willing to invest learning to use a new AI development tool before moving on?
Sample size: Enterprise AI Developers = 1,063n 12
TECHNOLOGY LANDSCAPE | DEVELOPER TOOLS
Developers are leaning into a variety of tools to streamline AI development
More than half are using no-code or low-code tools; 73% are leaning into pro-code tools
Types of Development Tools | Extent of Usage by Level
All Enterprise AI Developers, Showing T2B (Often + Very Often)
Pro code 73%
y
t
l
u
c Low code 65%
i
f
f
i
D
No code 59%
IBM11: To what extent are you using the following for AI development?
Sample size: Enterprise AI Developers = 1,063n 13
TECHNOLOGY LANDSCAPE | DEVELOPER TOOLS
AI-powered Coding Assistants are saving developers significant amounts of time
41% of developers said these tools saved them 1-2 hours per day
Extent of Coding Assistant Usage for AI Time Saved [Per Day] Using AI-Assisted Coding
Development Tools
All Enterprise AI Developers All Enterprise AI Developers, Showing % Selected
Very often Often Occasionally Rarely Not at all It doesn't save me time. 0%
Less than 15 minutes 2%
15 to 29 minutes 9%
35% 43% 15% 5%
30 to 59 minutes 25%
1 to 2 hours 41%
3 to 4 hours 18%
Nearly all enterprise AI developers are
using coding assistants – and 78% are
More than 4 hours 4%
using them often or very often.
I'm not sure 1%
I don't use AI-assisted coding tools 0%
IBM11: To what extent are you using the following for AI development? Coding assistants // IBM10: On average, how much time do you estimate you save per day by using AI-assisted coding tools?
Sample size: Enterprise AI Developers = 1,063n 14
TECHNOLOGY LANDSCAPE | DEVELOPER TOOLS
Exploration of new AI development tools is limited, with nearly 80% only occasionally
experimenting with new tools
Frequency of Experimenting with New AI Development Tools
All Enterprise AI Developers
Every month Every 1 to 3 months Every 3 to 6 months Rarely or never
21% 46% 31% 2%
Enterprise AI Developers experiment with ~6 new
tools on average during these time frames.
IBM7: How often do you experiment with new AI development tools? // IBM8 How many new AI development tools do you usually experiment with during that timeframe? [NUMERIC OPEN END] Showing Average
Sample size: Enterprise AI Developers = 1,063n, Enterprise AI Developers experimenting with new AI development tools at least every 3 to 6 months = 1,043n 15
SECTION 3
Agents
16
AGENTS
Just about everyone is exploring or developing AI agents
Exploration/Development of Use Cases for AI Agents
All Enterprise AI Developers
1% not currently exploring/developing use
cases for AI agents
99% exploring/developing use cases
for AI agents
IBM12: What use cases is your enterprise exploring or developing for AI agents?
Sample size: Enterprise AI Developers = 1,063n 17
AGENTS
Trustworthiness emerges as the top concern when it comes to scaling agents
Top Concerns for Scaling AI Agents in Enterprise
All Enterprise AI Developers, Showing % selected
Trustworthiness: Ensuring outputs are accurate and void of bias 31%
Introducing new attack vectors: AI Agents being compromised by
23%
malicious actors
Adhering to compliance and regulations 22%
Rogue AI agents aren't keeping
Developers up at night. Only 22% of
Becoming overly autonomous: Humans lose oversight and visibility
22%
into systems those surveyed said agents becoming
overly autonomous was a top concern.
I have no concerns 3%
IBM14: What are you most concerned about when it comes to AI agents scaling in the enterprise?
Sample size: Enterprise AI Developers = 1,063n 18
AGENTS
Customer service, project management, and content creation are top use-cases currently
being explored for agents
AI Agent Use Cases Being Explored
All Enterprise AI Developers, Showing % selected
Customer Service and Support 50%
Project Management / Personal Assistant 47%
Content Creation 46%
HR 43%
Transportation 32%
Healthcare 28%
Not currently exploring/developing 1%
IBM12: What use cases is your enterprise exploring or developing for AI agents?
Sample size: Enterprise AI Developers = 1,063n 19
© 2024 Morning Consult. All rights reserved."
150,ibm,Christina-Montgomery-Senate-Judiciary-Testimony-5-16-23.pdf,"Testimony of Christina Montgomery, Chief Privacy and Trust Officer, IBM
Before the
U.S. Senate Judiciary Committee
Subcommittee on Privacy, Technology, and the Law
Hearing on “Oversight of AI: Rules for Artificial Intelligence”
Tuesday, May 16, 2023
Chairman Blumenthal, Ranking Member Hawley, members of the Subcommittee:
Thank you for today’s opportunity to present before the subcommittee. My name is
Christina Montgomery, and I am IBM’s Chief Privacy and Trust Officer. I also co-
chair our company’s AI Ethics Board.
Introduction
AI is not new, but it has advanced to the point where it is certainly having a
moment. This new wave of generative AI tools has given people a chance to
experience it first-hand. Citizens are using it for help with emails, their homework,
and so much more.
While IBM is not a consumer-facing company, we are just as active – and have
been for years – in helping business clients use AI to make their supply chains
more efficient, modernize electricity grids, and secure financial networks from
fraud. IBM’s suite of AI tools, called IBM Watson after the AI system that won on
TV’s Jeopardy! more than a decade ago, is widely used by enterprise customers
worldwide. Just recently we announced a new set of enhancements, called
watsonx, to make AI even more relevant today.1 Our company has extensive
experience in the AI field in both an enterprise and cutting-edge research context,
and we could spend an entire afternoon talking about ways the technology is being
used today by business and consumers.
But the technology’s dramatic surge in public attention has, rightfully, raised
serious questions at the heart of today’s hearing. What are AI’s potential impacts
1 See https://newsroom.ibm.com/2023-05-09-IBM-Unveils-the-Watsonx-Platform-to-Power-Next-Generation-
Foundation-Models-for-Business.
2
on society? What do we do about bias? What about misinformation, misuse, or
harmful and abusive content generated by AI systems?
Senators, these are the right questions, and I applaud you for convening today’s
hearing to address them head-on.
IBM has strived for more than a century to bring powerful new technologies like
artificial intelligence into the world responsibly, and with clear purpose. We follow
long-held principles of trust and transparency that make clear the role of AI is to
augment, not replace, human expertise and judgement. We were one of the first in
our industry to establish an AI Ethics Board, which I co-chair, and whose experts
work to ensure that our principles and commitments are upheld in our global
business engagements.2 And we have actively worked with governments
worldwide on how best to tailor their approaches to AI regulation.
It’s often said that innovation moves too fast for government to keep up. But while
AI may be having its moment, the moment for government to play its proper role
has not passed us by. This period of focused public attention on AI is precisely the
time to define and build the right guardrails to protect people and their interests.
It is my privilege to share with you IBM’s recommendations for those guardrails.
Precision Regulation
The hype around AI has created understandable confusion among some in
government on where intervention is needed and how regulatory guardrails
should be shaped. But at its core, AI is just a tool, and tools can serve different
2 See https://www.ibm.com/artificial-intelligence/ethics.
3
purposes. A wrench can be used to assemble a desk or construct an airplane, yet
the rules governing those two end products are not primarily based on the wrench
— they are based on use.
That is why IBM urges Congress to adopt a “precision regulation” approach to
artificial intelligence. This means establishing rules to govern the deployment of AI
in specific use-cases, not regulating the technology itself.
A precision regulation approach that we feel strikes an appropriate balance
between protecting Americans from potential harms and preserving an
environment where innovation can flourish would involve:
• Different Rules for Different Risks – A chatbot that can share restaurant
recommendations or draft an email has different impacts on society than a
system that supports decisions on credit, housing, or employment. In
precision regulation, the more stringent regulation should be applied to the
use-cases with the greatest risk.
• Clearly Defined Risks – There must be clear guidance on AI end uses or
categories of AI-supported activity that are inherently high-risk. This
common definition is key to ensuring that AI developers and deployers have
a clear understanding of what regulatory requirements will apply to a tool
they are building for a specific end use. Risk can be assessed in part by
considering the magnitude of potential harm and the likelihood of
occurrence.
• Be Transparent, Don’t Hide Your AI – Americans deserve to know when
they are interacting with an AI system, so Congress should formalize
disclosure requirements for certain uses of AI. Consumers should know
when they are interacting with an AI system and whether they have recourse
4
to engage with a real person, should they so desire. No person, anywhere,
should be tricked into interacting with an AI system. AI developers should
also be required to disclose technical information about the development
and performance of an AI model, as well as the data used to train it, to give
society better visibility into how these models operate. At IBM, we have
adopted the use of AI Factsheets – think of them as similar to AI nutrition
information labels – to help clients and partners better understand the
operation and performance of the AI models we create.
• Showing the Impact – For higher-risk AI use-cases, companies should be
required to conduct impact assessments showing how their systems
perform against tests for bias and other ways that they could potentially
impact the public, and attest that they have done so. Additionally, bias
testing and mitigation should be performed in a robust and transparent
manner for certain high-risk AI systems, such as law enforcement use-
cases. These high-risk AI systems should also be continually monitored and
re-tested by the entities that have deployed them.3
IBM recognizes that certain AI use-cases raise particularly high levels of concern.
Law enforcement investigations and credit applications are two often-cited
examples. By following the risk-based, use-case specific approach at the core of
precision regulation, Congress can mitigate the potential risks of AI without stifling
its use in a way that dampens innovation or risks cutting Americans off from the
trillions of dollars of economic activity that AI is predicted to unlock.
Generative AI
The explosion of generative AI systems in recent month has caused some to call for
3 See https://www.ibm.com/policy/ai-precision-regulation/.
5
a deviation from a risk-based approach and instead focus on regulating AI in a
vacuum, rather than its application. This would be a serious error, arbitrarily
hindering innovation and limiting the benefits the technology can provide. A risk-
based approach ensures that guardrails for AI apply to any application, even as
this new, potentially unforeseen developments in the technology occur, and that
those responsible for causing harm are held to account.4
When it comes to AI, America need not choose between responsibility,
innovation, and economic competitiveness. We can, and must, do all three now.
Business’ Role
This focus on regulatory guardrails established by Congress does not – not by any
stretch – let business off the hook for its role in enabling the responsible
deployment of AI.
I mentioned that IBM has strong AI governance practices and processes in place
across the full scope of our global enterprise. We have principles grounded in
ethics and people-centric thinking, and we have strong processes in place to bring
them to life. This is also good business: IBM has long recognized ethics and
trustworthiness are key to AI adoption, and that the first step in achieving these is
the adoption of effective risk management practices.
Companies active in developing or using AI must have (or be required to have)
strong internal governance processes, including, among other things:
4 See https://newsroom.ibm.com/Whitepaper-A-Policymakers-Guide-to-Foundation-Models.
6
• Designating a lead AI ethics official responsible for an organization’s
trustworthy AI strategy, and
• Standing up an AI Ethics Board or similar function to serve as a centralized
clearinghouse for resources to help guide implementation of that strategy.
IBM has taken both steps and we continue calling on our industry peers to follow
suit.
Our AI Ethics Board plays a critical role in overseeing our internal AI governance
process, creating reasonable internal guardrails to ensure we introduce technology
into the world in a responsible and safe manner. For example, the board was
central in IBM’s decision to sunset our general purpose facial recognition and
analysis products, considering the risk posed by the technology and the societal
debate around its use. IBM’s AI Ethics Board infuses the company’s principles and
ethical thinking into business and product decision-making. It provides
centralized governance and accountability while still being flexible enough to
support decentralized initiatives across IBM’s global operations.
The board, along with a global community of AI Ethics focal points and advocates,
reviews technology use-cases, promotes best practices, conducts internal
education, and leads our participation with stakeholder groups worldwide. In
short, it is a mechanism by which IBM holds our company and all IBMers
accountable to our values, and our commitments to the ethical development and
deployment of technology.
We do this because we recognize that society grants our license to operate. If
businesses do not behave responsibly in the ways they build and use AI, customers
will vote with their wallets. And with AI, the stakes are simply too high, the
7
technology too powerful, and the potential ramifications too real. AI is not some
fun experiment that should be conducted on society just to see what happens or
how much innovation can be achieved.
If a company is unwilling to state its principles and build the processes and teams
to live up to them, it has no business in the marketplace.
Conclusion
Mr. Chairman, and members of the subcommittee, the era of AI cannot be another
era of move fast and break things. But neither do we need a six-month pause –
these systems are within our control today, as are the solutions. What we need at
this pivotal moment is clear, reasonable policy and sound guardrails. These
guardrails should be matched with meaningful steps by the business community
to do their part. This should be an issue where Congress and the business
community work together to get this right for the American people. It’s what they
expect, and what they deserve.
IBM welcomes the opportunity to work with you, colleagues in Congress, and the
Biden Administration to build these guardrails together.
Thank you for your time, and I look forward to your questions.
8"
151,ibm,IBM+Global+AI+Adoption+Index+Report+Dec.+2023.pdf,"IBM GLOBAL AI ADOPTION
INDEX – ENTERPRISE
REPORT
NOVEMBER 8 – 23, 2023
© 2023 Morning Consult, All Rights Reserved.
METHODOLOGY & AUDIENCE
REPRESENTATIVE SAMPLE OF IT PROFESSIONALS IN MARKET
• 2,342 IT Professionals at enterprises (organizations with > 1,000 employees)
• This study was conducted in Australia, Canada, China, France, Germany, India, Italy, Japan, Singapore, South Korea, Spain, UAE,
UK, US, and LATAM (Brazil, Mexico, Peru, Argentina, Chile, Colombia)
• Market sample sizes range from 92 to 316
• To qualify for this audience, participants must be employed full-time, work at companies with more than 1,000 employees, work in a manager
or higher level role, and have at least some knowledge about how IT operates and is used by their company.
• Survey conducted online through MC’s proprietary network of online providers.
COMPANY SIZE BREAKDOWN
• 50% of respondents came from firms with 1,001 to 5,000 employees
• 50% of respondents came from firms with more than 5,000 employees
RESPONDENTS REPRESENTED A MIX OF SENIORITY
• All respondents were required to have significant insight or input into their firm’s IT decision-making
• 20% of the sample was at a VP level or above (including CIOs, etc.)
• The remainder of the sample represented a mix of directors and senior manager-level employees
2
IBM GLOBAL AI ADOPTION INDEX
Key Findings
1. AI adoption and exploration, covering both general AI and 3. AI is contributing to multiple facets of organizational operations
generative AI, continues to be a substantial focus for enterprises at enterprises, with IT process automation and security and
globally one year after the release of GPT-3. Many of those large threat detection being the most popular applications. IT
companies already exploring or deploying AI have accelerated their Professionals are at the forefront of AI usage at their enterprises and
roll-out of AI in the past two years, with ‘Research and Development,’ note the importance of being able to build and run AI projects
‘Workforce Upskilling,’ and ‘Building Proprietary AI Solutions’ wherever their data resides. Confidence in these capabilities is high,
emerging as top investment priorities. In the dynamic landscape of as most IT Professionals are confident that their enterprise has the
generative AI, enterprises tend to utilize in-house technology over right tools to find data across the business.
open-source technology.
4. Trustworthy and responsible AI practices are of utmost
importance to both consumers and enterprises at various stages
2. As enterprises enter the AI landscape, many have already
of AI implementation. In fact, most large organizations already
established some form of an AI strategy. This adoption is fueled by
exploring or deploying AI are actively taking steps like safeguarding
factors such as increased accessibility, cost-cutting through
data privacy through the entire lifecycle to ensure that. Insufficient
automation, and growing AI integration in business apps. Globally,
expertise for reliable AI management and development and lack of an
Enterprise IT Professionals highlight accessible tools, %), the
AI strategy are among the biggest barriers enterprises face as they
increased prevalence of AI related skillsets, and AI-tailored solutions
strive to develop trustworthy AI.
as key industry changes. However, challenges like limited knowledge,
too much data complexity, and ethical concerns hinder adoption. In 5. AI has a predominantly positive influence on the workforce.
the context of generative AI, additional obstacles emerge, including Numerous enterprises are investing in AI training, and IT
data privacy and trust/transparency concerns. Professionals note employee enthusiasm for new AI and automation
tools. Additionally, AI plays a crucial role in addressing labor and skills
shortages by equipping large companies with the tools to streamline
tasks and automate self-service interactions.
Methodology: This poll was conducted from Nov. 8 – 23, 2023 among a sample of 2,342 IT Professionals at enterprises (organizations with > 1,000 employees) in Australia, Canada, China, France, Germany, India,
Italy, Japan, Singapore, South Korea, Spain, UAE, UK, US and LATAM (Brazil, Mexico, Peru, Argentina, Chile, Colombia). Global results have a margin of error of +/- 2 percentage points at a 95% confidence level.
3
AGENDA
AI AD OPT ION & IN VEST MENTS
D R IVER S & BAR R IER S OF AI
CURRENT USES OF AI
AI ET H IC S AN D R ESPON SIBIL IT Y
AI’S IMPAC T ON EMPL OYEES
AI ADOPTION & INVESTMENTS
Over the past four years, AI adoption at enterprises has remained steady, with 42% of IT Professionals reporting AI
deploying and an additional 40% reporting active exploration in November 2023.
Has your company adopted or explored using Artificial Intelligence (AI) as part of its business operations and digital transformation?
My company is not currently
My company is exploring, My company has actively
using, or exploring the
but has not deployed, AI in deployed AI as part of its Don't know/Not sure
use of, AI in its business
its business operations business operations
operations
Oct. 2019 16% 34% 45% 5%
Apr. 2021 12% 37% 44% 6%
Apr. 2022 12% 38% 46% 3%
Apr. 2023 16% 34% 45% 5%
Nov. 2023 15% 40% 42% 3%
Base IT Professionals at Enterprises (organizations > 1,000 employees): October 2019 = 1,358n, April 2021 = 1,550n, April 2022 = 2,362n, April 2023 = 2,247n, November 2023 = 2,342n 5
AI ADOPTION & INVESTMENTS
Although there is a similar global AI Adoption trend from April 2023, there are some country specific outliers worth
noting.
Has your company adopted or explored using Artificial Intelligence (AI) as part of its business operations and digital transformation?
My company is not currently My company has actively Increases in AI Adoption
using, or exploring the deployed AI as part of its
use of, AI in its business business operations
operations
My company is exploring,
Don't know/Not sure
but has not deployed, AI in
The UAE, UK, and LATAM all saw an uptick in
its business operations
enterprises deploying AI in November 2023.(UAE: 48%
Global Enterprise 15% 40% 42%
Apr. ‘23, 58% Nov. ‘23) (UK: 29% Apr. ‘23, 37% Nov.
Australia* 17% 50% 29%
‘23) (LATAM: 40% Apr. ‘23, 47% Nov. ‘23).
Canada 12% 48% 37%
China 14% 36% 50%
France 19% 45% 26% 10%
Decreases in AI Adoption
Germany 21% 44% 32%
India 13% 27% 59% China (66% Apr. ‘23 to 50% Nov. ‘23) and Japan (49%
Italy 23% 38% 36% 4% Apr. ‘23 to 34% Nov. ‘23) both experienced drops in AI
deployment, with larger proportions of IT Professionals
Japan 15% 46% 34% 5%
reporting AI exploration (China: 19% Apr. ‘23, 36% Nov.
Singapore 6% 41% 53%
‘23) (Japan: 27% Apr. ‘23, 46% Nov. ‘23).
South Korea* 6% 48% 40% 5%
Spain 18% 51% 28% AI deployment in Italy dropped from 52% in April 2023
UAE 10% 32% 58% to 36% in November 2023. Italian IT Professionals were
UK 17% 41% 37% 6% more likely to report in the second half of the year that
their business is not currently using or exploring AI
US 19% 38% 33% 10%
(13% Apr. ‘23, 23% Nov. ‘23).
LATAM 16% 34% 47%
Base IT Professionals at Enterprises (organizations > 1,000 employees): Global Enterprise = 2,342n, Australia = 92n, Canada = 147n, China = 316n, France = 151n, Germany = 154n, India = 215n, Italy = 112n, Japan = 169n, Singapore = 148n, South Korea = 94n, Spain = 6
101n, UAE = 168n, UK = 145n, US = 126n, LATAM = 204n
*Sample size is between 50 and 99
AI ADOPTION & INVESTMENTS
Enterprises within the financial services are most likely to be using AI, with nearly half of IT Professionals in that
industry reporting their enterprise has actively deployed AI.
Has your company adopted or explored using Artificial Intelligence (AI) as part of its business operations and digital transformation?
My company is not currently
My company is exploring, My company has actively
using, or exploring the
but has not deployed, AI in deployed AI as part of its Don't know/Not sure
use of, AI in its business
its business operations business operations
operations
Global Enterprise 15% 40% 42%
Financial Services Industry 15% 33% 49%
Telecommunications Industry 16% 45% 37%
24% 49% 18% 9%
Government Industry
Energy, Environment, Utilities Industry* 21% 51% 23% 5%
Automotive Industry* 13% 44% 37% 6%
Industrial Industry 11% 46% 42%
Healthcare Industry 20% 47% 25% 8%
Retail Industry 21% 42% 31% 7%
Travel & Transportation Industry* 13% 53% 31%
Base IT Professionals at Enterprises (organizations > 1,000 employees): Global Enterprise = 2,342n, Financial Services = 218n, Telecommunications = 103n, Government = 148n, Energy = 75n, Automotive = 68n, Industrial = 302n, Healthcare = 154n, Retail = 130n, Travel = 68n 7
*Sample size is between 50 and 99; Note: Media & Entertainment, Chemicals/Oil/Gas, and Aerospace & Defense Industry samples sizes are too low to show
AI ADOPTION & INVESTMENTS
About 2-in-5 IT Professionals indicate that their enterprise is implementing generative AI (38%), and another 42%
are currently exploring generative AI (42%).
ChatGPT has quickly raised awareness of generative AI. Is your company using generative AI?
We are actively We are currently exploring We are not exploring nor Don't know/Not sure
implementing generative AI generative AI actively implementing
generative AI
Apr. 2023 34% 40% 14% 12%
Nov. 2023 38% 42% 12% 8%
Base IT Professionals at Enterprises (organizations > 1,000 employees): April 2023 = 2,247n, November 2023 = 2,342n 8
AI ADOPTION & INVESTMENTS
Since April ’23, reported implementation of AI has gone up in Japan (+13%), Singapore (+14%), South Korea (+16%),
and the UK (+21%).
ChatGPT has quickly raised awareness of generative AI. Is your company using generative AI?
We are actively We are currently exploring We are not exploring nor Don't know/Not sure
implementing generative AI generative AI actively implementing
generative AI
Global Enterprise 38% 42% 12% 8%
Australia* 20% 50% 20% 11%
Canada 22% 55% 13% 10%
China 63% 34%
France 19% 44% 23% 15%
Germany 33% 46% 12% 8%
India 61% 34%
Italy 26% 41% 16% 17%
Japan 25% 47% 18% 10%
Singapore 43% 41% 11% 5%
South Korea* 27% 48% 16% 10%
Spain 30% 36% 21% 14%
UAE 52% 39% 7%
UK 32% 46% 14% 9%
US 29% 36% 14% 21%
LATAM 37% 45% 9% 10%
Base IT Professionals at Enterprises (organizations > 1,000 employees): Global Enterprise = 2,342n, Australia = 92n, Canada = 147n, China = 316n, France = 151n, Germany = 154n, India = 215n, Italy = 112n, Japan = 169n, Singapore = 148n, South Korea = 94n, Spain = 9
101n, UAE = 168n, UK = 145n, US = 126n, LATAM = 204n
*Sample size is between 50 and 99
AI ADOPTION & INVESTMENTS
Generative AI adoption is driven by enterprises already deploying AI in their business operations. 63% of IT
Professionals at large companies currently deploying AI also report that their company is implementing generative AI,
compared to only 17% of those at companies only exploring AI.
ChatGPT has quickly raised awareness of generative AI. Is your company using generative AI?
We are actively We are currently exploring We are not exploring nor Don't know/Not sure
implementing generative AI generative AI actively implementing
generative AI
Currently Deploying AI 63% 30% 4%
Exploring AI 17% 62% 12% 9%
Base IT Professionals at Enterprises (organizations > 1,000 employees): Currently Deploying AI = 984n, Exploring AI = 930n 10
AI ADOPTION & INVESTMENTS
4-in-10 or more of IT Professionals within the financial services, telecommunications, and industrial industries
indicate that their enterprise is implementing generative AI.
ChatGPT has quickly raised awareness of generative AI. Is your company using generative AI?
We are actively We are currently exploring We are not exploring nor Don't know/Not sure
implementing generative AI generative AI actively implementing
generative AI
Global Enterprise 38% 42% 12% 8%
Financial Services Industry 40% 42% 7% 11%
Telecommunications Industry 41% 40% 12% 8%
15% 45% 25% 15%
Government Industry
Energy, Environment, Utilities Industry* 27% 47% 17% 9%
Automotive Industry* 37% 46% 9% 9%
Industrial Industry 43% 45% 9% 4%
Healthcare Industry 23% 43% 21% 13%
Retail Industry 22% 41% 24% 14%
Travel & Transportation Industry* 37% 43% 16% 4%
Base IT Professionals at Enterprises (organizations > 1,000 employees): Global Enterprise = 2,342n, Financial Services = 218n, Telecommunications = 103n, Government = 148n, Energy = 75n, Automotive = 68n, Industrial = 302n, Healthcare = 154n, Retail = 130n, Travel = 68n 11
*Sample size is between 50 and 99; Note: Media & Entertainment, Chemicals/Oil/Gas, and Aerospace & Defense Industry samples sizes are too low to show
AI ADOPTION & INVESTMENTS
Companies with 1,000 or fewer employees are less likely than enterprises to be adopting general AI and generative AI.
Has your company adopted or explored using Artificial Intelligence (AI) as part of its business operations and digital transformation?
ChatGPT has quickly raised awareness of generative AI. Is your company using generative AI?
G ener al AI Adopt ion
My company is not currently using, or exploring the use of, AI in its business operations My company is exploring, but has not deployed, AI in its business operations
My company has actively deployed AI as part of its business operations Don't know/Not sure
Company ≤ 1,000 25% 46% 24% 5%
Enterprise (Company > 1,000) 15% 40% 42% 3%
G ener at ive AI Adopt ion
We are not exploring nor actively implementing generative AI We are currently exploring generative AI We are actively implementing generative AI Don't know/Not sure
Company ≤ 1,000 22% 44% 25% 9%
Enterprise (Company > 1,000) 12% 42% 38% 8%
Base IT Professionals: Enterprises (organizations > 1,000 employees) = 2,342n, Company ≤ 1,000 = 6,242n 12
AI ADOPTION & INVESTMENTS
Investment in AI has remained relatively stable since April 2022.
Which of the following best describes your company's AI investment over the last 24 months? [Among IT Professionals at companies currently exploring
or deploying AI]
We have There has been
We have paused
We have accelerated stopped/decreased no change in my
our rollout and/or None of the above
our rollout of AI our rollout and/or company's investment
investment in AI
investment in AI and/or rollout of AI.
Apr. 2022 60% 12% 6% 20%
Apr. 2023 59% 13% 6% 19% 2%
Nov. 2023 59% 12% 6% 21% 2%
Base IT Professionals at Enterprises (organizations > 1,000 employees) exploring/deploying AI: April 2022 = 2,005n, April 2023 = 1,767n, November 2023 = 1,914n 13
AI ADOPTION & INVESTMENTS
59% of IT Professionals at enterprises deploying or exploring AI indicate that their organization has accelerated the
AI rollout in the past 24 months, and only around 1-in-5 (21%) say that their investment has remained unchanged.
Which of the following best describes your company's AI investment over the last 24 months? [Among IT Professionals at companies currently exploring
or deploying AI]
We have There has been
We have paused
We have accelerated stopped/decreased no change in my
our rollout and/or None of the above
our rollout of AI our rollout and/or company's investment
investment in AI
investment in AI and/or rollout of AI.
Global Enterprise 59% 12% 6% 21%
Australia* 38% 10% 7% 41% 4%
Canada 35% 16% 9% 36% 4%
China 85% 5% 4% 6%
France 45% 10% 7% 36%
Germany 52% 16% 4% 24%
India 74% 11% 12%
Italy* 61% 10% 5% 24%
Japan 50% 7% 31% 9%
Singapore 60% 8% 4% 28%
South Korea* 49% 12% 11% 27%
Spain* 48% 28% 6% 14% 5%
UAE 72% 13% 5% 8%
UK 40% 25% 6% 25% 4%
US* 46% 17% 4% 28% 6%
LATAM 67% 10% 8% 15%
Base IT Professionals at Enterprises (organizations > 1,000 employees) exploring/deploying AI: Global Enterprise = 1,914n, Australia = 73n, Canada = 125n, China = 272n, France = 107n, Germany = 117n, India = 184n, Italy = 82n, Japan = 135n, Singapore = 138n, South 14
Korea = 83n, Spain = 80n, UAE = 151n, UK = 112n, US = 90n, LATAM = 165n
*Sample size is between 50 and 99
AI ADOPTION & INVESTMENTS
IT Professionals in the automotive and industrial industries are most likely to report their enterprise has accelerated
AI investments in the past two years.
Which of the following best describes your company's AI investment over the last 24 months? [Among IT Professionals at companies currently exploring
or deploying AI]
We have There has been
We have paused
We have accelerated stopped/decreased no change in my
our rollout and/or None of the above
our rollout of AI our rollout and/or company's investment
investment in AI
investment in AI and/or rollout of AI.
Global Enterprise 59% 12% 6% 21%
Financial Services Industry 54% 13% 4% 26%
Telecommunications Industry* 45% 14% 6% 33%
35% 15% 8% 38%
Government Industry*
Energy, Environment, Utilities Industry* 47% 24% 9% 15% 5%
Automotive Industry* 73% 18% 5%
Industrial Industry 65% 9% 6% 18%
Healthcare Industry 39% 22% 10% 25% 4%
Retail Industry* 49% 13% 6% 31%
Travel & Transportation Industry* 46% 18% 7% 28%
Base IT Professionals at Enterprises (organizations > 1,000 employees) exploring/deploying AI: Global Enterprise = 1,914n, Financial Services = 179n, Telecommunications = 84n, Government = 99n, Energy = 55n, Automotive = 55n, Industrial = 266n, Healthcare = 110n, 15
Retail = 94n, Travel = 57n
*Sample size is between 50 and 99; Note: Media & Entertainment, Chemicals/Oil/Gas, and Aerospace & Defense Industry samples sizes are too low to show
AI ADOPTION & INVESTMENTS
Research and development (44%), reskilling/workforce development (39%), and building proprietary AI solutions
(38%) are the top AI investments at large organizations exploring or deploying AI.
How does your company plan to invest in AI adoption over the next 12-months? Please select all that apply. [Among IT Professionals at companies
currently exploring or deploying AI]
Global South
Australia* Canada China France Germany India Italy* Japan Singapore Spain* UAE UK US* LATAM
Enterprise Korea*
Research & Development 44% 49% 41% 41% 36% 35% 67% 32% 27% 51% 51% 36% 45% 43% 51% 48%
Reskilling and workforce development 39% 36% 42% 42% 33% 32% 55% 24% 30% 43% 37% 22% 44% 36% 38% 38%
Build proprietary AI solutions 38% 30% 23% 53% 28% 39% 53% 40% 34% 37% 23% 30% 44% 33% 29% 35%
Augmenting human tasks with digital labor 33% 34% 26% 40% 16% 40% 40% 26% 24% 33% 33% 31% 39% 39% 33% 30%
Off-the-shelf AI applications 32% 21% 22% 39% 25% 36% 26% 26% 38% 25% 24% 32% 44% 21% 28% 45%
Embed AI into current applications and
29% 33% 28% 26% 26% 30% 42% 18% 24% 40% 27% 22% 24% 21% 18% 41%
processes
Off-the-shelf tools to build our own
29% 14% 19% 43% 16% 32% 32% 17% 31% 32% 25% 16% 30% 21% 20% 38%
applications and models
Don't know/Not sure 4% 8% 7% 0% 5% 3% 1% 5% 8% 3% 1% 5% 0% 7% 12% 2%
Other 0% 1% 1% 0% 0% 1% 0% 0% 1% 1% 0% 1% 0% 0% 0% 0%
Base IT Professionals at Enterprises (organizations > 1,000 employees) exploring/deploying AI: Global Enterprise = 1,914n, Australia = 73n, Canada = 125n, China = 272n, France = 107n, Germany = 117n, India = 184n, Italy = 82n, Japan = 135n, Singapore = 138n, South
Korea = 83n, Spain = 80n, UAE = 151n, UK = 112n, US = 90n, LATAM = 165n 16
*Sample size is between 50 and 99
Note: dark green shading indicates the most-chosen statements while light green shading indicates the least-chosen statements within a specific market
AI ADOPTION & INVESTMENTS
Among enterprises implementing or exploring generative AI, most are using either in-house technology (43%) or
open-source technology (32%), with reported use of each remaining relatively unchanged since April 2023.
Are you using in-house technology, open source technology, or working with technology partner/provider? [Among IT Professionals at companies
currently exploring or implementing generative AI]
We are using in-house technology We are using open source technology We are working with a technology
partner/provider
Apr. 2023 45% 32% 23%
Nov. 2023 43% 32% 25%
Base IT Professionals at Enterprises (organizations > 1,000 employees) exploring/implementing generative AI : April 2023 = 1,660n, November 2023 = 1,873n 17
AI ADOPTION & INVESTMENTS
IT Professionals at companies exploring or deploying generative AI in Australia, Italy, Japan, and the UK are more
likely than the global average to report that their companies are using open-source technology.
Are you using in-house technology, open source technology, or working with technology partner/provider? [Among IT Professionals at companies
currently exploring or implementing generative AI]
We are using in-house technology We are using open source technology We are working with a technology
partner/provider
Global Enterprise 43% 32% 25%
Australia* 22% 45% 33%
Canada 30% 35% 35%
China 50% 23% 27%
France* 43% 35% 22%
Germany 51% 30% 20%
India 46% 32% 22%
Italy* 29% 55% 16%
Japan 34% 47% 19%
Singapore 41% 25% 34%
South Korea* 31% 41% 27%
Spain* 50% 29% 21%
UAE 51% 25% 24%
UK 39% 42% 19%
US* 48% 32% 21%
LATAM 49% 24% 27%
Base IT Professionals at Enterprises (organizations > 1,000 employees) exploring/implementing generative AI : Global Enterprise = 1,873n, Australia = 64n, Canada = 113n, China = 305n, France = 95n, Germany = 122n, India = 204n, Italy = 75n, Japan = 122n, Singapore = 18
124n, South Korea = 70n, Spain = 66n, UAE = 153n, UK = 112n, US = 82n, LATAM = 166n
*Sample size is between 50 and 99
AI ADOPTION & INVESTMENTS
In-house technology is most likely to be utilized in the financial services, telecommunications, energy, and travel
industries.
Are you using in-house technology, open source technology, or working with technology partner/provider? [Among IT Professionals at companies
currently exploring or implementing generative AI]
We are using in-house technology We are using open source technology We are working with a technology
partner/provider
Global Enterprise 43% 32% 25%
Financial Services Industry 48% 28% 23%
Telecommunications Industry* 46% 39% 16%
37% 31% 31%
Government Industry*
Energy, Environment, Utilities Industry* 45% 36% 18%
Automotive Industry* 36% 32% 32%
Industrial Industry 41% 26% 33%
Healthcare Industry 34% 44% 23%
Retail Industry* 37% 40% 23%
Travel & Transportation Industry* 50% 24% 26%
Base IT Professionals at Enterprises (organizations > 1,000 employees) exploring/implementing generative AI : Global Enterprise = 1,873n, Financial Services = 179n, Telecommunications = 83n, Government = 89n, Energy = 55n, Automotive = 55n, Industrial = 265n, 19
Healthcare = 101n, Retail = 81n, Travel = 54n
*Sample size is between 50 and 99; Note: Media & Entertainment, Chemicals/Oil/Gas, and Aerospace & Defense Industry samples sizes are too low to show
AI ADOPTION & INVESTMENTS
Enterprises with more established generative AI practices are more likely to be using in-house technology over open-
source technology. Similarly, in-house technology is most likely to be used by companies with more than 1,000
employees exploring or implementing generative AI.
Are you using in-house technology, open source technology, or working with technology partner/provider? [Among IT Professionals at companies
currently exploring or implementing generative AI]
We are using in-house technology We are using open source technology We are working with a technology partner/provider
Company ≤ 1,000 38% 41% 21%
Enterprise (Company > 1,000) 43% 32% 25%
Enterprise Implementing Generative AI 59% 20% 21%
Enterprise Exploring Generative AI 29% 44% 28%
Base IT Professionals at companies exploring/implementing generative AI: Enterprises (organizations > 1,000 employees) = 1,873n, Company ≤ 1,000 = 4,288n, Enterprise Implementing Generative AI = 894n, Enterprise Exploring Generative AI = 979n 20
AGENDA
AI AD OPT ION & IN VEST MENTS
D R IVER S & BAR R IER S OF AI
CURRENT USES OF AI
AI ET H IC S AN D R ESPON SIBIL IT Y
AI’S IMPAC T ON EMPL OYEES
DRIVERS & BARRIERS OF AI
Most enterprises actively exploring or deploying AI have some form of AI strategy, with 27% reporting that their
company has an AI strategy for limited/specific use cases and about a third (32%) stating that their organization
already has a holistic strategy in place. 32% are in the process of developing an AI strategy.
Which of the following best describes your company's AI strategy? [Among IT Professionals at companies currently exploring or deploying AI]
My company had
My company has My company an AI strategy
an AI strategy, has a holistic but had to
My company does My company is
but it is strategy for discard it None of the
not have an AI developing an AI
focused on how it will use or have not above
strategy strategy
limited/specific AI across the been able to
use cases organization implement it
effectively
Global Enterprise 4% 32% 27% 32% 5%
Australia* 4% 44% 30% 19%
Canada 6% 37% 30% 25%
China 24% 27% 46%
France 7% 38% 33% 18% 4%
Germany 38% 32% 22% 4%
India 26% 19% 42% 11%
Italy* 5% 35% 32% 24% 4%
Japan 7% 30% 27% 30%
Singapore 33% 28% 28% 8%
South Korea* 31% 31% 34%
Spain* 5% 45% 24% 21% 4%
UAE 20% 25% 52%
UK 7% 40% 21% 19% 13%
US* 9% 38% 27% 19% 6%
LATAM 4% 34% 27% 31% 4%
Base IT Professionals at Enterprises (organizations > 1,000 employees) exploring/deploying AI: Global Enterprise = 1,914n, Australia = 73n, Canada = 125n, China = 272n, France = 107n, Germany = 117n, India = 184n, Italy = 82n, Japan = 135n, Singapore = 138n, South 22
Korea = 83n, Spain = 80n, UAE = 151n, UK = 112n, US = 90n, LATAM = 165n
*Sample size is between 50 and 99
DRIVERS & BARRIERS OF AI
Enterprises exploring AI are more likely to be in the beginning stages of AI strategy, while large organizations
deploying AI are more likely to have a holistic strategy in place.
Which of the following best describes your company's AI strategy? [Among IT Professionals at companies currently exploring or deploying AI]
My company had
My company has My company an AI strategy
an AI strategy, has a holistic but had to
My company does My company is
but it is strategy for discard it None of the
not have an AI developing an AI
focused on how it will use or have not above
strategy strategy
limited/specific AI across the been able to
use cases organization implement it
effectively
Currently Deploying AI 18% 23% 50% 7%
Exploring AI 6% 48% 31% 12%
Base IT Professionals at Enterprises (organizations > 1,000 employees) exploring/deploying AI: Currently Deploying AI = 984n, Exploring AI = 930n 23
DRIVERS & BARRIERS OF AI
Larger organizations exploring or deploying AI are more likely than smaller organizations to have a holistic AI
strategy in place (32% vs. 22%).
Which of the following best describes your company's AI strategy? [Among IT Professionals at companies currently exploring or deploying AI]
My company had
My company has My company an AI strategy
an AI strategy, has a holistic but had to
My company does My company is
but it is strategy for discard it None of the
not have an AI developing an AI
focused on how it will use or have not above
strategy strategy
limited/specific AI across the been able to
use cases organization implement it
effectively
Company ≤ 1,000 8% 36% 29% 22% 3%
Enterprise (Company > 1,000) 4% 32% 27% 32% 5%
Base IT Professionals at companies exploring/deploying AI: Enterprises (organizations > 1,000 employees) = 1,914n, Company ≤ 1,000 = 4,402n 24
DRIVERS & BARRIERS OF AI
Advances in AI making it more accessible (45%) is the top external driver of AI adoption at enterprises currently
exploring or deploying AI, followed by the need to reduce costs and automate key processes (42%) and the increasing
amount of AI embedded into standard off the shelf business applications (37%).
What external factors, if any, are helping drive AI adoption in your organization? Please select all that apply. [Among IT Professionals at companies
currently exploring or deploying AI]
Global South
Australia* Canada China France Germany India Italy* Japan Singapore Spain* UAE UK US* LATAM
Enterprise Korea*
Advances in AI that make it more accessible 45% 48% 46% 39% 32% 41% 59% 35% 42% 52% 52% 40% 41% 51% 42% 55%
Need to reduce costs and automate key
42% 48% 46% 35% 31% 40% 48% 35% 54% 49% 52% 31% 40% 37% 39% 41%
processes
The increasing amount of AI embedded into
37% 32% 34% 48% 29% 44% 47% 30% 25% 41% 27% 26% 35% 36% 27% 41%
standard off the shelf business applications
Competitive pressure 31% 41% 30% 24% 28% 33% 39% 28% 23% 41% 20% 16% 41% 30% 36% 27%
Directives from leadership 26% 30% 26% 20% 20% 23% 32% 13% 16% 33% 27% 20% 36% 26% 28% 35%
Labor or skills shortages 25% 32% 30% 22% 19% 32% 28% 9% 47% 24% 22% 19% 28% 29% 36% 9%
Pressure from consumers 25% 29% 20% 29% 18% 22% 34% 17% 9% 30% 20% 15% 33% 23% 24% 28%
Company culture 23% 19% 13% 28% 7% 21% 26% 27% 19% 26% 20% 29% 23% 25% 26% 26%
Environmental pressures 19% 15% 10% 23% 14% 13% 26% 15% 14% 20% 23% 15% 27% 26% 17% 16%
Legal and regulatory compliance pressures 18% 21% 16% 16% 18% 21% 22% 13% 15% 18% 18% 10% 19% 21% 23% 13%
Supply chain issues 18% 18% 19% 22% 12% 17% 28% 7% 13% 20% 14% 9% 25% 22% 22% 9%
Demands due to the Covid-19 pandemic 15% 10% 10% 21% 9% 9% 24% 5% 11% 19% 19% 11% 20% 17% 10% 14%
None of the above 1% 1% 1% 3% 1% 1% 1% 1% 1% 0% 1% 1% 0% 3% 2% 1%
Other 0% 1% 0% 0% 0% 0% 0% 0% 1% 1% 0% 1% 0% 1% 1% 1%
Base IT Professionals at Enterprises (organizations > 1,000 employees) exploring/deploying AI: Global Enterprise = 1,914n, Australia = 73n, Canada = 125n, China = 272n, France = 107n, Germany = 117n, India = 184n, Italy = 82n, Japan = 135n, Singapore = 138n, South
Korea = 83n, Spain = 80n, UAE = 151n, UK = 112n, US = 90n, LATAM = 165n 25
*Sample size is between 50 and 99
Note: dark green shading indicates the most-chosen statements while light green shading indicates the least-chosen statements within a specific market
DRIVERS & BARRIERS OF AI
Compared to AI projects 2 to 3 years ago, Enterprise IT Professionals consider accessible AI solutions (43%), the
increased prevalence of data, AI, and automation skills (42%), and AI tailored solutions (41%) the most important
changes in the industry.
Compared to AI projects 2-3 years ago, what are the most important changes you see in the industry? Please select no more than three.
Global South
Australia* Canada China France Germany India Italy Japan Singapore Spain UAE UK US LATAM
Enterprise Korea*
AI solutions are more accessible and easier to
43% 40% 41% 47% 38% 40% 57% 38% 36% 41% 49% 26% 48% 38% 40% 50%
deploy
Data, AI and automation skills are more prevalent,
teams are positioned to build, deploy, and manage 42% 35% 39% 53% 24% 34% 55% 27% 27% 49% 50% 41% 52% 43% 36% 43%
AI
AI solutions are better designed to fit the needs of
41% 34% 35% 51% 30% 37% 49% 38% 28% 47% 40% 38% 53% 32% 39% 46%
businesses
Businesses have clear data and AI strategies 31% 27% 24% 36% 21% 34% 38% 21% 21% 37% 29% 31% 48% 32% 22% 34%
Businesses have ethical guidelines in place for their
27% 32% 19% 35% 20% 21% 33% 21% 30% 26% 17% 16% 35% 32% 24% 25%
AI adoption
Don't know/Not sure 6% 10% 8% 1% 11% 8% 0% 5% 20% 3% 6% 6% 1% 4% 14% 2%
Other 0% 0% 0% 0% 0% 1% 0% 1% 1% 0% 1% 1% 0% 1% 1% 0%
Base IT Professionals at Enterprises (organizations > 1,000 employees): Global Enterprise = 2,342n, Australia = 92n, Canada = 147n, China = 316n, France = 151n, Germany = 154n, India = 215n, Italy = 112n, Japan = 169n, Singapore = 148n, South Korea = 94n, Spain =
101n, UAE = 168n, UK = 145n, US = 126n, LATAM = 204n 26
*Sample size is between 50 and 99
Note: dark green shading indicates the most-chosen statements while light green shading indicates the least-chosen statements within a specific market
DRIVERS & BARRIERS OF AI
Barriers to successful AI adoption have stayed consistent from April, although high prices are less likely to be a
hinderance in November (April ‘23 26% vs. Nov. ‘23 21%).
What, if anything, is hindering successful AI adoption for your business? Please select all that apply. [Among IT Professionals at companies currently
exploring or deploying AI]
33%
We have limited AI skills, expertise or knowledge
32%
25%
We have too much data complexity
24%
23%
We have ethical concerns
19%
22%
AI projects are too complex or difficult to integrate and scale
25%
21%
We have a lack of tools/platforms for developing AI models
23%
21%
The price is too high
26%
We do not have the use cases defined or the end user research 17% Nov. 2023
needed to get started 16%
Apr. 2023
17%
We do not have a holistic AI strategy in place
18%
17%
We do not have the ability to properly govern our AI models
17%
We are locked-in to one vendor (AI and Cloud tied to one single 13%
vendor) 16%
3%
None of the above
4%
0%
Other
1%
Nothing is technically hindering successful AI adoption for my 11%
business 10%
Base IT Professionals at Enterprises (organizations > 1,000 employees) exploring/deploying AI: April 2023 = 1,767n, November 2023 = 1,914n 27
DRIVERS & BARRIERS OF AI
Despite the increased prevalence in AI related skills, IT Professionals at enterprises exploring or deploying AI are
most likely to express that limited A"
153,mit_edu,Advanced_20Technology_20Adoption_20-_20Selection_20of_20Causal_20Effects.pdf,"AEA Papers and Proceedings 2023, 113: 210–214
https://doi.org/10.1257/pandp.20231037
ROBOT AND AUTOMATION: NEW INSIGHTS FROM MICRO DATA‡
Advanced Technology Adoption: Selection or Causal Effects?†
By Daron Acemoglu, Gary Anderson, David Beede, Catherine Buffington,
Eric Childress, Emin Dinlersoz, Lucia Foster, Nathan Goldschlag,
John Haltiwanger, Zachary Kroff, Pascual Restrepo, and Nikolas Zolas*
Advanced technologies, including robotics, Our work documented these facts:
artificial intelligence AI , and software sys-
( )
tems, are thought to be spreading rapidly in i The share of adopting firms remains
()
industrialized economies. In Acemoglu, Aaron, low for AI and robotics 3.2 percent
(
et al. 2022 , we used the 2019 Annual Business and 2 percent of firms, respectively and
( ) )
Survey ABS to provide a comprehensive over- rises to 19.6 and 40.2 percent for equip-
( )
view of the adoption of AI, robotics, dedicated ment and software, respectively.
equipment, specialized software, and cloud
computing for US firms in all sectors during ii Adoption is concentrated in large firms.
( )
2016–2018.
iii As a result, a high share of workers is
( )
exposed to these technologies, espe-
cially in manufacturing. For exam-
‡Discussants: Betsey Stevenson, University of Michigan; ple, 12–64 percent of US workers
James Bessen, Boston University; Gino Gancia, Queen and 22–72 percent of US manufac-
Mary University of London; Susan Helper, Case Western turing workers are exposed to these
Reserve University. technologies.
* Acemoglu: MIT email: daron@mit.edu; Anderson:
( )
National Center for Science and Engineering Statistics email: iv A significant share of adopters, rang-
ganderso@nsf.gov; Beede: US Census Bureau ( email: ( )
) ( ing from 30 percent for specialized
david.n.beede@census.gov; Buffington: US Census Bureau
) software to 65 percent for robotics by
email: catherine.d.buffington@census.gov; Dinlersoz:
( US Census Bureau email: emin.m.dinl) ersoz@census. employment weight, report using these
(
gov; Foster: US Census Bureau email: lucia.s.foster@cen- advanced technologies for automation.
) (
sus.gov ); Goldschlag: US Census Bureau (email: nathan. In total, 30.4 percent of US workers and
goldschlag@census.gov; Kroff: US Census Bureau email:
) ( 52 percent of manufacturing workers are
zachary.kroff@census.gov; Zolas: US Census Bureau
email: nikolas.j.zolas@c) ensus.gov; Childress: George employed at firms using these technolo-
( )
Mason University email: echildre@gmu.edu; Haltiwanger: gies for automation.
( )
University of Maryland email: halt@umd.edu; Restrepo:
( )
Boston University email: pascual@bu.edu. Any opinions and
( ) v Consistent with the use of these advanced
conclusions expressed herein are those of the authors and do ( )
technologies for automation, adopters
not reflect the views of the US Census Bureau. All results have
been reviewed to ensure that no confidential information is have higher labor productivity and lower
disclosed. The Census Bureau’s Disclosure Review Board and labor shares.
Disclosure Avoidance Officers have reviewed this data product
for unauthorized disclosure of confidential information and
vi Firms report that these technologies
have approved the disclosure avoidance practices applied to ( )
this release. DRB Approval Numbers: CBDRB-FY21-058, increase their demand for skills but do
CBDRB-FY21-316, CBDRB-FY22-057, CBDRB-FY22- not necessarily expand employment.
ESMD006-011, CBDRB-FY22-411, CBDRB-FY23-034,
CBDRB-FY23-112. DMS number 7508509.
This paper revisits the second fact—the rea-
† Go to https://doi.org/10.1257/pandp.20231037 to visit
sons why firms adopting advanced technologies
the article page for additional materials and author disclo-
sure statements. are larger. In principle, this could be for two
( )
210
VOL. 113 ADVANCED TECHNOLOGY ADOPTION: SELECTION OR CAUSAL EFFECTS? 211
different reasons. Either adoption of advanced
technologies causally expands employment, or 7
selection leads larger firms to more adoption.
6
For example, already-large firms may have a
greater likelihood of adopting advanced tech- 5
nologies because of fixed costs, or firms that are
growing fast for other reasons may also be better 4
at adopting and using these technologies.
These two explanations have different impli- 3
cations. The former would suggest that advanced
2
technologies contribute to employment growth,
at least at the firm level the i ndustry-level impli- 1
(
cations could differ from the firm-level ones, as
pointed out in Acemoglu, Lelarge, and Restrepo 0
2020 and Koch, Manuylov, and Smolka 2021 .
)
The latter would weigh in favor of limited
employment gains even in adopting firms and
would caution against firm-level explorations
using ordinary least squares or event study strat-
egies to uncover the effects of advanced technol-
ogy adoption.
Our results favor the selection interpretation.
Using data from the Longitudinal Business
Database LBD , we document that adopters
( ) were already large and growing faster before
AI, robotics, cloud computing, and specialized
software systems became broadly available.1
We also find that employment trends at adopt-
ing firms remained largely unchanged after the
widespread use of these technologies. Persistent
size and growth differences between adopters
and nonadopters imply that fi rm-level estimates
of the effects of advanced technologies must be
interpreted with caution.
I. Adoption and Firm Size
We first provide graphical evidence on the
relationship between firm size and the adoption
of AI and robotics. We focus on these technol-
ogies because they have received considerable
attention in recent empirical work. Figure 1 plots percentiles within detailed s ix-digit industries.2
adoption rates for firms in 36 size and age cate- The figure also reports the average adoption rate
gories, defined in terms of employment and age for firms in each size class.
2 We assign firms to their main six-digit North American
Industry Classification System industry in terms of payroll
1 These statements refer to employment. We document in across all its establishments. Employment percentiles are
Acemoglu, Anderson, et al. 2022 that firms’ adoption of defined based on the employment distribution in each indus-
( )
advanced technologies is associated with an increase in sales try. By construction, Figure 1 isolates differences in adop-
and a reduction in their labor share. The same pattern for tion rates across firms of different size operating in the same
French manufacturing is documented in Acemoglu, Lelarge, narrowly defined industry and controls for size differences
and Restrepo 2020. between manufacturing and nonmanufacturing firms.
( )
tnecrep
smrif
erahS
)
(
Share of firms using AI, 2016–2018
0–50 50–75 75–90 90–95 95–99 99–100
Firm size percentiles
8
7
6
5
4
3
2
1
0
tnecrep
smrif
erahS
)
(
Age percentiles
0–25 90–95
25–50 95–100 50–75 Overall
75–90
Share of firms using robotics, 2016–2018
Age percentiles
0–25 90–95
25–50 95–100
50–75 Overall
75–90
0–50 50–75 75–90 90–95 95–99 99–100
Firm size percentiles
Figure 1. Adoption of AI and Robotics for Firms in
Different Size and Age Categories
Notes: The figure plots adoption rates for AI and robotics by
firm age and size percentiles within detailed six-digit indus-
tries. See Acemoglu, Anderson, et al. 2022 for similar fig-
( )
ures for the remaining technologies.
Source: 2019 ABS
212 AEA PAPERS AND PROCEEDINGS MAY 2023
Adoption rises with size for all technologies
in the ABS: 5.5 percent of firms in the top per-
centile of their industries’ employment distribu-
4.8
tion use AI, 5.1 percent use robots, 31.4 percent
4.6
use dedicated equipment, 67.4 percent use spe-
4.4
cialized software, and 63.5 percent use cloud
4.2
computing. In contrast, the adoption rate among
4
firms in the fiftieth to seventy-fifth percentile
3.8
of industries’ employment distribution is much 3.6
lower: 3.1 percent for AI, 1.7 percent for robots, 3.4
18.6 percent for dedicated equipment, 39.6 per-
3.2 cent for specialized software, and 33.4 percent 3
for cloud. 2.8
II. Firm Employment Histories
The previous section documented sizable dif-
ferences in employment levels between adopt- Figure 2. Employment Trends for Establishments in
Robot-Using Firms and Others for 1978–2018
ing and nonadopting firms for robotics and AI .
( )
We now explore whether employment histories,
Notes: The figure plots the inverse hyperbolic sine of
in terms of both levels and trends, differ between employment in establishments associated with firms using
adopters and nonadapters. robots in the 2019 ABS lines with circles and those asso-
( )
Because LBD does not contain consistent ciated with nonrobot users in the 2019 ABS (dashed lines ).
For each cohort, we report employment numbers for the
information on firm-establishment histories, we
years following its entry into the LBD.
create a p seudo–firm establishment panel that
tracks employment in all establishments asso- Sources: 2019 ABS and 1978–2018 LBD
ciated with each firm in the ABS technology
module in 2018. We then conduct our empiri-
cal analysis at the level of these establishments
between 1978 and 2018.3 late 1990s and early 2000s. Third, employment
Figure 2 focuses on the differential employ- dynamics of adopters’ establishments seem
ment histories of adopters and nonadopters of unaffected by rising adoption of robots in recent
robotics for illustration purposes. It plots the decades.
evolution of average employment by cohort for To explore these patters for all technologies,
establishments in adopting and nonadopting we turn to the following regression model:
firms.4 The figure reveals three key patterns.
First, establishments in adopting firms are ini- 1 y Adopter
( ) j,i,c,t = αc+βi,t+γc× j
tially larger have higher employment than
( )
establishments in nonadopting firms. These size Adopter ,
+ δt× j+ϵj,i,c,t
differences are present at an early age and grow
over time, especially for early cohorts. Second, for an establishment j in industry i , cohort c ,
differences in employment levels and growth in year t . The left-hand-side variable is the
rates precede the period of rapid robot adoption inverse hyperbolic sine IHS of establishment
( )
in the United States, which took place in the employment, which allows us to include zeros in
our analysis. The right-hand-side variables are
cohort dummies ; industry-by-year dummies
αc
, which account for differences in employ-
3 In particular, this p seudopanel follows the same estab- βi,t
lishments over time, even though some of these establish- ment trends by four-digit industries; and cohort
ments may not have belonged to the firm in question in the and growth effects depending on adopter status
past. See Foster et al. 2016 for more details on this strategy as measured by the adopter dummy Adopter .
to track activity of firm( s bac) k in time. ( j)
These terms allow adopters to have different ini-
4 The first year in the LBD is 1976. We do not observe
tial levels differences by cohort and different
the exact age of establishments that existed at this point and ( )
assign them to a “ pre-77” cohort. growth dynamics different time effects .
( )
tnemyolpme
tnemhsilbatse
fo
SHI
Employment trends for establishments of
robot adopters and nonadopters, 1978–2018
Adopters Nonadopters
Pre 77
Pre 77 85–91
77–84
92–98
77–84 85–91 92–98 99–05 99–05
06–12 06–12
1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 2020
VOL. 113 ADVANCED TECHNOLOGY ADOPTION: SELECTION OR CAUSAL EFFECTS? 213
adopting firms is significantly greater than the
size of nonadopters at the same point in time.
35 For example, establishments at r obot-adopting
30
firms from the 1977–1984 cohort were initially
25 24.3 percent larger than establishments of firms
20 not adopting robotics technology. The same dif-
15 ference is 14.7 percent for robot-adopting firms
from the 1999–2005 cohort.
10
Panel B depicts the estimates of , which mea-
5 δt
sures the differential establishment employ-
0 ment growth of adoptin( g firms. It con) firms that
5 establishment employment for adopters grew
−
10 more rapidly than it did for n onadopters. For
−
example, from 1978–1984 to 1992–1998, estab-
lishments of robot-adopting firms expanded
their employment by 11.1 percent more than
nonadopters. Notably, for most technologies,
these differential growth experiences long pre-
dated the periods of high adoption in the United
States as a whole. Indeed, robotics, AI, special-
ized software systems, and cloud computing were
not spreading rapidly before the late 1990s.6 For
example, the adoption of AI concentrates in the
2016–2018 period see Acemoglu, Autor, et al.
( 2022 , while robot adoption gained prominence
)
in the late 1990s and the 2000s see Acemoglu
(
and Restrepo 2020 . Yet, establishments of AI
)
and robot-adopting firms were larger and grew
more rapidly than those of nonadopters decades
before these periods.
Panel B also shows that the differential
employment growth of adopters relative to
nonadopters is unaffected by the increased
adoption of these technologies in recent years.
If anything, establishments in adopting firms
grew at more comparable rates to establish-
ments in nonadopting firms in recent years. For
example, our estimates in Panel B imply that
the yearly growth differential for establishments
Figure 3 depicts estimates from equation 1 in robot-adopting firms relative to nonadopters
( )
separately for the five technologies in the ABS. went from 0.8 percent per year in 1978–1998 to
Panel A presents estimates of , which 0.4 percent in 1999–2018.
γc+δc
compare the initial establishment size of adopt-
ing firms of cohort c to the size of nonadopting III. Discussion
firms at the time of entry.5 The results in this
panel show that, consistent with our discussion Figures 2 and 3 show that establishments in
for robotics adoption in Figure 2, the initial adopting firms were initially larger and grew
size in terms of establishment employment of more rapidly than nonadopters, even before
( )
5 The interaction terms give employment differences at 6 The exception is dedicated equipment, such as
γc
the base period. Adding gives an estimate of employ- computer–numerically controlled machines, whose wide-
γc+δc
ment differences in the first period each cohort enters the spread adoption dates back to the early 1970s and is studied
LBD. in detail in Boustan, Choi, and Clingingsmith 2022.
( )
,secnereffid
tnemyolpme
laitinI
sretpodanon
susrev
sretpoda
)cδ
+
cγ(
Panel A. Differences in initial employment
Pre-7777–84 85–91 92–98 99–05 06–12 13–18
Cohorts
25
20
15
10
5
0
,secnereffid
htworg
tnemyolpmE
sretpodanon
susrev
sretpoda
)tδ(
Technology
AI
Robotics
Equipment
Software
Cloud computing
Panel B. Differences in employment growth
Technology
AI Robotics
Equipment
Software
Cloud computing
85–91 92–98 99–05 06–12 13–18
Period
Figure 3. Differential Employment Dynamics for
Establishments in Adopting Firms Relative to Others
Notes: Panel A plots estimates of from equation 1 ,
γc+δc( ( )) which measures the differential establishment employment
size for adopter firms relative to nonadopters. Panel B plots
, which measures the differential establishment employ- δt
ment growth for adopter firms relative to nonadopters.
Sources: 2019 ABS and 1978–2018LBD
214 AEA PAPERS AND PROCEEDINGS MAY 2023
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of what one would expect if advanced technolo- ligence and Jobs: Evidence from Online
gies caused adopting firms to expand their employ- Vacancies.” Journal of Labor Economics 40
ment. Instead, it points to small or negative effects S1 : S293–340.
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of adopters report using advanced technologies “Robots and Jobs: Evidence from US Labor
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of advanced technologies, automation reduces 6 : 2188–244.
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154,mit_edu,Regulating_20Transformative_20Technologies.pdf,"AER: Insights 2024, 6(3): 359–376
https://doi.org/10.1257/aeri.20230353
Regulating Transformative Technologies†
By Daron Acemoglu and Todd Lensman*
Transformative technologies like generative AI promise to acceler-
ate productivity growth across many sectors, but they also present
new risks from potential misuse. We develop a multisector technol-
ogy adoption model to study the optimal regulation of transforma-
tive technologies when society can learn about these risks over time.
Socially optimal adoption is gradual and typically convex. If social
damages are large and proportional to the new technology’s pro-
ductivity, a higher growth rate paradoxically leads to slower opti-
mal adoption. Equilibrium adoption is inefficient when firms do not
internalize all social damages, and sector-independent regulation is
helpful but generally not sufficient to restore optimality. JEL D21,
(
H21, H25, O31, O33
)
Recent breakneck advances in generative artificial intelligence have simultane-
( )
ously raised hopes of productivity gains in many sectors and fears that this technol-
ogy will be used for nefarious purposes, even posing an existential risk comparable
to nuclear war.1 Some experts have called to slow down or pause the development
and adoption of AI technologies,2 partly because a slower rollout might provide
time to identify danger areas and craft appropriate regulations. However, there is
little economic analysis of these issues, and it is unclear whether slowing the devel-
opment and adoption of a promising, transformative technology ever makes sense.
In this paper, we develop a framework to provide a first set of insights on these
questions. We consider a multisector economy that initially uses an old technology
but can switch to a new, transformative technology. This technology is transforma-
tive both because it enables a higher growth rate of output and because it is general
purpose and can be adopted across all sectors of the economy. It also poses new
risks. We model these by assuming that there is a positive probability of a disaster,
meaning that the technology will turn out to have many harmful uses. If a disaster
is realized, some of the sectors that had started using the new technology may not
be able to switch away from it, despite the social damages. Whether there will be a
* Acemoglu: Massachusetts Institute of Technology, Department of Economics email: daron@mit.edu;
( )
Lensman: Massachusetts Institute of Technology, Department of Economics email: tlensman@mit.edu. Peter
( )
Klenow was the coeditor for this article. We thank Glen Weyl for several useful discussions; Joshua Gans,
Chad Jones, and three anonymous referees for comments; and the Hewlett Foundation and the National Science
Foundation for financial support.
† Go to https://doi.org/10.1257/aeri.20230353 to visit the article page for additional materials and author
disclosure statements.
( )
1 https://www.nytimes.com/2023/05/30/technology/ai-threat-warning.html
2 https://futureoflife.org/open-letter/pause-giant-ai-experiments/
359
360 AER: INSIGHTS SEPTEMBER 2024
disaster is initially unknown, and society can learn about it over time. Critically, we
assume that the greater are the new technology’s capabilities, the more damaging it
will be when used for harmful purposes.3
In this environment, we study socially optimal and equilibrium adoption deci-
( )
sions. We first show that it is optimal to adopt the new technology gradually because
this enables greater learning. If all sectors immediately adopted and the disaster
transpired, many of them would not be able to switch back and avoid the social
damages. Gradual adoption allows society to gain from the new technology while
updating its beliefs about whether it will have socially damaging uses. As more time
passes without disaster, the belief that there will be a disaster declines “no news
(
is good news” . As society becomes more optimistic, it is optimal to adopt the new
)
technology across a larger number of sectors. Under weak conditions, this adop-
tion path is slow and convex, accelerating only after society is fairly certain that a
disaster will not occur. A simple quantitative example indicates that, for reasonable
parameters for the new technology’s growth advantage and disaster risk, optimal
adoption can be very slow.
Perhaps surprisingly, we demonstrate that adoption should be slower when the
new technology has a higher growth rate and damages from a disaster are large.
This is for two reasons. First, since damages after a potential disaster increase with
the new technology’s capabilities, a higher growth rate means that damages also
grow more quickly. Second, with a higher growth rate, the effective discount rate for
future output declines, so that short delays in adoption are not very consequential
for discounted utility.
Compared to optimal adoption, equilibrium adoption is inefficiently fast if private
firms internalize only part of the social damages from a disaster. Even the order in
which sectors adopt the new technology can differ between the equilibrium and the
optimum—sectors that have high social damages are not necessarily those that have
high private damages for adopters.4 Finally, we discuss how regulatory schemes can
help to close the gap between optimal and equilibrium adoption. Pigouvian taxes,
use taxes, or adoption taxes that are sector specific can fully implement optimal
adoption. When s ector-specific policies are not feasible, it is generally not possible
to implement optimal technology choices, but regulation can still increase welfare
by prohibiting use of the new technology in the sectors with the largest potential for
harm until the risk of a disaster is sufficiently low.
This paper is a first attempt to study the consequences and regulation of trans-
formative technologies that can be used for good or bad. Our conclusions naturally
depend on our modeling assumptions and should be interpreted with caution.
There are three literatures on which we build. The first is a growing literature
on economic disasters e.g., Rietz 1988; Barro 2006, 2009; Weitzman 2009, 2011;
(
Martin and Pindyck 2015, 2021 , which explores how the risk of rare economic
)
3 These assumptions can be motivated with generative AI applications. For irreversibility, once large language
models like ChatGPT are deployed in secondary education, it may be impossible to roll back their use, even after
it becomes clear that they harm student learning. For the damages rising with productivity, many experts fear that
these technologies either pose existential risks or will be misused, both of which would be more damaging when
they have greater capabilities e.g., Shevlane et al. 2023.
4 For example, if AI is us( ed to create pervasive dis) information on social media, this may be disastrous for
democracy but profitable for social media platforms.
VOL. 6 NO. 3 ACEMOGLU AND LENSMAN: REGULATING TRANSFORMATIVE TECHNOLOGIES 361
disasters affects asset prices and c ost-benefit analysis but does not focus on ques-
tions of technology adoption.
The second is a literature on technology adoption e.g., Katz and Shapiro 1986;
(
Parente and Prescott 1994; Foster and Rosenzweig 1995, 2010; Acemoglu, Aghion,
and Zilibotti 2006; Acemoglu, Antràs, and Helpman 2007; Comin and Mestieri
2014 . Early work touching on AI includes Galasso and Luo 2019 and Agrawal,
) ( )
Gans, and Goldfarb 2019 , but these papers do not focus on issues of learning about
( )
social damages from new technologies.
Third, there is a nascent literature focusing on damages from certain technologies
e.g., Bovenberg and Smulders 1995; Acemoglu et al. 2012 . Most closely related
( )
to our paper are a few works that discuss the dilemma between growth and existen-
tial risk from new technologies, including AI. Jones 2023 develops a one-sector
( )
growth model in which AI can be used to raise the aggregate growth rate but with
small probability causes human extinction. Whether it is optimal to use AI depends
crucially on the coefficient of relative risk aversion and whether consumption utility
is bounded. Aschenbrenner 2020 incorporates existential risk into Jones’s 2016
( ) ( )
model of growth and mortality and argues that existential risk rises with consump-
tion unless new mitigation technologies are developed. His model thus exhibits an
“existential risk Kuznets curve” in which existential risk optimally increases until
sufficient R&D resources are shifted toward mitigation. These two papers share
our focus on the costs and benefits of transformative technologies, but they do not
address the speed of adoption across sectors and do not feature learning about risks
over time.
The rest of the paper is organized as follows. Section I presents our benchmark
model. Sections II and III characterize optimal and equilibrium technology choices.
Section IV discusses the conditions under which optimal technology choices can
be restored through regulatory taxes, and Section V concludes. Omitted proofs and
extensions are in the online Appendix.
I. Setup
We consider a continuous-time economy that linearly produces a final good from
a continuum of sectors i 0,1 :
∈ [ ]
1
Y
= ∫ 0
Y
i
𝑑i
.
A representative household has r isk-neutral preferences defined over this final good
and discounts the future at rate 0 .
ρ >
Each sector can use an old technology O or a new, transformative technology N .
We write Q t 0 for the quality of technology j O,N at time t , x t 1 if
j ( ) > ∈ { } i ( ) =
sector i switches its production process to technology N and x t 0 otherwise.
i ( ) =
Sectoral output is
Y 1 x Q x Q ,
i = ( − i ) O + i αi N
362 AER: INSIGHTS SEPTEMBER 2024
where designates the comparative advantage of the new technology, which may
αi
vary if the new technology is b etter suited for some sectors than others. Given tech-
nology choices x x and qualities Q Q ,Q , final output is
= ( i ) i ∈[ 0,1 ] = ( O N )
1
Y x,Q 1 x Q x Q di
( ) = ∫0 ( − i ) O + i αi N .
The new technology is transformative, both because it is general purpose and can
be applied across all sectors and because it enables not just the production of more
output but a higher growth rate:
g g 0
N > O ≥ .
As a result of its restructuring impact on the economy, it also poses new risks. We
model these by assuming that there may be a disaster whereby the new technol-
ogy generates negative effects. If a disaster happens, then there will be damages of
Q 0 in units of the final good in the sectors that are using the technology.
δ i N > ( )
We assume that use of the new technology may be irreversible, so that with proba-
bility 0,1 , sector i cannot switch to technology O if it is using technology N
ηi ∈ ( )
when the disaster strikes. The realization of this reversibility event is independent
across sectors. We assume that damages are proportional to Q because the negative
N
effects correspond to misusing the better capabilities of the new technology.
In what follows, we reorder sectors so that is increasing and assume that i
δi
denotes the quantiles of the distribution, so that we can take this distribution to be
δ
uniform over some interval
[
_ , _
]
. Overall damages then become
δ δ
1
D (x, Q
) = ( ∫0 δi
x
i
𝑑i
)
Q
N .
The economy will experience a disaster with probability – 0,1 , and if there
μ ∈ ( )
is a disaster, its arrival time T is distributed exponentially with rate . We let t
λ μ( )
denote the planner’s or society’s posterior belief at t that there will be a disas-
( )
ter, assuming one has not yet arrived. We impose rational expectations, so that
0 – and the posterior belief evolves according to Bayes’ rule:
μ ( ) = μ
1 ˙ t t [1 t ]
( ) μ( ) = −λμ( ) −μ( ) .
A few comments are in order. First, we model damages in each sector i by the
reduced-form function Q to capture a broad range of potential harms. In the con-
δ i N
text of AI, these include the spread of disinformation that harms democracy; mass
unemployment; and the disruption of production in many sectors from AI-aided
cyber attacks.5 Second, as suggested above, the assumption that damages are pro-
portional to Q is related to the transformative nature of this new technology. For
N
5 Our functional form assumptions also impose that the rate of substitution between gross consumption and
damages in utility is constant and equal to one. Jones 2023 points out that this may not hold in the case of existen-
( )
tial risk and explores the implications for optimal use of a life-threatening new technology.
VOL. 6 NO. 3 ACEMOGLU AND LENSMAN: REGULATING TRANSFORMATIVE TECHNOLOGIES 363
example, damages from disinformation from AI will be higher when it can gen-
erate better language. Third, we assume that the arrival rate of the disaster—and
hence learning about the negative effects of the new technology—is independent
of how many sectors switch to the new technology. This is for simplicity but is
not unreasonable since many of the potential misuses of a new technology can be
gradually recognized without widespread adoption.6 Fourth, it can be verified that
our results remain identical if, instead of a single e conomy-wide disaster, there are
sector-specific disasters and beliefs about each sector’s disaster follow 1 .
( )
II. Socially Optimal Technology Choice
In this section, we set up, solve, and provide comparative statics for the social
( )
planner’s problem.
A. Social Planner’s Problem
Given risk neutrality, the planner’s objective is
(2
)
V (0
) =
E
μ ( 0 ) [ ∫0
∞ exp
(
−ρt
)
[Y (t
) −
D
(
t
) ]
𝑑t
]
,
where Y t and D t denote output and damages at time t and the expectation E is
( ) ( ) μ( 0 )
with respect to the prior belief 0 over the disaster’s arrival time T . To ensure that
μ ( )
the objective is well defined, we assume that
3 g ,
( ) ρ > N
which rules out the case in which the new technology grows so quickly that dis-
counted utility becomes infinite.
It is more convenient to work with the recursive formulation of 2 , which has
( )
the following state variables: the posterior belief of disaster, ; the time-varying
μ
qualities of the old and new technologies, Q ; and, after the disaster, the set of
sectors that were already using the new technology and for which this use is irre-
versible. We track these sectors using the vector x– x– , where x– 1 if
sector i uses technology N irreversibly and x– 0 o= th e ( r w i ) i i s∈ e[ 0 ., 1 L] et V ,Q i = de note
i = (μ )
predisaster social welfare, and let W x– ,Q denote postdisaster welfare. Then the
( )
Hamilton-Jacobi-Bellman HJB equations for the planner are
( )
(4
)
ρV (μ, Q
) =
max {Y
(
x, Q
) + μλ[
E
[
W
(
x– , Q
) |
x
] −
V
(
μ, Q
) ] } +
V˙
(
μ, Q
)
,
x 0,1
i ∈{ }
(5
)
ρW
(
x– , Q
) =
xma
x–
x
,1
{Y
(
x, Q
) −
D
(
x, Q
) } +
W˙
(
x– , Q
) .
i ∈{ i }
6 Alternative assumptions are discussed in Section V.
364 AER: INSIGHTS SEPTEMBER 2024
Equation 5 imposes that x cannot be less than x– because x– 1 implies that
( ) i i i =
sector i ’s use of the new technology is irreversible. V then depends on the condi-
tional expectation of welfare after a disaster given the current technology choices x ,
denoted by E [W
(
x– , Q
) |
x
]
.7 In (4 ), we also use the fact that the arrival rate of the
disaster, given the posterior , is .
μ μλ
To characterize the planner’s technology choices, suppose first that the disaster
has occurred. The planner’s problem in 5 is linear, so the solution is
( )
x
1 if x–
i =
1 or
( αi − δi )
Q
N >
Q
O
,
i = { 0 else
.
This expression assumes, without loss of generality, that the planner sticks with the
old technology if indifferent. It also imposes the constraint that x 1 when x– 1 .
i = i =
Even when unconstrained, it may be optimal to set x 1 if the output produced by
i =
technology N exceeds its damages plus the output that can be produced by technol-
ogy O . We first assume that damages are sufficiently large that, whenever possible,
the planner chooses technology O after a disaster:
6
( ) αi ≤ δi .
This enables us to focus on the most interesting case, where damages exceed the
benefits of the new technology. We return to the general case in Section IIC.
Integrating the HJB equation 5 and taking expectations with respect to x – , we
( )
have
E [W
(
x– , Q
) |
x
] = ∫0
1
[
(1
−
x
i ηi ) _ ρ
−1
g O
Q
O +
x
i ηi _ ρ
α i
−
−
g
δ
N
i Q
N ]
𝑑i
.
Before the disaster, it is optimal from 4 to use technology N in sector i if and
( )
only if
(7
) αi
Q
N −
Q
O > μληi [ _ ρ
−1
g O
Q
O − _ ρ
α i
−
−
g
δ Ni Q
N ] .
Intuitively, the left-hand side is the flow gain from using technology N in sector
i , while the right-hand side is the expected loss due to the disaster, including both
the discounted value of lost output and the irreversible damages. These losses are
multiplied by the posterior arrival rate of the disaster and the probability of irre-
μλ
versibility . Since is decreasing and Q Q is increasing, for any initial state
ηi μ N / O
0 ,Q 0 , there exists a time t such that technology O is used in sector i
(μ( ) ( ) ) i < ∞
before t and technology N is used thereafter.
i
7 To determine this conditional expectation, we use P rx– 1 x 1 and Prx– 1 x 0 0 .
( i = | i = ) = ηi ( i = | i = ) =
VOL. 6 NO. 3 ACEMOGLU AND LENSMAN: REGULATING TRANSFORMATIVE TECHNOLOGIES 365
B. Socially Optimal Technology Adoption
To determine how socially optimal use of technology N changes over time,
( )
denote the fraction of sectors that use technology N , or total adoption, by
1
X
(
μ, q
) = ∫0
x
i
(μ, q
)
𝑑i
.
Here, q log Q Q is the quality gap between the technologies and
= ( N / O )
x ,q 1 if and only if it is optimal to use technology N in sector i in state
i (μ ) =
,q . For simplicity, we assume that and are constant across sectors and
(μ ) αi ηi
equal to and the general case is studied in online Appendix B . This implies
α η ( )
that there exists a damage threshold L ,q such that it is optimal to adopt the new
(μ )
technology in sector i if and only if L ,q . Letting F denote the cumulative
δi < (μ )
distribution function of the uniform distribution over
[
_ , _
]
, total adoption is then
δ δ
just the fraction of sectors below the damage threshold:
X ,q F L ,q
(μ ) = ( (μ ) ) .
The following proposition is immediate from 7 , and we omit its proof.
( )
PROPOSITION 1: Suppose that 6 holds and and are constant across sectors.
( ) αi ηi
It is socially optimal to use technology N in sector i if and only if L ,q , where
δ i < ( μ )
L ,q exp q exp q
(8
) _
( μ
ρ
−) g−
N
α
= _
α
_
_−
_ μ__ λ _
η(
_
− ___)
− _ ρ
−( −
g O
)
.
L ,Q and thus X ,q is increasing in and q ; decreasing in g , , and ; and
(μ )( (μ )) α O λ μ
decreasing in g , provided that L ,q .
N (μ ) > α
Given 6 , the condition L ,q is satisfied as soon as there is any adoption.
( ) ( μ ) > α
Proposition 1 then implies that when the new technology enables faster growth, its
adoption should be slower. This is because of a precautionary motive—even though
the planner is r isk neutral, she would like to avoid irreversible damages from the
new technology. The faster the new technology grows, the greater are the potential
net output losses, strengthening this precautionary motive.
The comparative statics in Proposition 1 are partial because they hold the state
,q fixed. Full comparative statics must account for how parameter changes
(μ )
affect the evolution of the state t ,q t . The belief t does not depend on the
( μ( ) ( ) ) μ( )
growth rates g and g , but the quality gap q t q 0 g g t does. The
O N ( ) = ( ) + ( N − O )
damage threshold L ,q is increasing in the quality gap, so any change in growth
( μ )
rates affects adoption at each t 0 through both the direct effects described in
>
Proposition 1 and the indirect effects through changes in the quality gap q t . The
( )
next proposition characterizes these total effects.
366 AER: INSIGHTS SEPTEMBER 2024
PROPOSITION 2: Suppose that 6 holds and and are constant across sectors.
( ) αi ηi
i X t ,q t is decreasing in g .
( ) (μ( ) ( ) ) O
–
ii There exists an earliest time t such that X t ,q t is decreasing in
( ) – – < ∞ ( μ( ) ( ) )
g if t t . The time t is decreasing in g .
N > N
iii Adoption falls to zero as g approaches —that is, lim X t ,q t 0 .
( ) N ρ g N ↑ ρ ( μ( ) ( ) ) =
The first part of Proposition 2 establishes that the comparative static for g from
O
Proposition 1 generalizes in the presence of the indirect effects through q t —the
( )
quality gap q t is declining in g , reinforcing the direct effect and decreasing adop-
( ) O
tion. The second part shows that the new technology’s growth rate has more nuanced
implications: adoption is not always decreasing in g , but it is decreasing after some
–
N
critical time t , and this time itself is a decreasing function of g . This holds because
N
the precautionary motive highlighted above must compete with the fact that the
quality gap q t is increasing in g , but this indirect effect can dominate only at short
( ) N
time horizons.
The third part of the proposition establishes that as g increases toward the dis-
N
count rate, adoption almost stops. This might appear paradoxical initially but is also
intuitive. When g is approximately equal to , the benefits from the new technol-
N ρ
ogy are very high, leading to nearly infinite discounted utility provided no disaster
arrives. Delay in adoption thus has little effect on these benefits. However, a disaster
will have huge negative consequences, and avoiding it now takes precedence.
The next proposition further characterizes the shape of the adoption curve. Since
F is uniform, X ˙ ,q f L˙ ,q , where f is the constant density of F . Hence, the
( μ ) = ( μ )
curvature of technology adoption is
X¨ ,q L¨ ,q
( μ ) ( μ )
_ X˙_ _ ,_ q_ = _ L˙_ _ ,_ q_ .
( μ ) (μ )
We therefore have the following proposition.
PROPOSITION 3: Suppose that 6 holds.
( )
i L˙ ,q 0 is decreasing in g , and it is decreasing in g if and only if the
( ) ( μ ) > O N
quality gap is sufficiently large—that is,
g g g
α
exp
(
q
) −
1
> _
( ρ
_
_−
_ _
N
_
)
1_
−
_ − _
(
_ μ _
N
_
−
__
_O
_
)
( _
λ1
+ _ ρ
−μη
g O ) .
ii There exists a positive constant G ,q such that if exp q 1 ,
( ) (μ ) α ( ) >
L¨ ,q is positive if and only if g g G ,q . G ,q is independent
( μ ) N − O < ( μ ) ( μ )
of g and increases to infinity over time.
N
The intuition for the first part is the same as for Proposition 2. The damage
threshold increases as the posterior belief falls and the quality gap q grows. Faster
μ
growth for technology O slows the rate of increase of the quality gap and raises the
VOL. 6 NO. 3 ACEMOGLU AND LENSMAN: REGULATING TRANSFORMATIVE TECHNOLOGIES 367
1
0.75
0.5
0.25
0
0 10 20 30
t
opportunity cost of using technology N after the disaster. Consequently, the damage
threshold grows less quickly in each state. Faster growth for technology N raises
both the rate of increase in the quality gap and the net output losses from technology
N after the disaster. The latter effect dominates when the quality gap is sufficiently
large because additional improvements in technology N relative to O have only a
negligible impact on the planner’s technology choice.8
The second part of the proposition proves that adoption of the new technology
will eventually have a convex segment where adoption accelerates because eventu-
(
ally g g will be below G , q . This result holds even though the learning rate
| μ˙ | fa lN ls − at a O greater than expo(μ nent) i) al rate when μ < _1 2 (in particular, _ dd t | μ˙ | = −λ|
˙ 1 2 . This is because expected damages from technology N in sector i are
μ |( − μ))
proportional to the posterior , and as declines, larger increases in the damage
μ μ
threshold L ,q are needed to balance the expected damages and benefits in the
(μ )
“marginal” sector.9
To illustrate these results, we depict the time path of adoption in a couple of
parameterized cases in Figure 1. We set g 2 percent in line with trend GDP
O =
growth in developed economies and 0.04 to produce a risk-free interest rate
ρ =
of 4 percent. We choose two values for g based on Chui et al. 2023 , who fore-
N ( )
cast an increase in the growth rate of 0.6 –3.6 percent in the United States between
2023 and 2040 from AI and other automation technologies. We take the lower end
of this range, g g 0.6 percent , and a higher but still conservative estimate
N − O =
8 The latter effect also dominates regardless of the quality gap whenever L ,q 0 and g g g .
9 In online Appendix B, we verify this intuition by showing that learning dy( nμ am ) ic s> f avor con c N a − ve aO d o≥ pt ioρ n − w h eN n
sectors are heterogeneous according to instead of .
αi δi
t
X
)
(
% %
6 8
2. 3.
= =
gN gN
40 50 60
Figure 1. Socially Optimal Adoption Curves
Notes: Adoption curves X t X t,qt for different values of g . The remaining parameter values are
( ) ≡ ( μ( ) () ) – N
ρ =
0.04 ,
λ =
0.05 ,
η =
0.5 ,
α =
1 , g
O =
0.02 , _
δ =
1 , and
δ =
5 . The initial state is
μ (
0
) =
0.2 and
q0 0 .
( ) =
368 AER: INSIGHTS SEPTEMBER 2024
from the middle of the range, g g 1.8 percent while still satisfying 3 . We
N − O = ( ( ))
take the two technologies to have the same quality in year t 0 , thus q 0 0 .
= ( ) =
We supp_ose that damages range from one to five times gross sectoral output
_ 1 , 5 , and we set 0.5 so that half of all sectors using the new tech-
(δ = δ = ) η =
nology cannot switch back after a disaster. We set the expected arrival time of a
disaster if one exists to be 20 years, which gives 0.05 . Finally, a recent survey
( ) λ =
of AI experts reports a median estimate of existential risk of about 10 percent,10 and
since we are interested in nonexistential misuses of AI as well, we choose the initial
disaster probability to be twice as large, 0 20 percent. Figure 1 shows that
μ ( ) =
optimal adoption is slow, taking about 40 years until full adoption when g 2.6
N =
percent and almost 60 years when g 3.8 percent .
N =
C. Optimal Adoption with Small Damages
We have so far imposed 6 , ensuring that the postdisaster damages from the new
( )
technology are large and exceed its gross output within each sector. This is a natural
benchmark since our analysis is motivated by significant potential harms from AI.
We now relax this assumption and allow a sector’s damages to be small relative to
its output under the new technology .
( δ i < α)
In online Appendix C, we show that socially optimal adoption is again char-
acterized by a damage threshold L ,q , and we prove the following analogue to
(μ )
Proposition 2 for small damages.
PROPOSITION 4: Suppose that and are constant across sectors. For all t with
α i ηi
L t ,q t :
(μ( ) ( ) ) < α
i X t ,q t is decreasing in g .
( ) (μ( ) ( ) ) O
ii X t ,q t is increasing in g .
( ) (μ( ) ( ) ) N
iii If q 0 is sufficiently low and X t ,q t F , adoption is bounded
( ) ( ) (μ( ) ( ) ) < ( α)
below F as g approaches —that is, lim X t ,q t F .
(α) N ρ g N ↑ ρ ( μ( ) ( ) ) < ( α)
Adoption among sectors with small damages is still decreasing in g , but in con-
O
trast to the case with large damages, it is increasing in g . Gradual adoption remains
N
optimal even when g increases toward the discount rate . With small damages,
N ρ
using technology N is always optimal in the long run. Nevertheless, gradual adop-
tion is optimal to learn about the probability of a disaster before one occurs and
( )
to delay the adoption of technology N in case of a disaster until the quality gap
becomes sufficiently large. This strategy thus avoids temporary costs of irreversibil-
ity. Further analysis of this case is presented in online Appendix C.
Finally, we note that if damages are uncertain, any chance of large damages leads
to longer optimal delay, even if expected damages are small, in order to avoid the
possibility that damages turn out to be large and adoption is irreversible.
10 https://aiimpacts.org/2022-expert-survey-on-progress-in-ai
VOL. 6 NO. 3 ACEMOGLU AND LENSMAN: REGULATING TRANSFORMATIVE TECHNOLOGIES 369
In summary, the optimal adoption of a new, transformative technology should be
gradual, particularly when its superior capabilities also make its potential damages
greater and there is learning about the likelihood of misuse a “disaster” .
( )
III. Equilibrium Technology Choice
We now characterize equilibrium technology adoption when private firms do not
fully internalize social damages.
A. The Firm’s Problem
Suppose now that in each sector, the choice of technology is made by a private
representative firm that seeks to maximize expected discounted profits. To sim-
( )
plify, we assume that the firm in sector i appropriates all output of its intermediate
as profits but only internalizes private damages . This textbook externality
γ i ≤ δi
leads to excessively fast adoption of the new technology before the disaster, and
our main results below describe how the equilibrium and socially optimal adoption
curves differ.
Firm i ’s profit maximization problem can be formulated recursively in the same
way as the planner’s problem in the previous section. The state variables before the
disaster are again and Q , and after the disaster they are x – and Q . Let ,Q
μ i Π i (μ )
denote the firm’s predisaster value, x– ,Q its postdisaster value, and Y x,Q its
Φ i ( i ) i ( i )
gross output. The HJB equations for the firm are
( )
(9
) ρ Π i
(μ, Q
) =
max
{
Y
i (
x
i
, Q
) + μλ[
E
[ Φi (
x–
i
, Q
) |
x
i ] − Πi
(μ, Q
) ] }
x 0,1
i ∈{ }
˙ ,Q ,
+ Π i (μ )
10 x– ,Q max Y x,Q x Q ˙ x– ,Q
( ) ρ Φ i ( i ) = x x– ,1 { i ( i ) − i γi N } + Φ i ( i ).
i ∈{ i }
These value functions differ from the planner’s 4 and 5 because the firm internal-
( ) ( )
izes only a fraction of the flow damages from technology N .
γ i / δi
We now impose a stronger version of 6 : private damages are sufficiently large
( )
that firm i will always choose technology O after the disaster if possible:11
11
( ) αi ≤ γi .
Similar to the planner’s solution, it is privately optimal for firm i to use technology
N if and only if
Q Q 1 Q αi − γi Q
α i N − O > μληi [ _ ρ − g O O − _ ρ − g N N ] .
11 Without this assumption, an additional inefficiency would arise in equilibrium as firms would use the new
technology in some reversible sectors even after a disaster.
( )
370 AER: INSIGHTS SEPTEMBER 2024
The only difference between this condition and the planner’s optimality condition
7 is that private damages appear instead of social damages on the right-hand
( ) γi δ i
side. Firm i internalizes fewer damages from technology N and thus begins using it
earlier.
B. Equilibrium Technology Adoption
We denote total equilibrium adoption by
1
X
̃
(μ, q
) = ∫0
x
̃ i
(μ, q
)
𝑑i,
where x ,q 1 if and only if firm i uses technology N in state ,q . Again
̃ i (μ ) = ( μ )
assuming that and are constant across sectors, it is immediate that firm i will
αi η i
adopt the new technology if and only if private damages are lower than the damage
threshold, L ,q . Equilibrium adoption is then
γ i < (μ )
X ,q F L ,q ,
̃ (μ ) = γ ( (μ ) )
where F is the cumulative distribution function of .
γ
γi
This characterization implies that all comparative statics results from Section IIB
apply to equilibrium adoption. The results in Propositions 1 and 3 concern only the
damage threshold L ,q and hold exactly as stated, while Proposition 2 applies
( μ )
after replacing X ,q with X ,q .
(μ ) ̃ ( μ )
PROPOSITION 5: Suppose that 11 holds and and are constant across sectors.
( ) αi ηi
i X t ,q t is decreasing in g .
( ) ̃ (μ( ) ( ) ) O
ii There exists an earliest time t such that X t ,q t is decreasing in
( ) ̃ < ∞ ̃ (μ( ) ( ) )
g if t t . The time t is decreasing in g .
N > ̃ ̃ N
iii Adoption falls to zero as g increases to : lim X t ,q t 0 .
( ) N ρ g N ↑ ρ ̃ ( μ( ) ( ) ) =
In the remainder of this section, we characterize how the optimal and equilibrium
adoption curves differ. We first observe that similar adoption curves do not imply
that the equilibrium is optimal because the order in which sectors adopt the new
technology matters. For example, private and social damages may be negatively
affiliated, meaning that high social damage sectors have low private damages. In this
case, the order in which the new technology spreads in equilibrium is exactly the
opposite of the optimal order.
Even when the equilibrium and optimal orders of adoption coincide, the equilib-
rium can be inefficient. To see this, suppose that social and private damages are pos-
itively affiliated, so that there exists a n onnegative and strictly increasing function
( )
with . We can then write equilibrium adoption as
κ γ i = κ( δi ) ≤ δi
X ,q F 1 L ,q
̃ ( μ ) = ( κ − ( (μ ) ) ) .
VOL. 6 NO. 3 ACEMOGLU AND LENSMAN: REGULATING TRANSFORMATIVE TECHNOLOGIES 371
1
0.75
0.5
0.25
0
0 10 20 30
t
Figure 2. Comparing Socially Optimal and Equilibrium Adoption Curves
Notes: Socially optimal and equilibrium adoption curves, Xt and "
155,mit_edu,Dynamo20Case202024.pdf,"Case Study
Dynamo AI
Written by Audrey Woods
In a world of booming generative AI applications, business leaders across the economy worry that they need to
incorporate AI or risk falling behind. However, there are many factors to consider before designing, using, and/
or launching AI systems, including security, privacy, hallucinations and legal compliance. Between increasing
regulation and shifting consumer expectations, it’s more important than ever for companies to be aware of and
address the risks at every level of their evolving AI stack.
MIT CSAIL Startup Connect member Dynamo AI aims to help companies navigate this change with a suite of
products designed to offer end-to-end privacy, security, and compliance solutions. Broken into three pillars of
AI-focused support—DynamoEval, DynamoEnhance, and DynamoGuard—Dynamo AI seeks to support the
democratization of AI technologies by making them accessible, reliable, and safe to implement.
GETTING THEIR START
As CEO and Co-Founder Vaikkunth Mugunthan tells it, Dynamo AI began as the last chapter of his PhD thesis
at MIT. When he first started his graduate studies under CSAIL Principal Research Scientist Lalana Kagal,
he was focused on theoretical privacy. But when a CSAIL Alliances poster session landed him a summer
internship with JPMorgan, he was introduced to federated learning, which trains AI by sharing the model itself
instead of sharing data, thereby offering valuable privacy guarantees. There was a pressing industry demand
for this technology, especially in finance and healthcare, and the interest he experienced made him confident
enough to launch the company in 2021.
While Dynamo AI was originally focused only on providing a “plug and play” tool for federated learning—in fact
the company’s original name was DynamoFL—the team soon realized that there were many other aspects of
AI implementation that customers were concerned about. New AI-related laws emerging around the world, the
risks, both known and unknown, of launching AI systems, and the ongoing process of protecting users and
companies from those who might misuse AI programs either intentionally or not were all issues that Mugunthan
realized Dynamo AI could help with. Therefore, in late 2023 and early 2024, the company pivoted from a
focus exclusively on federated learning to a broader, more comprehensive set of features designed to assist
companies in both designing and launching safe and compliant AI systems.
Now Dynamo AI is partnering with Fortune 500 businesses to test their tools in various industry functions. They
went through the YCombinator startup accelerator and got “the first interview on the first day,” Mugunthan
says, and the company has since gone on to raise $19.4 million. Their two different TechCrunch features—one
in 2022 and one in 2023—each brought a fresh wave of publicity that helped grow the company to where it is
today, with 45 employes and customers such as Lenovo, Qualcomm, and Aisin. As a self-described “research-
heavy” company, Dr. Mugunthan explains how their priority is to “hire masters or PhDs from the best schools”
and become the most trusted name in the field of AI support.
DYNAMO AI: SECURING THE AI STACK
When explaining the need that Dynamo AI aims to meet in the industry, Head of Growth
and Strategic Partnerships Kavi Arora says, “if we really want to democratize these
technologies in different GenAI-based consumer-focused, internally focused applications,
and agent focused applications, there are a many risks that exist in privacy, security, and
hallucinations.” Dynamo AI, he explains, is “addressing the need for privacy, security, and
compliance throughout that AI stack [by] testing applications for risks, remediating those
risks, and then real-time guardrailing applications as they go into production.” These
services fall into three main “modules:” DynamoEval, DynamoEnhance, and DynamoGuard.
DynamoEval evaluates LLMs and generative AI programs as they’re being designed to make
sure a given program complies to emerging regulatory standards. Providing automated
stress testing, DynamoEval generates the needed documentation for regulatory audits and
checks a system’s weaknesses in privacy, security, and hallucination. DynamoEnhance
offers support at the next phase of development, fixing and remediating the identified risks.
This module offers several easy-to-use techniques to improve privacy (such as federated
learning), mitigate hallucinations, and bolster program safety. And finally, DynamoGuard
supports AI programs going into production by creating real time guardrails that are
customizable in natural language, for every organization’s bespoke policies. “For example,”
Arora says, ”multiple financial services institutions are deploying internal chatbots leveraged
by different portfolio managers. We’re enabling them to create guardrails where you can
allow certain levels of portfolio managers to drive certain types of insights and others
to not.” That level of granularity helps businesses enforce governance policies, prevent
misuse, and easily audit their LLMs.
Taken altogether, Dynamo AI’s various modules offer a platform for enabling secure, private,
hallucination-free, and regulation-compliant AI models.
THE CSAIL CONNECTION
One thing Dr. Mugunthan makes clear is how much he attributes his success to Lalana
and CSAIL. From the very beginning, it was Dr. Kagal’s encouragement that inspired him
to take a chance and apply to MIT, and he describes his time at CSAIL as “a fantastic
experience.”
“I really enjoyed the collaborative nature of projects,” he says, highlighting his ability to get
a minor from Harvard and his deep roots in the CSAIL community.
For Dr. Mugunthan, his link to CSAIL is more than nostalgic; it’s a pivotal part of his
company’s strategy. He says through MIT, “we have access to the best talents in the world,”
an advantage he’s used to hire some of the PhDs the company now employs. Dynamo AI
is also planning to launch internships and create a Dynamo AI ambassador program with
CSAIL, which would deepen this connection. Dr. Mugunthan adds, “I wouldn’t have been
at this stage [without CSAIL], so I want to give it back as well.”
continued Dynamo
AI
CASE
STUDY
For more information about CSAIL Alliances industry engagements, please visit:
cap.csail.mit.edu
Beyond recruitment, Dynamo AI is utilizing their connection with CSAIL Alliances to
maximize the company’s exposure and vet potential business partners. Dr. Mugunthan has
been invited to present at several Alliances conferences, which has led to “a good number
of client leads” and helped Dr. Mugunthan understand specifically which companies were
interested. “We were able to tailor our product toward what they needed,” he explains,
which helped Dynamo AI create even more market traction. Because of that, he’s eager to
take part in future CSAIL Alliances events.
“CSAIL Alliances has been super helpful,” Dr. Mugunthan says, calling Sr. Client Relations
Coordinator Philip Arsenault “a fantastic friend of mine.”
LOOKING FORWARD
When asked what they’re focused on next, Dr. Mugunthan and Arora explain that Dynamo
AI is now looking to expand the company’s reach into new sectors, exploring how their
suite can be applied to different industries. With the AI market growing in nearly every
economic sector, Dynamo AI hopes to use this momentum to their advantage and support
positive technological change.
Dr. Mugunthan says the end goal for Dynamo AI is “to make sure that when it comes to
privacy preserving machine learning, the first company that comes to anyone’s mind is us.”
With that in mind, Dr. Mugunthan calls his association with MIT an “added advantage,”
showing clients that Dynamo AI has the best people on the job.
continued Dynamo
AI
CASE
STUDY
For more information about CSAIL Alliances industry engagements, please visit:
cap.csail.mit.edu"
156,mit_edu,Integrating-AI-in-organizations-for-value-creation-through-Human-AI-teaming-A-dynamic-capabilities-approach.pdf,"JournalofBusinessResearch182(2024)114783
Contents lists available at ScienceDirect
Journal of Business Research
journal homepage: www.elsevier.com/locate/jbusres
Integrating AI in organizations for value creation through Human-AI
teaming: A dynamic-capabilities approach
Cristina Simo´ na,*, Elena Revillaa, Maria Jesús Sa´ enzb
aIE Business School, IE University, Spain
bMassachusetts Institute of Technology, United States
A R T I C L E I N F O A B S T R A C T
Keywords: Although the potentialities of artificial intelligence (AI) are motivating its fast integration in organizations, our
Human-AI teaming knowledge on how to capture organizational value out of these investments is still scarce. Relying on an
Productive dialogue approach to dynamic capabilities that focuses on the team level, we examine how humans and AI create in-
Dynamic capabilities
teractions that engage both agents in productive dialogue for value co-creation. Our analysis is based on a
Artificial intelligence
longitudinal case of the development of a recruitment algorithm at a national subsidiary of Santander bank. Our
results allow to identify three main sets of human-AI teaming interactions: achieving interoperability, building
trust, and producing mutual knowledge gains. We elaborate a set of propositions on how the value of AI is
increased when such interactions are created through productive dialogue, opening the scope for further research
on the teaming dynamics that turn the collaboration between both agents into a source of value creation for
companie
1. Introduction capabilities (DCs) framework to explain how firms can transform their
resources to persistently maintain value creation (Ambrosini &
The growing availability of artificial intelligence (AI) is stimulating Bowman, 2009; Teece, 2014). DCs have traditionally been studied both
its rapid drive for integration into organizations (Kinkel et al., 2022). AI at the macro-level of organizational routines (Felin & Powell, 2016;
is fundamentally related to autonomous decision-making (Berente et al., Fainshmidt et al, 2016; Bingham et al., 2015) and micro-level of exec-
2021), and it has therefore attracted companies’ interest because of its utive decisions (Day & Schoemaker, 2016; Kor & Mesko, 2013). How-
potential to extend their scope to domains that have been exclusively ever, scholars have proposed an emergent, meso-level of DC
human (Dwivedi et al., 2021), revolutionizing how business creates development that considers team learning as the source of their dyna-
value for organizations (Mikalef & Gupta, 2021). Thus, the imple- mism and acts as a link between the macro and micro ones (Harvey
mentation of these technologies is gaining momentum (Ångstro¨m et al., et al., 2022; Salvato & Vassolo, 2018). DCs are enabled at this meso-level
2023), and senior executives seem to agree on the criticality of AI as a when teams establish high-quality interactions through productive
game changer in the current business scenarios (Ångstro¨m et al., 2023; dialogue (Tsoukas, 2009). Thus, teammates are motivated to change
van de Wetering et al., 2022). However, organizations are still struggling how they work and produce joint learning that is translated into adap-
with the issue of how to capture value from AI (Berg et al., 2023), with tive organizational routines (Enholm et al., 2022; Harvey et al., 2020).
only 20 percent of companies declaring an impactful exploitation of AI In the present paper we contend that this meso-level of analysis be-
applications (Akter et al., 2021) and studies showing that investments in comes particularly consequential when examining how to capture value
this technology may even negatively impact market value (Lui et al., out of the integration of AI in organizations. In this scenario, AI is an
2022). In this context, scholars are shifting the focus from the benefits of agent in full who should collaborate closely with humans to improve
AI from a technological perspective toward a more holistic approach to performance through the augmentation of their capabilities (Raisch &
how to leverage AI and what its real sources of value are (Enholm et al., Krakowski, 2021). Considering AI’s peculiarities regarding volatility,
2022). opaqueness, or elusiveness of human control (Hassija et al., 2023;
From a theoretical perspective, scholars have relied on the dynamic- Mikalef & Gupta, 2021) we face unique inquiries in terms of how to team
* Corresponding author at: Maria de Molina, 13, 28006 Madrid, Spain.
E-mail addresses: cristina.simon@ie.edu (C. Simo´n), elena.revilla@ie.edu (E. Revilla), mjsaenz@mit.edu (M. Jesús Sa´enz).
https://doi.org/10.1016/j.jbusres.2024.114783
Received 25 July 2023; Received in revised form 8 June 2024; Accepted 12 June 2024
Availableonline27June2024
0148-2963/©2024ElsevierInc.Allrightsarereserved,includingthosefortextanddatamining,AItraining,andsimilartechnologies.
C. Sim´on et al. J o u r n a l o f B u s i n e s s R e s e a r c h 182(2024)114783
up with the technology to leverage its value (Ångstro¨m et al., 2023). 2. Theoretical background
While the scholar literature is examining how to develop DCs through
human-AI teaming both from a micro perspective (Weber et al, 2022; 2.1. Dcs
Brau et al, 2023) and also at a macro level (Mikalef & Gupta, 2021), to
the best of our knowledge there are no studies of human-AI teaming Recent studies propose that, when properly integrated into an or-
which place the focus on the meso-level of DCs. Following the call for ganization’s socio-technical system, AI creates value for the firm by
research on the mechanisms that reveal the interplay that underlies enacting DCs (Drydakis, 2022; Mikalef & Gupta, 2021; Schoemaker
human-AI teaming (Ångstro¨m et al., 2023; O’Neill et al., 2022), we et al., 2018). By contrast with ordinary capabilities, DCs are defined as
contend that this meso-level is key to explain how companies can “the firm’s ability to integrate, build, and reconfigure internal and
leverage the value of AI, and rely on the literature on productive dia- external competences to address rapidly changing environments.”
logue as a source of value creation (Keeling et al., 2021; Tjosvold et al., (Teece, 2007, p.316). Importantly, DCs cannot be acquired but can only
2014; Tsoukas, 2009) to discuss how to achieve quality teaming in- be developed internally (Easterby-Smith et al., 2009), and they do not
teractions between humans and AI. Given these considerations, the directly impact organizational performance but through a change of
following research question is posed: How should human-AI teams develop already-existing, ordinary enterprise capabilities (Chatterji & Patro,
through productive dialogue to create value for an organization? Based on 2014). In addition, DCs are path-dependent, going through iterative
the productive dialogue literature, we explore how humans and AI cycles of sensing-seizing-reconfiguration through which opportunities
create interactions that engage both agents at the team-based, meso-level are grasped and developed, thus leading to organizational learning and
of DC development in gaining leverage on the technology. Specifically, resource transformation (Easterby-Smith et al., 2009; Helfat & Peteraf,
we identify three categories of interactions, aiming at (i) negotiating the 2009). Therefore, the passage of time is critical for these capabilities to
terms of interoperability among the agents, (ii) nurturing trust to enrich reach their maturity stage and become embedded in organizational
the outcomes of the teaming relationship and (iii) creating mutual processes, learnings, and routines (Pan et al., 2022).
learning along the way. Along the lines of the previous arguments, among the premises
In attempting to deal with these issues, we rely on a longitudinal case derived from the DC framework is that value creation does not depend
study on how a human-AI team develops over a period of four years. on a company’s investment of resources but on how such resources are
Given the relevance of the evolving nature of these capabilities, we draw combined and deployed to create organizational learning and dynamic
on a process ontology (Tsoukas & Chia, 2002) to develop our case study, adaptation (Lockett, 2005). Until recently, DC scholars have examined
which emphasizes the temporal evolution of phenomena (Sharma & this phenomenon at two distinct levels. On the one hand, there is ample
Bansal, 2020). We focus on a particular type of AI application, a literature concentrated on the macro-level of operational routines and
machine-learning (ML), supervised-classification algorithm for decision-making systems, given their relevance in providing reliability
screening candidates in a large bank’s recruitment processes. The hiring to companies (Teece, 2007, 2014). For example, Helfat and Peteraf
function offers a particularly rich context for delving into human-AI (2003) examined the high-level origins of organizational capabilities
teaming (Chowdhury, Dey, et al., 2023) because experiences thus far and the specific sources of heterogeneity that support competitive
manifest how AI may destroy value through unintended consequences, advantage. Also from a macro perspective, DCs have been approached
thus demanding intensive interactions with humans for monitoring and from the view point of organizational learning (Denford, 2013; Kaur,
correction purposes (Soleimani et al., 2022; Teodorescu et al., 2021). In 2019) or of their role in adapting to disruptions at the company and
addition, only a small proportion of companies report being able to market levels (Karimi & Walter, 2015). Researchers have also adopted a
integrate these applications into their processes (Laurano, 2022), mainly distinct approach to DCs focusing on the micro-level of managerial
due to a lack of knowledge of what it represents for the human-resource (Adner & Helfat, 2003) or top executive decisions (Day & Schoemaker,
(HR) department in terms of assumed risks and work to be done (Hocken 2016). By contrast to the macro-level, this approach opens the scope for
& King, 2023). We could systematically monitor the implementation of change as decisions are flexible and subject to change; however, it limits
AI in a talent-acquisition department from its inception, focusing on the the view of the DC-creation process to individual decisions and therefore
quality of the interaction within the human-AI team that enabled a conceals our understanding of how routines are created (Helfat &
valuable integration of the technology into the organization. Peteraf, 2015; Salvato, 2021).
This study extends our current knowledge on the organizational Although both approaches to DC have led to relevant insights,
integration of AI-based applications in several ways. First, we contribute neither explains how individual decisions may be aggregated in a
to the development of the meso-level approach to DCs integrating AI as a manner that reconfigures resources and transforms routines at the
unique, new team member, therefore adding to the scarce literature in organizational level. To address this issue, further conceptual de-
this field (Harvey et al., 2022; Salvato & Vassolo, 2018). Our findings velopments propose a meso-level in which interpersonal connections
illustrate how, while engaged in cycles of interactions, the team facili- among employees act as connectors between the micro- and macro-
tated the dynamic sensing-seizing-reconfiguring pathway characteristic levels (Salvato & Vassolo, 2018). In this approach, companies leverage
of DCs (Chirumalla, 2021; Krakowski et al., 2023). Second, our research the joint effect of the adaptation to change triggered by top-level de-
points to the relevance of productive dialogue between humans and AI cisions and the stability created by organizational routines when em-
to allow for the creation of organizational value (Keeling et al, 2021; ployees are interconnected through teamwork, engaged to envision
Tjosvold et al, 2014). Thus, our study shows that it is not only the opportunities for improvement and willing to act, thus constituting the
quantity but also the quality of interactions between humans and AI that source of the dynamization of organizational capabilities (Peteraf et al.,
captures value out of the technology. We contribute to the human-AI 2013).
teaming literature by elaborating a set of propositions on how the This meso-level perspective emphasizes productive dialogue as a
value of AI is increased when such interactions are developed through critical aspect of the teaming-development process: “We see productive
productive dialogue. This way, our study suggests novel extensions on dialogue as the means through which individual employees’ proposals
the teaming strategies that engage both agents in value creation. for change become aggregated into a firm-level dynamic capability”
Finally, our study contributes to the practical approach to human-AI (Salvato & Vassolo, 2018, p. 1730). Productive dialogue assumes that
teaming at the meso-level of analysis by providing managerial insights agents recognize the “otherness” of their mates, act candidly, engage in
into how to design team interactions that facilitate the integration of AI, mutual interaction, and stimulate action out of collective learnings
augmenting the capacity of humans and leveraging the value that the (Berkovich, 2014; Tsoukas, 2009). As a result of productive dialogue,
technology can create for the organization. employees genuinely engage in improving current routines by devising
new reconfigurations of resources and prompting action, their joint
2
C. Sim´on et al. J o u r n a l o f B u s i n e s s R e s e a r c h 182(2024)114783
efforts resulting in changes in how a unit operates (Salvato & Vassolo, through flexible adaptation” (Kaplan & Haenlein, 2019a, p. 17). It is this
2018) and creating value (Keeling et al., 2021). Therefore, the sensing- capacity of AI to optimize itself through learning that shows the greatest
seizing of opportunities and final reconfiguration of resources that potential to dynamically transform organizations’ operating architec-
characterize DCs and create business value (Teece, 2014) emerge from ture and redefine how they capture and share value (Ångstro¨m et al.,
these inner teaming flows that evolve in collective learning cycles over 2023).
time (Harvey et al., 2022). Such a myriad of interactions brings about While these benefits are already apparent for companies, it is also
process and resource reconfigurations that are unique and difficult to accepted that the performance of AI applications to realize value is
imitate, thus reinforcing competitive advantage (Molloy & Barney, limited by several factors (Ångstro¨m et al., 2023; Revilla et al., 2023).
2015). One key factor concerns the quantity and quality of the data used for
This complex system of interactions through productive dialogue training it (Kaplan & Haenlein, 2019; Sarker, 2021; Vial et al., 2021).
that characterizes the meso-level reveals as particularly relevant when Although data collection is rapidly growing in organizational contexts,
exploring the integration of AI in organizations. The research literature available databases may not be appropriate for AI’s learning process
has growingly recognized that, to realize its benefits, AI cannot work in (Berente et al., 2021), and there are also data-privacy and related legal
isolation but only woven into human’s daily practices, which demands a issues that may limit its use (Van Den Broek et al., 2022). Furthermore,
close collaboration on the part of both agents (Moser et al, 2022; unlike humans, AI lacks the ability to interpret contextual cues and
Johnson & Vera, 2019). Additionally, the teaming between humans and anticipate the consequences of its decisions (Krakowski et al., 2023;
AI poses specific challenges involving the adaptation to the specificities Lindebaum & Ashraf, 2023), and this introduces relevant margins of
¨
of this new sociotechnological scenario (Musick et al, 2021; Angstrom et error when facing ill-structured problems under conditions of
al, 2023). Scholars have approached these aspects of human-AI teaming complexity, ambiguity, and scarce information (Madni & Madni, 2018).
in different ways as well. At the macro level, Mikalef et al (2021) AI may also produce senseless outcomes in problems involving social
identify the routines that companies should develop to best support B2B issues because it lacks the ability for moral deliberation (Hasija & Esper,
via AI, and others follow similar approaches at a more general level of 2022; Moser et al., 2022). Finally, a severe limitation of AI is its lack of
application of AI (e.g., Kemp, 2023). More recently, Akter et al. (2023) transparency and explainability regarding how data are integrated and
develop a framework for the application of AI to service innovation the knowledge that is gained by processing them (Chowdhury et al.,
focusing on the most relevant organizational capabilities that turn AI 2023). This “black boxing” prevents humans from understanding its
into a competitive advantage in the area. Conversely, working at the intentions, reasoning, and performance (Hasija & Esper, 2022; Vo¨ssing
micro level, Weber et al (2023) interview a group of experts in the field et al., 2022), and it consequently creates mistrust and reluctancy to
of AI with distinct background and degrees of experience regarding their collaborate with the technology (Dorton & Harper, 2022).
perspectives on the design of processes for developing organizational These unique characteristics of AI demand an intense, high-quality
capabilities that allow for an effective implementation of the technol- collaboration with humans to identify and correct flaws to make the
ogy. Similarly, Brau et al. (2023) analyze the effectiveness of different most of its potentialities (Balasubramanian et al., 2022; Weber et al.,
executive profiles to examine how their AI-based decisions determine 2023). However, scholars have claimed that, when this teaming is
the performance of digitized retail supply chains. A recent survey effective, AI can in turn augment human capabilities and improve de-
(Ångstro¨m et al., 2023) also collects the opinions of a wide sample of cision making through a mutual learning process (Weiss & Spiel, 2022).
executives with AI expertise on the challenges they face when inte-
grating this technology and the decisions that delineate a successful, 2.3. Human-AI teaming, sources of dynamism, and value creation
value-creating implementation. Finally, scanty studies deal partially
with human-AI teaming at the meso-level of analysis, e.g., examining the The application of a meso-level approach to the creation of DCs in the
patterns of interdependency that connect the different actors involved in context of AI calls for the above type of human-AI teaming, which in-
AI performance (Jacobides et al., 2021) or exploring specific aspects of volves the interaction of “at least one human and one autonomous agent
human-AI collaboration such as the effects of interactions being volun- where the autonomous agent has a significant role and is treated as a full
tary or hierarchically imposed (Bezrukova et al., 2023). teammate instead of a simple tool” (Schelble et al., 2022). Such a defi-
Our study focuses on how human-AI teaming develops at this meso- nition recognizes that human-AI teaming means not simply adding a
level perspective of DC creation. We argue that quality interactions new resource but also undergoing an internal redesign of the human
based on productive dialogue motivate effective teaming development. team operations in light of the new capacity (Mikalef & Gupta, 2021;
Through productive dialogue, teammates prepare to change how they Saenz et al., 2020), and accumulating evidence shows that the consid-
perform their work and based on such interactions, organizations enact eration of AI as one more group member significantly impacts the team’s
DCs. We contend that researching at this meso-level requires to first performance (Hauptman et al., 2023).
examine the unique features that AI may provide to organizations and Scholars have mostly tried to understand the terms of the interaction
then review the current body of knowledge on how such distinct but between humans and AI relying on the extensively studied field of
complementary agents engage in human-AI teaming for value creation. human teams (Endsley et al., 2022; Johnson & Vera, 2019). These an-
alyses reveal three main factors that have proven fully applicable to the
2.2. AI as a potential source of organizational value type of quality interaction that the meso-level model of DCs discusses
when it proposes productive dialogue as a core mechanism for enacting
Although scholars agree that there is not a single, univocal definition the dynamism of organizational capabilities (Salvato & Vassolo, 2018).
of AI, most concur on referencing it to human intelligence. Therefore, AI The first one is the recognition of interdependency between the team-
has recently been defined as “the ability of a system to identify, inter- mates (Kozlowski, 2015) to determine the workflow structure and terms
pret, make inferences and learn from data to achieve predetermined of the exchanges (Kozlowski & Ilgen, 2006). To engage in productive
organizational and societal goals” (Mikalef & Gupta, 2021, p. 3). dialogue, AI should integrate in a team’s activities while simultaneously
Operating in this way, AI speed and information-processing capacities demonstrating a level of agency in its outcomes that convinces humans
have proven to outperform those of humans in different scenarios, such of the unique value that this technology can provide beyond a tradi-
as in the management of routine and codifiable work (O’Neill et al., tional information and telecommunications (IT) application (Musick
2022), prediction tasks (Choudhury et al, 2020), and situations that et al., 2021; O’Neill et al., 2022). From this starting point, humans and
demand the fitting of models to large sets of alternatives (Weber et al., AI should share a profound understanding of each other’s capacities and
2023). Yet, the most distinct feature of AI is its ability to “learn from complementarities (Hauptman et al., 2023) and for this purpose, the
such data, and to use those learnings to achieve specific goals and tasks issues of explainability and transparency are critical for humans to
3
C. Sim´on et al. J o u r n a l o f B u s i n e s s R e s e a r c h 182(2024)114783
intervene in a timely manner (Endsley, 2023; Endsley et al., 2022). critical (Soleimani et al., 2022; Teodorescu et al., 2021). Furthermore,
Humans should be able to understand why a system makes specific de- the accessibility to multiple data sources allowed a synergistic collection
cisions, which represents a challenge because AI procedures and out- of evidence to warrant the validity of our findings (Eisenhardt, 1989).
comes are typically opaque (Ångstro¨m et al., 2023; Kellogg et al., 2020) From project inception, we could systematically and regularly observe
and AI can change its capacities in unpredictable, non-obvious ways the interactions leading to human-AI teaming. We combined these ob-
(Endsley, 2023). A recent review of empirical research on human-AI servations with interviews and archival data, mainly internal commu-
teaming (O’Neill et al., 2022) revealed that interdependence and nications and project presentations in public fora. This strengthened the
training in each other’s awareness were positive for the team (Johnson grounding of the theory, which is an important point because the
et al., 2021; Li et al., 2022; Xiong et al., 2023), and that low levels of theoretical development in the field is limited (Musick et al., 2021). The
reliability in AI could be balanced by increasing transparency (Chowd- longitudinal data allowed us to follow a process-based approach, which
hury, Joel-Edgar, et al., 2023; Vo¨ssing et al., 2022). is considered important for our objectives given the consideration of DCs
Another exportable aspect of human teams for achieving the pro- as evolving change-management phenomena and the need to focus on
ductive dialogue that creates efficient teaming with AI is trust, regarded the “know-how knowledge” of their development (Langley et al., 2013).
as an important antecedent of mutual understanding and team cohesion
(Feitosa et al., 2020). Trust, defined as “the attitude that an agent will 2.4. Case setting
help achieve an individual’s goals in a situation characterized by un-
certainty and vulnerability” (Dorton & Harper, 2022; Lee & See, 2004), In 2016, motivated by the fast-growing introduction of big data and
is fundamental for humans to become willing to accept AI as an equal analytics in business organizations, the TA manager of Santander Spain
partner (McNeese et al., 2021). Trust has also proven to be key to bank started to explore the application of these technologies in hiring
reinforcing interactions and extracting value from them (Hoff & Bashir, processes within the bank’s national market. As a result of the de-
2015). However, trust is not a binary phenomenon but rather operates as partment’s long experience in the practice, they were clear that the final
a continuum where there is a continuous calibration over interactions “hire” decision should be in the hands of a human. However, a whole set
between the agents (Yang et al., 2023), and how this works for AI is yet of opportunities emerged as some tasks could greatly benefit from the
to be fully explored (Dorton & Harper, 2022a). For example, trust grows use of AI. First of all, the volume of applications for junior positions was
with increased connection among humans; in human-AI teaming, very high due to the prestigious-employer branding of Santander bank
however, it may decline if interactions reveal flaws or malfunctions of and its widespread commercial network. When the project started, the
the technology (Glikson & Woolley, 2020); it may also rise if humans are department received an average of 4,000 applications per sales vacancy;
seen to identify and correct the failure to utilize an opportunity to learn under time pressure to fill the positions, they could review and reach out
more about the boundary conditions of the technology (Dorton & to only approximately 900, thus rendering a significant amount of po-
Harper, 2022). tential talent unexplored. Another relevant opportunity for the TA team
Finally, in the course of this continuous interdependency-based came up from the fact that candidates’ profiles were highly heteroge-
interaction reinforced by trust, learning emerges as a third decisive neous, given the applicants’ lack of experience. This made the CV-
attribute at the meso-level (Harvey et al., 2020). As a result of gaining screening process time-consuming; for every candidate considered
collective experience in decision-making and problem-solving through valid for a telephone interview, they had to go through more than 30
productive dialogue, team members engage in a process of mutual applications. Finally, the screening was conducted on a “first-come-first-
learning, in which two agents adapt their behavior and/or mental states served” basis, which might have left out talented late applicants. Due to
during continuous interaction (Peeters et al., 2020), and they in turn its unrelenting information-processing capability, the algorithm could
reinforce the acknowledgement of their respective agencies in the process the information on the candidates and update the ranking
decision-making processes (Weiss & Spiel, 2022). In the context of regardless of the exact time the application came in; the TA team would
human–human interaction, mutual learning is a natural phenomenon thus obtain the best matches at the top of the list in real-time throughout
because teammates recognize the need to co-adapt and become pre- the entire selection process, which constituted a huge opportunity to
dictable and explainable to facilitate collaboration (Harvey et al., 2022). improve the efficiency of the department’s operations.
In the case of human-AI interactions, it should be noted that learning After exploring the market, a decision was made to start collabora-
extends well beyond the training of the algorithm, and that every tion with IIC, a research-and-development (R&D) institute specializing
interaction is an opportunity to extract knowledge about how to best in big data. The objective was to develop an algorithm to support the
perform the task at hand by learning from each other’s mental models screening of the massive selection processes in the bank, focusing on the
and consequences of their chosen course of action (van Zoelen et al., sales force, who constituted the bulk of the positions in the more than
2021). 12,000 branches in the country. From the different types of AI-based
learning models, a decision was made to choose an ML supervised-
Research design learning application, mostly used by companies because its mode of
Case-selection strategy operation is relatively understandable and the final objective of the
process is more controllable by humans (Balasubramanian et al., 2022;
We adopted an interpretive case-study approach, which focuses on Kaplan & Haenlein, 2019). In the most extended uses of supervised ML,
revealing how a theory applies to a particular context (Eisenhardt & humans feed the algorithm with a set of predetermined categories and a
Graebner, 2007). Our setting for the case study is the Talent Acquisition large set of data, and the machine learns to estimate the correspondence
(TA) department of Santander bank in Spain, which provides recruit- of cases to one of the categories. The development of these algorithms
ment and selection services to source a national region of 20,000 em- comprises a training stage in which the AI builds the learning model, and
ployees. We followed several criteria for selecting this case. First, it is a further testing phase involving the AI making decisions autonomously
“particularly suitable for illuminating and extending relationships and with a human monitoring the quality of the outcomes (Campesato,
logic among constructs” (Eisenhardt & Graebner, 2007, p.27). The case 2020).
is theoretically representative of the organizational context of a large
company that plans for the implementation of an AI-based application; 2.5. Data collection
therefore, it faces the challenges and opportunities of developing
human-AI teaming to create value for the organization. Additionally, the The data collection included the entire process of AI-teaming
AI application is an algorithm that supports hiring processes, for which development over a period of four years. The most differential aspect
the collaboration between humans and developers has been regarded as of the case study was the monitoring of the development and
4
C. Sim´on et al. J o u r n a l o f B u s i n e s s R e s e a r c h 182(2024)114783
implementation of the algorithm from its inception by the first author, users’ feedback. Finally, attendance at conference presentations
who met with the TA manager by-monthly for a systematic follow-up. constituted a relevant source of observations of team members in a
This allowed us to meet one of the prior conditions in case study scenario in which they formally reflected upon the dynamics of the
research, which is “the development of testable, relevant and valid project and summarized what they jointly considered to be the key
theory requires intimate connection with the real world” (Verleye, takeaways from the project in terms of interactions, performance, and
2019). Our monitoring focused on the meso-level of analysis of DC mutual learning.
development, that is, the set of collective interactions based on pro-
ductive dialogue that might eventually translate into transformation of 2.6. Data analysis
the department’s routines.
The project team was composed of five persons: two on the side of the We followed a theory-elaboration model of analysis (Verleye, 2019)
developers (a project manager, psychologist with postgraduate training because we rely on theoretical insights of the meso-level DC framework
in AI-based applications, and a data scientist with experience in machine to identify the teaming mechanisms that create value but challenge them
learning), and two domain experts (selection technicians with a long by including AI as a unique member of the team. The data analysis was
experience in hiring processes) and the TA manager on the side on the conducted in three main stages using themati"
158,deloitte,data_ai_trends_report.pdf,"Data and AI
Trends Report
2024
The impact of generative AI
Data and AI Trends Report 2024
New opportunities,
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Gen AI is here – and it's a game-changer! This revolutionary Changes are rippling across the entire data stack in response
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Page 2
Data and AI Trends Report 2024
1 2
Top 5 trends
Gen AI will speed the
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5 minute read 6 minute read
3 4 5
Operational data
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3 minute read 4 minute read 5 minute read
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Data and AI Trends Report 2024
Trend 1 Trend 2 Trend 3 Trend 4 Trend 5
Gen AI will speed the
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Page 4
Data and AI Trends Report 2024
Trend 1 Trend 2 Trend 3 Trend 4 Trend 5
Nearly 2/3 of data decision
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Google Cloud Customer Intelligence Trends Research Survey, 2024. Page 5
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Nick Reed
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Chief Product Officer, Moody's Corporation
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Data and AI Trends Report 2024
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business data through natural language will be integrated with productivity tools and
a major force in bridging existing gaps in business applications. As many applications
organizational skill sets.
 allow users to see how others found
successful answers to questions, people will
Throughout 2024 and beyond, expect to see
also be able to benefit from aggregate
more business users ‘talking’ to their data
knowledge, as well as gaining insight into
using search and leveraging a conversational
which interactions have had the greatest
UI to create reports, dashboards and intuitive
impact over a day, a quarter, or a year.
Page 7
Data and AI Trends Report 2024
Trend 1 Trend 2 Trend 3 Trend 4 Trend 5
AI for all.
62%
Large language models connected to business data will allow even non-technical team members
to ‘talk’ to their data using search. Plus, a conversational UI can be used to create reports,
dashboards and intuitive intelligent visualizations, which will be integrated with productivity tools
and business applications.
47%
This shift is already underway. Here's how data decision makers responded to the question:
""Which type of non-technical users in your organization have been leveraging generative AI
42%
41%
to draw insights from your organization's data?""
37%
36%
33%
32%
23%
19%
16%
Security Logistics Administrative Business Customer & Finance & Human Product
 Operations Sales Marketing,
Development Account service Accounting Resources Management Advertising & PR
Page 8
Google Cloud Customer Intelligence Trends Research Survey, 2024.
Data and AI Trends Report 2024
Trend 1 Trend 2 Trend 3 Trend 4 Trend 5
“Wendy’s introduced the first modern pick-up window in the industry
more than 50 years ago, and we’re thrilled to continue our work with
Google Cloud to bring a new wave of innovation to the drive-thru
experience. Google Cloud’s gen AI technology creates a huge
opportunity for us to deliver a truly differentiated, faster, and
frictionless experience for our customers, and allows our employees
to continue focusing on making great food and building relationships
with fans that keep them coming back time and again.”
Todd Penegor
President and CEO, Wendy’s
Page 9
Data and AI Trends Report 2024
Trend 1 Trend 2 Trend 3 Trend 4 Trend 5
“Gen AI isn’t just enhancing BI, it’s reinventing it. In 2024, insights
won’t be uncovered – they’ll be proactively surfaced, with more
nuance and context than ever before.”
Irfan Khan
President & Chief Product Officer, SAP HANA Database & Analytics, SAP
Page 10
Data and AI Trends Report 2024
Trend 1 Trend 2 Trend 3 Trend 4 Trend 5
The roles of data
and AI will blur.
Page 11
Data and AI Trends Report 2024
Trend 1 Trend 2 Trend 3 Trend 4 Trend 5
80% of respondents agree
that the lines between data
roles are starting to blur.
Google Cloud Customer Intelligence Trends Research Survey, 2024. Page 12
Data and AI Trends Report 2024
Trend 1 Trend 2 Trend 3 Trend 4 Trend 5
Why should you care?
“When I was little, my mom
would spend hours with a travel
As the use of AI becomes more widespread, As processes are streamlined, the roles of
agent planning our vacations.
the speed at which companies can go from data and AI will become increasingly blurred;
Working with Google Cloud to
raw data to AI will become increasingly meaning formerly siloed teams will need to
important. work more closely together than ever before.
incorporate generative AI allows
The organizations that master this process will us to create a bespoke travel
be able to make better decisions, launch new
concierge within our chatbot.
products and services faster, and provide
superior customer experiences.
We want our customers to go
beyond planning a trip and help
them curate their unique travel
experience.”

Martin Brodbeck
CTO, Priceline
Page 13
Data and AI Trends Report 2024
Trend 1 Trend 2 Trend 3 Trend 4 Trend 5
According to research, more than half
Many data analysts are now taking on (54%) of digital leaders say skills
shortages prevent them from keeping up
responsibilities that were traditionally
with the pace of change.
reserved for data scientists and vice versa.
Nash Squared Digital Leadership Report, 2023.
Data analytics and engineering, AI, and This interlocking allows users to:
business analytics are the most scarce skills
within organizations. Gen AI presents an
Have a common workspace for data
opportunity to boost productivity of existing
engineers, analysts and scientists that
data teams and workloads, thus assisting with
supports multiple coding languages such as
this widening skills gap. To be able to
SQL, Python, and Spark.
seamlessly use data and AI platforms allows
organizations to improve productivity, and
Extend software development best practices
innovate faster by accelerating their data to AI
such as CI/CD, version history and source
journey.
control to data assets, enabling better
collaboration and hand-offs.
Data and AI tools are also becoming
increasingly interconnected in order to help
users streamline data and AI workflows.
Page 14
Data and AI Trends Report 2024
Trend 1 Trend 2 Trend 3 Trend 4 Trend 5
Gen AI is also providing employees with
ways to accomplish more technical tasks.
For instance, tools can suggest the new lines of
code required to update a financial-reporting
system or outline the A and B versions of a
marketing campaign or otherwise create first
drafts that human employees can take and
implement into live production environments.
The organization of the future: Enabled by gen AI, driven by people, McKinsey & Company, 2023.
Page 15
Data and AI Trends Report 2024
Trend 1 Trend 2 Trend 3 Trend 4 Trend 5
Bringing AI directly to data can activate its
full potential regardless of its format.
A significant challenge hindering organizations The advent of advanced AI and Machine
from fully utilizing the potential of data lies in Learning (ML) technologies has revolutionized
the substantial amount of untapped, the way organizations leverage their data.
unstructured data generated today. This These cutting-edge technologies offer
includes formats such as images, documents, unparalleled opportunities to unlock the full
and videos. It is estimated to cover roughly up potential of all data, regardless of its format;
to 80% of all data, which has so far remained structured, semi-structured, or unstructured.
untapped by organizations.
 Similarly, multi-modal AI has opened up a world
of possibilities for organizations, unlocking new
Structured data, characterized by its
levels of efficiency and accuracy when tuning 80% of the global datasphere
organization in fixed fields and columns, such
and grounding models in their enterprise data.
will be unstructured by 2025.
as in spreadsheets or databases, can be easily
Text embeddings enable vector searches
processed and analyzed using traditional
directly on data, without the need for complex
methods. However, unstructured data - think
and time-consuming preprocessing steps. This
VentureBeat, 2022.
social media posts, emails, customer call
simplifies the process of finding relevant
recordings, clinical documentation, and sensor
information, identifying patterns and trends,
readings - is often complex and challenging to
and clustering similar unstructured data in
interpret, making it difficult to extract
sources like documents.
meaningful insights.
Page 16
Data and AI Trends Report 2024
Trend 1 Trend 2 Trend 3 Trend 4 Trend 5
“The most transformative aspect of 2024's data and AI landscape
isn't just about efficiency – it's about democratization. By seamlessly
interconnecting these technologies, we empower not just data
scientists, but business users across the organization to unlock
actionable insights and drive innovation.”
Ali Golshan
CEO, Gretel
Page 17
Data and AI Trends Report 2024
Trend 1 Trend 2 Trend 3 Trend 4 Trend 5
AI innovation will hinge on
strong data governance.
Page 18
Data and AI Trends Report 2024
Trend 1 Trend 2 Trend 3 Trend 4 Trend 5
66% of organizations have at
least half of their data dark,
posing significant risk.
Five Factors For Planning A Data Governance Strategy, Forbes, 2023 & Gartner Glossary, Dark Data, 2024.
Page 19
Data and AI Trends Report 2024
Trend 1 Trend 2 Trend 3 Trend 4 Trend 5
Fewer than half of respondents
Why should you care?
(44%) are fully confident in their
organization’s data quality.
Google Cloud Customer Intelligence Trends Research Survey, 2024.
This explosion of new technology has its Similarly, most respondents (54%) consider
drawbacks, too. Many organizations are their organizations only somewhat mature
discovering new vulnerabilities and when it comes to data governance and only
weaknesses, especially when it comes to the 27% consider their organizations either
quality of their data. It’s not enough to just extremely or very mature.

apply LLMs to data – these models need to be
On the plus side, many organizations are
grounded in good quality enterprise data or
already taking steps to ensure data accuracy,
otherwise risk hallucinations. Organizations
data quality, and trust. The majority of
which take a practical approach to data
organizations surveyed 
governance, quality, and trust will be in a
strong position to deliver tangible business
believe they are building a data driven
outcomes with AI.

culture 
Most respondents are only somewhat
are centralizing data governance oversight 
confident (45%) in their organization's data
quality, and another 11% are even less than  are building centralized policy
somewhat confident. management, monitoring, and auditing.
Google Cloud Customer Intelligence Trends Research Survey, 2024. Page 20
Data and AI Trends Report 2024
Trend 1 Trend 2 Trend 3 Trend 4 Trend 5
Snap Inc uses Google’s Data Cloud to deliver a business domain-specific,
self-service data platform across distributed data, with decentralized data
ownership but centralized governance and visibility. With increased data
efficiency they can focus on improving the user experience and boosting
engagement.
Carrefour uses Google's Data Cloud to achieve zero trust network
protections, improving data security and strengthening secure access to
business-critical applications. Their data-centric infrastructure provides
flexibility to make changes very quickly and deliver the highest quality
service to their customers.
Page 21
Data and AI Trends Report 2024
Trend 1 Trend 2 Trend 3 Trend 4 Trend 5
What should
69%
organizations look for?
It’s key that organizations look for secure-by- End-to-end data lineage.
design data platforms that fully integrate data Automatically generated lineage to track data
encryption. The right platform should flows, perform impact analysis, and use
automatically catalog the data you own and lineage as a foundation for governance and 31%
give you the ability to logically unify and compliance across data and AI models.
organize your data leveraging metadata. This
Unified governance for data and AI assets.
enables you to centrally secure and govern
Central policy management, monitoring, and
data, based on your business context, and use
auditing for data authorization, retention, and
built-in automation and intelligence around
classification.
data profiling, quality, lineage, and more to
better manage data at scale. This enables:

Data quality. Auto-generate data quality rules 69% of employees had bypassed
to measure for completeness, accuracy, and
their organization’s cybersecurity
validity of your data.

guidance in the past 12 months.
Gartner Predicts Nearly Half of Cybersecurity Leaders Will Change Jobs by 2025, 2023.
Page 22
Data and AI Trends Report 2024
Trend 1 Trend 2 Trend 3 Trend 4 Trend 5
“2024 is a watershed moment for generative AI. Organizations that
fail to manage the risks throughout the AI development lifecycle will
be left behind. Those who proactively establish strong AI governance
practices are the ones who will unlock the true potential
of this technology.”
Felix Van De Maele
CEO and Co- Founder, Collibra
Page 23
Data and AI Trends Report 2024
Trend 1 Trend 2 Trend 3 Trend 4 Trend 5
Operational data will
unlock gen AI potential
for enterprise apps.
Page 24
Data and AI Trends Report 2024
Trend 1 Trend 2 Trend 3 Trend 4 Trend 5
71% of organizations plan
to use databases integrated
with gen AI capabilities.
Google Cloud Customer Intelligence Trends Research Survey, 2024. Page 25
Data and AI Trends Report 2024
Trend 1 Trend 2 Trend 3 Trend 4 Trend 5
Why should you care?
Businesses are excited about the potential Operational databases and warehouses with
with large language models (LLMs). They have vector support help bridge the gap between
all experienced the power of tools like Gemini LLMs and enterprise gen AI apps. This is why
and other large language models, but they we’re seeing so much interest in vector search
also recognize that the creative nature of and vector databases and why Retrieval-
these tools is not a good fit for most Augmented Generation (RAG) is an important
enterprise use-cases. Enterprise gen AI technique for enhancing and augmenting
applications face a variety of challenges that LLMs and gen AI models. We’re seeing a lot of
LLMs alone do not address; they need to innovation across the industry and much of it
provide accurate and up-to-date information, is driven by the open source community
offer contextual user experiences and do all including PostgreSQL, one of the most popular
this while not breaking the bank. databases for developers.
Page 26
Data and AI Trends Report 2024
Trend 1 Trend 2 Trend 3 Trend 4 Trend 5
What do organizations want
from AI-powered
databases?
Seamless connectivity to AI models, the ability to ground LLMs using techniques like RAG and the
ability to use natural language for database administration are the most important capabilities
when using AI in databases.
45%
40% 40%
38%
35%
33%
29%
Seamless Ability to ground LLMs using Ability to use natural language Ability to use natural Simplifying database Tight integration with AI Built-in, high-performance
connectivity to AI techniques such as retrieval for database administration, language to generate code migration and code tooling and frameworks vector search
models augmented generation (RAG) governance, and compliance conversion
Google Cloud Customer Intelligence Trends Research Survey, 2024. Page 27
Data and AI Trends Report 2024
Trend 1 Trend 2 Trend 3 Trend 4 Trend 5
Get it right and reap
the rewards.
The true power of gen AI is unlocked when Databases that fail to integrate
gen AI capabilities are likely to
operational data is integrated with gen AI to
become obsolete.

deliver real-time, hyper-personalized, and
contextually-relevant experiences across
Having AI closer to the operational data will
enterprise applications. Simply put, gen AI-
also allow developers to iterate quickly and
enabled operational databases holding
enhance the experience with all available data.
relevant business data will be the key to
You can do this where your data already lives
unlocking gen AI in the enterprise.
because databases are already powering all
applications, so organizations don’t have to
Successful databases will evolve to be AI-first,
learn or set up an entirely new system and it is
and deeply integrate technologies such as;
significantly more cost effective. In addition,
vector search, seamless connectivity to AI
with open source technologies like
models, support for natural language to SQL,
PostgreSQL, developers can get started
and tight integrations with AI tooling and open
sourc6e 9fra%me woorfk se. Amll thepselo wiyll bee enastiv ehlya d bquyicpklya wsitsh feamdil iar tools and capabilities.

built into operational databases and will
their organization’s cybersecurity
become table stakes.
guidance in the past 12 months.
IDC FutureScape Worldwide AI and Automation 2022 Predictions.
Page 28
Data and AI Trends Report 2024
Trend 1 Trend 2 Trend 3 Trend 4 Trend 5
“We explored several new entrants in the database market that focus on
storing vectors and ended up trialing several. And given Linear’s existing
data volume and our goals for finding a cost-efficient solution,
we opted for Cloud SQL for PostgreSQL once support for pgvector was
added. We were impressed by its scalability and reliability. This choice
was also compatible with our existing database usage, models,
ORM, etc. This meant the learning curve was non-existent for our team.”
Tom Moor
Head of US Engineering, Linear
Page 29
Data and AI Trends Report 2024
Trend 1 Trend 2 Trend 3 Trend 4 Trend 5
“Customers are looking to leverage the power of LLMs by
augmenting them with their domain knowledge and enterprise data.
To support these new use cases, cloud-based database solutions
that also embrace open-source gen AI orchestration frameworks will
provide application developers with the capabilities to help them
quickly and more efficiently build Retrieval Augmented
Generation (RAG) applications.”
Harrison Chase
CEO, Langchain
Page 30
Data and AI Trends Report 2024
Trend 1 Trend 2 Trend 3 Trend 4 Trend 5
2024 will be the year
of rapid data platform
modernization.
Page 31
Data and AI Trends Report 2024
Trend 1 Trend 2 Trend 3 Trend 4 Trend 5
Only 14% of organizations
are satisfied with their
legacy databases’ support
for AI, indicating there is a lot
of room for improvement.
Google Cloud Customer Intelligence Trends Research Survey, 2024. Page 32
Data and AI Trends Report 2024
Trend 1 Trend 2 Trend 3 Trend 4 Trend 5
Why should you care?
As more and more organizations seek to take The gen AI boom is bringing new urgency to
advantage of the opportunities gen AI brings, database modernization because the most
many are discovering their legacy databases popular AI tools for working with vectors,
are holding them back due to lagging models, and data run in the cloud and are
technology and poor user experience. In based on open source database technologies
addition to outdated technology and a poor such as PostgreSQL. In addition, the most
developer experience, legacy databases have advanced AI models run only on major cloud
also caught the attention of C-level executives platforms.
because of their expensive, unfriendly
licensing and vendor lock-in, which often
result in millions of dollars in unnecessary
annual costs.
Page 33
Data and AI Trends Report 2024
Trend 1 Trend 2 Trend 3 Trend 4 Trend 5
Top challenges with
legacy databases.
23%
19%
17%
13%
12%
10%
4%
Licensing costs Lack of cloud-first Lack of integration with Vendor lock-in Limited data model Limited pool of Lack of community
and practices architecture cloud services options practitioners motivated to support
work with this technology
Google Cloud Customer Intelligence Trends Research Survey, 2024. Page 34
Data and AI Trends Report 2024
Trend 1 Trend 2 Trend 3 Trend 4 Trend 5
Smooth transitions are more
possible than ever.
Thankfully, migrating from legacy databases is IT decision makers are now comfortable
becoming easier with database migration approving large modernization projects as
tools and programs continuing to improve and they look to embrace open technologies,
mature. We’re also seeing AI help to augment including gen AI, as part of their innovation
these tools, to the point where breaking free roadmaps.
from legacy databases is now much easier
with AI-assisted code conversion, code
completion, and improved efficiencies.
Page 35
Data and AI Trends Report 2024
Trend 1 Trend 2 Trend 3 Trend 4 Trend 5
“Data migration tools have been around forever, but more recently,
they've been getting smarter with the ability to do AI assisted code
conversion and code completion. The hardest part of the migration
is transforming the data and training the new applications to fit in
the new database. Both are made easier through gen AI. You can
use a model to look at a source database and find out how to
transform the data into the destination database. You can get
some quick wins, and ultimately get developer productivity. There's
still a ton of legacy stuff, and gen AI is bringing the bar down to
simplifying migrations.”
Andrew Storrs
VP Data Engineering, Aritzia
Page 36
Data and AI Trends Report 2024
Trend 1 Trend 2 Trend 3 Trend 4 Trend 5
“Character.AI is a pioneer in the design and development of open-
ended conversational applications. Our gen AI platform utilizes our
own advanced neural language model to generate human-like text
responses and engage in contextually relevant conversations.
When we found AlloyDB for PostgreSQL, we were stuck between a
rock and a hard place. Usage of our service had scaled
exponentially, putting unique stresses to various parts of our
infrastructure, especially our databases. Google Cloud's AlloyDB
and Spanner provide a solid foundation, delivering reliability,
scalability, and price performance for our workloads, from
engagement and operations, to AI and analytics.”
James Groeneveld
Research Engineer, Character.AI
Page 37
Data and AI Trends Report 2024
Trend 1 Trend 2 Trend 3 Trend 4 Trend 5
“Legacy systems were built for a different era. Gen AI's potential lies
in its ability to find novel connections and insights. In 2024,
organizations that liberate their data from outdated systems will be
best positioned to stay ahead of the curve.”
Gopal Srinivasan
Principal - Generative AI, Deloitte Consulting
Page 38
Data and AI Trends Report 2024
Trend 1 Trend 2 Trend 3 Trend 4 Trend 5
An entire generation of
developers is building AI
applications and leveraging AI
for more efficient coding,
improved database
performance insights, and
enhanced security posture.
Are you one of them?
Page 39
Data and AI Trends Report 2024
How Google
Cloud can help.
Google Cloud helps organizations unify data We also provide cutting-edge AI/ML and
and connect it with groundbreaking AI to generative AI capabilities that are readily
unleash transformative insights and available for your data, enabling all of your
personalized experiences. By harnessing the people to easily and quickly access the data
simplicity, scalability, security, and intelligence they need and unlock its true value. All of this
of Google's unified data and AI approach, is delivered with enterprise-grade efficiency.
businesses can unlock the full potential of It’s this unique combination that makes
their data in a single, streamlined solution.
 Google Cloud an unparalleled partner for
turning raw data into organizational value.

Because Google Data Cloud consolidates
workloads and manages the entire data life
cycle, data teams are empowered to develop
modern, data-driven applications using
popular open-source engines and models.
Page 40
Data and AI Trends Report 2024
Inside our one-of-a-kind approach.
Fully connected data and AI. A unified data foundation. The most open data platform for Enterprise-grade efficiency and
security at scale.
modernization.
As new ways of interacting with systems and Google Cloud's unified data foundation is built Google Cloud is committed to being the most Google Data Cloud is an industry leader in
data emerge, it’s clear that organizations need on BigQuery, and brings your data together open cloud provider, letting you build modern, efficiency, security, and scale; catering to
AI models grounded in quality enterprise data into one place, integrating structured and data-driven applications wherever your organizations of all sizes and adhering to the
that allows for analytical insights and unstructured data with AI to deliver insights workloads are. We support open source and most stringent enterprise requirements.
augmented experiences. across your entire data estate. This unified
open standards, and offer managed database
data foundation allows you to manage your We help make it easy for organizations to
services that are fully compatible with popular
With Google’s Data Cloud, data teams can use entire data lifecycle and help make data share data safely and securely across
open-source engines and models.

gen AI tools to activate their enterprise data access, management, governance, and organizational boundaries, run queries across
across BigQuery and AlloyDB, and use built-in analysis easier for different types of users With AlloyDB Omni and BigQuery Omni, you exabytes of data with blazing speed, and
features to easily apply AI/ML directly to their within an organization, effectively removing can utilize data and modernize your process billions of transactions – all with
data. For instance, BigQuery ML allows data data silos. applications across Google Cloud, AWS, Azure, generally lower cost.
teams to construct ML models straight on
and Google Distributed Cloud, without
their BigQuery data simply using SQL, and Our highly scalable architecture unifies
incurring the costs, security risks, and
even call foundation models in Vertex AI. Built- transactional and analytical systems, enabling
governance concerns associated with data
in vector embedding capabilities in AlloyDB tightly integrated data services across
migration. It’s now easier than ever to get
also allow users to store and generate BigQuery, AlloyDB, and Spanner. This allows
started with gen AI on a data platform that
embeddings within their data stores to help easy data analysis from Spanner to BigQuery,
meets you where you are on your
augment their LLMs and support their gen AI with virtually no impact to the underlying
modernization journey.
use cases. transactional workloads.
Page 41
Data and AI Trends Report 2024
So,
Ready to join
the party?
what’s next?
If you’ve got any questions about the content of this report, or
want to know more about how Google Cloud can support your
organization, our experts are always on hand.
Talk to an expert
Clearly, 2024 is shaping up to be an exciting - Naturally, many of these new opportunities
and pivotal - year for many organizations. require new skills, and for existing processes
Those who are able to prepare their people to be refined. Organizations that embrace the
and platforms to fully embrace new need to upskill and fully equip their people will
capabilities made possible with gen AI will not quickly find that this investment pays
only see short-term productivity gains, but dividends in the form of almost limitless
begin to effectively future-proof their potential.
organization against ever-evolving
competition.
You can also take our Data & AI Strategy Assessment to discover
how ready your organization is for AI-powered digital transformation
and receive expert recommendations to get you there faster.
Take the assessment
Page 42
Data and AI Trends Report 2024
Methodology.
The Google Cloud Customer Intelligence Region Company size Role
team conducted a global research study on
NORAM 180 1,000 to 4,999 19% Business Development 2%
Data & AI Trends with 410 Data Decision
Makers from 12/18/2023 - 1/17/2024. Active EMEA 104 5,000 to 9,999 20% IT or IS (Information Technology, 40%
Computer Engineering, Security,
recruitment was paused from 12/23 to 1/1 for etc.)
JAPAC 76 10,000 to 49,999 30%
winter break. Respondents included a mix of
Software Development 1%
LATAM 50 50,000+ 31%
Data, IT, and business leadership roles with
Technology Strategy or Product 11%
seniority ranging from C-level to Manager. All
Development
Industry Role level
respondents were employed at 1,000+
Marketing/Advertising/PR 16%
employee organizations currently using data Financial services 74 C-level 14%
Operations 3%
products & services. Respondents did not
Retail (e.g. Grocers, Stores, 42 VP or equivalent 20%
Boutiques, Franchises,
know Google was the research sponsor and Product Management 3%
Restaurants, etc.)
Director 43%
the identity of participants was not revealed
Research/Analytics/Strategic 6%
Technology 85
Manager 7% Planning
to Google.
Other 103
Lead / Head 4% Sales 1%
Data Science 15%
Interaction with data products and services
Hands on 40%
Strategic/oversight 60%
Page 43
Data and AI Trends Report 2024"
159,deloitte,amc-ai-the-future-of-public-engagement.pdf,"Artificial Intelligence (AI) and
the Future of Public Engagement
How government and public sector organizations are using AI to
streamline communication and serve increasingly diverse populations.
January 2025
As the use of AI continues to erupt populations they serve. In just over
across industries, constituent a year since the launch of OpenAI’s
engagement and targeted outreach GPT-4, our research1 showed that
are proven front-runners for early nearly 1 in 4 commercial organizations
adoption of the technology. This is had already begun using AI in their
good news across all industries, but marketing and outreach operations.
especially for Government, which relies This statistic, and many like it, reveal a
heavily on constituent engagement clear truth: AI isn’t merely a buzzword
for establishing trust, transparency, or a fleeting tech trend; it represents a
accessibility, and reliability. paradigm shift in how we understand,
process, and disseminate information.
Despite the importance of this field
in fulfilling mission requirements Despite this truth, the wealth of
for Government agencies, public information available on AI can make
sector organizations face unique it difficult to understand the use cases
challenges and expectations as it and applications that are right for you.
relates to how they engage with To cut through the noise, leaders from
the people they serve, including Deloitte’s GPS Advertising, Marketing,
heightened expectations among & Commerce2 (AM&C) practice have
consumers, expanded diversity of identified three key opportunities to
audiences served, demand for timely enhance existing outreach practices
and accurate communications, and with AI. The case studies that follow will
increased complexity of regulatory describe real-world examples of public
environments in which they operate. sector organizations that have taken
advantage of these AI opportunities
Fortunately, the AI revolution
and as a result have built trust with
presents exciting opportunities
the public, created efficiencies for
to improve efficiencies for public
their audiences and employees, and
sector organizations at all levels and
increased impact of their messaging
transform how they engage with the
and outcomes.
2
USING AI TO AI offers new opportunities for public Public sector organizations don’t have
sector communicators at each step of to re-invent the wheel — they can
ENHANCE the marketing and outreach process. infuse AI into their Existing Outreach
AI-powered analytics can uncover and Engagement Frameworks.
PUBLIC behavior patterns and preferences
with precision in real-time, Generative At Deloitte, we see three
OUTREACH
AI (GenAI) tools can produce hundreds clear ways for public sector
of tailored options, and Machine organizations to utilize AI in existing
AND
Learning (ML) technologies can now frameworks, reducing barriers to
make real-time data-driven decisions creating impactful outreach and
ENGAGEMENT
to maximize every dollar. engagement with their constituents.
AI AI AI
OPPORTUNITY OPPORTUNITY OPPORTUNITY
1 2 3
Gaining Deeper Developing Hyper- Optimizing Messaging
Audience Insights Personalized Content Delivery
EXISTING OUTREACH PROCESS: EXISTING OUTREACH PROCESS: EXISTING OUTREACH PROCESS:
UNDERSTAND DEVELOP CONTENT DISTRIBUTE CONTENT
YOUR AUDIENCES FOR YOUR AUDIENCES TO YOUR AUDIENCES
Public sector organizations Citizens demand messaging that It isn’t only about crafting the perfect
are charged with serving resonates with their individual lives and message — public sector organizations
incredibly diverse populations situations. Gone are the days of a “one- must also make sure that message
while simultaneously delivering size fits all” outreach approach. While lands in front of their primary targets at
personalized services. The key this challenge is a universal hurdle the right time. This goes beyond effort
to success is developing a deep for all communicators to overcome, it and money; it’s about guaranteeing
understanding of their audience, takes center stage with public sector that every ounce of investment results
but achieving this is not always as organizations that must, at times, in a maximum impact when reaching
feasible as it sounds. address a population of 334+ million. the public.
AI Injection: AI-powered AI Injection: GenAI tools and AI Injection: Machine-learning
analytics tools can provide AI-powered Chatbots can deliver models can analyze performance
insight into GPS organization’s hyper-personalized content to of content in real-time and
audiences that no human eye or audiences, more efficiently optimize distribution to maximize
focus group can conjure up. than ever. campaign impact.
3
AI
Gaining Deeper
OPPORTUNITY
1
Audience Insights
PUBLIC SECTOR ORGANIZATIONS and how best to reach them: How old are they? Do they
live in urban or rural areas? How do they get their news?
PROVIDE DEEPLY PERSONAL
What do they care about most?
SERVICES TO HIGHLY DIVERSE
POPULATIONS.
AI-POWERED ANALYTICS TOOLS CAN HELP PUBLIC SECTOR
Populations aren’t just growing — they’re aging and ORGANIZATIONS UNDERSTAND THEIR AUDIENCES’ UNIQUE
diversifying. Nearly 1 in 6 Americans are over the age of 65, NEEDS BETTER THAN EVER BEFORE.
a proportion projected to rise to 1 in 5 by 20303. Further, the
2020 Census4 showed that 67.8 million US residents (almost AI-powered analytics tools use advanced algorithms and
20%) speak another language other than English at home. GPS machine learning (ML) techniques to analyze large quantities
organizations serve this diverse population on the front lines, of both historical and current data, extracting meaningful
directly administering essential services that maintain the patterns and insights about audience behaviors and
welfare of communities. The need to cater to constituents preferences. Through examining these patterns, public sector
with varied reading levels, native languages other than English, organizations can better understand the constituents they’re
and a variety of accessibility requirements makes a one-size- serving to develop personalized strategies for better serving
fits-all communications approach insufficient. them. This approach allows for highly-accurate audience
Whether a public sector organization is seeking to segmentation and sets a solid foundation for developing
communicate about a newly developed policy or legislation, compelling content.
increase usage of their services, or send out an emergency
notice, it is critical to know what makes their audiences tick
4
OPPORTUNITY 1 Challenge: Universities across the country are experiencing increased
enrollment pressure in the face of a shifting higher education landscape.
IN ACTION
To combat this pressure and stay competitive, Michigan State University
(MSU) partnered with Deloitte to develop new enrollment strategies that
Michigan State
would balance MSU’s headcount, selectivity, diversity, and net tuition
University uses AI revenue goals. Before they developed these strategies, MSU needed a
deeper understanding of their current student population.
and ML Insights to
Increase Enrollment5 Solution: Deloitte helped to implement the Candidate360TM, 6 solution, which
used AI and ML predictive models to combine US lifestyle data from 250M+
households with MSU data and uncover meaningful behavior patterns and
preferences. The insights — which provided individualized profiles of in-state,
domestic, and international prospects — helped recruiters prioritize limited
time and resources. The data in-hand empowered recruiters and provided
insight into optimal communications channels for reaching students, helping
them increase responsiveness and avoid phone call screening.
Outcome: The Candidate360TM models enabled both recruiters and enrollment
directors to better understand their key geographic, demographic, and academic
student profiles when crafting their strategic and operational plans. In the first
year of utilizing the analytical models, MSU benefited from a 24% increase in out-
of-state student enrollment and a $5M increase in net tuition revenue.
DELOITTE AI CAPABILITY
DELOITTE’S DISCOVER.AI PLATFORM PRODUCES REAL-TIME, DEEP AUDIENCE INSIGHTS
To help public sector organizations track, measure, language processing (NLP), allowing the agency to see what
understand, and improve interactions audiences have with questions their audiences are asking most, and why. This AI
the services they provide, Deloitte has teamed up with technology can also integrate other audience interaction points
Qualtrics7 to provide an experience management platform (e.g., voice, text, survey, email, web, and social media) which
called Discover.ai. The platform, currently used to support would allow the state agency to have a more holistic picture of
a large state public health agency, combines real-time their targets and how to better serve them.
audience insights with powerful AI capabilities. Coupling those
With unparalleled insight into targeted audiences, public
capabilities with expert advisory services creates a powerful
sector organizations can reduce the time and resources
foundation for continuous improvement of the programs,
spent on broad, ineffective communication campaigns.
policies, and applications used by the public and workers.
Insights derived from these AI analytics tools can increase
Consider a state transportation agency, for example, that has the accuracy and relevance of information disseminated,
implemented a new vehicle policy and is receiving hundreds of fostering trust among the public. These insights can be
calls per day from concerned constituents. Audio recordings paired with generative functions of AI to develop meaningful
from more than 100,000+ customer service calls can be fed and receptive content that is personalized to the audience
into the Discover.ai platform, powered by Qualtrics AI. The specifications identified within the AI Analytics tools.
system can transcribe and analyze those calls using natural
5
AI
Developing Hyper-
OPPORTUNITY
2
Personalized Content
TWO-WAY COMMUNICATION IS In this landscape of heightened demand for personalized,
real-time content, the challenge lies in transcending
ESSENTIAL FOR BUILDING TRUST
traditional communication barriers to effectively listen and
IN GOVERNMENT.
cater to individual needs, ensuring meaningful engagement at
That said, engaging with the masses at the 1:1 level presents a scale never before possible.
a unique series of challenges. A recent report8 showed that
82% of survey respondents indicated if government wants
AI CAN HELP HUMANIZE GOVERNMENT SERVICES BY
to earn or keep their trust, governments need to hear the
DEVELOPING INDIVIDUALIZED CONTENT AT SCALE.
public’s concerns and let them ask questions. But this kind
of listening at scale is often resource-prohibitive. On the
Conversational and GenAI9 tools, powered by large language
outreach front, public sector organizations face an increasing
models (LLMs), are a cutting-edge branch of technology that
demand for content. And not just for any old content—but
create new, original content or data by learning from existing
they want increasingly dynamic, personalized, evolving
examples — transforming how we generate ideas, solve
content delivered in real time. A survey1 we conducted with
problems, and create across various fields. Adobe Firefly,
650 communications executives showed that the volume
Google’s Gemini, OpenAI’s ChatGPT and DALL-E, and many
of content that organizations need to meet demand has
others can analyze extensive data on text, images, and videos,
increased by 54% on average in the last year, and that
and create new, human-like content that speaks to a unique
organizations are only able to meet content demands 55%
individual’s needs at a superhuman pace. This technology
of the time on average.
synthesizes information about tone, structure, and visuals
from existing content to produce original, audience-specific
materials at scale.
6
OPPORTUNITY 2 Challenge: Every year, the Colorado Department of Human Services (CDHS)
is inundated with thousands of policy-related questions from case workers
IN ACTION
across the State about their Supplemental Nutrition Assistance Program
(SNAP) and Temporary Assistance for Needy Families (TANF) programs. To
Colorado Uses
answer each appropriately, policy analysts are forced to navigate hundreds,
GenAI to Produce if not thousands, of pages of technical, complex policy and process rules.
Personalized Solution: Deloitte collaborated with CDHS to implement the Program Area
Natural Dialogue Assistant (PANDA). PANDA is a Generative AI powered Policy
Answers to Policy
Engine that makes documents and internal knowledge available through an
Questions
AI-search. Rather than combing through documents, policy analysts can now
type their question into PANDA, which identifies relevant policy and provides
reasoning and references for support.
Outcome: PANDA has allowed policy staff to comb through 500+ pages of
content in an instant, and has equipped them with more tailored, personalized
responses to inquiries. In addition, the solution has significantly reduced
research times — on average it only takes between 20-30 seconds to search
all policy documents, formulate a response, and provide the customer with
references and reasoning.
DELOITTE AI CAPABILITY
DELOITTE’S CREATIVEDGE TOOL USES AI TO GENERATE ON-BRAND, AUDIENCE-TAILORED CONTENT
As GPS organizations continue to face increasing demand for Tools like CreativEdge can empower public sector outreach
more creative assets needed faster, in multiple languages, coordinators and communicators with the level of hyper-
and with decreasing budgets, tools that streamline content personalization required for trust building and effective
development are becoming more and more essential. communications with the public.
CreativEdge10 is designed to address these challenges by These types of AI systems foster two-way communication,
generating audience-specific, brand-compliant content which meets the communication preferences of a digitally
with the click of a button. After collecting a simple set of connected audience and helps humanize public sector
inputs from a public sector user — outreach goals, existing organizations, making them more relatable and accessible
audience insights, and an organization’s brand guidelines — to the populations they serve. In addition, through
CreativEdge develops an editable, one-paragraph description innovative content creation, agencies can bolster their public
of your target audience persona. Using this persona, the tool engagement strategies, ensuring relevance and effectiveness
can then generate a variety of tailored content in the form in a rapidly changing digital landscape. Combining these
of social media posts, printable assets, emails, and even generative AI tools with machine learning algorithms can
several creative brand campaign concepts that can serve ensure that personalized content is shared with audiences
as thought-starters for a public sector outreach team. It effectively and timely.
can even translate this content into 19 different languages.
7
AI
Optimizing
OPPORTUNITY
3
Messaging Delivery
UNDERSTANDING AUDIENCES AND they receive it at lunchtime. Some utilize Instagram to
consume short form video content across a variety of
CRAFTING THE PERFECT MESSAGE
platforms and genres.
IS ONE THING — EFFECTIVELY
DISSEMINATING AND ACTIVATING
AI SOLUTIONS CAN HELP PUBLIC SECTOR ORGANIZATIONS
THOSE MESSAGES OVER MULTIPLE
MORE ACCURATELY REACH THE RIGHT AUDIENCE WITH
CHANNELS IS AN ENTIRELY THE RIGHT MESSAGE AT THE RIGHT TIME.
DIFFERENT BEAST.
ML solutions are uniquely suited to address this challenge by
In 2023, a report11 estimated that as much as $20 billion
optimizing real-time outreach across digital media channels.
of global digital advertising spend was wasted by reaching
ML tools can learn and adapt based on performance of
consumers who weren’t their intended audiences. This figure
content in real-time, enabling public sector organizations
represents more than just a monetary loss — it means
to make data-driven decisions more quickly and efficiently.
that a significant amount of effort was put into outreach
These algorithms can process vast amounts of data from
that did not result in a real impact. Given the roles public
previous campaigns, including user engagement, click-
sector organizations play in stewarding taxpayer dollars, it is
through rates, and demographic information. They can then
important that campaigns target the right segments at the
identify which content performs best on which platforms
right time. Achieving this, however, is no small feat.
and at what times, and then automatically distribute those
Audiences engage with content on different channels in
messages or adjust the allocation of outreach resources on
a variety of mediums, and at different times — some are
different channels.
more likely to open an email and engage with content if
8
OPPORTUNITY 3 Challenge: Due to the difficulty in communicating complex policies to widely diverse
populations, constituents are not always aware of the programs for which they are
IN ACTION
eligible. Not only is it difficult to uniquely identify this underserved population, it can
also be difficult to reach them in a way that resonates.
A State Public
Solution: Deloitte is collaborating with a major state public health agency to enhance
Health Agency
the accuracy and effectiveness of educational outreach by utilizing AI capabilities,
Expands SNAP beginning with the Supplemental Nutrition Assistance Program (SNAP). By integrating
existing data on current SNAP enrollees with insights from HealthPrismTM, 12, a
Access Using AI-
proprietary Deloitte asset containing over 1,700 Social Determinants of Health (SDOH)
Powered Outreach
for more than 250 million adults in the United States, Deloitte can identify all adults
across the state who are predicted to be eligible for SNAP benefits but are not currently
enrolled. With this identified population, Deloitte assisted the state in conducting
hyper-targeted outreach and educating potential enrollees about the program and the
enrollment process. This was achieved by using Salesforce Marketing Cloud (SFMC), an
AI-enabled marketing automation platform, to perform direct-to-consumer outreach.
Outcome: By delivering direct communications tailored to each recipient’s
preferences with personalized information about the SNAP program and how to
check eligibility, the agency was able to successfully reach several underserved
populations, many of whom had children, to educate them on the nutrition assistance
program and how to enroll. To date, roughly 30% of the successfully contacted
individuals took action to either check eligibility or apply for the program.
DELOITTE AI CAPABILITY
DELOITTE’S ALLIANCES ALLOW CLIENTS TO MAXIMIZE IMPACT AND CAMPAIGN EFFECTIVENESS
Deloitte’s significant investment in unique AI capabilities adaptive content creation through AI, while powerful
is bolstered by strategic alliances with over 60 of the customer relationship management (CRM) platforms can use
world’s leading companies13, including many of the major AI AI to re-segment audiences based on demographics and past
innovators. This combination of our own in-house business campaign engagement. Deloitte can then connect clients with
acumen and technologies, paired with industry-leading tools search engine and social media platforms who then can use
from organizations like Salesforce, Google, Amazon, Medallia, AI targeting to pinpoint the precise digital channels and times
Adobe, Sprinklr, and many others, positions Deloitte to deliver to reach underserved communities. After deployment of the
tailored solutions for optimizing messaging delivery in all messaging, our strategic allies with AI-powered social media
forms — not just on a single messaging platform or medium. monitoring tools can analyze sentiment in real-time, allowing
the campaign to course-correct messaging based on
For example, Deloitte can help a public health agency working
public response.
to promote flu vaccinations to leverage a variety of messaging
optimization solutions in their end-to-end outreach process. This collaborative model gives public sector organizations an
Deloitte’s alliance with leading graphic design software unprecedented level of precision and agility, maximizing the
companies ensures clients have visually compelling and impact of every message.
9
Addressing the Risks of AI In Public
Sector Outreach and Engagement
While AI solutions may usher in radical efficiencies and deeper connections with the public, they must be adopted responsibly.
Deloitte is prepared to help agencies navigate the key considerations and risks related to successful adoption of AI-assisted
outreach solutions, including:
Data Security. AI applications often handle sensitive citizen data. Understanding where and how AI tools store and
protect data, and regularly auditing for compliance, can mitigate the risks of data breach or misuse.
Bias and Fairness. AI may inadvertently perpetuate existing discrimination. Identification and correction of biases,
employment of diverse datasets in model training, and continuous algorithm monitoring can promote inclusive
outcomes.
Transparency and Clarity. Advanced technologies often lead to questions about accountability. Choosing AI tools
that offer user-friendly explanations and clearly communicating when and how AI in used communications can build
trust and understanding with stakeholders.
Regulatory Compliance. Use of AI in government communications must conform to policy and regulations. It is
critical that AI deployment is sufficiently informed by legal and compliance expertise, particularly as the law evolves
with novel technologies.
Oversight. Reliance on AI could lead to reduced human oversight. A balanced approach, where AI augments human
expertise, insight, and creativity, is key to developing effective communications that get the best out of both the
workforce and the technology that aids it.
Deloitte’s Trustworthy AI™, 14 framework aims to help public sector organizations address these risks by using ethical safeguards
across seven dimensions related to fairness, transparency, accountability, robustness, privacy, safety, and security. The framework
analyzes these dimensions throughout an AI system’s lifecycle, from the initial design and training to the deployment and ongoing
monitoring, to ensure that bias is minimized across stages.
By acknowledging and addressing these risks, government and public sector organizations can leverage the
transformative potential of AI in a responsible and effective manner, ensuring their outreach and engagement
efforts are citizen-centric and aligned with ethical standards.
10
IN AN INFORMATION -AND By embracing AI carefully and responsibly, GPS organizations
aren’t just adopting new technology; they can lead the way on
CHANNEL- SATURATED
more personalized, insightful, and proactive communications.
ENVIRONMENT, THE CHALLENGE IS
Beyond increasing efficiency, leadership on AI demonstrates
NO LONGER JUST ABOUT GETTING dedication to better understanding and more effectively
serving the needs of citizens. Furthermore, the transformative
A MESSAGE OUT. ENSURING
potential of AI solutions can unlock communications barriers
MESSAGES REACH, INFORM,
and streamline public engagement.
RESONATE, AND INSPIRE IS
It’s clear that AI isn’t just a tool: it’s a formidable ally, capable
CRITICAL TO THE RELATIONSHIP of amplifying and augmenting the impact, reach, and depth of
BETWEEN GOVERNMENT AND THE how we communicate. Embracing this ally means embracing a
future where public sector organizations don’t just speak—they
CONSTITUENTS THEY SERVE.
listen, understand, and respond—with precision and empathy.
ENDNOTES
1) Gen AI powers content marketing advantage for early adopters
2) Deloitte Government Marketing Services
3) U.S. Older Population Grew From 2010 to 2020 at Fastest Rate Since 1880 to 1890
4) Nearly 68 Million People Spoke a Language Other Than English at Home in 2019
5) Deloitte Higher Education Client Success Story — Michigan State University
6) Candidate360™ Higher Education Enrollment Solutions
7) Qualtricsxm | Artificial Intelligence (AI) For Experience Management
8) 2024 Edelman Trust Barometer
9) Designing for the Public Sector with Generative AI | Deloitte US
10) CreativEdge™: A GenAI digital marketing campaign engine
11) ANA Provides “First Look” at In-depth Programmatic Media Transparency Study
12) Deloitte HealthPrism™
13) Deloitte’s Ecosystems & Alliances relationships: We’re Better Together
14) Trustworthy AI™ Bridging the ethics gap surrounding AI
11
GET IN TOUCH
RJ Krawiec
Principal
Deloitte Consulting LLP
rkrawiec@deloitte.com
Eric Uhlir
Studio Senior Manager
Deloitte Consulting LLP
euhlir@deloitte.com
Evan Tunink
Senior Manager
Deloitte Consulting LLP
etunink@deloitte.com
Peyton Marion
Senior Consultant
Deloitte Consulting LLP
pemarion@deloitte.com
As used in this document, “Deloitte” means
Deloitte Consulting LLP, a subsidiary of
Deloitte LLP. Please see www.deloitte.com/
us/about for a detailed description of our
legal structure. Certain services may not be
available to attest clients under the rules and
regulations of public accounting.
This publication contains general information
only and Deloitte is not, by means of
this publication, rendering accounting,
business, financial, investment, legal, tax,
or other professional advice or services.
This publication is not a substitute for such
professional advice or services, nor should it
be used as a basis for any decision or action
that may affect your business. Before making
any decision or taking any action that may
affect your business, you should consult a
qualified professional advisor. Deloitte shall
not be responsible for any loss sustained by
any person who relies on this publication.
Copyright © 2025 Deloitte Development LLC.
All rights reserved.
12"
160,deloitte,deloitte_ai-adoption-africa-2024.pdf,"Source: openai
AI for Inclusive Development in Africa –
Part I: Governance
A I for Inclusive Development in Africa | Governance
Introduction
AI in Africa
2400 AI Companies in Africa,
40% founded since 2017
In South Africa, scientists at the University of
Johannesburg used AI to forecast the peak periods of
COVID-19, helping develop more effective policy
measures.1 In Ghana, StarShea used AI to connect
women farmers globally, increasing their earnings by
50% within six months.2 In Kenya, a groundbreaking
startup, SohpieBot,3 employs AI-driven chatbots to
handle inquiries related to sexual and reproductive
health. These are just some of the many AI use cases
illustrating the potential of AI adoption on the continent
to address critical social, health, and economic
challenges.
Across the continent, public and private sector interest in AI has been growing rapidly,
spurred in part by the capabilities of large language models like ChatGPT. Africa currently
counts over 2,400 AI companies,4 out of which 40% were founded in the last five years.
For African nations to sustain and amplify this growth trajectory, governments and the
private sector must prioritize AI in their investments. This is essential not only for driving
Source: unsplash
economic expansion, but also for accelerating Africa’s progress towards the Sustainable
Development Goals (SDGs), especially considering the recently adopted UN resolution on
AI Governance that aims to promote safe, secure, and trustworthy AI systems.
UN Resolution on AI Governance
AI adoption in Africa does not come without costs or risk. Generative AI and large
language models ingest vast amounts of data, generating concerns around privacy, data
March 21st, 2024: The UN General
security and copyright infringement. Predictive AI models could upend traditional
Assembly adopted a landmark resolution
decision-making and raises ethical questions around biased and inaccurate data.
on the promotion of “safe, secure and
Proponents of AI development in Africa face nascent AI regulations, a large data deficit,
trustworthy” artificial intelligence (AI)
and high capital and operating expenses.
systems that will also benefit sustainable
development for all.
To create strong enabling environments across Africa that can realize AI’s immense
potential, while minimizing risks, we believe that public and private sector decision-
makers should focus on bolstering four pivotal enabling areas: 1) Governance, 2) Data
and Digital Infrastructure, 3) Talent, and 4) Funding.
Today, many African nations lack national strategies, institutions, and regulatory
frameworks that address AI technologies. This governance vacuum creates uncertainty –
stifling investment and hindering innovation in the AI sphere. In this paper we explore
these dimensions of AI governance, including challenges and opportunities in each.
Subsequent papers in this series will focus on the other three enabling areas.
1
AI for Inclusive Development in Africa | Governance
Governance
Trust is pivotal for the successful adoption and cultural
Status of National AI Strategy and
Planning
acceptance of AI in Africa.
•
Countries that have adopted an
It acts as the foundation for the sustainable and beneficial integration of AI into society. AI National Strategy
However, as AI solutions become more widespread on the African continent, a multitude
•
of risks arise that could undermine that trust, such as personal data misuse, inaccuracies Countries in the process of
in outputs of AI models, and systemic biases amplified by AI. To deliver on AI’s potential adopting an AI National Strategy
socioeconomic benefits, uphold human rights, and align with values such as fairness,
•
accountability, equity, transparency, inclusion, and responsible technology use, African
Countries with no national AI
governments and private sector actors must establish a strong governance foundation,
strategy but integrating AI into
including strategic direction, implementing mechanisms, and regulatory and ethical
digital strategy or legal framework.
frameworks for AI adoption.
•
Countries with limited or no
consideration of AI
AI Strategy and Planning
National and regional strategies can accelerate and sustain the adoption of AI, providing
roadmaps to guide its development, implementation and use in a way that respects
societal values and norms and contributes to inclusive growth. Both the United States5
and European Union6 have released AI strategies that set visions, policies, priorities, and
action plans for enabling AI development and commercial uptake and “ensuring that AI
works for the people.” These could be potential models for the African Union and
national governments on the continent.
In 2023, the African Union convened AI experts to draft the African Union Artificial
Intelligence (AU-AI) Continental Strategy for Africa, set to be released in 2024.7 Yet, most
countries in Sub-Saharan Africa have yet to develop national AI strategies or policy plans.
The lack of clear direction hinders collaboration and integration of AI across economic
sectors, impeding coordination, prioritization, and resource allocation of AI efforts within
each country and across the continent.8
Efforts to establish AI strategies in Africa vary, falling into the following categories: 1)
early AI adopters—countries with national AI plans or strategies adopted; 2) countries
with plans or strategies in development; 3) AI integrators – countries incorporating AI
governance into a comprehensive digital strategy or within an existing framework; and 4)
Sources:
non-adopters of AI –countries that have no mention of AI in plans, policies, or strategies
- AI governmental initiatives | Digital Watch Observatory
at the national level. In the first two categories, Egypt, Rwanda, Ghana, Senegal, Tunisia, - National AI Policy Summary II Nw (ictworks.org)
- Le Président sénégalais annonce la finalisation d'une
and Nigeria stand out as the early adopters and have either created AI national strategies
stratégie nationale sur l'IA - AITN (afriqueitnews.com)
or are currently in the process of doing so. - strategie-nationale-d'intelligence-artificielle-et-des-
megadonnees-2023-2027.pdf (gouv.bj)
- AI Readiness Index - Oxford Insights
2
AI for Inclusive Development in Africa | Governance
In 2021, Egypt launched its National AI Strategy and established a National Council
Egypt’s AI Journey
for Artificial Intelligence. This comprehensive strategy serves as a guiding
framework for the responsible and strategic adoption of AI technologies across
various sectors within Egypt. Egypt’s vision includes positioning itself as a thriving 7.7% Increase in Egypt’s GDP is
hub for innovation, drawing in investments, and effectively addressing critical expected from its new AI
societal challenges to stimulate economic growth. The implementation of this National Strategy
strategy is projected to yield a direct impact of $42.7 billion USD by 2030,
equivalent to 7.7% of the nation's GDP.
In the second category, AI integrators, lies South Africa, which lacks a national AI
strategy but has established a Presidential Commission on the Fourth Industrial
Revolution (4IR)9 to develop a strategic plan for the country's 4IR vision, to become a
leader in emerging technologies, notably, Artificial Intelligence, quantum computing,
and smart manufacturing. Similarly, Kenya, which also currently lacks a national AI
strategy, relies on existing laws related to AI and digital technologies as a regulatory
framework.10
Countries in the last group show limited or no consideration of AI. This is often due to
policymakers not perceiving it as a priority and questioning their capability to pursue it.
These countries may be prioritizing other underlying challenges to AI adoption, such as
digital divides, talent gaps, and limited data and digital infrastructure, all of which can
make AI seem like a distant goal.
Other reasons for the slow progress in adopting AI strategies and necessary governing
mechanisms are the limited knowledge and understanding of AI's potential and its
societal implications among the policymakers. This lack of awareness can impede
effective decision-making and the formulation of suitable policies and regulations. As a
result, it can lead to a slower pace of AI adoption and missed opportunities for using AI to
tackle social, economic, and developmental challenges.
Source : freepik
Opportunities for Consideration
African governments must craft national AI strategies to serve as a foundation for
o
effective AI governance. Public officials should consider engaging AI leaders, industry
experts, corporate and international partners to create robust AI strategies that define
objectives and encourage cross-sector collaboration, with clear, measurable roadmaps
for implementation. Strategies and roadmaps should ideally be developed within the
context and aligned to a comprehensive economic growth strategy, guiding long-term
objectives, and maximizing potential. These guiding documents should go beyond
articulating a vision; they should include analysis and decisions to set priorities, optimize
resource allocation, and identify implementation needs. National strategies should align
with and support implementation of the African Union’s forthcoming continental AI
strategy.11
3
AI for Inclusive Development in Africa | Governance
In a 2019 Deloitte article on AI strategy for government leaders, authors
explained that “strategy isn’t just a declaration of intent, but ultimately should
involve a set of choices that articulate where and how AI will be used to create
value, and the resources, governance, and controls needed to do so.” Based on
this premise, Deloitte developed an AI version of its classic strategic choice
cascade framework to reflect the questions and considerations required for AI
adoption (see figure below). To be effective a government AI strategy should
cover five core components – a vision, prioritized focus, a clear definition of
success, capabilities needed, and supporting management systems. This
framework can be applied at the national level, to use AI for improved
government performance or to advance an economic growth agenda, or from a
sectoral lens, for the AI strategy of a specific ministry or public agency.
Source: freepik
Donor partners can provide tailored training and capacity strengthening for
o
policymakers to bridge the knowledge gap. Enhancing domestic AI policymaking
capabilities will empower African countries to autonomously shape AI policies that align
with their distinctive requirements and ambitions. Donors can build on existing models
such as the FAIR Forward program, which initiated peer-learning activities to enhance the
capacity of policymakers from Africa and Asia to respond to the benefits and challenges
of AI. The program was implemented by the Human Sciences Research Council (HSRC)
from South Africa, working with researchers and policy experts from Ghana, Kenya,
Rwanda, South Africa, Uganda, among other countries.12
4
AI for Inclusive Development in Africa | Governance
Public-Private Coordination & Implementation Mechanisms Institutions with public-private
coordination mandate for AI adoption:
There is often a lag and lack of institutional coordination between national AI public
stakeholders and private sector players and initiatives, limiting the potential of such Egypt: Established a National Council for
strategies to be effective in dirving economic growth and social development. To fully Artificial Intelligence to implement the
harness AI's benefits, effective institutional frameworks for AI policy and strategy national AI strategy, which serves as a
implementation and collaboration between the public and private sectors, are essential. guiding framework for the responsible and
strategic adoption of AI technologies
Nigeria, Rwanda, and several other countries are taking a proactive approach on this across various sectors.
front. To promote research and development in emerging technologies, the Nigerian
government established the National Centre for AI and Robotics (NCAIR).13 This Nigeria: The Nigerian government
collaborative endeavor engages government agencies, businesses, and academic established the National Centre for AI and
institutions in a shared mission, to create an African Hub for AI and Robotics. Robotics (NCAIR) to engage government
Furthermore, in May 2023, the Federal Government of Nigeria made a resolute agencies, businesses, and academic
commitment to generate one million jobs in the digital economy by providing accessible institutions to create an African Hub for AI
courses for professionals, in Artificial Intelligence, Cloud computing, Game Programer, E- and Robotics.
commerce, Digital Marketing, etc.14
Rwanda: The AI Hub developed a training
Similarly, Rwanda launched the AI Hub initiative flagship program to focus on building program in collaboration with the Rwanda
vibrant AI ecosystems to support startup researchers and entrepreneurs. The AI Hub Space Agency (RSA), German Aerospace
developed a training program for young professionals in collaboration with the Rwanda Agency (DLR), and the private sector in
Space Agency (RSA), German Aerospace Agency (DLR), and the private sector in targeting targeting machine learning programs for
machine learning programs for earth observation. The program’s goal is to make use of earth observation.
AI and machine learning to leverage geospatial data in the sustainable development of
the country.15 In addition, the Rwanda Information Society Authority (RISA) and the AI
Hub established a natural language processing fellowship with the aim of bolstering the
state’s technical skillset among the local population.
Egypt has also created an AI council to oversee its national AI strategy, and Kenya has
entrusted its national innovation agency16 with developing and implementing IT-related
policies, including those pertaining to AI and data. Meanwhile, Senegal is the only African
country represented in the Global Partnership of Artificial Intelligence (GAPI), a standards
body convening experts from the public and private sectors to develop governance
models and shepherd global innovation.17 These initiatives illustrate the vital role that
public-private cooperation plays in advancing AI-driven economic growth.
Source: freepik
Opportunities for Consideration
Government strategies should empower new or existing institutions or coordination
o
mechanisms to drive implementation. These institutions, whether national AI agencies
or commissions, can carry the mandate of facilitating dialogue, co-creating policies, and
ensuring coherent AI ecosystems. African countries can actively learn from each other
and other contexts to tailor governance mechanisms to their specific needs. In the US, for
example, the government established the National Artificial Intelligence Research
Resource Task Force (NAIRR) to help drive implementation and facilitate dialogue among
stakeholders of AI adoption.18 The aim of the Task Force was to stand up the national
infrastructure for AI development and research. The task force brings together experts
from academia, industry, and government to provide insights and drive implementation.
African governments should actively participate in global AI standards bodies. Global
o
standards bodies are developing and proliferating governance guidance for AI research
5
AI for Inclusive Development in Africa | Governance
and activities—influencing AI product and service delivery models. African governments
should be an active voice at the table, allocating funding and time to these efforts to
influence global decision-making and advancing their own economic and national
security needs. This includes applying for membership, leadership roles in policy
committees and funding African experts at technical committees at these foras for
deeper global engagement.
o Implementation institutions should have a mandate to foster public-private Without the right processes and
partnership. Establishing collaborative governance structures that bridge the gap safeguards in place, the adoption of AI
between the public and private sectors is essential. We’re seeing this in other areas of can exacerbate existing digital divides,
opportunity for digital enablement. For example, in Senegal the National Meteorological including between expat and local
Agency was granted the mandate and liberty to establish PPPs as part of a weather and populations, men and women, urban and
climate data value chain from government to end users. Public agencies charged with rural residents, and along formal
implementing AI strategies should be similarly encouraged and equipped to partner with education level and income lines.
the private sector to strengthen the entire ecosystem.
Regulatory and Ethical Framework
As AI relies heavily on data, including personal and sensitive information, certain laws
and regulations related to data collection, transfer, protection, and cybersecurity have
significant impact on the ethical and safe adoption of AI. Much of the data employed for
AI tools and solutions comes from Global North users. In general, citizens in the Global
North typically have more advanced digital infrastructure. This easy access to digital
technologies leads to greater use of these technologies by Global North users, which in
turn creates greater representation of Global North users in the data within such
technologies. As a result, datasets may reflect the demographics, preferences and
behaviors of population in the Global North, potentially leading to biases when AI
systems are applied at the global level. AI has the potential to further exacerbate the
digital divide between the digitally underservedand the highly digitalized countries and
pose risk for disadvantaged or marginalized groups because these groups may not be
represented in AI training data.19
Similarly, across the African continent and within each country in the region, insufficient
infrastructure and other factors limit access and use of digital tools and technology for
certain groups more than others. Without the right processes and safeguards in place,
the adoption of AI can exacerbate existing digital divides, including between expat and
local populations, men and women, urban and rural residents, and along formal
education level and income lines.
Furthermore, AI as a tool can be co-opted for pernicious uses, such as for promulgating Source: freepik
mis-, dis- , or mal-information, which can have devastating effects on social and political
stability. For example, mis-,dis-, and mal-information have been weaponized to increase
instability in the Sahel region where low literacy rates, existing political tension, and and
uptick in social media use have combined to make an already complex situation even
more challenging.20
Additional challenges and gaps related to AI and data protection, data transfer, and
cybersecurity are significant. According to UNCTAD, most African countries have already
adopted a data protection law21 or are in the process of drafting legislation. However,
due to the rapid evolution of technology, regular updates are necessary. For example,
Tunisia enacted a personal data protection law in 2004. Despite being advanced, this law
6
AI for Inclusive Development in Africa | Governance
faces challenges, including the General Data Protection Regulation for the European
Union22 (GDRP) compliance, effective enforcement, raising awareness among businesses
and the public, and adapting to rapid technological changes.
Protection of inventions also stands as a crucial regulatory challenge for AI providers.
Indeed, AI's unique intellectual property challenges, particularly in algorithm protection
and AI-generated content, are also emerging concerns. Today, in the majority of
countries, AI software is protected only by copyright covering the source code. However,
this protection can be limited as it does not extend to the underlying ideas, methods, or
algorithmic functionalities.
One example of how this governance gap can manifest is in the level of caution used
by AI startups in their data collection. Several interviewed startups admitted that they
have used data from surveillance cameras to create their database and train their
models without the required authorizations from the national data protection
authorities due to a lack clarity and guidance on regulatory requirements. This reveals
a critical need for better regulatory understanding and support systems to ensure that
AI development aligns with legal and ethical standards.
Addressing these risks is essential to creating a safe, secure, inclusive, and transparent
enabling environment for ethical AI adoption and innovation in Africa. In some cases,
rapid adoption of a strict regulatory framework around AI might hamper innovation. For
example, in 2023 tech industry leaders in Kenya raised concerns about the government's
proposed bill for the Kenya Robotics and Artificial Intelligence Society, arguing that such
legislation could potentially stifle innovation, especially considering that the AI sector in
Kenya is still in its nascent stages.23 Conversely, policymakers recognize the importance
Source: freepik
of safeguarding consumers' interests by providing a legal framework for the
establishment and operation of AI technologies.24 This situation highlights the critical The European Union approved the AI Act:
need for a regulatory approach that safeguards users while also fostering technological The AI Act, approved on March 13th, 2024,
advancement and innovation. Recently the European Union approved an AI Act that “aims to protect fundamental rights,
establishes a foundation of safeguareds.25 democracy, the rule of law and
environmental sustainability from high-risk
Opportunities for Consideration AI, while boosting innovation and
establishing Europe as a leader in the field.
Prioritize the establishment of data protection, data transfer, cybersecurity and The regulation establishes obligations for
o
intellectual property regulations that are robust and aligned with international AI based on its potential risks and level of
standards. Adapting these laws is crucial to address the intersecting risks posed by AI, impact.”
such as algorithm protection, ownership and usage rights of AI-generated content and
the use of personal data with the appropriate protection. Data must not only be secured
properly, but ownership rights must also be clearly defined, recognizing both individual
and collective rights. Individuals should be allowed to opt-out or remove access of having
their data used as part of an AI solution. On ownership, resolving conflicts between data
sovereignty and accessibility can be challenging, especially regarding national
governments' control over data, but ethical use would ultimately place power into the
hands of those having their data collected. Reconciling data accessibility with privacy
protection is crucial. While AI innovation relies on diverse datasets, ensuring privacy
rights through measures like anonymization, masking, encryption, or differential privacy
is essential. Striking a balance between accessibility and privacy requires collaboration
among stakeholders and the development of clear ethical guidelines, security controls,
and regulatory frameworks. This not only boosts consumer confidence in African
organizations but also enhances their competitiveness on the international stage.
7
AI for Inclusive Development in Africa | Governance
Conversations about a unified regulatory framework should also be considered for
African nations. Just like the European Union, which last February approved the AI Act to
ensure that AI systems are safe, transparent, and accountable, fostering trust in this
rapidly evolving technology.
Equip local public actors with the knowledge to prevent and redress harm that results
o
from AI implemented with a global but not local perspective in mind. Engaging local
actors within the ecosystems strengthens capacity both to make ecosystem-informed
strategic investments and to develop effective safeguards for AI technology and data.
Local actors may include host country governments, technology companies, digital rights
activists, civil society organizations, local financial institutions, academic institutions, and
regulatory bodies. Local actors impacted by AI should be engaged throughout the design
and implementation processes. This means equipping local actors with the knowledge,
skills, and tools that allow them to analyze and understand when and how the use of AI
Source: freepik
tools might result in unfair or unjust outcomes.
Trustworthy AI™ framework promotes
Improve inclusivity and stakeholder representation in AI design, deployment, the ethical use of AI within organizations.
o
governance, or policymaking, especially for underrepresented or marginalized groups. Trustworthy AI™ requires governance and
Inclusivity and representation of African countries, contexts, and citizens is imperative to regulatory compliance throughout the AI
mitigating potential AI risks and harms on a global level. Within the African context, lifecycle from ideation to design,
inclusion of marginalized groups or those with limited access, such as persons with development, deployment, and machine
disabilities, rural residents, women, and girls,26 can better position AI technologies to learning operations anchored on the
address equity issues rather than exacerbating them. Designing AI using principles of seven dimensions in Deloitte's
gender equity and social inclusion can reduce AI bias27 in recruitment tools that may Trustworthy AI™ framework—transparent
reflect discriminatory hiring practices; in financing tools that determine discriminatory and explainable, fair and impartial, robust
credit scores or loan approvals; or with health tools that influence diagnosis and and reliable, respectful of privacy, safe
subsequent treatment. Knowledge sharing on AI risks and harms with the public enables and secure, and responsible and
active collaboration, learning, and idea sharing to shape an ecosystem where the public accountable.
and those traditionally excluded in other forums can engage on AI-related issues. The
collective perspective creates opportunities for surfacing problems and identifying
current and future risks of AI. This in turn allows for AI use that is more equitable,
inclusive, and rights-respecting; accounts for, and mitigates, potential harms; and is
reflective of a more global reality.
Adopt a human-centered, responsible, and ethical approach to AI. Including
o
considerations such as: 1) participation and inclusion, to engage and empower diverse
and affected stakeholder in the design, development, and governance of AI, so that their
needs, preferences, and values are respected and reflected; 2) accountability and
transparency to establish clear and enforceable rules and standards for the behavior and
performance of AI systems, as well as mechanisms for monitoring, auditing and
redressing any harms or errors; and 3) fairness and justice to enable AI systems to be fair,
equitable, and nondiscriminatory, so they are able to promote the social good and
human rights of all people, especially those who are marginalized, oppressed, and
perhaps lack access to the technology. This is an area of potential support from technical
partners. Tools such as Deloitte’s Trustworthy AI™ framework described below can be
useful in defining parameters and standards across the various dimensions that need to
Source: Deloitte
be considered.
8
AI for Inclusive Development in Africa | Governance
Conclusion
Recognizing the diverse landscape of Africa, each
country presents a unique context for AI adoption, with
varying levels of maturity across the continent.
However, across the board, it is imperative for African governments and businesses to
prioritize developing robust governance and risk mitigation frameworks, including
strategy, institutional implementation capabilities, public-private coordination, and
regulatory and ethical standards, to reap the potential of the technology. Read more
about other critical AI enabling areas in the region as we continue this series on AI
adoption, covering talent, data and digital infrastructure, and funding.
Source: Shutterstock
9
AI for Inclusive Development in Africa | Governance
About the Authors
Courtney Keene is a Senior Manager at Deloitte who brings 15 years of experience working as a trusted strategic advisor on
programming to strengthen local systems and promote inclusive growth. With a focus on the African continent, she has
advised and led programs covering 25 African countries on agriculture, energy, governance, health, and water and
sanitation. Email: ckeene@deloitte.com
Aïcha Mezghani is a Senior Manager at Deloitte Afrique in the International Donor practice, leading several projects on
strengthening entrepreneurial ecosystems and conducting the design and implementation of public policies, promoting
innovation and ICT sectors. Email: amezghani@deloitte.tn
Maryam Kyari is a Senior Consultant at Deloitte focused on conducting economic impact studies and devising corporate
social responsibility strategies for clients across energy, real estate, disaster recovery, and education. She aligns the
objectives of clients with regulatory guidelines and standard practices and develops governance structures to manage
investments and grants. Email: mkyari@deloitte.com
Ibrahim Almatri is a Senior Consultant in Deloitte’s Risk and Financial Advisory practice working with state and local entities
in the development and execution of risk management strategies and crisis recovery efforts. Email: ialmatri@deloitte.com
Get in Touch
Kathleen O’Dell is a principal with Deloitte's Government & Public Services team and leads Deloitte’s International
Development practice, which includes Deloitte’s work with the U.S. Agency for International Development, Millennium
Challenge Corporation, U.S. Export-Import Bank, and United Nations, among others. Email: kodell@deloitte.com
Adarsh Desai is a Principal in Strategy and Analytics practice within Deloitte’s Government and Public Services (GPS) group.
Adarsh leads strategy and implementation of Digital, AI, Generative AI, and Data Analytics solutions for International
Development Organizations and US Federal Government Agencies. Email: adadesai@deloitte.com
Mohamed Malouche is a Deloitte Advisory partner and business leader in the International Donors practice, leading work
on public sector modernization, local economic development, energy, entrepreneurship, and innovation across French-
speaking Africa. Email: momalouche@deloitte.tn
Carlton Jones is a Deloitte Consulting Leader in Tanzania, the Strategy Leader for East Africa, and the African Lead Client
Service Partner, responsible for the USAID work in economic growth, industry and cluster competitiveness, trade and
investment, and value chain development. Email: cjones@deloitte.co.tz
Acknowledgements
The authors wish to acknowledge Elizabeth Villarroel, Ramzi Maatoug, Francesca Cavalli, Helena Buckman, Aymen Mtimet,
John Millock, Kwame Antwi, Mohamed Baccar Fayache, and Rali Sloan for their extensive contributions to the development
of this report. We would also like to thank our colleagues Carlton Jones, Kathleen O'Dell, Mohamed Malouche, Shrupti
Shah, Mohamed Sylla, Wessel Oosthuizen and Mulaudzi Rudzani for their insights and guidance.
Finally, this report would not have been possible without the time and invaluable insights shared by startups and SMEs,
entrepreneurs, donors, experts, and support organizations. The authors extend their sincere thanks to Aya ElGebeely
(Talents Arena), Ali Mnif (Digital Africa), Celina Lee (Zindi Africa), Daniel Djaha (Orange), Jethro Datamwin Apeawini (Classic
Data Lab), Olivier Gakwaya (Smart Africa), Sinda Ben Salem (Instadeep), Richard Nii Lante Lawson, Moez Ben Hajhmida
(Fairconnect.ai), Nicolas David (AWS), and Wayan Vota (USAID).
10
AI for Inclusive Development in Africa | Governance
1 “AI Is Here to Stay! How Artificial Intelligence Can Contribute to Economic Growth in Africa”. UNU.
June 23, 2023. Link
2 “AI for Africa’s Socio-Economic Development”. African Union Development Agency AUDA-NEPAD.
Link
3 “SOPHIE BOT Artificial Intelligence”. Link
4 “AI Media Group: 2022 State of AI in Africa report”. Link
5 “Fact Sheet: Biden-⁠Harris Administration Takes New Steps to Advance Responsible Artificial
Intelligence Research, Development, and Deployment”. The White House. Link
6 “European approach to artificial intelligence”. EU. March 6, 2024. Link
7 “Artificial Intelligence is at the core of discuss"
161,deloitte,deloitte-cn-fsi-ai-in-banking-en-240805.pdf,"Changing the game:
the impact of artificial
intelligence on the banking
and capital markets sector
Contents
Overview: where are banks in the AI journey? 01
What impact can AI have on the bottom line and how? 04
How will the sector landscape change and who will be successful? 10
What is special about generative AI and where is this technology heading? 14
How will banks embed AI across the value chain? 17
What risks must be managed and how? 23
What are the key considerations for safe and effective execution? 26
How to get started, scale and drive adoption 28
Contacts 31
Endnotes 33
Changing the game: the impact of artificial intelligence on the banking and capital markets sector
1. Where are banks
in the AI journey?
01
Changing the game: the impact of artificial intelligence on the banking and capital markets sector
1. Where are banks in the AI journey?
Artificial intelligence Artificial Intelligence (AI) is already here and Considering the sector outlook more
shaping the wider world banks operate in. generally, the coming years continue to
will likely determine the
In automotive, Tesla and others delivered include macroeconomic and geopolitical
AI technology for sophisticated driver-assist uncertainty. Any number of unforeseen
banking and capital
functions, with an eventual end goal of events may emerge from an already cloudy
markets sector’s autonomous vehicles operating on public crystal ball. However, in a five-year timeline,
roads.1 The life sciences industry has been we in Deloitte Global Financial Services
winners and losers
realizing value from AI for drug research see AI as the single biggest controllable
and new molecule discovery, as it can draw opportunity for players to improve their
in the coming five
insights from massive data sets faster, competitiveness.
years. The journey process data and automate workflows
more efficiently, and convert insights into AI now allows banks to tackle challenges of
has already started.
actions to improve business performance scale in a way that, previously, would have
– from molecule to market.2 In public required many extra staff. If a particular
safety and security, for example in the function in a bank could be done better or
United Kingdom, London’s Metropolitan faster by adding one hundred extra trained
Police has trialled live facial recognition staff, it’s likely that AI can be transformative
(LFR)3 cameras in specific areas, to for that function. AI offers vast additional
accelerate identification of individuals the operational capacity, at low marginal
police are looking for. Regulating for the cost compared to hiring the equivalent
evolving use of AI is an ongoing challenge processing capacity as staff.
to lawmakers, for example the European
Union’s AI Act is intended to protect health, But more than that, the game in which
safety, fundamental rights, democracy players are competing will likely change.
and the rule of law, and the environment AI is on the threshold of a paradigm
from potential harmful effects – while shift. Through the work we do with
supporting innovation, particularly banks around the world we see leading
among European SMEs (small and innovators already making the step from
medium enterprises).4 AI as an ‘instrument of strategy’ (i.e.,
accelerating delivery of today’s business
Within this evolving societal context, AI is plan) to a ‘determinant of strategy’, where
not new to the banking and capital markets tomorrow’s business is planned around
(B&CM) sector. It has been in production new AI capabilities. JP Morgan Chase, which
for years in specific functions, including topped Evident Insights AI Index (which
algorithmic trading and trade surveillance. benchmarks how ready banks are for the
But the arrival of Generative AI (GenAI) incoming wave of transformation that
marks a new era, exploding the number of AI will bring) for a second year5, sees the
potential use cases and putting benefits in transformational impact that AI can have
the hands of the workforce. and plans to spend $1 billion or more
a year on AI capabilities.6
AI now allows banks to tackle
challenges of scale in a way that,
previously, would have required
many extra staff.
02
Changing the game: the impact of artificial intelligence on the banking and capital markets sector
An important point is that we do not see
FS sector Industry examples of AI-enhanced capabilities
AI displacing humans from the workforce
at large scale. Rather that AI augments
Retail banking NatWest reduced fraud by 6% as a share of UK
the workforce and drastically scales up
Industry (19% to 13%), including a 90% reduction in
processing capacity and quality. The role of
account opening fraud since 2019 which all contributed
the human workforce will naturally shift to a
to reducing operational costs. On the income side they
higher level, with a greater focus on design,
achieved a 5x increase in click-through for personalized
oversight and exceptions management,
lending on customized customer offers. 7
as well as having more bandwidth for the
relationship-based, customer-facing roles
Reduced credit card delinquency by 32% (brighterion
where human emotional intelligence is vital.
by Mastercard).8
Across financial services (FS) sectors,
Corporate and UK banks have been fully automating the loans
we are seeing the green shoots of AI value
transaction banking underwriting process up to US$100,000 (we have
being realized. Bloomberg was among the
seen up to US$250K).9
first to announce training their own model,
with BloombergGPT providing a means for
JPMorgan Chase developed a GenAI model to analyze
users to query and interact with complex
statements from the U.S. Federal Reserve to determine
financial data using natural language.
the nature of policy signals.10
Citigroup uses GenAI to assess the impact of new US
capital rules.11
Goldman Sachs is working on various projects which will
incorporate GenAI into its business practices. Among
An important the most mature of the projects include writing code in
English-language commands, and being able to generate
documentation.12
point is that
Morgan Stanley is using machine learning to identify
we do not see personalized investment ideas and suggest the “Next
Best Action”.13
AI displacing
Investment banking (IB) Projected 27% productivity increase across investment
and capital markets banks and 27%–35% front office employee
humans from productivity by 2026.14
Insurance Underwriting teams at a specialized insurer experienced
the workforce
a 113% productivity increase using generative
AI-supported workflows for underwriting submissions
at large scale. relating to bespoke policies.15
03
Changing the game: the impact of artificial intelligence on the banking and capital markets sector
2. What impact can AI
have on the bottom
line and how?
04
Changing the game: the impact of artificial intelligence on the banking and capital markets sector
2. What impact can AI have on
the bottom line and how?
Successful innovators The recent B&CM industry hype around AI will likely now act as the conduit that
AI could appear as the latest fad – another accelerates business impact and magnifies
can achieve a 5-15%
topic attracting much discussion but not value realization. We look at this more
ultimately leading to sustained operating closely later in this paper.
improvement in cost-
margin uplift. Most banks have invested
income ratio over the in strategic innovations in recent years as Ultimately, the significance of AI to the
cloud, data and digitization technologies sector will be assessed on the extent
next five years.
have advanced. Not all banks have yet that this innovation delivers sustained
achieved material improvement to their operating margin uplift. Here we consider
bottom line from these investments, the “size of the prize” given a typical cost-
particularly where they bolted new systems to-income ratio profile today and expected
and capabilities on to existing technology AI benefit themes.16 We see potential for a
estates, introducing additional cost and 5-7% positive contribution in 2-3 years, and
complexity without decommissioning 10-15% in 5-7 years. This view considers
legacy components. a wide range of banks, and smaller, more
nimble organizations including those with
However, the banks that have learned how currently high cost-income-ratios (CIRs)
to deliver innovation in their organization would find greater opportunity to achieve
will continue to outperform with AI, i.e., the higher end of this 5-15% range of
“the winners will keep on winning”. improvement.17
Successful cloud, data, analytics and
digitization initiatives have provided the
foundational capabilities for AI.
Figure 1. Cost reduction examples
Cost efficiency – examples Typical mid-sized universal bank Income growth – examples
Income = 100
Next generation market analysis/
Trading activities 10 predictive trading algorithms
5–7% uplift on trading income
Fees and
Improved customer retention
commissions 15
1–2% uplift fees/commissions
Workforce acceleration efficiencies
(more from less) across the board Cost = 60
Improved Customer
0–15% total staff cost
Staff Acquisition through
25 hyper-personalised marketing
IT development and
5-10% uplift Interest income and
maintenance acceleration
Technology staff fees/commissions
10–20% of IT staff cost 5–10
Improved credit-risk assessment Credit loss charges 5 Interest income (Net) Tailored loan pricing based
75
leading to fewer impairments Premises and on credit risk assessment
10–-15% saving in impairment charges equipment 10 2–3% increase on net
interest income
Other administrative/
Improved FinCrime/fraud
accounting
detection reduces litigation/redress
20
charges and fraud losses
Source: © 2024 Deloitte research. For information, contact Deloitte Global.
Note: this is an indicative ‘sizing’ view based on a typical cost/income profile in the industry and our ranged estimates of the potential of AI to improve performance in
specific areas. The examples shown are not exhaustive. Broad-brush costs for implementation/operating costs of AI, and for reduction/redeployment of headcount
are considered, while noting these may vary significantly across different organizations. The cost-income profile shown is informed by third party market data from
Refinitiv, Factica, Statista and selected publicly available Bank Annual Reports as available in Q4 2023.
05
Changing the game: the impact of artificial intelligence on the banking and capital markets sector
Where will the benefits come from? However, given AI risks and the evolving regulatory
AI, including GenAI, can bring advantages such as: landscape, AI without appropriate human supervision
may not be suitable for:
• Increased efficiency – automate repetitive tasks, freeing
human resources for more complex, creative or customer • Critical, fast-moving operations where timely human
facing engagement. supervision/intervention is not yet feasible.
• Improved accuracy – process vast amounts of data with • Customer/staff facing activities requiring human
greater precision and fewer errors than humans, leading to emotional intelligence (EQ).
more accurate predictions and outcomes.
• Regulatory-sensitive activities.
• Enhanced personalisation – analyze customer preferences
and behaviours to create tailored experiences, improving We see three key modes for achieving value through AI,
customer engagement. all of which combine AI and human strengths:
• Predict trends – make data driven decisions, detecting trends 1. A focus on productivity through personal agents;
and predicting changes in the market.
2. A focus on improving quality and process performance
• Creativity – new possibilities to create new possibilities for through specialist agents; and,
products, services and business models fostering innovation
and growth. 3. Large scale re-imagining of end-to-end processes
using the multi-modal capabilities of AI.
• Cost savings – streamlining operations, reducing errors, and
enabling better decision-making, AI can help save costs and The persona of core “agent modes” in which humans and AI
allocate resources more effectively. interact to implement the operating improvements that can
deliver financial impact. We stress the point that the benefit
• Protection – improving the effectiveness of financial crime in all three modes comes through combining human and AI
and loss prevention capabilities. strengths, not through large scale replacement of humans
with AI. Institutions should develop and strengthen the
• Accessibility – Make the services more accessible human skills to allow for adoption and value realization.
and affordable.
These modes will be leveraged in creating value across the
financial institution.
06
Changing the game: the impact of artificial intelligence on the banking and capital markets sector
Figure 2. Examples of AI personas
Personal Specialist Transforming
agent focus on agent focus on process focus
productivity improving quality on cost reduction
AI assist AI augmentation AI automation
10-20% potential 20-50% potential 50-80% potential
Executive and specialist roles Those with domain knowledge e.g. Customer facing and support roles
e.g. functional leaders, top levels investment manager, underwriters, e.g. contact centre agents,
of management relationship/account managers central services
Human strengths: Human strengths: Human strengths:
• Emotional intelligence • Relationship management • Problem solving and
• Creativity • Negotiation decision making
• Strategic planning • Domain knowledge • Compassion
• Persuasion and negotiation and experience • AI ethics and regulation
• Motivational leadership • Story-telling and making • AI-Human task management
• Ethical judgement and integrity insights relevant
• Critical thinking Machine strengths:
Machine strengths: • Fraud detection and prevention
• Analyze data and generate Machine strengths: • Data categorization
content • Speed in insight gathering • Quicker processing times
• Schedule meetings • Error checking and • Language translation
• Provide real-time assistance and validation exercises • Voice and text sentiment analysis
suggestions on documents • Trend spotting and
simple graph design
• Trading algorithms
• Predictive analytics
• Routine forecasting
Source: © 2024 Deloitte research. For information, contact Deloitte Global.
07
Changing the game: the impact of artificial intelligence on the banking and capital markets sector
Driving down cost through efficiencies and loss prevention
Most banks are currently building AI business cases around cost reduction, and this is no surprise.18 It is easier to get funding approved
for initiatives which drive out cost. The impact tends to be delivered quicker and benefits tend to be more directly attributable to the
investment made. As AI grows in its ability to take on the increasingly sophisticated tasks that previously required human action, the
opportunity grows for banks to perform a wider scope of activities faster and better, doing more with less.
Key cost reduction themes will likely include:
1. Workforce acceleration 2. Engineering transformation
A “marginal gains” approach to deploying Specifically, to benefit bank’s large
many productivity improvements across technology functions, GenAI can already
Cost
the human workforce. At the most generate and optimize software code,
efficiency
basic level, this will include automation reducing the time to write, while improving
of repetitive tasks such as data entry quality. As many software engineers examples
and analysis, search and query, draft in banking information technology (IT)
production of many varieties of tend to be relatively inexperienced and
operational content (meeting minutes, requiring oversight from senior engineers,
communications) and summarizing large GenAI “co-pilots” have the ability to
3. Loss avoidance
documentation. This is the type of “text and accelerate production releases and make
Risk management, fraud prevention, cyber,
images” productivity support perhaps most maintenance less onerous.
legal and other brand protection functions
associated with GenAI, particularly among
have high potential for improvement
newer users.
through AI. These functions tend to be
AI applied: a Portugal based improved by speeding up processes,
However, we see that the art of the institution has deployed an expanding scope of processes, and
possible is rapidly expanding, with more AI-powered converter tool that providing wider sets of data inputs to
specialist acceleration use cases including converts software code from legacy improve process performance – all of
data governance and management, COBOL-based systems to their which AI readily supports. Specifically,
data quality and remediation, model target Oracle platform to accelerate AI-enhanced credit risk management
development and analytics. a core platform modernization improvements can result in fewer loan
program. The large language impairments and write-off charges. Fraud
Workforce acceleration will likely require model (LLM) based converter prevention and financial crime (FinCrime)
widespread uplift in workforce skills with AI automatically generates functional processes can be accelerated and
in the same way as staff previously became documentation of the legacy expanded using AI to review a wider set of
proficient in typing, spreadsheets and COBOL code and creates a target input data sets to uncover new insights on
calendar management and other functions metadata schema to accelerate the actors and ultimately reduce losses.
which historically were performed by technical specification and build of
specialist resources only. the new data platform.
AI applied: Legal outcomes
A second use case is the ability for prediction. A Middle-East based
AI applied: various proprietary GenAI to consume millions of lines of bank is trialling a GenAI tool based
GenAI tools are being deployed in legacy code that is undocumented, on past contracts and litigation
compliance teams to summarize and rapidly extract business outcomes. The tool examines the
large sets of documentation issued rules/requirements to accelerate contracts and other documentation
by government and regulators.19 modernization. Deloitte practitioners involved in legal disputes and helps
This rapidly makes the key are already leveraging these the legal team better predict likely
takeaways and major insights capabilities to accelerate client’s outcomes of legal matters, as well
available to compliance teams and transformations and modernize as highlight potential risks in new
business staff in frontline roles. our own products internally.20 contracts.21
08
Changing the game: the impact of artificial intelligence on the banking and capital markets sector
Growing revenues through new capabilities and improved retention
While more difficult than cost-cutting, players will likely also invest to grow revenue. Revenue growth is a key challenge for banks due to the
relatively limited number of “opportunities to influence”. Consider supermarkets, which have practically limitless opportunities to influence
consumer purchasing behaviour through ranging, discounts, multi-buy offers and more. Unlike supermarket customers, how often do retail
banking customers re-mortgage, change current account or take on a new loan or credit card?
Conversion rate for any sales campaign is a critical metric for AI to improve. When consumers do switch financial products, pricing/rates
are a key factor in the decision, as is trust, and the quality of relationship that the consumer perceives with the bank – influenced by
service level and relevance of interactions and offers. AI can improve all of these factors, while reducing cost to deliver. We see a number
of key revenue-impacting themes:
2. Customer experience and retention
AI-powered digital agents (e.g., chatbots) AI applied: Advanced chatbot.
Income can reduce customer wait times by Bunq, a Netherlands-based
growth addressing an increasing range of neobank, has recently introduced
complexity of customer requests. While its very own generative AI platform
examples
certain customer journeys (e.g., those called Finn. This innovative platform
associated with large transactions, is designed to impress customers
bereavement etc.) must remain as person- with its exceptional ability to
to-person interactions, the improved provide answers to a wide range
1. New capabilities for growth responsiveness of digital customer service of money-related queries. Finn
We see that banks will invest in revenue- agents can improve customer experience features a chat-style text box that
generating capabilities across business and retention rates. Increasingly, the quality allows users to ask questions
lines, including: of AI interaction with humans will improve or seek advice about their bank
as AI technology develops–adjusting the AI account, spending habits, savings,
a. Insight-driven pricing: real-time agent’s behavior according to the behavior/ and other financial matters. The
customization of pricing (e.g., preferential emotions of the customer. platform is capable of combining
lending rates) to make highly competitive data to provide answers that
offers to target customers based on go beyond simple transactions,
enhanced measurement of their AI applied: Service content such as helping users recall past
credit risk. management. A Netherlands-based experiences like “What was the
institution has implemented a
name of that Indian restaurant I
b. Hyper-personalized marketing: natural language processing (NLP) visited with a friend in London?”23
improved conversion rates based on chatbot to support front-line staff
insightful identification of individual in delivering a more insightful
prospect and customer/client needs, customer experience. The tool
and highly-tailored communication. enables service staff to query
wide datasets in real-time based
c. Next generation trading algorithms: on live customer requests, rapidly
trading income uplift from enhanced returning relevant responses from
market insight and automated trading product catalogue, account fees,
decisions. terms and conditions, policies
etc. The next phase will enable
customers to interact directly with
AI applied: A UK-based universal
the chatbot as a digital agent.
bank has increased click-through
rates on its personal lending offers
by five times, through personalized
offer content and improved target
selection.22
09
Changing the game: the impact of artificial intelligence on the banking and capital markets sector
3. How will the sector
landscape change and
who will be successful?
10
3. How will the sector landscape change and
who will be successful?
The competitive landscape will likely be
redrawn, with sector’s probable winners
and losers determined by the speed and
effectiveness with which their AI initiatives
enable evolution of their business
operations, products and services.
laitnetop
eulaV
Changing the game: the impact of artificial intelligence on the banking and capital markets sector
AI is changing the game As mentioned earlier – for leading
institutions, AI is already making the
– who will be the new
paradigm shift from being an instrument
of strategy, to a determinant of strategy.
winners?
Figure 3. How AI is changing the game
Game 1 Game 2 Game 3
• Same processes – • New processes – • New business –
lower cost same business strategy, segments,
• Step change in • Transform products, service,
efficiency and customer experiences
productivity (cloud, experience, • Distinctive
automation) personalise definition of
products/services purpose /
(digital, data, AI) contribution to
society
• Expanding the Art
of the Possible (AI/
GenAI)
High Changing
the game...
Game 3
Intelligent
banking
Game 2
Digital
banking
Game 1
Classic
banking But how to jump
from Game 1 to
2 to 3?
Low Time High
Source: © 2024 Deloitte research. For information, contact Deloitte Global.
11
Changing the game: the impact of artificial intelligence on the banking and capital markets sector
So, who will be successful? How? Execution is critical
Banks expected to capture the biggest Key technical foundations The players which have realized benefits
benefits from prior waves of technology- • Cloud, where done well, has delivered from prior technical innovations have
enabled innovation (e.g., cloud, digital, data) readily-scalable computing power learned and refined the delivery methods
will continue to outperform in their value and accessible data provisioning, that work in their organization. Typically,
creation from AI. This is because leaders in that abstracted data away from the these have included consideration of:
innovation have already invested in the key complexity of legacy architectures while
organizational enhancements, including reducing total cost ownership of the • Governance – putting in place sufficient
culture, governance, data management IT estate. It also forced banks to learn oversight to adequately assess and
and agile delivery methods, needed to how to assess and manage the risks mitigate the spectrum of risks, without
capitalize on the AI opportunity. associated with introducing third-party unduly constraining delivery;
dependencies to the infrastructure
In many ways, substantial prior supporting core business processes. • Culture – benefits are well
investment in the innovations (cloud, data communicated, business function owners
management etc.) mentioned above has • Automation put in place the governance expect to embrace emerging technology
prepared the ground for value creation and risk management capabilities to to improve process performance;
from AI. All of these investments required oversee automated operations.
considerable capital expenditure that • Idea to value – strong processes
has constrained the bottom-line benefits • Data governance may have been are embedded to generate ideas for
realized to date. However, as above, implemented initially for compliance value delivery from innovation, assess
the organizations that have successfully purposes but has established the feasibility and investment case, rapidly
invested in these ambitious infrastructural organizational accountabilities, policies, deliver the best ideas into production
changes will find AI to be the conduit that quality improvement methods and and scale;
now accelerates the unlocking of value. understanding of organizational data
assets to provide trusted datasets as • Talent – hiring and learning/development
inputs to AI use cases. approaches that build adequate skills and
capacity; and,
• Digital banking has evolved customer
expectations to be more comfortable • Partnerships – engaging with the wider
with self-service, real-time, insight-driven market ecosystem, forming partnerships
and reduce reliance on bank staff for with technology and service providers
many interactions, while streamlining best placed to assist delivery.
key front-to-back processes e.g., client
onboarding, loan fulfilment.
12
Changing the game: the impact of artificial intelligence on the banking and capital markets sector
Realizing the value from AI will take The continuous upskilling of teams who The continuous
more than simply enabling the use these new tools to do more is not
technology. In recent history there a one-time effort, it should be built into
upskilling of
have been great expectations that the talent model and measured. Banks
technology transformation will drive who simply implement AI and GenAI to
significant efficiency gains only to deliver augment existing processes will likely teams who use
underwhelming results. Global chief not see the full value realization and
technology officer of Dell Technologies Inc, could in fact only see increased costs.
these new tools
captures the frustrations of many senior Banks who leverage AI and GenAI to
executives with the sustained investment support continuous transformation and
required: “I must’ve had ten conversations improvement can take the foundational to do more is
last week where CIOs were bemoaning investments already made (e.g., cloud and
that they had run out of money or blown data) and unlock further value.
not a one-time
their [cloud] budget off.24 ”Why could it be
different this time? The past investments Considering these points, the FinTech
(e.g., cloud, automation) have been parts subsector is likely to move quickest, due to effort, it should
of a solution but ultimately have not yet distinct execution advantages. Namely:
delivered transformational bottom-line
be built into the
value. In the case of cloud, organizations • The relative simplicity of their current
may have built the new capabilities but not operating models (considering
yet switched off what these cloud-based products, processes, technology, data talent model
solutions were intended to replace. In and organization) makes them less
the case of automation, it was possible to encumbered by the constraints of legacy
and measured.
automate parts of a process with great systems and processes. They still have
precision but the technology struggled the flexibility to jump straight to newly-
with inferencing and being intuitive; it was conceived processes without lengthy
a brittle solution in areas that required re-engineering of legacy.
elasticity to be effective.
• They typically have a culture tilted to
AI is already interacting with the workforce more rapid growth and innovation – their
in a more natural way and opens the doors greater risk appetite means they will be
for entirely different processes. Solutions willing to push AI capability to customers
for these processes can now be developed and into production processes sooner.
not as 1’s and 0’s but rather with natural But there are risks associated with doing
language providing great flexibility and this, before having the appropriate
speed to solution. guardrails and risk infrastructure in place.
Therefore, true value to banks will be
delivered when costly and long duration
processes are reconceived. As banks
evolve in maturity with AI and GenAI they
will begin to give front line employees
increasing autonomy and improved tooling
that will enable increasing revenue
(see “insight-driven pricing”) while also
reducing non-value add work (e.g., data
entry). But once that tooling is in place and
banks begin to reconceive processes there
must be a focus to continue to redeploy
staff to higher value roles.
13
Changing the game: the impact of artificial intelligence on the banking and capital markets sector
4. What is special about
generative AI and where is
this technology heading?
14
Changing the game: the impact of artificial intelligence on the banking and capital markets sector
4. What is special about generative AI
and where is this technology heading?
GenAI is a branch of AI currently attracting GenAI is about more than just text:
much attention, as it allows for the Gen AI is capable of working with multiple
generation of increasingly sophisticated “modalities” of content, with the ability to
content (e.g., text, code, audio, images, process one modality as input and generate
videos, processes) based on algorithms that another as output (not all combinations
imitate existing content, using statistical shown in figure 4 are currently possible).
predictions learned from large sources. Gen AI is able to produce sophisticated
content output including software code,
The fast-improving apparent quality of PowerPoint presentations and three
this content suggests that GenAI can dimensional (3D) models.
play a large role in business functions
traditionally considered to require
solely human intelligence.
GenAI is predicted to be the start
What is different about GenAI
and why all the excitement?
GenAI rapidly generates sophisticated of a new era for AI. The technology
content, based on vast bodies of source
information, designed to imitate what
will continue to evolve with focus
a skilled human being could produce.
This could be for example summarizing
large volumes of documentation, writing on multi-modal communication
an opinion piece, developing software
code, producing images/video to a and intelligence built into human
given specification, preparing a sales
presentation or defining rules to
interactions.
measure data quality.
Figure 4. AI modalities
Text Text
Code Code
Audio Audio
Image Image
Video Video
Read more about the next six
3D/Specialized 3D/Specialized
modalities in our recent publication
Generative AI Dossier.
Source: © 2024 Deloitte research. For information, contact Deloitte Global.
15
Changing the game: the impact of artificial intelligence on the banking and capital markets sector
The increasing sophistication and GenAI is already being used: According
apparent qu"
162,deloitte,gr-SEPE-Deloitte-Study-GenAI-eng-2024-noexp.pdf,"Federation of Hellenic ICT Enterprises (SEPE)
Gen AI - opportunities and prospects for the Greek economy
December 2023
Executive Summary
Introduction Objective and Results of the present study Alongside the survey, Deloitte assessed the degree of the
impact of Gen AI on the Greek economy and the
In the era of digital transformation both internationally In the above context SEPE, in cooperation with the global
employment of ICT specialists.
and in our Greece, the Information and Communication consulting firm Deloitte, conducted the present study,
Technologies (ICT) sector has experienced significant with the main objective of exploring the opportunities More specifically, with regard to the projected impact of
growth in recent years. and prospects of Gen AI in Greece while in particular it Gen AI on the country's GDP, the analysis concluded that
focuses on Greek entrepreneurship (examples of use its impact is projected to be very significant, with its
This trend is expected to intensify even further in the
cases as well as a primary survey), but also on the cumulative impact being estimated at +5,5% of the
coming years, with the proliferation of Generative
expected impact of this new technology on the Greek country's GDP by 2030 (i.e. €10,7 billion ), which under
Artificial Intelligence (Gen AI), which as a branch of
economy and the employment of ICT specialists. certain conditions can even reach +9,8%. It is noteworthy
Artificial Intelligence has the ability to generate original
that around 50% of this impact is estimated to be
content (such as code, images, video, audio, text and 3D In the context of the study, Deloitte, on behalf of SEPE and
contributed by 5 sectors of the economy: Financial &
models) using big data processing. with the support of the National Documentation Center
Insurance Services, Wholesale Trade, Manufacturing,
(NDC), conducted a survey in the private sector, in which a
Under certain circumstances and conditions, the benefits Services and Information & Communication Services.
large number of companies from both the ICT sector and
that can be achieved for businesses by adopting Gen AI
from other sectors of the economy participated, in order As for the impact of Gen AI on the ICT-specialists gap, it is
solutions are multi-level and relate to both the internal
to record their views regarding, on the one hand, the use also expected to be significant, with the projected gap
operations of corporations (e.g. better decision making,
of Gen AI technologies/solutions and, on the other hand, between supply and demand to increase by ~25.500
cost savings, higher productivity) and extroverted service
their strategic/immediate plans regarding the adoption of specialists, reaching a cumulative total of ~83.000 gap in
provision(e.g. improved customer experience).
this new technology. specialists by 2030*. The implementation of policy
In particular, Gen AI solutions can be deployed across the measures to reduce the ICT skills gap is becoming
As the survey shows, the adoption of Gen AI at Greek
entire spectrum of a company's operations, withmain imperative, with particular importance now being given
enterprises of all sectors of the economy, is still in early
categories of use cases identified relating to customer / to focused and fast-track skills development programs for
stages, although the majority of businesses believe that
public-facing services, content generation, code STEM graduates as well as other academic backgrounds
adopting Gen AI solutions can improve efficiency and
management, knowledge assistance and the extraction of leading to certifications sought by the labor market.
boost their growth. In addition, it was highlighted that
insights from unstructured data.
both the majority of companies in the ICT sector have not
yet adapted their strategy for the integration of Gen AI
solutions.
* The ~83.000 positions are the new estimate of the cumulative ICT
specialist gap until 2030, following last year's study by Deloitte for SEPE,
Copyright 2023Deloitte Business Solutions S.A. whereby the gap was estimated at ~57.500 specia2lists
Table of Contents
Introduction to Generative AI - Gen AI 4
5 Main Categories of Gen AI Use Cases 10
Presentation of results of the SEPE / NDC / Deloitte survey on Gen AI 24
Impact of Gen AI on the Greek economy 34
Conclusions 46
Copyright 2023Deloitte Business Solutions S.A. 3
Introduction to Generative AI - Gen AI
Copyright 2023Deloitte Business Solutions S.A. 4
Generative Artificial Intelligence (Gen AI) | Historical Review
The beginnings of Generative Artificial Intelligence can be traced back to 1943. This technological field developed further at the beginning
of the 21st century, and since 2018 has seen exponential growth, with the largest technology companies entering the field dynamically
First Steps Development Acceleration
(mid-20th century) (early 21st century) (2018 - today)
1943: Development mathematical model of 2003: Development intelligent voice 2018-2019: Open AIreleases ""GPT-1"", a groundbreaking
neural networks, basis for modern neural assistantson mobile phones advancement forlarge language models (LLM)
networks, by Warren McCulloch and Walter
Pitts 2012-2014:A computer cluster Google
Brain is trained to recognize a cat from
millions of images, using the large-scale
CNNs technique. At the same time, research
is published on new image recognition 2021-2022:DALL-E develops a creation tool image of 12 billion
technique and introduction to Generative parameters that uses only one sentence to create an image and
Adversial Networks (GANs) the Stable Diffusionlaunches an open source model for image
creation
1973:Development of a series of programs
2017: Google releases the first model
known as ""AARON"" focusing on autonomous
2023: Major technology companies are turning their attention to
Transformer, the foundation of many
art production, by Harold Cohen
Generative AI, such as Adobethrough Firefly, OpenAIthrough
popular AI generation tools today, such as
ChatGTP-4, Metathrough LLaMAandGooglefreeing up public
""Chat GPT""
access to Bard, an AI chatbot
Source: Deloitte Report “Dichotomies”
Copyright 2023Deloitte Business Solutions S.A. 5
Generative Artificial Intelligence (Gen AI) | Definitions and main use
Generative AI is a branch of AI capable of generating original content by performing a learning process, unlike traditional AI, which
does not allow the development of original content
Definition of Generative AI Generative AI (Gen AI) is a branch of AI that can generate
original content, such as: code, images, video, audio, text and
ArtificiaI Intelligence
3D models.
The science of creating intelligent machines, through special computer programs -e.g. personal
digital assistants (e.g. Google Assistant, Apple Siri, Amazon Alexa)
Machine Learning
The above is illustrated in the adjacent ""wheel"",
The implementation of algorithms that allow computer programs to automatically improve
which categorizes the generated “output” into
through experience -e.g. a movie recommendation system on a streaming platform, based
individual use cases of Gen AI. Until now, the
on consumer preferences creation of this type of content has been carried
out exclusively with human intervention.
Deep Learning
Subfield of machine learning algorithms based on artificial neural networks -e.g.
autonomous vehicles that recognize obstacles, other people and other vehicles on
the road
Generative AI has redesigned the way we communicate, work and innovate, with its
Gen AI adoption expected to open myriad of possibilities that previously seemed unlikely,
A branch of AI that relies on large language models (LLMs) to process large ushering in a new stage of creativity, efficiency and progress.
amounts of data and generate original content.
The diffusion of this technology is extremely fast if we consider that ChatGPThas
recorded1 million users in5 daysfrom the day it was made available to the public
(November 2022), and according to the latest statistics for November 2023, it has
more than 180 million registered users.
1960 1980 2010 2020
Source: Deloitte analysis
Copyright 2023Deloitte Business Solutions S.A. 6
Generative Artificial Intelligence (Gen AI) | Differentiation from Traditional AI
Generative AI is a branch of AI, which however presents important differences from the latter
Traditional Artificial Intelligence (AI) replicates human Generative Artificial Intelligence (Gen AI) performs
cognitive functions (learning, design, creativity) and deep learning, mimics brain function when
focuses on identifying and processing available and processing data and making decisions. Gen AI is
appropriate information in order to extract the based on machines or algorithms that have the
relevantrequired ""knowledge"" from their composition. ability to createprimary content, e.g. text, images,
audio, video.
Main features of traditional AI:
Main features of Gen AI:
It does not develop primary content
Develops / Generates primary content
Handles certain problems well, for specific business functions
Solves open-ended problems by performing
Solvesproblems defined on the basis of specific rules intelligent, human action
Human supervision and assistance in the learning process is Supports the increase of creativity and the
necessary improvement of the quality of primary ideas
Interprets the information for pattern recognition
Requires limited human supervision and has
Enhancespredictabilityin decision-making autonomous learning potential
Performs a learning process on the basis of existing
information
Source: Deloitte analysis
Copyright 2023Deloitte Business Solutions S.A. 7
Generative Artificial Intelligence (Gen AI) | Benefits for businesses
The adoption of Generative AI can bring users multiple benefits such as better decision making, improved customer experience, higher
productivity, cost savings and improved creativity / innovation
Better decision making
Generative Artificial Intelligence (Gen AI) makes recommendations through big data analysis,
facilitating multi-scenario simulations and exploration of alternative strategies, enhancing and
accelerating the complex decision-making process
Improved customer experience
Generative Artificial Intelligence (Gen AI) helps improve customer loyalty through
personalized service and support
Higher productivity
Through Generative Artificial Intelligence (Gen AI), routine operations (that may make up to
70% of human resources time) are automated, allowing focus on more complex and higher
value-added tasks
Cost savings
Lorem ipsuGmenerative Artificial Intelligence (Gen AI) leads to cost and money savings (≥30%), through
the automation of often repetitive tasks, thus freeing up human labor, while ensuring high
quality
Improved creativity and innovation
Lorem ipsum
Generative Artificial Intelligence (Gen AI), through the analysis of multiple data, can present
alternatives, offering inspiration to boost creativity and help increase the pace of development of new
products or services and bring them to market faster
Source: Deloitte analysis
Copyright 2023Deloitte Business Solutions S.A. 8
Generative Artificial Intelligence (Gen AI) | Critical success factors for adoption
The critical success factors for the integration of Gen AI relate both to how the relevant systems are developed and operated, and how
they are used by the human resources involved
The architecture of the systems that support Gen AI The training of human resources in Generative AI and, by
initiatives is crucial to the outcome and must be extension, in the development and use of these models is
chosen with great care so that the algorithms, a prerequisite for the proper integration of this technology
models and computing infrastructure are in business operations. This training should be given to all
appropriate and contribute to the efficient staff and should be followed by assessments of staff
operation of the whole system. readiness
In the context of ensuring all of the above, it is particularly
The datasets that Gen AI relies on to create content useful for the appropriate preparation of each company,
must be of high quality, multiple, extensive and up-to- through horizontal actions, such as: conducting a relevant
Formulating a well-defined Gen AI policy based on
date, as they are critical to the quality of the feasibility study per use case and corresponding prioritization
the principles of fairness, transparency,
“products” generated, while it is important to ensure of solutions of strategic importance, development of ""proofs
accountability and data protection and subsequent
the absence of ""biases"" of concept"" for the technical testing of Gen AI solutions and
compliance with all necessary legislative
gradual adaptation of the models adopted to larger data sets
frameworks should be a priority for businesses.
(scaling).
Source: Deloitte analysis
Copyright 2023Deloitte Business Solutions S.A. 9
5 Main Categories of Gen AI Use Cases
Copyright 2023Deloitte Business Solutions S.A. 10
5 main categories of use cases | Overview
Gen AI can contribute significantly to the development of 5 main categories of use cases that are of major importance for improving the
efficiency of many business processes
Public-facing services
More direct / more effective interaction with target audiences | more personalized user experience by providing customized responses
Content generation
Creating original texts, images, products, etc. Allows businesses to create content quickly and efficiently
Code management
Speed up and improve the code development process, saving time and eliminating human error
Knowledge assistance
Automation of processes for the capture and maintenance of knowledge. Easier navigation of users through the enterprise's
data and knowledge repositories
Extracting insights from unstructured data
Exploitation of information from unstructured data, achieving optimal understanding and utilization of the information containedtherein (e.g.
image and sound analysis)
Source: Deloitte analysis
Copyright 2023Deloitte Business Solutions S.A. 11
5 main categories of use cases | Benefits
Gen AI entails a number of benefits stemming from the 5 main categories of use cases that Gen AI supports
Extracting information
Public-facing services Content generation Code management Knowledge assistance
from unstructured data
Gen AI's processing of
Gen AI-powered analysis of data Gen AI can be used to create Gen AI as a code management Gen AI helps draw conclusions
unstructured data allows
Better decision from customer interactions can content with a more targeted tool can be leveraged when from ""complex"" information,
companies to make decisions
making contribute to better commercial focus, which helps better creating multidimensional helping to analyze data for
based on more complete
decision making decision making scenarios for decision making better decision making
information bases
Gen AI interacts with its users
Gen AI can contribute to the Gen AI allows the creation of Gen AI helps identify bugs in the Gen AI can analyze unstructured
Improved through natural language
delivery of personalized services original / customized content, code and provides suggestions data such as customer reviews
customer dialogues and can accurately
and therefore an improved adapted to the needs of each for fixes, contributing to a high and extract valuable insights to
experience identify the requested
customer experience client quality ""final"" output understand their preferences
information
Gen AI can help increase Gen AI provides insights and Gen AI makes knowledge
With the help of Gen AI more Analyses from unstructured data
productivity through faster supports the creative software management easier and faster,
Higher customer requests can be can reveal ways to improve
content development, enabling development process, leading to helping to improve the
productivity supported/processed end-to- business processes, helping to
more customers to be served at higher productivity of ICT productivity of a company's
end increase productivity
the same time specialists employees
The use of Gen AI for public- Gen AI enables businesses to Gen AI helps programmers Gen AI has the ability to analyze Gen AI enables automatic
facing service helps to automate create content quickly and reduce the time they spend on large amounts of information understanding and organization
Cost savings
tasks and therefore, to save efficiently, with less human certain activities such as code and synthesize it automatically of unstructured data, saving
costs intervention correction and very quickly time and human resources
Gen AI can analyze the profile of Gen AI can support the Gen AI helps draw conclusions Gen AI can discover new trends
Improved Content creation helps to quickly
the recipients and suggest more exploration of different, from complex information, from unstructured data,
creativity / generate a wide range of ideas -
creative ways of approaching / innovative approaches to code helping to create innovative supporting the creation of new
innovation vital for innovation
communicating development ideas and solutions innovative products
Copyright 2023Deloitte Business Solutions S.A. 12
5 main categories of use cases | Template for the Analysis
For a better understanding and deeper insight into the main categories of Gen AI use cases, for each of them an analysis is carried out in
6 dimensions: trends, type of extracted original content, points of differentiation from corresponding traditional AI solutions, potential
benefits, main sectors of the economy for application and indicative examples of use cases
For each of the five main categories of use cases, the
following pages provide an overview of the following:
Public-facing services
the trend towards the use of Gen AI
the possible forms of original content that can be
extracted (e.g. image, sound, code, etc.)
Content generation the main points of differentiation from corresponding
traditional AI solutions
the potential benefits that can be achieved
indicative examples of the most important sectors that
Code management are expected to be most applicable
Finally, for each use case category,
examples of use cases are provided
Knowledge assistance
Extracting insights from unstructured data
Copyright 2023Deloitte Business Solutions S.A. 13
5 main categories of use cases | ""Public-facing services"" - overview
A typical technological solution in the context of public service is chatbots, which can now use generative artificial intelligence to answer questions of the public,
Trend towards the use of solve problems and provide product and/or service recommendations. Gen AI solutions will bring about a significant evolution in public-facing services, as they
Gen AI have the potential for flexibility, offering solutions tailored to the audience they serve (customer experience personalization), even using customer interactions
to provide more comprehensive solutions.
Code code
Elements of ""import"" Elements of ""export""
Inputs / Prompts Output generated
Public-facing Public-facing
services services
Difference with other Today's chatbots have limited service capabilities, as they are based on traditional artificial intelligence (AI) systems andtherefore on predefined dialogues. AI is
technologies and used in public services mainly for automating tasks. The more sophisticated Gen AI can analyze data from customer interactions to suggest solutions, making it
traditional artificial easier for employees in customer support positions to perform their tasks. The high value of such solutions lies in their capabilities to respond to and service a
intelligence high volume of transactions, at high rates and by eliminating waiting times.
Benefits of Gen AI as a public-facing service tool Application to sectors of the economy
Did you know that...
Consumer goods, Retail trade Public Administration
Increase in customer Availability at all Lower call Strengthening Technology, 85%of executives say that
satisfaction hours, in real abandonment personalized Energy
Telecommunications Generative AI will interact
(CSAT Score) time rate service
directly with customers in
Financial Services Health
the next two years without
any human intervention
(Source: IBM)
Education Media
Reduction of Faster response Service in multiple Scalability
operating costs times languages
Copyright 2023Deloitte Business Solutions S.A. 14
5 main categories of use cases | ""Public-facing services"" - examples of use cases
TypLiocraelm G ipesnum AI use cases for public service in the Public Administration and Consumer Goods Industry are the Digital Public Servant and
Customer Service on demand, respectively
Digital Public Servant Customer service ""on demand""
Challenges Challenges
Public administration internationally -including in Greece -is significantly burdened by Many companies operating in the consumer goods industry have already integrated
bureaucracy and the large volume of documents stored in a variety of formats, which certain Artificial Intelligence (AI) capabilities into their systems in order to provide
makes it difficult to quickly access available information. As a result, the quality of automated and quick answers to their customers, should they seek information or
service often falls short of expectations, creating a climate of mistrust among citizens support about a product or service. Such automation, however, has a limited ability to
regarding the functionality and efficiency of public administration bodies. interpret customer questions and respond with absolute efficiency and accuracy.
The ""answer"" of Gen AI The ""answer"" of Gen AI
The Digital Public Officer(with the recent example of mAIgov.gr) can provide the
necessary interface between citizens and the services of the Public Administration, An interactive Gen AI ""assistant"" can foster a new climate of communication and
through the creation of an interaction system that can respond quickly and with high interaction with customers, as it can create personalized conversations during after-sales
quality to requests. support by providing immediate responses, offering relevant solutions and managing
The Digital Public Servant can rapidly identify and summarize information from multiple complaints. As customers can get faster responses to their questions through Gen AI,
sources on a multitude of issues in order to form appropriate responses to the queries of businesses are able to free up human resources to focus on more complex service issues.
requesters, restoring confidence in Public Administration.
Code Critical success factors Code Critical success factors
Ensuring the provision of accurate information/answers Ensuring the provision of accurate and personalized
advice or guidance
Continuous updating and updating of system Enhancing transparency regarding the functionalities
Public-facing information Public-facing of the model
services Services
A priori identification of customer expectations of the
Protecting sensitive data from cyber-attacks
business, for the best possible system response
3D 3D
Copyright 2023Deloitte Business Solutions S.A. 15
5 main categories of use cases | ""Content generation"" - overview
Existing AI solutions have the potential to focus on data categorization/recognition to support content development processes. The new achievement of Gen AI
Trend towards the use of
solutions is in the direct development of original content, thus enhancing creativity, the development of new ideas, and moreefficient focus and customization
Gen AI
to customer needs.
Code Code
Elements of ""import"" Elements of ""export""
Inputs / Prompts Content Output generated Content
generation generation
Difference with other Gen AI has the ability to create new versions of data in a variety of formats, not just text. This makes it useful for creating marketing materials, original artwork,
technologies and developing video games with dynamic and evolving content, and even creating synthetic data to train other Gen AI models, especially in scenarios where collecting
traditional artificial real data may be difficult or impractical. In addition, by analyzing existing market trends, consumer preferences and historicaldata, Gen AI models can propose
innovative insights that align with current market requirements in order to create new outputs.
intelligence
Benefits of Gen AI as a content generation tool Application to sectors of the economy
Did you know that...
Consumer goods, Retail trade Public Administration
The45% of employees in
Increased Improved user Trend analysis & Efficiency of available
Energy Technology, marketing departments
productivity experience research extraction resources
Telecommunications spend more than 50% of
the time within one
Financial Services Health
working week, for the
creation of content (Source:
Creating original Saving time Enhancing Compliance with Education Media Capterra’s 2022 AI Marketing
content accessibility regulations Survey)
Copyright 2023Deloitte Business Solutions S.A. 16
5 main categories of use cases | ""Content generation"" - examples of use cases
GenLo rAeIm t eipcshumnology can be applied to many industries, contributing significantly to the creation of content and products that respond to the
needs of each business customer/user
Marketing content assistant Product design assistant
Challenges Challenges
Businesses face a number of challenges when it comes to managing and optimizing
Product development is a time-consuming and demanding process for businesses. The
marketing content. With a large number of websites for their product portfolios,
need to fully understand customers' needs and preferences can be difficult and often
businesses spend a lot of time and resources creating product descriptions for specific
requires extensive research. Moreover, in an environment where competition is fierce,
customer groups, images, videos, etc. A major issue, too, is achieving consistency in
creating products that stand out and offer something unique can be challenging. In
descriptions, iconography, ads and other media. It is therefore imperative to deliver
addition, market needs can change rapidly, and businesses must adapt quickly to remain
personalized customer experiences quickly and in an automated manner, across a
competitive by creating innovative products.
multitude of ecosystems and touchpoints.
The ""answer"" of Gen AI
The ""answer"" of Gen AI
Gen AI technology can therefore be used to generate dynamic content (product Gen AI can be applied to a multitude of industries, allowing businesses to innovate and
descriptions, images, videos) based on user data. This dynamic content can be used to offer products that meet modern market needs. Machine learning algorithms can
create personalized ads / experiences and product recommendations, thus helping to analyze large data sets to discover trends, patterns and insights that can help create
increase business revenue / sales, but also to enhance customer / user engagement. products that meet consumer needs. The use of machine learning algorithms,
Creating targeted content for specific user segments also helps save time and costs. therefore, can help to optimize internal production processes, reduce costs or improve
efficiency.
Critical success factors
Critical success factors
Code
Code
Ensuring accuracy and relevance of content produced
Design innovative products that can be manufactured
and comply with the regulatory framework
Ensuring diversity and representativeness to avoid bias in
Content the content produced Content
generation generation
Protecting intellectual property rights when using Gen
Establish strong ethical guidelines regarding the use of
AI in the creative process
3D sensitive data 3D
Copyright 2023Deloitte Business Solutions S.A. 17
5 main categories of use cases | ""Code management"" - overview
Generative AI can be used in many aspects of software engineering such as managing, developing, completing, debugging, documenting and restructuring of
Trend towards leveraging
code. Images, sounds, texts and code can be fed into the Gen AI model from which, depending on the user's choice, a new form of code is produced in
Gen AI
programming languages such as Python, JavaScript, Java, Verilog, C, C++, TypeScript and more.
Code Code
Elements of ""import"" Elements of ""export""
Inputs / Prompts Code Output generated Code
Management Management
Difference with other A key difference in relation to other technologies such as traditional artificial intelligence (AI) is the possibility of developing new code after the descriptive capture
technologies and of the request. Artificial intelligence is mainly based on ""deterministic systems"" (""if-then"" conditions), which use a set of rules that lead to predetermined results
traditional artificial and are now suitable for generating code for repetitive tasks (e.g. GitHub Copilot, Amazon CodeWhisperer, etc). Therefore, the use of generative artificial
intelligence is suitable for applications where the main prompt is descriptions in natural language.
intelligence
Benefits of Gen AI as a Code Management tool Application to sectors of the economy
Did you know that…
Public Administration
Consumer Goods, Retail
Increased Error detection and Apply code Efficiency of time and Technology, Developers spend ~25-
Energy
productivity prevention standards resources Telecommunications 50% of their time per year
debugging. Gen AI greatly
Financial services Health improves this issue,
creating time for more
creative tasks
Education Media
Flexible decision Documentation Cost Data reliability (Source: Undo.io)
making management
Copyright 2023Deloitte Business Solutions S.A. 18
5 main categories of use cases | ""Code management"" - use case examples
GenLo rAeIm c iapnsu smignificantly support the overall process of code development by performing functions such as pattern synthesis, testing, and
documentation
Code Support for Developers What do Gen AI applications bring to code development?
Challenges
Through Gen AI, code development as a whole is done without the need for
Code development is a complex process, involving a number of challenges. It requires human intervention, as was required until now. Developer teams provide the
specialized staff and its lack of consistency or inadequacy leads to slow applications and system with descriptions or specifications, with Gen AI developing or suggesting
increased resource usage. The large amount of information and functions present code that meets the requested functionality. In this way, human resources are
significant problems for code review and testing to identify and correct errors. Additional focused on processes to achieve maximum quality and reliability of the models,
code maintenance issues are related to compatibility with other systems, lack of security, while minimizing the possibility of human error.
and lack of documentation.
The ""answer"" of Gen AI
The testing process, due to the large amount of data that requires testing, has
until now required significant human effort. Gen AI can automatically detect bugs
Using Gen AI to support code serves to offload ICT manpower and focus them on more or predict where they might occur, discover opportunities for optimization, and
complex and higher-value digital transformation tasks. suggest code restructuring points to upgrade its quality, allowing developers to
By using Generative Artificial Intelligence (Gen AI) faster completion of repetitive tasks is engage in the strategic decision-making and solutions they want to ""build"".
achieved, such as: developing, maintaining, documenting and checking code, adapting
functional code to different environments, data transformation, abstractions, etc.
Code Critical success factors Code documentation is a defining process of the overall process, which until now
has been mostly done manually. Gen AI can generate, without human
Ensuring accuracy and lack of errors intervention, comments / explanations / documentation summaries for specific
functions or even entire user manuals in order to make the code understandable
to others. Also, this technology has the ability to translate code into other
Ensure transparency and explainability of
Code documentation variables and comments programming languages, if there is a need to change or adopt the code in
management another environment.
Protection from cyber security risks
3D
Copyright 2023Deloitte Business Solutions S.A. 19
5 main categories of use cases | ""Knowledge assistance"" - overview
Gen AI models have access to a range of both structured and unstructured data which they can equally well read, understand, synthesize and extract useful
Trend towards leveraging
information to the u"
163,deloitte,in-deloitte-pov-safeguarding-gen-ai -with-cybersecurity-measures-noexp.pdf,"Safeguarding Generative
Artificial Intelligence (AI) with
cybersecurity measures
Risk insights and building blocks for secure
Generative AI solutions
2024
Safeguarding Generative Artificial Intelligence (AI) with cybersecurity measures
Table of contents
Introduction 02
Evolution of Generative AI 03
What constitutes Generative AI 04
How does Generative AI work? 05
Six categories of Cyber risks with Generative AI 06
Illustrative Cyber risks of Generative AI 07
Industry-wise use cases of Generative AI,
Cyber Risks, and Controls 08
Building blocks for secure Generative AI solutions 10
Way forward 13
Conclusion 14
Connect with us 15
01
Safeguarding Generative Artificial Intelligence (AI) with cybersecurity measures
Introduction
2022 was a watershed in the history of Artificial Intelligence As a result, it will lead to the following outcomes:
(AI). While the journey started in 1932, it has gradually become
1. Improving productivity
all-pervasive − first with digital assistants from various
2. Increased customer satisfactions
multinational technology conglomerates and now with the
3. Propelling Research and Development (R&D)
release of Generative AI. This phase is incredible because
it affects most businesses and personal interactions. While 4. Creating new revenue streams and business models
the world celebrates the coming of age of Generative AI, one
needs to make certain considerations to ensure that the scale While organisations and businesses adopt the Generative
and impact are progressive for individuals, organisations, and AI, Cybersecurity is paramount. Necessary controls should
society alike. be implemented to ensure that investments deliver the right
business results to organisations while maintaining individuals’
Generative AI has many positive implications and could have privacy and confidentiality. Additionally, a lack of adoption
the ability to transform the way we do business. Some of the of the Security by Design (SbD), Privacy by Design (PbD), and
most important aspects include the following: Ethical by Design (EbD) concepts could lead to exposure and
risks to data being used and training of models adopted.
Finally, security technology needs to keep pace with the
development of Generative AI.
Intelligent Information Technology (IT) – This point of view (POV) provides insights into certain
Transforming how IT is structured, how cybersecurity considerations for Generative AI and the
software development is done, and how IT is necessary controls organisations should consider while
enhanced and supported. building these systems.
Intelligent products – Enhancing sensor-
infused products using Generative AI, which
can have huge implications across several
industries.
Intelligent operations – Remodeling
operations with a greater emphasis on
Generative AI-derived inputs that could help
make operations nimble and agile.
02
Safeguarding Generative Artificial Intelligence (AI) with cybersecurity measures
Evolution of Generative AI
Most of the time, Generative AI is considered relatively new. Contrary to the
belief, Generative AI is deep-rooted in history and innovation.
Georges Artsrouni invented a machine
reportedly called the “mechanical brain”
1932
to translate languages on a mechanical Harold Cohen, a painter and professor,
computer encoded onto punch cards.1 collaborated with a programme called
1973 AARON to produce art autonomously.
Yann Lecun, Yoshua Bengio, and Patrick Paintings are done in Cohen’s style.
Haffner demonstrated how Convolutional
1989
Neural Networks (CNNs) can recognise
images. Researchers from the University of
Montreal published “a neural probabilistic
2000 language model”, which suggests a method
to model language using feed-forward
Data scientist Fei- Fei Li set up the ImageNet neural networks.
database that laid the foundation for visual 2006
object recognition.
Apple released Siri, a voice-powered
personal assistant that can generate
2011
responses and take actions in response to
Ian J. Goodfellow and colleagues published voice requests.
the first paper on Generative Adversarial
2014
Networks (GANs) that can determine if an
image is real or fake. Google researchers developed the concept
of transformers in the seminal paper
“Attention is all you need.” The paper
Google researchers implemented 2017
inspired subsequent research into tools that
transformers into BERT, trained on over 3.3
could automatically parse unlabeled text
billion words. It can automatically learn the
into Large Language Models (LLMs).
relationship between words in sentences,
2018
paragraphs, and even books, and predict
Open AI released ChatGPT in November
the meaning of text. Google DeepMind
to provide a chat-based interface to its
researchers developed AlphaFold to predict
2022 GPT 3.5 LLM. It attracted more than 100
protein structures that laid the foundation of
million users in two months, representing
Generative AI.
the fastest-ever consumer adoption of a
service.
Adobe launched Firefly, a family of Generative AI Google released Bard, a Generative AI
models tailor-made for creative professionals, 2023 chatbot built on 137 billion parameters and
with built-in guardrails for safety and copyright embeds Generative AI capabilities into its
standards. workshop products.
1 What is Generative AI? Everything you need to know
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Safeguarding Generative Artificial Intelligence (AI) with cybersecurity measures
What constitutes Generative AI
Generative AI learns from various inputs to generate its outputs.2
Data can vary from text and photos to videos, audio, and codes. Using these wide varieties of datasets, Generative AI creates novel
outputs. Generative AI models use technological elements, such as LLMs, diffusion networks, GANs, transformers and Variational
Auto Encoder (VAEs), and other novel techniques to identify patterns and structures within existing data to generate new content.
Text
Essays, speech, creating
questions asking
Large Language
Models (LLMs) Photos
New enhancements, such
as photo edits
GANs,
Video
Diffusion
transformers,
New videos, such as reels
networks
and VAEs
Audio
Music, clips, audio, etc.
Novel
techniques
Code
Self-learning models,
prompt engineering, etc.
2 Generative AI – What is it and How Does it Work? (nvidia.com)
04
Safeguarding Generative Artificial Intelligence (AI) with cybersecurity measures
How does Generative AI work?
A simplified view
Cloud and data Generative AI
AI infrastructure Generative AI models
platforms applications
Computer
Data Prediction Models Output
power
Why do these
Powering our Training on the What are Applications − What
applications seem
journey to tomorrow world’s knowledge foundation models? we see
so human?
The scale of compute Foundation models Similar to traditional OpenAI’s GPT-4 and Generative AI
capacity required are trained on AI, foundation NVIDIA’s Megatron applications
to train and process petabytes worth of models predict are two examples of generate content
foundation models global data to shape outputs based on foundation models, across various
necessitates the use understanding, tone, inferences on the specifically Large modalities (e.g., text,
of leading GPUs (e.g., and behaviour while inputs it receives. Language Models image, video, and
A100 NVIDIA) and considering human However, through (LLMs) that use deep audio)
TPUs (e.g., Google communication fine-tuning, prompt learning to process
TPU v4) on scalable styles. engineering, and huge amounts of
infrastructure. adversarial training, data. These models
these models form “memories”
generate outputs on the input
based on their datasets through
understanding tokenisation,
of human thereby shaping
communication. models’ parameters.
Common foundation
model architectures,
such as transformer
and diffusion, drive
modalities for each
model.
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Safeguarding Generative Artificial Intelligence (AI) with cybersecurity measures
Six categories of Cyber risks with
Generative AI
While there are many Cyber risks related to Generative AI, we have tried to
group them into six categories.
Generative AI model-based risks
Generative AI models are currently being developed by a few organisations. Most others end up using
those models. If not used wisely or ethically, these models can cause potential loss of confidential/
sensitive/copyright information or other intellectual property infringement.
Infrastructure risks
These include risks related to the infrastructure provided to support Generative AI models, applications,
and data. Traditional infrastructure Cyber risks, such as using components with known vulnerabilities,
insecure services, ransomware attacks, and DDoS attacks, are a few examples.
Data risk
While the data discovery and classification themselves have inherent risks, if the correct processes are
not followed, the right controls including those for privacy and confidentiality may not be present.
People risk
People risk is related to ethical use and bias aspects of Generative AI. It is equally important to ensure
that Generative AI systems do not cause harm to end users.
Application/algorithmic risk
These could include inherent algorithmic and coding risks in the applications developed on the
mentioned-above models.
Training and testing risk
These are related to lack of capability to create effective training and testing processes for Generative AI.
06
Safeguarding Generative Artificial Intelligence (AI) with cybersecurity measures
Illustrative Cyber risks of Generative AI
While there may be myriad risks from a cyber perspective, some key risks you
should be aware of while using Generative AI are provided below.
Membership inference Prompt injection
Inferring the presence of specific data points in Prompt Injection manipulates a Generative AI
the training set by querying the AI model and through crafty inputs, causing unintended actions
compromising data privacy. by the Large Language Models (LLMs). Direct
injections overwrite system prompts, while indirect
Insecure output Handling ones manipulate inputs from external sources.
This vulnerability occurs when a Generative AI
output is accepted without scrutiny, exposing Excessive agency
backend systems. Misuse may lead to severe Generative AI-based systems may undertake
consequences, such as XSS, CSRF, SSRF, privilege actions leading to unintended consequences.
escalation, or remote code execution. The issue arises from excessive functionality,
permissions, or autonomy granted to
Model denial of service LLMs-based systems.
Attackers cause resource-heavy operations on
Generative AI, leading to service degradation or Overreliance on Generated AI
high costs. The vulnerability is magnified due to Systems or people overly depending on Generative
the resource-intensive nature of LLMs and the AI without oversight may face misinformation,
unpredictability of user inputs. miscommunication, legal issues, and security
vulnerabilities due to incorrect or inappropriate
Supply chain vulnerabilities content generated by Generative AI.
Generative AI application lifecycle can be
compromised by vulnerable components or Model theft
services, leading to security attacks. Using third- Model Theft could involve unauthorised access,
party datasets, pre-trained models, and plug-ins copying, or exfiltration of proprietary Generative
can increase vulnerabilities. AI models. The impact includes economic losses,
compromised competitive advantage, and potential
Sensitive information disclosure access to sensitive information.
Generative AI may inadvertently reveal confidential
data in its responses, leading to unauthorised data Training data poisoning
access, privacy violations, and security breaches. This occurs when Generative AI training data is
Implementing data sanitisation and strict user tampered, introducing vulnerabilities or biases
policies to mitigate these risks is crucial. that compromise security, effectiveness, or ethical
behaviour. Sources include Common Crawl,
Insecure plug-in design WebText, OpenWebText, and books.3
Generative AI plug-ins can have insecure inputs and
insufficient access control. This lack of application Deepfakes
control makes them easier to exploit and can result Deepfake technology4 has advanced to the
in consequences such as remote code execution. point where it can be used in real-time, enabling
fraudsters to replicate someone’s voice, image, and
movements in a call or virtual meeting.
3 https://owasp.org/www-project-top-10-for-large-language-model-applications/assets/PDF/OWASP-Top-10-for-LLMs-2023-v1_0.pdf
4 https://www.latimes.com/business/technology/story/2023-05-11/realtime-ai-deepfakes-how-to-protect-yourself#:~:text=Cybersecurity%20experts%20
say%20deepfake%20technology,a%20call%20or%20virtual%20meeting.
07
Safeguarding Generative Artificial Intelligence (AI) with cybersecurity measures
Industry-wise use cases of Generative
AI, Cyber Risks, and Controls
Generative AI’s potential extends to almost every industry, as it provides a wide range of automation and enhances creative and
data-driven processes. However, with its various use cases, it also introduces Cyber risks.
A few use cases of Generative AI across key industries, along with the possible Cyber risks and mitigation steps, are provided below:
** This is not an exhaustive list.
Industry Use cases of Description of use case Cyber risk Mitigation/controls
Generative AI
Consumer Blog and social Content generation: Misinformation and fake Fact-checking and
media content Generative AI can be used content: Generative AI can verification: Establish
writing to generate a variety of produce false information, partnerships with
content types, including such as fake news articles, reputable fact-checking
text, photos, and videos. It photos, and videos. This organisations to verify the
can, for example, be used raises the possibility of accuracy of the content
to generate personalised distributing inaccurate generated. Develop
product descriptions, blog information, altering public automated systems
entries, or even fictional opinion, or carrying out to cross-reference the
stories. social engineering attacks. generated information
with reliable sources,
to help identify and flag
potential misinformation.
Government and Social services Generative AI can Bias and discrimination: Transparent decision-
Public Services and welfare assist in personalised Generative AI models making: Enhance
service delivery in trained on biased or the transparency of
healthcare, social incomplete data may Generative AI systems
welfare, or education. It inadvertently perpetuate by explaining decision-
can analyse individual biases or discrimination making processes. Use
data to recommend in social service delivery. techniques such as
suitable programmes, This could lead to unfair or Explainable AI (XAI) to
interventions, or support discriminatory outcomes, make the generated
services based on specific disadvantaging certain outcomes more
needs. individuals or groups based understandable to
on their demographic citizens, fostering trust
or socio-economic and accountability.
characteristics.
Energy, Resources, Energy Generative AI models Manipulation of Implement robust data
and Industrials demand can analyse historical forecasting data: security measures,
forecasting energy consumption Adversaries may attempt to including encryption,
data, weather patterns, manipulate or tamper with access controls, and
economic indicators, and the data used for energy secure storage to protect
other relevant factors, to demand forecasting. By the confidentiality and
help forecast future energy injecting false or misleading integrity of the data
demand. Accurate demand information into the dataset, used for forecasting.
forecasting helps utilities they could manipulate the Use anomaly detection
and energy providers forecast demand, potentially and outlier analysis
optimise resource allocation, leading to inefficient techniques to identify and
plan for peak demand resource allocation, financial mitigate potential data
periods, and enhance loss, or disruptions in energy manipulation attempts.
energy distribution.5 supply.
5 How generative AI can boost productivity in enterprises and industries
08
Safeguarding Generative Artificial Intelligence (AI) with cybersecurity measures
Industry Use cases of Description of use case Cyber risk Mitigation/controls
Generative AI
Financial Services Financial By learning from historical Model poisoning Regular model auditing
forecasting financial data, Generative threat actors may and monitoring of
AI models can capture manipulate the training adversarial activities are
complex patterns and process of Generative a few mitigating controls
relationships in the data, AI models by injecting to help combat the
enabling them to make malicious data or disturbing “poisoning of the data”.
predictive analytics about the training data to
future trends, asset prices, undermine the accuracy of
and economic indicators.6 forecasts.
Technology, User interface Generative AI can help SSRF vulnerabilities Rigorous input validation
Media, and design in User Interface (UI) allow the exploitation of and regular audit
Telecommunications design by providing Generative AI models by network/ application
automated suggestions performing unintended security.
for layouts, colour requests or accessing
schemes, and component restricted resources, such
placement based on as Application Programming
user requirements or Interfaces (APIs) or internal
predefined templates. This services that may lead to
can help developers in wrong designs.
rapid prototyping.7
Life Sciences and Drug discovery Generative AI can be Intellectual property Intellectual risks in
Health Care sector used to streamline drug theft: Generative AI models Generative AI can be
discovery and development are trained on extensive mitigated using multiple
by identifying potential datasets, which may include strategies together, such
drug candidates and proprietary or patented as encryption,
testing their effectiveness information. Unauthorised secure data hosting,
before moving them for access to these models or access controls,
other trials.8 their outputs could result water-marking, digital
in intellectual property signatures, or content
theft, where competitors fingerprinting.
could access confidential
drug discovery processes,
formulas, or compounds.
6 Generative AI in the Banking and Finance Industry
7 Generative AI: The Next Frontier in Telecom Innovation
8 Generative AI Healthcare Industry: Benefits, Challenges, Potentials
09
Safeguarding Generative Artificial Intelligence (AI) with cybersecurity measures
Building blocks for secure Generative
AI solutions
Business values layer
Growth and Operational Risk
Compliance
innovation efficiency management
Governance layer
Strategy and Risk and compliance Training and Policies and
roadmap management awareness standards
Organisation Integration with
and operating business and IT
model Processes
Five pillars of responsible Generative AI adoption for a secure AI ecosystem
Adoption Maintenance Scaling Customisation Decommissioning
Policy and principles Continuous evaluation Planning Impact assessment Decommissioning
Develop policies and and monitoring Create a scalability plan Conduct impact policies
principles focusing Regularly assess fairness, that outlines the steps, assessments to Create clear protocols
on transparency, bias, accuracy, and benchmarks, and risk evaluate the potential and procedures for
accountability, and impact on users. mitigation measures impacts, risks, and shutting down or
safety for developing/ to scale Generative AI considerations decommissioning
Infrastructure
buying Generative AI systems. associated with Generative AI systems.
monitoring and
technologies. customising Generative Ensure to address data
security Model robustness
AI systems. retention issues and
Governance Set up processes Verify the robustness
privacy problems.
Establish a governance for infrastructure and generalisation Transparency
programme to monitoring, to help capabilities of Maintain transparency Safeguard against
manage Generative AI address vulnerabilities Generative AI models while customising the malicious use
technology. and incorporate the most during scaling. Generative AI systems, of Generative AI
recent developments in to enable users to trust technologies, such
Risk assessment Risk assessment
Generative AI security. the customised system. as models, data,
Conduct risk Conduct a risk
and associated
assessments to identify Awareness assessment to identify
infrastructure after
potentials risks, safety Encourage the potential risks, such
termination.
and bias issues. responsible use and as data breaches,
discourage malicious or or unintended
Update your policy
unethical applications of consequences, as a
regularly, with
Generative AI. result of scaling.
mitigation controls.
Risk control layer
Confidentiality Integrity Availability Authenticity Authorisation Privacy
Regulatory compliance
10
Safeguarding Generative Artificial Intelligence (AI) with cybersecurity measures
The Generative AI risk management framework rests upon a solid foundation comprising four key layers:
Business values layer: It evaluates potential risks and benefits from AI implementation, aligning
projects with overarching strategic objectives, financial robustness, reputation management, and
competitive edge.
Governance layer: Governance of Generative AI involves managing and overseeing its application
across people, processes, and technology to ensure its responsible, secure, and ethical use.
Effective governance of Generative AI requires a multidisciplinary approach involving collaboration
between different teams and stakeholders. It should be an ongoing process that evolves with
advancements in Generative AI technology and dynamic in nature to keep up with societal norms
and regulation changes.
Five pillars: It encompass adoption, maintenance, scaling, customisation, and decommissioning,
offering a comprehensive roadmap for navigating the complete Generative AI lifecycle and
proactively identifying and mitigating risks at each stage.
Risk control layer: The culminating risk control layer bolsters the framework with its paramount
role in ensuring that AI technologies harmonise with data security, privacy imperatives, and
regulatory compliance, extending from established principles like the CIA triad to encompass the
full spectrum of privacy considerations and adherence to pertinent regulations.
11
Safeguarding Generative Artificial Intelligence (AI) with cybersecurity measures
12
Safeguarding Generative Artificial Intelligence (AI) with cybersecurity measures
Way forward
Some key cyber questions to help you assess your organisation’s readiness
for a secure, private and ethical use of Generative AI. Questions we want to
leave you with:
01
What are your key business use cases to make this programme a success?
02
Do you have the right cyber investments (tools, technologies, processes, and skillset) in your strategic roadmap?
03
Is there a plan to ensure that your Generative AI tools do not threaten your organisation’s end users and
customers?
04
How do you ensure your Generative AI tools are not using your sensitive data for training?
05
Do you have a process in place to ensure sensitive data is not used without the right controls?
06
Do you have policies that ensure the security of your Generative AI models?
07
Do you have a Security Operations Center (SOC) to monitor threats in your Generative AI landscape?
08
How does your business ensure that only authorised users can access Generative AI tools, models,
infrastructure, and data?
09
Will your Generative AI be used or built by a third party, and do you need to re-assess and re-look at your current
third-party risk programmes?
10
Are you prepared to use Generative AI with your organisation’s privacy and confidential controls (including
consent mechanism and data sanity)?
13
Safeguarding Generative Artificial Intelligence (AI) with cybersecurity measures
Conclusion
Generative AI has immense possibilities regarding content, These risks cut across the ecosystem’s foundation, bringing
which can help reduce the effort and bring out efficiencies in the in human capital, technology, and industry processes. While
system. Generative AI has applications across the ecosystem, the potential for Generative AI is undeniable, what will make
affecting individuals, organisations, and society alike. it a transformational force is balancing the risk and bringing
in the right controls for the global scale of adoption.
While we celebrate this quantum leap in technological
advancement, similar to any technology, the cybersecurity Generative AI will create immense growth opportunities in
perspective that needs to be considered would enhance key areas, such as intelligent IT, products, and operations.
the scale and application of Generative AI. The risks largely The next decade will be when AI will become mainstream and
lie in key areas, i.e. Generative AI models, applications, further enhance human potential and growth.
infrastructure, people, data, and the training and testing
methodologies.
14
Safeguarding Generative Artificial Intelligence (AI) with cybersecurity measures
Connect with us
Sathish Gopalaiah Deepa Seshadri
President, T&T, Deloitte India Partner, Deloitte India
sathishtg@deloitte.com deseshadri@deloitte.com
Gaurav Shukla Praveen Sasidharan
Partner, Deloitte India Partner, Deloitte India
shuklagaurav@deloitte.com psasidharan@deloitte.com
Vikram Venkateswaran
Partner, Deloitte India
vikramv@deloitte.com
Contributors
David George
Rajat Kothari
Vivekchandran N V
Titas Nath
15
Safeguarding Generative Artificial Intelligence (AI) with cybersecurity measures
16
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164,deloitte,in-strategy-a-gcc-leaders-guide-for-driving-gen-ai-adoption-single-page-web-version-v5-noexp.pdf,"A GCC leader’s guide
for driving Generative
AI adoption
December 2024
A GCC leader’s guide for driving Generative AI adoption
ii
A GCC leader’s guide for driving Generative AI adoption
Table of contents
1. Foreword 02
2. The role of Global Capability Centres (GCCs) in harnessing
GenAI capabilities 04
2.1 Accelerating GenAI adoption 05
3. How can GCCs gauge their GenAI adoption readiness: A strategic
assessment framework 09
4. How can GCCs identify, qualify and prioritise use cases? 13
4.1 Identification of use cases 13
4.2 Use case qualification 16
4.3 Prioritisation of use cases 19
5. How can GCCs implement GenAI use cases? 22
5.1 Ensuring readiness for successful deployment 24
5.2 Achieving success in scaling GenAI solutions 26
6. The emergence of AI agents and multi-agents 32
7. Key considerations for GCC leaders 34
7.1 Solution trustworthiness 34
7.2 Other considerations 36
8. Conclusion 37
9. Connect with us 38
01
A GCC leader’s guide for driving Generative AI adoption
Foreword
The transformative power of Generative AI GenAI's full potential while mitigating potential
(GenAI) is undeniable, and its potential to reshape challenges.
industries and business operations is significant.
As the GCC landscape evolves, it is clear that these As Deloitte India’s GCC and AI leaders, we are
centres are not just passive observers but active excited to witness the role GCCs will play in
catalysts in this AI-driven transformation. The shaping the future of GenAI. This thought paper
combination of strategic alignment, technological reflects our commitment to empowering GCCs
prowess, data-driven insights and a culture of with the knowledge they need to initiate and lead
innovation positions GCCs well to adopt GenAI. this transformative journey. We believe that by
embracing GenAI and using the insights within this
This report is a resource for GCC leaders to report, GCCs can unlock value, drive innovation and
assess readiness for GenAI adoption. It provides achieve success in the AI-powered era.
an approach for identifying and prioritising use
cases, a high-level approach to implementation
and metrics to measure success. Addressing key
considerations and risks helps GCCs harness
Rohan Lobo Anjani Kumar
Partner, GCC Leader Partner, Artificial
Technology & Intelligence & Data
Transformation Technology &
Deloitte India Transformation
Deloitte India
02
AA GGCCCC lleeaaddeerr’’ss gguuiiddee ffoorr ddrriivviinngg GGeenneerraattiivvee AAII aaddooppttiioonn
Generative AI is the single most
significant platform transition
in computing history. In the
past 40 years, nothing has
been this big. It is bigger than
PC, it is bigger than mobile, and
it is going to be bigger than the
internet by far.1
- Jensen Huang, CEO, NVIDIA
1. The Practical Impact Of AI For The Masses, Forbes, 28 November 2023
03
A GCC leader’s guide for driving Generative AI adoption
The role of Global Capability Centres
(GCCs) in harnessing GenAI capabilities
GCCs can be at the forefront of driving
enterprise-wide adoption of GenAI. Their Text
strategic role allows them to effectively
Creative
pilot and scale AI initiatives, unlocking new Chatbots Translation
writing
opportunities for their parent organisations.
With their robust technological ecosystems
Image
and unwavering commitment to innovation,
GCCs are poised to lead this transformation 2D and 3D Image Product
and not just participate. Images enhancement simulation
GCCs are strategic partners driving innovation
Code
and digital transformation for their parent
organisations. They use advanced tools, agile Code Code Bug
methodologies and external partnerships to generation compilation fixing
enhance efficiency and customer/employee
experiences. Initiatives such as innovation labs
Audio
and hackathons keep them at the forefront
of technology. While GCCs have long led
Text-to-Speech Music Voice
technology transformation, GenAI presents a
Generator composition assistants
new frontier, offering incremental digitisation and
transformative opportunities for new services and
business models. Video
2D and 3D Video Video
GenAI can be applied across various modalities,
Videos simulation processing
offering unique capabilities for automating and
enhancing business operations. The primary
modalities include text, image, code, audio and
video. The illustration below demonstrates
The ability of GCCs to integrate GenAI
GenAI’s significant potential to transform business
into the fabric of their service delivery
processes and create new opportunities for
will be a game-changer, enabling
innovation at the enterprise scale. GCCs are the
them to redefine processes and
perfect testbed for these capabilities and will play
improve efficiency, establishing them
a key role in the widespread adoption of this new
as leaders in the global enterprise
technology.
ecosystem.
– Yatin Patil, Partner, Leader - Enterprise
Technology and Performance, Deloitte
South Asia
04
A GCC leader’s guide for driving Generative AI adoption
Accelerating GenAI adoption
GCCs possess inherent strengths and capabilities, making them the perfect leaders for their parent
organisation's GenAI adoption initiatives.
Strategic alignment
By proactively collaborating with the parent organisation’s
leadership, GCCs can define a clear GenAI strategy that
complements the broader business objectives. This enables
GCCs to integrate their operations seamlessly into their global
strategy, enhancing the organisation's ability to innovate and
operate efficiently.
Tech-enabled ecosystem
GCCs have a tech-enabled ecosystem that enhances
their ability to harness GenAI. Their robust
infrastructure, extensive network of technology
vendors, access to digital talent, thriving ER&D
community, and mature tech start-up ecosystem create
fertile ground for the rapid adoption and development
of GenAI solutions.
Data access and management
As key data custodians for their parent organisations, GCCs have
extensive experience in managing vast, cross-functional datasets.
Their adoption of a Centers of Excellence (COE) approach ensures
robust data governance and infrastructure. The ability to gather,
clean and maintain high-quality datasets, along with their
proficiency in automation and custom workflows, makes GenAI a
natural extension, enabling insights and innovative operations.
05
A GCC leader’s guide for driving Generative AI adoption
Talent availability
GCCs have expertise in AI/ML, product engineering and
analytics essential for developing, deploying and maintaining
GenAI solutions. This is complemented by business/domain
knowledge and process ownership, enabling them to provide
a well-rounded solution to business problems using GenAI.
The opportunity to work on cutting-edge global projects and
the learning thereof enable GCCs to attract and retain
AI talent necessary to drive innovation.
Culture of experimentation
A culture of experimentation is key for GCCs, enabling
stakeholders to explore and pilot new GenAI applications
across business functions. This approach allows GCCs
to rapidly test, refine and implement AI solutions,
driving meaningful innovation. The parent organisation
also plays a vital role by creating a supportive and
empowering environment that encourages GCCs to
experiment freely, gain insights and apply new learnings.
Scalability and flexibility
Using advanced infrastructure and methodologies, GCCs ensure
GenAI solutions remain scalable and flexible, adapting efficiently
to project demands. Cloud-based platforms allow GCCs to scale
resources as needed. Their experience in Agile and DevOps
practices enables rapid development and iterative improvement
of AI models. Such enablers allow GCCs to deploy modular,
maintainable AI solutions capable of handling varying workloads.
06
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Several GCCs have successfully adopted and implemented GenAI across various use cases. The following
is a selection of Deloitte case studies that illustrate how these GCCs have applied GenAI effectively:
Context Solution Impact
A British-Dutch multinational consumer goods company
GenAI bot for Employees Implemented • Enhanced employee
enhancing experienced delays voice-enabled AI experience through 24/7
employee and inefficiencies bots integrated support.
experience in resolving payroll with ServiceNow • Achieved a 65 percent
and HR queries, to efficiently handle success rate and lowered
such as taxation and diverse payroll and ticket volumes by 5 percent.
benefits issues. HR-related inquiries. • Reduced dependency on
human intervention through
self-service options.
An American multinational technology company
GenAI-enabled Various customer Automated template • Reduce effort and time in
customer scenarios, such as generation using generating new templates.
communications declined payments GenAI to craft • Improve operational
or subscription personalised messages efficiency.
renewals, based on customer • Facilitate faster response
required tailored interactions. times to customer inquiries.
communications.
Manual drafting was
time-consuming and
inefficient.
A German luxury automotive company
Automated Instructions drafting Developed an AI-based • Reduce effort and time in
generation was manually system to automate generating instruction.
of assembly done, requiring the generation • Improve operational
instructions significant effort of assembly efficiency.
to prepare part- instructions based on • Reduce manual effort for
specific assembly car model BOM data. new part assembly.
instructions.
07
A GCC leader’s guide for driving Generative AI adoption
The nexus of enterprise systems, GenAI and GCCs for
enterprise transformation
Integrating GenAI into enterprise systems, unconventional GenAI experiences, enabling
especially those adopted by GCCs such as SAP, productivity improvements, increased
Oracle and ServiceNow, presents a compelling operations agility and better employee/
opportunity for GCCs to transform enterprise customer experiences for IT service and
operations. Using AI’s capabilities within these operations management, customer service
core systems, GCCs can automate routine tasks, management, HR services, portfolio
extract valuable insights from vast datasets and management, etc.
enable intelligent decision-making. The ability of
GenAI to understand natural language queries Deloitte’s Ascend5 platform for ERP systems
and generate contextually relevant responses can incorporates GenAI capabilities and process
significantly enhance user experiences within their accelerators to support clients' transformation
environments: initiatives. These advancements cater to various
use cases embedded with enterprise systems,
• Per SAP, SAP Joule2 has been integrated
enabling automation across critical functions such
across various SAP applications, including
as autonomous coding, configuration, design,
HR, finance, supply chain, procurement,
testing and project management. This integration
customer experience and into the SAP Business
streamlines operations and accelerates digital
Technology Platform. Joule aims to enhance
transformation for clients, positioning them to
user interaction by providing seamless
achieve greater efficiency and innovation in their
navigation, rapid information retrieval and
ERP-driven processes.
efficient execution of business tasks. It also
offers proactive recommendations and even AI-
assisted code generation.
• Oracle cites its OCI Generative AI Service,3 which
incorporates large language models into its
GCCs are experiencing rapid growth, becoming
modules. It supports use cases, such as writing
mature, efficient and innovative. As they continue
assistance, summarisation, data analysis and
this journey, GenAI presents a unique opportunity
interactive chat, helping businesses automate
that they must seize, lead and own.
and enhance various operations across their
– Deepak Mowdhgalya, Partner, Leader, Finance
systems.
Transformation, Deloitte India
• According to ServiceNow, GenAI has been
integrated into the workflows of its Now
Platform,4 called Now Assist. It provides
2. SAP Joule, SAP
3. Oracle Generative AI Service, Oracle
4. “Put Generative AI to work with Now Assist,” Service Now
5. Deloitte’s Ascend™, Deloitte
08
A GCC leader’s guide for driving Generative AI adoption
How can GCCs gauge their GenAI
adoption readiness: A strategic
assessment framework
As GCCs plan to use GenAI for business transformation, they must assess their readiness to implement
and adopt this technology effectively, focusing on two key dimensions:
Ecosystem enablers: Strategic factors Capabilities: Organisational and technical
that support GCCs’ overall readiness and elements that ensure the GCCs are
alignment with the parent organisation’s equipped to develop, deploy and sustain
objectives, focusing on fostering innovation GenAI solutions.
and ensuring leadership buy-in.
Ecosystem enablers Capabilities
1. Strategic alignment: Alignment of a GCC with 1. Technology infrastructure: Technology
the parent organisation’s goals and objectives, capabilities include computing power, scalable
demonstrating its ability to deliver strategic storage, advanced AI/ML tools, frameworks and
business outcomes and support the parent libraries, and networking to support end-to-
organisation in pursuit of its goals. end solutions.
2. S ervices/Processes delivered: The range 2. T alent pool: Expertise in AI, ML, data science
and depth of services and processes delivered and software development for strategising and
indicate the level of collaboration and demand implementing GenAI solutions at scale.
for GenAI use cases.
3. D ata management capability: Effective
3. L eadership buy-in: Align with the business/ data storage, processing and management
functional and regional leaders to obtain capabilities within GCC.
resources and sponsorship to drive GenAI
4. Change management and communications:
initiatives.
Effectively drive awareness of GenAI solutions
4. Culture of innovation: The extent to which a and ensure employee readiness through
GCC fosters an environment that encourages knowledge management initiatives and
experimentation, innovation and adoption of communication.
new technologies and methodologies.
5. R isk, compliance and security: Established
governance structures for data privacy and
processes to mitigate hallucinations and
unethical responses.
6. P artnerships: Third-party partnerships with
industry players, hyperscalers, academia,
research institutions or start-ups to enhance
GenAI capabilities.
09
A GCC leader’s guide for driving Generative AI adoption
Figure 1: GenAI adoption readiness assessment framework
High
High
Initiate groundwork: The GCC is in a nascent
stage and should work towards building the
ecosystem enablers and capabilities to deliver
GenAI initiatives. It must start by aligning with
the parent on how its contributions will enable
it to achieve the goals and objectives. GCCs
must seek sponsorship from leaders and strive
to build a pipeline for GenAI use cases. Based
on the role alignment with the parent, GCCs
must build/enhance capabilities to meet the
desired objectives.
srelbane
metsysocE
A GCC leader’s guide for driving Generative AI adoption
A quick mapping of GCC’s abilities across the two dimensions would reveal its readiness quotient to
successfully undertake the GenAI journey. This assessment identifies GCC's current positioning and
highlights the key focus areas.
Favourably placed: The GCC is well-positioned
Build capability: While there is clarity and
to implement GenAI initiatives effectively as
alignment on GCC contributions to the parent
it has clarity on its role and strategy in line
organisation and express support from the
with that of the parent organisation. The
leadership for driving GenAI initiatives, the GCC
GCC collaborates seamlessly with the parent
must look to ramp up capabilities across talent,
company and ensures there is sponsorship and
technology, etc., to successfully deliver the
demand from the parent leadership to drive
GenAI initiatives.
the GenAI initiatives. The GCC has also built
capabilities to deliver on the GenAI agenda.
Build Favourably
capability placed
Initiate Re-evaluate
groundwork strategy
Low
Capabilities
Re-evaluate strategy: While there could be
capabilities to deliver on GenAI, re-engage with
the parent and align on the strategy for the
GCC in driving GenAI initiatives. The GCC should
ensure clarity on how its initiatives will contribute
to the overall goals of the parent organisation
and seek leadership buy-in to ensure continuous
demand for GenAI driven from the GCC. As the
GCC has developed certain GenAI capabilities,
it should reassess, reorganise and redeploy its
capabilities in line with the strategy defined for
the GCC.
10
A GCC leader’s guide for driving Generative AI adoption
2. R e-evaluate strategy: While there could be capabilities to deliver on GenAI, re-engage with the parent and align on the strategy
for the GCC in driving GenAI initiatives. The GCC should ensure clarity on how its initiatives will contribute to the overall goals of
the parent organisation and seek leadership buy-in to ensure continuous demand for GenAI driven from the GCC. As the GCC has
developed certain GenAI capabilities, it should reassess, reorganise and redeploy its capabilities in line with the strategy defined
for the GCC.
Figure 2: Assessment criteria for parameters to evaluate GCC readiness
Ecosystem enablers
Low Parameter/Dimension High
Limited alignment with parent GCC goals and objectives,
organisation on goals and Strategic and its operations, methods
objectives, and its operations, alignment and practices are aligned and
methods and practices operating in unison with that of
the parent organisation
GCC supports a limited number GCC supports multiple functions
Services/
of functions and processes and a wide array of processes
Processes
delivered and sub-processes
Leaders not forthcoming in Leaders actively support shared
supporting and committing goals and vision and are willing
Leadership
resources for GCCs to undertake to contribute to success by
buy-in
new and bold initiatives committing effort and budget for
new and bold initiatives
Risk-averse mindset with a focus Strong focus on innovation with
on maintaining the status quo; Culture of consistent support, fostering
minimal focus on innovation innovation creativity and proactive adoption
of new technologies across GCC
11
A GCC leader’s guide for driving Generative AI adoption
Parameter/Dimension Low High
Strategic alignment Limited alignment with parent
organisation on goals and objectives, and
its operations, methods and practices
Services/Processes delivered GCC supports a limited number of GCC supports multiple functions and
functions and processes a wide array of processes and sub-
processes
Leadership buy-in Leaders not forthcoming in supporting Leaders actively support shared goals
Capabilities
and committing resources for GCCs to and vision and are willing to contribute
undertake new and bold initiatives to success by committing effort and
budget for new and bold initiatives
Low Parameter/Dimension High
Culture of innovation Risk-averse mindset with a focus on Strong focus on innovation with
maintaining the status quo; minimal focus consistent support, fostering creativity
on innovation and proactive adoption of new Basic infrastructure is limited Full stack infrastructure with
technologies across GCC Technology
by computing power, storage greater levels of computing
Technology infrastructure Basic infrastructure is limited by Full stack infrastructure with greater infrastructure
capacity, AI frameworks and power and storage, and access
computing power, storage capacity, AI levels of computing power and storage,
libraries, etc. to AI frameworks and libraries
frameworks and libraries, etc. and access to AI frameworks and
libraries
Talent pool Talent with skillsets in traditional tools and Availability of talent pool with
Talent with skillsets in traditional Availability of talent pool with
technologies; talent not geared for driving proficiency in new and advanced
tools and technologies; talent proficiency in new and advanced
new and cutting-edge technologies technologies, including AI/ML, analytics Talent pool
and data science not geared for driving new and technologies, including AI/ML,
Data management capability Inconsistent data governance, limited Robust data governance, advanced cutting-edge technologies analytics and data science
data handling and poor data quality data processing capabilities and high
data quality
Change management and Ad-hoc change management with limited Proactive change management with Inconsistent data governance, Data Robust data governance,
communications communication, training and employee effective communication, employee limited data handling and poor management advanced data processing
engagement involvement and comprehensive
data quality capability capabilities and high data quality
training and support
Risk, compliance and security Inadequate control and monitoring Actively enforced data regulation
mechanisms to comply with data privacy guidelines and controls with strong
Ad-hoc change management Proactive change management
regulations, relying only on basic data data encryption, access controls,
encryption and access controls network security measures, etc. with limited communication, Change with effective communication,
Partnerships Vendor relationships with the ecosystem Vendor relationships are long- training and employee management and employee involvement and
players are new, transactional and at a term, strategic and mature; hold engagement communications comprehensive training and
nascent stage; require significant effort to considerable bargaining power
support
gain bargaining power
Inadequate control and Actively enforced data
monitoring mechanisms to Risk, compliance regulation guidelines and
comply with data privacy and security controls with strong data
regulations, relying only on encryption, access controls,
basic data encryption and network security measures, etc.
access controls
Vendor relationships with the Vendor relationships are long-
ecosystem players are new, term, strategic and mature;
transactional and at a nascent Partnerships hold considerable bargaining
stage; require significant effort power
to gain bargaining power
After establishing the readiness levels of GCCs to adopt GenAI, the next step is to identify impactful use
cases for implementation. The next section will explore the methodology for identifying and evaluating
potential use cases.
12
A GCC leader’s guide for driving Generative AI adoption
How can GCCs identify, qualify and
prioritise use cases?
GCCs should adopt a systematic approach to identifying, qualifying and prioritising GenAI use
cases. This approach should be focused on achieving end-user adoption and ensuring alignment
with business goals and feasibility for maximum impact.
Identification Qualification Prioritisation
Analyse existing service Qualify the identified Identify the priority
catalogues or process use cases by evaluating order for the qualified
taxonomy to identify use key factors such as data use cases by evaluating
cases that use GenAI availability, technical value/benefits vis-à-vis
capabilities both within feasibility, financial the effort required.
the GCC and in the viability and associated
broader organisation. risks.
Identification of use cases
As part of the first step, GCCs need to identify potential use cases that can significantly benefit from
this technology by analysing existing service catalogues and process taxonomies and collaborating
with process leads/owners. During this assessment, it is important to identify the end user and the
benefits they realise. This process is guided by the following key questions, each aligned with different
applications of GenAI:
13
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Is the content being
summarised?
Condense large volumes of text
Is there any content being or data into concise and coherent Are there any conversations
generated? summaries, highlighting the most involved?
Original text, images, music critical information. Engages in human-like dialogue,
or other media created understanding and responding
E.g., Meeting minutes, workshop
to queries and maintaining
from scratch based on given
summary and document summary.
context over multiple
parameters or prompts.
interactions.
E.g., Report writing, email
E.g., FAQs, chatbots, internal
drafting, image banners and video
helpdesk support and non-textual
generation.
help.
GenAI
Is the content being
improves upon rule-
personalised?
based traditional AI
Tailor content,
by offering greater
recommendations,
creativity, contextual
or interactions to
understanding and
individual users based
nuanced output.
on their preferences,
behaviours and needs.
E.g., Personalised product
recommendations,
adaptive learning paths
and tailored marketing
content.
Is the content being analysed?
Is the content being Examine and interpret data, Is the content being
translated? text or other inputs to identify transformed?
Convert text or speech from patterns, insights and actionable Convert existing content into
one language to another while information. different formats or styles.
maintaining the original meaning
E.g., Forecasting (demand, supply, E.g., Text to code, style transfer
and context.
price), risk identification and and personalisation, text to table.
E.g., French to English, English to feedback sentiment analysis.
Hindi.
14
A GCC leader’s guide for driving Generative AI adoption
FINANCE
Procurement and
L2
purchasing
Supplier selection, Purchase order Document receipt, Approval routing
L3 onboarding and creation and data extraction and database
management delivery management and validation updates
15
seitivitcA
sesac
esU
IAneG
seitilibapaC
Figure 3: Methodology illustrating identification of use cases of two processes
ILLUSTRATIVE
L1
Invoice processing
and management
• Identify and • Create and approve • Receive invoices • Match invoices
evaluate potential purchase orders through channels to appropriate
suppliers • Track delivery of such as email, post approvers based
• Negotiate terms goods and services and fax; scan and on a predefined
and conditions • Address digitise physical approval hierarchy
• Finalise discrepancies, invoices • Update the
agreements and handle returns and • Extract key data company's
onboard suppliers manage supplier and convert various accounting system
performance inputs into one and archive
standard format paid invoices for
• Validate extracted auditing and future
data against reference
purchase
orders; identify
discrepancies, if any
• Supplier risk • Purchase order • Format • Smart approval
report and drafting standardisation routing
evaluation • Delivery forecasting • Error detection • Order and
summary • Personalised and discrepancy delivery query
generation supplier report generation bot for real-time
• Contract drafting communications updates
drafting
Content Content Content Content
generation generation summarisation analysis
Content analysis Content analysis Content analysis
Conversation
Content Content Content
summarisation personalisation transformation
By replicating the use case identification approach across processes and functions within the
organisation, a comprehensive list of potential use cases can be derived. This enables GCCs to
systematically uncover and harness the full spectrum of opportunities presented by GenAI.
AA GGCCCC lleeaaddeerr’’ss gguuiiddee ffoorr ddrriivviinngg GGeenneerraattiivvee AAII aaddooppttiioonn
Use case qualification
GCCs must next evaluate their list of identified use cases for GenAI to ensure they are feasible,
valuable and aligned with organisational goals. The following considerations outline the key
aspects necessary for an in-depth evaluation:
Fact ors to consider for qualification of use cases
Technical
feasibility
Data
availability
Infrastructure requirements
• A re robust API integration capabilities available?
• A re development frameworks such as ML and
NLP libraries available?
Requirement • A re other supporting hardware/software
Does the use case require domain-specific data for infrastructure available?
training the model?
Vendor availability
Quantity
Can use cases or clusters of use cases (with the
Is data sufficient for model building? Are there
same capability) be implemented using common
significant gaps or any missing data points?
vendors available in the market?
Quality
Is the data good enough for contextual
Reusability
understanding? Are multiple data sources available
Can the solution be reused or adapted for other
to enrich and diversify the dataset and remove
use cases requiring similar capability?
bias?
Validation
Are evaluation metrics in place to ensure GenAI
output accuracy and reliability? Is there a process
for identifying and addressing biases and for
comparing outputs to desired outcomes?
16
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Financial
feasibility
Risk,
compliance
and
regulatory
Cost Privacy and security
• W hat are the costs associated with infrastructure Are there data privacy or security concerns?
setup, maintenance, hiring and upskilling?
Ethical concerns
• W hat are the vendor partnerships, licensing fees, Could the use case trigger unethical responses?
integration downtime costs and ongoing support
Safety check
costs?
Can biases, errors and hallucinations be corrected?
Regulatory
Benefits
Are there regulatory requirements or compliance
• W hat tangible and intangible benefits can be
issues that must be addressed?
achieved, such as direct cost savings, operational
efficiency and resource optimisation? Use case exposure
Are the use cases being implemented for external
• H ow does it enhance quality, reduce errors/risks
customers with high exposure and risk compared
and improve overall performance?
with internal customers?
17
A GCC leader’s guide for driving Generative AI adoption
Figure 4: Methodology illustrating qualification of use cases of two processes
18
ECNANIF
The application of the use case qualification framework is illustrated through two finance processes.
This example demonstrates how use cases can be thoroughly vetted, ensuring only the most viable and
impactful ones are selected for implementation.
Content
personalisation
By performing these checks for each use case, a subset of qualified use cases can be derived. This
serves as a foundation for further prioritisation, ensuring that only the most valuable and practical use
cases are qualified.
gnisahcrup
dna
tnemerucorP
tnemeganam
dna
gnissecorp
eciovnI
L1 L2 L3 Use cases GenAI Data Technical Financial Risk, Qualification
capability availability feasibility feasibility compliance
and
regulatory
Supplier Supplier risk
selection, report and
onboarding evaluation
and summary
management generation
Contract drafting
Purchase Purchase order
order creation and order
and delivery confirmation
management drafting
Delivery
forecasting
Personalised
supplier
communications
drafting
Document Format
receipt, data standardisation
extraction
Error detection
and validation
and discrepancy
report generation
Smart approval
Approval
routing
routing and
database Order delivery
updates query bot for real
time updates
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A GCC leader’s guide for driving Generative AI adoption
Prioritisation of use cases
After qualifying use cases, GCCs must prioritise them by evaluating the expected benefits and effort
required. A framework provided below supports use case prioritisation across two dimensions: Benefits
and effort.
High
Low-hanging High-impact
fruits investments
Incremental
Resource drains
gains
Low
Low High
19
stfieneB
Effort
Benefits
Financial benefit: The net financial benefit enabled through a business case considering key metrics,
including run costs, Total Cost of Ownership (TCO) and payback period.
Strategic alignment: The extent to which the use case aligns with the organisation’s strategic
objectives.
Scalability and reusability: The ability of a solution to scale or use across multiple use cases (cross-
functional potential).
Non-financial benefit: The benefits such as improvement in productivity, agility, customer satisfaction
or employee experience.
Effort
Time: The time required to realise benefits.
Talent: Resources with multiple skillsets deployed across lifecycle.
Budget: Estimated budget, including development and run cost"
167,deloitte,Generative AI in Europe _ Deloitte Insights.pdf,"Now decides next. Is Europe ready for
generative AI?
Opportunities and hurdles: Europe's path in the Generative AI era
ARTICLE • 12-MIN READ • 18 JANUARY 2024
Since its debut in 2022, ChatGPT has rapidly seized the attention of businesses and societies worldwide, prompting organisations to rethink their
practices and strategies around tech and talent. Yet, as revealed in Deloitte’s The state of generative AI in the enterprise, regional disparities exist in the
adoption and readiness for such generative artificial intelligence tools as ChatGPT and Bard.
Factors such as investment levels, regulatory environments, risk appetite and talent availability vary significantly around the world, influencing
organisations’ ability to unlock the potential of generative AI. Europe, in particular, has the potential for growth in organisational preparedness,
adoption of generative AI tools and applications, risk management of generative AI and talent-related strategies. This article focuses on the
opportunities and challenges affecting Europe’s AI landscape, including labour shortages, skills gaps and stricter regulations.
Understanding generative AI: Deloitte's global research methodology
From 12 October to 5 December 2023, Deloitte surveyed over 2,800 global leaders (directors and above) to understand their views on generative AI. Participants were
required to have at least one working implementation of AI and a pilot of gen AI. The survey included respondents from the Americas (56%), Europe (27%) and Asia-
Pacific (17%).
There were 756 European business leaders from various countries and industries, with most representing organisations earning over US$1 billion annually. All
respondents have roles in their organisation’s AI and data science strategy decisions, investments, implementation approach and value measurement.
Generative AI: Transforming content generation, search and conversational
interfaces
Generative AI, a specific type of AI known for creating human-like outputs,1 is used to develop content across various formats like text, computer code,
audio and/or visual output.2 The most common applications reported by survey respondents globally included content generation, search/knowledge
management, virtual assistants/conversational chatbots and content summarisation. In terms of integrated generative AI resources, the top categories
are productivity applications, enterprise platforms, publicly available large language models (LLMs) and code generators.
The state of generative AI in the
enterprise
Read more
European perspective on generative AI
Balancing caution with opportunity in adoption of generative AI
While there are broad similarities in use cases globally, European leaders show less interest and attention towards generative AI than their counterparts
in the Americas and Asia-Pacific regions (figure 1). In line with this lower level of engagement, a significant portion of European respondents (over
20%) believe their industry and their own organisations are paying ‘too little attention’ to generative AI’s potential and implications.
This could relate to less perceived pressure for European respondents to adopt generative AI, with only 26% reporting significant pressure compared to
higher percentages in the Americas and Asia-Pacific. Additionally, they anticipate a more extended time frame for AI to significantly transform their
organisations, with a higher proportion of European leaders believing it will take more than three years, and only 9% currently seeing transformative
effects take shape. This contrasts with higher percentages in other regions where users believe in AI’s immediate transformative impact (figure 2).
Research from The Deloitte Global Boardroom Program found that almost half (48%) of European leadership teams and board members identified
their inability to show how technology enables growth as their biggest challenge when assessing the value of digital transformations.3 This reflects a
broader technology-literacy predicament for European executives resulting in their belief that their organisations are not ready for generative AI.
When asked about the emotions leaders associate with generative AI, excitement and fascination are common responses across regions. Still, European
leaders report notably lower trust concerning the technology. This mistrust may stem from cultural differences and concerns about AI-associated risks
like biases and copyright issues.4 European companies are focused on developing this new technology responsibly and ensuring its trustworthiness.
They aim to balance the potential and advantages of generative AI with the need for it to be regulated. This means ensuring that AI systems are fair,
impartial and accountable. They also want AI to be responsible, robust and dependable, while being safe and secure and protecting privacy and
confidentiality. Emphasising ethical AI practices could help organisations avoid reputational risk and enhance trust among customers and employees.
High expectations for productivity amidst slow adoption of generative AI tools
European leaders in our study highlight efficiency, productivity, cost reduction, innovation and growth improvements as the benefits of generative AI,
which mirrors global findings. These results are also consistent with previous reports such as the autumn 2023 edition of Deloitte’s European CFO
Survey.5 A significant 91% of European respondents expect generative AI to increase productivity, aligning with global results. This is particularly
significant for Europe, given the region’s recent productivity challenges, as highlighted by Deloitte Germany’s research into the economic effects of a
shrinking workforce.6
Despite such acknowledged benefits, European leaders face implementation challenges. Lower interest levels, trust gaps, slow implementation of
governing regulation and expectations of longer timelines for generative AI–driven change hinder organisational investment in and readiness for these
technologies. Compared to other regions, European leaders report less preparedness for adopting generative AI in business areas like risk management,
strategy, talent development and technology infrastructure.
Similarly, generative AI adoption in Europe is lower across all business functions compared to other regions (figure 3). Alongside regulatory
considerations, this may stem from Europe’s challenging economic conditions and ongoing geopolitical tensions impacting interest and slow adoption.
The survey took place against a backdrop of a US economy that had outperformed expectations and in which growth had accelerated. In contrast,
European growth had slowed sharply and Germany, although not the euro area as a whole, had fallen into recession. The US has also enacted policies
to enhance economic competitiveness, such as the Inflation Reduction Act and the CHIPS and Science Act. However, this does not necessarily explain
the higher levels of adoption in the Americas as the NextGenerationEU programme could provide similar incentives for European organisations to
adopt generative AI.7
Lower levels of generative AI adoption are certainly a result of European companies operating in a more complex and regulated environment than their
counterparts in the Americas and Asia-Pacific regions. In December 2023, the EU provisionally agreed on the EU AI Act, its landmark, world-first AI
regulation, which will introduce a comprehensive, legally binding, cross-sectoral framework for the technology to regulate its use and development.
Using a risk-based but prescriptive approach, the law will regulate AI, including generative AI, based on the potential risks of specific models or
applications. Certain AI use cases, such as behavioural manipulation, will be banned altogether. For AI systems and models deemed high-risk,
organisations providing or deploying them will be subject to stringent requirements, including pre-deployment fundamental rights impact assessments,
pre-market conformity assessments and transparency obligations, to name but a few.8 While the compliance implications are likely to be substantial,
the Act will also bring more accountability and fairer distribution of responsibilities across the AI value chain, as well as increased consistency across
sectors.
The Act will also have global implications, as it will apply to any AI providers or deployers whose systems are marketed or affect individuals residing in
the EU, regardless of their location. The final legal text, expected in early 2024, will give organisations further details to fully assess the Act's
operational and strategic impacts.9 It will be interesting to observe whether further clarity on the EU regulations will speed up the pace of
implementation of generative AI in Europe.
Walking the tightrope: As generative AI meets EU regulation, pragmatism is likely
Read more from TMT Predictions 2024
While there’s an expectation of comparable increased investment across the Americas, Asia-Pacific and Europe, European organisations in our survey
reported allocating less budget to generative AI than their peers in other regions. The wait for the final legal text of the EU AI Act may account for the
reluctance of European executives to move forward with investment as they wait to understand the regulatory trickledown of what the Act means for
them in practice. Further, Europe does not have the same legacy of investing in digital transformation and disruptive technologies (figures 4 and 5).
Historically, most external private investments in such technologies have been concentrated in the Americas, with the leading creators of generative AI
and the most notable LLMs in the world being based mainly in the US.10
Globally, our report shows that leaders tend to prefer buying over building generative AI tools, a trend particularly noticeable in Europe, where 37%
acknowledge this as their go-to strategy. In the Americas, it is 33%, and in Asia-Pacific 32%. This strategy is cost-effective for routine activities but
offers limited control and lacks a disruptive competitive advantage.11 However, this may not be a choice for many European organisations, who likely
do not have the resources to create and experiment with LLMs and lack access to the high-specification hardware needed to train models. It has been
widely reported that the graphic-processing units needed, for example, Nvidia A100/H100, have been stockpiled by various entities, especially in
Asia.12
Talent strategies
European organisations are less active in reskilling workers, educating their workforce and recruiting technical talent (figure 6). The latter is partially
due to the region’s more limited talent pool and existing skills shortages.13 More than a third of the EU’s labour force lacks necessary digital skills,14
and the UK seems to be in a similar position.15 These talent shortages, combined with modest efforts in educating and reskilling workers, are hindering
Europe’s ability to leverage the benefits of generative AI fully.
Europe’s cautious approach to reskilling its workforce may be influenced by its strong labour protection laws and high unionisation rates. In the case of
generative AI leading to job displacement, European businesses may perceive the immediate benefits of generative AI, like cost savings and productivity
gains, as less substantial compared to regions with less stringent labour laws.16 Additionally, robust labour protection and trade unions require
European companies to adopt a more deliberate approach when implementing technologies that could displace jobs as it can involve complex legal
considerations.
Yet there is also the possibility of generative AI leading to job augmentation, rather than job displacement via automation. A recent Deloitte report on
generative AI and the future of work17 suggests “there is a growing sense that generative AI will augment the human workforce rather than replace it.”
In other words, generative AI can enhance the workforce experience by eliminating routine tasks, allowing employees to focus on more meaningful
work and increasing employee job satisfaction and performance in the process.
As such, these rather limited efforts around talent might have adverse implications. The general-purpose nature of generative AI means that the demand
for skilled labour could increase across a broad range of occupations and industries. In addition, in countries with ageing workforces or declining
working-age populations, there’s often an increased drive towards automation to compensate for labour shortages.18 Firms in regions with a declining
number of middle-aged workers have historically turned to automation to make up for this demographic shortfall. With many European countries
dependent on declining working-age populations, the likelihood of widespread generative AI adoption increases.19
Completing such a transition means an increased demand for skilled workers at a time when demographic trends mean companies will be competing
for an ever-shrinking labour pool.20 This makes the lack of transparency of European businesses and reluctance to actively educate their workforce
about AI’s capabilities, benefits and value puzzling. Still, organisations will only realise generative AI’s potential with the understanding and acceptance
of employees. In particular, their fears about automation and job displacement need to be addressed.
Many European respondents in our study believe it will take up to two years to adjust their talent strategies for generative AI, with fewer feeling an
immediate need for change than counterparts in the Americas or Asia-Pacific. This may indicate a more cautious approach to organisational change
amid ongoing considerations of the technology’s risks, or it may simply be as a result of not yet knowing what the workforce implications will be as
this technology rolls out. Will generative AI replace jobs or make jobs easier and more enjoyable? Whether it plays more of a role in enhancing the
employee experience and enabling people to be more productive at work or taking over entire tasks and roles is yet to be determined as the potential of
this technology is explored.
Talent and skill gaps: Europe’s main challenge to maximising generative AI's potential
Across all regions, the technical talent shortage is a critical barrier to developing and deploying generative AI, with nearly 40% of European leaders
selecting this as a key obstacle This is consistent with previous Deloitte analyses that identify talent resources and capabilities as the main challenge in
Europe.21 European leaders also cite a lack of an adoption strategy and regulatory compliance concerns more than leaders in other regions (figure 7).
This is even though European organisations have less difficulty identifying use cases than peers in different regions.
Concerns common across regions include intellectual property issues, regulatory compliance, a lack of confidence in AI results, transparency, data
privacy and data misuse. European respondents more frequently see risk management as a barrier to implementing generative AI and are less convinced
about their organisation’s efforts in governing AI adoption and mitigating potential risks. Effective governance of generative AI is likely to be an
essential precursor to its scalable adoption across European organisations.
Respondents were also asked about strategies for managing generative AI risks. Top actions include monitoring and regulatory compliance, governance
frameworks and internal audits. European respondents particularly emphasised regulatory compliance as important, tying back to the need for a clearer
understanding of how the EU AI Act will impact organisations in practice.
Moreover, with generative AI, risk and regulation are no longer an exercise in technology management. Instead, when considered equally to other
strategic levers they can realise significant value. The relative novelty of LLMs in business applications can be a challenge, and the risks of LLMs are
dynamic and may change depending on their interactions with the user. However, development of guardrails, alongside proportionate deployment of
testing, controls and monitoring mechanisms can empower organisations to use generative AI safely and confidently.22
Generative AI: A strategic imperative for European businesses
This analysis shows that European leaders should prioritise preparing their organisations and workforce for the disruptive potential of generative AI.
Recent Deloitte reports indicate that, although generative AI is a new technology requiring time for adoption and benefits realisation, aligning it with
an organisational strategy is critical.23
Europe’s cautious approach to this emerging technology, characterised by a wait-and-see attitude, contrasts with the more proactive stances reported in
the Americas and Asia-Pacific regions. This difference in approach could see Europe lag in exploring the potential for generative AI, but it could also
result in a more responsible deployment environment that considers new responsibilities that are created when technologies are invented.
Balancing the need for trust with the urgency to remain competitive in the global market is critical. This involves taking a multi-disciplinary approach
to develop generative AI transformation strategies from the outset, and not just considering the technology potential itself. By approaching technology
investment responsibly, while also investing in the necessary training and development of the workforce, European organisations can better position
themselves to capitalise on the enormous benefits of generative AI, such as increased efficiency, innovation and competitive advantage.

Let’s make this work.
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BY Stacey Winters Richard Horton
United Kingdom United Kingdom
Roxana Corduneanu
United Kingdom
Endnotes
1. Deloitte, “Deloitte AI Institute UK,” accessed 11 January 2024.
View in Article
2. Ibid.
View in Article
3. Dan Konigsburg, William Touche, and Jo Iwasaki, Digital frontier: A technology deficit in the boardroom, Deloitte Insights, 13 June 2022.
View in Article
4. Caroline Atkinson, Europe and technology, Hoover Institution, 4 February 2019; Lukas Kruger and Michelle Seng Ah Lee, “Risks and ethical
considerations of generative AI,” blog, Deloitte, 6 June 2023.
View in Article
5. Jose Manuel Dominguez Carravilla, Richard Muschamp, Rolf Epstein, Dr. Pauliina Sandqvist, and Ram Krishna Sahu, European CFO Outlook
—Autumn edition, Deloitte Insights, accessed 11 January 2024.
View in Article
6. Deloitte Insights Magazine, To help bolster aging economies, boost workforce participation, data point, accessed 11 January 2024.
View in Article
7. Stefano Alfonso, Hilde Van de Velde, Miguel Eiras Antunes, Luca Bonacina, and Carlos Bofill, Futureproofing Europe: How the
NextGenerationEU programme is inspiring companies to transform, Deloitte Insights, 24 July 2023.
View in Article
8. Providers of general-purpose AI models and systems will be subject to specific requirements, based on the level of risk their products pose.
View in Article
9. Valeria Gallo and Suchitra Nair, “The EU AI Act: The finish line is in sight,” blog, Deloitte, 13 December 2023.
View in Article
10. Michael Chui, Eric Hazan, Roger Roberts, Alex Singla, Kate Smaje, Alex Sukharevsky, Lareina Yee, Rodney Zemmel, The economic potential of
generative AI: The next productivity frontier, McKinsey & Company, accessed 11 January 2024.
View in Article
11. Forbes, “Should you build or buy your AI?,” 22 May 2019.
View in Article
12. Qianer Liu and Hannah Murphy, “China’s internet giants order $5bn of Nvidia chips to power AI ambitions,” Financial Times, 10 August 2023.
View in Article
13. Martin Arnold and Valentina Romei, “Eurozone jobless rate hits record low of 7% as worker shortages spread,” Financial Times, 1 February
2022.
View in Article
14. European Union, “Plugging the digital skills gap,” accessed 11 January 2024.
View in Article
15. Jo Thornhill and Kevin Pratt, IT skills gap report 2023, Forbes, 13 September 2023.
View in Article
16. Ira Kalish and Michael Wolf, Generative AI and the labor market: A case for techno-optimism, Deloitte Insights, accessed 11 January 2024.
View in Article
17. Nicole Scoble-Williams, Diane Sinti, Jodi Baker Calamai, Bjorn Bringmann, Laura Shact, Greg Vert, Tara Murphy, and Sue Cantrell, Generative
AI and the future of work: The potential? Boundless, Deloitte AI Institute, accessed 11 January 2024.
View in Article
18. Daron Acemoğlu and Pascual Restrepo, Demographics and automation, MIT, accessed 11 January 2024.
View in Article
19. Kalish and Wolf, Generative AI and the labor market.
View in Article
20. Ibid.
View in Article
21. Carravilla, Muschamp, Epstein, Sandqvist, and Sahu, European CFO Outlook—Autumn edition.
View in Article
22. Deloitte, “Embedding controls and risk mitigations throughout the generative AI development lifecycle,” blog, accessed 11 January 2024.
View in Article
23. Gregory Dost and Diana Kearns-Manolatos, “Unleashing value from digital transformation: Paths and pitfalls,” blog, 14 February 2023; Brenna
Sniderman, Diana Kearns-Manolatos, and Nitin Mittal, Generating value from generative AI, Deloitte Insights, accessed 11 January 2024.
View in Article
Acknowledgments
The authors would like to thank Nancy El-Aroussy, Ralf Esser, Valeria Gallo, Ira Kalish, Paul Lee, Lucia Lucchini, Costi Perricos, Pauliina Sandqvist,
Michelle Seng Ah Lee, Sulabh Soral, Ben Stanton, Ian Stewart and Michael Wolf for their insights and contributions to this piece.
Cover image by: Mark Milward"
168,deloitte,in-ra-ai-risk-management-noexp.pdf,"AI Risk Management
Risk mitigation ""now"" and
strategic insights ""next""
March 2024
AI Risk Management | Risk mitigation ""now"" and strategic insights ""next""
2
AI Risk Management | Risk mitigation ""now"" and strategic insights ""next""
Table of contents
Introduction 04
Enhancing trustworthiness at every stage of
the AI lifecycle 04
A layered approach to building a trustworthy AI 05
AI risk universe—Illustrative 07
Deloitte’s Trustworthy AI framework 11
Need for governance structure across the AI lifecycle 16
Way forward 17
3
AI Risk Management | Risk mitigation ""now"" and strategic insights ""next""
Introduction
In today’s growing market, Artificial Intelligence (AI) is an imperative for various industries. Organisations are exploring the use of AI for
several solutions, including automation, to deliver value and bring efficiency to operations. If companies are relying heavily on AI, they
need to ensure ethical assurance and trustworthiness to make their AI systems dependable.
A solid framework can help organisations navigate this journey and gain confidence against various regulatory requirements as the AI
landscape evolves.
Enhancing trustworthiness at every stage of the AI lifecycle
Ideation and design
Accounting for applicable regulations for the business/industry and local or target geography to ensure
compliance by design from the initial stages of the AI project.
Model requirement
Establishing clear and comprehensive requirements during the model requirement stage of the AI lifecycle to
ensure successful development and deployment; data scientists and engineers could be involved proactively
during this stage to minimise the risk of project failures, costly rework, and potential ethical or legal issues.
Model development
Forming standards and best practice guidelines for developers, ensuring their technologies adhere to
compliance requirements at every stage of the AI lifecycle.
Model deployment
Demanding concrete and trustworthy demonstration from developers and/or vendors, ensuring their AI
systems adhere to relevant ethical, legal, and technical standards.
Data requirement
Certifying prerequisites in available data for AI models, such as adequacy, representativeness, and high quality
to prevent bias, discrimination, and unreliable results.
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AI Risk ManaTgheem ‘wehnat t|, wRihsky ,m aintidga htioown ’"" onfo twh""e a UndP Is ptraaytemgeicn itnss iegchotss y""snteexmt""
Data cleansing
Conducting data cleansing (error detection, standardisation, and normalisation) to eliminate errors, ensure
consistency, and optimise model performance in AI projects.
Data labelling
Ensuring accurate and detailed labels with bias mitigation to avoid errors and manage data effectively.
Model training and testing
Conduct thorough validation and testing of the model using diverse datasets, including both training and
validation data. Perform sensitivity analysis and stress testing to assess the robustness and reliability of the
model under different scenarios. Use adversarial testing to identify vulnerabilities and potential security
risks, such as adversarial attacks.
Model monitoring
Conducting continuous performance tracking, data drift detection, model retraining, maintaining
transparency, and confirming compliance with regulations and ethical standards to maintain model reliability
and accountability in decision-making.
A layered approach to building a trustworthy AI
To achieve a strong AI governance and risk management, it is crucial to establish multiple security layers when deploying AI programs.
The three Lines of Defence (3LoD) model is a fundamental framework that delineates three integral layers of defence, each with unique
responsibilities and accountabilities. At the core of this framework lies the pivotal role of personas, seamlessly integrated across these
lines of defence.
Through this process, organisations establish a resilient AI governance structure and foster transparency, accountability, and risk
mitigation throughout the AI lifecycle.
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AI Risk Management | Risk mitigation ""now"" and strategic insights ""next""
Lines of defence
Teams responsible:
• Independent AI assurance and audit team
• Internal auditors
Third
• Ethical AI review board
line of
defence
Teams responsible:
• AI governance team
• Compliance and ethics team
Second line of • Risk and compliance function
• Data privacy officer
defence
• Cyber security experts
Teams responsible:
• Business unit owners
First line of
• AI developers
defence • AI/ML engineers
Assurance checks
First line Second line Third line
• Enable increased first line of defence • Setting AI risk appetite • Enhance transparency and
testing by model owners through stress accountability with internal
• Identifying KRIs
testing and continuous testing audits by sharing model data
• Including forward-thinking risk and enabling audit trails, etc.
• Automating model validation and
taxonomies
monitoring
• Review model docs via governance
• Define model parameters and refine model
dashboards
development processes
• Establish AI risk strategy
• Enable real-time issue alerts
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AI risk universe—Illustrative
Awareness of the following risks in the AI development lifecycle is crucial for promoting responsible design, ensuring ethical
implementation, and fostering sustainable technological advancement.
Strategic
Risk category description
Risk of AI strategy/leadership not aligned to organisational/business objectives/leadership
Individual risks
Model requirements:
• AI strategy not coordinated with company strategies/value systems/risk appetite leads to
ineffective or even malicious/unethical models
Financial
Risk category description
Risk of inadequate and incorrect decisions/recommendations due to poor AI models, resulting in
direct and indirect losses or threats to the organisation, customer, brand, and reputation
Individual risks
Model evaluation:
• Financial losses, wastage of resources, and reputational losses because of wrong AI models
Data
Risk category description
Risk of unavailability of accurate, labelled, relevant, and unbiased data to develop, train, and deploy
models that meet its intended purposes
Individual risks
Data labelling:
• Inaccurate models from mismatched tests, production data, and improper data tagging
Data collection:
• Risk of biased or insufficient data for model development data cleaning
• Unauthorised access disintegrates solution alignment with business goals
Data labelling:
• Test data different from production data can result in inaccurate models, while inadequate data
tagging based on sensitivity can result in inappropriate safeguards.
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Technology
Risk category description
Risks associated with the technology used regarding auditability, scalability, and monitoring
Individual risks
Model monitoring:
• Tech constraints limit auditability and audit logs, hindering transparency
• Lack of monitoring and feedback loops delay corrections for model discrepancies
Model deployment:
• Single points of failure in deployment without redundancy and inflexible technology limit
scalability as the organisation grows.
Algorithmic
Risk category description
Risks associated with the algorithms leading to incorrect/inconsistent/biased/unethical decisions and
financial and reputational implications.
Individual risks
• Model training: Biased data begets biased and unreliable AI models
• Model evaluation: Inadequate risk-based stress testing and documentation can harm models
• Model deployment: Insecure coding and design flaws invite vulnerabilities
• Model monitoring: Absence of mechanisms for monitoring changing environments
Cyber ( including Data Privacy)
Risk category description
Risk of not identifying, labelling, storing, and securing Personally Identifiable Information (PII)
resulting in data privacy breaches, leading to reputational backlashes and regulatory repercussions.
Individual risks
Privacy: (Data labelling and data collection)
• Insufficiently secured data in AI models, lack of opt-in/opt-out options, and unauthorised data
use infringe on privacy rights.
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Cyber
Risk category description
Lack of adequate access controls in place to safeguard infrastructure, application, model,
and underlying code
Individual risks
Infrastructure:
• Risks pertaining to the underlying technology and resources that support the AI system. This
includes servers, networks, databases, and cloud services.
Application:
• Risks involving issues related to the AI application's functionality, usability, and integration
Model:
• Risks focussing on the AI model’s performance, interpretability, and generalisation capabilities
Underlying code:
• Risks involving challenges related to the quality, security, and documentation of the AI
system's codebase
People
Risk category description
Risk of unavailability of skilled people at each stage of the AI lifecycle and lack of clear segregation
of roles and responsibilities in terms of human-machine interface.
Individual risks
Talent:
• Risk on the company's talent culture (skills atrophy) due to AI implementation may lead to
employee resentment.
Governance:
• Insufficient AI skills
• Unclear roles and unapproved developments
• Missing human-machine interaction guidance (Override)
• Expertise loss risk
• Diversity prevents bias
Regulator
Risk category description
Risk of not catering to geographical or sectoral regulatory and compliance requirements with respect
to AI models, resulting in litigations, fines, and regulatory scrutiny.
Individual risks
Model evaluation:
• Lack of clarity on regulations and its changes around privacy and data security leads to the
creation of ambiguous models, financial penalties and regulatory scrutiny.
Model monitoring:
• Risks such as social engineering and privacy invasion without AI regulation
• Neglecting compliance may result in penalties and business continuity risks
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Third/Fourth-party
Risk category description
Risks arising due to the involvement of third/fourth parties in the AI deployment lifecycle
may lead to technology dependency and intellectual property loss.
Individual risks
• Unclear vendor roles hinder ownership
• Vague contract terms challenge risk management
• Inadequate security controls risk fines and reputation damage
Societal
Risk category description
Risk of incorrect, inconsistent, biased decisions and recommendations made by AI model
leading to issues, such as loss of jobs and exclusion of services causing socio-economic disparity.
Individual risks
• A lack of societal expectation management erodes trust in AI adoption.
• Non-transparent AI models contribute to societal bias and exclusion.
An independent assessor should address various risks associated with AI models, as meeting regulatory requirements will bolster the
entity’s trust:
Independent assurance:
To establish confidence and trust in AI systems, it is necessary to demand well-defined, consensus-
driven standards and credible evidence from developers, vendors, and executives. This evidence
should demonstrate the validity and suitability of the assurance for a specific use case. This can be an
internal and/or external assurance team (auditors, certification bodies, etc.)
Regulations and standards
compliance:
Seeking assurance involves the
essential reliability of AI systems
falling under their regulatory purview,
ensuring compliance with regulations
and best practice guidelines.
The control frameworks developed by
the organisation can use the existing
frameworks, such as ISO 27001,
ISO 42001, COBIT, GDPR, Fairness
Accountability and Transparency
in Machine Learning (FAT ML), and
implementation guidelines, along with
best practices, such as NIST SP 800,
NIST AI Risk Management Framework,
CIS Controls, and OWASP.
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Deloitte’s Trustworthy AITM framework
Governments, industries, and various other groups have struggled to set up an AI framework due to the challenging AI evolution across
industries. To bridge the gap, we have developed a Trustworthy AI framework, putting trust at the centre of everything we do.
This helps organisations set up governance structures for AI programmes and meet regulatory compliance throughout the AI lifecycle
from ideation to design, development, deployment, and Machine Learning Operations (MLOps) to empower employees, businesses,
customers, and industries.
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This trustworthy framework is based on the following seven dimensions
Transparent and explainable Fair and impartial Robust and reliable
AI models enable users to AI models prioritise inclusive AI models produce
make decisions that are easy design, promoting equitable consistent and accurate
to understand, auditable, application, access, and outputs, withstand errors,
and open to inspection. outcomes. An impartiality and recover quickly from
This involves assessing assessment examines system unforeseen disruptions and
system complexity, training design to ensure fairness, misuse. AI models must
methods, and efforts to by considering bias and maintain robustness and
enhance comprehension. cultural context. An integral reliability throughout their
It also examines how the part of this is to provide entire lifecycle. They should
system communicates comprehensive support for operate suitably in various
results, reasoning, displaced workers. Ongoing conditions, including
involvement in outcomes, user bias training and diverse normal, foreseeable, and
and avenues for recourse to fairness testing are conducted adverse scenarios.
users and data subjects. to address potential biases
using various definitions.
Private Safe and secure Accountable
AI models help respect user AI models are protected Policies dictate
privacy by limiting data use from risks that may responsibility for
to its intended purpose cause individual and/ AI-related decisions.
and duration. They provide or collective physical, Accountability is gauged
opt-in/out options for emotional, environmental, by transparent supervision
data sharing and evaluate and/or digital harm. of AI model creation and
transparency in user deployment. This ensures
communication regarding clarity and prevents
data policies, system risks, manipulation, with effective
Responsible
testing outcomes, and communication of system
appropriate use. They functions and limitations.
also scrutinise privacy by It includes validating
detailing sensitive data documented design
types used and strategies decisions, system failure
for data protection during reviews, and scenario
AI models are created
training and deployment. planning by the AI team.
and operated in a socially
responsible manner. They
put an organisational
structure in place that can
help determine who is
responsible for the output of
AI system decisions.
12
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AI Risk ManaTgheem ‘wehnat t|, wRihsky ,m aintidga htioown ’"" onfo twh""e a UndP Is ptraaytemgeicn itnss iegchotss y""snteexmt""
Enhancing reliability throughout the AI lifecycle
We explore stage-specific techniques to bolster reliability, linking each stage to its Trustworthy AI element, key stakeholders, guiding
principles, and crucial audit points to consider.
Trustworthy AI
AI lifecycle stage Associated persona Principles Audit focus
element
Ideation and design • Transparent and • AI architect • Traceability and • Assess traceability
explainability of and explainability
explainable
• AI developers significant decisions implementation
• Safe and secure taken by the system
• Review algorithm
• Usage of the simplest simplicity and
algorithm that meets decision override
performance goals mechanisms
• Ability to override • Verify security
the AI system's measures and
decision by third/fourth-party
designated people controls
• Security of users'
data
• Following secure
coding and security-
by-design practices
• Ensuring that
third/fourth-party
stakeholders
implement all the
necessary security
controls
• Alignment to the • Scrutinise
Model requirement • Robust and reliable • Business unit
principles of both alignment with
owners
• Accountable organisation and responsible AI
• AI/ML engineers responsible AI principles
• Reproducibility • Validate
and consistency of reproducibility and
outcomes grievance handling
• Implementation • Review human
of appropriate supervisory
grievance redressal control
and compensation implementation
mechanisms
• Quality assurance—
Human supervisory
control wherever
possible
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TAhI eR i‘swkh Mata, nwahgye,m aenndt h |o Rwis’ ko fm tihtieg aUtiPoIn p ""anyomwe"" nantsd e sctorastyesgtiec minsights ""next""
Trustworthy AI
AI lifecycle stage Associated persona Principles Audit focus
element
Data cleansing • Fair and impartial • AI governance • Ensuring system • Assess fairness
fairness and data quality
team
• Private maintenance
• Minimisation of the
• Data privacy
officers use of sensitive data • Review procedures
for sensitive data
• Usage of
handling
representative
datasets
• Ensuring the quality
and correctness of
data annotations
Data labelling • Fair and impartial • AI governance • Setting clear goals • Evaluate diversity
for diversity and and bias mitigation
team
• Private inclusion
• Data privacy • Review testing
• Countering various procedures with
officer
sources of bias diverse user groups
• Testing the AI
system with diverse
user groups
Model training • Robust and reliable • AI/ML engineers • Quality Assurance • Validate quality
assurance
• Monitor the feedback
• Risk and and feedback
to the system
compliance monitoring
functions • Implementation of
• Review failover
failover mechanisms
mechanisms and
• Optimisation of the stress testing
model’s inference implementation
speed
• Review the
• Proper integration documentation of
with data sources and the training process
other AI systems for transparency
and reproducibility
• Implementation of ML
Ops • Verify the
adherence to legal
• Usage of risk-based
and compliance
stress testing
requirements
techniques
during the model
training
14
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AI Risk ManaTgheem ‘wehnat t|, wRihsky ,m aintidga htioown ’"" onfo twh""e a UndP Is ptraaytemgeicn itnss iegchotss y""snteexmt""
Trustworthy AI
AI lifecycle stage Associated persona Principles Audit focus
element
Model deployment • Safe and secure • AI developers • Security of users’ data • Review security
protocols and data
• Adequate controls to
• Cybersecurity safety measures
prevent the possibility
experts
of a malicious attack • Validate measures
for preventing
• Ensuring the safety
attacks
and security of all the
stakeholders • Assess on-device
processing
• Usage of on-device
implementation
processing whenever
possible
Model monitoring • Robust and reliable • AI/ML engineers • Live monitoring in • Assess the efficacy
production to ensure of live monitoring
• Independent AI that the AI system is and diagnostic
assurance and operational capabilities
audit team
• Ability to trace, • Verify the
diagnose and existence and
rollback, if necessary, effectiveness of
in case of a failure disaster recovery
and business
• Disaster recovery
continuity plans
and business
continuity plans
• Resiliency of AI
systems
15
TAhI eR i‘swkh Mata, nwahgye,m aenndt h |o Rwis’ ko fm tihtieg aUtiPoIn p ""anyomwe"" nantsd e sctorastyesgtiec minsights ""next""
Need for governance structure across the AI lifecycle
To ensure AI development and deployments, it is essential to follow the ethical principles defined by the enterprise AI policy. A
governance structure at various levels ensures that AI systems are developed, deployed, and maintained responsibly, ethically, and
transparently. Following is a basic outline of an AI governance structure:
AI tracking Risk assessment and
Alignment with the Trustworthy AI measurement
framework, establishing clear role Methods to measure and
assignments and responsibilities, and understand AI risks using
outlining essential life-cycle criteria. This numbers and information, e.g.,
helps maintain uniformity across the metrics design and monitoring,
organisation, along with an updated risk categorisation, meaningful
inventory that includes attributes for the reporting, and analytics.
risk management programme.
Lifecycle standards Regulatory
Well-defined rules, tools, and Ability to adjust according to various
technology are needed to implement regulations set by different regulators in
the AI policy at every stage. They can different countries/regions. When it makes
be changed to fit different situations, sense, these adjustments should be added
such as using AI from other sources gradually to the existing programmes to
or creating new AI. This way, distinct manage risks related to models, data,
functions of the company can adjust cybersecurity, and legal matters.
requirements as needed.
In India, we do not have any regulations on AI for the development, classification, and use of non-personal and personal data in the
public domain.
In the recent B20 summit (G20 Business Forum) in India, the B20 task force recommended setting up a regulatory framework for
responsible AI, and the Indian government called for a global AI framework to promote the ethical development of AI.
Below are a few key considerations for setting up an effective governance structure for AI that could mobilise the people for
AI governance.
• Define goals and articulate objectives.
• Set up an ethics statement.
• Establish guardrails to guide, monitor, and assess AI solutions. For example, embedded controls in the AI model could prevent
specific actions from being completed.
• Define roles and responsibilities for the people responsible for the governance, development, deployment, management, and
monitoring.
• Set up an inventory of AI models and procedures for tracking and maintaining AI implementations.
• Create role-specific upskilling of stakeholders and employees to guide on AI solutions and their responsible development and
deployment.
• Define or optimise the existing data governance for the data.
• Develop KPIs to evaluate the AI models' performance.
16
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AI Risk ManaTgheem ‘wehnat t|, wRihsky ,m aintidga htioown ’"" onfo twh""e a UndP Is ptraaytemgeicn itnss iegchotss y""snteexmt""
Way forward
Maintaining trust in AI necessitates continuous monitoring of AI models to ensure they function as intended and align with trust criteria.
This is particularly challenging with opaque AI models.
Adequate awareness of AI Risk Management across the entire AI lifecycle and relevant stakeholders along with leveraging AI Risk
Management solutions to assess and validate model performance can restore balance in transparency and accuracy.
Beyond model evaluation, AI data management, privacy, cybersecurity, and post-deployment monitoring also benefit from such
solutions. These tech-enabled assessments enhance AI evaluations, fostering better governance and understanding of model
performance for comprehensive AI management.
17
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Connect with us
Anthony Crasto Peeyush Vaish
President, Risk Advisory Partner, Risk Advisory
Deloitte India Deloitte India
acrasto@deloitte.com peeyushvaish@deloitte.com
Nitin Naredi Samanth Aswani
Partner, Risk Advisory Partner, Risk Advisory
Deloitte India Deloitte India
nitinnaredi@deloitte.com saswani@deloitte.com
Key contributors
Manish Dayma Adarsh Mishra
Bharath Yellapu Sachin Arora
Acknowledgment
Akshay Dalvi Neha Kumari
18
18
19
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170,deloitte,DI_CGI-state-of-ai-for-gov.pdf,"A report from the
Deloitte AI Institute for Government and
the Deloitte Center for Government Insights
Scaling AI in government
How to reach the heights of enterprisewide adoption of AI
About the Deloitte AI Institute
The Deloitte AI Institute helps organizations connect all the different dimensions of the robust, highly
dynamic, and rapidly evolving AI ecosystem. The AI Institute leads conversations on applied AI
innovation across industries, using cutting-edge insights to promote human-machine collaboration in
the Age of WithTM.
The Deloitte AI Institute aims to promote dialogue about and development of artificial intelligence,
stimulate innovation, and examine challenges to AI implementation and ways to address them. The AI
Institute collaborates with an ecosystem composed of academic research groups, start-ups,
entrepreneurs, innovators, mature AI product leaders, and AI visionaries to explore key areas of
artificial intelligence including risks, policies, ethics, future of work and talent, and applied AI use cases.
Combined with Deloitte’s deep knowledge and experience in artificial intelligence applications, the
institute helps make sense of this complex ecosystem and, as a result, delivers impactful perspectives
to help organizations succeed by making informed AI decisions.
No matter what stage of the AI journey you’re in—whether you’re a board member or C-suite leader
driving strategy for your organization, or a hands-on data scientist bringing an AI strategy to life—the
Deloitte AI institute can help you learn more about how enterprises across the world are leveraging AI
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About the Deloitte Center for Government Insights
The Deloitte Center for Government Insights shares inspiring stories of government innovation, looking
at what’s behind the adoption of new technologies and management practices. We produce cutting-
edge research that guides public officials without burying them in jargon and minutiae, crystalizing
essential insights in an easy-to-absorb format. Through research, forums, and immersive workshops,
our goal is to provide public officials, policy professionals, and members of the media with fresh
insights that advance an understanding of what is possible in government transformation.
Connect
To learn more please visit www.deloitte.com/us/cir.
Contents
The power of AI at scale 2
Government organizations are making a strong start on AI 3
Progress can be stalled by an overreliance on pilots 5
Following the path of the trailblazers 9
Appendix—Respondent profile 14
Scaling AI in government
The power of AI at scale
ONE OF THE few bright spots to emerge primed to play an important role in the future of
from the difficult period of the COVID-19 government. To reap the transformative benefits of
pandemic has been the rapid development AI, the technology needs to be scaled and our
of an entirely new class of drug: the messenger global survey of 500 government leaders shows
RNA–based vaccine. While research into mRNA three key findings for organizations looking to
vaccines was not new, the pace with which multiple adopt AI at scale:
companies were able to use that approach to tackle
a new pathogen opens new doors into treating • Government organizations have made a strong
everything from other viruses to cancer. These start in exploring a wide variety of AI proofs
vaccines were not just the product of human genius of concept.
and resources; artificial intelligence (AI) also
played a key role. • The transformational benefits of AI require
adoption of AI at scales much larger than proofs
AI helped to identify potential molecular “targets” of concept.
on the virus where vaccines might act.1 As
researchers homed in on mRNA as a tool, AI • To move from pilots to at-scale AI,
helped to optimize the mRNA sequences for organizations need to not just adopt the
efficacy and ease of manufacture.2 Once vaccines technology, but to adapt their organizations
were developed, AI continued to help by predicting across six key dimensions.
the spread of the virus to help with testing.3 The
story of mRNA vaccines is a success story of Those organizational changes will help to drive AI
collaboration between government and industry from the fringes of an organization into the heart
that shows the world-transforming power of AI of the mission. There AI can bring its
when used at scale. transformational power to bear to improve the
lives of citizens.
Given the important mission and large data stores
in government organizations at every level, AI is
2
How to reach the heights of enterprisewide adoption of AI
Government organizations are
making a strong start on AI
THE TRANSFORMATIONAL POTENTIAL of AI With enthusiasm and a growing pool of resources,
is not lost on organizations at every level of many government organizations have launched
government. For example, in our recent pilots to explore how AI can help their
survey of government leaders, respondents at the organizations. Government organizations are
national, state, and local level all saw AI as exploring a range of AI use cases from speech
important to future mission outcomes (figure 1). recognition to predictive maintenance.
The fact that governments are serious about AI Government sectors such as defense and health
adoption is also reflected in the increasing share of that have a long history of AI experimentation are
AI investments—84% of agencies believe their AI among the leaders in fields such as responsible AI
investments will increase by 6% or more in the and data-sharing. For example, more than 35
next fiscal year.4 With budget analysis showing that countries have released AI strategies that include a
US Federal funding for AI research and focus on responsible AI—a finding backed by our
development alone is expected to have already respondents. Eighty-five percent of surveyed
grown by nearly 50% to more than US$6 billion in government executives indicated their organization
FY 2021, government leaders are clearly bullish on had an enterprisewide AI strategy.6
AI.5 As a result, they are making significant
investments and exploring new AI projects.
FIGURE 1
AI is important for mission outcomes across all levels of government over the
next five years
AI important for mission AI not important for mission
3%
8%
16%
Federal State Local
92% 97% 84%
Source: Deloitte's State of AI survey.
Deloitte Insights | deloitte.com/insights
3
Scaling AI in government
However, there is also a weakness in this pattern of (figure 2). This means that despite the significant
pursuing AI. While government respondents are effort and attention that government
exploring a wide range of AI use cases, they are organizations are paying to AI, most projects
fully deploying only a small fraction of them remain at the pilot scale.
FIGURE 2
Government organizations are pursuing a wide range of AI techniques, but most
of those efforts are developing rather than fully deployed
Percentage of respondents who are developing/have deployed each use case
Deployed Developing
Recommendations
31%
48%
Predictive maintenance
31%
4%7
Computer vision
29%
46%
NLP/NLG
26%
49%
Speech recognition
32%
41%
Biometrics
30%
43%
Pattern/anomaly detection
31%
41%
RPA
24%
47%
Sentiment detection
23%
45%
Intelligent robotics
25%
40%
Source: Deloitte's State of AI survey.
Deloitte Insights | deloitte.com/insights
4
How to reach the heights of enterprisewide adoption of AI
Progress can be stalled by
an overreliance on pilots
WHILE PILOTS PLAY a critical role in developing AI mostly through pilots and
developing successful AI, an exploration may be holding back
overreliance on them can be detrimental. further development.
To try to understand how, we analyzed the
reported capabilities and actions of respondent If organizations only pursue pilots, it can create a
organizations to evaluate how prepared they were sense of overconfidence. As small-scale pilot
for AI at scale. The analysis showed that while projects succeed, organizations may mistakenly
there are a significant number of mature think that they have all the capabilities they need
government organizations blazing a trail in AI to tackle AI at scale. We observed signs of this
(28%), a near majority are still beginners (48%, see overconfidence in our survey results. Seventy-three
figure 3). Being a beginner in AI is not necessarily a percent of government respondents believe that
problem. Even most trailblazing organizations they are ahead of the private sector in AI
were beginners at one point. The problem many capabilities. And as if to reinforce the optimism
governments face is that their pattern of
FIGURE 3
Despite experience with pilots, most government organizations are still
beginners in the journey to AI at scale
Trailblazers
High strategy/governance 28% High capabilities
(Three AI readiness
dimensions:
strategy, process, ethics)
Strategists Techies
17% 8%
Low capabilities Beginners Low strategy/governance
48%
(Three AI readiness dimensions:
data, technology and
platforms, people)
Source: Deloitte's State of AI survey.
Deloitte Insights | deloitte.com/insights
5
Scaling AI in government
bias, 80% believed they are also ahead of their CDO of a large US city describes initially being
public sector peers. surprised at the slow pace of AI development
among peers in the private sector. Only later did
the CDO begin to realize that the slower pace may
Pilot purgatory
be needed to tackle larger AI projects. The limited
scope of pilots may make them easier to pursue
The problem is that AI at scale requires different more quickly, but for larger-scale projects it takes
organizational capabilities than pilots or proofs of time to make sure that the right data is gathered,
concept. Pilots are typically smaller and narrower the appropriate use case is chosen, and costly
in focus than full-scale AI efforts. As a result, pilots mistakes are not made while developing
can often make use of different technologies and technological architecture. For those just starting
data sources than would be required for full-scale out in their AI journey, it can seem counterintuitive
use. They may not need to meet as rigorous that slowing down the process may be a way to
security and privacy requirements. Further, the achieving AI at scale quickly. Slow is smooth;
smaller scope of pilots means that they touch fewer smooth is fast.7
parts of an organization so that change
management is less of a factor in their success. In short, organizations that have only
experimented with pilot-scale AI cannot make it to
For these reasons, development of AI at scale just the heights of at-scale AI simply by doing more of
looks different than pilots. For example, a former what they are doing. Without intentional action to
acquire the organizational capabilities needed for
at-scale AI, organizations can easily become stuck
in “pilot purgatory” continually cycling through
promising AI pilots but never realizing the
transformational benefit that AI promises for their
core mission.
Adapt, don’t just adopt
The good news is that government leaders appear
to be increasingly aware of the gap between pilots
and at-scale AI. The respondents of our survey
again and again highlighted the gap between their
goals for AI and where they currently assessed
their AI capabilities (figure 4).
The US Department of Defense (DoD) is just one
example of the path leading government
organizations are taking to scale AI. In its 2018 AI
strategy, DoD outlined that, “The DoD will identify
and implement new organizational approaches,
establish key AI building blocks and standards,
develop and attract AI talent, and introduce new
operational models that will enable DoD to take
6
How to reach the heights of enterprisewide adoption of AI
FIGURE 4
Government leaders are aware of the gap between current and desired state
of AI capabilities
Strategy Ethics Process
91% No.1
91% of agencies believe AI the No.1 goal for AI reported by
...but
will be important to deliver respondents is “making internal
mission outcomes over the processes efficient” and not
next five years applying AI to the mission
72% 44%
72% of respondents say ...but 44% of respondents also
their organization is say that AI has negatively
prepared to deal with impacted the reputation of
issues related to ethical AI their organization
Top 3 33%
”Documented and enforced AI ...but only 33% of organizations
ops and governance follow documented
procedures” is among the top 3 MLOps procedures when
Strategy and governance
critical factors for successful developing an AI solution
AI implementations
State of AI in government
People Data Technology and platforms
78% 50%
78% of agencies say they 50% of respondents also cite a
...but
have sufficient skills to lack of skills as a major barrier
implement AI initiatives to taking advantage of AI
89% 41%
89% of public sector ...but only 41% of agencies consider
organizations say they public cloud as a data platform
have access to all neces- for various applications
sary data for AI
No.1 28%
”Lack of technology supporting ...but only 28% of respondents fully
AI” is the No.1 barrier for scale AI applications
agencies to take advantage of
Capabilities
AI
Source: Deloitte's State of AI survey.
Deloitte Insights | deloitte.com/insights
7
Scaling AI in government
advantage of AI systematically at enterprise • People. Agencies may face challenges around
scale.” 8 Since then, DoD has established the Joint accessing and recruiting necessary technical
Artificial Intelligence Center (JAIC) to better skills, as well as helping existing employees
govern AI use cases, set up the develop and deploy AI skills.
Joint Common Foundation (JCF) to provide ready-
to-use tools to experiment and scale AI use cases, • Process. AI can be a powerful new tool, but
and started offering new AI career paths to attract simply embedding it within existing business
and retain talent.9 processes designed for older tools will limit
its benefits.
While organizations like the US DoD are at the
forefront of AI in government, other organizations • Data. AI is only as good as the data upon which
may find it hard to replicate organizational change it is built, and its appetite for data is voracious.
of that nature. It is comparatively easy to adopt a
technology and graft it onto existing organizational • Ethics. While any technology’s deployment
structure and business processes. But it is much should be ethical, AI brings issues such as
harder to adapt the organization to allow it to take transparency, privacy, and bias into
full advantage of a new technology. To build the particular focus.
organizational capabilities needed for AI at scale,
organizations need to adapt their: • Technology and platforms. A variety of
models for pursuing AI exist that vary in terms
• Strategy. Because AI is a transformative of platforms and ownership of technology (e.g.,
technology, alignment on direction and level of internal or in partnership), but, in all cases, AI
ambition is crucial. requires a coherent approach that considers
future requirements as AI scales within the
organization and its usage evolves.
8
How to reach the heights of enterprisewide adoption of AI
Following the path of
the trailblazers
TRAILBLAZING GOVERNMENT
Organizations where senior leaders
ORGANIZATIONS such as many in the
communicate a clear vision for AI
defense and health sectors have already
are 50% more likely to achieve their
charted the way toward developing these six
organizational capabilities. Following their lead desired outcomes with AI.
can help other organizations to iteratively build
capabilities across those six dimensions and realize Drive AI into the heart of the mission: AI
the transformational benefits of AI at scale. should be about doing more and doing better.
However, our analysis found that organizations
that are just beginning their AI journey are more
Strategy
likely to use AI merely to improve internal
efficiency. As organizations gain experience and
Senior leaders should ensure AI strategy become more mature, they are more likely to use
supports the mission: The focus of an AI for mission-focused goals such as improving
organization’s AI strategy should not be merely to collaboration or creating new programs. In one
deploy AI for its own sake but rather should focus large-scale example, Singapore created a US$73
on how AI can be an enabler to deliver the million AI-enabled digital twin of the city, not to
organization’s mission outcomes. This means that make government more efficient, but to model
an organization’s AI strategy cannot be a product decision-making, experiment with service
purely of IT or technical teams but must be driven provision, and address some of the most pressing
by senior leaders. Our survey found that challenges facing the country.12
organizations where senior leaders communicate a
clear vision for AI are 50% more likely to achieve
As organizations gain experience
their desired outcomes with AI.10 In the early
and become more mature, they are
2010s, Jeff Bezos mandated that every leader
across Amazon develop a plan for how to use AI in more likely to use AI for mission-
their division. That mandate was instrumental in focused goals.
Amazon’s rise to become an AI leader today.11
9
Scaling AI in government
People Process
Balance outside hiring with reskilling: Our Reimagine processes and career paths: For
survey found that 69% of respondents would prefer government to truly revolutionize the lives of
to bring in new hires with required skill sets. Given citizens using AI, it will have to revolutionize the
the widespread shortage of AI talent,13 agencies way AI is deployed in its business processes and
should balance outside hiring with reskilling their workflows. After all, you cannot deliver new results
existing workforce. For example, both Denver and with old processes. Organizations that have
San Francisco city governments have established significantly changed workflows are 36% more
data academies to help train city workers and likely to achieve desired outcomes from their AI
others in the basic skills needed to harness AI.14 projects.17 Introducing new processes can also help
The National Security Commission on Artificial organizations create new career paths for workers
Intelligence (NCSAI) goes a step further, calling for who work with this technology, which can be a
establishing a digital service academy, modeled critical enabler to success.18 We found that agencies
after US service academies, to produce a trained that added new AI roles are 60% more likely to
workforce that caters to all federal agencies.15 achieve desired outcomes.19 While adding new
roles can help organizations, those benefits may be
Building technical skills is a clear benefit to temporary unless organizations can provide new
technical staff but can also help the wider career pathways for talent to grow and develop.
organization. Government will always need AI This is exactly what the Australian Public Service
specialists, but to adopt AI at scale, it should also and the country’s Digital Transformation Agency
improve data literacy for the workers who must collaborated on, defining over 150 new digital roles,
buy AI tools and services or use AI to deliver and creating the APS Career Pathfinder tool to help
services to citizens. For example, Abu Dhabi has people in those roles explore digital career options
created AI training workshops to help government in government.20
employees understand AI’s benefits and make
better decisions around its utility.16
Organizations that have significantly
changed workflows and added
new AI roles are 36% and 60%,
respectively, more likely to achieve
desired outcomes from their AI
projects.
10
How to reach the heights of enterprisewide adoption of AI
Data
Documenting and enforcing MLOps
makes organizations twice as likely
Identify relevant data and determine its
to achieve goals and three times
accessibility: Agencies that have access to the
more likely to be prepared for AI
necessary data are twice as likely to exceed
risks.
expectations in their AI initiatives.21 To make the
best use of AI, agencies need to identify relevant
Prioritize change management. If AI is to be
datasets and develop platforms to access that data.
successful, it will, by definition, be disruptive for
For instance, the US Air Force has adopted the
government organizations. AI can change not only
VAULT data platform which gives airmen access to
how processes are done, but even what services
the cloud-based data and tools they need to use AI
government delivers to its citizens. Our analysis
to improve readiness and mission success.22
indicates organizations that invest in change
management are 48% more likely to report that
Agencies that have access to the AI initiatives exceed expectations.25 However, the
more significant the change brought by AI, the
necessary data are twice as likely
more difficult it can be. Governments should
to exceed expectations in their AI
use the principles of behavioral economics to
initiatives.
understand the human impact of transformations
and how to provide appropriate support to
encourage change.26
Ethics
Organizations that invest in change
Document and enforce MLOps: Developing
management are 48% more likely
and deploying AI is not without ethical risks. That
to report that AI initiatives exceed
is why having clear documentation and enforceable
processes is important to having trustworthy and expectations.
transparent AI. This is where MLOps—the set of
automated pipelines, processes, and tools that
Technology and platforms
streamline all steps of AI model construction—can
help. After all, it is difficult to address ethical issues
with a model unless you know how that model was Build a diverse ecosystem: Every government
built and operated. In fact, our survey found that agency does not need to solve every problem itself.
documenting and enforcing MLOps makes From chatbots to speech-to-text, many solutions to
organizations twice as likely to achieve goals and technical problems already exist. Tapping into
three times more likely to be prepared for AI risks.23 other entities that have existing technical solutions
Organizations like the Internal Revenue Service or solved organizational challenges can accelerate
(IRS) have discovered that scaling AI beyond the progress toward AI at scale. In fact, our survey
pilot stage across the agency requires adopting found that continually cultivating a wide range of
different and rigorous processes for creating and relationships with industry, academia, and other
managing AI models.24 agencies dramatically improves the likelihood that
11
Scaling AI in government
an organization has what it needs to scale AI on where government needs help (figure 6).
(figure 5). As Eileen Vidrine, chief data officer at Partners don’t always need to be organizations at
the US Air Force says: “It’s really about working all. The City of LA’s Data Angels program brought
together, building collaborative, trusted volunteer data scientists into government on a
FIGURE 5
Agencies with diverse ecosystems are more likely to have what they need to
achieve their goals for AI
Diverse ecosystem Narrow ecosystem
81%
Sufficient skills
64%
Use AI to improve both front-end 69%
and back-end operations 36%
AI initiatives exceeded 39%
expectations 8%
Achieve outcomes set 37%
for AI initiatives 22%
Percentage of 33%
trailblazer organizations 9%
Source: Deloitte's State of AI survey.
Deloitte Insights | deloitte.com/insights
partnerships. It needs to be part of the part-time basis to help with a variety of tasks.27
conversation at the beginning and through the The program tapped into private sector data
whole life cycle about trying to optimize specialists who wanted to help the community
interoperability and avoiding what I would call while still retaining their jobs, bringing some of the
‘vendor lock’ as much as possible.” top data talent into public service with little cost to
the government.
Find partners that complement your need:
Find partners that provide the capabilities your AI is the future. Government leaders clearly
particular agency lacks. These partners should be a understand this. But getting to that future can be
wide variety of different organizations depending more difficult and more rewarding than it may
12
How to reach the heights of enterprisewide adoption of AI
FIGURE 6
Governments are partnering with a variety of players depending on their
unique needs
IT analyst
49%
Professional services/consulting
37%
Cloud vendors/hyperscalers
37%
Traditional IT firms
30%
Startups/boutique software providers
25%
Source: Deloitte's State of AI survey.
Deloitte Insights | deloitte.com/insights
seem at the start. Taking a realistic view of the right strategies, and the right governance to make
challenges inherent in developing AI at scale can sure that the AI of the future serves the citizens of
help government develop the right capabilities, the the future.
13
Scaling AI in government
Appendix—Respondent profile
FIGURE 7
Appendix: Respondent profile
SECTOR TITLE
9%
16%
9%
32% 31%
9%
13%
8%
12% 19% 19%
23%
Federal/central government—Civilian C-suite
Federal/central government—Defense Deputy or other top-level executive but below C-suite
Federal/central government—Health Deputy secretary/deputy agency head
Higher education Local government Manager level
State/provincial government Secretary/undersecretary/agency head Others
REGION
12%
19%
69%
Americas Europe APAC
Note: N = 517 respondents.
Source: Deloitte's State of AI survey.
Deloitte Insights | deloitte.com/insights
14
How to reach the heights of enterprisewide adoption of AI
Endnotes
1. Gunjan Arora et al., “Artificial intelligence in surveillance, diagnosis, drug discovery and vaccine development
against COVID-19,” Pathogens 10, 8 (2021): p. 1048.
2. MIT Sloan Management Review, “AI and the COVID-19 vaccine: Moderna’s Dave Johnson,” Me, Myself, and AI,
podcast, July 13, 2021.
3. Terri Park, “Behind COVID-19 vaccine development,” MIT News, May 18, 2021.
4. Based on analysis of government respondents of 2021 State of AI Survey.
5. Jon Harper, “Federal AI spending to top US$6 billion,” National Defense Magazine, February 10, 2021.
6. Future of Life Institute, “National and international AI strategies,” accessed December 6, 2021.
7. Phone interview with Sari Ladin-Sienne on September 15, 2021.
8. William D. Eggers et al., Crafting an AI strategy for government leaders: Does your agency have a holistic
AI strategy?, Deloitte Insights, December 4, 2019.
9. Ibid.
10. Based on analysis of government respondents of 2021 State of AI Survey. Respondents whose senior leaders
communicated a clear AI vision (78%) were 26 percentage points more likely in achieving desired AI outcomes
than respondents whose senior leaders did not communicate a clear AI vision (52%).
11. Steven Levy, “Inside Amazon’s artificial intelligence flywheel,” Wired, February 1, 2018.
12. Joe Mariani, Adam Routh, and Allan V. Cook, Convergence of technology in government: Power of AI, digital
reality, and digital twin, Deloitte Insights, March 11, 2020.
13. Joe McKendrick, “Artificial intelligence skills shortages reemerge from hiatus,” ZDNet, October 22, 2020.
14. Brian Elms, Peak Performance: How Denver’s Peak Academy is saving millions of dollars, boosting morale and just
maybe changing the world. (And how you can, too!), Washington DC: Governing, 2016, Data@SF, “Data Academy,”
accessed October 8, 2021.
15. National Security Commission on Artificial Intelligence, “Chapter 6: Technical talent in government,” accessed
October 8, 2021.
16. Eggers et al., Crafting an AI strategy for government leaders.
17. Based on analysis of government respondents of 2021 State of AI Survey. Respondents whose organizations
have significantly changed workflows (76%) were 20 percentage points more likely in achieving desired AI
outcomes than other respondents (56%).
1188.. NSCAI, “Chapter 6: Technical talent in government”; NSCAI, Final report, March 2021.
19. Based on analysis of government respondents of 2021 State of AI Survey. Respondents in whose organizations
new AI job roles/ functions were created (78%) were 28 percentage points more likely in achieving desired AI
outcomes than respondents in whose organizations no new AI roles were created (46%).
20. Digital Transformation Agency, “New APS career pathfinder tool,” October 19, 2020.
21. Based on analysis of government respondents of 2021 State of AI Survey.
15
Scaling AI in government
22. Eileen Vidrin, “Air Force CDO: Flying High With AI,” CIO Journal, August 20 2021; Secretary of the Air Force Public
Affairs, “Chief Data Office announces capabilities for the VAULT data platform,” US Air Force, October 11, 2019.
23. Based on analysis of government respondents of 2021 State of AI Survey.
24. Austin Price, Ashley Prusak, and Maria Wright, Tech Trends 2021: Peering through the lens of government,
Deloitte Insights, accessed October 8, 2021.
25. Based on analysis of government respondents of 2021 State of AI Survey.
26. William D. Eggers et al., Behavior-first government transformation: Putting the people before the process, Deloitte
Insights, August 25, 2020.
27. Harsha Mallajosyula, “Get to know a data angel,” Medium, February 15, 2019.
Acknowledgments
The authors would like to thank Thirumalai Kannan for his invaluable quantitative analysis of the
survey data. Kannan’s expertise was central to teasing out the signal from the noise in the data.
16
How to reach the heights of enterprisewide adoption of AI
About the authors
Edward Van Buren | emvanburen@deloitte.com
Edward Van Buren is the Strategic Growth leader—Artificial Intelligence (AI) for Deloitte Consulting
LLP’s Government & Public Services (GPS) Industry and the executive director of the Deloitte AI Institute
for Government. He works with technology companies and other strategic partners to develop
solutions harnessing the power of AI/ML for federal, state, local, and higher education clients. Van
Buren has more than 25 years of experience in consulting and the public sector. He has served diverse
clients such as the United States Postal Service, Internal Revenue Service, Office of Performance
Management, and the United States military, helping them transform their organizations to better
execute missions and utilize technologies.
William Eggers | weggers@deloitte.com
William Eggers is the executive director of Deloitte’s Center for Government Insights, where he is
responsible for the firm’s public sector thought leadership. His most recent book is Delivering on Digital:
The Innovators and Technologies That Are Transforming Government (Deloitte University Press, 2016). His
other books include The Solution Revolution, the Washington Post bestseller If We Can Put a Man on the
Moon, and Governing by Network. He coined the term “Government 2.0” in a book by the same name.
His commentary has appeared in dozens of major media outlets including New York Times, Wall Street
Journal, and Washington Post.
Tasha Austin | laustin@deloitte.com
Tasha Austin is a Principal in Deloitte’s Risk and Financial Advisory business and has more than 22
years of professional services experience involving commercial and federal financial statement audits,
fraud, dispute analysis and investigations, artificial intelligence and advanced data analytics. Tasha
serves as the Director of Deloitte’s Artificial Intelligence Institute for Government and is a leader in
Deloitte’s Artificial Intelligence and data analytics offering where she focuses on amplifying Deloitte’s
capabilities and services in key areas such as trustworthy/ethical AI, provides insight-driven solutions to
her clients, and is responsible for elevating Deloitte’s thought leadership and digital presence in AI to
the federal market. Tasha also leads Deloitte’s strategic firm-wide engagement initiatives with HBCUs.
She has a passion for bridging the data analytics and digital divide in under-resourced communities
and working with non-profit organizations to deliver and scale solutions that help advance equity and
promote social justice.
Joe Mariani | jmariani@deloitte.com
Joe Mariani is a research senior manager with Deloitte’s Center for Government Insights. His research
focuses on innovation and technology adoption for government organizations. His previous work
includes experience as a consultant to the defense and intelligence industries, high school science
teacher, and Marine Corps intelligence officer"
171,deloitte,Deloitte-Insights-Magazine-Issue-33.pdf,"Contents Deloitte INSIGHTS
Magazine 33
04 On the web / 07 Editor’s letter / 08 Contributors / 90 The end note
1. Data points
Bite-size insights from Deloitte research
18 Gen AI investments increasingly extend beyond the AI itself
19 While business leaders look inward for AI’s impact, tech leaders look outward
20 Few AI regulations across the globe address the outcomes rather than the tech
21 European organizations’ gen AI preparedness has increased, but few feel ready for the associated risks
22 A burgeoning ‘AI-generated’ market: Insurance safeguards against AI risk
23 More hands-on gen AI experience increases optimism—and caution—for millennials and Gen Z
24 Many tech leaders’ influence in the C-suite is growing, new Deloitte research suggests
25 Are new generative AI features in software a monetizable enhancement or table stakes?
26 A snapshot of AI adoption: Italy’s design sector
27 More US consumers think AI-generated health information should be left to the experts
2. Perspectives 3. Features
30 Better questions about generative AI 52 Predicting the unpredictable: Exploring how
Four scholars share critical questions leaders should ask technology could change the future of work
about generative AI, from concerns about bias to existential What does the future hold for worker and AI collaboration?
considerations about human values It depends less on the tech and more on the decisions we make
along the way.
34 Generative AI and the labor market:
A case for techno-optimism 60 Generative AI in Asia Pacific: Young employees
Generative AI can boost productivity and enhance the lead as employers play catch-up
labor market, yet it remains to be seen if everyone can reap A survey of more than 11,900 employees and students across
its many benefits the region finds that gen AI is already affecting 11 billion work
hours per week, but many employers likely aren’t optimizing
38 The more AI-enabled work becomes, that impact
the more important human imagination is
One of the most valuable skills you need to succeed in an 70 Designing for growth in the C-suite
AI-enabled working world you likely learned in kindergarten An analysis of over 46,000 job postings reveals the most
in-demand skills for C-suite roles like CFOs, COOs, and other
42 The democratization of deepfake technology brings executive leaders
new perils for business
A chief executive of a deepfake detection platform company and 78 Generative AI and government work:
Deloitte US’s chief futurist explore the growing deepfake risks, An analysis of 19,000 tasks
as well as mitigation strategies that can help organizations fight Deloitte US’s analysis reveals three criteria that can help
AI-enabled fraud with AI determine which tasks could be assigned to generative AI tools
and when different occupations could feel pressure to adopt them
46 Organizations talk about equity in AI, but are they
following through?
Diversity, equity, and inclusion leaders are in a unique position to
advocate for AI that works for everyone. Here’s where they have
opportunities to lead at the intersection of AI and DEI.
Issue 33 1
Masthead
Deloitte Insights Magazine
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EDITOR IN CHIEF Rupesh Bhat
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Corrie Commisso
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CREATIVE Rebecca Knutsen
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2 Deloitte Insights Magazine
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Deloitte Insights Magazine
EDITOR’S LETTER
Advancing the AI conversation
Over the past two decades, and then seemingly overnight, artificial intelligence has gone
from a fringe technology to what many consider to be must-have, market-making and
-shaping tech. And with each passing day, the AI conversation is evolving in real time, spurred
on, of course, by all things generative-AI–related and the more readily apparent impact AI could
have on organizations, industries, and economies.
In this issue, we’re featuring some of Deloitte’s latest proprietary research and insights to
help move the AI conversation forward, offering fresh perspectives and foresight on what those
organizational and economic impacts might be.
For instance, Deloitte researchers have sliced and diced a data set from a proprietary survey
of nearly 2,800 board members, C-suite executives, and other senior leaders in 14 countries for
insights into how generative AI budgets are being spent (page 18), what it takes to scale from
gen AI pilots to full implementation (page 90), and whether organizations feel prepared for
any risk and governance issues associated with the technology (page 21).
We look at which success metrics business and tech leaders turn to when determining
the impact of their AI investments (page 19), and which kinds of AI regulations could be
most effective for the ever-evolving technology, safeguarding the public while not hindering
innovation (page 20).
And we examine AI’s potential impact on work and the workforce from several angles—
considering gen AI’s potential impact on productivity and labor demand and, therefore,
economic outcomes (page 34); tracking the trend in C-suite roles requiring more data and
analytics skills (page 70); making the case for the key capability people could need as work
becomes more AI-enabled (page 38); and discussing why new prediction models might be
needed to determine how AI and other tech could change the future of work (page 52).
And we’re just scratching the surface. Deloitte is building a rich and diverse portfolio
of AI-related business research and insights—the kind of trustworthy, deeply researched
information that your AI-enabled searches rely on. Check out www.deloitteinsights.com for
lots more where this came from.
Best,
Elisabeth Sullivan
Editor in chief, Deloitte Insights
insights@deloitte.com
Issue 33 7
Contributors
Tasha Austin Andrew Blau
laustin@deloitte.com ablau@deloitte.com
Tasha Austin is a principal in Deloitte US’s government and public services Andrew Blau leads Deloitte US’s strategic futures practice, helping clients
practice whose experience includes commercial and federal financial statement think creatively and strategically about how they can successfully compete
audits, fraud, dispute analysis and investigations, artificial intelligence, and despite macro and market uncertainties in the world around them. He also
advanced data analytics. Austin helps lead the practice’s AI and data analytics leads eminence and insights for Deloitte Consulting, developing Deloitte
offering, helping clients with their financial management transformation. US’s perspectives on cross-cutting issues shaping organizations and markets.
Sonia Breeze Susan Cantrell
sbreeze@deloitte.co.nz scantrell@deloitte.com
Sonia Breeze leads Deloitte New Zealand’s human capital consulting prac- Susan Cantrell is vice president of products and workforce strategies at
tice and is also the internal talent partner. She’s committed to enabling Deloitte Consulting LLP, and a frequent speaker on human capital and
organizations to maximize the potential of their people. She leverages 25 the future of work. She is coauthor of the Harvard Business Press book
years of experience in human capital consulting and health care, and as a Workforce of One and has also been published widely in publications
CPO, to advise on and implement the people-oriented aspects of change including Harvard Business Review, The Wall Street Journal, and MIT Sloan
including technology enablement. Management Review.
Corrie Commisso Peter Evans-Greenwood
ccommisso@deloitte.com peter@evans-greenwood.com
Corrie Commisso is a senior editor at Deloitte Insights leading global content Peter Evans-Greenwood is an independent advisor and consultant, a senior
strategy for human capital and the future of work. She has more than 20 years fellow at the Australian Davos Connection, and a former fellow at the
of experience in writing, editing, and creative direction, and holds degrees in Deloitte Centre for the Edge in Australia. With over 20 years of experience
journalism and library and information science—a combination she credits at the intersection of business and technology, he combines systematic and
for her ability to tell deep and engaging stories. integrative thinking to help organizations make informed decisions in an
uncertain business environment.
Robert Hillard Susan C. Hogan
rhillard@deloitte.com.au shogan@deloitte.com
Robert Hillard leads consulting across Asia Pacific, including in China, Japan, Susan C. Hogan is the global leader of Deloitte’s finance and performance
Australia, India, Singapore, Malaysia, Indonesia, Thailand, Vietnam, New practice, and the leader of Deloitte US’s finance transformation practice.
Zealand, and South Korea. He has previously served as a member of Deloitte’s Hogan has nearly 30 years of transformation consulting experience, primarily
global board (Deloitte Touche Tohmatsu Limited), CTO for Deloitte Asia in finance and global business services.
Pacific, and the chief strategy and innovation officer for Deloitte Australia.
8 Deloitte Insights Magazine
Stuart Johnston Ira Kalish
stujohnston@deloitte.com.au ikalish@deloitte.com
Stuart Johnston is the Deloitte Global clients leader and a member of the global Ira Kalish is the chief global economist at Deloitte Touche Tohmatsu Ltd.
clients and industries executive. Based in Australia, he also serves as a lead client He is a specialist in global economic issues as well as the effects of economic,
partner and advisory partner, focusing on enhancing relationships with board demographic, and social trends on the global business environment. His
members and executives, and connecting Deloitte member firms globally. insights have appeared in publications including The Wall Street Journal, The
Economist, and The Financial Times.
Thirumalai Kannan Pankaj Kishnani
tkannand@deloitte.com pkamleshkumarkish@deloitte.com
Thirumalai Kannan is a researcher with the Deloitte Center for Government Pankaj Kishnani is a research manager with the Deloitte Center for Government
Insights. His research focuses on the quantitative analysis of cross-cutting Insights. His research focuses on identifying trends in emerging technolo-
issues in government, including emerging technologies like AI and advancing gies in the public sector. He closely tracks digital government transformation,
citizen trust. the regulation of emerging technologies, the role of government in catalyzing
innovations, and citizen-centric service delivery models.
Annalyn Kurtz Chris Lewin
ankurtz@deloitte.com chrislewin@deloitte.com
Annalyn Kurtz is the executive editor of Deloitte Insights. Prior to her role at Chris Lewin is a partner in the consulting practice at Deloitte Australia with a
Deloitte, she worked as a business journalist, newsroom leader, and educator. specialization in automation. He has extensive data and analytics experience
She has written about business and economics topics for CNN Business, driving IT transformation programs.
Fortune, and The New York Times, among others.
David Mallon Joe Mariani
dmallon@deloitte.com jmariani@deloitte.com
David Mallon is chief analyst and market leader for Deloitte US’s Joe Mariani is a senior research manager with Deloitte’s Center for Government
Insights2Action team, helping clients sense, analyze, and act at the ever- Insights. His research focuses on innovation and technology adoption for both
shifting intersection of work, workforce, workplace, and industry. He national security organizations and commercial businesses. His previous work
brings more than 20 years of experience in human capital, with expertise includes experience as a consultant to the defense and intelligence industries,
in organization design, organizational culture, HR, talent, learning, and a high school science teacher, and a US Marine Corps intelligence officer.
performance.
Issue 33 9
Timothy Murphy Kellie Nuttall
timurphy@deloitte.com knuttall@deloitte.com.au
Tim Murphy is a senior manager in Deloitte’s Center for Integrated Research. Kellie Nuttall is lead partner for strategy and business design and the
As a researcher and analytical scientist, he focuses on understanding how AI Institute at Deloitte Australia. Nuttall specializes in AI strategy and
organizations undergo large-scale transformations that grow the business, transformation initiatives, working with senior executives to build their AI
bolster operations, and make the enterprise more resilient against external fluency and their understanding of AI technologies. Prior to joining Deloitte,
shocks and disruptions. Nuttall built analytics capabilities within both government and private-sector
organizations.
John O’Mahony Julian Sanders
joomahony@deloitte.com.au jusanders@deloitte.com
John O’Mahony is a lead partner at Deloitte Australia with over 20 years Julian Sanders is a research lead in Deloitte’s DEI Institute with multi-industry
of professional economics experience. His skills and expertise include experience in education, public policy, program management, and research.
economic impact analyses, economic modelling, and economic policy. His In his current role, he manages and activates diversity, equity, and inclusion
specific industries of interest include telecommunications, media, technology, research, contributing to the institute’s thought leadership.
infrastructure, retail, housing, and manufacturing.
Nic Scoble-Williams Brenna Sniderman
nscoble-williams@tohmatsu.co.jp bsniderman@deloitte.com
Nic Scoble-Williams is a partner at Deloitte Tohmatsu Consulting LLC and Brenna Sniderman leads the Center for Integrated Research, where she
the Asia Pacific leader for the future of work. Based in Japan, and with more oversees cross-industry thought leadership for Deloitte. In this capacity,
than 20 years of cross-industry experience in IT services, talent strategy and Sniderman leads a team of researchers focused on global shifts in digital
advisory, and mergers and acquisitions, Scoble-Williams works with businesses transformation, innovation and growth, climate, and the future of work—
and governments to embed a “future of work” vision into enterprise how organizations can operate and strategize in an age of digital, cultural,
transformation strategies. environmental, and workplace transformation.
Peter Williams Michael Wolf
pewilliams@deloitte.com.au miwolf@deloitte.com
Peter Williams is a retired partner for Deloitte Australia. He served as the chief Michael Wolf is a global economist at Deloitte Touche Tohmatsu Ltd. He
edge officer at Deloitte’s Center for the Edge Australia and the chairman of began his career as an economist at the US Labor Department and has since
Deloitte Australia’s Innovation Council, and was a founder of Deloitte Digital. held economist positions at Moody’s Analytics, Wells Fargo Securities, and
PwC. His insights have been featured in media outlets including The Wall
Street Journal and National Public Radio.
10 Deloitte Insights Magazine
Artists
Jaime Austin Bose Collins
Jaime Austin is an art director at Deloitte Insights, a professional circle Bose Collins is a London-based design agency established in 1994, known for
designer, a design process expert, and an Excel enthusiast. Her design its use of state-of-the-art tools. Its capabilities extend from film direction,
approach is anchored in strategic problem-solving, where she leverages animation, and sound design, to computer graphics, 3D modeling, and AI
creativity and logic to craft beautiful artwork with meaningful narratives. whispering.
Sylvia Chang Matt Lennert
Sylvia Chang is Deloitte Insights’ creative director. She is a hunter and collector Matt Lennert led creative for Deloitte Insights and was the art director for
of trends, and serves as a repository of inspiration for the creative team. In Deloitte Insights Magazine. His work with artists and data visualization
a world full of noise and detritus, she’s able to see patterns. She treasures designers over the last two decades visually brought the stories to life. This
her Sundays jumping on the trampoline with her daughters at her home in issue marks the 33rd, and last, issue that he produced. Lennert and his wife
Connecticut. have retired and are off traveling the world.
Natalie Pfaff Molly Piersol
Natalie Pfaff is a senior graphic and data visualization designer at Deloitte Molly Piersol leads data visualization design at Deloitte Insights and is
Insights, passionate about crafting compelling visual stories. She believes that the designer for Deloitte Insights Magazine. She believes raw data creates
great design begins with a strong brand foundation and leverages it to push beautiful art and works to further expand the stories that live between the
creative boundaries. At home in Wisconsin, she cherishes everyday moments lines of figures. Piersol is a Virginia transplant to the Seattle area and her roots
with her husband and daughter. have grown deep enough that she’ll never go back.
Sofia Sergi Jim Slatton
Sofia Sergi is a Deloitte Insights senior graphic designer from New York. Her Jim Slatton is a Deloitte Insights designer and illustrator from Asheville, N.C.
passion for painting and drawing at a young age evolved into a love for design His graphic style is rooted in decades of branding work and a love of mid-
and storytelling. She draws inspiration from merging traditional art forms with century minimalism. He works with custom iconography and typography,
contemporary aesthetics to tell engaging and meaningful stories. photo collage, and data to distill complex information into simple visual stories.
Issue 33 11
Sonya Vasilieff Harry Wedel
Sonya Vasilieff is an art director at Deloitte Insights. She works in several Harry Wedel is a senior data visualization designer at Deloitte Insights. With
mediums including graphic design and illustration. Vasilieff is a native of a background in scientific research, he loves to find innovative ways to display
Seattle, which is as rare as the razor clams she digs for every year on her complex data through engaging information design. Based in New York,
beloved gray and rainy Washington coast. he credits his interests in music and photography for his passion for using
emotion in art to bring people together.
Alexis Werbeck
Alexis Werbeck is an art director and visual storyteller at Deloitte Insights.
Known for her bold, color-driven style, she loves to push creative boundaries
but will get irked if a single pixel is out of place. Although her dream of
becoming a pop star never transpired, you can find her singing karaoke and
rocking at least one article of clothing with rhinestones.
12 Deloitte Insights Magazine
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1
Gen AI investments increasingly extend
beyond the AI itself
Deloitte’s State of Generative AI quarterly survey explores where industry leaders are directing their
gen-AI–related funding
Generative AI’s near- and long-term success prioritization of those investments varies by indus- seem particularly focused on increasing their cyber-
hinges on continued co-investment in the try, the anticipated investments suggest that tech- security spending to support gen AI initiatives.
wider technology ecosystem, and recent Deloitte nology budgets may need to increase across the On average, 53% of respondents expect invest-
research signals that many early adopters are plan- board to take advantage of gen AI’s promise. ments in traditional AI and machine learning to
ning their AI-related investments accordingly. Strong data hygiene is a prerequisite for success- increase alongside investments in gen AI, sug-
According to the third installment of Deloitte’s ful AI and gen AI strategies, and 70% of leaders in gesting that those spending more on both will
quarterly State of Generative AI in the Enterprise the Deloitte State of Gen AI study are investing in be looking to combine predictive and generative
survey, which was fielded in May and June 2024, data management capabilities. Meanwhile, 73% of capabilities in powerful applications.
and gathered responses from nearly 2,800 lead- respondents expect their investment in cloud con-
ers whose organizations are further along in their sumption to increase along with investments in gen
adoption and implementation of gen AI solutions, AI. And, while cybersecurity capabilities are seeing Research and analysis by the Deloitte Center for
leaders across industries expect to be making criti- high co-investment levels from respondents across Integrated Research
cal investments in both gen AI and the intertwined all industries in the survey, averaging 75%, three
and AI-enabling capabilities of data management, industries—financial services; energy, resources, Read the full report at
cloud consumption, and cybersecurity. While the and industrials; and government institutions— www.deloitte.com/us/state-of-gen-ai
18 Deloitte Insights Magazine
losreiP
ylloM
yb
cihpargofnI
DATA POINTS
Q: “To what extent are technology investments in the following areas impacted
as a result of your organization’s enterprisewide generative AI strategy?”
Percentage of respondents who selected “increasing” or “significantly increasing”
100%
Data management
70% average
Traditional AI and
machine learning
capabilities
Cloud consumption 53% average
73% average
Communication
networks
42% average
Cybersecurity
75% average
Hardware
35% average
Note: Deloitte’s AI Institute and Center for Technology, Media & Telecommunications also contributed to this data collection and analysis.
Source: Deloitte Center for Integrated Research’s analysis of data from the Deloitte State of Generative AI wave 3 survey of 2,770 artificial
intelligence leaders, fielded in May and June 2024. These organizations should be considered more advanced users of artificial intelligence.
While
business
leaders look
inward for
AI’s impact,
tech leaders
look outward
A Deloitte Global study
examines the differences in
metrics used by organizations’
leaders to determine the success
of their AI investments
Strategies are being determined. Experimen-
tation is running rampant. Proofs of concept
abound. As generative AI quickly gains a foothold
across organizations and industries, there’s lit-
tle consensus yet about how best to determine its
impact—and whether C-level executives will reach
consensus, themselves. There are clues, however,
in how business and technology leaders meas-
ure value for traditional artificial intelligence, the
larger class of AI investments such as machine
learning, deep learning, and conversational AI for
which executives have established measurement
behaviors and preferences.
Using data from a global survey of 1,600 busi-
ness and technology leaders across 14 countries
conducted in February 2023,1 the Deloitte Center
for Integrated Research analyzed how technol-
ogy leaders and business leaders prioritize the key
performance indicators commonly associated with
digital investments when assessing the impact of
their organizations’ AI capabilities. The results
of this assessment proved to be counterintuitive:
Interestingly, while business leaders who partic-
ipated in the survey reported that they’re more
focused on AI’s process-related benefits within
their organizations, tech leader respondents said
they’re more often looking outward—at KPIs asso-
ciated with sales and customer satisfaction.
According to the survey, technology leaders
are 12 percentage points more likely than business
Issue 33 19
ledeW
yrraH
yb
cihpargofnI
KPIs for traditional Al, showing misalignment greater than or equal to 7 percentage points
between business and tech leaders
Tech leaders Business leaders Difference between responses
80% Overall
utilization
of KPI
10
9 9
7
10
12
10
40%
7
8
0%
Sales Sales of Net Intangible Share price Procurement Employee Employee Process
through new promoter assets as a volatility value for development utilization effectiveness
new digital score percentage money rate
digital products of long-term
platform assets
Used more by tech leaders Used more by business leaders
Notes: 1) N = 1,600; 2) Out of 1,204 respondents for traditional Al, 1,180 are technology and business leaders. The remaining are categorized
under “other.” 3) Business roles include administration, finance, human resources, marketing, operations, procurement, risk/compliance,
sales, strategy. Tech/transformation roles include digital, R&D, technology/IT, transformation.
Source: Deloitte Center for Integrated Research survey of global tech value leaders, conducted in February 2023.
leaders to be using the sales of new digital products Leadership’s alignment on AI success met-
as a KPI and 7 percentage points more likely to be rics could be less critical during an organization’s
focused on sales through new digital platforms, for experimentation or initial adoption phase, but it
instance. They also use net promoter scores and could, of course, become increasingly important
intangible assets more than business leaders.2 as the organization works to assess the technolo-
When it comes to all forms of AI, business and gy’s current and potential impact, and makes the
tech leaders alike might collectively be missing case for continued investment.
opportunities to consider innovation measures
and long-term value creation, the survey findings
suggest. Among those leaders who measure tradi- Research and analysis by the Deloitte Center for
tional AI, only about 30% use innovation-oriented Integrated Research
KPIs like the tech’s effect on an organization’s tol-
erance for experimentation or intelligent failure, Read the full report at
or the number of agile pods or teams.3 www.deloitte.com/insights/measuring-ai
DATA POINTS
Few AI regulations across the globe address
the outcomes rather than the tech
Outcome-based and risk-weighted regulations are an underused tool that can both protect the public
interest and encourage innovation, a Deloitte US analysis shows
When it comes to fast-moving technologies regulatory sandboxes that allow for prototyping risk-weighted, and no regulations included in the
like artificial intelligence, how can govern- and testing new methods; and collaborative regula- data set were both.
ments strike the balance between enabling innova- tion, which seeks alignment and engagement across This isn’t to say that outcome-based and risk-
tion and protecting the public interest? Innovation national and international players within the ecosys- weighted regulations don’t exist. They likely con-
and regulation tend to operate on two different tem. Second, the research center outlines principles stitute part of the regulatory structures of the 69
time frames, which can cause problems when gov- related to the regulations’ focus: outcome-based countries included in the analysis, according to
ernments are working to regulate rapidly evolving regulation, which focuses on the results rather than the researchers. It’s just that those regulations
technology like AI. And consider AI’s complexity the processes; and risk-weighted regulation, which aren’t considered “AI regulations,” so there’s an
and diversity: From computer vision finding pot- proposes a shift from one-size-fits-all regulation to opportunity for many governments’ AI-adjacent
holes in roads to generative pretrained transform- a data-driven, segmented approach. regulations to become more explicit. And these
ers answering people’s tax questions and more, it Outcome-based and risk-weighted regulations clarifications don’t just protect the public. They
could be a formidable challenge to find a single set can be powerful tools for regu"
172,deloitte,guidance-eo-cheat-sheet-2024.pdf,"A CDAO Perspective:
Safe, Secure, and Trustworthy AI
Updated: September 2024
On October 30, 2023, President Joe Biden signed an Executive Order (EO) on the Safe, Secure, and Trustworthy Development and Use of
Artificial Intelligence. The EO built on the administration’s work that led to voluntary commitments from leading companies to drive safe,
secure, and trustworthy development of AI and directed federal agencies to take certain actions to incorporate and govern AI in their
missions. Federal agencies have reported completing all the 150-day actions required by the EO since completing the 90-day actions. On
March 28, 2024, the Office of Management and Budget (OMB) produced its first government-wide policy for managing AI risks and
harnessing AI’s benefits. For key tenants, refer to the EO Fact Sheet and the OMB Guidance Fact Sheet. On August 14, 2024, final
guidance was released requiring agencies to submit their AI use case inventories to OMB for public reporting.
Government Chief Data Officers (CDO) and data leaders play a The following key themes for CDOs exist in an evolving
critical role preparing their organizations for the safe, secure, and landscape of federal guidance on AI adoption and do not capture
trustworthy application for AI. Government CDOs may face the extent of the data-specific requirements within the EO and
challenges and opportunities to improve their organization’s accompanying OMB Guidance.
data and operations for enhanced, equitable, and innovative
applications of AI.
DESIGNATING A CHIEF ARTIFICIAL INTELLIGENCE OFFICER (CAIO)
OMB POLICY REVIEW WHAT TO DO NEXT
“Within 60 days of the issuance of the The formal introduction of the CAIO prompts an immediate step to
October 2023 memorandum, the head ensure the careful coordination between the responsibilities
of each agency must designate a designated for CDOs, CDAOs and those required for CAIOs and to
Chief Artificial Intelligence Officer make the proper designations for the relevant department. This will
(CAIO).” (OMB M-24-10 Section 3.b.i) differ based on the agency’s current designations and
considerations and may involve designating the existing CDO as the
The primary responsibility for CAIOs
CAIO.
is “coordinating their agency’s use of
AI, promoting AI innovation, managing Expanded or new notable CAIO responsibilities include a) ensure AI
risks from the us of AI, and carrying code and data used for AI are “appropriately inventoried, shared,
out the agency responsibilities…” (OMB and released in agency code and data repositories”, b) developing
M-24-10 Section 3.b.ii) AI risk management guidance, c) instituting governance to remain
compliant, working on resourcing requirements and
recommending investment areas to build enterprise capacity, and
d) sharing relevant information with agency officials involved in
agency AI policymaking initiatives.
Action: Evaluate existing responsibilities and coordinate the role of
CDOs, CDAOs, and CAIOs to use, promote, and manage risks for the
agency’s use of AI
HIRING AI TALENT AND PROVIDING AI TRAINING
POLICY REVIEW WHAT TO DO NEXT
“…agencies are strongly encouraged to CDOs can take advantage of this focus on hiring AI talent and AI-
prioritize recruiting, hiring, developing, enabling talent to energize existing efforts towards building data
and retaining talent in AI and AI- fluency and upskilling their workforce for the use of AI. Data fluency
enabling roles to increase enterprise is a foundational element to prepare an organization’s workforce
capacity for responsible AI innovation.” and data for the effective use of AI solutions. CDOs can use OMB’s
(OMB M-24-10 4.c) forthcoming AI and Tech Hiring Playbook as a resource and
coordinate with the newly required AI Talent lead for the AI Talent
Task Force.
Action: CDOs can focus on incorporating AI fluency into existing
data literacy training programs to upskill their workforce and
prepare to engage new AI talent.
A CDAO Perspective:
Safe, Secure, and Trustworthy AI
CONNECTING NEW AI STRATEGIES TO DATA STRATEGY
POLICY REVIEW WHAT TO DO NEXT
“Within 365 days of the issuance of the To develop AI strategies and pursue high-impact AI use cases, CDOs
October 2023 memorandum, each should review existing data strategies with a focus on data
CFO Act agency must develop and infrastructure for AI and workforce readiness. Strategies should
release publicly…a strategy for connect on the agency’s top opportunities for AI, plans to increase
identifying and removing barriers to AI capacities and AI maturity, improvements for practitioner AI and
the responsible use of AI…” (OMB M- data literacy, and effective governance of AI usage.
24-10 Section 4.a).
To advance responsible AI innovation, the EO and accompanying
“Any data used to help develop, test, OMB guidance focus on several data actions CDOs can take to
or maintain AI applications, regardless remove barriers, including developing adequate infrastructure
of source, should be assessed for and curated agency data sets, maximizing access to internal
quality, representativeness, and data, and encouraging public access datasets.
bias.” (OMB M-24-10 Section 4.a.ii)
Action: Assess current data strategies and implementation efforts
to identify AI strategies and AI use cases, with attention to
organizational AI maturity, data literacy, and governance.
MANAGING RISKS FOR RIGHTS-IMPACTING AND SAFETY-IMPACTING AI
POLICY REVIEW WHAT TO DO NEXT
“Within 60 days of the issuance of the CDOs may participate in AI Governance Boards (with senior officials
October 2023 memorandum, each to govern the use of AI) as a representative responsible for data’s
CFO Act agency must convene an role as a key enabler and risk factor in AI adoption.
agency AI Governance board” (OMB
Agencies must review current or planned AI use to assess whether
M-24-10 Section 3.a.ii)
it meets the definition of safety or rights-impacting AI, part of which
“…all agencies are required to asks if the AI output “serves as a principal basis for a decision or
implement minimum practices…to action.” While is ultimately the determination of the CAIO, the CDO
manage risks from safety-impacting may support determining which AI is safety-impacting or rights-
AI and rights impacting AI.” (OMB M- impacting.
24-10 Section 5)
By December 1, 2024, Organizations will be required to document
This includes specific actions, for data assessments and other data-related activities in an AI Impact
example on terminating non- Assessment, required to be updated and leveraged throughout
compliant AI, determining which AI is the AI’s lifecycle. As part of the minimum practices prescribed for
safety-impacting or rights impacting, safety-impacting or rights-impacting AI, policy continues to focus on
and minimum practices for safety or properly documenting the agency’s data use for AI, the use of AI
rights-impacting AI. adequately representing communities and including activities
such as monitoring for improper bias and AI-enabled
“Agencies must assess the quality of
discrimination.
the data used in the AI’s design,
development, training, testing, and Action: CDOs can recognize their critical role in the AI impact
operation and its fitness to the AI’s assessment, ensuring the organization is prepared with the
intended purpose.” (OMB M-24-10 appropriate data infrastructure and data quality needed for
Section 5.c.iv.A.3) trustworthy AI.
FEDERAL CDO INSIGHTS
For or agencies that are already utilizing AI as an accelerator or preparing for its use, CDOs play a unique role across
innovating with AI and managing the potential barriers and risks tied to the safe, secure, equitable and trustworthy
utilization of AI. For more insights on the CDO role and CDO community needs, check out “The Mission-Driven CDO:
Insights from the 2023 Survey of Federal Chief Data Officers”.
A CDAO Perspective:
Safe, Secure, and Trustworthy AI
PUBLIC REPORTING OF AI USE CASES
POLICY REVIEW WHAT TO DO NEXT
“By December 16, 2024, each agency To support transparency, agencies must send an inventory of their
(except for the Department of Defense use cases to OMB. These use cases will be reported publicly.
and agencies in the Intelligence
Exceptions include research and development use cases, one-time
Community) must:
stand-alone use cases, use cases by the Department of Defense,
1. “Annually submit an inventory of use cases within a National Security System or within the
its AI use cases to OMB…” intelligence community, and use cases for which public reporting is
inconsistent with federal law or government wide policy.
2. “Subsequently post a
consolidated, machine-readable CSV of Action: For each qualifying use case in the inventory, complete a
all publicly releasable use cases on form at https://collect.omb.gov/site/212/home-page. Next post a
their agency’s website …”(EO 14110, machine-readable CSV of all publicly releasable use cases on their
2.a.1 and 2a.2) agency’s website at [agency.gov]/ai.
For non-reportable use cases, agencies must keep an inventory and
report aggregate metrics to https://collect.omb.gov/site/212/home-
page.
FEDERAL CDO INSIGHTS
For or agencies that are already utilizing AI as an accelerator or preparing for its use, CDOs play a unique role across
innovating with AI and managing the potential barriers and risks tied to the safe, secure, equitable and trustworthy
utilization of AI. For more insights on the CDO role and CDO community needs, check out “The Mission-Driven CDO:
Insights from the 2023 Survey of Federal Chief Data Officers”.
A CDAO Perspective:
Safe, Secure, and Trustworthy AI
CONNECT & INNOVATE ADDITIONAL SOLUTIONS
TOGETHER AND ACCELERATORS
CDAO Services Government AI Use Case Dossier
Support Chief Data Officers and other data leaders to See what’s working for other agencies and
enable and improve data-driven organizations through consider the ways AI can advance your
services like data governance, literacy, and strategy. mission with the Government and Public
Services Sector AI Use Case Dossier.
Identify: Data requirements, data sources,
insights, and value CAIO Transition Lab
Refine the CAIO role requirements, establish
Discover & Prep: Assess Risks, and
effective governance approaches, and create a
develop the Data and Insights Strategy
transition plan to empower a new CAIO through
Design & Build: Manage the data value a specialist guided experience.
chain; procure, ingest, and store
Trustworthy AI™
Launch & Integrate: Develop delivery
Understand seven key areas of risk for AI and
models, governance, and operations
keep your use of AI safe and ethical with Deloitte’s
Monitor & Mature: Support, enhance, Trustworthy AI ™ framework in line with NIST.
and scale data and insights services
AI and Data Strategy Services
Align on an organizational vision for AI, prioritize
Suite of CDO, Data, and AI Labs
AI use cases, and make strategic choices about
A one-day experience designed to—establish a
where to invest in AI, accelerated by Playbooks and
common understanding of the aspirations and
immersive Labs guided by experienced facilitators.
challenges of the CDAO’s team and key
stakeholders and develop a 180-day plan to drive AI Readiness & Management Toolkit
the CDAO’s priorities.
Apply our framework and tools to assess current
state, define future state, and chart a path
forward to build AI maturity across workforce,
CDO Playbook
data, and technology.
See the most recent thought leadership of CDOs in
the government based on trends and understanding GovConnect AI Ready Data Foundation
for AI priorities, AI Strategies and implementation of Suite of services designed to assist government
operating models agencies in building and managing modern,
cloud integrated data ecosystems, enabling the
delivery of AI at scale.
Contacts
Deloitte supports many Federal clients in the data and AI space. With best-in-class
AI advice and capabilities, We can help at each stage of the race, providing Chief
Data Officers with the CDAO Services they need to navigate changing regulation Ed Van Buren Adita Karkera
and the safe, secure, and trustworthy application of AI. Reach out for a consultation Principal, Deloitte Managing Director, Deloitte
Government and Public Services Government and Public Services
or to ask about our approach to the new executive branch AI guidance.
AI Strategic Growth Offering Leader Chief Data Officer
emvanburen@deloitte.com akarkera@deloitte.com
As used in this document, “Deloitte” means Deloitte Consulting LLP, a subsidiary of Deloitte LLP. Please see http://www.deloitte.com/us/about for a detailed description
of our legal structure. Certain services may not be available to attest clients under the rules and regulations of public accounting. This publication contains general
information only and Deloitte is not, by means of this publication, rendering accounting, business, financial, investment, legal, tax, or other professional advice or services.
This publication is not a substitute for such professional advice or services, nor should it be used as a basis for any decision or action that may affect your business. Before
making any decision or taking any action that may affect your business, you should consult a qualified professional advisor. Deloitte shall not be responsible for any loss
sustained by any person who relies on this publication. Copyright © 2024 Deloitte Development LLC. All rights reserved"
173,deloitte,DI_CIR_State-of-AI-4th-edition.pdf,"A report from the
Deloitte AI Institute and the
Deloitte Center for Integrated Research
Becoming an AI-fueled
organization
Deloitte’s State of AI in the Enterprise, 4th Edition
About the Deloitte AI Institute
The Deloitte AI Institute helps organizations connect all the different dimensions of the robust, highly
dynamic, and rapidly evolving AI ecosystem. The AI Institute leads conversations on applied AI
innovation across industries, using cutting-edge insights to promote human-machine collaboration in
the Age of WithTM.
The Deloitte AI Institute aims to promote dialogue about and development of artificial intelligence,
stimulate innovation, and examine challenges to AI implementation and ways to address them. The AI
Institute collaborates with an ecosystem composed of academic research groups, start-ups,
entrepreneurs, innovators, mature AI product leaders, and AI visionaries to explore key areas of
artificial intelligence including risks, policies, ethics, future of work and talent, and applied AI use cases.
Combined with Deloitte’s deep knowledge and experience in artificial intelligence applications, the
institute helps make sense of this complex ecosystem and, as a result, delivers impactful perspectives
to help organizations succeed by making informed AI decisions.
No matter what stage of the AI journey you’re in—whether you’re a board member or C-suite leader
driving strategy for your organization, or a hands-on data scientist bringing an AI strategy to life—the
Deloitte AI institute can help you learn more about how enterprises across the world are leveraging AI
for a competitive advantage. Visit us at the Deloitte AI Institute for the full body of our work, subscribe
to our podcasts and newsletter, and join us at our meetups and live events. Let’s explore the future of
AI together. L earn more.
About the Deloitte Center for Integrated Research
The Deloitte Center for Integrated Research (CIR) offers rigorously researched and data-driven
perspectives on critical issues affecting businesses today. We sit at the center of Deloitte’s industry and
functional expertise, combining the leading insights from across our firm to help leaders confidently
compete in today’s ever-changing marketplace.
Connect
To learn more please visit www.deloitte.com/us/cir.
Contents
Foreword: Becoming an AI-fueled organization 2
Executive Summary: Constantly transforming, never fully transformed 3
Strategy: What should your north star be? 7
Operations: How do you bring transformation into everyday work? 11
Culture & Change Management: Why is valuable change so elusive? 14
Ecosystems: How should you orchestrate your partnerships? 18
Our AI-fueled future: The pathway is clear 20
Methodology 21
Endnotes 23
Becoming an AI-fueled organization
Foreword: Becoming an
AI-fueled organization
RAPIDLY TRANSFORMING, BUT not fully Algorithms can independently balance financial
transformed—this is our overarching portfolios. Support centers often know customers’
conclusion on the market, based on the problems before they call.2 And these are still
fourth edition of our State of AI in the Enterprise early days.
global survey. Very few organizations can claim to
be completely AI-fueled, but a significant and In combination, these developments help enable
growing percentage are starting to display the businesses to increasingly liberate themselves from
behaviors that can get them there. the time constraints of human rhythms. Core
business operations can meet customer needs at a
To us, this is exciting and has reinforced our belief faster pace, while freeing up time and energy for
that now is one of the most opportunity-rich the workforce to use new tools to discover
periods in the history of AI technology. innovative avenues for value creation. Conversely,
Organizations are swiftly building capabilities and for those organizations lagging in AI capability
reaching enterprise scale: In fact, more than a development, it could pose an ever-increasing risk
quarter of our survey respondents have reached to their competitive viability in the not-too-distant
full-scale deployment of five or more types of AI future. The massive global disruptions over the last
applications within their organization.1 This year have only accelerated these trends beyond our
widespread enterprise experimentation has set a most aggressive predictions.3
solid foundation for many, making way for what
we believe could be a bumper crop of meaningful Fortunately, we’ve learned in recent years about
advancements and impact over the next few years. which practices can accelerate transformation, and
This is especially true for those who are already this knowledge can help fast-track outcomes. The
beginning to use AI to solve some of their most findings in this report aim to support organizations
business-critical and challenging problems. in navigating through the growing pains, in
whichever stage they may find themselves on the
Within just the last 18 months, AI capabilities have journey to becoming an AI-fueled organization.
advanced considerably, maturing from what was
often experienced as a bothersome critic—telling The Age of With is no longer on its way—it has
workers what to do or pointing out their mistakes— arrived. We hope you’ll join us as this story
to more frequently serving as a copilot, continues to unfold.
independently executing on insights and trends — Jason Girzadas, managing principal of
surfaced through the power and speed of cloud- Businesses, Global, and Strategic Services,
Deloitte LLP
based data hosting and computation. Today, some
supply chains are managing themselves.
2
Deloitte’s State of AI in the Enterprise, 4th Edition
Executive summary:
Constantly transforming,
never fully transformed
SINCE 2017, DELOITTE has documented the To learn how organizations across the globe are
increasing adoption of AI across the progressing toward this vision, we surveyed 2,875
enterprise. The third edition, published in executives from 11 top economies who have
2020, declared that we had entered the “age of purview into AI strategies and investments within
pervasive AI.”4 Pervasive AI adoption, however, their organizations. We asked them about a wide
does not mean that AI is being used to its full variety of behaviors—from their overarching AI
potential. And so, with the fourth edition of our strategy and leadership, to their technology and
global State of AI in the Enterprise report, we data approaches, and how they are helping their
explored the deeper transformations happening workforce to operationalize AI. Then, to
inside organizations that are using AI to drive understand which behaviors lead to the greatest
value. In other words, we wanted to know: outcomes, we analyzed the survey responses based
What are today’s most AI-fueled on how many types of AI applications a company
organizations doing differently to has deployed full-scale and the number of
drive success? outcomes achieved to a high degree (figure 1).
AI-fueled organizations leverage data as an asset to
deploy and scale AI systematically across all types
of core business processes in a human-centered
way. They use the power of rapid, data-driven
decision-making to enhance workforce and
customer experiences to achieve competitive
advantage and continuously innovate.5
“Becoming an AI-fueled
organization is to understand
that the transformation process
is never complete, but rather a
journey of continuous learning
and improvement.”
— Nitin Mittal, Deloitte AI coleader,
principal, Deloitte Consulting LLP
3
FIGURE 1
An organization’s AI maturity can be profiled based on the number of
applications deployed and outcomes achieved
AI application types fully
deployed
None 1–3 4–7 >7
""Pathseekers"" ""Transformers""
Low deployed/high High deployed/high
achieving: 26% achieving: 28%
N=753 N=794
Outcomes
achieved
(high degree)
""Starters"" ""Underachievers""
Low deployed/low High deployed/low
achieving: 29% achieving: 17%
N=832 N=496
4
7>
7–4
3–1
enoN
Becoming an AI-fueled organization
Transformers Pathseekers Underachievers Starters Total
6.8
6.2
5.9
5.5
4.0
3.5
1.9
1.6 1.4
1.0
Average number of AI apps in full deployment Average number of outcomes achieved to a high
degree
Source: The State of AI 4th Edition data analysis.
Deloitte Insights | deloitte.com/insights
Deloitte’s State of AI in the Enterprise, 4th Edition
This analysis revealed four key profiles: strong outcomes became evident. They fell into the
following categories: Strategy, Operations,
• Transformers (High outcome and high
Culture and change management, and
deployed—28% of survey respondents):
Ecosystems.
Transforming but not fully transformed, this
group has identified and largely adopted
Analysis of survey data and executive interviews
leading practices associated with the strongest
AI outcomes. They average 5.9 out of 10 revealed that success is built upon the foundation
possible full-scale deployments of different of a clear strategy that is communicated and
types of AI applications, and 6.8 out of 17 incentivized from the highest leadership—but that
possible outcomes achieved to a high degree. is not enough. With that clear strategy in place, two
They are the market leaders on their way to inter-related leading practices typically work
becoming AI-fueled organizations. together to support AI adoption and scale across
the enterprise: operations and culture plus change
• Pathseekers (High outcome and low
deployed—26% of survey respondents): management. And finally, the support of a robust
Pathseekers have adopted capabilities and set of ecosystem partners was shown to provide the
behaviors that are leading to success, but on technical foundations and outside perspectives
fewer initiatives. They are making moves but needed to deliver and perpetually innovate at scale.
have not scaled to the same degree as
Transformers. They average 1.9 out of 10 Our analysis also revealed not just what those
possible full-scale deployments of different
leading practices were, but how much of an effect
types of AI applications, and 6.2 out of 17
they had on organizational achievement:
possible outcomes achieved to a high degree.
• Strategy leading practice: AI-fueled
• Underachievers (Low outcome and high
organizations view AI as a key element of
deployed—17% of survey respondents): A
business differentiation and success, and
significant amount of development and
they set an enterprisewide strategy that
deployment activity characterizes this group;
is championed from the top. Organizations
however, they haven’t adopted enough leading
with an enterprisewide strategy and
practices to help them effectively achieve
leadership who communicate a bold vision are
meaningful outcomes. They average 5.5 out of
1.7 times more likely to achieve outcomes to a
10 possible full-scale deployments of different
high degree.
types of AI applications, and 1.4 out of 17
possible outcomes achieved to a high degree. • Operations leading practice: AI-fueled
organizations establish new operating
• Starters (Low outcome and low
models and processes that drive
deployed—29% of survey respondents):
sustained quality, innovation, and value
Getting a late start in building AI capabilities
creation. Organizations that document and
seems to characterize this group. They are the
enforce MLOps processes are approximately
least likely to demonstrate leading practice
two times as likely to achieve their goals to a
behaviors. They average 1.6 out of 10 possible
high degree. They are also about two times as
full-scale deployments of different types of AI
likely to report being extremely prepared for
applications, and 1.0 out of 17 possible
risks associated with AI and nearly two times
outcomes achieved to a high degree.
as confident that they can deploy AI initiatives
in a trustworthy way.
By analyzing these groups—the Transformers in
particular—the behaviors most associated with
5
Becoming an AI-fueled organization
• Culture and change management leading “By embracing AI strategically
practice: AI-fueled organizations
and challenging orthodoxies,
nurture a trusting, agile, data-fluent
culture and invest in change organizations can define a
management to support new ways of road map for adoption, quality
working. Organizations that invest in change
delivery, and scale to create
management to a high degree are 1.6 times as
likely to report that AI initiatives exceed or unlock value faster than
expectations and more than 1.5 times as likely
ever before.”
to achieve their desired goals, compared to
the rest. — Irfan Saif, Deloitte AI coleader, principal,
Deloitte & Touche LLP
• Ecosystems leading practice: AI-fueled
organizations orchestrate dynamic
ecosystems that help build and protect
competitive differentiation. Overall, In the following report, we explore each leading
organizations with more diverse ecosystems practice in detail, sharing critical and often
were 1.4 times as likely to use AI in a way that overlooked actions that leaders can take to avoid
differentiates them from their competitors. pitfalls on their transformation journey.
6
Deloitte’s State of AI in the Enterprise, 4th Edition
Strategy: What should
your north star be?
CORE LEADING PRACTICE: SET A CLEAR ENTERPRISEWIDE STRATEGY AT THE TOP THAT
ENABLES LEADERS TO HARNESS AI CAPABILITIES TO DRIVE NEW OPPORTUNITIES AND
COMPETITIVE ADVANTAGE.
Key findings:
• Set and communicate a bold vision. Organizations with an enterprisewide strategy and leaders who
communicate a bold vision are 1.7 times as likely to achieve outcomes to a high degree.
• Look for ways AI can help achieve a differentiated strategy. Only 38% of respondents believe their
use of AI differentiates them from competitors.
• Communicate your strategy transparently. Tell your workforce and the market about your strategy
and the implications and trade-offs along the way.
Pitfalls to avoid:
• Don’t ask data scientists or IT to drive your AI strategy. Senior business leaders should drive it based
on the core business strategy in partnership with data scientists.
• Don’t overindex on efficiency goals. Balance efficiency targets with growth- and
innovation-oriented goals.
ONE OF THE most frequently cited leading Lost in AI use cases: Leaders
practices for AI transformation is the need can forget to put their
for a bold, enterprisewide strategy that is business strategy first
set and championed by an organization’s highest
leadership. Our research confirmed this: To many leaders, it comes as a surprise to learn
Transformers are more than three times as likely to that the investment needed to develop AI solutions
have an enterprisewide strategy in place, and well cannot realize a return through the deployment of
over twice as likely as Starters to report their single, disconnected use cases, or even a handful.6
leaders communicate a vision for AI. However, This is why it’s so important to have an AI strategy
only 40% of our total survey respondents that is connected and coordinated across the
completely agreed that their company has one in enterprise, in tight alignment with the overarching
place. Meanwhile, even though a significant business strategy. All too often, however, business
majority (66%) of respondents view AI as critical to leaders get the planning process out of order,
success, only 38% believe their use of AI focusing too much on use cases or abdicating
differentiates them from competitors. What should leadership of the AI strategy to IT or data sciences.
organizations do differently to strengthen This can be a slippery slope, diminishing the
their approach? organization’s ability to use AI to create new ways
7
Becoming an AI-fueled organization
FIGURE 2
Leading AI strategy practices
Percentage of respondents who selected ""completely agree"" or ""very important"" to
these statements about strategy
Transformers Pathseekers Underachievers Starters Total
79%
68% 69% 66%
60%
55% 57%
48% 49%
44% 45%
38% 40% 40%
33% 33%
30%
22% 24%
19%
My company's use of My company has an My senior leaders Our AI initiatives are
AI differentiates us enterprisewide AI communicate a vision for important to our
from our competitors strategy AI that will significantly remaining competitive
change how we operate over the next five years
Source: State of AI 4th edition data analysis.
Deloitte Insights | deloitte.com/insights
of competing for customers, launching products, same key performance indicators (KPIs) that have
accelerating time-to-market, securing supply been crafted to incentivize and grow
chains, and beyond. competitive advantage.
In a now famous example from the early
To many leaders, it comes as a surprise
2010s, Jeff Bezos mandated that every
to learn that the investment needed
leader across Amazon plan for how they
to develop AI solutions cannot realize would use AI and machine learning (ML) to
a return through the deployment of help the company compete and win. This
imperative drove unparalleled innovation
single, disconnected use cases, or even
and was cited as the catalyst for the
a handful.
Amazon’s rise to become an AI leader
today.7 Many of the strongest AI strategies
start in this same way: by pushing clear
The strongest AI strategies tend to begin without objectives down to business leadership, so they can
ever mentioning AI. Instead, they should begin identify gaps and opportunities within their
with the organization’s north star: the core divisions and work backward from there to
business strategy. From there, the process requires apply AI as a solution.
tight collaboration with engaged leaders across all
business divisions and the focus of workers at all These local plans should then be brought back to
levels. Ultimately, AI strategy should function as the top, so that mutual goals and initiatives can be
the fuel to the business strategy, aligning to the aligned and unified with the core business strategy.
8
Deloitte’s State of AI in the Enterprise, 4th Edition
This step is often critical: It’s only when AI has “You have to go both for impact and build the
been integrated and proliferated throughout the foundations in parallel, and that is the most
enterprise that it can deliver the combination of challenging part,” advises Najat Khan, PhD, chief
efficiency- and value-creating outcomes needed to data science officer and global head of strategy &
fuel ongoing returns. operations for Janssen Research & Development.
“You have to pick the right questions, and have
what I call a diversified portfolio of questions to
Balance your goals:
drive impact, ensuring that you can demonstrate
Overindexing on efficiency can early value to build momentum for achieving
lead to missed opportunities longer-term, sustainable impact.”
It’s through the combination of both efficiency- and AI-fueled organizations can create durable
value-creation targets that organizations typically competitive advantage when the CEO and C-suite
achieve the most success. “When digitally collectively harness data, advanced analytics, and
transforming a company, you want greater degrees AI to shape strategic possibilities for both the near
of efficiency,” remarks Rajeev Ronanki, SVP and and long term in support of their
chief digital officer at Anthem. “But there is a corporate strategy.
second order of business: What new business
opportunities, what capabilities does AI open up
Communicate the
that allow for servicing adjacent or maybe entirely
new areas?” vision: Publicly signaling
transformation can
Our survey results reinforced this, demonstrating
build market value
that lower-achieving organizations (Starters and
Underachievers) tended to focus more on efficiency Chief executives of high-achieving organizations
or “cost out” goals, while high-achieving typically serve as the AI communicator-in-chief.
organizations (Transformers and Pathseekers) According to our survey data, those organizations
were more likely to emphasize growth-oriented that communicate a clear vision are 1.5 times as
goals, such as: improving customer satisfaction, likely to achieve desired outcomes compared to
creating new products and offers, and entering new those who do not. The most effective leaders tend
markets. In other words, high-achieving to use their platform not only to communicate and
organizations are more likely to maintain an eye champion their plans; they also clarify the
toward the art of the possible and a growth implications and trade-offs required along the way.
mindset, which allow them to take advantage of This is often essential for maintaining focus and
opportunities often missed by those who overindex ensuring that decisions made at all levels of the
on efficiency or supporting business as usual. organization remain aligned to the vision.
Leaders should also remember that value
“Envision what is possible in your
can be created by influencing perceptions
business, whether it’s been done before of the market and investors.
Communicating the company’s vision
or never been done before.”
publicly can amplify success, signaling to
— Michelle Lee, VP of Amazon Machine Learning
capital markets and the competitive talent
Solutions Lab
market that an organization is investing in
a bold and exciting future.8 If it’s not
9
Becoming an AI-fueled organization
important enough to merit such a forceful signal technology developments. As the organization’s
toward change, it’s highly likely that the core business strategy and AI capabilities mature
gravitational pull toward the status quo could over time, leaders should continually sharpen their
dampen outcomes for even the strongest strategy. goals, moving beyond staying competitive to
increasingly using AI and ML as competitive
differentiators.
Remain dynamic: Perpetually
iterate your AI strategy For more AI strategy recommendations:
Finally, developing an enterprisewide AI strategy • An innovation strategy powered by tech
that’s set up to fuel a differentiating core business
strategy is not a one-and-done exercise. • The AI Dossier: Top uses for AI in every major
Organizations should develop dynamic ways of industry — now and in the future
assessing their strategy to ensure it remains
responsive to ever-changing market and • A new language for digital transformation
10
Deloitte’s State of AI in the Enterprise, 4th Edition
Operations: How do you
bring transformation
into everyday work?
CORE LEADING PRACTICE DRIVE ONGOING QUALITY, INNOVATION, AND VALUE CREATION
THROUGH NEW OPERATING MODELS, ROLES, AND PROCESSES.
Key findings:
• Reimagine business workflows and roles. Organizations that have undergone significant changes to
workflows or added new roles are more than 1.5 times as likely to achieve outcomes to a high degree.
• No excuses: Document and follow MLOps. Organizations that document and follow MLOps processes
are twice as likely to achieve their goals to a high degree. They are also approximately two times as likely
to report being extremely prepared for risks associated with AI and nearly two times as confident that
they can deploy AI initiatives in a trustworthy way.
Pitfalls to avoid:
• Business leaders should allocate more time to solution design. Effectively redesigning processes and
how AI tools fit into workflows requires thoughtful attention.
• Don’t underestimate the unique maintenance needs of AI solutions. Establish and document robust
MLOps procedures to ensure ongoing quality and ethical delivery.
TECHNOLOGY CANNOT DELIVER documenting AI life cycle publication strategies,
transformative results unless organizations and updating workflows, roles, and team structures
reimagine how work gets done. Most leaders across the business.
today understand this intellectually; however,
survey results show a disconnect in putting it into To ensure quality AI solution development,
action: Across a variety of operational activities— enterprise adoption, and the most successful
both on the business side and within IT or data outcomes, organizations should rethink their
science teams—only about one-third of those operations from two key perspectives: across the
surveyed report that they have adopted leading business workflow, and within their IT and data
operational practices for AI. This includes adhering science team processes.
to a well-calibrated MLOps framework,
11
Becoming an AI-fueled organization
FIGURE 3
Leading AI operations practices
Percentage of respondents who selected ""completely agree"" to these statements
about operations
Transformers Pathseekers Underachievers Starters Total
56%
53%
49% 50%
44%
41% 42% 41%
38% 38%
32% 33% 31% 30%
24% 24% 25%
20%
15%
13%
My functional group My functional group My functional group has My functional group
follows a documented follows documented undergone significant changes has created new AI job
AI model life cycle MLOps procedures in how we create teams and roles/functions to
publication strategy when developing an manage workflows to take maximize AI
AI solution advantage of new technologies advancements
in the last five years
Source: The State of AI 4th Edition data analysis.
Deloitte Insights | deloitte.com/insights
When this happens, Michelle Lee, VP of Amazon
A call for leaders:
Machine Learning Solutions Lab, observes, “They
Business stakeholders then experience organizational inertia, because
should take ownership either the use case being addressed wasn’t
important enough, or there is an unwillingness to
of AI-fueled solutions
adopt a new and an unproven method.”
A successful AI solution should be conceived and
designed to fit within a new workflow created to Dr. Tian He, vice president and the head of JD
improve value delivery. To do this effectively, Logistics AI and Data Science, underscores that
business stakeholders should take a lead role, but “Most people learning especially machine learning
unfortunately, many misunderstand how to do this and deep learning just came out of school, and they
effectively. know the AI skills ... They’re technicians. But you
need to understand the business.”
This causes them to allocate too little
“We’ve seen a lot of AI projects where
time to rethink the broader operational
shifts needed to support successful people have implemented amazing
adoption of a value-creating solution.
models, but they’ve never seen the light
All too often, AI and ML development
of day because the business rejects the
teams are put in charge without a clear
view into the business processes they process changes that go along with it.”
are tasked with transforming.
— Rajen Sheth, VP of Google Cloud AI and Industry
Solutions
12
Deloitte’s State of AI in the Enterprise, 4th Edition
Only with an engaged partnership between the and operational managers is important to align the
business and AI and ML development teams, can a necessary processes for AI and ML to take hold.
new way of working emerge. Even when business
leaders understand their role, a lack of AI fluency While developing these processes is generally the
can inhibit their ability to collaborate effectively responsibility of IT and data science leadership, all
with the AI and ML development teams. Some stakeholders and senior leaders should be
organizations have found success in creating new concerned that these processes and standards are
roles to help translate between business in place and observed across the organization.
stakeholders and model development teams. In They are key to ensuring the ongoing quality of
these circumstances, an individual well-versed in models that are fueling critical business processes.
both business and analytics can serve as the bridge Data from our survey bear out just how important:
between overarching business strategic goals and Organizations that strongly agreed that MLOps
AI technical requirements.9 Our survey processes were followed were twice as likely to
demonstrates that efforts in creating new roles like achieve their goals, compared to the rest.
this can pay off. High-achieving surveyed Furthermore, these surveyed organizations were
organizations (Transformers and Pathseekers) also approximately two times more likely to report
were significantly more likely to create new roles feeling extremely prepared for risks associated
and functions to maximize AI advancements. with AI, and nearly twice as confident that they can
deploy AI initiatives in an ethical, trustworthy way.
MLOps: New capabilities
require new processes Rethinking ops: A catalyst
for AI transformation
In the early days of enterprise AI, initiatives took
place within localized teams and were contained Establishing the appropriate structures, roles, and
within business divisions. Models were frequently working relationships across an organization can
built on data scientists’ desktops and required be one of the most important steps in bringing an
relatively simple and smooth processes to AI transformation to life: “If I were to give one
maintain.10 Today, models are being deployed in piece of advice to a C-suite–level person looking at
the cloud and running mission-critical workloads. how to get this right in their organization, I would
As organizations reach this scale, the level and say, ‘Look at the organizational structure, because
complexity involved in perpetually developing, that can really facilitate the change,’” advises Phil
training, testing, deploying, monitoring, and Thomas, executive vice president of Customer
maintaining models have caught many Insights Data & Analytics at Scotiabank. “That for
organizations by surprise: Only 33% of all survey us was a massive accelerant in our journey—getting
respondents completely agree they have MLOps the org structure right and creating a culture of
processes in place. being a data-driven organization that’s accepting of
the use of AI.”
Not all data scientists are skilled in taking on an
engineering or operational mindset to manage this For more AI operations recommendations:
at scale. This is why a strong collaboration across
• ML Oops to MLOps
data scientists, engineering, application developers,
• Taking AI to the Next Level
13
Becoming an AI-fueled organization
Culture and change
management: Why is
valuable change so elusive?
CORE LEADING PRACTICE BUILD A TRUSTING, AGILE, DATA-FLUENT CULTURE AND INVEST IN
CHANGE MANAGEMENT TO SUPPORT NEW WAYS OF WORKING.
Key findings:
• Data fluency pays off. High-achieving organizations (Transformers and Pathseekers) are nearly three
times as likely to trust AI more than their own intuition, compared to low-achieving organizations
(Starters and Underachievers).
• Prioritize change management. Organizations that invest in change management to a high degree are
1.6 times more likely to report that AI initiatives exceed expectations and more than 1.5 times as likely to
achieve their desired goals.
• Fear can be an indicator of positive change if paired with supportive culture and
change management.
Pitfalls to avoid:
• Don’t take a one-size-fits-all approach to change management. Tailor your efforts to key audiences
and ensure a variety of resources are available to support new behaviors.
• Don’t expect change management to fix a poorly designed transformation. Thoughtfully designing
a new solution from the beginning can set the foundation for positive change.
OVER THE PAST few decades, the pace of Through interviews and survey data analysis, we
business and technology change has found the organizations with the strongest AI
quickened, requiring workers to adapt, outcomes tend to display some common
perpetually learn new skills, and make decisions characteristics, including high levels of
amid growing ambiguity. For many organizations, organizational trust, data fluency, and agility. And
these shifts have challenged a critical facet within to get there, investment in change management
their organization: their culture. has been key to successful AI transformation:
Executive interviewees repeatedly emphasized how
“Not to say that technical model
the cultural characteristics of their organizations
building is easy, but the biggest
either facilitate or hinder their AI-transformation
efforts. This aligned with a 2019 Deloitte Survey challenge is culture change.”
that found that organizations with the most data-
— Phil Thomas, executive vice president
driven cultures were twice as likely to significantly
of Customer Insights Data & Analytics at
exceed business goals.11 Scotiabank
14
Deloitte’s State of AI in the Enterprise, 4th Edition
Organizations that invest in change management machines replacing humans. But high-achievers
are 1.6 times as likely to report that AI initiatives also reported little desire to reduce employee
exceed expectations and more than 1.5 times as headcount as well as high investment in training
likely to achieve outcomes than those that don’t. A and change management. Wh"
178,deloitte,DI_State-of-AI-in-the-enterprise-2nd-ed.pdf,"State of AI in the Enterprise, 2nd Edition
Early adopters combine bullish enthusiasm with strategic investments
Early adopters combine bullish enthusiasm with strategic investments
Contents
Executive summary | 2
Activity, investment, and positive results | 3
To maximize value, early adopters should become
risk and change management experts | 9
Early adopters want more talent, and need a
better mix of it | 14
Enthusiastic early adopters can take the next step
by getting serious | 17
Endnotes | 21
1
State of AI in the Enterprise, 2nd Edition
Executive summary
FOR THE SECOND straight year, Deloitte risks. Project selection and managing return on
surveyed executives knowledgeable about cog- investment are also critical.
nitive technologies and artificial intelligence,1 3. Early adopters need the right mix of
representing companies that are testing and imple- talent—not just technical skills—to ac-
menting them today. We found that these early celerate their progress. They are short of
adopters2 remain bullish on cognitive technologies’ AI researchers and programmers but also need
value. As in last year’s survey, the level of support business leaders who can select the best use
for AI is truly extraordinary. Our analysis uncov- cases. To garner this talent, they are training
ered three main findings: their current workforce, but many feel the need
to replace existing workers with new people.
1. Early adopters are ramping up their AI Early adopters also may need a strategic ap-
investments, launching more initiatives, proach to talent that automates what machines
and getting positive returns. Cloud-based do best, while still capitalizing on human judg-
cognitive services are increasing adoption by ment and creativity.
reducing the investment and expertise required
to get started. These findings illustrate that cognitive tech-
2. Companies should improve risk and nologies hold enticing promise, some of which is
change management. This includes reducing being fulfilled today. However, AI technologies may
cybersecurity vulnerabilities—which can slow or deliver their best returns when companies balance
even stop AI initiatives—and managing ethical excitement over their potential with the ability to
execute.
METHODOLOGY
To obtain a cross-industry view of how organizations are adopting and benefiting from cognitive
computing/AI, Deloitte surveyed 1,100 IT and line-of-business executives from US-based companies
in Q3 2018. All respondents were required to be knowledgeable about their company’s use of
cognitive technologies/artificial intelligence, and 90 percent have direct involvement with their
company’s AI strategy, spending, implementation, and/or decision-making. The respondents
represent 10 industries, with 17 percent coming from the technology industry. Fifty-four percent are
line-of-business executives, with the rest IT executives. Sixty-four percent are C-level executives—
including CEOs, presidents, and owners (30 percent), along with CIOs and CTOs (27 percent)—and 36
percent are executives below the C-level.3
22
Early adopters combine bullish enthusiasm with strategic investments
Activity, investment, and
positive results
AY EAR LATER, AND the thrill isn’t gone. In
Deloitte’s 2017 cognitive survey, we were
struck by early adopters’ enthusiasm for
cognitive technologies.4 That excitement owed
much to the returns they said cognitive technolo-
gies were generating: 83 percent stated they were
seeing either “moderate” or “substantial” benefits.
Respondents also said they expected that cognitive
technologies would change both their companies
and their industries rapidly. In 2018, respondents
remain enthusiastic about the value cognitive tech- layers of abstract variables. Deep learning
nologies bring. Their companies are investing in models are excellent for image and speech recog-
foundational cognitive capabilities, and using them nition but are difficult or impossible for humans
with more skill. to interpret. New technologies are making it
easier for companies to launch deep-learning
projects, and adoption is increasing. Among
Higher adoption, multiple
our respondents, 50 percent said they use deep
options learning, a 16 point increase from 2017—the
largest jump among all cognitive technologies.
Compared with their counterparts in typical • Natural language processing is the ability to
companies,5 our early-adopter respondents have extract or generate meaning and intent from text
high—and growing—penetration rates of key cogni- in a readable, stylistically natural, and grammat-
tive technologies: ically correct form. NLP powers the voice-based
interface for virtual assistants and chatbots, and
• Machine learning is the ability of statistical the technology is increasingly used to query data
models to develop capabilities and improve sets as well.6 Sixty-two percent of respondents
their performance over time without the need to have adopted NLP, up from 53 percent last year.
follow explicitly programmed instructions. Most • Computer vision is the ability to extract
cognitive technologies are based on machine meaning and intent out of visual elements,
learning and its more complex progeny, deep whether characters (in the case of document
learning. That includes computer vision and digitization) or the categorization of content
natural language processing (NLP). Machine- in images such as faces, objects, scenes, and
learning adoption was already high at 58 percent activities. The technology behind facial recogni-
in 2017, and it grew by 5 percentage points in tion—computer vision—is a part of consumers’
2018. everyday lives. For example, some mobile
• Deep learning is a complex form of machine phones permit their owners to log in simply by
learning involving neural networks, with many
33
State of AI in the Enterprise, 2nd Edition
looking at them, via facial recognition.7 Com- finance, video analysis in brand management, and
puter vision technology “drives” driverless cars trouble ticket analysis in customer service. The
and animates cashier-less Amazon Go stores.8 need for companies to develop bespoke cognitive
Computer vision has also gone mainstream with initiatives will likely decline as similar services
our survey respondents, 57 percent of whom say enter the market.
their company uses it today. Off-the-shelf can go only so far, however. Many
companies will likely need to develop customized
What’s behind the growth of cognitive technolo- solutions to meet their lofty expectations for cog-
gies among early adopters,9 especially the popularity nitive technologies. Here, too, there are tools to
of sophisticated technologies such as deep learning? accelerate adoption. Many of the big cloud providers
One answer is investment. Thirty-seven percent of offer AI through an as-a-service model: Instead of
respondents say their companies have invested having to build their own infrastructure and train
US$5 million or more in cognitive technologies. algorithms, companies can tap into the technologies
Another reason is that companies have more ways
FIGURE 1
to acquire cognitive capabilities, and they are taking
Enterprise software represents the
advantage. Nearly 60 percent are taking what is
most popular—and easiest—path to AI
perhaps the easiest path:10 using enterprise soft-
ware with AI “baked in” (see figure 1). Respondents who report their company
uses this method of acquiring/developing AI
More respondents gain cognitive capabilities
through enterprise software, such as CRM or ERP
systems, than by any other method. These systems
Enterprise
59%
have the advantage of access to immense data sets software with AI
(often their own customers’ data), and can often be
used “out of the box” by employees with no special- 59%
Codevelopment
ized knowledge. 53%
with partners
The cognitive tools available through enterprise
59%
software are often focused on specific, job-related
tasks. While this can make them less flexible, they
Cloud-based AI 49%
may be impactful nonetheless. For example, Sales-
90%
force Einstein can help sales reps determine which
leads are most likely to convert to sales, and the 59%
Open-source
optimal time of day to contact those prospects. development tools 49%
Moreover, vendors continually develop advanced 59%
tools, which are gradually integrated into the soft-
ware. Salesforce recently developed an advanced Automated
46%
machine learning
NLP model for handling multiple use cases that
typically require different models.11
59%
The “easy path” will likely become even more
Data science
44%
attractive as software vendors and cloud providers modeling tools
develop AI offerings tailored to business functions. 59%
Google recently announced a set of prepackaged AI
services aimed at contact centers and HR depart- Crowdsourced 39%
development
ments.12 SAP’s AI capabilities, which it collectively
calls “Leonardo Machine Learning,” also include
specific solutions such as cash management in Source: Deloitte State of AI in the Enterprise, 2nd Edition, 2018.
44
Early adopters combine bullish enthusiasm with strategic investments
Cognitive technologies are
they need right away, and pay only for what they use.
a necessity, not an option
According to a recent Deloitte study, 39 percent of
companies prefer to acquire advanced technologies
such as AI through cloud-based services, versus Many early adopters are investing in cognitive
15 percent that prefer an on-premise solution.13 technologies to improve their competitiveness.
Indeed, the appeal of the AI-as-a-service model is Sixty-three percent of surveyed executives said their
reflected in its annual global growth rate, which is AI initiatives are needed to catch up with their rivals
estimated at a remarkable 48.2 percent.14 or, at best, to open a narrow lead (see figure 2).
Cloud-based deep-learning services can give And the linkage between adept application of AI
companies access to immense—and previously and competitive advantage appears to be growing
costly—computing power necessary to extract stronger. Eleven percent said that adopting AI is
insights from unstructured data. They can also of “critical strategic importance” today, but 42
manage large data sets and accelerate app develop- percent believe it will be critical two years from
ment with pretrained models. now. This is a small window for companies to hone
While there are myriad ways for companies to their AI strategies and skills, and they believe their
access ready-made AI or develop their own, many success depends upon getting it right. Executives
also seek outside expertise. Fifty-three percent of are becoming more realistic about the time this will
respondents codevelop cognitive technologies with require, however. In our 2018 survey, 56 percent
partners, and nearly 40 percent use crowdsourcing of respondents said cognitive technologies would
communities such as GitHub. transform their companies within three years, down
Through cloud services and enterprise soft- from 76 percent last year. The same was true of in-
ware, companies can try cognitive technologies dustrywide transformation: 37 percent of our 2018
and even deploy them widely, with low initial cost respondents think it will happen within three years,
and minimal risk. The growing number of cloud- 20 points lower than in 2017. We believe executives
based options may explain the spike in pilots and are acknowledging the complexity of using cogni-
implementations between 2017 and 2018. Fifty-five tive technologies to drive change across lines of
percent of executives say their companies have business, without despairing of attaining that goal.
launched six or more pilots (up from 35 percent in
2017), and nearly the same percentage (58 percent)
Earning while they learn
claim that they have undertaken six or more full
implementations (up from 32 percent). Many companies’ AI goals extend well beyond
ROI. Positive ROI, however, can build momentum
for future investment and generate support for ex-
FIGURE 2
AI helps organizations keep up with the (Dow) Joneses
Relative to competitors, respondents say their company’s adoption of AI has allowed them to . . .
16% 20% 27% 28% 9%
Catch up Stay on par Edge slightly ahead Widen a lead Leapfrog ahead
Source: Deloitte State of AI in the Enterprise, 2nd Edition, 2018.
Deloitte Insights | deloitte.com/insights
55
0 20 40 60 80 100
25%
27%
22%
24%
25%
24%
ecutive champions of AI, and the technologies seem tions and delivering superior customer experience.
to be delivering. In our survey, 82 percent said they Netflix found that if customers search for a movie
have gained a financial return from their AI invest- for more than 90 seconds, they give up. By using AI
ments. For companies across all industries, the to improve search results, Netflix prevents frustra-
median return on investment from cognitive tech- tion and customer churn, saving US$1 billion a year
nologies is 17 percent. Some are more adept than in potential lost revenue.17
others at turning investment into financial benefits Robust returns are not limited to tech companies.
(see figure 3). Both established manufacturers and innovative
While these returns are estimates based on self- startups are using AI to make manufacturing more
reported data, they show that executives across efficient. For example, industrial firms, such as GE
industries feel they’re getting value from cogni- and Siemens, are taking advantage of the data in
tive technologies. Tech companies are spending “digital twins” of their machines to identify trends
significantly on cognitive, and getting a strong and anomalies, and to predict failures.18
return. They are also the driving force behind cog- Companies such as these are using AI to improve
nitive technologies, developing them for a market business processes, which are prominent benefits
already estimated at US$19.1 billion globally.15 companies seek. In fact, our survey findings suggest
This includes giants such as Google, Microsoft, and that companies are placing increased emphasis on
Facebook, and literally thousands of startups.16 AI internal operations (see figure 4).
has also generated returns by improving opera-
Low AI investment/high returns High AI investment/high returns
Industrial products and services
Technology/media and
entertainment/telecommunications
Professional services
Financial services and insurance
Consumer products
Government/public sector
(including education)
Life sciences and health care
Low AI investment/low returns High AI investment/low returns
6
tnemtsevni
no
nruteR
State of AI in the Enterprise, 2nd Edition
FIGURE 3
Everyone’s winning, but some industries are winning bigger
AI investment and ROI: Relative landscape of industries
22%
20%
18%
16%
14%
12%
10%
Lower investment Median Higher investment
Note: The dotted lines in the graph represent the median ROI and median AI investment for all respondents, cross-industry.
Source: Deloitte State of AI in the Enterprise, 2nd Edition, 2018.
Deloitte Insights | deloitte.com/insights
6
Early adopters combine bullish enthusiasm with strategic investments
FIGURE 4
AI’s leading benefits are enhanced products and processes—
and better decisions
Respondents rating each a top-three AI benefit for their company
2017 2018
Enhance current products
51%
44%
Optimize internal operations
36%
42%
Make better decisions
35%
35%
Optimize external operations
30%
31%
55%
Free workers to be more creative
36%
31%
Create new products
32%
27%
Capture and apply scarce knowledge
25%
27%
Reduce headcount through automation
22%
24%
Pursue new markets
25%
24%
Source: Deloitte State of AI in the Enterprise, 2nd Edition, 2018.
Deloitte Insights | deloitte.com/insights
This shift toward internal operations has been Health care and life sciences companies are in-
accompanied by a somewhat reduced emphasis on vesting in AI but, according to our data, have less to
integrating AI into existing products and services, show for it. Certainly, some health care “big bang”
although that remains the most popular objective. projects have disappointed thus far. However, ad-
In fact, operational change is often required before vances in fields as diverse as radiology and hospital
such integration can take place. Our respondents claims management show that AI offers substantial
may be realizing that they should make operational potential for value in health care,19 despite some
changes first. high-profile stumbles. For example, in a recent
77
State of AI in the Enterprise, 2nd Edition
study, deep-learning neural networks identified Earlier, we noted that eight in 10 surveyed exec-
breast cancer tumors with 100 percent accuracy utives claim positive ROI from their companies’ AI
by analyzing pathology images.20 Such advances, efforts. However, we should view ROI claims with
however, are thus far only in the lab and will take a bit of caution: Less than 50 percent of surveyed
time before entering clinical practice. companies measure key performance indicators
necessary for gauging financial returns accurately.
These indicators include critical elements such as
(Mostly) rational exuberance
project budget/cost, ROI, and targets for produc-
Despite the hype AI generates, many executives tivity, cost savings, revenue, and customers (such
are excited—not wallowing in a trough of disil- as satisfaction and retention). This lack of mea-
lusionment. That’s translating into investment. surement gets to the heart of a significant problem
Eighty-eight percent of companies surveyed plan with cognitive implementations: They are often not
to increase spending on cognitive technologies managed with the same rigor that companies use
in the coming year; 54 percent say they will boost with more mature technologies.
spending by 10 percent or more.
8
Early adopters combine bullish enthusiasm with strategic investments
To maximize value, early
adopters should become risk
and change management
experts
BUSINESS AND TECHNOLOGY leaders con- low levels of experience with them, it’s unsurprising
front an array of challenges as they seek to that this was the most-cited challenge. Integration
create business value with artificial intel- into the business is a challenge for technologies
ligence. Many respondents cited implementation, in general, but it may be particularly problematic
integration into roles and functions, and measuring with AI given the impact it can have on knowledge-
and proving the business value of AI solutions as worker tasks and skills.
top challenges of AI initiatives (see figure 5). Imple- Companies sometimes struggle in AI projects
mentation can be a challenge with any technology, to navigate the “last mile” of behavior change.21 An
but given the relative newness of AI tools and the example we have seen is an organization that built a
FIGURE 5
Many early adopters struggle with the basics
Top challenges for AI initiatives: Ranked 1–3, where 1 is greatest challenge
Ranked 1 Ranked 2 Ranked 3 Ranked top three
Implementation challenges 13% 14% 12% 39%
Integrating AI into the company’s
14% 13% 12% 39%
roles and functions
Data issues (e.g., data privacy,
16% 13% 10% 39%
accessing and integrating data)
Cost of AI technologies/
13% 12% 11% 36%
solution development
Lack of skills 11% 10% 10% 31%
Challenges in measuring and
10% 11% 9% 30%
proving business value
Source: Deloitte State of AI in the Enterprise, 2nd Edition, 2018.
Deloitte Insights | deloitte.com/insights
99
State of AI in the Enterprise, 2nd Edition
machine-learning system to support the sales team system. (It is possible to automate this analysis, but
by predicting which prospects were likely to convert that would be an AI project of its own.)
and which customers were likely to churn. Though Getting the data required for an AI project,
the system worked as planned, the sales team was preparing it for analysis, protecting privacy, and
initially unprepared to accept its recommendations. ensuring security can be time-consuming and costly
The team had not been closely involved in the devel- for companies. Adding to the challenge is that data—
at least some of it— is
Twenty-three percent of respondents often needed before it is
even possible to conduct
ranked “cybersecurity vulnerabilities” as
a proof of concept. We
have seen companies
their No. 1 overall AI/cognitive concern.
that, because they had
not fully considered the
opment of the solution and neither understood nor difficulty of obtaining the data they need, decided to
trusted the results it produced. One way to avoid shelve projects and disband teams until they were
this problem is to involve business owners closely able to lay the proper data foundation.
throughout the development process so they can Some organizations also struggle to articulate
better understand what is being delivered. a business case or to define success for AI projects.
Anyone following business news about AI knows This may be because AI is viewed as experimental.
about the critical role played by data. Survey re- Sometimes it is because machine learning—one of
spondents consider “data issues” as one of the top the most widely used AI technologies—is inherently
challenges for their companies’ AI initiatives. There probabilistic, meaning that a new system’s ultimate
are numerous reasons for this. Some AI systems, performance can be difficult to estimate precisely.
such as virtual assistants to enable customer self- And sometimes it’s because the group charged with
service, require data from multiple systems that developing an AI solution is unaccustomed to devel-
may never have been integrated before. Customer oping business cases to justify its work.
information may reside in one system, financial
data in another, and virtual assistant training and
Managing risks of AI
configuration data in a third. AI creates a need for
data integration that a company may have managed It is a fact of life that novel situations often
to avoid until now. This can be especially chal- present new risks. The same is true of emerging
lenging in a company that has grown by acquisition technologies such as AI. Executives are concerned
and maintains multiple, unintegrated systems of about a host of risks associated with AI technologies
diverse vintages. (see figure 6). Some of the risks are typical of those
Another challenge for companies is that the type associated with any information technology; others
of data required for some AI projects is different are as unique as AI technology itself.
from the data with which they’re accustomed to
working. For example, some solutions depend on CYBER RISK
access to significant amounts of unstructured data Chief among the AI risks that concern executives
that may have been retained for record-keeping are cyber risks, which ranked as a top-three concern
but was never intended for analysis. In one virtual for half of our survey respondents (see figure 6).
assistant project we know of, the team needed to In fact, 23 percent of respondents ranked “cyber-
review thousands of recorded phone calls to identify security vulnerabilities” as their No. 1 overall AI/
common themes with which to derive rules for the cognitive concern. This apprehension is probably
1100
Early adopters combine bullish enthusiasm with strategic investments
FIGURE 6
Cybersecurity heads the lists of AI-related concerns
Potential AI risks of top concern to companies: Ranked 1–3, where 1 is greatest concern
Ranked 1 Ranked 2 Ranked 3 Ranked top three
Cybersecurity vulnerabilities
23% 15% 13% 51%
of AI
Making the wrong strategic
16% 13% 14% 43%
decisions based on AI
Legal responsibility for decisions/
11% 15% 13% 39%
actions made by AI systems
Failure of AI system in a mission-
13% 14% 12% 39%
critical or life-or-death context
Regulatory noncompliance risk 12% 15% 10% 37%
Erosion of customer trust from 11% 11% 11% 33%
AI failures
10% 12% 10% 32%
Ethical risks of AI
Source: Deloitte State of AI in the Enterprise, 2nd Edition, 2018.
Deloitte Insights | deloitte.com/insights
well placed: While any new technology has certain AI has also been used recently to create fake
vulnerabilities, the cyber-related liabilities sur- photos and videos of celebrities and politicians.
facing for certain AI technologies seem particularly While there are also techniques for identifying fakes,
vexing. it appears that technologies may fuel an arms race
Researchers have discovered that some ma- of fake image development versus detection. Given
chine-learning models have difficulty detecting the prominence of AI-based image recognition, this
adversarial input—that is, data constructed specifi- area is likely to be a cyber-risk battleground in the
cally to deceive the model. This is how one research future.
team fooled a vision algorithm into classifying as a There is evidence that cyber-risk concerns are
computer what appeared to be a picture of a cat.22 slowing or pausing AI projects at some compa-
The process of training machine-learning models nies. In addition, one in five respondents said they
can itself be manipulated with adversarial data. decided not to launch AI initiatives due to cyberse-
By intentionally feeding incorrect data into a self- curity worries (see figure 7).
learning facial recognition algorithm, for instance, Executives are commonly concerned about the
attackers can impersonate victims via biometric safety and reliability of AI systems as well. Forty-
authentication systems.23 In some cases, machine- three percent of respondents rated “making the
learning technology may expose a company to the wrong strategic decisions based on AI/cognitive rec-
risk of intellectual property theft. By automatically ommendations” as a top-three concern (see figure
generating large numbers of interactions with a 6). Nearly as many cited failure of an AI system in
machine-learning-based system and analyzing the a mission-critical or life-or-death situation. Placing
patterns of responses it generates, hackers could re- strategic decisions or mission-critical actions en-
verse-engineer the model or the training data itself. tirely in the hands of an AI system would certainly
1111
State of AI in the Enterprise, 2nd Edition
FIGURE 7
Cybersecurity threats are giving some companies pause
Effect of cybersecurity concerns on companies
Moved ahead with AI initiatives despite cybersecurity concerns
36%
Experienced a cybersecurity breach relating to AI initiatives within the last two years
32%
Slowed an AI initiative in order to address cybersecurity concerns
30%
Decided not to start an AI initiative due to cybersecurity concerns
20%
Canceled or halted an in-progress AI initiative due to cybersecurity concerns
16%
Source: Deloitte State of AI in the Enterprise, 2nd Edition, 2018.
Deloitte Insights | deloitte.com/insights
entail special risks. Entrusting AI systems with such and operational risks associated with these systems.
responsibilities remains rare today, however. A Complicating matters are questions surrounding
prominent exception is the use of AI in autonomous who can be held liable in the event of an AI-related
vehicles: The technology has been implicated in crime or mishap. How liability is assigned in these
several accidents, some fatal, during testing.24 cases is a topic of ongoing discussion.26
Another element of cyber risk that companies Two themes are particularly salient when
should consider is how much data—and what it comes to AI and regulatory risk: privacy and
kind of data—they are willing to put into public explainability. Because data is so critical to AI, com-
cloud environments, allowing them to use cogni- panies seeking to apply the technology are often
tive technologies to analyze much larger data sets hungry for the stuff. Privacy regulations governing
than private clouds. Analysis of sensitive customer personal data may dampen their appetite, though:
and financial data can yield valuable insights, but The General Data Protection Regulation (GDPR),
companies should weigh the perceived risks with which has recently come into force in Europe, sets
the benefits. A recent Deloitte study found that the privacy rules that require careful implementation.
more experience organizations have with cloud GDPR also mandates that companies using per-
computing, the more comfortable they are putting sonal data to make automated decisions affecting
sensitive data into public clouds.25 people must be able to explain the logic behind the
decision-making process.27 Guidance published by
LEGAL AND REGULATORY RISKS the US Federal Reserve (SR 11-7) affects US banking
Products and systems of all types, including IT similarly: It requires that the behavior of computer
systems, present a range of legal and regulatory risks. models be explained.28 What makes these regula-
As a result, it is unsurprising that four in 10 survey tions challenging for some AI adopters is the growing
respondents indicate a high degree of concern complexity of machine learning and the increasing
about the legal and regulatory risks associated with popularity of deep-learning neural networks, which
AI systems. Because not all methods of validating can behave like black boxes, often generating highly
AI systems’ accuracy and performance are reliable, accurate results without an explanation of how
companies will need to manage the legal, regulatory, these results were computed. Many tech companies
1122
Early adopters combine bullish enthusiasm with strategic investments
and government agencies are pouring resources Some of the ethical risks that resonated with
into improving the “explainability” of deep-learning our respondents are linked to the aforementioned
neural networks.29 cyber-safety and regulatory issues: unintended
consequences, misuse of personal data, and lack of
ETHICS AND REPUTATION explanation for AI-powered decisions. But there is
For most of our respondents, ethical risks are one concern that has achieved special prominence
not a top-of-mind information technology concern. in recent years, and ranked second among our re-
And while ethical risks ranked at the bottom of risk spondents’ ranking of ethical risks: bias.
concerns in our survey, about a third of executives Today, algorithms are commonly used to help
did cite them as a top concern. make many important decisions, such as granting
In a deeper look at potential ethical risks, sur- credit, detecting crime, and assigning punishment.
veyed executives revealed a wide range of concerns. Biased algorithms, or machine-learning models
At the top of the list is AI’s power to help create or trained on biased data, can generate discriminatory
spread false information. This may be due to the or offensive results. For example, one study found
attention that social-media-driven “fake news” re- that ads for high-paying jobs were shown more
ceived in the 2016 US elections. often to men than to women.30
1133
State of AI in the Enterprise, 2nd Edition
Early adopters want more
talent, and need a better
mix of it
DO EARLY ADOPTERS have the talent to need more skilled people. Thirty percent said they
develop and deploy cognitive solutions? face a major (23 percent) or extreme (7 percent)
The overall survey results suggest both a skills gap. Another 39 percent said their gap is
considerable amount of talent already and a strong “moderate.” Interestingly, the most advanced com-
demand for more. “Lack of AI/cognitive skills” was panies in our survey feel the skills gap acutely.31 The
a top-three concern for 31 percent of respondents— limitations of their technical skills may be exposed
below such issues as implementation, integration, as they launch more AI solutions, and as those solu-
and data. A skills shortage was identified as the tions increase in complexity and scale.
biggest challenge in moving from prototypes to Some skills are needed more than others (see
full production deployments for only 8 percent figure 8).32 Respondents report the highest level of
of respondents. need for AI researchers to invent new kinds of AI al-
Companies generally feel that they have sub- gorithms and systems. This suggests an aggressive
stantial AI capabilities. About four in 10 executives level of ambition for the technology. In addition, 28
report their companies have a high level of sophis- percent said they need AI software developers, 24
tication in managing and maintaining AI solutions, percent need data scientists, and roughly similar
selecting AI technologies and technology suppliers, percentages need user-experience designers,
integrating AI technology into the existing IT en- change-management experts, project managers,
vironment, identifying valuable applications of AI, business leaders, and subject-matter experts. Sixty-
building AI solutions, and hiring and managing one percent a"
179,deloitte,DI_government-execs-on-AI.pdf,"FEATURE
Government executives on AI
Surveying how the public sector is approaching an
AI-enabled future
William Eggers, Sushumna Agarwal, and Mahesh Kelkar
THE DELOITTE CENTER FOR GOVERNMENT INSIGHTS
Government executives on AI: Surveying how the public sector is approaching an AI-enabled future
As government uses artificial intelligence more, how can the experience of
early adopters guide other public sector organizations?
AS ONE OF the hottest technologies of recent exponential growth in processing power seem to
years, artificial intelligence (AI) has started finally be fast-tracking AI into the mainstream.
penetrating both the US public and the
private sectors—though to differing degrees. While This growing usage is reflected in the AI initiatives
the private sector seems bullish on AI, the public being undertaken by public sector organizations
sector’s approach appears tempered with more across levels. For instance, in February 2019, US
caution—a Deloitte survey of select early adopters President Donald Trump signed an executive order
of AI shows high concern around the potential risks to create the American AI Initiative, which aims to
of AI among public sector organizations (see the prioritize and guide AI development in the United
sidebar “About the survey”). The findings in this States.2 This builds on other federal AI initiatives,
study show the approaches and experiences of such as the Select Committee on AI.3 At the state
these early adopters of AI in the public sector. They level, the government of New Jersey has set up an
give a peek into how public sector organizations are innovation training platform to educate
approaching AI; and how the approaches, in many government workers about new technologies such
cases, differ from those of their private as AI and blockchain.4
sector counterparts.
As we have documented in previous studies,5 the
AI is not completely new to the public sector. The number of AI use cases in the public sector has
first AI contract was awarded in 1985 by the US increased manifold. As AI usage in the public
Social Security Administration,1 but the technology sector continues to grow, we sought to answer
still wasn’t advanced enough to become common in questions such as, how do early adopters in the
the following decades. Now, the growing ubiquity public sector perceive AI? What approaches are
of digital technologies, advances in the ability to these early adopters pursuing? Do these
store and analyze massive amounts of data, and approaches differ from those of the private sector?
ABOUT THE SURVEY
To gain insights into the experiences of early users of AI, Deloitte surveyed 1,100 executives from
US-based organizations across 10 industries currently using AI, in the third quarter of 2018. About 10
percent of the respondents were from the federal government, state government, higher education,
defense, international donor organizations, public health and social services, public transportation,
and security and justice—a collection of entities we refer to as “public sector.” This sample allowed
us to examine how the AI approach of early adopters in the public sector compares with that of
private industry.
The survey required the respondent’s organization to be using at least one AI technology and to have
built (or be building) at least one AI prototype system or full implementation/production system.
Also, respondents were required to be knowledgeable about their organization’s use of AI.
2
Government executives on AI: Surveying how the public sector is approaching an AI-enabled future
WHAT IS ARTIFICIAL INTELLIGENCE? This caution has led to new ways of developing AI
solutions in the public sector, such as prototyping
AI technologies are defined as those that
AI projects in controlled environments and
can perform or augment tasks, better
codeveloping AI solutions with partners.
inform decisions, and accomplish objectives
that have traditionally required human
intelligence, such as planning, reasoning
Early adopters believe AI can
using partial or uncertain information, and
learning.6 AI technologies include robotic be critical to organizational
process automation, natural language success
processing, machine learning, computer
vision, speech recognition, deep learning, Many early adopters in the public sector expect AI
and intelligent robotics.
technologies to become increasingly important in
the coming years. About 57 percent of early
adopters surveyed believe that AI is “very” or
The survey results reveal that the public sector “critically” important to their organization’s
early adopter respondents generally feel positive success today, and 74 percent of respondents
about their early AI experiences. They are using AI believe it will be in the next two years (figure 1).
to augment human capabilities, generating demand
for newer skills. However, many still lag other Some of the most popular AI uses cases in the
industries due to reasons such as lack of public sector focus on quality control issues
investment and skilled talent. Also, they tend to be (detecting defects and finding errors in software
understandably more cautious than other code), workforce management (recruiting and
industries due to ethical risks associated with AI. training), and cybersecurity (figure 2).
FIGURE 1
Early adopters in the public sector believe AI will become increasingly
important to their organization’s success
How would you rate the strategic importance of adopting/using AI/cognitive to your organization’s
overall success?
Critically important Very important None/minimal/somewhat important
NOW
Public sector
11% 46% 43%
Private sector 11% 60% 30%
IN TWO YEARS
Public sector 31% 43% 25%
Private sector 43% 40% 17%
Source: Deloitte analysis of the State of AI in the Enterprise Survey.
Deloitte Insights | deloitte.com/insights
3
Government executives on AI: Surveying how the public sector is approaching an AI-enabled future
FIGURE 2
The top AI uses cases in the public sector are quality control, workforce
management, cybersecurity
In which of the following areas is your organization using AI/cognitive technologies?
Quality control (e.g., detecting defects, finding errors in software code)
47%
Workforce management (e.g., recruiting and training)
38%
Cybersecurity
38%
IT automation (e.g., network and cloud management)
35%
Predictive analytics (e.g., predicting and preventing downtime, predicting medical outcomes)
35%
Risk management (e.g., detecting and preventing fraud)
31%
Customer service (e.g., chatbots and virtual assistants)
31%
Decision support (e.g., diagnosis)
30%
Tax, audit, and compliance (e.g., anomaly detection, document discovery)
28%
Connected equipment, devices, products (e.g., devices that learn user preferences, self-driving cars)
25%
Source: Deloitte analysis of the State of AI in the Enterprise Survey.
Deloitte Insights | deloitte.com/insights
Early adopters see AI as a Innovation Center, or iCenter, uses chatbots to aid
internal IT help desk personnel. The iCenter found
way to augment human
that 80 to 90 percent of the IT help desk tickets
capabilities
were for password resets. By leveraging chatbots
for such routine requests, the iCenter is allowing
The survey results suggest that AI is primarily
workers to deal with more complex issues.7
being used to make the work of humans more
effective rather than automate it altogether. As AI gets integrated into public sector
Freeing up workers to be more creative by organizations and routine tasks are automated,
automating tasks has been identified among the workers will need to learn to work with these
top three benefits of AI by early adopters surveyed,
technologies or will need to perform new and
while reducing headcount through automation is
different work. As many as 76 percent of early
near the bottom (figure 3).
adopter respondents in the public sector said
One area where AI is being used in many human workers and AI will augment each other to
governments to free workers from repetitive tasks produce new ways of working (figure 4).
is customer service chatbots. North Carolina’s
4
Government executives on AI: Surveying how the public sector is approaching an AI-enabled future
FIGURE 3
Freeing workers to be more creative is one of the top benefits of AI
What do you view as the primary benefit of AI/cognitive technology for your organization?
(Percent of respondents rating each category as one of the top three benefits of AI.)
Public sector Private sector
Enhances features, functions, and/or performance of our products and services
47%
43%
Optimizes internal business operations
41%
42%
Frees up workers to be more creative by automating tasks
35%
31%
Helps us make better decisions
34%
35%
Captures and applies scarce knowledge where needed
28%
27%
Reduces headcount through automation
21%
24%
Creates new products
20%
28%
Source: Deloitte analysis of the State of AI in the Enterprise Survey.
Deloitte Insights | deloitte.com/insights
The use of AI is already generating demand for new adopters. (See the sidebar “Starter, skilled, and
skills in the public sector, beyond technology and seasoned adopters of AI” to learn more about this
technical skills. While 34 percent of early adopters classification.)
surveyed are looking for software developers and
23 percent for data scientists, a sizable 30 percent
cite the need for business leaders, and 23 percent The use of AI is already
for change management experts (figure 5).
generating demand for
new skills in the public
The public sector lags other
sector beyond technology
industries in AI adoption
and technical skills.
Compared with other industries, the public sector
has the highest proportion of “starters,” those at
the beginning of their AI journey, and the lowest
proportion of “seasoned,” or experienced, AI
5
Government executives on AI: Surveying how the public sector is approaching an AI-enabled future
FIGURE 4
Early adopters in the public sector believe that AI will augment human labor
What do you view as the primary benefit of AI/cognitive technology for your organization?
Public sector Private sector
PERCENT SAYING THEY AGREE/STRONGLY AGREE
AI empowers people at our organization to make better decisions
68%
80%
Human workers and AI technologies will augment each other to produce new ways of working
76%
78%
Source: Deloitte analysis of the State of AI in the Enterprise Survey.
Deloitte Insights | deloitte.com/insights
FIGURE 5
Early adopters are seeking new skills to work with AI technologies
What kinds of skills/capabilities are most needed to fill your organization’s skills gap?
Software developers
34%
Business leaders
30%
Project managers
24%
Change management/transformation experts
23%
Data scientists
23%
AI researchers
23%
Subject matter experts
21%
User experience designers
15%
Source: Deloitte analysis of the State of AI in the Enterprise Survey.
Deloitte Insights | deloitte.com/insights
6
Government executives on AI: Surveying how the public sector is approaching an AI-enabled future
STARTER, SKILLED, AND SEASONED ADOPTERS OF AI
Some adopters of AI are further along in their efforts than others. To aid our comparison, we
identified three distinct segments at different levels of maturity. The “seasoned” (24 percent of all
respondents) is the most experienced cohort, at the leading edge of AI adoption maturity. They
have undertaken a number of AI production deployments; they also report that they’ve developed
a high level of expertise in selecting AI technologies and suppliers, identifying use cases, building
and managing AI solutions, integrating AI into their IT environment and business processes, and
hiring and managing AI technical staff. In the middle is the “skilled” cohort (45 percent). They have
launched multiple AI production systems but are not yet as AI-mature as the seasoned adopters.
They lag in their number of AI implementations, level of AI expertise, or both. At the low end of the
spectrum are “starters” (31 percent), which are just dipping their toes into AI adoption and have not
yet developed solid proficiency in building, integrating, and managing AI solutions.8
Only 14 percent of public sector adopters surveyed The public sector is also behind other industries
are classified as seasoned, whereas 45 percent are in integrating AI technology into business
still classified as starters. Meanwhile, in industries processes and the IT environment, and finding the
leading the way in AI such as financial services and right use cases for AI (figure 7). The one area in
technology, media, and telecommunications, which the public sector is on par with other
around 30 percent of respondents are classified as industries is selecting AI technologies and
seasoned AI adopters (figure 6). However, some technology suppliers, with about 44 percent of
pockets in the public sector, such as defense and respondents from both sectors saying they were
national security, are outliers, since they have been mature in this area (figure 7).
developing and using AI for many years.
7
Government executives on AI: Surveying how the public sector is approaching an AI-enabled future
FIGURE 6
Public sector is at the lower end of the AI maturity curve
Starters Skilled Seasoned
Overall
31% 45% 24%
Financial services and insurance
27% 42% 31%
Technology, media, and telecommunications
29% 41% 30%
Energy, resources, and utilities (including oil, gas, chemicals)
29% 41% 29%
Professional services
33% 41% 26%
Industrial products and services (aerospace, construction, industrial manufacturing)
37% 40% 22%
Consumer products
25% 57% 18%
Life sciences and health care
32% 52% 15%
Government/public sector
45% 41% 14%
Source: Deloitte analysis of the State of AI in the Enterprise Survey.
Deloitte Insights | deloitte.com/insights
FIGURE 7
The public sector’s AI activity is less sophisticated than that of the private sector
How would you characterize the level of sophistication in your organization when it comes to the
following tasks?
Public sector Private sector
PERCENT SELECTING HIGH SOPHISTICATION
Selecting AI technologies and technology suppliers
44%
44%
Identifying valuable applications for AI
30%
40%
Integrating AI technology into our existing IT environment
28%
40%
Integrating AI technology into business processes
24%
41%
Source: Deloitte analysis of the State of AI in the Enterprise Survey.
Deloitte Insights | deloitte.com/insights
8
Government executives on AI: Surveying how the public sector is approaching an AI-enabled future
Lack of investment and a skills operations, with the remaining amount going into
business enhancements (27 percent) and
gap could hinder adoption of
innovation and growth (26 percent).10
AI
Nevertheless, most public sector respondents plan
The survey shows that, among early adopters
to increase investments in the future. Some
across industries, the public sector has both the
40 percent of respondents said their organization
lowest level of AI investments and lower return on
plans to increase investment by more than
investments from their AI initiatives (figure 8).
10 percent, and only 4 percent said their
The low returns on AI investments could be due to
a focus on improving citizen services rather than
The one area in which
cost savings. The low investment itself could be
attributed to the high maintenance costs of
the public sector is on par
government legacy systems. In 2018, the US
government allocated 78.5 percent of its US$95.7 with other industries is
billion IT budget to operating and maintaining
selecting AI technologies
legacy systems.9 At the same time, CIOs of the most
innovative private sector organizations allocated a
and technology suppliers.
little less than half of their budgets (47 percent) to
FIGURE 8
Of all the sectors, the public sector invests the least in AI
Based on self-reported data
Source: Deloitte analysis of the State of AI in the Enterprise Survey.
Deloitte Insights | deloitte.com/insights
9
Government executives on AI: Surveying how the public sector is approaching an AI-enabled future
organization plans to reduce investment, implying Another factor seeming to hold back adoption in
an increased investment focus on AI technologies the public sector is the lack of needed skills. As
(figure 9). However, these numbers are still lower much as 71 percent of respondents cited the skills
than in the private sector, as 55 percent of private gap as a barrier—ranging from moderate to
sector respondents said their organization plans to extreme—to advancing AI projects in their
increase investment by more than 10 percent, and organization.
only 1 percent said their organization plans to
decrease investment.
FIGURE 9
Early adopters in the public sector have been increasing AI investments and plan
to continue doing so in the next fiscal year
How does your organization’s AI/cognitive investment in the current fiscal year compare with the
previous fiscal year’s investment?
Public sector Private sector
51%
44%
39%
36%
18%
8%
2% 2%
Decreased Stayed the +1–9% Increased by
investment same more than 10%
Thinking ahead, how do you expect your organization’s investment in AI/cognitive to change in
the next fiscal year?
55%
40% 40%
34%
16%
10%
4%
1%
Decreased Stayed the +1–9% Increased by
investment same more than 10%
Source: Deloitte analysis of the State of AI in the Enterprise Survey.
Deloitte Insights | deloitte.com/insights
10
Government executives on AI: Surveying how the public sector is approaching an AI-enabled future
Cybersecurity and ethical risks Early adopters are trying
are a major concern to find ways to balance AI
benefits and risks
About 46 percent of respondents from the public
sector said they have “major” or “extreme” While public sector early adopter respondents are
concerns about the potential risks associated with enthusiastic about the potential of AI technologies,
AI (figure 10). they are reportedly constrained by resource
shortages and are concerned about the risks
Of the different types of risk, ethical risk is the associated with these technologies.
second-highest concern cited by public sector early
adopters surveyed, but the lowest-ranked concern However, public sector organizations are making
for early adopters in other industries (figure 11). efforts to overcome these hurdles and move ahead
Further, recent media reports highlight how on their AI journey. Such efforts include:
government concerns around ethical risks of AI are
slowing and, in some cases, even halting the use of • Codevelopment with partners: Our survey
some AI technologies. San Francisco, for example, found public sector early adopters to be more
was the first major US city to block the use of facial inclined than the private sector to codevelop AI
recognition technology. The decision was rooted in solutions with partners (figure 12)—possibly
concerns about the invasion of citizen privacy as due to the advantages of tapping into market
well as potential racial bias.11 expertise and bringing in new capabilities to
mitigate technology risks. In fact, the
The concerns around cybersecurity vulnerabilities Interagency Select Committee on Artificial
are not surprising, considering the increasing Intelligence, which advises the White House on
number of cyberattacks on government systems. In AI research and development priorities,
2017 alone, federal civilian agencies reported more proposes this approach.13 Working with
than 35,000 security incidents.12 industry partners may require capabilities
within government for auditing algorithms.
FIGURE 10
Early adopters in the public sector are concerned about the potential risks
associated with AI
Overall, how concerned is your organization about the potential risks associated with your
AI/cognitive initiatives (e.g., cybersecurity, ethical, or legal risks)?
Public sector Private sector
MAJOR/EXTREME CONCERN ABOUT AI RISKS
46%
46%
Source: Deloitte analysis of the State of AI in the Enterprise Survey.
Deloitte Insights | deloitte.com/insights
11
Government executives on AI: Surveying how the public sector is approaching an AI-enabled future
FIGURE 11
Public sector early adopters are more concerned about ethical risks than any
other industry
Which of the following risks of AI/cognitive is your organization most concerned about?
Public sector Private sector
PERCENT RATING EACH CATEGORY A TOP-THREE AI RISK
Cybersecurity vulnerabilities of AI
47%
51%
Ethical risks of AI
44%
31%
Making the wrong decisions based on AI recommendations
43%
43%
Legal responsibility for decisions/actions made by AI systems
38%
39%
Failure of AI systems in a mission-critical for life-and-death context
35%
38%
Erosion of customer trust from AI failures
34%
33%
Regulatory noncompliance risk
31%
37%
Source: Deloitte analysis of the State of AI in the Enterprise Survey.
Deloitte Insights | deloitte.com/insights
• Testing new approaches: To foster sector early adopter respondents said that their
innovation, many early adopters surveyed in the organization had tested more than five
public sector are experimenting with new prototypes to date. In the US Department of
technologies and building prototypes. Defense’s AI strategy, released in February
Prototyping can help early adopters assess the 2019, prototyping is listed as one of the
vulnerabilities and test the impact of AI techniques to enhance the department’s
solutions in a controlled environment before AI capabilities.14
they are scaled. Around 41 percent of public
12
Government executives on AI: Surveying how the public sector is approaching an AI-enabled future
FIGURE 12
Early adopters in the public sector prefer codeveloping AI solutions with partners
Indicate whether your organization is already using or planning to use each of the following
ways of acquiring or developing AI/cognitive technologies
Public sector Private sector
Codevelopment with partners (e.g., IT and professional services firms)
62%
52%
Enterprise software with integrated AI
55%
60%
AI-as-a-service
55%
49%
Open source AI development tools
45%
49%
Data science modeling tools
39%
45%
Automated machine learning
35%
47%
Crowdsourced development communities
30%
40%
Source: Deloitte analysis of the State of AI in the Enterprise Survey.
Deloitte Insights | deloitte.com/insights
Learning from early adopters
should learn from the experiences of early adopters
within the government, as well as their private
The road to full-scale AI implementation may be a sector counterparts, to identify the use cases that
long one for many public sector agencies, but can be applied to their agencies and discover
pilots, experiments, and AI initiatives in different proven techniques to overcome challenges. With
pockets of government continue to grow. As more these learnings, the public sector can move up the
public sector agencies begin their AI journey, they AI adoption curve.
13
Government executives on AI: Surveying how the public sector is approaching an AI-enabled future
Endnotes
1. The Pulse, “Artificial intelligence’s impact on government contracting,” January 24, 2018.
2. James Vincent, “Trump signs executive order to spur US investment in artificial intelligence,” The Verge,
February 11, 2019.
3. The White House Office of Science and Technology Policy, “Summary of the 2018 White House Summit on
Artificial Intelligence for American Industry,” May 10, 2018.
4. Katya Schwenk, “New Jersey touts ‘first’ innovation training platform for state government,” Statescoop, June
18, 2019.
5. William D. Eggers, David Schatsky, and Peter Viechnicki, AI-augmented government: Using cognitive technologies to
redesign public sector work, Deloitte University Press, April 26, 2017; William D. Eggers, Neha Malik, Matt Gracie,
Using AI to unleash the power of unstructured government data, Deloitte Insights, January 16, 2019.
6. Deloitte Insights, Cognitive technologies: A technical primer, February 6, 2019.
7. Kevin C. Desouza and Rashmi Krishnamurthy, “Chatbots move public sector toward artificial intelligence,”
Brookings, June 2, 2017.
8. Jeff Loucks et al., Future in the balance? How countries are pursuing an AI advantage, Deloitte Insights, 2019.
9. David Wennergren et al., “Accelerating IT modernization in government,” Wall Street Journal, October 2, 2018.
10. Bill Briggs et al., Follow the money: 2018 global CIO survey, chapter 3, Deloitte Insights, August 8, 2018.
11. Kate Conger, Richard Fausset, and Serge F. Kovaleski, “San Francisco bans facial recognition technology,” New
York Times, May 14, 2019.
12. US Government Accountability Office, “Cybersecurity challenges facing the nation–high risk issue,” accessed
August 27, 2019.
13. Aaron Boyd, “Here’s what the White House’s AI Committee will focus on,” Nextgov, June 28, 2018.
14. Lauren C. Williams, “Pentagon outlines AI strategy,” GCN, February 13, 2019.
14
Government executives on AI: Surveying how the public sector is approaching an AI-enabled future
Acknowledgments
A special thanks to Susanne Hupfer from the Deloitte Center for Technology, Media &
Telecommunications for her support in analyzing the survey data and extracting insights for this study.
The authors would also like to thank Melissa Smith, David Noone, and Joe Mariani for reviews at
critical junctures and contributing their ideas and insights to this project.
About the authors
William Eggers | weggers@deloitte.com
William Eggers is the executive director of Deloitte’s Center for Government Insights, where he is
responsible for the firm’s public sector thought leadership. His most recent book is Delivering on Digital:
The Innovators and Technologies that Are Transforming Government (Deloitte University Press, 2016). His
other books include The Solution Revolution, the Washington Post best-seller If We Can Put a Man on the
Moon, and Governing by Network. He coined the term Government 2.0 in a book by the same name. His
commentary has appeared in dozens of major media outlets including the New York Times, the Wall
Street Journal, and the Washington Post. He can be reached at weggers@deloitte.com or on Twitter
@wdeggers. He is based in Rosslyn, Virginia.
Sushumna Agarwal | sushagarwal@deloitte.com
Sushumna Agarwal is a senior analyst with the Deloitte Center for Government Insights, Deloitte
Services LP. She researches workforce issues at the federal, state, and local government level. Her
primary focus is on applying quantitative techniques to enable data-driven research insights.
Mahesh Kelkar | mkelar@deloitte.com
Mahesh Kelkar of Deloitte Services LP is a research manager with the Deloitte Center for Government
Insights. He closely tracks the federal and state government sectors, and focuses on conducting
in-depth research on the intersection of technology with government operations, policy, and
decision-making.
15
Government executives on AI: Surveying how the public sector is approaching an AI-enabled future
Contact us
Our insights can help you take advantage of change. If you’re looking for fresh ideas to address your
challenges, we should talk.
Practice contact
Thomas Beyer
Principal | Deloitte Consulting LLP | + 1 619 237 6659 | thbeyer@deloitte.com
Thomas Beyer, principal, leads Deloitte Consulting’s Government and Public Services (GPS) Analytics &
Cognitive offering.
The Deloitte Center for Government Insights
William Eggers
Executive director | The Deloitte Center for Government Insights | Deloitte Services LP
+ 1 202 246 9684 | weggers@deloitte.com
William Eggers is the executive director of Deloitte’s Center for Government Insights, where he is
responsible for the firm’s public sector thought leadership.
16
About the Deloitte Center for Government Insights
The Deloitte Center for Government Insights shares inspiring stories of government innovation, looking
at what’s behind the adoption of new technologies and management practices. We produce cutting-
edge research that guides public officials without burying them in jargon and minutiae, crystalizing
essential insights in an easy-to-absorb format. Through research, forums, and immersive workshops,
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insights that advance an understanding of what is possible in government transformation.
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Deloitte Insights is an imprint of Deloitte Development LLC.
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None of Deloitte Touche Tohmatsu Limited, its member firms, or its and their respective affiliates shall be responsible for any
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About Deloitte
Deloitte refers to one or more of Deloitte Touche Tohmatsu Limited, a UK private company limited by guarantee (“DTTL”), its
network of member firms, and their related entities. DTTL and each of its member firms are legally separate and independent
entities. DTTL (also referred to as “Deloitte Global”) does not provide services to clients. In the United States, Deloitte refers to
one or more of the US member firms of DTTL, their related entities that operate using the “Deloitte” name in the United States
and their respective affiliates. Certain services may not be available to attest clients under the rules and regulations of public
accounting. Please see www.deloitte.com/about to learn more about our global network of member firms.
Copyright © 2019 Deloitte Development LLC. All rights reserved.
Member of Deloitte Touche Tohmatsu Limited"
180,deloitte,gps-hc-genai-powered-hr-final.pdf,"Power Human Resource
Service Delivery with AI
Public sector organizations and their HR functions are at the forefront
of creating opportunities to “mitigate the harms and maximize the
benefits of AI for workers,” as President Biden directed in his 2023
Executive Order on Safe, Secure, and Trustworthy AI. HR functions of
federal, state, and local government agencies have an opportunity to
leverage the power of AI to streamline their operations and enhance
the overall employee experience. By automating repetitive and time-
consuming tasks, HR professionals can free up their time to focus on
more strategic initiatives. Additionally, AI can enable data-driven
decision making, which can lead to better outcomes for the
organization and its employees.
Why now?
or 20,000 tasks performed by the federal of HR leaders believe they will be behind their peers
33% 76%
government can be completed by GenAI if they don’t implement AI solutions in 12-24 months
(HRD, 2023). (Gartner, 2023).
of organizations that have adopted AI for HR
43% 30% is saved by companies on their cost-per-hire through
are using AI for employee learning and
AI recruitment augmentation (Deloitte, 2023).
development (SHRM, 2024).
Integrating AI Across Key Human Resource Processes at your Organization
Leveraging AI to augment HR capabilities enables public sector organizations to address current workforce challenges and
make data-driven decisions that support their mission goals.
Where GenAI is Most Powerful in your HR Processes
Data-Driven Strategic Career &
Requisition Performance Learning &
Workforce Outreach & Succession
Creation Management Development
Planning Recruitment Planning
Infer skills of the existing Generate Position Identify new talent pools Detect and eliminate Provide AI-informed Analyze employee
workforce with future Descriptions, job (both active and passive unconscious bias from career recommendations performance data,
organizational needs for analyses, and job candidates) to optimize the performance including training path identify trends, and
targeted upskilling or announcements with the recruiting time and evaluation process to navigation for employees predict potential skill
recruiting efforts. use of an intelligent pinpoint prospects boost goal quality and and individual shortages using AI
assistant using existing inclined to accept an encourage achievement. development plan algorithms to cultivate
position management organization’s offer. suggestions. targeted training
data and light input from programs that address
hiring managers. specific skill gaps/org
priorities.
HR POLICY BOT
Provide case deflection, resolving customer questions at Tier 0 to reduce research and resolution times for HR.
HR DATA QUALITY & EMPLOYMENT MILESTONE PROCESSING
Provide insight into operational efficiencies through data to promote HR data quality while reducing burden on HR Quality Assurance teams; Automate HR tasks at
key milestones from offer letter generation to offboarding activities to improve the HR and employee experience.
Where Do I Start? The AI-Powered HR Maturity Curve
As organizations incorporate AI into their processes and applications, they progress along the maturity curve. Leaders
may want to consider the following HR initiatives, starting with HR automation and progressing to AI-driven strategies to
further their AI maturity goals.
Begin with establishing HR
data foundation, enabling AI-
driven data insights and HR
strategy
DEVELOP AN AI-
INFORMED HR STRATEGY
ESTABLISH in coordination with the IMPLEMENT AI-
organization’sn AI strategy
ESSENTIAL HR DATA EXPAND AI USE CASES INFORMED HR
FOUNDATION AUTOMATE HR TASKS across the talent lifecycle STRATEGY
for more accurate and identify AI use cases PILOT HR AI USE CASES informing the
outcomes across the talent lifecycle organization’s HR strategy
CREATE AI-INFORMED
HR AND DATA
GOVERNANCE
for strategy implementation
FOUNDATIONAL TRANSFORMATIVE INNOVATIVE EXPONENTIAL
How Deloitte Can Help Drive & Enable AI-powered HR Adoption:
Work with technical teams to create clean, structured, and consistent data that can be
1 Establish HR Data Foundation easily processed by AI systems while incorporating clear data governance and
management procedures to maintain data quality and integrity.
Collaborate with HR and organizational leaders to develop the roadmap to integrating AI
Develop AI-Powered HR
2 into your HR strategy and operations to prioritize AI use cases that can make immediate
Transformation Roadmap
impact and accelerate organizational success.
Implement Implement priority AI-use cases, start AI integration, and enhance your HR function with
3
feedback loops to integrate AI capabilities throughout talent lifecycle and HR processes.
AI-Use Cases
Ready your HR workforce for AI implementation by promoting AI literacy, upskilling and
4 Promote Workforce Readiness reskilling employees, helping employees adapt to new roles and skills, and promoting
continuous improvement.
BOTTOM LINE:
Leverage Gen AI to Streamline HR
Service Delivery and Enhance the
Employee Experience
AI provides the opportunity to think creatively about the tasks we ask
HR professionals to complete, versus the routine tasks AI can complete Kate Reilly Erin Schneider Lucy Melvin
Principal Managing Director Principal
to save HR (and their customers) time and energy. Build your strategy
Deloitte Consulting LLP Deloitte Consulting LLP Deloitte Consulting LLP
to enable AI-Powered HR today. kreilly@deloitte.com erschneider@deloitte.com lmelvin@deloitte.com
About Deloitte
Deloitte refers to one or more of Deloitte Touche Tohmatsu Limited, a UK private company limited by guarantee (“DTTL”), its network of member firms, and their related entities. DTTL and each of its member firms are legally separate
and independent entities. DTTL (also referred to as “Deloitte Global”) does not provide services to clients. In the United States, Deloitte refers to one or more of the US member firms of DTTL, their related entities that operate using the
“Deloitte” name in the United States and their respective affiliates. Certain services may not be available to attest clients under the rules and regulations of public accounting. Please see www.deloitte.com/aboutto learn more about
our global network of member firms.
Copyright © 2024 Deloitte Development LLC. All rights reserved."
181,deloitte,ai-readiness-and-management-framework.pdf,"AI Readiness & Management
Framework (aiRMF)
Navigating your AI journey
To implement artificial intelligence (AI) at scale, organizations need to build AI
maturity across the enterprise. Deloitte’s AI Readiness & Management
Framework integrates 10 capability areas to achieve enterprise AI readiness
and maturity. Deloitte partners with your organization to assess where you
are on your AI journey, define your target outcomes, and chart a path forward
to achieve your business and mission needs.
About Deloitte: As used in this document, a Deloitte means Deloitte Consulting LLP, a subsidiary of Deloitte LLP. Please see www.deloitte.com/us/about for a 1
detailed description of our legal structure. Certain services may not be available to attest clients under the rules and regulations of public accounting. Copyright © 2024 Deloitte Development LLC. All rights reserved.
Deloitte’s Integrated Approach to AI
Deloitte’s AI Readiness and Management Framework (aiRMF) is applied across three core functions: 1) Setting the Direction, 2) Building Core Capabilities to
Deliver AI Value, and 3) Managing AI Holistically.
Determine where and how AI can improve an organization’s operations and achieve mission/business needs
AI Exploration AI Strategy & Governance
Identify AI Opportunities & Use Cases Define Vision and Establish Governance
Develop foundational capabilities across data, technology, and people to enable AI solutions and deliver value
Data Technology People
Customer & User
Trustworthiness,
Experience* Data Readiness AI Apps and Solutions AI Enabled Workforce Security, & Risk*
Apply Customer-Centric Provide the Data Foundation Develop AI Solutions Prepare the Workforce Mitigate Risk and Instill
Design & Delivery
Confidence
AI Infrastructure & Platforms
Provide Technical Foundation
Continuously maintain, manage, and build upon AI capabilities
AI Delivery & Operations AI Sourcing Management
Scale, Maintain, and Operate AI Solutions Streamline Procurement
*Trustworthiness, Security, & Risk and Customer & User Experience are core to all AI capability areas and should be considered throughout the AI Journey
About Deloitte: As used in this document, a Deloitte means Deloitte Consulting LLP, a subsidiary of Deloitte LLP. Please see www.deloitte.com/us/about for a
detailed description of our legal structure. Certain services may not be available to attest clients under the rules and regulations of public accounting. Copyright © 2024 Deloitte Development LLC. All rights reserved.
aiRMF Capability Area Descriptions
AI Exploration AI Strategy & Governance
Defined mission and business needs, pin-pointed AI- Form a demand-driven strategy factoring the capabilities
enabled solutions, and discrete use cases outlining how AI necessary to implement AI technology responsibly,
could be applied throughout the organization to achieve securely, and consistently across the enterprise through
desired outcomes. plans, policies, procedures, and program alignment.
Trustworthiness, Security, & Risk Data Readiness
Mitigate risks and comply with AI regulations to Provide the foundation for accurate and impactful AI
create trust and confidence in the technology, solutions using high-quality, accessible, and labeled
while maintaining cybersecurity, the protection of data understood and trusted across the enterprise.
information, and the ethical use of data.
AI Delivery & Operations AI Infrastructure & Platforms
aiRMF
Scale and maintain AI solutions Implement a scalable architecture with the
and processes reliably and platform and tools needed to provide the speed,
CAPABILITY AREAS
efficiently in production. capacity, and processing power you need to
sustain AI-enabled solutions.
Customer & User Experience AI Apps & Solutions
Drive a human-centered AI experience and Implement AI software, models, and products across the
improve the adoption and value of AI organization to modernize, improve performance,
solutions with human-centered design and reduce total cost of ownership, and accelerate decision-
UI/UX techniques. making and workflows for mission-critical challenges.
AI Sourcing Management AI Enabled Workforce
Develop a sourcing strategy for effective procurement, Prepare your workforce to integrate AI into their
oversight, and management of vendor-provided AI solutions operational processes and determine the talent and skills
and services to meet your outcomes and advance your mission, they need to provide AI oversight and use it responsibly.
operations, and technology objectives.
About Deloitte: As used in this document, a Deloitte means Deloitte Consulting LLP, a subsidiary of Deloitte LLP. Please see www.deloitte.com/us/about for a
detailed description of our legal structure. Certain services may not be available to attest clients under the rules and regulations of public accounting. Copyright © 2024 Deloitte Development LLC. All rights reserved.
Contact the aiRMF Team for More Information
Aman Vij Leanna Pomponio Leigh Bechet Jordan Aulen
Principal Senior Manager Senior Consultant Consultant
avij@deloitte.com lpomponio@deloitte.com lbechet@deloitte.com jaulen@deloitte.com
About Deloitte: As used in this document, a Deloitte means Deloitte Consulting LLP, a subsidiary of Deloitte LLP. Please see www.deloitte.com/us/about for a
detailed description of our legal structure. Certain services may not be available to attest clients under the rules and regulations of public accounting. Copyright © 2024 Deloitte Development LLC. All rights reserved."
182,deloitte,deloitte-cn-trustworthy-ai-report-en-250113.pdf,"AI at a crossroads
Building trust as the path to scale
Deloitte Asia Pacific | AI Institute
AI at a crossroads | Building trust as the path to scale AI at a crossroads | Building trust as the path to scale
Contents
Report overview 4
01 Navigating the risks from rapid AI adoption 6
02 What does good AI governance look like? 8
03 AI Governance across Asia Pacific 12
04 The dividends from good AI governance 22
05 Building the foundations of trustworthy AI 25
Appendices 29
AI at a crossroads | Building trust as the path to scale AI at a crossroads | Building trust as the path to scale
AI at a crossroads:
Building trust as the path to scale
Report overview
As senior leaders move from experimenting to rolling Concerningly, more than half of technology workers
out AI solutions, a number of key risks – such as security do not believe their workplace can address AI related
vulnerabilities, privacy and legal risk – are experienced risks. To understand how effective AI governance can
by the organisation. While AI solutions offer powerful help to address these risks and unlock the potential
This report was co-developed by Deloitte Access Economics and productivity tools, they can lead to data breaches, of AI, Deloitte has surveyed nearly 900 senior leaders
loss of reputation and business and regulatory fines from 13 locations across the Asia Pacific region in one
the Deloitte AI Institute to provide insights to Asia Pacific C-suite
if the risks of these tools are not managed properly. of the most comprehensive stocktakes of AI governance
executives and tech leaders, on how they can improve their
maturity levels to date.
governance structures and organisation settings to develop
more trustworthy AI solutions.
There is a rising number of incidents from using AI across all industries
Over a quarter of organisations have experienced an increase of incidents related to AI in the past financial year.
Deloitte has created a Trustworthy AI Framework that
identifies seven dimensions necessary for organisations
Increase in incidents recorded in the past financial year, by industry
to have trust in their AI solutions – transparent and
explainable, fair and impartial, robust and reliable, 1
TRANSPARENT AND EXPLAINABLE
respectful of privacy, safe and secure, responsible
28% 31% 24% 42%
and accountable.
2
FAIR AND IMPARTIAL
But what needs to be in place for organisations
to achieve trustworthy AI? Good AI governance. Government and Life sciences and Technology sector Financial sector
public service health care
3
ROBUST AND RELIABLE
For C-suite executives and board members, activating
and supporting effective AI governance practices can be Good governance also leads to greater AI adoption and financial returns
challenging amidst competing priorities. To help address 4
RESPECTFUL OF PRIVACY
this ambiguity, we’ve developed an AI Governance
Maturity Index to identify what good AI governance looks
like in practice. This index contains a set of criteria to 5
SAFE AND SECURE
assess AI governance within an organisation and was 28% more staff 3x more likely 4.6 percentage points 45% of senior leaders
using AI solutions across to be using AI solutions higher in revenue believe good governance
applied to the responses of nearly 900 surveyed senior the business in areas such as R&D, growth from AI improves
leaders from Australia, China, India, Indonesia, Japan, 6 RESPONSIBLE operations and production, solutions reputation
Malaysia, New Zealand, Philippines, Singapore, South and customer service, among customers
marketing and sales
Korea, Taiwan (China), Thailand, and Vietnam.
A range of industries, organisation sizes and public 7 ACCOUNTABLE
sector organisations were included in the responses. Yet more than 90% of organisations can improve AI governance
The survey questions aimed to understand the maturity
level of AI governance across organisations, identify key Deloitte’s Governance Maturity Distribution of AI Trustworthy Index across Asia Pacific
Index uses 12 indicators to
enablers of effective AI governance and assess
the benefits to organisations from having these assess AI governance across 17% 74% 9%
organisations.
arrangements in place.
Basic In progress Ready
Actions to build Trustworthy AI
1 2 3 4
Prioritise AI governance to Understand and Build risk managers, Communicate and ensure AI
realise the returns from AI leverage the broader AI not risk avoiders transformation readiness
supply chain
4
AI at a crossroads | Building trust as the path to scale AI at a crossroads | Building trust as the path to scale
01 Figure 1
Top concerns about potential
Navigating the risks from rapid AI adoption
risks associated with using AI
The adoption of AI across the Asia Pacific region Security vulnerabilities can arise from AI solutions or
is transforming the business landscape. The rapid the vast amount of data used by the solutions, which Security vulnerabilities
86%
emergence of generative AI (GenAI) has only accelerated can become targets for theft or data breaches, and
this process, with investment in AI across the Asia can result in significant costs. The global average cost
Surveillance
83%
Pacific region expected to grow fivefold by the end of a data breach reached nearly $5 million USD in 2024,
of the decade, reaching $117 billion USD by 2030.1 a 10% increase from the previous year.4 Of course, for
Privacy
GenAI has quickly become the region’s fastest-growing large organisations, this cost can be significantly higher. 83%
enterprise technology.
There are also broader costs that are difficult to quantify,
Legal risk and copyright infringement
Behind the rapid pace of adoption are employees, such as damage to brand and loss of customers. The 80%
who often outpace their leaders. A previous Deloitte erosion of consumer confidence and the negative impact
study on Generation AI found that more than two in on brand reputation can have long-lasting effects, making Regulatory uncertainty
79%
five employees were already using generative AI at it crucial for businesses to manage AI and cybersecurity
work, with young employees leading the way.2 effectively. At the same time, there is a strong consumer
Reliability and errors
preference for businesses that use AI in a way that aligns
78%
This pace and scale of AI adoption means leaders with their ethical standards, such as transparency when
are encountering AI related risks in real time as AI is used. Research indicates that 62% of consumers Malicious content
78%
they experiment and roll out the technology. place higher trust in companies whose AI interactions
Our survey of nearly 900 senior leaders reveals that they perceive as ethical, and 53% are willing to pay
Regulatory burden
risks related to security vulnerability (86%), surveillance a premium for such products and services.5 76%
(83%) and privacy (83%) are the most common concerns
for senior leaders when using AI (Figure 1). These Organisations must also ensure that their use of AI Accountability
75%
risks have become even more pronounced since the is compliant with evolving legislative and regulatory
advent of GenAI, which has seen a step change in the requirements, which was a shared theme among
Transparency/explainability
capabilities of the technology alongside more user- the most common risks identified by senior leaders. 73%
friendly interfaces that have broadened the number While there has been a focus on developing and
of people who can use these powerful tools. enacting regulations and legislation across Asia Pacific Responsibility
73%
governments, these existing regulatory requirements
are usually a minimum standard for organisations to
Bias and discrimination
meet rather than comprehensive best practices. As a 71%
“Over half of technology workers
result, senior leaders must develop, adopt and enforce
believe their workplace does not have
organisational trustworthiness standards for AI solutions Job displacement
the appropriate settings to identify or
70%
and systems.6
address AI-related risks according
to a Deloitte study.”3 Addressing AI-related risks is essential: without
Source: Deloitte Trustworthy AI survey (2024)
proper management, these risks could lead to strained
customer relationships, regulatory penalties or public
backlash. Furthermore, fear of these risks can also deter
organisations from using AI. The State of AI Enterprise
survey found that three out of the four biggest challenges
to developing and using AI tools are risk, regulation and
governance issues.7 This highlights the importance of
effective AI governance for managing the ethical and
operational risks associated with AI and fully leveraging
this technology.
6 7
AI at a crossroads | Building trust as the path to scale AI at a crossroads | Building trust as the path to scale
02 Developing trustworthy AI solutions that meet AI governance can often feel elusive with constantly
these seven criteria does not happen automatically. shifting goalposts. To assist organisations to take
Organisations must have robust AI governance to practical steps to achieving trustworthy AI, we have
What does good AI governance look like?
provide the structure that ensures AI solutions align created an AI Governance Maturity Index.
with these principles.
This Index, based on 12 key indicators across five
At its core, good AI governance is required at all pillars (organisational structure, policy and principles,
Developing trustworthy AI solutions is essential for Deloitte has developed a Trustworthy AI Framework that
stages of the technology lifecycle and is embedded procedures and controls, people and skills and
senior leaders to successfully navigate the risks of outlines seven key dimensions that are necessary to build
across technology, processes, and employee monitoring, reporting and evaluation), assesses an
rapid AI adoption and fully embrace and integrate this trust in AI solutions – 1) transparent and explainable, 2) fair
training. Governance arrangements require tailoring organisation’s AI governance maturity (Table 1). Based
transformative technology. Trustworthy AI provides and impartial, 3) robust and reliable, 4) respectful of privacy,
to the sophistication of AI solutions used, location on these indicators, we categorise organisations as
a level of certainty that the technology is ethical, lawful 5) safe and secure, 6) responsible, and 7) accountable
and industry-specific regulations, and internal ‘Basic’, ‘In progress’ or ‘Ready’ in terms of their AI
and technically robust and provides confidence for senior (Figure 2). This framework and criteria should be applied to
organisational policy and standards. governance maturity. Further details about the Index
leaders to use AI solutions throughout their organisation. AI solutions from ideation through to design, development,
and the underlying questions are available in Appendix B.
procurement and deployment.
Figure 2: Deloitte Trustworthy AI framework
Table 1: Deloitte AI Governance Maturity Index
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Source: Deloitte (2024)
Source: Deloitte (2024)
8 9
AI at a crossroads | Building trust as the path to scale
CASE STUDY
The figure below depicts how each of the pillars in the Empowering the future:
Deloitte AI Governance Maturity Index is a foundational
element that can enable an organisation to achieve
Energy Queensland’s commitment to
trustworthy AI. Furthermore, the Index identifies the
practical arrangements and activities that an organisation
responsible AI and sustainable innovation
should undertake to achieve the seven dimensions
highlighted in the Trustworthy AI Framework.
Energy Queensland is Australia’s largest, wholly government-owned electricity
Figure 3: the Deloitte AU Governance Maturity Index company, servicing over 2.3 million customers and employing more than 9,300
people across its distribution, retail, and integrated energy solutions businesses.
Sharyn Scriven, CIO Energy Queensland expresses that incrementally’, according to Josh. This has involved
“AI is a game changer and as it matures will help aid trialling enterprise tools and building AI platform
Achieving
our business and people to achieve our vision and services to initially support corporate users with
Trustworthy AI 2032 Corporate Strategy.” heavy documentation, meetings and emails.
requires each organisation Josh Gow, General Manager of Customer and Emerging Effective and responsible use of AI requires team
Platforms, recognises that integrating AI is an important members with the right capabilities alongside
to develop:
focus area for Energy Queensland to drive operational powerful AI solutions. For this reason, ‘control group
excellence and enhance customer experience, releases’ are being conducted and reviewed, where
supporting the organisation’s ambitious strategy. While employees in different roles participate in a controlled
Energy Queensland has been using AI for several years, release, education and training program before further
there has been a shift from niche specialised use cases deployment.
The five pillars of the AI Governance Maturity Index
to broader use case evaluation and deployment.
“Ensuring we capture the value, opportunity and
Drafting an AI policy has been essential for Energy continue to manage the risk that AI will bring with
Queensland to ensure the right policies and settings further adoption is critical. It’s a matter of when,
are in place before introducing new AI solutions. This not if, AI will be in broader use across many more
has involved developing an AI Policy and a roadmap technologies. Not everyone will get the same AI and
for use case rollout across the organisation, along it may also be ‘under the hood’. We need to tailor
with necessary actions to establish appropriate how AI will aid our company to ensure it is effective,
Organisational Policy and Procedures People Monitoring,
guardrails. To ensure the AI policy adhered to industry responsible, and valuable.”
structure principles and controls and skills reporting and
best practices and was implemented correctly, Energy
evaluation
Queensland had the AI policy independently reviewed
Source: Deloitte (2024) by an external organisation, as well as internally.
Josh explains:
Key features to ensure trustworthy AI
“Our AI policy is under continued review, as a living,
There is no one-size-fits-all approach to AI governance. It should also be noted that higher levels of AI Governance
breathing document, given the rapidly changing
The specific governance structures will vary depending Maturity do not automatically lead to trustworthy AI
environment of AI and maturing industry standards
AI policy
on the industry, regulatory environment, AI ambition outcomes. If governance procedures are in place but are
and guidelines. Our monthly AI steering committee
and type of AI solutions being adopted. For instance, not effectively implemented, understood by staff or well-
includes senior executives who regularly discuss
AI steering committee
an AI-powered chatbot providing employees with tailored to the business context and strategy, trustworthy
the progress, risks and opportunities of AI.”
information about HR policies will require different AI outcomes may not be achieved. Effective AI governance
control processes compared to a bank’s AI-driven is different for every organisation. For this reason, it is Testing and piloting AI use cases before full Piloting and trialling
credit application solution that interfaces directly important for organisations to continuously evaluate and implementation is an important feature of Energy AI programs internally
with customers. Comparing common features of refine their AI governance framework to ensure that it is Queensland’s approach to AI. Trialling AI through
AI governance can help organisations identify areas right-sized to their unique needs and evolving regulatory internal use cases has been a strategic choice to Training programs
for improvement in their governance standards. requirements. create an environment where it has been ‘test and
learn focused to further evaluate risk and opportunity
10 1111
AI at a crossroads | Building trust as the path to scale AI at a crossroads | Building trust as the path to scale
03
PILLAR 1
AI governance across Asia Pacific Organisational structure
Having clearly identified roles within an organisation How organisations structure the teams responsible
Fewer than one in ten organisations across the Chart 1: Distribution of AI Trustworthy Index that are accountable for managing AI standards helps for ethical, legal and regulatory compliance related to
Asia Pacific have the governance structures necessary across Asia Pacific
to ensure any emerging AI-related issues are addressed AI may vary. Just over a quarter (28%) of organisations
to achieve trustworthy AI. Using our AI Governance
appropriately. For most organisations surveyed, this have a centralised ethics and risk team to monitor trends
Maturity Index, we classify 91% of organisations as 17% 74% 9%
responsibility lies with senior leadership, with 91% and detect risks related to AI use, while the majority (61%)
having ‘Basic’ or ‘In progress’ AI Governance structures
of organisations having a board member or C-suite of organisations have dedicated professionals working in
Basic In progress Ready
in place, highlighting substantial room for improvement
executive explicitly responsible. A further 7% nominated all or some departments or teams (Chart 3). The remaining
in AI governance (Chart 1). Source: Deloitte Trustworthy AI survey (2024)
a non-executive AI lead as responsible for managing risks organisations have either some teams with dedicated
and standards, while less than 2% of respondents were professionals or no dedicated roles for AI use.
Examining the five pillars of the AI Governance
not able to identify anyone primarily responsible in their
Maturity Index, organisations across Asia Pacific have Chart 2: Distribution of Trustworthy AI Index across pillars
organisation. More important than the structure of the team is having
the greatest opportunity for improvement in policies
clear responsibility and accountability for AI standards,
Organisational structure
and principles as well as procedures and controls.
yet this is less common in smaller organisations. For
Currently, 31% and 23% of organisations, respectively, 9% 73% 18%
organisations with more than 1,000 employees, only 3%
are categorised at ‘Basic’ levels in these two pillars.
have no dedicated AI risk roles, compared to 23% of those
In contrast, organisations performed better in the Policy and principles with fewer than 100 employees.
organisational structure and monitoring and evaluation
31% 56% 13%
pillars, with more than 90% achieving at least
‘In Progress’ status.
Procedures and controls
Achieving a ‘Ready’ status for the AI Governance Chart 3: Structure of team responsible for ethical, legal and regulatory compliance related to AI
23% 66% 10%
Maturity Index overall requires high performance
across all five pillars. While nearly one in five
organisations achieved a ‘Ready’ status in one of the People and skills
pillars, only half that shared achieved ‘Ready’ for their 28% 31% 29% 11%
22% 64% 14%
AI governance overall. This highlights the need to
consider AI governance in a holistic sense to develop A centralised team Every team / department Some teams / No dedicated roles
Monitoring, reporting and evaluation working across the has dedicated departments have for AI use
the conditions required for trustworthy AI.
organisation professionals dedicated
6% 77% 18% professionals
Basic In progress Ready
Source: Deloitte Trustworthy AI survey (2024)
Source: Deloitte Trustworthy AI survey (2024)
Addressing the overconfidence bias
Leaders may overestimate the maturity of AI Governance. Deloitte’s State of Generative AI in the Enterprise survey
found that 23% of organisational leaders rated their risk management procedures and governance as highly
prepared. However, this more detailed study, exploring the underlying structure of AI governance revealed only 9%
had actually achieved a ‘Ready’ level of governance.8 While the specific questions and sample differ, the extent of the
variation in these studies suggests that senior leaders need to have a detailed understanding of their AI governance
maturity. This is pertinent as overconfidence can represent a barrier to improving AI governance; if leaders believe
they have sufficient settings in place to manage AI risks, they are less likely to explore how they can improve.
12 13
AI at a crossroads | Building trust as the path to scale AI at a crossroads | Building trust as the path to scale
PILLAR 2 PILLAR 4
Policy and principles People and skills
Clear, broadly understood policies and principles are Chart 4: Implementation of trustworthy AI policies Employees play a crucial role in ensuring trustworthy Chart 6: Resources available to employees
a fundamental prerequisite for effective AI governance. AI. Yet, this remains a challenge for many organisations, to support them using AI
This AI policy differs from an AI strategy, with the latter Incident response and remediation plans where only 56% of employees, on average, have the skills 58%
including broader elements such as ambitions related and capabilities to use AI responsibly. 55% 55%
15% 38% 48% 52%
to AI and key metrics to measure progress. While most 49%
organisations across Asia Pacific have an AI strategy Training can be a powerful tool to bridge this gap.
Ethical guidelines and principles
in place, many are missing key elements of good Organisations that provide AI training see a 27% higher
governance in their AI policy. More than half of AI policies 11% 34% 55% share of employees equipped to use AI safely compared 36%
34%
lack timelines for implementing AI governance goals or to those that don’t – though just 52% of organisations 31% 32% 32%
contain ethical guidelines and principles related to AI. surveyed currently offer such programs. That said, 72%
AI policy for safe and responsible use of AI in the organisation
of organisations that currently don’t offer training are
Including these governance features in an AI policy is 8% 25% 67% actively developing programs for their teams.
key for employees to see the value. Among organisations
with an organisation-wide AI strategy, 30% report that The majority of organisations do offer guidelines
No plans/ Future plans Currently
not all employees see the strategy’s value. Where the Unsure implemented on responsible AI use, and 55% encourage on-the-
AI policy includes monitoring or auditing, i.e. having job learning and experimentation and slightly fewer
a defined risk appetite, response and remediation plan Source: Deloitte Trustworthy AI survey (2024) organisations have an advisory service or body for Guidelines on Advisory Training on AI security Encouraged
integrated with broader organisation policies, employees employees (49%). Private sector organisations lead in appropriate service for appropriate and privacy on-the-job
AI use employees AI use measures learning
are more likely to see the value in the strategy. offering AI use guidelines and training, whereas public
sector organisations are more likely to focus on security In development Currently available
measures and encourage on-the-job learning.
Source: Deloitte Trustworthy AI survey (2024)
PILLAR 3 PILLAR 5
Procedures and controls Monitoring, reporting and evaluation
The third pillar explores day-to-day practices for A key element of effective AI governance is a system for Having AI governance systems that are responsive
managing AI-related risks and standards in an employees to report queries or incidents related to AI use to changing requirements and emerging issues is Chart 7: Frequency of evaluating AI systems against
internal organisation standards
organisation. This includes an assessment procedure to in the workplace. Yet, two in five organisations lack such a critical to ensuring organisations can respond to risks
identify and manage AI-related risks, a comprehensive reporting mechanism. Organisations with formal reporting and incidents as they emerge. Overall, organisations
Less than
Unsure/Never
inventory of AI solutions used, and control frameworks systems see five times more queries and twice as many performed relatively well in this pillar, with the equal once a year
2%
that mitigate risks associated with the use of an AI reported incidents – indicating that those without these highest share (18%) achieving ‘Ready’ status. The majority 3%
At least yearly
solution. With the fewest organisations categorised as systems may be blind to emerging risks associated with (85%) of organisations evaluated their AI governance Ongoing or
‘Ready’ for this pillar, progress in this area will be key for AI. This issue is only growing more urgent, especially in against internal standards at least every six months real-time
10%
improving trustworthy AI performance across the region. Asia Pacific, where the number of queries and incidents (i.e. those evaluating at least every six months, three 47%
continues to rise (see Chart 5). months or in real-time). Monitoring and evaluating
whether AI governance is complying with any changes in
regulatory requirements is another element of this pillar. 27%
Chart 5: Change in the number of incidents related to AI in FY24 compared to FY23
Nearly three-quarters of organisations review legal and
regulatory requirements at least every six months.
30%
At least every
27% 35% 32%
six months
At least every
Increased Remained about Decreased
the same few months
Source: Deloitte Trustworthy AI survey (2024)
Source: Deloitte Trustworthy AI survey (2024) Note: excludes ‘unsure’ answers (6)
14 15
AI at a crossroads | Building trust as the path to scale AI at a crossroads | Building trust as the path to scale
CASE STUDY
Navigating innovation and governance:
How does trustworthy AI
Dai-Ichi Life Holdings’ approach
compare across industries?
to responsible AI
Dai-Ichi Life Holdings, Inc. is a leading insurance group, with 122 years
The results for the AI Governance Maturity Index and individual
of experience in providing life insurance and investment products across pillars vary by industry. We find that organisations within
the Asia Pacific region and to the global market. technology, financial services and professional services more
generally have the highest share of organisations that are
‘Ready’ for trustworthy AI. Meanwhile, public sector and life
Figen Ulgen is the Chief Data and AI Officer for Dai-Ichi Dai-Ichi Life Holdings views effective AI governance science and healthcare organisations have a lower share.
Life Holdings and oversees the organisation’s strategy as a collective responsibility. Notably, at the Dai-Ichi A high-level summary of four key industries is over the
for Artificial Intelligence and Data. Delivering high Global Data and AI Synergy Leadership forum, where following pages. A similar summary for key geographies
quality customer service and building a strong, leaders in the organisation and member companies across Asia Pacific is available in Appendix D.
trusting relationship with clientele are key values meet, AI governance was the voted to be the topic to work
for Dai-Ichi Life Holdings. Implementing AI solutions, on together. Furthermore, responsible AI is regarded as
in a responsible and ethical way, is key to achieving empowering for all stakeholders involved. The business
these goals. Dr. Ulgen explains: can comfortably roll out new AI solutions and internal
users can safely explore AI knowing that guards are in
“Our AI solutions need to be designed and place and flags will be raised if necessary. Importantly,
implemented in a way that reinforce our company’s Dr. Ulgen emphasised that processes should include
value around serving our customer. This requires how incidents are handled, if, and when they do occur.
time and patience to make sure our systems are
acting in the ways we expect. We know this is going “If we have the right framework and processes
to be a marathon, not a sprint.” in place, our staff don’t have to carry the burden
or feeling that they are taking a risk. They feel
Dai-Ichi Life Holdings is currently exploring the empowered to use the solutions we have designed
possibilities for innovation using generative AI, with confidence and knowing when to query a result.”
through digital agents, which are digital avatars
including chatbot capability. Accompanying Dai-Ichi Dr. Ulgen also highlighted organisational culture, and
Life Insurance agents to customer meetings, these specifically empathy, as foundational for delivering
digital agents help with note taking, extracting high quality customer service, which extends to the
relevant documents to the customer’s questions implementation of ethical AI. In the case of life insurance,
and later summarising the conversations. Dai-Ichi this looks like understanding that there is a “high level
Life Holdings is undertaking ongoing and long- of ethical responsibility toward the customer”.
term testing to ensure that the digital agents are
implemented in a responsible way. In fact, the digital
agents have been tested in multiple sales offices
for almost a year, with hundreds of sales agents.
Key features to ensure trustworthy AI
Additionally, critical to ensuring accuracy of answers,
for Dai-Ichi, is maintaining a ‘human touch’, with
every piece of information created by a digital agent
Long term approach
checked by employees.
Human touch
A collaborative effort
1166 17
AI at a crossroads | Building trust as the path to scale
Spotlight on Spotlight on
Financial services industry Technology industry
Being a knowledge and data intensive industry, Our AI Governance Maturity Index shows the financial The technology industsry is at the forefront of AI Based on the Deloitte Generation AI report, technology
financial services have been leading adopters services industry has higher levels compared with other disruption and a key enabler of developing AI solutions employees lead in adoption of GenAI into their workflow,
of digital innovation. The relatively higher levels industries. Demand for financial services is growing, for other industries. As long-time users of"
183,deloitte,gen-ai-multi-agents-pov-2.pdf,"The cognitive leap
How to reimagine work
with AI agents
December 2024
The cognitive leap | How to reimagine work with AI agents
Content
Key takeaways
• Multiagent AI systems can help transform traditional, rules-based
business and IT processes into adaptive, cognitive processes.
• Organizations should leverage key principles of AI agent and multiagent AI
system design and management, which borrow from tenets of composable design,
microservices architecture, and human resources deployment and teaming.
• The ability to scale AI agents and multiagent frameworks across a range of use
cases depends on developing a comprehensive reference architecture populated
with reusable core components.
• A systematic approach can make the difference between incremental,
isolated improvements and exponential enterprise transformation.
Vaulting ahead on the path to GenAI value 3
How agents deliver a cognitive advantage: 5
Principles of AI agent design and management
Adaptive processes for innovative outcomes: 8
Principles of multiagent AI system design and management
Expanding and scaling multiagent AI systems: 10
A reference architecture for agent-powered transformation
Multiagent AI systems in action: 12
An example use case for transforming traditional IT support processes
From generating to innovating: 14
Key considerations on the path to AI agent-enabled transformation
Making the cognitive leap 16
Get in touch & Endnotes 17
2
The cognitive leap | How to reimagine work with AI agents
Vaulting ahead on the
path to GenAI value
Everyone remembers that pivotal moment when we first saw
what large language models (LLMs) and Generative AI (GenAI)
Business executives say
could accomplish. Suddenly, the long-discussed theory of
conversational, intuitive, creative AI became a reality, right there deeply embedding GenAI
at our fingertips. Adoption of GenAI surged across industries:
into business functions and
By the end of 2023 most companies had embraced GenAI
solutions.2 By midyear 2024, 67% of companies using GenAI processes is the No. 1 way to
said they were increasing investments after seeing strong
drive value from the technology.1
results from the technology.3
But as companies dove into testing GenAI’s potential, many
came to recognize the limitations of standalone GenAI models.
Context and reasoning limitations of typical LLMs can make it
difficult to apply GenAI to complex, multistep workflows. As with
traditional AI, hallucination and bias can create significant barriers
to trust. And the creative outputs for which GenAI is celebrated
require continuous human monitoring for quality and accuracy.
For these and other reasons, early GenAI use cases were
mostly limited to isolated or narrowly defined tasks within larger
workflows. For example, a wealth management adviser may
quickly produce a meeting recap using a standalone, LLM-based
solution. But extracting rich post-meeting analytics based on
different information categories discussed in the meeting (e.g.,
client profile, client goals, retirement information, etc.) remained
too complex to achieve with a standalone GenAI solution.
AI agents and multiagent AI systems are helping
organizations hurdle these limitations and make
the cognitive leap into a new paradigm of business
process transformation and innovation.
AI agents enable organizations to tackle significantly more
complex tasks with GenAI across an expanded range of
processes and use cases. When AI agents work together in a
system, they can help collaboratively reason, plan, design and
execute novel workflows that amplify speed, differentiation
and efficiency across the enterprise.
In this paper we outline key design principles and a reference
architecture for scaling AI agent use cases that can help your
business seize the potential of AI agents now.
3
The cognitive leap | How to reimagine work with AI agents
“Each mind is made of many smaller
processes. These we’ll call agents.
Each mental agent by itself can only
do some simple thing that needs
no mind or thought at all. Yet when
we join these agents in societies—in
certain very special ways—this leads
to true intelligence.”
—Marvin Minsky, The Society of Mind4
4
The cognitive leap | How to reimagine work with AI agents
How agents deliver a
cognitive advantage
Determining the most appropriate roles and uses for AI agents Language, planning, reasoning, reflection, and the ability to use
begins with adopting a shared, enterprisewide understanding of tools, data and memory: These attributes are central to how AI
what they are and how they can fit into your organization. agents work and demonstrate cognitive abilities as well.
AI agents are reasoning engines that can understand context, In the realm of business, AI agents and human workers have other
plan workflows, connect to external tools and data, and execute broad similarities. Both must be carefully selected, well trained and
actions to achieve a defined goal. They do so by echoing some of well equipped to perform their jobs. And both should be smartly
the key qualities and advantages that have helped humans survive deployed and consistently managed in ways that help ensure
and flourish. efficient, value-adding performance.
As people, we can understand language and creatively articulate Not surprisingly then, our recommended principles of AI
responses. By employing specialized tools, we can amplify our agent design and management echo familiar themes from
physical and mental capabilities. By learning and remembering organizational design and human resource management.
information, we avoid mistakes and improve on what we’ve (Please see next page.)
already accomplished.
5
The cognitive leap | How to reimagine work with AI agents
Principles of AI agent design and management
• Domain-driven approach: Every area of expertise and function of your business
utilizes different processes, data and tools. While some AI agents may be able to serve
multiple domains and processes, most should be sourced and/or designed based on
specific domain requirements. To achieve this, each domain of your business should be
analyzed, subdomains and processes identified, and agents assigned based on specific
roles within the domain.
• Role-based design: Agents should be designed to perform roles rather than specific
tasks, grouping similar activities to avoid confusion and ensure efficient operation.
This approach—which aligns with the “single responsibility principle”5—can help your
organization reduce AI agent overlap and unnecessary technology complexity. It also
can help enable reusability of agents across systems and domains.
• Right balance: Related to the principle of role-based design, it is important to find
the proper balance between the number and the scope of responsibilities of individual
AI agents. Too many agents with too few responsibilities can result in unnecessary costs
as well as challenges related to consistent governance, maintenance, monitoring and
upgrades. Too few agents with too many responsibilities can result in bottlenecks and
poor performance.
• Controlled access to data, skills and tools: You wouldn’t give every employee in
your enterprise access to every application or data resource in your business. Similarly,
the tools, data and skills made available to a given AI agent should be limited to those
that are essential to its role. These constraints help reduce risk and improve outputs
from the agent. If an agent’s role requires more than five tools, consider how you might
separate its responsibilities across two or more agents.
• Reflective cycle: Agents—like people—get better and better when given an
opportunity to reflect on their own performance or receive constructive criticism.
That’s why it’s important to design a self-reflective pattern in which agents critically
evaluate their own output by referring to past examples or testing the results of its
output. Agents also receive feedback from other agents and humans. This combination
of self-assessment and external input creates a continuous loop of learning and
improvement that helps ensure compliance with quality, brand and risk standards.
6
The cognitive leap | How to reimagine work with AI agents
“Synergy (is) the bonus that
is achieved when things work
together harmoniously.”
—Mark Twain
7
The cognitive leap | How to reimagine work with AI agents
Adaptive processes for
innovative outcomes
The achievements of remarkable individuals—from Aristotle to Multiagent AI systems have the potential to impact every
Simone Biles—are often treated as proof of our boundless human layer of enterprise architecture—not just automating
potential. But as any leader today knows, individual strengths are existing processes and tasks, but also reinventing them.
no match for team synergy. Organized and managed well, teamwork By engaging with users and within workflows semantically rather
leverages and amplifies the strengths of each individual—making it than syntactically, AI agents can comprehend emerging needs
possible to achieve goals that no person could do alone. and address them in novel ways that obviate traditional, rules-
based processes. By continuously self-monitoring, multiagent AI
As with people, so too with AI agents. Research has shown that systems can improve their outputs in near real time. Meantime,
AI agents working together are more effective than individual the shared persistent state of AI agents in a system enables them
agents.6,7 By leveraging an “agency” of role-specific AI agents, to collaborate and coordinate activities in ways that continuously
multiagent AI systems can understand requests, plan workflows, streamline efficiency.
delegate and coordinate agent responsibilities, streamline actions,
collaborate with humans, and ultimately validate and improve The principles of agent design discussed in the previous section
outputs. Processes that were considered too complex for typical become especially important in this context. For example, dynamic
language models can be automated at scale—securely and workflow planning and task decomposition in a multiagent AI
efficiently. Projects that once took weeks can be completed in a system are critical to effectively automating and reinventing
small fraction of that time. Human workers who previously spent end-to-end processes—and are dependent on the right balance
precious hours performing routine, repetitive tasks can instead of domain-specific, role-based agents to perform each task.
focus on higher-level, higher-value activities. By providing each agent with controlled access to data, skills and
tools—and by providing checks and balances throughout the whole
So, while standalone AI agents can help accelerate the completion system—redundancies can be avoided and quality improved.
of individual tasks, multiagent AI systems can open new realms of
business process automation, speed and reliability. Agents within a When designing multiagent AI systems, we recommend a set of
system can interact and collaborate in various deployment patterns, principles to help ensure that these systems are robust, reliable
depending on the specific needs and complexity of the process. and trustworthy. (Please see next page.)
8
The cognitive leap | How to reimagine work with AI agents
Principles of multiagent AI system design and management
• Understandable and explainable systems: Good business leaders explain and justify their
decisions, and AI systems should do the same. The actions of your multiagent AI systems need to be
explainable, particularly in tasks related to perception and classification. Systems should be designed
to document each agent’s chain of thought,8 and not just the final output. (Think of it as “showing your
work” in math class.) Clarity and interpretability will help minimize biases originating from their design
or datasets.
• Composable design: Multiagent solutions should be designed with composability in mind.
A composable design can allow organizations to bring best-of-breed components together in a
microservices architecture to develop optimized and efficient multiagent systems. By orchestrating
custom and third-party agents that include different programming languages and agent frameworks,
your organization can design more complex agentic patterns that integrate with multiple internal
and external systems.
• Human in the loop: AI agents shouldn’t be solely responsible for critiquing their own or other
agents’ outputs. Knowledgeable humans must be essential parts of AI systems as a safeguard
against potential errors or biases. This isn’t just common sense; it’s a regulatory mandate in some
industries and/or US states. California, for example, recently required that AI-generated health
care-related decisions must be reviewed by a human before being shared with consumers.9
• Dynamic data patterns: In designing multiagent AI systems, data should be able to flow in
two distinct patterns: data to the agent and agent to the data. In the data-to-the-agent pattern,
unstructured data is typically captured into a vector or graph database. It’s important to include
not only the data itself but its hierarchy relevant to the specific use case. This enables agents to
apply the data appropriately within various contexts. In the agent-to-the-data pattern, the agent
uses suitable tools built into the model (such as search tools or API specifications) to determine
how to retrieve relevant structured data for the task at hand.
• Ecosystem integration: A multiagent AI system often needs to integrate with various existing
applications or processes to achieve its intended goals. Therefore, the design of these systems
should consider integration patterns with ecosystem processes and applications. Some integrations
may be achieved via application programming interfaces (APIs), while others may be event-driven.
For example, a multiagent system for post-meeting analytics may need to integrate with a CRM
platform through an API to upload client profiles or other information discussed during the meeting.
• Continuous improvement and adaptation: Performance improvement must be built into
the “DNA” of multiagent AI systems. Systems should be designed to learn from prior interactions
and evolve in response to new data and changing conditions. This capability can be implemented
through agent and workflow memory, which stores past interactions and workflow executions.
The stored information can later be leveraged to enhance future executions.
• Ethical considerations: The same ethical principles you apply to human capital decisions,
such as impact, justice and autonomy, should guide the design and deployment of multiagent AI
systems. In addition to prioritizing explainability, your organization should regularly assess AI system
outputs to ensure they contribute positively to society and avoid causing harm.
9
The cognitive leap | How to reimagine work with AI agents
Expanding and scaling
multiagent AI systems
Imagine you’re the chief transformation officer at a global
financial services company. You understand the principles of AI
agent and multiagent AI system design. You see the potential in
this next evolution of Generative AI technology everywhere in
your organization.
But where to apply it?
A multiagent AI system could help your HR team identify, recruit
and onboard talent by analyzing mountains of resumes against
job requirements, intelligently assessing candidates based on
skills and experience, even conducting initial screening interviews.
The benefits seem obvious: greater scalability and efficiency,
improved candidate matching, less bias …
Then again, AI agents could transform efficiency in your call
center by enabling plain-language conversations between clients
and chatbots. This could help digital self-service feel more like
old-fashioned client service—while your human support reps are
freed to focus on more sensitive, higher-value interactions.
Or maybe the place to focus is in improving personalization in
financial advisory services? Or in automating financial reports?
The list goes on—across every domain of the enterprise.
Thanks to the innate flexibility and scalability of multiagent AI
systems, your organization doesn’t have to limit its focus. While
it is true that no organization possesses the financial, talent or
technological resources to design and deploy bespoke multiagent
AI systems for every possible domain or use case—no longer are
these resources requisite to success.
The key is to treat a multiagent AI system as an
ecosystem of capabilities instead of solutions and to
develop a reference architecture that can support both
business and technical delivery processes. This approach
can allow your organization to more rapidly scale, expand and
reuse AI agents and multiagent frameworks across a range of use
cases—while also streamlining governance, monitoring, operation
and improvement of agentic outputs.
The essential layers of a reference architecture are shown in the
illustration on the next page. Each layer within the architecture
is loosely coupled with—but independent of—other layers.
Similarly, each component within a given layer can be leveraged
independently. This makes it possible to adapt, connect and
apply best-fit solutions for any use case that arises.
10
The cognitive leap | How to reimagine work with AI agents
A reference architecture for agent-powered transformation
Interaction layer
Purpose: Allow users, processes and Example elements for a financial services company:
existing applications to collaborate with
multiagent AI systems.
Actions for success: Develop defensive user
Mobile banking CRM Conversational IT support
interfaces that can anticipate and mitigate
app system IVR system portal
potential user errors or misuse, while guiding the
multiagent system(s) to respond contextually.
Workflow layer
Purpose: Ensure controlled flow engineering Example elements for a financial services company:
to help agents interact with each other
efficiently and in a more deterministic manner.
Actions for success: Implement value-stream
Know Risk Financial Software
analysis to monitor efficiency and effectiveness your control planning incident
of workflows. Identify governance guardrails customer testing workflow support
and touch points for human monitoring workflow workflow workflow
(“human in the loop”) to help reduce risks.
Human in Human in Human in Human in
Infuse long-term memory into workflows.
the loop the loop the loop the loop
Agents layer
Purpose: Create, manage, deploy Example elements for a financial services company:
and optimize role-specific AI agents.
Actions for success: Focus on MODEL GARDEN AGENT FACTORY TOOLS
industrializing the creation of role-
Multimodal Data retrieval
specific agents to accelerate speed Search engines
commercial LLM agent
to value.
Multimodal Recommendation Financial analysis
Each agent should be equipped with: open-source LLM agent tool
• A fit-for-use language model Fine-tuned Incident classification Code interpreter
model agent
• Tools that augment language model
capabilities with skills to perform Domain-skilled Incident analysis
specific tasks/roles SLMs agent DATA SOURCES
• Approved sources of authoritative data Incident resolution
Customer 360 record
agent
• Memory of past tasks to help improve
PROMPT REGISTRY
performance of new tasks Quality assurance Financial markets
Prompt agent data
• Access to effective prompts for engaging templates
with other agents and/or humans in a Agents from Incident history
given workflow Prompt third-party vendors
versioning
MEMORY
Prompt
testing Short-term
(current session)
Prompt
access management Long-term
(past sessions)
Agent operations layers
Purpose: Monitor outputs and metrics to help Example elements for a financial services company:
ensure agents are functioning as expected.
Actions for success: Implement
instrumentation and telemetry, along
Operational Qualitative Thought
with logs, traces and metrics, to gather data
metrics metrics metrics
about system activities. Activate alerts and
dashboards to simplify performance
monitoring against service-level objectives.
11
The cognitive leap | How to reimagine work with AI agents
Multiagent AI systems
in action
Continuing our exploration of the reference architecture layers and elements
that contribute to effective, efficient and scalable multiagent AI systems, let’s look
more specifically at an IT operations process—specifically, a support scenario for
a business software application.
Traditionally, this process involves multiple support team interventions and touch
points for the business user. The diagram below illustrates this resource-intensive,
inefficient and often time-consuming workflow.
Service desk rep (L1) gathers details Support analyst (L2) is assigned
from the business user and attempts to and then reaches out to the business
find a solution by searching knowledge user to collect details, analyze the issue
resources. If an existing solution is not and try to fix it. If the issue remains
available, the issue is escalated to the unresolved or may affect other users,
“I’m having a appropriate support specialist. it is assigned to L3.
problem with
a software app
that’s important 1 2
to my work.” Business user has to take time Business user often has to repeat the
to engage in a dialogue with L1. same information already provided to L1.
1 1 2
Business 3 Service desk rep Support analyst
user (L1) (L2)
2
Support technician (L3)
conducts a root cause
analysis to identify a
permanent fix.
3
3
Business user may be
engaged again to provide
Release Release manager is engaged to Support technician more information or test
manager plan deployment of the application (L3) potential solutions.
change to production so the issue
does not recur.
12
The cognitive leap | How to reimagine work with AI agents
Traditional L1 and L2 IT support workflows are primed for transformation through
multiagent AI system solutions. By leveraging an AI agent-enabled process, the user is
continuously updated—but can be much less actively engaged. Support personnel are engaged only
to monitor, review and approve rather than find and implement most solutions. This frees the human
support personnel to focus on the most complex and business-critical resolution of select issues.
And it frees business users to get back to the important work of generating enterprise value.
Here’s how it can work.
(This example shows one variation of IT support for illustrative purposes.
The most appropriate solution for your business may differ.)
REFERENCE ARCHITECTURE
“I’m having a problem with a software “Tell me more about LAYERS
app that’s important to my work.” your problem.”
Interaction
Workflow
Agents
Business A business user files a support ticket IT support
user through the enterprise IT support portal. portal Agent operations
The software incident support workflow
is triggered to resolve the incident.
Software incident Human
support workflow in the loop
The solution identified by the agents
is handed over to a “human in the
The workflow orchestrates the agents loop” to verify and execute the
for the resolution of the incident. resolution. This helps ensure that
human knowledge and judgment
remains part of the solution.
Incident An incident classification agent Software A specialized software incident analysis agent Software The solution
classification identifies the type of issue and incident reviews the ticket against existing data resources incident is implemented
agent engages the appropriate software analysis (knowledge base articles, SOPs, etc.). If a potential resolution for the business
agent agent
incident analysis agent. solution has already been developed the ticket passes user—who
to a software incident resolution agent, which has been
The incident classification agent’s either validates the solution or sends it back to the updated on
role fulfills typical L1 support. analysis agent for more information or other solutions. progress/status
throughout
The agents in this workflow fulfill typical L2 support. the process.
If no existing solution is found, the incident is elevated
to L3 (human) support.
As the workflow is executing, the traces and spans from the
agent interactions are continuously logged, processed and
aggregated through telemetry. This provides key operational and
response metrics for appraising performance of the workflow
“Back in
and each individual agent in the workflow. business!”
Operational Qualitative Thought
metrics metrics metrics Business
user
13
The cognitive leap | How to reimagine work with AI agents
From generating to innovating:
Key considerations on the path to
AI agent-enabled transformation
Every promising technology innovation comes with its own set of challenges. Multiagent AI systems are no
exception. Strategically, organizations need to identify priority areas and use cases where AI agents can have
the most rapid and valuable impact. Implications around change management also come into play, from
training employees in new skills to modifying existing processes. At Deloitte we’ve gleaned valuable lessons
that can help you realize the full value potential of this technology innovation.
As you explore the potential for multiagent AI systems for your organization, these considerations can help
provide a valuable head start.
1 4
Starting smartly Evaluating technologies
With so many potential use cases for multiagent AI There are numerous technology choices related to
systems, it’s important to be strategic about where each layer of the agentic architecture. To simplify the
to begin and how to move forward. Executive sponsorship process of selecting the right technology stack and agent
and appetite, rigorous cost/benefit analysis, and a development tool kit(s), consider leveraging an evaluation
clear understanding of the state of your underlying data framework that helps to objectively score the choices at
fabric form the foundation of use case prioritization and each layer to baseline the right-fit technology stack of
planning. To accelerate return on investment, proactive the agentic architecture.
and thorough change management should be a part of any
agent-powered transformation initiative, with an emphasis
on building trust across your organization and among your
stakeholders as new solutions are rolled out.
2
Pinpointing the right data, in the right context
Data forms the backbone of any agentic architecture.
For every use case, it’s essential to not only identify the
authoritative source of data that the agents will use but
also ensure that agents can evaluate the appropriate
context for that data. This is where knowledge engineering
comes into play: By organizing data (i.e., knowledge sources)
into a classification system or taxonomy, you make it easier
for agents to navigate and retrieve the right data.
3
Tapping talent
Your system’s design and development will require data
engineering, business process engineering, machine
learning and application architecture knowledge—in other
words, some of the most high-demand skills in today’s talent
market. Accessing the necessary human expertise typically
involves a combination of workforce upskilling and hiring,
combined with strategic outsourcing to fill the roles that
will be needed to support agentic AI transformation.
14
The cognitive leap | How to reimagine work with AI agents
5
Decomposing processes
Reimagining an existing process or developing new agent-
based workflows means breaking the overall process into
smaller, more manageable subprocesses. By decomposing
the process based on roles, each agent can specialize in a
clear set of tasks, ensuring there are no overlapping
responsibilities. To achieve this, consider using domain-driven
design principles in which the boundaries for each subprocess
are defined by and align with the organization’s domain and
team structure. This approach not only defines clear task
boundaries but helps pinpoint the right number of agents to
accomplish the overall process.
6
Scaling multiagent AI system impact with
sound reference architecture
A thoughtfully designed reference architecture allows your
organization to scale multiagent systems across a wide
range of use cases in trustworthy and transparent ways.
By embedding best practices and reusable components, this
approach establishes a standard and repeatable process for
design, deployment and continuous improvement. This not
only ensures interoperability and reduces redundancy but
also enables rapid adaptation and integration of best-fit
agents for any emerging use case. It also provides a solid
and ethical foundation for governance and optimization,
ensuring that the multiagent AI systems remain aligned
with enterprise goals and can evolve in response to
changing needs and technological advancements.
To design a reference architecture appropriate for
your whole organization, we recommend taking into
account industry best practices, market and customer
expectations, and the technology, process and data
realities of your own enterprise.
7
Embedding sound governance
It is very important to ensure that multiagent AI
systems, once deployed in production, consistently
generate quality outputs that do not introduce enterprise
risk. Continuous monitoring and analysis of system outputs
is critical to enabling timely identification of any potential
anomalies or inaccuracies. It’s important therefore to
ensure that every multiagent AI system be smartly
developed in ways that ensure multiple “checkpoints”
within the workflow—and that checks and balances are
engineered into each individual agent.
15
The cognitive leap | How to reimagine work with AI agents
Making the
cognitive leap
The rapid evolution of multiagent AI systems is transforming how
organizations address challenges and streamline processes.
This space is rapidly evolving as commercially available language
models, frameworks and agents continue to improve. Organizations
that adopt a systematic approach to multiagent AI system design
and management will be well positioned to scale these systems
effectively. Rather than limiting AI agent deployment to isolated
business processes, a comprehensive approach allows for the
expansion of AI capabilities across various use cases and domains.
By anchoring in the foundational principles we have outlined—
and by leveraging a robust reference architecture that enables
reuse and rapid adaptation of core components—organizations
can maximize the potential usage and scale of multiagent AI
systems. This approach helps empower organizations to derive
more value from their AI investments, putting them not just at
the forefront of technological advancement but giving them a
competitive advantage.
16
The cognitive leap | How to reimagine work with AI agents
Get in touch Endnotes
Prakul Sharma 1. Deborshi Dutt, Beena Ammanath, Costi Perricos and Brenna
Principal, Sniderman, Now decides next: Moving from potential to
AI & Data performance, Deloitte, August 2024, p. 10, https://www2.
Deloitte Consulting LLP deloitte.com/content/dam/Deloitte/us/Documents/consulting/
praksharma@deloitte.com
us-state-of-gen-ai-q3.pdf, accessed December 3, 2024.
2. Benjamin Finzi, Brett Weinberg and Elizabeth Molacek, Winter
Sanghamitra Pati
2024, Fortune/Deloitte CEO Survey, Deloitte, 2024, p. 11,
Managing Director,
https://www2.deloitte.com/content/dam/Deloitte/us/
US India AI Leader
Documents/us-winter-2024-fortune-deloitte-ceo-survey.pdf,
Deloitte Consulting LLP
spati@deloitte.com accessed December 3, 2024.
3. Dutt et al, Now decides next: Moving from potential to
performance, p. 8.
Abdi Goodarzi
Principal, 4. Marvin Minsky, The Society of Mind, New York: Simon & Schuster,
GenAI Innovation Leader March 15, 1988, ISBN 0-671-60740-5.
Deloitte Consulting LLP
5. Robert C. Martin, Agile Software Development: Principles,
agoodarzi@deloitte.com
Patterns, and Practices, Prentice Hall, 2003, p. 95. ISBN 978-
0135974445.
Vivek Kulkarni
6. KaShun Shum, Shizhe Diao and Tong Zhang, Automatic Prompt
Managing Director,
Augmentation and Selection with Chain-of-Thought from Labeled
AI Transformation
Data, Cornell University, February 27, 2024, https://arxiv.org/
Deloitte LLP
abs/2302.12822, accessed September 16, 2024.
vivkulkarni@deloitte.com
7. Boshi Wang, Sewon Min, Xiang Deng, Jiaming Shen, You Wu,
Luke Zettlemoyer and Huan Sun, Towards Understanding
Ed Van Buren
Chain-of-Thought Prompting: An Empirical Study of What
Principal,
Matters, Cornell University, June 1, 2023, https://arxiv.org/
GPS Applied AI Leader
pdf/2212.10001, accessed September 16, 2024.
Deloitte Consulting LLP
emvanburen@deloitte.com
8. Wang et al, Towards Understanding Chain-of-Thought Prompting.
9. California Legislative Informati"
184,deloitte,the-state-generative-ai-enterprise.pdf,"Now decides next:
Insights from the leading
edge of generative AI adoption
Deloitte’s State of Generative AI in the Enterprise
Quarter one report
January 2024
Table of contents
Foreword
Introduction
Now: Key findings
1 Excitement about generative AI remains 4 Current generative AI efforts remain more
high, and transformative impacts are focused on efficiency, productivity and cost
expected in the next three years. reduction than on innovation and growth.
2 M any leaders are confident about their 5 Most organizations are still primarily relying
organization’s generative AI expertise. on off-the-shelf generative AI solutions.
3 Organizations that report very high 6 Talent, governance and risk are critical areas
expertise in generative AI tend to feel more where generative AI preparedness is lacking.
positive about it—but also more pressured
7 Leaders see significant societal impacts on
and threatened.
the horizon.
8 Leaders are looking for more regulation and
collaboration globally.
Next: Looking ahead
Authorship & Acknowledgments
About the Deloitte AI Institute
About the Deloitte Center for Integrated Research
About the Deloitte Center for Technology, Media & Telecommunications
Methodology
22
Foreword
Now decides next
The arrival of generative AI heralds disruption and From these wave one insights, we can gain a clearer
opportunity across industries. Organizations are picture of how leaders are using generative AI,
exploring how generative AI can be used to unlock challenges, and lessons learned thus far. This helps
business value, supercharge efficiency and productivity, reveal some of the essential questions leaders should
and open the door to entirely new products, services be asking now and actions they should be taking to
and business models. As business leaders contend prepare their enterprise for what comes next.
with this new technology and make decisions about the
There is still much to discover with generative AI.
future of the enterprise with generative AI, it is helpful
As it matures and is deployed at scale for a litany of
to keep one’s finger on the pulse of adoption.
applications, new questions and challenges will become
To that end, The State of Generative AI in the Enterprise: clearer. Our quarterly reports will be available to help
Now decides next, captures the sentiments of 2,835 you make sense of this fast-moving space, consider
business and technology leaders involved in piloting or practical guidance based on what we have learned,
implementing generative AI in their organizations. In this and take a forward-looking view in your business
inaugural release of the quarterly report series, leaders future with generative AI.
indicated persistent excitement for using generative
Learn more about the series and sign up for updates at
AI and many expect substantial transformative
deloitte.com/us/state-of-generative-ai.
impacts in the short term. Yet, they also acknowledged
uncertainty about generative AI’s potential implications
Deborshi Dutt, Beena Ammanath, Costi Perricos and
on workforces and society as the technology is
Brenna Sniderman
widely scaled, calling for greater investment in talent,
governance and global collaboration.
3
Introduction
Now decides next: Insights from the leading edge of generative AI adoption
Will generative AI (gen AI) be the greatest, most impactful technology innovation in Generative AI seems to be following the same pattern, only much, much faster. ChatGPT
history? Will it completely transform how humans live and work? Or will it turn out to was publicly released on November 30, 2022, largely as a technology demonstration.
be just another technology du jour that promised revolutionary change but ultimately Two months later, it had already attracted an estimated 100 million active users—
delivered only incremental improvement? Right now, we can’t be certain. making it the fastest-growing consumer application in history.1
What we do know is that many breakthrough technologies of the past have followed Since then, generative AI has continued to advance by leaps and bounds and many new
a common adoption pattern: initial awareness; excitement that led to hype; mild tools and use cases have emerged—providing a powerful glimpse at the technology’s
disappointment as hype met reality; and then explosive growth once the technology vast potential to transform how people live and work.
reached critical mass and proved its worth.
4
Introduction
Insights from the leading edge (cont.)
About The State of
Generative AI in
During this frenzied period of generative AI advancement To help make smart decisions, leaders need objective, timely
and adoption, leaders in business, technology and information about current generative AI developments— the Enterprise
the public sector are under tremendous pressure to and where things are headed. Which is why Deloitte is
To help leaders in business, technology and the
understand generative AI—and to figure out how to harness conducting this ongoing quarterly survey. Our goal is to take public sector track the rapid pace of generative
AI change and adoption, Deloitte is conducting a
its capabilities most effectively (or at least avoid being the pulse of generative AI adoption, offer a view of what’s
series of quarterly surveys. The series is based
disrupted). They also sense that now decides next; that their happening, track evolving attitudes and activities, and deliver on Deloitte’s State of AI in the Enterprise reports,
which have been released annually five years
decisions and actions today will significantly affect how practical, actionable insights that can help leaders like you
running. The wave one survey was fielded to more
generative AI unfolds in the future, for better or worse. make informed and confident decisions about AI, strategy,
than 2,800 director- to C-suite-level respondents
across six industries and 16 countries between
investment and deployment.
It’s been said that people tend to overestimate the effect of October and December 2023. Industries included:
Consumer; Energy, Resources & Industrials;
a technology in the short run and underestimate its effect in In this report, we examine our first quarterly survey findings
Financial Services; Life Sciences & Health Care;
the long run. This phenomenon has occurred many times in in detail, supported by insights from Deloitte’s AI-related Technology, Media & Telecom; and Government
& Public Services. Learn more at deloitte.com/us/
the past and could very well happen again with generative AI. work with organizations across every major industry and
state-of-generative-ai.
Note here that given generative AI’s dizzying pace of change, many geographic regions. We also offer a forward-looking
the gap between the short run and long run might be view to help you decide what generative AI actions may make
measured in days, weeks or months—not years or decades. sense for your own organization and situation.
All statistics noted in this report and its graphics are derived from Deloitte’s
first quarterly survey, conducted October – December 2023; The State of
Generative AI in the Enterprise: Now decides next, a report series. N (Total
leader survey responses) = 2,835
Generative AI is an area of artificial intelligence and refers to AI that in
response to a query can create text, images, video and other assets.
Generative AI systems can interact with humans and are often built using
large language models (LLMs). Also referred to as “gen AI.”
5
Now: Key findings
This first pulse of our generative AI quarterly surveys was
completed in December 2023, and included more than
2,800 AI-savvy business and technology leaders directly
involved in piloting or implementing gen AI at major
organizations around the world. Here’s what they had to
say about sentiment, use cases, challenges and more.
66
Generative AI elicits a range
Now: Key findings
of strong emotions
1 Excitement about generative AI remains high, and
transformative impacts are expected in the next three years.
62% Excitement
Nearly two-thirds (62%) of the business and technology leaders surveyed reported excitement as a top
Fascination 46%
sentiment with regard to generative AI; however, that excitement was tinged with uncertainty (30%) (figure 1).
The vast majority of respondents (79%) said they expect generative AI to drive substantial transformation
30% Uncertaintly
within their organization and industry over the next three years—with nearly a third expecting substantial
transformation to occur now (14%) or in less than one year (17%) (figure 2). Trust 17%
The survey results suggest that many AI-fueled organizations are on the verge of scaling up their efforts
16% Surprise
and embracing generative AI in a more substantial way. This aligns with what we’re seeing in the marketplace,
where organizations around the world are racing to move from experimentation and proofs-of-concept
Anxiety 10%
to larger-scale deployments across a variety of use cases and data types—pursuing both speed and
value capture while managing potential downside risks and societal impacts. 8% Confusion
In future surveys, we will be closely monitoring progress in this area—particularly with regard to
Fear 6%
organizations’ expertise, capabilities, tangible outcomes, and responses to rapidly emerging advances in
generative AI technology. 4% Exhaustion
Anger
1%
31%
of the leaders we surveyed expect substantial transformation
Figure 1
in less than one year; 48% expect it in one to three years.
Q: Thinking about generative AI, what emotions do you feel most about the technology?
(Oct./Dec. 2023) N (Total) = 2,835
77
Now: Key findings
When is generative AI likely
to transform your organization?
1%
14%
Never
Now
20%
17%
Beyond
three years
Less than
one year
48%
In one to
three years
Figure 2
Q: When is generative AI likely to substantially transform your organization and your industry, if at all?
(Oct./Dec. 2023) N (Total) = 2,835
8
44% rate their organization’s generative AI expertise as
Now: Key findings high or very high, but is such expertise even possible
given the pace of the technology’s advancement?
2 Many leaders are confident about their organization’s
generative AI expertise.
Self-assessed expertise with
A large percentage of our survey respondents (44%) said they believe their organizations currently have generative AI runs high
high (35%) or very high (9%) levels of expertise with generative AI. This result is somewhat surprising given
how rapidly generative AI is evolving (figure 3).
1%
But within the specific context of our survey, high levels of confidence seem entirely reasonable since
No expertise
9%
we deliberately chose experienced leaders with direct involvement in AI initiatives at large organizations 10%
Very high
already piloting or implementing generative AI solutions. However, given how rapidly the field is unfolding, it Little
expertise
expertise
may be worth questioning the extent to which any leader should feel highly confident in their organization’s
expertise and preparedness. In fact, even today’s foremost AI experts who are personally developing
generative AI technologies at times seem genuinely surprised by their own creations’ capabilities.2
35%
High
Do some leaders consider their organizations to have high expertise based largely on the knowledge
expertise
and experience gained from small-scale pilots with a small number of generative AI tools? If so, leaders
45%
and organizations might actually become less confident over time as they gain experience with the larger
Some
challenges of deploying generative AI at scale. In other words, the more they know, the more they might
expertise
realize how much they don’t know. This is a trend we’ve seen time and again with other technological
advancements, and one we’ll be watching closely in our future surveys.
Figure 3
Q: How would you assess your organization’s current level of overall expertise
regarding generative AI?
(Oct./Dec. 2023) N (Total) = 2,835
9
Expertise with generative AI drives
attitudes toward adoption
Now: Key findings
3 Organizations that report very high expertise in generative
Very high Some
expertise expertise
AI tend to feel more positive about it—but also more
Trust prevails Rank trust
39% 9%
among top
over uncertainty
pressured and threatened.
emotions felt
11% 38% Rank uncertainty
among top
Relative to other respondents, leaders who rated their organization’s overall generative AI expertise as “very
emotions felt
high” tended to feel much more positive about the technology; however, they also feel more pressure to
adopt it—and see it as more of a threat to their business and operating models (figure 4).
Analysis showed this group using more modalities, deploying generative AI across more enterprise functions, Broad interest 78% 38% Say employees
show high interest
sparks
and pursuing more use cases. As you can see in the figure 4, leaders who reported very high levels of
in gen AI
transformation
expertise were also more likely to report higher levels of trust and lower levels of uncertainty. They also
31% 9% Say gen AI
tended to show broader interest in generative AI and expected faster transformation for their organizations.
is already
transformative
At the same time, these respondents’ greater understanding of generative AI appears to be shaping their
perspective on potential impacts—positive and negative. Many reported they viewed widespread adoption of
the technology as a threat to how their organizations operate and conduct business, amplifying the pressure
Widespread 33% 16% Feel widespread
and urgency they felt to adopt generative AI and scale it.
adoption is a
adoption
threat to business
generates
pressure
44% 25% Feel greater
Leaders of organizations with very high expertise are more likely to pressure to
adopt gen AI
view generative AI as a threat to their business and operating models.
Figure 4
(Oct./Dec. 2023) N (Total) = 2,835,
N (Very high) = 267; N (Some) = 1,273
10
Key benefits organizations hope
to achieve with generative AI
Now: Key findings
4 Current generative AI efforts remain more focused
Improve 56%
on efficiency, productivity and cost reduction than on
efficiency and
productivity
innovation and growth. 35% Reduce costs
Improve existing 29%
The majority of organizations surveyed are currently targeting tactical benefits such as improving
products and
services
efficiency / productivity (56%) and/or reducing costs (35%). Also, 91% said they expect generative AI to
29% Encourage
innovation
improve their organization’s productivity, and 27% expect productivity to increase significantly. A smaller
and growth
percentage of organizations reported targeting strategic benefits such as innovation and growth (29%)
Shift workers from 26%
(figure 5). lower to higher
value tasks
26% Increase speed
This is consistent with past technology adoption patterns. Initially, most organizations logically focus on
and/or ease of
developing new
incrementally improving their existing processes and capabilities—capturing value from low-hanging fruit
systems / software
Increase 25%
while building knowledge, experience and confidence with the new technology. Later, they expand or shift
revenue
their focus to improvements that are more innovative, strategic and transformational—using the new
technology to drive growth and competitive differentiation and advantage through capabilities that simply
23% Enhance
relationships
weren’t possible before.
with clients /
customers
Surveyed leaders that cited higher levels of AI expertise show earlier signs of moving up this curve. They Uncover new 19%
ideas and
are more focused on uncovering new ideas and insights (23% vs. 19% for the overall respondent pool),
insights
with less emphasis on efficiency and productivity (44% vs. 61% for the overall respondent pool) and cost
18% Detect fraud and
manage risk
reduction (26% vs. 38% for the overall respondent pool)—although those tactical benefits continue to be Figure 5
Q: What are the key benefits you hope to achieve through your generative AI efforts?
(Oct./Dec. 2023) N (Total) = 2,835
11
Now: Key findings
their bigger focus. In addition, nearly three-quarters of organizations that cited very high generative
AI expertise had already begun integrating the technology into their product development and R&D
activities, which are key drivers of innovation and growth.
As more organizations gain expertise and experience with generative AI, will they reinvest their dividends
from improving efficiency and productivity toward pursuing more strategic benefits such as innovation
and growth? Or will they use those dividends in other ways? This is another area we’ll be monitoring
closely in future pulse surveys.
Certainly, productivity and efficiency can be transformational, especially given the massive scale
generative AI has the potential to enable. However, the greatest value and strategic differentiation will
likely come from using the technology to innovate. First, by helping to generate new products, services
and capabilities that wouldn’t be possible otherwise. And, second, by enabling new business models and
ways of working across an enterprise.
In addition, organizations that cited very high generative AI expertise were already taking a much more
comprehensive approach than average, with significantly higher adoption levels across a broad range
of functional areas. In specific areas such as HR, and legal, risk and compliance, those organizations’
generative AI adoption rates were nearly three times higher than for the total respondent pool (figure 6).
91%
of all organizations expect their productivity to increase due
to generative AI.
12
Now: Key findings % of those who are using generative AI
Total Little expertise Some expertise High expertise Very high expertise
in a limited or at-scale implementation
Level of generative AI
adoption
IT / cybersecurity 22% 38% 57% 71%
46%
Marketing, sales and customer service 41% 16% 34% 50% 73%
57%
Product development / R&D 41% 14% 28% 73%
Strategy and operations 35% 10% 26% 47% 62%
37%
Supply chain / manufacturing 29% 9% 21% 61%
Finance 37% 63%
25% 5% 14%
Figure 6
Human resources 23% 6% 13% 29% 64%
Q: What is your organization’s
current adoption level of generative AI across
the following functions?
28%
(Oct./Dec. 2023) N (Total) = 2,835; Legal, risk and compliance 21% 7% 10% 60%
N (Very high) = 267; N (High) = 1,003;
N (Some) = 1,273; N (Little) = 274
1133
Generative AI: Have we seen this movie before?
The term “unprecedented” is often thrown around Generative AI’s speed factor may give organizations less help the workforce get accustomed to using generative
when talking about business and technology, to the time to ruminate or dabble with small-scale pilots— AI, and will show people how it can help make their
point of being cliché. However, in describing the pace of while reducing the margin for error—and increasing the jobs easier. Also, early wins will likely help produce cost
generative AI’s emergence and advancement—and its consequences of inaction. It also creates opportunities savings and momentum that then can be channeled into
massive potential impact on business (and humanity as a to generate extraordinary business value very quickly. higher value opportunities that are more strategic and
whole)—unprecedented could be an understatement. differentiated in nature, such as enabling new products,
Despite generative AI ’s greatly accelerated pace,
services, business models and ways of working that
Generative AI is already widely available to the public understanding typical adoption patterns based on
simply weren’t possible before generative AI.
and has a running start toward critical mass. Also, similar previous breakthrough technologies can provide
to smartphones, it’s easy for an average person to use valuable lessons that leaders can use to help them
without much training—and can help with activities they understand and fully capitalize on the technology’s rapid
already engage in every day—so the barriers to adoption advancement.
are low. What’s more, generative AI has the strong
As in the past, organizations’ initial efforts will likely
potential to assist with its own future development,
center around efficiency, productivity, cost savings and
which could trigger a cycle of exponential improvement
other incremental improvements. This is expected to
at exponential speed.
14
Now: Key findings
5 Most organizations are primarily relying on off-the-shelf
generative AI solutions.
Where off-the-shelf generative AI
In line with their current emphasis on tactical benefits from generative AI, the vast majority of respondents is used most
were currently relying on off-the-shelf solutions. These included productivity applications with integrated
generative AI (71%); enterprise platforms with integrated generative AI (61%); standard generative AI
71%
applications (68%); and publicly available large language models (LLMs) (56%), such as ChatGPT.
Productivity applications
Relatively few reported using more narrowly focused and differentiated generative AI solutions, such as
industry-specific software applications (23%), private LLMs (32%), and/or open-source LLMs (customized to
68%
Standard applications
their business) (25%).
Reliance on standard, off-the-shelf solutions is consistent with the current early phase of generative AI
61%
adoption, which is primarily focused on improving the efficiency and productivity of existing activities.
Enterprise platforms
However, as use cases for generative AI become more specialized, differentiated and strategic, the
associated development approaches and technology infrastructure will likely follow suit.
56%
Public LLMs
When will we see complex, high-value use cases that are truly differentiated and tailored to the specialized
needs of specific companies, functions and industries? How will organizations combine internal and
external resources to create customized generative AI tools that enable such strategic differentiation? In
particular, will we see off-the-shelf technology offerings be supplemented by private or hybrid public/private
development approaches and technology infrastructures capable of delivering and supporting those
differentiated solutions?
15
Now: Key findings
6 Talent, governance and risk are critical areas where
generative AI preparedness is lacking.
In this initial quarterly survey, 41% of leaders reported their organizations were only slightly or not at
all prepared to address talent concerns related to generative AI adoption, while 22% considered their
organizations highly or very highly prepared. Similarly, 41% of leaders reported their organizations were only
slightly or not at all prepared to address governance and risk concerns related to generative AI adoption,
while 25% considered their organizations highly or very highly prepared (figure 7).
Larger percentages of leaders reported high to very high levels of preparedness in technology infrastructure
(40%) and strategy (34%); however, the survey results show there is still significant room for improvement.
Generative AI barriers related to risk and governance
When it comes to risk and governance, generative AI is definitely not “just another technology.” The
fundamental challenge is how to capitalize on artificial intelligence’s power without losing control of it. After
all, the capability people seem to find most enthralling about generative AI is its ability to so convincingly
simulate human thinking and behavior. Of course, human thinking and behavior aren’t always perfect,
predictable or socially acceptable—and the same is true for the technology, itself.
16
Now: Key findings
Respondents claimed the highest levels of preparation in technology
Preparedness for generative AI
and strategy, while feeling far less prepared in risk and talent.
Technology infrastructure 4% 17% 38% 30% 10%
Strategy 5% 20% 41% 26% 8%
Not prepared
Slightly prepared
Risk & governance 13% 28% 34% 18% 7% Moderately prepared
Highly prepared
Talent 13% 28% 37% 17% 5% Very highly prepared
Figure 7
Q: Consider the following areas. For each, rate your organization’s level of preparedness with respect to broadly adopting generative AI tools / applications?
(Oct./Dec. 2023) N (Total) = 2,835
17
Managing generative AI
implementation risk
Now: Key findings
Monitoring regulatory
Specific generative AI risks and concerns include inaccurate results and information (i.e., “hallucinations”);
47%
requirements and
Establishing a governance
legal risks such as plagiarism, copyright infringement, and liability for errors; privacy and data ownership ensuring compliance
framework for the use
challenges; lack of transparency, explainability and accountability; and systemic bias. The latter of generative AI tools / 46%
applications
exemplifies another category of risk in which AI amplifies and exacerbates a problem that already exists,
such as propagating and systematizing existing social biases, facilitating and accelerating the spread of
Conducting internal
42%
audits and testing
misinformation, helping criminals commit crimes, or fanning the flames of political divisiveness.
on generative AI
tools / applications
Training practitioners
According to the business and technology leaders we surveyed during fourth quarter 2023, the biggest
37% how to recognize and
mitigate potential risks
concerns related to governance were: lack of confidence in results (36%), intellectual property issues
(35%), misuse of client or customer data (34%), ability to comply with regulations (33%), and lack of
36% Ensuring a human
explainability / transparency (31%). validates all generative
AI content
Some of the surveyed organizations were already actively managing generative AI implementation
34%
Using a formal group
risks through actions such as monitoring regulatory requirements and ensuring compliance (47%), or board to advise
on generative
establishing a governance framework for generative AI (46%), and conducting internal audits and testing
AI-related risks
32%
Keeping a formal inventory
on generative AI tools and applications (42%) (figure 8). However, such organizations are in the minority of all generative AI
implementations
and their actions barely scratch the surface of the challenge. This is especially true given that regulatory
26%
requirements typically lag behind the pace of technology innovation—although a US presidential Using outside vendors
to conduct independent
executive order and the European Union’s ambitious Artificial Intelligence Act are clear signs government
audits and testing
21%
Single executive
leaders in many parts of the world are taking the issue of AI risk very seriously.
responsible for managing
generative AI-related risks
Figure 8
Q: What is your organization currently doing to actively manage the risks around
your generative AI implementations?
(Oct./Dec. 2023) N (Total) = 2,835
18
Generative AI is impacting talent
strategies now
2%
Never
10% 17%
No formal Now
time frame
24%
16% Within 1 year
Now: Key findings 2+ years
Generative AI barriers related to talent and workforce
Generative AI has the potential to supplement human workers across a vast array of activities traditionally
thought of as uniquely human. As such, its impact on talent and workforce strategies could be immense.
How will it affect organizations and their workers in the short and long runs? Which types of skills will be
most affected, and when?
31%
The vast majority of leaders we surveyed (72%) said they expect generative AI to drive changes in their
1-2 years
talent strategies sometime within the next two years: now (17%), within 1 year (24%), or in 1-2 years (31%)
(figure 9).
Figure 9
However, less than half (47%) reported that they are sufficiently educating their employees on the
Q: When do you expect to make changes to your talent strategies because of
capabilities, benefits and value of generative AI—survey respondents also cited a lack of technical talent and
generative AI?
skills as the biggest barriers to adoption.
(Oct./Dec. 2023) N (Total) = 2,835
19
Now: Key findings
Against this backdrop, some respondents reported making a high or very high effort to: It should be noted, however, that these reported workforce-related efforts might be limited
recruit and hire technical talent to drive their generative AI initiatives (42%), educate the in scope. Deloitte’s experience suggests that most organizations have yet to substantially
workforce about generative AI (40%), and reskill workers impacted by generative AI (36%). address the talent and workforce challenges likely to arise from large-scale generative AI
Those numbers are much higher for leaders who viewed their organization’s generative AI adoption. A likely reason for this is that many leaders don’t yet know what generative AI’s
expertise as very high (74%, 74% and 73%, respectively) (figure 10). talent impacts will be, particularly with regard to which skills and roles will be needed most.
Preparing workforces for generative AI: Respondents making a high or very high effort in the following areas.
74% 74%
73%
All respondants
55% 55%
42% 50%
40%
Little expertise
36%
30%
27%
24%
Some expertise
16%
14%
10%
High expertise
Recruiting and hiring technical talent to drive Educating our broader workforce to raise Reskilling workers because of the impact
Very high expertise
our generative AI initiatives overall generative AI fluency of generative AI to their roles
Q: What level of effort is your organization taking regarding the following workforce-related areas?
Figure 10
(Oct./Dec. 2023) N (Total) = 2,835
20
“Generating confidence
in workers’ abilities to
collaborate with generative AI,
now, could elevate creativity
and job satisfaction, next.”
21
Now: Key findings
51%
expect generative AI to
7 Leaders see significant societal impacts on the horizon.
increase economic inequality.
Although the leaders we surveyed were generally excited and enthusiastic about generative AI’s potential
business benefits, they were less optimistic about its broader societal impacts. Specifically, 52% of
respondents said they expected widespread use of generative AI to centralize power in the global economy,
while 30% expected it to more evenly distribute global power. Similarly, 51% expected generative AI to
increase economic inequality, while 22% expected it to reduce inequality (figure 11).
What’s more, 49% of respondents believe the rise of generative AI tools / applications will erode the overall
level of trust in national and global institutions.
Is this pessimism or realism? Our survey results appear to reflect the broader moral and ethical debates
about artificial intelligence that are occurring in every corner of society—even in the boardrooms of the
technology companies driving AI development, where AI’s commercial value is being weighed against its
potential value to serve humanity and AI’s potential benefits are being weighed against its potential risks.
The challenges that generative AI poses in corporate governance and risk parallel those in societal
governance and risk. In both domains, the technology’s potential benefits and potential harms are high.
National and supranational organizations and governments will likely need to walk the tightrope of helping
to ensure that generative AI benefits are broadly and fairly distributed, without overly hindering innovation
or providing an unfair advantage to countries with different rules.
22
Now: Key findings
Expected societal impacts of generative AI
Distribution of economic power
5% 25% 18% 42% 10%
30% 52%
distribute centralize
Levels of economic inequality
3% 19% 27% 41% 140%%
22% 51%
decrease inequality increase inequality
Q: How will widespread use of generative AI shift the overall distribution of power in the global economy?
Figure 11
Q: How will widespread use of generative AI tools / applications impact global levels of economic inequality?
(Oct./Dec. 2023) N (Total) = 2,835
23
Support for increased regulation
and global collaboration
Now: Key findings
8 Leaders are looking for more regulation and
78%
more regulation
collaboration globally.
Agree the widespread
proliferation of generative
In a break from traditional business norms, the unique risks associated with generative AI are prompting
AI tools / applications will
many business leaders to call for increased government regulation and increased global collaboration require more regulation
of AI by governments
around AI technologies.
Among the leaders in our survey, 78% said that more governmental regulation of AI is needed, while 72%
said there is currently not enough global collaboration to ensure the responsible development of
AI-powered systems (figure 12).
These results seem to "
185,deloitte,state-of-gen-ai-report-wave-4.pdf,"Now decides next:
Generating a new future
Deloitte’s State of Generative AI in the Enterprise
Quarter four report
January 2025
deloitte.com/us/state-of-generative-ai
Table of contents
Introduction
Key findings
Looking back at 2024
Now: Where we are
Next: Looking ahead
Considerations
Case studies
Authorship & Acknowledgments
About the Deloitte AI Institute
About the Deloitte Center for Integrated Research
About the Deloitte Center for Technology, Media & Telecommunications
Methodology
22
Introduction
Foreword
It was only about 10 years ago when visionary tech leaders started talking about enterprises to be a lot more structurally agile to adapt, embrace and innovate to stay
a future powered by ubiquitous computing and ambient intelligence. Back then it relevant and differentiated.
sounded like science fiction. Today, it’s real. No where is this future more evident than
In the following report, we see that most companies are transforming at the speed
in the rapid advancement and adoption of AI technologies. New models and tools are
of organizational change, not at the speed of technology. This is not surprising but is
gaining greater and greater capabilities and performing more complex reasoning. Even
something that will need to be addressed. That said, many are also already using
what was state of the art a few years ago pales in comparison to what we have today.
GenAI to create business value that exceeds their expectations—with compelling new
In this AI era, many now believe that Moore’s Law is effectively dead. And we have use cases emerging every day.
every reason to believe that the AI flywheel will continue to accelerate with every week
So, what do I say to clients who are in the trenches of this transformation? Don’t lose
and year—often referenced as the greatest secular shift of this quarter century.
focus. Stay curious, and challenge the orthodoxies of your organizations. GenAI and
Despite the technology’s rapid pace, I hear from clients and business leaders who are AI broadly is our reality—it’s not going away. While there are more questions than
wondering when it will meet their transformational expectations—when will business answers, but to stay in the game, leaders must be willing to try, do unconventional
leaders see the value and innovation that has been promised? things, learn and help mature.
Just like the internet, cloud, or even mobile, the transformational opportunities weren’t State of GenAI in the Enterprise is a snapshot in time of this great transformation. An
uncovered overnight. But as they became pervasive, they drove significant disruption opportunity for you to see where and how organizations across industries are finding
to business and technology capabilities, and also triggered many new business their way. I hope it serves to spark new ideas and new approaches that help illuminate
models, new products and services, new partnerships, and new ways of working and the path to your organization’s AI-fueled future.
countless other innovations that led to the next wave across industries. As we have
–Ranjit Bawa, Principal, US Chief Strategy and Technology Officer
experienced the half-life of these waves continues to be shorter. As such, it requires
3
Introduction
Generating a new future
For the past year, Deloitte has been conducting quarterly global survey reports and
executive interviews focused on Generative AI (GenAI) in the enterprise. We titled
our study Now decides next because we believed in GenAI’s potential to dramatically
transform how businesses operate—and that the actions companies take today will
have a decisive impact on their ability to succeed with GenAI in the future. And that’s
exactly what we found.
As with previous transformational technologies, the initial excitement and hype about
GenAI has gradually given way to a mindset of positive pragmatism. Many companies
are already seeing encouraging returns on their early GenAI investments. However,
those companies and others have learned that creating value with GenAI—and
deploying it at scale—is hard work. Although the technology at times seems like magic,
there is no magic wand when it comes to GenAI adoption, deployment, integration
and value creation.
44
Introduction
Key findings
There is a speed limit. Barriers are evolving. Some uses are outpacing others.
GenAI technology continues to advance at incredible Significant barriers to scaling and value creation are still Application of GenAI is further along in some business
speed. However, most organizations are moving at the widespread across key areas. And, over the past year areas than in others in terms of integration, return on
speed of organizations, not at the speed of technology. regulatory uncertainty and risk management have risen in investment (ROI) and expectations. The IT function is
No matter how quickly the technology advances—or organizations’ lists of concerns to address. Also, levels of trust most mature; cybersecurity, operations, marketing and
how hard the companies producing GenAI technology in GenAI are still moderate for the majority of organizations. customer service are also showing strong adoption and
push—organizational change in an enterprise can only Even so, with increased customization and accuracy of results. Organizations reporting higher ROI for their
happen so fast. models—combined with a focus on better governance— most scaled initiatives are broadly further along in their
adoption of GenAI is becoming more established. GenAI journeys.
All statistics noted in this report and its graphics are derived from Deloitte’s fourth quarterly survey, conducted July – September 2024; The
State of Generative AI in the Enterprise: Now decides next, a report series. N (Total leader survey responses) = 2,773. Percentages in this report
and its charts may not add up to 100, due to rounding.
Generative AI is an evolving area of artificial intelligence and refers to AI that in response to a query—a prompt—can create new text,
images, video and other assets. Generative AI systems can interact with humans and are built—or “trained”—on datasets that range in
size and quality from small language models (SLMs) to large language models (LLMs). Generative AI is also referred to as “GenAI.”
Evolving upon GenAI technologies, emerging AI agents are software systems that can complete complex tasks and meet objectives with little
or no human intervention. They are called “agents” because they have the agency to act independently, planning and executing actions to
achieve a specified goal. Related, the vision for agentic AI is that autonomous AI agents will be able to execute assigned tasks consistently and
reliably by acquiring and processing multimodal data, using various tools to complete tasks, and coordinating with other AI agents—all while
remembering what they’ve done in the past and learning from their experience.
5
Introduction
Key findings
The focus is on core business value. The C-suite sees things differently. Agentic AI is here.
A strategic shift is emerging, from technology catch-up Relative to leaders outside of the C-suite, CxOs tend Agentic AI is gaining interest as a breakthrough
to competitive differentiation with GenAI. Beyond the to express a rosier view of their organization’s GenAI innovation that could unlock the full potential of GenAI,
IT function, organizations tend to focus their deepest investments—and how easily and quickly GenAI’s with GenAI-powered systems having the “agency”
GenAI deployments on parts of the business uniquely barriers will be addressed and value achieved. It’s critical to orchestrate complex workflows, coordinate tasks
critical to success in their industries. that CxOs move on from being cheerleaders to being with other agents, and execute tasks without human
champions for achieving organizational efficiency and involvement. However, agentic AI is not a silver bullet and
market competitiveness. all the broad challenges currently facing GenAI still apply.
6
Introduction
Key findings
Our previous quarterly report said the clock was ticking model improvements—including domain and industry
to prove value—and this remains true today. Senior customization—and the promise of AI agents could
decision-makers might not be demanding tangible value help overcome inherent challenges and accelerate
About the State of Generative
and financial results from GenAI yet, but they soon will be. the creation of business value. However, it might be a
AI in the Enterprise:
multiyear journey for some organizations to reach full-scale
More and more organizations are moving from GenAI Wave four survey results
deployment and achieve the ROI they are looking for.
experimentation to deployment and scaling—with
The wave four survey covered in this report was fielded
proven use cases emerging and significant ROI being With GenAI, some level of uncertainty is unavoidable
to 2,773 director- to C-suite-level respondents across six
achieved through the most advanced GenAI initiatives. and the technology will likely continue to advance at
industries and 14 countries between July and September
a rapid pace. Business and technology leaders, for
2024. Industries included: consumer; energy, resources and
What’s more, despite some feelings of disillusionment
their part, should focus on what they can control—
industrials; financial services; life sciences and health care;
and unmet expectations, the vast majority of
namely, organizational readiness, particularly in areas technology, media and telecom; and government and public
organizations we surveyed are taking a realistic
services. The survey data was augmented by additional
such as data, risk management, governance, regulatory
perspective and showing sustained commitment in their insights from 15 interviews with C-suite executives and AI and
compliance and workforce / talent. Addressing issues
quest for value from GenAI, and they seem willing to data science leaders at large organizations across a range of
in these key areas will help position organizations for
industries. For details on methodology, please see p. 45.
do the hard work that needs to be done. Foundation
success with GenAI no matter how the future unfolds.
This quarterly report is part of an ongoing series by the
Deloitte AI InstituteTM to help leaders in business, technology
and the public sector track the rapid pace of Generative AI
change and adoption. The series is based on Deloitte’s
State of AI in the Enterprise reports, which have been
released annually the past five years. Learn more at
deloitte.com/us/state-of-generative-ai.
7
Real-world case studies
The case studies featured in this report are a small subset of the insights from our ongoing
in-depth interviews with business and AI leaders from a wide range of industries. The goal is
to build on the quantitative findings from our quarterly surveys by capturing practical, real-
world insights directly from leaders and organizations on the front lines of GenAI adoption.
Our interviews explore how leading organizations in diverse industries are
using GenAI to create value. Most notably, we are seeing initiatives focused
on applying GenAI to business-specific challenges in areas critical to success
in that organization’s industry. Examples include using GenAI for:
• B rand promotion and integrated business planning in the consumer products industry
• Predictive maintenance for physical assets in the energy industry
• Drug discovery and clinical trial tracking in the pharmaceutical industry
• Cybersecurity and portfolio management in the financial services industry
• S ales enablement, chip development and improved search in the technology industry
• A rchive management and music source separation in the media and entertainment industry
This focus on mission-critical activities suggests a broad strategic shift in the GenAI
landscape, from technology catch-up to competitive differentiation.
Go to case studies
8
Looking back at 2024
999
Now: Looking back at 2024 Level of interest in GenAI
(high + very high)
Looking back at 2024
Q1 Q4
Our first global quarterly survey, conducted in late 2023, revealed great excitement and expectations for GenAI.
However, those feelings were tempered by uncertainty and fear about the technology’s potentially negative impacts
Board 62% 46% -16 pts
on workers and society. Our second and third quarterly surveys focused more deeply on how organizations were
prioritizing tangible results and value creation from their GenAI investments, and on understanding and tackling
the barriers to successful scaling.
C-suite /
-15 pts
executive 74% 59%
A key finding during the year was that promising results from early GenAI pilots were raising expectations and
leaders
driving increased investment in the technology.
Today, interest and excitement about GenAI remain high. However, the initial fervor has gradually given way to a
Technical
86% 86%
leaders
positive yet pragmatic mindset—especially among business leaders at all levels. Meanwhile, technology leaders’
interest and excitement have remained high and steady (figure 1).
LOB /
Although this shift among business leaders might seem like a step backward for GenAI, it is entirely consistent with
functional 64% 56%
the usual life cycle for transformative technologies. It is also a net positive in terms of helping organizations move leaders
past the hype stage so they can directly tackle the serious work of using GenAI to create real business value.
Employees 49% 50%
A key finding during the year was that promising results
from early GenAI pilots were raising expectations and
Figure 1 Q: For the following groups in your organization, rate their
driving increased investment in the technology.
overall level of interest in Generative AI.
State of Generative AI in the Enterprise Survey,
Q1 (Oct./Dec. 2023) N (Total) = 2,774; Q4 (July/Sept. 2024)
N (Total) = 2,773; 14 countries common to both data sets
1100
Now: Looking back at 2024
Over the past year, as organizations gained experience with GenAI, they began to better “Data emerged as the central factor for [our GenAI] success,” said a former software
understand both the rewards and challenges of deploying the technology at scale— engineering manager for one of the world’s leading technology companies. “While
and adjusted their plans and expectations accordingly. Budgets have risen, and the the models and computing power existed, accessing the right data proved to be the
need for C-suites and boards to spur their organizations into action has diminished. biggest bottleneck. To address this, the company implemented a centralized data
At the same time, the need for disciplined action has grown. Technical preparedness strategy, managed by a single data leader, to streamline data acquisition and minimize
has improved, while regulatory uncertainty and risk management have become bigger redundancy—enabling faster model development.”
barriers to progress. Talent and workforce issues remain important; however, access to
specialized technical talent no longer seems to be the dire emergency it once was, at least
in comparison to other priorities. There has been one constant, however: improved data
management continues to be a top priority, even for companies that live and breathe data.
“Data emerged as the central
factor for [our GenAI] success …”
— Former software engineering manager for leading technology company
11
Now: Looking back at 2024
From a technology perspective, the capabilities of The vast majority of respondents (78%) reported they
foundation models and applications have improved expect to increase their overall AI spending in the next
dramatically over the past year. There are smaller, more fiscal year, with GenAI mostly expanding its share of
efficient models; better latency; bigger access windows; the overall AI budget relative to our first-quarter survey
expanded modalities; greater autonomy; and increased results. In particular, the percentage of organizations
model specialization. investing 20%–39% of their overall AI budget on
GenAI climbed by 12 points, while the percentage of
Reliability and trust have improved as well, although both
organizations investing less than 20% of their AI budget
still have a long way to go. Meanwhile, the adoption rate
on GenAI fell by 6 points.
for customized, open-source and/or proprietary large
language models (LLMs) remains limited at 20%–25% of “The way we do business has not changed,” said the VP of
those surveyed. artificial intelligence at a major media and entertainment
company. “For every project, our objective is always to do
Over the past year, respondents reported they
something that has a positive impact on the business. This
believe their organizations have most improved their
has not changed and is not going to change because it’s
GenAI preparedness in the critical areas of technology
what makes sense. However, a large proportion of project
infrastructure (+7 points) and strategy (+5 points). However,
proposals now have a [GenAI] component to them.”
preparedness has seemingly not improved in the other
critical areas of risk and governance and talent.
78%
of respondents expect to increase their overall AI spending
in the next fiscal year.
12
Now: Looking back at 2024
View from the C-suite
Relative to other respondents, the C-suite leaders (CxOs) in our survey generally demonstrated higher levels
of excitement and optimism about their organizations’ GenAI implementations. For example, 21% of C-suite
survey respondents reported they feel GenAI is already transforming their organization, compared to only 8%
of non-C-suite respondents. C-suite executives surveyed are comparatively less worried about barriers such
as trust, risk management, governance and regulatory compliance. They also have a rosier view of how quickly
their organization is moving, and how quickly the barriers to scaling and value creation will be addressed.
Sixty percent of non-C-suite respondents believe it will take 12 months or more to overcome scaling barriers,
compared to only 47% of C-suite respondents.
This doesn’t necessarily mean CxOs are out of touch with the challenges of adopting and deploying GenAI.
It could be they are still playing the primary role of catalyst or cheerleader and are in the process of learning
what it really takes to implement and scale GenAI. What will be important going forward is for CxOs to direct
that enthusiasm to removing barriers and enabling scaling.
Now that GenAI in the enterprise is moving past its infancy, CxOs should take on new roles, including those
of guide, counselor and challenger. Chief executive officers should show top-down support for GenAI, be the
champions for governance and risk initiatives, and foster an environment of trust and transparency. Chief
information officers, chief technology officers and chief data officers should sharpen their focus on identifying and
overcoming the barriers to large-scale GenAI deployment within their domains. Chief financial officers should
ensure responsible spending without stifling innovation. And chief human resource officers should promote
training, reskilling and other human capital investments.
13
Now: Looking back at 2024
The uneven pace of change
With transformational technologies, For businesses, embracing and integrating GenAI back from developing and deploying GenAI tools and
there are always gaps between the pace is much harder—and takes much longer—due to a applications (figure 2). This highlights respondents’
complex mix of factors. This could include dealing with unease about which use cases will be acceptable,
of technological change and the ability of
competing transformational priorities. However, policy, and to what extent their organizations will be held
individuals, businesses and policymakers
legislative and regulatory changes might be more accountable for GenAI-related problems.
to keep up. GenAI is no exception.
challenging overall.
This uneven pace of change creates friction for
Incredible advances in GenAI technology, fueled by
Governments today face the monumental task organizations, which likely contributes to the relatively
massive capital and intellectual investments from
of regulating a technology whose capabilities are moderate pace of transformation we are seeing as
tech companies, are already manifesting in individuals’
still taking shape. One direct consequence is that businesses work through their challenges on the path
everyday lives—through smarter smartphones,
regulatory compliance has emerged from the pack to creating sustained value with GenAI.
improved customer service, AI-enhanced search
to become the top barrier holding organizations
engines, and more.
Barriers to developing and deploying GenAI
Q1 Q4
+10 pts. +6 pts. -10 pts.
38%
36%
32%
28% 26% 26% 27% 26% 27%
25%
24%
22% 21% 20%
19%
18% 17% 17% 17%
15% 15%
14%
Worries about Difficulty Implementation Lack of technical Lack of a Difficulty Lack of an Trouble choosing Cultural Not having the Lack of executive
complying with managing risks challenges talent and skills governance identifying use adoption the right resistance from right comp. commitment
regulations model cases strategy technologies employees infrastructure / and/or funding
data
Figure 2 Q: What, if anything, has most held your organization back in developing and deploying Generative AI tools / applications? (Select up to three challenges)
State of Generative AI in the Enterprise Survey, Q1 (Oct./Dec. 2023) N (Total) = 2,774; Q4 (July/Sept. 2024) N (Total) = 2,773; 14 countries common to both data sets
14
Now: Where we are
1155
Now: Where we are
For our fourth wave report, we wanted to answer
several questions about scaling and value realization.
1
W here do things stand with workforce adoption?
2 H ow many experiments are organizations pursuing, and what are
their success rates?
3
W hich benefits are GenAI initiatives targeting?
4 A re some types of GenAI initiatives / use cases showing more promise
than others?
5
A re they meeting ROI expectations?
16
Now: Where we are
1
Where do things stand with workforce adoption?
Our latest survey results show that access to GenAI is still largely limited to less than enterprise, it generally makes sense to offer broad workforce access to sanctioned
40% of the workforce. Also, for most organizations, fewer than 60% of workers who GenAI tools, supported by clear guidelines for proper use.
have access to GenAI actually use it on a daily basis. This suggests many companies
“Currently, GenAI adoption is driven by internal demand, with early adopters seeking
have yet to integrate GenAI into their standard business workflows. It also raises the
to use the tools to meet their specific needs,” said the head of GenAI in product
chicken-and-egg question of whether limited access to GenAI is inhibiting comfort and
management at a major technology company. “However, we expect a shift towards
uptake with the technology (and stifling innovation), or whether the lack of high-value,
push-driven adoption in the next year, where all business units will be required to
innovative use cases is limiting interest and adoption.
integrate the platform as it becomes an approved and proven tool. This shift will create
For GenAI to become truly transformational, it will likely require greater numbers of pressure for teams to leverage the technology or risk missing out on the benefits it offers.”
workers experimenting and leveraging the technology to identify new, high-impact use
cases within the business. “Within our organization, the demand for GenAI use cases
and innovation primarily comes from middle management and employees, rather than
being driven by the C-suite,” said the director of product management for GenAI, cloud
“ Currently, GenAI adoption is driven
and data centers at a leading semiconductor company. “While the C-suite has been
slower to engage in AI implementation, teams across the company are developing by internal demand, with early
proofs-of-concept and driving AI adoption through internal boards and governance
adopters seeking to use the tools to
structures. This bottom-up approach emphasizes improving workflows and test cases,
with leadership providing support as needed for broader integration.”
meet their specific needs …”
Of course, access alone does not equate success. Providing access to GenAI does
not mean workers will use it. Conversely, workers with a burning desire to use GenAI
— H ead of Generative AI, project management at major technology company
will likely find a way to do so, with or without approval. However, in order to foster
transformation and maintain some level of control over how GenAI is used within the
17
Volume of experiments / POCs
Now: Where we are
2
35%
What is the state of
29%
24%
GenAI experimentation?
7%
We found organizations are still heavily experimenting
3% 3%
with GenAI, and scaling tends to be a longer-term goal.
Over two-thirds of respondents said that 30% or fewer
More than 100 51 to 100 21 to 50 11 to 20 Less than 10 Don’t know
of their current experiments will be fully scaled in the
Volume of experiments / POCs
next three to six months. This suggests companies are
taking time to test GenAI’s capabilities and to figure
out where it can help the most (figure 3).
The lion’s share of organizations are currently
pursuing 20 or fewer GenAI experiments or proofs of
concept (POCs) and expect to fully scale 10%–30% of
those experiments in the next three to six months.
As expected, individual company actions vary, with
larger numbers of experiments being conducted by
organizations that are large, advanced in their use of
AI, and/or operating in key industries of technology,
media and telecommunications; life sciences and
health care; or financial services.
Figure 3 Q: Approximately how many Generative AI experiments or proofs of concept is your organization currently pursuing? What percentage of these AI
experiments or proofs of concept do you anticipate will be fully scaled in the next three to six months?
State of Generative AI in the Enterprise Survey, (July/Sept. 2024) N (Total) = 2,773
snoitazinagro
fo
%
Scaling progress (next 3-6 months)
27%
26%
16%
13%
9%
5%
2% 2%
1%
80%
% of experiments / POCs
snoitazinagro
fo
%
70% 60% 50% 40% 30% 20% 10% 0%
18
Now: Where we are
Which benefits are GenAI
60%
initiatives targeting?
“Improved efficiency and productivity” continue to be
50%
the most commonly sought benefits from GenAI, and
many organizations (40%) reported they are already
achieving their expected benefits in this area to a large
40%
or very large extent. However, our respondents cited
slightly higher levels of success in a small handful of
more strategic benefit areas, particularly “new ideas
and insights” (46%) and “innovation and growth”
30%
(45%) (figure 4).
20%
10%
10% 15% 20% 25% 30% 35% 40% 45% 50% 55% 60%
deveihca
stfieneB
taht
%
eht
,ti
thguos
taht
seinapmoc
gnoma(
)tnetxe
egral
yrev
ro
egral
a
ot
ti
deveihca
Benefits achieved vs. benefits sought
Detect fraud and manage risk
Benefit sought
(% hoping to achieve the benefit)
Figure 4 Q: What are the key benefits you hope to achieve through your Generative AI efforts? (Select up to three benefits) To what extent are you achieving
those benefits to date?
State of Generative AI in the Enterprise Survey, (July/Sept. 2024) N (Total) = 2,773
gniveihca
3
Uncover new ideas and insights
Encourage innovation and growth
Enhance relationships with clients / customers
Improve
Increase speed / ease of developing new systems
efficiency and
productivity
Improve existing products and services
Shift workers from lower- to higher-value tasks
Increase revenue
Reduce costs
46%
seeking
of respondents (seeking the benefit)
reported that they are uncovering
new ideas and insights with GenAI.
19
Now: Where we are GenAI initiatives are most
4
advanced within these functions
Are some use cases showing more promise?
IT 28%
To understand where GenAI is having the deepest impact on organizations, we asked respondents to consider one
of their most advanced GenAI initiatives—an initiative that is most fully scaled—and then to identify which function or
Operations 11%
department it targets.
Marketing 10%
Since GenAI is a highly advanced technology—and one of its best capabilities is generating
Customer service 8%
computer code—it’s no surprise that the IT function came out on top (28%).
Cybersecurity 8%
However, the survey data also shows GenAI being deployed deeply in many other parts of the business as well, including
Product development 7%
operations (11%), marketing (10%), and customer service (8%) (figure 5a).
R&D 6%
5%
Sales
5%
Strategy
Supply chain 4%
Finance 4%
HR 2%
Manufacturing 2%
Legal, risk, compliance 1%
Figure 5a Q: Consider one of your organization’s most advanced (scaled)
GenAI initiatives. In which function or department is this initiative?
State of Generative AI in the Enterprise Survey, (July/Sept. 2024)
N (Total) = 2,773
20
Now: Where we are
Even more revealing, we found that the products company said: “Value creation is measured relatively mundane tasks secondary to the core business.
operationally by the acceleration of development “Our company has an enterprisewide AI leadership
most advanced GenAI applications outside
timelines, with AI providing faster results while staying team, but I think they’re really focused on a co-pilot
of IT overwhelmingly target critical business
within set performance and output quality constraints. strategy and helping all individuals use AI tools to improve
areas that are fundamental to success in a
Our focus is on development speed, rather than their productivity,” said the director of organizational
company’s specific industry (e.g., marketing in the
outperforming human capabilities. And while a tenfold transformation and change at a leading consumer
consumer industry; operations in energy, resources and
acceleration without human involvement remains products company. “We’re a little bit behind the eight
industrial; cybersecurity in financial services).
aspirational, a three- to five-fold increase in speed has ball on internal processes, and AI is sort of on the fringe.
already been realized.” I don’t think business-facing case studies have been
For example, in the life sciences and health care industry,
weaved into an overall enterprise AI strategy.”
where R&D is strategically important, the associate
This is a crucial insight since many business leaders still
director of artificial intelligence at a leading health care
associate GenAI with personal productivity and other
Top three most advanced (scaled) GenAI initiatives by industry
Color of the bubble represents the function
Industry Consumer Energy, resources & industrial Financial services Life sciences & health care Tech, media & telecom Government
Top 3 functions IT 20% Operations 23% IT 21% IT 23% IT 34% IT 96%
using GenAI
Marketing 20% IT 17% Cybersecurity 14% R&D 21% Product dev 17%
applications and
the percentage of Operations 3%
Customer service 12% Strategy 11% Finance 13% Operations 11% Cybersecurity 12%
initiatives in each
Figure 5b Q: Consider one of your organization’s most advanced (scaled) GenAI initiatives. In which function or department is this initiative?
State of Generative AI in the Enterprise Survey, (July/Sept. 2024) N (Total) = 2,773
21
Now: Where we are
5
Are advanced GenAI initiatives meeting ROI expectations?
Return on investment for organizations’ most advanced GenAI initiatives has been
generally positive. Almost all organizations report measurable ROI, and one-fifth of respondents say their most advanced
74%
(20%) report ROI in excess of 30%. Similarly, nearly three-quarters (74%) say their
Generative AI initiative is meeting or
most advanced initiative is meeting or exceeding their ROI expectations (43%
exceeding their ROI expectations.
meeting, 31% exceeding). Also, two-thirds (67%) say their most advanced initiative is
at least moderately integrated into their broader work processes (figure 6).
Most advanced (scaled) GenAI initiatives
ROI to date ROI expectations Level of integration
51% or more 6% Significantly above 7% Completely integrated 4%
31% to 50% 14% Somewhat above 24% Large extent 20%
11% to 30% 41% Meeting 43% Moderate extent 43%
6% to 10% 23% Somewhat below 19% Small extent 25%
Less than 5% 9% Significantly below 5% Not at all, but intend to 7%
Not measuring 5% No intention to integrate 2%
Figure 6 Q: ROI to date: Estimate the ROI to date for this specific initiative. / ROI expectations: How is the ROI from this Generative AI initiative meeting your organization’s expectations? / Level of integration: To what level is the Generative AI initiative integrated
into the broader organization"
186,deloitte,sea-cons-genai-centre-of-adoption.pdf,"GenAI Centre of
Adoption
Scaling GenAI for everybody
Deloitte Workforce Transformation SEA
October 2024
© 2024 Deloitte Consulting Pte Ltd 1
Contents
How GenAI has lowered the barrier to AI 4
Where organisations fall short of leveraging GenAI 5
What organisations can do 6
Measuring GenAI adoption 7
The Centre of Adoption 8
Contact us 9
2
© 2024 Deloitte Consulting Pte Ltd 2
Introduction
Generative AI, or ""GenAI,"" is transforming the landscape of artificial intelligence.
Built on large language models (LLMs) that can create content – text, images,
videos – based on simple prompts, it is rapidly adopted particularly across the
Asia-Pacific (APAC) region, where organisations are moving beyond experiments
and proofs-of-concept to focus on scaling GenAI at an enterprise level.
With investments surging and the Generative AI market projected to hit $200
billion by 20321, its momentum is evidently accelerating.
Despite its rapid growth, many organisations still struggle to fully leverage the
benefits of GenAI.
The urgency for mass adoption, rather than fragmented efforts, is proving critical
to unlocking its full potential.
By doing so, businesses can rapidly prove value through next level innovations,
efficiency, and competitiveness. For this to happen, it is essential that all
employees actively participate in this transformation.
In this paper, we explore what’s truly happening within organisations—
where employees are already using GenAI, regardless of formal management’s
endorsement. We identify four critical gaps in scaling GenAI, particularly from a
people perspective, and discuss how organisations can elevate employees’
proficiency and maturity around GenAI adoption through targeted capability
building.
We introduce the Center of Adoption (CoA) as an approach to drive safe, scalable,
and effective outcomes around GenAI adoption across key functional areas.
Source: 1Generative Artificial Intelligence | Deloitte US
©©© 222000222444 DDDeeellloooiiitttttteee CCCooonnnsssuuullltttiiinnnggg PPPttteee LLLtttddd 333
How GenAI has lowered the barrier to AI
Generative AI has democratised advanced AI by putting it in the
hands of every employee
It has put advanced AI in the hands of everyone
Did you know?
GenAI tools and trainings are readily accessible at little to no cost
(or infrastructure investment), thanks to an abundance of platforms,
tutorials and communities
43% of employees across Asia
Pacific are using GenAI for
work. Southeast Asia ranks
2ndout of 9 locations in APAC
for GenAI use
It takes the form of things we already know
In APAC, GenAI daily users save
6 hours a week through
No formal training is required to start using GenAI tools, which are
increased speed, qualityof
often modelled on familiar user interfaces (e.g., chat bots)
work, and the ability to generate
new ideas
19%of GenAIusers in Southeast
Asia are Daily Active Users.
This is expected to increase by
It makes people faster – and better – at what they do
232% in the next five years
The more you use GenAI, the better you will get, as it learns
how you work and how to provide ever better responses
Source:
Deloitte Generative AI in Asia Pacific Report
©© 22002244 DDeellooiittttee CCoonnssuullttiinngg PPttee LLttdd 44
Where organisations fall short of leveraging GenAI
GenAI makes AI more accessible than ever, but its full potential can only be
realised if four critical gaps are addressed
The LEADERSHIP Gap: The GOVERNANCE Gap: The EFFICIENCY Gap: The SCALE Gap:
Not creating an environment Undefined ethical standards Not fully leveraging the Not scaling use cases beyond
that encourages, celebrates, and rules of use technology and its capabilities individual application
and drives adoption
• Lack of awareness and urgency, as • Unmonitored or unchecked • Inefficient or sub-optimal use of • Involves uneven and isolated use of
decision-makers do not frequently platform usage prompts/tools/platforms tools/platforms
engage with GAI platforms
• Negative consequences (e.g., data • Increased human error, poor • Benefits only select individuals
• Lack of institutional mechanisms leak) machine responses and at negligible scale
to drive adoption
• Significant riskfor the organisation • Modest efficiency gains (e.g., bad • Neglects multiplier gains that can
prompts) only be reaped through function or
organisation wide application
• While 94% of employees are ready to • A study by Layer X found that 6% of • A multidisciplinary study found that • 70%of organisations with scaled GAI
learn new skills to work with GenAI, workers havecopy-pasted sensitive sub-optimal GenAIusage can reduce capabilitiesreport improved products
only 5% reported that their employers informationinto GAI tools3 worker performance by 19%4 and services2
were providing training on a large
scale1 Alarmingly, 4% of employees were • Workers expect 61% of current tasks 63% of similar organisations have
found to do so weekly3
to be impacted by GenAIin the next 5 reported being able to encourage
years5 innovationand growth2
Sources: 1Harvard Business Review | 2Deloitte State of Generative AI in the Enterprise | 3Layer X | 4MIT Sloan | 5Deloitte Asia Pacific Generative AI Report
©© 22002244 DDeellooiittttee CCoonnssuullttiinngg PPttee LLttdd 55
What organisations can do
Focus on your employees to drive adoption and address critical people gaps,
before investing in platforms
Bottom-up Top-down
1 2
(Enabling the workforce) (Starting with use cases for the business)
ALIGN GUIDE UPLIFT TRAIN SECUR E ARCHIT EC T
Create a common Demonstrate how Provide practical Make it easy for Ensure safe and Establish the
understanding of GenAI can be learning pathways employees to reliable GenAI infrastructure to
GenAI concepts, beneficial to all and opportunities embed GenAI use across all support
language, and employees, and to bring up into their day-to- teams and increasingly
guardrails across help them to see employees’ core day ways of functions advanced use
all employee the business GenAI skills working cases and data
groups impact needs
Focus of this whitepaper
©© 22002244 DDeellooiittttee CCoonnssuullttiinngg PPttee LLttdd 66
Measuring GenAI adoption
Ground your approach in five levels of maturity
L5: ENTERPRISE
ENABLER
L4: TEAM ENABLER
Champions department
and enterprise-wide
L3: ACTIVE USER
Drives and checks efficient
GenAI adoption and
and ethical GenAI use within
integration
L2: LEARNER
the team
Proficient daily use of GenAI
• Focuses on scaling GenAI
to perform strings of tasks, as
L1: NOVICE Trained to effectively and • Ensures AI tools are applied capabilities into strategic
part of BAU workflows
initiatives
efficiently perform specific responsibly, with attention to
Minimal engagement and Gen AI use cases ethical considerations and • Ensures that adoption aligns
• Fully proficient in using GenAI
experimentation of Gen AI for data privacy with business goals
• Actively builds GenAI skills for across various tasks
everyday use
specific use cases (e.g., • Mentor teams to elevate their
• Integrates GenAI into daily
• Little engagement with GenAI content generation) GenAI use and foster
workflows to optimise
responsible GenAI innovation
• Limited understanding of • Understands the potential of processes and enhance
how GenAI can help them GenAI in their role productivity
All employees should strive to be at L2, while functional leaders are expected to be at L5. This may vary between organisations, depending on the strategic,
business and organisational objectives
©© 22002244 DDeellooiittttee CCoonnssuullttiinngg PPttee LLttdd 77
The GenAI Centre of Adoption (CoA)
Through a dedicated and centralised team, organisations can accelerate GenAI
adoption while bridging critical gaps
Key Outcomes
Adoption Speed of Adoption Proficiency
% increase in adoption over period Time taken to go from one level to the next # target employees at a discrete level
WHAT IS A COA?
Customer & Corporate
Technology
Marketing Affairs
Dedicated unit designed to drive the mass adoption of GenAI across all
functions and levels within the organisation.
Central hub focused on building a culture of continuous learning and
Sales Finance
GenAI CoA
innovation to maximise GenAI’s value.
CHRO
People & Regulatory KEY ACTIVITIES
Org. / HR Compliance
Develop and drive a shared GenAI taxonomy, practical standards (non-
Head of AI
technical), governance, and ethical guardrails
Operations Legal
Help employees to identify areas where GenAI will benefit them,
experiment safely, and help them to redefine their roles
Supply Enterprise
Data Privacy Identify skill and competency gaps across leadership and employee
Chain Risk
groups; develop learning pathways, experiences and programs
©© 22002244 DDeellooiittttee CCoonnssuullttiinngg PPttee LLttdd 88
Contact Us
Let’s unlock GenAI’s full potential in your organisation
Indranil Roy Christopher Lewin Clarissa Turner Miri Takakura
Executive Director Executive Director Executive Director Director
Workforce Transformation AI & Data Leader Workforce Transformation Workforce Transformation
Southeast Asia
Southeast Asia Malaysia Singapore
indroy@deloitte.com chrislewin@deloitte.com clturner@deloitte.com mtakakura@deloitte.com
+65 9636 8024
+65 6232 7128 +60 3 7610 7233 +65 9181 7044
Authors & Contributors
Christian Teo Nadiah Johari Chanette Teoh Claire Ng
Editor Sub-Editor Author Author
GenAI CoA Workforce Transformation Workforce Transformation Workforce Transformation
David Ng Pui Leung Cyndi Chan
Author Designer
AI & Data CoRe Creative Services (C&M)
©© 22002244 DDeellooiittttee CCoonnssuullttiinngg PPttee LLttdd 99
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© 2024 Deloitte Consulting Pte Ltd 10"
187,deloitte,Q3 StateOfGenAI_Report_Wave3_v6.pdf,"Now decides next:
Moving from potential
to performance
Deloitte’s State of Generative AI in the Enterprise
Quarter three report
August 2024
deloitte.com/us/state-of-generative-ai
Table of contents
Foreword
Introduction
Now: Key findings
1 Building on initial success
2 Striving to scale
3 M odernizing data foundations
4 M itigating risks and preparing for regulation
5 M aintaining momentum by measuring value
Next: Looking ahead
Authorship & Acknowledgments
About the Deloitte AI Institute
About the Deloitte Center for Integrated Research
About the Deloitte Center for Technology, Media & Telecommunications
Methodology
22
Introduction
Foreword
In the rapidly evolving landscape of artificial intelligence (AI), the connection between The complex discussions around creating value and managing risk makes it clear to me
technology and value has become increasingly apparent. What is known about major that we need to keep humans at the center of all this decision-making. It is the human
technology innovations in the past holds true with Generative AI (GenAI). stakeholders who impact how applications are conceived and developed, how they are
adopted and used, and how they are managed for trust and security. In this, employee
Technology application on its own is not enough. Results and business outcomes
upskilling and change management remain indispensable elements of value-driving
matter. The real measure of success for GenAI will be how it enables enterprise
GenAI programs.
strategies and drives tangible value.
With a focus on business outcomes and human-centered change, I feel the future with
As organizations are scaling, and learning from, their GenAI pilots, I have heard the
GenAI grows brighter by the day, even as the journey ahead will continue to surprise
discourse around GenAI shift from unbridled excitement to a more nuanced and
and challenge us.
critical evaluation of its real impact on business outcomes. I am also beginning to see
organizations think more about tailored GenAI tools—evolving from large language Learn more about the series and sign up for updates at
models (LLMs) to small language models (SLMs) for more targeted needs. They are http://deloitte.com/us/state-of-generative-ai.
also exploring how the rise of AI agents can redefine interactions within their digital
–Jim Rowan, Applied AI SGO Leader
environments, offering new avenues for automation and personalization.
Amid this maturation, regulatory considerations are coming to the fore. Our past
survey results indicated a strong market appetite for smart GenAI regulation and
oversight. Businesses and governments alike are navigating a dynamic landscape
and are struggling to keep pace with the rate of technology innovation. The challenge
is to unlock the benefits of GenAI while facing regulatory uncertainty, orchestrating
governance and building trust. No small task.
33
Introduction
Moving from potential to performance
The clock is ticking for organizations to create significant cases with strong return on investment (ROI) and a clear Generative AI-powered applications? Is regulatory
and sustained value through their Generative AI path to scale will be essential. They’ll need to address uncertainty holding them back? Are they developing a
initiatives. Promising pilots have led to more investments, challenges across the board: people, process, data and comprehensive set of financial and nonfinancial measures
escalating expectations and new challenges. During this technology. Change management and organizational to form a complete picture of benefits achieved? These
pivotal phase, C-suites and boards are beginning to look transformation will need to be given as much consideration questions must be explored in-depth as organizations
for returns on investment. There is a chance that their as technology. journey from Generative AI promise to performance.
interest in Generative AI could wane if initiatives don’t
In this quarter’s survey, we focused on two critical areas to
pay off as much, or as soon, as expected.
scaling—data and governance, and risk and compliance—
Will organizations demonstrate the patience and and how organizations are measuring and communicating
perseverance needed to unlock the transformational value. Are data-related issues hindering efforts? How
potential of Generative AI? To get there, value-led use are organizations ensuring the right oversight of
44
Introduction
Moving from potential to performance (cont’d)
Building on initial success Striving to scale
• I mproved efficiency and productivity and cost reduction are still the top benefits • T wo of three surveyed organizations said they are increasing their investments in
sought by organizations. Those are also cited by 42% of respondents as their most Generative AI because they have seen strong early value to date.
important benefits achieved to date.
• H owever, many are still challenged to successfully scale that value—nearly 70% of
• H owever, 58% reported they realized a more diverse range of most important respondents said their organization has moved 30% or fewer of their Generative AI
benefits, such as increased innovation, improved products and services, or experiments into production.
enhanced customer relationships.
• R espondents said that embedding Generative AI deeply into critical business
functions and processes is the top way to drive the most value from their
Generative AI initiatives.
All statistics noted in this report and its graphics are derived from Deloitte’s third quarterly survey, conducted May
– June 2024; The State of Generative AI in the Enterprise: Now decides next, a report series. N (Total leader survey
responses) = 2,770. Percentages in this report and its charts may not add up to 100, due to rounding.
Generative AI is an area of artificial intelligence and refers to AI that in response to a query can create text,
images, video and other assets. Generative AI systems can interact with humans and are often built using large
language models (LLMs). Also referred to as “GenAI.”
5
Introduction
Moving from potential to performance (cont’d)
Modernizing data foundations Mitigating risks and preparing Maintaining momentum
for regulation by measuring
• Three-quarters of respondents said their organizations
have increased investment around data life cycle
• O rganizations feel far less ready for the challenges • M ore than 40% of respondents said their companies
management to enable their Generative AI strategy.
Generative AI brings to risk management and governance— are struggling to define and measure the exact impacts
Top actions include enhancing data security (54%)
only 23% rated their organization as highly prepared. of their Generative AI initiatives.
and improving data quality (48%).
• In fact, three of the top four things holding organizations • Less than half said they are using specific KPIs to
• Data issues are limiting options—55% of organizations
back from developing and deploying Generative AI tools measure Generative AI performance, and many
reported avoiding certain Generative AI use cases
and applications are risk, regulation (such as the European standard measures of success aren’t currently
because of data-related issues. Top data-related
Union’s AI Act, in effect August 1), and governance issues. being applied.
concerns include using sensitive data in models
and managing data privacy and security. • T o deal with regulatory uncertainty, about half of
organizations reported they are preparing regulatory
forecasts or assessments.
About the State of Generative AI in the Enterprise:
Wave three survey results
The wave three survey covered in this report was fielded to 2,770 director- to C-suite-level respondents across six industries and 14 countries
between May and June 2024. Industries included: Consumer; Energy, Resources & Industrials; Financial Services; Life Sciences & Health Care;
Technology, Media & Telecom; and Government & Public Services. The survey data was augmented by additional insights from 25 interviews
with C-suite executives and AI and data science leaders at large organizations across a range of industries. This quarterly report is part of an
ongoing series by the Deloitte AI InstituteTM to help leaders in business, technology and the public sector track the rapid pace of Generative AI
change and adoption. The series is based on Deloitte’s State of AI in the Enterprise reports, which have been released annually the past five
years. Learn more at deloitte.com/us/state-of-generative-ai.
66
Now: Key findings
77
Now: Key findings Top benefit achieved through
Generative AI initiatives
1
Building on initial success
Organizations say they are seeing value from their early Generative AI forays and those successes are driving more
investment. Two-thirds of the organizations we surveyed (67%) said they are increasing investments in Generative AI Improved 34%
efficiency and
because they have seen strong value to date. A head of AI strategy and governance in the banking industry has seen
productivity
this first-hand: “Before GenAI, most senior leaders only had a vague understanding of what AI was or what it can do. Now,
they have AI at their fingertips, and it has opened their eyes to the possibilities. We have applied for additional resources.”
12% Encouraged
innovation
As in our prior quarterly surveys, improved efficiency and productivity and cost reduction continue to be the most
Improved 10%
common benefits sought from Generative AI initiatives. Those benefits were cited by 42% of wave three respondents
existing
products and 9% Reduced
as their single, most important benefit achieved to date (figure 1).
services costs
However, for most wave three respondents (the other 58%), the top benefit achieved through the new technology is Enhanced 9%
relationships
something other than efficiency, productivity or cost reduction. This includes increased innovation (12%), improved 7% Increased speed
with clients /
and/or ease
products and services (10%), and enhanced customer relationships (9%). The diversity of possible sources of value from
customers
of developing
Generative AI initiatives is exciting to many leaders and shows the potential and versatility of this new technology.
new systems /
Increased 6%
software
revenue
This distribution could mean a couple of different things. Organizations may be seeking efficiency, productivity and
6%
Developed
cost reduction, but aren’t seeing it materialize yet; they may be getting unexpected value from less tangible areas; or
new products
they may be prioritizing these other types of value. There is no one-size-fits-all approach to employing Generative AI, Shifted 4% and services
workers from
and there is a wide range of benefits that could be gained. It is important for organizations to be clear about what
4% Better
lower- to higher-
detection of
kind of value they are seeking before embarking on any Generative AI initiatives—start with value first. value tasks
fraud and risk
management
67%
of organizations we surveyed said they are increasing
Figure 1 Q: What is the most important benefit your organization
investments on Generative AI given strong value seen to date. has achieved to date through your Generative AI initiatives?
(May/June 2024 ) N (Total) = 2,770
88
Now: Key findings
Our executive interviews provided examples of Generative AI use cases that are already and greater innovation and market differentiation, most projects further along in the
delivering real-world value across a wide range of industries. Although they are working scaling process are still focused on improving productivity (figure 2).
toward things like automated decision-making, accelerated research and development,
Generative AI use cases delivering real-world value by industry
Banking Transportation Telecom Insurance Consumer Technology Finance Pharmaceuticals
A customer service A system to provide Support tools An internal medical Customer Continuous Project management Internal tool that
tool that handles customer support deployed for retail claims appeal review segmentation tools improvement tools that quickly provides instant
messages, using both and handle simple and technical field tool that provides leveraged to create processes enhanced create summary enterprise information
chat and voice, and support tickets. staff, and systems increased response more precise and by directly leveraging materials for key (such as standard
provides cross-sell The system can for troubleshooting quality and a decreased customized segments customer feedback stakeholders. operating procedures)
opportunities based automatically pull and preventive time to respond. across geographies. to inform product for thousands of staff.
on the interaction. data for human maintenance, all to development
agents to use for reduce costs. road maps.
more complex tasks.
Figure 2
9
Now: Key findings Behaviors driving the most value
for Generative AI initiatives
What do organizations think will most help drive greater “CEOs and executive leadership teams are getting much
value for their Generative AI initiatives? While many more excited and interested in what’s possible and are
Deeply embedding GenAI into
22%
different factors contribute to Generative AI value looking for use cases to demonstrate the value and
functions / processes
creation, the action cited most often by the leaders benefit,” said the global head of AI, machine learning,
we surveyed is embedding the technology deeply into analytics and data at a pharmaceutical company. “There is Effectively managing risks 13%
business functions and processes (figure 3). a lot of willingness to test, experiment and scale. However,
Deploying the latest
the potential danger is that people might get disappointed
11%
“An LLM is like an engine,” said a VP at a bank’s AI center technology
and lose attention if it’s not paying off fast enough.”
of excellence. “No one just wants the engine of a car
Developing creative and
10%
or a plane; they want a car or a plane. So, there are all C-suite and board members are still intrigued, but there
differentiated applications
these things you need to do to make it part of business are some potential signs of enthusiasm beginning to
Tailoring / customizing
processes, so the business can use it.” The value from wane as the “new technology shine” wears off. Survey
10%
models with proprietary data
any Generative AI initiative won’t be fully realized if it sits respondents said that interest in Generative AI remains
apart. As with other technologies, it will only reach its “high” or “very high” among most senior executives
Hiring the best talent 9%
potential when it is embedded in everyday tasks. Many (63%) and boards (53%); however, those numbers
organizations are already employing enterprise tools have declined since the Q1 2024 survey, dropping 11
Completely measuring
8%
enhanced with this emerging technology resource to percentage points and 8 percentage points respectively. performance
try and make this happen. Time is of the essence as organizations look to scale
their early achievements. Providing enough budget 8%
Although many have seen promising results from
early projects and are increasing investment in
Providing access to as much
7%
of the workforce as possible
Generative AI, it is important that organizations
show sustained and significant value as quickly
as possible.
Figure 3 Q: Which behavior / action do you think will drive the most
value for the Generative AI initiatives in your organization?
(May/June 2024 ) N (Total) = 2,770
10
Now: Key findings
2 Striving to scale A large majority of organizations have deployed less than
a third of their GenAI experiments into production
Selecting and quickly scaling the Generative AI projects with the most potential to create
value is the goal. However, many Generative AI efforts are still at the pilot or proof-of-
Organizations
26%
concept stage, with a large majority of respondents (68%) saying their organization has 24%
GenAI experiments
19%
moved 30% or fewer of their Generative AI experiments fully into production (figure 4).
moved into production
14%
This isn’t necessarily surprising—despite rapid and impressive advances in Generative AI’s
7%
capabilities, its applications are still relatively new and organizations are figuring out what it 4%
3%
1% 1%
can (and can’t) do well. Many organizations are learning through experience that large-scale
Generative AI deployment can be a difficult and multifaceted challenge. As with a lot 0% 10% 20% 30% 40% 50% 60% 70% 80%
of digital transformation efforts, projects can fail or struggle for a variety of reasons.
Figure 4 Q: In your estimation, what percentage of your Generative AI experiments have been deployed to date into your
“Most of our applications are still in the minimum-viable-product or proof-of-concept organization (moved into production)?
phase,” said a senior specialist for AI compliance in the automotive industry. (May/June 2024 ) N (Total) = 2,770
“Scaling across an organization where
Successfully scaling may mean different things to different organizations—based
on their goals, what approach they are taking with Generative AI, and to what
you have thousands of employees
extent scaling is actually necessary. They could be expanding from one market
to multiple markets, from a small group within a function to the entire function,
has several basic requirements, and
or from a portion of a process to multiple, integrated processes. It also depends
they’re quite challenging.” on what Generative AI-powered tools and applications are being used: scaling a
code generator across an IT department is going to be different than scaling a
customized LLM for the finance function, or a new enterprise customer relationship
-Senior specialist for AI compliance in the automotive industry
management application with Generative AI features.
11
Now: Key findings
Despite these differences, some fundamentals are consistent. More broadly, organizations should invest in the foundations of Generative AI
and concurrently assess and advance their strategy, processes, people, data and
“Foremost, you need a strategy,” the senior specialist for AI compliance continued.
technology (figure 5).
“Strategy means you can’t start by purchasing separate solutions ... if you really want
to scale, first you need to base your strategy on platforms.” Many of the fundamentals may look similar to prior digital transformation
efforts, but due to the unique nature of Generative AI, things like robust
This platform-centric approach could include establishing centers of excellence, technology
governance, transparency for building trust, transforming talent, and
platforms to enable multiple use cases, and centralized teams of experts. In our Q2 report
mature data life cycle management take on increased importance.
we advocated for centralized resources that can accelerate deployment of similar use
cases and enable organizations to make the most of scarce Generative AI expertise.
Essential elements for scaling Generative AI initiatives from pilot to production
Figure 5
Strategy Process Talent Data & technology
Ambitious Modular
Integrated Transparency Provisioning
strategy & value Robust architecture
risk to build trust the right AI
management governance and common
management in secure AI infrastructure
focus platforms
Clear, Agile Acquiring Effective
Strong Transformed
high-impact operating model (external) and Modern data model
ecosystem roles, activities
use case and delivery developing foundation management
collaboration and culture
portfolio methods (internal) talent and operations
12
Now: Key findings
How do organizations feel like they are doing across these areas—are they prepared the LLMs still needs to be improved … Data readiness; data is going to be problem
to scale? We asked how highly prepared respondents thought their organizations were forever ... Deep Generative AI understanding as well. There’s not enough people who
across some of the essential scaling elements (figure 6). Technology infrastructure understand and can drive transformation.”
(45%) and data management (41%) fared the best, followed by strategy (37%), risk
To help start a conversation on how to overcome some of these barriers, in
and governance (23%), and talent (20%).
this quarter’s survey we focused on two areas critical to scaling—exploring
This indicates that there are still some fundamental challenges holding organizations how organizations are approaching data and governance, and risk
back from successfully scaling their Generative AI initiatives. A senior director and and compliance.
head of a Generative AI accelerator in the pharmaceutical industry identified a
With respect to data, more organizations’ leaders reported they are initially prepared.
number of pressing issues: “The heritage of our processes and approaches, that
For risk and governance, they know they are not. Both need attention.
is what’s really holding us back right now. Number two is that the performance of
Do organizations think they are ready?
Percentage of organizations that are highly prepared
for GenAI across the following areas 45% 41%
37%
23%
20%
Figure 6 Q: For each area, rate your organization’s level of preparedness Technology Data Strategy Risk & Talent
with respect to broadly adopting generative AI tools / applications?
infrastructure management governance
(May/June 2024 ) N (Total) = 2,770
13
Now: Key findings
3 Modernizing data foundations
75%
of organizations have increased their technology investments
around data life cycle management due to Generative AI.
Compared with the other aspects of Generative AI However, even those executives who consider themselves
readiness, survey respondents judged that their highly prepared will likely need to do more as they progress
organizations are fairly mature with respect to data life in their journeys. Some we interviewed said that as they
cycle management (as a reminder, survey respondents moved from proof of concept to scale, unforeseen data
are from more AI-savvy organizations). This could be issues were exposed—highlighting a need to be agile.
because they had a good foundation to start with or
These issues could be because of the Generative AI-
that, according to our survey, 75% of organizations have
specific demands to data architecture and management.
increased their technology investments around data life
More robust governance—quality, privacy, security,
cycle management due to Generative AI.
transparency—is needed overall, especially around using
This increased focus was evident in our executive data that doesn’t already exist inside the organization (e.g.,
interviews. “There’s a whole series of questions GenAI public domain, synthetic and licensed third-party data).
is triggering about data strategy, that in the past Documenting data sources and labeling has an increased
were far less important,” said the chief technology importance. With more people potentially leveraging
officer at a manufacturing company. “I think we’re data, data access frameworks and literacy require more
probably spending as much time on data strategy and attention. It may change approaches toward cloud or on-
management as on pure GenAI questions, because premises data services. For more advanced LLM users,
data is the foundation for GenAI work.” working with synthetic data may eventually come into play.
1144
Now: Key findings
Levels of concern around data management
Figure 7 Q: For the following, how much concern does your organization have with respect to its data management for
Generative AI implementations?
(high + very high)
(May/June 2024 ) N (Total) = 2,770
58% 58% 57% 49% 38%
Using sensitive data Managing data privacy- Managing data security- Complying with data- Using our own proprietary
in models related issues related issues related regulations data in models
One of these challenges was highlighted by a former vice president of data and intelligence That could be because of data-quality issues, intellectual property concerns, not having
for a media and entertainment company: “The biggest scaling challenge was really the the right data, or worries about using certain kinds of data (e.g., public domain, synthetic
amount of data that we had access to and the lack of proper data management maturity. or licensed third-party data). The concerns that organizations were worried about the
There was no formal data catalog. There was no formal metadata and labeling of data most in our survey included using sensitive data in models (58% had at least a high
points across the enterprise. We could go only as fast as we could label the data.” level of concern), data privacy issues (58%), and data security issues (57%) (figure 7).
Organizations were much more worried about using sensitive data (e.g., customer
Data-related issues could be hindering organizations in their quests for getting
or client data) than they were using their own proprietary data (e.g., sales,
the levels of value that they are seeking. Data-related issues have caused 55%
operational, financial).
of the organizations we surveyed to avoid certain Generative AI use cases.
15
Now: Key findings Improving data-related capabilities
Consistent with those concerns, the top actions The value from Generative AI initiatives will increasingly
organizations are taking to improve their data-related come from organizations leveraging their differentiated
capabilities are enhancing data security (54%), improving data in new ways (whether for fine-tuning LLMs, building
Enhanced 54%
data quality practices (48%), and updating data an LLM from scratch or utilizing enterprise solutions).1 data security
governance frameworks and/or developing new For Generative AI to deliver the kind of impact executives
48%
data policies (45%) (figure 8). expect, companies will likely need to increase their Improved
data quality
comfort with using their proprietary data, which may
practices
be subject to existing and emerging regulations.
Updated 45%
governance
frameworks /
Developed new
43% Collaborated
data policies
with cloud
service provider
“Data quality is key. Understanding what data is or IT integrator
Upgraded IT 37%
to improve
infrastructure
capabilities
good data. Where is that data held? How is it
34% Hired new
talent to fill
secured? How is it permissable? All those things
data-related
Integrated 27%
skill gaps
data silos
are key to making [Generative AI] scalable.”
24% Moved to a
more flexible,
-Chief operations officer & chief of strategy for a financial services firm
open data
architecture
Figure 8 Q: What specific actions has your organization taken
to improve its data-related capabilities to support its
Generative AI initiatives?
(May/June 2024 ) N (Total) = 2,770
16
Now: Key findings
4 Mitigating risks and preparing for regulation
According to our survey respondents, Likely driving these concerns are new and emerging as highly prepared. These issues will be increasingly
risks specific to the new tools and capabilities—like important as activities shift from small-scale pilots to
three of the top four barriers to successful
model bias, hallucinations, novel privacy concerns, trust large-scale deployments and Generative AI becomes
development and deployment of
and protecting new attack surfaces. This environment more deeply embedded into the fabric of organizations.
Generative AI tools and applications are:
may be why organizations feel far less ready for the Highlighting the importance, respondents selected
challenges Generative AI brings to risk management and effectively managing risks as the second-most reported
worries about 36%
governance—since only 23% rated their organization way to drive the most value for Generative AI initiatives.
regulatory compliance
30%
difficulty managing risks
29%
lack of a
governance model
Currently, these are considered even more significant
than other critical barriers such as implementation
challenges, a lack of an adoption strategy, and difficulty
identifying use cases.
17
Now: Key findings
The chief operations officer and chief of strategy in a To help build trust and ensure the responsible use for using Generative AI tools and applications (51%),
financial services company summed up the challenge: of Generative AI-powered tools and applications, monitoring regulatory requirements and ensuring
organizations are generally working to establish compliance (49%), and conducting internal audits /
“How do you democratize Generative AI across your
new guardrails, educate their workforces, conduct testing on Generative AI tools and applications (43%)
business while having all of the right controls in place?
assessments, and build oversight capabilities. (figure 9). Despite their importance for effective scaling,
We have an AI board, we have an ethics framework, we
each of these actions is only being taken by less than
have an accountability model. We want to know who’s
Specific actions surveyed organizations are currently
roughly half of the organizations we surveyed.
using it for what, and that it’s being used in the right way.”
taking include establishing a governance framework
Actions to manage risk
51%
49%
43%
37%
35%
33%
30%
23%
19%
Establishing Monitoring regulatory Conducting internal Training practitioners Ensuring a human Keeping a Using a formal Using outside Single executive
a governance requirements and audits and testing how to recognize validates all GenAI- formal inventory group or board to vendors to conduct responsible for
framework for the ensuring compliance of GenAI tools / and mitigate created content of all GenAI advise on GenAI- independent audits managing GenAI-
use of GenAI tools / applications potential risks implementations related risks and testing related risks
applications
Figure 9 Q: What is your organization currently doing to actively manage the risks around your Generative AI implementations?
(May/June 2024 ) N (Total) = 2,770
18
Now: Key findings
78%
of leaders surveyed in Q1 agreed that more
governmental regulation of AI was needed.
Implementing new processes and controls is rarely easy and will likely require active
change management to build support within the organization. “Before launching
anything, we have strict AI governance,” said the chief analytics officer at a professional
services firm. “In the past we had a bit of a siloed approach, but today, at a minimum,
everything has to go through privacy and compliance because we have a methodical
way of managing risk. This is new and challenging to some.”
On top of risk and governance issues, Q3 surveyed organizations were exceedingly
uncertain about the regulatory environment that may exist in the future (depending on
the countries they operate in). In our first quarterly report, 78% of leaders agreed that
more governmental regulation of AI was needed. However, there is a difference between
theory and practice. Organizations are struggling with regulatory uncertainty, and
worries about interpretation and enforcement may be preventing them from pursuing
certain use cases in specific geographies.
The uncertainty around AI regulation may make it feel like there could be many varied
outcomes, but our research suggests most countries are following a similar path
concerning AI policies.2 Governments are working to balance protection, innovation
and economic benefit, so future actions will likely be in line with the regulatory
traditions of each country and region.
1199
Now: Key findings Insights from our executive interviews
How some real-world organizations are dealing with compliance, risk management
Some organizations reported taking action to prepare
and governance issues
for potential regulatory changes. Top areas include
preparing regulatory forecasts or assessments (50%),
An increasing number of organizations are making risk a central factor when selecting Generative AI use
monitoring by the general counsel (48%), and working
cases and investments. However, many are walking a tightrope—trying to minimize risk without being too
with external partners (46%) (figure 10). However, some
risk averse, which could lead to missed opportunities and open the door to competitors.
organizations aren’t doing anything to prepare; 14%
said they aren’t making any specific plans.
Here are some risk-related actions revealed through our in-depth executive interviews:
How organizations are preparing
for regulatory changes Avoiding
Avoid use cases that could require additional regulatory scrutiny
specific tools
and use cases Shut off access to specific Generative AI tools for staff
For organizations that rely heavily on owned intellectual property, be extremely cautious when
Corporate 50% Limitin"
189,deloitte,us-ima-next-gen-controllership-2025.pdf,"Next-gen controllership
Harnessing AI and emerging technologies
to transform finance and accounting
Table of contents
Laying the groundwork: Global research to trace the controllership technology journey �����������������������������1
Section 1: Artificial intelligence: How AI is reshaping accounting and finance ���������������������������������������������������1
Section 2: Beyond AI: Technologies leading change in controllership ��������������������������������������������������������������10
Section 3: From traditional to tech: How emerging technologies are affecting controllership ������������������15
Section 4: How to thrive: A framework for future decision-making �������������������������������������������������������������������19
Conclusion: A way forward ���������������������������������������������������������������������������������������������������������������������������������������������23
End notes ����������������������������������������������������������������������������������������������������������������������������������������������������������������������������24
About the authors ������������������������������������������������������������������������������������������������������������������������������������������������������������25
About the survey ���������������������������������������������������������������������������������������������������������������������������������������������������������������26
Next-gen controllership | Harnessing AI and emerging technologies to transform finance and accounting
Deloitte’s1 Center for Controllership™
Deloitte’s Center for Controllership is a research, resource, and
collaboration center that helps chief accounting officers (CAOs)
corporate controllers, and others in the controllership function�
Deloitte helps organizations effectively navigate business risks
and opportunities—from strategic, reputation, and financial risks
to operational, cyber, and regulatory risks—to gain competitive
advantage. We apply our experience in ongoing business
operations and corporate life cycle events to help clients become
stronger and more resilient. Our market-leading teams help
clients embrace complexity to accelerate performance, disrupt
through innovation, and lead in their industries. For more
information about Deloitte’s Center for Controllership, please visit
www�deloitte�com/us/cfc
IMA® (Institute of Management Accountants)
IMA® is one of the largest and most respected associations
focused exclusively on advancing the management accounting
profession. Globally, IMA supports the profession through
research, the CMA® (Certified Management Accountant) and
CSCA® (Certified in Strategy and Competitive Analysis) programs,
continuing education, networking, and advocacy of the highest
ethical business practices� Twice named Professional Body of the
Year by The Accountant/International Accounting Bulletin, IMA
has a global network of about 140,000 members in 150 countries
and 350 professional and student chapters. Headquartered in
Montvale, N.J., USA, IMA provides localized services through its
four global regions: the Americas, Asia/Pacific, Europe, and Middle
East/India. For more information about IMA, please visit
www�imanet�org�
Next-gen controllership | Harnessing AI and emerging technologies to transform finance and accounting
Laying the groundwork: Global research to trace
the controllership technology journey
Emerging technologies are transforming the finance and This report presents the findings from this survey alongside
accounting industry. With the adoption of artificial intelligence (AI) considerations from industry experts and professionals, offering
and new functionalities available in the convergence of data insights into common emerging technologies used in the
location, process automation, and data analytics technologies, controllership function, the benefits these technologies may have on
financial institutions can now process transactions faster, more core accounting processes, and how technology may transform the
accurately, and with seemingly greater efficiency. However, the function of controllership. We explored how professionals apply new
integration of these new technologies comes with a set of tools, the challenges in adopting technology solutions and insights into
challenges� Legacy systems, which are often outdated and lack the overcoming these challenges, optimizing the positive impacts, and
necessary compatibility with newer technologies, can make the meeting expectations for the future of controllership.
adoption of new technology innovations difficult and expensive.
The insights gleaned from this research form the bedrock of our
Additionally, the implementation of new technology requires
guidance on how controllers and their teams can leverage a
significant investment in training, infrastructure, and cybersecurity
practical framework for navigating the unpredictable landscape of
measures. Despite these challenges, the benefits of emerging
emerging technology. This framework aims to assist finance and
technology in finance and accounting can be promising, and
accounting professionals to optimize the functional value of
companies that integrate these technologies effectively are likely to
technologies that are set to become a staple in the next generation
gain a competitive advantage in the industry.
of controllership�
From the winter of 2023 to the spring of 2024, IMA and Deloitte’s
Center for Controllership conducted a global survey of more than
900 finance and accounting analysts, managers, directors,
controllers, and CFOs. The global survey aimed to read the pulse of
how the finance and accounting functions are navigating the influx of
emerging technology available against expectations for future
implementations, possible applications, and controllership impact�
Section 1: Artificial intelligence: How AI is reshaping
accounting and finance
What is AI? For a term that is consistently in headlines and at the Traditional AI is often rule-based and provides outputs such as
forefront of business discussions, you may find yourself wondering numbers, labels, or classifications. If you have ever used a virtual
what exactly is AI? assistant on a website or leveraged predictive analytics
technologies for your data, you were using traditional AI. This
The standard answer: Artificial intelligence (AI) is the theory and
category of AI is optimized for processing large amounts of data
development of computer systems able to perform tasks normally
following predefined rules that train the AI to respond to a given set
requiring human intelligence. AI can be categorized into two main
of circumstances. It is distinguished by its response within
categories: traditional and generative (figure 1).
prescribed parameters, but it does not adapt to situations outside
its training set�
Generative AI (GenAI) has the ability to generate new content, as
the name suggests. It is AI that can create content across various
modalities, such as text, images, and code, which would have
previously taken human skill and expertise to create.
1
Next-gen controllership | Harnessing AI and emerging technologies to transform finance and accounting
Figure 1: Defining artificial intelligence: Traditional AI vs. Generative AI
AI overview – Artificial intelligence (AI) is the theory and development of
computer systems able to perform tasks normally requiring human intelligence.
Traditional Generative
Traditional AI is Generative AI is artificial
artificial intelligence intelligence applications that
that is often role-based create new content across
and provides outputs various modalities (e.g., text,
such as numbers, images, audio, code, voice,
labels, or classifications video) that would have
without the ability to previously taken human skill
generate new content. and expertise to create.
Traditional Generative
applications applications
Machine learning Text, image,
video generation
Natural language
processing Synthetic data
generation
Virtual assistants
Automated content
Deep learning
moderation or translation
Predictive analytics
Chatbots
Robotics process
Content creation
automation
Automated education
Speech recognition
Image recognition
2
Next-gen controllership | Harnessing AI and emerging technologies to transform finance and accounting
But what does that mean? The current state of AI
Let’s simplify. AI encompasses many technologies that work AI tools and other rule-based innovations are pervasive, but AI is
together to build innovative solutions that transform society and entering a new era. The hype around AI innovation over the past
business alike� year has reached new levels, and for good reason. What changed?
In short, AI is graduating. It is transforming from rule-based
In the finance function, that can include machine learning, natural
traditional models to foundational data and language models that
language processing, deep learning, predictive analytics, robotic
can generate its own rules�
process automation, and speech recognition�
While a rule-based model focuses on predictions and patterns
Why AI matters
using massive amounts of historical data and language models,
The question is not if AI will affect your work, but when. Our global
GenAI can generate content and insights that builds upon
survey showed that the implementation and use of AI in the
foundational data. AI has advanced technological capabilities that
controllership function is expected to nearly double in the next
can empower controllers and transform how business is done�
three to five years. Furthermore, AI was ranked as the second most
With tools from intelligent automation to machine learning, natural
important technology skill for controllers to have training on in the
language processing, and GenAI, organizations are presented with
next three to five years.
both opportunities and risks in finance and accounting.
GenAI captured the public’s imagination when it burst onto the
There are many AI tools available that the accounting and finance
scene in the second half of 2022. Few technologies have ever
function can leverage. When asked which AI products are currently
debuted to such fanfare. Adoption and use of GenAI have been
being used the most in controllership, respondents identified
sudden and rapid among the public. In one example, OpenAI
Microsoft products such as Azure Synapse as the highest used AI
reported reaching 100 million users within 60 days of releasing
tool. Azure Synapse, like most of the tools in our survey, is mostly
ChatGPT to the public�2
used for analytics purposes�
GenAI may be the next great chapter in the history of information.3
This tool was closely followed by OpenAI, with over 30% of
For businesses, the opportunity to augment professionals and
respondents who use AI claiming to use OpenAI in their organizations.
controllers with machine-assisted intelligence is a generational
opportunity. It’s a paradigm shift that may be poised to unlock Rounding out the top three AI tools that respondents mentioned
doors to new business opportunities and fundamentally change using was Snowflake, which has AI capabilities to understand
how the enterprise organizes and operates� unstructured data, answer free-form questions, and provide
intelligent assistance. Other AI tools also used within controllership
include, Domo, Oracle, Sage Intacct, SAP Concur, and ThoughtSpot.
Deloitte’s insights
While the interest in traditional AI and GenAI is reaching
new heights, organizations are adopting AI tools at a lower
rate than many may have expected. Organizations seem to
be waiting for more niche tools to enter the market or
more advanced out-of-the-box technologies to emerge
with practical applications for the finance and
controllership space�
3
Next-gen controllership | Harnessing AI and emerging technologies to transform finance and accounting
GenAI adoption challenges
The top reported challenge for implementing GenAI tools was AI Other challenges noted by respondents included lack of skilled labor,
integration with existing systems, with 19% of survey respondents limited use cases, trust concerns, reliance on bad data, a lack of
citing this has been a challenge with past implementations� This leadership support, and problems with funding (figure 2).
challenge was followed by security concerns, data governance, and
lack of skilled labor for the top implementation challenges� For
future planned AI implementations, integration with existing systems
remained the biggest concern; however, respondents ranked
challenges with data governance and lack of skilled labor higher for
future expected challenges. It is expected to see challenges with
data governance become a significant lift for many organizations as Deloitte’s insights
AI has been receiving much attention in the current climate.
they plan to implement AI (figure 2).
As it introduces a paradigm shift to accelerating
While specific implementation challenges may vary, one common transformation, finance leaders have been more engaged in
barrier is the alignment of system architecture� This relates to the the excitement, likely driving a willingness to fund
noted challenges around data inconsistencies across applications� implementations. However, that excitement may outperform
Inconsistent data governance across the organization leads to the current impact of AI in the finance and accounting space.
challenges in implementing integrated solutions. Therefore, the willingness, or perceived willingness, to fund AI
tools may focus on more long-term or future investments
Lack of funding and lack of leadership support remained the smallest
until the impact aligns with the hype or offers more
challenges for respondents, both for previous and future
assurance for a return on investment.
implementations; however, respondents identified that lack of
funding was becoming more of a concern going forward�
4
Next-gen controllership | Harnessing AI and emerging technologies to transform finance and accounting
Figure 2: Top AI implementation challenges in the past five years
1 2 3
Integration with
Security concerns Data governance
existing systems
4 5 6
Lack of Lack of Lack of trust
skilled labor use case in technology
7 8 9
Lack of
Availability Lack of
leadership
of clean data funding
support
5
Next-gen controllership | Harnessing AI and emerging technologies to transform finance and accounting
AI is an operations force multiplier for human ambitions in finance
Benefits of GenAI in finance
According to respondents, the top reported benefits of AI are Deloitte’s insights
increased automation enablement and the reduction of monotonous Organizations have historically utilized predictive (forecast
responsibilities (figure 3). The global survey showed that 20% more based on historical data) and prescriptive (forecast-driven
recommendations) analytics in more simplistic use cases.
respondents identified predictive and prescriptive analytics
However, professionals have noted GenAI can increase the
as a benefit in the next five years compared to the previous
power of predictive analytics. With GenAI, the model can
three to five years.
offer a prediction with the additional benefit of context and
explanations around that prediction. Natural language
models will make this more accessible� The adoption of
built-in prescriptive analytics into larger offerings will also
likely drive accessibility.
GenAI has the potential to reduce the burden of manually
intensive tasks on humans, freeing them up to focus on higher
value and more ambitious strategies. It is rocket fuel for
operations that can enable a workforce to utilize technologies to
guide decisions and focus more on critical or strategic tasks�4
Figure 3: Top AI benefits in the next five years
1 2 3
Reduce monotonous
Increase automation Ease of data analysis
responsibilities
4 5 6
Reduce Higher Predictive and
human error productivity prescriptive
analytics
6
Next-gen controllership | Harnessing AI and emerging technologies to transform finance and accounting
GenAI use cases in accounting and finance
Deloitte’s insights - Controllership AI use cases5 GenAI’s broad applicability makes it a useful tool across personas
and functions and throughout businesses. For example, using
Increase automation – Automate journal entries; reduce manual
GenAI, the controllership function can systematize recurring
tasks in order-to-cash cycle; process, match, and pay in
entries and reconciliations, perform source-to-target chart of
procure-to-pay process to support touchless invoice processing
account mapping, review and analyze contract terms, and prepare
Reduce monotony – Create, track, and manage close activities; internal and external financial reporting that includes commentary
produce automatic smart accounting reports based on and insights�7
predefined template
Finance leaders can use GenAI to maintain a pulse on the business
Data analysis – Automatically analyze data and provide optional and adapt to changing market conditions, predict and preempt
solutions; improve variance analysis with unstructured data; strategic macroeconomic blockers, enhance organizational
generate insights in video, text, or image format structure, and provide quick answers to evolving questions and
real-time information. Controllers and finance leaders can use
Reduce human error – Reconcile inconsistent journal entries;
GenAI to run intelligent searches of knowledge bases, standard
assess reliability of accounting entries
operating procedures, and regulatory documents; generate control
Productivity – Reduce time spent on manual processes such as compliance reports to provide domain-specific expertise to
risk interviews, in which GenAI can analyze unstructured data business decisions, and monitor compliance, ethics, and control
sources such as discussions to uncover takeaways, themes, and across the business�8
insights; produce diagrams, slides, and other insight material from
With this ability, GenAI could create a more profound relationship
datasets, allowing humans to focus on any identified exceptions
between humans and technology. AI can be a force multiplier or
Predictive and prescriptive analysis – Validate actuals in the assistant for workers—liberating them from more repetitive tasks
close; provide trend analysis and insights for accountants; identify and enabling the workforce to focus on more creative or strategic
biggest drivers of cash flow; analyze historical fulfillment rates for aspects of their jobs�9
inventory management6
7
Next-gen controllership | Harnessing AI and emerging technologies to transform finance and accounting
Implementation considerations
The use of traditional AI and GenAI in accounting and finance may Deloitte’s insights
Many experts identify the record-to-report process for internal or
vary across functions. Respondents identified that advanced
management reporting as one of the strongest use cases for
analytics and intelligent systems, such as data science and AI, are
FP&A. In addition, flux analysis and managing the close process
being implemented the most in financial reporting and financial
could benefit from GenAI implementations.
planning and analysis (FP&A) within controllership. Other areas
leveraging advanced analytics and intelligent systems include
controls and compliance, treasury, general ledger and close
accounting, and operational accounting�
Figure 4: Generative AI adoption in controllership
1–2 years away Currently
from adopting adopting
No plans to
ever adopt
15% 8%
38% 22% 8% 9%
2–5 years away 6–12 months away Currently
from adopting from adopting using
8
Next-gen controllership | Harnessing AI and emerging technologies to transform finance and accounting
While traditional AI tools will likely continue to exponentially Key takeaways
increase in finance and accounting use cases, it is important to
• The ongoing adoption of traditional AI will likely continue to grow
note that GenAI adoption is quickly gaining traction across finance.
as it becomes standard technology in business. Per the survey
Our global survey showed that 16% of respondents are either
results, emerging adoption of GenAI is also likely to increase over
currently using or currently adopting GenAI, and almost half of
the next few years.
respondents (44%) plan to adopt GenAI in the next five years
(figure 4). • When looking at the emerging AI tools and their various
generative applications, the opportunities they present to finance
and accounting are multifaceted. While many tools currently have
analytics applications, GenAI tools are a paradigm shift to the
finance and controllership landscape because of their broad
applicability and convergence with other emerging technologies.
• With the challenges to AI implementations and concerns over
governance and security, stepping into the opportunity to
maximize benefits may be achieved with a successful AI
implementation framework� This is discussed further in Section 4�
9
Next-gen controllership | Harnessing AI and emerging technologies to transform finance and accounting
Section 2: Beyond AI: Technologies leading change
in controllership
The big players: Next-gen tech First, let’s define the key areas in emerging technology outside of AI:
While the novelty of GenAI brings AI to the forefront of many
Data location and management technology
emerging technology discussions, it’s crucial to note that AI is not
Data location and management refers to systems, methodologies,
the only emerging technology taking up real estate in the
and infrastructure used to store, manage, and retrieve data across
next-generation accounting landscape. Other technologies such as
various physical devices and geographical locations. This
process automation, data analytics, and data location continue to
technology encompasses both the hardware and software
evolve and play a big role in accounting and finance. In this section,
components necessary to ensure data is securely saved and
we will identify the most used technologies, implementation trends,
efficiently accessible when needed. In controllership, this
and emerging functionalities for each category according to the
technology can include on-premise, cloud, or data mesh
survey (figure 5).
approaches to storage and data management�
Figure 5: Emerging technology areas in controllership
Common Process Data analytics Artificial
locations of data automation and visualization intelligence (AI)
10
Next-gen controllership | Harnessing AI and emerging technologies to transform finance and accounting
Data analytics and visualization technology Process automation technology
Data analytics tools convert raw data into actionable insights. It Process automation refers to the use of technology to automate
includes a range of tools, technologies, and processes used to find repetitive and manual tasks within a business process. It includes
trends and solve problems by using data. technologies like robotic process automation (RPA), intelligent
document processing (IDP), workflow orchestration, AI, system
Data visualization technologies enable the graphical representation
integrations, and business rules. Its practical applications include
of information and data, often through visual elements like charts,
automating financial processes such as data validation, forecast
graphs, and maps. Its practical application can include the
reports, and reconciliations�
visualization of ad hoc or strategic analysis, compelling
presentations of context underneath typical variance analyses, and
heightened understanding of data to communicate a wide variety
of use cases including daily sales, revenue analytics, variance
decomposition, and growth trends�
Section 2.1: Data location and management survey trends
With most technology implementation initiatives trending upward, Respondents identified the most used data location technology as
it may come as a surprise that results from our survey SAP, with 18% of respondents implementing SAP within the next
demonstrated the implementation of data location technology five years. Other notable data location technologies include SQL,
within accounting and finance is expected to decrease by 32% in and Oracle. While most data location implementations are
the next five years (figure 6). expected to decrease compared to the previous five years, the
survey showed that Amazon Web Services (AWS) will have a 25%
increase in implementations in the next three to five years
compared to current use, the highest increase compared to other
data location technologies (figure 6).
Deloitte’s insights
While this trend deviates from other technologies, there are
some possible explanations for this perception. The
marketplace is moving toward modern ERPs—a wave that
started about five years ago and likely has five years left.
While ERPs typically involve an on-premise data warehouse,
many organizations are moving toward a modern
cloud-based warehouse. Seeking to participate in the cloud
Deloitte’s insights
data warehouse trend, some traditional ERP vendors have
As noted previously, with the emergence of more
created their own offerings as well. Another emerging trend
cloud-based systems and the data mesh trend, the
is the data mesh strategy, in which individual corporate
perception is that finance and accounting professionals may
functions can own their respective data and then publish to a
experience less involvement in the IT side of data
data catalog for consumption in analytics by other functions�
implementation� Some considerations from professionals in
With the move toward cloud-based solutions and emerging the marketplace note that SAP is geared more to larger
data mesh technology, data location implementations may companies and has a stronger footprint in manufacturing
be moving more toward IT ownership. As a result, finance rooted in its strength in ERP integration. Oracle, however,
and accounting leaders may have less visibility or may have a stronger presence in other industries.rooted in
involvement in data location implementations. its strength in ERP integration. Oracle, however, may have a
stronger presence in other industries�
11
Next-gen controllership | Harnessing AI and emerging technologies to transform finance and accounting
Section 2.2: Data analytics and visualization survey trends
The implementation of data analytics and visualization technology
is expected to remain steady, with approximately 24% of Deloitte’s insights
SAC is a native SAP visualization tool, and with an increased
respondents stating they have implemented this type of
interest in SAP S4HANA and Central Finance, SAC will likely see
technology in the past five years and expect to implement this
an uptick in the opportunity for its use. In addition to these
technology in the next three to five years.
traditional reporting and visualization tools identified in the
The survey results found that PowerBI is the most used data survey, we are seeing organizations use desktop analytics
analytics and visualization tool in controllership, with 35% of toolkits to transform, enhance, and improve quality insight and
respondents stating their organization is using PowerBI and 33% data. Other tools offer visualization capabilities as well as tools
planning to implement the tool in the next three to five years to automate business rules, apply criteria, and pull reports�
(figure 6). This is consistent with what Deloitte has seen in the
marketplace . Native and naturally integrated tools have the added AI impact on data analytics and visualization
While natural language generation has been around in some
benefit of ease of use and larger platform integration.
form for many years, the next-gen AI capabilities may offer
Other high-use data analytics tools include Python, SAP Analytics new applications for analytics and data visualization in
Cloud (SAC), and Tableau . We found that the use of SAC is controllership. Organizations will likely see an increase in AI
expected to increase by 28% in the next three to five years when integrations or add-on capabilities with the data analytics
compared to the past five years, the highest increase in change of and visualization tools on the market. There are multiple ways
the analytics technologies� this could present itself with transformative applications.
Notably, GenAI will likely be an innovative tool for producing
prompt-based data and visualization analytics—including
automated or generative language prompts that can produce
new visualizations, stories, and analyses of data.
Figure 6: Most used data analytics and visualization tools in controllership for the next 3–5 years
1 2 3
PowerBI SAP Analytics Cloud Tableau
An interactive data visualization A cloud solution for business intelligence, A data visualization tool
software product developed by enterprise planning, and predictive that allows users to
Microsoft with a primary focus analytics that provides all analytics connect, analyze, and
on business intelligence capabilities for all users in one product visualize any data
Other technologies available in survey question include Alteryx, Python, Qlik, R, and SAS.
12
Next-gen controllership | Harnessing AI and emerging technologies to transform finance and accounting
Section 2.3: Process automation survey trends
The implementation of process automation technology is expected Other common automation tools used in controllership include
to remain steady with over one-fourth of respondents (26%) stating Power Query and Tableau, with 16% and 13% of respondents
that they plan to implement automation technology in the next five currently using these tools in the accounting and finance function.
years (figure 7). This is consistent with automation implementation (figure 7).
trends Deloitte has seen in the past five years. With automation
Automation Anywhere use has the highest expected growth, with
tools becoming increasingly available and user friendly,
an increase by more than 50% over the next three to five years
organizations are reviewing manual processes more frequently to
compared to current use according to the survey.
identify automation opportunities�
Automation Anywhere is utilized to automate transactional
The global survey showed that the most used tool for data
workflows, such as customer service and service management.
preparation and automation is SQL server-enabled automation
tools, with over 22% of respondents stating their accounting and
finance function has used SQL-enabled automation in the past
three to five years. Forward looking, SQL-enabled automation tools
will continue to be the most used tools with 18% of respondents
stating their organization plans to implement SQL in the next five
years (figure 7).
Deloitte’s insights
What we have seen in the marketplace aligns with our view that
RPA and intelligent automation will continue to grow� These
technologies leverage a synthetic keyboard and mouse to
execute business processes.
AI impact on on process automation
AI is having, and will continue to have, a transformative impact
on process automation. The convergence of these two
emerging technology solutions has wide-ranging applications in
Deloitte’s insights
the finance environment.
SQL’s popularity may be due to the broad applicability of
multiple tools that leverage SQL data. SQL acts as a Process automation tools are already starting to leverage AI for
reconciling source to ‘hub’ systems, so businesses that user-generated automations, allowing all users to create
leverage multiple data tools can use SQL to reconcile automation without the need for deep tech knowledge� For
multiple sources of data or match source to target example, a user can leverage AI to automate a process by
data. Other tools also utilize SQL servers, which further specifying various inputs to produce an output.
drives its expansive implementation. For example, SQL can
Another example may be reporting automation—where a user
be used in ERP systems to automate data validation
can generate reports with a prompt or question using GenAI.
processes. It is levered by various applications to perform
Finance and accounting professionals may notice some
automated reconciliations and for financial forecasting and
prominent automation providers already offering AI integrations
data imaging�
with broad use cases. Some of these may look like utilizing AI to
What sets SQL apart from other systems on the market may accelerate the user experience or generating content, context,
also be how well known it is. It is also noted as being very and output to accelerate reviews. Other applications may be
user friendly, explainable, and traceable. SQL offers a creating and handling customer queries "
190,deloitte,us-ai-institute-generative-ai-agents-multiagent-systems.pdf,"Prompting for action
How AI agents are reshaping
the future of work
Expanded capabilities, use cases and
enterprise impact from Generative AI
November 2024
Deloitte AI Institute
Prompting for action | How AI agents are reshaping the future of work
About the Deloitte AI Institute
The Deloitte AI InstituteTM helps organizations connect the different dimensions
of a robust, highly dynamic and rapidly evolving AI ecosystem. The Institute leads
conversations on applied AI innovation across industries, with cutting-edge insights,
to promote human-machine collaboration in the “Age of With.”
The Deloitte AI Institute aims to promote a dialogue and development of artificial
intelligence, stimulate innovation, and examine both challenges to AI implementation
and ways to address them. The Institute collaborates with an ecosystem composed of
academic research groups, startups, entrepreneurs, innovators, mature AI product leaders
and AI visionaries to explore key areas of artificial intelligence including risks, policies,
ethics, future of work and talent, and applied AI use cases. Combined with Deloitte’s deep
knowledge and experience in artificial intelligence applications, the Institute helps make
sense of this complex ecosystem, and as a result delivers impactful perspectives to help
organizations succeed by making informed AI decisions.
No matter what stage of the AI journey you’re in, whether you’re a board member or a
C-suite leader driving strategy for your organization or a hands-on data scientist bringing
an AI strategy to life, the Institute can help you learn more about how organizations across
the world are leveraging AI for a competitive advantage. Visit us at the Deloitte AI Institute
to access the full body of our work, subscribe to our podcasts and newsletter, and join us
at our meetups and live events. Let’s explore the future of AI together.
www.deloitte.com/us/AIInstitute
2
Prompting for action | How AI agents are reshaping the future of work
Content
Key takeaways
• AI agents are reshaping industries by expanding the potential applications
of Generative AI (GenAI) and typical language models.
• Multiagent AI systems can significantly enhance the quality of outputs
and complexity of work performed by single AI agents.
• Forward-thinking businesses and governments are already implementing
AI agents and multiagent AI systems across a range of use cases.
• Executive leaders should make moves now to prepare for and embrace
this next era of intelligent organizational transformation.
Introduction 4
AI agents: 5
What makes them different—and why they matter
Multiagent AI systems: 7
Amplifying the potential of AI agents
Key benefits of AI agents and multiagent AI systems: 7
Advantages that AI agents are unlocking for organizations today
Transforming strategic insights: 8
A real-world example of a multiagent AI system
Achieving impact through targeted use cases: 11
How AI agents are changing industries and enterprise domains
Enabling new ways of working and new horizons of innovation: 13
Implications for strategy, risk, talent, business processes and technology
The road ahead: 15
What we expect as AI agents continue to evolve
Charting a course into the next era of organizational transformation: 16
Recommended actions for leaders to take now
Get in touch & Endnotes 17
3
Prompting for action | How AI agents are reshaping the future of work
Introduction
How can we operate faster and
more efficiently?
This question has always been at the forefront of strategic
agendas—but Generative AI (GenAI) is helping unlock new
answers. With its ability to produce novel outputs from plain-
language prompts, GenAI has enabled enterprises to significantly
enhance speed and productivity across a range of business tasks.
However, use cases for typical language models have only just
begun to show GenAI’s transformative potential. In this time of
rapid AI evolution, it’s time to think bigger and bolder: from
streamlining routine tasks to redesigning entire workflows.
Now the question for business and government leaders
is becoming:
How can we rethink our business
processes with GenAI?
Large language models (LLMs) and GenAI-powered tools used
by most organizations today serve as helpful assistants: A human
worker enters a prompt, GenAI quickly produces an output.
However, this interaction is largely transactional and limited
in scope.
What if GenAI could be more like a skilled collaborator that will
not only respond to requests but also plan the whole process
to help solve a complex need? What if GenAI could also tap into
the necessary data, digital tools and contextual knowledge to
orchestrate the process end to end, autonomously?
This vision is becoming a reality with the emergence of AI agents
and multiagent AI systems—a powerful advancement in what’s
possible through human-AI partnership. Leading companies and
government agencies are already seeing the value of AI agents
and putting them into practice.
Adapt or fall behind
In this paper, we explore what makes AI agents so groundbreaking.
We then reveal how they are reshaping industries, including At the end of 2023, nearly 1 in 6
government and public services, by enabling new use cases,
surveyed business leaders said
enhancing automation and accelerating the future of intelligent
organizational transformation. GenAI had already transformed
their businesses.1
4
Prompting for action | How AI agents are reshaping the future of work
AI agents: What makes them
different—and why they matter
To grasp the potential value of AI agents and their role in As a result, early GenAI use cases have mostly been limited to
expanding the automation horizon, it is important to understand standalone applications such as generating personalized ads
how they differ from the language models and GenAI applications based on a customer’s search history, reviewing contracts and
familiar to business leaders today. legal documents to identify potential regulatory concerns,
or predicting molecular behavior and drug interactions in
AI agents are reasoning engines that can pharmaceutical research.
understand context, plan workflows,
AI agents excel in addressing these limitations while also
connect to external tools and data, and leveraging capabilities of domain- and task-specific digital tools
to complete more complicated tasks effectively. For example,
execute actions to achieve a defined goal.
AI agents equipped with long-term memory can remember
customer and constituent interactions—including emails, chat
While this may sound broadly like what standalone LLMs or sessions and phone calls—across digital channels, continuously
GenAI applications can do, there are key distinctions that learning and adjusting personalized recommendations. This
make AI agents significantly more powerful. (See table, page 6.) contrasts with typical LLMs and SLMs, which are often limited to
Typical LLM-powered chatbots, for example, usually have limited session-specific information. Moreover, AI agents can automate
ability to understand multistep prompts—much less to plan and end-to-end processes, particularly those requiring sophisticated
execute whole workflows from a single prompt. In essence, they reasoning, planning and execution.
conform to the “input-output” paradigm of traditional applications
and can get confused when presented with a request that must AI agents are opening new possibilities to drive enterprise
be deconstructed into multiple smaller tasks. They also struggle to productivity and program delivery through business process
reason over sequences, such as compositional tasks that require automation. Use cases that were once thought too complicated
consideration of temporal and textual contexts. These limitations for GenAI can now be enabled at scale—securely and efficiently.
are even more pronounced when using small language models
(SLMs), which, because they are trained on smaller volumes of In other words: AI agents don’t just interact. They more
data, typically sacrifice depth of knowledge and/or quality of effectively reason and act on behalf of the user.
outputs in favor of improved computational cost and speed.
5
Prompting for action | How AI agents are reshaping the future of work
A new paradigm for
human-machine collaboration
Through their ability to reason, plan, remember and act,
AI agents address key limitations of typical language models.
Typical language models AI agents
Use case Automate tasks Automate entire workflows/processes
scope
Planning Are not capable of planning or Create and execute multistep plans to achieve
orchestrating workflows a user’s goal, adjusting actions based on
real-time feedback
Memory & Do not retain memory and have limited Utilize short-term and long-term memory to
fine-tuning fine-tuning capabilities learn from previous user interactions and
provide personalized responses; Memory may
be shared across multiple agents in a system
Tool Are not inherently designed to integrate with Augment inherent language model capabilities
integration external tools or systems with APIs and tools (e.g., data extractors, image
selectors, search APIs) to perform tasks
Data Rely on static knowledge with fixed training Adjust dynamically to new information and
integration cutoff dates real-time knowledge sources
Accuracy Typically lack self-assessment capabilities and Can leverage task-specific capabilities, knowledge
are limited to probabilistic reasoning based on and memory to validate and improve their own
training data outputs and those of other agents in a system
6
Prompting for action | How AI agents are reshaping the future of work
Multiagent AI systems:
Amplifying the potential of AI agents
While individual AI agents can offer valuable enhancements, the
truly transformative power of AI agents comes when they work
together with other agents. Such multiagent systems leverage Key benefits of AI agents
specialized roles, enabling organizations to automate and optimize and multiagent AI systems
processes that individual agents might struggle to handle alone.
Capability—AI agents can automate interactions with
Multiagent AI systems employ
multiple tools to perform tasks that standalone language
multiple, role-specific AI agents to models were not designed to achieve (e.g., browsing a
website, quantitative calculations).
understand requests, plan workflows,
coordinate role-specific agents, Productivity—Whereas standalone LLMs require constant
human input and interaction to achieve desired outcomes,
streamline actions, collaborate with
AI agents can plan and collaborate to execute complex
humans and validate outputs. workflows based on a single prompt—significantly speeding
the path to delivery.
Multiagent AI systems typically involve standard-task agents
Self-learning—By tapping short- and long-term contextual
(e.g., user interface and data management agents) working with
memory resources that are often unavailable in a pre-trained
specialized-skill and -tool agents (e.g., data extractor or
language model, AI agents can rapidly improve their output
image interpreter agents) to achieve a goal specified by a user.
quality over time.
At the core of every AI agent is a language model that provides
Adaptability—As needs change, AI agents can reason
a semantic understanding of language and context—but
and plan new approaches, rapidly reference new and
depending on the use case, the same or different language models
real-time data sources, and engage with other agents to
may be used by agents in a system. This approach can allow some
coordinate and execute outputs.
agents to share knowledge while others validate outputs across
the system—improving quality and consistency in the process.
Accuracy—A key advantage of multiagent AI systems is
That potential is further enhanced by providing agents with shared
the ability to employ “validator” agents that interact with
short- and long-term memory resources that reduce the
“creator” agents to test and improve quality and reliability
need for human prompting in the planning, validation and iteration
as part of an automated workflow.
stages of a given project or use case.
Intelligence—When agents specializing in specific tasks
This concept extends what’s possible with individual AI agents
work together—each applying its own memory while utilizing
by taking a team or agency approach. By decomposing a detailed
its own tools and reasoning capabilities—new levels of
process into multiple tasks, assigning tasks to agents optimized
machine-powered intelligence are made possible.
to perform the tasks, and orchestrating agent and human
collaboration at each stage of the workflow, this type of system
Transparency—Multiagent AI systems enhance the ability
has proven much more likely to produce higher quality, faster
to explain AI outputs by showcasing how agents communicate
and more trustworthy outcomes.2, 3
and reason together, providing a clearer view of the collective
decision-making and consensus-building process.
In other words: Multiagent AI systems don’t just reason
and act on behalf of the user. They can orchestrate complex
workflows in a matter of minutes.
7
Prompting for action | How AI agents are reshaping the future of work
Transforming strategic insights
No matter the industry, every organization engages in research, analysis and reporting—whether about
economic conditions, customer and constituent preferences, policy and pricing strategies, or other topics.
Traditionally, these projects require skilled human analysts to perform multiple steps, which can be
time-consuming, utilizing research and analysis tools along with in-house subject matter expertise.
Here’s what a traditional research project typically looks like.
Analyst Analyst identifies topic and Analyst Analyst selects sources, Analyst
scope: A report on the top 5 searches and compiles
Analyst synthesizes themes
GenAI trends in financial services, relevant information, and
and perspectives, outlines a
based on publicly available data organizes materials and notes.
plan for the report and sends to
from the prior 3 months.
business stakeholder for review.
Stakeholder Analyst drafts the report Analyst Stakeholder provides Stakeholder
and sends to stakeholder, feedback on outline.
who provides feedback
and iterates with analyst. Analyst or designer researches images,
Analyst sends approved develops graphics and designs report.
report to designer.
Analyst Proofer reviews report and Proofer Risk & compliance
or Designer provides feedback, which analyst
and/or designer incorporate.
Risk & compliance professionals
are engaged as needed.
Final report
is delivered.
While effective and repeatable, this approach is …
Time-consuming Inefficient Difficult to scale
Completing a single report can take Skilled analysts must perform many Companies and government agencies
days or weeks, making it difficult to repetitive activities that take their can struggle to hire and retain enough
seize emerging opportunities. focus away from higher-level analysis. skilled, experienced analysts to grow
their research capacity.
8
Prompting for action | How AI agents are reshaping the future of work
Deloitte has developed a multiagent AI system that can streamline
AI AGENT TYPES
and improve each step of research and reporting. Here’s how it works.
“I need to write a report about “Please tell me about
GenAI trends in my industry.” your request ....” Standard-task Specialized-skill
agent(s) & -tool agents
One or more agents Role-specific agents
that perform tasks that execute specific
common to all tasks within the
workflows workflow
Analyst Analyst and interface agent discuss and define report User
scope, sources and timeframe for data collection, target industry interface All agents can access …
and audience, etc. Through this process, the analyst defines the • Language models (shared or separate)
deliverable: A report on the top 5 GenAI trends in financial services,
based on publicly available data from the prior 3 months. • External tools & data sources as needed
• Shared short- and long-term memory
Planning agent breaks the goal into
subprocesses, develops a workflow and
identifies necessary tools and specialized
agents to execute the workflow.
File Multimodal Planning
management processing
Prompt Data Web Content Topic Report
expanding sourcing browsing summarization modeling writing Analyst reviews the report and
requests changes. The system
iterates and refines the report.
Report Data Data Image Quality
formatting structuring visualizing selection assurance
Analyst
Specialized agents expand prompts, conduct research, compile and analyze results, identify themes
and draft the report outline. As needed, the multimodal processing agent translates and interprets data
collected from visual and audio sources. Once the outline is approved/adjusted by the analyst, additional
specialized agents draft and design the report complete with customized charts and illustrations.
Throughout the process, the quality assurance agent checks for accuracy, quality and regulatory/brand
compliance, while the data management agent ensures source materials and report iterations are Final report
documented for reference/review. is delivered.
In addition to being effective and repeatable, this AI agent-powered approach is …
Fast Efficient Highly scalable
A single, quality report can be Skilled professionals can focus on In essence, this system provides an instantly
produced in less than an hour. validating, iterating and refining the report. available team of skilled digital workers.
9
Prompting for action | How AI agents are reshaping the future of work
Effective and efficient work
depends on creativity and
knowledge augmented by
well-planned processes and
task-appropriate tools.
That’s what AI agents and
multiagent AI systems can
bring together.
10
Prompting for action | How AI agents are reshaping the future of work
Achieving impact through
targeted use cases
Organizations across industries and sectors are already leveraging the potential of AI agents and multiagent
systems to transform processes, improve efficiency, and expand impact. Let’s explore four use cases
that are possible today—two in specific industries, and two that can be applied in any business.
1 USE CASE 2 USE CASE
Individualized financial advisory Dynamic pricing and
and wealth management personalized promotions
INDUSTRY: Financial services INDUSTRY: Consumer
Financial advisory services often have relied on broad Standard pricing strategies often involve static models that do
categorizations of customers based on age, income and risk not account for real-time market conditions, customer behavior
tolerance. This approach can often miss the complexities of or inventory levels. Multiagent AI systems can rapidly integrate
individual financial situations and goals. In today’s rapidly changing analysis based on vast amounts of real-time data—such as
financial landscape, there is an increasing demand for personalized, competitor pricing, customer purchase history and seasonal
adaptive financial advice. Multiagent AI systems can analyze diverse trends—to dynamically adjust prices. Additionally, they can
data sources—including the customer’s financial history, real-time personalize promotions based on individual customer
market data, life events and even behavioral patterns—to help preferences, attributes and shopping habits with the goal of
advisers create financial plans and investment strategies tailored improving conversion rates and elevating customer satisfaction.
for the specific individual. AI agents can then continuously monitor
and adjust recommendations as circumstances change. POTENTIAL ADVANTAGES ACHIEVED WITH AI AGENTS:
POTENTIAL ADVANTAGES ACHIEVED WITH AI AGENTS:
Faster adaptation
Adjust prices instantly in response to
Hyperpersonalization market changes, inventory levels or
Customize financial advice to each customer’s customer demand—optimizing revenue.
specific needs and goals, considering factors
Personalized offers
that other methods might overlook.
Tailor promotions to each customer’s
Continuous fine-tuning preferences and behavior, increasing the
Automatically update financial plans and likelihood of purchase.
strategies in response to changes in market
Greater profitability
conditions or personal circumstances.
Maximize margins and minimize discounting
Improved customer satisfaction by optimizing pricing and promotions on an
Strengthen customer relationships by ongoing basis.
providing more relevant and timely advice,
leading to higher retention and satisfaction.
Enhanced scalability
Serve a larger number of customers with
high-quality, personalized advice without
raising costs to deliver.
11
Prompting for action | How AI agents are reshaping the future of work
3 USE CASE 4 USE CASE
Talent acquisition and recruitment Personalized customer support
DOMAIN: Human resources (HR) DOMAIN: Customer and beneficiary service
Traditional recruitment processes often involve manual resume Traditional customer and beneficiary support systems often rely on
screening, repetitive candidate assessments and significant scripted interactions, which can fail to resolve complex or unique
administrative work—which can lead to inefficiencies. AI agents inquiries—leading to customer frustration and escalation.
can automate the end-to-end recruitment process by using natural In contrast, multiagent AI systems can understand plain-language
language processing to analyze resumes, assess candidates based requests and generate relevant and natural responses that
on skills and experience, and conduct initial screening interviews consider the customer’s history, preferences and real-time context.
via GenAI-powered avatars. These systems can collaborate with These advanced systems can handle many complex inquiries
HR professionals to ensure that qualified candidates are identified, effectively—reducing the need for escalation to live agents while
prioritized and moved through the hiring pipeline efficiently while improving customer/beneficiary satisfaction.
adhering to relevant regulations.
POTENTIAL ADVANTAGES ACHIEVED WITH AI AGENTS:
POTENTIAL ADVANTAGES ACHIEVED WITH AI AGENTS:
Greater consistency and scalability
Increased efficiency AI agents can operate 24/7 without fatigue,
Automate tasks to allow HR teams maintaining a consistent quality of service
to focus on strategic activities, shortening no matter the volume of inquiries.
the time to hire.
Improved customer experiences
Improved candidate matching Each customer interaction can be adjusted
Analyze a broader range of data points to help to individual needs, improving satisfaction
match candidates to roles more accurately, and engagement.
improving the quality of hires.
Compounding efficiencies
Reduced bias The ability to learn from each interaction can
By standardizing candidate assessments and help reduce response times, improve quality,
focusing on skills and experience, AI agents can help and free up human service agents to focus on
address unconscious bias in the recruitment process. more nuanced customer requests.
Dynamic scalability
Handle large volumes of applications, making it
easier to manage hiring campaigns or recruit for
multiple roles simultaneously.
12
Prompting for action | How AI agents are reshaping the future of work
Enabling new ways of working
and new horizons of innovation
As language models continue to evolve, AI agents and systems are Risk implications
likely to become strategic resources and efficiency drivers for core
business and government activities such as product development, AI agents introduce new risks that necessitate robust security
regulatory compliance, customer service, constituent engagement, and governance structures. A significant risk is potential bias in AI
organizational design and others. We see a future in which algorithms and training data, which can lead to inequitable decisions.
agents will transform foundational business models and Additionally, AI agents can be vulnerable to data breaches and
entire industries, enabling new ways of working, operating cyberattacks, compromising sensitive information and data integrity.
and delivering value. The complexity of AI systems also presents the risk of unintended
consequences due to AI agents behaving unpredictably or making
That’s why it’s important for C-suite and public service leaders decisions not aligned with organizational goals.
to begin preparing now for this next chapter in the evolution of
human-machine collaboration and business innovation. To manage these risks, it is important to set clear parameters for
agent interactions, monitor operational metrics, and continually
Let’s explore some of the new ways of thinking and leading that ensure data ethics, privacy, security and integrity. As AI agents
should be considered during this time of rapid change. are integrated into core business processes, an enterprisewide
governance framework with guidelines on data usage, ethics and
security can further help mitigate risks. This framework should
Strategy implications ensure compliance with relevant regulations and include continuous
monitoring of AI agent interactions. Advanced security measures,
Leaders should begin integrating AI agents and multiagent
such as encryption and multifactor authentication, can help protect
AI systems into their overall strategies and future road maps.
against data breaches and cyberattacks. Training and awareness
This involves reimagining business processes, investing in AI
programs for employees can provide an additional defense
capabilities, and fostering cultures of innovation. Organizations
by helping employees understand the ethical and operational
should develop their own clear road map for AI agent adoption,
considerations of working with AI agents.
identifying key areas where they can drive the most value and
impact on broader business goals.
Effective change management will be crucial for successful FOCUS AREAS
integration. Leaders should think carefully through how they will
• Identify brand and operational risks that may arise around
address organizational resistance, provide training, and ensure
data usage, AI agent interactions with each other and with
that employees understand the value and benefits of AI agents.
tools, and ethics.
This includes developing a comprehensive communication strategy
to keep employees and other stakeholders informed and engaged
• Ensure model outputs are effectively tested and validated.
throughout the adoption process.
• Implement an AI agent governance framework that is
regularly reviewed and updated as AI technologies evolve.
FOCUS AREAS • Monitor emerging risks specific to AI agents such as “agent
autonomy”—i.e., the risk of unintended consequences when
• Identify and prioritize business and service areas agents make decisions with minimal human oversight.
where AI agents can have the most immediate and
measurable impact.
• Develop robust training programs to help employees
understand and use AI agents in ways that improve
productivity and efficiency.
13
Prompting for action | How AI agents are reshaping the future of work
Talent implications Business process implications
The implementation of AI agents is likely to change the traditional AI agents and multiagent AI systems demand careful human
workforce structure. As AI agents take over routine and lower-value evaluation of business processes—sometimes from the
tasks, there will likely be a high demand for human skills related to ground up. While agents will redefine many core processes over
designing, implementing and operating these systems. Leaders time, AI agents can be integrated into existing operating models
should think through what new roles, job descriptions and job today, enhancing the efficiency of current processes without the
architectures are involved in building out the capability and then need for complete system overhauls. This approach makes it
how to identify, recruit, train and retain this specialized talent. easier for organizations to adopt lower-risk agent solutions
incrementally—but requires careful planning, management
Beyond the implications for tech talent, enterprise leaders should and alignment to ensure that AI agents are improving what
be ready to help employees across a wide variety of roles learn people and/or other technology solutions already do well.
how to work with AI agents and even identify new use cases
where they could improve processes. Deployed and managed In use cases where AI agents do make sense to implement,
well, AI agents can open up new realms of potential for human- human involvement will remain vitally important for tasks
machine collaboration—but that potential depends on workers requiring judgment, validation and critical decision-making.
understanding, embracing and being able to perform new roles. This collaboration is important to help ensure that AI outputs
are accurate, reliable and effective. In this paradigm, everyone
working with AI agents serves as a manager—giving orders (via
prompts), clarifying requests, monitoring progress, reviewing
FOCUS AREAS
outputs and requesting or making changes as necessary.
• Communicate the benefits of AI agents, and help
employees adapt to new ways of working.
• Foster a culture of innovation and continuous learning. FOCUS AREAS
Leaders should instill a mindset of innovation and
• Ensure that where agents are implemented into existing
adaptability related to AI agents.
business processes, those processes remain effective
• Explore a redesign of job architectures, workflows while driving greater efficiency and value.
and performance metrics to reflect the new reality
• Establish processes for continuously monitoring and
of humans and AI agents working in tandem.
improving the performance of AI agents. This includes
collecting and analyzing data on the performance of
AI agents, identifying opportunities for improvement, and
making changes as needed to optimize their performance.
14
Prompting for action | How AI agents are reshaping the future of work
Technology and data implications
Implementing AI agents can be costly, requiring substantial The road ahead
investment in technology and infrastructure. Organizations
should carefully evaluate the value proposition and return on The era of AI agent collaboration is still in its early stages.
investment; and develop a phased approach to use cases, with Interest is growing among businesses and technology
a focus on “low-hanging fruit” (i.e., simpler use cases) that can providers, but comprehensive solutions are not yet common.
lay the groundwork for more complex activations. There is much technical work to be done—particularly in
terms of the reasoning and planning capabilities that will
Quality data is the foundation for AI agents to work effectively. enable AI agents.
If data is inaccurate, incomplete or inconsistent, the agents’
outputs and actions may be unreliable or incorrect—creating Improvements are likely to come fast. In recent months
both adoption and risk issues. It’s therefore essential to invest GenAI tools have shown significant improvements in
in robust data management and knowledge modeling. reasoning and agent orchestration capabilities. Many venture
capital firms are investing heavily across the spectrum of AI
Adopting trustworthy AI practices is a key to mitigating risks agent-related technologies, as are many of today’s leading
and ensuring ethical deployment. This includes developing GenAI and technology providers. What is available today is
AI agent solutions that are fair, transparent and accountable, only a glimpse of what’s to come. Indeed, we anticipate a
and addressing potential biases in AI models. significant evolution of core language models, AI agents, and
agent orchestration platforms within the next 12 months.
Future-focused leaders aren’t waiting on the sidelines.
FOCUS AREAS
Across industries, companies are already designing, testing
• Put the right technology infrastructure in place to and—in some cases—implementing agents.
support the adoption and implementation of AI agents
(e.g., AI orchestration platforms and scalable data lakes).
• Ensure data is properly organized, up to date and
accessible to AI agents. This includes having well-defined
data governance policies and procedures as well as
continuous access to real-time data feeds to enable
dynamic, accurate decisions.
• Establish processes for monitoring and managing the
performance and ethics of AI agents and multiagent
AI systems. Without transparent and trustworthy AI,
customer trust and regulatory compliance are at risk.
15
Prompting for action | How AI agents are reshaping the future of work
Charting a co"
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customer care
Deloitte AI InstituteTM
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About the Deloitte AI InstituteTM
The Deloitte AI Institute helps organizations Deloitte’s deep knowledge and experience in AI
connect the different dimensions of a robust, highly applications, the Institute helps make sense of this
dynamic and rapidly evolving artificial intelligence complex ecosystem and as a result provides impactful
(AI) ecosystem. The AI Institute leads conversations perspectives to help organizations succeed by making
on applied AI innovation across industries, with informed AI decisions.
cutting-edge insights, to promote human-machine
collaboration in the “Age of WithTM”. No matter what stage of the AI journey you’re in—
whether you're a board member or a C-Suite leader
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Institute collaborates with an ecosystem composed of for a competitive advantage. Visit us at the Deloitte AI
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22
AI-powered Communications Service Providers
Communication Service Providers (CSPs)
can leverage AI-enabled solutions to
build a competitive advantage through
operational efficiencies and enhanced
customer experiences.
For CSPs, the need to optimize The business benefits of artificial transformation of business functions
and automate processes is intelligence (AI) are becoming more and moving from a traditional to an
increasing rapidly, especially apparent each day. With AI—and augmented workforce. Way too
given the challenge of managing especially Generative AI—CSPs can often companies hastily pursue
and servicing complex networks automate mundane tasks through individual, disjointed AI and
while still delivering a seamless prediction and decision-making, Generative AI use cases that
and personalized customer derive insights that improve customer eventually waste resources and
experience. Automating processes, experiences, and generate data prove ineffective in achieving the
improving business insights, and that can be used to train machine goals of the business. CSP leaders
creating value require flexible learning models and simulate should first develop a sound AI
strategies, a creative approach and real-world scenarios. strategy for transforming their
data-driven methodologies that can operations in alignment with
prescribe custom solutions at the Capitalizing on this value is no their business objectives.
right place and at the right time. small feat—achieving AI-enabled
operations involves end-to-end
3
AI-powered Communications Service Providers
What is AI-enabled
CSP operations?
AI-enabled CSP operations embed AI solutions to optimize and
automate field service and network operations, and enhance customer
service. By automating complex tasks, improving decision-making,
optimizing network strategy and performance, and enabling
personalized customer experiences, CSPs can:
Improve Reduce Increase Generate
efficiency costs customer loyalty new revenue
44
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Why is Generative AI a game-changer?
Here are a few examples where CSPs Network ops and maintenance
Generative AI is a subset of artificial
can utilize AI and Generative AI to Identify network faults through digital
intelligence in which machines create
drive value in the enterprise: twins and provide prompt-based
new content in the form of text, code,
remediation solutions for on-field
voice, images, videos, and processes.
Personalized customer
technicians for faster resolution.
Generative AI has massively expanded
self-service
the scope of what value AI can Advanced decision making
Enable on-the-fly customer support
bring because it can generate net in the field
based on local language and propose
new content based on past data Augment technicians with advanced
new product/service recommendatio
and patterns, as well as assist in problem-solving to provide them with
ns, generating offers to increase
formulating new solutions. Generative additional solutions they might
satisfaction and retention.
AI also allows users to retrieve not have thought of or to test new
Network planning
data across complex and siloed solutions prior to implementation.
and deployment
data sources to gain insights with
Create simulations of service quality
ease. Other AI techniques such as Amidst an ever-changing
and subscriber experience at
supervised and unsupervised learning business and technology
potential deployment sites based on
models can be used to predict landscape, CSPs who effectively
user behavior, environmental and
outcomes and uncover anomalies. embed Generative AI across
historical usage data.
Together with Generative AI, they their operations may be better
represent a significant opportunity Network stress testing able to adapt quickly and remain
for CSPs to reinvent their operations. Generate scenarios to simulate future competitive. Here's more on what
network consumption patterns AI can do for customer service,
to stress test load and provision field services, and network
resources given specific bandwidth operations.
and network constraints.
5
AI-powered Communications Service Providers
Elevating customer care
Technology innovations are pushing CSPs to reimagine how they
deliver customer care experiences. The low costs of switching from one
provider to another and increased competition require companies to
build loyalty through delivering a differentiated customer experience.
Next best action/Next best offer. Proposes
Customer experiences can now be improved
new product/service recommendations and
through next-generation conversational
sales offers based on customer preferences to
AI, which can create unique, personalized
increase satisfaction and retention.
encounters that are brought to life by
avatars and Generative AI models. Imagine Smart routing. Quickly connects customers
empathetic, multilingual chatbots instantly to the best live agents based on specific needs
solving technical issues, providing personalized when issues are beyond what the system
recommendations, and understanding can do.
the nuances of every customer question.
Additionally, predictive models can anticipate For agents
problems before they arise, proactively solving Generative AI can support customer service
the issue to improve their experience. Need associates by generating suggested scripts,
to speak to an agent? They are better armed solutions, and recommendations based on
with chatbots that can summarize complex current customer issues and past interactions
information at a glance. This seamless, AI- and trends. Agent performance can be
driven approach boosts customer satisfaction improved by creating a one-stop-shop hub
and fosters loyalty through personalized and where agents have fast access to information
delightful experiences. and tools, such as:
Agent assist. Enables agents to collaborate
For customers
with Generative AI virtual assistants trained on
Generative AI can improve outcomes through
internal data in real-time via voice and text to
direct customer interactions and enable
help resolve customer inquiries more efficiently.
customer service associates to be more
Issue summarization. Automatically
efficient and customer centric. Generative AI
summarizes, tags, and logs customer history
virtual assistants can enhance the self-service
for agents' future reference.
experience and effectiveness by interpreting
customer intent and available data via: Agent training. Evaluates customer sentiment,
resolution steps, and KPIs to initiate coaching
Knowledge search. Quickly interprets context
and training.
and searches for relevant information from
complex and siloed customer history and
knowledge management systems to deliver
tailored responses in real-time.
6
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Demonstrated customer care benefits
AI can help CSPs
improve customer
experiences with
Increased Reduced Labor Increased Better new- purposeful human-to-
personalization operating costs effectiveness compliance customer machine interactions
with more calls with live agents acquisition and conversations can
deflected to handling more and improved be streamlined and
virtual agents complex issues retention personalized.
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AI-powered Communications Service Providers
Transforming field service
and logistics
A heavier reliance on at-home networks and increased customer
expectations of network reliability have exacerbated the challenges
for field service and logistics teams. The added strain can lead
to inefficient operations, higher costs, and a less-than-ideal
customer experience.
To address these challenges, CSPs can leverage For technicians
AI across the field service value chain. AI tools Once technicians are on site, Generative AI
can help CSPs optimally source, manage, can serve as a copilot. The technology
and pick/pack inventory that field service enables them to more efficiently complete
technicians bring to job sites. Logistics teams jobs, improving technicians’ experience and
can also leverage optimization algorithms to productivity while bettering the customer
efficiently equip and schedule technicians experience. For example:
based on skillsets in addition to dynamically
Issue resolution. Leverage Generative AI
routing them based on unpredictable
to power a search for personalized solutions
constraints such as schedule changes and
to customer network issues and accelerate
weather disruptions.
troubleshooting. Retrieval augmented
generation and summarization from internal
For logistics managers
databases and customer chat history can
Generative AI can support logistics managers
generate the recommended resolution steps
and help them make more informed, proactive
and explanations for network engineers.1
decisions. Look into solutions such as:
Vision AI for image and video analysis can
Field technician dispatch. Optimize your fleet further enable technicians to diagnose issues
productivity with faster dispatch and dynamic through visual inspection.
routing, significantly reducing the organization's
carbon footprint. The advanced scheduling
can route field technicians based on skillsets,
equipment required, and availability.
Predictive maintenance. Avoid untimely
vehicle and mechanical warehouse
breakdowns, as machines alert managers
of potential problems before they happen.
8
AI-powered Communications Service Providers
Demonstrated field service and logistics benefits
These improvements
can not only result in
reduced costs, but also
Increased Reduced Decreased Lower costs Decreased Increased new revenue through
field service time to dispatch for gas and time to train customer an enhanced brand
technician resolution processing maintenance and onboard retention and greater customer
productivity for jobs time technicians loyalty by serving the
customer more quickly
and effectively.
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AI-powered Communications Service Providers
Optimizing network operations
Network downtime and service degradations cost CSPs tens of
billions of dollars in losses per year.2 The ability to move from reactive
to proactive asset maintenance can deliver significant savings.
Network Operations start to look different when enabled with AI
technologies. There aren’t as many fire drills to answer.
Many CSPs are now enabling their Network Network operations optimization. Move
Operations Centers (NOCs) with AI. These centers from reactive to proactive automation of Tier-1
serve as the nerve center of telco networks and operations that can deliver significant cost savings
can enable downstream AI use cases that can while minimizing network downtime and service
drive value for CSPs. NOCs of the Future will rely degradations. Generative AI can help technicians
less on human wherewithal and manual processes correlate alarms with meaningful insights and
by implementing AI-enabled solutions that bring take action by automating resolution steps.3
greater agility, precision, and proactiveness.
Predictive surveillance. Predict potential
Building AI-enabled NOCs are not only more
future issues before they occur and monitor
efficient, but also more resilient, scalable, and
network equipment in-real time in order to detect
future-ready.
anomalies that might indicate potential issues.
Leveraging AI helps maintain the physical layer
From an operational efficiency perspective, this
of the network, and proactively avoids sudden
includes leveraging historical maintenance and
equipment malfunction and expense truck rolls.
fault data to train predictive and network-specific
Automated data enrichment. Dramatically
large language models that can evaluate real-time
improve ticket resolution. Technicians have to
streaming data to identify potential network
parse through siloed information sources in
issues and raise alarms for network engineers to
order to resolve issues that arise. Generative AI
quickly intervene. AI can help to reduce operations
can enrich issue data by automatically pulling
costs through preventative actions and improve in relevant information from network service,
the customer experience through proactive configuration, and performance metrics. It then
notifications and backup solutions. correlates it with past incidents and resolution
approaches, which can lead to a quicker
For network operators understanding of each problem, more certainty
Deloitte EMEA's Telecom Engineering Centre around the most effective fix, and faster mean-
time-to-diagnose and mean-time-to-resolve.
of Excellence outlines significant return on
investment for automation in its paper,
The Age of Telecom Network Automation.
Both AI and it's counterpart Generative AI are
emerging as game changers, saving critical
time in such areas as:
10
AI-powered Communications Service Providers
Demonstrated network operations benefits
These results
demonstrate that
AI-enabled operations
Reduced Reduced network Increased network Preventative care can improve network
operational downtime outage & incident for network access infrastructure by
complexity predictability decreasing disruptions,
reducing risk of
network failure, and
improving productivity
of network technicians.
1111
AI-powered Communications Service Providers
Create a winning AI strategy
When developing a sound AI strategy for transforming operations,
CSP leaders should align closely with their business objectives.
1 Develop a strategic Generative AI ambition
Your AI strategy should align to your core business objectives and goals and be clear on the
value it aspires to realize as well as the route to get there via considered, achievable action.
2 Produce a compelling case for transformation
Considerations should be made across economic viability, technology viability, privacy,
risk appetite, capacity required and competitive advantage for the case taken to the board.
3 Establish a purposeful approach to prioritizing use cases
Use a risk versus reward metrics to help determine where to start. AI use cases not only
need to aspire to business objectives, but also be evaluated for risks.
4 Identify key players to inspire and drive transformation
A cultural shift will be necessary to upend current business processes, bring people
along on the journey early.
5 Evolve talent to keep pace
For humans and machines to collaborate effectively, both business and technical teams
need to be fluent in and adaptable to new AI technologies.
6 Assess the technical landscape
The technology required to deliver, monitor, evaluate, and improve AI models should
all be evaluated.
7 Develop and efficient data governance approach
The traditional data capabilities built for traditional analytics can support AI with
additional attention on quality, governance, availability, and ownership clarity.
8 Ensure robust controls
Risks and their corresponding mitigations should be built into AI delivery
for every use case, not as an afterthought.
9 Put risk, privacy and ethical considerations at the forefront
Design governance and control mechanisms to ensure ethical and accountable
AI development aligned to policies and customer expectations.
10 Adapt operating models for AI development
Operating models across core and edge businesses should drive safe and consistent
delivery of AI solutions, instilling confidence in the decisions and insights that result.
12
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AI is a transformative opportunity for CSPs. Many organizations are already
realizing significant benefits, both cost saving and revenue generating, by
automating processes and augmenting workforces with tools that can accelerate
and improve performance. The CSPs that look at the technology in a broad way,
with a clear path, across operations may better their long-term position.
As we progress through the AI However, it is imperative that the If CSPs do this correctly, smooth
era, CSPs have an opportunity to journey begins with a cohesive operations, delighted customers,
transform their operations with AI strategy in order to avoid and a differentiated competitive
enhanced automation, precision, a siloed approach that fails to advantage could lie ahead.
and personalization. extract the value from this
revolutionary technology.
1133
AI-powered Communications Service Providers
14
4 1
AI-powered Communications Service Providers
Reach out for a conversation.
Howie Stein Mohamad Said
Baris Sarer
Deloitte Consulting LLP Deloitte Consulting LLP
Deloitte Consulting LLP
howiestein@deloitte.com bsarer@deloitte.com msaid@deloitte.com
Endnotes
1 Beena Ammanath et al, The Generative AI Dossier, Deloitte, 2023, p. 137.
2 Anubhav Mohanti et al, “The hidden costs of downtime: The $400B problem facing the Global 2000,”
Oxford Economics in partnership with Splunk, 23 July 2024.
3 Beena Ammanath et al, p. 137.
14
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action that may affect your business. Before making any decision or taking any action
that may affect your business, you should consult a qualified professional advisor.
Deloitte shall not be responsible for any loss sustained by any person who relies on
this publication.
About Deloitte
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192,deloitte,us-ai-institute-generative-artificial-intelligence.pdf,"A new frontier
in artificial intelligence
2
Implications of Generative AI for businesses
Deloitte AI Institute
Implications of Generative AI for businesses
Implications of Generative AI for businesses
Executive summary Contents
The year 2022 was a watershed year for artificial intelligence Even so, Generative AI is in its infancy and not without risk. Section I
(AI), with the release of several consumer-facing applications Some of the most important risks to address relate to privacy and Decoding the Generative AI magic trick 5
like ChatGPT, DALL.E, and Lensa. The common theme the use security, managing bias, transparency and traceability of results,
of Generative AI–a paradigm shift in the world of AI. While current IP ownership, and equal access, especially for those at greater Section II
generations of AI use pattern detection or rule-following to help risk of job displacement. As such, participants should balance Consumer and enterprise use cases
analyze data and make predictions, the advent of transformer commercialization, regulation, ethics, co-creation, and even for Generative AI 9
architectures has unlocked a new field: Generative Artificial philosophy, as well as expand the group of stakeholder thinkers
Intelligence. Generative AI can mimic the human creative and contributors beyond technologists and enthusiasts. Section III
process by creating novel data similar to the kind it was Commerce and competition in Generative AI 17
trained on, elevating AI from enabler to (potentially) Ultimately, Generative AI could
co-passenger. In fact, Gartner estimates that more than 10% Section IV
create a more profound relationship
of all data will be AI-generated by as early as 2025,1 heralding a new Adopting and commercializing Generative AI 27
age, the Age of With™. between humans and technology,
even more than the cloud, the
Although early traction has been through consumer releases, which
About the Deloitte AI Institute
could be era-defining, Generative AI also has the potential to smartphone, and the internet
add contextual awareness and human-like decision-making
to enterprise workflows, and could radically change how we
did before.
Various analysts estimate the market for
The Deloitte AI Institute helps organizations connect the different
dimensions of a robust, highly dynamic and rapidly evolving
do business. We may be only just beginning to see the impact Generative AI at $200B by 2032.7 This represents ~20% of total
AI ecosystem. The AI Institute leads conversations on applied
of solutions like Google’s Contact Center AI (CCAI), which is designed AI spend, up from ~5% today.8 Said another way, the market
AI innovation across industries, with cutting-edge insights, to
to help enable natural language customer service interactions,2 and will likely double every two years for the next decade.
promote human-machine collaboration in the “Age of With”.
industry-specific solutions like BioNeMo from NVIDIA, which can Numbers aside, we believe the economic impact could be
accelerate pharmaceutical drug discovery.3 As such, Generative AI far greater. To help understand the potential, this paper
The Deloitte AI Institute aims to promote a dialogue and
has attracted interest from traditional (e.g., Venture Capital (VC), is equal parts primer and provocateur, adding structure
development of artificial intelligence, stimulate innovation, and
Mergers & Acquisitions (M&A)) and emerging (e.g., ecosystem to a rapidly changing marketplace. We start with a brief
examine challenges to AI implementation and ways to address
partnerships) sources. In 2022 alone, venture capital firms explainer of the foundational elements, delve into enterprise and
them. The AI Institute collaborates with an ecosystem composed
invested more than $2B,4 and technology leaders made significant consumer use cases, shift focus to how players across the market
of academic research groups, start-ups, entrepreneurs,
investments, such as Microsoft’s $10B stake in OpenAI5 and can build sustainable business models, and wrap up with some
innovators, mature AI product leaders, and AI visionaries, to
Google’s $300M stake in Anthropic.6 considerations and bold predictions for the future of Generative AI.
explore key areas of artificial intelligence including risks, policies,
ethics, future of work and talent, and applied AI use cases.
The far-reaching impacts and potential value when deploying
Combined with Deloitte’s deep knowledge and experience in
Generative AI are accelerating experimental, consumer, and soon,
artificial intelligence applications, the Institute helps make sense
enterprise use cases. And even though much media coverage
of this complex ecosystem, and as a result, deliver impactful
has focused on consumer use cases, the opportunities are
perspectives to help organizations succeed by making informed AI
widespread–and some are already here. Still, questions remain
decisions.
about how individuals and enterprises could use Generative AI to
deliver efficiency gains, product improvements, new experiences,
No matter what stage of the AI journey you’re in; whether you’re
or operational change. Similarly, we are only beginning to see how
a board member or a C-Suite leader driving strategy for your
Generative AI could be commercialized and how to build sustainable
organization, or a hands on data scientist, bringing an AI strategy
business models.
to life, the Deloitte AI institute can help you learn more about how
enterprises across the world are leveraging AI for a competitive
advantage. Visit us at the Deloitte AI Institute for a full body of our
work, subscribe to our podcasts and newsletter, and join us at our
meet ups and live events. Let’s explore the future of AI together.
www.deloitte.com/us/AIInstitute 3
2
Implications of Generative AI for businesses 2
IImmpplliiccaattiioonnss ooff GGeenneerraattiivvee AAII ffoorr bbuussiinneesssseess Implications of Generative AI for businesses
Section I: Decoding the Generative AI magic trick
SECTION I
Decoding the Generative AI
magic trick
The lofty expectations for Generative AI depend
on continued progress and innovation across
an interconnected hardware, software, and data
provider ecosystem.
The tech stack underlying Generative AI,
however, is in some ways similar to others
that came before. It consists of three layers:
t
infrastructure, platform, and applications.
Infrastructure is generally accepted s
as the most established, stable, and
commercialized layer. Incumbents
offer compute, networking, and storage,
including access to specialized silicon
(microprocessors) like NVIDIA’s GPUs and
Google’s TPUs optimized for AI workloads.
Meanwhile, the application layer is evolving
rapidly and consists of leveraging and
extending foundation models, which is s
Generative AI’s equivalent of a platform.
44 5
Implications of Generative AI for businesses Implications of Generative AI for businesses
Section I: Decoding the Generative AI magic trick
Generative AI Tech Stack Foundation Models, however, are what While this framework is applicable across AI
differentiate the Generative AI tech architectures, state-of-the-art Foundation
End-users stack from AI that came before. At its Models today (e.g., GPT-3, Stable Diffusion,
core, a Foundation Model, a term coined by Megatron-Turing) are based on a neural
Stanford University’s Center for Research on network architecture called transformers,
Application Development
3 Foundation Models, is a machine learning invented by a team at Google Brain in
User-facing B2B and B2C apps (ML) model pre-trained on a broad dataset 2017.10 Transformers represent a step
developed in partnership with that can be adapted to solve a range of change in ML performance and differ from
or on top of proprietary models problems.9 Just as Microsoft’s Win32 offers prior architectures in their ability to assign
Application APIs for developers to access base-level context, track relationships, and predict
Ecosystem hardware and OS functions, and NVIDIA’s outcomes. The most mature Foundation
Vertically Integrated CUDA allows graphic-intensive applications Models today are in the text domain,
Open/Closed APIs Foundation Models like game engines simplified access to GPU primarily driven by vast quantities of
Fine-tuned Models resources, the model layer is designed to available training data, which accelerated
Niche proprietary models
Refined models for targeted use case connect ambitious application developers to the development of Large Language Models
with pre-built user-facing
2 optimized hardware to help accelerate the (LLMs), a type of Generative AI foundational
B2B or B2C apps.
adoption of and democratize Generative AI. model. LLMs are trained to generate
text by predicting the next word in
Foundational Models
Model Layer These models are often available to a sequence or missing words within
Open or Close-source models
developers via closed and open APIs, where a paragraph.
developers can fine-tune models with
additional training data to improve context,
relevance, and performance to specific use
Hyperscale Compute
1 cases, all while optimizing delivery costs. Compute, networking, storage, and middleware
Foundation models are typically developed
in four stages, which are illustrated below.
Infrastructure/
Hardware Silicon
Specialized microprocessors/accelerators for training & interference Development of Foundation Models
1 2 3 4
Source: Deloitte
Architecture Pre-training Fine-tuning Production
The structure Training on Adjusting Deployment to
and design of the a massively large parameters production where
model and the dataset to create to improve the model is
algorithm used a defined set performance accessible via APIs
for training of parameters on specific tasks
Training Dataset Curated Dataset
Process Elements: Process Input
6 7
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Output
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detsujdA
sretemaraP
Section I: Decoding the Generative AI magic trick
Moreover, Generative AI can create
artifacts across various modes—code,
images, video, audio, and 3D models.
This could both disrupt and drive step
changes in productivity across a range
of capabilities, from copywriting to
research and software engineering. For
example, in advertising, Generative AI could
create original copy, product descriptions,
and images in seconds. It can generate
synthetic X-ray images in healthcare, helping
physician diagnostic training.
Indeed, Generative AI
could transform how
businesses operate and
interact with customers
and may even redefine
an “employee” as we
know it. This transformation
is already underway
in some consumer and
enterprise spaces.
Source: Deloitte
Implications of Generative AI for businesses Implications of Generative AI for businesses
Section II: Consumer and enterprise use cases
for Generative AI
SECTION II
Consumer and enterprise
use cases for Generative AI
In 2022, OpenAI’s DALL·E 2 captured the world’s
attention with its text-to-image capabilities.11
The model creates images from simple prompts,
from something as direct as “a lion in a jungle”
to something more comical like “two lions playing
basketball in the style of Picasso.”
Ever since, Generative AI has occupied the Efficiency | Optimizing tasks like planning,
news cycle, punctuated by other launches research, and product discovery
like ChatGPT and previews like MusicLM. No
wonder we’ve seen broad-market consumer Instruction | Providing personalized
use cases, like Bing’s internet search guidance or learning content
powered by OpenAI’s ChatGPT.12 These
are emblematic of a Cambrian explosion in Creation | Generating or enhancing
consumer apps, touching everything from content, replicating the creative process
search to therapy.
Entertainment | Building games,
To help contextualize this explosion, virtual personas, and other entertainment
we group consumer use cases—those
that individuals invoke in their personal This is just an early view of the market; there
lives—into four broad categories based will likely be overlapping categories as work
on the utility provided: evolves. Moreover, new, category-defining
use cases are expected to emerge as future
generations of AI (e.g., those that enable
multi-model engagement or run entirely
on-device) mature.
8 9
Implications of Generative AI for businesses Implications of Generative AI for businesses
Section II: Consumer and enterprise use cases
for Generative AI
A Sampling of Consumer Use Cases Available Today
Efficiency Instruction Creation Entertainment
Creating a health Conversing with Generating & editing
Creating original games
& wellness plan virtual companions video files
Creating personalized Creating interior Chatting with pop
Discovering new products
financial plans design mockups culture figures
Conducting research Curating outfits
Teaching new languages Rendering 3D environments
with citations & fashion ideas
Modifying & editing Remixing or
Curating content Synthesizing research papers
design files sampling music
Guiding & informing Creating art Generating original fictional
Answering general questions
personal writing & editing images short stories
Sample vendors
Synthesis.ai Grammarly Luminar AI Jasper
Consensus Lingostar.ai Lensa Scenario
10 11
ytixelpmoC
hgiH
ytixelpmoC
woL
Section II: Consumer and enterprise use cases
for Generative AI
The pace of change can make predictions challenging, Consumer use cases can also be indicators These efficiencies may even redefine
of the possibilities in the enterprise. job expectations, making prompt
but as of early 2023, we expect consumer use cases
However, unlike consumers, enterprises engineering (i.e., asking AI the right
with the following aspects as having staying power: require advanced features, proven ROI, questions) a differentiating skill set.
customization, organizational content, Ultimately, horizontal use cases will
security, and technical support. In today’s create a commercial foundation for more
formative era of Generative AI, the most specialized applications. Enterprises must
popular enterprise use cases—invoked start deploying these early to help build
to drive internal or B2B outcomes—will capabilities and a knowledge base, making
be general purpose or applicable across the value case for vertical applications
industries or functions (“horizontal”). over time.
However, like technologies that came before,
Speed to market Occupational utility Seamless integrations
there are often more sustainable value- Today, some enterprises are already driving tangible
Consumer awareness, increasingly through Products that create value in the workplace, Solutions that integrate into platforms could
creation opportunities in industry-specific
social media, could lower acquisition like writing assistants, may be easier be discovered through existing workflows, returns from investments in horizontal use cases.
enterprise use cases (“vertical”).
costs, allowing companies to piggyback on to fit into a sustainable business model, driving more “sticky” adoption. Grammarly
We’ve seen research teams summarize
coverage, work out product kinks, and scale as opposed to products attached to was early to market with this on PCs and,
Potential targets of horizontal use third-party information, product managers
efficiently with an active and contributory a “hype cycle,” like social media filters. more recently, OpenAI with Bing.
cases are well-established automation write requirements documentation, social
user base.
centers, offer a substantial volume of media marketers refine copy, and customer
training data (e.g., knowledge base, service teams create case summaries and
support chat logs), and are the focus suggested resolutions. However, tangible
of cost optimization and productivity ROI could depend on proprietary
improvement efforts. For example, and serviceable data, secure model
creative marketing tasks like writing partitioning, talented product leaders
advertising copy, blogs, or social media and ML engineers, enabling MLOps
captioning can take hours or days for tooling, and new commercial and operating
humans to author. In contrast, Generative models. These are investments that
AI can complete workable drafts in minutes, enterprises should evaluate, whether they
requiring only editing from humans. see themselves as early adopters, fast
followers, or late entrants.
Source: Deloitte
Implications of Generative AI for businesses Implications of Generative AI for businesses
Sampling of Vertical and Horizontal Enterprise Use Cases
Consumer & Life Sciences & Banking & Fin. Technology Media & Industrial & Government &
Retail Health Care Services Telecom Manufacturing Public Sector
Personalized AR/VR Content Fraud Simulation Personalized AR/ Original Games Geological Academic 24/7
Conversational Generation for & Pattern VR Experience Creation Assessment Office Hours
Retail Experience Digital Therapy Detection Generation • • • • • • for Oil Exploration Virtual Assistant
• • • • • • • • • • • • • • • • • • • •
Customized Predictive & Tax and Automated Trailer & Summary Generative Infrastructure
Product Design & Virtual Patient Compliance Audit Product & Generation Simulation Mapping
Recommendation Triage & Scenario Testing Hardware Design • • • • & Safety Testing & Planning
• • • • • • • • • • • • • • •
Product Details 3D Images of Retail Banking Personalized & Script/Score 3D Env. Disaster Recovery
& Photography Anatomy for Transaction Automated UI/UX Design Rendering: Well Simulation
Generation Education Support Design & Subtitle Sites, Pipelines, • •
• • • • • • • • • Generation etc.
• • •
Fashion Outfit Healthy & Personalized Product Testing Personalized Automated Tech. Fraud, Waste &
Curation Wellness Plan Virtual Financial & Feedback News Equipment Abuse Prevention
• • • Creation Advisor Generation & Content Training Reports
• • • • • • • Generation • • • • •
• • •
Personal Art Drug Discovery Financial Software Sales, CX Original Fictional Generative Research
Creation Through Molecule Reporting & Retention Short Stories Automation w/ Citations
& Edits Simulation Analysis & Insight Support Generation for Smart & Explainers
• • Gen. • • • Factories •
• • •
Personalized Self-serve HR & IT End-to-end Customer Feedback Automated Code Dialogue Generation
Conversational Functions Automated Sentiment Debugging for Virtual Assistants
Retail Experience • • Customer Service Classification & Issue Resolution •
• • • • • • •
Enterprise Search 3D Environment Marketing/Sales Accessibility Support Autonomous Code Personalized
& Knowledge Mgmt. Rendering: Content Generation (text-to-speech & Generation Targeted Ads
• • Metaverse • • speech-to-text) & Completions across platforms
• • • • • • • • •
12 13
gnigremE
erutaM
• Text • 3d Model
• Image • Code
• Audio • Others
• Video
LACITREV
LATNOZIROH
Section II: Consumer and enterprise use cases Section II: Consumer and enterprise use cases
for Generative AI for Generative AI
In contrast, vertical use cases target industry-specific workflows
that require domain knowledge, context, and expertise.
For these, foundation models may need to be Ease of use | Integrations into systems and
fine-tuned or may even require new special- workflows via out-of-the-box connections and
purpose models. For instance, Generative AI low/no code tooling, reducing expensive IT
can be used to create a customized portfolio resources and enabling frontline users.
of securities based on risk-reward descriptions
or recommend personalized treatment plans Security and privacy | Compliance with data
based on a patient’s medical history and security standards (e.g., SOC 2, HIPAA, GDPR)
symptoms. However, delivering performant and role/persona-level access control over
vertical use cases requires a nuanced confidential data.
understanding of the field. In software, for
example, Generative AI can design composable Robust ecosystems | Broad set of
blocks of code based on simple prompts, which development and service partners to extend,
requires tacit knowledge of efficient coding, customize, and co-develop specialized data
coding languages, and an understanding sets, use cases, and applications.
of technical jargon.
Transparency and explainability |
Enterprise buyers have unique purchase Understanding how model outputs and
decisions relative to consumers, as model responses are derived and the ability
performance (speed, relevance, breadth to perform root cause analysis
of sources) is not expected to exclusively on inaccurate results.
drive vendor selection. on early opinions
from both advocates and naysayers, frequently Flexibility and customization | Ability to
cited criteria to adopt Generative AI are: create parameters, train on proprietary data,
and customize embeddings while maintaining
privacy and ownership
of data and tuning.
Generative AI Modality
Source: Deloitte
Implications of Generative AI for businesses Implications of Generative AI for businesses
Section II: Consumer and enterprise use cases Section II: Consumer and enterprise use cases
for Generative AI for Generative AI
Despite its promise, myriad challenges should
be overcome before Generative AI can be deployed
Even as new use cases emerge at an accelerating pace,
at scale. We discuss these in more detail, but there
we believe the market will unfold in six ways:
is also the question of commercial viability. In other
words, for all the fascinating possibilities and use cases
for Generative AI, it still needs to be determined how
vendors will build a sustainable business model.
Today, there are ethical concerns with Generative AI, While horizontal use cases will likely be the first to
including its potential for workforce displacement.16 deliver value, vertical-specific use cases could
However, like previous generations of AI, this command a premium due to the dependence on
technology will likely primarily augment human proprietary data. As such, data will be a currency,
performance. Indeed, AI could be commonplace creating new economies for access to proprietary
in worker’s toolkits, like Workspace among analysts, and synthetic data.
GitHub among coders, or Creative Cloud
among marketers.
6
1
Regulatory actions will likely vary
in speed, reach, oversight, and 5
reporting requirements across 2 All industries can benefit from
major markets (e.g., US AI Bill Generative AI. However, data-rich
of Rights,13 EU AI Act,14 China sectors (e.g., banking, retail,
Cyberspace Administration15). As hospitality) or those whose
such, vendors and enterprises will products leverage data (e.g.,
need to proactively establish information services) may move
practices that ensure data 4 —and should move—faster.
quality, transparency, fairness, 3 Conversely, those based on
safety, and robustness, which will judgment (e.g., law, medicine)
be critical to Trustworthy AI. may be more cautious about
adopting but nevertheless see
the benefit by accelerating the
synthesis of prior knowledge.
Text-based use cases will be commercialized
first, but the potential cost and productivity Given the shift away from low-interest rates, costs will
gains may be greater when commercializing increase, pushing enterprises to invest in use cases with
higher-order tasks as these skills can be more clear ROI. As such, use cases that directly impact cost
expensive to recruit, take longer to train, and (e.g., chatbots), productivity (e.g., search), or revenue
are right-brain (creative) versus left-brain (e.g., marketing copy) could have greater adoption than
(logical), making success subjective. those that eliminate humans.
14 15
Implications of Generative AI for businesses Implications of Generative AI for businesses
Section III: Commerce and competition
in Generative AI
SECTION III
Commerce and competition
in Generative AI
The battle for value capture will be fought
on multiple fronts, and each layer of the stack
will have its competitive dynamics driven
by things like scale, data access, brand,
and a captive customer base.
However, we see two primary competitor To begin, the infrastructure layer, which
archetypes: pure-play providers operating is the most mature of the Generative AI
within a single layer–infrastructure, technology stack, is where hyperscalers
model, and application - and integrated dominate the market. The business
providers that play in multiple layers. As model here is proven: provide scalable
with incumbent technology, we expect compute with transparent, consumption-
consumer pricing to be simple (e.g., per based pricing. To help make Generative
user, per month) and enterprise pricing AI workloads “sticky,” hyperscalers
to be more complex (e.g., per call, per have entered commitments with model
hour, revenue share). However, pricing providers to guarantee future workloads,
simplicity, predictability, and value including Azure with OpenAI,17 Google with
will be important to scaling within the Anthropic,18 and AWS with Stability.ai,19
enterprise beyond early adopters or alongside their proprietary models.
edge use cases.
16 17
Implications of Generative AI for businesses Implications of Generative AI for businesses
Section III: Commerce and competition Section III: Commerce and competition
in Generative AI in Generative AI
While the cloud service providers (CSP) deliver abstracted Next is the model layer, where the market Another less-considered path to monetization could
is evolving fast. This area can be resource
services, there is another enabling layer within be developing and licensing model architectures
intensive; model builders must continually
infrastructure that is rapidly evolving: silicon. revisit architectures (e.g., parameters, or development platforms.
embeddings) to maintain performance.
Here, NVIDIA is a leader with their Ampere They have to attract and retain AI talent In other industries, like semiconductors,
and Hopper series GPUs purpose-built (i.e., architects, engineers, and data ARM (CPU) and Qualcomm (wireless
for training and inference workloads, scientists) to design the frameworks, networking) create large, stable business
respectively, coupled with their Selene guardrails, and learning mechanisms to models built on licensing fees.
supercomputing clusters that speed ensure the robustness and reliability of
up training time.20 Similarly, AMD’s models. Finally, Generative AI workloads
CDNA2 Architecture is purpose-built for can run up large bills due to their
exascale computing on machine learning compute-heavy nature and need for
applications, advancing competition in the specialized silicon.22 No wonder we’ve
high-performance computing market.21 seen players start to recoup the investment
by charging fees or integrating into
monetized products (e.g., GPT-3.5 into Edge,
LaMDA into Google Search).
Infrastructure Layer Model Layer
Offering Description Examples Primary Customer Primary Monetization Offering Description Examples Primary Customer Primary Monetization
Enterprise Developer Consumer Model Metric Enterprise Developer Consumer Model Metric
Hosted and managed Co:here
Amazon Closed-source Model Per token
models built on a vast Google Yes Yes No Consumption
Hyperscale and purpose- Baidu Per minute Providers Per API call
data corpus OpenAI
Cloud Service Provider built compute, storage, Yes Yes No Consumption By CPU/GPU
Google
and networking type
Microsoft Foundation models
Open-source Model Meta Monetized via fine-tuned
maintained by No Yes No
Providers Stability.ai models or model hubs
communities
Specialized services to Amazon
Per hour
Generative AI Service accelerate deployment Use case-specific
Co:here Yes Yes No Consumption Per generation Fine-tuned Model Co:here Per token
Providers (e.g., security, monitoring, versions of foundation Yes No No Consumption
Per embedding Providers C3.ai Per API call
testing, model isolation) Google models
Github
Marketplace, community Subscription
Hugging Per month
Model Hubs or hosting services for Yes Yes No Consumption
Purpose-built Face Per hour
AMD One-time models Rev. share
Chip Provider semiconductors, Yes No No Per component Replicate
NVIDIA lease
including GPUs and CPUs
Proprietary architectures, Co:here One-time Per embedding
Model Service
synthetic data, weights, MostlyAI No Yes No Subscription Per month
Providers
and embeddings RealAI License Per user
18 19
Implications of Generative AI for businesses ImplIimcaptliiocantsio onfs G oef nGeernaetriavteiv Ae IA fIo fro rb buussinineesssseess
Section III: Commerce and competition Section III: Commerce and competition
in Generative AI in Generative AI
Finally, the application layer serves as the gateway Competition within the application layer could
between models and end users. unfold within several markets. However, given the
Today's apps are typically monetized wide range of applications and use cases that may
through subscriptions and recurring
emerge, we should look at “micro-markets.” Broadly,
transactions, a model that will likely persist,
albeit with modifications suited to today’s real and predicted enterprise use cases fall into
Generative AI.
five categories where competitive lines could be drawn:
Application Layer
Offering Description Examples Primary Customer Primary Monetization
Enterprise Developer Consumer Model Metric Accelerate
Improve productivity by speeding up outcomes. These do not eliminate human
Google
SDKs, frameworks, intervention but provide high-quality inputs upon which to build.
Hugging License
Platforms and tools to build and Yes Yes No Per user
Face Rev. share
distribute apps
Microsoft
Personalize
Create intimacy and personalization, which previously would have taken significant
Boomy Subscription Per user
Full-feature solutions to effort. Here, models can leverage personal data to tailor content.
Standalone application Canva Yes No Yes Consumption Per month
modify workflows
Lensa One-time Per service
Automate
Extensions and features AI Art
Subscription Per user Deliver business and technical workflows and, in certain instances, replace humans.
Plugins to supplement tasks and Grammarly Yes No Yes
Consumption Per month Vendors often demo these due to the immediate cost-saving potential.
workflows Jasper
Create
Push the boundaries of intellectual property development, leveraging prompts (a new
art form unto itself) to generate novel content like images, video, text, and media.
Simulate
Create environments in which workflows, experiments, and experiences can be simulated
before being pushed into production, saving time, cost, and physical resources.
20 2211
Implications of Generative AI for businesses Implications of Generative AI for businesses
Accelerate Personalize Automate Create Simulate
Calendar mgmt./
Email outreach Social media marketing Image/logo creation 3D modeling
Admin assistant
Gaming environment
Note taking Keynote speaker notes Advertising copy Marketing campaigns
design
Short-form video
Content marketing Physical goods design Support chatbots Medical testing (R&D)
generation
NLP-based email/ Product ideation
Advertising video editing Content summarization Chemical interactions
app. responses & PRD authoring
Basic code generation Disaster response
Code completion Personal assistant Music scoring
& documentation management
Anthropic Facebook OPT BigScience BLOOM OpenAI DALL.E 2 Cradle
Co:here GATO OpenAI Codex Soundify DreamFusion
OpenAI GPT-3 Microsoft X-CLIP Tabnine Stable Diffusion NVIDIA GET3D
22 23
SNOITACILPPA
SLEDOM
Section III: Commerce and competition Section III: Commerce and competition
in Generative AI in Generative AI
Sampling of Enterprise Micro-Markets A second archetype, in contrast to pure-play We see integration happening in two
providers who monetize through first- ways. First, companies like Anthropic and
and third-party channels, are vertically Midjourney have released applications
integrated or multi-layer players. for specific use cases. Lower in the stack,
These players lead with bundled pricing, companies like NVIDIA have released
proprietary data, special-purpose specialized models, including BioNeMo,
clouds, or cross-domain expertise a pharmaceutical pipeline development
to gain a competitive advantage. accelerator that is optimized
to run on NVIDIA GPUs.
Integrated Players
Offering Description Examples Primary Customer Primary Monetization
Enterprise Developer Consumer Model Metric
Anthropic Per month
Applications built on
Model Co:here Subscription Per user
proprietary, first-party Yes No Yes
and application Midjourney Consumption Per service
models
OpenAI Per download
Fully managed Per hour
Model and Google
infrastructure and model- Yes Yes No Consumption Per API call
infrastructure NVIDIA
Source: Deloitte as-a- service Per embedding
Amazon
Purpose-built horizontal Per minute
This may have implications for the model Silicon and Azure
and vertical clouds for ML Yes "
193,deloitte,us-cbe-nacd-2024-governance-outlook-deloitte.pdf,"This document was prepared solely for your internal use only, and it is the sole property of its copyright owner. Further distribution of the content (in whole or in part)
in any form is prohibited without written permission from NACD. Copyright © 2023 National Association of Corporate Directors. All rights reserved.
Artificial Intelligence: An Emerging
Oversight Responsibility for
Audit Committees?
By Brian Cassidy, Ryan Hittner, and Krista Parsons, Deloitte & Touche LLP
The audit committee has many discrete duties, including overseeing financial reporting and
related internal controls, the independent and internal auditors, and ethics and compliance,
to name just a few. However, these and other duties are part of a broader audit committee
responsibility: risk oversight. While the audit committee does not manage all risks, it is responsible for
overseeing the procedures and processes by which the company anticipates, evaluates, monitors,
and manages risks of all types. Recent developments in artificial intelligence (AI), including the
emergence of generative AI, are leading businesses to evaluate AI’s potential impact to their business
technology strategy. As businesses expand their use of AI, especially into core business processes,
the audit committee will need to understand the challenges and opportunities presented by AI to
address risks related to governance and stakeholder trust.
WHO’S MINDING THE AI STORE NOW?
According to a 2023 survey conducted by Deloitte and the Society for Corporate Governance,
corporate secretaries see AI strategy and oversight as still evolving. The findings show that few
respondents (13%) had a formalized AI oversight framework, although many (36%) were considering
the development and implementation of AI oversight policies and procedures.
22
2024 Governance Outlook
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These results are particularly interesting when compared to a 2022 Deloitte survey, in which 94
percent of respondents said AI was critical to their company’s short-term success.1 This may suggest
some level of information asymmetry between management and the board, congruent with the
notion that AI is in a state of flux. Thus, at least for now, the AI landscape might best be characterized
as an abstract governance puzzle.2
THE AI GOVERNANCE PUZZLE
Oversight Structure [Not] on the Agenda Risky Lack of Opinion
29% reported that AI over- 44% indicated that AI has not 68% didn’t know (or didn’t
sight was not assigned to been on any agenda (full respond) when asked how
any committee or the full board or committee); 37% the company mitigates
board; 16% placed it with the have discussed on an ad hoc AI-related risk.
audit committee. or as needed basis.
RISKS AND OPPORTUNITIES
FAMILIAR AND DIFFERENT SET OF RISKS
With new technology comes the possibility of new risks. Some AI risks present well-trodden chal-
lenges that arise in other technology areas and can be overseen and understood in the context of an
ongoing enterprise risk management (ERM) process,3 such as the COSO ERM framework. However,
other risks may be unfamiliar and/or amplified. A few illustrative examples are highlighted below.
X Shadow IT Environments: Use of IT assets by personnel without the knowledge or
oversight of IT security professionals can occur with any type of software or hardware.
However, unauthorized use of generative AI by personnel may compound data-related
risks. This risk may be increased given the lack of AI policy in many organizations.
Further, employees leveraging generative AI to write code may inadvertently introduce
vulnerabilities through code generated by AI.
1 Business leaders were defined as company representatives who met one or more of the following qualifiers: (1)
responsible for AI technology spending or approval of AI investments, (2) responsible for the development of AI
strategy, (3) responsible for implementation of AI technology, (4) acting as AI technology subject-matter specialist,
or (5) otherwise stated they were influencing decisions around AI technology. See Nitin Mittal, Irfan Saif, and Beena
Ammanath, Fueling the AI transformation: Four key actions powering widespread value from AI, right now, State of AI
in the Enterprise, 5th Edition report, Deloitte, October 2022.
2 Natalie Cooper, Bob Lamm, and Randi Val Morrison, “Future of tech: Artificial intelligence (AI),” Board Practices Quar-
terly, Deloitte, August 2023.
3 Alexander J. Wulf and Ognyan Seizov, “‘Please understand we cannot provide further information’: Evaluating content
and transparency of GDPR-mandated AI disclosures,” AI & Society (2022).
23
2024 Governance Outlook
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in any form is prohibited without written permission from NACD. Copyright © 2023 National Association of Corporate Directors. All rights reserved.
X IP Ownership and Infringement: Generative AI users can input confidential or
protected data, which may result in an array of adverse outcomes, including
disclosure of such confidential or protected data to third parties. Outputs using this
type of data may also constitute infringement of intellectual property.4 Furthermore,
as generative AI applications are used to craft increasingly sophisticated media
across multiple formats, it may not be clear who owns the rights to any resulting
intellectual property.
X Cybersecurity Bad Actors: A frequent concern across many types of technology stems
from malicious actors who circumvent security protocols. Generative AI use cases may
amplify some types of cybersecurity risks. For example, hackers may use generative AI
to write code for purposes of infiltrating data environments or create phishing messages
that more accurately mimic human language and tone.
Finding the appropriate balance between AI’s benefits and risks depends on a constellation of
factors. Outputs produced by generative AI change over time as the technology learns from data.
But just like with humans, it is possible for this subcategory of AI technology to learn things that are
incorrect. For that reason, traditional risk management strategies may not be well-equipped for the
challenges that arise from generative AI use.
GENERATIVE AI RISK EXAMPLES
Low Transparency Hallucination Bias Potential Value Alignment
How generative AI Generative AI When trained on Even with safe-
derives its output products and services nonrepresentative guards, generative
can be a “black box,” may generate data, generative AI AI output may
making it difficult to output that seems output could exhibit contradict its
explain and/or audit. accurate but is systematic errors. intended purpose.5
actually false or
cannot be justified.
4 Christian Heinze, “Patent infringement by development and use of artificial intelligence systems, specifically artificial
neural networks,” in A Critical Mind: Hanns Ullrich’s Footprint in Internal Market Law, Antitrust and Intellectual Property,
eds. Christine Godt and Matthias Lamping, MPI Studies on Intellectual Property and Competition Law, vol. 30 (Hei-
delberg, Germany: Springer, 2023), pp. 489–515.
5 Vic Katyal, Cory Liepold, and Satish Iyengar, “Artificial intelligence and ethics: An emerging area of board oversight
responsibility,” On the Board’s Agenda, Deloitte, 2020.
24
2024 Governance Outlook
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in any form is prohibited without written permission from NACD. Copyright © 2023 National Association of Corporate Directors. All rights reserved.
Regardless of whether the risk is familiar, completely new, and/or amplified, the resultant conse-
quences may be notable. Failure to mitigate any subcategory of AI-related risks may lead to many
adverse outcomes such as reputational damage, financial losses, legal action, and regulatory
infractions. A starting point for addressing such concerns might include using mitigation strategies
that are already known to work in other contexts, such as the COSO ERM framework referred to
earlier. For AI-centric guidance related to implementation and scaling, it may be worth considering
the benefit of systems such as the NIST AI Risk Management Framework.
WITH RISKS COME BENEFITS, TOO
If AI presented nothing but risk, it seems unlikely that it would have emerged as “the” technology of
the future. Clearly, AI has benefits, some of which may not be known for some time. One particular
set of benefits is squarely in the audit committee’s wheelhouse—namely, the potential to streamline
and enhance a company’s internal audit, financial reporting, and internal control functions. There
are also aspects of generative AI technology that, while still evolving, may one day fundamentally
change an organization’s financial systems. While there is much uncertainty, the future transforma-
tive potential of generative AI may add much to the current array of use cases. In the shorter term,
various subcategories of AI are already capable of improving the quality of financial reporting via
reviewing transactions, identifying errors, addressing internal control gaps, and detecting fraud.
If AI isn’t being used within these areas, the audit committee might ask if the company is exploring
potential use cases—and if the company is not, the committee might ask to hear the reasons behind
that decision.
USE OF AI TECHNOLOGY MAY HAVE MANY BENEFITS
Cost Savings Boosted Revenues Development Time New Insights
Process automations AI-infused products AI may shorten time Appropriate
and improvements and services may to market by increas- generative AI
may improve task provide new growth ing the speed of use may bolster
efficiency. opportunities. early-stage testing. employee creativity.
25
2024 Governance Outlook
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COMMON AI USE CASE EXAMPLES
USE CASE DESCRIPTION OPPORTUNITIES RISKS
Use of intelligent The technology may Poorly designed or
automation to match reduce costs by maintained systems
invoices to payments, processing a large may generate errors
including classification volume of transactions that are time consum-
Invoices
of expenses with a high degree of ing to undo.
and Payments
accuracy.
Leverage of natural By producing the Natural language and
language and gener- initial drafts or identi- generative AI trained
ative AI processing fying common errors, on biased data may
to create legal docu- generative AI may misapply the law or
Contract Review ments or review them create efficiencies and make up precedent.
or Generation for errors lower legal liability in a
cost-effective manner.
Incorporating Modeling and analytics Lack of robust testing
predictive analytics to AI technology may be and regular updates
improve the accuracy capable of identifying can cause modeling
of functions like inven- patterns at a speed that and analytics AI to
Forecasting
tory management and outpaces human-led become more inaccu-
and Modeling
revenue forecasting data analysis efforts. rate over time.
Use of generative AI Employees may use The technology may
to develop models generative AI to drive expose confidential
or applications efficiencies in day-to- data with generative
that create effi- day tasks and help AI inputs or may
Code ciencies for routine identify possible gener- create outputs that
Development personnel activities ative AI use cases. involve intellectual
property infringement.
AI AND THE AUDIT COMMITTEE
The tendency to assign oversight of emerging risks to the audit committee means it is sometimes
described as the “kitchen sink” of the board. However, as noted earlier, this is consistent with the
audit committee’s overarching role in risk oversight. It’s also worth considering that it is common for
topics taken on by the audit committee at the outset to eventually be overseen by other committees.
Some aspects of AI oversight seem more aligned with the audit committee’s work than others. And
when it comes to considering such congruence questions, it may be helpful to think about the audit
committee’s current levels of technology fluency and comfort. For instance, given the audit commit-
tee’s traditional governance areas, it may be prudent for it to oversee AI use in financial reporting.6
6 The audit committee may want to also think about indirect impacts. Depending on the use case, AI technology may
have an array of indirect effects on financial measures (GAAP or otherwise).
26
2024 Governance Outlook
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In other parts of AI oversight, it may be less clear whether the audit committee is a “good fit.” For
example, the impact of generative or natural language AI on the workforce may be more aligned
with the oversight of the compensation/talent committee or the full board.
The “temporary assignment” of AI to the audit committee may make sense for other reasons, as well.
First, AI remains an emerging technology and is likely to continue to change rapidly. Second, there is
extensive governmental interest in AI, which may result in legislation that will require adjustments in
its oversight. Thus, determining now that AI, or aspects of AI, should be overseen by another commit-
tee or committees may turn out to be premature.
An audit committee might choose to assess its AI risk tolerance across oversight areas such as auditing,
financial reporting, and internal control functions. It may be helpful to contextualize that analysis
by comparing it to other areas of the company. For example, company divisions that routinely use
technology enhancements in client-facing operations may have a higher appetite for risk. But a
higher risk tolerance in operational settings does not necessarily correlate with how risks are viewed
when it comes to financial reporting impacts.
An important part of the AI governance puzzle for the audit committee is assessing risk. But, at least
for now, this task is currently made more difficult by a shifting regulatory landscape. Governments
and regulators around the world are considering whether regulation and policy can address AI risks.
Their progress toward developing and enacting policies and regulations over AI is uneven across the
globe and in different stages of development and enactment. And to make things more complex,
stakeholder groups—shareholders, customers/clients, employees, suppliers, and community—all have
varying and sometimes conflicting expectations around use and governance of AI. For these reasons,
there may be a benefit to continuously assessing AI risks and benefits over waiting for emerging
and future legislative proposals or regulatory guidance. But to accurately make such continual
assessments, it’s important that the audit committee and the board have sufficient knowledge to ask
questions around the organization’s adoption and use of AI.
POTENTIAL AUDIT COMMITTEE OVERSIGHT QUESTIONS
TO CONSIDER
X What are the company’s current and potential future use cases for AI, and do any of
them have an impact on financial reporting or other audit committee oversight areas?
X Has management considered opportunities to use AI that may enhance or improve
financial reporting processes?
X What processes are, or will be, used to evaluate dependencies that may arise in other
areas where the audit committee may have primary oversight, like cybersecurity or
data management?
X Are processes for use of AI congruent with the company’s risk appetite in terms of level
of proactiveness and mitigation strategy?
X Given the speed of AI technology development, are existing processes being assessed
and updated with appropriate frequency?
27
2024 Governance Outlook
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in any form is prohibited without written permission from NACD. Copyright © 2023 National Association of Corporate Directors. All rights reserved.
Brian Cassidy Ryan Hittner Krista Parsons
Brian Cassidy is an Audit & Assurance partner with Deloitte & Touche LLP and the US Audit & Assurance Trustworthy
AI leader.
Ryan Hittner is an Audit & Assurance principal with Deloitte & Touche LLP and the US Artificial Intelligence & Algorithmic
Assurance coleader.
Krista Parsons is an Audit & Assurance managing director with Deloitte & Touche LLP. She is also the Governance
Services coleader and the Audit Committee Program leader for Deloitte’s Center for Board Effectiveness.
This publication contains general information only and Deloitte is not, by means of this publication, rendering account-
ing, business, financial, investment, legal, tax, or other professional advice or services. This publication is not a substitute
for such professional advice or services, nor should it be used as a basis for any decision or action that may affect your
business. Before making any decision or taking any action that may affect your business, you should consult a qualified
professional adviser. Deloitte shall not be responsible for any loss sustained by any person who relies on this publication.
Deloitte refers to one or more of Deloitte Touche Tohmatsu Limited, a UK private company limited by guarantee (“DTTL”),
its network of member firms, and their related entities. DTTL and each of its member firms are legally separate and
independent entities. DTTL (also referred to as “Deloitte Global”) does not provide services to clients. In the United
States, Deloitte refers to one or more of the US member firms of DTTL, their related entities that operate using the
“Deloitte” name in the United States and their respective affiliates. Certain services may not be available to attest clients
under the rules and regulations of public accounting. Please see http://www.deloitte.com/about to learn more.
28
2024 Governance Outlook"
194,deloitte,us-ai-institute-responsible-use-of-generative-ai.pdf,"Proactive risk management
in Generative AI
Deloitte AI Institute
A call for proactive risk management in Generative AI
About the AI Institute
The Deloitte AI Institute helps organizations connect
the different dimensions of a robust, highly dynamic
and rapidly evolving AI ecosystem.
The AI Institute leads conversations on helps make sense of this complex ecosystem,
applied AI innovation across industries, with and as a result, deliver impactful perspectives
cutting-edge insights, to promote human- to help organizations succeed by making
machine collaboration in the “Age of With”. informed AI decisions.
The Deloitte AI Institute aims to promote No matter what stage of the AI journey
a dialogue and development of artificial you’re in; whether you’re a board member
intelligence, stimulate innovation, and or a C-Suite leader driving strategy for your
examine challenges to AI implementation organization, or a hands on data scientist,
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2
A call for proactive risk management in Generative AI 3
Generative AI is dominating public
interest in artificial intelligence
By some estimations, Generative AI is the end of the
Internet search and the tool that will revolutionize
many aspects of how we work and live. We’ve heard
that before in AI. The newest applications often
conjure public excitement.
Yet, Generative AI is different than most In the business realm, there is growing
other kinds of AI in use today. Large intrigue around how Generative AI can be
language models, for example, can respond used in the enterprise. As with all cognitive
to user prompts with natural language tools, the outcomes depend on how they are
outputs that convincingly mimic coherent used, and that includes managing the risks,
human language. What is more, there is which for Generative AI have not been as
effectively no barrier to using some of these deeply explored as the capabilities.
models because they do not require any Some primary questions are, can business
knowledge of AI, much less an understanding users trust the outputs of this kind of
of the underlying math and technologies. AI application, and if not, how can that
be achieved?
3
A call for proactive risk management in Generative AI
New bots on the block
To this point, AI has broadly been used CIOs and technologists may already know
to automate tasks, uncover patterns that Generative AI is not “thinking” or being
and correlations, and make accurate creative in a human way, and they also likely
predictions about the future based on know that the outputs are not necessarily
current and historical data. Generative AI is as accurate as they might appear. Non-
designed to create data that looks like real data. technical business users, however, may not
Put another way, Generative AI produces digital know how Generative AI functions or how
artifacts that appear to have the same fidelity much confidence to place in its outputs.
as human-created artifacts. Natural language The business challenge is magnified by the
prompts, for example, can lead the neural fact that this area of AI is evolving at a rapid
network to generate images that are in some pace. If organizations and end users are
cases indistinguishable from authentic images. challenged just to keep up with Generative
For large language models that create text, AI’s evolving capabilities, how much more
the AI sometimes supplies source information, difficult might it be to anticipate the risks
underscoring to the user that its outputs are and enjoy real trust in these tools?
factually true, as well as persuasively phrased.
“Trust me,” it seems to say.
4
A call for proactive risk management in Generative AI
Trust is not an inherent quality of AI but instead the product
of AI governance, risk mitigation, and the intentional alignment
of people, processes, and technologies across the enterprise.
The trustworthiness of Generative AI
depends on how an organization uses it,
and as enterprises wade into this fast-moving
field of AI, there are factors of trust and ethics
that should be addressed.
5
A call for proactive risk management in Generative AI
1| Managing hallucinations
and misinformation
A Generative model references its dataset to There is also the risk of inherent bias within
concoct coherent language or images, which is the models, owing to the data on which they are
part of what has startled and enticed early users. trained. No single company can create and curate
With natural language programs, while the all of the training data needed for a Generative
phrasing and grammar may be convincing, AI model because the necessary data is so
the substance may well be partially to expansive and voluminous, measured in tens of
entirely inaccurate, or sometime, when terabytes. Another approach then is to train the
representing a statement of validity, model using publicly available data, which injects
false. One of the risks with this kind of natural the risk of latent bias and therefore the potential
language application is that it can “hallucinate” for bias in the AI outputs.
an inaccurate output in complete confidence.
It can even invent references and sources that A fundamental risk is that users may place
are non-existent. The model would be forgiven complete confidence in erroneous or
as its function is to generate digital artifacts that biased outputs and make decisions and
look like human artifacts. Yet, coherent data take actions based on a falsehood. One
and valid data are not necessarily the same, way to help mitigate this risk is through AI
leaving end users of large language models to governance, and many of the leading practices
contend with whether an eloquent output is associated with other kinds of AI also apply
factually valuable at all. to generative models: workforce upskilling,
waypoints for decision making across the
AI lifecycle, structured oversight, ubiquitous
documentation, and the many other activities
that promote Trustworthy AI™.
36
A call for proactive risk management in Generative AI
2|The matter of attribution
Generative AI outputs align with the original How do we contend with attribution when a
training data, and that information came from tool is designed to mimic human creativity by
the real world, where things like attribution and parroting back something drawn from the data
copyright are important and legally upheld. it computes? If a large language model outputs
Data sets can include information from online plagiarized content and the enterprise uses that
encyclopedias, digitized books, and customer in their operations, a human is accountable
reviews, as well as curated data sets. Even when the plagiarism is discovered, not
if a model does cite accurate source the Generative AI model. Recognizing
information, it may still present outputs the potential for harm, organizations may
that obscure attribution or even tread implement checks and assessments to help
across lines of plagiarism and copyright ensure attribution is appropriately given. Yet,
and trademark violations. if human fact-checking of AI attribution
becomes a laborious process, how much
productivity can the enterprise actually
gain by using Generative AI?
Finding the balance between trust in attribution
and human oversight will be an ongoing
challenge, with significant legal and brand
implications for the enterprise.
37
A call for proactive risk management in Generative AI
3|Real transparency
and broad user explainability
End users can include people who have Enterprise-wide AI literacy and risk awareness is
limited understanding of AI generally, much becoming an essential aspect of any company’s
less the complicated workings of large day-to-day operations. This is perhaps even
language models. The lack of a technical more important with Generative AI. Business
understanding of Generative AI does not users should have a real understanding of
absolve the organization from focusing on Generative AI because it is the end user
transparency and explainability. If anything, (and not necessarily the AI engineers and data
it makes it that much more important. scientists) who contends with the risks
and the consequences of trusting a tool,
Today’s Generative AI models often come regardless of whether they should. To promote
with a disclaimer that the outputs may be the necessary AI understanding, CIOs and
inaccurate. That may seem like transparency, business leaders may look to existing workforce
but the reality is many end users do not training and learning sessions, explanatory
read the terms and conditions, they do not presentations to business users, and fostering
understand how the technology works, and an enterprise culture of continuous learning.
because of those factors, the large language
model’s explainability suffers. To participate in
risk management and ethical decision making,
users should have accessible, non-technical
explanations of Generative AI, its limits and
capabilities, and the risks it creates.
38
A call for proactive risk management in Generative AI
Accountability
on the road ahead
Even as Generative AI becomes better able No matter how powerful it becomes, we
to mimic human creativity, we should still need the analysis, scrutiny, context
remember and carefully consider the awareness, and the humanity of people at
human side of this equation. Everyone the center of our AI endeavors.
will be affected by Generative AI in one
way or another, from outsourced labor to This AI era is the Age of
layoffs, changing professional roles, and even
potentially legal issues. Generative AI will have With™, where humans
real impact, and because an AI model has
work with machines to
no autonomy or intent, it cannot be held
accountable in any meaningful sense.
achieve something neither
At scale, the possibility of transparency
could do independently.
with Generative AI becomes elusive and
“keeping the human in the loop” becomes Now is the time to
a growing problem. It is also unclear at this
derive viable methods
point the degree of consequences that may
result from mass adoption of Generative AI,
of accountability, trust,
such as the proliferation of fake facts to the
detriment of objective and complete truth. and ethics, linking the
These challenges are unlikely to hinder
Generative AI product
Generative AI’s adoption.
and its outcomes with its
creator, the enterprise.
9
A call for proactive risk management in Generative AI
Reach out for a conversation.
Beena Ammanath Wessel Oosthuizen Dr. Kellie Nuttall Jefferson Denti Audrey Ancion Jan Hejtmanek Roman Fan Anne Sultan
Global Deloitte AI Institute Deloitte AI Institute Africa, Deloitte AI Institute Deloitte AI Institute Brazil, Deloitte AI Institute Deloitte AI Institute Deloitte AI Institute China, Deloitte AI Institute France,
Leader Lead Australia, Lead Lead Canada, Lead Central Europe, Lead Lead Lead
Deloitte AI Institute Deloitte Africa Deloitte Australia Deloitte Brazil Deloitte Canada Deloitte Central Europe Deloitte China Deloitte France
United States, Lead woosthuizen@deloitte.com knuttall@deloitte.com jdenti@deloitte.com aancion@deloitte.ca jhejtmanek@deloitte.com rfan@deloitte.com asultan@deloitte.com
Deloitte Consulting, LLP
bammanath@deloitte.com
Dr. Bjoern Bringmann Prashanth Kaddi Masaya Mori Nicholas Griedlich Naser Bakhshi Tiago Durao Sulabh Soral
Deloitte AI Institute Deloitte AI Institute India, Deloitte AI Institute Japan, Deloitte AI Institute Deloitte AI Institute Deloitte AI Institute Deloitte AI Institute
Germany, Lead Lead Lead Luxembourg, Lead Netherlands, Lead Portugal, Lead United Kingdom, Lead
Deloitte Germany Deloitte India Deloitte Japan Deloitte Luxembourg Deloitte Netherlands Deloitte Portugal Deloitte United Kingdom
bbringmann@deloitte.com pkaddi@deloitte.com mmori@deloitte.com ngriedlich@deloitte.com nbakhshi@deloitte.nl tdurao@deloitte.com ssoral@deloitte.com
10
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195,deloitte,us-ai-institute-scaling-GenAI-final (1).pdf,"Scaling Generative AI
13 elements for sustainable growth and value
Scaling Generative AI | 13 elements for sustainable growth and value
About the Deloitte AI InstituteTM
The Deloitte AI Institute helps organizations connect
the different dimensions of a robust, highly dynamic
and rapidly evolving AI ecosystem. The AI Institute leads
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with cutting-edge insights, to promote human-machine
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2
Scaling Generative AI | 13 elements for sustainable growth and value
Near the top of every enterprise agenda is a question of how to leverage Generative AI (GenAI).
With use cases proliferating horizontally across functions and vertically within business units,
the next step is...sustainably scaling GenAI for strategic business value.
Generative AI, like origami, transforms a resource
(data and paper, respectively) into a compelling
output. Just as origami artists fold paper to
resemble interesting shapes, Generative AI
computes data to approximate human cognition
and creativity.
3
Scaling Generative AI | 13 elements for sustainable growth and value
Getting more GenAI into production
Deloitte’s State of GenAI in the Enterprise Q3
report revealed that many businesses are
encountering challenges when making the
transition from GenAI proof-of-concept to
scaled deployment.1
Seventy percent of surveyed organizations indicate that less than one third of their
GenAI experiments have made it to production. This suggests that while enterprises are
investing in GenAI, they are not yet seeing the full potential ROI. A common challenge is
defining what is required to achieve GenAI scale at a practical level.
We define scale broadly as the ability of a system to handle a growing amount of
work or its potential to be enlarged to accommodate growth with steadily
decreasing unit costs. For GenAI specifically, scaling also means moving from
experimentation to implementation in a way that is sustainable, secure, and
aligned with business goals.
GenAI at scale generates more diverse and representative outputs, it can handle more
complex tasks, and its speed, output quality, and accuracy are enhanced. As a result,
operational costs become more efficient and business impact is governed, measured,
and communicated.
4
Scaling Generative AI | 13 elements for sustainable growth and value
At the highest level, GenAI scaling factors can be grouped into the familiar areas of strategy, process, talent, and
data and technology. Each area presents challenges to be navigated and contains leading practices that help point the way
to GenAI value realization.
Strategy Process Talent Data and Technology
Ambitious Modular
Integrated Transparency Provisioning
strategy and value Robust architecture
risk to build trust the right AI
management governance and common
management in secure AI infrastructure
focus platforms
Clear, Agile Acquiring Effective
Strong Transformed
high-impact operating model (external) and Modern data model
ecosystem roles, work,
use case and delivery developing foundation management
collaboration and culture
portfolio methods (internal) talent and operations
Essential elements for scaling Generative AI initiatives from pilot to production
5
Scaling Generative AI | 13 elements for sustainable growth and value
STRATEGY
Ambitious strategy and
value management focus
An organization’s GenAI strategy and vision need to be comprehensive, integrated with
broader business objectives, and aligned with other existing AI programs. Executive
buy-in and a top-down mandate are essential for aligning functions and decision-making.
Leadership sets priorities and strategy, and without an executive mandate, it is difficult
to coordinate change across multiple teams. A cohesive GenAI strategy defines business
objectives, sets measurable goals, identifies valuable areas for application, and measures
realized value. As a part of strategy development, it’s important to show progress against
short-term goals and inform any iterative improvements needed to the strategy.
Establish a
comprehensive vision
with a top-down mandate
6
Scaling Generative AI | 13 elements for sustainable growth and value
STRATEGY
Clear, high-impact use
case portfolio
There are six common macro archetypes for GenAI: Q&A-based search, summarization,
content generation, content transformation, virtual agent, and code generation. In seeking
value-driving applications, organizations should look across the archetypes for low-
barrier, high-impact use cases for core business domains. These drive efficiencies and
savings that can be reinvested in innovation. Other high-impact use cases may be more
transformational and differentiating with enterprise-wide applicability. Whether deploying
a proven application or striving for something novel, all applications require technical
feasibility and a viable business case. What is more, existing processes will likely need to be
reimagined to incorporate and leverage the capabilities of GenAI use cases in workflows.2
At its core, the use case portfolio needs to be focused on answering business questions
and meeting quantified goals. We see leading organizations create business cases that
weave together the value GenAI can provide to multiple teams, rather than evaluating
the value of individual applications. This is done most effectively by running a number
of use cases in parallel. It makes efficient use of resources and allows for rapid portfolio
management should a specific use case prove less compelling without sacrificing
momentum of the overall Gen AI portfolio.
Explore low-barrier, high-
impact use cases to drive
efficiencies and savings
7
Scaling Generative AI | 13 elements for sustainable growth and value
STRATEGY
Strong ecosystem
collaboration
GenAI is maturing rapidly, with existing providers and new market entrants alike driving
capabilities and lateral applications. The array of GenAI solutions and the speed with which
they are evolving can make it challenging to select the appropriate tools and platforms
that enable enterprise strategy. To reach target outcomes, enterprise leaders should build
strategic relationships with technology and data ecosystem stakeholders and keep pace
with GenAI development. By monitoring elements like product roadmaps, total cost of
ownership, and labor delivery models, business leaders can gain an understanding as
to how their GenAI programs should evolve and how ecosystem players can accelerate
progress and results as strategic partners, rather than as transactional vendors. Use a
framework to support a structured approach to evaluating solutions based on factors such
as data readiness, AI maturity, risk appetite, and total cost of ownership.
Evolve with existing
providers and new market
entrants alike
8
Scaling Generative AI | 13 elements for sustainable growth and value
PROCESS
Robust governance
Inconsistent processes can lead to risks and inefficiencies, while consistent governance
processes help standardize workflows for data collection, solution engineering, output
validation, and performance monitoring. Common delivery frameworks (e.g., LLMOps)
bring together GenAI development and deployment into a unified, governed lifecycle that
is secure and compliant. A common misconception is that strong processes can hinder
speed and creativity. Our experience suggests the opposite. By understanding how work
needs to be done and the accompanying guardrails, teams are empowered to explore
ways to generate value without fear that they may be making a mistake. Clear boundaries
allow freedom for bold action and innovation, while a lack of clarity may lead to more
conservative approaches. Governance includes documented roles and responsibilities
that drive stakeholder accountability in decision-making across the AI lifecycle, and
inform the controls for risk identification and mitigation. Governance also standardizes
how stakeholders identify, prioritize, and approve GenAI applications. As processes are
amended, organizations need to be mindful about disrupting existing automated or
manual controls and take steps to establish assurance in those amended processes.
Even as the regulatory landscape is in flux, organizations should proactively establish
governance processes that meet existing or likely regulatory requirements.
Create repeatable
governance processes to
help standardize work
9
Scaling Generative AI | 13 elements for sustainable growth and value
PROCESS
Integrated risk management
For GenAI to reach its full potential business value and adoption, it must be trusted
and secure.3 Attempting to scale without accounting for trust in data and the machine
that consumes it can have implications for regulatory compliance, finance and strategy,
cybersecurity and privacy, adoption and change management, and brand reputation—
the consequences of which can limit or even erase GenAI’s intended value. Risk and
trust need to be considered and addressed across the GenAI lifecycle, from design and
development through deployment and scaled implementation. This includes validation
processes and feedback loops for human oversight to manage solution performance
and accuracy. It also includes guardrails to ensure privacy, drive ongoing compliance, and
promote agility in proactively responding to emerging risks. Data security is particularly
essential. Differentiated GenAI applications are fueled by sensitive, proprietary enterprise
data. Thus, training and usage can potentially expose or leak business-critical data and
create risks to the organization. This is not a one-time event—organizations must make
this part of regular work, rather than a separate consideration.
Address risk and data
security across the
GenAI lifecycle
10
Scaling Generative AI | 13 elements for sustainable growth and value
PROCESS
Agile operating model
and delivery methods
The operating model impacts how the enterprise aligns technology, processes, and roles
and responsibilities to create strategic business value. An integrated model connects the
blueprint for value with AI business cases to inform how work is delivered and helps drive
alignment across the enterprise. As the marketplace matures and new capabilities and
risks impact AI lifecycles and governance, the organization needs to be agile in matching
internal opportunities with the right technologies. To help, organizations may turn to
technical experts or an AI Center of Excellence (COE) that equips decision makers with
the insight to align the vision for success with the organization’s AI maturity and ambition.
This supports a cohesive approach to orchestrating the elements of GenAI development
and application. It helps avoid AI and data silos and instead drive toward reusable
building blocks, coordinated sourcing strategy, informed build-versus-buy decisions, and
security and risk management.
Support a cohesive
approach to orchestrating
the components
11
Scaling Generative AI | 13 elements for sustainable growth and value
TALENT
Transparency to build trust
in secure AI
Trust in GenAI is essential to increasing workforce adoption and realizing benefits. With
GenAI, employees may have existing biases, inhibitions, skills gaps, or even a fear that they
could be replaced by a machine. Trust in GenAI grows out of transparency, where every
stakeholder understands how the enterprise is pursuing GenAI applications, how they are
intended to create value, and how the workforce can leverage these tools as efficiency and
productivity enhancers. Transparency around the benefits targeted by GenAI solutions
helps correct misinformation and creates an opportunity to improve the workforce
experience. Trust is also important for external stakeholders, third parties, and customers,
and a transparent approach to GenAI use includes consent for data collection, notification
of how GenAI outputs may impact users, and documentation across the AI lifecycle to
inform audits and compliance.
Help stakeholders understand
the GenAI vision and how it
creates value for them
12
Scaling Generative AI | 13 elements for sustainable growth and value
TALENT
Transformed roles, work,
and culture
Deployments at scale can disrupt the status quo, transforming employee responsibilities and
how work is accomplished. As an enterprise reimagines strategy, processes, and technology
to drive GenAI value, the workforce needs to be brought on the journey as value is created
through individuals doing work differently. Organizations should nurture adoption by
documenting and communicating responsibilities and process amendments to workflows.
Poor communications may cause misunderstanding about GenAI’s potential and limitations,
leading to unrealistic expectations or resistance. Conversely, effective communications
align stakeholders around the same vision for scale and value, including as they relate to
governance, policy, IT security, risk, and funding. Topics to communicate include outcomes
and lessons learned, the organization’s AI roadmap, the impact on end users (e.g., customers
or employees), and guidance to the workforce on how to balance day-to-day tasks with
AI skills development. Ongoing adoption should be measured to identify optimization
opportunities and internal leading practices. This should inform the overall use case roadmap
and activation strategy. Simply put, upstream conversations should take place before
continuing to build technical solutions that are underdelivering against expectations.
Nurture adoption by
documenting responsibilities
and process amendments
13
Scaling Generative AI | 13 elements for sustainable growth and value
TALENT
Acquiring (external) and
developing (internal) talent
Organizations deploying GenAI need to consider the skilled human talent required
across the GenAI lifecycle. Skills mapping can reveal where the enterprise needs to
expand or improve the workforce. Recruiting new talent is one avenue, such as by
attracting new employees from educational facilities (e.g., universities). In reimagining
work with GenAI, the organization may attract new leaders who are eager to use
technology to deliver business value, as well as top talent seeking opportunities to
learn and develop. Yet, most of a company’s GenAI capabilities will grow out of training
and upskilling existing employees, and as GenAI touches every part of the enterprise,
the entire workforce requires training to adopt and use it. To this end, businesses may
create overall AI literacy programs, training plans tailored to employee personas (e.g.,
technical, functional, sales, marketing, etc.), and opportunities (e.g., hackathons and
digital playgrounds) for employees to apply new knowledge and build competence in
GenAI application, management, and monitoring. A GenAI COE can help orchestrate
human-centered continuous learning to promote adoption.
Balance talent
acquisition with
workforce upskilling
14
Scaling Generative AI | 13 elements for sustainable growth and value
DATA & TECHNOLOGY
Modular architecture and
common platforms
IT architecture needs to evolve as technologies mature and as the organization’s needs
change. Flexibility in modular systems includes leveraging microservices and APIs
(Application Programming Interfaces) for tech stack integration, as well as techniques for
improving output reliability (e.g., retrieval augmented generation, fine-tuning). This enables
platform and model “lift and shift” and supports partnerships with hyperscalers that can
provision on-prem or cloud-based environments via contracts that reward increased
volume with lower unit costs. In prioritizing a modular architecture, organizations can
facilitate user growth with a cost-per-user model, automate guardrails for managing GenAI
risk, leverage GenAI capabilities in enterprise software platforms, and establish an internal
marketplace where users can select models, access prompt catalogs, and leverage
existing solutions. Modular architecture and delivery also accommodate low-code
platforms for business users and provide a clear pathway to industrializing capabilities.
Prioritize a flexible
IT architecture to
facilitate enhancements
15
Scaling Generative AI | 13 elements for sustainable growth and value
DATA & TECHNOLOGY
Provisioning the right
AI infrastructure
GenAI infrastructure includes reusable assets, data pipelines, solution development
environments, and a range of post-deployment management and feedback capabilities.
Bringing the right secure infrastructure to the right place in the GenAI value chain is
necessary for sustainable, cost-effective scale. Taking an AI Factory approach enables
reusable components and data products while also integrating sourcing strategy,
cybersecurity considerations, demand generation, prioritization, governance, and business
outcomes. While focusing on speed to value and taking an agile, incremental approach
to infrastructure development, organizations can look to iterative design and continual
evaluation of cost mechanisms against a per-user or per-use model. One important
consideration is that executives are likely to be more comfortable funding enhancements
to existing capabilities, as opposed to building net-new systems. Using existing investments
and approaching scale as building incremental capabilities can help encourage investments
by overcoming a misperception that a GenAI endeavor is starting from scratch.
Take an agile
approach to enable
continuous improvement
16
Scaling Generative AI | 13 elements for sustainable growth and value
DATA & TECHNOLOGY
Modern data foundation
As organizations increasingly shift to hybrid-cloud environments, data integration challenges
may increase, with proprietary and third-party data sources existing on disparate platforms.
In addition to master data, GenAI applications consume other forms of data (e.g., reference,
unstructured first-party) that traditionally sit in the realm of knowledge management. Value
creation opportunities from GenAI are blending knowledge and data management capabilities.
Data quality and accessibility issues can limit value and potentially create a perception that
scaled solutions are not viable nor valuable. A GenAI-ready data foundation includes the
processes, philosophies, approaches, and approvals for data sharing and use. As a part of this,
evaluate the organization’s data findability, accessibility, interoperability, reusability, and storage.
Rather than starting from scratch, the organization’s existing data governance efforts can likely
be extended and adjusted to accommodate unstructured data. Data should also be curated
and integrated across departmental lines. Consider a parallel workstream for data readiness
evaluation and progression focused on clean and organized data, efficient data pipelines,
and robust data governance practices. By ensuring systems are secure and foundational
data capabilities are aligned with the GenAI strategy and governance, enterprises can evolve
data availability, engineering, and management to enable adoption and scale. At the same
time, it is worth noting that interim value can be harvested, albeit at a lower potential, while
comprehensive and foundational data modernization activities are underway.
Align data capabilities and
processes with GenAI strategy to
support quality and accessibility
17
Scaling Generative AI | 13 elements for sustainable growth and value
DATA & TECHNOLOGY
Effective model management
and operations
Trustworthy, compliant GenAI applications require coordinated solution management,
including continuous monitoring for impartial output accuracy, waypoints for decision-
making, and data feedback loops for continuous improvement. Cost management is also a
factor. GenAI deployment raises questions around variable and fixed costs, and business
leaders need visibility into managing and forecasting end-to-end costs for infrastructure,
tools, personnel, maintenance, and models. Insourcing key functions may permit
differentiation or better economics over time, and insourcing decisions need to be balanced
against the cost to build a capability, the ramifications of moving to a fixed versus variable
cost, and the expenses associated with capability management (e.g., hiring and training,
oversight, technology acquisition, facilities).
Monitor for impartial
output accuracy and focus
on cost management
18
Scaling Generative AI | 13 elements for sustainable growth and value
Measuring success with GenAI at scale
The value of scaled GenAI deployments is found
in how they advance an integrated enterprise
strategy and drive toward business goals.
Establishing realistic goals for quantitative KPIs (beyond productivity and efficiency metrics,
such as hours saved) allows the enterprise to assess whether the scaled deployment is
achieving its intended business impact. With a use case portfolio that balances cost- and
revenue-oriented value levers, there are key indicators that reveal whether the
enterprise is on the right track:
• Increased speed to market, from ideation to deployment
• A decline in proof-of-concept demand, as demand shifts to low-code environments
available to business users
• A decrease in unit cost for new capabilities/solutions, with technical solutions and code
being reusable, thus reducing development efforts
• An increase in the number of foundational capabilities that help the organization access
GenAI advancements as they emerge
• An increase in domain-specific models allowing for more use cases and broader
application across the organization
• Increased use of capabilities and solutions, owing to a growing number of users in the
enterprise
• An increase in stated value realization on a cumulative basis due to GenAI
• An increase in internal certification/badging of existing employees in GenAI capabilities,
both functional and technical
• Use of GenAI to redefine a business process, rather than embedding GenAI in existing
business processes
19
Scaling Generative AI | 13 elements for sustainable growth and value
GenAI capabilities are improving and multiplying,
and at this point, few organizations are likely to
have achieved each element of scale to their
greatest capacity. The leading practices, governed
processes, and ecosystem of complementary
technologies are still being developed and defined.
While change is inevitable, pursuing the
elements of scale today positions the
organization to go live with GenAI for
business value as this transformative
technology evolves.
20
Scaling Generative AI | 13 elements for sustainable growth and value
Let’s connect
Reach out for a conversation on scaling Generative AI
Lou DiLorenzo Jr. Edward Van Buren Rohit Tandon
US AI & Data Strategy Government & Public Services US AI & Insights
Practice Leader Leader – Applied AI Practice Leader
US CIO & CDAO Programs Deloitte Consulting LLP Deloitte Consulting LLP
Executive Sponsor emvanburen@deloitte.com rotandon@deloitte.com
Deloitte Consulting LLP
ldilorenzojr@deloitte.com
Acknowlegements
The authors would like to thank the following leaders and colleagues for their contributions to this effort.
Kevin Abraham, Beena Ammanath, Aniket Bandekar, Kevin Byrne, Ricky Franks, Justin Hienz, Kevin Hutchinson, David Jarvis,
Carissa Kilgour, Lena La, Geoff Lougheed, Parth Patwari, Brittany Rauch, Jim Rowan, Kristin Ruffe, Baris Sarer, Dean Sauer, Laura
Sangha Pati Aditya Kudumala Jenn Malatesta
Shact, Brenna Sniderman, Ian Thompson, and Saurabh Vijayvergia.
USI AI & Insights Life Sciences Global AI Leader Commercial Officer
Practice Leader Deloitte Consulting LLP Deloitte & Touche LLP Endnotes
Deloitte Consulting LLP adkudumala@deloitte.fr jemalatesta@deloitte.com
1 Jim Rowan, Beena Ammanath, Brenna Sniderman et al, “Now decides next: Moving from potential to performance, Deloitte’s
spati@deloitte.com
State of Generative AI in the Enterprise,” Quarter three report Deloitte, August 2024.
2 Rowan, Ammanath, Sniderman et al, “Now decides next.”
3 Deloitte, “TrustworthyAITM, Bridging the ethics gap surrounding AI,” accessed 3 October 2024.
21
Deloitte refers to one or more of Deloitte Touche Tohmatsu Limited, a UK private company limited by guarantee, and its
network of member firms, each of which is a legally separate and independent entity. Please see www.deloitte.com/about
for a detailed description of the legal structure of Deloitte Touche Tohmatsu Limited and its member firms. Please see
www.deloitte.com/us/about for a detailed description of the legal structure of Deloitte LLP and its subsidiaries. Certain
services may not be available to attest clients under the rules and regulations of public accounting.
This publication contains general information only and Deloitte is not, by means of this publication, rendering accounting,
business, financial, investment, legal, tax, or other professional advice or services. This publication is not a substitute
for such professional advice or services, nor should it be used as a basis for any decision or action that may affect your
business. Before making any decision or taking any action that may affect your business, you should consult a qualified
professional advisor. Deloitte shall not be responsible for any loss sustained by any person who relies on this publication.
Copyright © 2024 Deloitte Development LLC. All rights reserved."
196,deloitte,us-artificial-intelligence-and-mergers-and-acquisitions.pdf,"Artificial intelligence and
mergers and acquisitions
Observations from the frontlines and how
to prepare for the coming shift
Artificial intelligence and mergers and acquisitions: Observations from the frontlines and how to prepare for the coming shift
Artificial intelligence (AI) has a powerful new variant,
Generative AI (GenAI). GenAI shows promise as game-
changing technology given its combination of novel features,
accessibility by nontechnical users, and scalability across an
enterprise. This combination of traits has the potential to un-
lock new sources of value across the enterprise.
Unsurprisingly, organizations want to know what this all 4. This is only the beginning, and when it comes to
means for dealmaking and, most importantly, how to GenAI, winners will be defined by their ability to
realize its value to mergers and acquisitions (M&A). navigate early challenges and identify the right
choices needed to win.
In an M&A context, it is easy to see how GenAI could
create a competitive edge for early adopters. Its ability to What about the second question: How can enterprises
ingest, interpret, and summarize significant quantities of capitalize on this opportunity? Given the indications
data; automate manual and labor-intensive processes; above, we recommend serious consideration of the
and uncover new insights and questions are all potential following actions:
avenues for enhancing returns during M&A. The
1. Stand up or strengthen sensing capabilities using
opportunities are numerous, but there is a clear risk:
internal and external resources, to keep a pulse on
Leaders who choose to defer action may lose ground to
AI and GenAI activity, considering direct and indirect
those who seize first-mover advantage.
competitors and partners.
Before diving in headfirst, M&A leaders and executives
2. Recast the M&A strategy by taking into consideration
should ask: How will GenAI affect M&A, and how can
how AI and GenAI might affect existing value chains
we capitalize on this opportunity?
and opportunities to capitalize on disruption and drive
To answer the first question, four key predictions are greater growth and value creation throughout the
well-founded: portfolio.
1. Deals will increasingly focus on (i) the acquisition of 3. Identify and invest in experts that can help
AI- and GenAI – capabilities, assets, and data, and (ii) validate and amplify AI and GenAI opportunities
the divestiture of business models vulnerable to and that bring a blend of commercial, operational, and
AI disruption. technical perspective.
2. Meaningful application of GenAI will enhance the 4. Prioritize and test AI use cases to develop a deeper
M&A process across the entire life cycle, improving understanding of capabilities and limitations and to aid
speed, quality of insights, and financial outcomes with identifying the most promising opportunities to
during execution. implement across the enterprise.
3. GenAI will continue to gain momentum in M&A as
early adopters employ it as a key lever to create value With the stage set, let us dive into the details.
from the top line to “heart of the business” functions.
2
Artificial intelligence and mergers and acquisitions: Observations from the frontlines and how to prepare for the coming shift
Four key predictions
1. Deals will increasingly focus on (i) the acquisition and look for ways to further exploit opportunities.
of AI- and GenAI – capabilities, assets, and data, and Similarly, for enterprises that find the basis of their
(ii) the divestiture of business models vulnerable to competition has fundamentally changed, some may
AI disruption. elect to divest noncore businesses to reinvest in new
and differentiated capabilities.
Private equity and strategics are increasingly buying AI
and GenAI capabilities. According to Crunchbase, GenAI 2. Meaningful application of GenAI will enhance the
and AI startups raised almost $50 billion in 2023, a 9% M&A process across the entire life cycle, improving
increase over 2022 levels.1 Furthermore, we see AI and speed, quality of insights, and financial outcomes
GenAI deal activity across nearly all sectors. See below during execution.
for recent industry examples:
With its ability to digest large quantities of data,
• Technology: Acquisition of AI based capabilities to synthesize and summarize findings quickly, develop
enhance AI Business offerings to improve customer quantitative and qualitative analyses, provide
experience and productivity. recommendations and predictions based upon pattern
recognition, and refine outputs through deep learning,
• Life sciences: Acquiring products from a clinical-stage
AI and GenAI can make an impact across the full M&A
drug discovery firm, which uses AI for a proprietary
life cycle.
drug discovery engine.
To date, much of the focus has been earlier in the
• Insurance tech: Acquisition of AI-driven cyber risk
life cycle. This is likely driven by companies starting to
analytics and GenAI-enhanced underwriting and
apply it where they are most comfortable and feel the
quoting.
least risk. Key examples include using AI and GenAI to
evaluate markets, products, and technologies to inform
• Oil and gas: M&A and investment in AI-enabled digital
strategies, identify gaps or vulnerabilities in product
models that can increase operational efficiency by
portfolios, and prioritize targets that fill those gaps. In
enhancing reservoir characterization.
fact, several private equity funds are already engaged
As these acquisitions show, AI and GenAI are a rising in exercises to understand how GenAI could have an
trend in multiple sectors. Goldman Sachs Research impact on their portfolio of investments.
predicts that AI investment could approach $200 billion
globally by 2025,2 which is likely to set the stage for AI is also helping to identify targets in a novel way. In
place of typical screening tools and criteria, AI and
future business strategies in an increasingly AI-driven
machine learning (ML)-enabled screening tools help
global market.
uncover previously “hidden” options by presenting new
Additionally, there has been a significant increase in targets that resemble their short list of top targets.
private equity deal activity focused on AI and GenAI, In fact, one client is training AI on what “successful”
considering rapid growth potential. To avoid being left portfolio companies look like—without defining inputs
behind, industry leaders will need to create or defend or outputs. Instead, the AI has established its own
competitive advantage with new investments in assets, criteria to uncover targets with a higher probability of
data, and AI capabilities. We are already seeing the value capture. Lastly, GenAI is helping clients review and
first signs of disruption across multiple sectors, which summarize critical supplier or customer contracts to
creates opportunities for the disrupters and potentially inform execution, integration, or separation strategies.
significant challenges for the disrupted.
Figure 1 shows the five key stages of the M&A life cycle
As the promise of GenAI becomes more real, new and associated key use cases identified by Deloitte.
entrants will pose competitive threats for incumbents
3
Artificial intelligence and mergers and acquisitions: Observations from the frontlines and how to prepare for the coming shift
Figure 1: AI and GenAI use cases across the M&A life cycle
Examples of AI and GenAI M&A use cases
• Product portfolio analysis: Assess product mix and recommend growth strategies
• Nested wargaming: Game out competitive moves and counter-moves by a key competitor
Target
• (Un)structured data analysis: Automated data extraction and transference of deal financial data into analytics and tools
screening/M&A
• Market sensing and analytics in PE: Identify and summarize PE investment thesis, leveraging market research and
strategy & planning
valuation • Deal sourcing/target screening: Identify and prioritize high performing assets for future deals to correlate with investment
strategy
• Valuation global standards chatbot: GenAI Engine that answers valuation questions based on global standards
• Diligence observations/focus: Evaluate financial and operational data to identify key risks
• Management interview preparation: Prepare management interview guides and additional data requests
• CDD prep and voice of customer execution: Examine target customer segments, preliminary trends
and summarize themes
Due
• Functional due diligence (HR, IT, Ops, etc.): Analyze and compare HR practices and policies (e.g., leaves, severance)
diligence
• Culture diligence: Use public data sources to gather information on the target company’s culture
• Management EBITDA drafting: Create management adjusted EBITDA build and draft description of adjustments
• Report tie-out: Compare draft report with finance workbook schedules, and note where values do not reconcile
• Working capital optimization: Generate insights about payment terms for customer/vendors and working capital
Negotiations & • Term sheet analysis: Analyze and summarize key agreement and financing terms
deal structure • Deal closing conditions: Draft Day 1 criteria and closing conditions based on sell-and buy-side objective
• Blueprinting, Day 1 checklists, TSAs: Automate operating model designs and draft of Day 1 planning deliverables
• Benchmarking analysis: Prepare benchmarking of financial and operational KPIs
• IT landscape analytics: Generate summary of comparison between seller and buyer applications, infrastructure and IT
Post-deal services
planning & • Contract analysis: Encapsulate key terms in contracts for contract harmonization
execution • Day 1 communications: Generate Day 1 communications (e.g., deal announcements, stakeholder FAQs, and social media
posts)
• Culture analysis: Synthesize survey and focus group data and recommend actionable steps to address culture differences
• Chatbot for Day 1 support: Leverage GenAI chatbot to answer questions related to deal and Day 1 readiness
• Synergy assessment: Quantify operational and financial synergies through transformation initiatives
Restructuring &
• Value creation and synergies: Analyze value creation levers, and prioritize cost savings initiatives
transformation
• Critical path management: Develop and track critical path for transformation and value realization
4
laed-erP
laed-tsoP
Artificial intelligence and mergers and acquisitions: Observations from the frontlines and how to prepare for the coming shift
While much of the focus has been on the earlier stages As summarized in figure 2, the degree of readiness,
of the life cycle, companies will build on early learnings value potential, stakeholder engagement, and security
and apply those learnings to reduce the risk and reaches its greatest strength at various parts of the
increase the value during downstream M&A activities. M&A life cycle.
Figure 2: Focus areas for GenAI in M&A based on Deloitte’s sponsored survey of M&A executives
Target Initial Detailed Integration/
identification assessment due diligence separation planning
Lower readiness
Market
readiness
Higher readiness
Lower value
creation potential
M&A value
potential
Higher value
creation potential
Limited stakeholder
engagement
Stakeholder
engagement
Extensive stakeholder
engagement
Lower security
concern
Security
Higher security
concern
Early developments in GenAI have created a higher readiness in early M&A life cycle use
cases. While there may be higher value creation potential in later life cycle use cases,
there are also additional considerations with stakeholder engagement and security.
Note: Figure 2 includes responses from a brief study conducted with M&A executives across industries.
5
Artificial intelligence and mergers and acquisitions: Observations from the frontlines and how to prepare for the coming shift
The costs, risks, and rewards of building and executing The question is not whether GenAI will affect M&A, but
GenAI use cases are still taking shape. But the instances rather at what pace? The technology’s potential to recast
that produce meaningful return on investment in the look and feel of dealmaking is significant, but several
the form of better insights, increased productivity, challenging headwinds must be navigated to bring that
and accelerated execution will emerge as real potential to fruition.
differentiators—and likely pave the way for applications
GenAI suffers from hallucinations: making incorrect
later in the M&A life cycle.
inferences from its source data that may seem
3.GenAI will continue to gain momentum in M&A correct. As with any tool, results and quality must
as early adopters employ it as a key lever to create be validated. GenAI is likely to open a gap and lag in
value from the top line to “heart of the business” understanding and development for the average or early
functions. career employee. Additionally, regulatory and ethical
complexities continue to evolve and at a seemingly
While we anticipate acquisitions of AI- and GenAI-
slower pace than AI. We also see access to or ownership
augmented business will continue to be a focus, we also
of large, high-quality, proprietary data increasing in
see that early experimentation with AI is uncovering
importance as a source of advantage.
opportunities to improve top-line growth, reduce costs,
and minimize execution risk. In fact, a recent Deloitte Perhaps GenAI will come to differentiate M&A winners
survey3 found that 79% of CEOs believe AI will increase from laggards. On the other hand, AI technologies
efficiencies, and 52% believe AI will drive revenue growth may simply become mission-critical capabilities that all
for their enterprises. companies adopt equally—tomorrow’s analog to the
internet or electricity.
As the survey signals, top-line growth is not the only
consideration coming into focus. The associated cost Having sketched the likely developments and the
opportunities and operational benefits are becoming remaining areas of uncertainty, what should an M&A-
clearer as well. Some buyers are already incorporating oriented organization do today to prepare for an AI-
modest cost savings associated with more well-founded fueled future?
use cases such as deploying advanced chatbots to
reduce customer service costs, automating coding and
documentation tasks to lower software development
costs, or even personalizing marketing content while
trimming associated spend.
We anticipate that as buyers gain more experience,
they will likely gain confidence in their ability to estimate
and deliver the impact of these use cases. With that
confidence, they will naturally expand their AI repertoires
to more “heart of the business” functions, such as cost of
goods sold, along with back-office functions such as IT,
finance, HR, and legal.
4.This is only the beginning, and when it comes
to GenAI, winners will be defined by their ability
to navigate early challenges and identify the right
choices needed to win.
6
Artificial intelligence and mergers and acquisitions: Observations from the frontlines and how to prepare for the coming shift
From strategy to action:
Four steps to take now
Strategy and deal teams that have not yet acted should an enterprise needs, what businesses or capabilities it
consider four moves to inform their strategy. We see no longer needs, and what to acquire or divest based on
each move as a “no regrets” decision that can help that intelligence.
position a company for effective and sustainable growth
Step 3. Identify and invest in experts that can help
in parallel with the AI revolution.
validate and amplify opportunities.
Step 1. Stand up or strengthen sensing capabilities.
As companies pursue AI acquisitions, they will have to
In a world of increasing “unknown unknowns” and identify internal talent or access external expertise, or
accelerating rate of advancement, companies can benefit both, to assess AI and GenAI targets.
by formalizing their approach to sensing.
That expertise will be critical to evaluating the quality of
For example, companies should examine sources the underlying technology and impact to existing assets.
of new potential threats that may arise from GenAI Increasing the level of understanding across business
disruption. Such sources may expand the definition of leaders, including commercial and R&D will be critical
competitors to include smaller, nontraditional entrants to identifying new internal diligence leads and sourcing
and markets beyond existing products and offerings. deals.
New technological developments should be tracked
In tandem, diligence teams will need to build and employ
and “scored” to indicate level and type of impact on
a framework that evaluates the suitability of a target’s
existing businesses or relative attractiveness of new
AI capabilities and its potential as a disrupter or enabler
opportunities. Additionally, insights from secondary
of enterprise strategy. Teams will also need to evaluate
sources should be validated through firsthand knowledge
and quantify future investment needed to enable their
of developments, either via third parties or direct
strategy. This can include detailed software due diligence,
conversations with those who possess knowledge of
including evaluation of source code and data, and
emerging capabilities. Increased awareness will not only
product testing for accuracy of underlying technology.
provide valuable perspective on the current state of play
but should enhance abilities to see where things may
Step 4. Prioritize and test M&A AI use cases.
go in the future and better position companies to make
smarter bets earlier. Planning can only take you so far, and we believe that
“learning by testing” is critical in this early stage of new
Step 2. Recast M&A strategy through an AI lens.
technology adoption.
Companies should reexamine their industry structures
Companies should leverage a cross-functional team’s
and reimagine their business models through an AI lens.
perspective to assist with prioritization of AI and GenAI
use cases. Regarding prioritization, teams should
The first consideration is to understand and challenge
consider evaluation of use cases based on their customer
existing assumptions that the industry will operate in
value, business impact, feasibility, and investment needs.
the future as it does today. The second consideration
Teams will also need to establish measures of success
involves disaggregating the value chain and pushing on it:
and identify learnings that will help make use cases
Where could AI both disrupt the chain and create shifts in
effective at scale. Lastly and most importantly, teams
power? Lastly, given these dynamics, companies should
should begin to test these use cases and avoid analysis
make deliberate choices in a “where to play and how
paralysis. The key is to get started on the journey and not
to win” strategic choice framework. This approach can
to overthink which foot to make the first step with.
assist with uncovering the specific AI capabilities or data
7
Artificial intelligence and mergers and acquisitions: Observations from the frontlines and how to prepare for the coming shift
New tools, new rules
AI presents unparalleled capabilities that can super- Deloitte has seen these shifts at work as we advise and
charge productivity, identify value in novel ways, gen- serve our clients during this technological revolution. As
erate rapid insights, and assist with identifying and we have observed these trends and forces firsthand, it
mitigating risks. These capabilities are likely to rapidly seems certain that enterprises across all industries will
change the work we do today and reshape how we think have lessons ahead. They will come from experience,
about M&A. GenAI not only has the potential to change not theory—and those who learn them earliest stand to
M&A from a process standpoint, but to also influence reap the greatest benefits.
the deals we seek, the way we compete, and the sourc-
es of value we identify across the enterprise. Lastly, the
pace of adoption is increasing, and those who wait face
disruption from those who act.
8
Artificial intelligence and mergers and acquisitions: Observations from the frontlines and how to prepare for the coming shift
Endnotes
1. Gené Teare, “Global startup funding in 2023 clocks in at lowest level in 5 years,” Crunchbase, January 4, 2024.
2. Abhinandan Jain, “2023: The year AI took over investments – What to expect in 2024?,” Alltech Magazine, January 6, 2024.
3. Deloitte, “The majority of CEOs surveyed believe Generative AI will increase their organizations’ efficiencies: ‘Summer 2023 Fortune/Deloitte CEO
Survey’,” press release, July 24, 2023.
9
Artificial intelligence and mergers and acquisitions: Observations from the frontlines and how to prepare for the coming shift
Authors
Will Engelbrecht Erik Dilger
Principal Managing Director
Deloitte Consulting LLP Deloitte & Touche LLP
wiengelbrecht@deloitte.com edilger@deloitte.com
Jeffrey Canon Sean McKenzie
Managing Director Manager
Deloitte Consulting LLP Deloitte Consulting LLP
jcanon@deloitte.com smckenzie@deloitte.com
Sandeep Dasharath
Senior Manager
Deloitte Consulting LLP
sdasharath@deloitte.com
About Deloitte
Deloitte refers to one or more of Deloitte Touche Tohmatsu Limited, a UK private company limited by guarantee, and its network
of member firms, each of which is a legally separate and independent entity. Please see www.deloitte.com/about for a detailed
description of the legal structure of Deloitte Touche Tohmatsu Limited and its member firms. Please see www.deloitte.com/us/
about for a detailed description of the legal structure of Deloitte LLP and its subsidiaries. Certain services may not be available to
attest clients under the rules and regulations of public accounting.
This publication contains general information only and Deloitte is not, by means of this publication, rendering accounting,
business, financial, investment, legal, tax, or other professional advice or services. This publication is not a substitute for such
professional advice or services, nor should it be used as a basis for any decision or action that may affect your business. Before
making any decision or taking any action that may affect your business, you should consult a qualified professional adviser.
Deloitte shall not be responsible for any loss sustained by any person who relies on this publication.
Copyright © 2024 Deloitte Development LLC. All rights reserved.
10"
197,deloitte,us-ai-institute-scaling-GenAI-final.pdf,"Scaling Generative AI
13 elements for sustainable growth and value
Scaling Generative AI | 13 elements for sustainable growth and value
About the Deloitte AI InstituteTM
The Deloitte AI Institute helps organizations connect
the different dimensions of a robust, highly dynamic
and rapidly evolving AI ecosystem. The AI Institute leads
conversations on applied AI innovation across industries,
with cutting-edge insights, to promote human-machine
collaboration in the “Age of With”.
The Deloitte AI Institute aims to promote a dialogue
and development of artificial intelligence, stimulate
innovation, and examine challenges to AI implementation
and ways to address them. The AI Institute collaborates
with an ecosystem composed of academic research
groups, start-ups, entrepreneurs, innovators, mature AI
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Combined with Deloitte’s deep knowledge and experience
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No matter what stage of the AI journey you’re in; whether
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Let’s explore the future of AI together.
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2
Scaling Generative AI | 13 elements for sustainable growth and value
Near the top of every enterprise agenda is a question of how to leverage Generative AI (GenAI).
With use cases proliferating horizontally across functions and vertically within business units,
the next step is...sustainably scaling GenAI for strategic business value.
Generative AI, like origami, transforms a resource
(data and paper, respectively) into a compelling
output. Just as origami artists fold paper to
resemble interesting shapes, Generative AI
computes data to approximate human cognition
and creativity.
3
Scaling Generative AI | 13 elements for sustainable growth and value
Getting more GenAI into production
Deloitte’s State of GenAI in the Enterprise Q3
report revealed that many businesses are
encountering challenges when making the
transition from GenAI proof-of-concept to
scaled deployment.1
Seventy percent of surveyed organizations indicate that less than one third of their
GenAI experiments have made it to production. This suggests that while enterprises are
investing in GenAI, they are not yet seeing the full potential ROI. A common challenge is
defining what is required to achieve GenAI scale at a practical level.
We define scale broadly as the ability of a system to handle a growing amount of
work or its potential to be enlarged to accommodate growth with steadily
decreasing unit costs. For GenAI specifically, scaling also means moving from
experimentation to implementation in a way that is sustainable, secure, and
aligned with business goals.
GenAI at scale generates more diverse and representative outputs, it can handle more
complex tasks, and its speed, output quality, and accuracy are enhanced. As a result,
operational costs become more efficient and business impact is governed, measured,
and communicated.
4
Scaling Generative AI | 13 elements for sustainable growth and value
At the highest level, GenAI scaling factors can be grouped into the familiar areas of strategy, process, talent, and
data and technology. Each area presents challenges to be navigated and contains leading practices that help point the way
to GenAI value realization.
Strategy Process Talent Data and Technology
Ambitious Modular
Integrated Transparency Provisioning
strategy and value Robust architecture
risk to build trust the right AI
management governance and common
management in secure AI infrastructure
focus platforms
Clear, Agile Acquiring Effective
Strong Transformed
high-impact operating model (external) and Modern data model
ecosystem roles, work,
use case and delivery developing foundation management
collaboration and culture
portfolio methods (internal) talent and operations
Essential elements for scaling Generative AI initiatives from pilot to production
5
Scaling Generative AI | 13 elements for sustainable growth and value
STRATEGY
Ambitious strategy and
value management focus
An organization’s GenAI strategy and vision need to be comprehensive, integrated with
broader business objectives, and aligned with other existing AI programs. Executive
buy-in and a top-down mandate are essential for aligning functions and decision-making.
Leadership sets priorities and strategy, and without an executive mandate, it is difficult
to coordinate change across multiple teams. A cohesive GenAI strategy defines business
objectives, sets measurable goals, identifies valuable areas for application, and measures
realized value. As a part of strategy development, it’s important to show progress against
short-term goals and inform any iterative improvements needed to the strategy.
Establish a
comprehensive vision
with a top-down mandate
6
Scaling Generative AI | 13 elements for sustainable growth and value
STRATEGY
Clear, high-impact use
case portfolio
There are six common macro archetypes for GenAI: Q&A-based search, summarization,
content generation, content transformation, virtual agent, and code generation. In seeking
value-driving applications, organizations should look across the archetypes for low-
barrier, high-impact use cases for core business domains. These drive efficiencies and
savings that can be reinvested in innovation. Other high-impact use cases may be more
transformational and differentiating with enterprise-wide applicability. Whether deploying
a proven application or striving for something novel, all applications require technical
feasibility and a viable business case. What is more, existing processes will likely need to be
reimagined to incorporate and leverage the capabilities of GenAI use cases in workflows.2
At its core, the use case portfolio needs to be focused on answering business questions
and meeting quantified goals. We see leading organizations create business cases that
weave together the value GenAI can provide to multiple teams, rather than evaluating
the value of individual applications. This is done most effectively by running a number
of use cases in parallel. It makes efficient use of resources and allows for rapid portfolio
management should a specific use case prove less compelling without sacrificing
momentum of the overall Gen AI portfolio.
Explore low-barrier, high-
impact use cases to drive
efficiencies and savings
7
Scaling Generative AI | 13 elements for sustainable growth and value
STRATEGY
Strong ecosystem
collaboration
GenAI is maturing rapidly, with existing providers and new market entrants alike driving
capabilities and lateral applications. The array of GenAI solutions and the speed with which
they are evolving can make it challenging to select the appropriate tools and platforms
that enable enterprise strategy. To reach target outcomes, enterprise leaders should build
strategic relationships with technology and data ecosystem stakeholders and keep pace
with GenAI development. By monitoring elements like product roadmaps, total cost of
ownership, and labor delivery models, business leaders can gain an understanding as
to how their GenAI programs should evolve and how ecosystem players can accelerate
progress and results as strategic partners, rather than as transactional vendors. Use a
framework to support a structured approach to evaluating solutions based on factors such
as data readiness, AI maturity, risk appetite, and total cost of ownership.
Evolve with existing
providers and new market
entrants alike
8
Scaling Generative AI | 13 elements for sustainable growth and value
PROCESS
Robust governance
Inconsistent processes can lead to risks and inefficiencies, while consistent governance
processes help standardize workflows for data collection, solution engineering, output
validation, and performance monitoring. Common delivery frameworks (e.g., LLMOps)
bring together GenAI development and deployment into a unified, governed lifecycle that
is secure and compliant. A common misconception is that strong processes can hinder
speed and creativity. Our experience suggests the opposite. By understanding how work
needs to be done and the accompanying guardrails, teams are empowered to explore
ways to generate value without fear that they may be making a mistake. Clear boundaries
allow freedom for bold action and innovation, while a lack of clarity may lead to more
conservative approaches. Governance includes documented roles and responsibilities
that drive stakeholder accountability in decision-making across the AI lifecycle, and
inform the controls for risk identification and mitigation. Governance also standardizes
how stakeholders identify, prioritize, and approve GenAI applications. As processes are
amended, organizations need to be mindful about disrupting existing automated or
manual controls and take steps to establish assurance in those amended processes.
Even as the regulatory landscape is in flux, organizations should proactively establish
governance processes that meet existing or likely regulatory requirements.
Create repeatable
governance processes to
help standardize work
9
Scaling Generative AI | 13 elements for sustainable growth and value
PROCESS
Integrated risk management
For GenAI to reach its full potential business value and adoption, it must be trusted
and secure.3 Attempting to scale without accounting for trust in data and the machine
that consumes it can have implications for regulatory compliance, finance and strategy,
cybersecurity and privacy, adoption and change management, and brand reputation—
the consequences of which can limit or even erase GenAI’s intended value. Risk and
trust need to be considered and addressed across the GenAI lifecycle, from design and
development through deployment and scaled implementation. This includes validation
processes and feedback loops for human oversight to manage solution performance
and accuracy. It also includes guardrails to ensure privacy, drive ongoing compliance, and
promote agility in proactively responding to emerging risks. Data security is particularly
essential. Differentiated GenAI applications are fueled by sensitive, proprietary enterprise
data. Thus, training and usage can potentially expose or leak business-critical data and
create risks to the organization. This is not a one-time event—organizations must make
this part of regular work, rather than a separate consideration.
Address risk and data
security across the
GenAI lifecycle
10
Scaling Generative AI | 13 elements for sustainable growth and value
PROCESS
Agile operating model
and delivery methods
The operating model impacts how the enterprise aligns technology, processes, and roles
and responsibilities to create strategic business value. An integrated model connects the
blueprint for value with AI business cases to inform how work is delivered and helps drive
alignment across the enterprise. As the marketplace matures and new capabilities and
risks impact AI lifecycles and governance, the organization needs to be agile in matching
internal opportunities with the right technologies. To help, organizations may turn to
technical experts or an AI Center of Excellence (COE) that equips decision makers with
the insight to align the vision for success with the organization’s AI maturity and ambition.
This supports a cohesive approach to orchestrating the elements of GenAI development
and application. It helps avoid AI and data silos and instead drive toward reusable
building blocks, coordinated sourcing strategy, informed build-versus-buy decisions, and
security and risk management.
Support a cohesive
approach to orchestrating
the components
11
Scaling Generative AI | 13 elements for sustainable growth and value
TALENT
Transparency to build trust
in secure AI
Trust in GenAI is essential to increasing workforce adoption and realizing benefits. With
GenAI, employees may have existing biases, inhibitions, skills gaps, or even a fear that they
could be replaced by a machine. Trust in GenAI grows out of transparency, where every
stakeholder understands how the enterprise is pursuing GenAI applications, how they are
intended to create value, and how the workforce can leverage these tools as efficiency and
productivity enhancers. Transparency around the benefits targeted by GenAI solutions
helps correct misinformation and creates an opportunity to improve the workforce
experience. Trust is also important for external stakeholders, third parties, and customers,
and a transparent approach to GenAI use includes consent for data collection, notification
of how GenAI outputs may impact users, and documentation across the AI lifecycle to
inform audits and compliance.
Help stakeholders understand
the GenAI vision and how it
creates value for them
12
Scaling Generative AI | 13 elements for sustainable growth and value
TALENT
Transformed roles, work,
and culture
Deployments at scale can disrupt the status quo, transforming employee responsibilities and
how work is accomplished. As an enterprise reimagines strategy, processes, and technology
to drive GenAI value, the workforce needs to be brought on the journey as value is created
through individuals doing work differently. Organizations should nurture adoption by
documenting and communicating responsibilities and process amendments to workflows.
Poor communications may cause misunderstanding about GenAI’s potential and limitations,
leading to unrealistic expectations or resistance. Conversely, effective communications
align stakeholders around the same vision for scale and value, including as they relate to
governance, policy, IT security, risk, and funding. Topics to communicate include outcomes
and lessons learned, the organization’s AI roadmap, the impact on end users (e.g., customers
or employees), and guidance to the workforce on how to balance day-to-day tasks with
AI skills development. Ongoing adoption should be measured to identify optimization
opportunities and internal leading practices. This should inform the overall use case roadmap
and activation strategy. Simply put, upstream conversations should take place before
continuing to build technical solutions that are underdelivering against expectations.
Nurture adoption by
documenting responsibilities
and process amendments
13
Scaling Generative AI | 13 elements for sustainable growth and value
TALENT
Acquiring (external) and
developing (internal) talent
Organizations deploying GenAI need to consider the skilled human talent required
across the GenAI lifecycle. Skills mapping can reveal where the enterprise needs to
expand or improve the workforce. Recruiting new talent is one avenue, such as by
attracting new employees from educational facilities (e.g., universities). In reimagining
work with GenAI, the organization may attract new leaders who are eager to use
technology to deliver business value, as well as top talent seeking opportunities to
learn and develop. Yet, most of a company’s GenAI capabilities will grow out of training
and upskilling existing employees, and as GenAI touches every part of the enterprise,
the entire workforce requires training to adopt and use it. To this end, businesses may
create overall AI literacy programs, training plans tailored to employee personas (e.g.,
technical, functional, sales, marketing, etc.), and opportunities (e.g., hackathons and
digital playgrounds) for employees to apply new knowledge and build competence in
GenAI application, management, and monitoring. A GenAI COE can help orchestrate
human-centered continuous learning to promote adoption.
Balance talent
acquisition with
workforce upskilling
14
Scaling Generative AI | 13 elements for sustainable growth and value
DATA & TECHNOLOGY
Modular architecture and
common platforms
IT architecture needs to evolve as technologies mature and as the organization’s needs
change. Flexibility in modular systems includes leveraging microservices and APIs
(Application Programming Interfaces) for tech stack integration, as well as techniques for
improving output reliability (e.g., retrieval augmented generation, fine-tuning). This enables
platform and model “lift and shift” and supports partnerships with hyperscalers that can
provision on-prem or cloud-based environments via contracts that reward increased
volume with lower unit costs. In prioritizing a modular architecture, organizations can
facilitate user growth with a cost-per-user model, automate guardrails for managing GenAI
risk, leverage GenAI capabilities in enterprise software platforms, and establish an internal
marketplace where users can select models, access prompt catalogs, and leverage
existing solutions. Modular architecture and delivery also accommodate low-code
platforms for business users and provide a clear pathway to industrializing capabilities.
Prioritize a flexible
IT architecture to
facilitate enhancements
15
Scaling Generative AI | 13 elements for sustainable growth and value
DATA & TECHNOLOGY
Provisioning the right
AI infrastructure
GenAI infrastructure includes reusable assets, data pipelines, solution development
environments, and a range of post-deployment management and feedback capabilities.
Bringing the right secure infrastructure to the right place in the GenAI value chain is
necessary for sustainable, cost-effective scale. Taking an AI Factory approach enables
reusable components and data products while also integrating sourcing strategy,
cybersecurity considerations, demand generation, prioritization, governance, and business
outcomes. While focusing on speed to value and taking an agile, incremental approach
to infrastructure development, organizations can look to iterative design and continual
evaluation of cost mechanisms against a per-user or per-use model. One important
consideration is that executives are likely to be more comfortable funding enhancements
to existing capabilities, as opposed to building net-new systems. Using existing investments
and approaching scale as building incremental capabilities can help encourage investments
by overcoming a misperception that a GenAI endeavor is starting from scratch.
Take an agile
approach to enable
continuous improvement
16
Scaling Generative AI | 13 elements for sustainable growth and value
DATA & TECHNOLOGY
Modern data foundation
As organizations increasingly shift to hybrid-cloud environments, data integration challenges
may increase, with proprietary and third-party data sources existing on disparate platforms.
In addition to master data, GenAI applications consume other forms of data (e.g., reference,
unstructured first-party) that traditionally sit in the realm of knowledge management. Value
creation opportunities from GenAI are blending knowledge and data management capabilities.
Data quality and accessibility issues can limit value and potentially create a perception that
scaled solutions are not viable nor valuable. A GenAI-ready data foundation includes the
processes, philosophies, approaches, and approvals for data sharing and use. As a part of this,
evaluate the organization’s data findability, accessibility, interoperability, reusability, and storage.
Rather than starting from scratch, the organization’s existing data governance efforts can likely
be extended and adjusted to accommodate unstructured data. Data should also be curated
and integrated across departmental lines. Consider a parallel workstream for data readiness
evaluation and progression focused on clean and organized data, efficient data pipelines,
and robust data governance practices. By ensuring systems are secure and foundational
data capabilities are aligned with the GenAI strategy and governance, enterprises can evolve
data availability, engineering, and management to enable adoption and scale. At the same
time, it is worth noting that interim value can be harvested, albeit at a lower potential, while
comprehensive and foundational data modernization activities are underway.
Align data capabilities and
processes with GenAI strategy to
support quality and accessibility
17
Scaling Generative AI | 13 elements for sustainable growth and value
DATA & TECHNOLOGY
Effective model management
and operations
Trustworthy, compliant GenAI applications require coordinated solution management,
including continuous monitoring for impartial output accuracy, waypoints for decision-
making, and data feedback loops for continuous improvement. Cost management is also a
factor. GenAI deployment raises questions around variable and fixed costs, and business
leaders need visibility into managing and forecasting end-to-end costs for infrastructure,
tools, personnel, maintenance, and models. Insourcing key functions may permit
differentiation or better economics over time, and insourcing decisions need to be balanced
against the cost to build a capability, the ramifications of moving to a fixed versus variable
cost, and the expenses associated with capability management (e.g., hiring and training,
oversight, technology acquisition, facilities).
Monitor for impartial
output accuracy and focus
on cost management
18
Scaling Generative AI | 13 elements for sustainable growth and value
Measuring success with GenAI at scale
The value of scaled GenAI deployments is found
in how they advance an integrated enterprise
strategy and drive toward business goals.
Establishing realistic goals for quantitative KPIs (beyond productivity and efficiency metrics,
such as hours saved) allows the enterprise to assess whether the scaled deployment is
achieving its intended business impact. With a use case portfolio that balances cost- and
revenue-oriented value levers, there are key indicators that reveal whether the
enterprise is on the right track:
• Increased speed to market, from ideation to deployment
• A decline in proof-of-concept demand, as demand shifts to low-code environments
available to business users
• A decrease in unit cost for new capabilities/solutions, with technical solutions and code
being reusable, thus reducing development efforts
• An increase in the number of foundational capabilities that help the organization access
GenAI advancements as they emerge
• An increase in domain-specific models allowing for more use cases and broader
application across the organization
• Increased use of capabilities and solutions, owing to a growing number of users in the
enterprise
• An increase in stated value realization on a cumulative basis due to GenAI
• An increase in internal certification/badging of existing employees in GenAI capabilities,
both functional and technical
• Use of GenAI to redefine a business process, rather than embedding GenAI in existing
business processes
19
Scaling Generative AI | 13 elements for sustainable growth and value
GenAI capabilities are improving and multiplying,
and at this point, few organizations are likely to
have achieved each element of scale to their
greatest capacity. The leading practices, governed
processes, and ecosystem of complementary
technologies are still being developed and defined.
While change is inevitable, pursuing the
elements of scale today positions the
organization to go live with GenAI for
business value as this transformative
technology evolves.
20
Scaling Generative AI | 13 elements for sustainable growth and value
Let’s connect
Reach out for a conversation on scaling Generative AI
Lou DiLorenzo Jr. Edward Van Buren Rohit Tandon
US AI & Data Strategy Government & Public Services US AI & Insights
Practice Leader Leader – Applied AI Practice Leader
US CIO & CDAO Programs Deloitte Consulting LLP Deloitte Consulting LLP
Executive Sponsor emvanburen@deloitte.com rotandon@deloitte.com
Deloitte Consulting LLP
ldilorenzojr@deloitte.com
Acknowlegements
The authors would like to thank the following leaders and colleagues for their contributions to this effort.
Kevin Abraham, Beena Ammanath, Aniket Bandekar, Kevin Byrne, Ricky Franks, Justin Hienz, Kevin Hutchinson, David Jarvis,
Carissa Kilgour, Lena La, Geoff Lougheed, Parth Patwari, Brittany Rauch, Jim Rowan, Kristin Ruffe, Baris Sarer, Dean Sauer, Laura
Sangha Pati Aditya Kudumala Jenn Malatesta
Shact, Brenna Sniderman, Ian Thompson, and Saurabh Vijayvergia.
USI AI & Insights Life Sciences Global AI Leader Commercial Officer
Practice Leader Deloitte Consulting LLP Deloitte & Touche LLP Endnotes
Deloitte Consulting LLP adkudumala@deloitte.fr jemalatesta@deloitte.com
1 Jim Rowan, Beena Ammanath, Brenna Sniderman et al, “Now decides next: Moving from potential to performance, Deloitte’s
spati@deloitte.com
State of Generative AI in the Enterprise,” Quarter three report Deloitte, August 2024.
2 Rowan, Ammanath, Sniderman et al, “Now decides next.”
3 Deloitte, “TrustworthyAITM, Bridging the ethics gap surrounding AI,” accessed 3 October 2024.
21
Deloitte refers to one or more of Deloitte Touche Tohmatsu Limited, a UK private company limited by guarantee, and its
network of member firms, each of which is a legally separate and independent entity. Please see www.deloitte.com/about
for a detailed description of the legal structure of Deloitte Touche Tohmatsu Limited and its member firms. Please see
www.deloitte.com/us/about for a detailed description of the legal structure of Deloitte LLP and its subsidiaries. Certain
services may not be available to attest clients under the rules and regulations of public accounting.
This publication contains general information only and Deloitte is not, by means of this publication, rendering accounting,
business, financial, investment, legal, tax, or other professional advice or services. This publication is not a substitute
for such professional advice or services, nor should it be used as a basis for any decision or action that may affect your
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professional advisor. Deloitte shall not be responsible for any loss sustained by any person who relies on this publication.
Copyright © 2024 Deloitte Development LLC. All rights reserved."
198,deloitte,us-advisory-ai-data-readiness.pdf,"AI data readiness (AIDR)
Getting your data ready for AI adoption at scale
July 2024
AI data readiness (AIDR) | July 2024
Introduction
Data is becoming increasingly important for the success of a
business as organizations adopt to changes in the business
environment; become more digital, data-driven, and use data
to influence decision-making; and become more responsive to
customer needs. Data has historically been used to drive various
aspects of business and has been an enabler for emerging
technologies, including artificial intelligence (AI), which has been
a game-changer in recent years.
AI, at its core, is a sophisticated and multifaceted concept,
intricately woven from three fundamental components:
Each of these elements plays a crucial role in the functioning and
impact of AI and has specific risks and challenges that need to be
mitigated through an effective set of implemented AI
Business context Technique/ algorithm Data governance requirements.
What makes up AI? Common risks/challenges for governing AI
BBuussiinneessss ccoonntteexxtt •• PPuurrppoossee aanndd vvaalluuee o off A AI I ••OOppeeraratitoionnala cl ocnotnrtorlosls
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aencdo seycsotesymstem
TTeecchhnniiqquuee//aallggoorriitthhmm •• AApppplliiccaabbiilliittyy ttoo uussee c caassee ••LLifiefe c yccylcele c oconntrtorlosls
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aarree uusseedd ttoo aaddddrreessss aa ssppeecciiffiicc u ussee c caassee o or rb buussinineesss sp proroblbelmem • Vendor/ platform dependency • Data and model drift
• Vendor/ platform dependency • Data and model drift
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DDaattaasseettss ((iinntteerrnnaall oorr eexxtteerrnnaall)) u usseedd t too b buuilidld a anndd t rtarainin A AI I astnadn sdtaarnddsards ••DDaatata m moovevmemenetnt
mmooddeellss//aallggoorriitthhmmss,, aanndd tthheeirir l elevveel lo of fc cuuraratitoionn a anndd f ifit-tf o-fro-ur-sues e •• DDaattaa eetthhiiccss aanndd pprrivivaaccyy ••DDaatata u usese/f/itfi tfo fro rp uprupropsoese
((ee..gg..,, aavvaaiillaabbiilliittyy ooff vveeccttoorrss,, wweeigighhttss, ,r reessuultlsts)) •• D Da at ta a q qu ua al li it ty y ••TThhiridrd-p-paartryt yd daatata
1
AI data readiness (AIDR) | July 2024
Defining the business problem is a linchpin in maintaining Identifying the appropriate algorithm or technique is another
a sharp focus on requirements throughout the creation of an critical step in implementing an AI solution, once the business
AI model. This initial step serves as a compass, guiding the requirements have been identified. This involves considering
development process by helping to clearly articulate the business factors such as scalability, interpretability, and computational
efficiency. Consequently, this step assists in laying the
requirements (i.e., specific challenges and/or opportunities) that the
groundwork for subsequent phases of model development,
AI model aims to address.
including data pre-processing, feature engineering, and model
evaluation, to reasonably ensure that the AI model is effective in
addressing the targeted business challenge.
Data availability
Typical data-related challenges for organizations
Data quality and fit for purpose
Typical data-related challenges for organizations
• Is the required data associated with the business
problem available within the organization?
• In case of availability of data, what are the insights into
• Is the data on which the AI model is constructed,
nuances of data availability that assist practitioners in
capable of providing meaningful insights
making informed decisions regarding data collection,
or predictions?
pre-processing, and augmentation?
• Are there potential challenges related to data quality
or quantity requiring measures to address these
issues throughout the model development process?
The effective implementation of AI hinges on adeptly managing
various data challenges, especially in the context of heightened
complexity in data life cycle management for AI applications.
Typical challenges include:
• The quality and availability of data, with poor data quality
potentially impeding AI system development.
• Ethical considerations, including privacy and security,
which highlight the importance of regulatory compliance.
• Data governance, standards, regulatory compliance, and
data resilience are emphasized to help minimize risks and
reasonably ensure accountability in AI decision-making.
To mitigate the errors and inefficiencies, it is crucial to implement
effective data quality processes, including data cleansing,
validation, and monitoring. Data quality standards and practices
are essential to reasonably ensure that the data used for training AI
models is accurate, representative, and unbiased.
2
AI data readiness (AIDR) | July 2024
Use cases related to AI over the years and associated data concerns1
AI usage Data concerns examples
1. Personalized customer service • Availability of historical transactional data
(e.g., AI powered chatbots and virtual assistants) • Accuracy of data and use of data that is fit for purpose
• False positives in data used to train the fraud detection model
2. Real-time fraud detection and security
• Manual reviews vs. level of automation to validate data quality
• Compliance to data privacy rules
3. AI powered robo-advisor
• Source/method for acquisition of data used for advanced analytics models
• Processes to manage sourcing, evaluation, procurement, integration, and
4. Credit risk assessment maintenance of third-party datasets
• Bias in data uses for assessments and decisions
1. Todd Bigham et al., AI and risk management: Innovating with confidence, Deloitte, 2018.
3
AI data readiness (AIDR) | July 2024
What is AI data readiness (AIDR)?
An organization’s preparedness in implementing strategies to help guide effective AI deployment by reasonably ensuring that its data is
available, high quality, properly structured, and aligned with its AI use cases.
That depends. What
How can I benefit data do we have to Well, what are your
from AI? use for AI? data requirements?
Considerations for AIDR:
• Understanding of the business
context and objective for AI
• Structuring a process to identify
and evaluate available data
Business Users Data scientists Data & tech. teams
• Articulating data readiness gaps
and improvement opportunities
Use AI to achieve business Identify data requirements for Provide data based on data
needs and drive decision making business needs, and ensure data requirements from the • Facilitating a common
is ready for AI use data scientists taxonomy across stakeholders
for AI data readiness
4
AI data readiness (AIDR) | July 2024
What are the steps to implement and reasonably
ensure the readiness of AI data?
Identifying the data scope, evaluating data readiness, and implementing improvements for data readiness are pivotal in creating an effective
AI model.
Define Evaluate data Improve data
data scope readiness readiness
Define the scope of data whether it Utilize aspecialized Data Develop and execute a plan to
focuses on a single, specific use case Readiness Assessment Tool to improve data readiness through
or aims for an enterprise-wide evaluate the readiness of in-scope combination of near and
transformationto determine targeted data for the intended AI application longer-term actions to accelerate
and effective planning. and use. AI build and deployment.
5
AI data readiness (AIDR) | July 2024
Define data scope
Defining the data scope is a crucial initial step for financial
institutions embarking on the journey into artificial intelligence.
Specifically, the scope for AI data readiness involves evaluating
risk tolerance, harnessing the insights of use-case owners through
strategic collaboration, and ultimately identifying key characteristics
to help articulate the problem or objective addressed by the
AI model.
This scope can range from a focused application like fraud detection
to a broader, enterprisewide embrace of artificial intelligence.
Several activities are involved in creating a well-defined data scope:
Activities Considerations
• Required data inputs designed to optimize AI model performance
1. Identify required data inputs • Data availability
• Specific data types, both structured and unstructured
• Data sources available and understanding how to apply them to the AI model
2. Define data sources • Identification and documentation of the data sources to be leveraged (e.g.,
internal databases, external APIs, third-party datasets, or acquired data)
3. Establish data collection and • Data cleaning, normalization, feature engineering, and augmentation
pre-processing requirements as required
• Adherence to data privacy regulations and safeguarding sensitive information
4. Consider data privacy and security • Access control to reasonably ensure that authorized personnel with specific
roles can view or modify sensitive data
• Definition of time frame and scale of datasets
5. Define data scope boundaries
• Limitations or exclusions to be imposed on the datasets
In the context of a fraud detection use case for a bank, establishing • Geographical information: Considering the geographic
a precise data scope is paramount. The data scope for this scenario location of transactions can be crucial for identifying anomalies.
could include:
Unusual transactions in locations not typically associated with
the customer’s behavior could be red flags.
• Financial transactions: The primary focus may likely be on
data related to financial transactions encompassing details such
as transaction amounts, time stamps, and transaction types.
• Customer behavior patterns: Analyzing historical customer
behavior is essential. This includes studying spending patterns,
transaction frequency, and typical transaction sizes associated
with each customer.
6
AI data readiness (AIDR) | July 2024
Evaluate data readiness: Five dimensions
In the realm of financial services, where data is as valuable as Having an AI data readiness approach allows for a structured
currency, the readiness of this data for AI implementation is not process to evaluate the preparedness of a client’s data landscape
just a technical requirement but a strategic imperative. across five critical dimensions:
Dimensions Capabilities for evaluation
1.Data Availability
Data that’s well-organized, structured, and easily accessible in a • Data Management
timely manner to boost efficiency in storage, retrieval, and • Data Integration and Utilization
processing,while promoting reusability along with abstraction. • Advanced Analytics
• Data Storage
2.Data volume & diversity
Sufficient and diverse datasets (e.g., representing real-world • Historical Data
scenarios) allow for AI solutions to identify complex patterns • Data Sourcing
and deliver more accurate predictions. • Data Diversity (Features)
3.Data quality & integrity
By adopting and upholding leading data quality standards • Data Accuracy and Fitness
and processes, AI models can work with accurate, • Standardization & Protocols
consistent & fit-for-purpose data, leading to reliable and • Metadata
• Documentation & Reporting
accurate outcomes.
4.Data governance
Implementing a robust governance framework for data and • Data Strategies
AI can help manage data throughout its lifecycle, establish • AI Governance and Documentation
policies, standards,data ownership,and set guidelines • Data Collaboration
• Data Assessment
around use of data for AI.
5.Data ethics & responsibility
Data ethical considerations incorporated in data policies for • Regulatory Compliance
use of data for AI to drive improvement in accountability of • Data Protection & Access Monitoring
AI-based decision-making, emphasize safety, and foster • Use Case Specific Data Rules
transparency around data usage for AI processes.
#
Our AIDR questionnaire can help measure an AIDR score to facilitate a
AIDR GO / NO-GO DECISION to move forward with the AI Build.
score
7
AI data readiness (AIDR) | July 2024
Each dimension is a pillar that upholds the integrity and efficacy of many essential building blocks and that the necessary factors are
of AI applications. The capabilities for evaluation listed above can considered for constructing the initial AI model.
reasonably ensure that these pillars are strong both individually and
This meticulous approach not only facilitates the ease of
cohesively to support the overarching goal of
implementing subsequent models, but also reasonably ensures
AI-driven transformation.
the ongoing health and performance of the initial AI model.
A highly effective strategy for achieving data readiness is to dedicate
ample time to thoroughly analyze the existing landscape across each
dimension outlined above. This process helps ensure the availability
Improve data readiness
Improving AI data readiness is important because high-quality accuracy and reliability of data is crucial for training AI models.
and well-structured data is one of the foundations of successful AI
The following are five tactical steps to improve AI data readiness,
models and algorithms. By improving data readiness, organizations
based on the evaluation of capabilities across each of the
can unlock the full potential of AI and derive meaningful insights, as
five domains .
5. Communicate
and monitor
4. Develop • Meet with
key stakeholders
improvement plans
to evaluate progress
and resolve blockers
3. Go/ No-Go • Develop initiatives to • Create data readiness
workshop help address the top scorecard to measure
findings while AI project KPIs to
2. Determine • Hold go/no-go considering the risk demonstrate impact
workshops with tolerance levels of improved
risk tolerance stakeholders established in the data readiness
previous step
to gauge
1. Analyze • Collaborate which AIDR • Establish clear
results with client to dimensions from steps in improving
determine the risk the Assessment defining a data
tolerance levels of Tool need to be governance framework
• Review the implementing a
addressed based
outcomes of the solution to the
on the risk Target
Assessment use case for
tolerance levels
Tool and identify each areas of State vision
the top findings improvement
and areas
• Consider client’s
of improvement
risk appetite
within the
business context
Expected outcomes:
• Improved data environment maturity
• Accelerated AI model development
8
AI data readiness (AIDR) | July 2024
Conclusion
AI has emerged as a transformative force in today’s data-driven
world. While readiness of data is critical for harnessing the
potential of AI applications, several key takeaways have emerged,
including challenges, prospects, and considerations for adoption.
As organizations are investing in data infrastructure and
formulating AI strategies, the continuous advancements, and a
commitment to addressing data related concerns will assist in
driving the future of AI applications.
AI data readiness will thus be the foundation for unlocking AI’s
full potential in a wide range of applications. Your data is not just
information; it can be the key to your potential opportunities in
leveraging AI solutions.
Are you ready to unleash the power of AI?
How can we help?
AI data readiness approach
Deloitte’s AIDR approach is a tool for assessing data
readiness in preparation for AI implementation.
AI Data Readiness Assessment Tool
The Data Readiness Assessment Tool is leveraged to
evaluate the current state of a company’s data
environment in the five key dimensions of
datareadiness.
AI Data Readiness Score/Results
The AIDR Assessment Toolaggregates the responses
from each question to show a score for each dimension,
rolled up into an aggregate score.
9
AI data readiness (AIDR) | July 2024
Reach out to get started:
Our team is standing by to help and is excited for the opportunity to assist with your AI data readiness journey.
Vic Katyal Cory Liepold Satish Iyengar
Risk & Financial Advisory Risk & Financial Advisory Risk & Financial Advisory
Principal and Chief Operating Officer Principal Managing Director
Deloitte & Touche LLP Deloitte & Touche LLP Deloitte & Touche LLP
Ajay Ravikumar Akiva Ehrlich Shlomi Cohen
Risk & Financial Advisory Digital Controls Artificial Intelligence and Data
Senior Manager Advisory Partner Managing Director
Deloitte & Touche LLP Deloitte Israel & Co. Deloitte Israel & Co.
Amit Golan
Financial Industry Risk and Regulatory
Senior Manager
Deloitte Israel & Co.
Contributors:
Soumya Gollamudi, Risk & Financial Advisory, Manager, Deloitte & Touche LLP
Brian Nam, Risk & Financial Advisory, Senior Consultant, Deloitte & Touche LLP
Amanda Schwartz, Risk & Financial Advisory, Analyst, Deloitte & Touche LLP
Sahana Gurumurthy, Risk & Financial Advisory, Senior Solution Advisor, Deloitte & Touche Assurance & Enterprise Risk Services India Pvt. Ltd.
10
Data health reporting | Insights and actions
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11"
199,deloitte,us-ai-institute-gen-ai-for-enterprises.pdf,"Generative AI
is all the rage
Deloitte AI Institute
GGeenneerraattiivvee AAII iiss aallll tthhee rraaggee
Implications of Generative AI for businesses
About the Deloitte AI Institute
The Deloitte AI Institute helps organizations connect Combined with Deloitte’s deep knowledge and
the different dimensions of a robust, highly dynamic experience in artificial intelligence applications,
and rapidly evolving AI ecosystem. The AI Institute the Institute helps make sense of this complex
leads conversations on applied AI innovation across ecosystem, and as a result, deliver impactful
industries, with cutting-edge insights, to promote perspectives to help organizations succeed by
human-machine collaboration in the “Age of With”. making informed AI decisions.
The Deloitte AI Institute aims to promote a dialogue No matter what stage of the AI journey you’re in;
for and development of artificial intelligence, whether you’re a board member or a C-Suite leader
stimulate innovation, and examine challenges to AI driving strategy for your organization, or a hands
implementation and ways to address them. The AI on data scientist, bringing an AI strategy to life, the
Institute collaborates with an ecosystem composed of Deloitte AI institute can help you learn more about
academic research groups, start-ups, entrepreneurs, how enterprises across the world are leveraging AI
innovators, mature AI product leaders, and AI for a competitive advantage. Visit us at the Deloitte AI
visionaries to explore key areas of artificial intelligence Institute for a full body of our work, subscribe to our
including risks, policies, ethics, future of work and podcasts and newsletter, and join us at our meet ups
talent, and applied AI use cases. and live events. Let’s explore the future of AI together.
www.deloitte.com/us/AIInstitute
2
22
GGeenneerraattiivvee AAII iiss aallll tthhee rraaggee
Let’s take a moment to cut
through the hype.
The AI field took a turn with the release
of powerful Generative Artificial Intelligence
(AI) models, and as a result, the world is
seeing the automation of some skills around
creativity and imagination sooner than many
expected. For some organizations, Generative
AI holds valuable potential for higher order
opportunities, like new services and business
models. Deloitte offers a method for
selecting Generative AI use cases, as well
as some next steps for business leaders in
the Age of With™.
33
Implications of Generative AI for businesses 2
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The rise of Generative AI
Generative AI has captured attention
in global media and the public square,
prompting questions and discussions
around this transformative technology.
Businesses, research organizations, and Generative AI in general and LLM-
even lay users are experimenting with powered chatbots in particular
Generative AI, and given the excitement are not without risks, and this
and interest, it is important to look is prompting discussions around things
more closely at the potential capabilities like the potential for job losses and
and implications for business. legal questions around intellectual
property and ownership. What is more,
Generative AI is a subset of artificial because the chatbot mimics coherent
intelligence in which machines create human phrasing, it may give some the In this article, we look closely at the
new content in the form of text, code, impression that the AI understands the potential benefits and limitations
voice, images, videos, processes, and prompts to which it responds, which of Generative AI, introduce a method
even the 3D structure of proteins. Some can lead users to anthropomorphize to qualify if, where and how these
forms of Generative AI have been well the chatbot (i.e., the ELIZA effect, cognitive tools could be used, and
established in this decade, but it was as seen in the work of computer offer important factors for business
a large language model (LLM) powering scientist Joseph Weizenbaum). leaders to weigh when adopting
an easily accessible chat interface Generative AI.
that enabled Generative AI to have its Deloitte is working on a variety of
breakthrough moment and surprise projects exploring the opportunities In a prior article, “Implications
even specialists in the field. and business value Generative AI of Generative AI for businesses,”
can create for our clients. From Deloitte offered a deep dive on
As with other types of AI before it, experiences and conversations thus the qualities and capabilities
this new poster child of AI is stimulating far, the clear path ahead, as with of Generative AI, the state of the
the imagination as organizations and all AI, is to attempt to discover and market, and what that means for
individuals consider how to use this tool capitalize on capabilities while also organizations wading into this fast-
to benefit both business and society. responsibly managing the risks that evolving technology field. And in
Generative AI can help in incremental forthcoming articles, we will discuss
are already emerging.
digitization and basic productivity use questions from legal, ethics, risk, and
cases (e.g., more effective text-based talent and technology perspectives
channels). But its grander potential and provide insights into industry
is in the higher order opportunities, use cases.
such as new services or business models
that were previously uneconomical.
444
Generative AI is all the rage
The rise of Generative AI While this is still the
beginning, it’s moving fast.
Among organizations across industries, In late 2022, with the release of an
there is interest in differentiating AI use easy-to-use Generative AI chatbot, more
cases, from public service applications people began to discover and imagine
to addressing climate change and how this new technology can be used
transforming business functions (see in the creative space. The chatbot use
Deloitte’s AI Dossier). AI is viewed as a case opened the door for thinking more
tool that can automate skills and tasks broadly about how Generative AI can
performed by humans, and AI can be so be used for tasks, ranging from writing
successful in this regard that humans can copy to generating 3D structures and to
forget skills that have been automated. outputting organizational processes. As
Examples include writing assistants, such, we are now seeing the automation
home automation, and automotive of some skills around creativity and
navigation systems. Would most people imagination sooner than many expected.
have the ability to navigate a new city
without a mobile phone? There is a lot left to discover. In this
Age of With™, the era of humans
We have seen these kinds of automations working with intelligent machines
emerge across a variety of areas and to achieve things greater than what
skillsets. The assumed roadmap for either could do alone, Generative AI
AI was that, in the shorter term, AI is will impact the future of work and
most valuable as a way to automate become a common tool throughout
operational skills, and creative skills will various aspects of our daily lives.
remain the exclusive province of human In some cases, the applications may
thinking for the foreseeable future. With be clearly visible, but more often
Generative AI, this roadmap has taken an than not, they may be working in
unexpected turn. the background.
5
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The evolution of Generative AI
The ability of Generative AI to create a convincing (albeit low-
quality and greyscale) image of a human face emerged in 2014.
Since then, the image quality has increased, and today, almost
anything that can be described in words can also be generated
as an image, using a textual description called a “prompt.”
Throughout 2022, social media users tinkered with Generative
AI platforms and shared the results. We have seen avocado
armchairs and photorealistic images of astronauts riding horses
on the Moon. Cosmopolitan magazine was the first to publish
a cover page created by an AI-based image generation tool,
and there has even been a case of a user who submitted an
AI-generated image to a fine art competition—and won first
place. Today, we are seeing similar improvements in other kinds
of Generative AI. You may even have found this article via a chat
with an AI system that integrates with a search engine.
Images created with Generative AI.
66
Generative AI is all the rage
How Generative AI works:
Understanding the basics
To understand how Generative AI will impact
business and life, we need to understand what
it is, what it can do, and what it cannot do, yet.
Machine learning has dominated the field In a process referred to as training,
of AI for decades. Generally, this approach the algorithm is supplied with a large
to AI development is rooted in the dataset of input/output examples to
concept of learning from examples, rather extract patterns from the input, which
than following explicitly programmed allow conclusions about the expected
rules. This is important as there are many output. Spam filters, for example, use
tasks that humans perform based on these patterns to identify similarities in
tacit knowledge (and thus can provide data points and relate those to different
examples) but cannot describe the classes (i.e., sorting email to a spam
underlying rules to do so. For example, folder). While the input data has become
humans know how to recognize a face, more complex over time, from simple
but the rules that would instruct an AI arrays of numbers to high-resolution
system to do the same are much less photos, the output side of a model has
clear. The approach of learning from to this point been mostly limited to
examples has led to the development of categories like “spam” or “not spam,”
powerful tools that can identify intricate “cat” or “dog,” or numerical values such
patterns in complex data. as 7°C or $29. This approach powers
nearly all AI that has been deployed so far,
the result is “single purpose” applications
that can only perform one task.
7
Generative AI is all the rage
INPUT OUTPUT
Figure 1:
Used car data An example of
a single purpose
Type Engine Milage Year machine learning
model, using a
Limo Electric 70k 1996
Predicted price regression model
Regression to predict the
S. wagon Petrol 100k 2005 $17,000
model resale value for
a given car.
Truck Diesel 150k 2016
Figure 2:
INPUT OUTPUT
An example of how
Email Predicted label a single purpose
model can be used
Hi Bjorn,
No spam to sort e-mails
Have a look at this: A generative adversarial network
Classification by “spam” or
(GAN) is a class of machine learning frameworks
model Spam “not spam.”
designed by Ian Goodfellow
Enter Generative AI
The main difference between “traditional quality data processed over weeks
AI” and Generative AI is that in the latter, on large-scale, GPU-enabled, high
the output is of a higher complexity. performance computing clusters.
Rather than just a number or a label, the Only a few institutions have the necessary
output can be an entire high-resolution resources and talent to build such
image, a full page of newly written text models. Running a model also requires
(which is generated word by word), or any a lot of compute, which is why access to
other digital artifact. This introduces an these kinds of models is often provided
interesting new element: There is usually via an application programming interface
more than one possible correct answer. (API). This allows developers to use the
This results in a large degree of freedom models with their existing software
and variability, which can be interpreted products without need for additional
as creativity. infrastructure. These models are versatile
and can be fine-tuned for specific tasks,
Generative AI models are typically hence they are called Foundation Models.
large and resource hungry. Creating Unlike single-purpose AI, they are suited
them requires terabytes of high- for multi-purpose tasks.
Figure 3:
INPUT OUTPUT
With Generative AI,
Generated text user prompts lead
Prompt
to artifacts that
Generative AI is a broad field of computer sicence focused on can contain a large
What is AI?
model creating intelligent machines that can perform tasks degree of freedom
that typically require human-like congnitive abilities,
and variability.
such as perception, reasoning, learning, problem-
solving, and decision-making.
8
Generative AI is all the rage
Regarding risks and limitations
Current Generative AI models have Similar to other AI models, Foundation
limitations. Perhaps the most well-known Models can reproduce latent bias in the
is termed “hallucination,” which refers to training data, and of course, they lack
a high-confidence response that is not comprehension and the ability to reason
grounded in the training data. In other as humans do. This has implications for
words, the response is fictional. For the broader concept of Trustworthy
some applications, like art generation, AI™. After all, they are language models,
this is a non-issue and perhaps even image models, or voice models but not
a desired “creative” feature of Generative knowledge models.
AI. For other applications, however,
such as copywriting or computer code Despite limitations, Foundation
generation, hallucinations can result in Models can function at such a high
artifacts that are not entirely valid or true, quality that many new use cases
which undercuts the potential value of become possible.
Generative AI.
Another limiting factor is that today’s
Generative AI models generate artifacts
based on the model itself and the
Some known limitations
input prompt. Other additional sources
of current Generative AI
and datasets cannot currently be
integrated directly into the model’s
internal information processing without
Hallucination | Generative AI systems create
costly re-training or fine-tuning, which
confident responses that cannot be grounded in any
means Generative AI models can only
of its training data.
access information extracted from the
data on which they were trained. For
similar reasons, they cannot provide Bias | Similar to other learned models, Foundation
references and sources for the generated Models inherit the bias contained in the training data.
content, which complicates validation.
Furthermore, current models have
Lack of human reasoning | Generative AI systems
a context window of a few thousand
are based on statistical features, which is not a solid
words, which is the limit for the size
of the combined input and output. foundation for logical reasoning.
However, Generative AI models can
be combined with other systems (e.g.,
Limited context window | Current models have
search, conversational AI) to leverage the
a context window of a few thousand words, which is the
benefits of both parts. For example, with
limit for the combined input and output of the model.
a chatbot, a conversational AI system can
serve as an orchestration layer between
the Generative AI model, a search engine,
and the user, which helps to amplify the
user experience.
9
Generative AI is all the rage
Generating revenue
using Generative AI
Using this technology for business benefit can
be conceived along two distinct approaches.
First, the models can be used as they a job advertisement or a floor plan,
are available today, a simple interface all the way to the 3D model of an engine
that allows near-direct access to the part, a molecule with certain properties,
underlying model in the form of a chat or a workflow, to name a few. Use cases
for text or an image generation tool. with high usage frequency are preferred,
The second approach is to integrate as there will be more example data to
Generative AI with other technologies fine-tune and improve a model, and
to automate processes. For example, subsequently a more substantial impact.
Generative AI can allow for human- Other factors to consider in selecting
level expressive interactions, while high-value use cases are existing skill and
a conversational AI system (i.e., a chat- cost bottlenecks with human generated
or voicebot) controls the flow and artifacts. The quality of the artifact may
ensures factual accuracy. An example in some cases require human effort, but
is an automated, Generative AI-powered if it can be created with Generative AI to
call center. We expect the second a commensurate quality, then the human
approach will provide the most value. can be liberated to work on higher quality
tasks. By turning lower-level creative tasks
A good start to identifying use cases over to Generative AI, we could see things
is to find processes or tasks where like databases providing stock content
a digital artifact of some kind is created (e.g., images, sounds, or texts) turned
or processed. This could range from upside down as these digital artifacts can
be created instantly with a prompt.
If a task requires effort to execute but is easy
to validate, it might be a good use case.
Deloitte has designed a Digital Artifact a task without Generative AI; and the
Generation/Validation method to help necessary effort to validate or fact check
innovation leaders determine whether the output from the Generative AI. This
an idea can be turned into a beneficial leads to a two-dimensional classification,
use case leveraging Generative AI. At categorizing use cases based on the
the core of this method are two of the required human effort and the ability
most critical elements to consider: of the user to validate the results.
the human effort required to complete
10
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There is a sweet spot for Generative AI use cases
Generative AI is useful where the artifact generation effort is high and validation is easy
Digital Artifact Generation/Validation method
Identifying desirable use cases
HIGH
Generative AI assists best in use cases
where human effort is high, while
validation is easy.
CONSIDER Generation effort
3 How much human effort is required to
2
achieve the desired result
1
Validation effort
4 How much human effort is required to
check the plausibility or correctness of
Generation the result
effort
ASSESS
NEGLECT
LOW
DIFFICULT Validation effort EASY
Examples plotted above
1 Create a joke 2 Draw an image of 3 Draft a contract 4 Draft a contract
an elephant under (without legal expertise) (with legal expertise)
While creating a good
a palm tree
joke requires some effort If you do not have legal If you do have legal
into designing the punch- Drawing any sophisticated expertise, drafting expertise, drafting
line and best storytelling- image requires reasonable a contract is very hard and a contract still requires
style, it is easy to validate effort for most people validating it difficult. effort, but validating it is
simply by reading it. regardless of the tools Generative AI is useful signifacantly easier.
available. On the other where the artifact
hand, validation is easy generation effort is high
since you can just look at and validation is easy.
the picture.
1111
Generative AI is all the rage
For example, re-writing text can be There is an additional complication
a daunting and time-consuming task for that should be considered. If the model
a human. Generative AI tools can take outputs are consistently correct, users
original text and quickly produce may, over time, become less rigorous in
a re-written result, a shorter text, fact checking. Inevitably, however, the
a summary, or even a different writing model will make an error, and part of the
style. A user who is familiar with the challenge is that the errors may not be
original content can validate the accuracy obvious, particularly when Generative AI
or correctness of the output. Thus, this is used to create more complex things,
could be a promising application of like programming code. Thus, when
Generative AI. assessing the ease of validation, weigh
the importance of ongoing attention
Yet, if the user is reviewing outputs that to fact checking.
are outside of their area of expertise,
validation becomes more complicated.
The Generative AI output may
read as coherent and convincingly
accurate, but the potential for a
“hallucination” remains. If users lack
the knowledge to validate the output
and spot hallucinations, the use case is
revealed to require high levels of effort
for validation and mitigating the risks
from hallucinations.
12
Generative AI is all the rage
Insights from Deloitte projects on Generative AI:
Reaping benefits from Generative AI requires
more than identifying a good use case
Identifying use cases is only part of the avoid the temptation to go forward alone
challenge. Whenever a transformative and instead find support and knowledge
technology emerges, some organizations from partners, colleagues, and third-party
are spurred to experiment for the sake organizations operating in this space.
of its novelty, which can lead to “random
acts of digital” that do not deliver the The inherent complexity in current
anticipated return. Driving business projects leveraging Generative AI
results with Generative AI requires a requires a cross-disciplinary team
strategy and collaboration from a cross- to guide and govern the AI lifecycle.
disciplinary team. In addition, with Professionals from a variety of domains
a technology that is advancing and can help the business answer critical
maturing as quickly as Generative AI, questions, including:
DOMAIN
Ideation Business Customer & Enterprise Human Risk Regulations
& Product Operations Marketing Technology Capital Management & Laws
Development
STAKEHOLDERS
Creatives, CEO, COO, CMO CIO, CTO, IT CHRO Risk officers Legal &
designers Line of Business Compliance
leaders
KEY QUESTIONS
What can How does the How can the use Can the existing Does the What risks emerge What current and
Generative AI Generative AI fit case be leveraged MLOps-tech stack workforce possess when deploying expected laws
permit that into and enhance to build customer and platform the skills to use Generative AI (e.g., and regulations
reduces human existing processes engagement, licenses fuel Generative AI, jailbreaks, prompt- concern the use
effort and can be and enterprise and how much Generative AI, or and what are the spoofing), and of Generative AI,
rapidly validated? strategy? transparency is are third-party implications for how do these risks and are existing
appropriate? services required? talent acquisition impact Generative governance and
and upskilling? AI value? MLOps processes
sufficient to meet
those laws and
regulations?
13
Generative AI is all the rage
Based on our observations and experience, we
recommend business leaders avoid jumping head-first
into the hype. Instead, we encourage decision makers to:
1 2 3
Develop a strategy for Become familiar with the Bring together a cross-
Generative AI and integrate and underlying technologies that make disciplinary team of people with
harmonize it with the enterprise’s Generative AI possible, as well as the the domain knowledge to think
existing AI strategy. The same current capabilities and limitations. creatively about potential use cases.
principles that guide an AI-fueled Educate your workforce in the When business leaders, technology
organization apply to the use of usage, risks, and capabilities of AI leaders, and creatives work together
Generative AI (e.g., access to curated to establish a baseline of knowledge with external experts, they are able
enterprise data; AI governance; through training. Also monitor over to identify valuable applications
process transformation to leverage time how the technology advances and also design Generative AI
cognitive workers, etc.). With and the impact on business risks deployments, to mitigate business
a technology evolving this quickly, and opportunities, as they emerge. and cyber risks and meet applicable
avoid the temptation to go forward This article series may support laws and regulations.
alone. Find support and knowledge your efforts.
from partners and third-party
organizations operating in this space.
4 5 6
Leverage Deloitte’s Ensure the collection and Assess use cases against
Digital Artifact Generation/ curation of proprietary data, Trustworthy AI™ principles,
Validation method as this is key for tailored use cases including challenges around bias
to identify where Generative AI that provide a differentiator or and misinformation, attribution,
might impact your value chain, competitive advantage. transparency, and enterprise
with incremental digitization from accountability for the impact from
basic productivity use cases to Generative AI.
higher order opportunities, such
as new, differentiating services
or business models.
14
Generative AI is all the rage
Deloitte is excited to move into the future with
our clients and colleagues, as well as with our
connections in academia and the broader
AI ecosystem around the world.
The discussions so far show that there There is a lot
is a need for a deeper understanding
of Generative AI, from the underlying to cover and the
technology to its impact on the future
conversations are far
of work. As such, it is important to look
closely at the implications for risk, trust,
from over. Deloitte
and governance, which is investigated
in a forthcoming article, “Proactive risk is a trusted advisor
management in Generative AI.” There are
also legal considerations for Generative as we push beyond
AI, which we plan to cover in “Legal
the initial buzz
implications of using Generative AI
(What the AI System won’t tell you).”
around this new
technology and into
how Generative AI
can be used for good
in the Age of WithTM.
15
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Reach out for a conversation.
Beena Ammanath Wessel Oosthuizen Dr. Kellie Nuttall Jefferson Denti Audrey Ancion
Global Deloitte AI Institute Deloitte AI Institute Africa, Deloitte AI Institute Deloitte AI Institute Brazil, Deloitte AI Institute
Leader Lead Australia, Lead Lead Canada, Lead
Deloitte AI Institute Deloitte Africa Deloitte Australia Deloitte Brazil Deloitte Canada
United States, Lead woosthuizen@deloitte.com knuttall@deloitte.com jdenti@deloitte.com aancion@deloitte.ca
Deloitte Consulting, LLP
bammanath@deloitte.com
Jan Hejtmanek Roman Fan Anne Sultan Dr. Bjoern Bringmann Prashanth Kaddi
Deloitte AI Institute Deloitte AI Institute China, Deloitte AI Institute France, Deloitte AI Institute Deloitte AI Institute India,
Central Europe, Lead Lead Lead Germany, Lead Lead
Deloitte Central Europe Deloitte China Deloitte France Deloitte Germany Deloitte India
jhejtmanek@deloitte.com rfan@deloitte.com asultan@deloitte.com bbringmann@deloitte.com pkaddi@deloitte.com
Masaya Mori Nicholas Griedlich Naser Bakhshi Tiago Durao Sulabh Soral
Deloitte AI Institute Japan, Deloitte AI Institute Deloitte AI Institute Deloitte AI Institute Deloitte AI Institute
Lead Luxembourg, Lead Netherlands, Lead Portugal, Lead United Kingdom, Lead
Deloitte Japan Deloitte Luxembourg Deloitte Netherlands Deloitte Portugal Deloitte United Kingdom
mmori@deloitte.com ngriedlich@deloitte.com nbakhshi@deloitte.nl tdurao@deloitte.com ssoral@deloitte.com
Special thanks to contributors:
Jakob Nikolaus Moecke, Senior Consultant, Deloitte AI Institute Germany Elon Allen, Partner, Monitor Deloitte Australia
Philipp Joshua Wendland, Senior Consultant, Deloitte AI Institute Germany Bram den Hartog, Partner, Monitor Deloitte Australia
Alexander Mogg, Lead Partner, Monitor Deloitte Germany Aisha Greene, Senior Manager, Deloitte AI Institute Canada
Kate Fusillo Schmidt, Senior Manager, Deloitte AI Institute US Anke Joubert, Senior Manager, Deloitte AI Institute Luxemburg
Erica Dodd, Senior Manager, Deloitte AI Institute Australia Dr. Gordon Euchler, Director, Deloitte Germany
Jessica Carius, Senior Consultant, Deloitte AI Institute Australia
111666
This publication contains general information only and Deloitte is not, by means of
this publication, rendering accounting, business, financial, investment, legal, tax, or
other professional advice or services. This publication is not a substitute for such
professional advice or services, nor should it be used as a basis for any decision or
action that may affect your business. Before making any decision or taking any action
that may affect your business, you should consult a qualified professional advisor.
Deloitte shall not be responsible for any loss sustained by any person who relies on
this publication.
About Deloitte
Deloitte refers to one or more of Deloitte Touche Tohmatsu Limited, a UK private
company limited by guarantee (“DTTL”), its network of member firms, and their related
entities. DTTL and each of its member firms are legally separate and independent
entities. DTTL (also referred to as “Deloitte Global”) does not provide services to
clients. In the United States, Deloitte refers to one or more of the US member firms of
DTTL, their related entities that operate using the “Deloitte” name in the United States
and their respective affiliates. Certain services may not be available to attest clients
under the rules and regulations of public accounting. Please see www.deloitte.com/
about to learn more about our global network of member firms.
Copyright © 2023 Deloitte Development LLC. All rights reserved."
200,deloitte,us-ai-institute-ceo-guide-to-generative-ai-enterprises.pdf,"A CEO's guide
to envisioning the
Generative AI enterprise
Leading a Generative AI-fueled enterprise:
A CEO series
Deloitte Global CEO Program
Deloitte AI InstituteTM
AA CCEEOO''ss gguuiiddee ttoo eennvviissiioonniinngg tthhee GGeenneerraattiivvee AAII eenntteerrpprriissee
About the About the
Deloitte Global CEO Program Deloitte AI Institute
The Deloitte Global CEO Program is The Deloitte AI Institute helps sense of this complex ecosystem, and as
dedicated to advising chief executive organizations connect the different a result, deliver impactful perspectives
officers throughout their careers—from dimensions of a robust, highly dynamic to help organizations succeed by making
navigating critical points of inflection, to and rapidly evolving AI ecosystem. The AI informed AI decisions.
designing a strategic agenda, to leading Institute leads conversations on applied AI
through personal and organizational innovation across industries, with cutting- No matter what stage of the AI journey
change. The program offers innovative edge insights, to promote human-machine you’re in; whether you’re a board member
insight and immersive experiences to help: collaboration in the “Age of With”. or a C-Suite leader driving strategy for your
organization, or a hands on data scientist,
• Facilitate the personal success of
The Deloitte AI Institute aims to promote bringing an AI strategy to life, the Deloitte
individual executives, new or tenured,
a dialogue and development of artificial AI institute can help you learn more about
throughout their life cycle.
intelligence, stimulate innovation, and how enterprises across the world are
• Elevate the relationships between them, examine challenges to AI implementation leveraging AI for a competitive advantage.
their leadership teams, and their boards and ways to address them. The AI Institute Visit us at the Deloitte AI Institute for a
collaborates with an ecosystem composed full body of our work, subscribe to our
• Support the strategic agenda for their
of academic research groups, start-ups, podcasts and newsletter, and join us at
organizations in times of disruption
entrepreneurs, innovators, mature AI our meet ups and live events. Let’s explore
and transformation.
product leaders, and AI visionaries, to the future of AI together.
explore key areas of artificial intelligence
www.deloitte.com/us/ceo
including risks, policies, ethics, future of www.deloitte.com/us/AIInstitute
work and talent, and applied AI use cases.
Combined with Deloitte’s deep knowledge
and experience in artificial intelligence
applications, the Institute helps make
22
A CEO's guide to envisioning the Generative AI enterprise
Few technologies have debuted to as much
consumer and media fanfare as Generative AI,
especially upon the November 2022 launch of the
first conversational Generative AI chatbot.
Over the past year, user adoption,
experimentation, and awareness of
Generative AI’s seemingly boundless
possibilities have continued to expand.
This exponential growth has instilled
a growing belief among businesses
and CEOs that Generative AI has the
potential to significantly augment,
if not substitute, even the most
intricate and unstructured avenues
of value creation.
For example, computer code, once
considered the work of specialized
masters, can now be easily created by
Generative AI. Even elements of the
nuanced art of strategy development,
In a Generative AI-driven Moreover, the hard investment
a most critical executive
autonomous enterprise, such tradeoffs that CEOs have had
discipline, can be increasingly
capabilities will likely become more to face have limited their ability
augmented by AI. Consider the
commonplace over time. to develop critical capabilities,
application and use of scenarios or
including foundational technology
strategic foresight in the formulation
At the risk of adding fuel to an already and workforce investments. The
and refinement of enterprise strategy.
frenetic hype cycle, Generative AI Generative AI shift requires
Ever more powerful and generative
opens up possibilities for entirely new business leaders—most acutely,
AI could:
business models and market capture. CEOs—to alter how they lead
• Dramatically broaden and structure But the reality of the past year is the enterprise.
the basis of evidence by which to stark: There has been a lot of activity
anticipate the future of markets and interest, and plenty of proofs of
concept and demos, but a disjointed
• Create rich and divergent stories
approach has prevented most
about different plausible futures
companies from fully harnessing
• Continuously monitor the the potential of Generative AI.
environment for signals regarding
the critical uncertainties that
underpin the plausible futures
and their likelihood
• Assess the suitability of strategic
positions and options in the
context of the different futures and
suggest adaptations
3
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eeaarrllyy mmoovveerrss ccaann ttaakkee aaddvvaannttaaggee..
44
A CEO's guide to envisioning the Generative AI enterprise
Our analysis has shown that Generative AI is much more than the
successful digital transformation can evolution of a chatbot—it can be the
result in up to $1.25 trillion (USD) in compressed digital representation
additional market cap, and Generative of the entire enterprise, capturing
AI is proving to be a powerful knowledge and communicating it
accelerant for transformation.1 Over through natural language (as opposed
the next decade, productivity gains to programming languages). To truly
and capabilities enabled by AI are capture its actual value, rather than
expected to increase global GDP by focusing on accomplishing discrete
$7 trillion, while the Generative AI tasks or “shallow” use cases that
market doubles every other year.2,3 are disconnected from the core
business, CEOs have an opportunity
CEOs can capture this value by to envision how to align Generative
setting the right vision, drawing AI to their overall business strategy.
their perspective from both a Generative AI embodies the potential
strategic understanding of the to encapsulate and disseminate the
technology and its potential to drive entirety of an enterprise, not merely in
value and marketplace advantage. completing tasks but in reshaping the
Indeed, Generative AI represents fundamental business framework.
the unequivocal catalyst reshaping
industries and redefining business We call this vision of the future the
strategies, empowering forward- autonomous enterprise: a future-
thinking CEOs as architects of an state organization that capitalizes
AI-infused future. on the unique advantages of pairing
humans with AI to help people
become far more effective and the
work more fulfilling as digital agents
complement and support them.
55
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Realizing a future-state
autonomous enterprise
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aammoouunnttss ooff uunnssttrruuccttuurreedd ddaattaa,, wwhhiicchh rreessttrriicctteedd
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llooww ccoonntteexxtt vvaarriiaabbiilliittyy,, aanndd hhiigghh aaccccuurraaccyy..
TThhaatt’’ss nnoo lloonnggeerr tthhee ccaassee wwiitthh GGeenneerraattiivvee AAII..
66
A CEO's guide to envisioning the Generative AI enterprise
As we know from studying the Technical teams in the background In the autonomous enterprise of
progression of information technology are constantly monitoring the data the future, the blueprints of the
over time, cognitive automation and models the digital agents are organization, its complex ways of
systems are only going to become using to compile such assessments. working, and years of institutional
more intelligent.4 Generative AI Doing so maintains confidence in knowledge are at our fingertips,
accessible through sophisticated AI
capabilities could enable the use of the integrity and accuracy of the
models. The transformative potential
digital bots or agents that operate forecasts. After minor modifications,
of Generative AI transcends prior
throughout an enterprise in a the CEO and executive team agrees
limitations in value creation through
supportive role. Such bots could on next steps. Humans then execute
automation, heralding a future where
be given goals instead of specific the agreed vision by activating
fully autonomous bots operate
commands and could develop plans, additional agents that independently
within enterprises, helping humans
execute tasks, and even assign other assist them with making project plans,
formulate strategies, execute tasks,
digital agents tasks. designing products and digital twins,
and adapt to market dynamics,
and generating marketing content.
Imagine a planning meeting in an The digital agents can even alert fundamentally altering the landscape
autonomous enterprise. Digital agents humans when they should adjust of business operations.
are tasked with synthesizing the strategies to changes in market
company’s prior fiscal year sales and conditions to ensure resiliency.
creating a forecast based on current
and expected market conditions.
The CEO and the executive team
interrogate the enterprise AI model
about its forecasting methods
and assumptions, which are
communicated with clear rationales.
7
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Humans with AI
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ccaann hheellpp aa CCEEOO oouuttppaaccee tthhee ccoommppeettiittiioonn..
88
A CEO's guide to envisioning the Generative AI enterprise
First and foremost, the CEO should to create differentiation; and it will
be specific about how Generative AI be the role of its leaders to find that
can increase human employees’ skills, “differentiation” by designing unique
efficiency, and productivity, thanks ways for humans and AI to interact.5
to new interfaces that ease human Humans will continue to excel at
interaction and allow for engagement ensuring that a strategy balances
through natural language. Current the conflicting goals of multiple
Generative AI capabilities enhance stakeholders, meeting new customers
individual productivity by partnering face-to-face, or leveraging outside-
with humans to serve three primary the-box creativity to overcome
roles: synthesizing disparate data seemingly unsurpassable obstacles.
sources, copiloting as virtual assistants Because Generative AI is more
for complex tasks, and creating adept at churning out iterations than
content much more efficiently than generating breakthrough ideas,6 the
humans. In this way, Generative benefits of Generative AI may only
AI democratizes capabilities and be limited by our imaginations: The
acts as a great equalizer to level World Economic Forum, surveying
up talent: New or inexperienced 800 global business leaders, predicts
workers can immediately increase that “leadership and imagination skills
their contributions and value while will be largely unaffected by AI.”7 If this
experienced workers reach new prediction holds, the CEO’s role will
productivity levels. be to imagine how the creativity of
their people can be combined with the
Second, CEOs should recognize capabilities of AI to build competitive
that an autonomous enterprise advantage. In fact, CEOs can strive to
frees humans to focus on problems create an environment in which the
requiring a human touch. In fact, human workforce is incentivized to
organizations competing in a create and share content. Otherwise,
marketplace where every company AI models will over time begin to be
has access to the same Generative trained on their own content, creating
AI tools will likely need to rely on an “AI echo chamber” that could
enduring human capabilities, such markedly degrade the quality and
as curiosity, empathy, and creativity diversity of output.8
9 9
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The CEO’s role: Set the vision,
tell the story, and invest well
To better capture value and realize the full potential of the autonomous
enterprise, CEOs play a vital role in three significant areas:
setting the vision, communicating it, and making the right investments
to accelerate the journey toward that future.
1 | Set the vision. discrete use cases, pilots, and
projects to scale AI in order to
The CEO is not only critical to driving
realize its full value. To do this, they
digital transformation, but also
can lean in on three drivers of AI
sets the tone for how ambitious
scale: community, commonality,
a transformation will be.9 CEOs’
and coordination.11
most unique role is to develop
and articulate a clear vision—an
opportunity for a radically enhanced,
augmented, and eventually
automated business model that can
bring value to employees, customers,
and other stakeholders. But the Nurture a community Discover and exploit Coordinate through
autonomous enterprise will look of workers who commonality in order a central group in
different for each organization, and are enthusiastic to to build capabilities order to gain singular
CEOs must determine the salience, explore the potential across the enterprise visibility to all initiatives
as the application, speed, pace of of Generative AI tools. on integrated platforms, and to better prioritize
change, and potential for advantage In doing so, these rather than delivering high-impact and
will vary by business.10 That said, communities will be a set of disparate transformational
the aggregate pace of change is able to identify areas initiatives or capabilities. investments.
only accelerating. where there may be
duplicate efforts or
To shape their vision, CEOs may be
similar structures.
inclined to take a technology and
By building communities and better
apply it directly to their business
coordinating, organizations can find
model, systematically examining the
commonalities that lead to more integrated
opportunities for AI to be infused at
every step of the existing enterprise platforms and well orchestrated use cases.12
value chain. But, like the shift from This will likely lead to opportunities to
candles to light bulbs, Generative AI simplify the value chain and create a more
could provide CEOs the opportunity integrated enterprise.
to fully reshape and redefine their
business models, thinking beyond
10
AA CCEEOO''ss gguuiiddee ttoo eennvviissiioonniinngg tthhee GGeenneerraattiivvee AAII eenntteerrpprriissee
2 | Communicate the vision. experience and the values of the as needed to adapt to new ways
people it serves—from how humans of working. That’s not to mention
There are multiple barriers to
create, connect, and make decisions tackling concerns around privacy,
Generative AI that only a clearly
to how they consume, learn, and security, trust, explainability,14
articulated vision can unlock. In
grow. Leaders must emphasize how and regulation.
addition, our research shows that
their employees and customers can
AI starts out in a trust deficit. When flourish with machines, rather than In the coming articles of Leading a
customers know a brand is using AI, against them. Intentional, humanity- Generative AI-fueled enterprise: A CEO's
their trust in the brand declines by a powered augmentation of AI will series, we’ll help CEOs navigate these
factor of 12, and they are significantly create autonomous enterprises, challenges by guiding them through
more likely to rate a brand as low productive environments, and organizational readiness, ecosystem
in reliability. From a workforce flourishing futures that reflect what strategy, and leadership imperatives.
perspective, workers perceive it really means to be human. This series is intended to support
employers as less empathetic when CEOs on their AI journeys as their
AI tools are offered. Furthermore, 3 | Invest to accelerate organizations evolve from digital
it's not uncommon that leaders transformation. enterprises to intelligent enterprises,
have their own concerns with AI, and finally, to the autonomous
The CEO’s path to enterprise
ranging from privacy, security, and enterprise that is right for them.
adoption should give teams
transparency of results, to the loss of
confidence as well as resources
human connection—a critical factor Paul Graham, co-founder of
and freedom to experiment,
in bridging the trust gap with AI and technology startup accelerator Y
with commitments to hard
employee experiences.13 Failing to Combinator, has noted “When you’re
investments. The journey to an
address these trust risks can lead to dealing with exponential growth,
autonomous enterprise means
significant potential for value erosion. the time to act is when it feels too
building a foundation in digital and
early.”15 A year after the earliest
For CEOs at AI-fueled organizations, AI capabilities, such as technology versions of Generative AI have made
trust is imperative to building a infrastructure, for the flexibility and their big debut, few are likely to
narrative that inspires confidence computing power needed to properly claim that it’s too early to act. It’s
in employees and customers empower AI; data management, for not too late for CEOs to act—yet—
alike. Powerful narratives rooted feeding the organization’s digital on a bold vision to drive value and
in trust start with envisioning a blueprint into AI models; and change competitive advantage through the
positive future where AI enables management through upskilling, autonomous enterprise.
and complements the human cultural changes, and restructuring
11
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Reach out for a conversation
Benjamin Finzi Nitin Mittal Bill Briggs
Global CEO Program Leader Global Generative AI Leader Chief Technology Officer
Deloitte Consulting LLP Deloitte Consulting LLP Deloitte Consulting LLP
bfinzi@deloitte.com nmittal@deloitte.com wbriggs@deloitte.com
Anh Nguyen Phillips Louis DiLorenzo Jr.
Global CEO Program AI & Data Strategy Practice Leader
Research Director National US CIO Program Leader
Deloitte Touche LLP Deloitte Consulting LLP
anhphillips@deloitte.com ldilorenzojr@deloitte.com
Contributors
A special thanks goes to the following colleagues for their work in this effort.
Deborshi Dutt | AI Strategic Growth Offering Leader, Florian Klein | Strategic Foresight Leader, The Center for the
Deloitte Consulting LLP | debdutt@deloitte.com Long View, Deloitte Consulting Germany | fklein@deloitte.de
Jas Jaaj | Managing Partner, Generative AI | Abhijith Ravinutala | Writer, Novel and Eponential
jjaaj@deloitte.ca Technologies (Office of the CTO) | aravinutala@deloitte.com
Baris Sarer | Principal, Telecom, Media & Technology, Caroline Brown | Eminence Team Lead, Office of the CTO,
Strategy & Analytics | bsarer@deloitte.com Deloitte Consulting LLP | carolbrown@deloitte.com
Jonathan Goodman | Global Chair, Monitor Deloitte,
Deloitte Canada | jwgoodman@deloitte.ca
1122
AA CCEEOO''ss gguuiiddee ttoo eennvviissiioonniinngg tthhee GGeenneerraattiivvee AAII eenntteerrpprriissee
About the CEO series:
Leading a Generative AI-fueled enterprise
A veritable ocean of content exists in regard to Generative AI adoption for
enterprises. Through Leading a Generative AI-fueled enterprise: A CEO series,
we aim to provide a ship for CEOs and leaders to navigate that ocean.
Not all companies may need to board this ship, but for industries that
involve knowledge work, Generative AI is poised to have widespread
impact, and CEOs can take advantage.
Endnotes
1 Diana Kearns-Manolatos, “Unleashing Value from Digital Transformation: Paths and Pitfalls,” Monitor Deloitte.
February 14, 2023.
2 Goldman Sachs, “Generative AI Could Raise Global GDP by 7%.” April 5, 2023.
3 Gopal Srinivasan et al, “A New Frontier in Artificial Intelligence,” Deloitte AI Institute. 2023.
4 Mike Bechtel, “Prepare for the Future of Information Technology,” Deloitte Consulting LLP.
5 Michael Griffiths et al, “Human Inside: How Capabilities Can Unleash Business Performance,” Deloitte Insights. 2020.
6 David Meyer, “’Generative AI Is Not Yet an Automation Technology’: A Decade later, the Authors of a Seminal Paper on Job
Risks Are Back with a Reevalution,” Fortune. September 18, 2023.
7 Mark Rayner, “AI: 3 Ways Artificial Intelligence Is Changing the Future of Work,” World Economic Forum. August 14, 2023.
8 Oguz A. Acar. ""Has Generative AI Peaked?"" Harvard Business Review. November 8, 2023.
9 Ibid.
10 Dr. Kellie Nuttall and Stuart Scotis, ""Generation AI: Ready or not, here we come!"" Deloitte Access Economics & AI Institute.
11 Mike Walsh and Nitin Mittal, “To Scale GenAI, Companies Need to Focus on 3 Factors,” Harvard Business Review.
December 1, 2023.
12 Ibid.
13 Deloitte analysis. TrustID and AI: Measuring the Impact of AI on Customer and Employee Trust. August 2023.
14 In reference to AI, explainability refers to a layer of transparency in AI systems, allowing users to understand how data is
used and how AI systems and algorithms make decisions. See: Lukas Kruger, “Explaining explainable AI,” Deloitte UK.
15 Paul Graham, “When you’re dealing with exponential growth, the time to act is when it feels too early,” Post on X. March 11,
2020. Accessed November 13, 2023.
1133
This publication contains general information only and Deloitte is not, by means of
this publication, rendering accounting, business, financial, investment, legal, tax, or
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that may affect your business, you should consult a qualified professional advisor.
Deloitte shall not be responsible for any loss sustained by any person who relies on
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201,deloitte,us-ai-institute-ceo-guide-to-scaling-generative-ai.pdf,"Three roles CEOs need to play
to scale Generative AI
Leading a Generative AI-fueled enterprise:
A CEO series
Deloitte Global CEO Program
Deloitte AI InstituteTM
TThhrreeee rroolleess CCEEOOss nneeeedd ttoo ppllaayy ttoo ssccaallee GGeenneerraattiivvee AAII
About the About the
Deloitte Global CEO Program Deloitte AI Institute
The Deloitte Global CEO Program is The Deloitte AI Institute helps sense of this complex ecosystem, and as
dedicated to advising chief executive organizations connect the different a result, delivers impactful perspectives
officers throughout their careers—from dimensions of a robust, highly dynamic to help organizations succeed by making
navigating critical points of inflection, to and rapidly evolving AI ecosystem. The AI informed AI decisions.
designing a strategic agenda, to leading Institute leads conversations on applied AI
through personal and organizational innovation across industries, with cutting- No matter what stage of the AI journey
change. The program offers innovative edge insights, to promote human-machine you’re in; whether you’re a board member
insight and immersive experiences to help: collaboration in the “Age of With”. or a C-Suite leader driving strategy for your
organization, or a hands on data scientist,
• Facilitate the personal success of
The Deloitte AI Institute aims to promote bringing an AI strategy to life, the Deloitte
individual executives, new or tenured,
a dialogue and development of artificial AI institute can help you learn more about
throughout their life cycle.
intelligence, stimulate innovation, and how enterprises across the world are
• Elevate the relationships between them, examine challenges to AI implementation leveraging AI for a competitive advantage.
their leadership teams, and their boards and ways to address them. The AI Institute Visit us at the Deloitte AI Institute for a
collaborates with an ecosystem composed full body of our work, subscribe to our
• Support the strategic agenda for their
of academic research groups, start-ups, podcasts and newsletter, and join us at
organizations in times of disruption
entrepreneurs, innovators, mature AI our meet ups and live events. Let’s explore
and transformation.
product leaders, and AI visionaries, to the future of AI together.
explore key areas of artificial intelligence
www.deloitte.com/us/ceo
including risks, policies, ethics, future of www.deloitte.com/us/AIInstitute
work and talent, and applied AI use cases.
Combined with Deloitte’s deep knowledge
and experience in artificial intelligence
applications, the Institute helps make
22
Three roles CEOs need to play to scale Generative AI
The strategic opportunities presented
by Generative AI require CEOs to dive deep
into their organizations’ technology agenda.
For many CEOs, that means becoming
tech-savvy enough to detect how
Generative AI could redefine
their business models, including
understanding disruptions to their
industries, identifying the competitive
advantage in their enterprise’s AI
adoption, and understanding where
this advantage would likely erode the
fastest. We last wrote about the CEO’s
need to set a vision for adopting
Generative AI, communicate that
vision, and invest in transformation.1
However, the path from vision to
action is not always clear. While many
executives recognize the importance
CEOs have always had multiple roles,
of AI, up to 87% don’t feel equipped
at times serving as skilled dealmakers
to transform their business with
with the acumen for favorable
it, according to recent surveys.2
negotiations; as venture capitalists
Given the types of Generative AI
who place bets on winning strategies
choices that need to be made, their
and manage portfolios; or as
outsized impact, and the significant
champions who evangelize important
organizational change demanded by
business priorities. In the context
such transformation, CEOs should
of Generative AI, CEOs must apply
dive into key decisions they would
their experiences from these
normally delegate. That’s because
roles to three distinct areas:
they are actively shaping their
securing computing power,
organization’s vision and defining its
selecting an ecosystem for their
ambitions: whether to be a first mover
large language models (LLMs),
or fast follower, whether AI is needed and standing up centers
for innovation or productivity, and of excellence.
whether they should be building or
buying AI capabilities.
3
TThhrreeee rroolleess CCEEOOss nneeeedd ttoo ppllaayy ttoo ssccaallee GGeenneerraattiivvee AAII
The buck stops with the CEO
Yesterday’s white-hot innovations are prone to
becoming today’s modernization or efficiency headaches.
44
Three roles CEOs need to play to scale Generative AI
Take multicloud for example. Therefore, CEOs need to take time upfront to make key decisions,
Many enterprises rushed into such as:
piecemeal cloud agreements without
establishing a central decision-making
hub, and years later found themselves
with technical sprawl that desperately
needed streamlining.3 Without
caution, Generative AI adoption
could take the exact same path. With Should we How can our How will we How do we
more and more players rolling out focus our organization measure direct embed trust
AI options and the hype burgeoning, investments build flexibility and indirect and guardrails
CEOs should learn from the lessons of on a few key in our execution cost and in the AI model
the past. Given how new Generative choices, or approach? performance development?
AI is, not even your technical leaders should we implications?
may have the expertise necessary maximize
to navigate this field alone. optionality while
the competitive
market for
Generative AI
plays out?
Some enterprises we’ve interviewed have
already run into issues of making AI investments
without reaping strong benefits. The time is
now for CEOs to make effective decisions.
55
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Securing access
to computing power
With the widespread adoption of Generative AI, the
necessity for swift model training and execution
has emerged as a critical business requirement.
66
Three roles CEOs need to play to scale Generative AI
Conventional compute infrastructure Business models are bound chip models, they are negotiating
relies on central processing units to evolve as AI increasingly with the CEOs of chipmakers to
(CPUs) that handle data sequentially. becomes a part of knowledge understand and secure the right level
However, for highly parallel workloads, work, which further emphasizes of resources for their company.
especially when dealing with LLMs, the importance of the CEO
the use of graphics processing in shepherding that change. As dealmakers, CEOs should be sure
units (GPUs) and other specialized Regardless of the level of a CEO’s to consider investor sentiments
AI chips enable massively parallel AI ambitions, they will likely need to and engage with their executive
processing, a crucial element for think creatively about partnerships leadership team, specifically their
efficiently processing terabytes of to secure computing power for chief information officer (CIO) and
data through algorithms based on Generative AI. As the dealmaker- chief technology officer (CTO),
Generative AI. As we discussed in in-chief, they play a pivotal role in to facilitate alignment between
Tech Trends 2024, companies are ensuring access to critical resources hardware procurement strategies
actively tackling this challenge by that can redefine their enterprise. and overarching business objectives.
embracing GPUs as the primary Additionally, emerging technologies
For some organizations, their needs
resource for training AI models.4 such as edge computing present
may be met by niche cloud providers
The integration of such dedicated AI new opportunities for decentralized
who specialize in GPUs, a number
chips is poised to become standard AI processing. By staying abreast
of which are cropping up in global
practice in enterprises, offering of technological advancements
markets.7 However, those with higher
early adopters a competitive edge, and market trends, CEOs can
AI ambitions of pursuing innovation
particularly in a fragile supply chain.5 make informed decisions to
and competitive advantage may want
Research predicts that the market future-proof their organization’s
to secure more robust computing
for specialized chips will be well over computing infrastructure.
power. Many such CEOs are engaged
US$60 billion in 2024 and climb up to
in conversations with Generative AI
US$120 billion by 2027.6
hardware companies. Without delving
into the technical details of the actual
7
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Selecting an AI ecosystem
for LLMs
The value of Generative AI hinges upon the data
it consumes. Or, as the adage says, “Garbage In, Garbage
Out.” This presents a problem for CEOs, as most LLMs
available today are not built with out-of-the-box domain,
industry, or organization-level specificity.
88
Three roles CEOs need to play to scale Generative AI
Though CEOs may face challenges determines whether it will be a first adoption. For example, the utility
with data regulations and standards, mover in building custom LLMs company Enbridge built separate
private LLMs can deliver clear or buy them later.9 Adopting a copilot tools for developers to
advantages in choice, cost, and venture-capitalist-like mindset, the code and for office staff to navigate
control, while enabling enterprises CEO can leverage understanding productivity applications, thereby
to retain their strategic intellectual of the marketplace and established offering diverse benefits to each
property.8 To capture this value and relationships with major players team.10 As we’ve written previously,
scale, enterprises need to select the to determine which bets can be finding commonality in AI needs can
LLMs and broader AI ecosystems that made safely, while considering ensure that the enterprise builds a
suit their specific needs. their broader portfolio and being cohesive platform for scaling AI, as
thoughtful about every round of opposed to buying one-off products
CEOs can also look at their investment. for disparate use cases.11
organization’s data as a bargaining
chip. Many enterprises have an In this pursuit, CEOs need to ensure Furthermore, CEOs must recognize
untapped trove of data, which could the LLM selection process isn’t the evolving nature of AI models and
be highly valuable to companies technologies, necessitating
purely a technical endeavor.
building AI products. CEOs can They must go beyond traditional continuous evaluation and
consider valuing current and future- procurement methodologies, relying optimization of LLM solutions. This
state data assets as potential inputs instead on strategic relationships might entail engaging with industry
to new business models, as long and market insights to identify thought leaders, cultivating strategic
as they concurrently secure or ideal partners. Given the pace of partnerships with research
anonymize data to avoid trust and innovation, today’s exciting AI model institutions, and more. CEOs can tap
regulatory concerns. They will likely can become obsolete tomorrow, their CTOs and CIOs to maintain and
need to keep all options in mind as and organizations can’t rely on check in on key AI relationships that
they make investment decisions. lengthy vendor assessments. But can drive business growth
by fostering the right collaborations and innovation.
As with securing hardware, the CEO
across the Generative AI ecosystem,
need not delve too deep into the
organizations can start to build
technical specifics of different AI
custom models for specific functions
models. Instead, the CEO calibrates
(e.g., Finance) and then scale up their
the company’s AI ambition and
9 9
TThhrreeee rroolleess CCEEOOss nneeeedd ttoo ppllaayy ttoo ssccaallee GGeenneerraattiivvee AAII
Standing up AI centers
of excellence
A recent Deloitte and Fortune CEO survey
found that 80% of organizations are already
implementing or likely to implement
Generative AI to accelerate innovation,
while a whopping 96% are doing so to
increase efficiencies.12
1100
Three roles CEOs need to play to scale Generative AI
To reap these expected benefits, unwavering support and Case in point: Generative AI adoption
organizations are standing up AI resources to facilitate inception. can be seen as a new frontier in
centers of excellence (COEs). These They can ensure that critical cognitive efficiency, enabling us
organizational hubs serve as catalysts elements of standing up the COE— to tap into the power of a human
for innovation, enabling organizations such as hardware, data needs, intelligence unburdened by repetitive
to conceptualize, develop, and deploy and governance—are sufficiently activities and able to focus on
AI solutions at scale. funded, while delegating the exploring, connecting, and elevating
remaining aspects. the human experience.15 In the era
A COE can bring together a cross- of Generative AI, valuable work is
The CEO also has to engage in winning
functional group of AI experts and not just repeating known tasks, but
hearts and minds of all stakeholders,
stakeholders to focus organizational asking the right questions, developing
including customers, employees,
efforts and create a consistent and innovating new solutions,
the board, and society at large.
approach to governance and assessing the generated outputs,
Working with their chief legal or risk
guardrails (e.g., by hiring ethicists), and fine-tuning your model for better
officer, CEOs can help ensure that
which are increasingly salient topics performance—uniquely human tasks.
all members of the enterprise, from
to consumers and employees.13
the executive leadership team to
Moreover, this organizational Finally, the CEO’s role in articulating
middle managers and beyond, feel
structure may help organizations the vision, mission, and purpose of AI
prepared for what’s on the horizon.
differentiate their AI transformation extends beyond internal stakeholders
Many employees are fearful of AI
from that of their competitors— to external partners, investors, and
transformation in their organizations
partnering with a consulting provider industry peers. By leveraging AI
and are eager to understand what
may provide the outside perspective centers of excellence as platforms
future jobs and skills may look like.14
and network needed to succeed. for knowledge exchange and
CEOs should foster a culture of AI
collaboration, CEOs can position their
fluency and innovation and champion
CEOs need not lead the organizations as frontrunners in the
a clear purpose for AI adoption, as a
implementation of these centers. race to Generative AI advantage.
matter of supercharging humans (not
Rather, their role is to champion
replacing them).
establishment and provide
11
TThhrreeee rroolleess CCEEOOss nneeeedd ttoo ppllaayy ttoo ssccaallee GGeenneerraattiivvee AAII
The AI revolution is bound to alter CEO roles
for the years to come. Already, CEOs have to
be more tech-savvy than ever, given how
important technology is to competitive advantage
and ways of working.
1122
Three roles CEOs need to play to scale Generative AI
As AI becomes even more embedded This is especially true when AI
into knowledge work, the details of adoption is still nascent. CEOs can
AI adoption are expanding out of the bring their experience to bear on
tech leader’s domain to become a the many macro and micro decisions
CEO priority. that will need to be made when a
technology is both very new and
very impactful.
As we continue our series on leading an
AI-fueled organization, we’ll delve into
more aspects of the CEO’s role in preparing
their organizations to pivot to the
Generative AI future.
13
TThhrreeee rroolleess CCEEOOss nneeeedd ttoo ppllaayy ttoo ssccaallee GGeenneerraattiivvee AAII
Reach out for a conversation
Benjamin Finzi Nitin Mittal Bill Briggs
Global CEO Program Leader Global Generative AI Leader Chief Technology Officer
Deloitte Consulting LLP Deloitte Consulting LLP Deloitte Consulting LLP
bfinzi@deloitte.com nmittal@deloitte.com wbriggs@deloitte.com
Anh Nguyen Phillips Deborshi Dutt
Global CEO Program AI Strategic Growth Offering Leader,
Research Director Deloitte Consulting LLP
Deloitte Touche LLP debdutt@deloitte.com
anhphillips@deloitte.com
Contributors
A special thanks goes to the following colleagues for their work in this effort.
Kate Schmidt | COO, AI Strategic Growth Offering,
Deloitte Consulting LLP | kateschmidt@deloitte.com
Abhijith Ravinutala | Writer, Office of the CTO,
Deloitte Consulting LLP | aravinutala@deloitte.com
Caroline Brown | Eminence Team Lead, Office of the CTO,
Deloitte Consulting LLP | carolbrown@deloitte.com
1144
TThhrreeee rroolleess CCEEOOss nneeeedd ttoo ppllaayy ttoo ssccaallee GGeenneerraattiivvee AAII
About the CEO series: Leading a Generative AI-fueled enterprise
A veritable ocean of content exists in regard to Generative AI adoption for enterprises. Through
Leading a Generative AI-fueled enterprise: A CEO series, we aim to provide a ship for CEOs and
leaders to navigate that ocean.
Not all companies may need to board this ship, but for industries that involve knowledge work,
Generative AI is poised to have widespread impact, and CEOs can take advantage.
Endnotes
1 Benjamin Finzi et al, “A CEO’s guide to envisioning the Generative AI enterprise,” Deloitte. November 30, 2023.
2 Weber Shandwick, “Un/Predictions 2024.” February 22, 2024.
3 Mike Bechtel and Bill Briggs, “Above the clouds: Taming multicloud chaos,” Deloitte Insights. December 06, 2022.
4 Bechtel and Briggs, “Tech Trends 2024,” Deloitte Insights. December 06, 2023.
5 Lucas Mearian, “Chip industry strains to meet AI-fueled demands—will smaller LLMs help?,” Computer World.
September 28, 2023.
6 Gartner, “Gartner Forecasts Worldwide AI Chips Revenue to Reach $53 Billion in 2023.” August 22, 2023.
7 Krystal Hu, “CoreWeave raises $2.3 billion in debt collateralized by Nvidia chips,” Reuters. August 03, 2023.
8 Chris Arkenberg et al, “Taking control: Generative AI trains on private, enterprise data,” Deloitte Insights. November 29, 2023.
9 Tanmay Chopra, “When it comes to large language models, should you build or buy?,” TechCrunch. January 25, 2023.
10 Bechtel and Briggs, “Genie out of the bottle: Generative AI as growth catalyst,” Deloitte Insights. December 06, 2023.
11 Mike Walsh and Nitin Mittal, “To Scale GenAI, Companies Need to Focus on 3 Factors,” Harvard Business Review.
December 01, 2023.
12 Benjamin Finzi and Brett Weinberg, “Fall 2023 Fortune/Deloitte CEO Survey Insights,” Deloitte. November 13, 2023.
13 Brad Hise and Jenny Dao, “Ethical considerations in the use of AI,” Reuters. October 02, 2023.
14 Heather Stockton, “Inspiring Employees To Trust The Coming GenAI-Fueled Workplace,” Forbes. January 18, 2024.
15 Walsh and Mittal, “Industry 5.0 will be fueled by minds, not just machines,” Fortune. January 15, 2024.
1155
This publication contains general information only and Deloitte is not, by means of
this publication, rendering accounting, business, financial, investment, legal, tax, or
other professional advice or services. This publication is not a substitute for such
professional advice or services, nor should it be used as a basis for any decision or
action that may affect your business. Before making any decision or taking any action
that may affect your business, you should consult a qualified professional advisor.
Deloitte shall not be responsible for any loss sustained by any person who relies on
this publication.
About Deloitte
Deloitte refers to one or more of Deloitte Touche Tohmatsu Limited, a UK private
company limited by guarantee, and its network of member firms, each of which is a
legally separate and independent entity. Please see www.deloitte.com/about for a
detailed description of the legal structure of Deloitte Touche Tohmatsu Limited and
its member firms. Please see www.deloitte.com/us/about for a detailed description
of the legal structure of Deloitte LLP and its subsidiaries. Certain services may not be
available to attest clients under the rules and regulations of public accounting.
This publication contains general information only and Deloitte is not, by means of
this publication, rendering accounting, business, financial, investment, legal, tax, or
other professional advice or services. This publication is not a substitute for such
professional advice or services, nor should it be used as a basis for any decision or
action that may affect your business. Before making any decision or taking any action
that may affect your business, you should consult a qualified professional advisor.
Deloitte shall not be responsible for any loss sustained by any person who relies on
this publication.
Copyright © 2024 Deloitte Development LLC. All rights reserved."
202,deloitte,us-scaling-artificial-intelligence.pdf,"Scaling artificial intelligence (AI)
Navigating the horizons of innovation
Scaling artificial intelligence (AI) | Navigating the horizons of innovation
AI technology is creating new opportunities and transforming To help answer these questions and guide this transformative
industries in this fast-changing world. For insurers, using AI journey, we have formulated a three-phase framework that provides
effectively is not only a way to keep up with the competition; a road map for success. It is based on our extensive knowledge of
it’s also a way to excel in a digital age where innovation is essential the industry and the opportunity and potential value to be achieved.
for success. With so many possible options and challenges, scaling This framework provides a systematic approach to shape your
AI across your enterprise can seem overwhelming. ambition and strategy, enabling insurers to contextualize their
current actions and plan a way forward in scaling AI efficiently.
Today, many executives are wondering: How do you deal with By dividing the journey into three separate horizons—optimize,
the difficulties of this journey? How do you move from testing differentiate, and disrupt—insurers can navigate the complexities
to deploying to achieving scale? How do you generate and of scaling AI with confidence and clarity.
demonstrate value and keep the momentum?
It’s one thing to explore the potential of AI through small-scale
experiments and prototypes and another to incorporate AI solutions
smoothly into your operations, delivering real benefits and driving
lasting growth. Making this transition to scale successfully requires
a structured approach and a clear understanding of where
to prioritize your efforts.
Aspirational
Stage of journey Opportunity
Disrupt
Next wave: Reimagining • Transform business models
Horizon 3
the business
Disrupt • Transform ways of working
Ex: Autonomous agents to help manage
products/policies/portfolios, multi-
agent conversations, automated fraud
investigation, insurance-specific LLMs
Horizon 2 Differentiate
Differentiate Holistic business impact • Increase revenue
Ex: Call center virtual agents, automated
• Improve experiences
end-to-end support, anomaly-based
cybersecurity and identity verification, • Reduce costs
automated policy coverage review
Horizon 1
Optimize
Optimize
Internal experimentation • Improve productivity
and optimization
• Lower risk profile
Ex: Intelligent document processing,
document contextualization and
Today summarization, improved chatbots,
synthetic data and code generation
2
Scaling artificial intelligence (AI) | Navigating the horizons of innovation
Horizon 1: Optimize Horizon 2: Differentiate
Internal experimentation Holistic enterprise
and prototyping transformation
Horizon 1 is the first step in scaling AI, where the main goal is to As your organization moves toward Horizon 2, the aim is to
experiment, test, and verify AI applications internally. It focuses on achieve comprehensive enterprise transformation and scale.
evaluating how to adopt, scale, and manage new technologies that Horizon 2 prepares the ground to combine traditional AI and
have low risk. Organizations in Horizon 1 are still emerging in terms generative AI solutions and makes the case for a wider enterprise
of AI maturity, are developing foundational capabilities (e.g., soft AI strategy. To get tangible value from AI, organizations need
launch production environment), and are establishing AI policy to change their focus from creating point solutions to building
and governance. integrated solutions using a variety of AI technologies with a full view
to enhance how they deliver experience and value for their internal
Additionally, as part of this step, the goal is to verify productivity and customers (i.e., employees) and external customers (i.e., advisers,
efficiency improvements through successful internal point solutions agents, policyholders).
across the insurance value chain, enhancing operational efficiency
and preparing for future use-case scalability. Examples include For instance, a comprehensive solution to assess the effectiveness
AI-driven intelligent document processing for simplified of marketing messages can use several AI technologies, such
information extraction and validation, and generative AI coding as descriptive analytics to measure audience reach and quality,
tools for non-developers. machine learning to measure the effectiveness of content,
and generative AI to measure and summarize insights and
recommendations for improvement. At Deloitte, we have developed
a systematic approach, called a “string of pearls,” on how to scale
successfully across an organization using AI solutions.
This horizon involves major changes in the organization, with
targeted investments and solution development across the business
functions, and it takes a holistic view of how data, business assets,
and technologies work together at the enterprise level—thus
creating value across multiple dimensions to increase revenue,
enhance experiences, and reduce costs.
3
Scaling artificial intelligence (AI) | Navigating the horizons of innovation
Horizon 3: Disrupt
Next wave: Reimagining
the business
The ultimate goal of effective AI scaling and adoption occurs in
Horizon 3, where AI can help transform traditional business models
by creating new ways of using technology. This horizon is where the
vision of AI innovators to build AI-powered enterprises becomes
reality. The goal is to achieve a “humans with machines” way of
working, where humans and machines collaborate to create the
opportunity to rethink the business model and/or operating model.
For example, with AI’s cognitive abilities that resemble human
intelligence, autonomous AI decision-making can mimic expert
judgment without human involvement for some applications
like reinforcement learning and deep learning techniques; and
improvements in causal AI enabled by evolving models and pattern
recognition techniques. Many of the most potential use cases
emerge from the combination of leading AI techniques (autonomous
agents, reinforcement learning) with advancements in technology
(transformer/probabilistic models) and hardware (Internet of Things
devices). These state-of-the-art solutions will not only change ways
of working and business models but entire industries, opening new
possibilities for innovation and growth.
4
Scaling artificial intelligence (AI) | Navigating the horizons of innovation
Embracing the
AI journey
Each horizon builds on the previous one, creating a base for
sustainable and scalable AI development. Horizon 1 allows
processes to be fine tuned, preparing the ground for more extensive
transformations in Horizon 2. Horizon 3, then, becomes the domain
of innovation, where organizations can explore the limits of what
AI can do, reinventing their business models and operations.
Scaling AI is a dynamic and complex journey that requires a careful
and strategic approach. Our scaling AI horizon framework offers a
road map for organizations to go through the different stages of AI
development, from optimizing internal processes to differentiating
their enterprise and ultimately disrupting traditional business
models. As technology keeps evolving, the ability to scale AI
effectively becomes not just a competitive edge but a necessity
for staying relevant in an increasingly digital and intelligent world.
5
Scaling artificial intelligence (AI) | Navigating the horizons of innovation
Contacts
Sandee Suhrada
Principal
Deloitte Consulting LLP
ssuhrada@deloitte.com
Udit Narula
Manager
Deloitte Consulting LLP
unarula@deloitte.com
Vishvam Raval
Senior consultant
Deloitte Consulting LLP
viraval@deloitte.com
6
Scaling artificial intelligence (AI) | Navigating the horizons of innovation
7
About Deloitte
Deloitte refers to one or more of Deloitte Touche Tohmatsu Limited, a UK private
company limited by guarantee, and its network of member firms, each of which is a
legally separate and independent entity. Please see www.deloitte.com/about for a
detailed description of the legal structure of Deloitte Touche Tohmatsu Limited and
its member firms. Please see www.deloitte.com/us/about for a detailed description
of the legal structure of Deloitte LLP and its subsidiaries. Certain services may not
be available to attest clients under the rules and regulations of public accounting.
This publication contains general information only and Deloitte is not, by means of
this publication, rendering accounting, business, financial, investment, legal, tax, or
other professional advice or services. This publication is not a substitute for such
professional advice or services, nor should it be used as a basis for any decision
or action that may affect your business. Before making any decision or taking any
action that may affect your business, you should consult a qualified professional
adviser. Deloitte shall not be responsible for any loss sustained by any person who
relies on this publication.
Copyright © 2024 Deloitte Development LLC. All rights reserved.
8861248"
203,deloitte,us-realizing-transformative-value-from-ai-and-genAI-in-life-sciences-040924.pdf,"Realizing Transformative Value
from AI & Generative AI
in Life Sciences
Life Sciences companies have an opportunity to unlock
$5-7 billion (Bn) dollars in value from the use of artificial
intelligence (AI). We estimate that nearly 90% of value will be
derived from three functional areas: research and development
(R&D), manufacturing and supply chain, and commercial.
Over the past 18 months, Generative AI (GenAI) has broadened the breadth of value that AI could deliver.
Deloitte conducted a study of 20, end-to-end AI use cases, which when linked together like ‘pearls on a string’
can transform value streams (e.g., product launch, clinical development) across functional areas. Supported by
specialist interviews, benchmarking surveys, and pro forma forecasts, the objective was to assess the total
enterprise value opportunity of AI from cost reduction, cost avoidance, and revenue generation.
We estimate a top 10 biopharma company with average revenue of $65-75 Bn could capture
between $5-7 Bn of peak value by scaling the use of AI over 5 years. This varies based on organization
size (e.g., $35 Bn in revenue could achieve $2.5-3.5 Bn in peak value).
Estimated Enterprise Opportunity: $5–$7Bn
FIGURE 1. VALUE CREATION BREAKDOWN BY FUNCTION
Example Levers Impacted by AI Value Creation Breakdown
Research & Revenue Uplift Levers Cost Reduction Levers: 40%
Development • Time to market • Time to market
30 - 45% of Value • Revenue from drug repurposing • Cost to pre-clinical trial 60%
10%
Manufacturing Revenue Uplift Levers Cost Reduction Levers:
& Supply Chain • Revenue from surplus • # of deviations
90%
15 - 25% of Value manufacturing yield • Production cycle time
Commercial Revenue Uplift Levers Cost Reduction Levers: 45%
25 - 35% of Value • Patient conversion rate • Marketing content creation
• Time to/on therapy • Payer contract administration 55%
5%
Enabling Areas Revenue Uplift Levers Cost Reduction Levers:
5 - 15% of Value • Reduction in contract revenue • Overall SDLC cycle time
95%
leakage • Media / PR spend
Revenue Uplift
Cost Reduction
2 Realizing Transformative Value From AI And GenAI In Life Sciences
R&D represents the top value opportunity at 30-45%. AI applied to novel drug identification and accelerating
drug development could provide both cost savings and revenue uplift. This is followed by commercial at
25-35%, where marketing costs could be optimized and activities such as script utilization could be enhanced
by AI. Manufacturing, supply chain, and enabling areas (including IT, HR, and Finance) are primarily candidates
for cost transformation through efficiency realization and vendor cost reductions using AI.
We estimate a 5-year timeline for an enterprise to capture peak value from use of AI. The value accretion
schedule also differs by functional area due to each area’s inherent characteristics. Typically, enabling areas
(including IT, HR, and Finance) have the most opportunities for cost savings and avoidance, which results in
faster time to value realization. In contrast, R&D and supply chain have long-term capital requirements that
are difficult to amend, thus extending the timeline to reaching peak value.
FIGURE 2. AVERAGE 5-YEAR VALUE ACCRETION SCHEDULE OF AI IMPACT (PERCENTAGE OF
PEAK VALUE REALIZED)
$0.9B - $1.2B $1.8B - $2.5B $3.1B - $4.3B $4.1B - $5.7B $5.0B - $7.0B
Research & Development 22%
30% 32% 34% 35%
Supply Chain 13%
14%
17%
Commercial 19% 20%
38%
Enabling Areas 34%
33%
31% 30%
28%
21% 18% 16% 15%
Year 1 Year 2 Year 3 Year 4 Year 5
ASSUMPTIONS
1Foundational data and infrastructure are in place to enable transformational use case development
2Each function implements the full portfolio of transformational AI use cases (e.g., AI clinical trials, AI manufacturing, AI marketing)
METHODOLOGY
• Each bar represents the value a top 10 biopharma company could capture from AI over a 5-year time frame
• A top-down (% of revenue) and bottom-up (% of operating margin) approach was applied to evaluate value potential based on an
individual organization’s growth potential and operational efficiency
• The peak value range is a blended average using the two evaluation approaches
• For the top 10 biopharma companies average total revenue were $65Bn to $75Bn and average operating margin of$20Bn to $25Bn in
2022
3 Realizing Transformative Value From AI And GenAI In Life Sciences
Can GenAI live up to its predicted
value potential?
Anecdotal success stories and endless media attention have generated hype around the utility of GenAI.
Deloitte has spent the last year not only thinking critically about the value of GenAI, but also implementing the
technology, driving its adoption, and monitoring its value. This includes scaling dozens of AI and GenAI use
cases for our life sciences clients and launching our own GenAI platform for our internal colleagues.
Our experience suggests that there is realizable value from GenAI. But, it’s critical for companies to separate
hype from reality to best understand the true impact GenAI could deliver.
FIGURE 3. DEBUNKING MYTHS AROUND GEN AI
Value Realization
GenAI should deliver immediate bottom- Typically, cost reductions are likely to occur within 1-2 quarters
line value. of deployment primarily from efficiency gains and cost avoidances.
Revenue gains could take 3-4 quarters to materialize.
Talent Disruption
GenAI could be a lever to quickly right- In the short-term, GenAI could drive individual FTE productivity
size organizations. gains.
Adoption
If you build it, they will come. GenAI Adoption of GenAI is more likely to be successful when the technology
tools, once launched could be embraced is embedded in existing ways of working / tools in concert with
and utilized to the maximum. purposeful upskilling of users with prompt engineering skills.
Market Speed
Innovation is moving so quickly that LLMs We have reached critical milestones with LLMs. Within the next 1-2
and AI strategy should be updated every years, incremental shifts (e.g., multimodal processing) rather
6 months. than paradigm shifts are likely.
Enterprise Structures
Setting up a Center of Excellence is the A strong enterprise mandate, governance, and value capture
only path to adoption and success. methodology are the pathways to success regardless if the model is
centralized or federated.
4 Realizing Transformative Value From AI And GenAI In Life Sciences
What are the GenAI ‘no regret bets?’
To de-risk investments and accelerate progress, organizations should kickstart their GenAI programs with ‘no
regrets bets’ that can deliver value in a relatively short timeframe. This not only serves as a proof point to
catalyze enterprise adoption, but it also creates opportunities to fund additional investments with realized
gains.
There isn’t a one-size-fits-all approach when it comes to ‘no regrets bets.” However, based on our recent work
in implementing GenAI programs, we have learned that the following ‘bets’ are likely to reflect a low
complexity, high value profile for most organizations.
FIGURE 4. POTENTIAL VALUE FOR KEY 'NO REGRETS BETS'
VALUE TO VALUE TO WHY THIS IS
DESCRIPTION THE BUSINESS UNIT THE ENTERPRISE NO REGRETS
Research & Scientific Literature
Development Summarization
Generate easy-to- Greater productivity + Cost reduction GenAI can cut through
consume summaries of from faster hypotheses research noise and go
+ Revenue Uplift
scientific literature testing straight to insights with
minimal resource
investment
Intelligent Study
Deliverable Authoring
Automate the drafting Greater speed + Cost reduction Companies have a
of clinical study report from less rework and massive treasure
+ Cost avoidance
(CSR) deliverables automated drafting trove of past
documents that can
be tapped into to
automate creation
Supply Chain & SOP Management AI
Manufacturing Assistant
Automate updating Greater productivity + Cost reduction GenAI can help avoid
all relevant SOPs with from automated and costly quality control
+ Cost avoidance
simple prompts cascaded updates to issues by improving
SOPs how employees
perform their job
Amplified Quality
Events Mgmt.
Identify, investigate, Enhanced compliance + Cost avoidance Misclassifications
and remediate quality from faster identification can result in huge
events using AI and remediation of quality penalties that can be
events mitigated using GenAI
5 Realizing Transformative Value From AI And Gen AI In Life Sciences
FIGURE 4B. POTENTIAL VALUE FOR KEY NO REGRETS BETS
VALUE TO VALUE TO WHY THIS IS
DESCRIPTION THE BUSINESS UNIT THE ENTERPRISE NO REGRETS
Commercial AI-Generated Content
Generate ideas and Improved outcomes + Cost reduction Small productivity gain
design artifacts using AI from more personalized in this large cost bucket
+ Revenue uplift
content and faster can result in outsized
adaptation to customer bottom-line impact
needs
Contract
Performance Advisor
Intelligent investigation Minimized leakage + Cost reduction Pharma companies
of payer contracts to of revenues through spend billions in
+ Revenue uplift
identify discrepancies better monitoring rebates, GenAI can
identify deviations
which could result in
large payoffs
Enabling Areas MLR Optimization
Identify high-risk Streamlined process + Cost reduction Marketing pieces can
claims in marketing of incident management be quickly reviewed
+ Cost avoidance
materials for review and from preemptive risk resulting in faster time
automated adjustment remediation to market
Competitive
Intelligence
Generate competitive Improved insights + Cost reduction GenAI can tap into
insights through market/ from faster and more market data that is
+ Revenue uplift
industry data accurate data synthesis currently underused
to facilitate more
informed decisions
In future articles, we will dive deeper into how organizations
should approach 'no regrets bets' identification and
execution.
6 Realizing Transformative Value From AI And Gen AIIn Life Sciences
The time to act is now
The life sciences industry is at an inflection point – and harnessing AI and GenAI as a catalyst for
transformation is vital. Winning tomorrow requires organizations to take the right steps toward embracing this
technology today.
Here are top 5 actions that you could take in order to initiate momentum on your AI
and GenAI value journey:
Establish a Empower a leader(s) with a mandate to own and drive an
1
Leadership Mandate enterprise AI + GenAI agenda
Align on a Prioritize 2 – 3 strategic opportunity areas to serve as
2
Strategic Blueprint enterprise north stars
Identify Activate business units and IT/Digital to identify initial
3
No Regrets Bets “no regrets bets” that align to priority areas
Create Minimum Establish a governance function that can manage AI + GenAI
4
Viable Governance risks, investments, ethical use, and progress while
encouraging innovation
Launch Deliver solutions that can demonstrate tangible value
5
Pilot Solutions and prove out adoption
However, successfully driving large-scale AI transformation programs requires organizational
evolution. We list 4 major changes required as companies move forward on their AI value journey...
Mindset Leadership Investment Cultural Execution
Evolution Evolution Evolution Evolution Evolution
Move beyond the Goal leaders Treat AI investments AI should not be Evolve beyond front
endless cycle of against measurable as core enablers looked at like a and back office
near term proof AI targets and value of enterprise tool, but as a skill methods and adopt
of concepts, and goals in order to business strategies that all employees a “two in the box”
place long term drive AI evangelism and not as will need to approach where
bets on AI in key and accountability experimental possess to business & IT are
areas investments maximize efficiency goaled together
7
Realizing Transformative Value From AI And Gen AI In Life Sciences
Authors
Aditya Kudumala
Deloitte Consulting LLP
akudumala@deloitte.fr
Adam Israel
Deloitte Consulting LLP
adisrael@deloitte.com
Sai Lella
Deloitte Consulting LLP
slella@deloitte.com
Jonathan Fan
Deloitte Consulting LLP
jonafan@deloitte.com
Wendell Miranda
Deloitte Services LLP
wmiranda@deloitte.com
About Deloitte
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8"
205,deloitte,us-navigating-the-artificial-intelligence-frontier.pdf,"Navigating the
artificial intelligence
frontier
An introduction for internal audit
Navigating the artificial intelligence frontier | An introduction for internal audit
Insights to ground your AI strategy
During 2023, artificial intelligence (AI) captured the imagination of the world, fueling discussion among businesses and policymakers by
demonstrating the transformative power of how these technologies could redefine work. Whether interacting with vast sources of knowledge
and business data through humanlike interactions, accelerating how people work, or revealing new opportunities that were not previously
possible through manual efforts, the benefits of AI are far reaching.
AI is a broad domain. However, significant attention has been given to a specific field of AI known as Generative Artificial Intelligence (GenAI)
following the mass global interest in applications like OpenAI’s ChatGPT. Adoption and use of (GenAI) has been sudden and rapid among the
public. OpenAI reported reaching 100 million users within 60 days of releasing ChatGPT to the public.1 Given the opportunity GenAI presents,
and the fact employees are using GenAI “side of desk” for work tasks, it is no wonder that organizations are investing heavily in enterprise use
cases. With the rapid acceleration and integration of GenAI into business functions, AI and GenAI risk management will continue to be a hot
topic for internal audit teams throughout 2024 and beyond.
For internal audit, this presents two key considerations: how to provide assurance and assess the risks associated with AI (including
GenAI), and how to leverage its potential to evolve and innovate internal audit’s own ways of working. In this publication, we explore
these two faces of AI.
Internal audit’s role in assurance over AI
AI and GenAI offer significant opportunities for organizations. At the same time, they present a frontier of new risks for boards and audit
committees to navigate. To mitigate and minimize these risks, organizations are actively investing in the development of risk
management frameworks and controls to enable them to innovate with confidence.
These new AI controls will be needed to help manage data privacy and security risks, as well as ethical considerations and concerns
about the reliability of outputs created by GenAI. Internal audit functions are also looking at the developing regulatory landscape to
ensure that their organization is preparing for the arrival of regulations across all geographies they operate in.
In conjunction with the publication of the regulations and guidance, the pace of AI development and deployment for the United States is
expected to intensify as the US government pushes to be a global leader in AI development and innovation.
Harnessing the power of AI to reimagine internal audit’s ways of working
Alongside their organizations’ efforts to leverage AI, internal audit leaders are also trying to understand the potential impact or
opportunity and art of the possible for their own functions.
As a firm, we believe the integration and use of enabling technologies in internal audit, such as AI, is critical to helping functions
maximize their impact and value. The digital landscape is broad, covering many other domains including automation, audit
management systems, cloud-based solutions, visualization, data analytics, and process mining. While they can be deployed in
isolation, the power of digital is in their combination. As such, internal audit functions need a strategic and coordinated approach
across both the function and the internal audit life cycle.
GenAI will play an important role within internal audit functions’ digital strategies, not only in providing new capabilities but also in
helping to engage leadership and staff in continuous improvement and innovation by reimagining traditional approaches. For those who
are successful in digitizing their functions, the rewards are clear—enhanced quality, increased assurance and better insights, new levels
of productivity, increased staff satisfaction levels, and greater impact on the organization and for their broader stakeholders. Yet digital
capabilities remain a significant gap and the number-one opportunity for many functions.
1 Krystal Hu, “ChatGPT sets record for fastest-growing user base – analyst note,” Reuters, February 2, 2023.
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Navigating the artificial intelligence frontier | An introduction for internal audit
Decoding the jargon: Useful AI terminology to know
Before internal audit functions can hope to assess and assure the risks of AI or look to explore the art of the possible from its use, internal
auditors must acquire a baseline of AI fluency. To help you with your AI 101, we outline some of the key terminologies and basics below.
Artificial intelligence
Artificial intelligence is a broad “umbrella” term
given to the field of computer science that
focuses on creating systems that can perform
tasks requiring human intelligence.
Machine learning
Machine learning refers to algorithms that make informed decisions and
learn over time without being explicitly programmed to do so. Machine
learning helps to train AI models to identify and predict patterns based on
human-processed data, rather than relying on hard-coded rules.
Deep learning
Deep learning is a powerful and advanced machine learning paradigm
that leverages neural networks to improve model performance.
The models simulate human reasoning to make intelligent decisions
and learn over time based on observed results.
Generative artificial intelligence
GenAI is a highly sophisticated subset of AI using foundation models to
create content across a variety of modalities. The models which support
the generation of content (often referred to as foundation models) are
underpinned by advanced machine and deep learning capabilities.
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Navigating the artificial intelligence frontier | An introduction for internal audit
Types of GenAI (modalities)
The primary GenAI foundation models are focused around generating new content using our primary forms of communication,
such as text and imagery. However, there are many variations, and models continue to develop at pace. For simplicity, it can be
helpful to think of the models as being able to produce outputs across an increasing combination of the following data
modalities (e.g., multimodal):
Explain to my In Python, code a A bowl of soup A teddy bear Play ""we have
colleagues the program that that is a portal painting to reduce
business impact of predicts the to another a portrait the number
Generative AI likelihood of dimension as of plastic
in 50 words customer digital art bags"" in a
conversion sleepy tone
Text Code Image Video Audio
Generative AI, by creating new
content or predicting future
trends, can drive innovation,
optimize operations, enhance
customer interactions, and
enable personalized offerings.
This results in improved
business efficiency, customer
satisfaction, and potentially
opens up new revenue streams.
Large language models
Of all the modalities, large language models (LLMs) have gained most of the attention from organizations for their ability to generate
text. Popularized through tools like OpenAI’s ChatGPT, LLMs are a specific type of text-based model that have been trained on
petabytes worth of global data. The parameters within these models represent the model’s level of understanding about each word and
their context within the training datasets. In the case of LLMs, more parameters allow them to capture more complex patterns in the
data they were trained on, typically leading to improved accuracy on language-related tasks.
At a simplistic level, LLMs predict an output based on inferences built on their training and the inputs they receive. Given the level of
data they have been trained on, their ability to provide highly convincing and compelling responses in a humanlike interaction is why
they have gained such attention.
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Navigating the artificial intelligence frontier | An introduction for internal audit
The mechanics of GenAI
GenAI is a highly sophisticated subset of AI. While the vast majority of internal auditors will not need a deep technical understanding akin to
data scientists, it can be helpful to appreciate the general mechanics of GenAI to consider where risks can arise and to determine the level of
technical skills that an internal auditor may need to provide assurance over the organizations’ use of these tools.
How GenAI works
Applications … what we see
GenAI applications generate content from user prompts across various
modalities (e.g., text, image, video, audio) based on how the underlying model
GenAI application Outputs was trained.
Why do these applications seem so human?
Like traditional AI, foundation models are models (1) that predict outputs
based on inferences built on the inputs they receive. However, through
fine-tuning (2), prompt engineering (3), and adversarial training (4), these
models can generate outputs geared toward meeting human intent.
Prediction What are foundation models?
GenAI models OpenAI’s GPT-4 and NVIDIA’s Megatron are two examples of foundation
models, specifically large language models, which use deep learning to
Models process massive amounts of data to form “memories” on the input
datasets through tokenization (5), thereby shaping the models’ parameters
Cloud and data (6). There are common foundation model architectures—for example,
platforms
Data Transformer (7), Diffusion (8)—which drive the modalities for each model.
Training on the world’s knowledge
AI
Foundation models are trained on petabytes worth of global data to shape
infrastructure
Compute power understanding, tone, and behavior while considering human communication styles.
Powering our journey to tomorrow
The scale of the compute capacity required to train and process
foundation models necessitates the usage of leading GPUs (graphics
processing units) (e.g., A100 NVIDIA) and TPUs (tensor processing units)
(e.g., Google TPU v4) on scalable infrastructure.
Key terms
1. GenAI model 4. Adversarial training 7. Transformer model
A neural network that has undergone The technique of pitting different deep A model that can “transform” words into
training to generate outputs based on a learning models against each other in a context-aware representations that Google and
given input prompt. training game or competition. University of Toronto invented in 2017.
2. Fine-tuning 5. Tokenization 8. Diffusion model
The process of refining foundation models to The process of splitting text into smaller units. Construction of high-resolution images from
make them suitable for specific applications. noise. Mostly used in speech-to-image and text-
6. Parameters to-image models.
3. Prompt engineering Trainable values within the model that are
The act of creating or modifying the prompt adjusted based on the training data to optimize
given to a model to obtain an optimal answer the output.
or output.
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Navigating the artificial intelligence frontier | An introduction for internal audit
What can AI do today?
The capabilities that AI can provide today are allowing organizations to challenge their ways of working and reveal new possibilities. Not all of
these will be relevant to internal audit, but some could be applied across the internal audit life cycle to evolve and innovate approaches.
Example AI capabilities include:
SENSE PERCEIVE
Sense physical Sense visual
See objects See faces See actions Hear voices
data data
Sense screen Convert speech
Sense light Detect objects Detect faces Detect motion
pixels to text
Sense Identify
Sense sound Classify objects Recognize faces Identify actions
keystrokes speaker
Determine
Sense Sense mouse
Perform OCR Determine age Hear sounds gender from
temperature clicks
voice
Identify
Determine Recognize
emotion in
gender sounds
voice
Recognize
emotion
LEARN KNOW
Represent
Learn by Learn facts Retrieve
and store
technique and skills information
knowledge
Populate global Retrieve
Learn from
Learn skills knowledge relevant
examples
base documents
Populate
Retrieve
Learn by trial contextual
Learn facts relevant
and error knowledge
answer units
base
Learn by
analyzing Retrieve
Maintain truth
structure specific facts
Capabilities with most relevance and potential application to internal audit.
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Navigating the artificial intelligence frontier | An introduction for internal audit
COMMUNICATE PLAN ACT
Act in
Understand and generate Plan
Understand language physical
language production
environment
Detect Translate Generate Plan robot Convert text
Classify text
language languages narrative motion to speech
Analyze Generate
Answer Move robot
Extract entities sentiment in image and Plan routes
questions limbs
text video captions
Analyze
Recognize Act in virtual
emotion in Dialogue
relationships environment
text
Generate
mouse clicks &
keystrokes
Generate
animated
avatar
CREATE REASON AND SOLVE PROBLEMS
Create text Create videos Infer
Create
Create custom Make logical
marketing Cluster
videos inferences
content
Make
Create sales
probabilistic Recommend
content
inferences
Create support Predict numeric
Classify
content value
Create images Create speech Solve problems
Search for
Create general Create custom
optimal Optimize
images voices
solution
Create
Satisfy
advertising
constraints
images
Create
Create models
chemicals
Capabilities with most relevance and potential application to internal audit.
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Navigating the artificial intelligence frontier | An introduction for internal audit
AI is not as new as you think … but with GenAI we are
heading into unchartered waters
Before becoming too caught up in the GenAI hype, it is worth recognizing that most people are already using AI in their daily lives without
realizing it. For example, tools like auto-complete, spellcheck, smart calendar scheduling, and suggestions on the most effective ways to
visualize data in applications, such as Power BI, are all using forms of AI. Natural language processing (e.g., chatbots, sentiment analysis),
speech recognition (audio to text), robotics, and perception sensing (e.g., object detection) have been in existence for some time. Chances are
your organization, and potentially your own internal audit function, are already engaged in forms of machine learning. If you have not yet
explored existing AI capabilities, there are significant opportunities and benefits that can be gained before heading into the world of GenAI.
The clear potential from democratizing access and the acceleration in development of GenAI tools is creating very significant opportunities
and brings with it new areas of risk that many organizations have yet to understand.
Internal audit’s role in assurance over AI
GenAI presents a broad spectrum of risks, many of which are still emerging. Among the main concerns raised by GenAI are:
Risk Description
Privacy Personal information shared with third-party Software-as-a-Service AI may not comply with
privacy laws and puts customer/employee data at risk of exposure.
Information gathered (e.g., by web scraping) may contain IP protected content, and prompts
Intellectual property must be carefully written not to leak any secret know-how. There are also challenges with
protecting IP of content generated by AI.
GenAI tools may be targeted by adversaries to reveal sensitive information and/or take malicious
Malicious behavior
actions on networks and data.
GenAI tools may be used in an unintended manner and to circumvent organizational policies,
Ethical use
laws, and regulations (e.g., submitting content in competitive events).
Hallucination Models might output facts that are false. Sources and citations are unavailable for most models.
Bias in training data (e.g., over/under-representation of a population cohort, sexism, racism) can
Bias
generate biased outputs.
Lack of considerations for model performance limitations (dependent on training data used)
Model performance
could lead to sub-optimal business outcomes (e.g., poor quality reports).
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Navigating the artificial intelligence frontier | An introduction for internal audit
The regulatory landscape
There have been several developments in the AI regulatory landscape, which continues to move at pace. Guidance has been published to aid
organizations as they navigate the use not only of GenAI, but of all forms of AI. Some of the key voices in the regulatory landscape include:
• EU AI Act (latest development from December 2023) – The European Union AI Act, which was provisionally agreed to among member
states and is expected to come into action in the first quarter of 2024, is a regulatory risk-based approach to classify AI systems and manage
the development, distribution, and use of AI systems.
• US White House Executive Order on AI (issued October 2023) – The Biden-Harris administration has released an executive order (EO)
aimed at enhancing safe, secure, and trustworthy development and use of AI throughout the federal government. This EO, while not a law
or regulation, encourages federal agencies to explore AI uses responsibly and manage associated risks, and could lead to new policies
impacting AI developers. To advance security and safety in AI’s development and use, the EO invokes emergency authority to require
disclosure of powerful AI systems and large-scale computing operations. It also addresses concerns around GenAI, encouraging the
identification of synthetic content and the use of labels to distinguish between authentic and AI-generated content.
• International Organization for Standardization (ISO) and International Electrotechnical Commission (IEC) published two key
standards for managing AI risks and systems (published in February 2023 and December 2023) – ISO/IEC 23894:2023 (published in
February 2023) provides risk management guidance for organizations developing, deploying, or using AI systems. It outlines principles and
processes for integrating risk management practices throughout the AI life cycle. ISO/IEC 42001:2023 (published in December 2023) focuses
on establishing, implementing, maintaining, and continually improving an AI management system. It specifies requirements to facilitate the
responsible development, deployment, and use of AI.
• NIST (National Institute of Standards and Technology) framework (published in January 2023)2 – NIST has collaborated with
organizations from both public and private sectors to develop the NIST AI risk management framework. The guidance is voluntary and aims to
help organizations understand the considerations that should be made during the design, development, use, and evaluation of AI systems.
2 Marilena Do Rosario, ""What you need to know about NIST's AI Risk Management Framework published in January 2023,"" Deloitte Financial Services Blog, February 2, 2023.
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Navigating the artificial intelligence frontier | An introduction for internal audit
What should internal audit be doing for their boards and
audit committees?
While GenAI technology is still developing, it is already being adopted by organizations at pace. Internal audit functions are seeking to
understand to what extent their organization is using this technology and to what extent they are planning to invest in it. As internal audit
functions grapple with this new risk domain, we recommend the following activities:
AI strategy and governance
Internal audit should consider its organization’s approach to the governance of AI. This should include
01
a review of the organization’s AI strategy, business case(s), and to what extent AI risks have been
considered. Consideration should be given to what extent senior executives have been involved in
defining the AI strategy and associated guardrails, given they can have organizational consequences.
Policy, standards, and guidelines
02
Internal audit should consider reviewing any AI policy the organization has developed, including
acceptable usage guidance and/or policy that defines the parameters of AI system development.
AI inventory
Internal audit should consider whether an AI inventory has been developed by the business
03
including both active and developing AI projects with details on their status and risk management
considerations. Organizations are taking differing approaches to this, but ultimately AI risks cannot
be managed unless there is clarity over AI use.
Regulatory readiness
Internal audit should understand how the organization is staying up to date with the fast-moving
04
regulatory environment. Organizations need to consider regulations in all the geographies they
operate in. If this assessment is not thorough, they run the risk of having to “roll back” deployed AI
use cases, which could cause significant business disruption.
AI risk management and culture
Current risk management processes may need to be amended to ensure that risks associated with AI are
proficiently covered.3 AI risk management frameworks and risk assessments are being developed and
05
should be integrated into the current risk management processes and procedures to ensure systems
utilizing AI are effectively managed, governed, and monitored. Risk appetite statements may also need to
be updated, and many organizations are adapting existing governance arrangements to be fit for AI, such
as AI ethics councils and the creation of AI centers of excellence.
3 Lukas Kruger and Michelle Seng Ah Lee, ""Embedding controls and risk mitigations throughout the GenAI development lifecycle,"" Deloitte, accessed March 28, 2024.
9
Navigating the artificial intelligence frontier | An introduction for internal audit
Harnessing the power of AI to reimagine
internal audit’s ways of working
What about internal audit’s own use of AI?
The use of more established AI capabilities (e.g., natural language processing and machine learning) have been present within more advanced
internal audit functions for some time, often found within analytics teams. As access to GenAI and data security issues are overcome, we
expect to see internal audit functions of all shapes and sizes to significantly scale their use of GenAI. For now, the reality is that most internal
audit functions have not engaged in GenAI beyond exploration of ChatGPT or conceptual applications. Only very few are actively developing
proofs of concept, but this is just a matter of time. As AI continued to grow in popularity, it is hard to imagine a world where it is not at the
forefront of our businesses. The good news is that AI is not as scary as it seems.
Enabling technologies are becoming increasingly accessible and this is only being accelerated through the wider efforts of organizational
Information Technology (IT) functions looking at the same challenging questions. Organizations do not need to become digital experts
overnight or start replacing auditors with a team of data scientists (although increasing digital fluency and being able to access some of these
skill sets will be important).
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Navigating the artificial intelligence frontier | An introduction for internal audit
A glimpse into the GenAI-driven internal audit life cycle
The application of GenAI on internal audit’s life cycle is only limited by the imagination and creativity of teams. From our discussions with
internal audit functions, the following applications and use cases are where many in the industry see potential:
Risk assessment Plan development Engagement planning Execution Reporting
Supporting auditor Supporting auditor Supporting auditor Analysis of data through Initial draft report.
research and research and research and natural language Initial draft report
understanding of risk understanding of risk, understanding of risk and questioning. review and QA.
for a specific industry. business processes, and business processes in
expected controls in advance of planning. Suggested interview Editorial QA, e.g.,
Supporting audit advance of engagement questions for different simplifying language,
universe creation, e.g., planning. Suggested control stakeholders’ personas. sentiment analysis.
guidance on universe objectives and test
design and process Suggested audits procedures based on Critical assessment of risk Summation of reports
universe. against the risk- assessed in-scope risk areas. and control descriptions, for audit committee
audit universe. e.g., if it covers who, what, summaries.
Suggested data where, and when.
Suggested scheduling sources and potential Generation of video/
and resource allocation analytics tests. Initial draft of workpaper. audio reporting.
based on known
constraints, e.g., Generated scripts for Drawing themes from Customized stakeholder
number of staff, their data extraction and interview notes/audio. communications.
skills and seniority. analytics execution.
First draft of scope/terms Summation/ Report language
of reference. interrogation of audit translation.
evidence documents.
Drafting emails to
Initial workpaper review communicate the audit
and quality assurance. report.
Initial draft of issue/
observations.
AI is only one element of internal audit’s digital landscape. Significant benefits can be achieved through automation, audit management
systems configuration and design, cloud-based solutions, visualization, data analytics, and process mining. While they can be deployed in
isolation, the power of digital is in their combination. As such, internal audit functions need a clear digital strategy and coordinated approach
across the function. For further information on how functions should approach a purpose-driven and digitally powered future, we
recommend reading our Internal Audit 4.0 framework.
11
Navigating the artificial intelligence frontier | An introduction for internal audit
What should internal audit be doing to accelerate
its adoption of AI?
Increase your digital fluency
01 Start engaging with learning and development now. You do not need everyone to become data scientists,
engineers, or digital experts. However, being familiar with the terminology, types of capability, and
potential for these tools will help accelerate adoption.
Determine your digital strategy and potential
Determine how GenAI can help you achieve your broader functional strategy and outcomes.
Systematically review your ways of working to identify potential use cases. But do not limit your digital
02 strategy to just GenAI; there are many applications and use cases relating to other areas of machine
learning, such as natural language processing, sentiment analysis, topic modeling, linear regression, and
neural networks that can already be harnessed and provide opportunities for experimentation. Equally,
do not overlook the opportunities that exist from maximizing functionality from audit management
systems and embracing analytics, visualization, and other tools such as process mining.
Engage with your technology teams
03
Understand your organization’s stance toward AI, both from a data privacy and security perspective and
its appetite for shaping existing solutions within the safety of your organization’s environment.
Clean up your data
The quality of AI both in terms of its training and its output will be a product of the quality of data it is
given. Many organizations (including internal audit) have poor data quality, version control, or outdated
04 versions of documents that have not been removed from intranets for years. While you are waiting for
some of the tools to become more accessible, getting your house in order will pay dividends to the value
AI can deliver. For example, analyzing your risk and control frameworks, scope documents, findings, and
recommendations to create a tokenized database of internal audit content could help turn currently
untapped information into a goldmine of knowledge and insight.
Work through and manage the risks
The risks outlined in this publication are as relevant to internal audit’s use of GenAI as they are to the
05
business. Good governance is critical, and functions should be challenging themselves to put in robust
governance processes and controls around the use, development, testing, access, and ongoing
monitoring of AI within internal audit.
Develop a culture of innovation
Organizational culture can make or break the success of innovative technology and new ways of working.
06 The limits of what GenAI could be used for are only contained by the imagination of individuals. Functions
that have a culture of innovation, curiosity, and the willingness to experiment have usually fared better
than those that were less willing to embrace change. Functions should consider innovation programs,
encourage experimentation, and reward the right behaviors.
12
Navigating the artificial intelligence frontier | An introduction for internal audit
Where do we go from here?
Whether it is the assurance over risks posed by AI or exploring how you might use these technologies in your own internal audit
function, learning and improving your digital fluency is key. GenAI will also require a mindset shift. Its prevalence and the speed at which
it is evolving will drive a need to reimagine the human-technology relationship. Interacting with GenAI will become part of the daily
routine, enabling new possibilities but bringing the potential for overreliance on AI outputs.
Organizations, including internal audit functions, will need to assess the risks and opportunities associated with GenAI, balancing the
benefits from efficiencies gained through reduced manual effort with the need to check and verify accuracy. What is clear is that tools
this powerful offer so much potential that they will be here for the long term. The attention given to GenAI and the investments being
made means internal audit will need to engage and do so quickly.
GenAI is a fast-moving and developing field of AI. As an organization, we are taking the same journey as many of our clients. We believe
GenAI has the potential to transform the internal audit profession and have already made significant investments in both our approach
to assuring GenAI and supporting organizations in their use of these technologies. If you would like to talk to our dedicated team of
specialists, please get in touch.
Internal audit contacts
Sarah Fedele Neil White Michael Schor Geoffrey Kovesdy
Principal Principal Principal Principal
US Internal Audit Leader Deloitte & Touche LLP Deloitte & Touche LLP Deloitte & Touche LLP
Deloitte & Touche LLP nwhite@deloitte.com mschor@deloitte.com gkovesdy@deloitte.com
sarahfedele@deloitte.com
GenAI internal audit contacts
Explore Internal Audit 4.0
framework for further
insights on the power of
Alex Vorpahl Madeline Mitchell
digital for internal audit
Senior Manager Senior Manager
Deloitte & Touche LLP Deloitte & Touche LLP
avorpahl@deloitte.com mademitchell@deloitte.com
13
This publication contains general information only and Deloitte is not, by means of this publication,
rendering accounting, business, financial, investment, legal, tax, or other professional advice or
services. This publication is not a substitute for such professional advice or services, nor should it be
used as a basis for any decision or action that may affect your business. Before making any decision or
taking any action that may affect your business, you should consult a qualified professional adviser.
Deloitte shall not be responsible for any loss sustained by any person who relies on this publication.
As used in this document, “Deloitte” means Deloitte & Touche LLP, a subsidiary of Deloitte LLP. Please
see www.deloitte.com/us/about for a detailed description of our legal structure. Certain services may
not be available to attest clients under the rules and regulations of public accounting.
Copyright © 2024 Deloitte Development LLC. All rights reserved."
207,deloitte,us-lshc-ai-medtech-2024.pdf,"Is Generative AI changing
the game for medtech?
Deloitte research suggests while GenAI has
started delivering value, its potential is far greater
Is Generative AI changing the game for medtech?
Contents
Executive summary 3
AI and GenAI have started to deliver value to
medtech companies across functions 4
GenAI could help medtech firms achieve cost-efficiencies
of up to 12% of their revenue 6
Building blocks to maximize value from AI and GenAI 8
Conclusion 10
Authors 12
2
Is Generative AI changing the game for medtech?
Executive summary
Across industries, artificial intelligence (AI) and Generative However, using AI and GenAI in isolation may not drive this
AI (GenAI) discussions are moving from potential to value transformative value. Adopting a string-of-pearls approach—
realization.1 For medtech companies, this value can be in integrating multiple GenAI use cases with other AI technologies,
the form of cost reductions, cost avoidance, and new data, and digital tools—is important for achieving cost
revenue generation. efficiencies and other benefits. This requires appropriate
“building blocks” to scale AI and GenAI, including creating an
To assess where medtech companies have realized value2
enterprise ambition for AI use and operating structures such as
from AI and GenAI and what could be next, the Deloitte
a Center of Excellence. These should be accompanied by new
Center for Health Solutions surveyed 85 leaders from medtech
ways of working, governance for responsible and ethical AI use,
organizations during the summer of 2024 and conducted
and proactive communication of an AI value narrative.
follow-up interviews. We found that:
• AI and GenAI have begun to deliver value across functions,
with 42% of surveyed executives reporting benefits in product
development and 35% in IT and cybersecurity functions.
• GenAI could enable medtech companies to achieve cost
efficiencies of 6% to 12% of their total revenue in the next two
to three years. This could be in the form of cost reductions,
cost avoidances, and other benefits. For a medtech company
with $20 billion to $26 billion in revenue, this translates to an
estimated $1.2 billion to $3.2 billion.
3
Is Generative AI changing the game for medtech?
AI and GenAI have started
to deliver value to medtech
companies across functions
Using AI to transform processes is not new to medtech organizations “ Our strategy is to utilize AI and GenAI in
(see sidebar, “AI in action at medtech companies”). But beyond this
our products and across all functions as
anecdotal evidence at the organizational level, the industrywide
impact of AI and GenAI is not well known. Our survey results aim much as possible.”
to bridge this gap by illustrating where and how these technologies
have affected medtech companies. —VP, large medtech company
Our research suggests that, overall, medtech companies have
moved quickly to leverage GenAI’s capabilities. Fifty-seven percent of
surveyed leaders reported that their organization is implementing or
scaling GenAI use cases or “quick wins” that have provided benefits
across functions. The pace of GenAI adoption also appears to have
accelerated the use of other AI, as our survey data indicates.
AI in action at medtech companies
Medtech companies have used AI to save time,
reduce costs, avoid additional expenses, and
create revenue. Here are some examples:
Digital asset procurement optimization: Siemens Personalized technology: Meticuly uses AI and 3D
Healthineers developed an AI-powered digital asset printing to deliver custom medical implants within
management repository, enabling its 60,000 employees two to seven days. Meticuly’s ML algorithms analyze
to search for, use, and reuse digital assets, such as stock the patient’s natural bone structure, including any
imagery, for marketing. This initiative saved the company an defects or irregularities and the impacted area,
estimated EUR 3.5 million by reducing the need to purchase to design a bespoke implant. Such implants could
new digital assets.3 reduce the likelihood of interoperative challenges.5
People analytics and workforce development: Johnson
and Johnson leveraged AI to transform and modernize
its HR operations. AI-driven models predict employee
attrition based on industry trends, performance, and career
progression. Machine learning (ML) is applied to human
capital data to assess the state of skills in the workforce and
help create personalized development plans and learning
curricula for employees.4
4
From code to cure, how Generative AI can reshape the health frontier | The shifting health care market landscape
Respondents reported that their organizations have generated the
greatest value from AI and GenAI in product development (42%)
and IT and cybersecurity (35%) functions (see figure 1). For product
development, medtech companies have used AI for concept design
and prototyping.6 Beyond device design, GenAI has benefited some
organizations in summarizing scientific literature and drafting trial
documents, such as vendor contracts and site checklists.
For IT and cybersecurity, organizations have used GenAI to generate
high-quality code and improve data management processes. This
includes creating meta labels, cleaning data, and anonymizing data
to enhance security and usability.7
While close to one-third of surveyed leaders said they have already
realized value from AI and GenAI across functions, another third
expects to realize value across most functions over the next two
to three years. In commercial operations, AI could help analyze
sales and customer data to provide the next-best engagement
recommendations to sales teams and improve conversion rates.
Leveraging AI-driven platforms could accelerate the creation, review,
and deployment of marketing and sales content to cut marketing
spending.8 One interviewee reported that his organization is using AI
5
ROF
ESU
IANEG
Is Generative AI changing the game for medtech?
Figure 1: Value realized and expected from AI and GenAI use across functions
Product IT and Commercial HR and other Supply chain and Finance
Functions development cybersecurity internal services manufacturing
Have already realized
value from AI and GenAI 42% 35% 30% 23% 22% 20%
Expect to realize value 35% 28% 37% 31% 37% 36%
from AI and GenAI in the
next 2 to 3 years
GenAI use cases being scaled
Study document Report generation Optimizing marketing Training content Quality event Financial insight
generation from unstructured content creation creation management generation enterprise data sources
Clinical study
summarization and Data management Medical, legal, and HR portal content Inventory Collection
insight generation regulatory review creation optimization automation
Scientific writing A du et bo um ga git ne gd K sO uL m p me ars rip ze ac tit oiv ne Ma ck oi mng m c uo nn it ce an tit o a nn d Pr ao nd du c dt e t lr ia veck rying M&A due diligence
more inclusive
Q. In which of the following areas has your organization realized value from the use of AI including GenAI? In which of these areas does it expect to realize value in
the next 2 to 3 years? Respondent to select only one option. [N=85]
Q. Where has GenAI helped your organization to realize benefits? Is your organization scaling the use of GenAI for this purpose? Please select all that apply.
Respondents answered this question based on their visibility into GenAI use across functional areas as assessed by our screener questions. Figure shows the top 3
GenAI use cases being scaled across functional areas.
Commercial [N=58], Supply chain and manufacturing [N=63], R&D [N=69], Finance [N=54], HR and other internal services [N=55] and IT and cybersecurity [N=68]
chatbots to automate order intake and solve customer
queries. Given its contextualization abilities, GenAI could further
improve self-service capabilities for medtech customers across
digital channels.9
As part of digitalized supply chains, AI could automate activities
such as supplier checks and management of quality events
while optimizing inventory management, distribution, and
warehousing. For a faster path to net-zero, AI could provide insights
to optimize capital investment and energy usage and cost-effectively
reduce emissions.10
AI can also assist as a self-service partner for business leaders
to support agile financial decision-making. For instance, AI
could provide quick access to complex financial insights such as
scenario analysis for budget planning. Continuous financial data
monitoring using AI and advanced analytics could also help uncover
valuable investment and savings opportunities, which could
improve margins.11
Is Generative AI changing the game for medtech?
GenAI could help medtech firms
achieve cost-efficiencies of up to
12% of their revenue
By effectively deploying GenAI and other AI technologies, medtech Overall, our survey respondents anticipate AI and GenAI to
companies could achieve cost efficiencies, including cost reductions, reduce SG&A costs by 7% to 19% over the next two to three years,
cost avoidances, and other benefits, ranging from 6% to 12% of which could benefit the commercial and shared services functions,
revenue. For instance, a large medtech company with $20 billion to including HR, finance, and IT. For a large medtech company, this
$26 billion in revenue could realize cost efficiencies of $1.2 billion to could mean an estimated savings of up to $1.5 billion through
$3.2 billion through AI implementations across functions in the next efficiency gains and vendor cost optimizations. Similarly, a large
two to three years (figure 2). Actual efficiencies gained could vary medtech company could save up to $1.4 billion (5% to 12%) in
depending on the scale and integration of AI within an organization. COGS by applying AI to activities such as predictive maintenance,
contract creation, and vendor management in supply chain
To arrive at these estimates, we analyzed anticipated cost savings
and manufacturing.
percentages from AI and GenAI use across the various categories—
selling, general, and administrative costs (SG&A); R&D expense; As discussed previously, more surveyed leaders reported
and cost of goods sold (COGS)—as reported by surveyed medtech realizing value from AI and GenAI in product development than
leaders. We applied these percentages to publicly reported financial other functions. According to survey respondents, applying AI and
data from the top 10 medtech companies by revenue in 2023.12 GenAI could save up to 20% of R&D costs, translating to $0.3 billion
in savings for a large medtech company over the next two to
three years.
Figure 2: Estimated potential efficiency gains via AI use for a large medtech company in the next 2–3 years
Potential savings %
Top 10 medtech firm Functional areas likely
Type of costs from use of AI in the Potential savings estimate
average cost* to benefit
next 2–3 years*
Selling, general and
Commercial, Shared
administrative costs $7.2B 7%-19% $0.5B - $1.5B
services
(SG&A)
Supply chain and
Cost of goods sold (COGS) $11.7B 5%-12% $0.6B - $1.4B
manufacturing
R&D costs $1.5B 7%-20% $0.1B - $0.3B Product development
Estimated potential savings for a large medtech company
$1.2B - $3.2B
in the next 2–3 years
Methodology:
* Potential dollar savings are based on applying savings estimates collected through our survey to the average of different costs—R&D expenses, cost of
goods sold (COGS), and selling, general, and administrative (SG&A)—reported in the financial statements of the top 10 medtech companies in 2023. Cost
savings percentages applied are based on interquartile ranges from the survey data.
Q. To your best estimate, what share of cost savings could AI including GenAI enable your organization to achieve in the next 2 to 3 years? Please pick the %
of impact AI has had across the following cost categories. (N=85)
6
Is Generative AI changing the game for medtech?
However, using AI and GenAI in isolation may not drive one another to drive efficiencies and compound value (see sidebar,
transformative benefits. Medtech companies may need to undertake “Enhancing health care practitioner engagement with a string-of-
a string-of-pearls approach. By stringing together multiple workflows pearls approach”). Such an approach can be applied to processes
through GenAI and other AI, data, and digital technologies, across functions—from R&D to commercial and shared services.
companies could transform entire processes. This approach
creates a series of business process enhancements that build on
Enhancing health care practitioner engagement with a string-of-pearls approach
A string-of-pearls approach with GenAI at the core could enable companies to deliver appropriate content to
health care practitioners (HCPs) through appropriate channels at an appropriate time.
As part of this approach, GenAI and ML could analyze prescriptions, sales data, and digital interaction data to
micro segment and target HCPs for engagement. Based on this segmentation and AI-driven insights on content
consumption patterns, GenAI could categorize, organize, and create new content. ML algorithms could then
determine the sequence and frequency of content delivery across channels and measure the effectiveness of
these tactics to optimize omnichannel engagement (figure A).
Figure A: Orchestrating omnichannel engagement with GenAI
“Next-best action” objectives Use cases GenAI ML Descriptive analytics Data
Understand the
1 e wn hv icir ho n thm ee bn rt a i nn d C ino tm elp lige eti nti cv ee mar bS k rc e ar t na , dp co e im
n
a tn p ed e llit gd it ei is v nt e cil , el and Geo-lev ree pl i on rs ti ig nh gt , trend pubW lie cb d- is sc cr la op se ud re d s,a Ata P; LD
operates
Find and engage with
HCPs based on hidden HCP micro- Identify “latent” drivers Segment HCPs based on APLD data, digital
2 potential and segmentation of customer behavior prescribing patterns HCP 360 profiles interactions, sales
disposition
actionable insight
3 C o pr ea g rte a sog no nizr aei lz ic ze o a a n tin t oed nn t for cat (teC ago gon grt iie nzn a gt )t ion Tag con tt ae xn ot n a os mse yts using Content i nc so in gs hu tmption Standard ci oz ned te t na txonomy,
4 D b coue nnv te d el lo nep t n a en wd geC no en rt ae tn iot n tailG oe ren de r ta ot e p rc eo fn et re en nt c es Conte pn rt e e fff ere ec nti cv eeness/ maM pe ps is na gg te o/ c so en gt men et n ts Firs pt- up ba lr isty h ea rn ad l lt ih ai nr cd e-p sarty
5 D e cuxe p sli tev ore mir e enta rc sr eg se tt oe d O enm cu gn asi t gc o eh m man e en r n e tl Behavio dr ea tl e-b cta is oe nd signal fC rh eqa on u pn e tne iml c, y s i, z e c aq a tu id oe e nn n c ce e, Call plan ps l, a p na sid media loC ig nR s s,M i gc h ud tsa st ,t o a mm, c eea drl il s a c e pe gn lm at ne e sr n t
Measure Closed loop Automated cross- Measure effectiveness of Measure audience reach APLD data, digital
6 effectiveness of measurement channel, cross-audience tactics and quality marketing data, HCP
tactics interpreter MTA interaction data
Source: Deloitte analysis
7
Is Generative AI changing the game for medtech?
Building blocks to maximize value
from AI and GenAI
Interviewees highlighted that though many medtech organizations leveraging less than 10% of AI’s potential.” We outline six key building
have kick-started their AI and GenAI journey, they may just be blocks for medtech companies to leverage AI and GenAI to a greater
scratching the surface. As one interviewee commented, “I feel we are extent (figure 3).
FigurFei 3g:u Brueil d4i:n Bg ubiloldckins gfo br lloevcekrsa gfionrg lAeI vaenrda GgeinnAgI AI and GenAI
1 Craft a strategic blueprint: Anchor AI investments to meet overarching business
objectives and focus on 3 to 5 high-value opportunity areas to scale AI.
2 Build operating structures: Establish measurable AI targets and possibly a Center of
Excellence (COE) to align investments and resources, supporting prioritized AI and
GenAIuse cases.
3 Focus on value realization: Take a disciplined approach to evaluate, approve, and
adopt AI and GenAIto help ensure value.
4 Create tech and scaling capabilities: Select an appropriate technology platform to
leverage LLMs while forging ecosystem partnerships to scale AI and GenAI.
5 Build new ways of working: Emphasize AI's role in enhancing human capabilities, and
develop training programs to improve AI literacy and optimize workflows.
6 Promote responsible AI use: Establish governance to address data privacy, security,
and bias, and foster the accountable and ethical deployment of AI.
Source: Deloitte analysis
Source: Deloitte analysis
1. Craft a strategic blueprint 2. Build operating structures
Scaling AI across the enterprise may no longer be a question of Building distinct operating structures, such as establishing an
“when” but “to what extent.” Medtech companies should view AI AI Center of Excellence (COE), could help medtech companies
and GenAI investments as core enablers of their enterprise strategy cross-leverage talent and resources to weave AI and GenAI into
rather than as experimental projects. While executing a multitude of their business. Such COEs, while not necessarily a requirement
use cases can deliver value, a focus on depth versus breadth could for success, should enable prioritizing and managing AI and GenAI
be more beneficial. investments and should include an explicit capability for value
realization.
Action steps:
Action steps:
• Frame a strategic blueprint: Define the business ambition
for AI use and anchor investments to meet short- and long-term • Source and prioritize use cases: Identify and prioritize
business goals. a portfolio of AI and GenAI use cases relevant to identified
opportunity areas.
• Identify bold plays: Determine three to five high-value
opportunity areas where AI and GenAI use could provide the most • Create a roadmap: Develop a plan to align investments and
value and focus on scaling for impact. resources to support use-case development and scaling.
• Set measurable AI targets: Build explicit metrics of value to be
captured through use-case enablement. Assigning measurable
targets to AI leaders can further promote AI advocacy and ensure
8
accountability.
Is Generative AI changing the game for medtech?
3. Focus on value realization 5. Build new ways of working
Sustaining the enthusiasm and momentum to scale AI requires While initial experiments may have progressed, large-scale AI
a disciplined approach to assessing and making appropriate deployment involves a stronger focus on bridging the trust gap
investments and communicating their value. AI appears to be just with AI, which GenAI could likely exacerbate. As one interviewee
another step on the digital transformation path for companies. By pointed out, “We want to make AI a part of our culture and part
focusing on building and communicating an ongoing value narrative and parcel of the way we do things going forward.” This involves
from AI use, companies could prevent disillusionment, especially as fostering willingness among business users by clearly demonstrating
newer digital technologies become the next “shiny thing.” value, providing adequate training, and creating guardrails for the
responsible use of AI and GenAI tools.
Action steps:
Action steps:
• Track and realize value: Create mechanisms to measure
outcomes and optimize investments while assessing progress • Focus on human augmentation to encourage adoption:
toward AI ambitions. During rollouts, demonstrate how AI enhances human capabilities
rather than just automates tasks.
• Articulate an enterprise value story: Create and promote a
narrative of AI impact on identified opportunity areas to relevant • Build AI fluency: Develop training programs to improve AI
stakeholders—from the board and CEO to business leaders. literacy, covering AI concepts, applications, and limitations.
Optimize workflows to ensure seamless AI integration and user
proficiency.
4. Create technology and scaling capabilities
Build enterprise architecture that collates key components required
6. Promote responsible AI use
to quickly create and deploy AI and GenAI applications, tools, and
capabilities. Such architecture should include access to open-source Medtech companies should establish clear guidelines and promote
and proprietary platform capabilities to utilize large language ethical practices around AI to help ensure responsible deployment.
models, sandbox environments, and common solution archetypes. The string-of-pearls approach can only be effectively leveraged if
companies ensure AI use is harmonized with the evolving regulatory
Action steps:
environment.
• Determine an appropriate platform(s): Select a cost-effective
Action steps:
platform, or platforms, to support capability development (e.g.,
semantic search, information extraction, classification, simulation • Establish ground rules: Govern the ethical use of AI by
exercises) tailored to execute multiple GenAI use cases. addressing issues such as data privacy, security, and bias.
• Build ecosystem partnerships: Identify and work with industry • Shape internal governance: Enable business users to
consortia and working groups as they emerge. Continuously understand and contend with risks from AI use (e.g., data security
evaluate platforms and partnerships to enhance AI and GenAI and privacy) and model outputs (e.g., hallucinations and biases,
capabilities and infrastructure. lack of transparency).
• Promote transparency and accountability: Clearly document
processes and communicate openly about AI model/tool
capabilities and limitations.
99
Is Generative AI changing the game for medtech?
Conclusion
The medtech industry appears to be at an inflection
point—scaling AI and GenAI for transformation is
important. Organizations should consider making
“bold plays” that integrate these technologies into
their operations.
In upcoming publications, we will focus on how
medtech companies can execute bold plays through
a string-of-pearls approach and the impact these
could have on functions such as R&D, supply chain,
and commercial.
10
Is Generative AI changing the game for medtech?
Endnotes
1. Jim Rowan et al., Now decides next: Moving from potential to performance, August 2024.
2. F or our survey respondents, we defined value as cost savings, cost eliminations, new sources of revenue, improvements or additions to
products and services, improved customer relations, and other benefits organizations may have achieved through AI and GenAI use.
3. Bynder, “Siemens Healthineers saves millions in costs with Bynder’s AI-powered DAM,” accessed August 8, 2024.
4. Finn Bartram, “AI and automation: Suresh Raman of Johnson & Johnson on how to effectively harness AI technology in people operations,”
Authority Magazine, November 20, 2023.
5. Y Consulting, “Meticuly: Revolutionizing the future of medical implants with AI and 3D printing,” August 25, 2024.
6. Mark Crawford, “Artificially intelligent design for orthopedic devices,” Orthopedic Design & Technology, March 10, 2023.
7. Vikram Agarwal, “2024 J.P. Morgan Healthcare Conference: As GenAI Expands Medtech Capabilities, the Industry Gains Momentum,”
LinkedIn, January 25, 2024.
8. Anthill, “AI in pharma marketing: Meaning, strategy and best practices,” accessed September 20, 2024.
9. DigitalOcean, “How to use AI for sales: Techniques and tools,” accessed September 19, 2024.
10. Tanya Gupta, “AI in supply chain: Use cases, examples and how AI [sic] used in supply chain management?,” Clear, updated August 8, 2024.
11. Michael Abramov, “The human-AI collaboration: How humans and AI can work together in finance,” Keymakr Blog, August 2, 2024.
12. Medical Device and Diagnostic Industry (MD+DI), “Top 40 medical device companies,” February 29, 2024.
13. Nitin Mittal, “Getting real about GenAI,” Deloitte Insights, April 1, 2024.
11
FIsr oGmen ceordaeti tvoe cAuIr ceh, ahnogwin Gge tnheer gaatimvee AfoI rc amne rdetsehcahp?e the health frontier | Unlocking new levels of efficiency, effectiveness, and innovation
Authors
Sheryl Jacobson Dr. Jay Bhatt
US Consulting Medtech Practice Leader Managing Director
Deloitte Consulting LLP Deloitte Center for Health Solutions and
shjacobson@deloitte.com Deloitte Health Equity Institute
Deloitte Services LP
jaybhatt@deloitte.com
Mukund Lal Wendell Miranda
Senior Manager Deputy Manager
Deloitte Consulting LLP Deloitte Services LP
muklal@deloitte.com wmiranda@deloitte.com
Aditya Kudumala Apoorva Singh
Principal Senior Research Analyst
Deloitte Consulting LLP Deloitte Services LP
adkudumala@deloitte.fr apoorvasingh@deloitte.com
Srivathson Chennakesavan Leena Gupta
US Life Sciences MedTech Analytics & Life Sciences Research Leader
Cognitive Leader Deloitte Center for Health Solutions
Deloitte Consulting LLP Deloitte Services LP
schennakesavan@deloitte.com legupta@deloitte.com
Dr. Asif Dhar
Global Life Sciences and Health Care
Consulting Leader
Deloitte Consulting LLP
adhar@deloitte.com
Acknowledgments:
The authors would like to thank Maulesh Shukla, Natasha Elsner, Dana Schmucker, Tomislav Medan, Spencer Hanson,
Adam Israel, and Rob Jacoby for their insights, expertise, and critical feedback on the research. Additionally, the authors
would like to thank Rebecca Knutsen, Laura DeSimio, Chris Giambrone, Jesse Daniels, Deb Asay, and many others who
contributed to the success of this project.
12 12
This publication contains general information only and Deloitte is not, by means of
this publication, rendering accounting, business, financial, investment, legal, tax, or
other professional advice or services. This publication is not a substitute for such
professional advice or services, nor should it be used as a basis for any decision or
action that may affect your business. Before making any decision or taking any action
that may affect your business, you should consult a qualified professional advisor.
Deloitte shall not be responsible for any loss sustained by any person who relies on
this publication.
About Deloitte
Deloitte refers to one or more of Deloitte Touche Tohmatsu Limited, a UK private
company limited by guarantee (“DTTL”), its network of member firms, and their related
entities. DTTL and each of its member firms are legally separate and independent
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Copyright © 2024 Deloitte Development LLC. All rights reserved.
9709314"
208,deloitte,us-state-of-gen-ai-report-q2.pdf,"Now decides next:
Getting real about
Generative AI
Deloitte’s State of Generative AI in the Enterprise
Quarter two report
April 2024
deloitte.com/us/state-of-generative-ai
Table of contents
Foreword
Introduction
Now: Key findings
1 Value creation
2 Scaling up
3 B uilding trust
4 E volving the workforce
Next: Looking ahead
Authorship & Acknowledgments
About the Deloitte AI Institute
About the Deloitte Center for Integrated Research
About the Deloitte Center for Technology, Media & Telecommunications
Methodology
22
Introduction
Foreword
We have traveled a long way since the Generative AI space race kicked off in November 2022—and
yet, we know we are still at the beginning of this long and exciting transformation. Every day, we
talk with clients about how much there is to focus on in the moment, how explosive the pace of
change is, and how challenging it can be amid the excitement to take a longer-term view.
We see organizations starting to achieve benefits and move toward a near future where this
“We are in the first inning of a
early stage of Generative AI tools is widely dispersed and driving new value. But there are also
some hard realities to deal with as business leaders look to scale and realize the potential of this thousand-inning game and there’s
powerful technology.
so much to be figured out.”
The State of Generative AI in the Enterprise: Getting real about Generative AI captures a new snapshot
of this transformative time from the perspectives of nearly 2,000 business and technology leaders,
all from organizations that are actively deploying and scaling Generative AI today. Echoing our -Chief analytics officer in financial services
many clients, from these executives we hear that while excitement persists it may be at its peak as
leaders come up against cultural challenges, questions about how to manage their workforces, and
issues with trust that—at least for now—stand in the way of unlocking Generative AI’s full value.
All told, it is exciting that Generative AI’s potential is beginning to weave its way deeper into the
foundations of how organizations operate and we continue to learn more about emerging leading
practices. Amid those developments, we also continue to see that achieving value with Generative
AI connects hand in hand with keeping humans at the center.
Learn more about the series and sign up for updates at http://deloitte.com/us/state-of-generative-ai.
Nitin Mittal, Costi Perricos, Kate Schmidt, Brenna Sniderman and David Jarvis
3
Introduction
Getting real about Generative AI
Is the infatuation phase over? Quarter two of Deloitte’s Our research shows that organizations are increasingly Two of the most critical challenges for scaling are
global quarterly survey found many organizations prioritizing value creation and demanding tangible building trust (in terms of making Generative AI
beginning to get down to the serious work of making results from their Generative AI initiatives. This requires both more trusted and trustworthy) and evolving
Generative AI’s vast potential a reality. them to scale up their Generative AI deployments— the workforce (addressing Generative AI’s potentially
advancing beyond experimentation, pilots and proofs massive impact on worker skills, roles and head count).
This report presents findings from the second in
of concept. Transitioning to large-scale deployments
Deloitte’s ongoing series of quarterly global surveys Here we’ll take an in-depth look at all four of these
will increase Generative AI’s impact on the business
on Generative AI in the enterprise. To gain additional areas—value, scaling, trust and workforce—to help
and expand its reach to a much larger segment of the
context for our wave two research, we also organizations move forward more effectively on their
workforce. Successful scaling, in turn, presents a wide
conducted a series of in-depth interviews with senior Generative AI journeys. Future survey reports will
range of challenges, encompassing everything from
executives from a broad range of industries. focus selectively on other key challenges to successful
strategy, processes and people to data and technology.
Generative AI scaling and value creation.
4
Introduction
Getting real about Generative AI (cont’d)
Value creation Scaling up
• T he percentage of organizations reporting they were already achieving their expected • L eaders see scaling as essential for creating value, increasing Generative AI’s impact
benefits to a “large” or “very large” extent is 18%–36%, depending on the type of on the business and expanding the technology’s user base. The scaling phase is when
benefit being pursued. Generative AI’s potential benefits are converted into real-world value. It’s also, however,
when an organization’s potential concerns can become real-world barriers to success.
• O rganizations that reported “high” or “very high” levels of Generative AI expertise
are scaling Generative AI much more aggressively—and are achieving their desired • Common areas of concern include data security and quality, explainability of Generative AI
benefits to a much greater degree than others. outputs, and worker mistrust or lack of familiarity with Generative AI tools.
• O rganizations primarily plan to reinvest the savings from Generative AI into innovation • W orkforce access to approved Generative AI tools and applications remains quite low,
(45%) and improving operations (43%)—addressing the value equation from both sides. with nearly half of surveyed organizations (46%) reporting they provided approved
Generative AI access to just a small portion of their workforces (20% or less). However,
most workers with internet access will have access to public Generative AI tools and
could be using them without consent.
All statistics noted in this report and its graphics are derived from Deloitte’s second quarterly survey, conducted
January – February 2024; The State of Generative AI in the Enterprise: Now decides next, a report series. N (Total
leader survey responses) = 1,982.
Generative AI is an area of artificial intelligence and refers to AI that in response to a query can create text,
images, video and other assets. Generative AI systems can interact with humans and are often built using large
language models (LLMs). Also referred to as “GenAI.”
5
Introduction
Getting real about Generative AI (cont’d)
Building trust Evolving the workforce
About the State of
• Lack of trust remains a major barrier to large-scale • M ost organizations (75%) expect the technology to Generative AI in the
affect their talent strategies within two years; 32%
Generative AI adoption and deployment. Two key
Enterprise: Wave two
of organizations that reported “very high” levels of
aspects of trust we observed are: (1) trust in the quality
survey results
Generative AI expertise are already making changes.
and reliability of Generative AI’s output and (2) trust
from workers that the technology will make their jobs
The wave two survey covered in this report was fielded
• T he most expected talent strategy impacts are process
easier without replacing them. to 1,982 director- to C-suite-level respondents across
redesign (48%) and upskilling or reskilling (47%).
six industries and six countries between January
• T rust issues have not prevented organizations from and February 2024. Industries included: Consumer;
• Generative AI is expected to increase the value of
Energy, Resources & Industrials; Financial Services;
rapidly adopting Generative AI for experiments
some technology-centered skills (such as data analysis) Life Sciences & Health Care; Technology, Media &
and proofs of concept, with 60% reporting they
Telecom; and Government & Public Services. Our
as well as human-centered skills (such as critical thinking,
are effectively balancing rapid implementation with Q2 survey findings are augmented with over 20 executive
creativity and flexibility), while decreasing the value of
interviews. This second report is part of a yearlong
risk management. Trust is likely to become a bigger
other skills. series by the Deloitte AI Institute to help leaders
issue, however, as organizations transition to large-
in business, technology and the public sector track
scale deployment. Many reported they are currently • I n the short term, more organizations said they expect the rapid pace of Generative AI change and adoption.
The series is based on Deloitte’s State of AI in the Enterprise
investing significant time and effort into building the technology to increase head count (39%) than to
reports, which have been released annually the past five years.
guardrails around Generative AI. decrease head count (22%)—perhaps due to increased
Learn more at deloitte.com/us/state-of-generative-ai.
needs for Generative AI and data expertise.
• Organizations that reported “high” or “very high” levels of
expertise recognize the importance of building trust in
Generative AI across numerous dimensions (e.g., input /
output quality, transparency, worker empathy) and are
implementing processes to improve it to a much greater
extent than are other organizations.
6
Now: Key findings
77
Now: Key findings
1
Value creation
Proving, measuring and communicating value is crucial Value objectives and priorities for Generative AI can— Therefore, many forward-thinking organizations are
to an organization’s Generative AI journey. In our survey and should—vary by organization, industry and use case. implementing Generative AI without specific ROI targets
and interviews, many organizations reported they Where the technology’s potential impact is strategic as they realize they can’t afford to get left behind in this
were increasingly emphasizing the need for Generative and truly game-changing, the need and latitude for critical and fast-moving market.
AI initiatives and investments to have clear value experimentation, learning and innovation are much
objectives and deliver tangible results, rather than simply greater (with less emphasis on immediate payback) than
being viewed as experiments or learning experiences. in situations where productivity and cost savings are the
primary expected benefits.
As one executive at a Fortune 500 manufacturing
company noted: “We have a very strict internal rule Moreover, Generative AI is so new—and advancing so
that if we don’t see value from our Generative AI quickly—that accurately estimating benefits is much
solutions, we won’t do it or we won’t scale it.” harder than for an established technology with a proven
track record.
That said, there are many ways to define and
measure value—especially for a technology with the “Any technology that’s a little over a year old, nobody’s
transformational potential of Generative AI. Although going to have a year’s worth of data to do a backward-
financial return on investment (ROI) is important, value looking ROI,” said one tech company executive we
drivers such as innovation, strategic positioning and interviewed. “And with the fundamental and foundational
competitive differentiation can be even more important. changes Generative AI offers, it’s very hard to even offer
a forward-looking [total cost of operating] or ROI because
there’s so many possibilities of impact and varied ways
to integrate it into your business.”
88
Now: Key findings
Organizations are starting to demand tangible “large” or “very large” extent is 18%–36%, depending on Generative AI “experts” are achieving their desired
business value from Generative AI, and some the type of benefit being pursued. benefits to a much greater degree.
are beginning to achieve real-world benefits.
As one public sector executive told us, “The big selling In every category, organizations that rated themselves
point is if I make an investment and do something like
The organizations we surveyed expect Generative AI to as having “high” or “very high” levels of Generative AI
this, what’s the tangible return and what are some easy
deliver a broad range of benefits, with the most common expertise reported much greater success at achieving their
returns? And then what are more complicated longer-term
objective—at least in the short term—being improved desired benefits. Their advantage was greatest in strategic
returns that take more investment money? If I can do some
efficiency and productivity (56%), which is consistent with and growth-related areas such as improving products and
of the easier ones and build on them, it can translate into
the results from last quarter’s survey. The percentage of services and encouraging innovation and growth.
‘I think this would be worth it to invest a lot more money.’ I
respondents who said their organizations’ Generative AI
believe a lot of entities in our sector are at that point.”
initiatives were already achieving expected benefits to a
Achieving benefits
Of those seeking the benefit, the percentage of respondents achieving the benefit to a large extent or more
Very high expertise 70% 63% 54% 55% 48% 48% 48% 40% 36% 42%
Overall 28% 35% 27% 18% 36% 30% 25% 29% 30% 22%
Improve existing Encourage Improve Reduce Increase speed / Uncover new Increase Enhance Detect fraud / Shift workers
products innovation efficiency and costs ease of dev new ideas and revenue relationships with manage risk from lower- to
and services and growth productivity systems / software insights clients / customers higher-level tasks
Figure 1 Q: What are your anticipated benefits and to what extent are you achieving those benefits to date?
(Jan./Feb. 2024); N (Total) = 1,982; N (very high) = 96
9
Now: Key findings
“Expert” organizations are scaling Generative AI According to our survey, organizations reporting “very high” expertise reported, on average, implementing
much more aggressively. high” levels of Generative AI expertise are deploying AI at scale in 1.4 functions, out of eight total functions,
much more rapidly and extensively than others. In fact, while those with “some” expertise are doing so in only
Generative AI expert organizations are likely having
73% said they are adopting the technology at a “fast” 0.3 functions. Further, 38% of those with “very high”
more success at capturing benefits because they are
or “very fast” pace (versus only 40% of organizations expertise reported implementing Generative AI at
scaling up much more aggressively, compared to the
with “some” level of expertise). They are also scaling scale in marketing, sales and customer service—
other categories, which provides a larger base for
Generative AI at higher rates across functions and using versus only 10% of organizations with “some” level of
generating benefits.
it more within functions. For example, those with “very expertise.
Companies that report expertise are moving quickly.
80%
73%
66% 64%
61% 62%
47%
48%
40%
39%
34%
33%
23%
19%
Adopting at a Providing more of their Adopting at higher levels Investing more in Investing more in Using code Using open-source
faster pace workforce access to across functions hardware cloud consumption generators more LLMs more
GenAI
Adopting Generative AI Implementing Generative Increasing hardware Increasing cloud Currently using Generative Currently using
“fast” or “very fast” >40% of workforce has AI for marketing, sales investment because of investment because of AI code generator open source large
access to Generative AI and customer service Generative AI strategy Generative AI strategy language models
tools / applications
Figure 2 (Jan./Feb. 2024 ) N (Total) = 1,982; N (Very high) = 96; N (Some) = 1,021 Very high expertise Some expertise
10
Now: Key findings
Insights from our executive interviews align closely with and has saved us significant amounts of money … and that are industry-specific and narrowly focused but more
survey findings, showing that leading organizations are we have scaled very broadly across many of our sites and strategically impactful (e.g., Generative AI tools for
aggressively scaling up their Generative AI efforts both continue to scale further across more equipment across semiconductor design that are used only by a small
horizontally (across multiple functions or domains) more sites.” subset of workers but have a very large impact on
and vertically (within a single function or domain). This the business).
Similarly, from a broad market perspective we are seeing
combination of horizontal and vertical scaling may help
an increasingly sharp distinction between horizontal use
achieve value creation more effectively.
cases that cut across industries (e.g., office productivity
As one chief transformation officer in manufacturing noted, suites and enterprise resource planning systems with
“[We have] an application that is being incredibly successful integrated Generative AI) and vertical use cases that
11
Now: Key findings Areas to reinvest time and cost savings
Organizations primarily plan to reinvest the strategic objectives such as innovation and growth,
savings from Generative AI into innovation and
and are likely already working more aggressively to
additional operations improvements.
develop strong Generative AI capabilities.
Among the overall respondent pool, organizations Driving 45%
By contrast, organizations in industries that are
innovation
said they primarily planned to reinvest cost
currently not being disrupted by Generative AI are opportunities
and timesavings from Generative AI into driving
more likely to focus on benefits such as individual
innovation (45%) and improving operations (43%), 43% Improving
worker productivity and operations improvement,
operations
addressing the value equation from both sides. It’s
areas with less of a sense of urgency and less Developing 29% across the
interesting to note that a significant percentage new products organization
tolerance for risk. Such organizations can still benefit
and services
of organizations (27%) also planned to reinvest in
greatly from Generative AI—just in a different way. 28% Expanding
our market
scaling Generative AI adoption, creating a cycle of
They also have a valuable opportunity to watch Scaling GenAI 27%
Generative AI reinvestment and growth. adoption across
and learn from the experiences of other industries
the organization
28% Improving
that are currently being disrupted—lessons that
Organizations with “very high” Generative AI cybersecurity
could serve them well if and when Generative AI infrastructure
expertise are even more focused than others on
Training and 23%
disruption reaches their own industry. upskilling
driving innovation (51%). They are also less inclined
employees 20% Enhancing risk
than others to reinvest savings from Generative
management
“To enable GenAI value in our business, we need to
systems
AI into improving operations and more inclined to Enhancing IT 19%
change our mindset and develop R&D capabilities
infrastructure
prioritize developing new products and services. 16% Exploring new
to realize a long-term vision enabled by GenAI,” said
business models
Creating a 13%
the CEO of a digital media company. “Right now, [our
The right reinvestment approach depends on an return for 9% Creating
mindset] is short-term and just about tangible cash shareholders new jobs
organization’s specific needs. Organizations currently
value for one-off use cases.”
facing strategic disruption or transformation from
Generative AI have a greater imperative to focus on Figure 3 Q: Where does your company plan to reinvest cost or timesavings
generated through implementation of GenAI capabilities (select top 3)?
(Jan./Feb. 2024 ) N (Total) = 1,982
12
Now: Key findings
2 Scaling up
A key to value creation, scaling increases Generative potential issues become real-world barriers. And with organization, incorporating more datasets, expanding the
AI’s impact on the business and expands its user Generative AI, many of those barriers are still being user base (internal and external) to improve upon existing
base—both of which have a strong multiplier effect on identified and understood. results, and refining the current solution for more value.
Generative AI’s benefits. Yet, many organizations find it This phased approach gives us a sense of assurance the
“There are always issues that emerge through the
challenging to make the leap from pilots and proofs of investment is worthwhile before we commit significantly
adoption and scaling transition that aren’t expected—
concept to large-scale deployment. more resources.”
the question we have to consider is how hard are they to
Scaling is complex and requires effort across a variety of overcome,” said a chief technology officer we interviewed. Off-the-shelf Generative AI solutions for common use
interrelated elements spanning strategy, process, people, “For example, [one of our] use cases had some technical, cases such as office productivity are arguably the easiest
data and technology. Although the challenges associated policy and cybersecurity issues, but they were relatively to deploy at scale, but they still require substantial
with scaling Generative AI are common to many digital easy to overcome, so we scaled. Conversely, for [two investment, effort and training. For unique and/or more
transformation initiatives, issues such as risk management other] use cases more issues emerged linked to the skill strategic Generative AI solutions and use cases, the
and governance, workforce transformation, trust and data level to work with the outputs of the AI solution. These complexity and challenges increase by leaps and bounds,
management take on even greater importance. What have been harder to address, so scaling has been slower.” along with the potential for greater returns.
worked well in the past might not work the same way with
A public sector chief information officer outlined another
this new technology.
approach: “[For us, successful scaling is] building on
The scaling phase is when potential benefits are previous successes and then taking those initiatives
converted into real-world value. It is also, however, when to another level. Expanding to other areas of the
13
Now: Key findings
Workforce access to approved GenAI tools and applications remains low. Our executive interviews pointed to a number of reasons for this overall low penetration
rate, mostly revolving around risk versus reward—especially data-related risks. Do the
Nearly half of our respondents (46%) reported they provided approved Generative AI
potential rewards of Generative AI justify the risks, and can the risks be mitigated? In
access to just a small portion of their workforces (20% or less). Organizations reporting
particular, we found widespread concern that allowing workers to use public large language
“very high” levels of Generative AI expertise are further along, with nearly half (48%)
models (LLMs) and Generative AI tools might lead to problems with protection of intellectual
providing approved Generative AI access to at least 40% of their workforces. Even for these
property and customer privacy.
“expert” organizations, worker access to approved tools remains the exception, not the rule.
Percentage of workforce with access to Generative AI
76%
Overall
49%
46%
36%
Little expertise
31% 29% 28%
27%
25%
23% 24%
16% 16%
Some expertise 14% 16%
8%
5% 3% 3% 4% 6% 6% 7%
1% 2%
High expertise
Up to 20% 20%–40% 40%–60% 60%–80% More than 80%
Very high expertise
Percentage of the workforce
Figure 4 Q: How much of your overall workforce, do you estimate, have access to your organization’s sanctioned (approved) Generative AI tools/applications?
(Jan./Feb. 2024) N (Total) = 1,982, N (Very high) = 96, N (High) = 606, N (Some) = 1,021, N (Little) = 257
14
Now: Key findings
Other concerns that came up in our executive sensitive data and intellectual property into public LLMs
interviews include: in an entirely uncontrolled way. This status is likely to
continue in the absence of practical policies for allowing
• Generative AI outputs that can be unpredictable and
and managing widespread Generative AI access.
subject to inaccuracies (i.e., “hallucinations”)—which
undermine trust, particularly when combined with Organizations should be actively developing sustainable
lack of transparency and explainability processes and policies for enabling ubiquitous but
responsible Generative AI use and managing the
• Potential loss of control over what Generative AI
associated risks at scale. Widespread but controlled
apps are being used within the organization and
access to Generative AI will help people get more
who is using them
comfortable with the technology and enable them to
understand what it can and cannot do—giving them a
• Worker resistance to using Generative AI due to lack
more realistic and informed perspective while opening
of familiarity or concerns about being replaced
the door to new opportunities for Generative AI value
Given the potential challenges and risks, a cautious
creation across the enterprise.
approach to allowing workers to use Generative AI tools
arguably makes sense. However, tight restrictions on
Generative AI are best viewed as a temporary stopgap
measure—not a viable long-term solution. Logically,
any worker with internet access will have access to
public Generative AI tools and could be using them
without their employer’s consent—potentially leaking
1155
“It has been surprising to see how
low the bar is to do something
quick and dirty—this is both
exciting and scary, but the big
challenge is to scale—this is a
whole new ball game … but scaling
is hard without centralization.”
-Director of data science and AI in the technology industry
1166
Now: Key findings Areas of strength
3 Building trust Growing trust
said their organization’s trust in
Lack of trust continues to be one of the biggest barriers According to a chief technology officer we interviewed,
72%
all forms of AI has increased since
to large-scale adoption and deployment of Generative AI. “The explainability piece is really holding us back right now
Generative AI emerged in late 2022
In this context, two key aspects of trust are: (1) trust in the … once we get a better handle on that, I think we will really
quality and reliability of Generative AI’s output (supported be able to accelerate our adoption.”
Balancing speed and risk
by improved transparency and explainability), and (2) trust
Ultimately, most organizations will likely each end up using
reported their organization is effectively
from workers that Generative AI will make their jobs easier
LLMs customized and fine-tuned for their specific balancing integrating Generative AI
and won’t replace them. 60%
rapidly while implementing processes
domain, industry and use case, rather than just scaling
that mitigate potential risks
Regarding worker trust, one executive we interviewed up a general-purpose LLM. This specificity will help
noted that “once people start seeing efficiencies and Generative AI produce outputs that are more precise,
the benefits the tools have to their work, that will drive transparent and explainable.
Opportunities for improvement
adoption and sustained success.” In other words, greater
Lack of trust and related risks have thus far not
exposure to Generative AI tools will help people become
Lacking confidence
prevented organizations from rapidly adopting
more comfortable with the technology and understand
Generative AI for experiments and proofs of
how it can help them do their jobs.
selected “lack of confidence in results” as
concept; however, this will likely change as
33% one of their top risks related to Generative
As for trusting Generative AI’s outputs, the technology’s organizations transition to large-scale deployment.
AI tools / applications (#3 of 10 overall)
fallibility in the form of “hallucinations” is well known and
According to our wave two survey, 60% of respondents
is actively being addressed through improved training
Measuring trust
believed their organization is effectively balancing
and guardrails. For many organizations, transparency and
rapid integration of Generative AI while implementing of organizations said they are measuring
explainability are even bigger issues. In its current form,
processes that mitigate potential risks. Also, 72% said their worker trust and engagement as part
36%
Generative AI still operates largely as a black box—taking
of altering their talent strategies because
organization’s trust in Generative AI has increased since the
an input and producing an output with no real way for
of the adoption of Generative AI
technology emerged in late 2022.
humans to understand how that output was reached.
Figure 5 (Jan./Feb. 2024 ) N (Total) = 1,982
17
Now: Key findings
Our executive interviews suggest, however, that addressing trust issues is likely to “Expert” organizations recognize the importance of building trust in
become critically important as organizations transition from experimentation to Generative AI and are putting effort into it.
large-scale deployment—especially for organizations where the imperative to deploy
Despite the importance of trust for successful Generative AI deployment and scaling, 40%–
Generative AI is more tactical than strategic, and thus less time sensitive.
45% of our overall respondents said they are, to a “large” or “very large” extent, implementing
Generative AI when deployed at scale becomes far more important to the business processes to improve trust in their Generative AI initiatives through various aspects (such as
and affects a much larger pool of human users, making trust a much bigger issue. Trust data quality, output reliability and organizational empathy). However, among organizations that
related to data quality, LLM training and output reliability becomes particularly important. reported “very high” Generative AI expertise, the focus on trust is much higher across every
aspect (59%–73%). This likely reflects both their greater appreciation for the importance of trust
“If you don’t have the right dataset or data quality, it is very hard for the application to be
and their greater reliance on Generative AI as an integral and crucial part of the business.
helpful,” said a chief technology officer we interviewed. “GenAI solutions are very sensitive
to good quality and well-structured data. If the data is not correct, it is very hard to know
that the output is wrong.”
Companies implementing processes to generate trust in GenAI
In our survey, 33% of respondents cited lack of confidence in results as one of Generative
To a “large” or “very large” extent
AI’s top risks (third in the list of top 10 risks). Only 36% of the organizations surveyed were
measuring worker trust and engagement as part of adapting their talent strategies to Overall Very high expertise
Generative AI.
45% 60%
Transparency with employees
Demonstration of consideration,
40% 59%
empathy and kindness in use of GenAI
43% 73%
Quality Generative AI input data
41% 67%
Reliable Generative AI output
Figure 6 (Jan./Feb. 2024 ) N (Total) = 1,982; N (Very high) = 96
18
Now: Key findings
4 Evolving the workforce
Workforce challenges affect Generative AI scaling on both the front and back ends. On the Most organizations expect Generative AI to affect their talent strategies.
front end, organizations need valuable and scarce talent with expertise in Generative AI
Three-quarters of survey respondents (75%) expect to change their talent strategies
(and data management) to develop and refine their solutions. They also need the overall
within two years in response to Generative AI. Organizations reporting “very high”
workforce to be comfortable enough with the technology to be willing to use it for improving
Generative AI expertise expect to change their talent strategies even faster, with
efficiency and effectiveness. On the back end, organizations need to understand how the
32% already making changes. This is consistent with our broader finding that such
workforce could be affected by large-scale Generative AI deployment and then develop
organizations are scaling up their initiatives much more aggressively than are others,
talent strategies, programs and policies that make sense for the business and workers alike.
leading to greater and more immediate talent impacts.
Addressing these critical and complex workforce issues is an urgent enabler for Generative
AI adoption and scaling, even as organizations work to figure out the technology side
of the problem.
Timeline for change in talent strategies
18% 26% 31% 16% 10%
Now Within 1 year 1-2 years 2+ years Don’t
know /
no formal time
frame
Figure 7 Q: When do you expect to make changes in talent strategies because of generative AI?
(Jan./Feb. 2024 ) N (Total) = 1,982
19
Now: Key findings
The most common talent strategy responses are with “very high” expertise were much more focused on resistance"
209,deloitte,us-state-of-gen-ai-report.pdf,"Now decides next:
Insights from the leading
edge of generative AI adoption
Deloitte’s State of Generative AI in the Enterprise
Quarter one report
January 2024
Table of contents
Foreword
Introduction
Now: Key findings
1 Excitement about generative AI remains 4 Current generative AI efforts remain more
high, and transformative impacts are focused on efficiency, productivity and cost
expected in the next three years. reduction than on innovation and growth.
2 M any leaders are confident about their 5 Most organizations are still primarily relying
organization’s generative AI expertise. on off-the-shelf generative AI solutions.
3 Organizations that report very high 6 Talent, governance and risk are critical areas
expertise in generative AI tend to feel more where generative AI preparedness is lacking.
positive about it—but also more pressured
7 Leaders see significant societal impacts on
and threatened.
the horizon.
8 Leaders are looking for more regulation and
collaboration globally.
Next: Looking ahead
Authorship & Acknowledgments
About the Deloitte AI Institute
About the Deloitte Center for Integrated Research
About the Deloitte Center for Technology, Media & Telecommunications
Methodology
22
Foreword
Now decides next
The arrival of generative AI heralds disruption and From these wave one insights, we can gain a clearer
opportunity across industries. Organizations are picture of how leaders are using generative AI,
exploring how generative AI can be used to unlock challenges, and lessons learned thus far. This helps
business value, supercharge efficiency and productivity, reveal some of the essential questions leaders should
and open the door to entirely new products, services be asking now and actions they should be taking to
and business models. As business leaders contend prepare their enterprise for what comes next.
with this new technology and make decisions about the
There is still much to discover with generative AI.
future of the enterprise with generative AI, it is helpful
As it matures and is deployed at scale for a litany of
to keep one’s finger on the pulse of adoption.
applications, new questions and challenges will become
To that end, The State of Generative AI in the Enterprise: clearer. Our quarterly reports will be available to help
Now decides next, captures the sentiments of 2,835 you make sense of this fast-moving space, consider
business and technology leaders involved in piloting or practical guidance based on what we have learned,
implementing generative AI in their organizations. In this and take a forward-looking view in your business
inaugural release of the quarterly report series, leaders future with generative AI.
indicated persistent excitement for using generative
Learn more about the series and sign up for updates at
AI and many expect substantial transformative
deloitte.com/us/state-of-generative-ai.
impacts in the short term. Yet, they also acknowledged
uncertainty about generative AI’s potential implications
Deborshi Dutt, Beena Ammanath, Costi Perricos and
on workforces and society as the technology is
Brenna Sniderman
widely scaled, calling for greater investment in talent,
governance and global collaboration.
3
Introduction
Now decides next: Insights from the leading edge of generative AI adoption
Will generative AI (gen AI) be the greatest, most impactful technology innovation in Generative AI seems to be following the same pattern, only much, much faster. ChatGPT
history? Will it completely transform how humans live and work? Or will it turn out to was publicly released on November 30, 2022, largely as a technology demonstration.
be just another technology du jour that promised revolutionary change but ultimately Two months later, it had already attracted an estimated 100 million active users—
delivered only incremental improvement? Right now, we can’t be certain. making it the fastest-growing consumer application in history.1
What we do know is that many breakthrough technologies of the past have followed Since then, generative AI has continued to advance by leaps and bounds and many new
a common adoption pattern: initial awareness; excitement that led to hype; mild tools and use cases have emerged—providing a powerful glimpse at the technology’s
disappointment as hype met reality; and then explosive growth once the technology vast potential to transform how people live and work.
reached critical mass and proved its worth.
4
Introduction
Insights from the leading edge (cont.)
About The State of
Generative AI in
During this frenzied period of generative AI advancement To help make smart decisions, leaders need objective, timely
and adoption, leaders in business, technology and information about current generative AI developments— the Enterprise
the public sector are under tremendous pressure to and where things are headed. Which is why Deloitte is
To help leaders in business, technology and the
understand generative AI—and to figure out how to harness conducting this ongoing quarterly survey. Our goal is to take public sector track the rapid pace of generative
AI change and adoption, Deloitte is conducting a
its capabilities most effectively (or at least avoid being the pulse of generative AI adoption, offer a view of what’s
series of quarterly surveys. The series is based
disrupted). They also sense that now decides next; that their happening, track evolving attitudes and activities, and deliver on Deloitte’s State of AI in the Enterprise reports,
which have been released annually five years
decisions and actions today will significantly affect how practical, actionable insights that can help leaders like you
running. The wave one survey was fielded to more
generative AI unfolds in the future, for better or worse. make informed and confident decisions about AI, strategy,
than 2,800 director- to C-suite-level respondents
across six industries and 16 countries between
investment and deployment.
It’s been said that people tend to overestimate the effect of October and December 2023. Industries included:
Consumer; Energy, Resources & Industrials;
a technology in the short run and underestimate its effect in In this report, we examine our first quarterly survey findings
Financial Services; Life Sciences & Health Care;
the long run. This phenomenon has occurred many times in in detail, supported by insights from Deloitte’s AI-related Technology, Media & Telecom; and Government
& Public Services. Learn more at deloitte.com/us/
the past and could very well happen again with generative AI. work with organizations across every major industry and
state-of-generative-ai.
Note here that given generative AI’s dizzying pace of change, many geographic regions. We also offer a forward-looking
the gap between the short run and long run might be view to help you decide what generative AI actions may make
measured in days, weeks or months—not years or decades. sense for your own organization and situation.
All statistics noted in this report and its graphics are derived from Deloitte’s
first quarterly survey, conducted October – December 2023; The State of
Generative AI in the Enterprise: Now decides next, a report series. N (Total
leader survey responses) = 2,835
Generative AI is an area of artificial intelligence and refers to AI that in
response to a query can create text, images, video and other assets.
Generative AI systems can interact with humans and are often built using
large language models (LLMs). Also referred to as “gen AI.”
5
Now: Key findings
This first pulse of our generative AI quarterly surveys was
completed in December 2023, and included more than
2,800 AI-savvy business and technology leaders directly
involved in piloting or implementing gen AI at major
organizations around the world. Here’s what they had to
say about sentiment, use cases, challenges and more.
66
Generative AI elicits a range
Now: Key findings
of strong emotions
1 Excitement about generative AI remains high, and
transformative impacts are expected in the next three years.
62% Excitement
Nearly two-thirds (62%) of the business and technology leaders surveyed reported excitement as a top
Fascination 46%
sentiment with regard to generative AI; however, that excitement was tinged with uncertainty (30%) (figure 1).
The vast majority of respondents (79%) said they expect generative AI to drive substantial transformation
30% Uncertainty
within their organization and industry over the next three years—with nearly a third expecting substantial
transformation to occur now (14%) or in less than one year (17%) (figure 2). Trust 17%
The survey results suggest that many AI-fueled organizations are on the verge of scaling up their efforts
16% Surprise
and embracing generative AI in a more substantial way. This aligns with what we’re seeing in the marketplace,
where organizations around the world are racing to move from experimentation and proofs-of-concept
Anxiety 10%
to larger-scale deployments across a variety of use cases and data types—pursuing both speed and
value capture while managing potential downside risks and societal impacts. 8% Confusion
In future surveys, we will be closely monitoring progress in this area—particularly with regard to
Fear 6%
organizations’ expertise, capabilities, tangible outcomes, and responses to rapidly emerging advances in
generative AI technology. 4% Exhaustion
Anger
1%
31%
of the leaders we surveyed expect substantial transformation
Figure 1
in less than one year; 48% expect it in one to three years.
Q: Thinking about generative AI, what emotions do you feel most about the technology?
(Oct./Dec. 2023) N (Total) = 2,835
77
Now: Key findings
When is generative AI likely
to transform your organization?
1%
14%
Never
Now
20%
17%
Beyond
three years
Less than
one year
48%
In one to
three years
Figure 2
Q: When is generative AI likely to substantially transform your organization and your industry, if at all?
(Oct./Dec. 2023) N (Total) = 2,835
8
44% rate their organization’s generative AI expertise as
Now: Key findings high or very high, but is such expertise even possible
given the pace of the technology’s advancement?
2 Many leaders are confident about their organization’s
generative AI expertise.
Self-assessed expertise with
A large percentage of our survey respondents (44%) said they believe their organizations currently have generative AI runs high
high (35%) or very high (9%) levels of expertise with generative AI. This result is somewhat surprising given
how rapidly generative AI is evolving (figure 3).
1%
But within the specific context of our survey, high levels of confidence seem entirely reasonable since
No expertise
9%
we deliberately chose experienced leaders with direct involvement in AI initiatives at large organizations 10%
Very high
already piloting or implementing generative AI solutions. However, given how rapidly the field is unfolding, it Little
expertise
expertise
may be worth questioning the extent to which any leader should feel highly confident in their organization’s
expertise and preparedness. In fact, even today’s foremost AI experts who are personally developing
generative AI technologies at times seem genuinely surprised by their own creations’ capabilities.2
35%
High
Do some leaders consider their organizations to have high expertise based largely on the knowledge
expertise
and experience gained from small-scale pilots with a small number of generative AI tools? If so, leaders
45%
and organizations might actually become less confident over time as they gain experience with the larger
Some
challenges of deploying generative AI at scale. In other words, the more they know, the more they might
expertise
realize how much they don’t know. This is a trend we’ve seen time and again with other technological
advancements, and one we’ll be watching closely in our future surveys.
Figure 3
Q: How would you assess your organization’s current level of overall expertise
regarding generative AI?
(Oct./Dec. 2023) N (Total) = 2,835
9
Expertise with generative AI drives
attitudes toward adoption
Now: Key findings
3 Organizations that report very high expertise in generative
Very high Some
expertise expertise
AI tend to feel more positive about it—but also more
Trust prevails Rank trust
39% 9%
among top
over uncertainty
pressured and threatened.
emotions felt
11% 38% Rank uncertainty
among top
Relative to other respondents, leaders who rated their organization’s overall generative AI expertise as “very
emotions felt
high” tended to feel much more positive about the technology; however, they also feel more pressure to
adopt it—and see it as more of a threat to their business and operating models (figure 4).
Analysis showed this group using more modalities, deploying generative AI across more enterprise functions, Broad interest 78% 38% Say employees
show high interest
sparks
and pursuing more use cases. As you can see in the figure 4, leaders who reported very high levels of
in gen AI
transformation
expertise were also more likely to report higher levels of trust and lower levels of uncertainty. They also
31% 9% Say gen AI
tended to show broader interest in generative AI and expected faster transformation for their organizations.
is already
transformative
At the same time, these respondents’ greater understanding of generative AI appears to be shaping their
perspective on potential impacts—positive and negative. Many reported they viewed widespread adoption of
the technology as a threat to how their organizations operate and conduct business, amplifying the pressure
Widespread 33% 16% Feel widespread
and urgency they felt to adopt generative AI and scale it.
adoption is a
adoption
threat to business
generates
pressure
44% 25% Feel greater
Leaders of organizations with very high expertise are more likely to pressure to
adopt gen AI
view generative AI as a threat to their business and operating models.
Figure 4
(Oct./Dec. 2023) N (Total) = 2,835,
N (Very high) = 267; N (Some) = 1,273
10
Key benefits organizations hope
to achieve with generative AI
Now: Key findings
4 Current generative AI efforts remain more focused
Improve 56%
on efficiency, productivity and cost reduction than on
efficiency and
productivity
innovation and growth. 35% Reduce costs
Improve existing 29%
The majority of organizations surveyed are currently targeting tactical benefits such as improving
products and
services
efficiency / productivity (56%) and/or reducing costs (35%). Also, 91% said they expect generative AI to
29% Encourage
innovation
improve their organization’s productivity, and 27% expect productivity to increase significantly. A smaller
and growth
percentage of organizations reported targeting strategic benefits such as innovation and growth (29%)
Shift workers from 26%
(figure 5). lower to higher
value tasks
26% Increase speed
This is consistent with past technology adoption patterns. Initially, most organizations logically focus on
and/or ease of
developing new
incrementally improving their existing processes and capabilities—capturing value from low-hanging fruit
systems / software
Increase 25%
while building knowledge, experience and confidence with the new technology. Later, they expand or shift
revenue
their focus to improvements that are more innovative, strategic and transformational—using the new
technology to drive growth and competitive differentiation and advantage through capabilities that simply
23% Enhance
relationships
weren’t possible before.
with clients /
customers
Surveyed leaders that cited higher levels of AI expertise show earlier signs of moving up this curve. They Uncover new 19%
ideas and
are more focused on uncovering new ideas and insights (23% vs. 19% for the overall respondent pool),
insights
with less emphasis on efficiency and productivity (44% vs. 61% for the overall respondent pool) and cost
18% Detect fraud and
manage risk
reduction (26% vs. 38% for the overall respondent pool)—although those tactical benefits continue to be Figure 5
Q: What are the key benefits you hope to achieve through your generative AI efforts?
(Oct./Dec. 2023) N (Total) = 2,835
11
Now: Key findings
their bigger focus. In addition, nearly three-quarters of organizations that cited very high generative
AI expertise had already begun integrating the technology into their product development and R&D
activities, which are key drivers of innovation and growth.
As more organizations gain expertise and experience with generative AI, will they reinvest their dividends
from improving efficiency and productivity toward pursuing more strategic benefits such as innovation
and growth? Or will they use those dividends in other ways? This is another area we’ll be monitoring
closely in future pulse surveys.
Certainly, productivity and efficiency can be transformational, especially given the massive scale
generative AI has the potential to enable. However, the greatest value and strategic differentiation will
likely come from using the technology to innovate. First, by helping to generate new products, services
and capabilities that wouldn’t be possible otherwise. And, second, by enabling new business models and
ways of working across an enterprise.
In addition, organizations that cited very high generative AI expertise were already taking a much more
comprehensive approach than average, with significantly higher adoption levels across a broad range
of functional areas. In specific areas such as HR, and legal, risk and compliance, those organizations’
generative AI adoption rates were nearly three times higher than for the total respondent pool (figure 6).
91%
of all organizations expect their productivity to increase due
to generative AI.
12
Now: Key findings % of those who are using generative AI
Total Little expertise Some expertise High expertise Very high expertise
in a limited or at-scale implementation
Level of generative AI
adoption
IT / cybersecurity 22% 38% 57% 71%
46%
Marketing, sales and customer service 41% 16% 34% 50% 73%
57%
Product development / R&D 41% 14% 28% 73%
Strategy and operations 35% 10% 26% 47% 62%
37%
Supply chain / manufacturing 29% 9% 21% 61%
Finance 37% 63%
25% 5% 14%
Figure 6
Human resources 23% 6% 13% 29% 64%
Q: What is your organization’s
current adoption level of generative AI across
the following functions?
28%
(Oct./Dec. 2023) N (Total) = 2,835; Legal, risk and compliance 21% 7% 10% 60%
N (Very high) = 267; N (High) = 1,003;
N (Some) = 1,273; N (Little) = 274
1133
Generative AI: Have we seen this movie before?
The term “unprecedented” is often thrown around Generative AI’s speed factor may give organizations less help the workforce get accustomed to using generative
when talking about business and technology, to the time to ruminate or dabble with small-scale pilots— AI, and will show people how it can help make their
point of being cliché. However, in describing the pace of while reducing the margin for error—and increasing the jobs easier. Also, early wins will likely help produce cost
generative AI’s emergence and advancement—and its consequences of inaction. It also creates opportunities savings and momentum that then can be channeled into
massive potential impact on business (and humanity as a to generate extraordinary business value very quickly. higher value opportunities that are more strategic and
whole)—unprecedented could be an understatement. differentiated in nature, such as enabling new products,
Despite generative AI ’s greatly accelerated pace,
services, business models and ways of working that
Generative AI is already widely available to the public understanding typical adoption patterns based on
simply weren’t possible before generative AI.
and has a running start toward critical mass. Also, similar previous breakthrough technologies can provide
to smartphones, it’s easy for an average person to use valuable lessons that leaders can use to help them
without much training—and can help with activities they understand and fully capitalize on the technology’s rapid
already engage in every day—so the barriers to adoption advancement.
are low. What’s more, generative AI has the strong
As in the past, organizations’ initial efforts will likely
potential to assist with its own future development,
center around efficiency, productivity, cost savings and
which could trigger a cycle of exponential improvement
other incremental improvements. This is expected to
at exponential speed.
14
Now: Key findings
5 Most organizations are primarily relying on off-the-shelf
generative AI solutions.
Where off-the-shelf generative AI
In line with their current emphasis on tactical benefits from generative AI, the vast majority of respondents is used most
were currently relying on off-the-shelf solutions. These included productivity applications with integrated
generative AI (71%); enterprise platforms with integrated generative AI (61%); standard generative AI
71%
applications (68%); and publicly available large language models (LLMs) (56%), such as ChatGPT.
Productivity applications
Relatively few reported using more narrowly focused and differentiated generative AI solutions, such as
industry-specific software applications (23%), private LLMs (32%), and/or open-source LLMs (customized to
68%
Standard applications
their business) (25%).
Reliance on standard, off-the-shelf solutions is consistent with the current early phase of generative AI
61%
adoption, which is primarily focused on improving the efficiency and productivity of existing activities.
Enterprise platforms
However, as use cases for generative AI become more specialized, differentiated and strategic, the
associated development approaches and technology infrastructure will likely follow suit.
56%
Public LLMs
When will we see complex, high-value use cases that are truly differentiated and tailored to the specialized
needs of specific companies, functions and industries? How will organizations combine internal and
external resources to create customized generative AI tools that enable such strategic differentiation? In
particular, will we see off-the-shelf technology offerings be supplemented by private or hybrid public/private
development approaches and technology infrastructures capable of delivering and supporting those
differentiated solutions?
15
Now: Key findings
6 Talent, governance and risk are critical areas where
generative AI preparedness is lacking.
In this initial quarterly survey, 41% of leaders reported their organizations were only slightly or not at
all prepared to address talent concerns related to generative AI adoption, while 22% considered their
organizations highly or very highly prepared. Similarly, 41% of leaders reported their organizations were only
slightly or not at all prepared to address governance and risk concerns related to generative AI adoption,
while 25% considered their organizations highly or very highly prepared (figure 7).
Larger percentages of leaders reported high to very high levels of preparedness in technology infrastructure
(40%) and strategy (34%); however, the survey results show there is still significant room for improvement.
Generative AI barriers related to risk and governance
When it comes to risk and governance, generative AI is definitely not “just another technology.” The
fundamental challenge is how to capitalize on artificial intelligence’s power without losing control of it. After
all, the capability people seem to find most enthralling about generative AI is its ability to so convincingly
simulate human thinking and behavior. Of course, human thinking and behavior aren’t always perfect,
predictable or socially acceptable—and the same is true for the technology, itself.
16
Now: Key findings
Respondents claimed the highest levels of preparation in technology
Preparedness for generative AI
and strategy, while feeling far less prepared in risk and talent.
Technology infrastructure 4% 17% 38% 30% 10%
Strategy 5% 20% 41% 26% 8%
Not prepared
Slightly prepared
Risk & governance 13% 28% 34% 18% 7% Moderately prepared
Highly prepared
Talent 13% 28% 37% 17% 5% Very highly prepared
Figure 7
Q: Consider the following areas. For each, rate your organization’s level of preparedness with respect to broadly adopting generative AI tools / applications?
(Oct./Dec. 2023) N (Total) = 2,835
17
Managing generative AI
implementation risk
Now: Key findings
Monitoring regulatory
Specific generative AI risks and concerns include inaccurate results and information (i.e., “hallucinations”);
47%
requirements and
Establishing a governance
legal risks such as plagiarism, copyright infringement, and liability for errors; privacy and data ownership ensuring compliance
framework for the use
challenges; lack of transparency, explainability and accountability; and systemic bias. The latter of generative AI tools / 46%
applications
exemplifies another category of risk in which AI amplifies and exacerbates a problem that already exists,
such as propagating and systematizing existing social biases, facilitating and accelerating the spread of
Conducting internal
42%
audits and testing
misinformation, helping criminals commit crimes, or fanning the flames of political divisiveness.
on generative AI
tools / applications
Training practitioners
According to the business and technology leaders we surveyed during fourth quarter 2023, the biggest
37% how to recognize and
mitigate potential risks
concerns related to governance were: lack of confidence in results (36%), intellectual property issues
(35%), misuse of client or customer data (34%), ability to comply with regulations (33%), and lack of
36% Ensuring a human
explainability / transparency (31%). validates all generative
AI content
Some of the surveyed organizations were already actively managing generative AI implementation
34%
Using a formal group
risks through actions such as monitoring regulatory requirements and ensuring compliance (47%), or board to advise
on generative
establishing a governance framework for generative AI (46%), and conducting internal audits and testing
AI-related risks
32%
Keeping a formal inventory
on generative AI tools and applications (42%) (figure 8). However, such organizations are in the minority of all generative AI
implementations
and their actions barely scratch the surface of the challenge. This is especially true given that regulatory
26%
requirements typically lag behind the pace of technology innovation—although a US presidential Using outside vendors
to conduct independent
executive order and the European Union’s ambitious Artificial Intelligence Act are clear signs government
audits and testing
21%
Single executive
leaders in many parts of the world are taking the issue of AI risk very seriously.
responsible for managing
generative AI-related risks
Figure 8
Q: What is your organization currently doing to actively manage the risks around
your generative AI implementations?
(Oct./Dec. 2023) N (Total) = 2,835
18
Generative AI is impacting talent
strategies now
2%
Never
10% 17%
No formal Now
time frame
24%
16% Within 1 year
Now: Key findings 2+ years
Generative AI barriers related to talent and workforce
Generative AI has the potential to supplement human workers across a vast array of activities traditionally
thought of as uniquely human. As such, its impact on talent and workforce strategies could be immense.
How will it affect organizations and their workers in the short and long runs? Which types of skills will be
most affected, and when?
31%
The vast majority of leaders we surveyed (72%) said they expect generative AI to drive changes in their
1-2 years
talent strategies sometime within the next two years: now (17%), within 1 year (24%), or in 1-2 years (31%)
(figure 9).
Figure 9
However, less than half (47%) reported that they are sufficiently educating their employees on the
Q: When do you expect to make changes to your talent strategies because of
capabilities, benefits and value of generative AI—survey respondents also cited a lack of technical talent and
generative AI?
skills as the biggest barriers to adoption.
(Oct./Dec. 2023) N (Total) = 2,835
19
Now: Key findings
Against this backdrop, some respondents reported making a high or very high effort to: It should be noted, however, that these reported workforce-related efforts might be limited
recruit and hire technical talent to drive their generative AI initiatives (42%), educate the in scope. Deloitte’s experience suggests that most organizations have yet to substantially
workforce about generative AI (40%), and reskill workers impacted by generative AI (36%). address the talent and workforce challenges likely to arise from large-scale generative AI
Those numbers are much higher for leaders who viewed their organization’s generative AI adoption. A likely reason for this is that many leaders don’t yet know what generative AI’s
expertise as very high (74%, 74% and 73%, respectively) (figure 10). talent impacts will be, particularly with regard to which skills and roles will be needed most.
Preparing workforces for generative AI: Respondents making a high or very high effort in the following areas.
74% 74%
73%
All respondents
55% 55%
42% 50%
40%
Little expertise
36%
30%
27%
24%
Some expertise
16%
14%
10%
High expertise
Recruiting and hiring technical talent to drive Educating our broader workforce to raise Reskilling workers because of the impact
Very high expertise
our generative AI initiatives overall generative AI fluency of generative AI to their roles
Q: What level of effort is your organization taking regarding the following workforce-related areas?
Figure 10
(Oct./Dec. 2023) N (Total) = 2,835
20
“Generating confidence
in workers’ abilities to
collaborate with generative AI,
now, could elevate creativity
and job satisfaction, next.”
21
Now: Key findings
51%
expect generative AI to
7 Leaders see significant societal impacts on the horizon.
increase economic inequality.
Although the leaders we surveyed were generally excited and enthusiastic about generative AI’s potential
business benefits, they were less optimistic about its broader societal impacts. Specifically, 52% of
respondents said they expected widespread use of generative AI to centralize power in the global economy,
while 30% expected it to more evenly distribute global power. Similarly, 51% expected generative AI to
increase economic inequality, while 22% expected it to reduce inequality (figure 11).
What’s more, 49% of respondents believe the rise of generative AI tools / applications will erode the overall
level of trust in national and global institutions.
Is this pessimism or realism? Our survey results appear to reflect the broader moral and ethical debates
about artificial intelligence that are occurring in every corner of society—even in the boardrooms of the
technology companies driving AI development, where AI’s commercial value is being weighed against its
potential value to serve humanity and AI’s potential benefits are being weighed against its potential risks.
The challenges that generative AI poses in corporate governance and risk parallel those in societal
governance and risk. In both domains, the technology’s potential benefits and potential harms are high.
National and supranational organizations and governments will likely need to walk the tightrope of helping
to ensure that generative AI benefits are broadly and fairly distributed, without overly hindering innovation
or providing an unfair advantage to countries with different rules.
22
Now: Key findings
Expected societal impacts of generative AI
Distribution of economic power
5% 25% 18% 42% 10%
30% 52%
distribute centralize
Levels of economic inequality
3% 19% 27% 41% 140%%
22% 51%
decrease inequality increase inequality
Q: How will widespread use of generative AI shift the overall distribution of power in the global economy?
Figure 11
Q: How will widespread use of generative AI tools / applications impact global levels of economic inequality?
(Oct./Dec. 2023) N (Total) = 2,835
23
Support for increased regulation
and global collaboration
Now: Key findings
8 Leaders are looking for more regulation and
78%
more regulation
collaboration globally.
Agree the widespread
proliferation of generative
In a break from traditional business norms, the unique risks associated with generative AI are prompting
AI tools / applications will
many business leaders to call for increased government regulation and increased global collaboration require more regulation
of AI by governments
around AI technologies.
Among the leaders in our survey, 78% said that more governmental regulation of AI is needed, while 72%
said there is currently not enough global collaboration to ensure the responsible development of
AI-powered systems (figure 12).
These results seem to r"
210,deloitte,us-the-new-data-duo-for-ai-powered-growth.pdf,"Marketing and IT:
The new data duo for
AI-powered growth
How marketers can bridge the data
Subtitle, date or author second line
divide to leverage the full power of AI
Marketing and IT: The new data duo for AI-powered growth
WHAT’S INSIDE
Increasing consumer complexity 1
Data + AI + marketing 3
Benefits of a unified data ecosystem combined with AI 7
How to create a unified data ecosystem and enable 9
AI-powered marketing
Technologies to create a unified data ecosystem 12
Resources to guide your next steps 16
Marketing and IT: TheC naenw S VdOatDa sduurov ifvoer t AhIe- pfuotwuerere odf gmroedwitah?
INCREASING CONSUMER COMPLEXITY
Consumer behaviors are constantly evolving, and their So perhaps it’s not surprising that creating and
high expectations for speed, convenience, and tailored delivering personalized messages to customers is
experiences make it complex for brands to effectively something that marketers have worked on for many
understand and meet their needs. To make a substantial years. Over time, advancements in technology have
connection with their target audience and satisfy those made the collection, processing, and activation of data,
expectations, organizations must not only be flexible especially first-party data, more influential in supporting
and agile, but stay up to date with changing consumer this strategy. As marketers explored more advanced
values and preferences. solutions, such as machine learning (ML) and artificial
intelligence (AI), to power hyper-personalization, they
From a marketing perspective though, you could enabled more automated processes, which helped their
argue that this thinking is nothing new. For as long as organizations increase efficiency and cut costs. Although
marketers have honed their craft, they’ve understood marketers have relied on AI for some time now (maybe
that the organizations that know their customers and without even realizing it), the generative AI revolution is
most effectively provide a personalized experience creating lots of excitement, numerous questions, and
are the ones that can drive engagement, acquisition, some trepidation about what this technology means
and lifelong loyalty. In fact, research shows that a well- for marketing. For many organizations, it can still be
executed, hyper-personalized marketing strategy can very difficult to power a customer-centric and hyper-
deliver eight times the return on investment (ROI) and lift personalized marketing strategy that effectively links
sales by 10% or more (figure 1).1 back to and connects with its customers.
Figure 1. Research shows ROI and lift sales
Source: Deloitte, Connecting with meaning, accessed 2023
41
Marketing and IT: The new data duo for AI-powered growth
But why is this the case? For years, organizations have Traditionally siloed across most organizations, there
collected massive amounts of customer data to analyze has been a rise of marketing and IT leaders teaming
and inform their decision-making (figure 2). Additionally, up and using innovative data approaches to provide
the rapid advancement of AI solutions should be a new and improved experiences to their customers.
catalyst for change, greatly enhancing an organization’s Throughout this paper, we explore the benefits of
ability to create marketing content and engage with the marketing and IT duo and how this partnership
customers more efficiently and effectively. So why are leverages data and AI to improve customer experiences
marketers continuing to struggle? and unlock business outcomes.
To answer this question, we surveyed a diverse group of
marketing and IT leaders throughout the world to:
• Identify the use cases that marketers prioritize
highly but struggle to execute.
• Understand the common challenges that
marketers experience with use case execution.
• Define what marketers truly mean by
“personalization” and “bringing their customer
experiences to life.”
Figure 2. Survey results involving artifical intelligence models with predictive AI and generative AI
2
Marketing and IT: The new data duo for AI-powered growth
DATA + AI + MARKETING
Data lies at the core of every modern organization, This may be easier said than done though, as we see
and it’s being created, stored, and analyzed at an organizations continuing to grapple with the challenge
unprecedented rate. Especially for marketers, this of effectively implementing critical and highly prioritized
explosion of data presents enormous opportunities use cases. For example, over half of the marketing
for those who are prepared to take advantage of it. leader respondents to our survey indicated the following
With the rapid growth of AI, it’s become increasingly marketing capabilities as high priorities for their
critical for organizations to ensure that the necessary organizations, but more than 40% of those respondents
data to inform their AI solutions is sound and readily also said that their organizations lacked maturity in
available. Organizations can combine their data actually executing each of these capabilities.
foundation and AI capabilities with human expertise
to understand people’s needs and the external factors When exploring the major challenges that organizations
that influence them. Establishing this holistic picture run into when implementing or using AI/ML to support
can help marketers become more efficient in both their their marketing use cases, our research uncovered an
investment strategy and time to execute, ultimately impactful collection of barriers (figure 4).
enabling them to deliver the hyper-personalized
experiences that their customers expect (figure 3).
Figure 4. What are the major challenges or
Figure 3. Marketers focus to deliver hyper- barriers that your organization experiences
personalized experiences by establishing a when implementing and using AI/ML to support
holistic picture marketing and advertising use cases?
63
Marketing and IT: The new data duo for AI-powered growth
Although not an exhaustive list, there are several There are many opportunities for organizations that are
factors that can contribute to these struggles and an properly prepared to collect, process, and unify their
organization’s inability to overcome them. data, particularly as the volume of gathered data grows.
The ability to organize, access, and act on data is critical.
LIMITED DATA SHARING AND INTEROPERABILITY Organizations that fail to address data management and
BETWEEN SYSTEMS unification requirements may find future data influxes to
be more of a challenge than a chance for innovation.
While many organizations collect and store first-party
data (and may combine it with second- or third- FRAGMENTED NETWORK OF CONTENT CHANNELS
party data) to use in marketing campaigns, a lack of
connectivity and interoperability between the data can be Consumers have seemingly endless ways to research
problematic and limit the impact of marketing analytics their interests, be inspired, or make a purchase.
or activation efforts. Although not the only culprit, Conversely, marketers are dealing with a larger and
siloed or independent data systems often contribute more fragmented network of content channels to
to an organization’s lack of data unification and sharing, reach and engage people. This only amplifies the
especially as the volume of collected data grows. challenges that organizations and marketers must
navigate—especially since, as research shows, people
Based on our research, the top challenge organizations expect to be consistently treated as individuals across
face when sharing data internally mirrors a top challenge all of these channels. Organizations need to adapt to
associated with organizations using AI/ML for their consumers’ changing interests in real time and serve up
marketing use cases: ensuring sufficient integrations or personalized content no matter where they are.
interoperability across data platforms.
Figure 5. Customers expect to be consistently treated as indivuals across all content channels.
Source: Deloitte, Connecting with meaning, accessed 2023
4
Marketing and IT: The new data duo for AI-powered growth
Figure 6. how teams within an organization believe
SLOW AND COMPLEX TECH ADOPTION
first-party data is used to support an organization’s
marketing use cases.
The slow and oftentimes complex orchestration of
technologies within organizations can hinder the
development of a project before it even starts.
In recent years, marketing teams have advanced
their tech fluency. Since 2015, the fastest-rising skills
mentioned in job postings for marketing leaders are “key
performance indicators (KPIs)” and “cloud solutions,”
two areas that fit comfortably in the world of data and
technology.2 Additionally, our research revealed that
72% of marketers possess either primary decision-
maker or executive approver authority within their
organizations when it comes to choosing marketing or
data technology solutions.
This shift in skill set and responsibility has moved the
focus of IT more toward system integration support and
away from its traditional role as the primary enterprise
technology decision-maker. For a while, this worked out,
but over time and with the growth of first-party data and
AI, conflicting priorities and perspectives between IT and
Ultimately, this can lead to misalignment on the core
marketing teams surfaced.
objective(s) for an AI project, which can encourage
both teams to continue to work independently. This
On the IT side, there’s a strong will to lead enterprise
can create an environment for AI adoption projects
data and AI projects. It makes sense: This team oversees
to sputter along or fail altogether as both teams
the organization’s data governance and security policies,
struggle to agree on important project components,
and often builds and trains AI/ML models. From the
such as the business case, technical and financial
marketing team’s side, the desire to spearhead these
requirements, and evaluation criteria.
data and AI projects stems from the fact that they
are the ultimate consumers of these data assets (for
Through our research, we continue to see that today
instance, AI/ML model scores). Marketers need them
in how teams within an organization believe first-party
to understand customers and build better marketing
data is used to support an organization’s marketing
activation tactics, such as personalized content
use cases (figure 6).
generation or an optimized media buying strategy.
85
Marketing and IT: The new data duo for AI-powered growth
IMPACT OF DATA PRIVACY POLICIES These changes can impact everything from campaign
planning to post-campaign reporting, and they can affect
Ever-changing data privacy regulations require privacy the accuracy of third-party ad platforms and brand
leaders to constantly update and conform their measurement systems. A marketer’s ability to target,
organization’s data compliance and governance rules. measure, and understand its consumers is directly
Additionally, the expansive but often fragmented affected, which makes it vital that organizations align on
collection of data privacy laws propels many and establish a privacy-centric approach to marketing.
organizations to adopt internal policies that apply
regulations, such as the California Consumer Privacy This can be especially challenging for global brands
Act of 2018 (CCPA) or the General Data Protection that must untangle a web of regional, national, and
Regulation (GDPR), across any consumer that interacts international data privacy laws. Regardless of the
with the organization, regardless of that individual’s complexity, owning and building a foundation of
location. Additionally, heightened controls among consented first-party data is crucial to AI-powered
internet browsers and hardware companies are marketing. Our research indicates that high-growth
impacting traditional data collection, and with these companies are focused on using AI to support their
disruptions in gathering valuable data, marketers are marketing processes because AI is the business
facing significant disruption in how they drive business multiplier that can help organizations keep up with
impact and measure ROI. shifting consumer demands and gain necessary insights
in an efficient and privacy-centric way (figure 7).
Figure 7. High-growth companies are focused on using AI to support their marketing processes.
6
Marketing and IT: The new data duo for AI-powered growth
BENEFITS OF A UNIFIED DATA ECOSYSTEM
COMBINED WITH AI
It’s important to remember that consumers expect create actionable insights through conversation. In turn,
organizations to understand their wants and needs as this can make insights commonplace and reduce the
well as adapt to changes in them almost instantaneously. time needed for organizations to transform their vast
Providing a superior customer experience is increasingly amounts of data into insights and decisions.
important for organizations, especially in today’s
crowded marketplace. In fact, 97% of organizational For years, marketers have utilized analytics applications
leaders agree that customer experience management that have AI/ML built in as a core component to help
is an integral business strategy for creating loyal and unlock insights. The introduction of generative AI has
long-lasting customer relationships.3 However, to meet created the opportunity to take this output to the next
or even anticipate and exceed their customers’ needs, level by democratizing access to generated insights and
organizations should adopt a unified data ecosystem data-driven decision-making.
that integrates with their existing data systems, as
well as harnesses the power of AI to create authentic Imagine asking a generative AI application to “Tell me
customer experiences. This unified data ecosystem which online products underperform and how that
serves as the infrastructure that helps organizations impacts revenue.” After a few moments, the application
take advantage of AI. In today’s dynamic world, speed returns a list of the 10 products with the highest declines
and predictability can be game-changing tools for in forecasted revenue for the next quarter. Finding ways
organizations as they design and deliver the hyper- to make it faster to get to insights regardless of technical
personalized experiences that customers expect. or analysis skills will be incredibly impactful.
By building a foundation with organized and trusted INCREASE ADOPTION OF AI IN MARKETING
data, you build confidence across your organization that
everyone is operating from the same source of truth. As part of our research, we discovered that 78% of
From there, you can enable AI to make data-driven markets are planning to increase the use of AI to
decisions quicker and unlock forecasting capabilities enhance their marketing capabilities and processes
that let you better predict and adapt to the needs of the over the next 12–18 months. Conversely, only 31% of
business. In addition to positioning your organization marketers indicated that their organizations have a
to be successful with AI, you may realize the following well-defined strategy in place that balances generative AI
benefits from a unified data ecosystem: capabilities with robust data privacy measures to enable
AI-powered marketing. This signifies that while many
MAKE EVERYONE AN ANALYST organizations are aware of—and some even actively
utilize it—generative AI still remains a new territory
The unification of data, AI, and business intelligence that may not be fully understood, but marketers are
(BI) enables marketers to develop a dynamic collection intrigued by its potential.
of self-service BI dashboards that a broad group of
end users—not just analysts—can easily use and For example, content guidelines and policies can be
manipulate. The ability to access, analyze, and act on up- effective tools for marketers but sometimes difficult
to-date data can empower marketers, business leaders, to design and deploy. Content strategists can utilize
and other important users with real-time insights that generative AI to brainstorm and develop the content
drive value. pillars that incorporate their organization’s mission,
vision, and brand values. Additionally, generative AI can
This concept of “everyone is an analyst” introduces be an effective tool to enforce the brand guidelines
a new way to work by making data and marketing that ensure an organization’s voice is consistent in
analytics capabilities more accessible to all interested content and community engagements across numerous
stakeholders. Essentially, it can empower an platforms and channels.
understanding of data at a superficial level by enabling
marketers and other business users with the ability to
7
Marketing and IT: The new data duo for AI-powered growth
Figure 8. Surveyed marketers are interested in implementing
generative AI capabilities.
Marketers can also use generative AI to tailor a conversational experiences that retrieve information
message’s tone for different audiences, or create custom from a wide variety of relevant data sources, such as
imagery based on an individual’s specific characteristics localized weather, product and media catalogs, or
or behavior—it can act as a helping hand in the effort current events. This AI-powered capability can help
to create hyper-personalized marketing. In fact, 73% decrease the time-to-service and enhance the speed
of marketers surveyed indicated an interest in using and accuracy with which customer questions are
generative AI in this way (figure 8). addressed, resulting in reliable support that helps you
stand out to your customers.
Working in parallel with ad platform technology that’s
powered by AI, marketers can create sustainable EXPERIMENT AND ITERATE FASTER WITH AI
frameworks to facilitate consistency and resonance
and make ongoing decisions about their content and A structured, test-and-learn approach to experiment
its impact. For example, marketers can integrate AI with AI can supercharge your innovation for quicker and
and data into their digital content creation process to more efficient results.
create quick-turn digital assets that activate across
multiple channels. Organizations can use ad platform technology powered
by AI to help optimize their in-platform metrics to
COMBINE AI AND HUMAN EXPERTISE TO realize a high ROI. Marketers can use multivariate
OPTIMIZE RESOURCES testing strategies to identify cohorts, maximize
engagement, and iterate with increasing improvements,
AI is naturally adept at tackling questions where the all while relying on the technology to do the heavy
answers are precise and the logic is clear. To that end, lifting when it comes to creative experimentation. For
it can be especially beneficial to utilize AI to automate instance, AI can generate content based on different
tasks, especially the time-consuming or less strategic versions of metadata descriptors to create new
ones. This can help drive better campaign performance iterations to be tested.
or improve team efficiency by reallocating people to
focus on tasks less suited for AI, such as critical problem- Organizations can build and deploy AI models, often
solving or strategic decision-making on how to market a provided by a cloud-based platform, with their first-
new product or service. party data, to enable predictive analytics capabilities,
such as customer lifetime value modeling or propensity
For example, some advertising platforms provide AI- to convert, which can be activated across marketing
powered features, and marketers can utilize these to channels to help marketers optimize their strategy for
augment their targeting strategies and capture growth hyper-personalized customer engagement and serve
opportunities or drive incremental conversions across the right message to the right customer at the right
their marketing channels. time. By enabling these audiences, marketers can test
personalized messaging and experiment with more
Generative AI can also be used to improve loyalty and parts of the customer journey.
engagement on your website by automating advanced
181
Marketing and IT: The new data duo for AI-powered growth
HOW TO CREATE A UNIFIED DATA ECOSYSTEM
AND ENABLE AI-POWERED MARKETING
By now, you may be convinced of the benefits of a Figure 9. Who in your company is championing the
unified data ecosystem and AI-powered marketing. The adoption and use of AI to support marketing and
next step: realizing this dream in your organization. advertising use cases?
The transformation required for this process represents
an opportunity to start fresh with the way you work with
data, and not just optimize a broken process. It will be a
journey, but over time, changes can be made that better
align IT and marketing teams. You can move toward a
centralized vision, reduce data silos to encourage more
data collaboration, and democratize access to analytical
insights for all interested stakeholders.
FIND AN IMPLEMENTATION APPROACH THAT
WORKS FOR EVERYONE
Perhaps the best place to start this journey is with
the teams and individuals who will be responsible for
the design, implementation, use, and support of the
new data and AI capabilities. The objective here is to
bring the marketing, business, and IT teams together
to align on a data and AI implementation that fits with
the organization’s culture, values, and growth strategy.
Below are some suggestions that can help this go
more smoothly.
ACKNOWLEDGE MISALIGNMENT BETWEEN
ORGANIZATIONAL LEADERS
Our research shows that executive leaders and IT
Delivering transformative projects, such as building managers are active champions for AI adoption. We
a unified data ecosystem with AI capabilities, can found that when digital transformation is driven from
be a sizable investment that creates tension and the top down, it tends to be more successful (figure 9).
disagreements among all invested stakeholders.
Accepting this conflict is a crucial first step. In fact, research from Deloitte’s State of AI in the
Unsurprisingly, our research indicated a healthy spread Enterprise, 5th Edition reports tthat a vision or plan
of teams that are championing the adoption of AI to from an organization’s executive leadership for how
support marketing use cases. When those situations AI will be used is the most important factor in the
occur, embrace the conflict and use it to identify the root development of an AI-ready culture. Additionally, the
causes that prevent harmony among the key decision- research indicates these “high-outcome organizations,”
makers. This practice may require teams and individuals which adopt leading practices associated with the
to tweak their mindsets about data and AI, but doing so strongest AI outcomes, are significantly more likely to
can help each team, especially marketers, to develop report revenue-generating results—such as entering
new disciplines that bring a strategic, full-funnel, and new markets, expanding services to new constituents,
cross-channel view to how an organization can use data creating new products or services, or enabling new
and AI. business and service models. The rewards can be
lucrative for organizations that are aligned and execute
on a common vision for the use of AI.
192
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IDENTIFY TEAMS TO INCLUDE IN PROCESS
With an emphasis on creating
new growth, we’ve leveraged out
Although many teams will benefit from a data and AI
foundation, some teams who would benefit more, such e-commerce and customer profile
as digital advertising teams, don’t have the IT or technical data for ML-driven audience
resources to support them. Many organizations work
segmentation and driving rapid
with external partners to build their marketing use
experimentation of personalized
cases, and they use something like predictive audience
building and activation to quickly demonstrate ROI and customer experience.
prioritize more strategic use cases. The North Face is
a great example of this. The company saw a need to
Frank Tingley
better understand its customers, and it collaborated
Senior Director of Analytics
with Deloitte to develop a cloud-based solution with AI/
ML capabilities that used its e-commerce and customer The North Face
profile data to increase purchase frequency and drive
member acquisition.
Figure 10. Revenue-generating outcomes—High- vs. low-outcome organizations
(Selecting “Achieved to a high degree”)
Source: Deloitte, State of AI in the Enterprise, 5th Edition, October 2022
1130
Marketing and IT: The new data duo for AI-powered growth
When thinking about the teams to include and building a Figure 11. Types of partners organizations used to
road map for teams to benefit quickly, there are a handful develop their customer data and AI strategies.
of important, people-related questions to consider.
1. Between our IT and marketing leadership teams, do
we have a clear and aligned understanding of what a
unified data ecosystem combined with AI can solve?
2. In order for this investment to drive value, which
teams will ultimately access and utilize these
capabilities?
3. Which teams can take advantage of these data and
AI capabilities to drive experimentation and growth?
4. How can the organization ensure people readiness?
What AI skill levels are present among current
employees?
5. How do the marketing, business, and IT leaders align
on this journey? As the growth and financial security
of organizations continues to be scrutinized, how
can the infusion of data and AI help leaders better
understand, plan for, and exceed their target metrics?
6. With respect to multi-partner orchestration, what
external organizations need to be involved in the
process to maximize value?
While many marketing leaders may be more familiar with
LEVERAGE MULTIPLE PARTNERS
the process of collaborating with a media or creative
agency of record, the importance of data in marketing is
External support can be quite valuable for a
so crucial that some forward-thinking marketing leaders
transformational project with data and AI. After
are hiring a “data partner/agency of record” to help
identifying the internal teams that should be involved,
bridge the data divide with IT.
consider which partners could meaningfully contribute
to your project.
Developing and implementing an impactful strategy
around unified data and AI can be challenging. A plan
When we asked about the types of partners
that calls on the expertise of a variety of partner types is
organizations used to develop their customer data and
critical for success. Especially with the recent explosion
AI strategies, each type listed below was selected by
of generative AI, the AI landscape and ecosystem is
more than 45% of marketing and IT leaders (figure 11).
rapidly evolving with new technology and partnerships.
Organizations need reliable guidance on strategies
When this question was changed to inquire about
that can help them build and deploy applications that
the implementation of their customer data and AI
successfully balance safety, responsibility, and ROI.
strategies, creative and media agencies were the only
partner types to not be selected by at least 45% of
marketing and IT leaders.
1114
Marketing and IT: The new data duo for AI-powered growth
TECHNOLOGIES TO CREATE A
UNIFIED DATA ECOSYSTYEM
Another key piece of the puzzle is identifying the right that enable data analysts to build and operationalize
technology platform to build the data foundation ML models on structured, semi-structured, and even
that powers your AI engines. Data and AI are highly unstructured data using simple SQL—in a fraction of
interdependent, so a sound data foundation and the time it would take to build a model from scratch.
strategy heavily influences the ability to develop and These ML models can be shared with a managed AI/ML
deploy initial AI use cases and gain traction toward platform that allows for more advanced AI use cases
reaching a mature state of AI adoption. Organizations designed to help you build, deploy, and scale machine
need to build AI into their data foundation and strategy learning models faster, for any use case, including
and embed AI into the data life cycle. This can be building generative AI apps.
accomplished through a customer data platform (CDP)
or data cloud solution, which can be built internally using Specific features like this are designed and well-
cloud platform technology, engaging with a consulting or positioned to make AI/ML development faster, easier,
delivery partner, or making a purchase off the shelf from and more accessible than ever before.
a technology partner.
Ultimately, organizations can work with a data cloud
BUILD A DATA CLOUD provider to help design and drive use case testing that
can quickly prove value without needing a new, full-
With a collection of cloud-based, data, and AI-powered scale enterprise system implemented. From that point,
systems, data cloud providers can help organizations organizations can help build an appropriate business
manage every stage of the data life cycle and transform case, assess the output, and evolve scale machine
their marketing efforts through the benefit of a learning models faster, for any use case, including
connected, open, and intelligent data ecosystem. With building generative AI apps.
this collection of cloud-based services, organizations
can implement a data platform that unifies the data, Specific features like this are designed and well
business intelligence, and AI capabilities needed to positioned to make AI/ML development faster, easier,
provide transformative experiences for customers, and more accessible than ever before.
unlock timely insights across various data sources, and
enable organizations to act on data-derived decisions Ultimately, organizations can work with a data cloud
that drive impact. provider to help design and drive use case testing that
can quickly prove value without needing a new, full-
Many data cloud providers also offer preconfigured AI/ scale enterprise system implemented. From that point,
ML frameworks and toolsets so developers don’t have organizations can help build an appropriate business
to start from scratch when they begin their AI projects. case, assess the output, and evolve.
More specifically, some data warehouses offered by
data cloud providers have built-in AI/ML capabilities
1152
Marketing and IT: The new data duo for AI-powered growth
LICENSE AN ENTERPRISE CDP Additionally, some organizations are turning toward a
“dual-zone” CDP approach (as introduced in Deloitte
CDPs have become a fundamental tool for organizations Digital’s Bridge the customer data divide with a dual-
to gain real insight into their customers’ preferences zone CDP) to create a structure for IT and marketing
and intent. A CDP can provide a solution built on top of leaders to collaborate and align on the organization’s
existing technology and infrastructure, which can enable technology—from data storage and processing to
organizations to quickly integrate their existing data marketing analysis and activation. Dual-zone unbundles
sources and leverage those insights to drive efficiency. CDP capabilities from one or more sources and
reorganizes them into two distinct (but still connected)
Of course, a CDP is not a one-size-fits-all piece of zones, each with clear ownership and responsibilities.
technology, and understanding how to proceed requires
some upfront discussions among IT and business As a result, organizations can deepen their
leaders to research and understand the different CDP understanding of customers, reduce risk through
options emerging in the marketplace today and define a greater privacy compliance, elevate the experiences of
clear path forward (figure 12). customers, and drive new revenues.
Figure 12. Common CDP archetypes
Four basic CDP archetypes are emerging in the marketplace today—each with its own strengths and gaps.
1136
Marketing and IT: The new data duo for AI-powered growth
Figure 13. Solving needs across the customer divide
Enterprise IT and marketing typically have different priorities when it comes to customer
data-related capabilities. Identifying the right solution for your whole organization begins
with understanding where those needs diverge—and where they overlap.
Source: Deloitte, Bridge the customer data divide with a dual-zone CDP, November 2022
EXPERIMENT WITH AI-POWERED SOLUTIONS
Generative AI solutions are
With high-quality data in place, it’s possible to transform not just about keeping up with
key marketing capabilities, such as content marketing, content demands; they are about
in real time and craft inspiring creative content using AI
"
211,deloitte,us-state-of-gen-ai-q3.pdf,"Now decides next:
Moving from potential
to performance
Deloitte’s State of Generative AI in the Enterprise
Quarter three report
August 2024
deloitte.com/us/state-of-generative-ai
Table of contents
Foreword
Introduction
Now: Key findings
1 Building on initial success
2 Striving to scale
3 M odernizing data foundations
4 M itigating risks and preparing for regulation
5 M aintaining momentum by measuring value
Next: Looking ahead
Authorship & Acknowledgments
About the Deloitte AI Institute
About the Deloitte Center for Integrated Research
About the Deloitte Center for Technology, Media & Telecommunications
Methodology
22
Introduction
Foreword
In the rapidly evolving landscape of artificial intelligence (AI), the connection between The complex discussions around creating value and managing risk makes it clear to me
technology and value has become increasingly apparent. What is known about major that we need to keep humans at the center of all this decision-making. It is the human
technology innovations in the past holds true with Generative AI (GenAI). stakeholders who impact how applications are conceived and developed, how they are
adopted and used, and how they are managed for trust and security. In this, employee
Technology application on its own is not enough. Results and business outcomes
upskilling and change management remain indispensable elements of value-driving
matter. The real measure of success for GenAI will be how it enables enterprise
GenAI programs.
strategies and drives tangible value.
With a focus on business outcomes and human-centered change, I feel the future with
As organizations are scaling, and learning from, their GenAI pilots, I have heard the
GenAI grows brighter by the day, even as the journey ahead will continue to surprise
discourse around GenAI shift from unbridled excitement to a more nuanced and
and challenge us.
critical evaluation of its real impact on business outcomes. I am also beginning to see
organizations think more about tailored GenAI tools—evolving from large language Learn more about the series and sign up for updates at
models (LLMs) to small language models (SLMs) for more targeted needs. They are http://deloitte.com/us/state-of-generative-ai.
also exploring how the rise of AI agents can redefine interactions within their digital
–Jim Rowan, Applied AI SGO Leader
environments, offering new avenues for automation and personalization.
Amid this maturation, regulatory considerations are coming to the fore. Our past
survey results indicated a strong market appetite for smart GenAI regulation and
oversight. Businesses and governments alike are navigating a dynamic landscape
and are struggling to keep pace with the rate of technology innovation. The challenge
is to unlock the benefits of GenAI while facing regulatory uncertainty, orchestrating
governance and building trust. No small task.
33
Introduction
Moving from potential to performance
The clock is ticking for organizations to create significant cases with strong return on investment (ROI) and a clear Generative AI-powered applications? Is regulatory
and sustained value through their Generative AI path to scale will be essential. They’ll need to address uncertainty holding them back? Are they developing a
initiatives. Promising pilots have led to more investments, challenges across the board: people, process, data and comprehensive set of financial and nonfinancial measures
escalating expectations and new challenges. During this technology. Change management and organizational to form a complete picture of benefits achieved? These
pivotal phase, C-suites and boards are beginning to look transformation will need to be given as much consideration questions must be explored in-depth as organizations
for returns on investment. There is a chance that their as technology. journey from Generative AI promise to performance.
interest in Generative AI could wane if initiatives don’t
In this quarter’s survey, we focused on two critical areas to
pay off as much, or as soon, as expected.
scaling—data and governance, and risk and compliance—
Will organizations demonstrate the patience and and how organizations are measuring and communicating
perseverance needed to unlock the transformational value. Are data-related issues hindering efforts? How
potential of Generative AI? To get there, value-led use are organizations ensuring the right oversight of
44
Introduction
Moving from potential to performance (cont’d)
Building on initial success Striving to scale
• Improved efficiency and productivity and cost reduction are still the top benefits • Two of three surveyed organizations said they are increasing their investments in
sought by organizations. Those are also cited by 42% of respondents as their most Generative AI because they have seen strong early value to date.
important benefits achieved to date.
• However, many are still challenged to successfully scale that value—nearly 70% of
• However, 58% reported they realized a more diverse range of most important respondents said their organization has moved 30% or fewer of their Generative AI
benefits, such as increased innovation, improved products and services, or experiments into production.
enhanced customer relationships.
• Respondents said that embedding Generative AI deeply into critical business
functions and processes is the top way to drive the most value from their
Generative AI initiatives.
All statistics noted in this report and its graphics are derived from Deloitte’s third quarterly survey, conducted May
– June 2024; The State of Generative AI in the Enterprise: Now decides next, a report series. N (Total leader survey
responses) = 2,770. Percentages in this report and its charts may not add up to 100, due to rounding.
Generative AI is an area of artificial intelligence and refers to AI that in response to a query can create text,
images, video and other assets. Generative AI systems can interact with humans and are often built using large
language models (LLMs). Also referred to as “GenAI.”
5
Introduction
Moving from potential to performance (cont’d)
Modernizing data foundations Mitigating risks and preparing Maintaining momentum
for regulation by measuring
• Three-quarters of respondents said their organizations
have increased investment around data life cycle
• Organizations feel far less ready for the challenges • More than 40% of respondents said their companies
management to enable their Generative AI strategy.
Generative AI brings to risk management and governance— are struggling to define and measure the exact impacts
Top actions include enhancing data security (54%)
only 23% rated their organization as highly prepared. of their Generative AI initiatives.
and improving data quality (48%).
• In fact, three of the top four things holding organizations • Less than half said they are using specific KPIs to
• Data issues are limiting options—55% of organizations
back from developing and deploying Generative AI tools measure Generative AI performance, and many
reported avoiding certain Generative AI use cases
and applications are risk, regulation (such as the European standard measures of success aren’t currently
because of data-related issues. Top data-related
Union’s AI Act, in effect August 1), and governance issues. being applied.
concerns include using sensitive data in models
and managing data privacy and security. • To deal with regulatory uncertainty, about half of
organizations reported they are preparing regulatory
forecasts or assessments.
About the State of Generative AI in the Enterprise:
Wave three survey results
The wave three survey covered in this report was fielded to 2,770 director- to C-suite-level respondents across six industries and 14 countries
between May and June 2024. Industries included: Consumer; Energy, Resources & Industrials; Financial Services; Life Sciences & Health Care;
Technology, Media & Telecom; and Government & Public Services. The survey data was augmented by additional insights from 25 interviews
with C-suite executives and AI and data science leaders at large organizations across a range of industries. This quarterly report is part of an
ongoing series by the Deloitte AI InstituteTM to help leaders in business, technology and the public sector track the rapid pace of Generative AI
change and adoption. The series is based on Deloitte’s State of AI in the Enterprise reports, which have been released annually the past five
years. Learn more at deloitte.com/us/state-of-generative-ai.
66
Now: Key findings
77
Now: Key findings Top benefit achieved through
Generative AI initiatives
1
Building on initial success
Organizations say they are seeing value from their early Generative AI forays and those successes are driving more
investment. Two-thirds of the organizations we surveyed (67%) said they are increasing investments in Generative AI Improved 34%
efficiency and
because they have seen strong value to date. A head of AI strategy and governance in the banking industry has seen
productivity
this first-hand: “Before GenAI, most senior leaders only had a vague understanding of what AI was or what it can do. Now,
they have AI at their fingertips, and it has opened their eyes to the possibilities. We have applied for additional resources.”
12% Encouraged
innovation
As in our prior quarterly surveys, improved efficiency and productivity and cost reduction continue to be the most
Improved 10%
common benefits sought from Generative AI initiatives. Those benefits were cited by 42% of wave three respondents
existing
products and 9% Reduced
as their single, most important benefit achieved to date (figure 1).
services costs
However, for most wave three respondents (the other 58%), the top benefit achieved through the new technology is Enhanced 9%
relationships
something other than efficiency, productivity or cost reduction. This includes increased innovation (12%), improved 7% Increased speed
with clients /
and/or ease
products and services (10%), and enhanced customer relationships (9%). The diversity of possible sources of value from
customers
of developing
Generative AI initiatives is exciting to many leaders and shows the potential and versatility of this new technology.
new systems /
Increased 6%
software
revenue
This distribution could mean a couple of different things. Organizations may be seeking efficiency, productivity and
6%
Developed
cost reduction, but aren’t seeing it materialize yet; they may be getting unexpected value from less tangible areas; or
new products
they may be prioritizing these other types of value. There is no one-size-fits-all approach to employing Generative AI, Shifted 4% and services
workers from
and there is a wide range of benefits that could be gained. It is important for organizations to be clear about what
4% Better
lower- to higher-
detection of
kind of value they are seeking before embarking on any Generative AI initiatives—start with value first. value tasks
fraud and risk
management
67%
of organizations we surveyed said they are increasing
Figure 1 Q: What is the most important benefit your organization
investments on Generative AI given strong value seen to date. has achieved to date through your Generative AI initiatives?
(May/June 2024 ) N (Total) = 2,770
88
Now: Key findings
Our executive interviews provided examples of Generative AI use cases that are already and greater innovation and market differentiation, most projects further along in the
delivering real-world value across a wide range of industries. Although they are working scaling process are still focused on improving productivity (figure 2).
toward things like automated decision-making, accelerated research and development,
Generative AI use cases delivering real-world value by industry
Banking Transportation Telecom Insurance Consumer Technology Finance Pharmaceuticals
A customer service A system to provide Support tools An internal medical Customer Continuous Project management Internal tool that
tool that handles customer support deployed for retail claims appeal review segmentation tools improvement tools that quickly provides instant
messages, using both and handle simple and technical field tool that provides leveraged to create processes enhanced create summary enterprise information
chat and voice, and support tickets. staff, and systems increased response more precise and by directly leveraging materials for key (such as standard
provides cross-sell The system can for troubleshooting quality and a decreased customized segments customer feedback stakeholders. operating procedures)
opportunities based automatically pull and preventive time to respond. across geographies. to inform product for thousands of staff.
on the interaction. data for human maintenance, all to development
agents to use for reduce costs. road maps.
more complex tasks.
Figure 2
9
Now: Key findings Behaviors driving the most value
for Generative AI initiatives
What do organizations think will most help drive greater “CEOs and executive leadership teams are getting much
value for their Generative AI initiatives? While many more excited and interested in what’s possible and are
Deeply embedding GenAI into
22%
different factors contribute to Generative AI value looking for use cases to demonstrate the value and
functions / processes
creation, the action cited most often by the leaders benefit,” said the global head of AI, machine learning,
we surveyed is embedding the technology deeply into analytics and data at a pharmaceutical company. “There is Effectively managing risks 13%
business functions and processes (figure 3). a lot of willingness to test, experiment and scale. However,
Deploying the latest
the potential danger is that people might get disappointed
11%
“An LLM is like an engine,” said a VP at a bank’s AI center technology
and lose attention if it’s not paying off fast enough.”
of excellence. “No one just wants the engine of a car
Developing creative and
10%
or a plane; they want a car or a plane. So, there are all C-suite and board members are still intrigued, but there
differentiated applications
these things you need to do to make it part of business are some potential signs of enthusiasm beginning to
Tailoring / customizing
processes, so the business can use it.” The value from wane as the “new technology shine” wears off. Survey
10%
models with proprietary data
any Generative AI initiative won’t be fully realized if it sits respondents said that interest in Generative AI remains
apart. As with other technologies, it will only reach its “high” or “very high” among most senior executives
Hiring the best talent 9%
potential when it is embedded in everyday tasks. Many (63%) and boards (53%); however, those numbers
organizations are already employing enterprise tools have declined since the Q1 2024 survey, dropping 11
Completely measuring
8%
enhanced with this emerging technology resource to percentage points and 8 percentage points respectively. performance
try and make this happen. Time is of the essence as organizations look to scale
their early achievements. Providing enough budget 8%
Although many have seen promising results from
early projects and are increasing investment in
Providing access to as much
7%
of the workforce as possible
Generative AI, it is important that organizations
show sustained and significant value as quickly
as possible.
Figure 3 Q: Which behavior / action do you think will drive the most
value for the Generative AI initiatives in your organization?
(May/June 2024 ) N (Total) = 2,770
10
Now: Key findings
2 Striving to scale A large majority of organizations have deployed less than
a third of their GenAI experiments into production
Selecting and quickly scaling the Generative AI projects with the most potential to create
value is the goal. However, many Generative AI efforts are still at the pilot or proof-of-
Organizations
26%
concept stage, with a large majority of respondents (68%) saying their organization has 24%
GenAI experiments
19%
moved 30% or fewer of their Generative AI experiments fully into production (figure 4).
moved into production
14%
This isn’t necessarily surprising—despite rapid and impressive advances in Generative AI’s
7%
capabilities, its applications are still relatively new and organizations are figuring out what it 4%
3%
1% 1%
can (and can’t) do well. Many organizations are learning through experience that large-scale
Generative AI deployment can be a difficult and multifaceted challenge. As with a lot 0% 10% 20% 30% 40% 50% 60% 70% 80%
of digital transformation efforts, projects can fail or struggle for a variety of reasons.
Figure 4 Q: In your estimation, what percentage of your Generative AI experiments have been deployed to date into your
“Most of our applications are still in the minimum-viable-product or proof-of-concept organization (moved into production)?
phase,” said a senior specialist for AI compliance in the automotive industry. (May/June 2024 ) N (Total) = 2,770
“Scaling across an organization where
Successfully scaling may mean different things to different organizations—based
on their goals, what approach they are taking with Generative AI, and to what
you have thousands of employees
extent scaling is actually necessary. They could be expanding from one market
to multiple markets, from a small group within a function to the entire function,
has several basic requirements, and
or from a portion of a process to multiple, integrated processes. It also depends
they’re quite challenging.” on what Generative AI-powered tools and applications are being used: scaling a
code generator across an IT department is going to be different than scaling a
customized LLM for the finance function, or a new enterprise customer relationship
-Senior specialist for AI compliance in the automotive industry
management application with Generative AI features.
11
Now: Key findings
Despite these differences, some fundamentals are consistent. More broadly, organizations should invest in the foundations of Generative AI
and concurrently assess and advance their strategy, processes, people, data and
“Foremost, you need a strategy,” the senior specialist for AI compliance continued.
technology (figure 5).
“Strategy means you can’t start by purchasing separate solutions ... if you really want
to scale, first you need to base your strategy on platforms.” Many of the fundamentals may look similar to prior digital transformation
efforts, but due to the unique nature of Generative AI, things like robust
This platform-centric approach could include establishing centers of excellence, technology
governance, transparency for building trust, transforming talent, and
platforms to enable multiple use cases, and centralized teams of experts. In our Q2 report
mature data life cycle management take on increased importance.
we advocated for centralized resources that can accelerate deployment of similar use
cases and enable organizations to make the most of scarce Generative AI expertise.
Essential elements for scaling Generative AI initiatives from pilot to production
Figure 5
Strategy Process Talent Data & technology
Ambitious Modular
Integrated Transparency Provisioning
strategy & value Robust architecture
risk to build trust the right AI
management governance and common
management in secure AI infrastructure
focus platforms
Clear, Agile Acquiring Effective
Strong Transformed
high-impact operating model (external) and Modern data model
ecosystem roles, activities
use case and delivery developing foundation management
collaboration and culture
portfolio methods (internal) talent and operations
12
Now: Key findings
How do organizations feel like they are doing across these areas—are they prepared the LLMs still needs to be improved … Data readiness; data is going to be problem
to scale? We asked how highly prepared respondents thought their organizations were forever ... Deep Generative AI understanding as well. There’s not enough people who
across some of the essential scaling elements (figure 6). Technology infrastructure understand and can drive transformation.”
(45%) and data management (41%) fared the best, followed by strategy (37%), risk
To help start a conversation on how to overcome some of these barriers, in
and governance (23%), and talent (20%).
this quarter’s survey we focused on two areas critical to scaling—exploring
This indicates that there are still some fundamental challenges holding organizations how organizations are approaching data and governance, and risk
back from successfully scaling their Generative AI initiatives. A senior director and and compliance.
head of a Generative AI accelerator in the pharmaceutical industry identified a
With respect to data, more organizations’ leaders reported they are initially prepared.
number of pressing issues: “The heritage of our processes and approaches, that
For risk and governance, they know they are not. Both need attention.
is what’s really holding us back right now. Number two is that the performance of
Do organizations think they are ready?
Percentage of organizations that are highly prepared
for GenAI across the following areas 45% 41%
37%
23%
20%
Figure 6 Q: For each area, rate your organization’s level of preparedness Technology Data Strategy Risk & Talent
with respect to broadly adopting generative AI tools / applications?
infrastructure management governance
(May/June 2024 ) N (Total) = 2,770
13
Now: Key findings
3 Modernizing data foundations
75%
of organizations have increased their technology investments
around data life cycle management due to Generative AI.
Compared with the other aspects of Generative AI However, even those executives who consider themselves
readiness, survey respondents judged that their highly prepared will likely need to do more as they progress
organizations are fairly mature with respect to data life in their journeys. Some we interviewed said that as they
cycle management (as a reminder, survey respondents moved from proof of concept to scale, unforeseen data
are from more AI-savvy organizations). This could be issues were exposed—highlighting a need to be agile.
because they had a good foundation to start with or
These issues could be because of the Generative AI-
that, according to our survey, 75% of organizations have
specific demands to data architecture and management.
increased their technology investments around data life
More robust governance—quality, privacy, security,
cycle management due to Generative AI.
transparency—is needed overall, especially around using
This increased focus was evident in our executive data that doesn’t already exist inside the organization (e.g.,
interviews. “There’s a whole series of questions GenAI public domain, synthetic and licensed third-party data).
is triggering about data strategy, that in the past Documenting data sources and labeling has an increased
were far less important,” said the chief technology importance. With more people potentially leveraging
officer at a manufacturing company. “I think we’re data, data access frameworks and literacy require more
probably spending as much time on data strategy and attention. It may change approaches toward cloud or on-
management as on pure GenAI questions, because premises data services. For more advanced LLM users,
data is the foundation for GenAI work.” working with synthetic data may eventually come into play.
1144
Now: Key findings
Levels of concern around data management
Figure 7 Q: For the following, how much concern does your organization have with respect to its data management for
Generative AI implementations?
(high + very high)
(May/June 2024 ) N (Total) = 2,770
58% 58% 57% 49% 38%
Using sensitive data Managing data privacy- Managing data security- Complying with data- Using our own proprietary
in models related issues related issues related regulations data in models
One of these challenges was highlighted by a former vice president of data and intelligence That could be because of data-quality issues, intellectual property concerns, not having
for a media and entertainment company: “The biggest scaling challenge was really the the right data, or worries about using certain kinds of data (e.g., public domain, synthetic
amount of data that we had access to and the lack of proper data management maturity. or licensed third-party data). The concerns that organizations were worried about the
There was no formal data catalog. There was no formal metadata and labeling of data most in our survey included using sensitive data in models (58% had at least a high
points across the enterprise. We could go only as fast as we could label the data.” level of concern), data privacy issues (58%), and data security issues (57%) (figure 7).
Organizations were much more worried about using sensitive data (e.g., customer
Data-related issues could be hindering organizations in their quests for getting
or client data) than they were using their own proprietary data (e.g., sales,
the levels of value that they are seeking. Data-related issues have caused 55%
operational, financial).
of the organizations we surveyed to avoid certain Generative AI use cases.
15
Now: Key findings Improving data-related capabilities
Consistent with those concerns, the top actions The value from Generative AI initiatives will increasingly
organizations are taking to improve their data-related come from organizations leveraging their differentiated
capabilities are enhancing data security (54%), improving data in new ways (whether for fine-tuning LLMs, building
Enhanced 54%
data quality practices (48%), and updating data an LLM from scratch or utilizing enterprise solutions).1 data security
governance frameworks and/or developing new For Generative AI to deliver the kind of impact executives
48%
data policies (45%) (figure 8). expect, companies will likely need to increase their Improved
data quality
comfort with using their proprietary data, which may
practices
be subject to existing and emerging regulations.
Updated 45%
governance
frameworks /
Developed new
43% Collaborated
data policies
with cloud
service provider
“Data quality is key. Understanding what data is or IT integrator
Upgraded IT 37%
to improve
infrastructure
capabilities
good data. Where is that data held? How is it
34% Hired new
talent to fill
secured? How is it permissable? All those things
data-related
Integrated 27%
skill gaps
data silos
are key to making [Generative AI] scalable.”
24% Moved to a
more flexible,
-Chief operations officer & chief of strategy for a financial services firm
open data
architecture
Figure 8 Q: What specific actions has your organization taken
to improve its data-related capabilities to support its
Generative AI initiatives?
(May/June 2024 ) N (Total) = 2,770
16
Now: Key findings
4 Mitigating risks and preparing for regulation
According to our survey respondents, Likely driving these concerns are new and emerging as highly prepared. These issues will be increasingly
risks specific to the new tools and capabilities—like important as activities shift from small-scale pilots to
three of the top four barriers to successful
model bias, hallucinations, novel privacy concerns, trust large-scale deployments and Generative AI becomes
development and deployment of
and protecting new attack surfaces. This environment more deeply embedded into the fabric of organizations.
Generative AI tools and applications are:
may be why organizations feel far less ready for the Highlighting the importance, respondents selected
challenges Generative AI brings to risk management and effectively managing risks as the second-most reported
worries about 36%
governance—since only 23% rated their organization way to drive the most value for Generative AI initiatives.
regulatory compliance
30%
difficulty managing risks
29%
lack of a
governance model
Currently, these are considered even more significant
than other critical barriers such as implementation
challenges, a lack of an adoption strategy, and difficulty
identifying use cases.
17
Now: Key findings
The chief operations officer and chief of strategy in a To help build trust and ensure the responsible use for using Generative AI tools and applications (51%),
financial services company summed up the challenge: of Generative AI-powered tools and applications, monitoring regulatory requirements and ensuring
organizations are generally working to establish compliance (49%), and conducting internal audits /
“How do you democratize Generative AI across your
new guardrails, educate their workforces, conduct testing on Generative AI tools and applications (43%)
business while having all of the right controls in place?
assessments, and build oversight capabilities. (figure 9). Despite their importance for effective scaling,
We have an AI board, we have an ethics framework, we
each of these actions is only being taken by less than
have an accountability model. We want to know who’s
Specific actions surveyed organizations are currently
roughly half of the organizations we surveyed.
using it for what, and that it’s being used in the right way.”
taking include establishing a governance framework
Actions to manage risk
51%
49%
43%
37%
35%
33%
30%
23%
19%
Establishing Monitoring regulatory Conducting internal Training practitioners Ensuring a human Keeping a Using a formal Using outside Single executive
a governance requirements and audits and testing how to recognize validates all GenAI- formal inventory group or board to vendors to conduct responsible for
framework for the ensuring compliance of GenAI tools / and mitigate created content of all GenAI advise on GenAI- independent audits managing GenAI-
use of GenAI tools / applications potential risks implementations related risks and testing related risks
applications
Figure 9 Q: What is your organization currently doing to actively manage the risks around your Generative AI implementations?
(May/June 2024 ) N (Total) = 2,770
18
Now: Key findings
78%
of leaders surveyed in Q1 agreed that more
governmental regulation of AI was needed.
Implementing new processes and controls is rarely easy and will likely require active
change management to build support within the organization. “Before launching
anything, we have strict AI governance,” said the chief analytics officer at a professional
services firm. “In the past we had a bit of a siloed approach, but today, at a minimum,
everything has to go through privacy and compliance because we have a methodical
way of managing risk. This is new and challenging to some.”
On top of risk and governance issues, Q3 surveyed organizations were exceedingly
uncertain about the regulatory environment that may exist in the future (depending on
the countries they operate in). In our first quarterly report, 78% of leaders agreed that
more governmental regulation of AI was needed. However, there is a difference between
theory and practice. Organizations are struggling with regulatory uncertainty, and
worries about interpretation and enforcement may be preventing them from pursuing
certain use cases in specific geographies.
The uncertainty around AI regulation may make it feel like there could be many varied
outcomes, but our research suggests most countries are following a similar path
concerning AI policies.2 Governments are working to balance protection, innovation
and economic benefit, so future actions will likely be in line with the regulatory
traditions of each country and region.
1199
Now: Key findings Insights from our executive interviews
How some real-world organizations are dealing with compliance, risk management
Some organizations reported taking action to prepare
and governance issues
for potential regulatory changes. Top areas include
preparing regulatory forecasts or assessments (50%),
An increasing number of organizations are making risk a central factor when selecting Generative AI use
monitoring by the general counsel (48%), and working
cases and investments. However, many are walking a tightrope—trying to minimize risk without being too
with external partners (46%) (figure 10). However, some
risk averse, which could lead to missed opportunities and open the door to competitors.
organizations aren’t doing anything to prepare; 14%
said they aren’t making any specific plans.
Here are some risk-related actions revealed through our in-depth executive interviews:
How organizations are preparing
for regulatory changes Avoiding
Avoid use cases that could require additional regulatory scrutiny
specific tools
and use cases Shut off access to specific Generative AI tools for staff
For organizations that rely heavily on owned intellectual property, be extremely cautious when
Corporate 50% Limiting
exposi"
212,ey,ey-gen-ai-for-lending-brochure.pdf,"How pursuing
GenAI can transform
mortgage lending
By applying GenAI innovations across the lifecycle,
mortgage lenders can gain a strategic advantage.
IN BRIEF In this era of unprecedented technology, mortgage
• Mortgage lenders previously lenders have a transformative opportunity to drive
have embraced artificial
operational efficiencies while enhancing customer
intelligence (AI) and machine
learning (ML). Yet they experiences by leveraging generative AI (GenAI). With
have been slow to adopt
artificial intelligence (AI) and machine learning (ML)
generative AI (GenAI).
already underpinning technological advancements in
• For mortgage lenders
embarking on their GenAI this space, we would expect to see lenders flocking
journey, multiple use cases
to GenAI to gain a tactical advantage. GenAI makes
impacting key operational
components offer a path extracting insights and automating processes connected
forward.
with unstructured data easier than ever before, and
the mortgage industry is rich with data across the loan
lifecycle, including credit, marketing, servicing and
back office.
Yet according to the Fannie Mae Mortgage Lender
Sentiment Survey released in October 2023, only 7%
of mortgage lenders are currently using GenAI; 71%
are either just beginning to explore this technology
or are not considering it at all. The complexity of the
technology, evolving regulations, concerns over data
privacy and intellectual property issues have made
adoption a challenge. But those who find a way to
navigate the complexities and drive successful adoption
will have the opportunity to outperform their industry
peers in revenue, profitability and customer experiences.
01 | How pursuing GenAI can transform mortgage lending
“
Lenders can explore and invest in GenAI capabilities starting
with use cases that have already shown a significant positive
impact in other industries. Starting on a small scale allows
lenders to identify immediate gains, thereby providing a
valuable learning experience. Moreover, this measured
approach boosts the confidence to implement broader and
more ambitious GenAI applications while maintaining a
sustainable pace of progression.
ADITYA SWAMINATHAN
EY Americas Consumer Lending and Mortgage Leader
Mortgage lender adoption of AI/ML
Source: Mortgage Lender Sentiment Survey, Fannie Mae, October 2023
7%
29%
Current users
22%
Trial users
Investigating
Not used or explored
42%
02 | How pursuing GenAI can transform mortgage lending
1
CHAPTER 1
How to remediate concerns around potential GenAI risks
Mortgage lenders are taking a measured approach to exploring GenAI.
The reluctance around GenAI is rooted more in perceived On the plus side of the ledger, GenAI could counteract
obstacles and concerns than a lack of awareness of the some of the market pressures that mortgage lenders
technology’s potential. In our interactions with clients, are facing. In the EY 2023 Annual Mortgage Executive
lenders have expressed concerns that the implementation Research Report, 60% of leaders across 20 top global
of GenAI will be a complex, expensive process that banks, midsize regional banks, and nonbank and FinTech
disrupts their existing infrastructure. Mortgage lenders lenders reported the need to increase origination volume.
also worry about data security, privacy issues, and Historically high interest rates dampened new loan and
regulatory compliance. refinancing activity, which in turn has created greater
urgency to gain market share. Among the lenders
While the risks and the concerns are real, they can be
surveyed, 70% also cited reducing operating costs as
addressed through a deliberate, measured approach
a top challenge. The mortgage lifecycle – origination,
that selects the right mix of technology and implements
servicing and default – involves time-consuming tasks
governance processes. Integration often does not require
that require reviewing cumbersome unstructured content
a drastic technological overhaul, as existing systems can
including loan applications, title report, and appraisal
be enhanced with AI capabilities, making the transition
reports, as well as extensive human interaction. GenAI
more achievable and less intimidating.
has the potential to streamline these tasks, automate
routine processes, facilitate swift and accurate decision-
making, and ultimately generate substantial cost savings.
Perceived value in GenAI
Source: Mortgage Lender Sentiment Survey, Fannie Mae, October 2023
Improve operational efficiency 73%
Reduce human error 9%
Enhance consumer/borrower experience 7%
Better control risks 5%
Other 5%
03 | How pursuing GenAI can transform mortgage lending
2
CHAPTER 2
Applying GenAI across the mortgage lending value chain
Mortgage lenders can gain significant advantages in critical areas.
Opportunities to boost efficiency across key operational
components span the entire mortgage lifecycle.
These uses are extensive and scalable, evolving with
technological advancements and incorporating more
sophisticated applications over time.
Origination: Servicing: Default: Cross-functional
applications:
By analyzing existing GenAI can serve as a GenAI can analyze
customer metadata, virtual loan assistant, borrower data and GenAI can reduce the
GenAI has the potential handling a large previous interactions need for manual data
to increase origination volume of requests to determine the most entry by automating,
volume by generating simultaneously and effective communication organizing, and
personalized loan identifying complex or strategies with the categorizing underwriting
offerings and creating sensitive issues that customer. It can optimize and servicing
tailored products for require the expertise payment allocation and documentation, including
specific customer groups. of a human agent. predict the likelihood credit reports, income
The prospect pool can be Through extensive use of payments on statements, tax returns,
expanded by tapping into of chatbots, GenAI can delinquent accounts. and insurance policies.
a wider range of sourcing improve call deflection AI and ML models can Back-office operations
channels, including social and containment. also proactively identify also can be automated
media. Establishing By automating key borrowers who are at to complete regulatory
an enriched prospect tasks, including call increased risk of default. compliance checks, such
profile can allow for dispositioning and notes as loan file completeness
more effective outreach capture, productivity of reviews, disclosures and
through customized the servicing workforce certification. Customer
loan offers, a higher can be increased. complaints can be
propensity to close the Agents also benefit from identified and logged
loan, and an increased enhanced knowledge with greater accuracy,
likelihood that the tools that help them which accelerates
borrower will secure the query the complex the resolution. GenAI
mortgage. state-level regulations also can channel
and internal rules and unstructured data from
procedures. various resources into a
searchable knowledge
management hub.
04 | How pursuing GenAI can transform mortgage lending
In addition to these specific use cases, lenders can benefit
from GenAI adoption by their vendors who are moving
to embed the technology in support systems, platforms
and applications.
GenAI use cases and opportunities across the mortgage lifecycle
Origination Servicing Default Cross-functional
• Personalized loan • Early intervention • Personalized • Document
offerings and default collection automation
prevention communications
• Social media lead • Searchable,
generation • Loss mitigation and • Payment allocation synthesized
catastrophic events optimization knowledge center
• Fraud detection and
credit assessment • Virtual loan assistant • Default prediction • Regulatory
and account models compliance
management
• Customer complaints
Source: EY Consumer Lending Team
05 | How pursuing GenAI can transform mortgage lending
3
CHAPTER 3
GenAI use cases forge the path to lending modernization
Where mortgage lenders should start making initial investments in GenAI.
For lenders starting their GenAI journeys, the following • Knowledge center: When receiving a customer query,
three use cases offer an ideal entry point. agents in traditional organizations often have to
access multiple databases to find the answer. Agents
• Personalized loan offerings: Traditional lending
are further challenged by the possibility that the
institutions primarily offer standardized loan products
information in the database may be outdated. Difficult
with minimal customization, which may not cater
interfaces also can create bottlenecks by requiring
to the specific needs of all customers. The lack of a
the use of different tools to find information. Once
personalized approach that aligns the loan offerings
the answer is found, organizations run the risk of
with the customer’s financial condition also increases
agents interpreting or conveying the same information
the chance of default. Standardized loan products
differently.
come with high operational costs due to the manual
process of scrutinizing individual loan applications GenAI can help connect and combine knowledge across
and underwriting them. different databases, creating a knowledge center for
agents to access. It can also parse and summarize
GenAI can customize loans, leveraging customer data
new laws, rules, and regulations applicable to the
to design products tailored to individual needs, which
organization. Having dashboards and interfaces that
in turn enhances customer satisfaction and retention.
are easier to navigate simplifies the information-
It can increase loan origination rates by analyzing
gathering process and saves time. With all agents
existing customer metadata. Based on a customer’s
working from the same information, they can provide
personal and financial situation, GenAI can offer data-
a consistent customer experience.
based insights that would allow the lender to adjust
the loan terms, potentially decreasing the chances of
Answering customer queries for information
default. Through greater automation of the entire loan
sanctioning process, operational costs can be reduced
Future state with GenAI —
and efficiency improved.
Difference in customer loan offerings • Connecting data from disparate sources
• Accurate and relevant content
Future state with GenAI —
• Simplified dashboards and interfaces
• Consistent voice
• Customized loan products
• Accurate targeting of specific customer groups
Source: EY Consumer Lending Team
• Personalized approach
• Operational efficiency
Source: EY Consumer Lending Team
06 | How pursuing GenAI can transform mortgage lending
• Customer complaints: When a customer calls with a
complaint, the traditional organization has agents who Authors:
manually draft complaint summaries, a time-consuming
task that can lead to inaccuracies and errors. Without
clear parameters, the manual categorization of
Aditya Swaminathan
complaints can be difficult. While logging the complaint,
EY Americas Consumer Lending
the agent receiving the call is also expected to treat the and Mortgage Leader
caller with empathy, providing a human touch to the Ernst & Young LLP
customer experience. aditya.swaminathan@ey.com
An application that leverages a Large Language Model
Sameer Gupta
(LLM) can help transcribe and summarize the complaint
EY North America Financial
into call notes. By using predefined categories, GenAI
Services Organization Advanced
can classify complaints, increasing organization and
Analytics Leader
making it easier to identify trends. Freed up from Ernst & Young LLP
manual tasks, agents can focus on extending empathy sameer.gupta@ey.com
and apologies, which improves customer satisfaction.
William Coe
Handling customer complaints
Senior Manager,
Business Transformation Consulting
Future state with GenAI — Ernst & Young LLP
william.coe@ey.com
• AI-transcribed complaints increase efficiency
• AI complaint categorization saves time and allows
for easier identification of complaint trends
Contributors:
• Agent attention is on the customer, improving
customer satisfaction
Dan Thain
Source: EY Consumer Lending Team Principal, Financial Services
1 Mortgage Lender Sentiment Survey, Fannie Mae, October 2023 Business Consulting
Ernst & Young LLP
Conclusion daniel.thain@ey.com
With advancing technology and evolving consumer
expectations, a transformative opportunity awaits Joe Owen
forward-thinking mortgage lenders. By exploring and Senior Manager, Consulting
Ernst & Young LLP
investing in GenAI technologies, lenders stand to gain a
joseph.owen@ey.com
first-mover advantage and play a pivotal role in shaping
the future of the consumer lending space.
Luke Caussade
Manager, Consulting
Ernst & Young LLP
luke.caussade@ey.com
07 | How pursuing GenAI can transform mortgage lending
EY | Building a better working world
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to create long-term value for clients, people and
society and build trust in the capital markets.
Enabled by data and technology, diverse EY
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Working across assurance, consulting, law,
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EY refers to the global organization, and may refer to one or more,
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please visit ey.com.
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© 2024 Ernst & Young LLP.
All Rights Reserved.
US SCORE no. 23260-241US_2
2402-4424343 BDFSO
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This material has been prepared for general informational purposes only and
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ey.com"
213,pwc,wef-leveraging-generative-ai-for-job-augmentation-and-workforce-productivity-2024.pdf,"In collaboration with
PwC
Leveraging Generative AI
for Job Augmentation and
Workforce Productivity:
Scenarios, Case Studies
and a Framework for Action
I N S I G H T R E P O R T
N O V E M B E R 2 0 2 4
Images: Unsplash.com
Contents
Foreword 3
Executive summary 4
Introduction 6
1 GenAI’s potential for promoting job augmentation and 7
workforce productivity
2 The unwritten future of GenAI in the workforce 11
3 Insigths from early adopters 16
4 Framework for action 21
Conclusion 27
Appendix: Scenario methodology 28
Acknowledgements 29
Contributors 30
Endnotes 31
References 32
Disclaimer
This document is published by the
World Economic Forum as a contribution
to a project, insight area or interaction.
The findings, interpretations and
conclusions expressed herein are a result
of a collaborative process facilitated and
endorsed by the World Economic Forum
but whose results do not necessarily
represent the views of the World Economic
Forum, nor the entirety of its Members,
Partners or other stakeholders.
© 2024 World Economic Forum. All rights
reserved. No part of this publication may
be reproduced or transmitted in any form
or by any means, including photocopying
and recording, or by any information
storage and retrieval system.
2
November 2024 Leveraging Generative AI for
Job Augmentation and Workforce Productivity:
Scenarios, Case Studies and a Framework for Action
Foreword
Till Leopold
Peter Brown
Head, Work, Wages and Job
Partner, PwC UK; Global
Creation, World Economic
Workforce Leader, PwC
Forum
Generative artificial intelligence (GenAI) is geographies, we found that successful deployment
transforming the world of work. According to the of GenAI depends as much, or more, on people
World Economic Forum’s latest Future of Jobs than the technology itself. Workers need to
survey, within the next five years, employers expect understand, trust, and adopt GenAI. This requires
GenAI advancements to reshape a substantial not only training and support but also a cultural shift
number of jobs, potentially affecting up to 40% of within the organization to embrace new ways of
total global working hours. working.
As the capabilities of this transformative technology This report covers insights from the early adopters
continue to evolve, organizations are wondering we interviewed, as well as four different scenarios
how they can use GenAI to drive job augmentation for how the deployment of GenAI in organizations
and workforce productivity, and the actions they could play out. It also offers an actionable
can take to harness its full potential. framework which organizations can use to shape
their GenAI workforce approach.
To find out, the World Economic Forum and PwC
embarked on a new piece of research focusing We would like to thank all the organizations and
on how early adopters of GenAI are leveraging it experts who generously shared their time and
across the workforce, the impact it is having and experience with us. We hope this report will be a
the lessons they have learned along the way. useful resource for organizations across the world
as they navigate the opportunities and challenges
Based on interviews with more than 20 that GenAI brings for job augmentation and
organizations across a wide range of industries and workforce productivity.
3
Executive summary
Generative artificial intelligence (GenAI) has conversely, because of concern over its fast
the potential to drive significant improvements progress and potential for job disruption—is
in workforce productivity at the level of tasks, one which misses out on the opportunities of
organizations and economies. Delivering those productivity gains and job augmentation. A world
gains depends, among other things, on the of high trust but limited improvements in GenAI
deployment of GenAI to augment jobs, i.e. contains significant risks; while one where both
to partially perform tasks in such a way that trust and quality and applicability improve in tandem
technology effectively supports or enhances human is likely to see the biggest gains in workforce
capabilities through human-machine collaboration. productivity and job augmentation.
Drawing on a review of existing research, scenario
analysis and case studies of early adopters, this Insights from early adopters
report proposes a framework for action that fosters
job augmentation. The four near future scenarios outlined provide
a useful background to insights derived from
Global context interviews with more than 20 early adopters
from a wide range of industries and regions across
What sets GenAI apart from previous developments the world. These organizations are pursuing GenAI
in artificial intelligence is its ability to widen access partly out of confidence in productivity gains. They
to the use of AI and eliminate the barrier of also believe that GenAI will improve the quality
specialized knowledge. GenAI has the potential to of work, and the experience of their employees.
contribute to economic and productivity growth A different motivation is a desire to pre-empt the
by creating efficiencies through freeing up working potential disruption of their business.
time spent on lower-value tasks to engage in higher
value-added activities. Moreover, GenAI has the The organizations quickest to adopt GenAI in their
potential to augment human workers by enhancing workforce are those that could be described as
their skills and capabilities, thereby increasing their ‘data-driven’. They emphasize the need to develop
productivity and enabling new and diverse forms of and test GenAI solutions in small groups before
value creation. rolling them out to the rest of the organization,
allowing for issues to be identified and addressed
However, GenAI’s potential to enhance productivity before wider implementation. They also put
may vary across countries, industries, and significant emphasis on risk management, including
organizations. To effectively deploy GenAI in the designing processes that have ‘humans in the
workforce, organizations must also address a loop’, forming internal committees or councils that
range of factors including trust, skills, culture and establish internal rules, standards, and frameworks
the demonstration of business value from GenAI and assess use cases and consider sustainability
investments. implications of using GenAI at scale.
Scenario analysis To identify the potential for workforce productivity
gains and job augmentation, early adopters
With such a fast-moving technology, it is hard to combine both bottom-up and top-down
predict how even the relatively near-term future will approaches, with strong support from leadership
play out. To help think through the possibilities, it and reliance on the innovative capabilities of their
is useful to think in terms of scenarios based on workforce. It is in day-to-day practice where most
two key uncertainties that will shape the near future use cases are identified and developed. According
of GenAI-enabled job augmentation, productivity to this perspective, the most promising use
and innovation. The first core uncertainty relates cases are those embraced and championed by
to the level of trust in GenAI, which refers to the employees themselves.
confidence that employees and organizations have
in GenAI-driven tools and their outputs as well Framework for action
as employee trust in their employers, technology
providers, and governments. The second core Combining insights from the scenarios and
uncertainty relates to whether the applicability and lessons learned from early adopters, the report
quality of GenAI will continue to improve in the proposes an actionable framework for promoting
short-term or remain the same. job augmentation and workforce productivity
growth with GenAI. Focusing on factors within an
Any combination of these two dimensions is organization’s control, it is designed to be useful
possible, leading to very different outcomes. A both to organizations just starting out on their
world where trust is low—either because GenAI GenAI workforce deployment journey as well as
does not progress significantly from today; or, those seeking to scale existing efforts.
4
The framework highlights a number of key elements Engage elements focus on facilitating that GenAI
around two core themes: Enable and Engage. The workforce applications are effectively adopted and
Enable elements focus on establishing foundations integrated into workflows to generate the desired
and guiding principles and include: GenAI vision benefits. These elements include: Culture and
and strategy; Data and technology infrastructure; change management; Skills development and
and Regulatory compliance and governance. The redeployment; and Use case management.
5
Introduction
This report aims to unearth the experience of early Section 4 builds on the previous sections to offer
adopters of generative artificial intelligence (GenAI) an actionable framework that organizations may
deployment in the workforce to derive lessons adapt for their own use to augment jobs and
learned and provide an actionable framework enhance productivity through GenAI adoption.
for promoting job augmentation and enhancing The framework aims to enable organizations to
workforce productivity. It examines the key harness the potential of GenAI while adhering to
elements that organizations must have in place ethical standards, emerging legal requirements,
to facilitate these outcomes. Research interviews and considering the development and well-being of
conducted for this report are global in scope, employees using the technology.
encompassing a wide range of geographies,
industries and organizations including commercial, The findings suggest that, with the right enabling
public-sector and social entities. conditions, GenAI has the potential to augment
jobs and enhance productivity. However, this
The emergence of GenAI in the workplace has requires organizations to go through a phase of
created significant interest, from the boardroom understanding the technology’s value for their
to the breakroom. Section 1 examines these specific needs, identifying appropriate use cases,
expectations, hopes and concerns, and outlines and thoroughly testing the solutions. Moreover,
current barriers for individuals and organizations to safeguarding that workers understand, trust and
effectively leverage the technology to achieve better adopt GenAI is essential before use cases can be
people and business outcomes. scaled; thus, in addition to training and support, a
cultural shift within the organization is also critical
At present, the future of GenAI in the workforce to embrace new ways of working. Based on
remains uncertain and undefined. Despite rapid the insights from the interviews, the successful
developments, the technology is still in its infancy, deployment of GenAI depends as much or more on
making it impossible to extrapolate the extent to people than on the technology itself.
which productivity gains and job augmentation
may be achieved in the near future. Acknowledging This report has been developed as part of The
the unpredictable nature of the future, Section Jobs Initiative, coordinated by the World Economic
2 considers four scenarios to enable various Forum, which aims to build the jobs of tomorrow
stakeholders to think through the multiple ways in and ensure good jobs for all in the context of
which GenAI in the workforce could evolve. ongoing labour market disruptions. One key
focus area for the initiative is promoting strategies
Section 3 presents findings from interviews with for leveraging GenAI for job augmentation and
more than 20 early adopting organizations that workforce productivity growth. It is one of a series of
have generously shared their experiences, lessons current World Economic Forum reports that explore
learned and expectations regarding the emerging the transformative role of artificial intelligence across
impact of GenAI on productivity gains and job industries and a variety of key themes.
augmentation to provide valuable insights into the
practical implications and potential returns of GenAI
workforce deployment.
6
GenAI’s potential
1
for promoting job
augmentation and
workforce productivity
This section provides an overview of the debate several other prominent LLMs shortly thereafter,
about generative artificial intelligence (GenAI) in public interest in GenAI has surged, raising
the workforce and its potential for promoting job expectations about its potential to transform the
augmentation and productivity growth. It also global labour market. According to the World
highlights current expectations and assessments Economic Forum’s latest Future of Jobs survey,
surrounding GenAI as well as barriers to its more within the next five years, employers expect a
widespread workforce adoption, two years after the substantial number of jobs to be reshaped due to
public launch of one of the most prominent large GenAI advancements1, potentially affecting up to
language models (LLMs). 40% of total global working hours.2
By leveraging natural language processing This first section of the report will review the current
technology, GenAI enables users to interact with state of the debate on GenAI’s potential, with a
it as though they were conversing with a human, particular focus on job augmentation, workforce
reducing barriers to usage and the need for productivity growth and barriers to the technology’s
specialized technical knowledge. Since the public more widespread workforce adoption.
launch of ChatGPT 3.5 in November 2022, and
1.1 GenAI and job augmentation
Like other recent advances in automation and AI More frequently, GenAI may partially automate
technologies, the rise of GenAI has led to concerns some tasks of a job role but simultaneously improve
about possible job displacement. This apprehension human workers’ ability to perform other tasks. In
is partly rooted in the technical potential of the line with recent research, this paper refers to this
technology itself and partly in skepticism about process as job augmentation (see Box 1 and Fig.
employers’ and governments’ ability to support 1).6
individuals through AI-induced job disruptions.3 One
recent survey indicated that 47% of employees who As GenAI technologies and labour markets
had used GenAI expressed concerns that it may continue to evolve, it is likely that some job roles
affect the nature of their work in a negative way.4 may become more fully automated while others
may be further augmented in the future. In similar
Research examining the potential impact of GenAI ways to earlier industrial transformations, both
on jobs commonly operates on the premise that job automation and job augmentation may be
job roles and occupations are composed of expected to lead to additional job creation – both
various tasks, some of which may be susceptible directly, creating wholly new jobs in various fields,7
to varying degrees of automation by GenAI. For and indirectly through macroeconomic spillover
instance, tasks that are repetitive or routine are effects from increased productivity and additional
more exposed to automation than those requiring economic value creation. The focus of this report
significant human interaction. While a wide range of is on the immediate term and in putting into place
tasks may be fully automated by GenAI, research enabling conditions for job augmentation now and
to date has found very few examples of jobs that in the next years.
could be displaced in this way in their entirety.5
7
BOX 1 Automation and augmentation
This report distinguishes between the following collaboration. Job augmentation may go beyond
definitions: technical productivity increase to also enhance job
quality and worker well-being.2
Job automation refers to the use of GenAI to fully
1. Raisch, S. and S. Krakowski, “Artificial
perform tasks that were previously performed by
Intelligence and Management: The Automation–
humans in a given occupation.1
Augmentation Paradox”, Academy of
Management Review, vol. 46, no. 1, 2021.
Job augmentation refers to the use of GenAI
to partially perform tasks in such a way that 2. World Economic Forum, Augmented
technology effectively supports or enhances Workforce: Empowering People, Transforming
Manufacturing, 2022.
human capabilities through human-machine
FIGURE 01 GenAI: Example of a more exposed and less exposed job
Software Developers (more exposed) Human Resource Managers (less exposed)
28.7% 43.2% 28% 16.1% 22.2% 61.7%
Higher potential for automation: Higher potential for automation:
— Analyse data to improve operations — Determine resource needs of projects or operations
— Analyse performance of systems or equipment — Manage budgets or finances
Higher potential for augmentation: Higher potential for augmentation:
— Prepare informational or instructional materials — Explain regulations, policies or procedures
— Evaluate the characteristics, usefulness or performance of — Train others or operational or work procedures
products or technologies
Lower potential for automation or augmentation: Lower potential for automation or augmentation:
— Coordinate with others to resolve problems — Interview people to obtain information
— Communicate with others about business strategies — Coordinate group, community or public activities
Automation Augmentation Lower potential Non-language tasks
Source
World Economic Forum, Jobs of Tomorrow: Large Language Models and Jobs, 2023.
1.2 GenAI and workforce productivity growth
GenAI’s potential impact on productivity is one of knowledge. GenAI has the potential to contribute
its most anticipated benefits, particularly because to economic and productivity growth by creating
of the slowdown in productivity growth in many efficiencies through freeing up working time
economies.8 Although current forecasts vary spent on lower-value tasks to engage in higher
widely, it has been suggested that GenAI’s impact value-added activities. For instance, automating
on productivity could add trillions to the global help desk queries may allow customer service
economy over the next decade.9 workers to focus on more complex issues that
increase customer satisfaction. One recent study,
What sets GenAI apart from previous developments surveying more than 100,000 workers from 11
in AI is its ability to widen access to the use GenAI-exposed occupations, found that workers
of AI and eliminate the barrier of specialized estimated ChatGPT could reduce working times
8
by 50% for one-third of their job tasks.10 As may disproportionally boost productivity for workers
respondents interviewed as part of the research with less experience or skill, thereby reducing entry
for this report highlighted, to realize these potential barriers to the digital economy.16
productivity gains it is important to capture time
saved as value at an organizational level. At an industry level, exposure to GenAI-driven
task automation and augmentation varies widely
Moreover, GenAI has the potential to augment across sectors, with not all industries being equally
human workers by enhancing their skills and impacted or standing to benefit from GenAI. As
capabilities, thereby increasing their productivity described above, previous research has identified
and enabling new and diverse forms of value which tasks are most exposed to LLMs, highlighting
creation.11 For instance, GenAI may augment their higher or lower potential for automation or
human capabilities in creative tasks, though it augmentation. For example, one recent study
does not currently surpass human creativity on its found that software developers from three large
own.12 Research also suggests that GenAI may technology firms increased the number of tasks
help narrow productivity gaps between lower- and completed by over 26% using GenAI.17 When these
higher-skilled workers.13 exposure levels are aggregated at the industry level,
it becomes evident that the impact of GenAI may
GenAI’s potential to enhance productivity may vary vary significantly across industries. For instance,
across countries, industries and organizations. At the technology and financial sectors could face
a country level, more developed economies may substantial task automation, while the healthcare
face higher disruption risk due to prevalence of and education sectors may benefit more from task
knowledge work, but they are also better equipped augmentation.18
to adopt GenAI more quickly and at scale.14 Many
of these countries also face a decrease in labour Importantly, as discussed in Section 3 of this
supply, which may boost the demand for new report, productivity growth is not the only driver
technologies such as GenAI to seek efficiency for organizations to deploy GenAI. Many also
improvements.15 Emerging economies may similarly expect improved quality of work and better work
benefit from productivity growth by addressing experiences for their employees, increasing
infrastructure constraints and shortages in basic employee engagement and talent retention.
digital skills. Early research indicates that GenAI
1.3 Current barriers to scaling GenAI adoption in
the workforce
Historically, slow and inconsistent adoption of To build trust and facilitate the ethical use of GenAI,
AI technologies has restricted their impact and there is strong demand for transparency and
effectiveness.19 As of mid-2024, only 12% of responsible deployment. Increasingly, organizations
workers report that they use GenAI at work on are implementing responsible GenAI principles
a daily basis.20 Current barriers to GenAI uptake to build trust in decision-making processes by
encompass concerns related to trust, skills improving explainability and mitigating risks. At both
acquisition, change in culture and unclear business national and supranational levels, some territories
value. are tightening regulations on AI to promote trust
and ethical use by setting clear boundaries and
enforcing accountability. For example, the European
Trust AI Act includes the “human-in-the-loop” principle,
emphasizing human accountability in decision
making. This principle may help to increase trust in
Trust is a crucial factor that must be considered GenAI by establishing accountability and respecting
when embracing new technologies. GenAI models human values.24
are sometimes referred to as “black box” systems
due to the complexity of their algorithms, raising At an industry level, concerns have been raised
concerns about the outcomes they generate about a comparatively small number of industry
and transparency.21 In line with this, CEOs see players holding significant influence over the
cybersecurity, spread of misinformation, legal or development of GenAI as well as its regulatory
reputational damage, and increased levels of bias environment.25 Government regulation is partly
as primary concerns related to the adoption of aimed at creating a level playing field where all
GenAI.22 In addition to bias and discrimination, parties follow the same regulations and criteria.
workers are specifically worried about the lack Nevertheless, inconsistent AI laws worldwide
of oversight, transparency, explainability and could also have the reverse effect, disadvantaging
accountability.23 organizations that are operating in the most strictly
regulated jurisdictions.
9
Skills 60% over the next three years, reaching 7.6%
of IT budgets by 2027.30 Others exhibit greater
reluctance, questioning the assuredness of the
At a workforce level, two out of five employers returns these investments may bring. Companies
report that a lack of adequate AI-related skills is often cite costs as a significant barrier to GenAI
an obstacle to the integration of GenAI at work.26 adoption, with a number of them unsure of the
Increased demand for GenAI skills outside of tech technology’s potential benefits.31 The uncertainty is
roles is evident when examining the share of job amplified by the limited evidence available on the
postings in non-tech roles that now request these impact of GenAI on firm performance.32 Preliminary
skills.27 Yet, there is a prevailing concern among outcomes show promise, but the conclusive
78% of senior executives that their companies benefits remain unclear. For instance, the edge
may fail to train their employees rapidly enough to from adopting GenAI could diminish with increasing
keep pace with technological advancements in the competitive pressures in the market.33
coming years.28 This concern is reinforced as 37%
of more than 56,000 workers from 50 countries Preliminary findings indicate that the most significant
and regions surveyed by PwC have not used promise of GenAI in revolutionizing business models
GenAI applications for work in the past year, and an could reside at the intersection of specialized
additional 25% have only done so once or twice. expertise and innovative problem-solving, propelling
Even in the highly exposed financial sector almost rapid advancements in proposition innovation that
one-quarter of workers reported not to have used encompass new offerings, customers, markets,
GenAI for work. In the telecommunication sector, channels and customer relationships. Interviews
which showed the highest overall use of GenAI, conducted for this report with various early
19% of workers have never used GenAI in their adopters of GenAI show that organizations remain
work.29 cautious about the use of GenAI in externally facing
products and services. While ultimately something
they may strive for, so far, most organizations are
Culture experimenting and scaling up GenAI within the
comparatively safer walls of their own internal
organization.
The culture of an organization is a crucial factor in
the adoption of new technologies such as GenAI. To shift the focus from smaller-scale incremental
Organizations interviewed for this report stress the improvements to new business models, the
importance of change management: successful advancement of GenAI, in combination with
introduction of GenAI depends on experiments and other emerging technologies, will be one of the
finding use cases. This requires a stimulating and most important determinants. For instance, the
supportive culture. For example, it is important to emergence of multimodal LLMs enables the
cultivate mindsets such as a future-positive attitude, concurrent processing and generation of various
growth mindset and agility, which are crucial for data forms (including text, imagery and audio),
employees to embrace GenAI in the workplace. integrating these elements to create a thorough
Young (technology) companies and data-driven understanding. It can bring new strategic business
organizations tend to adopt new technology easier benefits such as improved decision-making,
and faster because they are less hampered by enhanced user experience and operational
existing, established ways of working and embody efficiency.34 With more clarity on the speed and
a data culture and digital literacy. magnitude of such developments, the potential of
GenAI to lead to business model reinvention will
become more apparent.
Business value
Certain enterprises are readily committing
substantial resources to GenAI technologies,
with investments in GenAI projected to grow by
10
The unwritten future of
2
GenAI in the workforce
There remains a high degree of uncertainty about productivity and innovation. These scenarios are
the future trajectory of GenAI in the workforce not forecasts or idealized visions but illustrate what
and the extent to which its potential for job realistically could happen.
augmentation and productivity growth may be
realized. This section presents four different near This section presents four scenarios for the near-
future scenarios for how the deployment of GenAI term future of GenAI based on future studies
in organizations could play out. Organizations, methods (Figure 2; see the Appendix for a more
leaders and workers alike will need to consider detailed description of the report’s scenario
these alternative futures when forming their hopes, methodology). Scenarios were developed through
expectations and strategies regarding GenAI workshops and trend analysis, focusing on two
adoption. key uncertainties that will shape the near future of
GenAI-induced job augmentation, productivity and
“Amara’s law” states that observers tend to innovation: 1) trust in GenAI, and 2) improvements
overestimate the short run impacts of new in applicability and quality of the technology. These
technology and underestimate the longer- scenarios are applicable to organizations that are
term ones. The long-term impact of GenAI on exposed to GenAI, with leadership aiming to deploy
productivity, augmentation and innovation remains GenAI, regardless of current workforce adoption or
uncertain.35 While some current task- and firm-level external influences. The analysis excludes situations
use cases for tools like ChatGPT have shown a where governmental entities may force or restrict
40% decrease in task time and an 18% increase GenAI use.
in quality,36 experience has shown that it can take
a long time for technology to become sufficiently
widespread to affect productivity at an economy- GenAI-induced job augmentation
wide scale.37 Applying GenAI in tasks, processes
and productivity growth: Two
and structures requires experimentation and finding
core uncertainties
and applying use cases – and this simply takes
time.38 This is also confirmed by the case study
interviews in Section 3. The first core uncertainty relates to the level of trust
in GenAI, which refers to the confidence that
Moreover, many tasks that humans currently employees and organizations have in GenAI-driven
perform, for example in the areas of transportation tools and their outputs. It also refers to the trust
and manufacturing, are multifaceted and require of employees in the organization, the technology
real-world interaction, which GenAI is not currently provider and the government to prevent issues such
able to improve upon.39 The question is whether as privacy breaches, exploitation and information
organizations will reach a point where massive leaks. As outlined in Section 1, trust is crucial
scaling-up may take place, leading to productivity for GenAI adoption and is influenced by different
gains and job augmentation on a macroeconomic factors.
level in the longer term.
The second core uncertainty relates to whether the
applicability and quality of GenAI will continue to
Scenario thinking: navigating an improve in the short term or remain the same. High
applicability means GenAI tools are practical and
uncertain future
useful across various use cases and industries.
High quality means the outputs are accurate,
Studies of the future recognize its unpredictability reliable and have a low percentage of errors. When
and aim to anticipate and prepare for the impact combined, these qualities make GenAI valuable
of potential developments, in this case: the and dependable. Improved applicability and quality
impact GenAI could have on job augmentation, would lead to new use cases, user models and
productivity and innovation in the near future. The functionalities, allowing GenAI to (further) augment
scenarios presented in this section are tools to and automate tasks, enhance jobs, create new
navigate uncertainty and inform strategic decisions. industries and serve as a foundation for future
They explore uncertainties and present possible technologies.
outcomes of GenAI-induced job augmentation,
11
High
Scenario 1 Scenario 4
High Hopes Shifting Gears
High trust High trust
Current applicability/quality Expanding applicability/quality
Scenario 2 Scenario 3
Broken Promises Lost Opportunities
Low trust Low trust
Current applicability/quality Expanding applicability/quality
Low
Remains at current level Improves
Applicability and quality of GenAI
2.1 Scenario 1: High Hopes
not able to effectively interpret or validate the results
High trust, current applicability & it produces. This leads to inaccurate decision-
making or reliance on flawed insights. So, high trust
quality
does not result in increasing productivity; on the
contrary, it leads to work having to be redone (for
In this scenario, enthusiasm for GenAI workforce example, one recent study showed that participants
adoption is high. Leadership hopes GenAI will who used an LLM to solve a particular business
contribute to the solving of labour shortages and problem exhibited a 23% lower correctness of the
anticipates it will improve the quality of work. There response compared to those who completed the
is a fear of missing out on opportunities as well. task without GenAI, due to ineffective use of the
Organizations, fearful of disruption, are afraid of t"
214,pwc,28-ceo-survey-regional-report.pdf,"Capturing opportunities today,
reinventing for tomorrow
28th Annual CEO Survey:
Middle East findings
Snapshot of the Middle East findings
Resilient GCC economies spark Reinvention, urgent - accelerated Industries converge and compete
01 02 03
optimism amid uncertainty by AI, climate and regulation: over new domains of growth:
90% 60 72%
%
of GCC CEOs are confident about of regional CEOs think their businesses will of regional CEOs expect to do a
growth in their company revenue not be viable within 10 years or less deal outside of their industry or
over the next 12 months without adaptation, with the majority citing sector in the next three years
regulatory change as a major factor
77% 70% 53%
of KSA CEOs and 80% of UAE CEOs of GCC CEOs believe GenAI will increase of regional companies have targeted a new
confident in economic growth in their profitability within 12 months customer base within the last five years
territory
43%
61% 79%
of regional CEOs are already competing in
of regional CEOs expect to increase of regional CEOs have initiated climate
new sectors or industries
headcount within 12 months, although friendly investments in the last five years
34% of GCC CEOs cited skills shortages
as a major concern
40%
of GCC CEOs cited that cyber risk is the
top threat for the next 12 months,
followed by geopolitical conflict
Foreword
Our annual survey of CEOs globally and across the Middle East reflects the collective voice of business leaders -
offering valuable insights into the opportunities they see, the challenges they face and the path forward. This year
we captured more responses than ever before, with almost 300 CEOs sharing their views. In our region, hearing
these voices has never been more important, as we grapple with the profound megatrends of climate change and,
technological disruption, increasing regulation and an evolving geopolitical landscape.
What we’ve heard is clear: CEOs in our region are among the most confident globally about economic growth in
their territories and their own revenue growth in the coming year, with many planning to expand their workforces.
Businesses here are already investing in new technologies and strategies, particularly around AI and sustainability;
and with the region amplifying its voice in the global climate conversation, there is a clear commitment to driving
sustainable growth.
However, for these leaders, the urgency to reinvent is clear. A striking 60% of regional CEOs now believe their
businesses will not survive ‘within the next 10 years or less’ without significant adaptation - a notable increase
from last year, when less than half expressed similar concerns.
CEOs in the Middle East recognise that traditional models of business are increasingly unsustainable in the face
of transformative catalytic shifts. Alongside the climate crisis and AI-driven disruption, they also recognise there is
a battle to capture value in new domains as industry lines blur and companies face fierce new competitors,
reshaping market dynamics. These are forcing CEOs to rethink how they can innovate to secure that ever-critical
advantage. The imperative is evident: CEOs must balance the opportunities of today while also reinventing their
businesses for tomorrow.
I want to sincerely thank the chief executives who provided their valuable time in participating in this survey. The
rich insights provided have enabled us to chart a clear picture of the opportunities and challenges shaping the
future of business in our region.
Hani Ashkar
Middle East Senior Partner
PwC Middle East
01
Resilient GCC
economies spark
optimism amid
uncertainty
Resilient GCC economies spark optimism amid uncertainty
CEOs in the Middle East remain among the most confident globally about their company’s revenue growth and the Unsurprisingly, in the wider Middle East,
region's economic outlook for the year ahead, despite the geopolitical unrest unfolding in 2024. confidence is lower than in GCC countries as the
economic impact of regional conflicts have
According to the regional findings of our 28th PwC Annual Global CEO Survey, based on responses from chief extended to neighbouring countries, such as
executives representing 11 countries across the Middle East, this sentiment is the strongest amongst CEOs in the Jordan, Egypt and Lebanon. Jordan recorded a
Gulf Cooperation Council (GCC) countries, with 90% optimistic about short-term revenue growth over the next 12 6.6% year-on-year decrease in tourist arrivals
months. 71% of CEOs in these countries also indicate optimism in their own territory’s economic growth - ahead of through August due to its proximity to conflict
their peers in the wider region and globally - with 80% of CEOs in the UAE and 77% in Saudi Arabia forecasting zones in the region, while there was a 62% drop in
economic growth in the next 12 months, followed by CEOs in Oman (69%) and Qatar (63%). Expectations of Suez Canal revenue in Egypt as a result of the
growth from CEOs globally when talking about their own territories sat at a more modest 57%. reduction in Red Sea traffic in the first half of this
year.1
Despite its proximity to regional conflicts and
ongoing challenges from inflation and the currency
Q. How do you believe
Global Middle East GCC crisis, Egypt, however, has experienced a
economic growth (i.e. gross
remarkable economic turnaround this year, with a
domestic product) will change, US$35bn investment from the UAE providing a
57% 64% 71%
if at all, over the next 12 major boost. This has supported the
implementation of critical reforms, including the
months in your territory?
liberalisation of the currency regime, which has
played a key role in reducing inflation and boosting
economic growth.2 As a result, 63% of CEOs in
Improve Egypt remain optimistic about economic growth in
their market. In contrast, only 45% of CEOs in
Stay the same Jordan share this optimism, significantly trailing
their regional and global peers.
Decline 21%
16%
Looking at confidence through an industry lens,
14%
the consumer markets sector is the most confident
22%
19% about revenue growth in the short term (within the
14%
next 12 months), while the technology, media, and
telecommunications sector leads in confidence
2025 2025 2025
over the more medium term.
Note: Percentages may not total 100% due to rounding.
Q. How confident are you about your company’s prospects for revenue growth?*
Richard Boxshall
Consumer
91%
Chief Economist and
Markets
Leader of the PwC
91%
Global Economics
Network
Health
90%
93%
Variations in CEO sentiment across
the region reflect the differing
Technology, Media 90% growth prospects between the
Telecommunications
GCC and other territories. While
98% the outlook for the GCC remains
stronger, supported by substantial
investments in the non-oil sector
Financial 90% driving economic diversification
Services and resilience, geopolitical
90% uncertainties and their impact on
inflation and supply chains remain
a concern for CEOs.
Transport
87%
and Logistics
93%
Energy, Utilities
79%
& Resources
88%
Over the next 12 months Over the next three years *Net confidence for Middle East companies
Note: Percentages may not total 100% due to rounding.
Non-oil investment fuels growth
Given the Middle East’s investment in the non-oil sectors, its green economy initiatives and technological
accelerations, and an emphasis on localisation, it’s not surprising that non-oil investment is fuelling growth. Non-oil
GDP increases across the GCC averaged 3.7%, easily surpassing the overall economic growth rate of 1.8%.3 This
has helped offset the impact of OPEC+ oil production cuts. The Middle East also continues to emerge as a thriving
hub for dealmaking, where we see the region’s sovereign investment funds playing a pivotal role in driving private
equity deal volumes, particularly in emerging sectors as highlighted in our 2024 TransAct Middle East mid-year
update.4
In fact, according to our regional findings, the UAE is the seventh most likely country where global CEOs are
planning to invest, outside of their home territory - while within the region, Saudi Arabia, the UAE and Egypt are the
top three countries that regional CEOs are planning to invest into outside of their existing territories.
Anticipating revenue increases, CEOs in the region are looking to scale their operations to capture growth
opportunities at even higher rates than last year. Our survey data indicates that they are more likely to increase
headcount than their global peers: 61% of regional CEOs expect to add headcount within the next 12 months,
compared to just 42% globally, and up from 55% in 2024. In the GCC, 64% of CEOs plan to increase headcount,
despite a third (34%) citing skills shortages as a major concern. This reiterates the need for organisations to
prioritise workplace upskilling to adapt to technological advancements and tackle challenges such as supply chain
disruptions, driven by geopolitical tensions and the climate crisis.
From an employee perspective, regional workforce responses to our 2024 Hopes and Fears survey reflect the same
sentiment - 63% said technological change, especially the rise of AI, GenAI and robotics, would impact their jobs in
the next three years, compared to only 46% globally. Additionally, more than half (54%) of respondents stated that
climate change would impact their jobs, compared to 37% globally.5
Global Middle East
17%
39%
42%
Looking at headcount trends by sector, 70% of CEOs in the healthcare industry plan to increase employee numbers next year, alongside over 60% in consumer markets,
transportation and logistics and technology, media and telecommunications. Additionally, more than half of CEOs in the energy, utilities and resources and financial services
sectors anticipate workforce growth, reflecting a strong commitment to expansion across key industries.
Sector-specific concerns include inflation for consumer
markets (47%) and health industries (40%), while cyber
risks and geopolitical tensions are prominent in
transportation and logistics (53%) and technology,
media and telecommunications (50%). In the energy,
utilities and resources sector, cyber risks (47%) and
macroeconomic volatility (39%) dominate concerns,
while financial services leaders are worried about
technological disruptions (42%) and economic
instability (19%).
5202
Q. To what extent will your company increase or decrease headcount in the next 12 months?
GCC
Decrease 11% 12%
Little to no
28% 24%
change
Increase 61% 64%
Note: Percentages may not total 100% due to rounding.
Confidence in the face of ongoing threats: Geopolitical conflicts, cyber risks and skills shortages
Alongside this picture of varying levels of confidence, Middle East CEOs identified a range of issues set to be top of
mind as they approach decisions in the year ahead. Geopolitical conflict (41%), cyber risks (36%) and inflation
(30%) were cited as their top threats, while a lower availability of skilled workers emerged as a main concern,
particularly for business leaders in the GCC (34%). Notably, inflation, which was the top concern last year at 38%,
has now been overtaken by geopolitical conflict as the top risk.
The perceived magnitude of these threats varies somewhat across the region. Geopolitical conflict remains a
significant concern, particularly for CEOs in Jordan, where 55% say that their organisation will be ‘highly’ or
‘extremely’ exposed to this threat in the coming year, compared to 41% of the regional average.
In the UAE, cyber risks are identified as the biggest threat by CEOs, with 38% of business leaders anticipating high
exposure to such risks in 2025. Meanwhile skills shortages are a primary concern for 41% of CEOs in Oman and
rank among the top three threats for CEOs in the UAE and Egypt.
“The key challenge will be the lack of talent across the board, whether it is in IT, digital, relationship management or
compliance. Our only competitive advantage lies with our people, and we must ensure to recruit and retain the best
talent.” CEO, Financial Services
02
Reinvention,
urgent -
accelerated by AI,
climate and
regulation
Despite a robust short-term confidence in business
growth among regional business leaders, the impetus Q. If your company continues running on its current path, for how long do you think your business
to reinvent is stronger than ever this year - and more will be economically viable?
immediate. CEOs in the Middle East anticipate greater
pressure to evolve in the next decade or even less,
driven mostly by continuing emerging technologies, Global
42%
climate change, anticipated increased regulation and an
intensifying competition over new domains of growth as
55%
industry lines blur. They understand that the next
decade will bring profound change, and they must be
ready to adapt. This sentiment resonates strongly
Middle East
through our survey findings. 60%
Last year, almost half of Middle East CEOs expressed 37%
concerns about their organisation’s economic viability
over the next 10 years if they failed to evolve. This year,
our survey reveals an increased urgency to reinvent - GCC
64%
64% of GCC CEOs and 60% of overall Middle East
CEOs believe they will need to adapt their businesses
3%
in 10 years or less to remain viable. This heightened
concern not only surpasses last year’s levels but also
far exceeds the current global average of 41%.
10 years or less More than 10 years
Note: Percentages may not total 100% due to rounding.
Recognising the significant impact of transformative shifts on the region, CEOs in the Middle East are driven by an
urgent need to rethink strategies, looking to embrace innovation and build resilience for sustainable growth. The
message is clear: evolve or face irrelevance.
A closer look at key industries in the region reveals that the imperative for reinvention is widely felt. Our survey data
indicates that over 70% of CEOs in healthcare industries and the energy, utilities and resources sectors believe their
organisations will not be viable within the next decade without adapting. Similarly, more than 60% of leaders in
transport and logistics and technology, media and telecommunications share this sentiment.
Additionally, over half of CEOs in consumer markets and nearly half in the financial services sector recognise the
same critical need for transformation, reiterating that the need to evolve is no longer a choice – it's a necessity.
So how have business leaders in the region been driving change?
For CEOs in the Middle East, this has been an
opportunity to radically transform their Q. To what extent has your company taken the following actions in the last five years?
fundamental approach to creating, delivering
and capturing value. According to our
findings, more than half of our regional CEOs
have driven change by innovating products 54%
53% 53%
52%
and services over the past five years, while
50%
53% have focused on targeting a new
customer base to expand their market reach.
Examples of such innovation include 43%
UAE-based Detectiome's Revonco,6 an 40%
39%
38%
AI-powered multi-cancer early detection test
that is redefining healthcare, and Tabby, 34% 35%
MENA's first fintech unicorn,7 which is 32%
revolutionising financial services in the region.
26%
25%
24%
Additionally, 43% have collaborated with
other organisations, 39% have targeted new
routes to market and 34% have implemented
new pricing models - higher than the global
averages. This underscores the fact that
regional CEOs have more proactively
embraced innovation, diversification and
strategic partnerships to ensure their
organisations are better equipped for future Targeted a new Developed innovative Collaborated with Targeted new Implemented new
success. customer base products or services other organisations routes to market pricing models
53% Global Middle East GCC
Note: Percentages may not total 100% due to rounding.
Reinvention is equally critical from an investor’s perspective, as they seek to understand how the companies they invest in
have targeted a new
are navigating crises, strengthening resilience and ensuring long-term value creation. According to PwC’s 2024 Global
Investor Survey8 investors are closely examining the reinvention imperative - especially the adoption of emerging
customer base
technologies - to assess whether businesses they are investing in are positioned to capitalise on innovative opportunities.
in the last five years Four in five investors who invest in companies in the Middle East indicated technological change as the most fundamental
driver compelling companies to rethink how they create, deliver and capture value.
CEOs focus on regulation, strategy and innovation for future viability
For business leaders in the region, anticipated Additionally, almost a third (29%) of CEOs highlighted rising product and service costs as a key external driver for
regulatory changes were identified as the most critical evaluating economic viability, slightly below the global average of 32%. Embracing disruptive technologies was
external factor influencing economic viability over the cited by 27% of respondents as another main external driver of economic viability within the next decade, followed
next decade or less. Over a third of Middle Eastern closely by strong incumbent competition at 26%. On the internal front, 22% of regional CEOs pointed to a lack of
CEOs cited these expected changes, compared to 42% skills as a major factor impacting their company’s potential viability within the next 10 years or less.
globally. Over the next decade, GCC countries are
expected to implement significant regulatory changes in
the areas of AI, technology and climate, among others.
Q. What are the top five external factors influencing a Middle East company's
This will be key to shaping future enterprise and will
economic viability within the next 10 years?
offer transformative opportunities for businesses to
drive innovation, enhance competitiveness and achieve
sustainable growth. Robust frameworks for AI Changes in the
38%
governance, data protection and cybersecurity will regulatory environment
foster a secure environment for technological
advancements, while climate-focused policies will
Increasing
enable businesses to leverage sustainability 29%
products/services costs
opportunities. The region also anticipates economic
diversification initiatives, labour market reforms and
Disruptive
trade regulations, including digital trade platforms and 27%
technology
free trade agreements that will further reduce barriers,
attract foreign investments, and open new markets.
Strong incumbent
26%
“Compliance with advanced regulations remains competition
essential. Achieving a balance between innovation and
regulatory requirements will require a proactive Decreasing demand
23%
approach to ensure that our initiatives comply with both for products/services
local and international standards.” - CEO, Technology,
Media and Telecommunications
Among those Middle East CEOs looking beyond a 10-year horizon, more than half identified a growing demand for
products and services as a critical external factor, followed by regulatory changes (52%) and disruptive technologies
at (34%).
Internally, more than half (59%) emphasised making bold strategic choices as the most crucial factor for long-term
viability, slightly ahead of their global peers at 55%. These strategic decisions will enable businesses to address
future disruptions and seize emerging opportunities - enabling them to build resilience in a dynamic regional
landscape.
Other key internal factors impacting potential long-term economic viability included organisational efficiency (39%)
and having the right skills for a competitive environment (35%), underscoring the need for adaptability and
readiness to thrive.
Megatrends redefine industries: AI and climate creating new domains of growth
Middle East countries are adopting Artificial Intelligence (AI) at an unprecedented pace, fuelled by ambitions to
diversify economies and build future-ready industries. Business leaders see AI as a transformative catalyst for
innovation, with GenAI tools optimising processes and accelerating outcomes. And trust in having AI embedded into
key processes is particularly high, with half of GCC CEOs trusting it to a ‘large’ or ‘very large’ extent, compared to
Ali Hosseini only one third of their global peers.
Chief AI and
Technology Officer This growing confidence is backed by investments by regional governments and private enterprises in AI research,
PwC Middle East development and innovation hubs, while fostering responsible AI adoption. Saudi Arabia has made substantial
progress in the Global AI Index, climbing 17 positions to rank 14th globally, while national strategies have facilitated
a deep trust in AI - including the Saudi Vision 2030 and the UAE’s National AI Strategy 2031.
Our regional findings also indicate that in the GCC, a notable 88% of CEOs have adopted GenAI in the last 12
months, exceeding global averages and reflecting greater confidence in the technology’s potential.
The Middle East has benefited from
a high rate of AI adoption and at a
greater pace than competitors Q. Did your company adopt generative AI to any degree in the last 12 months?
globally, which has led to increases
in time efficiencies, profitability,
revenue and a tech-savvy
workforce. To maintain their edge,
88%
companies need to accelerate
86%
AI-led innovation and integration, 83%
with a particular focus on GenAI to
unlock new value opportunities and
be future ready
We adopted generative AI to any degree in the last 12 months
Global Middle East GCC
Note: Percentages may not total 100% due to rounding.
In fact, over the next three years, AI, including GenAI, is set to become a core component of technology platforms,
business processes, and the development of new products and services in the region. For example, Falcon 3,
developed by the Technology Innovation Institute (TII),9 delivers high-quality results with low compute requirements,
while Jais, a collaboration between G42’s Inception and Mohammad Bin Zayed University of Artificial Intelligence
(MBZUAI),10 preserves Arabic heritage and democratises AI access. In the GCC, 93% of CEOs predict AI will be
systematically integrated into tech platforms, compared to 78% globally. Additionally, 90% expect AI to enhance
business processes and workflows (vs. 76% globally), 85% to embed it in workforce and skills (vs. 68% globally),
and 81% anticipate its use in new product and service development (vs. 63% globally).
This reiterates the agility and proactivity of regional business leaders in adopting AI to drive digital transformation,
maintain competitiveness and foster growth. This sentiment is only set to grow stronger, with the region expected to
prioritise investments in AI infrastructure, forge global partnerships with leading tech giants, and establish robust
data security frameworks to drive sustainable AI growth in 2025.11
GenAI adoption is also rapidly accelerating across industries in the Middle East, with adoption rates exceeding 85%
in sectors such as consumer markets, transport and logistics, health industries, energy, utilities and resources,
technology, media and telecommunications and financial services within the past 12 months. Trust in embedding AI
and GenAI was particularly strong amongst CEOs from the consumer markets, transport and logistics and
technology, media and telecommunications industries.
As CEOs in the region embrace GenAI at scale, a striking 70% of business leaders in the GCC have indicated that it
will increase profitability in the next 12 months, up from last year - and higher than the global average of just 49%.
Q.To what extent will generative AI increase or decrease the profitability of your company in the
next 12 months? (Net increase)
49%
Global
Middle East 67%
GCC 70%
This confidence is reinforced by the tangible benefits observed over the past year, with GCC CEOs reporting that
GenAI has driven greater efficiencies, increased revenue and profitability, and facilitated job creation.
The most notable findings on GenAI this year were as follows:
01 02 03
68% of GCC CEOs More than half of GCC CEOs 36% of GCC business leaders
acknowledged improved reported revenue growth (vs. highlighted job creation through
efficiencies in their own time at 32% globally) while 53% saw GenAI, more than double the
work (vs. 53% globally) and an increase in profitability (vs. global average of 17%.
63% reported efficiencies in 34% globally).
employees’ time (vs. 56%
globally). In fact, 72% of CEOs
in technology, media and
telecommunications, 69% in
healthcare industries and 65%
in financial services have
expressed strong confidence in
GenAI’s potential to enhance
employee efficiency.
Q.To what extent did generative AI increase or decrease the following in your company in the last 12 months? (Net increase)
68% 68% 65%
63%
56% 52% 53% 51% 52%
53%
36%
32%
34%
32%
17%
Efficiencies in my own Efficiencies in my Profitability Revenue Headcount
time at work employees' time at work
Global Middle East GCC
Note: Percentages may not total 100% due to rounding.
Investor confidence also echoes these findings, with regional data from the PwC Global Investor Survey 2024
revealing that investors remain optimistic about the promise of GenAI - 74% of respondents to this survey believe
that GenAI will increase productivity in the companies they invest in or cover in the Middle East, compared to 66%
of global respondents believing the same about the territories they are investing in. And 67% of respondents believe
that GenAI will increase profitability in the companies they invest in or cover in the region, compared to 62% of
global respondents. This optimism aligns with the broader trend of key regional economies positioning themselves
as global frontrunners in AI adoption and innovation.
“In the near future, disruptive technologies will drive the economy. The challenge is that we don't know
what technologies will come and what disruption they will make. We must be ready to adapt to these new
67%
technologies and think out of the box.” - CEO, Consumer Markets
of respondents believe that
GenAI will increase
profitability in the companies
they invest in or cover in the
region, compared to 62% of
global respondents.
Climate change, the other critical megatrend - a powerful catalyst for reinvention
Climate change, the other critical megatrend, has also been a powerful catalyst for reinvention. Its combination of
opportunities and challenges have prompted CEOs to rethink strategies, adopt sustainable practices and position
their organisations for long-term resilience and growth. This is evident in our survey findings which indicate that
Yahya Anouti nearly 80% of CEOs in the GCC have initiated climate-friendly investments in the past five years, signaling a positive
Partner, Strategy& PwC regional momentum towards sustainability. Notably, we see particularly strong commitments from CEOs in transport
Middle East Sustainability and logistics (90%), consumer markets (84%) and financial services (84%) sectors, reflecting a growing focus on
Platform Leader sustainable practices in some of the region’s fastest growing sectors. The Saudi Investment Bank’s debut Tier 1
Sustainable Sukuk Issuance of US$ 750 million,12 for example, has been a milestone in the bank’s commitment to
sustainable finance and reinforces its position as a leader in responsible banking practices in the Kingdom of Saudi
Arabia.
Our survey demonstrates a clear In our latest Sustainability in the Middle East report13 published in 2024, four in five executives indicated that their
picture for Middle East CEOs: companies now have a formal sustainability strategy in place – with more than half saying that this strategy is fully
sustainability can drive economic embedded across their organisations.
opportunity and deliver measurable
Our regional CEO Survey findings have revealed that for more than half of business leaders in the GCC and nearly
benefits. With almost 80% initiating
half in the Middle East - climate investments are yielding returns that are higher than global averages, despite the
climate-friendly investments in the last
high upfront costs. This indicates that there is now a growing acknowledgment among regional CEOs that
five years, the region is proving that
sustainability can align with profitability and presents an opportunity to explore the factors enabling these higher
bold action on climate can align with
returns, such as government incentives, technological adoption or strategic investments in renewable energy.
profitability. The challenge now is for
leaders to accelerate innovation, push However, the balance between managing costs and maximising revenue continues to pose a substantial challenge
boundaries, and turn sustainability into for business leaders. When it comes to CEO buy-in on sustainable investments, only 14% of CEOs in the region
a cornerstone of their competitive say they have accepted returns below the minimum acceptable rate for other investments in the past 12 months,
advantage. compared to 25% globally - the case for the second consecutive year.
And while almost 80% of regional CEOs have made climate friendly investments in the last five years (particularly in
the transport and logistics, consumer markets and financial services sectors at 90%, 84% and 84% respectively),
they are less likely than their global peers to accept significantly lower rates of return on climate-friendly
investments. In a world increasingly shaped by environmental challenges, business leaders must recognise the need
to further integrate sustainability into core strategies - including investment decisions - to align profitability
with purpose.
Q. To what extent have climate-friendly investments initiated by your company in the last five years
caused increases?
54%
49%
48%
44%
36%
33%
Revenue from products/services sales Costs
Global Middle East GCC
Note: Percentages may not total 100% due to rounding.
Findings from our survey have also indicated that among Middle East CEOs who have not made any climate-friendly investments in the last 12 months, several key barriers
hinder their ability to decarbonise. Regulatory complexity emerges as a key challenge due to the lack of mandatory regional sustainability regulations, impacting companies
engaged in cross-border trade with jurisdictions like the European Union and the United States, where such regulations are enforced.14 Similarly, the perception of lower returns
on climate-friendly investments remains a greater challenge regionally, with the percentage of CEOs concerned about this nearly doubling the global average. The lack of
available financing is also a challenge, with regional CEOs reporting this issue at more than twice the rate of global leaders.
I aim to strengthen our focus on sustainability by deeply integrating climate-friendly investments into our business model. This aligns with global trends and
stakeholder expectations, ultimately contributing to a positive environmental impact and long-term profitability.” CEO, Financial Services
Addressing these obstacles requires a reinvention of traditional business models, enabling business leaders to advocate for regulations that can support climate action agendas,
embrace innovative financing strategies and relook at climate-friendly investments as opportunities for long-term value creation.
Q. To what extent, if at all, are the following factors inhibiting your company’s ability to decarbonise its business model?
[NET: To a large extent & to a very large extent]
Global
Middle East
36% 36%
35%
GCC 34% 33% 34%
31%
31%
24%
23%
20%
20%
18%
14%
6%
Lower returns for Regulatory complexity Lack of demand from Lack of available finance Lack of buy-in from
climate-friendly (e.g. policy changes, external stakeholders my management
investments inconsistent local (e.g. customers, team or the board
requirements) investors)
Note: Percentages may not total 100% due to rounding.
5202
03
Industries
converge and
compete over new
domains of growth
As CEOs in the Middle East evaluate the impact of the transformative forces of AI and climate change on their
existing industries and businesses, they are focusing on unlocking new value streams. This is driving sector
convergence, breaking down traditional boundaries and fostering collaboration. For example, AI-powered solutions
are linking healthcare with technology to create precision medicine and advanced diagnostics as in the case of the
Ahmad Abu Hantash partnership between G42 Healthcare and Mubadala Health in the UAE.15 Meanwhile, the climate crisis is driving
Partner, Technology energy companies such as ADNOC"
215,pwc,ai_jobs_barometer_2024.pdf,"PwC’s 2024
AI Jobs Barometer
How will AI affect jobs, skills, wages,
and productivity?
pwc.com/aijobsbarometer
PwC’s 2024 AI Jobs Barometer goes beyond
predictions about AI’s impact to find evidence by
analysing over half a billion job ads. The Barometer
reveals how AI is transforming the world of work,
making people and businesses more productive
while changing what it takes for workers to succeed.
Headline Findings
4.8x 25%
Sectors with highest AI penetration are Jobs that require AI specialist skills carry up
seeing almost fivefold (4.8x) greater labour to a 25% wage premium in some markets.
productivity growth. Rising labour productivity
can generate economic growth, higher
wages, and enhanced living standards.
3.5x 25%
Growth in jobs that require AI specialist skills Skills sought by employers are changing at a
has outpaced all jobs since 2016 (well before 25% higher rate in occupations most able to
ChatGPT brought fresh attention to AI), with use AI. To stay relevant, workers in these jobs
numbers of AI specialist jobs growing 3.5 will need to build or demonstrate new skills.
times faster than all jobs.
PwC’s 2024 AI Jobs Barometer 2
Half a billion job ads reveal
AI’s impact
AI is the Industrial Revolution of knowledge work, transforming how all workers can apply
information, create content, and deliver results at speed and scale. How is this affecting
jobs? With the AI Jobs Barometer, PwC set out to find empirical evidence to help sort fact
from fiction.
PwC analysed over half a billion job ads from 15 countries to find evidence of AI’s impact at
worldwide scale through jobs and productivity data.
PwC tracked the growth of jobs that demand specialist AI skills (such as machine learning
or neural networks) across countries and sectors as an indication of AI penetration.1 We
find that AI penetration is accelerating, especially in professional services, information &
communication, and financial services. Workers with specialist AI skills command significant
wage premiums, suggesting that their abilities to deploy AI are valuable to companies.
1 AI’s true penetration into the economy may be even greater than reflected in this analysis. By focusing on job ads, this analysis captures AI’s impact on
job changers, but does not capture AI usage or upskilling for existing employees.
PwC’s 2024 AI Jobs Barometer 3
The AI Jobs Barometer uses half a billion job ads from 15 countries to
examine AI’s impact on jobs, skills, wages, and productivity
But AI’s impact is not limited to only those workers who have specialist AI skills. Many,
if not most, workers who use AI tools in their work do not have or need these specialist
skills. For example, a limited number of workers with specialist AI skills may design
an AI system or tool for a company that is then used by hundreds or thousands of the
company’s customer service agents, analysts, or lawyers - none of whom have specialist
AI skills. In fact, one thing that makes a well-known form of AI - generative AI - such a
powerful technology is that typically it can be operated using simple everyday language
with no technical skills required.
To capture AI’s impact on all jobs, PwC analysed all jobs (and sectors) by their level of ‘AI
exposure.’ A higher level of AI exposure means that AI can more readily be used for some
tasks. Examples of occupations with higher AI exposure are financial analysts, customer
service agents, software coders, and administration managers. The analysis revealed that
sectors with higher AI exposure are experiencing much higher labour productivity growth.
At the same time, the skills demanded by employers in AI-exposed occupations are
changing fast. Read on to learn more.
PwC’s 2024 AI Jobs Barometer 4
Key Terms
‘AI specialist skills’: Specialist, technical AI skills like deep learning or
cognitive automation. See Appendix One for AI skills list.
‘AI specialist jobs’: Jobs that require specialist, technical AI skills.
‘All jobs’: All jobs in all occupations.
‘AI-exposed’: Describes all jobs or sectors in which AI can readily be used
for some tasks (based on definition of AI Occupational Exposure
developed by Felten et al.)
PwC’s 2024 AI Jobs Barometer 5
AI penetration is
accelerating
Attention to AI’s impact on the jobs market exploded in November 2022 with the launch of
ChatGPT 3.5. However, the data shows that AI had quietly exerted a growing impact on the
jobs market years before. Growth in AI specialist jobs has outpaced growth in all jobs since
2016, well before ChatGPT brought fresh focus to AI.
Today, there are seven times as many postings for specialist AI jobs as there were in 2012.
In contrast, postings for all jobs have grown more slowly, doubling since 2012. Put another
way, openings for jobs that require specialist AI skills have grown 3.5 times faster than
openings for all jobs since 2012.
Growth in Al jobs has outpaced all jobs since at least 2016
Number of Job Postings, relative to 2012
12
10
8
6
4
2
0
2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023
AI Jobs All Jobs
Source: PwC analysis of Lightcast data. The analysis represents six of the fifteen countries: US, UK, Singapore, Australia, Canada, and New Zealand.
Nine countries have been excluded due to data prior to 2018 being unavailable: France, Germany, Belgium, Denmark, Spain, Italy, Netherlands, Norway,
and Sweden.
The 2022 peak in job postings above represents exceptionally high demand for workers
which gradually eased in 2023 as job market conditions returned toward normal.
PwC’s 2024 AI Jobs Barometer 6
Knowledge work sectors have higher
AI penetration
Knowledge work sectors in particular are seeing growing demand for jobs that require
specialist AI skills. The share of job ads requiring these skills is higher in professional
services, information & communication, and financial services - precisely those sectors
predicted to be most exposed to AI.2 Financial services has a 2.8x higher share of jobs
requiring AI skills vs other sectors, professional services is 3x higher, and information &
communication is 5x higher.
Share of job postings by sector requiring Al skills
5%
4%
3%
2%
1%
0%
2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023
Information and Communication Professional Services Financial Services Other sectors
Sources: PwC analysis of Lightcast data, UK Government Impact of AI on Jobs 2023. “Other sectors” includes Agriculture, Mining, Power, Water, Retail
trade, Transportation, Accommodation, Real Estate, Administrative activities, Arts and Entertainment, Household activities, Construction, Manufacturing,
Education, Health and Social Activities and Extraterritorial Activities sectors. Chart includes all 15 countries in this study.
2 AI Occupational Exposure (AIOE), constructed by Felten et al (2021), scores and measures the degree to which occupations rely on abilities in
which AI has made the most progress in recent years, meaning AI can more readily be used for some tasks in those occupations.
PwC’s 2024 AI Jobs Barometer 7
AI specialist jobs command up to a 25%
wage premium on average
AI’s value to companies is made clear by what is happening with the wages of workers
with AI specialist skills - the very people who are making the AI revolution possible.
25%
Up to 25% wage premium for
workers with specialist AI skills
PwC’s 2024 AI Jobs Barometer 8
As we have seen, growth in jobs demanding AI specialist skills has outpaced growth in all
jobs since 2016. What’s more, these jobs carry up to a 25% wage premium on average,
underlining the value of these skills to companies.
Below are average AI wage premiums for five countries for which there is sufficient
data to perform the analysis. To show how this wage premium can affect individual
occupations, wage premiums for selected occupations are given.
Wage premium for job vacancies which require Al skills by country
Country Al Wage Premium
Occupation USA UK Canada Australia Singapore
Database Designers
+53% +58% +8% +14% +35%
and Administrators
Lawyers +49% +27% - - -5%
Sales and Marketing
+43% +14% +3% +7% +3%
Managers
Financial Analysts +33% +32% - - +11%
Applications Programmers +32% +24% - +7% +34%
Systems Analysts +30% +34% +15% +7% +28%
Accountants +18% +5% +17% - +4%
Average wage premium
+25% +14% +11% +6% +7%
across all jobs
Sources: PwC analysis of Lightcast data, ISCO-08 Occupation Codes (4-digit level). 2023 data. These findings do not demonstrate a causal relationship.
These estimates are calculated by comparing the average salaries of AI job postings to those of non-AI postings for the same occupations. Two filters
are applied to ensure (1) the count of AI job postings and (2) the ratio of AI jobs:non-AI jobs being compared is above a certain threshold. The analysis
provided represents five of the 15 countries: UK, USA, Singapore, Canada and Australia. The remainder of the countries have been omitted from this
analysis as the data was less extensive: New Zealand, Italy, France, Germany, Spain, Belgium, Netherlands, Denmark, Norway and Sweden.
For example, job ads for US sales managers that require AI specialist skills offer wages that
are on average 43% higher than job ads for sales managers that do not require AI skills.
Canada’s accountants can enjoy a 17% wage premium if they have AI specialist skills, and
UK employers are willing to pay a 27% premium for lawyers equipped with AI skills.
PwC’s 2024 AI Jobs Barometer 9
AI appears to be driving a
productivity revolution
So far this report has discussed jobs which require specialist AI skills like deep learning
or natural language processing. But many, if not most, workers who use AI tools in their
work do not have these skills. To understand how AI is affecting all jobs, PwC examined
jobs and sectors by their levels of ‘AI exposure’ which means the degree to which AI can
readily be used for some tasks. PwC’s analysis revealed how higher levels of AI exposure
appear to be affecting workers’ productivity, numbers of job openings, and the skills that
jobs require. First, let’s see how AI may be affecting productivity.
Labour productivity growth has been sluggish in many nations for years. OECD countries
have experienced a lost decade of labour productivity growth with weak average annual
rises of 1.1% from 2011 to 2020, followed by declines in both 2021 and 2022.3
4.8x
higher growth in labour
productivity in Al-exposed
sectors
3 OECD, Labour Productivity and Utilisation. The pandemic had a negative impact on productivity in 2020-2022.
PwC’s 2024 AI Jobs Barometer 10
Al exposure and labour productivity growth rate by sector. Each dot
represents a country.
Sources: PwC analysis of OECD data, Felten et al. (2021). The AI Occupation Exposure (AIOE) constructed by Felten et al’s (2021) AI Occupational
Exposure (AIOE) scores and measures the degree to which occupations rely on abilities in which AI has made the most progress in recent years, meaning
AI can more readily be used for some tasks. The AIOE score is a relative measure, where higher numbers indicate greater exposure to AI, meaning that
even negative values still imply a certain degree of exposure to AI. To measure the growth rate in labour productivity, PwC used the OECD’s GVA per
person employed metric, indexed on 2018. Due to the availability of the OECD data, PwC focused on just six sectors. The 2023 OECD labour productivity
data has not been released.Therefore the labour productivity growth rate between 2018 and 2022 is considered. If the view that AI is increasing productivity
is correct, it would be expected that the pattern of stronger productivity growth for AI-exposed sectors would continue or accelerate in 2023. The ‘4.8x
higher growth’ is a comparison of averaged labour productivity growth rates; absolute growth rates are 0.9% and 4.3%.
PwC’s 2024 AI Jobs Barometer 11
2202
-
8102
etar
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ytivitcudorp
ruobaL
30%
20%
10%
0%
-10%
-20%
-1.5 -1 -0.5 0 0.5 1 1.5 2 2.5
Sectors less exposed to Al Al Sectoral Exposure Sectors more exposed to Al
Average .9% Average 4.3%
noitcurtsnoC
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,liateR
&
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noitamrofnI ygolonhceT
laicnaniF secivreS
This stagnant labour productivity is a serious problem because it is a drag on economic
growth, reducing potential tax revenues, chipping away at investment in public services
and flatlining living standards.
Recently there has been much speculation that AI can supercharge workers’ productivity.
The good news is there is now evidence to suggest that this is not just wishful thinking,
and is already fast becoming reality.
We have seen that three sectors - financial services, IT, and professional services - have
higher AI exposure and higher AI penetration. How is this affecting productivity?
The data shows that these three sectors are seeing nearly 5x faster productivity growth
than sectors with lower AI exposure (such as transport, manufacturing and construction).
While it is not possible to prove causation, this is an intriguing pattern. Unlike the
computer revolution which took significant time to enhance productivity (economist
Robert Solow once observed that the impact of the computer age was evident
everywhere but in the productivity statistics), the data suggests AI is already doing so,
right now. AI may be compressing the ‘productivity J-curve’4 in which new technologies
can take significant time to cause a sharp uptick in productivity.
PwC’s 2024 Global CEO Survey confirms that 84% of CEOs whose companies have
begun to adopt AI believe it will increase efficiency in their employees’ time at work.5
Increasing productivity means more than just doing the old things faster. It also means
finding new, AI-powered ways to create value. In fact, 70% of CEOs say that AI will
significantly change the way their company creates, delivers and captures value over the
next three years.
Al does more than help workers do the old things
faster. Al opens the door to new business models
and ways of creating value.
The implications for business are huge. Global CEOs anticipate that one form of AI -
generative AI - will deliver significant top and bottom line benefits, with 46% saying it will
increase profitability, and 41% saying it will increase revenue.
Investors agree. PwC’s 2023 Global Investor Survey shows that investors believe
accelerated adoption of AI is critical to the value equation, with 61% of investors saying
faster adoption is very or extremely important. When responses indicating ‘moderately
important’ are included, the proportion jumps to 85%.
All of this adds up to a positive story for the global economy: a revolution in productivity
and value creation.
4 Productivity J-curve,’ Brynjolfsson et al., National Bureau of Economic Research.
5 Around a third of the respondents in our 2024 Global CEO Survey have begun to adopt AI. Of these, 84% believe it will increase employees’ efficiency.
These findings suggest that companies leading the way on AI deployment are seeing the benefits.
PwC’s 2024 AI Jobs Barometer 12
AI is helping to ease
labour shortages
In AI-exposed occupations such as customer services and IT - a number of which have
acute labour shortages - jobs are still growing, but 27% more slowly on average.
This could be good news for many nations facing shrinking working age populations and
vast unmet needs for labour in many sectors. AI can help to ensure that the labour supply
is available for the economy to reach its full potential.
27%
Lower job growth in
Al-exposed occupations
(though jobs still growing overall)
PwC’s 2024 AI Jobs Barometer 13
Job openings are still growing in Al-exposed occupations, but more slowly
Sources: PwC analysis of Lightcast data, ISCO-08 Occupation Codes (2-digit level) and Felten et.al AI Occupation Exposure. The cross-country
comparison on the right hand side considers the difference in the growth in job postings between the occupations most exposed to AI and those least
exposed to AI.
It is important to emphasise that job numbers in AI-exposed occupations are still growing.
The data suggests that AI does not herald an era of job losses but rather more gradual
jobs growth, helping to enable companies to find the workers they need.
PwC’s 2024 AI Jobs Barometer 14
3202-9102
sgnitsop
ni
etar
htworG
250%
Cleaners and Helpers
200%
Construction and
150% Manufacturing Labourers
Sales and Service Workers
Plant and Machinery
Operators
Clerical Support Workers
100%
Administrative &
Commercial Managers
50% Agriculture Labourers
Business Professionals
IT Professionals
0%
-2.00 -1.50 -1.00 -0.50 0.00 0.50 1.00 1.50 2.00
-50%
Occupation less Occupation is more
-100% exposed to Al exposed to Al
Average growth 80% Al Occupation Exposure Average growth 58%
What this means for workers:
Build skills for an AI age
The skills required by employers in AI-exposed occupations are changing fast. Old skills
are disappearing from job ads - and new skills are appearing - 25% faster than in roles
less exposed to AI.
25%
higher skills change in Al-exposed
occupations
PwC’s 2024 AI Jobs Barometer 15
Change in skills demanded by employers for occupations more (and less)
exposed to Al
Sources: PwC analysis of Lightcast data, ISCO-08 Occupation Codes (2-digit level), Felten et al. (2021). The net skill change is based on Deming and
Noray (2020) and is calculated by using the difference between 2019-2023 in the total number of skills required by job occupations using the ISCO-08 4-
digit occupational codes. The AI Occupation Exposure is from Felten et al’s (2021) and measures the degree to which occupations rely on abilities in
which AI has made the most progress in recent years, meaning AI can more readily be used for some tasks. The correlation coefficient is .31 and is the
statistical measure that quantifies the strength and direction of a linear relationship between net skill change and AI Exposure. To calculate the average net
skill changes for the most and least exposed occupations to AI,an average of the net skill change of the top and bottom quartile of occupations is taken
based on their exposure to AI. See Appendix Two for formula.
Workers in AI-exposed roles may need to demonstrate or acquire new skills to stay relevant
in a jobs market that is fast-evolving. PwC’s 2024 Global CEO Survey makes it clear that
69% of CEOs anticipate that generative AI will require most of their workforce to develop
new skills, rising to 87% of CEOs who have already deployed generative AI. Workers need
to take ownership of their learning, rapidly developing the skills to remain relevant and to
embrace the opportunity AI brings.
PwC’s 2024 AI Jobs Barometer 16
3202-9102
neewteb
egnahc
lliks
teN
14
Greater
change
in skills 12
demanded Web and multimedia developers
by
employer
10
Software
Web
developers
8 technicians
6
Athletes and
sports players Mathematicians,
actuaries and
4 statisticians
Smaller
Hand
change
in skills launderers Judges
demanded 2
by
employer Psychologists
Roofers
0
-2 -1.5 -1 -0.5 0 0.5 1 1.5 2
Al Occupation Exposure
Occupation less Occupation is more
exposed to Al exposed to Al
Average: 2.7 Average: 3.4
(Least exposed quartile) (Most exposed quartile)
There are clues to which skills workers may want to build to prosper in an AI age. Some
of the skills rising fastest in demand are those which cannot easily be performed by AI.
Below are four of the skills categories rising fastest in demand, and for each category a few
examples are provided of specific skills with growing demand. From dam construction to
sports instruction, some skills with booming demand are relatively hard for AI to perform.
FASTEST growing skill categories
Skill sub-category Growth in skills
sub-category
Yoga +426%
Performing
Sport Instructors +178%
Arts, Sports, +155%
Swimming +20%
and Recreation
Creative Arts +18%
Child Safeguarding +156%
Personal Care +82% Laser Hair Removal +84%
and Services Skin Treatments +41%
Funeral Arrangements +11%
Solar Development +87%
Energy and +58% Water Metering +58%
Utilities Energy Trading +44%
Dam Construction +33%
Sediment Sampling +84%
Ecological Restoration +57%
Environment +48%
Waste Collection +32%
Flood Controls +17%
Sources: PwC analysis of Lightcast data. Data based on 2019-2023. The overall growth in skill categories is calculated as the change in the average
share of the skill category for all countries between 2019 and 2023.
On the other hand, what skills are declining in demand? Below are four skills categories
with the steepest declines in employer demand, with a few illustrative examples of
specific skills with falling (or rising) demand within each category.
The AI transformation is clear to see in categories like Information Technology where
demand for AI-related skills like ‘AI/Machine Learning Inference’6 is flourishing, while
6 AI/Machine Learning Inference means applying a machine learning model to a dataset to generate an output, insight, or prediction.
PwC’s 2024 AI Jobs Barometer 17
demand for some skills that may be more readily replaced by AI (such as coding in
Javascript) is falling. The Analysis category shows a similar pattern with soaring demand
for Natural Language Programming (an AI skill) and declining demand for Regression
Analysis, a type of analysis AI can help to perform.
SLOWEST growing skill categories
Skill sub-category Growth in skills
sub-category
AI/ML Inference +113%
Information -26% Smart Devices +81%
Technology Cloud Operations -7%
Javascript -37%
Game Design +12%
Visual Effects -11%
Design -23%
Computer Graphics -30%
Interface Design -46%
Pipeline Management +6%
Consumer Sales -11%
Sales -20%
Online Auctions -23%
Cold Calling -37%
Natural Language +64%
Programming
Analysis -14% Asset Analytics +3%
Data Synthesis -8%
Regression Analysis -21%
Within the slowest growing skills categories, some sub-categories buck the trend and are growing fast. Some of these
(like Al/ML Inference) are Al skills.
Sources: PwC analysis of Lightcast data. Data based on 2019-2023. The overall growth in skill categories is calculated as the change in the average
share of the skill category for all countries between 2019 and 2023.
No going back to yesterday’s jobs market -
but vast opportunities await those who
adapt to an AI age
AI is redefining what it means to be a financial analyst, a software coder, a customer
service agent (and many more roles), opening up whole new possibilities for workers to
deliver impact. Workers who learn to harness AI are likely to have bright futures in which
they can generate greater value and could consequently have greater bargaining power
for wages - all within a context of rising societal prosperity.
Workers agree. PwC’s 2023 Global Workforce Hopes and Fears Survey shows workers
expect mostly positive benefits from AI with 31% expecting AI to increase their
productivity/efficiency and 21% expecting AI to create new job opportunities.
Many who predict AI will cause a sharp decline in job numbers are asking the wrong
question. Those who predict AI will have a negative impact on total job numbers often
look backward, asking whether AI can perform some tasks in the same way as they
have been done in the past. The answer is yes. But the right question to ask is this:
How will AI give us the power to do entirely new things, generating new roles and even
new industries?
PwC’s 2024 AI Jobs Barometer 19
AI makes human labour more relevant and valuable, opening
up new opportunities for people to develop new skills and enter new
roles. AI will create new jobs for people that we haven’t yet begun
to imagine. Many of the fastest-growing jobs of today - from cloud
engineer to digital interface designer - didn’t exist 10 or 20 years ago
and have been generated by technology. Like a spreadsheet or a saw,
AI is a tool that makes people more powerful and capable. Workers
who build the skills to harness AI will be more valuable than ever.
Pete Brown, Global Workforce Leader, PwC UK
AI often performs best in partnership with people. Without oversight, AI can miss context
and nuance or give poorer quality output. Only humans can fully appreciate and navigate
the people, processes, and context of individual organisations and situations.
As technology gets better at being technology, humans can get
better at being humans. There is clear evidence that AI often delivers
the best outcomes when used in partnership with people. The AI era
requires a new style of leadership, an openness to bold transformation
and inventive thinking about how AI and people together can create
new forms of value.
Carol Stubbings, Global Markets and TLS Leader, PwC UK
Our analysis (particularly the finding about AI’s potential impact on productivity) suggests
that AI’s effect on jobs may be similar to that of the internal combustion engine in the 20th
century which reduced numbers of some jobs (such as horse trader) while at the same time
creating far more jobs than it displaced (from truck driver to road engineer to traffic police).
PwC’s 2024 AI Jobs Barometer 20
AI provides much more than efficiency gains. AI offers
fundamentally new ways of creating value. In our work with clients,
we see companies are using AI to amplify the value their people
can deliver. We don’t have enough software developers, doctors,
or scientists to deliver all the code, healthcare, and scientific
breakthroughs the world needs. There is a nearly limitless demand for
many things if we can improve our ability to deliver them.
Scott Likens, Global AI and Innovation Technology Leader, PwC US
Far from heralding the end of jobs, AI signals the start of a new era in which workers can
be more productive and valuable than ever.
Instead of focusing only on how AI can take on some tasks formerly done by people,
we should think inventively about how to make the most of AI to create new industries
and new roles for people. Embracing AI in this way is one way to bring about continued
positive outcomes for workers. Economist Eric Brynjolfsson observed, ‘If AI is used
mainly to mimic humans, to replace humans with machines, it is likely to lead to lower
wages and more concentration of wealth. If we use AI mainly to augment our skills, to do
new things, then it is likely to lead to widely shared prosperity and higher wages.’7
7 The Second Machine Age, Eric Brynjolfsson
PwC’s 2024 AI Jobs Barometer 21
Next steps for companies, workers,
policymakers
There is no going back to yesterday’s jobs market, but - if carefully managed - the AI
revolution could bring a bright future for workers and companies. Below are steps that
companies, workers, and policymakers can take to help realise AI’s promise to grow
productivity and fuel rising shared prosperity.
Here is what companies can do. Business leaders can embrace, experiment, and create
new uses of AI. They can think beyond using AI to do things the way they have been done
in the past and instead use AI to generate new ways to create value. While AI can help to
make existing processes more efficient, companies can realise even more benefit from
AI by using it to reinvent business models or pioneer new product lines. Thinking
inventively about how to use AI helps the company to be the disruptor rather than the
disrupted, and it helps to create new opportunities for people.
PwC’s 2024 AI Jobs Barometer 22
Business leaders should view AI as a complement to people that is best used with
human oversight. Leaders should track the ever-shifting ‘jagged frontier’8 which marks
where AI performs brilliantly versus where AI lacks capabilities or works best with human
assistance. Companies can support employees to make the most of AI by offering
training and helping them see how AI empowers them (and can even make their jobs
more enjoyable by freeing them to work more autonomously and be more confident in
their roles)9.
Firms can consider hiring on the basis of candidates’ skills rather than focusing solely
on their degrees, job history, or previous job titles. This helps firms find the workers they
need, and it helps workers more readily adapt to a fast-changing jobs market. A study
by PwC and the World Economic Forum conducted across 18 economies shows that
a skills first approach has the potential to expand the talent pool by 100 million people.
Companies can take a skills first approach for existing employees too, treating workers as
people with sets of skills and talents that can be fluidly applied across the organisation.10
These ‘skills based organisations’ can more flexibly deploy workers, helping both
companies and workers adapt to the AI transformation while opening up broader talent
pools, developing more resilient talent pipelines for the jobs of tomorrow, and achieving
enhanced levels of employee motivation, satisfaction, performance, and retention.11, 12
Workers, for their part, should embrace AI, experimenting with it and seeking ways it can
complement and enable them in their work.13 Workers should build the skills to be sought
after in an AI age (for example, skills that either complement AI or are hard for AI to do).
Some workers may need to adapt more than others to succeed in an AI era; for example,
some workers may need only a little training to adopt AI tools while other workers may
need to move to new occupations which require more extensive retraining or upskilling.
Workers, companies, and policymakers share responsibility for helping all workers adapt
to an AI era.
8 ‘Navigating the Jagged Technological Frontier: Field Experimental Evidence of the Effects of AI on Knowledge Worker Productivity and Quality,’
Fabrizio Dell’Acqua et al, Harvard Business School working paper
9 MIT Sloan Management Review: ‘Achieving Individual - and Organisational - value with AI,’ 2022.
10 Skills based organisations are integrating skills throughout the talent management lifecycle by implementing skills-based training programs for
upskilling and reskilling, as well as establishing skills-based career pathways for redeployment.
11 Skills-based sourcing & hiring playbook, Rework America Alliance, 2022
12 AI can help with skills based hiring by, for example, automatically generating and updating skills profiles and working out adjacent skills people are
likely to have or could readily learn.
13 Workers whose companies do not offer AI tools can experiment with public AI tools like ChatGPT. Workers should not use proprietary company data
on public tools, but public tools still provide a wealth of opportunities to get to know AI’s power.
PwC’s 2024 AI Jobs Barometer 23
Policymakers can encourage the use of AI to grow productivity and prosperity, for
example by building the supportive policy environment, digital infrastructure, and skilled
workforce to help realise AI’s potential. Countries with the strongest growth in jobs that
demand AI skills (an indicator of AI usage and penetration) offer lessons for policymakers
in how to create an environment conducive to making the most of AI. The three countries
in this study with the highest proportion of jobs that require AI skills are Singapore,
Denmark, and the US. These are the same three countries that top the IMF’s AI
Preparedness Index ranking which measures areas such as digital infrastructure, human-
capital and labor-market policies, innovation and economic integration, and regulation
and ethics. Policymakers who would like their people to benefit from the AI revolution
should take note.
Proportion of total job vacancies requiring Al related skills by country,
2012-2023
Singapore has the highest proportion of Al related
2.5%
job vacancies increasing to 4.8% in 2023
2.0%
1.5%
1.0%
0.5%
0.0%
2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023
Singapore Denmark United States New Zealand Germany United Kingdom France All countries
Sources: The IMF’s AI Preparedness Index ranks countries’ preparedness to adopt AI based on four pillars: Digital Infrastructure, Innovation & Integration,
Human Capital & Policies and Regulation & Ethics.
Policymakers can support workers with training/retraining and safety nets, and shape
the education system to help prepare workers for an AI age in which critical thinking,
creativity, and adaptability are likely to be key skills. Finally, policymakers can strive to
make sure that growing prosperity from AI adoption is widely shared.
PwC’s 2024 AI Jobs Barometer 24
Key areas for action
Policymakers
E ncourage the use of Al to grow productivity and prosperity
Ensure growing prosperity from Al adoption is widely shared
Support the use of Al to augment rather than replace workers
Support workers with training/retraining, worker protections, and safety nets
Shape the education system to help prepare workers for an Al age
Ensure the responsible use of Al with PwC’s Responsible Al framework
Businesses
E mbrace, exper"
217,pwc,revolutionising-Customs-with-AI.pdf,"Revolutionising Customs with AI
Dream big, start small
Foreword
Rajat Chowdhary
Partner, Technology Consulting
PwC Middle East
customs authorities are at a critical crossroad. Global trade is
becoming more complex and security demands are intensifying.
AI offers a transformative opportunity to revolutionise customs
operations – yet its adoption remains fragmented.
This thought leadership on Revolutionising customs with AI: Dream
big, start small, explores how the technology can move beyond
siloed applications to drive end-to-end transformation across the
customs value chain.
By harnessing AI’s capabilities holistically, customs administrations
can achieve greater operational efficiency, enhance security and
gain deeper insights into trade flows, ultimately building a
next-generation customs system that is agile and resilient in the face
of modern trade demands.
Bastian Vomhof
Director, Customs Consulting
PwC Luxembourg
AI is on everyone's mind. For customs administrations, the path to fully
realising the transformative potential of AI begins with elevating their digital
maturity. This journey towards digital transformation is essential for
achieving simplification, enhanced security, and improved trade facilitation
through AI.
At PwC, we bring unparalleled experience and expertise in guiding and
upskilling customs administration through this transformation. We offer
access to global best practices, skills, and support you in experimentation
and innovation to enable AI.
Join us as we envision a future where AI redefines the next generation of
customs excellence.
PwC | Revolutionising customs with AI 2
Introduction
In an interconnected and complex global economy,
customs administrations find themselves at a critical
juncture. As trade volumes surge due to e-commerce,
supply chains diversify, and illicit activities become more
sophisticated. Traditional customs operations struggle to
keep pace, highlighting the urgent need for
transformation. AI is at the forefront of this evolution,
poised to revolutionise customs by driving operational
efficiency, enhancing security, and simplifying
processes.
Currently, many customs administrations explore AI
through fragmented, siloed efforts, such as
experimenting with machine learning for risk
management or using automation for document
processing. While these isolated use cases may offer
localised benefits, they do not provide a cohesive
strategy for unlocking AI’s full potential when applied
across the entire customs value chain.
This paper highlights that AI adoption starts with
experimentation and prototyping, which ultimately lead
to a broader strategic vision. It examines the challenges
in this journey and demonstrates how AI can streamline
and automate customs processes, enhance security,
and facilitate legitimate trade.
customs authorities can embrace a new era of
human-centric AI to simplify and automate their work,
leveraging this momentum to transition to a higher level
of digital transformation for greater efficiency and
effectiveness. In its final sections, the paper outlines
strategies for accelerating AI adoption, offering practical
steps to drive full-scale transformation.
In this journey, AI is not just a solution; it is the
foundation of a next-generation customs system that is
faster, more secure, and capable of adapting to the
evolving demands of modern trade.
PwC | Revolutionising customs with AI 3
Evolving challenges in customs
Customs administrations face challenges in adapting to the digital age. Many have outdated
IT systems and regulations and struggle to analyse ever increasingly volumes of data.
These issues are compounded by the fierce competition for the skills needed to experiment
with emerging technologies. Nevertheless, there are now tangible examples of Customs
Administrations effectively leveraging the benefits of AI implementation.
Challenge Description Case Example
DG TAXUD built a
customs administrations must perform risk collaborative AI platform that
assessments based on hundreds of millions of allows EU Member States to
records gathered through the EU’s Import collaborate and enhance
Control System before loading in a third country their national risk
Safety &
or arrival in the EU. assessment capabilities.
security
In a complex regulatory landscape, customs
In Brazil, customs declaration
authorities must manage diverse international
and verification processes
trade agreements and compliance
were manual and error-prone
requirements. Evolving standards require
Compliance until automated with an
real-time monitoring and reporting, with
& regulation AI-enabled verification tool.
non-compliance risking revenue and security.
Recent global events like pandemics and
To solve supply chain
geopolitical tensions have exposed the fragility disruptions, the UAE, in
of supply chains. customs authorities need to partnership with WEF,
address disruptions in the flow of goods and launched the TradeTech
Supply chain adapt to the unique challenges posed by initiative to harness AI to
improve customs procedures.
& logistics smaller, high-volume e-commerce shipments.""
As customs processes become more advanced, Indian customs developed an
a substantial amount of data is generated. AI-enabled system to store
customs authorities require solutions to and analyse data effectively,
integrate this data from various sources and providing valuable insights
Data
analyse it for improved operational efficiency. for decision-making.
management
PwC | Revolutionising customs with AI 4
Transitioning to cognitive customs:
Enhancing efficiency, security, and trade
To manage the evolving challenges in international trade and customs, authorities should aim
to transition towards a cognitive customs state, characterised by advanced technological and
operational capabilities. This transformation requires substantial investment, eventually
embedding AI in the customs operations DNA.
While many customs authorities are currently at the experimental or opportunistic stages of AI
adoption, moving towards a cognitive stage can help custom authorities to achieve many
intermediate benefits, such as develop simpler, fully automated systems that leverage
predictive analytics, optimise processes autonomously, and learn from historical data to
improve decision-making.
This cognitive shift ensures greater efficiency, security, and adaptability in managing the
increasingly complex global trade environment. Additionally, it allows customs administrations
to be proactive in addressing emerging risks, improve trade facilitation, and create a more
transparent and streamlined supply chain, ultimately fostering economic growth and
international cooperation.
Customs AI Maturity Framework
Adopts emerging Cognitive shift: AI as
technologies with part of the Customs
flexible architecture Administration DNA
Process-driven
Basic capabilities operations
PwC | Revolutionising customs with AI 5
ygolonhceT
ytirutam
Non exhaustive
Where do you AI use cases
stand today?
High
Increase container scanning using
AI
Detect abnormal packages with
unsupervised learning
Innovator Leader
Enabled Established Virtual chatbot assistants for trade
and compliance queries
Auto-classify goods from free text
descriptions
Low
Low Operational Maturity High
Real-time translation with any
language of traders and travelers
AI will help customs organisations
unravel untapped potential
Customs authorities should invest in and experiment with AI-enabled technologies to advance
towards a cognitive customs state. This can be done by strategically deploying AI for different
customs use cases, helping unlock new levels of operational efficiency, enhance security, and
streamline processes. While the benefits may take some time to materialise, AI will eventually
drive exponential growth in capabilities, leading to transformative changes in customs
operations over the long term.
After overcoming initial setbacks and adapting to AI
technologies, customs operations begin to see
transformative changes, leading to high transformative
and operational maturity
What actually happens
Cognitive
capabilities
Time
Valley of disappointment What you think should happen
There might be initial challenges in AI adoption that
can be attributed to the complexities involved in Indicates initial expectations, where stakeholders
integrating AI into existing systems. This might might expect a direct and steady improvement in
result in the delay in realisation of AI benefits, cognitive capabilities as AI technologies
slowing growth in the development of cognitive are implemented
capabilities
PwC | Revolutionising customs with AI 6
Integrating AI across the value chain
Customs Administrations should evaluate their entire value chain and pinpoint specific use
cases where AI can Revolutionise and transform their operations.
Non exhaustive
Value chain What it entails Countries Technology Outcomes
Improved security
AI identifies high-risk by enhancing the
Assessing risk along
cargo shipments and detection of
with aiding customs in
Pre-arrival forecasts trade trends, dangerous
proactive planning EU, Canada, enhancing security shipments
Hong Kong,
and efficiency
South Korea
Increased detection
Detecting cargo AI detects anomalies
accuracy and
anomalies in X-ray and fraud patterns,
expedited the
scans and combating improving contraband
Arrival clearance process
smuggling and tax Canada, Hong identification and
evasion by spotting Kong, South combating smuggling
fraud patterns Korea and tax evasion
Automates the
Customs duties and process of
AI-enabled
taxes are calculated determining the HS
Custom Harmonised System
based on the goods' code of goods and
clearance type and value World Customs (HS) Codes calculates their duty
Organisation classification tool
rate in real time
Faster audit
AI ensures trade
Analysing invoices compliance by processes, reduced
Post and certificates analysing invoices and human error, and
automatically to certificates, detecting increased
clearance combat smuggling and UAE fraud to combat compliance
tax evasion smuggling and tax
evasion
Improved
AI-enabled analysis tool compliance and
Analysing customs
is utilised to examine simplified customs
Policy- data for insights,
customs data, providing clearance process
making aiding data-driven Brazil
insights for data-driven
policy decisions
policy decisions
PwC | Revolutionising customs with AI 7
Impact of enablers on AI adoption
A comprehensive business impact analysis, based on various criteria such as business
model, ease of adoption, competitive advantage, and technological disruptions, can help to
focus on specific needs and requirements.
Non exhaustive
Impact on
Enablers Key component Description Investment
adoption
Manage innovation in an
adaptive manner,
Innovation
ensuring the required
management
Agile agility to cope with Low Medium High Low Medium High
changes.
Place customs officers
User centricity and trade at the centre of
UX design the AI integration process. Low Medium High Low Medium High
Understand and govern
own data, make future
Data centricity reforms more data-driven,
Data governance and address privacy Low Medium High Low Medium High
and management concerns.
Invest in capacity building
of customs staff to foster
Skills
a cultural shift. Forming
development
Training interdisciplinary teams to Low Medium High Low Medium High
avoid siloed initiatives.
Break silos within
customs administrations
and between technology
Collaboration
providers to share best
Low Medium High Low Medium High
Strategic
practice, tools and
partnerships
resources.
Leveraging internationally
Technology supported standards to
integration simplify integration and
Interoperability Low Medium High Low Medium High
reduce costs.
PwC | Revolutionising customs with AI 8
How can PwC help you?
• Capture the vision and ambition for AI.
Current state
• Gap assessment of the existing customs capabilities in
and vision three aspects: people, process, data and technology.
• Benchmarking study for alignment and best practice.
• Identifying and prioritising use cases.
• Design user journeys and organisational structure.
Feasibility and • Carry out feasibility studies.
prioritisation • Define governance structure, including key performance
indicators, decision rights, and Responsible,
Accountable, Consulted, and Informed (RACI) matrices.
• Rapid prototyping.
• Develop strategic choices.
Strategy and
• Action planning and roadmapping – for example, on how
action planning
the customs administrations can move into the desired
future state, outlining key milestones and objectives.
• Shape technical descriptions, including requirements,
desired functionalities, and visual prototypes.
• Leverage key enablers that can support your journey,
Prototyping
such as technology, resources, and partnerships.
• Design customs ecosystem architecture, including
integration requirements.
• RFP preparation including pre-qualification, technical
evaluation, scope of work, and technical/functional
Tender and specifications.
• Define Service-Level Agreement (SLA) parameters for
supplier evaluation
different components.
• Support on pre-bid meeting and clarification response.
• Vendor response analysis (technical and commercial).
• Project management activities, including risk mitigation
and daily project coordination.
Programme • Supply installation testing (use case testing) and go-live
monitoring.
management
• SLA monitoring for edge devices, applications, and IT
infrastructure.
• Evaluation of change requests across project life cycle.
PwC | Revolutionising customs with AI 9
eniltuO
metsyS
smotsuC
neG
txeN
tnemssessA
poleved
dna
ngiseD
tnemelpmI
evil
oG
Conclusion
Customs authorities are increasingly adopting AI to boost efficiency, security, and compliance, yet
much of AI’s potential remains untapped due to foundational gaps. To unlock AI’s power, customs
authorities must establish essential enablers to translate technology into tangible gains.
Experimental, proof-of-concept pilot projects, focused on real impacts, are essential for
determining how AI can best fit customs operations.
Alongside this experimental approach, better innovation management is crucial, with an agile
workflow that includes end-user input. While rapid results should remain a priority, AI’s broader
role in driving digital transformation, including improvements in data governance and technology
infrastructure, must not be overlooked.
Establishing mixed AI teams that combine data scientists, risk managers, and other departments
will improve engagement and buy-in across the organisation, reducing the risks of siloed
initiatives. Additionally, advancing customs legislation to allow data standardisation will make AI a
more effective, versatile tool for customs.
To fully harness AI in customs, these initial steps toward a larger vision are essential. Building a
foundation for better coordination, stakeholder involvement, and a results-driven mindset,
customs administrations can leverage AI to deliver safer, more efficient, and reliable processes for
themselves and end-users.
References
1. https://www.wto.org/english/res_e/booksp_e/wcotech22_e.pdf
2. https://www.elibrary.imf.org/display/book/9798400200120/9798400200120.xml?code=imf.org
4. https://www.wto.org/english/res_e/booksp_e/wco_wto_annex_the_case_studies.pdf
5. https://www.apec.org/docs/default-source/groups/sccp/compendiumofsmartcustomspracticesforapec
economies_0424.pdf?sfvrsn=acc161e1_2
6. https://mag.wcoomd.org/magazine/wco-news-104-issue-2-2024/automating-image-analysis-
china-customs-implements-new-model-for-the-development-and-deployment-of-algorithms
7. https://taxation-customs.ec.europa.eu/document/download/bf00c70a-df7f-475f-a6ab-6194b3b89efb_en
Contact Us
PwC Middle East PwC Luxembourg
Rajat Chowdhary Philippe Pierre
Partner, Technology Partner, EU Global Leader
Mobile: +971504293733 Mobile: +352 621 334 313
Email: rajat.c.chowdhary@pwc.com Email: philippe.pierre@pwc.lu
Sharang Gupta Bastian Vomhof
Director, Technology Director, Customs Consulting
Mobile: +971 504326559 Mobile: +352 621 334 109
Email: sharang.g.gupta@pwc.com Email: bastian.vomhof@pwc.lu
Dipesh Guwalani
Senior Manager, Technology
Mobile: +971 565205132
Email: dipesh.g.guwalani@pwc.com
Contributors
Xavier Lisoir
Managing Director, Customs & AI Ravi Jhawar
Director, Customs Architecture
Mobile: +352 621 334 114
Email: xavier.lisoir@pwc.lu Mobile: +352 621 334 430
Email: ravi.jhawar@pwc.lu
Soham Rane
Manager, Technology
Mobile: +966 543697178
Email: soham.r.rane@pwc.com
About PwC
PwC is a global network operating in 151 countries, with over 364,000 professionals dedicated to delivering excellence in Assurance, Advisory, and Tax Services. Beyond our traditional Customs Compliance services for Economic
Operators, we are recognized experts in transformation and modernisation of Customs Administrations worldwide. Our international perspective allows us to serve a diverse clientele, including the EU Commission, GCC public safety,
border security, and Customs Authority, as well as numerous national Customs administrations. Our consulting projects span from strategy to implementation, supporting Customs Administrations in their digital transformation journeys. We
deliver large-scale digital programmes that make Customs more data-driven, efficient and save. Beyond strategic advice, we design and implement innovative Customs solutions in-house, leveraging cutting-edge technologies such as data
analyPticsw, cClou d| c oRmepuvtinog,l auntdi oartnificisiali nintgell igcenucse. tOoumr cosm pwrehiethns ivAe Iapproach ensures practical, hands-on solutions that drive tangible results for Customs Administrations.
12
© 2024 PwC. All rights reserved
Sensitivity: Public"
218,pwc,AI-insurance_21.08.2023_END.pdf,"Artificial Intelligence
for insurance
companies
Presentation by
Petr Novák, Radek Hendrych,
Magdalena Kardela-Wojtaszek
Agenda
1.
Who we are? 3
2.
AI in the current world 6
3.
Selected use cases 10
4.
Our approach and capabilities 14
5.
Summary 22
© PwC Risk Management & Modeling 2
1
Who we are?
© PwC Risk Management & Modeling
Introducing our Team
Petr Novák Radek Hendrych
Director Senior Manager
#datastrategy #actuary #modeling
We are a newly established team within PwC CZ, with a #dataanalysis Data Actuarial #IFRS17 #SolvencyII
strong focus on digital, data-driven services and products. #datadelivery
Insight Excellence
Together, we bring insurance business-subject-matter #datagovernance
experts that can deliver complex transformation projects
from business design and data management up to
regulatory and actuarial consequences.
Our competence is bolstered by an expansive teams of
senior specialists, who provide crucial support to each
Business
project.
Development
Insurance
risk team
100+
Magdalena
Kardela-Wojtaszek
Manager
experts from PwC with
#insurance #productdesign
extensive experience in #businessdevelopment
data, risk and business
© PwC Risk Management & Modeling 4
Meet the Team supporting Us
40+
Our strong skills are supported by large teams of experienced specialists including insurance business experts, risk managers,
data managers, data modelers, actuaries, and more. In total, there are over 40 specialists based in the Czech Republic, with the
potential to expand to over 100 specialists across the Central and Eastern European region.
experts in PwC CZ
100+
Risk managers
Operations Efficiency specialists Data analysts
experts in PwC CEE
Data engineers
Risk modelers
Product Development Managers
Data scientists
Business analysts
Actuaries
Data architects
Project managers
Insurance experts
Business Intelligence specialists
© PwC Risk Management & Modeling 5
2
AI in the
current world
© PwC Risk Management & Modeling
AI in the current world
Artificial Intelligence has been making great strides. It has shifted from an incomprehensible subject of a chosen few “Einsteins” to a daily used assistant.
Companies invest enormous amounts of money in AI to revolutionize various aspects of their operations and gain a competitive edge in the market.
Why is it important to think about AI ?
Cost Reduction
Innovation and Enhanced
Improved
and Process
Competitive customer Economic growth
efficiency
Optimization
Advantage experience
© PwC Risk Management & Modeling 7
PwC's support in achieving
AI-powered business goals
How to effectively use
AI to build up your
company? How to
transform your business
to stay ahead?
PwC can help you to answer those questions
as well as define the right AI vision and
strategy of your company.
© PwC Risk Management & Modeling 8
Clear path to the successful AI implementation
▪1 Management awareness - We will show ▪5 Way of work - We will define the
Our approach the managers what the current and changes in your way of working to
expected AI capabilities are and how include or enhance AI use
they are used/can be used in insurance
Our proven approach ▪6 Platform and tool - We will define the
sector. We will moderate unreasonably
contains a set of deliverables main functionalities needed to use AI
high expectations and challenge low
that help you to define a expectations ▪ 7 Regulation and risks - We will articulate
reasonable path of AI the main risks related to AI
▪2 Use case ideation - We will prepare a
implementation for your implementation in your company and
workshop to think up relevant AI use
explain the existing and emerging
business.
cases
regulations
▪3 Vision - We will help you to formulate the
▪8 Roadmap - We will place all the main
company AI vision
activities onto a roadmap
▪4 Organization and team - We will
propose to you variants of organizational
setup, services, competencies and roles
to implement AI and benefit from AI
© PwC Risk Management & Modeling 9
3
Selected
use cases
© PwC Risk Management & Modeling
AI models | Use cases
AI deployment can benefit insurers across various domains, encompassing pricing, underwriting, claims handling, customer service, and fraud prevention.
Presented below are a exemplary use cases that illustrate how AI can impact internal processes and customer service quality.
Pricing and underwriting Distribution Claims handling
• Analyze volumes of data and make accurate • Understand your customers better and tailor their products, • Optimize internal processes, reduce manual errors,
predictions about risk factors and prices services, and marketing efforts accordingly improve operational efficiency, and lower costs
• Analyze various factors, such as individual • Offer personalized product recommendations and increase • Analyze historical data and patterns and identify
behavior, driving patterns, or health data, to cross-selling opportunities suspicious claims
create personalized pricing models (in terms
• Analyze customer behaviors, market trends, and other
of risk premium or profit margin)
relevant data to predict future outcomes
Analysis & monitoring
• Automate the underwriting process to make
better underwriting decisions
• Identify patterns, trends, and anomalies
Customer service
• Analyze large datasets and perform complex
• Analyze vast amounts of market data, including
actuarial model / calculations (e.g., in terms of
reserving, pricing, CLV) • Use AI-powered chatbots and virtual assistants to handle competitor pricing, customer preferences to stay
customer inquiries competitive in a rapidly evolving market
• Analyze customer feedback from various channels to • Automate compliance monitoring and ensure
understand sentiment, identify issues, and improve adherence to regulations and reduce human error
customer satisfaction
• Automate the generation of reports, dashboards,
and insight
© PwC Risk Management & Modeling 11
Use cases | Claims handling
AI use cases AI use cases
Explore the example of how • Document and photo analysis • Letter preparation for client
• Verification of policy conditions for claims • Client communication
artificial intelligence (AI) • Claim anti-fraud check
integration revolutionizes • Providing claim decisions
• Adjusting case reserves
claim processing, reviews,
• Settlement settings
and settlements.
Automated
Claim Review Claim Rejected
& Investigation
Benefits
Claim Revision
Claim Event Claim Reported
(Ex-post)
• Claim process optimization
Manual • Reduction of manual mistakes
Claim Review Claim Settled • Operational efficiency improvement
& Investigation • Cost reduction
• Enhanced customer experience
• Accurate reserve estimations and
calculations
AI use cases AI use cases AI use cases AI use cases
• Real-time support for claim • Document and photo analysis • Letter preparation for client • Fraud detection
registration • Verification of policy conditions • Client communication • Process monitoring
• Analysis of claim form and for claims • Payment processing (automated/manual)
attached documents for • Claim anti-fraud check • Claim portfolio analysis:
completeness and correctness • Providing claim validity patterns, trends, anomalies
• Claim / client scoring recommendations. identification
(automated / manual)
• Adjusting case reserves • Automated report, dashboard,
• Case reserving and insight generation
AI use cases
• Real-time claim status support
• Actuarial loss reserving
© PwC Risk Management & Modeling 12
Use cases | Pricing
Premium
composition
AI use cases
•Advanced statistical / machine
Profit margin learning techniques to optimize
Benefits
profit margin (e.g., dynamic
discounts) -competitiveness
•Accurate risk assessment
•Customized premiums based on
customer/product risk profile
Expenses
•Competitive pricing
(costs & commissions)
AI use cases
Risk premium •Advanced statistical / machine
learning techniques to model
(expected claim outgo) expected risk more precisely
© PwC Risk Management & Modeling 13
4
Our approach
and capabilities
© PwC Risk Management & Modeling
Data science team
Usually, at the center of AI implementation, there is a data science team. The team should not be
composed only from data scientists, but also data engineers, analysts and DevOps engineers should
be part of it.
These required roles are highly sought after in the job market, making recruitment and retention
challenging. We will provide you with job descriptions and the proper mix of employees and
contractors. To effectively attract and retain these experts, several critical factors must be considered:
the specific use cases, the tools employed, the methodology applied, and the team's composition.
Not all AI development must be realized from scratch by the data science team.
A lot of ready-made AI solutions and knowledgeable suppliers are on the market. But it is important,
data science team provides AI solutions to the company, and it should be their right to decide about
developing the solution themselves or with the help of a supplier. We will address this situation in
your case and bring the elements to decide on whether to Make or Buy.
What are the sources of dissatisfaction among data scientists, and what factors could erode their
loyalty and enthusiasm? One critical aspect is investing time and effort into developing an AI solution
that ends up unused by the company. Surprisingly, this situation is quite common. Another
demotivating factor is navigating through bureaucratic processes involving multiple levels of approval
beyond their control, often leading to extended delays and inefficiencies in project progression.
Additionally, the extended duration needed for data preparation can be discouraging; although they're
keen to create AI models, the necessary data isn't readily available. Lastly, the presence of low-
quality data makes it difficult for data scientists to perform their tasks effectively.
© PwC Risk Management & Modeling 15
Functions of modern data science framework
Modeling data Modeling
availability environment
• Data easily accessible from modeling environment • Modern programming language (e.g., Python)
• Data science team in control of data extraction • Collaboration tools for smooth cooperation inside team
– Shared virtual storage
• Long-term data storage and versioning
– Code version control (e.g., GIT)
– Libraries version control (e.g., conda, poetry)
• Data storage separate from modeling environment – Experiments and models version control (e.g., What does this
• Data extraction controlled by a different team mean?)
• Data provided in flat files without any version control • Virtual machines for computation / memory-intensive tasks
• Outdated programming languages (e.g., SAS)
• Complicated and messy collaboration inside the team
– File sharing through emails
– No code version control
– Different version of libraries on local machines
– No control over experiments and models
• Only local machines with limited computation power
© PwC Risk Management & Modeling 16
Functions of modern data science framework
Documentation Deployment
and outputs to production
• Versioning of outputs (model registry) • Production code in the same framework as development
• Documentation integrated in the data science environment
framework • Project structure created taking deployment into consideration
• Integrated testing
• No systematic control over model versions • Containerization and CI/CD pipeline
• Documentation in separate files without versioning • Scheduling and automation of tasks (e.g., monthly scoring)
• Data science team in charge of production settings
• Production system separated from development environment (or in
different programming language)
• No consideration of deployment during modeling
• Complicated testing procedure
• Difficult integration (and updates) in production systems
• Manual running of production scripts
• Separate DevOps / MLOps team in charge of production
© PwC Risk Management & Modeling 17
Functions of modern data science framework
Monitoring Planning and
and validation project management
• Real-time monitoring / validation of model performance • Modern project management tools (e.g., Jira, Azure DevOps)
• Monitoring / validation integrated in data science platform • Time estimation and real-time tracking for individual tasks
• Alerts and early warnings in case of unexpected behavior • Integration with documentation, code, and outputs
• Fully automated reporting interface
• Project management outside the data science framework
• Ad hoc one-time monitoring / validation reports • Complicated tracking and governance
• Reports has to be created outside data science platform
• No integration with documentation, codes, or outputs
• No real-time information about potential problems
• Requiring manual effort, consuming time and human resources
© PwC Risk Management & Modeling 18
Functions of modern data science framework
Modeling
environment
• Faster project delivery
• Increased quality of delivery
• Higher effectiveness and costs savings
• Reduction of risk of error / miscommunication
• More extensive control over the product
• Improved project management
• Flexibility and ability to quickly react to new situations
• Support experimentation and innovation
© PwC Risk Management & Modeling 19
Building data science framework/platform
On top of traditional Example technical solution implemented in MS Azure: External raw data
data solutions, we
implement a platform • IDE: Jupyter notebooks*
dedicated to machine • Computation framework: Azure Machine Learning Studio Data lake DWH
learning and AI use • AutoML: Azure Machine Learning**
cases. The platform • Model registry: Azure Machine Learning** (MLFlow)
consists of all the tools
• Experiments: Azure Machine Learning** (MLflow) Consumers Data science platform
data scientists would
• Feature store: Azure Machine Learning** (MLFlow)
need to accomplish
• Monitoring & logging: Azure Machine Learning** (MLflow) + Azure Monitor
their tasks. Artifact Project Model
• Orchestration: Azure Data Factory (Airflow), Azure Databricks* Workflows
CI/CD
store management registry
• Artifact store: Azure DevOps Artifacts
The platform could be
• Project management: Azure DevOps Boards + Wiki
implemented on- Code Computation
• Code versioning: Azure DevOps Repos Orchestration
premise or in-cloud - versioning framework
• CI/CD: Azure DevOps Pipelines
e.g., Microsoft Azure.
• Storage: Azure Data Lake Storage + Delta Lake + Azure SQL Database Monitoring &
AutoML Storage
* or other open source IDE (e.g., VS Code) logging
** or Azure Databricks (Spark)
IDE Experiments Feature store
© PwC Risk Management & Modeling 20
Building data science framework/Way of working
The ultimate goal of data scientists is to prepare solutions that
automatically provide smart advice from data or that automatically
answer questions or can just conversate.
The important role to benefit from data science is the business or
product owner. They are atheperson who can connect business
opportunities with data science capabilities and has the power to
decide where to use data science.
There are 3 related processes to achieve the goal:
1. Business delivery - from idea, through cost/benefit analysis,
objective specification, changes in the organization, product and
client service up to business monitoring
2. Data science delivery - from data preparation, through model
training and deployment up to model monitoring
3. DevOps delivery - automation of data processing for the model,
creation of CI/CD pipelines, encapsulation of the model to an
application, operation and technical support
© PwC Risk Management & Modeling 21
5
Summary
© PwC Risk Management & Modeling
Data culture strategy
Having the best of breed data platforms and excellent data specialists does not mean the company
will succeed in gaining maximum value from data. Something that is required to win in the
competitive fight in the digital world.
No endeavors by data specialists can cover all the company’s and the employees’ needs to get
responses from data.
The employees must learn how to understand and use data to find answers to their basic questions:
so-called BI self service. There are effective tools in the market powered by AI, which can help them
in this effort. It is also important the employee experience the difference between a decision based on
data and a decision based on their intuition. In addition, the employees should recognize and
understand the problem when using low-quality data.
Another part of data culture lies in the employees’ understanding how they can benefit from and
work with data specialists, mainly data scientists and what AI can bring to them.
PwC has the experience and know-how to focus and structure data educationand how to use the
tools to achieve a steep learning curve and the best results.
© PwC Risk Management & Modeling 23
Interested?
Contact us.
Petr Radek Magdalena
Novák Hendrych Kardela-Wojtaszek
Data Delivery, Actuarial and Risk Modeling, Insurance business development,
Risk Management and Modeling Risk Management and Modeling Risk Management and Modeling
PwC Czech Republic PwC Czech Republic PwC Czech Republic
T: +420 602 282 972 T: +420 734 542 531 T: +420 732 999 650
M: petr.novak@pwc.com M: radek.hendrych@pwc.com M: magdalena.kardela.wojtaszek@pwc.com
Thank you
PwC Czech Republic
Risk Management & Modeling
pwc.cz/rmm
© 2023 PricewaterhouseCoopers Česká republika, s.r.o. All rights reserved. “PwC” is the brand under which member firms of PricewaterhouseCoopers International Limited (PwCIL) operate
and provide services. Together, these firms form the PwC network. Each firm in the network is a separate legal entity and does not act as agent of PwCIL or any other member firm. PwCIL
does not provide any services to clients. PwCIL is not responsible or liable for the acts or omissions of any of its member firms nor can it control the exercise of their professional judgment or
bind them in any way.
© PwC Risk Management & Modeling"
219,pwc,SEE-NextGen-Survey-Report-2024.pdf,"PwC’s Global NextGen
Survey 2024 – SEE
Report
Transformation, succession and the next generation of family business leaders
The mid-2020s are particularly significant for the Central and Eastern European (CEE) region. May
2024 heralded the 20th anniversary of the accession of Czech Republic, Estonia, Hungary, Latvia,
Lithuania, Poland, Slovakia and Slovenia to the European Union, joined three years later by Bulgaria
and Romania. This growing maturity is reflected in the current challenges faced by family businesses,
which are of crucial importance to the regional economy, with up to half of private sector
employeesbeing employed by family-owned and run companies.
The majority of Southeastern European (SEE) family businesses are still run by the first generation of
post-communist era entrepreneurs who formed them in the early 1990s. According to the 2023 CEE
Family Business Survey, 64% of CEE family businesses (double the 32% global figure) are in the
hands of the first generation and 36% have passed to second or further generations (vs. 68%
globally). However, with many of the first wave now in their 60s or 70s, this decade is seeing the
approach of a changing of the guard in leadership in family businesses. NextGen, or the next
generation of family business leaders who are typically aged between young adulthood and their early
40s, are getting closer to attaining senior executive roles.
The global PwC Global NextGen Survey is particularly pertinent to SEE, illustrated by the record
number of participants this year of respondents from SEE and the wider CEE—and the high
percentage of family businesses still run by their founders. There are clear signs that these pioneering
businesses are at a crossroads—and are looking inward and outward to plan a strategy to remain
viable and prosper in the coming decades.
The contribution of family businesses to SEE economies cannot be ignored and
underestimated. In fact, in many countries in our region, these companies are the backbone
of the national economies. Succession planning is key to future-proofing all businesses but
has particular significance for family businesses. NextGen in many cases are already in
managerial positions—preparing to take over as CEOs or Board members. The SEE edition
of the report explores the key challenges and priorities on the horizon NextGen. It also shows
that local traditions and business mentality have their impact on how these companies are
increasingly seeing the imperative to transform.”
Bojidar Neytchev
Partner, Entrepreneurial and Private Business Leader
PwC SEE
PwC | Global NextGen Survey 2024 2
Key findings
Only have a minute? Consider these notable findings from the SEE edition of the
NextGen survey.
NextGen, or the next generation of family business leaders, are typically aged between young
adulthood and their early 40s, and are getting closer to assuming control of family-run
businesses in the SEE region.
Achieving growth is a key priority for NextGen in the next two years. Over half (52%) of
respondents see achieving growth as a top priority, followed by expanding into new sectors or
markets and talent management. There is a reasonably good match between business
priorities and where NextGen are engaged.
NextGen globally and in SEE feel they can add most value in terms of professionalising and
modernising management practices (22% in SEE vs. 21% globally), diversifying the services
and products that the business offers (16% in SEE vs 9% on a global level) and international
expansion (14% in SEE vs. 8 % globally).
NextGen in SEE are reasonably positive on issues related to clarity of roles and
responsibilities (63%). Also, 55% of NextGen in SEE believe that their company has a clear
governance structure, compared to 68% of the current generation. At the same time, they are
less positive about their company’s digital capabilities and confess that in more than one-third
(33% vs. 36 % globally) of their companies there is a resistance to embracing changes.
Despite a realisationthat there is a need to harness generative AI, SEE family businesses
have been slow to implement the technology. Although 47% (compared to 30% globally) are
in the early stages of exploration, 33% of family businesses in SEE have yet to begin their
generative AI journey. Only 6% have implemented AI in some or many parts of their
organisations. Furthermore, only 12% of family businesses in SEE have a person or team in
the company directly responsible for generative AI.
Responses show a clear difference in how much NextGen in SEE are engaged in generative
AI now - and how much they expect to be in the future. Only 12% of NextGen in SEE are
currently engaged in generative AI - but a further 53% expect to be engaged in it in the future.
PwC | Global NextGen Survey 2024 3
Key findings
Only have a minute? Consider these notable findings from the SEE edition of the
NextGen survey.
NextGen is overall positive about the technology’s potential, despite being aware of the risks
of AI adoption. However, only 10% of NextGen in SEE believe that generative AI will increase
their company profitability in the next 12 months (half of the global figure) and only 29% (44%
globally) believe that in the next three years, generative AI will significantly change the way
their companies create, deliver and capture value.
A slightly greater majority of NextGen in SEE (86% vs 82%) are personally interested in
generative AI than global averages. Perceived knowledge levels in the region are high. 59%
of Nextgen in SEE feel they are personally knowledgeable about generative AI vs 53%
globally.
Only 4% of NextGen in SEE report that their family businesses have already defined
governance around using AI responsibly, although a further 59% believe they need to do this.
NextGen in SEE generally feel more positive than global averages about family trust levels.
However, only 27% believe there are high levels of trust between family members and non-
family members within the business. Less than half (47%) believe there are high levels of
trust between NextGen family members and the current generation.
NextGen is concerned about succession planning. Just over half (51%) of NextGen in SEE
are aware of a succession plan in their family business, but many of those were not involved
in its development and further 6% do not know if there is a succession plan.
53% of NextGen in SEE believe the ability or readiness of the current generation to retire is a
difficult aspect of succession and 45% believe proving themselves as a new leader is will be
also be difficult.
PwC | Global NextGen Survey 2024 4
Achieving growth is the key
priority
Achieving business growth is comfortably the key priority for NextGen in SEE over the next two
years, with well over half of respondents citing growth as most important. Given that the second
biggest priority is expanding into new sectors, this chimes with the results of last year’s Family
Business Survey, where 91% of CEE respondents reported that growth is important because it
enables investment in their company’s future. This suggests that both current and NextGen leaders
are doing all they can to adapt to uncertain times in order to pursue growth.
Question
From your own personal point of view, what would be your top three priorities for the company over the next two
years?
In which areas, if any, are you personally actively engaged at present or likely to be engaged in the future?
SEE Global
59
Achieving business growth
67
41
Expanding into new sectors or markets
43
Talent management -attracting/retaining the best 41
young talent 43
Improving the working conditions/practices of our 24
employees 27
Ensuring we offer the right products and services 22
for today’s customers 51
22
Investing in innovation and R&D
27
20
Adopting new technologies
37
Increasing our focus on investments for 20
sustainability and impact 27
8
Upskilling the digital capabilities of our workforce
22
Supporting our local community via increased 8
investment or business activity 16
Reconsidering our asset allocation and 8
investments 14
Reducing the organisation's environmental impact 4
18
Increasing our focus on privacy and cybersecurity 2
2
PwC | Global NextGen Survey 2024 5
In today's dynamic market, family businesses must strategically focus on
both organic growth and new business growth to ensure long-term
success. For organic growth, staying vigilant to structural changes in
established markets is essential. Embracing digital transformation can
optimize operations and enhance customer experiences. Investing in
talent and fostering a culture of innovation can drive continuous
improvement and adaptability. For new business growth, identifying
opportunities in emerging markets experiencing structural changes can Luis Ndreka
provide a significant edge. Exploring new sectors and forming strategic
CEO of Lufra
partnerships can open additional growth avenues. By focusing on these
Foods, Albania
dual strategies, family businesses can navigate challenges and secure a
prosperous future.”
Despite the current generation of family business leaders citing changing market conditions,
innovation and development as fundamentally important, family businesses have a reputation for
having somewhat traditional and conservative mindsets. Both globally and in the SEE region,
NextGen reveals a willingness to explore new ideas and business practices. The principal areas
where they believe they can add the most value are: professionalisation and modernisationof
management practices and international expansion.
This more than hints that NextGen has one eye on leading business transformation once they assume
the roles of key decision-makers. This is especially relevant considering diversifying the services and
products that family businesses offers and separating family ownership from management can be two
of the key actions that NextGen may bring to the top tables.
Orbico started as a logistical and distribution startup in the late eighties and early nineties. At
that time, we could develop and grow by adding new partners, services, and territories to our
portfolio. Last twenty years we have been considered as a reliable partner of global leading
producers and are encouraged to acquire existing players in our region of Central and
Southeast Europe.”
Stjepan Roglic
Deputy Chairman of the Supervisory Board
Orbico d.o.o, Croatia.
PwC | Global NextGen Survey 2024 6
Question
Where do you think that you can personally add the most value to your family business?
SEE Global
Professionalising and modernising management practices 22
21
Diversifying the services and products that the business 16
offers 9
14
International expansion
8
10
Separating family ownership from management
8
Having a clearly defined purpose, i.e.ensuring the business 8
is not just about making profits 11
8
Investing in new business ideas
10
8
Reinvesting more profit into developing the business
8
Having a business strategy fit for the digital age 6
10
Starting my own venture (supported/financed by the family or 2
operating under the family holding)) 4
2
Attracting and retaining talent
4
Starting my own venture (not supported/financed by the 2
family) 2
Upskilling staff 3
Partnering with start-ups 2
NextGen in SEE is notably less positive than current leadership on issues related to clarity of roles
and responsibilities, governance structures and digital structures. There is a clear generation gap in
terms of role clarity and governance, with NextGen both globally and in SEE over 10% more
pessimistic. Furthermore, one-third (33%) of NextGen in SEE and slightly more of their global
counterparts (36%) believe there is institutional resistance in their family business to embracing
change. Additionally, less than one-quarter (24% vs. 34% globally) see appropriate protocols or a
constitution in place. The survey suggests, therefore, that NextGen clearly feels there is work to be
done in transforming the governance and the strategic direction of SEE family businesses.
Question
How strongly do you agree that…?
SEE Global
Global
There are clear roles and responsibilities for those involved in running the business 63 63
We have a clear governance structure 55 51
We have strong digital capabilities 41 31
There is a resistance within the company to embrace change 33 36
We have family protocols/a constitution in place 24 34
The current generation does not fully see opportunities related to technology
24 29
transformation within the business
PPwwCC || GGlloobbaall NNeexxttGGeenn SSuurrvveeyy 22002244 7
A gap between NextGen personal
interest and company action on
generative AI
In any business or sector, implementing GenAI is a marathon, not a
sprint, requiring a careful balance between urgency and prudence: move
too slowly, and you lose out to competitors; move too quickly, and the
risks increase significantly. To bridge the gap between awareness and
value creation, family businesses should treat GenAI implementation as
a strategic initiative rather than an operational one centered solely on
functions, tools, or technology. They should focus on building a
trustworthy, traceable data foundation and appropriate governance which Snezhana Ilieva
are essential for responsible implementation, with humans playing a key
Director AI and
role in the process. An emphasis should be placed on building
Data Science,
awareness amongst employees - effective use of generative AI depends
PwC SEE
on staff proficiency. Finally, businesses should build a balanced
ecosystem of partners to address GenAI needs across the tech stack
while avoiding vendor lock-in.”
High interest and knowledge surround AI but limited
action so far
The recent CEE edition of the Global CEO Survey alluded to CEOs being somewhat more neutral than
their global counterparts on generative AI, with 10% less believing the technology will significantly
change the way their company creates, delivers and captures value over the next three years.
Nonetheless, CEOs still see a clear direction of travel in terms of AI, with well over half (59%) predicting
that generative AI will be vital to transformation.
The NextGen Survey identifies comparable patterns. Most NextGen in SEE (86% vs 82% globally) are
personally interested in generative AI—and a majority perceive their knowledge levels to be high. 59%
of NextGen in SEE consider themselves knowledgeable about generative AI, which is higher than the
53% global figure.
Despite an awareness that there is a need to harness generative AI, SEE family businesses have been
slow to implement the technology. Although 47% (compared to 30% globally) are in the early stages of
exploration, 33% of family businesses in SEE have yet to begin their generative AI journey. Only 6%
have implemented AI in some or many parts of their organisations. Furthermore, only 12% of family
businesses in SEE have a person or team in the company directly responsible for generative AI.
PwC | Global NextGen Survey 2024 8
These results point to two things. Firstly, there may be more evidence of a generation gap between
current leaders and NextGen. NextGen clearly recognise the transformational potential of generative
AI—but also report that their family businesses have been slow off the mark in terms of adoption.
Secondly, connected to the above—it is clear that the pace of generative AI technological
advancement is relentless, but the reaction of many family businesses hasn’t gotten even close to
matching this speed.
Despite their broad realisation that there is a need to harness AI technology, SEE family businesses
have moved towards generative AI adoption at a fairly pedestrianpace in many cases. They have to
pick up speed in order to benefit from the technology—and quickly.
Question
How would you describe your family business’s current level of adoption of generative AI?
SEE Global
8
No activity: Our company has currently prohibited its use
9
33
No activity: We have not yet started to explore 40
47
Early stages of exploration
30
2
Currently testing and piloting 7
2
Tested and paused
1
6
Currently implemented in a few areas
6
Currently implemented in many areas 1
2
Other
1
5
Don`t know
Digitialisation and automation of processes is an inevitable part of business. We are actively
investing in this direction to be more efficient, to create more convenience for customers, and
to make the work of our teams even more precise. No matter how fast generative AI
develops, it will not replace humans, their intelligence, and their creativity. I believe in people
and the immense potential of each succeeding generation.”
Vladimir Nikolov
Operational President at FANTASTICO, Bulgaria
PwC | Global NextGen Survey 2024 9
Forecasting growth in the deployment of AI in the medium,
rather than short term
Given the slow pace of AI implementation in SEE family businesses, it is perhaps unsurprising that
just over one in ten (12%) of NextGen report their organisation has a person or team directly
responsible for generative AI. Also, responses show a clear difference in how much NextGen in SEE
are engaged in generative AI now—and how much they expect to be later. Only 12% of NextGen in
SEE are currently engaged in generative AI—but a further 53% expect to be engaged in the future.
Question
To what extent do you agree with the following statements about generative AI?
SEE Global
In the next three years, generative AI will require most of our 41
workforce to develop new skills 48
In the next three years, generative AI will significantly change 29
the way our company creates, delivers and captures value 44
Generative AI will increase our company’s profitability in the 10
next 12 months 21
Generative AI will mean a reduction in our company’s 8
headcount in the next 12 months 18
Generative AI has already changed our company’s technology 4
strategy
15
There is an important question that
these results pose—especially
when considering that over one-
third of SEE NextGen believe
generative AI will require the
majority of their workforce to learn
new skills. Will there be a significant
reconfiguration of strategic priorities
towards generative AI once
NextGen assumes executive roles
in family businesses?
PwC | Global NextGen Survey 2024 10
The 41% difference between NextGen who are currently engaged in AI and those who believe they
will be in the future certainly points to an acceleration towards AI once NextGen takes the reins.
Additionally, only 10% of NextGen in SEE believe that generative AI will increase the company’s
profitability in the next 12 months—which is half of the 21% reported globally.
This gives the impression that the handover from the first wave of SEE entrepreneurs to a more tech-
savvy NextGen is seen by the latter as a catalyst for more transformation and profitability in the
medium to long term through AI adoption.
The transition from interest to implementation in generative AI in family businesses involves strategic
planning, education and integration efforts. Family businesses should start educating their leaders
and employees about what generative AI is, its potential and how it can benefit their specific
businesses.
A clear majority of NextGen in SEE are generally positive about what generative AI can potentially
bring to their family businesses. There are also clear signals that there is some trepidation, primarily
about the risks surrounding AI—in particular cybersecurity—and also concerning the sheer pace of
the technology’s evolution. Approaching half (41%) of NextGen see phishing attacks, data breaches
and other cyber risks as likely to increase due to generative AI. Significant numbers of between one-
quarter and one-fifth see risks in the spread of misinformation, bias towards specific groups of
customers or employees, and legal liabilities and reputational risks.
Question
To what extent do you agree that generative AI is likely to increase the following in your company in the next 12
months?
SEE Global
48
Cybersecurity risk (e.g.phishing attacks, data breaches)
41
33
Spread of misinformation
27
29
Legal liabilities and reputational risks
22
25
Bias towards specific groups of customers or employees
20
PPwwCC || GGlloobbaall NNeexxttGGeenn SSuurrvveeyy 22002244 11
Despite being aware of the inherent risks in AI adoption, there is overall positivity about the
technology’s potential among NextGen. This is demonstrated by a clear majority of 76% (compared to
73% globally and 68% in other parts of CEE) seeing AI as a powerful force for business
transformation. The perceived key benefits of AI by NextGen in SEE are increased operational
efficiencies, cost savings and improved decision making. AI is also seen as being capable of helping
SEE businesses adopt new technologies, upskill the digital capabilities of the workforce and achieve
growth.
Question
To what extent do you agree with the following statements about AI generally? And to what extent do you agree
or disagree with the following statements about AI and your family business?
SEE Global
76
AI is a powerful force for business transformation
73
65
AI seems to evolve so quickly that it’s hard to keep up
66
61
It’s difficult to know how to capitalise on AI
51
In the next three years, AI will lead to more competitor pressure 61
(e.g., new products, entrants, prices changes) 64
I feel I can personally help the business to navigate emerging 49
technologies / AI 42
Being an AI champion will help me move into a leadership 39
position 40
There is an opportunity for family businesses to take a leading 29
role in the responsible use of technology and AI 50
This optimism is tempered, however, by two-thirds (66%) of NextGen reporting they believe that AI
seems to evolve so quickly that it's hard to keep up, and that well over half (58%) feel that AI will bring
more pressure from competitors.
The survey statistic that perhaps speaks loudest about the extent of work to be done by SEE family
businesses is that only 4% of NextGen report their family business has already defined governance
around using AI responsibly, although a further 59% believe they need to do this. SEE family
businesses need to quickly get moving in the AI direction that NextGen clearly indicates as the one
they must travel on.
PwC | Global NextGen Survey 2024 12
Current leaders and NextGen
have work to do in building and
maintaining trust
It is not a surprise that as young and dynamic persons NextGen
understand the trust issue that the consumers of their family business
may have in emerging technologies used. On the other hand, given this
response we may expect that NextGen will prioritise building consumers'
trust in technologies when they get more involved and in control of the
family operations. So, I would rather see this as a positive sign indicating
a potential future development of family businesses. We may expect that
more attention will be devoted in those businesses on technology issues Miroslav
as well as on transparency and integrity matters. Depending on the Marchev
family businesses' profile they can approach big technology providers to
Country
help them introduce new solutions, team up with startups to explore new
Managing
opportunities or eventually try to develop new and more trustful smart
Partner, PwC
solutions internally. Anyway, interesting times in this respect are ahead
North Macedonia
of us and NextGen will clearly try to lead the way on technology related
topics.”
NextGen in SEE generally feel more positive than the global
average about family trust levels. However, only 27% believe
there are high levels of trust between family members and
non-family members within the business and less than half
(47%) believe there are high levels of trust between NextGen
family members and the current generation. This apparent
lack of trust among different groups within the business is not
the best basis for some companies to undertake succession
planning, which is discussed in the section below.
PwC | Global NextGen Survey 2024 13
Question
How much trust would say there is between…?
5 –High levels of trust 4 1-3 –Lower levels of trust
Global
5 –High Levels
NextGen family members and the current
47 31 22 32
generation
Family members outside the business and family
45 27 29 28
members working in the business
Family owners and non-family management 31 29 41 22
Family members and non-family members within
27 37 37 23
the business
*Based on CEE editions of the 2023 Family Business Survey and the 2024 NextGen Survey.
These issues around trust also have consequences for the business as a whole—as family
businesses in the SEE region tend to build their reputation upon trust. There is an acceptance that
consumers may also have trust issues. Although approaching half of NextGen in SEE (45%) believe
their consumers have medium levels of trust in businesses to responsibly use emerging
technologies, only 12% of NextGen believe consumers have high levels of trust in this context.
As explained in the 2024 Edelman Trust Barometer Global Report, while family businesses remain
the most trusted type of business, implementation of innovation is just as important as invention.
Mismanaged innovations are more likely to create a backlash than build consumer trust.
With the apparently slow pace of AI experimentation and even slower implementation generally in
SEE family businesses, NextGen (and the current generation of leadership) has a major challenge on
their hands. This challenge isn’t only around increasing their companies' understanding of the
business benefits and risks of AI—but implementing it in a way that retains and increases the
confidence of consumers.
PPwwCC || GGlloobbaall NNeexxttGGeenn SSuurrvveeyy 22002244 14
In order to earn consumers’ trust over time, businesses need to focus on open communication
and transparency when starting to implement emerging technologies. They need to provide
detailed information about data collection and processing, as well as about all data protection
measures undertaken. Education and awareness will foster the process and ensure
consumers are aware of the benefits of emerging technologies and how the business is using
them to provide a higher quality product or service. Finally, CEE family businesses have built
a strong reputation and trust throughout time that can be positively leveraged in the
implementation process.”
Mihaela Kozanova
Cluster Business Development Manager of Sofia Hotels Management
Bulgaria
PPwwCC || GGlloobbaall NNeexxttGGeenn SSuurrvveeyy 22002244 15
More effective succession planning
is needed to bridge the generation
gap
PwC can help to smooth up the transfer of the business to the next
generation. This includes helping to establish a legal structure that
supports the family business's unique needs and the unwritten rules that
govern family interactions within the business context. We can also
assist in defining managerial roles for family members, complementing
these with external experts where necessary to bring in fresh
perspectives and specialised knowledge. PwC ensures that the
succession process does not negatively impact continuance of the family Milivoje
business and or business executives, making all parties involved in the Nesovic
process and with mutual respect and recognition.”
Partner, EPB
Leader, PwC
Serbia, Bosnia
and Montenegro
While emotional, social and personal issues play important roles in all businesses, intergenerational
and interfamilial ties greatly heighten these dynamics in family companies. Given the advancing years
of the first generation of family business entrepreneurs, it follows that handing over to the next
generation of leaders is at the forefront of their thoughts. Last year’s CEE Family Business Survey
showed that succession planning is a growing challenge for many family-owned businesses in the
region.
In the CEE Family Business Survey, almost three-quarters of leaders (74%) reported that ensuring
their business stays in the family is a key personal long-term goal. However, the survey also
suggested that many first-generation leaders had not taken appropriate steps to achieve this goal.
Only 69% of them have some form of governance policy in place within the business compared to
81% globally—and 10% fewer than global averages had a document including a last will and
testament (25% in CEE, 35% globally).
This concern over succession planning is shared by NextGen. Just over half (51%) of NextGen in
SEE are aware of a succession plan in their family business. However, a significant minority of 18%
were not involved in its development. Concerningly for the family businesses’ importance to SEE
economies, as many as 43% reported that their business had no succession plan in place, and a
further 6% said they were unaware of a plan.
PwC | Global NextGen Survey 2024 16
Question
Are you aware of your family having a succession plan in place?
I don`t know if No, there is Yes, bit I was not involved Yes, and we have developed
there is a plan no plan in its development the plan together
SEE Global
6
12 17
43 28 22
20 22
18
33 39 39
2024 2024 2022
*Text
A number of factors may be at play in regard to this apparent lack of planning in numerous family
businesses. A majority of NextGen identify the ability or readiness of the current generation to retire
as a key issue in succession planning. 53% see this as a fairly or very difficult aspect of succession
and 45% believe proving themselves as a new leader/board member will be challenging.
Our whole family has always been involved in the activities of FANTASTICO GROUP. There
is currently a third generation of the family working in the company, and I would say that
continuity is more of a natural process as we all, including myself, have been working as a
team since our youth. We have gone through various positions to know the processes well,
including in a supermarket. At the same time, we rely on experience, but we are also brave
enough to accept the new ideas of younger colleagues. This applies both to our family and to
the whole company's team. About 3,400 people are part of our team, and 84 of them have
over 20 years of experience in the company.”
Vladimir Nikolov
Operational President at FANTASTICO, Bulgaria
PwC | Global NextGen Survey 2024 17
The question perhaps is—does the current generation share NextGen’s confidence that they are
ready to lead family businesses into the coming decades? There may also be intergenerational
communication issues, as well as the ones of trust. SEE family businesses would do well to
remember that passing on ownership and responsibility is not a straightforward matter of passing on
the torch upon retirement. Rather, the process hinges upon the establishment of an appropriate legal
framework. As well as ownership and executive decision-making, this structure should also pay
attention to the values and mission of the company, how knowledge is passed on, and importantly—
how all of this is communicated based on established rules.
Succession planning isn’t, however, just about intergenerational succession, it is also about
opportunities. NextGen in SEE generally feel positive about their career opportunities and ambitions
in their family business. A vast majority (92%) moderately or strongly agree that they have the
opportunity to learn and grow within their family business. This perhaps points to an engaged, hungry
NextGen who are slightly unsure, but also excited and positive about their and their family
businesses’ futures.
Question
In your view, how easy or difficult are the following aspects of succession?
Difficult Easy
SEE 24 76
Learning about business essentials (i.e.
accounting, finance, operations) Global 29 68
SEE 29 65
Discovering your own strengths and
passions
Global 31 66
SEE 33 63
Understanding and getting insights on your
own Family Business / Office
Global 32 64
SEE 45 49
Proving yourself as a new leader or board
member
Global 48 46
SEE 53 41
Ability or readiness of the current generation
to retire
Global 55 37
PPwwCC || GGlloobbaall NNeexxttGGeenn SSuurrvveeyy 22002244 18
NextGen should be drivers
of sustainability goals
With the introduction of CSRD and sustainability reporting family
business will be impacted on different scales. Large companies under
local laws and regulations will be directly impacted with an obligation to
prepare sustainability reports, however impact will be felt by small and
medium private enterprises as it impacts their customers and suppliers
which will require them to adopt throughout their value chain. Family
Slaven Kartelo
businesses which recognise this as an opportunity and investment
Partner,
(instead of cost) and set smart sustainable strategy and operations will
Sustainability
have multiple benefits, including conti"
220,pwc,healthcare-reboot-for-the-gcc.pdf,"A Healthcare Reboot
for the GCC
How AI can supercharge your
healthcare ecosystem
Introduction
Artificial Intelligence (AI) in healthcare is no longer a distant dream. Natural language processing (NLP) can, among
other things, now record doctors’ notes, interpret medical histories, and analyse scientific literature more thoroughly
than humans. Deep-learning algorithms applied to wearable sensors, genomic data, blood work, scans, and other
medical information can develop personalised treatment plans. And virtual medical assistants powered by AI can
offer health coaching, shape diets, and even help prevent and predict illness.
AI in healthcare has evolved from basic data patterns to complex predictive models. Leading institutions use AI to
analyse vast amounts of clinical data, improving diagnosis and workflows. Mayo Clinic’s virtual primary care system
in the United States, for example, uses AI to optimise patient interviews and diagnostic processes, with physicians
selecting AI-recommended diagnoses in 84% of cases.1
In 2008, the Singapore Ministry of Health established Synapxe (originally the Integrated Health Information System)
to connect people and systems for a healthier Singapore. Partnering with major technology providers, Synapxe
developed the AI-powered Health Discovery Plus (HD+) system, enhancing healthcare services and workflows. HD+
supports remote monitoring and management of conditions like hypertension and chronic kidney disease. Developed
during the pandemic, it significantly improved patient care by processing over 660,000 vital sign reports and issuing
over 45,000 clinical alerts. Innovations like HD+ demonstrate AI’s potential in remote patient monitoring, diagnostics,
and patient-managed treatment planning, driving global healthcare toward AI-augmented care.
Figure 1: Moving from Advanced Analytics to Large Language Models
Range of techniques that aim to understand and predict /
Advanced Analytics forecast data patterns through statistical analysis and other
complex methodologies
Computer systems designed to replicate human
Artificial Intelligence intelligence, enhancing human cognitive functions and
decision-making processes
Subset of AI that enables machines to analyse and learn
Machine Learning from existing datasets, to make decisions and predictions
more accurately
Algorithms that use prompts or existing data and learned
GenAI patterns to generate new and original content, such as text,
code, images, videos, audio
Large Subset of GenAI, trained on massive datasets to process
Language and interpret human language and translate it to
Models computational embeddings
Although interest in healthcare AI opportunities has been growing exponentially over the past few years, its roots
began in some of healthcare’s greatest challenges. For the past five decades, healthcare technology has been
plagued by a lack of interoperability - getting data and people to communicate intelligently, quickly, and efficiently.
The more recent emergence of Generative AI (GenAI) addresses a major challenge in healthcare: the overwhelming
amount of unstructured data from diverse sources. It does this by extracting insights from complex datasets, such
as millions of whole genome sequences or human brain intricacies. As leading physician Eric Topol explains in his
published work, Deep Medicine, these advancements will ultimately enhance the health outcomes for individuals and
improve the efficiency of healthcare providers.2
01
The need for AI is clear: by integrating patients, providers, payers, and pharmaceutical companies through AI,
a unified front is created to overcome some of the sector’s greatest challenges. Providers leverage AI-powered
diagnostics and treatment planning for personalised patient care. Payers benefit from proactive risk management
and cost optimisation. Pharmaceutical companies streamline drug discovery and development, accelerating the
journey from research to market. This holistic integration ensures a collaborative approach, where information flows
seamlessly across the sector, fostering innovation, efficiency, and a collective commitment to advancing the quality
and accessibility of healthcare services.
Effectively deploying advanced healthcare analytics also fits in with the growing global focus on population health
and value-based care, and the widely accepted Quintuple Aim of healthcare improvement,3 which encompasses the
following goals:
Figure 2: The Quintuple Aim of healthcare improvement
Patient
Clinical
experience
outcomes
The Quintuple
Aim of
healthcare
improvement
Health
equity
Financial
sustainability
Clinician
wellbeing
02
The GCC: a region ripe for AI-enabled
technological innovation
The Gulf Cooperation Council (GCC) governments are aligning with the global digital revolution, shifting their focus to
e-commerce, smart cities, e-services, and digital health. These indicators and growing enthusiasm have paved the
way for AI to be integrated within businesses from all sectors, including a leapfrog adoption cycle for AI across the
healthcare sector.
To realise the full potential of AI, societies and organisations must commit to advanced healthcare analytics. This
commitment is already underway in the GCC region. In fact, our research highlights the GenAI market opportunity
across the GCC, estimating potential overall economic impact of $23.5 billion per year by 2030.4 The research also
indicated that GenAI-fueled improvements in efficiency and effectiveness would have the greatest impact in Saudi
Arabia and the UAE, with significant benefits also seen in Qatar, Kuwait, Oman, and Bahrain and healthcare was one
of the key industries to be affected.
GCC AI readiness indicators
High internet usage Global cybersecurity World digital Government AI-readiness
index competitiveness ranking index
GCC has one of the highest KSA and UAE ranked among UAE ranks 12th among 64 The UAE ranks 18th globally
internet usage rates globally, the top five countries in the economies in 2023 ranking in 2023, reflecting significant
with 99% of the population world by the International by the International Institute strides in AI implementation
online, compared to the Telecommunication Union for Management in public services. Saudi
world average of 63%.5 (ITU).6 Development (IMD).7 Arabia and Qatar rank 29th
and 34th respectively, out of
193 countries.8
Recent examples of AI healthcare initiatives in the region:
The UAE has been making great strides in leveraging AI in healthcare - from Abu Dhabi’s Department of Health
becoming the first entity to develop the Policy on Use of Artificial Intelligence in the Healthcare Sector,9 through to
the Ministry of Health and Prevention (MoHAP) establishing the health sector’s inaugural Centre of Excellence for AI
in 2023.10 Earlier this year, Thumbay Institute of AI in Healthcare hosted the region’s first international conference on
AI training and upskilling for healthcare professionals.11
The Dubai Health Authority’s EJADA AI system has also achieved remarkable results through a preemptive
disease prevention system, while the UAE healthcare firm M42, a joint venture between Mubadala Health and G42
Healthcare, has recently launched Med42, a clinical Large Language Model that provides high-quality answers to
medical questions. This follows the earlier release of their Arabic-enabled ‘Jais’ model, showcasing the country’s
commitment to advancing healthcare through AI.
Recently in Saudi, the Saudi Data and AI Authority established the AI Ethics Principles to integrate AI technologies,
regulate AI data, safeguard data privacy, promote responsible AI development, and minimise risk.12 And in 2022 the
Saudi Food and Drug Authority published guidance on the regulation of AI and ML-based medical devices, to ensure
the safety and reliability of these technologies.13
With governments laying the groundwork for AI governance, healthcare entities now have an urgent responsibility to
integrate these technologies within their businesses, to make sure they are on track with the global mobilisation as
they strive to meet enterprise challenges.
03
Stabilisation, value-based care, and regulation
drive AI direction across the GCC
In the current environment, healthcare demand is largely well-served by high-quality, specialised providers, and
market expansion is tapering as it approaches a steady state. As business value growth potential levels off for
healthcare providers across the GCC, the focus for players is reorienting towards differentiation and improving
efficiencies.
Stabilisation is also inevitable with the various government reforms in place aimed at expanding private health
insurance coverage. Abu Dhabi and Dubai have witnessed this with the rollout of mandatory insurance, resulting in
steady hospitalisation rates of around 10% following full implementation.14 Other GCC countries are expected to
follow a similar trend.
With the increased focus on value-based care — succinctly defined as the overall cost of improving outcomes that
are valued by patients and populations — the overarching message is evident: providers are incentivised for the
value of integrated care they provide. In Abu Dhabi, downward pressure on facility multipliers has been witnessed
from a reimbursement standpoint, thus impacting overall business top line growth. Similar to the case in Dubai,
the potential introduction of service-based packages in the region is also expected to exert negative pressure on
expected reimbursement rates. Providers are thus forced to deliver care more effectively by leveraging operational
efficiencies — an approach that can be facilitated through the use of AI. By applying AI algorithms, processes such
as resource allocation, including staff scheduling, room utilisation, and equipment maintenance, can be streamlined,
thus improving overall efficiency.
Growing regulatory requirements and maturing insurance markets have also increased the imperative for physicians
and other clinicians to accurately document patient information, thereby increasing the administrative burden and
limiting the time they have available to fulfil their true purpose: treating patients face to face. This is amplified by
the shortage of clinical staff in the GCC and the heavy reliance on external staff to fill the resource gap, with recent
findings showing close to 60% of physicians and nurses in KSA and Oman being expatriates (this figure rises to more
than 90% in Dubai alone, highlighting how expat-driven the workforce is).15 To mitigate this issue, GenAI offers the
solution for auto-generating necessary clinical documentation and relieving the administrative burden.
Given the challenge presented by governments moving forward with AI governance and the market pressures in the
region, how does the healthcare sector move forward with exploiting the opportunities inherent in AI technology? As
with many disruptive technology adoptions, it begins by making the business case.
04
Getting started in healthcare AI implementation
Implementing an AI programme can be an exciting yet daunting endeavour for leadership teams looking to harness
its transformative power. In healthcare today, AI offers unprecedented opportunities to drive innovation, enhance
decision-making and unlock new levels of efficiency and competitive advantage.
By structuring AI work into three phases, leadership can establish a clear plan from strategy through execution and
measurement. This structured approach aligns teams and maximises AI’s impact on business operations, customer
experiences and technology innovation. To drive this process, an AI Centre of Excellence (CoE) can be established,
which will serve as a stepping stone to capture, ideate and develop use cases, and build internal capacity in a
responsible and sustainable way.
Figure 3: AI implementation programme overview
Monitoring &
AI transformation blueprint
evaluation
Business
strategy
Business use
cases
Roadmap and
delivery
Voice of the
customer
Technology
capabilities
Technology strategy
Monitoring of data
Data governance
management practices &
policies
alignment with regulations
Data structure for Effectiveness of data
data management governance policies
Data quality, security Assessment of data
& compliance with quality & integrity to ensure
industry regulations accuracy & reliability
Data management & governance
Enterprise program management Enterprise architecture Agile delivery
Delivery excellence (CI/CD
Change management and training
pipeline, ML ops)
05
Phase 1: Ambition
Leadership starts by identifying appropriate business and technology team members. The Ambition phase sets the
overarching business, technology, and data strategy for the AI programme, taking an outside-in, customer/product-
centric approach. Key activities include defining AI vision and goals, aligning customer needs and market trends,
and establishing clear success criteria for measuring AI’s impact on customer experiences. This phase assesses
the readiness and investment requirements for AI adoption, and establishes governance structures and stakeholder
engagement mechanisms to ensure alignment and accountability throughout the AI journey.
Leadership teams should define quantifiable business goals using key performance indicators (KPIs) to demonstrate
AI’s impact, ranging from improving patient outcomes and operational efficiency to driving revenue growth and
ensuring regulatory compliance. By setting clear, measurable objectives, leadership can track progress, evaluate
efficacy, and demonstrate tangible value to stakeholders.
Figure 4: Example of AI goal setting and KPI’s
Enhanced patient
Operational efficiency Clinical decision support
outcomes
•Goal: Reduce readmission rates by x% •Goal: Reduce average patient wait times •Goal: Improve diagnostic accuracy rates
with a specified timeframe by X minutes through AI-powered by X% using AI-driven imaging analysis
•Goal: Improve patient satisfaction scores scheduling and resource allocation and decision support tools
by X points over next year •Goal: Increase utilisation rate of hospital •Goal: Decrease medication errors by X%
•Goal: Increase early detection rates for facilities by X% through predictive through AI-powered medication
chronic conditions by X% through maintenance and optimisation reconciliation/prescription assistance
AI-driven predictive analytics •Goal: Reduce admin overhead costs by •Goal: Increase adherence to
X% by automating repetitive tasks (e.g., evidence-based guidelines by X% with
billing, coding) using AI AI-driven clinical decision support
systems
Population health
Revenue growth Regulatory compliance
management
•Goal: Increase revenue per patient •Goal: Reduce incidence of preventable •Goal: Achieve compliance with regulatory
encounter by X% through AI enabled diseases by X% through AI-driven requirements (e.g., HIPAA, GDPR)
personalised treatment plans targeted population health analytics and targeted through AI-driven data
interventions interventions security/governance measures
•Goal: Expand service offerings and •Goal: Increase patient engagement and •Goal: Reduce audit risk/penalties related
market reach by X% through AI-enabled adherence to treatment plans by X% to compliance violations by X% through
telemedicine/remote patient monitoring using AI-powered patient education AI-enabled risk management and
solutions programs monitoring systems
•Goal: Achieve higher reimbursement •Goal: Improve community health
rates by X% through improved outcomes by X% through proactive
documentation accuracy & compliance health monitoring and intervention
facilitated by AI strategies enabled by AI
It is also important to thoroughly assess what data is available, including the different sources, formats and quality of
the data that will need to be leveraged. Identify any gaps or concerns that might exist, and develop a plan to source,
prepare and integrate the data required in a robust and responsible way.
06
Phase 2: AI transformation blueprint
The AI transformation blueprint phase builds a roadmap that balances value and risk, and flows from the business
strategy and KPIs of the Ambition phase into developing AI business use cases and priorities for engaging AI
technology capabilities. Leadership assesses where the greatest opportunities lie for optimising existing processes
within their current workflows. Leaders may also choose to prioritise certain initiatives based on unique needs and
priorities, such as customer expectations or specific products and services in the enterprise’s portfolio.
Potential business use cases
The figure below illustrates a selection of potential business use cases for AI in healthcare, highlighting a spectrum of
applications.
Figure 5: Risk-impact assessment for potential use cases
•Aid patients with tasks such as appointment scheduling, physician / service
Care journey automation availability, registration, benefits and eligibility checks, etc.
Remote patient monitoring •Monitoring (e.g., vitals monitoring) and engaging with the patients (e.g., medication
& engagement adherence, reminders) remotely and outside of traditional care delivery settings
•Auto-generated document that summarises some, or all of the patient's existing
Shared health summaries
conditions, clinical notes, discharge summaries, medications, etc.
Patient
engagement Patient education and •Provision of educational content and tools to patients to help manage their health
consumerism and allow them to be part of the healthcare decision-making process
•Engage with a primary care virtual assistant, providing an initial assessment and
Virtual clinician
recommendations without oversight from a clinician
•Automating clinical documentation (e.g., discharge summaries, clinical notes, referral
Clinical documentation letters, prescription, etc.) based on live interaction between patient and physician
during an episode of care
•AI algorithms that leverage vast amounts of medical database and patient data (e.g.,
Clinical diagnosis and
electronic health records, transcripts, natural human speech, written material,
personalised medicine
Clinical decision images, videos) for clinical diagnosis and generation of personalised treatment plans
support and •Assign diagnostic and procedural codes to patient diagnoses, treatments,
Clinical coding and
documentation procedures or equipment used, based on analysed clinical information from health
charge entry
records
Clinician performance •Feedback on clinician performance (e.g., adherence to clinical guidelines, billing
evaluation and training procedures) based on analysis of clinical encounter transcripts
Payment verification & •Verify payments automatically by matching with patient accounts and update patient
payment posting accounts automatically
•Analyse refunds, adjustments, write-offs and overpayments, identify any
Account reconciliation
discrepancies, and process financial corrections
Mid-office and
Insurance pre-approval & •Extract patient information, verify coverage and generate and submit
back-office
benefits authorisation pre-authorisation requests to insurance companies
administrative
Claim submission & denial •Review claims and denials by identifying any errors or missing information
functions
management (minimising risk of rejected claims) and streamline claim submission process
High Risky bets Priority wins
R me om nio tote ri np ga t aie nn dt C aa ur te o mjo au tr in oe ny veP ria fiy cm ate ion nt &
engagement payment posting
Clinical diagnosis Patient Shared Account reconciliation
& personalised education & health Insurance pre-approval &
medicine consumerism summaries benefits authorisation
Claim submission &
Virtual Clinical denial management
clinician documentation
Clinical coding
and charge entry
Clinician
performance
evaluation and
training
Deprioritised risks Incremental gains
Low
High Risk Low
of implementing AI to the use case
07
In developing and prioritising business use cases, leaders must define them in detail, including their objectives, target
user groups, data requirements, and expected outcomes. Enabling technology needs are concurrently assessed,
and a technology stack is selected or developed to effectively support AI use cases. The rapid advancement of AI
technologies can lead to significant changes in risk and feasibility within a few months, necessitating continuous
reassessment to ensure alignment with the latest developments and opportunities.
Partnership strategies
Partnership strategies also play a key to this analysis, covering both technological capability and the regulatory sides.
Technology partners can provide access to cutting-edge, off-the-shelf AI solutions and domain-specific accelerators.
Partnering with regulatory bodies and standards organisations can help mitigate legal and ethical risks associated
with AI implementation.
Workforce and skills
A thorough review of the existing workforce skill sets must take place to identify any gaps where upskilling is
required, or new roles to be created calling for fresh skills and experience entirely. Our latest CEO Survey found that
67% of Healthcare CEO’s believe that GenAI will require most of their workforce to develop new skills.16 Workforce
preparation is essential to successful execution.
Risk assessment
Leadership should also assess the potential for unintended consequences or disruptions to existing workflows and
revenue cycle operations. By conducting a thorough risk assessment and weighing these factors, business leaders
can make informed decisions about which use cases to prioritise and mitigate risks effectively. Risk also has to
be measured against value to determine priority. High-risk factors, such as external versus internal customers (i.e.
patient facing vs. employee facing), regulatory compliance, data security, and stakeholder buy-in, must be carefully
evaluated to mitigate potential challenges and ensure successful implementation.
Once potential use case areas have been identified, leaders can evaluate which elements can be served using
in-house technology capabilities versus which will require strategic partnerships. At this point teams can also
collaborate to define clear objectives, success criteria, and KPIs for each use case.
By following a structured approach to use case development, leadership teams can ensure that AI initiatives are
aligned with strategic objectives and positioned for success in driving tangible business value and innovation. This
phase also involves building or enhancing data infrastructure, sourcing and preparing training data, and developing
or acquiring AI models and algorithms. Finally, the AI Transformation phase outlines a comprehensive implementation
plan, including timelines, resource allocation, risk management strategies, and milestones for measuring progress
and achieving key deliverables.
For payers and pharma players, a similar approach can be adopted — prioritising individual use cases based on
their value addition as well as their risk of implementation. Rather than having customer centres answering benefits-
related questions, AI can automatically generate Explanation of Benefits tables that outline services and cost-
sharing amounts, streamlining the workflow and reducing admin work. Payers can also facilitate prior authorisation
processes through automated intake, validation and triaging of PA request documentation. This will help reduce
waiting times for approvals, allowing patients quicker access to necessary treatments. Also, given the significant
number of grievances and complaints insurers receive, automating the response process can expedite the workflow,
as well as help in highlighting potential risks to minimise litigation and maintain brand reputation.
Pharma players can also leverage AI across different processes. By predicting and visualising molecular
conformations, new molecule generation is enabled with data-informed models. AI can also enhance search and
analytics capabilities through pretrained LLMs on available drug safety data and real-world cases.
08
Phase 3: Monitoring and evaluation
This phase focuses on evaluating the outcomes of AI initiatives and measuring their impact against the overarching
strategy. This involves defining KPIs and metrics to assess the effectiveness, efficiency, and ROI of AI programmes.
These metrics may include customer satisfaction scores, revenue growth, cost savings, operational efficiency
gains, and other relevant indicators. It also includes conducting regular assessments to track progress, identify
improvement areas, and adjust strategies and tactics as needed. Additionally, this phase aims to communicate
results and insights to key stakeholders, including senior leadership, business units, and external partners to
recognise successes, address challenges, and reinforce the value of AI in transforming customer experiences and
driving business outcomes.
09
With great power comes great responsibility:
building an enterprise governance framework for
responsible AI
For business leaders, there are plenty of reasons to be excited about AI, starting with its power and ease of use. But,
as with any emerging technology, there are also potential new risks. Some of these hazards may come from your
company’s use, others from malicious actors.
To manage both kinds of risks and harness AI’s power to drive sustained outcomes and build trust, you will need
responsible AI - a methodology designed to enable AI’s trusted, ethical use. It has always been important, but it has
become crucial in the dawning era of generative AI.
Healthcare leaders will start with a responsible AI end-to-end enterprise governance framework which focuses on the
risks and controls that will guide the AI journey. The elements of such a framework are shown below:
Figure 6: Responsible AI framework
The responsible use of AI necessitates an integrated approach that encompasses technical, ethical, social and legal considerations, all
of which are essential to maximise the advantages of AI use while substantially reducing any associated risks.
Ethical principles Human oversight Data integrity
AI technologies must be developed and AI technologies must be overseen by Datasets employed in training AI models
adopted in accordance with core ethical humans to guarantee their adherence to must be of high quality and diverse to
standards, including transparency, ethical and legal standards and to prevent bias and promote fairness.
fairness, privacy, and accountability. mitigate unforeseen outcomes. Developers must address potential biases
that could arise from both the algorithms
and their application.
Regulation &
Privacy & security Education & awareness
governance
AI systems must be designed, prioritising Individuals and organisations must be Governments and industries must
privacy and security, safeguarding well-informed about the capabilities & collaborate to establish robust regulations
against unauthorised access and misuse limitations of AI, as well as its potential and standards that promote the ethical
of personal data. effects on society and the environment. development and application of AI
systems.
10
What’s next?
AI is no longer a technology of the future: it is very much here and in use today. It holds the promise of empowering
healthcare leaders to overcome some of the sector’s most historically daunting challenges—detecting diseases at
their earliest stages, streamlining patient care, reducing medical errors, decreasing clinician burnout, lowering system
costs, and improving outcomes that matter to the populations it serves.
It is not a question of whether healthcare will implement AI, but of how the implementation will proceed and continue
to evolve. This raises a host of other questions for healthcare players in the GCC:
01 Where should healthcare players place their bets?
Should players take immediate action and invest in high-cost technologies, following global counterparts and
02
best practices, even if they may pose some form of risk?
Or should players progress in small steps focused on low risk and low cost solutions (quick wins) to check
03
the box for AI integration?
04 Do organisations have the minimum level of data quality, completeness, and maturity to enable the use of AI?
05 How will healthcare providers gain the trust of patients in clinical diagnosis and data privacy?
How smoothly can they transform existing healthcare systems and workflows into digitised, AI-enabled
06
processes?
The answers lie in a structured approach like the one we have detailed, driven by establishing a robust AI CoE from
strategy to execution that can prioritise the right use cases while empowering the workforce with necessary skills and
tools to augment their knowledge and capabilities.
The faster leadership teams can embrace AI and start their journey, the faster they can reap the many advancements
that are driving costs down while improving quality of care. Achieving the delicate balance between innovation and
risk mitigation is key to unlocking the full potential of this powerful and transformative technology
AI stands poised to reimagine healthcare. Where will your imagination lead your enterprise?
11
References
1. https://nuscriptmed.com/ai-achieves-high-diagnostic-accuracy-in-virtual-primary-care-setting/
2. https://drerictopol.com/portfolio/deep-medicine/#:~:text=In%20Deep%20Medicine%2C%20leading%20physician,medicine%20and%20
reducing%20human%20mortality.
3. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8608191/
4. PwC. Reshaping the Middle East: A CEO’s playbook to win the $23.5 billion Generative AI opportunity. https://www.strategyand.pwc.com/m1/en/
strategic-foresight/sector-strategies/technology/reshaping.html
5. World Bank/ (2021). Internet users (% of population). World Bank Data. https://data.worldbank.org/indicator/IT.NET.USER.ZS
6. International Telecommunication Union (ITU) Global Cybersecurity Index. https://www.itu.int/en/ITU-D/Cybersecurity/Pages/global-cybersecurity-
index.aspx
7. IMD World Digital Competitiveness Ranking 2023 Report. https://worldcompetitiveness.imd.org/countryprofile/AE/digital
8. Oxford Insights. (2023). Government AI Readiness Index 2023. https://oxfordinsights.com/ai-readiness/ai-readiness-index/
9. https://www.doh.gov.ae/en/news/the-department-of-health-becomes-region
10. Ministry of Health and Prevention. (2023). MoHAP Launches Health Sector’s First National Centre of Excellence for AI. https://mohap.gov.ae/en/
media-center/news/19/10/2023/mohap-launches-health-sectors-first-national-centre-of-excellence-for-ai
11. https://gulfnews.com/uae/health/uae-thumbay-institute-of-ai-in-healthcare-to-host-regions-first-international-conference-driving-ai-training-for-
health-professionals-1.102298294
12. https://sdaia.gov.sa/en/SDAIA/about/Documents/ai-principles.pdf
13. https://beta.sfda.gov.sa/en/regulations/87661
14. Dubai Health Authority. Annual Health Statistic Book. https://www.dha.gov.ae/en/open-data Department of Health Abu Dhabi. https://www.doh.gov.
ae/en/resources/opendata
15. Frost Sullivan/Mashreq. 2020 Annual Overview of Healthcare in the GCC Growth opportunities for 2021 and beyond. https://www.mashreq.com/-/
jssmedia/pdfs/corporate/healthcare/2020-Annual-Overview-of-Healthcare-in-the-GCC.ashx
16. https://www.pwc.com/m1/en/ceo-survey/27th-ceo-survey-middle-east-findings-2024.html
Contact us
Amar Patel Timur Korshlow
Partner, Healthcare Sector Lead Partner, Advanced Analytics,
for Deals, PwC Middle East
PwC Middle East Email: tkorshlow@pwc.com
Email: a.patel@pwc.com
At PwC, our purpose is to build trust in society and solve important problems. We’re a network of firms in 151
countries with nearly 364,000 people who are committed to delivering quality in assurance, advisory and tax
services. Find out more and tell us what matters to you by visiting us at www.pwc.com.
Established in the Middle East for over 40 years, PwC Middle East has 30 offices across 12 countries in the region
with around 11,000 people. (www.pwc.com/me).
PwC refers to the PwC network and/or one or more of its member firms, each of which is a separate legal entity.
Please see www.pwc.com/structure for further details.
© 2024 PwC. All rights reserved"
221,capgemini,Capgemini-Custom-Private-Gen-AI-Assistants-POV-1.pdf,"Enterprise-specific
AI agents keep the
Gen AI promise
Custom enterprise-specific Gen AI makes it possible to reach business outcomes faster with
more insightful and original output. It can be the most effective way for a company to
maximize the value of its own structured and unstructured data.
The awarding of the 2024 Nobel Science Prizes in public data and adding processing power is not
physics and chemistry for artificial intelligence- enough to deliver domain-specific improvements
related discoveries confirmed its significance for and the expansion of capabilities needed to justify
investment. They lack the specialized, high-quality
modern life. According to Nobel laureate Geoffrey
data needed to operate with expertise in a company’s
Hinton, one aspect of AI related to generative AI -
specialism. This data is locked behind corporate
machine learning - will “have a huge influence. It will
firewalls, or otherwise unavailable for generalist
be comparable with the Industrial Revolution, but
LLM training. As a result, even top-tier chatbots like
instead of exceeding people in physical strength, it’s
ChatGPT or Gemini, which are based on LLMs, can
going to exceed people in intellectual ability.”1 produce flawed answers, or “hallucinations”.
Nevertheless, generative AI has not been in popular Accordingly, there are sound reasons to view the
use for long, with some well-publicized instances of generative AI phenomenon with some detachment,
unintended generative AI mistakes. Consequently, but its fundamental benefits to enterprise have
organizations are cautiously optimistic about not yet been widely or fully tapped in the economy.
Enterprise-specific, i.e., custom private Gen AI, also
the potential benefits of integrating it into their
known as agentic AI, can be improved to achieve
strategies, processes, and business models.2
higher accuracy using transparent, accessible
While large learning models (LLMs) hold potential sources and verifiable results. This form has an
to re-shape business, they run into a performance enhanced ability to focus on specific areas and
ceiling when dealing with specialist areas that they produce context-sensitive results through training on
have not been trained on. Users are cautioned not refined data from specific business domains. Agentic
to trust their responses outright, especially for implementation allows expanded use cases in more
questions involving unique, organization-specific complex and sophisticated scenarios.
information. Simply training them with more
1Babbage from The Economist, The 2024 Nobel prizes: a triumph for AI, Oct. 9, 2024
2Capgemini Research Institute, Harnessing the value of generative AI, p. 27
2 Enterprise-specific AI agents keep the Gen AI promise © Capgemini 2024. All rights reserved
Specialize for better outcomes
General purpose LLMs can be effective tools to dormant, siloed and forgotten. Meanwhile, the
increase productivity, but their lack of specificity company reinvents the wheel, wastes time and
reduces the relevance and quality of their output. budget, and misses opportunities.
They tend to produce generic results that do
To achieve generative AI’s maximum benefit to
not deliver the full potential achievable from
enterprise productivity and creativity, a custom-
an organization’s own intellectual property and
designed language model can be trained on
data, which should lead to actionable insights and
an organization’s own dataset in its entirety
productivity gains.
using structured and unstructured data. This
Innovation that sets a company apart is most likely form of enterprise AI agent has a higher level of
to come from an organization’s own proprietary reasoning, producing more refined responses on
insights. According to business intelligence provider, specialist subjects.
Gartner, 68% of enterprises struggle to integrate AI
The ideal generative AI model boosts productivity
into workflows that rely heavily on internal data.3
across the entire workforce. For instance, it can
Custom private Gen AI makes it possible to respond streamline the process a business development
faster to business opportunities, with more team follows to respond to RFPs, speed up selection
insightful and original content. It can be the most of qualified talent for HR, or enhance the quality of
effective way for a company to maximize the value customer interactions in a contact center.
of its IP in various forms. Without the mass data
interpretive power of Gen AI, much of this will lie
3 Gartner, RAG in enterprise data strategy
3 Enterprise-specific AI agents keep the Gen AI promise © Capgemini 2024. All rights reserved
Safeguarding Gen AI data
Along with making Gen AI a more effective business resource, there are
significant ethical and compliance reasons to prefer a custom private Gen
AI system. Its closed loop structure allows tighter governance, security
protocols, and continuous monitoring to keep it in line with a company’s
own ethical and security guidelines, and in compliance with data privacy and
sovereignty, where applicable.
Knowing the methods used for data training are key to managing legal risks.
These risks are real, with credible copyright and trademark infringement
cases already underway.4 Proprietary LLMs are safer due to their closely
controlled, transparently-sourced training data.
By striking a balance between robust security controls and proactive
safeguards for ethical AI performance, organizations can protect their critical
assets while cultivating trust and strengthening operational resilience.
Data security has climbed higher up the board agenda due to tighter
operational resilience regulation. A custom private Gen AI assistant allows
companies to impose strict data security measures, retain full ownership
of their intellectual property, and gain clearer insights into potential
vulnerabilities.
4Reuters, AI companies lose bid to dismiss parts of visual artists’ copyright case, August 13, 2024
4 Enterprise-specific AI agents keep the Gen AI promise © Capgemini 2024. All rights reserved
The data ingestion
framework - the key
to delivering full
Gen AI value
The decisive component of a custom private LLM for a query, making knowledge management and on a large, known dataset, RAG is an information
is its Data Ingestion Framework (DIF). The DIF is domain-specific assistance more productive. Queries retrieval system that picks out specific, relevant data
crucial for analysis of and attributing meaning to are thus answered with data from the correct from available databases and documentation. An LLM
documents and other materials in a dataset and documents and the most applicable sections, a critical is static, while the RAG model is dynamic. The LLM’s
thus its productiveness. It extracts, organizes, and capability for domain-specific use cases and effective strength is in specific, well-defined scenarios while
prepares data for future retrieval. It applies metadata knowledge management. RAG is capable of broad matching with up-to-date
for ontological purposes, ensuring that the model information, but with less precision, making guardrails
Supporting a trained LLM with customized retrieval-
can access the right information at the right time. The critically important. 5
augmented generation (RAG) improves the accuracy
aim is for better targeting of the required information
of responses. Where the LLM is an AI system trained
5Towards Data Science, The Practical Limitations and Advantages of RAG, April 15, 2024
5 Enterprise-specific AI agents keep the Gen AI promise © Capgemini 2024. All rights reserved
Multi-stage guardrails
RAG systems rely on guardrails at multiple stages Intermediate guardrails then act as checkpoints
to ensure quality and mitigate risks. Guardrails during the retrieval process, validating selected
also keep Gen AI systems in alignment with data for alignment with policies and the context of
organizational values by eliminating harmful and the query.
biased outputs.
A final output filter evaluates the response once the
The first layer of control begins with input information is retrieved, but before it is delivered
filtering, which examines the specific content of to the user or downstream business processes.
user prompts for compliance and risk factors. For This filter ensures that the response adheres
instance, a query requesting sensitive strategies to key requirements, such as confidentiality,
or methodologies, e.g., how to redesign internal appropriateness, e.g., absence of toxic or harmful
processes, could inadvertently expose confidential content, and compliance with company policies
information. This is particularly important in and regulatory standards. It also confirms that the
regulated sectors like banking, healthcare, and output meets user expectations of accuracy and
defense, where strict compliance standards prohibit relevance, serving as a final quality control step.
sharing sensitive data with external AI systems
or vendors.
6 Enterprise-specific AI agents keep the Gen AI promise © Capgemini 2024. All rights reserved
Cost controls
The cost of running LLMs can spiral if left unchecked summaries or context-specific outputs rather than
due to their reliance on intensive computing processing entire datasets. This hybrid approach can
resources and token-based pricing models. This is cut compute expenses by 25–35%.7
directly tied to the volume of prompts processed
Cost control measures in custom LLMs integrated
and the length of generated responses. This
with RAG systems with token usage monitoring
makes extensive or complex queries exponentially
enable enterprises to harness the power of
expensive. 65% of enterprises in 2024 reported
generative AI without financial surprises. By
difficulty in forecasting LLM usage costs, with some
focusing on task-specific applications and
companies experiencing over 30% unanticipated
streamlined workflows, organizations can better
budget overruns due to insufficient monitoring and
forecast expenses and align their AI investment with
control.6
business priorities.
A major benefit of using RAG with custom LLMs
is the ability to shift computational burden to
retrieval mechanisms, which are cheaper to run.
RAG frameworks retrieve enterprise data in real-
time, allowing LLMs to focus solely on generating
6Gartner, Enterprise AI cost control report
7Forrester, Build Efficient And Robust GenAI Apps With Prompt Engineering And
Advanced LLM App Architectures
7 Enterprise-specific AI agents keep the Gen AI promise © Capgemini 2024. All rights reserved
Examples of Gen AI solutions using custom private generative AI agents tailored to
domain and business function
Banking and Healthcare Research and
Finance Sales
insurance providers development
Automating data Credit memo Drafting longitudinal Identifying cross- Synthetic data
analysis across generation patient summaries selling opportunities generation
financial reporting, by using a Gen AI
marketing, Covenant monitoring Clinical trial package agent as a portfolio Advanced drug
operations, supply generation sales executive discovery and
chains Suspicious Activity therapeutics
Report and other Personalized medicine Generating first-shot
Proactive data-driven financial crime report RFP/RFQ responses Novel protein design
recommendations filings Research assistant
to reduce manual agent Customer intent/ Clinical trial and
analysis time Pitchbook generation insights agent research facilitation
Claims processing, e.g,
legal case package
creation
Underwriting
assistant
8 Enterprise-specific AI agents keep the Gen AI promise © Capgemini 2024. All rights reserved
Use case -
Streamlining an
insurance underwriting
workflow
A market-leading US insurance underwriter saw the opportunity to use
generative AI to improve underwriting workflows. Their implementation
of a custom private generative AI model analyzed historical claims, policy
data, and external risk factors to draft underwriting recommendations. It
also generated detailed explanations for its recommendations, enabling
underwriters to then make informed decisions faster.
This reduced underwriting case turnaround times by 40%, improved risk
assessment accuracy, and supported the creation of personalized policies.
This ultimately enhanced customer satisfaction and profitability.
9 Enterprise-specific AI agents keep the Gen AI promise © Capgemini 2024. All rights reserved
Our experience
Capgemini has proven expertise in activating data and AI to its full potential
for data-driven businesses. Building on our partnerships with hyperscalers
and the AI innovation ecosystem, we help our clients deliver value and
generate competitive advantage with a portfolio of tailored, scalable Gen
AI solutions.
We help maximize the value of your enterprise data by creating accurate,
context-aware Gen AI assistants. They empower your employees and
customers using your own data for specific business needs, while
safeguarding data. These agents are typically used to streamline customer
service, marketing, contract management, content generation, financial
analysis, and more, at controlled cost of use.
10 Enterprise-specific AI agents keep the Gen AI promise © Capgemini 2024. All rights reserved
Experts to contact
Pinaki Bhagat
AI & Generative AI Solution Leader,
Financial Services
Ashvin Parmar
Vice President, Generative AI CoE Leader,
Financial Services
11 Enterprise-specific AI agents keep the Gen AI promise © Capgemini 2024. All rights reserved
About Capgemini
Capgemini is a global business and technology transformation partner, helping organizations to
accelerate their dual transition to a digital and sustainable world, while creating tangible impact for
enterprises and society. It is a responsible and diverse group of 340,000 team members in more than
50 countries. With its strong over 55-year heritage, Capgemini is trusted by its clients to unlock the
value of technology to address the entire breadth of their business needs. It delivers end-to-end
services and solutions leveraging strengths from strategy and design to engineering, all fueled by
its market leading capabilities in AI, cloud and data, combined with its deep industry expertise and
partner ecosystem. The Group reported 2023 global revenues of €22.5 billion
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© 2024 Capgemini. All rights reserved."
224,capgemini,2024_07_10_Capgemini-News-Alert_Generative-AI-for-Software-Engineering-1.pdf,"Press contact:
Florence Lievre
Tel.: +33 1 47 54 50 71
Email: florence.lievre@capgemini.com
Generative AI is set to be adopted by 85% of the software workforce over the
next two years
Three in five organizations see innovative work as the biggest benefit of generative AI use in software
engineering; software professionals say generative AI will boost their comms with business teams
Paris, July 10, 2024 – Generative AI (Gen AI) is expected to play a key role in augmenting the
software workforce, assisting in more than 25% of software design, development, and testing
work in the next two years. According to the Capgemini Research Institute’s latest report
“Turbocharging software with generative AI: How organizations can realize the full potential of
generative AI for software engineering”, a large majority (80%) of software professionals
believe that, by automating simpler repetitive tasks, Gen AI tools and solutions will significantly
transform their function, freeing up time for them to focus on higher-value-adding tasks. More
than three quarters of software professionals are confident that generative AI has the potential
to boost collaboration with non-technical business teams.
While the generative AI adoption for software engineering is still in its early stages, with 9 in 10 organizations
yet to scale, the report found that organizations with active Gen AI initiatives are already reaping multiple
benefits from its adoption – fostering innovation coming first place (61% of organizations surveyed) followed
by improving software quality (49%). They saw also an improvement of between 7 to 18% (on average) in
the productivity1 of their software engineering functions. For certain specialized tasks, time saving was as
high as 35%.
Organizations surveyed highlighted that they plan to leverage the additional time freed up by generative AI
for innovative work such as developing new software features (50%) and upskilling (47%); while reducing
headcount being the least-adopted route (just 4% of responding organizations). New roles, such as
generative AI developer, prompt writers or generative AI architect are also emerging.
Improved collaboration between tech and business teams
From better communication to explaining what the code is doing in natural language, Gen AI makes the
connection between software engineers and other business teams more effective. 78% of software
professionals are optimistic about Gen AI’s potential to enhance collaboration.
Augmented software workforce and employee satisfaction
According to the survey, generative AI tools are used today by 46% of software engineers for assisting them
on tasks. Almost three quarters agree that generative AI's potential extends beyond writing code. While
coding assistance is the leading use case, generative AI also has applications in other software development
lifecycle activities, such as code modernization or user experience (UX) design.
1 Overall improvement in the productivity of the individual from all types of tasks accelerated by generative AI. Productivity advantage
increasing with organization size.
Capgemini News Alert
Both senior and junior software professionals also report higher levels of satisfaction from using Gen AI
(respectively 69% and 55%). They see generative AI as a strong enabler and motivator.
However, according to the report 63% of software professionals declare using unauthorized Gen AI tools to
assist them in tasks. This rapid take-up, without proper governance and oversight in place, exposes
organizations to functional, security, and legal risks like hallucinated code, code leakage, and IP issues.
Pierre-Yves Glever, Head of Global Cloud & Custom Applications at Capgemini, said: “Generative AI has
emerged as a powerful technology to assist software engineers, rapidly gaining adoption. Its impact on
coding efficiency and quality is measurable and proven, yet it holds promise for other software activities.
However, we must remember that the true value will emerge from a holistic software engineering approach,
beyond deploying a single ‘new’ tool. This involves addressing business needs with robust and relevant
design, establishing comprehensive developer workspaces and assistants, implementing quality and security
gates, and setting up effective software teams. The focus should be on what genuinely generates value.
Exciting times lie ahead!”
To access the full report: Link
Methodology:
The Capgemini Research Institute surveyed 1,098 senior executives (director and above) and 1,092 software
professionals (architects, developers, testers, and project managers, among others). 20 in-depth interviews
were conducted with leaders from the industry, partners, and startups, along with several software
professionals.
About Capgemini
Capgemini is a global business and technology transformation partner, helping organizations to accelerate
their dual transition to a digital and sustainable world, while creating tangible impact for enterprises and
society. It is a responsible and diverse group of 340,000 team members in more than 50 countries. With its
strong over 55-year heritage, Capgemini is trusted by its clients to unlock the value of technology to address
the entire breadth of their business needs. It delivers end-to-end services and solutions leveraging strengths
from strategy and design to engineering, all fueled by its market leading capabilities in AI, cloud and data,
combined with its deep industry expertise and partner ecosystem. The Group reported 2023 global revenues
of €22.5 billion.
Get The Future You Want | www.capgemini.com
About the Capgemini Research Institute
The Capgemini Research Institute is Capgemini’s in-house think-tank on all things digital. The Institute
publishes research on the impact of digital technologies on large traditional businesses. The team draws on
the worldwide network of Capgemini experts and works closely with academic and technology partners. The
Institute has dedicated research centers in India, Singapore, the United Kingdom and the United States. It
was recently ranked #1 in the world for the quality of its research by independent analysts.
Visit us at https://www.capgemini.com/researchinstitute/
Capgemini News Alert"
226,capgemini,22Oct2024-Conversations-for-tomorrow_Edition_9_Report-V1.pdf,"Gener(AI)ting
the future
Quarterly review
N°9 — 2024
Gener(AI)ting the future
Generative AI today is at the threshold of an outburst of creative exuberance. The cover
for this edition of Conversations for Tomorrow represents the myriad possibilities that
arise at the intersection of light and shadow – not too dissimilar to the possibilities that
generative AI creates. At the same time, it also highlights fleeting patterns and dark
spots which one must bear in mind as they move forward. The content and design of
this issue reflect the opportunities, the challenges, and the risks that generative AI is
now throwing up in front of organizations.
2
Capgemini Research Institute Gener(AI)ting the future
Foreword At Capgemini, we But adopters have also had to acknowledge
help organizations AI’s significant carbon footprint. Over
prepare for tomorrow one-third of organizations in our research
by distilling the are already tracking their Gen AI carbon
unique insights and perspectives of leaders emissions.
from global business, academia, the startup
community, and wider society. Organizations globally are rapidly
embedding Gen AI across functions, with
Gener(AI)ting the Future a ripple effect for wider society. In this
edition of Conversations for Tomorrow we
In Conversations for Tomorrow, the focus on this AI-generated future.
Capgemini Research Institute identifies
the strategic imperatives for the future of We would like to thank all the leaders and
business and the society it serves. In this experts who have enriched this edition of
ninth edition of the journal, among other the journal with their insights. By sharing
areas, we explore: the perspectives of such a diverse range of
accomplished individuals, we aim to present
• The rapid rise of generative AI (Gen AI) a comprehensive overview of Gen AI and its
contribution to generating a new future.
• The rate at which organizations across
industries are adopting the technology
• The CEO of one of the hottest AI startups
• The use cases that it enables
• One of the most-respected AI scientists
globally, who is also a board member at
• Its impact on sustainability journeys
Amazon
• How it is likely to change work and the
• A former member of the European
workforce
Parliament who played a key role in the
• How and why it needs to be regulated EU AI Act
Our annual research into the state of Gen • A leading professor at Stanford
AI shows that organizations are embracing
• Senior executives from Adobe,
generative AI, this is reflected by an uptick in
Salesforce, OECD, Telefónica, and Itaú
investment levels. The vast majority (80%) of
Unibanco
organizations in our survey increased their
investment in 2023; 20% maintained their
• Capgemini’s own subject-matter experts
investment levels; and no organization has
decreased its investment in Gen AI from last Pulling together such a wide range of views
year. was an extremely instructive exercise for
us. We hope you enjoy reading this edition
In the past year, every organizational as much as we enjoyed putting it together
domain, from sales and marketing to IT, for you.
operations, R&D, finance, and logistics, has
seen an increase in the rate of adoption.
Early adopters are seeing benefits from
improved operational efficiency to
enhanced customer experience. Moreover,
generative AI adoption among employees
is robust in most organizations, with the
majority allowing its use.
33
Gener(AI)ting the future Capgemini Research Institute
Contents
4
Capgemini Research Institute Gener(AI)ting the future
PP..1144
THE CEO CORNER
P.16 P.16
Arthur Mensch Aiman Ezzat
CEO CEO
Mistral AI Capgemini
5
Gener(AI)ting the future Capgemini Research Institute
PP..2266
EXECUTIVE CONVERSATIONS WITH…
P.28 P.64
Erik Brynjolfsson Clara Shih
Professor, Stanford CEO, Salesforce AI
P.36 P.76
Audrey Plonk Andrew Ng
Deputy Director, Directorate CEO, LandingAI
for Science, Technology and
Innovation (STI), OECD
P.44 P.86
Dragoş Tudorache Chema Alonso
Former MEP, EU AI Act Chief Digital Officer,
co-rapporteur, European Telefónica
Parliament
P.54 P.94
Scott Belsky Ricardo Guerra
Chief Strategy Officer, Chief Information Officer,
Adobe Itaú Unibanco
Contents
66
Capgemini Research Institute Gener(AI)ting the future
PP..110044
PERSPECTIVES FROM CAPGEMINI
GENERATIVE AI FOR
MANAGEMENT
P.106
PP.. 113300
Elisa Farri
and
Gabriele Rosani INSIGHTS
from The Management Lab
by Capgemini Invent FROM THE CAPGEMINI
RESEARCH INSTITUTE
P.131
Gen AI in enterprise
GENERATIVE AI: THE The rise of generative AI investments in
ART OF THE POSSIBLE organizations
P.116 P.138
Robert Engels Gen AI in software engineering
Head of Generative AI Lab, How software engineering is being
Capgemini shaped by generative AI
OPERATIONAL AI IS
CHANGING HOW WE LOOK
AT DATA
P.124
Anne Laure Thibaud
Executive Vice President, Data,
AI & Analytics Group Offer
Leader, Capgemini
and
Steve Jones
Executive Vice President, Data
Driven Business & Gen AI,
Capgemini
77
Gener(AI)ting the future Capgemini Research Institute
Executive Summary
Executive
Summary
Generative AI (Gen AI) is making rapid Andrew Ng, CEO, LandingAI, emphasizes the
inroads into organizational structures, boost to productivity: “For many jobs, AI will
transforming them rapidly from the inside only automate or augment 20-30% of tasks.
out. As businesses across industries begin So, there's a huge productivity boost, but
to implement Gen AI, several key themes people are still required for the remaining
emerge that highlight its potential to impact 70% of the role.”
organizations, workforce, and society.
Gen AI has the potential
to reimagine the future
We are most creatively
of the workforce
confident when we
Gen AI has potential to unlock human are five years old.
creativity, allowing employees to focus on
We lose our creative
complex strategic tasks. Clara Shih, CEO,
Salesforce AI, comments: “AI will allow confidence as we
workers to move away from repetitive tasks
get older because
to focus on doing what humans do best,
which is building relationships, unlocking of the skills gap,
creativity, making connections, and
exposure to criticism,
addressing higher-order problems.”
and just the lack of
Scott Belsky, Chief Strategy Officer and
access to creative
Executive Vice President, Design and
Emerging Products, Adobe, adds: “We are tools. Generative
most creatively confident when we are five
AI is fundamentally
years old. We lose our creative confidence
as we get older because of the skills gap, changing this.”
exposure to criticism, and just the lack of
access to creative tools. Generative AI is
Scott Belsky
fundamentally changing this.” Chief Strategy Officer, Adobe
8
Capgemini Research Institute Gener(AI)ting the future
Executive Summary
Whenever a
traditional activity
gets replaced or
augmented with
one based on bits,
it usually brings
significant energy
and environmental
benefits.”
Erik Brynjolfsson
Stanford
This shift is fostering a culture of continuous In the article, Generative AI for
learning and adaptability. Erik Brynjolfsson, Management, Elisa Farri, Vice-President
Professor at the Stanford Institute for at Capgemini Invent, and Gabriele Rosani,
Human-Centered AI and Director of the Director at Capgemini Invent, comment:
Stanford Digital Economy Lab, discusses the “AI's capability to collaborate on a cognitive
importance of workforce skill enhancement: and emotional level, offering insights and
“AI requires significant changes in the contributing to complex decision-making
economy to create full impact, particularly processes, is an area that many managers
in terms of organization and skills of the have yet to fully realize or integrate into
workforce. Identifying which skills are their strategic thinking.”
important, followed by self-learning and
training programs, are required to prepare They add: “Executives need to cultivate the
the workforce. Secondly, businesses will ability to adopt the co-thinking mindset.
need to restructure and adapt to capitalize Whether in individual tasks or team
on new technologies.” endeavors, mastering this shift will become
a vital competitive advantage.”
Gen AI also has a significant impact
on managers and leadership.
9
Gener(AI)ting the future Capgemini Research Institute
Executive Summary
In Gen AI deployment, Sharing Capgemini perspectives, Anne Laure
Thibaud, Executive Vice President, Data
ethical considerations
AI and Analytics Group Offer Leader, and
are paramount Steve Jones, Executive Vice President, Data-
driven Business and Gen AI, remark: “The
assumption is that Gen AI cannot be trusted
Gen AI has emerged as a transformative
in the same way as a human employee and
innovation. However, its potential
given the opportunity, will act outside its
for misuse emphasizes the need for
boundaries. Organizations are compelled to
organizations to uphold strong ethical
build all the information about their culture,
standards.
mission, and guardrails into the AI they use
to retain control of it.”
Aiman Ezzat, CEO, Capgemini, stresses
the importance of safe use of Gen
AI: “Organizations should establish
employee guidelines for safe use of Gen
AI and validating outputs to eliminate
bias.”
Arthur Mensch, CEO, Mistral AI,
underscores the importance of managing
AI-driven products within ethical
boundaries: “When an organization is
making an AI-driven product, it must
consider the decisions and outputs the
system will make. These decisions and
these outputs should be constrained to
respect the company's role.”
Audrey Plonk, Deputy Director of
the OECD Directorate for Science,
Technology, and Innovation, is
responsible for the OECD’s digital
policy portfolio. She elaborates on data
concerns: “Data privacy considerations
are a key aspect that organizations and
individuals are exploring extensively.
There is a lot of work to be done to
improve transparency and determine
which sources of data should be used to
train AI models. It is essential to put the
appropriate safeguards in place, including
data protection considerations.”
10
Capgemini Research Institute Gener(AI)ting the future
Executive Summary
The path to responsible Ricardo Guerra, CIO, Itaú Unibanco,
highlights the responsibility of
AI
organizations to use AI ethically:
“Organizations have to take a lot of the
To secure the future of AI, comprehensive responsibility for use and governance of AI
safeguards and collaborative efforts are and other technologies. But governments
essential. Unified actions from policymakers must still stay informed and try to
and organizations are crucial to ensure the implement supportive regulation.”
responsible use of Gen AI.
Steering towards a
Dragos Tudorache, former member of the
sustainable future with
European Parliament and Rapporteur on the
EU AI act, says: “Most companies working Gen AI
with AI already had general principles or
codes of conduct or self-regulation in place.
Gen AI will play a pivotal role in addressing
There were guidelines outlined by UNESCO,
climate change. Additionally, businesses
OECD, and even by the European Parliament.
must adopt sustainable practices that align
But we realized these measures were
with environmental goals.
insufficient to mitigate the very real risks,
such as discrimination bias.”
On climate engineering, Andrew Ng
elaborates: “Given the world's collective
inability to move CO2 emissions in the way
we know it needs to, I think it is past time
to take climate engineering more seriously.
Organizations should
I think AI, specifically large AI foundation
establish employee models of climate, have a large role to play
in that.”
guidelines for safe
use of Gen AI and Ricardo Guerra talks about sustainable
data centers: “We're learning when to
validating outputs to
use different solutions and emphasize
eliminate bias.” investing in sustainable data centers and
green technologies. We're also closely
Aiman Ezzat monitoring the market, prioritizing
CEO, Capgemini providers that offer green solutions.”
Erik Brynjolfsson says: “Whenever a
On the need to draft the EU AI Act, he adds:
traditional activity gets replaced or
“We needed to put stronger safeguards in
augmented with one based on bits, it
place that command respect and, ultimately,
usually brings significant energy and
help society to trust in the interaction with
environmental benefits.”
this technology, hence the decision to
formulate the policy.”
11
Gener(AI)ting the future Capgemini Research Institute
Executive Summary
The rise of open-source allow everyone to scrutinize the model for
potential sources of bias, demystifying the
and small language
‘black box’ nature of AI models.”
models
Arthur Mensch comments: “Smaller models
also mean the applications are less costly
Small language models (SLMs) are highly
to run and, more importantly, if you have
cost-effective, resource-efficient, and
a model that is 100 times smaller, you can
have minimal environmental impact.
call it 100 times more for the same cost,
Open-source models promote innovation,
bringing a little more intelligence to your
enhance collaboration, and ensure greater
application with each call.”
transparency in development and usage.
Clara Shih adds: “The future of AI will be a
Aiman Ezzat says: “There are clear
combination of both large and small models
advantages to both open and closed
because of climate impact, as well as for cost
approaches. Openness boosts innovation
and performance reasons.”
and drives collaboration. Open models also
When an organization
is making an AI-driven
product, it must
consider the decisions
and outputs the system
will make. These
decisions and these
outputs should be
constrained to respect
the company's role.”
Arthur Mensch
CEO, Mistral AI
12
Capgemini Research Institute Gener(AI)ting the future
Executive Summary
Ricardo Guerra says: “Adopting Gen AI
requires a culture of innovation. With Gen
AI, we need to engage the business and
The future of AI will design teams actively, as they must identify
opportunities beyond mere tech adoption.”
be a combination of
both large and small
Innovative startups
models because of
point to the future of
climate impact, as
Gen AI
well as for cost and
From content creation to multi-agent
performance reasons.""
systems, alternative computing, and hybrid
AI, emerging tech startups are pushing the
Clara Shih
boundaries of the next generation of AI
CEO, Salesforce AI
applications.
Getting Gen AI right
Synthesia uses AI to create customizable
requires sound video content featuring realistic avatars,
allowing businesses to generate engaging
technical strategy and a
video presentations. Soundraw offers
culture of innovation an AI-powered platform for generating
original music without the risk of copyright
infringement.
Effective Gen AI adoption requires a well-
coordinated strategic approach with a
In the realm of alternative computing,
strong technical foundation, support from
Mythic develops analog chips for faster,
leadership, and an organizational culture of
more efficient AI tasks such as matrix
innovation.
multiplications, while Groq creates AI-
optimized language processing units (LPUs)
Chema Alonso, Chief Digital Officer,
designed for running LLMs.
Telefónica, suggests: “You need to have a
robust technical strategy based on cloud
Liquid AI is pioneering the development of
and sound data, and the rest will fall into
highly efficient, task-specific models using
place. Secondly, you need to have strong
liquid neural networks, with applications
support from top management. Finally,
such as drone navigation, showcasing Gen
you need sufficient budget. Once you have
AI’s wide range of possibilities.
that, you need to make sure that your whole
organization is very well trained on Gen AI –
what can and cannot be done.”
13
Gener(AI)ting the future Capgemini Research Institute
The CEO Corner
Arthur Mensch
CEO
Mistral AI
The CEO
in discussion with
Corner
Aiman Ezzat
CEO
Capgemini
14
Capgemini Research Institute Gener(AI)ting the future
15
Gener(AI)ting the future Capgemini Research Institute
The CEO Corner
Arthur Mensch Aiman Ezzat
CEO, Mistral AI CEO, Capgemini
Arthur Mensch is a French entrepreneur and With more than 20 years’ experience
scientist. at Capgemini, Aiman Ezzat has a deep
knowledge of the Group’s main businesses.
In 2023, Arthur Mensch, along with Guillaume
He has worked in many countries, notably
Lample and Timothée Lacroix, founded Mistral
the UK and the US, where he lived for more
AI with the mission of making generative AI
than 15 years.
ubiquitous and pioneering a new approach to AI
- one that is more open, portable, independent, Aiman was appointed CEO in May 2020.
and accessible to all. Prior to that, from 2018 to 2020, he served
as the Group’s COO and, from 2012 to
After more than 10 years of academic work
2018, as CFO. Aiman is also on the Board
focused on the possibilities of machine learning
of Directors of Air Liquide and is a member
in the field of brain imaging and on optimization
of the Business Council and the European
of machine learning, he joined DeepMind Paris in
Round Table (ERT) for Industry.
2020 as a researcher, where he spent three years
and played a key role in the development and
deployment of flagship projects in generative AI.
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Capgemini Research Institute Gener(AI)ting the future
The CEO Corner
What inspired you to form a new player [in
Mistral] in the generative AI (Gen AI) space –
and why in Europe?
— Arthur: My co-founders and I have been working in the
Gen AI space for over 10 years, previously in large US-based
organizations. When development accelerated at end-2022,
it gave us an opportunity to create some very strong models
in a short period of time. We secured funding, assembled
a dedicated team and the GPUs [graphic processing units]
required to train the LLMs [large language models], and
were ready to go.
Why Europe? Europe is a great place to start a company. The
education systems in France, Poland, or the UK, for example,
are great for training AI scientists. We brought in recent
PhDs from Paris; we were able to get the most important
thing to get started – the team. As the only player in Europe
in the field of conventional language models, we had some
strong geographical business opportunities.
We use both an open-source
model and a portable platform
for model deployment.""
Arthur Mensch
1177
Gener(AI)ting the future Capgemini Research Institute
The CEO Corner
What do you see as the advantages of the open-source gen
AI model?
— Arthur: We use both an open-source model and a portable platform for
model deployment. Even our commercial models are licensed. This allows
users to customize the models to their needs.
It offers portability and comfort. With a model that you can deploy on any
platform, on a private cloud or on-premise or on dedicated instances on
the cloud, you can use the technology where your data is. So, this adapts to
the data-governance constraints of the enterprises, and our customers very
much appreciate this flexibility.
— Aiman: There are clear advantages to both open and closed approaches.
Openness boosts innovation and drives collaboration. Open models also
allow everyone to scrutinize the model for potential sources of bias,
demystifying the “black box” nature of AI models. There are also challenges.
Customizing open models for a particular industry or organization is tricky,
but using open models out of the box can lead to suboptimal performance.
Fine-tuning any foundation model, open-source or proprietary, is a time-
consuming, resource-intensive process that requires significant financial
investment. Hence, it is important for enterprises to assess ROI carefully
before pushing out the Gen AI boat.
""Open models also allow
everyone to scrutinize
the model for potential
sources of bias.""
Aiman Ezzat
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Capgemini Research Institute Gener(AI)ting the future
The CEO Corner
Do you see organizations using a generalized Gen AI model going
forward or many different specialized models?
— Arthur: We see the field moving in these two directions simultaneously. A strong
generalized model gives a good platform for testing solutions. But this can be a
slow and costly process, offering poor ROI for specific tasks. You want your LLM
to offer an intelligent, dynamic solution for a specific issue, whether that’s parsing
the logs of an IT system or parsing the conversation between a customer and a
customer agent.
From a scientific point of view, smaller models
can solve specific issues, but they must be
finely tuned. We want to bring solutions to
market that develop the smallest possible
If you have a model
model to solve a specific defined task, which
will allow for low-latency applications. that is 100 times
smaller, you can call it
Smaller models also mean the applications
are less costly to run and, more importantly, 100 times more for the
if you have a model that is 100 times smaller,
same cost, bringing a
you can call it 100 times more for the same
cost, bringing a little more intelligence to little more intelligence
your application with each call. We call this
to your application
“compressed knowledge”. We specialize
models in order to make differentiated with each call.""
applications that go fast, that call LLM often
and that are cost-controlled. Arthur Mensch
— Aiman: There’s a very clear market for both generalized and specialized models.
A generalized model can serve those use cases that don’t require extensive
customization. These are “low-hanging fruit” that rapidly demonstrate the power of
Gen AI.
Developing and training specialized models for some basic use cases might even
be counterproductive in terms of cost and sustainability. That said, there are use
cases that benefit from specialized models, for instance, in terms of performance
characteristics or in detecting and responding to specific nuances of the industry
or use case. Any use case that requires high performance or deep domain
expertise will likely continue to go down the path of specialized models. At the
same time, specialized models potentially require significant resources in terms
of maintenance and regular updates, so organizations might prefer a generalized
model for use cases with less stringent requirements. I see a future where both
types of models coexist harmoniously.
1199
Gener(AI)ting the future Capgemini Research Institute
The CEO Corner
Any use case that
requires high
performance or deep
domain expertise
will likely continue to
go down the path of
specialized models.""
Aiman Ezzat
What are the most innovative use cases that you are
seeing in Gen AI?
— Arthur: In financial services, for instance, Mistral has built models that
extract information from financial reports and summarize it for bankers to
analyze. This harnesses the power of generative AI to process a large amount
of text and detect weak signals, which is very much the core business of
banks. The other successful deployment is in customer services.
— Aiman: We have been working on several innovative cases using Gen
AI across industries. In life sciences for instance, we have developed with
generative AI a solution to design new drug molecules. This method
significantly boosts the process of generating new structures, offering
researchers a potent tool for designing molecules aimed at specific
biological targets. It illustrates AI's transformative potential in accelerating
and refining drug discovery, particularly in the preliminary phases.
2200
Capgemini Research Institute Gener(AI)ting the future
The CEO Corner
Given the energy required to create and train the large
models, what are the sustainability implications for Gen
AI?
— Arthur: Most of the compute and energy resources required to run these
systems are used at inference time rather than at training time. So you train
for a couple of months, and when the models are deployed on many, many
GPUs, then the large energy consumption is more linked to the usage than
to the training itself. There are trade-offs between the amount you spend
on training and the compression that you can achieve. If you invest more in
training, you can make smaller models, achieving the same performance as a
larger model with less compute. These smaller models consume less energy
to deploy at inference time.
At Mistral, we focus on
compressing knowledge and
making models that are smaller
Most of the compute and
than those the competition
produces. Limiting carbon energy resources required to
emissions is a cause that is
run these systems are used
very dear to our heart and the
reason why we deployed our at inference time, rather than
solutions in Europe. In Sweden,
at training time.""
in particular, renewables
compose a high proportion of
energy consumption. Arthur Mensch
— Aiman: Our research shows that more than three-quarters of organizations are
conscious of environmental concerns around Gen AI. As a leader in the eco-digital
revolution, we at Capgemini recognize the need to weigh the immense potential of Gen
AI against its cost to the planet and society. We are committed to taking a “sustainable
by design” approach to developing Gen AI solutions that harness cutting-edge data, AI,
and climate tech to maximize business outcomes in a sustainable manner.
Mitigation strategies include optimizing the amount of data required to train the
models, working on smaller, task-specific energy-efficient models that employ more
efficient training and operating algorithms, and powering the AI infrastructure with
renewable energy as well as using more energy-efficient datacenters. We also promote
transparency in AI development and operation by monitoring and disclosing the energy
consumption and carbon footprints of Gen AI models. Our Gen AI lifecycle analysis tools
help organizations to mitigate environmental impact.
2211
Gener(AI)ting the future Capgemini Research Institute
The CEO Corner
How should large organizations address ethical considerations and
potential bias in deployment of AI models?
— Arthur: When an organization is
making an AI-driven product, it has to
consider the decisions and outputs the
system will make. So, these decisions ""The rare
and these outputs should be constrained
talent that we
to respect the company's role. What it
means is that before deployment of a recommend
new AI product, the first thing to think
every
about is how do you evaluate success.
How do you ensure that the model is organization
behaving as it should and not producing
look for is
unwanted outputs? And is it able to
provide a nuanced but unbiased answer the software
to complex questions?
engineer who
Owing to our open approach, the can also do data
customer can make their own editorial
science.""
choices from these evaluations.
— Aiman: Large organizations should be Arthur Mensch
conscious of a variety of risks: Inherited
risk, intellectual property, correctness,
data leakage, and user privacy.
Organizations should establish employee
guidelines for the safe use of Gen AI
and validating outputs to eliminate
bias. At Capgemini, we have applied a
governance model to ensure this.
22
Capgemini Research Institute Gener(AI)ting the future
The CEO Corner
How are you bridging the AI talent demand-supply gap?
— Arthur: It has been a challenge to get the best AI scientists. We
recommend hiring very strong data scientists who can undertake software
development. Since we are making the tools and the foundation for the
model itself, training the model is not a necessity within the enterprise
setting. To make the most interesting products, clients must understand
how to use the platform.
So, the capacity for doing this is really adjacent to what we used to call
data science a decade ago. It's the ability to run experiments, to evaluate
certain systems, to see what is failing, and to see how to try and improve it.
This scientific mindset, running experiments on a computer and measuring
success, which is really the data scientist's job. The changes with the data
scientist's job today is that the software requirement is stronger because,
if you want to make an interesting application, you also need to dive deep
into the way you assemble the software, connect it to the LLM, the LLM to
the database, and an LLM to tools. Having a system mindset is necessary to
create successful applications. The rare talent that we recommend every
organization look for is the software engineer who can also do data science.
— Aiman: We are investing over €2 billion over three years in Gen AI and
have already trained over 120,000 team members on generative AI tools
thanks to our Gen AI Campus. We have also launched a dedicated platform
to industrialize our custom generative AI projects. We will also focus on
obtaining certifications and building centers of excellence, as well as specific
go-to-market skills. Ultimately, Gen AI training will be a key requirement in
all of our development and training curricula.
2233
Gener(AI)ting the future Capgemini Research Institute
The CEO Corner
How do you see gen AI driving transformation in large
organizations?
— Arthur: The first step is to take a model – a Mistral model, for instance
– and connect it to the enterprise context. The enterprise context is
located across different databases or SaaS [Software-as-a-Service]
systems. You can then generate assistants with access to the enterprise
context, to help every employee navigate the enterprise processes and
organization. That's typically what our customers do first. They create a
knowledge management tool or general assistant for employees.
— Aiman: Driving transformation with generative AI goes beyond the
technology. Success depends on a broad strategic vision that covers
everything from applying it to the right use cases, potentially adapting
internal processes, to optimizing customer-facing operations. In addition,
the value of generative AI depends on two key foundations: the data and
the human elements. Leaders need to have the right data foundations
in place to ensure they are realising the full potential of Gen AI. Equally
important is training employees to not only use AI effectively but also to
trust it, which is key to adoption.
2244
Capgemini Research Institute Gener(AI)ting the future
The CEO Corner
Arthur Mensch Aiman Ezzat
CEO, Mistral AI CEO, Capgemini
""The first step ""There’s a very
is to take a clear market
model – a for both
Mistral model, generalized
for instance and specialized
– and connect models.""
it to the
enterprise
context.""
222555
Gener(AI)ting the future Capgemini Research Institute
Executive Conversations
Executive
conversations
with…
26
Capgemini Research Institute Gener(AI)ting the future
Executive Conversations
STANFORD SALESFORCE AI
Erik Brynjolfsson Clara Shih
Professor CEO
h p.28 h p.64
OECD LANDINGAI
Audrey Plonk Andrew Ng
Deputy Director, Directorate for CEO
Science, Technology and Innovation (STI)
h p.76
h p.36
EUROPEAN PARLIAMENT TELEFÓNICA
Dragoş Tudorache Chema Alonso
Former MEP, EU AI Act co-rapporteur Chief Digital Officer
h p.44 h p.86
ADOBE ITAÚ UNIBANCO
Scott Belsky Ricardo Guerra
Chief Strategy Officer Chief Information Officer
h p.54 h p.94
27
Gener(AI)ting the future Capgemini Research Institute
Executive Conversations
ERIK
BRYNJOLFSSON
Professor at the Stanford Institute
for Human-Centered AI, and
Director of the Stanford Digital
Economy Lab
28
Capgemini Research Institute Gener(AI)ting the future
Executive Conversations
GENERATING
GROWTH
THROUGH AI
Erik Brynjolfsson is the Jerry Yang the Co-founder of Workhelix. One of the most
and Akiko Yamazaki Professor cited authors on the economics of information,
and Senior Fellow at the Stanford he was among the first researchers to measure
Institute for Human-Centered AI the productivity contributions of IT and the
(HAI), and Director of the Stanford complementary role of organizational capital and
Digital Economy Lab. He is also the other intangibles. He is the author of nine books,
Ralph Landau Senior Fellow at the including the bestseller The Second Machine
Stanford Institute for Economic Age: Work, Progress, and Prosperity in a Time
Policy Research (SIEPR), Research of Brilliant Technologies (2014) with co-author
Associate at the National Bureau Andrew McAfee, and Machine, Platform, Crowd:
of Economic Research (NBER), and Harnessing Our Digital Future (2017).
29
Gener(AI)ting the future Capgemini Research Institute
Executive Conversations
""AI – IN PARTICULAR GENERATIVE
AI – IS THE ELECTRICITY OF OUR
ERA, INCREASINGLY UBIQUITOUS
AND SPAWNING COUNTLESS
COMPLEMENTARY INNOVATIONS.""
How is generative AI transformational?
The biggest driver of productivity growth for
businesses and the economy as a whole, is what
economists call “general-purpose technologies”
or GPT, the same initialism AI researchers now use
for “generative pre-trained transformers.” AI – in
particular generat"
227,capgemini,CRI_Turbocharging-Software-with-Gen-AI-1.pdf,"Turbocharging software with Gen AI: How organizations can realize the full potential of generative AI for software engineering
Turbocharging
software with Gen AI
How organizations can realize the full potential
of generative AI for software engineering
#GetTheFutureYouWant
#GetTheFutureYouWant
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Turbocharging software with Gen AI: How organizations can realize the full potential of generative AI for software engineering
Capgemini Research Institute 2024
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Turbocharging software with Gen AI: How organizations can realize the full potential of generative AI for software engineering
Capgemini Research Institute 2024
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Turbocharging software with Gen AI: How organizations can realize the full potential of generative AI for software engineering
Organizations are reaping multiple benefits • Organizations are utilizing these productivity gains on
innovative work such as developing new software features
from leveraging generative AI for software
(50%) and upskilling (47%). Very few aim to reduce
engineering.
headcount (4%).
• Generative AI is having a positive impact on software
• The leading benefits for organizations are enabling
professionals’ job satisfaction.
more innovative work, such as developing new software
features/services (observed by 61% of surveyed • 69% of senior software professionals and 55% of junior
organizations), improving software quality (49%), and software professionals report high levels of satisfaction
increasing productivity (40%). from using generative AI for software.
• 78% of software professionals are optimistic about
• Organizations using generative AI have seen a 7–18% generative AI’s potential to enhance collaboration
productivity improvement1 in the software engineering between business and technology teams.
function as per early estimates. This is highest for
specialized tasks such as coding assistance2 (34% as the
maximum potential for time savings with 9% on average)
and creating documentation (35% as the maximum
potential for time savings with 10% on average). This
research analyzed time savings in various software
engineering tasks using generative AI tools and not cost
savings which can be significantly different.
Capgemini Research Institute 2024
%
78
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Turbocharging software with Gen AI: How organizations can realize the full potential of generative AI for software engineering
Generative AI adoption is at an early stage • Generative AI is expected to play a key role in augmenting
the software workforce with better experience, tools and
but will accelerate sharply.
platforms, and governance (assisting in more than 25% of
software design, development, and testing work by 2026).
• Adoption of generative AI for software engineering is still
in its early stages, with 9 in 10 organizations yet to scale. • Coding assistance is the leading use case, but generative
AI also finds applications in other software development
• 27% of organizations are running generative AI pilots,
lifecycle (SDLC) activities (test case generation,
and 11% have started leveraging generative AI in their
documentation, code modernization, UX design assistance,
software functions.
etc.)
• Three in four (75%) large organizations (annual revenue
greater than $20 billion) have adopted (piloted/
• Most use cases have yet to be adopted by a majority of
scaled) generative AI compared to 23% of their smaller
organizations (39% are focusing on coding assistance and
counterparts (annual revenue between $1–5 billion).
37% on UX design assistance as top adopted use cases).
• Adoption (including pilots) is expected to increase
significantly in the next two years from 46% of software
workforce using generative AI tools today (for any kind of
training, experimenting, piloting, and implementing, with
authorized or unauthorized access) to an estimate of 85%
in 2026.
Capgemini Research Institute 2024
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Turbocharging software with Gen AI: How organizations can realize the full potential of generative AI for software engineering
Lack of foundational prerequisites and • Using unauthorized tools without proper governance and
oversight exposes organizations to functional, security,
unofficial usage of generative AI pose
and legal risks like hallucinated code, code leakage, and
significant functional, security, and
IP issues.
legal risks.
• 27% of organizations have the platforms & tools, and 32%
have talent prerequisites in place, to implement generative
AI for software engineering.
• Over 60% lack governance and upskilling programs for
generative AI for software engineering.
• Of those software professionals who use generative AI,
63% use unauthorized tools.
• Nearly a third of the workforce is self-training on
generative AI for software as less than 40% of employees
are receiving training from their organizations.
Capgemini Research Institute 2024
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Turbocharging software with Gen AI: How organizations can realize the full potential of generative AI for software engineering
How can organizations harness the full retention, solving complex issues, and collaborating
with business.
potential of generative AI for software
engineering? • Identify requirements for new capabilities and
source them.
• Prepare for generative AI use by delivering technology
• Select and prioritize high benefit use cases. prerequisites:
• Build a repository of platforms and tools for a seamless
• Mitigate risks around security, IP/copyright issues, and and augmented software engineering experience.
code leakage using a thorough risk management approach.
• Privately and safely contextualize generative AI
assistants with organization’s own content.
• Transform your software organization to ensure optimal
usage of generative AI:
• Adopt a measurement protocol for generative AI impact
• Augment your software teams with a generative AI monitoring and use case prioritization.
assistant. A majority of junior (53%) as well as senior
professionals (58%) believe that generative AI tools
will augment their day-to-day work within the next • Put people at the heart of this transformation by creating
two years. For instance, generative AI tools can help a learning culture at your organization.
junior professionals learn faster and come up to speed
• Provide upskilling and cross-skilling opportunities.
quickly, while they allow senior professionals to focus
on grooming juniors by ensuring their learning and • Address employees’ work displacement concerns.
Capgemini Research Institute 2024
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Turbocharging software with Gen AI: How organizations can realize the full potential of generative AI for software engineering
Who should This report provides insights into the use of the full potential of generative AI for software
generative AI for software engineering and engineering.
offers recommendations that will be useful to
read this Business leaders in technology, IT, product,
organizations across industries in harnessing
strategy, R&D/engineering, general management,
and innovation who have responsibility for –
report and
and oversight of – their organization’s software
engineering function will find it particularly useful.
why? 1000+ This report draws on insights from a
comprehensive multi-sectoral survey of 1,098
senior executives (director level and above)
and 1,092 software professionals (including
architects, developers, testers, and project
organizations with annual revenue
managers) from organizations with over $1 billion
greater than $1 billion, represented by a
in annual revenue. The report covers the major
minimum of one software professional
considerations for implementing generative AI
and one software leader, are part of this
in software engineering and includes in-depth
research.
qualitative insights from 20 industry leaders,
professionals, and entrepreneurs.
Capgemini Research Institute 2024
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Turbocharging software with Gen AI: How organizations can realize the full potential of generative AI for software engineering
This report is a part of Capgemini Research
Institute’s series on Generative AI
Gen AI in organizations - annual research
Gen AI for management*
Gen AI in supply chain* Gen AI for marketing Gen AI for software Gen AI in R&D
engineering and engineering
Gen AI and
consumers
Gen AI and Gen AI and Gen AI and
Gen AI in business operations* Gen AI in manufacturing* Gen AI in customer service* sustainability* ethics/ cybersecurity*
trust*
Data mastery*
Special edition of our premium journal Conversations for tomorrow on Gen AI*
To find out more, please go to https://www.capgemini.com/insights/research-institute/ *Upcoming reports
Capgemini Research Institute 2024
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Turbocharging software with Gen AI: How organizations can realize the full potential of generative AI for software engineering
Since the dawn of the modern computer age, there Today, by leveraging the power of large language models
has been a disconnect between natural language and (LLMs), generative AI can enhance developers’ productivity,
machine language. With hardware and software advances, improve software quality, and accelerate time to market.
programming has evolved in waves over time and this gap has Marco Argenti, Chief Information Officer at Goldman Sachs:
begun to close (see Figure 1). “Goldman Sachs is using artificial intelligence to turn software
developers and others into superhumans.” 4
This evolution now appears near complete, as natural
language becomes the lingua franca. With recent rapid In generative AI, the software workforce has a tool to
advances in AI and high-performance computing, we can accelerate key tasks (such as design, coding, migrating,
now simply “chat” with computers and – through human testing, deploying, support and maintenance) with minimal
supervision and accountability – let the AI assistant augment effort and a minimal learning curve.
tasks ranging from programming, generating test cases
and user stories, to documenting, among others. As Andrej
Karpathy, one of the founders of OpenAI and former director
of AI at Tesla, famously quipped following the introduction of
ChatGPT: ""The hottest new programming language is English”.3
Capgemini Research Institute 2024
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Figure 1.
Increasing levels of value creation from evolution of software development languages and platforms
Evolvement of software development languages & platforms
ENIAC
1940
Source: Capgemini analysis
noitaerc
eulaV
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Turbocharging software with Gen AI: How organizations can realize the full potential of generative AI for software engineering
“The hottest new
programming
language is English”
Low code/No code
Cloud native
DevOps Automation
Python
Java
C++
C-Programming
Cobol
IBM 704
Assembler
Generative AI boost
1950 1960 1970 1980 1990 2000 2010 2020 2030
Machine & assembly language High-level programming language Object-oriented language Development platforms
Capgemini Research Institute 2024
However, generative AI brings risks and challenges. implementation approach to harness the potential of generative
Uncontrolled use can lead to hallucinated code, IP issues, AI while managing its risks. With this research we attempt to assess
private data leakages, and security vulnerabilities. Software the impact of generative AI on the software engineering function,
engineering organizations need a new strategy and covering such questions as:
• How will generative AI impact the various stages of software
development lifecycle (SDLC)?
• How can organizations quickly adopt and scale generative AI to
drive productivity and innovation?
• How will generative AI impact software engineers’ ways of
working?
• What are the challenges for software engineering and how best
can we manage the risks associated with generative AI?
• How can organizations continuously measure and optimize
impact of generative AI on their software engineering function?
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Turbocharging software with Gen AI: How organizations can realize the full potential of generative AI for software engineering
Capgemini Research Institute 2024
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Turbocharging software with Gen AI: How organizations can realize the full potential of generative AI for software engineering
What do we Defining the term “software” • Custom software: Specific, advanced programs
developed for a specific purpose for an individual
Software is a strategic capability, transforming the way or company, which can be modified or changed.
mean by businesses design their products and services, function Custom software is not commercially available
overall, compete, and provide value to customers. Software but is built and operated for internal purposes.
is vital to modern business, whether as a product itself or
“Generative AI
integrated into enterprise apps or products. • Consumer software: Sold directly to end users,
consumer software includes apps, web portals,
There are three main categories of software: and information tools such as maps, financial data,
for software
news, games, and music players.
• Business software: Used by organizations to run,
scale, and optimize day-to-day business functions and • Embedded software: A piece of software to
engineering”? processes and/or interact with their customers and program hardware or non-PC devices to facilitate
partners. functioning. These are specialized environments
and applications for a specific hardware stack with
There are two broad types of business software: performance, power, and functionality requirement
• Packaged software: Third-party standard programs and constraints.
grouped to provide different tools from the same
family in a package, commercially available under the
licensor’s standard terms, payable with either a one-
off or annual fee.
Capgemini Research Institute 2024
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Turbocharging software with Gen AI: How organizations can realize the full potential of generative AI for software engineering
Generative AI has Generative AI’s potential for software Generative AI’s impact on the SDLC
engineering
potential for all With the increasing proliferation of software in products,
services, operations; software teams are under pressure
Software engineering has shifted strongly towards greater
categories of software,
to deliver more, better, faster. Generative AI has the
automation and simplification, particularly with the advent
potential to yield benefits across the SDLC. Figure 2
but this research focuses of generative artificial intelligence (generative AI). The rise
shows some of the tasks and activities in SDLC that can
of large language models (LLMs) has been key. LLMs are
benefit from the use of generative AI tools. It is worth
largely on software deep-learning AI algorithms that can recognize, summarize,
noting that it is a subset of all activities encompassing
translate, predict, and generate content by building on
engineering for custom, very large datasets. They have facilitated the increasing SDLC. It can be integrated at any stage – from business
needs analysis and writing agile user stories to software
adoption by consumers and organizations of software
embedded, or consumer design, coding, documentation, packaging, deployment,
engineering.
testing, and operations – augmenting the work of
software which goes Generative AI has the potential to transform the software software engineers and helping increase efficiency,
engineering process, as it can be integrated into tech stacks improve quality, and enhance job satisfaction.
through the entire
to unlock new features and updates for software currently
Generative AI also touches the roles of many data
in use. Many leaders are striving to integrate AI-enabled
software development analysts, business analysts, platform/software designers,
plug-ins or incorporate AI-powered technology into their
and software engineers, developers, and tester.
own enterprise and software engineering platforms. Our
lifecycle.
previous research shows that generative AI will assist in
writing one out of every five lines of code in the coming
year.5
Capgemini Research Institute 2024
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Turbocharging software with Gen AI: How organizations can realize the full potential of generative AI for software engineering
Figure 2.
Potential application areas of generative AI in the SDLC
(DevOps) Software Lifecycle
Business Use case modeling Coding assistance (code generation, Legacy code Code explanation Platform provisioning Software observability with
demand/requirement User stories generation completion) modernization Code documentation & configuration analysis and recommendations
analysis and writing Reverse engineering Unit tests generation (migration, conversion, Code vulnerabilities
etc.)
Business
...............................................D....e...s...i.g...n...........................................................C...o...d....i.n...g............................................................B....u...i..l.d................................................................T...e...s...t...........................................................R....e...l.e...a...s..e..........................................................D....e...p...l.o....y.......................................................O.....p...e...r..a...t..e.......................................................M.....o...n....i.t..or
demand
UX/UI design Software architecture Software refactoring Software packages Test Case generation Software packages assembly Incidents resolution
configuration Test Data sets Release notes Tickets assistance
(Agile) Product Teams / (Waterfall) Development Teams
Backlog and roadmap planning Product value stream performance Team effectiveness analysis and improvement Team communication and collaboration
Effort estimations recommendations Process facilitation (plannings, retrospective, burndown, etc.)
Industrialized Software Engineering Platform
Agile Process Management/ALM Developer workplace (IDE) DevOps automation Tests automation Generative AI foundations
Source: Capgemini Research Institute analysis.
Capgemini Research Institute 2024
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Turbocharging software with Gen AI: How organizations can realize the full potential of generative AI for software engineering
01
Organizations are reaping
significant benefits from
leveraging generative AI for
software engineering.
Capgemini Research Institute 2024
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Turbocharging software with Gen AI: How organizations can realize the full potential of generative AI for software engineering
Figure 3. Augmenting innovation and
One in two organizations adopting generative AI sees improvements in enabling innovative work and quality of software.
improving software quality
are the leading benefits.
Percentage of organizations seeing benefits through the adoption of generative AI,
as mentioned by software leaders
Three in five organizations see innovative work – for
Enabling innovative work example, developing new features and services using
61%
(e.g., developing new features, services etc.) software – as the biggest benefit of generative AI use
in software engineering (see Figure 3). Of software
Quality of software 49%
professionals surveyed, 80% believe that, by automating
simpler repetitive tasks, generative AI will free up time
Productivity 41%
for them to focus on innovation and value-adding tasks,
fostering greater creativity.
Collaboration 36%
Akram Sheriff, Senior Software Engineering Leader (Gen
Security 34% AI, AI, ML) at Cisco Systems elaborates: “One of the biggest
drivers of generative AI adoption is innovation. Not just on
Time to market/reduction in lead-time 33% the product side but also on the process side. While senior
professionals are leveraging generative AI combined with their
Cost of software development 25% domain expertise for product innovation, junior professionals
see value in AI process and tool innovation, and in automation
Technical debt 12%
and productivity optimization.”
Compliance and risk management 9%
Source: Capgemini Research Institute, Generative AI in Software Engineering, Senior Executive Survey, April 2024, n =
412 software leaders that have scaled up or are running pilots with generative AI in software engineering.
Capgemini Research Institute 2024
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Turbocharging software with Gen AI: How organizations can realize the full potential of generative AI for software engineering
Generative AI also enables improvements in software
quality. It can help deliver higher-quality code with fewer
errors and improvements in test coverage and quality. Both
factors give organizations a productivity boost at team and
organizational levels. For example, Emirates NBD, a large
banking group in the Middle East, not only accelerated
developer productivity by up to 20% in complex tasks, but
also improved the company’s code quality by 20% by using
GitHub Copilot’s code suggestions.6
Head of AI at a leading Australian telco, explains: “With use of
generative AI for software engineering, the number of test cases
could be increased by 30%, greatly enhancing test coverage and
quality.”
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Turbocharging software with Gen AI: How organizations can realize the full potential of generative AI for software engineering
Figure 4. For telecom businesses, generative AI can play a significant
Telecom and retail sectors see enablement of innovative work as a top benefit from generative AI. role in the development of such data-powered, innovative
applications as network management and maintenance
as well as customer service/sales apps offering hyper-
Percentage of organizations by sector, who have active initiatives and see enable- personalization. BT Group’s Digital unit has an AI-powered
ment of innovative work as a top benefit, as per software leaders product lifecycle management strategy. Within four months
of deploying Amazon’s CodeWhisperer, it had automated
Telecommunications 86% nearly 12% of repetitive work, allowing the pilot workforce to
focus on more strategic goals.7
Retail 76%
Similarly, the retail industry is leveraging generative AI
Life sciences and healthcare 74% to gather and analyze customer preferences, competitor
insights, past sales history, etc., and create robust and precise
Consumer products 71%
requirements documentation as the basis of engaging
Global 61% customer-facing apps. Wayfair, a home goods company, is
considering using generative AI to reduce the technical debt
High tech 58%
accumulated in their software stack over years.8
Energy transition & utilities 56%
Banking 55%
Aerospace & defence 53%
Automotive 52%
Public services 52%
Insurance 45%
Source: Capgemini Research Institute, Generative AI in Software Engineering, Senior Executive Survey, April 2024, n = 412 senior
executives that have scaled up or running pilots with generative AI in software engineering.
Capgemini Research Institute 2024
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Turbocharging software with Gen AI: How organizations can realize the full potential of generative AI for software engineering
Organizations with active Figure 5.
Larger organizations have seen greater productivity improvement with generative AI.
generative AI initiatives
have seen an average
Productivity improvement range of a software professional, grouped by organization revenue size
7–18% improvement in
productivity across the 19% 19% 19%
18%
18%
SDLC.
15%
11%
Those organizations actively using generative AI in software
9% 9%
engineering have seen an average total productivity
7% 6%
improvement of 7–18% across the SDLC today, compared to
4%
non-usage of generative AI. The increasing maturity of tools
and processes along with growing professional experience,
means productivity is likely to continue to improve.
We also found that productivity advantage increases with Global Average USD 1 billion to USD 5 billion to USD 10 billion to USD 20 billion to More than USD 50
< USD 5 billion < USD 10 billion < USD 20 billion < USD 50 billion billion
organization size (see Figure 5).
Top range of productivity improvement Bottom range of productivity improvement
Source: Capgemini Research Institute, Generative AI in Software Engineering, Senior Executive Survey, April 2024, n =
412 software leaders that have scaled up or running pilots with generative AI in software engineering. Top and bottom
productivity ranges are found by the 80th and 20th percentile respectively of individual productivity improvement data.
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Turbocharging software with Gen AI: How organizations can realize the full potential of generative AI for software engineering
Ancileo, Singapore-based insurance software-as-a-service
(SaaS) company, used generative AI to increase developer
productivity. Sylvain Dutzer, Chief Technical Officer, Ancileo:
“Ancileo is using Amazon Q to supercharge our developers by
helping them understand existing codebase and troubleshoot
directly in their integrated development environment (IDE). This
allows our team to reduce time resolving coding-related issues by
30%. Even our architects use it to help find the best solutions to
specific problems based on context.” 9
Nitin Tandon, Chief Information Officer of financial services
firm Vanguard: “We are enabling productivity gains for
developers by experimenting ‘rapidly and safely’ with generative
AI tools — with human oversight and expertise.” 10
Improvements in coding speed (78%) and testing speed (54%)
are the top reasons cited for this improvement. Generative
AI can produce test cases directly from requirements, with
significant time savings. Where testing an app requires
certain application programming interfaces (APIs), AI test
code generators can create these snippets quickly.
Capgemini Research Institute 2024
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Turbocharging software with Gen AI: How organizations can realize the full potential of generative AI for software engineering
Generative AI yields Figure 6.
Generative AI shows significant productivity improvement in terms of time savings for documentation and coding assistance tasks.
productivity improvement
for a set of specialized Maximum and average time savings through generative AI usage for a set of activties (current estimates)
software tasks
35%
34%
We analyzed specific tasks from a software professional’s
daily work to understand the impact of generative AI.
Some tasks are better suited for generative AI, given the
maturity of the tools available and the experience of the 20% 20%
workforce. As shown in Figure 6, creating literature and
documentation, and writing code and scripts show the
greatest timesaving. This tapers off for the remaining 10%
9%
major task categories in the SDLC. However, as toolchains
5%
and platforms improve, this benefit is likely to spread. It is
1%
important to note that saving time using generative AI tools
is significantly different from saving cost. Assessing cost
savings was not a part of the scope of this research. Creating literature Writing code and Debugging and Project management
and documentation scripts testing
Maximum improvement Average improvement
Source: Capgemini Research Institute, Generative AI in Software Engineering, Software Professionals Survey, April 2024, n = 368
software professionals that are actively using generative AI. Maximum improvement is represented by the 95th percentile’s results,
while an average user is represented by the statistical average.
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Turbocharging software with Gen AI: How organizations can realize the full potential of generative AI for software engineering
Organizations are utilizing Figure 7.
Innovative work and upskilling are the top areas where organizations are channelizing productivity gains.
productivity gains on
innovative work and How is your organization planning to leverage the additional time freed up by generative AI?
upskilling, not headcount
Focus software professionals' efforts on innovation for e.g.,
50%
reduction. developing new features, services etc.
Upskill software professionals on business skills and understanding 47%
According to our survey, 79% believe generative AI will
significantly reduce the workload and free up additional time Focus software professionals' efforts on complex, high-value tasks 46%
for software professionals. This freed up time is being used
for higher-value-adding tasks including enhanced innovation
Upskill software professionals on advanced technical capabilities 45%
and upskilling, as shown in Figure 7.
Mousumi Bhattacharya, Director of IT at Centene, a US-based Invest in cross-skilling of software professionals 37%
managed care company: “Generative AI has tremendous
potential to improve productivity by shifting professional Train software professionals to ensure quality, security,
31%
efforts and time from mundane and repetitive things to more IP, ethical issues standards are being met
meaningful, creative and challenging tasks.”
Reducing technical debt 26%
Stephane Dupont, EVP and Head of Operations at Airbus,
the leading European aerospace company: “I see it as a
Reduce the size of workforce 4%
coding assistant, giving developers more time to think about
the architecture, the new features, next steps, quality, etc., and
spending less time on pure code development.”
Source: Capgemini Research Institute, Generative AI in Software Engineering, Senior Executives Survey, April 2024,
N = 870 senior executives who believe that generative AI will free up additional time for software professionals
Capgemini Research Institute 2024
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Turbocharging software with Gen AI: How organizations can realize the full potential of generative AI for software engineering
Brian Lanehart, president, and CTO of financial technology
provider Momnt: “Being able to completely communicate an
entire application request to generative AI will reduce a task
timeline significantly. That means an engineer or team is freed up
to think creatively or strategically.” 11
Reducing headcount is the least-adopted route (taken by
only 4% of responding organizations); and new roles, such as
generative AI developer, generative AI Architect, AI platform
architect, prompt engineer, etc. have evolved. The head of
AI at a leading Australian telco: “Even as autonomous vehicles
“I see it as a coding assistant, giving developers more time to think
are a reality, human supervision and ability to take control is
still required. Similarly, software engineers won’t be replaced by about the architecture, the new features, next steps, quality, etc.,
generative AI – they will start thinking about the actual design
and spending less time on pure code development.”
process, long-term strategy, next phase of software, etc. rather
than spending a year writing code.“
Stephane Dupont
EVP and Head of Operations at Airbus,
the leading European aerospace company
Capgemini Research Institute 2024
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Turbocharging software with Gen AI: How organizations can realize the full potential of generative AI for software engineering
Generative AI benefits in Figure 8.
Senior and junior professionals see positive impacts of generative AI on their job satisfaction
software engineering
extend to job satisfaction
Extent to which generative AI impacts these areas within your organization’s
software engineering function
and happiness.
69%
Our research shows that generative AI has a positive impact
on software professionals’ job satisfaction and reduces 55%
51%
attrition rates (see Figure 8). Fabio Veronese, Head of ICT 47%
Industrial Delivery at ENEL Group: “We are more ambitious. For
us, improving development productivity with generative AI is not
just about lines of code. It is also about developer experience.”
%
69
Senior software professionals Junior software professionals
(having experience > 3 years) (having experience <= 3 years)
Senior software professionals believe that Improve job satisfaction Reduce attrition
generative AI will have a positive impact on job
satisfaction
Source: Capgemini Research Institute, Generative AI in Software Engineering, Software
Professionals Survey, April 2024, N = 215 software professionals
Capgemini Research Institute 2024
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Turbocharging software with Gen AI: How organizations can realize the full potential of generative AI for software engineering
Most of the current workforce sees generative AI as a strong Figure 9.
enabler and motivator – 35% associate it with being “assisted Most of the workforce feels positive about generative AI tools for software engineering.
and augmented,” and 24% feel “excited and happy” about its
adoption (see Figure 9).
How does the workforce feel as regards to the adoption of generative AI
While there is currently an emphasis on generative AI’s
utility in code completion and writing, three in four senior
executives believe it will significantly transform their
software engineering organization.
Tommy MacWilliam, Engineering Manager for Infrast"
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industry in the AI era
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The semiconductor industry in the AI era
Snapshot of research Downstream industries
methodology express concerns
over semiconductor
supply
Who should read this Organizations The semiconductor
report and why? anticipate surging industry is innovating,
semiconductor demand but softwarization
remains a challenge
Capgemini Research Institute 2025
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The semiconductor industry in the AI era
Resilience and Partnerships are taking Conclusion
sustainability gather the industry forward
momentum
Exploring big tech's How the semiconductor Research
shift to in-house
industry can capitalize methodology
chip design
on emerging
opportunities
Capgemini Research Institute 2025
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The semiconductor industry in the AI era
AI adoption is powering a surge in demand for customization, introduce more comprehensive application
semiconductors: programming interfaces (APIs) and software development kits
(SDKs), as well as reinforcing security features. Consequently,
While semiconductor industry organizations forecast a 15%
one in three downstream organizations is exploring or is
rise in two years, the downstream organizations (those reliant
actively engaged in in-house chip design, enabling greater
on semiconductor supply for their products or services and
customization while also gaining more control over
operations) anticipate their demand for chips to increase at
their supply chains. Further, sustainability, supply chain
a higher growth rate of 29%. Increased adoption of artificial
resilience, and security are critical concerns for downstream
intelligence (AI) and generative AI (Gen AI) is driving the
organizations going forward.
need for specialized neural processing units (NPUs) and
high-performance graphics processing units (GPUs) that can Amid “softwarization” challenges, innovation shines
handle massive computations and large datasets efficiently. through:
Additionally, downstream organizations expect their demand
The semiconductor industry continues to excel in innovation
for AI chips, custom silicon chips, and memory-intensive chips
across a number of areas. Although various players stand out
to increase over the next 12 months.
in specific areas, our analysis reveals three types of innovation
Amid buoyant demand lies concern: that are consistently prioritized across the industry:
Over half of downstream organizations doubt the • Design innovation: Advances in chip architectures, such as
semiconductor industry's ability to meet their needs. 3D integrated circuit (IC) design and multi-die integration
Technological advancements, including GPU computing for AI are pushing the boundaries of performance and energy
and machine learning and inference acceleration are important efficiency, while half of design organizations are investing in
to these industries, which are continually seeking to enhance Gen AI to shorten design cycles.
Capgemini Research Institute 2025
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The semiconductor industry in the AI era
• Manufacturing innovation: Advances in extreme ultraviolet extend the chip lifecycle, and enhance customization in an
(EUV) lithography and the shift towards smaller process evolving market, the industry finds monetizing its software
nodes (i.e., 3 nanometer and 2 nm) enable the production a challenge.
of more powerful and efficient chips. Nearly half of
Focus on supply chain resilience and sustainability:
manufacturers also rely on AI and ML to optimize processes.
Only two in five semiconductor organizations are confident
• Packaging innovation: 3D packaging and the use of chiplets
in the resilience of their supply chains. Organizations focus
(tiny integrated circuits that can be combined to create
on onshoring and “friendshoring” (basing supply chains in
complex components) are enhancing functionality and
countries that are geopolitical allies) to enhance stability and
performance without increasing physical footprint.
reduce single-region dependency. Consequently, the industry
Hardware security remains paramount, with significant anticipates domestic sourcing will improve by 17% over the
investment in secure chip design, hardware-based encryption, next two years. Three-quarters (74%) of organizations expect
and root of trust (RoT, a trusted source within a cryptographic to increase their US investments, and 59% will increase
system) technologies. But while there is steady progress in investment in the EU.
integrating software and hardware to create more adaptable
Besides its continued focus on improving power efficiency,
and programmable semiconductor solutions, monetization
the industry is becoming more eco-friendly by cutting
remains a hurdle.
energy consumption, implementing water recycling and
However, the “softwarization of semiconductors” is falling reuse systems, using less toxic alternative chemicals, and
short of the industry's expectations. While this innovation is minimizing waste.
crucial for semiconductor companies to expand use cases,
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The semiconductor industry in the AI era
Recommendations for the semiconductor industry: • Coordinate strategies with governmental initiatives
To capitalize on emerging opportunities, semiconductor such as grants for R&D and collaborate within the industry
organizations should consider the following: ecosystem to drive shared innovation and standardization.
• Utilize AI and Gen AI to automate design processes, • Enhance cybersecurity measures to protect data
improve production efficiency, and optimize performance to integrity, use advanced security to safeguard proprietary
meet the specialized needs of emerging applications. technologies, and advocate for stronger intellectual
property (IP) laws to deter infringement and protect
• Invest in cutting-edge fabrication methods such as 3D chip
innovation-led competitive advantage.
stacking and accelerate research in emerging fields such as
advanced silicon photonics integration. • Adopt open standards and open-source collaboration to
drive semiconductor innovation.
• Diversify supplier networks across multiple regions while
investing in R&D for alternative materials and technologies.
Implement sustainable manufacturing practices such as
green chemistry and utilize renewable energy sources to
minimize carbon footprint.
Capgemini Research Institute 2025
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The semiconductor industry in the AI era
Who should read this report and why?
This report will be relevant for decision-makers topics like cybersecurity, softwarization, supply silicon, in-house chip design, sustainability, and supply chain
across the semiconductor ecosystem and its chain resilience, and sustainability—enabling dynamics provide guidance for aligning technology adoption
down-stream industries. Specifically, this will be executives to align their strategies for the future. with organizational goals.
useful for:
2. Leaders in downstream industries: By connecting perspectives from both semiconductor
1. Executives in the semiconductor industry: Professionals in automotive, consumer executives and downstream organizations, this report equips
Integrated device manufacturers, fabless design electronics, retail, telecom, aerospace and stakeholders with the knowledge and strategies needed to
firms, foundries, OSAT companies, material defense, high tech, medical devices/medical thrive in a rapidly evolving landscape.
and subsystem companies, and semiconductor electronics, industrial equipment, financial
capital equipment manufacturers will gain a services, and energy industries will gain a deeper
strategic outlook on industry trends, including understanding of how semiconductor trends
advancements in design, manufacturing, and impact their industries. Insights into custom
packaging. The report also addresses critical
Capgemini Research Institute 2025
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The semiconductor industry in the AI era
Snapshot of research methodology
1. Survey of 250 semiconductor industry organizations 2. Survey of 800 downstream organizations
6%
11%
3% 1%
Aerospace and defense
8% 11%
Automotive
Integrated device manufacturers 10%
Consumer electronics
10% Fabless design
6%
Energy
Outsourced semiconductor assembly and
test (OSAT) companies Financial services
48%
Foundries 10% High tech
10%
12% Material and subsystem companies Industrial equipment
Semiconductor capital equipment companies Medical devices/medical electronics
Electronic design automation (EDA) companies 10% Retail
13% Telecom
18%
13%
3. Twelve in-depth interviews with executives from the
semiconductor industry and downstream industries.
Capgemini Research Institute 2025
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The semiconductor industry in the AI era
Definitions – A rich set of flexible and programmable specific purpose, such as image processing or inference,
acceleration engines that offload and improve at a lower monetary and resource cost than a general-
applications performance for AI and ML, purpose processor. ASICs enable ML and other typically
• Neural processing units (NPUs):1 NPU zero-trust security, telecommunications and high-cost functionality in situations where it would
architecture simulates the neural network of the storage, among others. otherwise be impractical. ASICs will not always be the
human brain. It processes large amounts of data appropriate solution, but are worth consideration.
simultaneously, performing trillions of operations • Graphics processing units (GPUs):3 A GPU is an
per second. It uses less power and is far more electronic circuit that can perform mathematical • Softwarization:6 Softwarization is the concept of
efficient than a CPU or GPU, while freeing these calculations at high speed. Computing tasks developing a more standardized, limited set of base
up for other tasks. Combining an NPU with such as graphics rendering, ML, and video chips that can be customized for various industries and
machine learning (ML) offers lightning-fast, high- editing require similar operations on a large solutions by reducing the number of unique hardware
bandwidth AI in real time. dataset. GPUs can perform the same operation designs and instead using software to provide industry-
on multiple data values simultaneously. This specific functionalities – essentially moving the “logic”
• Data processing units (DPUs):2 A DPU is a increases processing efficiency for many from silicon to software.
“system-on-a-chip” (SoC) that combines: compute-intensive tasks.
• Downstream industries: While nearly all industries
– An industry-standard, high-performance, • AI chips:4 AI chips are specialized computing rely on semiconductors for their products or services
software-programmable, multi-core hardware used in the development and and operations, the scope of this research includes
CPU, tightly coupled with the other SoC deployment of AI systems. AI chips are essential automotive, consumer electronics, retail, telecom,
components, for meeting the demand for greater processing aerospace and defense, high tech, medical devices/
– A high-performance network interface power, speed and efficiency. medical electronics, industrial equipment, financial
capable of parsing, processing, and efficiently services, and energy.
transferring data at line rate, or at least • Custom chips:5 Custom or application-specific
network speed, to GPUs and CPUs, integrated circuit (ASIC) chips are designed for a
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The semiconductor industry in the AI era
01
Organizations
anticipate surging
semiconductor demand
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The semiconductor industry in the AI era
Semiconductors are the backbone of the digital world, two years. The main drivers of this increase are the growing
powering smartphones, Gen AI, computers, cars, usage of sophisticated electronics, the rise in electric and
satellites, and virtually every electronic device in use driverless cars, and the development of smart technologies
today. In 2023, nearly 1 trillion semiconductors – more and high-speed internet. Growth in data-driven applications
than 100 times the number of people on the planet – and demand for energy-efficient solutions are also helping to
were sold worldwide.7 Despite a cyclical market decline perpetuate this trend.
in the first half of 2023, worldwide sales recovered in
While the semiconductor industry is cyclical by nature, our
the second half of the year to $527 billion.8 By 2030, it is
research indicates that the semiconductor industry
anticipated that the semiconductor market size will
anticipates demand to increase by 15% by 2026, while
surpass $1 trillion.9
downstream organizations expect an increase of almost 30%
(see Figure 1). While we did not ask the respondents for their
Semiconductor
projections beyond 2026, extrapolating from the current
increase of 15% suggests that the market size could reach
manufacturers and approximately $930 billion by 2030, nearing the $1 trillion
estimate mentioned earlier. The semiconductor industry's
% downstream industries cautious outlook reflects a slow recovery in markets like
29
automotive despite strong AI-driven demand.
expect demand to rise
The semiconductor industry and downstream industries
expected increase in demand for anticipate significant growth in demand over the next
semiconductors by downstream
organizations, to the end of 2026
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Gen AI at wTTohhreek :ss Seehmmaiipccioonnngdd tuuhccett oofurr tiinnuddreuu ssottfrr oyy riingn a ttnhhieez aAAtIIi oeenrraas
Figure 1.
Downstream industries estimate demand for semiconductors to increase at double the rate of the semiconductor industry’s
expectation
Expected semiconductor demand increase in two years to the end of 2026
29%
15%
Semiconductor industry Downstream industries
Source: Capgemini Research Institute, Semiconductor survey, November 2024, N = 190 semiconductor industry organizations
(includes Integrated Device Manufacturers (IDMs),fabless design firms and foundries), N = 800 downstream organizations.
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The semiconductor industry in the AI era
Rapid technological progress is driving demand for more Figure 2.
powerful, efficient, and customized chips, prompting
Nearly three in five semiconductor organizations say that 5G (or other next-gen communication protocols) and Gen AI are
semiconductor manufacturers to invest heavily in R&D. Our
impacting their strategy
research indicates that 58% of semiconductor organizations
believe that 5G or other next-gen communication protocols
are impacting their strategies, while 56% believe that Gen
AI is a strong influence. 5G and other next-generation
Technology domains impacting semiconductor manufacturing strategy
communication protocols are foundational to enabling a wide
range of emerging technologies and markets, including IoT,
autonomous vehicles, AR/VR, and edge computing. They drive
demand for advanced semiconductor solutions that require
higher performance, energy efficiency, and integration.
58% 56%
51%
44% 42%
21%
17% 16%
5G or other next-gen Edge computing AR/VR Blockchain and
communication cryptocurrency
protocols
Generative AI Wearables Quantum Space
computing technology
Source: : Capgemini Research Institute, Semiconductor survey, November 2024, N = 250 semiconductor organizations.
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The semiconductor industry in the AI era
The adoption of AI/Gen
compute engine, and the network subsystem enhances memory solutions such as Micron's HBM3E (one of many
throughput, whether in small or large clusters.” products on the market), which can optimize performance
AI is driving GPU/NPU and reduce CPU offload during AI processing, allowing faster
Qualcomm‘s NPU, designed specifically to take on AI
training and greater responsiveness to queries.12
workloads, is an essential enabler of on-device Gen AI
demand
capabilities. The NPU offers optimal performance, power, and In our research, 58% of semiconductor organizations
space efficiency to handle complex ML operations.10 ChatGPT mentioned they expect higher demand for NPUs to
The adoption of AI and Gen AI is driving demand for model exemplifies the transformative role of GPUs in AI. Leveraging accompany growth in Gen AI adoption, with 57% anticipating
training and inference capabilities, and data centers, while thousands of NVIDIA GPUs, the training and inference increased need for high-performance chips and 56% for
the growth of on-device AI applications further underscores processes for its large language model (LLM) demonstrate memory-intensive chips, signaling a shift toward advanced
the need for specialized semiconductor solutions. Subi the unparalleled efficiency and scalability GPUs bring to AI processing solutions.
Kengeri, VP of AI Systems Solutions at Applied Materials, workloads. This infrastructure supports Gen AI services for
says, ""The AI era marks a new wave of growth for the over 100 million users, underscoring the critical contribution
semiconductor industry, propelled by the high returns on of GPU technology to cutting-edge semiconductor
investment generated by AI's economic value. For AI systems, applications in AI-driven innovation.11 A Senior Director at a
the key metric remains Total Cost of Ownership, while for US-based IDM explains, “One of the key advancements in the
Silicon, it is Perf/Watt/$.” semiconductor industry is performance enhancement through
parallel computing, exemplified by GPUs. As AI accelerators
To supply the demand rising from AI/ Gen AI, organizations evolve, various parallelization techniques, including expert
are ramping up their NPU, GPU, and memory capabilities. A parallelism, pipeline parallelism, tensor parallelism, and
Senior Director at a US-based IDM says, “We focus on context parallelism, are being employed. These optimization
optimizing the interplay of compute, memory, and network and architectural innovations are designed to boost overall
components to achieve system-level efficiency as customers throughput, whether for training or inference workloads.”
adopt AI to unlock its benefits. Computational efficiency
minimizes the time required for matrix multiplication, the Furthermore, today's Gen AI models require more data to
memory subsystem ensures data is readily available for the enhance outcomes and exploit new opportunities.
Inferencing LLMs such as ChatGPT benefits from advanced
Capgemini Research Institute 2025
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The semiconductor industry in the AI era
Figure 3.
Due to Gen AI adoption, nearly three in five semiconductor organizations are seeing increased demand for NPUs, high-performance GPUs, and memory-intensive chips
Areas where organizations anticipate demand for their semiconductor products will be
impacted in the next two years by use of Gen AI applications
58% 57% 56%
40% 39%
16%
NPUs Memory-intensive Custom chips
chips
High performance DPUs Power and MEMS
GPUs chips
Source: Capgemini Research Institute, Semiconductor survey, November 2024, N = 250 semiconductor organizations.
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The semiconductor industry in the AI era
Organizations project high
demand for AI chips and
custom silicon chips
The market is also witnessing a surge in diverse applications
for AI chips and custom-designed chips, underlining their
transformative potential. Intel and AWS announced a multi-
year co-investment in custom chip design that would
encompass Intel products and wafers.13 Sam Geha, EVP of IoT,
Compute and Wireless business at Infineon Technologies,
explains, “Just a few years ago, our role was simply to produce
chips, leaving it to customers to determine how to use them.
Today, however, we are expected to deliver customized
solutions. Software has emerged as a critical differentiator,
particularly as our chips have become increasingly complex.
Beyond general-purpose software, we now provide specialized
solutions for AI and edge AI, enabling customers to effectively
train and deploy models. Alternatively, we can offer services to
manage the training and deployment for them.”
Our research shows that most downstream organizations
foresee heightened demand in the next two years: 88%
anticipate a rise in AI chip needs, 81% foresee heightened
calls for custom chips, and 79% predict an upturn in demand
for memory-intensive chips. As figure 3 shows, according to
39% of semiconductor organizations, Gen AI is expected to
drive demand for custom chips in the next two years.
Capgemini Research Institute 2025
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The semiconductor industry in the AI era
Figure 4.
Nearly four out of five downstream organizations anticipate increased demand for AI chips, custom silicon chips,
and memory-intensive chips over the next 12 months
Expected demand for chips
88%
81% 79% ""The AI era marks a new wave
of growth for the semiconductor
industry, propelled by the high
returns on investment generated by
AI's economic value. For AI systems,
21% the key metric remains Total Cost
of Ownership, while for Silicon, it is
15% 14%
Perf/Watt/$.”
AI chips/chips designed Custom silicon chips Memory-intensive chips
for AI acceleration (custom ASIC) (e.g., HBM, GDDR6)
Subi Kengeri
Proportion of organizations expecting Average expected increase over
VP of AI Systems Solutions,
an increase in demand the next 12 months
Applied Materials
Source: Capgemini Research Institute, Semiconductor survey, November 2024, N = 800 downstream organizations.
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The semiconductor industry in the AI era
Rise of custom silicon
Figure 5.
As AI workloads ramp up energy demand, electricity and Two in three downstream organizations are either already using custom silicon chips or are considering using them in their
infrastructure suppliers must adapt quickly to support the products
growing needs of data centers. Meanwhile, companies such
as NVIDIA with their commanding market share in AI chips,
Usage of custom silicon chips in products
continue to exerts significant influence over pricing. The
need for optimized performance, energy efficiency, and
differentiation in competitive markets such as automotive,
high-tech, and medical devices is driving the use of custom
18%
silicon. Custom chips allow organizations to better meet 56%
specific application demands, reduce costs at scale, and
leverage advances in AI and IoT, fueling widespread adoption
across industries. Yes, we use custom silicon chips in our products
Yes, we are considering using custom silicon chips in our products
According to our research, 56% of downstream organizations
16% No, we may consider using custom silicon chips in the future
are already using custom chips, while 11% are considering
the possibility. A large majority of industrial equipment No, we have no such plans
organizations (85%), and telecom organizations (82%)
currently rely on custom silicon chips. Tech giants Microsoft,
Amazon, and Meta are also developing in-house chips
tailored for AI inferencing. 11%
Source: Capgemini Research Institute, Semiconductor survey, November 2024, N = 800 downstream organizations.
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The semiconductor industry in the AI era
“Advanced platforms and software are no longer just enablers but critical differentiators in the semiconductor
industry, driving efficiency and scalability in design, manufacturing, and deployment. With the growing complexity of
AI, IoT, and edge computing applications, the ability to integrate domain-specific software with hardware accelerators
will define leadership. To stay competitive, semiconductor players must embrace co-optimization across the stack,
from chip architecture to application interfaces, ensuring they can meet the escalating demands of data-intensive,
low-latency markets.”
Jiani Zhang
EVP, Chief Software Officer,
Capgemini Engineering
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The semiconductor industry in the AI era
02
Downstream industries
express concerns over
semiconductor supply
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The semiconductor industry in the AI era
Over half of all
Figure 6.
More than three out of five organizations believe that geopolitical tensions and inadequate fab capacity impact the reliability
downstream organizations
of the semiconductor supply chain
are uncertain that the
semiconductor industry
Factors impacting the reliability of semiconductor supply chain
can cope in 2025
69%
As nations compete for control over vital technologies and 65%
resources, geopolitical tensions continue to impact the 52%
49%
global semiconductor supply chain. The flow of components, 46% 43%
materials, and completed semiconductor products has been
hindered by international trade disputes, export restrictions,
and tariffs. For example, Taiwan’s TSMC, the world's largest
semiconductor manufacturer, with a market share of about
55%, is the producer of the world’s most advanced chips.14
Consumer supply networks that rely on TSMC could be Geopolitical tensions Reliance on Push for sovereignty
seriously disrupted by any military escalation involving China small number of
semiconductor
and Taiwan. Deteriorating US-China ties have also given rise Fab capacity Pandemic Availability of
suppliers
to setbacks in the form of prohibitions on certain products natural resources
and more stringent controls. In 2022, the US introduced
export controls that restrict the People’s Republic of China’s
(PRC’s) ability to obtain advanced computing chips, develop
and maintain supercomputers, and manufacture The The Source: Capgemini Research Institute, Semiconductor survey, November 2024, N = 800 downstream organizations.
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The semiconductor industry in the AI era
advanced semiconductors.15 These rules were revised in The COVID-19 pandemic exposed vulnerabilities in the global
2023, and in December 2024, the US Commerce Department semiconductor supply chain, disrupting logistics, demand,
further expanded the list of Chinese technology companies and production, while heightening concerns over product
subject to export controls and included many that make availability and rising costs. Our research shows that 49%
equipment used to make computer chips, chipmaking tools, of downstream organizations consider this impact to be
and software. China, as a response, announced that it is ongoing, while 47% had to curtail some product/feature
banning exports to the United States of gallium, germanium, launches due to chip shortages during the pandemic.
antimony and other key high-tech materials with potential
Downstream industries share this uncertainty. Around 59%
military applications.16 Our research indicates that 69% of
of downstream organizations believe that suppliers’ ability
downstream organizations believe that geopolitical tensions
to meet their semiconductor demand is an ongoing concern.
significantly impact the reliability of the semiconductor
Similarly, only around one-quarter (26%) feel that supply is
supply chain.
sufficient. This is particularly prominent among sectors such
Additionally, 65% of downstream organizations consider fab as A&D (14%) and organizations headquartered in Sweden
capacity to have a strong impact, while 52% feel that reliance (10%) and the United States (11%).
on a small number of semiconductor suppliers impacts their
reliance on the semiconductor supply chain.
%
69
Percentage of downstream
organizations that believe
geopolitical tensions impact the
reliability of the semiconductor
supply chain
Capgemini Research Institute 2025
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The semiconductor industry in the AI era
GPU computing and AI/ML
Figure 7.
Fewer than three in ten downstream organizations believe chip supply is sufficient
acceleration are the most
relevant advancements
Downstream industries’ perception of chip supply/demand
for downstream
organizations
59%
Semiconductor technology breakthroughs have spurred
47%
innovation in consumer industries, breeding smarter, more
efficient products. AI/ML acceleration and GPU processing
26%
have the potential to revolutionize downstream operations.
GPUs outperform because they provide high throughput
and parallel processing, streamlining real-time inference and
model training, in particular for AI and ML applications.
Suppliers’ ability to meet During the COVID-19 The semiconductor
Our research suggests that 54% of downstream
our semiconductor demand pandemic, we had to industry is supplying chips
organizations (those that rely on fast data processing and
is an ongoing concern curtail some product/feature at a rate sufficient
AI-powered automation) believe that GPU computing and
for our organization launches due to chip shortages for our needs
AI/ ML accelerations are the most relevant advancements for
them. Alessandro Miranda, Senior Director of Radio Access
Network (RAN) Design and Optimization, at ZTE, explains,
“We need specialized hardware and architectures designed
Source: Capgemini Research Institute, Semiconductor survey, November 2024, N = 800 downstream organizations. to accelerate processing and optimize algorithms. Graphics
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The semiconductor industry in the AI era
processing units (GPUs), for instance, which were originally Figure 8.
developed solely for rendering images and videos in video More than half of downstream organizations believe that advancements in GPU computing and AI/ML acceleration can bring
games, have now become the cornerstone of AI processing most value
due to their ability to handle parallel data processing
efficiently.“
Most relevant semiconductor advancements for downstream industries
Dell Technologies showcases the practical application of
GPU computing and AI/ML acceleration by integrating
NVIDIA's AI-ready GPUs, networking solutions, and tools
like AI Enterprise and Omniverse with its own hardware
GPU computing 54%
and expertise. This collaboration offers communications
service providers (CSPs) the tools needed to efficiently run AI and ML acceleration 54%
AI workloads across networks.17
5G/ next-gen communication technologies 49%
Advancements in GHz/watt 47%
NPU computing 27%
DPU 24%
Chiplets 24%
Wearables 19%
Next-generation memory chips (MRAM, ReRAM) 17%
Source: Capgemini Research Institute, Semiconductor survey, November 2024, N = 800 downstream organizations.
Capgemini Research Institute 2025
2255
The semiconductor industry in the AI era
“We need specialized hardware and architectures designed to accelerate processing and optimize algorithms. Graphics
processing units (GPUs), for instance, which were originally developed solely for rendering images and videos in video
games, have now become the cornerstone of AI processing due to their ability to handle parallel data processing
efficiently.”
Alessandro Miranda
Senior Director of Radio Access Network
(RAN) Design and Optimization, ZTE
Capgemini Research Institute 2025
26
The semiconductor industry in the AI era
Downstream
Figure 9.
Nearly half of downstream organizations are looking for enhanced customization and more comprehensive APIs and SDKs
organizations expect
enhanced customization,
Top ranked technology innovations/improvements desired by downstream organizations
more comprehensive APIs
and SDKs, and stronger
Enhanced customization 47%
security
Availability of more comprehensive APIs, SDKs, kits, boards, etc. 47%
Enhanced security 41%
The semiconductor industry has seen significant
enhancements in recent years, particularly in areas such as Higher computation capacity 36%
customization, security, APIs, software development kits AI support 29%
(SDKs), kits, and boards. These advancements are helping
businesses develop more tailored, secure, and efficient Lower power consumption 27%
products. Our research indicates that 47% of downstream Smaller size 25%
organizations have ranked enhanced customization and
Improved integration 25%
availability of more comprehensive APIs, SDKs, kits, boards,
etc., in the top three innovations that they look forward to in Drive customer experience 21%
the semiconductor industry, while 41% have ranked enhanced
security among the top three.
Companies such as Intel18 design custom ASICs for their
CPUs and GPUs, integrating both digital logic and analog Source: Capgemini Research Institute, Semiconductor survey, November 2024, N = 800 downstream organizations.
components to meet the specific performance and power
requirements of their products.
Capgemini Research Institute 2025
27
The semiconductor industry in the AI era
One in three downstream
Figure 10.
More than three out of ten downstream organizations are either already designing their chips in-house or are considering it
organizations is designing
chips in-house "
230,capgemini,Top-tech-trends-2025_Infographic.pdf,"AI is powering
all the top tech
trends of 2025
In 2025, AI is the biggest
current tech influencer
Top five trends for 2025
Industry by Capgemini experts Investors
executives (VCs and private equity)
Generative AI: From copilots to reasoning AI agents
AI & Gen AI in cybersecurity: New defenses, new threats
AI-driven robotics: Blurring the
Multimodal AI Multimodal AI
line between human and machine
The surge in AI is driving
AI in software engineering HealthTech
nuclear resurgence
New-generation supply chains: Agile, greener, and AI-assisted AI in software engineering
Generative AI: From copilots to
reasoning AI agents
At Capgemini, we believe the use of AI agents – autonomous AI systems
capable of independently handling end-to-end tasks and collaborating as
multi-agent systems – will be one of the biggest tech trends for 2025.
2023 - Early generative AI systems 2024-25 – AI agentic systems
<Task description>
interpretation
AI
Agent
• “One-size-fits-all”
tools
• Can’t handle abstraction
• Prone to hallucination and errors
• Does not scale well
• Not sustainable
Database Statistical Computational Network
• Autonomous task execution
• Improved efficiency
• But security challenges in autonomy
Example: ChatGPT by OpenAI, an AI chatbot for Example: DSO Go by Bayer, an AI agent designed to
personalized assistance, customer support, and combine the strengths of a guided conversation in a
content generation. pre-defined pattern with model-based on generative A.
of executives of investors (VCs) that follow the AI
70% and 85% and data tech domain pick
AI agents
as a top three
impactful trend for 2025.
AI & Gen AI in cybersecurity:
New defenses, new threats
While Gen AI offers transformative potential to enhance security measures, malicious
actors have quickly recognized its capacity for evil, employing it for sophisticated
attacks that target both human vulnerabilities and machine defenses
Industry executives ranked AI & Gen AI in cybersecurity as the top tech trend of 2025
and investors ranked it third overall
Governments across the world are responding to threats with stricter laws:
In August 2024, the Singapore government launched In 2024, the EU promulgated its Cyber Resilience Act
Operational Technology (OT) Cybersecurity Masterplan (CRA), requiring manufacturers to embed enhanced
to enhance the security and resilience industrial control cybersecurity measures across a broad range of everyday
systems and its technologies. hardware and software products.
AI-driven robotics: Blurring the line
between human and machine
LLMs are transforming robotic capabilities and have accelerated the
development of next-gen robotics to handle complex, interconnected tasks,
enhancing operational efficiency, personalizing customer experiences,
and improving decision-making across industries.
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rroobboottiiccss RR&&DD cceenntteerr iinn TTookkyyoo,, JJaappaann..
ooff iinnvveessttoorrss (rVanCkse adnd private equity) ranked
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8899%% AAII--ppoowweerreedd hhuummaannooiidd rroobboott JJeettssoonn TThhoorr
iinn tthhee fifirrsstt hhaallff ooff 22002255..
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iinndduussttrryy aanndd eennggiinneeeerriinngg ddoommaaiinn..
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rroobboott ffoorr hhoouusseehhoolldd cchhoorreess..
The surge in AI is driving
nuclear resurgence
The energy sector is transforming at an unprecedented pace, driven by mounting pressure to respond to the
climate crisis and supported by innovation across sectors, from renewables and biofuels to low-carbon
hydrogen and beyond. Nuclear energy stands out as a focal point for 2025
SMRs and AMRs will lead the way for new nuclear as they are poised for
rapid industrialization
Small Modular Reactors (SMR’s) Advanced modular reactors (AMR’s)
Offer safer, scalable, and Also known as Generation IV
cost-effective alternatives to reactors, use innovative fuels,
traditional reactors, using an coolants, and technology to
established fuel supply chain generate low-carbon electricity,
without needing ultra-heavy and are intrinsically safe,
forging capacity. compact, and portable.
Key activities in this space:
Google announced plans to Meta has announced a planned Amazon has signed agreements
purchase electricity RFI for 1-4 GW of new nuclear. to support the development of
generated using SMRs. nuclear energy projects
New-generation supply chain:
Agile, greener, and AI-assisted
By harnessing cutting-edge technologies such as digital twin and AI-powered algorithms in their supply chains,
businesses can simulate various scenarios to optimize operations for agility and resilience. Sustainable supply
chains and product passports enable transparency and accountability in sourcing and production
General Motors (GM) integrates
sustainability into its supply chain
of CTOs, heads of innovation/CIOs, and through the BrightDrop platform for EV
heads of R&D, engineering, and product logistics, sustainable sourcing practices,
85%
agree that ‘new-generation supply chain’ and advancements in EV technologies.
is among the top three technology
trends for 2025. Pfizer uses AI to optimize its supply chain,
enhance drug development, clinical trials,
and vaccine distribution
By 2030, several groundbreaking trends
are poised to revolutionize our lives.
Programmable Quantum Genome Artificial general
Hyperconnectivity
new materials computing therapy intelligence
New materials Quantum computing This involves AGI can understand, Offers seamless
engineered to uses the unique modifying an learn, and apply combination of
change their properties of tiny individual’s genetic knowledge across a terrestrial and
properties, such as particles to solve material to treat or wide range of tasks non-terrestrial
shape or color, at problems much prevent disease, at a human level, networks to facilitate
molecular assembly faster than classical potentially offering enabling and communication and
level in response to computers can, cures for genetic allowing machines to collaboration on a
external stimuli or helping with tasks disorders and potentially perform global scale,
programming such as encryption, personalized many intellectual enhancing
optimization, and medicine tailored to tasks that a human connectivity and
simulations an individual could do integration across
platforms and devices
Download report Subscribe to our research
the Capgemini Group. Copyright © 2025 Capgemini. All rights reserved."
231,capgemini,INVENT-PoV_GenAI-Mobility_EN.pdf,"Generative AI for
sustainable mobility
Introduction 3
A few reminders about generative AI 4
Our vision of GenAI use cases
in relation to mobility 7
Generative AI for operational efficiency 8
Revolutionizing the traveler experience 10
Interview client 12
Conclusion 14
Our expertise 15
Auteurs & contributeurs 17
2
Introduction
Transport decarbonization, which is essential to the fight against
climate change, will not only require the electrification of vehicles,
but also a substantial shift in traffic towards collective, shared and/or
active mobility. But facilitating and accelerating this modal shift is
not just a question of developing networks or increasing financial
incentives: travelers also need to be attracted by an offer that is
clear, appropriate, practical and economical. From the moment they
choose their route to the moment they arrive at their destination,
their experience must be as personalized and contextualized as
possible, ensuring maximum fluidity before, during and after their
journey. This seamless process is likely to create a positive perception
of the transport modes used, the brand and its offering, and thus a
strong motivation to repeat
the experience and
recommend it.
70%
1 Attracting and retaining new
customers so that they abandon
their private car is one of the
main challenges facing public
transport authorities (PTA) and
operators (PTO) today.
Technological innovations, in
of respondents to a survey of the
particular generative AI, will
Autonomy community of experts
have a decisive role to play in
believe that LLMs will help meeting this challenge. We are
reduce the number of privately convinced that this form of
owned cars and increase modal artificial intelligence,
shift by enabling mobility popularized in 2022 by ChatGPT,
operators to offer more is capable of removing a number
of major obstacles to the
convenient solutions.
accessibility and attractiveness
of sustainable mobility. This is
also the opinion of a majority of
the members of the Autonomy
community, 70% of whom
believe that it will help to reduce the number of privately owned cars and
increase modal shift by enabling mobility operators to offer more
practical solutions (although 33% see it more as a fad than a genuine
lever for change)1.
As suggested by the first use cases that have been implemented or are
still being studied, and as we will see in this document, generative AI will
have a very significant impact on two key areas: operational efficiency
and the traveler experience, with, as a result, significant benefits for
mobility companies, their employees, their customers… and the
environment.
1 Survey addressed to members of the Autonomy community in January 2024.
3
A few reminders about
generative AI
Generative AI, or GenAI, refers to a form of artificial intelligence capable, as its
name suggests, of generating text, images, video, audio, or a mixture of these
media.
It is based on statistical models trained on an extremely large body of data,
including computer code, equivalent to several billion pages of documents, images
and thousands of hours of video or audio. On a basis of a written query (prompt),
GenAI is able to generate original and unique content, which is similar – but not
identical – to the content it has assimilated.
Text / Speech Code Image / Video
Summarising a text/
Autofill code Increasing resolution
a conversation
Modifying/styling
Translating Translating a code
an image
Analysing a corpus/ Optimising an Translating an image
a conversation existing code into a photo
understanding a question/ Documenting an Detecting an anomaly
an instruction existing code in an image/video
Re(writing) according Writing code based Creating a 2D image based
to instructions on instructions on instructions
Describing an Testing an existing code
Transforming 2D into 3D
image/video by units
Answering questions Orchestrating
in a factual manner a workflow
Solving logical/
mathematical problems
Mature Emerging Arising
Figure 1-Maturity of fields of application depending on the nature of the data supplied to GenAI.
4
Generative AI can be applied to any
type of data (text, image, video,
sound, code, etc.) both as input and
output to the query, so the fields of
application are theoretically
innumerable: creating articles,
personalizing content, producing
computer code, generating data sets,
correcting images, 3D animation, and
many more.
Large Language Models (LLMs), a
sub-category of GenAI specializing in
language, are now the most mature,
robust and widely used GenAIs. They
are first trained to predict the next
word in a given sequence of words.
They are then specialized to perform
tasks other than their primary function.
In this way, they excel at understanding
and producing text across a fairly wide
range of applications: classification,
research, synthesis, conversation,
translation, writing, etc.
On the other hand, because of their
probabilistic nature, and despite their
often impressive performance, LLMs
have intrinsic limitations that should
not be overlooked:
• Bias: the model depends on the training
data set from which it draws its
inspiration. It mechanically reproduces
any weaknesses, such as biases,
stereotypes, prejudices, errors,
obsolescence, etc.
• Reliability: as models predict the next
word in a sequence based on the
previous words, they can generate
information that seems logical in
context, but which is not actually true or
accurate. There is therefore never
absolute certainty that the answer is
accurate, relevant or appropriate. This
can even lead to gross factual errors
called hallucinations.
5
To overcome these limitations as
much as possible, it is essential to
implement techniques to control
the model according to the
intended use case, and to set up a
certain number of safeguards:
ensuring the quality of the data;
improving the relevance, accuracy
and way of processing the query
(prompt-engineering); forcing the
model to respond only on the basis
of sources provided and traceable in
the response (context-engineering);
and possibly – although, in practice,
much more complex – adapting the
model (fine-tuning). In all cases,
automated and human controls
need to be put in place before the
solutions are deployed on a large
scale.
All this means that LLM
performance can be significantly
improved for the application in
question. Even so, they will not be
able to perform tasks completely
autonomously, without user
supervision. The user will have to
treat them as an assistant,
constantly keeping a critical eye on
them.
Finally, whatever the project is, we
must not forget to consider the
issues of security, compliance and
environmental footprint that
generative AI and LLMs sometimes
raise so acutely.
6
Our vision of GenAI use cases
in relation to mobility
Operational efficiency Passenger experience
Technical documentation
Situational analysis Customer feedback
analysis
Customer complaint
Documentaion production Conversational tools
management
Personalising and
Knowledge capitalisation
Instant translation contextualising passenger
and transfer
journeys and information
Support functions
Synthesis of financial or Audits and verification of Follow-up of
HR documentation procedures medical visits
Figure 2 - Major GenAI use cases in the mobility sector.
7
Generative AI for
operational efficiency
In the mobility and transport sector, operational efficiency is the sinews of
war. In all sector businesses, improving efficiency in the field means helping to
provide a service that is less costly, more reliable, more responsive and more
resilient in the face of unforeseen events, and that offers travelers a more
satisfactory experience. In this area, generative AI is a powerful lever for
improvement, as numerous use cases already illustrate.
Analysis of technical and legal
documentation
The mobility and transport sector operates and orchestrates equipment of
extremely varied nature, technology and era. It is also a particularly regulated
sector, governed by complex provisions and standards that frequently evolve. As a
result, the technical and legal documentation is abundant, fluctuating and
heterogeneous and needs to be constantly taken into account. Particularly
well-suited to enriched and personalized documentary research, generative AI can
bring considerable time savings and improve accuracy when it comes to obtaining
the right information, so helping you to make the right decision in real time.
Production of reporting
documents
In all professions, employees produce numerous reports to provide information on
their activity, share their experiences and alert others to any difficulties or
incidents they may have encountered. Generative AI can relieve this valuable but
time-consuming task, and improve its quality, by assisting employees with data
entry, pointing out missing information and even, in the future, directly
transcribing voice recordings. It can also facilitate the use of these reports by
making unsuspected comparisons, detecting imperceptible similarities, and
proposing new categorizations. In this way, generative AI will be able to carry out
root cause analyses and suggest ways of resolving problems much more quickly.
8
Situational analysis
The new generative AIs are multimodal, meaning that they can process all kinds of media
simultaneously, text and images for example. They have this fine-tuned ability to describe
images and therefore to depict a context. Integrated into surveillance systems, they can be
used to detect problem situations that require both object or person detection and context
analysis. For example, they can be used to detect high-risk situations (such as illness or
aggression, or crowds of people) and assist a security guard to intervene more quickly. It is
also possible to characterize damage or quality defects, warn of the presence of obstacles
on or near tracks (intruders, vegetation, landslides, etc.), check the cleanliness of premises
and equipment to specify cleaning operations, or certify compliance with safety procedures.
These capabilities could be combined with traditional computer vision-type AI for greater
efficiency and to limit the large energy and environmental impact of generative AI.
Feedback from experience
SNCF Réseau, National Company of the French Railways,
improves document-retrieval for its customers
With the opening of rail traffic to competition, SNCF Réseau will
be approached by a growing number of operators to handle
regulatory technical issues. To provide these customers with a
fast and relevant response, Capgemini has helped SNCF Réseau
to develop the demonstrator of a search assistant based on
generative AI. This solution takes the form of a conversational
agent, whose ergonomics and path have been optimized to offer
users a simple, fluid and personalized experience. Queried in
natural language via this interface, possibly in several languages,
the generative AI engine, owned by Capgemini, draws its
information from a database of technical documents that has
been compiled and qualified in advance. One of the special
features of this model is that it displays the sources that support
the users’ response, which reinforces the users’ confidence and
enables them, if necessary, to deepen, validate or share their
research. Finally, metrics have been put in place to monitor the
model’s performance and ensure that it durably meets the
expectations of SNCF Réseau’s customers.
« The implementation of an initial solution based on generative AI
will enable our sales forces, and soon our customers, to save time,
whether they are railway companies or public transport
authorities. The solution we have developed will enable them to
find all the information they need in the regulatory railway
documentation (the Network Reference Document) in just one
minute. Generative AI opens up new creative possibilities for
simplifying the day-to-day lives of actors involved in the rail
industry. »
Olivia Fischer, Head of Markets, Offer and Customer
Experience at SNCF Réseau
9
Revolutionizing the
traveler experience
Transport may be collective, but the experience is individual. All travelers have
their own itinerary, their own needs, their own control of the offer and the
tools at their disposal. And conditions are constantly changing, so no two
itineraries are ever the same. So how can we offer everyone a satisfactory
experience when no two travelers expect exactly the same information, at the
same time and in the same form? Generative AI can help solve this complex
equation of the traveler experience. In push or pull mode, it can provide each
customer with precisely the information they need, when they need it, and on the
channel that suits them best. In this way, it can help to deliver the personalized,
contextualized and optimized experience that is likely to convince as many
passengers as possible to opt for greener mobility.
Conversational tools
Generative AI makes it possible to set up conversational agents that are much
more advanced than they are today, and capable of communicating in a language
comparable to the language of humans. At a time when the search for information
(a fare, a timetable, an itinerary, a possible connection, etc.) occupies a
predominant place in the customer journey, the possibility of a conversational
interface will be a huge advantage. Without necessarily having to change the
underlying algorithms of existing chatbots, this will enable passengers to easily
express their needs, constraints and criteria, without having to go through a
multitude of screens and filters. The tool will also be able to add personalized
recommendations to the response, depending on the profile (foreign visitor,
person with reduced mobility, cyclist, etc.) and suggestions (directing passengers
towards more sustainable solutions, offering custom subscription packages, etc.).
70% of the members of the Autonomy community believe that conversational
tools will make it easier to take account of the diversity of needs, thereby creating
more inclusive mobility.
70% of respondents to a survey of Autonomy
2
community of experts believe that
conversational tools will make it possible
to take better account of the diversity of
needs and create more inclusive mobility.
2 Survey addressed to members of the Autonomy community in January 2024.
10
Customer feedback
Listening to and taking into account the “voice of the customer” is essential for identifying
the problems encountered, the needs and the expectations of travelers, and therefore
improving the experience on an ongoing basis. Today, however, this is a fairly laborious
process, both for passengers who want to express their opinions and for the staff
responsible for processing them. Generative AI can considerably help both: the travelers, by
enabling them to express themselves in natural language, or even orally, and the teams, by
automatically sorting, categorizing, and qualifying the opinions gathered. AI is capable of
identifying the key points despite the diversity of formulations, and even of detecting irony.
It can then offer an immediate, targeted and personalized response to each individual,
create regular summary reports to measure and monitor customer satisfaction; and finally –
in the longer term – detect and escalate similar and recurring problems.
Instant translation
Thanks to its translation capabilities, generative AI can remove the language barrier which,
for foreign tourists, is often the main obstacle to a positive transport experience. In
anticipation of an influx of visitors of all nationalities at the Paris Olympic and Paralympic
Games, Paris public transport operator RATP and National Company of the French Railways
SNCF are preparing several systems. One of these systems, which is currently being tested,
will make it possible to instantly translate the audio announcements broadcast in stations
into several languages and then to pronounce them using a synthetic voice. Another solution
will provide agents with a specialized instant translation application: the queries, expressed
by passengers in their own language, will first be translated into French for the agent,
who will be able to formulate their response in French before it is in turn translated into
the passenger’s language. This will result in smoother and more efficient exchanges,
for both staff and customers, and will improve the traveler/visitor experience.
1111
Customer interview
Mathilde Villeneuve
Project Director at the RATP
Data Factory
Can you tell us about RATP’s approach to
generative AI?
RATP is taking a pragmatic, value-driven approach by integrating
generative AI into its sustainable mobility strategy. This initiative
explores two major areas: improving the quality of working life and
efficiency of its agents, and developing solutions tailored to its
business units and strategic challenges. Generative AI solutions are
becoming an essential pillar of RATP’s toolbox, aimed at accelerating
the exploitation of data and improving operational efficiency..
Which ones have you identified?
Several priority themes have already been identified, such as increased
control of mobility needs in order to plan transport solutions,
improving operational performance and service rendered to
passengers (e.g. incident analysis, chatbot assisting agents in stations),
and improving the quality of working life for agents (e.g. control of
purchasing processes in the context of public procurement contracts).
RATP is industrializing a first use case: a virtual assistant for station
agents. Station agents are the first point of contact for passengers and
station guards. They need to be particularly versatile in order to
answer all the questions asked by travelers, ensure compliance with rail
safety standards and implement the requirements of public transport
1122
authorities. This system enables agents to be more efficient in carrying
out their daily tasks, such as providing clear and precise information to
passengers on fares, refunds and access procedures. As well as
optimizing work processes and improving service quality, this virtual
assistant saves time for RATP’s 5,500 agents.
How is RATP taking up this solution?
RATP is adopting an approach based on creating value for users by
rapidly industrializing practical applications for its business units. It is
focused on supporting business units in identifying relevant use cases,
and on involving and training users from the earliest stages of
development. This user-oriented approach also means that the risks
and limitations associated with the use of generative AI (impact on
employment, algorithmic biases, organizational changes, etc.) can be
accelerated and taken into account, while keeping the human element
at the center of the loop.
RATP, aware that the democratization of generative AI within a large
company requires an iterative and collaborative approach, is building
its approach by mobilizing all the necessary skills internally and via its
partners (data/AI expertise, cloud provider, etc.). To achieve its
objectives, RATP relies on its data platforms/AI. It is gradually putting in
place a technical basis that will enable it to control and rationalize
practices and solutions, secure the data and company’s know-how, and
guarantee the ethical and reliable use of these technologies
(transparency of algorithms, evaluation of models, governance of
technologies, etc.).
1133
Conclusion
Even as the transport actors imagine and develop
their first applications of generative AI, the
extremely rapid progress of the technology will very
quickly enable them to envisage other use cases,
with an even greater impact. For example, the ability
to bring more energy- and data-efficient language
models to mobile terminals (smartphones, tablets,
etc.) will improve the performance and experience of
applications, for example for train crews or network
maintenance agents. Another expected advance is
that multimodal models, capable of processing text,
images and/or sound at the same time, will open a
vast field of possibilities, enabling, for example, the
creation of composite reports combining photos and
audio commentary.
However, whether for these applications of the future
or those already under development, we must always
bear in mind the limitations of generative AI in general
and LLMs in particular. The purpose of these tools is to
assist, help and accelerate, but not to carry out tasks or
IMAGE make decisions without human approval. The users,
whether agents or travelers, must therefore be made
aware of the fact that they will need to systematically
check the information communicated to them. When
they are implemented, technological solutions must
always be accompanied by a framework for use and
appropriate communication. Despite these precautions,
the gains in terms of time, ergonomics and experience
are usually considerable, at least if the product has been
properly designed and optimized by specialists.
The mobility and transport sector is still in its early
stages in terms of the use of generative AI, but the first
use cases suggest its immense potential for improving
both operational efficiency and the passenger
experience. Progress in these two key areas will help to
make green mobility more pleasant, more accessible,
and more attractive, thereby encouraging modal shift.
Generative AI is emerging as a major instrument in the
transformation towards sustainable mobility, and public
transport operators and authorities alike must seize its
tremendous potential without further delay.
14
Finally, of course, all these
benefits depend on users
adopting the solutions.
For example, there is no
43%
guarantee that customers will
accept this technology, even if it
meets their needs.
Will LLMs
The experts in the Autonomy
community are very divided on replace mobility
the subject: 57% of them think applications?
that travelers will be searching
for their itinerary using an LLM
in the future, compared with
43% who believe that 57%
traditional applications still
have a bright future ahead of
them.
Major educational efforts are
therefore essential if generative
AI is to become the powerful
Yes No
accelerator of sustainable
mobility envisaged in this report. AI will enable us to identify We will still use mobility
the best itinerary by having apps to choose and book
a spoken conversation. an itinerary.
Figure 3 - Survey addressed to members of the Autonomy community in January 2024.
Our expertise
Within Capgemini Invent, our R&I Lab is organized
around research and innovation programs applied to
our customers’ challenges, including AI and mobility,
drawing on expertise and best practices in research
and innovation brought by Quantmetry, an
acquisition by Capgemini Invent back in 2023.
15
Capgemini authors
Mehdi Essaidi,
Vice-President Smart Mobility,
Capgemini Invent
Lucile Ramackers,
Senior Manager Sustainable Mobility,
Capgemini Invent
Toscane Berberian,
Senior Mobility Consultant,
Capgemini Invent
Alexandre Lapene,
Data Science Director,
Generative AI Specialist,
Capgemini Invent
Capgemini sponsor
Alex Marandon,
Vice-President & Global Head of Invent
Generative AI Accelerator,
Capgemini Invent
Capgemini contributors
Philippe Cordier,
Chief Data Scientist and Vice-President of
Artificial Intelligence and Data Engineering,
Capgemini Invent
Farès Goucha,
Rail Industry Director,
Capgemini Invent
Sophie Poulin,
Automotive, Mobility, Transport & Travel
Client Director,
frog part of Capgemini Invent
Hugo Cascarigny,
Vice-President, Data & Analytics Intelligent
Industry,
Capgemini Invent
Autonomy contributor
Ross Douglas,
CEO,
Autonomy Paris
17
About Capgemini Invent
As the digital innovation, design and transformation brand of the Capgemini Group,
Capgemini Invent enables CxOs to envision and shape the future of their businesses.
Located in over 30 studios and more than 60 offices around the world, it comprises
a 12,500+ strong team of strategists, data scientists, product and experience
designers, brand experts and technologists who develop new digital services,
products, experiences and business models for sustainable growth.
Capgemini Invent is an integral part of Capgemini, a global business and technology
transformation partner, helping organizations to accelerate their dual transition to
a digital and sustainable world, while creating tangible impact for enterprises and
society. It is a responsible and diverse group of 340,000 team members in more than
50 countries. With its strong over 55-year heritage, Capgemini is trusted by its
clients to unlock the value of technology to address the entire breadth of their
business needs. It delivers end-to-end services and solutions leveraging strengths
from strategy and design to engineering, all fueled by its market leading capabilities
in AI, cloud and data, combined with its deep industry expertise and partner
ecosystem. The Group reported 2023 global revenues of €22.5 billion.
Get the future you want
Visit us at www.capgemini.com/invent
About Autonomy
Autonomy, based in Paris produces an annual Expo, Global Decarbonization Expo
which is focused on Commercial and Industrial solar, battery storage autonomous
and electric vehicles. In addition to the expo, Autonomy produces bespoke summits
on specific subjects in key cities and content on decarbonizing the economy.
Visit us at www.autonomy.paris
Copyright © 2024 Capgemini. All rights reserved."
232,capgemini,2024_11_19_-Capgemini_AI-and-Generative-AI-in-Cybersecurity-report.pdf,"Press contact:
Florence Lievre
Tel.: +33 1 47 54 50 71
Email: florence.lievre@capgemini.com
AI and Gen AI are set to transform cybersecurity for most organizations
While Gen AI heightens vulnerabilities, more than half of organizations also anticipate faster threat
detection and increased accuracy through its use
Paris, November 19, 2024 – The Capgemini Research Institute’s new report, “New defenses, new
threats: What AI and Gen AI bring to cybersecurity”, published today, suggests that while new
cybersecurity risks are emerging, due to the proliferation of AI and generative AI (Gen AI), these
technologies represent a transformative shift in reinforcing cyber-defense strategies for the long
term to predict, detect, and respond to threats. Two thirds of organizations are now prioritizing
AI within their security operations.
According to the report, while AI is considered by organizations as a strategic technology to strengthen their
security strategies, the increased adoption of Gen AI across various industries1 brings heightened
vulnerability. Gen AI introduces three major risk areas for organizations: more sophisticated attacks with
more adversaries, the expansion of the cyber-attack surface, and a growth in vulnerabilities in the entire
lifecycle of custom Gen AI solutions. These risks are also compounded by the misuse of AI and Gen AI by
employees which can significantly increase the risk of data leakage.
Two in three organizations are wary of increased exposure to threats
Almost all organizations surveyed (97%) say they have encountered breaches or security issues related to
the use of Gen AI in the past year. Gen AI also brings additional risks, including hallucinations, biased,
harmful, or inappropriate content generation, and prompt injection attacks2. Two in three organizations
(67%) are worried about data poisoning and the possible leakage of sensitive data through the training
datasets used to train Gen AI models.
Moreover, Gen AI's ability to generate highly realistic synthetic content is posing additional risks: more than
two in five organizations surveyed (43%) said they have suffered financial losses arising from the use of
deepfakes.
Nearly 6 in 10 believe they need to increase their cybersecurity budget to bolster their defenses
consequently.
AI and Gen AI are paramount for detecting and responding to attacks
Surveying 1,000 organizations3 that have either considered AI for cybersecurity or are already using it, the
report finds that most rely on AI to strengthen their data, application and cloud security due to the
technology’s ability to rapidly analyze vast amounts of data, identify patterns and predict potential breaches.
1 Nearly one-quarter (24%) of organizations have enabled Gen AI capabilities in some or most of their functions and locations - Capgemini
Research Institute, “Harnessing the value of generative AI 2nd edition: Top use cases across sectors,” July 2024.
2 This involves using malicious inputs to manipulate AI and Gen AI models, compromising their integrity.
3 1,000 organizations across 12 sectors and 13 countries in Asia Pacific, Europe, and North America, with annual revenues of $1 billion and
over.
Capgemini Press Release
More than 60% of them reported a reduction of at least 5%, in their time-to-detect, and nearly 40% said
their remediation time fell by 5% or more after implementing AI in their security operations centers (SOCs).
Three in five organizations surveyed (61%) believe AI to be essential to effective threat response, enabling
them to implement proactive security strategies against increasingly sophisticated threat actors. In addition,
the same proportion of respondents foresee Gen AI strengthening proactive defense strategies in the long
term, anticipating faster threat detection. Over half of them believe also that the technology will empower
cybersecurity analysts to concentrate more on strategy for combating complex threats.
“The use of AI and Gen AI has so far proved to be a double-edged sword. While it introduces unprecedented
risks, organizations are increasingly relying on AI for faster and more accurate detection of cyber incidents.
AI and Gen AI provide security teams with powerful new tools to mitigate these incidents and transform
their defense strategies. To ensure they represent a net advantage in the face of evolving threat
sophistication, organizations must maintain and prioritize continuous monitoring of the security landscape,
build the necessary data management infrastructure, frameworks and ethical guidelines for AI adoption, and
establish robust employee training and awareness programs,” said Marco Pereira, Global Head
Cybersecurity, Cloud Infrastructure Services, Capgemini.
Methodology
The Capgemini Research Institute surveyed 1,000 organizations that have either considered AI for
cybersecurity or are already using it, across 12 sectors and 13 countries in Asia Pacific, Europe, and North
America. They have annual revenues of $1 billion and over. The global survey took place in May 2024.
Organizations surveyed represent a diverse range of sectors including automotive; consumer products;
retail; banking; insurance; telecom; energy and utilities; aerospace and defense; high-tech; industrial
equipment manufacturing; pharma and healthcare and public sector.
About Capgemini
Capgemini is a global business and technology transformation partner, helping organizations to accelerate
their dual transition to a digital and sustainable world, while creating tangible impact for enterprises and
society. It is a responsible and diverse group of 340,000 team members in more than 50 countries. With its
strong over 55-year heritage, Capgemini is trusted by its clients to unlock the value of technology to address
the entire breadth of their business needs. It delivers end-to-end services and solutions leveraging strengths
from strategy and design to engineering, all fuelled by its market leading capabilities in AI, cloud and data,
combined with its deep industry expertise and partner ecosystem. The Group reported 2023 global revenues
of €22.5 billion.
Get the Future You Want | www.capgemini.com
About the Capgemini Research Institute
The Capgemini Research Institute is Capgemini’s in-house think-tank on all things digital. The Institute
publishes research on the impact of digital technologies on large traditional businesses. The team draws on
the worldwide network of Capgemini experts and works closely with academic and technology partners. The
Institute has dedicated research centers in India, Singapore, the United Kingdom and the United States. It
was recently ranked #1 in the world for the quality of its research by independent analysts.
Visit us at https://www.capgemini.com/researchinstitute/
Capgemini Press Release"
233,capgemini,17_10_Generative-AI-at-Work-CRI-Report_Group-press-release-2.pdf,"Press contact:
Mollie Mellows
Tel.: + 44 (0) 7342 709384
E-mail: mollie.mellows@capgemini.com
Generative AI expected to accelerate entry-level career progress across
industries
• Employees believe generative AI will facilitate a third (32%) of entry level tasks over the
next 12 months.
• 6 in 10 managers and most employees (71%) expect entry-level roles across functions to
evolve from creation to reviewing generative AI outputs, over the next 3 years.
• Over three-quarters (78%) of leaders and managers predict generative AI will augment
their problem-solving and decision-making in the next three years, and over half think
manager-level positions will evolve towards specialization.
Paris, October 17, 2024 – The Capgemini Research Institute’s new report on generative AI (Gen
AI) in management, ‘Gen AI at work: Shaping the future of organizations’, published today,
suggests that Gen AI could have a positive impact on early-stage careers. In the longer-term,
the report finds that Gen AI has the potential to create new job roles, transform organizational
structures, drive human-AI fusion teams, and make managerial roles more specialist. However,
adoption remains low and nascent. The report finds most employees lack the training they need
to develop Gen AI skills.
Whilst the impact of Gen AI on careers has been hotly debated, this new research finds the majority of
business leaders believe that entry level roles could become more autonomous and evolve into frontline
managerial roles within the next three years. With this in mind, the proportion of managers in teams across
functions could expand from 44% to 53% in the next three years; only 18% of leaders and managers believe
that Gen AI will reduce middle management.
Employees think that, over the next 12 months, generative AI tools could lead to an average time saving of
18% for entry-level workers, implying there could be significant productivity improvements for junior
employees. However, the cost of the Gen AI tool must also be taken into account, cites the report.
Furthermore, 81% of leaders and managers expect new roles such as data curators, AI ethics specialists
and algorithm trainers to emerge at the entry level.
“Generative AI tools are becoming more adept at assisting with complex managerial tasks, which could
challenge the status quo of organizational structure and ways of working,” said Roshan Gya, CEO of
Capgemini Invent and member of the Group Executive Committee. “Generative AI has the potential to shift
from a co-pilot to a co-thinker, capable of strategic collaboration, adding new perspectives and challenging
assumptions. This shift could unlock significant value when tailored to specific business use cases but is
dependent on several factors, including organizations prioritizing building the skills and readiness of
employees, taking proactive steps around talent acquisition and development.”
Potential to redefine management but still a significant gap on actual usage
The report finds that Gen AI is transitioning the view of future leadership and managerial roles toward
becoming more strategic, focusing on decision making and fostering innovation. In fact, many managers
and leaders currently believe that Gen AI tools could act as co-thinkers for them. 65% of the leaders and
Capgemini Press Release
managers surveyed see high potential in Gen AI for complex strategic tasks, and more than half of leaders
believe managers will play a critical role as catalysts of Gen AI-driven change. The technology could also
save leaders and managers up to seven hours each week, with nearly 8 in 10 leaders believing that Gen AI
will positively impact their productivity in the next 12 months.
Gen AI has the potential to amplify the strategic scope of leaderships roles. Currently, managers spend more
than one-third of their time on administrative tasks. However, AI’s ability to automate much of this work
provides opportunities to focus on strategic-planning and problem-solving tasks. In the next three years,
over three-quarters (78%) of leaders and managers expect Gen AI to augment their problem-solving and
decision-making, and over half believe manager-level positions will evolve towards specialization. 57% of
leaders at organizations advanced in their Gen AI implementation already see their roles becoming more
strategic.
While adoption of Gen AI in management has good potential, there is a significant gap between potential
and actual usage. Although 97% of leaders and managers say that they have experimented with Gen AI
tools, only 15% use Gen AI tools at least once a day in their work.
Organizational structures need to transform to enable cohesive human-AI collaboration
For nearly half (46%) of teams, AI is used simply as a tool to enhance existing capabilities and workflows.
However, human-machine partnerships are starting to be embraced. One in three teams are currently using
AI as a ‘team member’, for example by enhancing human performance or using AI agents to complete
predefined tasks without human intervention. According to the research, today AI is used as a supervisor in
only 1% of teams i.e., it is directing, allocating, or prioritizing work for humans. Yet, in the next 12 months,
13% of teams expect to use AI in this role. In an AI-led environment, human judgment is increasingly
important, and the majority of leaders, managers and employees in the research acknowledge this.
Training and managerial guidance required to secure the future of Gen AI at work
Despite the potential of Gen AI to boost productivity across job functions, adoption remains nascent. While
almost two-thirds (64%) of workers already use Gen AI tools for their work, only 20% of employees use
Gen AI tools daily.
Employees also lack proficiency in key skills, with only 16% believing they are getting the support they need
to develop Gen AI skills. Only 13% of employees say they are well-versed in machine conversational skills;
only a third say they can manage Gen AI systemic risks; and less than half claim to have prompt engineering
skills. The report suggests that team members should be equipped with the right AI skills, defining rules and
responsibilities for cohesive human and Gen AI collaboration, ensuring accountability when Gen AI systems
make mistakes, and adapting workflows and processes for the new era of Gen AI.
Report Methodology
Capgemini Research Institute conducted a global quantitative executive survey in May 2024 across 15
different countries and 11 key industries, surveying 1,500 respondents from 500 organizations, with annual
revenue of more than $1 billion. Each unique organization is represented by three executives, one each at
leadership level, middle-management level, and front-line management level (the three respondents can be
from different functions or locations). The report is also based on an entry-level employee survey to take
their perspective on Gen AI adoption by their managers and leaders. The survey targeted 1,000 entry-level
employees from the same 500 organizations as in the executive survey. Hence, overall, each organization,
irrespective of location or function, is represented by five respondents – three executive-level (leaders and
managers) and two entry-level employees. In addition to these executive and entry-level employee surveys,
the report also draws on 15 in-depth interviews with independent experts from various industries across the
globe to validate and substantiate findings. Please note, the study findings reflect the views of the
respondents and are aimed at providing directional guidance.
Capgemini Press Release
About Capgemini
Capgemini is a global business and technology transformation partner, helping organizations to accelerate
their dual transition to a digital and sustainable world, while creating tangible impact for enterprises and
society. It is a responsible and diverse group of 340,000 team members in more than 50 countries. With its
strong over 55-year heritage, Capgemini is trusted by its clients to unlock the value of technology to address
the entire breadth of their business needs. It delivers end-to-end services and solutions leveraging strengths
from strategy and design to engineering, all fueled by its market leading capabilities in AI, cloud and data,
combined with its deep industry expertise and partner ecosystem. The Group reported 2023 global revenues
of €22.5 billion.
Get The Future You Want | www.capgemini.com
About the Capgemini Research Institute
The Capgemini Research Institute is Capgemini’s in-house think-tank on all things digital. The Institute
publishes research on the impact of digital technologies on large traditional businesses. The team draws on
the worldwide network of Capgemini experts and works closely with academic and technology partners. The
Institute has dedicated research centers in India, Singapore, the United Kingdom and the United States. It
was recently ranked #1 in the world for the quality of its research by independent analysts.
Visit us at https://www.capgemini.com/researchinstitute/
Capgemini Press Release"
234,capgemini,Final-Web-Version-Report-Sustainable-Gen-AI-2.pdf,"Developing sustainable Gen AI
Developing
sustainable Gen AI
#GetTheFutureYouWant
2
Developing sustainable Gen AI
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Generative AI has a significant environmental impact: perform real-time functions, requires an equal or greater
Generative AI (Gen AI) relies on processing huge volumes of amount of energy. Data centers also consume a huge amount
data, which necessitates enormous computational power, of energy and water for cooling purposes. Running an
rendering it an energy-intensive technology. The production inference of 20–50 queries on an LLM uses about 500 ml of
of graphics processing units (GPUs), integral to the water each time.
functioning of Gen AI, requires rare earth metals, the mining
Gen AI has contributed to increased GHG emissions:
of which contributes to greenhouse gas (GHG) emissions.
In our research, we surveyed executives from 2,000
Additionally, the frequent hardware upgrades that Gen AI
organizations that have Gen AI initiatives underway.
requires put a great deal of stress on natural resources, as
well as further polluting the environment. Recent estimates • 48% of executives believe that their use of Gen AI has
suggest that, Gen AI could create between 1.2 to 5.0 million driven a rise in GHG emissions.
metric tons of e-waste by 2030, which is around 1,000 times
• Organizations that currently measure the environmental
more e-waste than was produced in 2023.1
impact of their use of Gen AI project the share of Gen
Estimates suggest that training a GPT-3 model (which AI-driven emissions as a proportion of total organizational
includes 175 billion parameters) consumes an amount of carbon emissions to rise, on average, from 2.6% to 4.8%
electricity equivalent to the annual consumption of 130 over the next two years.
US homes.2 Moving to the next model size up, GPT-4 (with
• 42% of executives have had to relook at their climate goals
1.76 trillion parameters), power consumption of training is
due to Gen AI’s growing footprint.
estimated to be equivalent to yearly power consumption of
5,000 US homes.3 After training, which is a one-time event in
the model’s lifecycle, the inferencing phase, where models
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Developing sustainable Gen AI
However, many organizations continue to ignore Gen AI’s How to create responsible Gen AI for sustainable
growing carbon footprint: business value?
Just 12% of the executives in our research confirm that their Gen AI has the potential to accelerate sustainable business
organizations measure Gen AI footprint. Furthermore, just priorities, control costs, and drive growth. However,
20% rank “environmental footprint of Gen AI” among the organizations must weigh these benefits against the
top five factors when selecting or building Gen AI models, technology’s environmental costs. We suggest the following
with performance, scalability, and cost dominating their approach to make informed, responsible business decisions:
consideration process. Only 27% of executives say they
• Identify the right technology that addresses your
compare energy consumption levels of Gen AI models.
business needs: It is important to note that Gen AI is
Organizations are currently taking only a partial view of costs,
just one element of the broader tech landscape, and
effectively ignoring the energy costs of model deployment
solving most business problems requires a combination of
and inferencing. As Gen AI models become more complex
different techniques. “Hybrid intelligence” – a convergence
and pervasive, careful management of both financial and
of traditional AI, Gen AI, and technologies such as
environmental costs will be crucial to scalability.
automation, robotic process automation (RPA), etc. –
Organizations are just beginning to incorporate
can unlock new levels of ingenuity and efficiency. Nearly
sustainability measures into the Gen AI lifecycle, with 31%
three-quarters (74%) of executives believe that choosing
of executives saying their organization has taken steps
the appropriate technology (be it AI, Gen AI, analytics, or a
to this end. As many as 74% of executives find measuring
combination of different technologies) that addresses your
Gen AI’s environmental impact challenging due to limited
business needs is crucial to reducing the environmental
transparency from hyperscalers and model providers. They
footprint of Gen AI and harnessing its full potential.
expect the tech sector to lead efforts to normalize and
streamline measurement and transparent reporting of the
environmental impact of Gen AI.
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• Assess and mitigate Gen AI's environmental impact: – Monitor and report your Gen AI footprint: Analysis
and accurate measurement, monitoring, and tracking
are paramount. Organizations should also communicate
– Build the business case for sustainable Gen
their sustainability intentions clearly to stakeholders,
AI: In addition to being more responsible from an
disclosing emissions levels, detailing progress
environmental perspective, prioritizing sustainable Gen
transparently, and setting definite goals. Of those
AI initiatives offers significant business advantages,
measuring the environmental footprint of Gen AI (12%
notably cost reductions and acceleration of work.
of our survey sample), only 28% disclose it, and only 24%
It is important to showcase the business case to
have set targets to reduce it.
top leadership and take into account the incurred
environmental cost. – Implement sustainable practices throughout Gen
AI’s lifecycle:
– Evaluate Gen AI partners and models on
sustainability parameters: More than half (55%) of 1. Hardware-related measures: Prioritize partners
executives believe that including sustainability as a with more energy-efficient and recyclable hardware
key criterion in vendor selection for all Gen AI-related specifically designed for AI/Gen AI.
requirements would reduce environmental footprint. It
2. Model architecture and algorithm-related
is crucial that organizations select the most appropriate
measures: Use smaller, task-specific pre-trained
model. For example, when comparing large and small
models. Consider optimizing model size through
models, organizations must decide on the optimum
techniques such as model compression, pruning,
balance between performance and energy consumption.
quantization, and knowledge distillation to
Decisions on using prebuilt or custom models also
significantly lower cost and energy consumption.
impact computational power and carbon footprint, with
the former demanding greater resources.
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Developing sustainable Gen AI
3. Sustainable-infrastructure measures: Consider them at the pilot stage currently. An additional 37% say
providers which use low-carbon energy sources such they have started to explore its potential for sustainability.
as solar, wind, geothermal, and nuclear to power Moreover, two in three (66%) executives say they expect a
AI/Gen AI infrastructure and data centers. Select reduction of more than 10% in GHG emissions in the next
cloud providers that utilize energy-efficient green 3–5 years as an output of Gen AI-led sustainable business
data centers. Choose a region for your server that initiatives. However, this assumption needs to be taken
ensures smaller environmental impact. Additionally, with caution, given the limited number of organizations
consider utilizing edge computing devices to which measure the environmental footprint of their Gen
reduce data transfer and distribute associated AI use.
energy usage.
When it comes to using Gen AI to accelerate progress
4. Sustainable usage: Tracking and quantifying toward business and sustainability goals, it is key to
the carbon footprint of Gen AI applications is identify suitable use cases. Organizations should
critical to eliminating unnecessary usage. Consider carefully identify and prioritize the most appropriate
implementing batch processing and prompt sustainability use cases for Gen AI based on financial and
optimization techniques such as prompt caching or environmental costs and the expected sustainability and
concise chain of thought (CCOT) to ensure efficient business benefits.
processing.
We looked at more than 100 use cases across functions
and sectors and assessed them across two dimensions:
• Tap into Gen AI's potential by investing in the right use the complexity of implementation and potential to create
cases to accelerate sustainable business value: One- a sustainable business impact. A few quick-wins emerged
third (33%) of executives say they have already started from our analysis, including ESG reporting, sustainable
using Gen AI for sustainability initiatives – with half of product design, life cycle assessment (LCA), supplier
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sustainability reporting, virtual assistance, sustainable IT • Govern for sustainable Gen AI: Implementing a
governance, and ESG scenario planning among others. governance model for safe, transparent, sustainable, and
These use cases need to be weighed on environmental ethical usage is also imperative. Most executives (62%)
cost (energy, water, carbon) of using Gen AI and the believe robust guardrails and governance can effectively
business and environmental benefits the technology mitigate Gen AI’s environmental impact. Nearly half
offers. Our analysis also reveals the potential of Gen AI (49%) also rank the lack of clear governance models
to accelerate progress on UN Sustainable Development among the top five challenges of implementing Gen
Goals (SDGs). It should be noted that in many cases AI for sustainability. Partner with technology partners,
organizations plan to deploy a combination of startups, research institutions, sustainability experts, and
technologies to achieve a more comprehensive approach governments to share best practices, develop sustainable
to problem solving and innovation. Gen AI standards, and harness Gen AI to accelerate
sustainable business goals.
• Develop the right data and technology foundations:
Only 37% of executives claim their organization has the
right data-management tools and technologies for Gen
%
AI, and only one-third (33%) evaluate and monitor data 42
quality for Gen AI. Building the right data foundations
and developing the required skillsets are the keys to
deriving maximum benefits from Gen AI. Organizations
can also evaluate the potential of AI agents to create
sustainable business value in ESG reporting and of executives have had to relook at their
compliance-related areas. climate goals due to Gen AI’s growing
environmental footprint.
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Who should read this report and why?
Who? Why?
This report should speak to technology and This report explores the environmental impact, as annual revenue above $1 billion and are already
business leaders across functions, but data, well as the potential of Gen AI to drive sustainable working on Gen AI initiatives. These organizations
digital, and sustainability leaders will find it business value. We analyze organizational awareness are based in 15 countries: US, Canada, Brazil, UK,
particularly helpful. and priorities and look at potential use cases. We France, Germany, Italy, Spain, the Netherlands,
recommend a series of actions for organizations Norway, Sweden, India, Australia, Singapore and
Gen AI models are resource-hungry – with a
to minimize Gen AI’s environmental impact and Japan. The survey spans 12 key industries and
substantial carbon, energy, water, and material
maximize its sustainability potential, while managing sectors: aerospace and defense, automotive,
footprint. It is imperative that, as the technology
cost and performance, and sustaining the impetus of banking and capital markets, consumer products,
develops, it remains within the guardrails of
technological change. energy and utilities, insurance, life sciences,
environmental sustainability.
manufacturing, public sector/government, retail,
The report presents a detailed five-step approach
telecom, and high tech. The report also includes
to developing a sustainable Gen AI. It draws on
qualitative findings from industry leaders.
comprehensive research building on our internal
expertise and a survey of 2,000 senior executives
(director level and above) at organizations that have
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01
Gen AI has a
significant
environmental impact
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Generative AI (Gen AI) has rapidly gained traction in the business and consumer world. Our Throughout its lifecycle,
recent report on consumer trends highlights that 58% of consumers in 2024 have replaced
traditional search engines by Gen AI tools for product recommendations – up from 25% in Gen AI has a considerable
2023.4 In the business world as well, Gen AI can replicate – and, in some respects, surpass –
human thought processes, synthesizing tailored content with far-reaching implications for environmental impact
driving innovation, enhancing customer experience (CX), streamlining operational efficiency,
and boosting growth. Our recent research reveals that organizations recognize the vast
potential of Gen AI: 80% have increased investment in the technology in the past 12 months. From manufacturing (encompassing materials and hardware
Moreover, while only 6% had integrated Gen AI across their business functions and locations as impact), model training, and usage (including data centers’
of the end of 2023, that figure had risen to 24% as of October 2024.5 energy, water, and carbon impact) to end-of-life (e-waste),
Gen AI consumes vast quantities of resources and leaves
However, these valuable advantages come at a cost that goes beyond the monetary. It is
notable financial and environmental footprints. Figure 1
important to recognize and address the energy consumption, carbon footprint, water usage,
highlights Gen AI's environmental impact throughout its
and e-waste entailed in the implementation of Gen AI throughout its lifecycle.
lifecycle, based on secondary sources.
%
80
of the organizations have
increased investment in Gen AI
in the past 12 months.
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Developing sustainable Gen AI
Figure 1.
Gen AI’s environmental impact across its lifecycle
Gen AI’s environmental impact across its lifecycle
Manufacturing Model pre-training Model fine-tuning Model reinforcement Model inferencing and End-of-life
carbon footprint footprint footprint learning footprint usage footprint footprint
Materials and hardware Software development and optimization Ongoing usage e-waste
• Around half of the GHG • Training a model of size GPT-4 (1.76 trillion parameters) consumes • In 2022, 60% of Google’s ML energy went to inference, with • Gen AI could create
emissions from producing between 51,772 and 62,319 MWh of electricity – enough to the remaining 40% to training between 1.2 to 5.0 million
the graphics cards power at least 5,000 US homes for a year • Just a single query on ChatGPT consumes almost ten times metric tons of e-waste by
required for Gen AI the energy a Google search requires 2030, which is around
operations come from the 1,000 times more e-waste
• Running an inference of 20-50 queries on an LLM uses about
mining of rare earth than was produced in 2023
500 ml of water
elements
• The International Energy Agency (IEA) forecasts global electricity demand for data centers to more than double, from 460 TWh in 2022 to
1,000 TWh in 2026 (roughly equivalent to the electricity demand of Japan)
• Water consumption at IT infrastructure facilities in Virginia’s ‘data center alley’ in 2023 increased by 69% from 2019 levels
Source: Capgemini Research Institute analysis, Harvard Business Review, ""How to make generative AI greener,"" July 2023, IEA, Electricity 2024: Analysis and forecast to 2026, January 2024, Financial
Times, “US tech groups’ water consumption soars in ‘data centre alley’,” August 2024, Vox, “AI already uses as much energy as a small country. It’s only the beginning,” March 2024, OECD, ""How much
water does AI consume? The public deserves to know,"" November 2023, ARXIV, “The carbon footprint of machine learning training will plateau, then shrink,” April 2022, Frontline Magazine, ""E-waste
from AI computers could ‘escalate beyond control’: study,“ October 2024.
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Gen AI’s hardware requirements put a Gen AI models are energy-hungry where the model is deployed. This demands as much or more
energy than the training phase, with the energy requirement
strain on natural resources and habitats
Srini Koushik, President of AI, Technology, and Sustainability for inferencing expected to increase exponentially as higher
Gen AI is consuming significant amounts of energy and at Rackspace Technology, a US-based multi-cloud solution numbers of people become regular users of Gen AI. As per
resources in data centers around the world. For example, provider across apps, data, and security, says: “As it exists estimates, 60% of Google’s machine learning (ML) energy
it uses thousands of graphics processing units (GPUs). GPU today, AI and sustainability take you in opposite directions. use in 2022 went to inference, with the remaining 40% to
chips require 10–15 times more power to operate than a AI consumes a lot of power, whether it’s training large training.14 According to the International Energy Agency (IEA),
traditional central processing unit (CPU).6 (We note, however, language models (LLMs) or running inference. And this just a single query on ChatGPT consumes 2.9 watt-hours of
that this usage could be at least partially offset by the greater power consumption is growing exponentially.” 11 Larger electricity – almost ten times what a Google search requires.
energy efficiency of GPUs due to their ability to perform models (that include more parameters and therefore require If we assume around 9 billion daily searches (the estimated
many more calculations simultaneously.) 7 more training data) generally consume more energy and total daily searches conducted on Google), running these
generate more carbon in the training process: searches on ChatGPT would require an additional 10 terawatt-
GPU chips and the other hardware that Gen AI requires hours of electricity annually.15 This is equivalent to the annual
• Estimates suggest that training a GPT-3 model with
are often made from copper, cobalt, tungsten, lithium, electricity consumption of 1.5 million EU citizens.16
175 billion parameters consumes nearly 1,300 MWh of
germanium, palladium, lead, chromium, cadmium, mercury,
and other earth metals. Around half of the greenhouse gas electricity, roughly the same amount of power consumed Reinforcement learning methods, such as reinforcement
(GHG) emissions in the production of the graphics cards by 130 US homes in a year.12 learning from human feedback (RLHF) and reinforcement
required for the operation of Gen AI comes from the mining • Moving to the next model size up, GPT-4, with 1.76 trillion learning from AI feedback (RLAIF), also leave a considerable
of earth metals.8 parameters, power consumption of training is estimated at environmental footprint.
between 51,772 and 62,319 MWh – enough to power 5,000
Gen AI typically requires a wider array of hardware than other
US homes for a year (at a conservative estimate).13
types of computing and cycles through that hardware at a
faster rate, requiring more frequent replacement.9 These Following the training process (a one-time event in the
shorter use phases will naturally accelerate the harmful model’s lifecycle) is the inferencing phase – essentially
effects on habitats and more rapidly deplete resources.10
Capgemini Research Institute 2025
14
Developing sustainable Gen AI
Gen AI training and inferencing drive up Figure 2.
energy requirements in data centers Global electricity demand is on a steep upward curve
Global electricity demand is on a steep upward curve
IEA forecasts global electricity demand for data centers to
more than double, from 460 terawatt-hours in 2022 to 1,000
terawatt-hours in 2026 (roughly equivalent to the electricity
demand of Japan), driven primarily by AI (see figure 2).17 1 200
Goldman Sachs estimates that the share of power demand
of data centers will form 3–4% of the global power demand
by 2030.18
This surging electricity demand for AI workloads in data
centers is already impacting the GHG emissions levels of
hyperscalers. Microsoft reported a 31% increase in Scope
3 emissions since 2020, primarily due to the expansion of
data centers.19 Google also reported a 48% increase in GHG
emissions from 2019 levels, owing to rising data-center
energy consumption and supply chain emissions.20 To try to
sate the ever-growing energy demand of their data centers,
organizations are planning to focus on nuclear energy
projects.21 Over the last year, Google Cloud,22 AWS,23 and
Microsoft24 announced plans to use small modular reactors
(SMRs) to power their data centers.
Note: Includes traditional data centers, dedicated AI data centers, and cryptocurrency consumption; excludes demand
from data-transmission networks.
Source: IEA.
Capgemini Research Institute 2025
hWT
Projected electricity demand – data center, AI, and cryptocurrencies
1 000
800
600
400
200
0
2019 2020 2021 2022 2023 2024 2025 2026
Low case Base case High case
15
Developing sustainable Gen AI
Gen AI models are also water-thirsty Widespread Gen AI adoption will see
e-waste levels shoot up
Another consequence of the growth of data center
operations is the huge increase in fresh, clean water Mark Kidd, EVP and General Manager at Iron Mountain
demand to prevent overheating. Water consumption at IT
Data Centers, says: “E-waste is one of the fastest-growing
infrastructure facilities in Virginia’s “data center alley” in
waste streams in the world. Annual e-waste production
2023 increased by 69% from 2019.25 Running an inference of
is on track to reach a staggering 75 million metric tons
20–50 queries on an LLM such as GPT-3 uses about 500 ml
by 2030. Just 17% of global e-waste is documented to be
of water each time.26 Our previous research shows that, as
collected and properly recycled each year.” 32
of 2023, around 11% of consumers had replaced traditional
search engines with Gen AI tools27– a trend that we expect to As the use of Gen AI becomes more widespread and
grow. If GPT-3 took over all 9 billion daily Google searches,28 profound, the e-waste challenge and the cost associated
it would require 4.5 billion liters of water daily to cool the with it will grow correspondingly. The limited lifespan
ensuing data-center operations. This anticipated daily water of Gen AI hardware will fuel this issue. Some estimates
requirement looks quite substantial, considering almost suggest that Gen AI could create between 1.2 to 5.0 million
half the world’s population may face severe water stress as metric tons of e-waste by 2030, which is around 1,000
soon as 2030.29 With Gen AI model training and inferencing times more e-waste than was produced in 2023.33 E-waste
pushing up the focus on nuclear energy, the amount of water largely ends up in landfills, where harmful chemicals
required for cooling nuclear power plants should also be such as mercury, lead, bromine, and arsenic leach out
taken into consideration. Moreover, estimates suggest that from the electronics, polluting soil and consequently
the production of a single microchip, which is extensively endangering the health of wildlife, livestock, and people
used within the Gen AI landscape, requires approximately in the surrounding area. The adoption of circular economy
2,200 gallons (8,328 liters) of ultra-pure water (UPW).30 measures among hardware manufacturers and data center
Approximately 10 million gallons (39 million liters) of ultra- operators is imperative to tackle this.
pure water is used per day by an average chip manufacturing
facility, which is equivalent to water used by 33,000 US
households every day.31
Capgemini Research Institute 2025
16
Developing sustainable Gen AI
Gen AI is one of the Figure 3.
Half of executives agree that Gen AI has increased their organization’s GHG emissions
reasons for the rise in
Half of executives agree that Gen AI has significantly increased their
organization’s GHG emissions
GHG emissions in nearly
Percentage of executives who agree with the statement:
half of organizations
""Gen AI has increased the GHG emissions of our organization,""by sector
In our research, we surveyed executives from 2,000
organizations that are already working on Gen AI initiatives.
A en m via rojo nr mity e n(7 t2 a% l im) o pf a t ch te o e f x Ge ec nu t Aiv I e iss ha ig gr he ee r t th ha at n t th re a ditional 48% 53% 52% 51% 50% 50% 48% 47% 46% 46% 45% 44% 42%
AI models. Nearly half (47%) say their organization’s GHG
emissions have increased in the past 12 months by nearly
6% on average. Executives believe that Gen AI is one of the
reasons for this rise in emissions. As figure 3 shows, a similar
proportion of executives say Gen AI has driven a rise in their
organization’s overall GHG emissions. Global Life sciences Manufacturing Aerospace Telecom Public sector Automotive
and defense
High tech Energy and utilities Retail Consumer products Insurance Banking and
capital markets
Source: Capgemini Research Institute, Gen AI and Sustainability survey, August-September 2024, N = 2,000 executives from
organizations that are working on Gen AI initiatives.
Capgemini Research Institute 2025
17
Developing sustainable Gen AI
Among the organizations that measure the environmental Figure 4.
impact of Gen AI, more than half (51%) say Gen AI is one
Nearly half of executives from advanced organizations say they have had to relook at
of the reasons for the rise of GHG emissions of their
Nearlyt hhaelifr osuf setxaeincaubtiilviteys cforommm aitdmvaennctes dd uoerg taon Gizeant iAonIs say they are reassessing
organizations. They also expect the technology’s emissions as
sustainability commitments due to Gen AI
a proportion of carbon emissions from internal operations to
increase from 2.6% to 4.8% in the next two years.
Percentage of executives who agree with the statement:
""With Gen AI, we have had to relook at our original sustainability commitments/goals,""
Consequently, organizations that have already started
by stage of Gen AI implementation
working with Gen AI are re-evaluating their climate goals;
47%
42% of executives in our research agree. Within organizations 42% 44%
40%
advanced in Gen AI implementation (those implementing
Gen AI across most functions/locations), nearly half (47%)
have had to relook at their sustainability commitments (see
figure 4). Google’s Chief Sustainability Officer, Kate Brandt,
explained in a press interview: “Reaching the net zero goal
by 2030 is extremely ambitious. It will require us to navigate
a lot of uncertainty, including around the future of AI’s
environmental impacts.” 34 All organizations Organizations that have enabled Gen AI
capabilities in some of their functions/locations
Organizations that have begun Organizations that have enabled
%
48 working on some pilots of Gen AI Gen AI capabilities in most/all of
their functions/locations
Source: Capgemini Research Institute, Gen AI and Sustainability survey, August-September 2024, N = 2,000 executives
of the executives say that Gen AI is from organizations that are working on Gen AI initiatives, N = 1,236 executives from organizations working on Gen AI
one of the reasons for rise in GHG pilots, N = 636 executives from organizations enabling Gen AI capabilities in some of their functions/locations and N =
128 executives from organizations enabling Gen AI capabilities in most of their functions/locations.
emissions of their organization.
Capgemini Research Institute 2025
18
Developing sustainable Gen AI
02
The sustainability
of Gen AI remains a
low priority
Capgemini Research Institute 2025
19
Developing sustainable Gen AI
Most organizations
of executives say a lack of
don't measure the 74%
transparency from Gen AI providers
makes measurement challenging
impact of Gen AI
Our research confirms that only a few executives are
currently aware of the extent of the overall environmental
64% cite the complexity of tracking energy
impact of Gen AI. For example, only 28% of executives in
consumption across various applications “First, you must
our research were aware that, on average, a Gen AI query
requires nearly 10 times as much electricity to process as a identify the impact
Google search. Only 31% were aware that training an LLM
so you can track and
at a US-based data center consumes around 700,000 liters
of fresh water. Moreover, only 38% claim to be aware of the
reduce it.”
environmental impact of Gen AI they use. 58% say driving efficiency is more important
than measuring impact
Mauli Tikkiwal, a board member at UK-based Orchard Hill
College and Academy Trust, says: “First, you must identify
the impact so you can track and reduce it.” However, only
one in ten executives (12%) says that their organization
actively measures their Gen AI footprint, while a majority
Mauli Tikkiwal
(82%) plan to start in the next 12–24 months (see figure 5).
They give a range of reasons for this omission: Board member at UK-based
Orchard Hill College and
Academy Trust
Capgemini Research Institute 2025
20
Developing sustainable Gen AI
Figure 5.
OOOnnlynl y1ly2 1% 12 2o%f% oo rogf fao norizgragatianonnizisza mateitoiaosnunsrs em mtheeea aesnsuvurireroe nt htmheee ne tenanlv ivmiriporoancnmt moefe nGnteatnal A li Imimppaacct to of fG Geenn A AII
PPeercrecenntataggee o of fe exexecucutitvieves ss asayiyningg PPeercrecenntataggee o of fe exexecucutitvieves sc ictiitningg t hthee b beeloloww r ereaasosonns sf ofor rn noot t
ththeeiri ro orgrgaanniziazatitoionn m meeaasusureres st htheeiri r mmeeaasusurirningg G Geenn A AI fI ofoootptprirnintt
GGeenn A AI fI ofoootptprirnintt
LaLcakc ko of ft rtarnanspsparaerenncyc yf rforomm h hypypeersrcsaclaelersr/sG/Geenn A AI I
77%% 1122%% mmooddeel pl prorovivdideersr sin in d disicslcolosisningg t hthee e ennvivrioronnmmeenntatla l 7744%%
NNoo a nandd n noot t YeYess
fofoootptprirnint to of fm mooddeelsls
pplalnannniningg t oto
stsatratr t
LaLcakc ko of fa wawaraerenneesss sin in le leadadeersrhshipip t eteamam o of fs usustsatianinababiliitliyt y 6688%%
mmeeasausurirningg
imimppacatc to of fG Geenn A AII
4433%% 3399%% ToTooo c ocommpplelex xt oto m meeasausurere
6644%%
NNoo, p, plalnannniningg t oto NNoo, p, plalnannniningg t oto OOrgrgananiziaztaitoionnala pl priroiortritziaztaitoionn o of fd drirviivningg
5588%%
mmeeasausurere in in t hthee mmeeasausurere in in t hthee eeffifficiceiennciceies st hthrorouugghh G Geenn A AI rI artahtheer rt hthanan it ist s
nneextx t2 244 m moonnththss nneextx t1 122 m moonnththss eennvivrioronnmmeenntatla ilm imppacatct
Source: Capgemini Research Institute, Gen AI and Sustainability Source: Capgemini Research Institute, Gen AI and Sustainability survey, August-September 2024, N = 1,767 executives
survey, August-September 2024, N = 2,000 executives from from organizations that are currently not measuring their Gen AI footprint.
organizations that are working on Gen AI initiatives.
Capgemini Research Institute 2025
21
Developing sustainable Gen AI
Organizations look to the tech sector
to drive sustainable Gen AI
A majority (78%) of the executives in our research Organizations also expect the tech sector to
%
say their organization is using pre-trained Gen AI develop innovative mitigation measures. Eszter 74
models, and only 4% have built their own models Haberl, Sustainability Business Strategy Director
from scratch. Among those using pretrained at India-based auto component manufacturing
models, 63% contract them as a service through company, Motherson Group, says: ""The
hyperscale cloud providers, giving rise to a landscape of interaction between generative AI,
reliance on tech providers for measurement and sustainability, and ESG goals is very complex, of executives cite lack of
tracking. However, as figure 5, above, highlights, particularly in manufacturing. While generative transparency in disclosure and
nearly three-quarters (74%) of executives cite AI offers transformative potential for optimizing reporting of Gen AI's environmental
lack of transparency in disclosure and reporting produ"
235,capgemini,Safeguarding-Europe-s-security-in-the-age-of-AI_Final_digital-version.pdf,"Software is eating Defense
Safeguarding
Europe’s Security
in the age of AI
The world is undergoing a
profound geopolitical and
technological transformation.
Artificial Intelligence has
changed defense, and post-
quantum cryptography will be
essential to protecting its future.
This report provides leaders
with a roadmap to navigate this
critical juncture and harness
the potential of technology to
safeguard Europe’s strategic
autonomy and resilience.
Published on the occasion of the Munich
Security Conference 2025
Disclaimer: This report is not an official
publication of the Munich Security Conference
(MSC). The contents of this paper do not purport
to reflect the opinions or views of the MSC and
is meant to provide input to and stimulate the
debate at the MSC.
2 Safeguarding Europe’s Security in the age of AI
Table of contents
FOREWORD........................................................................................4
EXECUTIVE SUMMARY......................................................................5
SUMMARY OF RECOMMENDATIONS...............................................6
EUROPE AT A TECHNOLOGICAL CROSSROADS..............................7
INTRODUCTION.................................................................................8
SECURING AI TO SECURE EUROPE..................................................10
NATO’S VIEW ON ARTIFICIAL INTELLIGENCE ................................17
GLOBAL TRENDS IN AI R&D.............................................................18
SHAPING SECURITY FOR THE QUANTUM AGE..............................20
SECURITY IN THE QUANTUM AGE..................................................21
NATO’S VIEW ON QUANTUM TECHNOLOGIES..............................25
GLOBAL TRENDS IN QUANTUM AND POST-QUANTUM
CRYPTOGRAPHY R&D.....................................................................26
AI AND QUANTUM TECHNOLOGIES AT THE SERVICE
OF DEMOCRATIC STABILITY AND SECURITY.................................28
STRATEGIC RECOMMENDATIONS..................................................29
CONCLUSION...................................................................................35
CONTRIBUTORS..............................................................................36
BIBLIOGRAPHY................................................................................41
END NOTES......................................................................................45
3 Safeguarding Europe’s Security in the age of AI
Foreword
Andreas Conradi,
Head of Defense Europe
& Executive Vice President, Capgemini
National security and defense have never been more Securing the future of our democracies requires proactive
critical in the face of unpredictable, rapidly evolving steps, as conflict will persist, adversaries will mobilize,
threats. And it is the role of cutting-edge technologies, and AI and QC will continue to evolve. To best prepare for
seamlessly integrated across every aspect of defense, that future challenges, we can call on the strategic, innovative,
is enabling the industry to address emerging challenges and technological excellence available in the industry to:
with unmatched agility and precision.
Accelerate innovation and embrace
The concept of ‘software eats defense’ underscores the
transformative technologies like AI and QC
growing importance of software and digital solutions as
strategic drivers of innovation. Governments and armed Strengthen technology sovereignty to create
forces now face not only traditional weaponry but also the the right conditions for technology to thrive
weaponization of technology—tools readily exploited by
adversaries and increasingly embedded within domestic Enhance trust and interoperability by
security infrastructures. Staying ahead and securing maintaining the highest security standards
the future calls for a forward-looking vision where
digital solutions are central to strategic advancements, Act with urgency to ensure readiness for the
enhancing capabilities, resilience, and agility in a rapidly future state of conflict
evolving landscape.
Nowhere is this more evident than in the emergence of
artificial intelligence (AI) and quantum computing (QC).
In a relatively short time, the velocity and volume at which Dr. Benjamin Schulte,
AI processes information have become critical to military
Strategy & Innovation Lead Defense
decision-making, serving as a guiding force in helping
Europe, Capgemini
armed forces navigate the fog of war. Meanwhile, QC is a
rapidly emerging technology capable of obliterating even
the most robust security defenses by today’s standards.
The transformation of defense and security, driven by
new software-defined capabilities, is characterized by
The convergence of these disruptive yet transformative
a critical tension: the imperative for openness and rapid
technologies provide national defense with two invaluable
experimentation with emerging technologies clashes
assets. However, it also presents adversaries with two
with the necessity of securing these advancements
powerful tools for attack—attacks that can unfold quickly,
against adversaries, inherent risks, and new occurring
are difficult to detect, and can cause widespread damage
vulnerabilities. Proactive adoption and continuous
in an instant.
innovation are vital to deter aggression, protect our
democracies, and ensure enduring security in an
To capitalize on the opportunities presented by AI and
increasingly complex world.
QC, while future-proofing defense capabilities, we need
to operate with the most stringent security standards.
While new technologies should be explored and integrated
Understanding the origins and training of AI, as well as
swiftly, safeguarding their integrity and operational
ensuring it remains immune to adversarial influence,
security is equally essential. Against this dynamic backdrop,
is more critical than ever. With the arrival of QC on the
our report examines the transformative potential of AI and
horizon, we should confidently act to secure critical and
quantum technologies in defense, emphasizing how AI can
sensitive information. This should not be measured by
revolutionize decision-making and autonomous operations
today’s encryption standards, but rather with the question:
while quantum advancements promise unbreakable
“Is this ‘quantum safe’?”
encryption and enhanced secure communications.
4 Safeguarding Europe’s Security in the age of AI
Executive summary
Our world is in the midst of profound societal,
technological and geopolitical change.
Our world is in the midst of profound societal, advances, it threatens current cryptographic systems,
technological and geopolitical change. European leaders endangering secure communications, critical infrastructure,
are being required to rethink their strategy to reflect a shift and operational continuity. Unlike AI, PQC may not
to a multipolar world, the re-emergence of high-intensity revolutionize security operations but provides the essential
conflict, and the transformative impact of technology on backbone for safeguarding their integrity. The interplay
security, strategic autonomy, and European resilience. The between these technologies is clear: while AI catalyses
stakes are high, with implications ranging from strategic transformative capabilities, its effectiveness depends on
planning and innovation management to battlefield tactics. the foundational security provided by PQC. Without this
protection, AI’s power to enhance security and resilience
The notion of “software is eating the world” also holds becomes a potential liability.
true for defense and security as the recent events in Ukraine
have demonstrated. Thus, it can be said that “software Europe’s strategic autonomy in a multipolar world will
is eating defense”, with a growing appetite and ever- hinge on its ability to navigate the convergence of AI’s
accelerating pace. Defense innovation historically focused transformative impact and PQC’s protective potential.
on hardware, especially platform centric capabilities such The secure integration of these technologies will ensure
as tanks, aircraft, and ships. seamless coordination among allies and fortify Europe
against hybrid threats and adversarial capabilities. As
Today’s alpha and omega is the interplay of software- digital transformation accelerates, the interplay between
defined and hardware-enabled capabilities, shaping future AI and PQC should be harnessed to strengthen Europe’s
systems, operations, and decision-making. This shift technological sovereignty and resilience.
unlocks unprecedented opportunities and introduces new
vulnerabilities that should be proactively managed. The This report assesses the security implications of AI
transformative power of Artificial Intelligence (AI) and integration and PQC adoption in defense and security,
the emerging threats from quantum computing demand emphasizing their interconnected roles in securing
an urgent, coordinated and strategic response. Without Europe’s strategic future. It concludes with actionable
robust defense, societies face greater risks of manipulation, recommendations for Europe’s political, military, and
threatening stability, sovereignty, and democracy. industrial leaders to:
Artificial Intelligence is transforming the operational
landscape across critical domains, serving as a catalyst for
national security, public safety, infrastructure resilience,
Accelerate innovation and
and crisis management. It enhances decision-making,
situational awareness, and predictive capabilities, reshaping operational integration.
how governments, organizations, and industries address
security challenges. AI brings with it complex challenges
related to data management, supply chains, cybersecurity Strengthen technological sovereignty.
and human oversight, demanding increasing attention to
the secure uses and implementation of the technology.
Enhance trust and interoperability.
In parallel, post-quantum cryptography (PQC) offers
the protection of the digital foundation upon which AI
and other critical systems rely. As quantum computing
5 Safeguarding Europe’s Security in the age of AI
Summary of
recommendations
Accelerate innovation and integration Target Audiences
Adopt a balanced approach between risk-tolerance and ethics
to testing emerging technological solutions
Adapt procurement procedures to the short development
cycle of information technologies
Train and develop AI systems with realistic, high-quality synthetic data
Strengthen technological sovereignty Target Audiences
Increase domestic production of critical components to reduce
external dependencies
Task an EU agency to coordinate and centralize expertise, streamline
adoption, and drive standardization in emerging technologies
Improve the training and anticipate the need for security and defense workforce
in line with the requirements of a rapidly evolving technological landscape
Enhance trust and interoperability Target Audiences
Develop a transatlantic “common data strategy” to facilitate
the sharing of AI training data
Develop a transatlantic shared approach to AI and quantum ethical
development and use
Establish a standardized, robust AI development and management
framework for interoperability between allies
Key Armed forces Policy-makers Defense industry
6 Safeguarding Europe’s Security in the age of AI
Europe at a
technological
crossroads
General (ret.) (OF-9) Eric Autellet
Former Major General of the French
Defense Staff
We stand today at a critical juncture, witnessing a
convergence of societal transformations, technological
breakthroughs and geostrategic changes. Europe’s strategic
context is undergoing a significant transformation and
evolving dynamics which require new mindsets, strategies
and partnerships. While some principles remain unchanged,
tangible aspects like recent technological advances (AI,
cloud computing, big data), changing geopolitical context,
and societal development are transforming the security
and military sphere.
Power and interactions are shifting from global to regional
scales, redefining international relations and prompting
leaders to prioritize regional security,
autonomy and resilience.
The integration of emerging technologies without
strategic foresight risks undermining Europe’s sovereignty,
potentially leading to serious technological and strategic
disruption. A balanced and cohesive approach to
technology deployment is therefore essential.
The increasingly widespread use of digital assets is
now enabling permanent competition, unexpected
confrontations and new ways of fighting. Europe has yet
to master the digital domain, which will be the next arena
for confrontation and war between states. In the near
future, the bloc’s efforts must focus on on mastering new
technologies, in particular the transition to post-quantum
cryptography, ensuring that it is not caught off-guard by
progress in this domain. It is also urgent to cohesively map
European research and development efforts to address
these challenges head-on.
7 Safeguarding Europe’s Security in the age of AI
Introduction
Is software eating defense? AI: transformation or upheaval?
The world is undergoing a profound transformation driven AI is transforming the operational landscape in numerous
by rapid technological change. Technology is reshaping areas, acting as a catalyst for innovation. To enable
society and accelerating change at an unprecedented pace, it to continue being driving force for innovation and
bringing both significant benefits and challenges. As global transformative change in national security, public safety,
power dynamics shift, nations race to secure an edge in infrastructure resilience and crisis management, its uses
this new context of high intensity and high technology. should be secured in the long term.
The military domain is increasingly defined by software.
While tanks, aircraft, and ships were once the core focus However, this technological advance also introduces
of innovation, software now drives transformation, complex challenges ranging from AI-powered cyber-attacks
shaping operational systems, decision-making processes, to algorithmic biases, either inherent to the data used to
and overall defense strategies. train AI systems or maliciously introduced by adversaries
to “poison” the data and render the AI ineffective. Secure
Where should we focus today to secure our way of life in implementation of AI-driven systems is thus fundamental
the future? New challenges arise every day, some of which to mitigate associated risks and ensure that AI improves
may not have even been envisaged just a few years ago. operational efficiency while protecting and being protected
Are we sufficiently aware of these changes, and, more against potential vulnerabilities. These foundational
importantly, able to tackle them effectively? What can transformations are forcing a profound rethink of our
we do today to mitigate future threats? security and defense.
Time to act
This report addresses the strategic, future-defining
It is essential to balance investments
challenges posed by secure AI and PQC for security and
in Gen AI with those in cybersecurity
defense, offering a roadmap for political, military, and
industrial leaders to act decisively to secure Europe’s and quantum technologies to
future. It provides a comprehensive understanding of address current risks effectively.”
current and emerging challenges and presents actionable
recommendations to inspire proactive measures. As Patrice Duboé
Benjamin Franklin aptly noted, “By failing to prepare, Executive Vice President / Chief
you are preparing to fail.” The time to act is now. Technology & Innovation Officer -
Aerospace & Defense, Capgemini
The future of European security
hinges on our mastery of
transformative technologies.
AI and quantum innovations
hould be deployed with precision
and responsibility.”
Dr. Cara Antoine
Chief Technology, Innovation & Portfolio
Officer / Executive Vice President |
Capgemini
8 Safeguarding Europe’s Security in the age of AI
Quantum: the next challenge
The evolution of the AI-augmented battlefield makes
secure communication essential. PQC will play a pivotal
role in securing the digital infrastructure that AI and
digital systems rely on against emerging threats posed
by quantum capabilities. While these applications for
now remain theoretical, they hold the potential to
disrupt secure communications on a massive scale,
rendering current encryption protocols obsolete and
jeopardizing military operations. PQC will provide the
necessary foundation for maintaining the integrity
the digital backbone of future operations.
Quantum computing could render secure communication
impossible overnight. Command would thus no longer
be possible and secure operations would collapse.
Such a catastrophic scenario is not inevitable. PQC can
be deployed now on IT and communications systems,
reducing the threat to data lost now and systems in the
future. PQC operates on classical rather than quantum
computers, and thus provides a practical solution today
to address the significant threats posed by tomorrow’s
quantum and computation power advances.
This report will examine these two key trends, their
potential impact on the future of software-defined
capabilities, and the strategic responses leaders should
consider capitalizing on opportunities while addressing
associated risks. Drawing on qualitative research,
including expert interviews with distinguished defense
professionals from European armed forces, NATO,
the European Defense Agency, and technology and
innovation experts at Capgemini, the report will offer
actionable recommendations for strengthening AI
security and safeguarding quantum technologies
in the years to come.
9 Safeguarding Europe’s Security in the age of AI
Securing AI to
secure Europe
AI is a catalyst for transformation and has led to a
revolution in national security, public safety, critical
infrastructure, and military operations. It enhances
decision-making, situational awareness, and predictive Artificial Intelligence refers to the ability
capabilities, enabling proactive responses to evolving of machines to perform tasks traditionally
threats. AI applications span autonomous systems,
requiring human intelligence, such as recognizing
targeting and decision support, predictive analytics,
patterns, learning from experience, drawing
and cyber defense, among others.
conclusions, making predictions, or generating
The conflicts in Ukraine and the Middle East have recommendations. These applications may guide
highlighted the growing pervasivness of AI and its or alter the behavior of autonomous physical
accelerated integration into a variety of systems systems (like automated vehicles) or operate
and platforms, such as Unmanned Aerial Vehicles
entirely within the digital domain (e.g. ChatGPT),
(UAVs), targeting processes, or the analysis of satellite
with autonomy ranging from partial human
imagery. The conflicts in Ukraine and the Middle East
have showcased the increasing pervasivness of AI and intervention to full independence post-activation.
accelerated integrationinto into a variety of systems
and platforms, such as Unmanned Aerial Vehicles Source: U.S. Department
(UAVs), weapons targeting systems, or the analysis of State (2023)
of satellite imagery. This chapter looks at current
and upcoming applications of AI and focuses on
how to ensure safe and secure uses of AI.
Four elements of AI
Hardware Software
Disks, computers, chips Algorithms, models
Connection Data
Networks Text, figures, images etc
10 Safeguarding Europe’s Security in the age of AI
AI applications in security
and defense
The conflict in Ukraine:
a testbed for AI
Artificial intelligence is already a major tool in a range of
critical security and defense areas. It is already transforming
all operational domains (land, sea, air, space, cyber,
AI is playing a central role in supporting
electromagnetic spectrum) and the way missions are
Ukrainian forces in intelligence, operational
conducted (from anticipation to detection and reaction). By
support and targeting. In the field of counter-
multiplying effects (e.g. swarming) and increasing battlefield
espionage, AI systems, in collaboration
transparency, AI is offering added value across all functions.
with companies such as Palantir, analyze
Its applications span military operations, military support,
vast datasets to identify threats to national
disaster prevention and humanitarian aid, intelligence,
security, flagging suspicious behavior of
homeland security and border management.
Ukrainian citizens or their potential links
with Russia. Moreover, AI is integrated
AI will specially improve decision support in all the areas
with voice translation tools that process
of security and defense. At the strategic level, AI will be
intercepted enemy communications,
able to analyze action plans, issue early warnings and help
extracting actionable intelligence to
produce simulations to guide operational planning. At
anticipate adversary movements.
the operational level, it already processes intelligence
to prioritize and validate targets. At the tactical level,
In the field of operational support, the
AI provides real-time data and actionable intelligence
Operations Centre for Threat Assessment
to optimize immediate responses.
(COTA), leveraging AI, integrates various data
streams, providing real-time information
to guide logistics and strategy. Finally, AI
improves target acquisition, analyzing drone
As AI continues to mature, we and social media data to locate and neutralize
targets of strategic value on a daily basis.
can expect further disruptions in
military operations. The journey
towards AI integration is already
well underway.”
Dr. Bryan Wells
NATO Chief Scientist
11 Safeguarding Europe’s Security in the age of AI
Current, emerging, and future AI applications
Online threat Intelligence Operational Complex
detection processing simulations AI agent decision
and analysis support
Now New Next
Supply chain Predictive Drone swarming Autonomous C2 automation
optimization behavior cyber attack
response
Risks to secure AI uses
and mitigation strategies
AI impacts all operational domains, military functions if
not the very nature of warfare. Securely implementing and
using AI poses specific challenges linked to technology,
people and process. This also requires a clear balance
between the need to rapidly implement AI in the fields
of defense and security while navigating the challenges
related to this implementation. These challenges can be
structured in four main categories: cybersecurity, supply
chain security, data, as well as expertise and human
resources, and require targeted mitigation strategies.
12 Safeguarding Europe’s Security in the age of AI
Challenges and mitigation strategies
Key challenges to the integration of AI in defense and security can be tackled by a
number of mitigation strategies, structured in four main categories: cybersecurity,
supply chain security, data, as well as expertise and human resources.
Domain Challenge Mitigation strategy
System AI is vulnerable to attack • Harmonize security standards to mitigate threats
on AI systems, including model poisoning, oracle attacks,
security
and input perturbation
• Increase collaboration between the public and private
sectors to strengthen AI against adversarial attacks and
improve the cyber security of hardware and software
from the R&D to the implementation phase
Supply chain Strain on the • Invest in developing European semiconductor production
supply chain/disruptions capabilities to reduce external dependency.
security
• Encourage partnerships with European industry,
academia, and research institutions to mitigate risks
from strategic competition
Data • Lack of quality and • Develop a sovereign Cloud for securing sensitive data while
quantity of data guaranteeing compliance with national security protocols
• Difficulty obtaining and • Integrate, in the future, fully homomorphic encryption,
sharing data (encrypted, enabling classified data to be shared and processed
classified, incomplete) securely without decrypting
• Data requiring advanced • Create data centers to meet the sector’s growing
storage and processing computing and storage needs.
capabilities • Use synthetic data in situations where it is impossible to
• Data poisoning obtain data, in particular to anticipate future scenarios
Expertise • Cognitive biases Enhance human oversight and
(e.g. automation bias) expertise through:
and human
• Excessive reliance
resources • (Re)training programs
on AI outputs
• High-level expertise cultivation
• Obligation to respect • Integration of technical specialists
international into military and security operations
humanitarian law
• Maintaining meaningful
human control over the
use of force
• Necessity to trust
the AI system
13 Safeguarding Europe’s Security in the age of AI
Cyber and supply chain security
The secure implementation of AI for defense and security We need to get end users
faces important obstacles in cybersecurity and supply
and operators into capability
chain security. For instance, hostile actors could exploit
development, for clearer
vulnerabilities in AI systems through deceptive data inputs
(“data poisoning”) during the development stage, or by operational needs and agile
targeting the model itself.1 Because these techniques are capability development with direct
not only persistent and evolving threats, but also highly
feedback from the theater. This
sophisticated and difficult to detect, NATO’s AI Strategy
highlights that they put critical infrastructure and sensitive means rethinking how we develop
operations at risk.2 software-defined, hardware-
enabled capabilities.”
Rising global demand for semiconductors and microchips
further strains AI supply chain security, as production is
Dr. Benjamin Schulte
limited by long lead times, complex and capital-intensive
Strategy & Innovation Lead Defense Europe,
design and manufacturing processes, and can be subject
to geopolitical tensions.3 The high costs associated with Capgemini
designing new chips mean that economies of scale are
essential, leading to the concentration of production
between a few leading companies. A few countries
dominate these supply chains, raising concerns about
reliance, strategic leverage, and espionage. The U.S., for
example, has restricted exports of advanced chips and
Europe needs to strengthen its
manufacturing equipment to China,4 highlighting the
importance of controlling critical supply chains for the European champions to remain
secure use of AI in security and defense, especially for digitally sovereign.”
states without independent supply chains of their own.
Dr. Christian Weber
AI systems’ security should be strengthened throughout
Principal, Partner Lead and Client Manager
their lifecycle. This includes fortifying AI against hostile
actors’ attacks and improving the (cyber) security of the Defense, Capgemini Insights & Data Germany
associated hardware and software. Governments are
investing heavily in domestic semiconductor production to
reduce dependence on foreign suppliers.5 The European
Union’s adoption of the €43 billion European Chips Act,
which aims to produce 20% of the world’s semiconductors
by 2030 in the EU, is one such example.6
Cooperation between the public and private sectors is Integrating the results of start-ups
essential to securing AI for security and defense. NATO’s AI into the traditional procurement
strategy highlights partnerships with industry, academia,
and industrial world remains
and research institutions to advance technological
a major Challenge.”
capabilities, safeguard intellectual property, and mitigate
risks from adversarial use or strategic competition.7
Andreas Conradi
These efforts, aligned with the Munich Security
Head of Defense Europe / Executive Vice
Conference’s call for strengthened semiconductor
and AI coordination, aim to foster innovation and ensure President, Capgemini
Europe’s access to vital AI components.8
14 Safeguarding Europe’s Security in the age of AI
Data
Data is another major challenge, first and foremost the The use of synthetically generated data effectively
quantity and quality of available data. Training military AI addresses many challenges. It can fill the gap where
systems relies on accurate, relevant and AI-ready data for real data is unavailable or provide lower-classification
the adequate fulfillment of their functions, but this can data for initial model development, enabling a smoother
be difficult to obtain.9 Furthermore, the vast quantities of transition to higher-classification environments.
data generated (for example by sensors and collaborative
combat operations) require advanced storage and Additionally, synthetic data is often indispensable for
processing capabilities, which are not always available, preparing AI to handle real-world scenarios that have yet to
especially at the edge. These limitations pose significant occur, such as zero-day cybersecurity threats. By simulating
operational challenges on platforms such as submarines, battles or unprecedented situations, synthetic data enables
tanks, and other vehicles where computational resources training for both AI systems and personnel. To be effective,
are highly constrained. Military AI systems depend on however, this data should closely mirror real-world
access to encrypted or classified data, which adds another conditions, requiring a high degree of “equivalence”
layer of complexity and raises the question of who can to ensure reliability.15
access and use this sensitive data. Finally, the risks of data
poisoning and adversarial manipulation—where attackers
corrupt training or test datasets to reduce the performance
of AI models—further raise the stakes because of the
grave consequences that erroneous AI outputs can have
in military settings.10
A commonly used solution to
One solution is to develop sovereign cloud infrastructure11
to secure sensitive defense data in compliance with mitigate paucity of data is the use
national and regional security protocols.12 The future of synthetically generated data.”
integration of fully homomorphic encryption is another
significant step, as it will enable classified data to be shared Dr. Mark Dorn
and processed securely without decryption, protecting Director Defense, Cambridge Consultants
critical information even in cooperative situations.13 The
creation of vast data centers with a capacity in excess of
one gigawatt is another crucial milestone towards meeting
the sector’s growing computing and storage needs. These
facilities would make it possible to process operational data
at an unprecedented scale, while guaranteeing its security
and availability.14 Investing in these strategies could
vastly improve data security management and lay a solid
foundation for the implementation of secure AI across
the defense and security sectors.
15 Safeguarding Europe’s Security in the age of AI
Expertise and human resources
The integration of AI decision-support systems into military
and security applications raises issues around human-
machine interaction. Secure AI implementation requires
high ethical, legal, and human decision-making standards,
which should provide the flexibility required to continually In defense, the trust factor is crucial.
incentivize and nourish innovation rather than stifle it.
Soldiers and military personnel need
Key concerns center on human control over the use of to trust the AI systems they’re using,
force and the mitigation of cognitive biases, such as just as they would trust any other
over-reliance on automated systems while disregarding
tool in combat. That’s why it’s
contradictory information (automation basis), which may
important to invest in AI literacy
distort decision-making.16 Operators may inadvertently
ignore the legal and strategic implications of their decisions and ensure users understand how
and cause errors or unintended outcomes. Integrating AI to use these systems responsibly.”
will also expand internal attack surfaces, as personnel may
misuse AI.17 On the technology end, AI systems can have
Martijn van de Ridder MSc
difficulty adapting to dynamic wartime conditions.18 The
Vice President | Lead Data & AI Defense
U.S. DoD’s Project Maven is a case in point, struggling to
Europe
independently identify an enemy vehicle under different
weather conditions than those it was originally trained
on.19 AI adoption in a military context risks contravening
international humanitarian law, in particular the principles
of distinction, necessity, humanity and proportionality,
which underpin the lawful conduct of hostilities.20
Strategies to enhance huma"
237,forrester,786b1a49-7a44-4f8b-94de-2cae08ac8903.pdf,"To shareholders and all members of the Forrester community,
Against the backdrop of an uncertain economy and continued layoffs in the tech industry, we continued
our voyage of transitioning clients to Forrester Decisions in 2023. Our target was to migrate two-thirds
of our contract value (CV) to the new platform, and I am happy to report that we achieved that
important milestone. By the end of 2024, our three-year product transition to a single, powerful, and
scalable Forrester Decisions research product will be complete.
We also made progress on two other business imperatives: 1) creating a high-performance sales
organization and 2) capturing opportunities opened by generative artificial intelligence (genAI).
While progress was made, our financial performance did not meet plan. We managed through these
challenges by carefully controlling expenses and staying laser-focused on building a CV growth engine.
A different kind of research and advisory partner
With the advent of Forrester Decisions and its supporting infrastructure of advisory, consulting, and
events, Forrester is filling a unique market gap. We are no longer serving the old research library model
in which mid-level executives in companies received research and built a “library” to answer one-off
questions. And we are not in the consulting business, which focuses on transactions and transitory
relationships.
Forrester is a new type of research and advisory partner serving C-level executives and their teams. We
uniquely help our clients focus on winning, serving, and retaining their customers to drive growth. We
tailor our engagement model to enable our customers to achieve their ongoing initiatives and
outcomes. We are always with our clients — on their side and by their side — through their multi-year
projects and transformations. And we help companies align their marketing, sales, product, technology,
digital, and customer experience functions to operate most efficiently.
Forrester Decisions brings together three powerful research and advisory components:
1. Bold vision to help clients stay ahead of changing customer and market dynamics and better
plan for the future. Forrester Decisions clients get access to customer obsession research,
customer insights, trends and predictions, market forecasts, and technology and service
provider landscapes.
2. Curated tools and frameworks to empower clients to execute on their vision with proven
strategic models and plug-and-play templates. This includes access to key performance
indicators and peer benchmarks, assessments, strategic models, templates, Forrester Wave™
evaluations for specific functions, and certification courses.
3. Hands-on guidance to enable leaders to de-risk decisions, leverage best practices, and set
their teams up for success. These are delivered through guidance sessions with analysts, peer
discussions, and events.
Clients have embraced the new Forrester model. In 2023, we conducted 15,000 client guidance sessions,
driven by initiatives and outcomes that we have recorded for over 85% of clients. Clients report that
Forrester Decisions improves the success rate of transformational initiatives by 26%, accelerates time to
value for transformational initiatives by 50%, and delivers a return on investment of 259%.
Sharpening our go-to-market team
The new Forrester is designed to be sold to C-level executives in user companies larger than $500 million
in revenue and technology vendors and service providers with more than $50 million in revenue. Selling
higher allows us to land and expand across functions. Our goal is to win the leader, win the team, and,
ultimately, to win the organization.
Under the leadership of Nate Swan, Forrester’s chief sales officer, the Forrester sales force is well
positioned to achieve high performance. Nate has brought new leaders into key sales roles, boosted
sales operations and enablement, put a new emphasis on improving sales process and methodology,
increased sales activity and strengthened sales pipelines, and standardized the way that we sell. These
efforts are improving sales efficacy and preparing the company to scale the sales force more quickly.
This experienced, confident, and resolute team is making the changes that will enable the company to
expand contract value.
Additionally, in 2023, we refocused our consulting and events businesses to help drive contract value.
These non-CV businesses are becoming a smaller fraction of our overall revenue mix — in 2023, 70% of
total revenue was in research services, and we expect this share to increase in future years. Our contract
value portfolio is more predictable, scalable, and profitable — and over time, the changing mix will
improve the overall quality of our business.
Seizing the genAI opportunity
Generative AI represents an extraordinary opportunity for Forrester. In meetings with clients, I have
described genAI as the most significant technology change of my lifetime. It represents three
opportunities for Forrester:
1)(cid:3) Research. Forrester was built for exactly these types of moments. When new technology
arrives, large companies need guidance and research to plot the best deployment strategies.
Generative AI was a major focus of our research in 2023, as more than 85 analysts published
reports on how to harness the potential of genAI and provided guidance sessions for clients.
These insights help business and technology leaders separate the massive hype from reality —
and understand how they can leverage AI to augment business practices in ways that were
previously impossible.
2)(cid:3) Product. We believe that genAI will revolutionize the research industry. To meet the genAI
moment, we introduced Forrester’s client-facing generative AI tool, Izola, representing the most
significant step forward in delivering content to our clients since we launched our website 30
years ago. As of April 2024, Izola is available to all Forrester Decisions clients.
3)(cid:3) Operations. In addition to Izola, we have developed four genAI tools that improve internal
efficiency and processes. These systems provide automation for our customer success
managers, account executives, and analysts — enabling them to work more efficiently with
speed.
I am very proud of how quickly Forrester grabbed the genAI opportunity. In a challenging year, we
stayed on offense.
ESG impact
In 2023, we continued to make progress on our environmental, social, and governance (ESG) journey. We
hired our first-ever director of diversity and inclusion to operationalize and build on the strong
foundational work we’ve completed in recent years. We continued to strengthen ties with our local
communities through donations, volunteer work, and outreach.
Outlook for 2024
In a 2023 survey of research and consulting decision-makers that Forrester commissioned from an
independent market research firm, Forrester ranked as one of the most recognizable research and
consulting companies in the world. Our brand had advanced from previous years, and its recognition
and quality was grouped in the top five with much larger players like McKinsey and Bain. Despite our
challenges over the last two years, we continue to punch way above our weight, a good harbinger of the
company’s potential.
I have never been more positive and optimistic about the future of Forrester. We have taken bold action
over the last four years — acquiring SiriusDecisions, focusing the business on net contract value
increase, simplifying our product portfolio, launching our research power platform, Forrester Decisions,
and doubling down on helping our clients use customer obsession to accelerate business growth. It has
been a difficult journey which has been harder than we had expected — but we are confident that we
have made the changes that position Forrester for a better future. The foundational work of 2023 will
enable us to capture a growing share of our substantial total addressable market in 2025 and beyond.
I want to thank all Forresterites who are with me on this journey — your dedication to our clients and
your will to win are inspiring. And I want to thank our investors for their patience and vision as we move
forward — I can assure you that we are working diligently every day to increase shareholder value and
leverage our already strong market position.
Thank(cid:3)you.(cid:3)
(cid:3)
George(cid:3)F.(cid:3)Colony
Form 10-K
2023
UNITED STATES
SECURITIES AND EXCHANGE COMMISSION
Washington,D.C.20549
FORM 10-K
(MarkOne)
☒ ANNUALREPORTPURSUANTTOSECTION13OR15(d)OFTHESECURITIESEXCHANGEACTOF1934
ForthefiscalyearendedDecember31,2023
OR
☐ TRANSITIONREPORTPURSUANTTOSECTION13OR15(d)OFTHESECURITIESEXCHANGEACTOF1934
FORTHETRANSITIONPERIODFROM TO
CommissionFileNumber000-21433
Forrester Research, Inc.
(ExactnameofRegistrantasspecifiedinitsCharter)
Delaware 04-2797789
(Stateorotherjurisdictionof (I.R.S.Employer
incorporationororganization) IdentificationNo.)
60AcornParkDrive
Cambridge,Massachusetts 02140
(Addressofprincipalexecutiveoffices) (ZipCode)
Registrant’stelephonenumber,includingareacode:(617)613-6000
SecuritiesregisteredpursuanttoSection12(b)oftheAct:
Titleofeachclass TradingSymbol(s) Nameofeachexchangeonwhichregistered
CommonStock,$0.01ParValue FORR NasdaqGlobalSelectMarket
SecuritiesregisteredpursuanttoSection12(g)oftheAct:None
IndicatebycheckmarkiftheRegistrantisawell-knownseasonedissuer,asdefinedinRule405oftheSecuritiesAct.YES☐NO☒
IndicatebycheckmarkiftheRegistrantisnotrequiredtofilereportspursuanttoSection13or15(d)oftheAct. YES☐NO☒
IndicatebycheckmarkwhethertheRegistrant:(1)hasfiledallreportsrequiredtobefiledbySection13or15(d)oftheSecuritiesExchangeActof1934duringthe
preceding12months(orforsuchshorterperiodthattheRegistrantwasrequiredtofilesuchreports),and(2)hasbeensubjecttosuchfilingrequirementsforthepast90
days. YES☒NO☐
IndicatebycheckmarkwhethertheRegistranthassubmittedelectronicallyeveryInteractiveDataFilerequiredtobesubmittedpursuanttoRule405ofRegulationS-T
(§232.405ofthischapter)duringthepreceding12months(orforsuchshorterperiodthattheRegistrantwasrequiredtosubmitsuchfiles). YES☒NO☐
Indicatebycheckmarkwhethertheregistrantisalargeacceleratedfiler,anacceleratedfiler,anon-acceleratedfiler,smallerreportingcompany,oranemerginggrowth
company.Seethedefinitionsof“largeacceleratedfiler,”“acceleratedfiler,”“smallerreportingcompany,”and“emerginggrowthcompany”inRule12b-2ofthe
ExchangeAct.
Largeacceleratedfiler ☐ Acceleratedfiler ☒
Non-acceleratedfiler ☐ Smallerreportingcompany ☐
Emerginggrowthcompany ☐
Ifanemerginggrowthcompany,indicatebycheckmarkiftheregistranthaselectednottousetheextendedtransitionperiodforcomplyingwithanyneworrevised
financialaccountingstandardsprovidedpursuanttoSection13(a)oftheExchangeAct. ☐
Indicatebycheckmarkwhethertheregistranthasfiledareportonandattestationtoitsmanagement’sassessmentoftheeffectivenessofitsinternalcontrolover
financialreportingunderSection404(b)oftheSarbanes-OxleyAct(15U.S.C.7262(b))bytheregisteredpublicaccountingfirmthatpreparedorissueditsauditreport.
☒
IfsecuritiesareregisteredpursuanttoSection12(b)oftheAct,indicatebycheckmarkwhetherthefinancialstatementsoftheregistrantincludedinthefilingreflectthe
correctionofanerrortopreviouslyissuedfinancialstatements.☐
Indicatebycheckmarkwhetheranyofthoseerrorcorrectionsarerestatementsthatrequiredarecoveryanalysisofincentive-basedcompensationreceivedbyanyofthe
registrant’sexecutiveo(cid:31)cersduringtherelevantrecoveryperiodpursuantto§240.10D-1(b).☐
IndicatebycheckmarkwhethertheRegistrantisashellcompany(asdefinedinRule12b-2oftheExchangeAct). YES☐NO☒
Theaggregatemarketvalueofthevotingandnon-votingcommonequityheldbynon-affiliatesoftheRegistrant,basedontheclosingpriceofthesharesofcommon
stockonTheNASDAQStockMarketonJune30,2023,wasapproximately$340,000,000.
ThenumberofsharesofRegistrant’sCommonStockoutstandingasofMarch4,2024was19,394,000.
DOCUMENTSINCORPORATEDBYREFERENCE
Portionsoftheregistrant’sProxyStatementrelatedtoits2024AnnualStockholders’MeetingtobefiledsubsequentlyareincorporatedbyreferenceintoPart
IIIofthisForm10-K.
FORRESTERRESEARCH,INC.
INDEXTOFORM10-K
Page
PARTI
Item1. Business 3
Item1A. RiskFactors 7
Item1B. UnresolvedStaffComments 9
Item1C. Cybersecurity 10
Item2. Properties 10
Item3. LegalProceedings 11
Item4. MineSafetyDisclosures 11
PARTII
Item5. MarketforRegistrant’sCommonEquity,RelatedStockholderMatters,andIssuerPurchasesofEquity
Securities 12
Item6. [Reserved] 13
Item7. Management’sDiscussionandAnalysisofFinancialConditionandResultsofOperations 14
Item7A. QuantitativeandQualitativeDisclosuresAboutMarketRisk 23
Item8. ConsolidatedFinancialStatementsandSupplementaryData 24
Item9. ChangesinandDisagreementsWithAccountantsonAccountingandFinancialDisclosure 55
Item9A. ControlsandProcedures 55
Item9B. OtherInformation 55
Item9C. DisclosureRegardingForeignJurisdictionsthatPreventInspections 55
PARTIII
Item10. Directors,ExecutiveOfficers,andCorporateGovernance 56
Item11. ExecutiveCompensation 57
Item12. SecurityOwnershipofCertainBeneficialOwnersandManagementandRelatedStockholderMatters 57
Item13. CertainRelationshipsandRelatedTransactions,andDirectorIndependence 57
Item14. PrincipalAccountantFeesandServices 57
PARTIV
Item15. ExhibitsandFinancialStatementSchedules 58
Item16 Form10-KSummary 58
SIGNATURES 61
2
ThisAnnualReportonForm10-Kcontainsforward-lookingstatementswithinthemeaningofthePrivateSecuritiesLitigation
ReformActof1995.Wordssuchas“expects,”“believes,”“anticipates,”“intends,”“plans,”“estimates,”orsimilarexpressionsare
intendedtoidentifytheseforward-lookingstatements.Referenceismadeinparticulartoourstatementsaboutchangingstakeholder
expectations,migrationofourclientsintoourForresterDecisionsproducts,productdevelopment,holdinghybridevents,possible
acquisitions,futuredividends,futuresharerepurchases,futuregrowthrates,operatingincomeandcashfromoperations,future
deferredrevenue,futurecompliancewithfinancialcovenantsunderourcreditfacility,futureinterestexpense,anticipatedincreases
in,andproductivityof,oursalesforceandheadcount,theadequacyofourcash,andcashflowstosatisfyourworkingcapitaland
capitalexpenditures,andtheanticipatedimpactofaccountingstandards.Thesestatementsarebasedonourcurrentplansand
expectationsandinvolverisksanduncertainties.Importantfactorsthatcouldcauseactualfutureactivitiesandresultsofoperations
tobemateriallydifferentfromthosesetforthintheforward-lookingstatementsarediscussedbelowunder“RiskFactors.”We
undertakenoobligationtoupdatepubliclyanyforward-lookingstatements,whetherasaresultofnewinformation,futureevents,or
otherwise.
PARTI
Item1.Business
General
ForresterResearch,Inc.isaglobalindependentresearchandadvisoryfirm.Wehelpleadersacrosstechnology,customer
experience,marketing,salesandproductfunctionsusecustomerobsessiontoaccelerategrowth.ThroughForrester’sproprietary
research,consulting,andevents,leadersfromaroundtheglobeareempoweredtobeboldatwork,navigatechange,andputtheir
customersatthecenteroftheirleadership,strategy,andoperations.Ouruniqueinsightsaregroundedinannualsurveysofmorethan
700,000consumers,businessleaders,andtechnologyleadersworldwide,rigorousandobjectiveresearchmethodologies,over100
millionreal-timefeedbackvotes,andthesharedwisdomofourclients.
OurcommonstockislistedonNasdaqGlobalSelectMarketunderthesymbol""FORR"".
MarketOverview
Webelievethatmarketdynamics—fromempoweredcustomerstotheemergenceofgenerativeAI—havefundamentally
changedbusinessandtechnology.Thesedynamicscontinuetochangestakeholderexpectations.
Consumersandbuyershavenewdemandsandrequirements.Towin,serve,andretaincustomersinthisenvironment,we
believethatcompaniesrequireahigherlevelofcustomerobsession.Customerobsessedfirmsputtheircustomersatthecenteroftheir
leadership,strategy,andoperations.Ourresearchhasshownthatcustomer-obsessedfirmsgrowfasterandaremoreprofitable.
Organizationsandleadersrequireacontinuousstreamofguidanceandanalysistoadapttotheseever-changingbehaviorsand
realities.Webelievethatthereisanincreasingneedforobjectiveexternalsourcesofthisguidanceandanalysis,fuelingwhatwecall
the“goldenageofresearch.”
Forrester’sStrategyandBusinessModel
Thefoundationofourbusinessmodelisourabilitytohelpbusinessandtechnologyleaderstackletheirmostpressingpriorities
anddrivegrowththroughcustomerobsession.Forresterhelpsclientssolveproblems,makedecisions,andtakeactiontodeliver
results.Withourproprietaryresearch,consulting,andevents,ourbusinessmodelprovidesmultiplesourcesofvaluetoourclientsand
createsasystemtoexpandcontractvalue(""CV""),whichweviewasourmostsignificantbusinessmetric.
Generallyspeaking,wedefineCVproductsasthoseservicesthatourclientsuseoverayear’stimeandthatarerenewable
periodically,usuallyonanannualbasis.OurCVproductsprimarilyconsistofoursubscriptionresearchproducts,whileournon-CV
businesses,consultingandevents,playcriticalcomplementaryrolesindrivingourCVgrowth.
Withrespecttoourclients,webelievethatithasbecomedifficultforlargecompaniestorunmulti-yearstrategyandchange
managementprojectsontheirownascustomersarechangingfasterandcompetitorsareincreasinglyaggressive.Multi-yearCV
productrelationshipsenableustohelpourclientsformulatetheirvisionforthefutureandthentranslatethoseplansinto
implementationandoutcomesovertime.Forourinvestors,webelievethatCVgrowthwillresultinpredictableandprofitablerevenue
streams.
OurbusinessmodelisbuiltonthepremisethatanincreaseinCVgeneratesmorecashwhichcanthenbeinvestedinimproving
ourgo-to-marketstructure(activitiesincludingsales,product,marketingandacquisitions)andcreatingCVproductsthatclientsrenew
yearafteryear—repeatingthecycleanddrivingthemodelforward.Werefertothismodelasour""CVgrowthengine.""
3
OurProductsandServices
Westrivetobeanindispensablesourcethatbusinessandtechnologyleadersacrossfunctions,includingtechnology,customer
experience,digital,marketing,sales,andproduct,worldwideturntoforongoingguidancetoplanandoperatemoreeffectively.
Wedeliverourproductsandservicesgloballythroughthreebusinesssegments–Research,ConsultingandEvents.
Research
Formorethan40years,Forresterhasbeenprovidingobjective,independentanddata-drivenresearchinsightsutilizingboth
qualitativeandquantitativedata.Weadheretorigorous,unbiasedresearchmethodologiesthataretransparentandpubliclyavailable
toensureconsistentresearchqualityacrossmarkets,technologies,andgeographies.
OurprimarysubscriptionresearchservicesincludeForresterDecisions,ForresterResearch,andSiriusDecisionsResearch. This
portfolioofresearchservicesisdesignedtoprovidebusinessandtechnologyleaderswithaprovenpathtogrowththroughcustomer
obsession.Keycontentavailableviaonlineaccessincludes:
• futuretrends,predictions,andmarketforecasts;
• deepconsumerandbusinessbuyerdataandinsights;
• curatedbestpracticemodelsandtoolstorunbusinessfunctions;
• operationalandperformancebenchmarkingdata;and
• technologyandservicemarketlandscapesandvendorevaluations.
Ourresearchservicesalsoincludetimewithouranalyststoapplyresearchtotheircontext.
Launchedin2021,ForresterDecisionsisaportfolioofstandardizedresearchservicescombiningkeyfeaturesofForrester
ResearchwithkeyfeaturesofSiriusDecisionsResearch.WeintendtomigrateourexistingclientsthatpurchaseForresterResearch
andSiriusDecisionsResearchproductstotheForresterDecisionsproducts,andasofJanuary1,2023,ForresterDecisionsbecameour
onlysubscriptionresearchproductavailableformostnewclients.AsofJanuary1,2024,approximately66%ofourCVwas
composedofForresterDecisionsproducts.
Consulting
OurConsultingbusinessincludesconsultingprojectsandadvisoryservices.Wedeliverfocusedinsightsandrecommendations
toassistclientsindevelopingandexecutingtheirtechnologyandbusinessstrategies.Ourconsultingprojectshelpclientswith
challengesaddressedinourpublishedresearch.Ourconsultingprojectsincludeconductingmaturityassessments,prioritizingbest
practices,developingstrategies,buildingbusinesscases,selectingtechnologyvendors,structuringorganizations,anddeveloping
contentmarketingstrategiesandcollateral,andsalestools.ConsultingplaysanimportantroleinsupportingourCVgrowth,aswe
havefoundthatclientsthatpurchaseconsultingprojectsfromusrenewtheirCVcontractsathigherratescomparedtoclientsthatdo
notpurchaseconsulting.
Events
WehostmultipleeventsacrossNorthAmerica,Europe,andtheAsia-Pacificregionthroughouttheyear.ForresterEventsare
thoughtfullydesignedandcuratedexperiencestoprovideclientswithinsightsandactionableadvicetoachieveacceleratedbusiness
growth.ForresterEventsfocusonbusinessimperativesofsignificantinteresttoclients,includingbusiness-to-businessmarketing,
salesandproductleadership,customerexperience,securityandrisk,newtechnologyandinnovation,anddatastrategiesandinsights.
OneoftheprimarypurposesofourEventsbusinessistohelpdriveourCVgrowth,andwehavefoundthatprospectiveclientsthat
haveattendedoneofoureventsconvertintoclientsathigherratescomparedtothosethathavenotattendedanevent.
Weholdallofoureventsashybridevents,consistingofbothin-personandvirtualexperiencesthatallowustoofferadded
attendeebenefitssuchasondemandsessions,morenetworkingopportunitiesandmorecontent,leadingtohigherattendee
engagement.
SalesandMarketing
Webelievewehaveastrongalignmentacrossoursales,marketingandproductfunctions.
WesellourproductsandservicesthroughourdirectsalesforceinvariouslocationsinNorthAmerica,EuropeandtheAsia
Pacificregion.Oursalesorganizationisorganizedintogroupsbasedonclientsize,geography,andmarketpotential.OurPremier
4
groupsfocusonourlargestvendorandenduserclientsacrosstheglobewhileourEmergingandMid-SizeTechgroupfocuseson
smalltomid-sizedvendorclients.OurEuropeanandAsiaPacificgroupsfocusonbothenduserandvendorclientsintheirrespective
geographies.OurInternationalBusinessDevelopmentgroupsellsourproductsandservicesthroughindependentsalesrepresentatives
inselectinternationallocations.Wealsohaveteamsfocusedonnewbusiness,revenuedevelopment,andeventsales.
Weemployed601salespersonnelasofDecember31,2023comparedto709salespersonnelemployedasofDecember31,
2022.
WealsosellselectResearchproductsdirectlyonlinethroughourwebsite.
OurmarketingactivitiesaredesignedtoelevatetheForresterbrand,differentiateandpromoteForrester’sproductsandservices,
improvetheclientexperience,anddrivegrowth.Weachievetheseoutcomesbycombiningthevalueofreputation,demand
generation,customerengagement,andsalesandcustomersuccessenablementprogramstodelivermultichannelcampaignsandhigh-
qualitydigitalexperiences.Ourcustomersuccessorganizationconductspost-saleengagementactivitiesthataredesignedtoalignto
clientoutcomes,acceleratetimetovalue,anddrivehigherretention.
AsofDecember31,2023,ourproductsandservicesweredeliveredtomorethan2,400clientcompanies.Nosingleclient
companyaccountedformorethan4%ofour2023revenues.
PricingandContracts
Wereportourrevenuefromclientcontractsinthreecategoriesofrevenue:(1)research,(2)consulting,and(3)events.We
classifyrevenuefromsubscriptionsto,andlicensesof,ourresearchproductsandservicesasresearchrevenue.Weclassifyrevenue
fromourconsultingprojectsandstandaloneadvisoryservicesasconsultingrevenue.Weclassifyrevenuefromticketstoand
sponsorshipsofeventsaseventsrevenue.
Contractpricingforannualsubscription-basedproductsisprincipallyafunctionofthenumberoflicensedusersattheclient.
Pricingofcontractsisafixedfeefortheconsultingprojectorshorter-termadvisoryservice.Weperiodicallyreviewandincreasethe
listpricesforourproductsandservices.
Wetrackcontractvalueasasignificantbusinessindicator.Contractvalueisdefinedasthevalueattributabletoallofour
recurringresearch-relatedcontracts.Contractvalueiscalculatedastheannualizedvalueofallcontractsineffectataspecificpointin
time,withoutregardtohowmuchrevenuehasalreadybeenrecognized.Contractvaluedecreased4%to$332.1millionat
December31,2023from$345.4millionatDecember31,2022.
Competition
Webelieveourfocusonhelpingbusinessandtechnologyleadersusecustomerobsessiontodrivegrowthsetsusapartfromour
competition.Inaddition,webelievewecompetefavorablydueto:
• ourabilitytoofferforward-lookingresearch,toolsandframeworksaswellashands-onguidance;
• ourfocusonprovidingteamswithinourclients'organizationswiththeconfidencetoexecuteeffectivelywithend-to-end
guidance,valuableknowledge,know-how,andasharedvocabulary;
• ouruseofrigorousresearchmethodologiestoofferobjectiveinsights;and
• ourbrandpromisetobe“onyoursideandbyyourside,”meaningthatwestrivetobeobsessedaboutourclients'needs
andprioritiesandalignedtotheirstrategies.
Ourprincipaldirectcompetitorsincludeotherindependentprovidersofresearchandadvisoryservices,suchasGartner,aswell
asmarketingagencies,generalbusinessconsultingfirms,survey-basedgeneralmarketresearchfirms,providersofpeernetworking
services,anddigitalmediameasurementservices.Inaddition,ourindirectcompetitorsincludetheinternalplanningandmarketing
staffsofourcurrentandprospectiveclients,aswellasotherinformationproviderssuchaselectronicandprintpublishingcompanies.
WealsofacecompetitionfromfreesourcesofinformationavailableontheInternet,suchasGoogle.Ourindirectcompetitorscould
choosetocompetedirectlyagainstusinthefuture.Inaddition,therearerelativelyfewbarrierstoentryintocertainsegmentsofour
market,andnewcompetitorscouldreadilyseektocompeteagainstusinoneormoreofthesemarketsegments.Increasedcompetition
couldadverselyaffectouroperatingresultsthroughpricingpressureandlossofmarketshare.Therecanbenoassurancethatwewill
beabletocontinuetocompetesuccessfullyagainstexistingornewcompetitors.
IntellectualProperty
Ourproprietaryresearch,methodologiesandotherintellectualpropertyplayasignificantroleinthesuccessofourbusiness.We
relyonacombinationofcopyright,trademark,tradesecret,confidentiality,andothercontractualprovisionstoprotectourintellectual
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property.Weactivelymonitorcompliancebyouremployees,clientsandthirdpartieswithourpoliciesandagreementsrelatingto
confidentiality,ownership,andtheuseandprotectionofForrester’sintellectualproperty.
Employees
Attracting,retaining,anddevelopingthebestandbrightesttalentaroundtheglobeiscriticaltotheongoingsuccessofour
company. AsofDecember31,2023,weemployedatotalof1,744persons.Oftheseemployees,1,257wereintheUnitedStatesand
Canada;282inEurope,MiddleEastandAfrica(“EMEA”);and205intheAsiaPacificregion.
Culture. Ourcultureemphasizescertainkeyvalues—includingclient,courage,collaboration,integrity,andquality—thatwe
believearecriticaltodeliverForrester’suniquevaluepropositionofhelpingbusinessandtechnologyleadersusecustomerobsession
todrivegrowth.Inaddition,weseektofosteraculturewhereemployeescanbecreative,feelsupportedandempowered,andare
encouragedtothinkboldlyaboutnewideas.
DiversityandInclusion(D&I).Wefocusonattracting,hiring,andtheinclusionofallbackgroundsandperspectives,withthe
goalsofimprovingemployeeretentionandengagement,strengtheningthequalityofourresearch,andimprovingclientretentionand
customerexperience.Wefieldregularall-employeesurveystomeasureourprogressagainstourgoals.In2023,inadditiontothe
ongoingtrainingtoequipemployeestoplayanactiveroleinfosteringasafe,respectful,productive,andinclusiveworkenvironment,
examplesofoureffortswithrespecttoD&Iincluded:
• introducinganewD&ILeadershipAdvisoryCounciltohelpaccelerateourD&Igoals;
• increasingemployeeself-identificationwithinhumanresourcesystemprofiles;
• ensuringthatoureventsanddigitalexperiencesareinclusiveandaccessibletoall;and
• ourcontinuationofvariouspartnershipstoattractandaccessmoretalentfromunderrepresentedgroups.
LearningandDevelopment.WehavearobustlearninganddevelopmentprogramandcelebrateandenrichtheForresterculture
throughfrequentrecognitionofachievements.Tokeepemployeesandteamsconnectedandinspiredtodotheirbestworkina
distributedworkenvironment,wehaveenhancedthelearninganddevelopmentopportunitiesforouremployeesacrossabroadrange
ofinitiativesincludingnewhireandonboarding,D&I,andleadershiptraining.
AvailableInformation
ForresterResearchInc.wasincorporatedinMassachusettsonJuly7,1983andreincorporatedinDelawareonFebruary16,
1996.Forrester’scorporateofficesarelocatedinCambridge,Massachusetts.
OurInternetaddressiswww.forrester.com.Wemakeavailablefreeofcharge,onorthroughtheinvestorinformationsectionof
ourwebsite,annualreportsonForm10-K,quarterlyreportsonForm10-Q,currentreportsonForm8-K,andamendmentstothose
reportsfiledorfurnishedpursuanttoSection13(a)or15(d)oftheSecuritiesExchangeActof1934assoonasreasonablypracticable
afterweelectronicallyfilesuchmaterialwith,orfurnishitto,theSEC.TheSECmaintainsaninternetsite(http://www.sec.gov)that
containsreports,proxyandinformationstatementsandotherinformationregardingissuersthatfiledocumentselectronically.
6
Item1A.RiskFactors
Weoperateinarapidlychangingandcompetitiveenvironmentthatinvolvesrisksanduncertainties,certainofwhicharebeyond
ourcontrol.Theserisksanduncertaintiescouldhaveamaterialadverseeffectonourbusinessandourresultsofoperationsand
financialcondition.Theserisksanduncertaintiesinclude,butarenotlimitedto:
RiskFactorsSpecifictoourBusiness
ADeclineinRenewalsorDemandforOurSubscription-BasedResearchServices. Oursuccessdependsinlargepartupon
retaining(onbothaclientcompanyanddollarbasis)andenrichingexistingsubscriptionsforourResearchproductsandservices,
includingthemigrationofourexistingclientsfromourlegacyForresterResearchandSiriusDecisionsproductsintoourForrester
Decisionsportfolioofservices.Futuredeclinesinclientretentionandwalletretention,orfailuretogeneratedemandforandnewsales
ofoursubscription-basedproductsandservices,includingForresterDecisions,duetocompetition,changesinourofferings,or
otherwise,couldhaveanadverseeffectonourresultsofoperationsandfinancialcondition.
DemandforOurConsultingServices. Consultingrevenuescomprised25%ofourtotalrevenuesin2023and28%ofourtotal
revenuesin2022.Consultingengagementsgenerallyareproject-basedandnon-recurring.Adeclineinourabilitytofulfillexistingor
generatenewconsultingengagementscouldhaveanadverseeffectonourresultsofoperationsandfinancialcondition.
OurBusinessMaybeAdverselyAffectedbytheEconomicEnvironment. Ourbusinessisinpartdependentontechnology
spendingandisimpactedbyeconomicconditionssuchasinflation,slowinggrowth,risinginterestrates,threatofrecessionand
supplychainissuesthatmayimpactusandourcustomers.Theeconomicenvironmentmaymateriallyandadverselyaffectdemand
forourproductsandservices.IfconditionsintheUnitedStatesandtheglobaleconomyweretoleadtoadecreaseintechnology
spending,orindemandforourproductsandservices,thiscouldhaveanadverseeffectonourresultsofoperationsandfinancial
condition.AlthoughwedonothaveanyemployeesormaterialclientrelationshipsinRussiaorUkraineandonlyalimitedpresencein
theMiddleEast,ifthecurrentconflictsinUkraineandtheMiddleEastweretoescalateorspreadtootherregions,theremaybe
negativeeffectsonboththeUnitedStatesandtheglobaleconomythatcouldmateriallyandadverselyaffectourbusiness.
OurInternationalOperationsExposeUstoaVarietyofOperationalRiskswhichCouldNegativelyImpactOurResultsof
Operations. AsofDecember31,2023,wehaveclientsinapproximately76countriesandapproximately22%ofourrevenuescome
frominternationalsales.Ouroperatingresultsaresubjecttotherisksinherentininternationalbusinessactivities,includinggeneral
politicalandeconomicconditionsineachcountry,challengesinstaffingandmanagingforeignoperations,changesinregulatory
requirements,compliancewithnumerousforeignlawsandregulations,differencesbetweenU.S.andforeigntaxratesandlaws,
fluctuationsincurrencyexchangerates,difficultyofenforcingclientagreements,collectingaccountsreceivableandprotecting
intellectualpropertyrightsininternationaljurisdictions,andpotentialdisruptionscausedbyforeignwarsandconflicts.Furthermore,
werelyonlocalindependentsalesrepresentativesinsomeinternationallocations.Ifanyofthesearrangementsareterminatedbyour
representativesorus,wemaynotbeabletoreplacethearrangementonbeneficialtermsoronatimelybasis,orclientssourcedbythe
localsalesrepresentativemaynotwanttocontinuetodobusinesswithusorournewrepresentative.
AbilitytoDevelopandOfferNewProductsandServices. Ourfuturesuccesswilldependinpartonourabilitytooffernew
productsandservices.Thesenewproductsandservicesmustsuccessfullygainmarketacceptancebyanticipatingandidentifying
changesinclientrequirementsandchangesinthetechnologyindustryandbyaddressingspecificindustryandbusinessorganization
sectors.Theprocessofinternallyresearching,developing,launching,andgainingclientacceptanceofanewproductorservice,or
assimilatingandmarketinganacquiredproductorservice,isriskyandcostly.Wemaynotbeabletointroducenew,orassimilate
acquired,productsorservicessuccessfully.Ourfailuretodosowouldadverselyaffectourabilitytomaintainacompetitiveposition
inourmarketandcontinuetogrowourbusiness.
TheUseofGenerativeAIinourBusinessandbyOurClientsandCompetitorsCouldNegativelyAffectourBusinessand
Reputation. InOctoberof2023,weintroducedIzola,agenerativeAItoolthatallowsourclientstoqueryourresearchdatabase.We
arealsointheprocessofimplementingvariousothergenerativeAIinitiativeswithinourcompany.Whilewebelievethatgenerative
AItechnologiesoffersignificantopportunities,theyarerapidlyevolvingandtheintegrationofgenerativeAItechnologiesintoour
andourvendors’systems(potentiallywithoutthevendordisclosingsuchusetous"
245,mckinsey,rewired-in-action-case-collection-2024.pdf,"Eyebrow
Title on two lines
Rewired
Lorem ipsum dolor sit amet, consectetur adipiscing elit nullam
rutrum tempus.
in Action
Real-world examples of Digital and AI transformations
and how leading companies succeed
Month Year
Contents
04
Introduction
05
About McKinsey Digital
06
Recipe for capturing value from Digital and AI transformations
08
Lighthouse success stories
0260
CReocnatapc ot fU tsransformation recipe and lessons learned
Eyebrow
Title on two lines
Lorem ipsum dolor sit amet, consectetur adipiscing elit nullam
rutrum tempus.
Month Year
Introduction
When we published Rewired, McKinsey’s in-depth guide to digital and AI transformations, we wanted to show more
examples of how the best companies found success. So, we put together this booklet showcasing companies that have
successfully rewired themselves to turn digital and AI solutions into transformative value.
These companies reflect many of the core lessons of Rewired, including how to align the top team around change that
matters, how to develop technology and data that distributed teams can use to innovate, and how to unlock scale to get the
full financial benefits that are available. By rewiring themselves, these companies have developed the ability to constantly
innovate with digital and AI across the entire business to improve customer experiences and reduce unit costs.
Rewired In Action is a collection of stories highlighting how McKinsey has helped companies get value from their digital and
AI transformations. We hope you find this useful and informative. Please don’t hesitate to reach out to us with any questions.
Robert Levin Johannes-Tobias Lorenz Rodney Zemmel
Senior Partner Senior Partner Senior Partner
Boston Düsseldorf New York
4
About McKinsey Digital
McKinsey Digital is a collection of leaders, experts, and practitioners who help clients create transformative value with
technology. We work with leading companies across the world to drive transformations and build new businesses by
bringing together the capabilities they need. We help our clients create value by harnessing the power of data and artificial
intelligence, modernizing core technology, optimizing and automating operations, building stunning digital experiences, and
developing digital talent and culture.
Our global team includes more than 6,700 strategists, data scientists, designers, architects, product managers, agile
coaches, and software, data, and cloud engineers. Using the latest technology and proven methodologies, we design
digital strategies and build robust software and digital products tailored to our clients’ needs—driving transformations that
accelerate sustainable and inclusive growth.
We’ve served clients in every sector on digital and analytics:
2,000+
companies served on digital and analytics last year.
500+
new businesses built since 2019.
6,700+
practitioners globally, including:
400+ 1,430+ 840+
designers. software and cloud engineers. product owners and agile coaches.
1,500 730+ 1,800+
data scientists. data engineers. integrative digital and analytics
consultant.
5
Recipe for capturing value from digital
and AI transformations
Learnings from serving 2,000+ companies on digital and analytics topics
Strategy
Creating the Transformation Roadmap
Successful transformations start with the CEO and top leadership reimagining their business in the
digital age. The resulting decisions are translated into a detailed strategic roadmap that is both rooted in impact
and clear about the new capabilities needed to deliver it. Leading companies develop transformation roadmaps
focused on business domains that are big enough to generate meaningful value but small enough that it doesn’t
disrupt large parts of the business.
Capabilities
Building Your Talent Bench
You can’t outsource your way to digital excellence. Companies need the capabilities to build and evolve their
proprietary digital solutions, and that requires quality digital and AI talent. Top organizations create a detailed
talent roadmap to hire the best and create an environment where they thrive. This requires understanding what
really motivates top talent and adjusting the company’s culture and approach to excite them.
Adopting a New Operating Model
Building and scaling digital and AI solutions across hundreds of working teams require companies to be much
faster and more flexible in the way they develop technology, so having an agile operating model is critical.
Developing that operating model, however, is perhaps the most complex aspect of a transformation because it
touches the core of the organization and how people work together. It requires determining the right operating
model for you, and building up core capabilities in product management and experience design.
Technology for Speed and Distributed Innovation
The objective for technology is to make it easy for your pods to constantly develop and release digital and AI
innovations to customers and users. Achieving this requires building a distributed technology environment for
easy access to data, applications, and software development tools pods need to rapidly innovate and deliver
secure, high-quality solutions.
Embedded Data Everywhere
The ability of the technology solutions to generate value is dependent on the quality, relevance, and availability
of data. That’s why it’s critical to architect data thoughtfully for easy consumption, reuse, and scaling. The goal
is to have the data teams need so they can use it to make better decisions and build better data-enabled
solutions. The key is to build a set of data products that can be easily consumed by any team or application
across the organization.
Change Management
The Keys to Unlock Adoption and Scaling
Getting customers or business users to adopt solutions as part of their day-to-day activities, and scaling them
across your customer base, markets, or organizational units are often a massive challenge. Companies need
to address the technical, process, and human issues at a sufficiently granular level, have clear KPIs to track
progress, and ensure teams are capturing the value.
6
7
Lighthouse Case Examples
Grupo Mariposa:
Harnessing connected technology in the LatAm food
and beverage market
Latin America, Consumer
Mariposa believed that with the right technology, digital tools, and capabilities, it
could transform the way it serves store owners while overcoming the challenges
of a fragmented LatAm food and beverage market. It created an ecosystem
centered around store owners and powered by AI and digital solutions, such as
conversation commerce, commercial frontline planning, and route optimization.
Charles River Labs:
Accelerating drug development as a digitally-enabled
trusted partner
North America, Life Sciences
Charles River Laboratories, a prominent pre-clinical contract research
organization, embarked on a digital transformation to enable customers to
accelerate development of high-quality medicines for patients. Over a 3-year
journey, they have scaled across all enterprise and stood up an at-scale digital
factory that has dramatically accelerated the speed of delivering new products
and services.
Allianz Direct:
Advancing as Europe’s leading digital insurer
Europe, Insurance
Determined to shape the future of digital insurance and revolutionize the level
of service provided, Allianz Direct embarked on an ambitious journey. They
transformed their processes and leveraged modern technology and advanced
analytics such as AI-based loss assessment and evaluation to become “digitally
unbeatable” in every aspect of their value chain and ensure strong growth for
years to come.
Xcel Energy:
Driving toward net zero with the power of digital
North America, Energy/consumer
Faced with the imperative to replace its aging IT infrastructure and meet
increasing customer demand, Xcel Energy followed a clear roadmap to reform
its technology architecture and use digital to provide affordable, safe and
de-carbonized energy in a highly regulated environment. With initial success
in multiple business units, Xcel Energy continues to scale the program to keep
their plants cost competitive and advance toward zero-carbon baseload.
8
Kiwibank:
Building a better bank for the future of New Zealanders
Asia-Pacific, Banking
With the commitment to provide the highest level of customer service and
grow consistently, Kiwibank, New Zealand’s largest state-owned bank, set a
bold vision for digital transformation and core technology replacement. After
implementing a number of key foundational technology elements, Kiwibank is on
its way to become the top banking choice in the region.
BCP:
Taking banking to new heights on a digital rocketship
Latin America, Banking
BCP, the largest bank in Peru, recognized the potential to enhance customer
experiences and operational efficiency through digital initiatives. Their goals
were twofold: reimagining the customer experience and improving efficiency.
Harnessing new digital techniques, leveraging data and advanced analytics,
adopting new ways of working, and building new capabilities became the path
to realizing their vision of becoming the top bank in Peru.
DBS:
Transforming a banking leader into a technology leader
Asia-Pacific, Banking
In a rapidly changing digital landscape, Singapore-based DBS bank aspired
to transform into a truly digital bank with a clear vision: “Make banking joyful.”
DBS created a best-in-class platform-operating model with joint leadership
between business and technology with a firm focus on customers. It also made
fundamental shifts in its culture and built-up in-house technology capabilities
through innovative recruiting and retention strategies.
Freeport-McMoRan:
Unlocking new mining production through AI
transformation
North America, Mining
Freeport’s expectations for growth required significant capital and lengthy
permitting and construction efforts. Seeking another path, leadership turned
to artificial intelligence (AI) to see if it was possible to get more out of the assets
they already had. By aligning leadership, thoughtfully building out scaling
capabilities, and adopting an agile operating model, Freeport mined AI to drive
new value.
9
Grupo Mariposa:
Harnessing connected technology in the LatAm
food and beverage market
The opportunity The solution
Disrupting a fragmented Creating a new digital ecosystem that puts store owners
CPG market at the center
Guatemala-based Grupo Mariposa traces Grupo Mariposa partnered with McKinsey to create an end-to-end
its roots back to 1885 when it started with ecosystem powered by AI and digital solutions to help overcome the
a single soft drink factory. Today, through challenges of a market with over 3 million points of sale.
its CBC, Bia, and Beliv subsidiaries, it has
At the heart is a new platform powered by advanced analytics and strategic
become a major Latin American food and
partner Yalo, offering “conversational commerce,” which allows store owners
beverage company with operations in more
to conveniently connect with the brand and manage order inventory. The
than 16 countries.
easy-to-use digital tool also gives store owners greater agency by supplying
In Latin America, the food and beverage them with personalized recommendations based on market trends and
ecosystem is highly fragmented, with over supporting them to better serve their customers and rotate inventory. Instead
5 million points of sale. This fragmentation of relying only on a salesperson to tell them what to order, store owners
creates complexity on multiple levels, from (“tiendas”) are advised by digital tools which learn from micro-segments in
sharing data with store owners to planning their own neighborhoods to help them place and track orders digitally.
efficient delivery routes. Companies need
The platform also includes modular solutions for customer service,
to manage the commercial activities of
microloans, loyalty programs, and other services. It enables shopkeepers
thousands of salesforce, merchandisers, and
and sales teams to receive stock-out predictions and order suggestions,
delivery personnel that serve local stores.
place and track orders, and participate in customized loyalty programs. It also
Mariposa believed that with the right helps drive growth by offering business intelligence and knowledge to build
technology, digital tools, and capabilities, it management skills, contributing to the evolution of small shopkeepers into
could transform the way it serves store owners micro-business owners.
while overcoming some of the challenges of
McKinsey and Mariposa built the platform with open technology and
fragmentation. With technology, Mariposa
microservice architecture, enabling future integration with external partners.
aimed to change the beverage ecosystem and
Ultimately, the partners intend to make the platform available to additional
progress on its purpose of becoming the best
companies as a SaaS offering. The vision is to bring lenders, food and
solution for store owners and the first choice
consumer goods suppliers, delivery services, and other companies that
at the point of sale.
create value for store owners into an open ecosystem.
Just as important as the connection with store owners, Mariposa’s
transformation digitizes its go-to-market model. Mariposa co-developed
a proprietary platform with McKinsey to provide digital tools to transform
the commercial frontline roles. This platform is also being marketed to other
CPGs and distributors. It includes an atomic task module that provides each
salesperson with a prioritized list of each day’s tasks. Reps check off tasks on
10
The impact their mobile devices as they finish them., doing away with paperwork. Reps can
also review an AI-generated suggested order list based on consumer behavior
+100k data for each store they visit and place orders digitally. Delivery drivers, whether
in-house or through a vendor, can instantly call up the most efficient route for the
Points of sale that are touched so day’s stops.
far by the digital service channel in
All these technology solutions were made possible by a transformation of
Mariposa’s new ecosystem.
Mariposa’s internal culture. Mariposa integrated digital transformation as part
of its identity and communicated the goals and rationale of the transformation
8-10%
throughout the organization. The company created an implementation playbook
that spelled out the elements of the transformation and then formed a change
Sales uplift from store owners
management committee to oversee the process and support the new agile
who use the solution daily.
operating model. It cemented the changes with a revamped performance
management system and incentives.
5,000+
With these supports in place, Mariposa hired more than 50 highly skilled
digital team members in eight countries across all digital domains. Working in
Sales employees using new digital tools
global agile teams, they helped build tools and capabilities while redesigning
for commercial and deliver management.
and streamlining processes. The final cornerstone of the success was the
engagement of the senior leadership team. Mariposa’s CEO and group president
met weekly with the transformation team for over two years to bring the vision to
action.
“We believe our superpower lies in
fostering strong relationships with SMEs
and harnessing the potential of connected
technology to generate prosperity in Lessons learned
communities. Our ambition is to serve our
clients by providing them with the tools and Align the digital solution to the actual needs of clients
capabilities to make our vision a reality.” (store owners)
– Juan Pablo Mata, CEO of Apex by Grupo Mariposa Mariposa created the new platform with a focus on store owners
Strategic and shifted the go-to-market approach to “pull.” For example,
Roadmap recognizing that storeowners already used WhatsApp, the
“Our biggest learning is that digital
Mariposa team leveraged it for conversational commerce, rather
transformation is not solely about the
than imposing a new platform.
technology. It is about the people – having
the right set of talent, knowing how to recruit Recruit and retain the right tech talent to drive mindset shifts
people, and how to retain them. It’s the people Mariposa focused to attract, recruit, and assemble the right teams
that create the technology that will come and hired over 50 dedicated digital experts in data, technology,
Talent
closer to the business. ” agile, and UX in eight countries. These people became digital
ambassadors to pollinate the culture and mindset shifts
- Alfredo Jose Castañeda, Digital Transformation Leader,
Grupo Mariposa throughout the organization.
Scale the transformation across the organization with
“In the digital transformation process, a
a top-down approach
leader must have two key elements. The first
Along with a clear strategic vision, Mariposa established a change
one is a growth mindset. Once you begin to Adoption
management committee to implement its plan and change
and
understand this different perspective of how
Scaling the operating model. Top leaders consistently and relentlessly
you can bring different capabilities to the
communicated the change plan to internal and external
business, you begin a whole growth mindset
stakeholders, including visits to stores and directly working with
process. And then the second one is servant
salesforce.
leadership. Servant leadership is to accept
that the way that we used to work, it just Video link and case story
doesn’t apply to the modern business”
- Alfredo Jose Castañeda, Digital Transformation Leader,
Grupo Mariposa
11
Charles River Labs:
Accelerating drug development as a digitally-enabled
trusted partner
The opportunity The solution
Accelerating drug Becoming a digitally-enabled trusted partner, putting
development and gaining customers at the core to better serve patients
efficiencies with digital
Charles River Labs set a goal to become a “digitally-enabled trusted partner”
Charles River Laboratories, a prominent pre- that integrates expertise, seamless offerings, and digital delivery to enable
clinical contract research organization (CRO), customers to accelerate the development of high-quality medicines for
plays a pivotal role in the drug development patients. To achieve this, they recognized the need to rethink their approach
ecosystem. It conducts research, in three key areas: customer engagement, internal employee interactions,
development, and safety testing before a life- and their technology foundation.
saving drug ever gets to market. Partnering
With McKinsey’s support, Charles River started with a Digital Diagnostic
with pharma, biopharma, biotech, industry,
that was focused on understanding its starting point for how it engages with
and academia, Charles River contributes to
customers externally and what customers thought about that experience,
over 85 percent of FDA-approved therapies,
internal operations required to deliver customer impact, and its capabilities
supporting companies in bringing novel
on critical enablers of these processes – from technology to talent to
treatments to market.1
data and beyond. Critical to this work was identifying the value at stake,
The world has seen innovation and uncovering customer pain points and unmet needs along the buying journey,
acceleration of therapies like the COVID- and providing digital, design, engineering, and data capabilities. These inputs
19 vaccines in ways that are extraordinary. helped build the business case across multiple initiatives and prioritize where
Historically, these therapies took 5 to 10 to start. Charles River completed this three-month diagnostic phase with
years to develop.2 Yet, during the COVID- a vision for a new digital enterprise and a minimal viable product (MVP) to
19 pandemic, society united to achieve the deliver an online customer engagement and interaction platform.
same feat in just 9 months.3 Charles River
The company spent the next three months mobilizing for the digital business
played a central role in this, which reinforced
build. The team prepared the technical foundations, user stories, and user-
its aspirational goal: what if they were able
tested digital experience to be ready for the first sprint. To ensure they had
to universally subtract a year or more out of
the right set of capabilities going forward, the team set up an “agile talent win
the drug development process? What kind
room” to quickly source new talent and upskill existing employees on agile
of scalability would that take? What kind of
methods, sprint cadence, and ceremonies, as well as the specifics of new
fundamental reimagining of processes would
roles such as product owner, product designer, and scrum master.
it take? To achieve its goals, Charles River
embarked on a transformation into a digital
enterprise, providing their pharmaceutical
clients with expertise, seamless offerings, and
digital delivery.
1. “Biologics Testing Solutions.” Charles River Labs.
2. “Vaccine Research & Development.” Johns Hopkins Coronavirus Resource Center.
3. “FDA Approves First COVID-19 Vaccine.” U.S. Food and Drug Administration, 2021.
12
The impact From there, the “digital factory” was launched—the MVP was called “Apollo,”
representing an entirely new way of engaging digitally with customers and
6 collaborating as an organization, all enabled by a new technical architecture,
including a cloud environment and master data architecture stood up by the team.
Days marketing needs to turnaround
Apollo provides customers the ability to track their projects in one place, and have
a project, down from 28 days.
access to near real-time data, so they know how each project is progressing.
This platform has enabled Charles River to build a richer relationship with clients,
200
becoming a true adviser and thought partner on their drug development journey.
Employees and leaders trained in agile In six short months, the team completed and launched the MVP. Apollo is now
work methods in less than six months. on a scale-up release phase, and in the spirit of lasting change, the company
thinks of it as a “lifestyle rather than a diet.” They continue to deploy the new agile
$100 million+
methodology across the organization to solve problems in innovative ways.
It has been three years and Charles River is successfully scaling across the entire
Annual run-rate impact identified
enterprise, including customer-facing interactions, e-commerce, employee
over an initial three years.
collaboration, lab operations, and automation in finance. They have an at-scale
3 digital factory and have expanded from 3 agile pods to more than 20 across
multiple business units and functions, dramatically accelerating the speed
Months to launch new digital products of delivering new products and services. It also has a best-in-class customer
and services, down from 12 to 18. enablement platform. And at Charles River, they remain customer-focused , using
design thinking in each product and service launched to meet customer and
employee needs. Ultimately, Charles River has successfully shifted from being a
science organization to a science and technology organization and better able to
support patients by accelerating drug development.
“Adopting design thinking and becoming
customer-centric is crucial for reimagination.
We need to start with understanding how Lessons learned
customers work with us, their environment,
Set a North star with customers and patients at the center
challenges, and successful interactions, and
Charles River found inspiration in other companies’
use that information to determine how best
transformations, using them as a North Star. Leveraging insights
to reimagine the process to meet their needs
while achieving our objectives.” Strategic from diverse industries like banking and high tech, they set
Roadmap goals, developed a rapid roadmap, and kept customers’ (and
– Mark Mintz, Corporate Senior Vice President & Chief
their patients’) needs at the center to become a valuable partner,
Information Officer, Charles River Laboratories
gaining a competitive edge.
“One thing the digital transformation has
Creating an unrivaled experience for their digital talent
done for our employees at Charles River is to
Charles River believed that providing a great experience for
allow our employees to become innovators.
clients should also extend to employees, and so the company
They can recognize aspects that could be
Agile aimed to create an outstanding environment for digital, scientific,
optimized, reconsidered from different
Operating
perspectives, and gather feedback from Model and business talent. The organization prioritized exceptional
experience by hosting lively and enjoyable agile meetings,
each other to develop improved tools and
exploring innovative ways to collaborate virtually, and celebrating
processes that significantly impact our
successes.
customers’ experience positively.”
– Pam Walker, Corporate Vice President & Global Head of
Build a digital-first mindset
Operations, Charles River Laboratories
To embrace a digital-first mindset, Charles River established a
“Adopting agile ways of working and design new digital organization with a product-centric agile model. A
Talent
thinking changed the way that we think about transformation office supported the shift to agile practices, while
technology, transforming us from long 12- to a “digital talent win room” facilitated recruitment of new expertise.
18-month deliveries to short, frequent delivery The company also engaged their best business talent, adapting
that is regularly reviewed with customers so roles and establishing external partnerships where needed.
we can quickly pivot based on the value that
we’re creating.”
Video link and case story
– Mark Mintz, Corporate Senior Vice President & Chief
Information Officer, Charles River Laboratories Coming soon
13
Allianz Direct:
Advancing as Europe’s Leading Digital Insurer
The opportunity The solution
Launching a new era for growth Transforming a digital disruptor with state-of-the-art
technology and new ways of working
Allianz Direct, the pan-European digital
insurer of global insurance leader Allianz Allianz Direct had three cornerstone goals: a fully digital business model,
Group, wanted to shape the future of online highly competitive market positioning, and an agile corporate culture,
insurance and provide a new level of service radiating the engineering mindset throughout the organization’s activities.
that could galvanize the organization
With support from McKinsey, Allianz Direct built a state-of-the-art,
and propel it to a new era of growth. To
digital platform that can be scaled across all countries in record time.
outcompete and ensure strong growth well
The platform allows teams to learn from one another as they launch new
into the future, it embarked on a daunting
products, improvements, and plug-and-play software. For customers, the
journey: it would transform ways of working
online experience is easy to use and features many time- and cost-saving
and use modern, cutting-edge technology
innovations with maximum self-service capabilities. In one example,
and advanced analytics to reimagine the end-
Allianz Direct built a flagship service—the “60-second claim”— enabled
to-end user experience, from buying the first
by AI-based loss assessment and evaluation, allowing customers to
product to filing a claim. The North Star was
process a claim in less than a minute by uploading photos and documents.
to become “digitally unbeatable” in all areas of
the value chain and thus Europe’s number one Allianz Direct built momentum in the direct insurance market in Europe
digital insurer. in just a few years by targeting two important market segments: “smart
shoppers” and “price seekers.” The business provides them with the
features they value most, including competitive pricing and a broad online
“With a combination presence.
of technical excellence, All of this was enabled by a foundational change in the organization’s
culture, operational and technical excellence, and a disrupting operating
sophisticated IT, and digital
model. McKinsey helped Allianz Direct create a talent strategy built
marketing capabilities, around hiring the best engineers. This infusion of talent was crucial to
we’ve created a strong building an agile, engineering-focused corporate culture. Today, a third
of Allianz Direct’s employees work in technology or data roles. The Allianz
foundation that will act
team created an operating model based on best-in-class technology
as the innovation engine capabilities and cross-functional agile squads responsible for creating
and marketing insurance products. The result is a highly adaptive and
for the Allianz Group.”
scalable operating model that fosters cross-market collaboration.
– Philipp Kroetz, Chief Executive Officer, Allianz Direct
14
The impact Lessons learned
15% Create a clear roadmap for deploying digital services
Allianz Direct focused its strategic roadmap on a full suite of digital
Year-over-year revenue growth self-service assets (for claims notification, claims management,
momentum (in selected countries). Strategic policy administration) equipped with best-in-class tools such as
Roadmap AI-based loss assessment and claims segmentation.
Work toward rapid implementation
30-50%
Allianz Direct teams worked in biweekly sprints. New products
were tested and implemented immediately whenever practical.
Reduction in costs due to the
Agile More than 40,000 deployments on the platform per year
scalable platform strategy. Operating
Model underline this approach.
Aim for consistency and reusability of digital assets
+90%
By building a platform that could be used across Europe, Allianz
Direct is able to scale its services and continuously improve the
Technology
Customer satisfaction ratings, after customer experience while lowering costs.
reimagining the customer experience.
Make data widely available and easy to use
Allianz Direct committed to instill a data-driven decision-making
culture, so it created easy-to-use dashboards and data-enabled
Data
performance management systems along the full value chain.
“The successful transformation can be attributed to the combination of
technical excellence, sophisticated IT infrastructure, and advanced digital
marketing capabilities, along with robust execution and global delivery in a
complaint way. We dedicated utmost attention, allocating 150% of our focus
to launch and establish our platform as a solid foundation. In addition, we
complemented the approach by emphasizing key aspects such as market
analysis, retail marketing strategies, pricing optimization, efficient damage
management, and streamlined operations to maximize our competitiveness
within the industry.”
- Christoph Weber, Chief Transformation Officer, Allianz Direct
“The most impactful decision was to be stubborn about the outcome, and to
never waiver on what good looks like. And that means you need to invest in the
best technology and in the best people, and be really stubborn about it”
– Philipp Kroetz, CEO, Allianz Direct
“We are disrupting at scale and will continue to work consistently on the
transformation of our business model, always questioning industry standards
and looking beyond our category.”
- Christoph Weber, Chief Transformation Officer, Allianz Direct
Click here to view case story
15
Xcel Energy:
Driving toward net zero with the power of digital
The opportunity The solution
Delivering a tech-enabled, Combining technology and innovation to provide safe,
sustainable future in a highly clean, and reliable energy at an affordable price
regulated environment
Xcel Energy started by developing a path forward and aspirational vision and,
Imagine it’s your first day on the job as chief then worked backward to define a set of technology investments. McKinsey
technology officer (CTO) for one of the brought technical expertise and deep experience with the nuclear power
largest electric and natural gas utilities in sector to help guide the transformation. The work centered on three clear
North America, and suddenly, one of your goals: cost savings through AI and automation; operational excellence and
core systems goes down, leading to a loss of safety; and more efficient regulatory compliance through transparency,
revenue every hour when 5 million customers accelerating to meet its baseload energy needs with zero carbon electricity.
cannot pay their bills. This is what happened
Instead of starting small, Xcel Energy took a bold approach by beginning
to Tim Peterson when he joined Xcel Energy
with one of its most complex and highly regulated domains, nuclear power.
in late 2019 as CTO. Upgrading the utility’s
The utility ini"
246,mckinsey,generative-ai-and-the-future-of-work-in-america-vf1.pdf,"McKinsey Center for Government
Generative
AI and the
future of work
in America
July 2023
Authors
Kweilin Ellingrud
Saurabh Sanghvi
Gurneet Singh Dandona
Anu Madgavkar
Michael Chui
Olivia White
Paige Hasebe
Editor
Lisa Renaud
Cover illustration by Matt Murphy
About the McKinsey Global Institute
The McKinsey Global Institute was established in 1990. Our mission is to provide a fact base to
aid decision making on the economic and business issues most critical to the world’s companies
and policy leaders. We benefit from the full range of McKinsey’s regional, sectoral, and functional
knowledge, skills, and expertise, but editorial direction and decisions are solely the responsibility
of MGI directors and partners.
Our research is grouped into five major themes:
— Productivity and prosperity: Creating and harnessing the world’s assets most productively
— Resources of the world: Building, powering, and feeding the world sustainably
— Human potential: Maximizing and achieving the potential of human talent
— Global connections: Exploring how flows of goods, people, and ideas shape economies
— Technologies and markets of the future: Discussing the next big arenas of value
and competition
We aim for independent and fact-based research. None of our work is commissioned or paid for
by any business, government, or other institution; we share our results publicly free of charge;
and we are entirely funded by the partners of McKinsey. While we engage multiple distinguished
external advisers to contribute to our work, the analyses presented in our publications are MGI’s
alone, and any errors are our own.
You can find out more about MGI and our research at www.mckinsey.com/mgi.
MGI Directors MGI Partners
Sven Smit (chair) Marco Piccitto Michael Chui Jan Mischke
Chris Bradley Olivia White Mekala Krishnan Jeongmin Seong
Kweilin Ellingrud Jonathan Woetzel Anu Madgavkar Tilman Tacke
About the McKinsey Center
for Government
With its independent and analytical approach, the McKinsey Center for Government (MCG) is
a dedicated center of excellence that helps government leaders deliver better outcomes and
experiences for their people.
Backed by a network of global experts, MCG works alongside many of the world’s leading public
sector stakeholders and organizations to enable them to operate at the highest level.
©Eloi Omella/Getty
Contents
At a glance iv
Executive summary 1
Introduction 13
1. A robust recovery marked by job switching
and labor shortages 15
2. Job gains and losses through 2030 23
3. New forces changing labor demand:
Generative AI and federal investment 31
4. Who’s vulnerable? 43
5. Preparing for the future of work 53
Methodology brief 63
Acknowledgments 67
At a glance
— During the pandemic (2019–22), the US labor market saw 8.6 million occupational
shifts, 50 percent more than in the previous three-year period. Most involved people
leaving food services, in-person sales, and office support for different occupations.
— By 2030, activities that account for up to 30 percent of hours currently worked across
the US economy could be automated—a trend accelerated by generative AI. However, we
see generative AI enhancing the way STEM, creative, and business and legal professionals
work rather than eliminating a significant number of jobs outright. Automation’s biggest
effects are likely to hit other job categories. Office support, customer service, and food
service employment could continue to decline.
— Federal investment to address climate and infrastructure, as well as structural shifts,
will also alter labor demand. The net-zero transition will shift employment away from
oil, gas, and automotive manufacturing and into green industries for a modest net gain in
employment. Infrastructure projects will increase demand in construction, which is already
short almost 400,000 workers today. We also see increased demand for healthcare workers
as the population ages, plus gains in transportation services due to e-commerce.
— An additional 12 million occupational transitions may be needed by 2030. As people
leave shrinking occupations, the economy could reweight toward higher-wage jobs. Workers
in lower-wage jobs are up to 14 times more likely to need to change occupations than those in
highest-wage positions, and most will need additional skills to do so successfully. Women are
1.5 times more likely to need to move into new occupations than men.
— The United States will need workforce development on a far larger scale as well as
more expansive hiring approaches from employers. Employers will need to hire for skills
and competencies rather than credentials, recruit from overlooked populations (such as
rural workers and people with disabilities), and deliver training that keeps pace with their
evolving needs.
McKinsey Global Institute | Generative AI and the future of work in America iv
Web 2023
future-of-work
Exhibit 1 of 21
We expect an additional 12 million occupational transitions through 2030.
US job growth, index (0=2016 levels)
40
Resilient and growing
occupations1 30
+17%
9.9M jobs 20
Stalled but rising
occupations² 10
+7%
2.8M jobs
0
–10%
Hit and declining –6.0M jobs
–10
occupations³
2016 2019 2022 2030
Growth • Healthcare demand increase • Investments in • Automation adoption
trajectory as the population ages infrastructure and the • Sustained e-commerce trend
driven by • The push toward digitization net-zero transition • Reduced need for
and technology • Demand for reskilling and customer-facing roles
• Demand for last-mile lifelong learning
delivery
Projected 1M 1M 10M
transitions⁴ From a
to new resilient and
occupations,⁵ growing
2022–30 occupation
to any other
occupation
Occupational 36% of US workers in 2022: 25% of workers: 39% of workers:
categories
• Health professionals • Builders • Production work
within each
profile • Health aides, technicians, • Creatives and arts management • Food services
and wellness
• Property maintenance • Customer service and sales
Occupations • STEM professionals
• Mechanical installation and repair • Office support
where
• Managers
generative AI • Community services
could accelerate • Transportation services • Education and workforce
automation • Business and legal training
significantly professionals
• Agriculture
1Resilient during the pandemic, 2019–22, and expected to grow between 2022 and 2030.
2Stalled during the pandemic, 2019–22, and expected to rise between 2022 and 2030.
3Hit during the pandemic, 2019–22, and continuing to decline between 2022 and 2030.
4Job transitions are defined as jobs in net declining occupations across sectors compared with the 2030 baseline.
5Even in categories that are growing overall, employment may decrease in specific occupations, requiring some workers to find new roles.
Source: O*NET; US Bureau of Labor Statistics; Current Population Survey, US Census Bureau; McKinsey Global Institute analysis
McKinsey & Company
McKinsey Global Institute | Generative AI and the future of work in America v
© Nitat Termmee / Getty
Executive summary
The US labor market is going through a rapid evolution in the way people work and the work
people do. Months after MGI released its last report on the future of work in America, the world
found itself battling a global pandemic.1 Since then, the US job market has come roaring back
from its sudden drop. The nature of work has changed as many workers have stuck with remote
or hybrid models and employers have sped up their adoption of automation technologies. More
recently, the accelerated development of generative AI, with its advanced natural language
capabilities, has extended the possibilities for automation to a much wider set of occupations.
Amid this disruption, workers changed jobs at a remarkable pace—and a subset made bigger
leaps and moved into entirely different occupations. Some 8.6 million occupational shifts took
place from 2019 through 2022. Now even more change is in store. We expect an additional
12 million occupational shifts by 2030. The total number of transitions through 2030 could be
25 percent higher than we projected a little over two years ago.2
Multiple forces are set to fuel growth in certain occupations and erode jobs in others. They
generally fall into three categories: automation, including generative AI; an injection of federal
investment into infrastructure and the net-zero transition; and long-term structural trends such
as aging, continuing investment in technology, and the growth of e-commerce and remote work.
We do not forecast how aggregated employment may be affected by the business cycle in the
short term; instead, we focus on how these forces may reshape the composition of labor demand
over the long term.
Across a majority of occupations (employing 75 percent of the workforce), the pandemic
accelerated trends that could persist through the end of the decade. Occupations that took a hit
during the downturn are likely to continue shrinking over time. These include customer-facing
roles affected by the shift to e-commerce and office support roles that could be eliminated
either by automation or by fewer people coming into physical offices. Declines in food services,
customer service and sales, office support, and production work could account for almost
ten million (more than 84 percent) of the 12 million occupational shifts expected by 2030.
Multiple forces are set to fuel
growth in certain occupations
and erode jobs in others.
1 The future of work in America: People and places, today and tomorrow, McKinsey Global Institute, July 2019.
2 The future of work after COVID-19, McKinsey Global Institute, February 2021.
McKinsey Global Institute | Generative AI and the future of work in America 1
By contrast, occupations in business and legal professions, management, healthcare,
transportation, and STEM were resilient during the pandemic and are poised for continued
growth. These categories are expected to see fewer than one million occupational shifts
by 2030.
For the other categories that account for the remaining one million occupational shifts still to
come, the pandemic was a temporary headwind. Employment in fields like education and training
should rise in the years ahead amid a continuous need for early education and lifelong learning.
Demand for construction workers also stalled during the height of the pandemic but is expected
to rebound strongly.
The changes estimated in our earlier research are happening even faster and on an even bigger
scale than expected. It is becoming even more urgent to solve occupational and geographic
mismatches and connect workers with the training they need to land jobs with better prospects.
The fact that workers have been willing to pivot and change career paths, while a tighter labor
market encouraged companies to hire from broader applicant pools, gives cause for optimism—
but not complacency. The future of work is already here, and it’s moving fast.
In a tighter labor market, workers have been moving
into new roles, accelerating occupational shifts
By the end of 2022, employment had bounced back to its 2019 level. But a great deal was in flux.
Are pandemic-era labor shortages here to stay?
The quits rate soared to new heights during the pandemic, with roughly 48 million Americans
leaving their jobs in 2021 and 51 million in 2022. What people did next is not fully evident from
the data. Some moved into better jobs with higher pay. Others left the labor force, whether out of
discouragement or for personal or health reasons, and it is unclear if or when they will return.
Total employment hit an all-time high after the pandemic, with many employers encountering
hiring difficulties. As of April 2023, some ten million positions remained vacant; labor force
participation had ticked up but was 0.7 percentage point below its prepandemic level. That
translates into roughly 1.9 million workers who are neither employed nor actively looking for jobs.
This erosion comes after an extended 20-year trend of steadily falling participation.
Labor supply may continue to be constrained, given that one in four Americans will be of
retirement age or older by 2030. Without higher participation rates, increased immigration,
or meaningful productivity growth, labor shortages could be a lasting issue as the economy
and the population grow. This remains an open question confronting markets, economists,
and employers.
Workers have shown a willingness
to change career paths, while
a tighter labor market has
encouraged companies to hire
from broader applicant pools.
McKinsey Global Institute | Generative AI and the future of work in America 2
Web 2023
future-of-work
Exhibit 2 and 7 of 21
Exhibit E1
More than 50 percent of recent occupational shifts in the United States involved workers
leaving roles in food services, customer service, office support, and production.
Estimated shifts to another occupation,
by category,¹ 2019–22, %
(XX) — Number of occupational shifts in each
occupational category, 2019–22
Health aides, technicians,
Food services (1.3M)
and wellness (700K)
>75% low-wage jobs
Hit and Resilient >75% low-wage jobs
>75% workers without college degree
declining and growing >75% workers without college degree
Number of shifts Health aides
Top 3 occupations over 2019–22 occupations Food occupations
Nursing assistants 93K
services Business
Fast food and counter workers 529K
Hit during COVID-19 and legal Resilient during COVID-19 Recreation workers 87K
Waiters and waitresses 397K and continuing to 8 professionals and continuing to grow Childcare workers 85K
Cooks 96K decline 16
6 Business and legal professionals (600K)
STEM
professionals <25% low-wage jobs
Customer service and sales (1.3M)
5 25–50% workers without college degree
>75% low-wage jobs
~8.6M total
Project management specialists 110K
>75% workers without college degree
Retail salespersons 447K Customer occupational 7 Others Sales representatives 100K
service and 15 Business operations specialists 38K
Cashiers 158K sales
shifts
Hairdressers, hairstylists, 96K
STEM professionals (400K)
and cosmetologists
5
50% faster rate of change Educators <25% low-wage jobs
Office support (1.2M) than in previous 3 years 25–50% workers without college degree
4
>75% low-wage jobs Computer systems analysts 66K
Builders
>70% workers without college degree 14 3 Computer programmers 56K
Electrical and electronic engineering 21K
Office clerks, general 443K Office 7 Community technologists and technicians
Secretaries and administrative 96K services
support 10
assistants
Others (600K)
First-line supervisors of office and 70K Others
administrative support workers <25% low-wage jobs
Production
Stalled
work 25–50% workers without college degree
but rising
Production work (900K) Light truck drivers 62K
occupations
>75% low-wage jobs Bus drivers, transit and intercity 35K
Stalled during COVID-19 School psychologists 25K
>75% workers without college degree
but starting to rise
Other categories include health professionals, managers, and
Laborers and freight, stock, 126K transportation services.
and material movers
Production helpers 68K
Machinists 66K
Education and workforce training (400K) Builders (300K) Community services (300K) Others (600K)
25–50% low-wage jobs 25–50% low-wage jobs 25–50% low-wage jobs 50–75% low-wage jobs
<25% workers without college degree >75% workers without college degree 50–75% workers without college degree >75% workers without college degree
Note: Figures may not sum to 100%, due to rounding.
1“Occupational shifts” refers to net declines in employment in specific occupations Substitute teachers 154K Carpenters 40K Correctional officers and jailers 65K Maids and housekeeping cleaners 134K
between 2019 and 2022. However, we do not know exactly how individuals moved from
one occupation to another or if they made multiple moves; for that reason, we refer to the Tutors 81K Painters, construction and maintenance 25K Lifeguards, ski patrol, and 36K Coaches and scouts 26K
n chu am nb ge er s o . f occupational shifts rather than specifying the number of workers making those Preschool teachers 25K Drywall and ceiling tile installers 14K other recreational protective Computer, automated teller, 23K
service workers
Source: O*NET; US Bureau of Labor Statistics; Current Population Survey, US Census and office machine repairers
Bureau; McKinsey Global Institute analysis
Rehabilitation counselors 25K
Other categories include agriculture, creatives and art
management, mechanical installation and repair, and
McKinsey & Company property maintenance.
McKinsey Global Institute | Generative AI and the future of work in America 3
The Great Attrition obscured deeper shifts
While most attention was focused on soaring quits rates during the pandemic, something more
structural was also occurring. A subset of people did more than change employers; they moved
into different occupations altogether. Based on net increases and decreases in employment,
some 8.6 million occupational shifts took place from 2019 through 2022—50 percent more than
in the previous three-year period (Exhibit E1).3 While it is impossible to trace individual moves,
many people left their previous roles and landed better-paying jobs in other occupations.
The majority of these shifts came from people leaving jobs in food services, customer service and
sales, office support, and production work (such as manufacturing). At the same time, managerial
and professional roles plus transportation services collectively added close to four million jobs
from 2019 to 2022. Our previous research had anticipated these types of changes over a longer
time frame, but the pandemic suddenly accelerated matters. The past few years have been a
preview of trends we expect to continue through the end of the decade.
More high-wage jobs—and fewer workers taking lower-wage service jobs
Overall employment in low- and middle-wage occupations has fallen from prepandemic levels,
while occupations that pay more than $57,000 annually added about 3.5 million jobs. However, it
is unclear how many higher-paying roles were filled by people who moved up and how many were
filled by new entrants to the labor force. Meanwhile, the number of lower-wage job openings has
not declined. Demand for lower-wage service work remains, but fewer workers are accepting
these roles.
What is clear from the job switching and occupational shifts of the past three years is that the
US labor market accommodated a higher level of dynamic movement. Spiking demand and
labor scarcity forced many employers to consider nontraditional candidates with potential and
train them if they lacked direct experience. While this may not hold in the future, employers and
workers alike can draw on what they have learned about the potential for people to make quick
pivots and add new skills.
Automation and other forces will continue to reshape the labor market
Automation, from industrial robots to automated document processing systems, continues to
be the biggest factor in changing the demand for various occupations. Generative AI is both
accelerating automation and extending it to an entirely new set of occupations. While this
technology is advancing rapidly, other forces are also affecting labor demand. Overall, we expect
significant shifts in the occupational mix in the United States through the end of the decade.
The effects of automation and generative AI
Automation has taken a leap forward with the recent introduction of generative AI tools.
“Generative” refers to the fact that these tools can identify patterns across enormous sets of data
and generate new content—an ability that has often been considered uniquely human. Their most
striking advance is in natural language capabilities, which are required for a large number of work
activities. While ChatGPT is focused on text, other AI systems from major platforms can generate
images, video, and audio.
Although generative AI is still in the early stages, the potential applications for businesses are
significant and wide-ranging. Generative AI can be used to write code, design products, create
marketing content and strategies, streamline operations, analyze legal documents, provide
customer service via chatbots, and even accelerate scientific discovery. It can be used on its own
or with “humans in the loop”; the latter is more likely at present, given its current level of maturity.
3 Measured as net job losses for individual occupations across sectors, net of estimated retirements; derived from US Bureau
of Labor Statistics (BLS) data. An administrative assistant who takes a similar position with another employer has simply
switched jobs and is not part of this analysis. If that person becomes an office manager, they have changed occupations
within the same category (office support). If they become a computer systems analyst, they have moved into a different
occupational category (STEM professionals). The latter two moves are the kind of occupational shifts that we measure. Since
we are unable to trace exactly how individual workers moved, we use net declines as a broad proxy. In our forward-looking
scenario, we refer to people needing to make transitions if demand is projected to decline in their current occupation.
McKinsey Global Institute | Generative AI and the future of work in America 4
All of this means that automation is about to affect a wider set of work activities involving
expertise, interaction with people, and creativity. The timeline for automation adoption could be
sharply accelerated. Without generative AI, our research estimated, automation could take over
tasks accounting for 21.5 percent of the hours worked in the US economy by 2030. With it, that
share has now jumped to 29.5 percent (Exhibit E2).4
4 Note that this is the midpoint, representing the average of a very wide range, from 3.7 to 55.3 percent.
Web 2023
future-of-work
Exhibit 3 and 13 of 21
Exhibit E2
With generative AI added to the picture, 30 percent of hours worked today
could be automated by 2030.
Midpoint automation adoption¹ by 2030 as a share of time spent on work activities, US, %
Automation adoption without Automation adoption with XX — Percentage-point acceleration in
generative AI acceleration generative AI acceleration automation adoption from generative AI
0 10 20 30 40
STEM professionals 16
Education and workforce training 16
Creatives and arts management 15
Business and legal professionals 14
Managers 9
Community services 9
Office support 7
Health professionals 6
Builders 6
Property maintenance 6
Customer service and sales 6
Food services 5
Transportation services 5
Mechanical installation and repair 5
Production work 4
Health aides, technicians, and wellness 4
Agriculture 3
All sectors² 8
¹Midpoint automation adoption is the average of early and late automation adoption scenarios as referenced in The economic potential of generative AI: The next
productivity frontier, McKinsey & Company, June 2023.
²Totals are weighted by 2022 employment in each occupation.
Source: O*NET; US Bureau of Labor Statistics; McKinsey Global Institute analysis
McKinsey & Company
McKinsey Global Institute | Generative AI and the future of work in America 5
Other forces affecting future labor demand
Automation is not occurring in a vacuum, of course. Other trends are affecting the demand for
certain occupations, and we expect the employment mix to change significantly through 2030,
with more healthcare, STEM, and managerial positions and fewer jobs in customer service, office
support, and food services.
— Federal investment: Recent federal legislation is driving momentum and investment in
other areas that will affect jobs.5 Reaching the net-zero emissions goal is one of these
priorities. Some 3.5 million jobs could be displaced through direct and indirect effects across
the economy. But at the macro level, these losses should be more than offset by gains of
4.2 million jobs, primarily led by capital expenditures on renewable energy. The net-zero
transition will likely be a net positive for jobs, but those jobs may be located in different places
and require different skills.
Similarly, major investment in infrastructure projects across the country will bolster
construction jobs, which could see employment growth of 12 percent from 2022 through
2030. However, the sector already had some 383,000 unfilled positions in April 2023. This
shortage will have to be addressed to bring infrastructure projects to life from coast to coast.6
The CHIPS and Science Act is putting additional funding into semiconductor manufacturing
as well as R&D and scientific research.7 This comes at a time when some companies have
been adjusting their supply chains, leading to an uptick in domestic manufacturing. While
manufacturing is likely to boost employment demand overall in the years ahead, the sector is
becoming more high-tech. It will involve fewer traditional production jobs than in the past but
more workers with technical and STEM skills.8
— Other structural trends: At the same time, other trends like rising incomes and education
levels will sustain jobs. An aging population will need more healthcare workers in multiple
roles, while the ongoing process of digitizing the economy will require adding more tech
workers in every sector.
Putting it all together, the mix of jobs is changing, and we
anticipate an additional 12 million occupational shifts
One of the biggest questions of recent months is whether generative AI might wipe out jobs. Our
research does not lead us to that conclusion, although we cannot definitively rule out job losses,
at least in the short term. Technological advances often cause disruption, but historically, they
eventually fuel economic and employment growth.
This research does not predict aggregated future employment levels; instead, we model various
drivers of labor demand to look at how the mix of jobs might change—and those results yield
some gains and some losses.9 In fact, the occupational categories most exposed to generative
AI could continue to add jobs through 2030 (Exhibit E3), although its adoption may slow their
rate of growth. And even as automation takes hold, investment and structural drivers will support
employment. The biggest impact for knowledge workers that we can state with certainty is that
generative AI is likely to significantly change their mix of work activities.
5 While our scenario includes the impact of federal investment in the net-zero transition and infrastructure, it does not include
the full impact of the CHIPS and Science Act and the Inflation Reduction Act, since implementation remained unclear at the
time of this analysis. However, both pieces of legislation point to the possibility of additional upside.
6 Garo Hovnanian, Adi Kumar, and Ryan Luby, “Will a labor crunch derail plans to upgrade US infrastructure?” McKinsey &
Company, October 2022.
7 Note that both the CHIPS and Science Act and the Inflation Reduction Act create room for additional upside in employment.
But since there is still uncertainty about their implementation as of this writing, their effects on jobs are not explicitly
incorporated into our scenario.
8 For more on this topic, see Asutosh Padhi, Gaurav Batra, and Nick Santhanam, The titanium economy: How industrial
technology can create a better, faster, stronger America, Public Affairs, 2022.
9 We rely on employment projections from the US Bureau of Labor Statistics for 2030 employment levels.
McKinsey Global Institute | Generative AI and the future of work in America 6
Web 2023
future-of-work
Exhibit E3
Exhibit 4 and 8 of 21
While STEM, healthcare, builders, and professional fields continue to add jobs,
generative AI could change work activities significantly for many occupations.
Estimated labor demand change and generative AI Midpoint automation Employment,
automation acceleration by occupation, US, 2022–30 adoption¹ by 2030, % absolute
15– 25– 35–
35 25 35 40 5M 10M
Health
professionals
30
Health aides,
technicians,
25 and wellness
STEM
professionals
20 Increasing labor demand Increasing labor demand
and modest change of and high change of work
work activities activities
15
Builders Managers Creatives and
Change
arts management
in labor
demand,² % 10 Transportation
Property
services
maintenance
Business and
legal professionals
5 Mechanical
Community
Agriculture installation services Education and
and repair workforce
training
0
Production 5 10 15 20
work Food
services
–5
Decreasing labor demand
with modest change of
work activities
–10
Customer
service
–15 and sales
Office
support
–20
Increase in automation adoption driven by generative AI acceleration, percentage points
¹Midpoint automation adoption is the average of early and late automation adoption scenarios as referenced in The economic potential of generative AI: The next
productivity frontier, McKinsey & Company, June 2023.
2We consider multiple drivers affecting demand: rising income, aging populations, technology investment, infrastructure investment (including Bipartisan
Infrastructure Law), rising education levels, net-zero transitions, marketization of unpaid work, creation of new occupations, automation (including generative AI),
increased remote working and virtual meetings, and e-commerce and other virtual transactions.
Source: US Bureau of Labor Statistics; Current Population Survey, US Census Bureau; McKinsey Global Institute analysis
McKinsey & Company
McKinsey Global Institute | Generative AI and the future of work in America 7
Resilient and growing occupational categories
The largest future job gains are expected to be in healthcare, an industry that already has an
imbalance, with 1.9 million unfilled openings as of April 2023. We estimate that there could be
demand for 3.5 million more jobs for health aides, health technicians, and wellness workers, plus
an additional two million healthcare professionals.10
By 2030, we further estimate a 23 percent increase in the demand for STEM jobs. Although
layoffs in the tech sector have been making headlines in 2023, this does not change the longer-
term demand for tech talent among companies of all sizes and sectors as the economy continues
to digitize. Employers in banking, insurance, pharmaceuticals, and healthcare, for example,
are undertaking major digital transformations and need tech workers with advanced skills.11
In addition, the transportation services category is expected to see job growth of 9 percent
by 2030.
Declining occupational categories
The biggest future job losses are likely to occur in office support, customer service, and food
services. We estimate that demand for clerks12 could decrease by 1.6 million jobs, in addition to
losses of 830,000 for retail salespersons, 710,000 for administrative assistants, and 630,000
for cashiers. These jobs involve a high share of repetitive tasks, data collection, and elementary
data processing, all activities that automated systems can handle efficiently. Our analysis also
finds a modest decline in production jobs despite an upswing in the overall US manufacturing
sector, which is explained by the fact that the sector increasingly requires fewer traditional
production jobs but more skilled technical and digital roles.13
We estimate that 11.8 million workers currently in occupations with shrinking demand may need
to move into different lines of work by 2030. Roughly nine million of them may wind up moving
into different occupational categories altogether. Considering what has already transpired, that
would bring the total number of occupational transitions through the decade’s end to a level
almost 25 percent higher than our earlier estimates, creating a more pronounced shift in the mix
of jobs across the economy.
Overall, we expect more growth in demand for jobs requiring higher levels of education and skills,
plus declines in roles that typically do not require college degrees (Exhibit E4).
Almost 12 million additional
occupational transitions may be
needed by the end of the decade.
10 Note that registered nurses, nurse practitioners, and nurse anesthetists are in the healthcare professionals category; nurse
midwives and licensed practical and licensed vocational nurses are in the health aides category.
11 Jon Swartz, “As Big Tech cuts workers, other industries are desperate to hire them,” MarketWatch, February 18, 2023; and
Steve Lohr and Tripp Mickle, “As Silicon Valley retrenches, a tech talent shift accelerates,” New York Times, December 29,
2022.
12 Note that clerks include receptionists and information clerks, general office clerks, bookkeeping, accounting, and auditing
clerks, and shipping, receiving, and inventory clerks
13 Building a more competitive US manufacturing sector, McKinsey Global Institute, April 2021.
Mc"
247,mckinsey,mckinsey-technology-trends-outlook-2023-v5.pdf,"Technology Trends
Outlook 2023
July 2023
McKinsey & Company
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Contents
Introduction 4
The AI revolution 11 Cutting-edge engineeering 63
Applied AI 12 Future of mobility 64
Industrializing machine learning 16 Future of bioengineering 69
Generative AI 21 Future of space technologies 74
Building the digital future 26 A sustainable world 79
Next-generation software development 27 Electrification and renewables 80
Trust architectures and digital identity 32 Climate technologies beyond
electrification and renewables 80
Web3 37
Compute and connectivity frontiers 42
Advanced connectivity 43
Immersive-reality technologies 48
Cloud and edge computing 53
Quantum technologies 58
Technology Trends Outlook 2023 3
Introduction
After a tumultuous 2022 for technology investment and New and notable
talent, the first half of 2023 has seen a resurgence of
All of last year’s 14 trends remain on our list, though some
enthusiasm about technology’s potential to catalyze
experienced accelerating momentum and investment,
progress in business and society. Generative AI deserves
while others saw a downshift. One new trend, generative
much of the credit for ushering in this revival, but it
AI, made a loud entrance and has already shown
stands as just one of many advances on the horizon
potential for transformative business impact.
that could drive sustainable, inclusive growth and solve
complex global challenges. This new entrant represents the next frontier of AI.
Building upon existing technologies such as applied
To help executives track the latest developments, the
AI and industrializing machine learning, generative
McKinsey Technology Council has once again identified
AI has high potential and applicability across most
and interpreted the most significant technology trends
industries. Interest in the topic (as gauged by news and
unfolding today. While many trends are in the early
internet searches) increased threefold from 2021 to
stages of adoption and scale, executives can use this
2022. As we recently wrote, generative AI and other
research to plan ahead by developing an understanding
foundational models change the AI game by taking
of potential use cases and pinpointing the critical skills
assistive technology to a new level, reducing application
needed as they hire or upskill talent to bring these
development time, and bringing powerful capabilities
opportunities to fruition.
to nontechnical users. Generative AI is poised to add
Our analysis examines quantitative measures of as much as $4.4 trillion in economic value from a
interest, innovation, and investment to gauge the combination of specific use cases and more diffuse
momentum of each trend. Recognizing the long-term uses—such as assisting with email drafts—that increase
nature and interdependence of these trends, we also productivity. Still, while generative AI can unlock
delve into underlying technologies, uncertainties, and significant value, firms should not underestimate the
questions surrounding each trend. This year, we added economic significance and the growth potential that
an important new dimension for analysis—talent. We underlying AI technologies and industrializing machine
provide data on talent supply-and-demand dynamics learning can bring to various industries.
for the roles of most relevance to each trend. (For more,
please see the sidebar, “Research methodology,”
on page 9.)
About the McKinsey Technology Council
Technology is changing everything in our work and home lives. The McKinsey Technology Council helps
understand what is coming and how it will affect us all—taking a look around the corner toward the futures
that technology change can unlock as well as the tough questions it raises.
We look at a spectrum of technologies, from artificial intelligence to computing to biology, and their
applications across all sectors, from mining to entertainment. We also look at the science, how it
translates into engineering, and when it will accelerate to impact—at scale and around the world.
The McKinsey Technology Council brings together a global group of more than 100 scientists,
entrepreneurs, researchers, and business leaders. We research, debate, inform, and advise, helping
executives from all sectors navigate the fast-changing technology landscape. Together, we are
shaping the future.
—Lareina Yee, senior partner,
McKinsey; chair, McKinsey Technology Council
Technology Trends Outlook 2023 4
++1125%% Investment- in m1ost 4tech tre%nds tightened automotive, chemicals, financial services, and
year over year, but the potential for future life sciences—stand to potentially gain up
growth remains high, as further indicated by to $1.3 trillion in value by 2035. By carefully
the recent rebound in tech valuations. Indeed, assessing the evolving landscape and
tech trends job postings absolute ingvelsotmbeanlt sj orebma pineods sttrionngg sin considering a balanced approach, businesses
2022, at more than $1 trillion combined, can capitalize on both established and
from 2021 to 2022 from 2021 to 2022
indicating great faith in the value potential of emerging technologies to propel innovation
these trends. Trust architectures and digital and achieve sustainable growth.
identity grew the most out of last year’s 14
+12% -14%
−13% trends, increasing by nearly 50 percent as
Tech talent dynamics
security, privacy, and resilience become
increasingly critical across industries. We can’t overstate the importance of
Investment in other trends—such as applied talent as a key source in developing a
tech trends job postings global job postings AI, advanced connectivity, and cloud and competitive edge. A lack of talent is a top
from 2021 to 2022 from 2021 to 2022 edge computing—declined, but that is likely issue constraining growth. There’s a wide gap
due, at least in part, to their maturity. More between the demand for people with the skills
mature technologies can be more sensitive needed to capture value from the tech trends
to short-term budget dynamics than more and available talent: our survey of 3.5 million
nascent technologies with longer investment job postings in these tech trends found that
time horizons, such as climate and mobility many of the skills in greatest demand have
technologies. Also, as some technologies less than half as many qualified practitioners
become more profitable, they can often scale per posting as the global average. Companies
further with lower marginal investment. Given should be on top of the talent market, ready
that these technologies have applications to respond to notable shifts and to deliver a
in most industries, we have little doubt that strong value proposition to the technologists
mainstream adoption will continue to grow. they hope to hire and retain. For instance,
recent layoffs in the tech sector may present
Organizations shouldn’t focus too heavily
a silver lining for other industries that have
on the trends that are garnering the most
struggled to win the attention of attractive
attention. By focusing on only the most hyped
candidates and retain senior tech talent.
trends, they may miss out on the significant
In addition, some of these technologies
value potential of other technologies and
will accelerate the pace of workforce
hinder the chance for purposeful capability
transformation. In the coming decade, 20 to
building. Instead, companies seeking
30 percent of the time that workers spend on
longer-term growth should focus on a
the job could be transformed by automation
portfolio-oriented investment across the
technologies, leading to significant shifts
tech trends most important to their business.
in the skills required to be successful. And
Technologies such as cloud and edge
companies should continue to look at how
computing and the future of bioengineering
they can adjust roles or upskill individuals
have shown steady increases in innovation
to meet their tailored job requirements. Job
and continue to have expanded use cases
postings in fields related to tech trends grew
across industries. In fact, more than 400 edge
at a very healthy 15 percent between 2021 and
use cases across various industries have been
2022, even though global job postings overall
identified, and edge computing is projected to
decreased by 13 percent. Applied AI and next-
win double-digit growth globally over the next
generation software development together
five years. Additionally, nascent technologies,
posted nearly one million jobs between
such as quantum, continue to evolve and
2018 and 2022. Next-generation software
show significant potential for value creation.
development saw the most significant growth
Our updated analysis for 2023 shows that
in number of jobs (Exhibit 1).
the four industries likely to see the earliest
economic impact from quantum computing—
Technology Trends Outlook 2023 5
Web <2023>
<ETxehchibTrietn 1d s-L1>
Exhibit <1> of <3>
Job postings for fields related to tech trends grew by 400,000 between 2021
and 2022, with generative AI growing the fastest.
Tech trend job postings,1 2021–22, thousands
700
600
+6%
500
+29%
400
+12%
300
+16% +15%
200
100
2021 2022
0
Applied AI Next-generation Cloud and edge Trust architectures Future of
software development computing and digital identity mobility
300
200 +27%
100 +8% +7%
+10% +23%
0
Electrification and Climate tech beyond Advanced Immersive-reality Industrializing
renewables electrification and connectivity technologies machine learning
renewables
200
+40% +16% +44% +12%
100
–19%
0
Web3 Future of Future of space Generative AI Quantum
bioengineering technologies technologies
1Out of 150 million surveyed job postings. Job postings are not directly equivalent to numbers of new or existing jobs.
Source: McKinsey’s proprietary Organizational Data Platform, which draws on licensed, de-identified public professional profile data
McKinsey & Company
This bright outlook for practitioners in most fields The talent crunch is particularly pronounced for trends
highlights the challenge facing employers who are such as cloud computing and industrializing machine
struggling to find enough talent to keep up with their learning, which are required across most industries.
demands. The shortage of qualified talent has been a It’s also a major challenge in areas that employ highly
persistent limiting factor in the growth of many high- specialized professionals, such as the future of mobility
tech fields, including AI, quantum technologies, space and quantum computing (Exhibit 2).
technologies, and electrification and renewables.
Technology Trends Outlook 2023 6
Exhibit 2
Most fields related to these tech trends require skills where talent supply is
low, while only a few fields have a talent surplus.
Availability of qualified talent, by skill required per
tech trend,¹ ratio of profiles to job postings Rank 1 2 3
Talent Talent
deficit Machine learning (ML) surplus
Data science
Applied AI
TensorFlow
Amazon Web Continuous
Services integration
Next-generation
software development Infrastructure Cloud
managementAmazon Web Services computing
Cloud and edge computing
Cloud
Risk Regulatory Computer computing
Trust architectures and analysis compliance security
digital identity
Maintenance
Manufacturing
Future of mobility
Automotive industry
Contract management
Electrification and renewables
Photovoltaics Renewable
energy
Climate tech beyond
electrification and renewables Sustainability Energy
Regulatory efficiency
compliance Kubernetes
Advanced connectivity
Telecommunications
Internet of Things
Immersive-reality technologies
Product Computer Graphic
engineering vision design
Industrializing machine learning
PyTorch TensorFlow
ML
Web3
Stakeholder Cloud
management computing Blockchain Molecular
biology
Future of bioengineering
Pharmaceuticals Gene therapy
Remote
Aerospace engineering sensing
Future of space technologies
Aerospace industries
Python ML
Generative AI
Regulatory
Python
compliance
Cloud computing Quantum computing
Quantum technologies
<0.1:1 0.1:1 0.2:1 0.4:1 0.6:1 1:1 2:12 4:1 6:1 8:1
¹The ratio of online profiles claiming each trend’s most needed tech skills to all job postings requiring skill (logarithmic scale).
²Benchmark: 2 profiles with skill per job posting. Average talent supply–demand ratio benchmark based on skills listed for the 20 most common jobs.
Source: McKinsey’s proprietary Organizational Data Platform, which draws on licensed, de-identified public professional profile data
McKinsey & Company
Technology Trends Outlook 2023 7
The 15 tech trends combinations, there’s significant power and potential in
looking across these groupings.
This report lays out considerations for all 15 technology
trends. We grouped them into five broader categories To describe the state of each trend, we developed
to make it easier to consider related trends: the AI scores for innovation (based on patents and research)
revolution, building the digital future, cutting-edge and interest (based on news and web searches). We also
engineering, compute and connectivity frontiers, and a counted investments in relevant technologies and rated
sustainable world. Of course, when considering trend their level of adoption by organizations (Exhibit 3).
Exhibit 3
We described each trend by scoring innovation and interest, and we also
counted investments and rated their level of adoption by organizations.
Innovation, interest, investment, and adoption, by technology trend, 2022
1.0
Adoption rate, score
(0 = no adoption; 5 =
Applied AI mainstream adoption)
0.8 0 1 2 3 4 5
Future of Advanced
0.6 bioengineering connectivity
Innovation,1 score
(0 = lower;
1 = higher) Electrification
Quantum technologies
and renewables
0.4 Industrializing machine learning
Next-generation software development
Cloud and edge computing
Future of
Immersive-reality technologies
mobility
0.2
Climate tech beyond electrification & renewables
Equity investment, $ billion
Trust architectures and digital identity
Future of space tech
Web3
Generative AI
250 150 75
0
0 0.01 0.10 1.00
0 0.2 0.4 0.6 0.8 1.0
Interest,2 score
(0 = lower; 1 = higher)
Note: Innovation and interest scores for the 15 trends are relative to one another. All trends exhibit high levels of innovation and interest compared with
other topics and are also attracting significant investment.
1The innovation score combines the 0–1 scores for patents and research, which are relative to the trends studied. The patents score is based on a measure
of patent filings, and the research score is based on a measure of research publications.
2The interest score combines the 0–1 scores for news and searches, which are relative to the trends studied. The news score is based on a measure of news
publications, and the searches score is based on a measure of search engine queries.
McKinsey & Company
Technology Trends Outlook 2023 8
Research methodology
To assess the development of each technology trend, our team collected data on five tangible
measures of activity: search engine queries, news publications, patents, research publications,
and investment. For each measure, we used a defined set of data sources to find occurrences of
keywords associated with each of the 15 trends, screened those occurrences for valid mentions
of activity, and indexed the resulting numbers of mentions on a 0–1 scoring scale that is relative
to the trends studied. The innovation score combines the patents and research scores; the
interest score combines the news and search scores. (While we recognize that an interest
score can be inflated by deliberate efforts to stimulate news and search activity, we believe that
each score fairly reflects the extent of discussion and debate about a given trend.) Investment
measures the flows of funding from the capital markets into companies linked with the trend.
Data sources for the scores include the following:
— Patents. Data on patent filings are sourced from Google Patents.
— Research. Data on research publications are sourced from the Lens (www.lens.org).
— News. Data on news publications are sourced from Factiva.
— Searches. Data on search engine queries are sourced from Google Trends.
— Investment. Data on private-market and public-market capital raises are sourced
from PitchBook.
— Talent demand. Number of job postings is sourced from McKinsey’s proprietary
Organizational Data Platform, which stores licensed, de-identified data on professional
profiles and job postings. Data is drawn primarily from English-speaking countries.
In addition, we updated the selection and definition of trends from last year’s study to reflect the
evolution of technology trends:
— The generative-AI trend was added since last year’s study.
— We adjusted the definitions of electrification and renewables (previously called future of
clean energy) and climate technologies beyond electrification and renewables (previously
called future of sustainable consumption).
— Data sources were updated. This year, we included only closed deals in PitchBook
data, which revised downward the investment numbers for 2018–22. For future of
space technologies investments, we used research from McKinsey’s Aerospace &
Defense Practice.
Technology Trends Outlook 2023 9
About the authors
Michael Chui Mena Issler Roger Roberts Lareina Yee
McKinsey Global Institute Associate partner, Partner, Senior partner, Bay Area; chair,
Partner, Bay Area Bay Area Bay Area McKinsey Technology Council
The authors wish to thank the following McKinsey colleagues for their contributions to this research:
Bharat Bahl Jonathan DePrizio Naomi Kim Tanya Rodchenko
Soumya Banerjee Ivan Dyakonov Jesse Klempner Lucy Shenton
Arjita Bhan Torgyn Erland Kelly Kochanski Henning Soller
Tanmay Bhatnagar Robin Giesbrecht Matej Macak Naveen Srikakulam
Jim Boehm Carlo Giovine Stephanie Madner Shivam Srivastava
Andreas Breiter Liz Grennan Aishwarya Mohapatra Bhargs Srivathsan
Tom Brennan Ferry Grijpink Timo Möller Erika Stanzl
Ryan Brukardt Harsh Gupta Matt Mrozek Brooke Stokes
Kevin Buehler Martin Harrysson Evan Nazareth Malin Strandell-Jansson
Zina Cole David Harvey Peter Noteboom Daniel Wallance
Santiago Comella-Dorda Kersten Heineke Anna Orthofer Allen Weinberg
Brian Constantine Matt Higginson Katherine Ottenbreit Olivia White
Daniela Cuneo Alharith Hussin Eric Parsonnet Martin Wrulich
Wendy Cyffka Tore Johnston Mark Patel Perez Yeptho
Chris Daehnick Philipp Kampshoff Bruce Philp Matija Zesko
Ian De Bode Hamza Khan Fabian Queder Felix Ziegler
Andrea Del Miglio Nayur Khan Robin Riedel Delphine Zurkiya
They also wish to thank the external members of the McKinsey Technology Council.
Technology Trends Outlook 2023 10
The AI revolution
Technology Trends Outlook 2023 11
Applied AI
The trend—and why it matters McKinsey Global Survey on the state of AI shows that the
proportion of responding organizations adopting AI more
With AI capabilities, such as machine learning (ML),
than doubled from 20 percent in 2017 to 50 percent in 2022.
computer vision, and natural-language processing (NLP),
The 2022 survey also indicated that adopting AI can have
companies in all industries can use data and derive insights
significant financial benefits: 25 percent of respondents
to automate processes, add or augment capabilities, and
attributed 5 percent or more of their companies’ EBIT to AI.
make better decisions. McKinsey research estimates the
However, organizational, technical, ethical, and regulatory
potential economic value at stake from applied AI to be
issues should be resolved before businesses can realize the
$17 trillion to $26 trillion, and the share of companies
technology’s full potential.
pursuing that value has been increasing. The annual
Applied AI Score by vector (0 = lower; 1 = higher)
Scoring the trend
Talent demand News
High innovation and investment scores for
applied AI are commensurate with its large
potential impact. Each year from 2018 to 2022,
applied AI has had the highest innovation scores
of all the trends we studied, and its investment
score also ranks in the top five. Perhaps Equity Searches
unsurprisingly, in 2022, demand for talent in investment 0.2
0.4
applied AI was also highest among all trends. 0.6
0.8
1.0
Adoption rate score, 2022
Patents Research
0 1 2 3 4 5
None Mainstream
1.0
Equity investment, Job postings,
2022, 2021–22, 0
2018 2022
$ billion % difference
104 +6
Talent demand Ratio News Press reports
Industries affected: Aerospace and defense; of actual skilled people featuring trend-
Agriculture; Automotive and assembly; Aviation, to job vacancies related phrases
travel, and logistics; Chemicals; Construction and
Equity investment Searches Search
building materials; Consumer packaged goods;
Private- and public- engine queries for
Education; Electric power, natural gas, and utilities;
market capital raises for terms related to
Financial services; Healthcare systems and
relevant technologies trend
services; Information technology and electronics;
Media and entertainment; Metals and mining; Oil Patents Patent Research Scientific
and gas; Pharmaceuticals and medical products; filings for technologies publications on topics
Public and social sectors; Real estate; Retail related to trend associated with trend
Telecommunications
Technology Trends Outlook 2023 12
Latest developments — Global AI adoption plateaus—for now. While AI adoption
globally is more than double that in 2017, the proportion
These are some recent developments involving applied AI:
of organizations using AI has leveled off to around
— Investment fuels enhanced AI capabilities. Although 50 percent to 60 percent in recent years. However,
investments in AI were down to $104 billion in 2022 companies that have already adopted AI nearly doubled
from a high of $146.8 billion in 2021, they continue to the number of capabilities they use, such as natural-
pace ahead of 2018–20 levels, which averaged language generation or computer vision, from 1.9 in 2018
$73.5 billion. With investments flowing, AI continues to 3.8 in 2022.2
to post state-of-the-art results with continuous
improvements in areas such as model accuracy. For
example, the cost to train image classification systems
has decreased by 63.6 percent, and training times
have improved by 94.4 percent since 2018.1 However,
additional potential for applied AI could be unlocked
by combining it with new emerging AI technology. For
example, the foundation models underlying generative
AI could process large amounts of unstructured
manufacturing data, such as notes and logs, to enrich
current AI solutions that optimize performance.
— Policy makers accelerate regulatory actions to curb
AI misuse. As AI technology advances, so too has its
potential for misuse: the AIAAIC Repository, which
tracks incidents related to the ethical misuse of AI,
algorithms, and automation, indicates that the number
of controversies involving AI has increased by 26 times
since 2012. Algorithmic fairness, bias, and misuse have ‘We haven’t found an industry
become mainstream concerns. An analysis of legislative
or business function that couldn’t
records in 127 countries shows that the number of laws
passed containing the words “artificial intelligence” enhance its performance through
grew from one in 2016 to 37 in 2022. Prompted by the
applying AI. But capturing the
accelerated development of AI by private firms, the
European Union’s AI Act—which regulates foundational value of AI is a journey that
AI models—is nearing law status following parliamentary
requires taking action across
committee approval. Meanwhile, the McKinsey Global
multiple dimensions, from
Survey on the state of AI indicates that there has been
no substantial increase in organizations’ reported talent to technology.’
mitigation of AI-related risks relative to the increase
in AI use. – Michael Chui, partner, Bay Area
1 Daniel Zhang et al., Artificial Intelligence Index Report 2022, AI Index Steering Committee, Stanford Institute for Human-Centered AI, Stanford University,
March 2022.
2 “The state of AI in 2022—and a half decade in review,” QuantumBlack, AI by McKinsey, December 6, 2022.
Technology Trends Outlook 2023 13
Talent market
Applied AI
Demand
Applied AI has seen rapid growth in demand for talent, with job postings more than tripling since 2018. Demand
for data scientists and software engineers grew significantly in 2021 and saw moderate growth in 2022.
Job postings by title, 2018–22, thousands
100 Data scientist
Software engineer
80 Data engineer
Software developer
60
Customer service representative
Project manager
40
Scientist
Product manager
20
0
2018 2022
Skills availability
The demand for practitioners of machine learning, data science, NLP, and some associated tools is high
compared with supply.
Talent availability, % share of postings requiring skill
7
21 3 3 2 2 2
Machine Data TensorFlow Computer Natural-language Deep PyTorch
learning science vision processing learning
Talent availability, ratio of talent to demand
13.2
1.5 1.7 2.0
0.9 0.8 1.3
Machine Data TensorFlow Computer Natural-language Deep PyTorch
learning science vision processing learning
Technology Trends Outlook 2023 14
‘Applied AI has the potential to become more valuable and
useful to companies in combination with generative AI. A key
marker for the future will be how synergies between the two
are captured to maximize value capture across organizations.’
– Carlo Giovine, partner, London
In real life Key uncertainties
Real-world examples involving the use of applied AI The major uncertainties affecting applied AI include
include the following: the following:
— Emirates Team New Zealand dramatically accelerated — Lack of available resources, such as talent and funding,
hydrofoil design and testing by using AI to train a “digital might affect the pipeline of AI applications, despite
twin”—a digital replica of a sailor—to test designs in technical advances in solutions for industrializing ML
a simulated environment. By using the AI “sailor” to and in IT infrastructure.
remove the bottleneck of human sailors performing the
— Cybersecurity and privacy concerns, notably on data
tests, the team reduced costs by 95 percent and was
risks and vulnerabilities, are prevalent—51 percent of
able to test ten times as many designs.
survey respondents cited cybersecurity as a leading
— Freeport-McMoRan deployed a custom-built AI model risk in 2022.
loaded with three years’ worth of operating data to
— Regulation and compliance might affect AI research
optimize production processes and total output at a
and applications.
copper mill. In doing so, it increased production by
10 percent while reducing capital expenditures on a — Ethical considerations—including data governance,
planned expansion. equity, fairness, and “explainability”—surround the
responsible and trustworthy use of AI.
— Telkomsel built a new data analytics platform
supplemented by AI-driven tools to better understand
customers across thousands of microsegments. Using Big questions about the future
9,000 data points per customer across more than
Companies and leaders may want to consider a few
50 models, the company drives personalization by
questions when moving forward with applied AI:
identifying the right way to interact with customers and
offering the most relevant products and services. — How might companies better determine which
AI applications benefit them and their
stakeholders most?
Underlying technologies
— What features make AI trustworthy and responsible
AI comprises several technologies that perform cognitive-
and how should they be integrated into applications?
like tasks. These include the following:
— What checks should companies put in place to guard
— Machine learning (ML). This term refers to models that
against AI-related risks associated with data privacy
make predictions after being trained with data rather
and security, equity, fairness, and compliance?
than following programmed rules.
— How will companies use generative AI in combination
— Computer vision. This type of ML works with visual
with applied AI to maximize potential synergies or
data, such as images, videos, and 3-D signals.
differentiate when it makes sense to use one approach
— Natural-language processing (NLP). This type of ML over the other?
analyzes and generates language-based data, such as
text and speech.
— Deep reinforcement learning. This type of ML uses
artificial neural networks and training through trial and
error to make predictions.
Technology Trends Outlook 2023 15
Industrializing machine learning
The trend—and why it matters solutions, identify and resolve issues in production, and
improve teams’ productivity. Experience suggests that
Industrializing machine learning (ML), commonly referred
organizations that industrialize ML successfully can shorten
to as ML operations, or MLOps, refers to the engineering
the production time frame for ML applications (from proof of
practices needed to scale and sustain ML applications in
concept to product) by about eight to ten times and reduce
an enterprise. These practices are enabled and supported
development resources by up to 40 percent.3 Industrialized
by an ecosystem of technical tools that is rapidly improving,
ML was pioneered by a small number of leading companies,
both in functionality and interoperability. MLOps tools can
but adoption is now spreading as more companies use AI for
help companies transition from pilot projects to viable
a wider range of applications.
business products, accelerate the scale-up of analytics
Industrializing machine Score by vector (0 = lower; 1 = higher)
learning
Talent demand News
Scoring the trend
Scores across news, searches, publications,
and patents increased significantly, while
demand for talent has nearly quadrupled in
the same time frame. These increases suggest Equity Searches
0.1
investment
that the use of methods for industrializing ML 0.2
0.3
could widen in the years ahead 0.4
0.5
Adoption rate score, 2022
Patents Research
0 1 2 3 4 5
None Mainstream 1.0
0.5
0
Equity investment, Job postings, 2018 2022
2022, 2021–22,
$ billion % difference
3 +23
Talent demand Ratio News Press reports
of actual skilled people featuring trend-
to job vacancies related phrases
Industries affected: Aerospace and defense;
Equity investment Searches Search
Automotive and assembly; Electric power,
Private- and public- engine queries for
natural gas, and utilities; Financial services;
market capital raises for terms related to
Information technology and electronics;
relevant technologies trend
Media and entertainment; Metals and
mining; Oil and gas; Pharmaceuticals and Patents Patent Research Scientific
medical products; Telecommunications filings for technologies publications on topics
related to trend associated with trend
3 Based on observations from ML operations deployment in a series of large-scale analytics transformations supported by McKinsey.
Technology Trends Outlook 2023 16
Latest developments
These are some recent developments involving
industrializing ML:
— Companies increasingly commit to industrializing ML.
Investments into companies in the ML industrialization
space reached a high of $4.7 billion in 2021 and
remained strong throughout 2022 at a cumulative
$3.4 billion. With investments flowing, ML decision
makers have also doubled down on their commitments:
85 percent of respondents to a ClearML survey
‘We are at an inflection point
indicated that they had a dedicated MLOps budget in
2022. IDC predicts that 60 percent of enterprises will with artificial intelligence.
have implemented MLOps by 2024. Such investments
Generative AI has captured
could prove wise, as our own research finds that
companies seeing higher returns from AI are more both mainstream and business
likely to engage in ML industrialization.
imaginations. Organizations
— The ecosystem rapidly evolves through acquisitions and
that are willing to continuously
new offerings. The year 2022 was marked by significant
consolidation, partnerships, and new releases. Altair learn and adapt their processes,
acquired RapidMiner, Snowflake acquired Myst AI,
ways of working, and technology
McKinsey acquired Iguazio, and Hewlett Packard
Enterprise acquired Pachyderm. Databricks announc"
249,mckinsey,EN_McKinsey_GenAI_Implications_Germany_Labor_Market.pdf,"Effects of GenAI
on the German
labor market
An opportunity to mitigate skilled labor shortages
November 2023
CONFIDENTIAL AND PROPRIETARY
Any use of this material without specific permission
of McKinsey & Company is strictly prohibited
An opportunity to
mitigate skilled
labor shortages
1
Germany is experiencing  Share of businesses affected by skilled labor shortageshave increased 5-fold p.4
since 2009.
skilled labor shortages
 General open positions have increased 4-fold since 2004 p.5
2
GenAI can unlock and boost  GenAI has the potential to greatly enhance Germany's competitiveness by p.12
boosting productivity growth by an estimated 18%. GenAI can also help
productivity to mitigate
address skilled labor shortages through innovation.
these shortages
 This primarily concerns: Professions in workforce training, STEM, and p.15
healthcare that have both the greatest need (represented by the share of job
vacancies per share of employment in an occupation group – of >0.9) and
greatest GenAI potential (>17 pp) for labor shortage mitigation
 Greatest profitable effects for a) employees in highly professionalized careers p.14
(e.g., legal and business, 36 pp) and b) higher education (e.g., tertiary p.13
education, 24 pp), as well as c) high-earning employees (e.g., top earners, 12 p.16
pp)
3
 Germany boasts the highest number of GenAI startups (>500) in the EU p.20
Germany has a promising
private sector
landscape for GenAI adoption
 Germany holds a top-five global ranking in computing power, academic
p.22
publications, and patents, demonstrating its competitiveness in tech and
research.
 Germany ranks second among OECD countries in AI skill penetration, with p.23
1.7 out of every 100 workers reporting AI skills, only slightly behind the United
States.
McKinsey & Company 2
The skilled labor shortage in
Germany
Agenda
The potential of GenAI to
increase productivity
The GenAI landscape in Germany
McKinsey & Company 3
At the end of 2022, ~50% of businesses reported they had been
affected by skilled labor shortages, marking a 5x increase since 2009
Manufacturing industry Retail Services Construction Wholesale
Share of enterprises affected by skilled labor shortages according to sectors in Germany,
Reported to the ifo Institute, percent
60
50
40
5x increase
30
20
10
0
2004 06 08 10 12 14 16 18 20 2022
Source: ifo Institute, ifo Konjunkturumfragen McKinsey & Company 4
Skilled labor shortage sentiment is corroborated by reported
open positions quadrupling between 2004 and 2022
Absolute number of open positions in Germany,
Reported to the Federal Employment Agency in Germany, thousands
900
800 4x increase
700
600
500
400
300
200
100
0
2004 06 08 10 12 14 16 18 20 2022
Source: Federal Employment Agency (Bundesagentur für Arbeit), labor market in numbers McKinsey & Company 5
The skilled labor shortage in
Germany
Agenda
The potential of GenAI to
increase productivity
The GenAI landscape in Germany
McKinsey & Company 6
Background: GenAI is the natural evolution of analytical AI,
addressing a novel set of challenges to realize large automation
potential, thus unlocking meaningful productivity potential
Analytical AI Generative AI
Analytical AI algorithms are used GenAI algorithms are used to
to solve analytical tasks faster either create new content on par
and more efficiently than with humans, or greatly enhance
humans — e.g., being able to humans' abilities — e.g.,
classify, predict, cluster generating audio, code, images,
or evaluate data text, and videos
Forecasting Segmenting Conducting Designing Creating Generating
sales customers sentiment concepts marketing or code
analyses social media copy
McKinsey & Company 7
Example: By unlocking productivity potential, GenAI can address skilled
labor shortages in manufacturing, resulting in fewer vacancies due to
more internal task completion
Illustrative – computer engineer
01
Sara’s current job as a computer engineer
Sara is a computer engineer for a manufacturing company who shifts
between 17 unique activities, including testing the performance of
electrical equipment and collaborating with technical personnel. Her
company is struggling to find skilled personnel.
03 Sara’s time rearrangement and productivity
gains
02
Sara’s company adopts new technologies With automation, the resulting free time creates increased
productivity and innovation: Sara can now operate an
Sara's company invests in real-time data analytics and
adjacent workstation, which is underutilized, as her company
machine-learning software to help monitor the computer
has not been able to recruit a suitably skilled new colleague.
systems in the manufacturing plant. The company also
Moreover, she invents a novel solution to a computer-design
purchases several robotics and automation systems to
problem at the plant.
streamline production.
Fewer vacancies and more innovation in 2030 04
Various workflows have been optimized. Thus, numerous positions are now covered
internally where the company had previously struggled to find suitably skilled
colleagues. Moreover, Sara's company has implemented various computer-design
improvements, which speeds up production.
McKinsey & Company 8
Example: By unlocking productivity potentials, GenAI can meet skilled
labor shortages in workforce training resulting in less vacancies and
better apprentice performance and satisfaction
Less vacancies and better apprentice
04
Illustrative – educator and workforce training
performance and satisfaction in 2030
Various individualized courses and modules have been
implemented across the organization's workforce training portfolio.
Hence, now significantly less trainer input is required. Therefore,
the average performance of apprentices has increased, and the
personal satisfaction of apprentices has improved as they now
receive tailored training while having more space for deeper
exchange with trainers on a more personal level..
John's organization has adopted
02
new technologies
03
John's organization invests in educational John's time rearrangement and productivity
generative AI software which can analyze the
gains
needs, constraints, and preferences of each
apprentice, and subsequently offers tailored With automation, the resulting free time creates increased
content and learning styles. Moreover, the productivity and innovation: John can now increase the
new software can create simulation-based number of apprentices under his supervision from 20 to 30
and individualized trainings with much less which is great for the organization, as it has been struggling to
input from John. recruit another workforce trainer. Moreover, he implemented
data-driven informed development conversations and
additionally introduced a new innovative course offering
individualized remote work simulation.
01
John's current job as a workforce trainer
John is a workforce trainer in a vocational school who shifts between 13 unique
activities, including frontal teaching, preparing individual work samples, development
conversations, and assessing the apprentices' individual outputs. His organization
struggles to find skilled trainers.
McKinsey & Company 9
To assess GenAI productivity potentials,
we analyzed around 2,100 distinct work
activities and ~850 professions
ILLUSTRATIVE
Capability requirements
Physical
Answers about
Professions  Fine motor skills/dexterity
products and services
 Gross motor skills
 Navigation
 Mobility
Employees in retail and sales Greet customers
Sensory
 Sensory perception
Employees in food and
Clean and maintain work areas Cognitive
beverage service
 Retrieving information
 Recognizing known patterns/categories (supervised learning)
 Generating novel patterns/categories
Teachers Demonstrate product features
 Logical reasoning/problem solving
 Optimizing and planning
 Creativity
Health practitioners Process sales and transactions
 Articulating/display output
 Coordinating with multiple agents
Natural language processing (NLP)
 ...  ...
 …  …  Understanding natural language
 …  …  Generating natural language
Social
~850 ~2,100 activities assessed across all  Social and emotional sensing
professions
 Social and emotional reasoning
professions
 Emotional and social output
Source: National Labor Offices, Occupation Information Network; McKinsey Global Institute analysis McKinsey & Company 10
In Germany, GenAI promises greater productivity potential in
complex processes, such as decision making and collaboration…
With GenAI Without GenAI1
Overall technical automation potential, comparison by midpoint scenarios, percent
Activity groups
55
Decision Applying expertise2
19 +36 pp
making and Disclaimer: Technical automation
collaboration potential implies the availability of
50
Managing3 technological capabilities required to
16 +34 pp
automate a particular work activity, hence,
affecting hours spent on that work activity
Interfacing with 50
stakeholders 25 +25 pp
Data 92
Processing data
management 75 +17 pp
79
Collecting data
65 +14 pp
Physical Performing unpredictable 34
physical work4 +1 pp 33
Performing predictable 70
physical work5 +2 pp 68
1. Previous assessment of work automation before the rise of GenAI, including analytical AI, 3. Managing and developing people
machine leanrning, and deep learning 4. Performing physical activities and operating machinery in unpredictable environments.
2. Applying expertise to decision making, planning, and creative tasks 5. Performing physical activities and operating machinery in predictable environments
Note: Figures may not sum, because of rounding
McKinsey & Company 11
Source: McKinsey Global Institute analysis
…thus, GenAI makes it possible to contribute significantly to
Germany's competitiveness
With GenAI Without GenAI2
Productivity impact from automation by scenario, 2022-40, CAGR,1 percent
Developed economies Key implications
USA France Austria Global3 Germany for Germany
3.6 3.7 3.7 3.9 Early (vs. late) adoption of
0.7 0.7 0.6 automation potential will lead
0.6
to an additional ~EUR
3.3
2.9 3.0 0.8 3.1 0.9 2,600bn in GDP by 2040
0.6
0.2 0.2 0.6
0.4 0.3 0.6 0.7 Early additional adoption of
Early Late Early Late Early Late GenAIalone can increase
Germany's GDP by
Emerging economies
~EUR 585bn (13%) by 2040
China India Mexico 3.4
GenAI can increase
1.3
3.8 2.6 automation impact on
0.2
0.6 2.3 2.9 productivity growth by
0.5 0.6
~18%, significantly advancing
3.2 0.8 1.1 Germany's competitive
2.3 0.2
0.1 1.8 position
0.7 0.1
0.1
Early Late Early Late Early Late Early Late
Early4 Late4
1. Based on the assumption that automated work hours are reintegrated into work at today's productivity level
2. Previous assessment of work automation before the rise of GenAI
3. Based on 47 countries, representing about 80% of global employment
4. Automation scenarios (early: early adoption of GenAItechnology capabilities; late: late adoption of GenAItechnology capabilities, expert based)
Note: Figures may not sum, because of rounding.
Source: Oxford Economics; The Conference Board Total Economy Database; McKinsey Global Institute analysis McKinsey & Company 12
Education: Greatest labor shortage mitigation potential for tertiary
education level while societally for high school education level
With GenAI Without GenAI1
Key implications
Impact of GenAI on technical automation potential in midpoint scenario, 2023, percent for Germany
Overall technical Additional Population-weighted Additional impact of GenAI is
Education automation potential, automation Share of skilled labor shortage expected to be highest for
level comparison2 potential,2 pp population, % mitigation potential those with tertiary-level
education (24 pp)
Tertiary
60 Example: Computer engineers
education
24 19 Medium (STEM) like Sara or workforce
(Bachelor and
36 trainers like John
above)
The population-weighted
skilled labor shortage
High school 64
mitigation potential is highest
(Diploma or 13 56 Large
for high-school-degree holders
equivalent) 51
(55.9% population share)
Example: Community health
63 care worker or pharmacy
None
9 25 Small technician
(No degree)
54
1. Previous assessment of work automation before the rise of GenAI
2. Based on US extrapolation
Source: StatistischesBundesamt(DeStatis); McKinsey Global Institute analysis McKinsey & Company 13
Professions: GenAI holds the greatest opportunities for workforce
training, business and legal, and STEM
With GenAI Without GenAI1
Impact of GenAI on automation potential sorted by additional GenAI potential, percent
Employment-weighted
Overall technical automation potential, comparison Automation Share of German skilled labor shortage Worldautomation
Professions bymidpoint scenarios, 2023,% potential shift, pp employment % mitigation potential potential shift,pp Key implications
Educator and workforce training 14 54 40 3 Medium 39 for Germany
Business/legal professionals 32 68 36 6 High 30
Greatest skilled labor shortage
51
Creatives and arts management 22 29 1 Medium 25
mitigation potential in Germany in
57
STEM professionals 28 29 7 High 29 the areas of workforce training
Office support 85 22 19 High 21 (40 pp), business and legal
63
(36 pp), and STEM (29 pp)
67
Community services 45 22 6 High 26
49 Employment-weighted labor
Managers 29 20 3 Medium 17
shortage mitigation potential in
Health professionals 45 17 2 Medium 14
28
Germany is largest for
70
Customer service and sales 60 10 9 Medium 12 business and legal, STEM,
Production work 67 75 8 13 Medium 9 office support, and community
Property maintenance 37 7 4 Low 9 services
30
59 Based on relative employment in
Transportation services 53 6 3 Low 7
Germany and the world,
40
Health aides, technicians, and wellness 34 6 9 Medium 9
STEM and community services
Builders 55 5 5 Low 4
50 might profit more in Germany,
Food services 61 66 5 4 Low 8 while workforce training and
68 customer service might profit less
Mechanical installation and repair 63 5 4 Low 6
than the global average
Agriculture 6266 4 1 Low 4
Total 49 65 16 100 12
1.Previous assessment of work automation before the rise of GenAI. | Note: Figures may not sum, because of rounding. McKinsey & Company 14
Source: McKinsey Global Institute analysis
Profession: GenAI has greatest labor shortage mitigation potentials in high
job vacancy concentration areas, such as educator training, STEM, and Health
Job-vacancy concentration2 and corresponding automation shift in Germany
Additional technical automation potential, pp Key implications
40 for Germany
Medium need – high potential High need – high potential
Educator and
workforce training
35
Business/  Highest need (≥0.9) and
legal professionals potential for GenAI for
30
skilled labor shortage
Creatives and arts management STEM professionals
mitigation in workforce
25 training (40 pp), STEM (29
Community services pp), and health
20 Office professions (17 pp)
Managers support
Health professionals
 This applies to workforce
15
trainers like John or
Health aides, Customer service Production computer engineers (STEM)
10 technicians, and sales work
like Sara
and wellness Transportation Property maintenance
5 Agriculture services Food services Builders  High potential (≥20 pp) but
Mechanical medium need for business,
Medium need – limited potential High need – limited potential
installation and repair creatives, or office support,
0
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 1.1 1.2 1.3 1.4 1.5 1.6 1.7 1.8 1.9 2.0 2.1 2.2 and high needs but limited
potential (≤10 pp) for builders
Share of job vacancies per share of employment in an occupation group2
Note: Figures may not sum because of rounding
1. Previous assessment of work automation before the rise of GenAI. | 2. Share of job vacancies divided by the share of employment within an occupation
group indicating the concentration of open job positions per actual employment
Source: McKinsey Global Institute analysis McKinsey & Company 15
Wages: GenAI is expected to have the biggest labor shortage
mitigating impact in areas with high wages
Largest increase in automation adoption from GenAI
Largest automation adoption without GenAI1
Additional GenAI automation adoption vs. without GenAI per wage group,22030, percentage points
Key implications
USA France Austria Germany for Germany
Developed 15 12 12 In Germany, the
14
13
economies 12 highest wage group
11
10 (quintile) will
9
8 experience the
6 6 greatest additional
5 5
4 automation potential
3 3
(12 pp) and the
corresponding labor
shortage mitigation
6
China India Mexico
potential from GenAI
Largest automation
Emerging
4
adoption without
economies
3 GenAI is highest for
8
7 7 7 7 7 4th wage group
(quintile), with 37%
4
3 3 post-GenAI versus
2 2 2 2 2
1 33% pre-GenAI
Lower Lower Mid Upper Upper Lower Lower Mid Upper Upper Lower Lower Mid Upper Upper Lower Lower Mid Upper Upper
mid mid mid mid mid mid mid mid
1. Previous assessment of work automation before the rise of GenAI
2. Difference between automation potential without GenAIand additional automation potential with GenAI
Source: McKinsey Global Institute analysis McKinsey & Company 16
Upskilling and attracting the right tech talent is the core task of
public and private organizations in mitigating labor shortages
Building on existing capabilities
and competencies Recruiting of new tech talent
1 Upskilling/ • Developing requirements for the • Analyzing the skills and competence
reskilling for AI building and leadership development of profiles of current employees and
roles GenAI core competencies existing open positions that cannot be
• Determining the cohorts with upskilling recruited from the labor market
needs • Establishing employer branding and
• Establishing a boot camp approach to targeted recruiting to attract best-in-class
GenAI training talent
2 Training and • Implementing improvements for the • Identifying the remaining necessary
coaching training program based on early findings qualifications
3 Establishing a • Involving senior management to ensure • Creating a short-term hiring target for
'learning culture' support is provided recruiting the required role profiles
• Defining the behavioral and mindset • Developing a mid-term road-map for
changes required for a learning culture strategic recruiting
• Designing of competency building
initiatives (e.g., on feedback, coaching)
McKinsey & Company 17
The skilled labor shortage in
Germany
Agenda
The potential of GenAI to
increase productivity
The GenAI landscape in Germany
McKinsey & Company 18
Public and private actors must work toward a scenario with both
the right operating environment and the availability of skills
Ensuring availability Key implications
Providing the right environment of skills for Germany
The two key enablers
Procurement
Private German for making use of
sector Government GenAI in Germany are:
The right operating
environment and the
availability of relevant
skills in Germany
R&D Creates and As they both have a
collaboration enforces reinforcing effect
towards the respective
Skills
Data for
counterpart they must
GenAI1
be pursued
simultaneously.
Public policy
Academia
(Germany/EU)
Supports
Quantitative deep dive on every stakeholder (group) on the following pages
1. We apply a GenAIfocus to this framework because GenAIbuilds on the workforce, skillsets, and capabilities, which grew the AI market
Source: Oxford Publication McKinsey & Company 19
Germany is an entrepreneurial but underfunded country with
great potential to becoming a European leader
Private sector
European perspective Leader Emerging
Total funding
Estonia Switzerland UK
of AI companies
proportional
to GDP
Finland
France
Portugal
Iceland Sweden
Belgium Germany
Norway
Austria
Ireland
Denmark
Hungary
The Netherlands
Luxembourg
Lithuania Spain
Malta Greece
Czech Republic Poland
Slovenia
Slovak Republic Russia Italy
Absolute number of AI start-ups
Source: The Global AI, Index, Tortoise AI Report, Tortoise Media McKinsey & Company 20
Germany’s expenditure on R&D is increasing, whereas its
contribution to AI projects has stagnated for years
Private sector Academia
Gross domestic expenditure on R&D, Number of AI projects by global comparison,
% of GDP % total AI projects1
Germany EU-27 Germany Switzerland Austria China EU-27 UK India US
3.5 35
30
3.0
25
20
2.5
15 This is a relative representation;
it doesn't indicate a declining
number in AI projects
10
2.0
5
1.5 0
2011 12 13 14 15 16 17 18 19 20 2021 2011 12 13 14 15 16 17 18 19 20 21 2022
Number of Al projects (i.e., Al-related GitHub ""repositories"") as a
fractional count based on the share of contributions (i.e.,
""commits"") by country and over time
1. fractional count based on contributions
Source: Eurostat.-November 2022; GitHub; Preqin; oecd.ai McKinsey & Company 21
Germany is a leader in technology, but only keeping up
with equally large countries in investments
Private sector Academia
Global perspective – relating to both AI and GenAI in 2022
Absolute
Funding positioned Leader US
private and
public
China
investments UK
in AI
Japan
companies India France
Germany
Spain
Canada Italy
Sweden
South Korea
Australia
The Netherlands
Singapore
Finland
Switzerland
Austria
Mexico Brazil
Aspirational Technology skilled
Uganda Russia
Technology and research1
1. Technology and researchcontains country ranks by theoretical peak computer performance, number of processing cores, number of supercomputers, and maximal LINPACK performance achieved; the country ranks for the number of
conference papers and journal papers; and the country rank for the number of patents
Source: Brookings, 2022 McKinsey & Company 22
Germany has a high proportion of self-
reported AI capabilities compared to the
OECD average, trailing only the US
Skills
Key implications
AI skill penetration of workforce
for Germany
Prevalence of workers with AI skills as self-reported by LinkedIn members from 2015-2022 by country1
United States 2.2 Germany has the 2nd
Germany 1.7
Israel 1.7
second highest AI skill
Canada 1.6
United Kingdom 1.5
penetration (1.5) in its
Korea 1.4
Japan 1.2
workforce, which is only
Turkey 1.2
France 1.1
surpassed by the US with a
OECD average 1.0
Spain 1.0
penetration factor of 2.2
Netherlands 1.0
Italy 0.9
Switzerland 0.9 German workers are 1.7x
Greece 0.9
Australia 0.9 likely to report AI skills
Poland 0.8
Ireland 0.7 than workers in the OECD
Sweden 0.7
Norway 0.7 benchmark
Finland 0.6
Belgium 0.6
Hungary 0.6 Germany is thus in the
Lithuania 0.6
Austria 0.6 group of leading AI
Denmark 0.5
Estonia 0.5 nations, like the US, Israel,
Mexico 0.5
New Zealand 0.4 Canada, and the UK
Slovenia 0.4
Czech Republic 0.4
Portugal 0.4
Luxembourg 0.4
Chile 0.4
Slovak Republic 0.4
1. A Country’s AI skills penetration of 1.5 means that workers in that country are 1.5X more likely to report AI skills than workers in the benchmark
Source: Data from LinkedIn 2015-2022 accessed on Sep 20, 2023; self-reported; OECD.AI (2023) McKinsey & Company 23"
250,mckinsey,adopting-ai-at-speed-and-scale-the-4ir-push-to-stay-competitive-v2.pdf,"Operations Practice
Adopting AI at speed
and scale: The 4IR push
to stay competitive
AI has brought the Fourth Industrial Revolution to an inflection point,
and manufacturers must choose a path forward: innovate, accelerate,
or follow fast.
by Henry Bristol, Enno de Boer, Dinu de Kroon, Forest Hou, Rahul Shahani, and Federico Torti
February 2024
The world has changed for manufacturers. capabilities they have built to deploy it with both
Preparation for uncertainty has become an industry speed and scale. In this first installment, we’ll
norm, with executives expecting the impact of explore how the maturity of AI marks a 4IR inflection
disruption—whether from geopolitical tensions, point; examine how leading manufacturers are
climate change effects, technology breakthroughs, redefining the leading edge of manufacturing with
or supply chain vulnerabilities—to increase by 15 to this technology; and finally, consider three types
25 percent over the next five years.1 of strategic responses—to innovate, to accelerate,
or to follow fast—that manufacturers will need to
At the same time, advanced manufacturing is now consider as industry becomes more competitive.
flourishing in markets where stagnation had seemed The second and third articles, respectively, will
intractable. Growth in the US manufacturing sector, focus on the at-scale impact of AI within the
for example, had languished at 1.4 percent over manufacturing sector and the essential capabilities
the past two decades. More recently, AI, digital that drive AI adoption.
technologies, sustainable features, and higher skill
have reinvigorated the market: over the past five
years, US industrials companies have generated The S-curves of industrial revolution
total shareholder returns about 400 basis points Global industry transformation has never been
higher than in the previous 15 years. instantaneous. Each “revolutionary shift” saw a lag
period between the introduction of the enabling
The accelerating pace of the Fourth Industrial foundation and widespread adoption. Consider the
Revolution (4IR) can help enable this type of steam engine. The Roman architect Vitruvius made
next-level performance while also increasing mention of a rudimentary steam-powered device
workforce inclusivity and sustainability. The Global as early as 15 BC. Why, therefore, did widespread
Lighthouse Network,2 now in its fifth year, provides adoption take more than 1,800 years? The answer
an expanding pool of examples. In effect, each is simple: steam was neither practical nor cost-
Lighthouse cohort provides a three- to five-year effective until breakthrough engine technologies—
look ahead at the future of operations across the along with the infrastructure of coal supply
value chain. chains—made it so. This tipping point essentially
eliminated the learning curve, allowing the “doing
The most recent cohort affirms a 4IR inflection curve” to steepen. The front-runners had done the
point, marked by two factors. First, machine learning. It wasn’t until the late 18th century that, in
intelligence technologies—AI that, rather than the space of just 20 years, steam engine adoption in
seeking to simulate human intelligence, empowers industry increased from practically nothing to nearly
machines with the specialized intelligence needed 80 percent.
to perform complex tasks in the cyber-physical
world of production—are reaching unprecedented What steam was to the First Industrial Revolution
levels of maturity. Second, leading companies is what AI will be to the fourth. And much as coal
are redefining the concept of a pilot as they scale supply chains and factory infrastructure were the
impact by using entire factories, rather than tipping point that enabled steam power to race
individual use cases, as pilots. up the adoption curve, data collection and data
infrastructure are doing the same in the fourth.
This series of three articles will explore: (1) the Already, some of the world’s leading factories
current status of global manufacturing, with a generate multiple petabytes of data a week. If all
particular focus on (2) what AI looks like among ten million factories in the world operated at this
today’s leading manufacturers, and (3) the level, they would double all human information in
1 “The great acceleration: CIO perspectives on generative AI,” MIT Technology Review, July 18, 2023.
2 The Global Lighthouse Network is a World Economic Forum initiative cofounded with McKinsey and counseled by an advisory board of industry
leaders, including Contemporary Amperex Technology, Foxconn Industrial Internet, Henkel, Johnson & Johnson, Koc Holdings, Siemens, and
Schneider Electric. Lighthouses in the network are designated by an independent panel of experts.
Adopting AI at speed and scale: The 4IR push to stay competitive 2
less than a month (see sidebar “The evolution of Revolution—is that we believe them to be three
the revolutions”). to five years further along 4IR’s adoption curve
than are other manufacturers. Today, they aren’t
focused on piloting use cases. Instead, they’ve built
The adoption S-curve the capabilities to get new use cases right quickly
We can see a pattern as we look back upon each and without trials. For companies with multiple
industrial revolution: it has always taken the shape Lighthouses, entire factories serve as pilots for
of an “S-curve.” The first phase is a learning curve, networkwide deployment at scale. Leaders are now
which tends to be long, and is marked by trial and capturing the value of 4IR technologies ten and
error as early front-runners figure out how to make 50 factories at a time, where others are still working
things work. It then moves on to the next phase, to find value within a single factory.
the “doing” part of the curve. This is when the
foundational technology has been established, Lighthouses are accelerating, and so too is the
and organizations work to deploy it across their maturity chasm they have put between themselves
production networks. Finally, we see an optimization and everyone else. This chasm is evident in the
curve, which is when industries align around what wake of recent disruption and volatility. Consider
works best. New standards and protocols become that 85 percent of Lighthouses saw revenue
ingrained, and costs start to stabilize (Exhibit 1). reductions of less than 10 percent during the
One need only look back at a now-ubiquitous height of the COVID-19 pandemic; this was true
technology, such as the smartphone, to recognize for only 14 percent of other manufacturers.
this three-phase S-curve in play. Lighthouses could react quicker: although they
faced the same supply chain risks, 65 percent
One of the most significant differentiators for of Lighthouses were already dual-sourcing and
members of the Global Lighthouse Network—the increasing inventory by 2022, compared with only
153 factories at the forefront of the Fourth Industrial 24 percent of other companies.
Web <2024>
E<Axdhoipbtiintg 1 AI at speed and scale: the 4IR push to stay competitive >
Exhibit <1> of <4>
From learning to doing: Lighthouses are rapidly climbing the Fourth
Industrial Revolution adoption curve.
Fourth Industrial Revolution (4IR) adoption curve, illustrative
Learning Doing Optimizing
Factory pilots, characterized by Network adoption, where proven Industry new normal, when costs
trial-and-error refinements technologies rapidly scale optimize and standards coalesce
around best-in-class solutions
HIGH
Adoption Scaling
and impact slump Lighthouses, 2024
Pilot
purgatory
Lighthouses, 2018
LOW
Time and investment
Source: Global Lighthouse Network: Adopting AI at speed and scale, World Economic Forum, December 2023
McKinsey & Company
Adopting AI at speed and scale: The 4IR push to stay competitive 3
AI is defining the Fourth “pyramid” of 4IR technologies—it is playing the role
Industrial Revolution of conductor for 4IR technologies, which together
perform a symphony of impact (Exhibit 2).
The true power of AI for the Fourth Industrial
Revolution lies with the fact that it sits at the top of a
Web <2024>
E<Axdhoipbtiintg 2 AI at speed and scale: the 4IR push to stay competitive >
Exhibit <2> of <4>
The full value of Fourth Industrial Revolution technologies comes from a
suite of technology solutions.
Fourth Industrial Revolution (4IR) technology pyramid
Machine intelligence to optimize, augment, or automate
decision making, such as heuristic models, applied AI,
and generative AI
Machine
Digital-worker productivity tools at the operator or
intelligence
process level (eg, augmented/virtual reality, wearables,
and exoskeletons)
Path to Path to Automation and disruption of processes, such as
Worker Production
implementation exponential co-bots and flexible robots, automated guided vehicles
connectivity robotics and
impact and drones, and 3-D printing
and digitization automation
System-level digitization of planning and management
Digital planning and processes, such as the manufacturing execution
management tools system, product life cycle management, customer
relationship management, and other enterprise tools
Connectivity and Underlying data, connectivity, and computing tools
infrastructure tools (eg, cloud and edge hosts, 5G communication, and
data lakes)
McKinsey & Company
The evolution of the revolutions
Late 18th century — Unlock: mass production — Enabling infrastructure:
semiconductors and transistors
— Revolutionary shift: steam power and — Enabling infrastructure: electrical grid
mass production techniques Today
Mid-20th century
— Unlock: mechanization — Revolutionary shift: machine
— Revolutionary shift: programmable
intelligence to make trade-off
— Enabling infrastructure: coal logic and control loops for automation
decisions enabling augmentation
supply chain of robotics and reduction in manual
and optimization
tasks
Late 19th century
— Unlock: advanced manufacturing
— Unlock: standardization and
— Revolutionary shift: electrification,
automation — Enabling infrastructure: big data
assembly line production, new
resources, and synthetic materials
Adopting AI at speed and scale: The 4IR push to stay competitive 4
AI is defining the Fourth Industrial
Revolution, and Lighthouses are
showing us that AI has myriad new use
cases and possibilities for unimaginable
performance improvements.
Consider the example of a rapid changeover at a transparent data connectivity and visualization
production site (Exhibit 3). This requires flexible dashboards, and similar digital-lean solutions.
robotics to handle different products, automated These use cases took much longer for Lighthouses
guided vehicles to move materials and parts, 3-D to implement than they do today; where most
printing to customize line fixtures, and wearable Lighthouses say it took ten to 20 months average
technology to keep managers and technicians time to implement their first five use cases,
informed with real-time data. What orchestrates 75 percent say they can now do it in less than six
this complex interplay of elements, each of which is months. Even more impressively, 30 percent claim
individually complex? The answer: AI. they can do it in less than three months.
But AI needs terabytes of data generated by This is because for early use cases, factories first
and collected from a broad range of sources: had to rewire their data collection and connectivity
enterprise systems, machine sensors, connectivity layers, design tech stacks that added to or
infrastructure, and human workers. That’s why the upgraded legacy infrastructure, train their people
most advanced front-runners are ahead. They had on how to use advanced new tools, and reorganize
the foresight to make investments and take on risks themselves to deploy digital solutions quickly
involved in building the data foundations that are and with strong feedback. Once built, these
needed to power AI technologies and unlock their capabilities became the foundation for the rapid
potential impact (see sidebar “Understanding AI: deployment of new use cases. One Lighthouse, for
How it actually works). example, says it was able to implement a gen-AI-
based technician adviser in just days and weeks,
With AI, machine intelligence can orchestrate highly not months and years.
complex technologies for rapid solutions.
Ongoing challenges see Lighthouses move from
pilot purgatory to scaling slump. At the factory
Capabilities to tackle the scaling slump level, capabilities are a solved problem, at least
AI is defining the Fourth Industrial Revolution, and for Lighthouses. Now, many Lighthouses are
Lighthouses are showing us that AI has myriad in the valley that follows the “false peak” of the
new use cases and possibilities for unimaginable learning curve, stalling on impact while they rewire
performance improvements. themselves for network scale. This isn’t easy. Taking
a technology that works in one place and extending
it across an entire production network introduces
Use cases inform capabilities, and capabilities
massive new challenges: data, technology, talent,
lead scale. Back in 2018, cutting-edge use cases
and organizational challenges that exist at a single
looked like localized applications of advanced
site are not the same at the macro level—and neither
analytics and autonomous vehicles, or radically
Adopting AI at speed and scale: The 4IR push to stay competitive 5
Web <2024>
E<Axdhoipbtiintg 3 AI at speed and scale: the 4IR push to stay competitive >
Exhibit <3B> of <4>
With AI, machine intelligence can orchestrate highly complex technologies
for rapid solutions.
Implementation of a “one click” changeover
1
3
5
2
4
1 2 3 4 5
Machine intelligence Wearables and devices Flexible robotics Integrated systems, Cloud and edge hosts
“conducts” an give critical alerts to are designed to be including the connect all systems
orchestra of Fourth technicians based on easily reprogrammed manufacturing wirelessly and execute
Industrial Revolution real-time data so they can handle execution system and critical computations
technologies, multiple, diverse distributed control
maintaining system, products system, inform and
timing, sequencing, control all machines
and responsiveness needed to make the
next product
McKinsey & Company
Adopting AI at speed and scale: The 4IR push to stay competitive 6
are the solutions. For that gen-AI-based technician to integrate ChatGPT into Bing mere months after
adviser to see the light of two dozen factories, those its launch.
factories must first be ready to receive it.
And like both the steam engine in the First
Those that overcome the scaling slump can Industrial Revolution and AI technologies in tech
define entire industries. This is because they and banking, we expect 4IR’s breakthrough
set standards. The adoption of steam engines in technologies to catapult from single digit
manufacturing is an early example. For a more to widespread adoption within the decade.
modern-day example: consider Toyota, which Lighthouses are leading the way. Already, AI-based
managed to scale its production system at the use cases make up over 60 percent of the use
macro level. Shortly after, lean manufacturing and cases presented by new Lighthouse applicants, up
Six Sigma became standard fare for companies from just 11 percent in 2019.
all across the world—with the accompanying
emergence of new standards, protocols,
certifications, and regulatory measures. The Lighthouses are accelerating the
innovations became institutionalized; Toyota leading edge: How will you respond?
defined the new normal, which is reflective of Although it may be some time before gen AI and
the optimization phase of the adoption curve. In other highly advanced emerging technologies
other sectors outside of manufacturing, such as see networkwide adoption in manufacturing,
tech and banking, AI is already at this stage, so Lighthouses are already achieving factory-scale
the conversations are focused on standards and adoption. All of the newly recognized Lighthouses
regulatory compliance. These sectors can deploy have at least one gen AI pilot in process, and
AI at scale, fast—for example, Microsoft was able several have implemented, tested, and iterated
Understanding AI: How it actually works
Before exploring in detail how A subset field in AI, machine learning, large language models (with hundreds
Lighthouses have been rapidly adopting began developing traction by the 1980s. of billions of neurons) that can learn
analytic AI and piloting its emergent It focused on teaching machines to especially abstract patterns (exhibit).
offshoot, generative AI, it’s first crucial learn relationships hidden in data and
Each of these breakthroughs has
to gather a basic understanding of what to build approximate models of real
followed its own accelerating adoption
underpins AI. Everyone is talking about it, systems. Within two decades, a branch
curve. Today, both machine learning and
which raises a worthy question: Do of machine learning called deep learning
deep learning techniques, excluding
you actually know the fundamentals of emerged, as “neural networks” became
generative adversarial networks (gen
how AI works? popular methods to model real systems
AI) and encompassing such methods as
by mimicking how the human brain
Pioneered in the 1950s, AI now refers to gradient and adaptive boosting, random
works, with millions of computational
the broad field of developing machines, forests, convolutional and recurrent
“neurons.” 2017 saw the popularization
applications, and tools that approximate neural networks, decision trees, support
of transformers and the advent of
human behavior, including all aspects vector machine algorithms, and more
generative adversarial networks, a type
of perceiving, reasoning, learning, and are collectively referred to as analytic
of deep learning known as generative
problem solving. The first instances AI. This family of technologies has seen
AI (gen AI), which has enabled use of
included statistical analyses and rapid maturity and pace of adoption
exceptionally large neural nets called
predictions enabled by early computers. by Lighthouses.
Adopting AI at speed and scale: The 4IR push to stay competitive 7
Understanding AI: How it actually works (continued)
The next evolution: Gen AI unstructured data sets, like a human brain. the database, often referred to as the large
Gen AI is projected to add between It leverages a “transformer” architecture language model or foundation model.3
$2.6 trillion and $4.4 trillion in annual value to generate “embeddings”—an approach
To pick or generate sequences of tokens,
to the global economy1—nearly a quarter initially designed for natural language
one deep learning model predicts
of which could be captured by productivity processing tasks. Embeddings are
subsequent tokens, while another
improvements of up to two times and task massive vectors representing hundreds
analyzes and scores the selection—which
automations of nearly 70 percent across of thousands of parameters for any given
is exactly why gen AI is often referred
activities related to manufacturing and “token,” or piece of information. (For text-
to as a generative adversarial network.
supply chains.2 That’s why it makes sense based models, a token might be as small as
This unique approach is what enables
that, as of mid-2023, nearly one-third the prefix “un.”) It can predict or “generate”
gen AI to begin to process troves of
of all companies surveyed said that they content by identifying a probability that
unstructured data to emulate true human
have implemented gen AI in at least one any one token will sequentially follow
reasoning and connection, synthesize
business function. another token. This probability calculation
insights, generate content, and generally
accounts for the proximity of that token’s
Gen AI’s differentiating factor is that it can “humanize” user interactions.
vector embedding with others stored in
pay attention to patterns across immense
Web <2024>
E<Axdhoipbtiintg AI at speed and scale: the 4IR push to stay competitive >
Sidebar <1> of <1>
Generative AI is the next new frontier of a long AI journey.
Artificial intelligence timeline
1950s 1960s 1970s 1980s 1990s 2000s 2010s 2020s
Artificial intelligence Machine learning Deep learning Generative AI
The broad field of Major approach to achieve Branch of machine Branch of deep learning that uses
developing machines AI by teaching machines learning that uses “neural exceptionally large neural networks
that can replicate human to learn relationships networks” to model real called large language models (LLMs)
behavior, encompassing hidden in data and build systems by mimicking (with hundreds of billions of neurons)
perceiving, reasoning, approximate models of how the human brain that can learn especially abstract pat-
learning, and problem real systems works, utilizing millions terns; applying these LLMs to interpret
solving of computational “neurons” and create text, images, video, and data
has become known as generative AI
McKinsey & Company
1 “The economic potential of generative AI: The next productivity frontier,” McKinsey, June 14, 2023.
2 “The great acceleration: CIO perspectives on generative AI,” MIT Technology Review, July 18, 2023.
3 “Embeddings: The Language of LLMs and GenAI,” Medium, October 4, 2023.
gen AI use cases for impact in as little as days or They’ve coupled these enablers with clear business
weeks—not months or years. Again, they’re able to strategy and effective change management.
do so because they’ve already built the necessary Notably, Lighthouses avoid the trap of investing
enablers: solid data and tech infrastructure, a strong in technology for its own sake, instead ensuring
talent base, and agile operating models. that every use case presents clear business value.
Adopting AI at speed and scale: The 4IR push to stay competitive 8
Paradoxically, they’ve surged ahead by taking a This moment compels a decision:
patient, measured approach—typically between Lead, innovate, or follow fast
ten and 20 months for use case implementation,
What does an inflection point mean for
with an ROI period of approximately two and a half
manufacturers? It compels a crucial decision
years. This takes patience and a steady hand, but
about how to respond. The global front-runners—
the returns are worth it. Lighthouses’ 4IR use cases
Lighthouses—along with an increasing number of
have averaged between two and three times ROI
other leading organizations are pushing through the
within three years and between four and five times
scaling slump, achieving network-level impact. What
ROI within five years.
will other companies do?
As Lighthouses continue to push through the
There is more than one intelligent response. An
scaling slump, their ability to implement use cases
organization might choose to be a network-level
more rapidly is improving. The most recent three
digital innovator. This is an industry pathfinder
Lighthouse cohorts implemented use cases
that takes a risk on—and proves, at the factory
26 percent faster than the first three did, and
level—the next big thing. This is the path that
75 percent of Lighthouses report that they’re able
many Lighthouses have taken. But there’s also a
to deploy a new advanced use case in under six
smart path forward for the industry accelerator.
months; 30 percent can do so in less than three
This company focuses on network-level impact,
months. Technology adoption is self-perpetuating.
changing the landscape for an entire industry.
The more companies progress, the faster they
Finally, there’s great opportunity that lies with
progress. This also provides them with added
being a fast follower. This company embraces
agility and ability to respond to disruption—a major
the playbook already written by the innovators
factor in the expanding performance gap between
and accelerators, capturing value while skipping
leaders and laggards.3
the costs and tribulations of the learning curve
altogether (Exhibit 4).
Web <2024>
E<Axdhoipbtiintg 4 AI at speed and scale: the 4IR push to stay competitive >
Exhibit <4> of <4>
As Fourth Industrial Revolution technologies accelerate and Lighthouses
tackle the scaling slump, others will need to take a strategic response.
Response archetypes
Innovator securing Accelerator achieving Fast follower rapidly Laggard falling behind on
competitive advantage by competitive advantage deploying off-the-shelf digitization and potentially
piloting new technologies with speed and scale of solutions once proven to losing relevance as a
and proving their impact technology adoption, with be impactful, scalable, and competitive manufacturer
production network–level cost effective
impact
McKinsey & Company
3 Global Lighthouse Network 2023 Research Survey, August 2023.
Adopting AI at speed and scale: The 4IR push to stay competitive 9
Each of these three responses can comprise an The next two articles in this series will explore what
Find more content like this on the
intelligent strategic response. This exciting juncture advanced AI and gen AI look like among today’s
McKinsey Insights App
of the Fourth Industrial Revolution provides a leading manufacturers, and the capabilities that
momentous opportunity for manufacturers to are powering Lighthouses to scale advanced
choose a course of action. There’s freedom in that, technologies across full production networks.
and it means that companies can take the approach
that best suits their circumstances and business This article originally appeared in the Global
needs. But make no mistake: this inflection point Lighthouse Network whitepaper Adopting AI at
also means that inaction is a sure-fire path to failure. speed and scale, published on December 14, 2023.
Scan • Download • Personalize
Henry Bristol is a consultant in McKinsey’s Dallas office, Enno de Boer is a senior partner in the New Jersey office, Dinu de
Kroon is a partner in the Zurich office, Forest Hou is a partner in the Shanghai office, and Rahul Shahani is a partner in the
New York office. Federico Torti is the advanced manufacturing and supply chains initiatives lead at the World Economic Forum.
Designed by McKinsey Global Publishing
Copyright © 2024 McKinsey & Company. All rights reserved.
Adopting AI at speed and scale: The 4IR push to stay competitive 10"
251,mckinsey,technologys-generational-moment-with-generative-ai-a-cio-and-cto-guide.pdf,"Technology’s generational
moment with generative AI:
A CIO and CTO guide
CIOs and CTOs can take nine actions to reimagine business and technology
with generative AI.
This article is a collaborative effort by Aamer Baig, Sven Blumberg, Eva Li, Douglas Merrill, Adi Pradhan,
Megha Sinha, Alexander Sukharevsky, and Stephen Xu, representing views from McKinsey Digital.
© Getty Images
July 2023
Hardly a day goes by without some new CTO, the generative AI boom presents a unique
business-busting development related to opportunity to apply those lessons to guide the
generative AI surfacing in the media. The C-suite in turning the promise of generative AI
excitement is well deserved—McKinsey research into sustainable value for the business.
estimates that generative AI could add the
equivalent of $2.6 trillion to $4.4 trillion of value Through conversations with dozens of tech
annually.¹ leaders and an analysis of generative AI initiatives
at more than 50 companies (including our own),
CIOs and chief technology officers (CTOs) have a we have identified nine actions all technology
critical role in capturing that value, but it’s worth leaders can take to create value, orchestrate
remembering we’ve seen this movie before. New technology and data, scale solutions, and
technologies emerged—the internet, mobile, manage risk for generative AI (see sidebar, “A
social media—that set off a melee of experiments quick primer on key terms”):
and pilots, though significant business value
often proved harder to come by. Many of the 1. Move quickly to determine the company’s
lessons learned from those developments still posture for the adoption of generative AI,
apply, especially when it comes to getting past and develop practical communications to, and
the pilot stage to reach scale. For the CIO and appropriate access for, employees.
A quick primer on key terms
Generative AI is a type of AI that can create new content (text, code, images, video) using patterns it has learned by training on exten-
sive (public) data with machine learning (ML) techniques.
Foundation models (FMs) are deep learning models trained on vast quantities of unstructured, unlabeled data that can be used for
a wide range of tasks out of the box or adapted to specific tasks through fine-tuning. Examples of these models are GPT-4, PaLM 2,
DALL·E 2, and Stable Diffusion.
Large language models (LLMs) make up a class of foundation models that can process massive amounts of unstructured text and
learn the relationships between words or portions of words, known as tokens. This enables LLMs to generate natural-language text,
performing tasks such as summarization or knowledge extraction. Cohere Command is one type of LLM; LaMDA is the LLM behind
Bard.
Fine-tuning is the process of adapting a pretrained foundation model to perform better in a specific task. This entails a relatively short
period of training on a labeled data set, which is much smaller than the data set the model was initially trained on. This additional training
allows the model to learn and adapt to the nuances, terminology, and specific patterns found in the smaller data set.
Prompt engineering refers to the process of designing, refining, and optimizing input prompts to guide a generative AI model toward
producing desired (that is, accurate) outputs.
Learn more about generative AI in our explainer “What is generative AI” on McKinsey.com.
1 “The economic potential of generative AI: The next productivity frontier,” McKinsey, June 14, 2023.
2 Technology’s generational moment with generative AI: A CIO and CTO guide
2. Reimagine the business and identify use 1. Determine the company’s posture for
cases that build value through improved the adoption of generative AI
productivity, growth, and new business As use of generative AI becomes increasingly
models. Develop a “financial AI” (FinAI) widespread, we have seen CIOs and CTOs respond
capability that can estimate the true costs and by blocking employee access to publicly available
returns of generative AI. applications to limit risk. In doing so, these
companies risk missing out on opportunities for
3. Reimagine the technology function, and focus innovation, with some employees even perceiving
on quickly building generative AI capabilities in these moves as limiting their ability to build
software development, accelerating technical important new skills.
debt reduction, and dramatically reducing
manual effort in IT operations. Instead, CIOs and CTOs should work with risk
leaders to balance the real need for risk mitigation
4. Take advantage of existing services or adapt with the importance of building generative AI
open-source generative AI models to develop skills in the business. This requires establishing
proprietary capabilities (building and operating the company’s posture regarding generative AI
your own generative AI models can cost tens by building consensus around the levels of risk
to hundreds of millions of dollars, at least in the with which the business is comfortable and how
near term). generative AI fits into the business’s overall strategy.
This step allows the business to quickly determine
5. Upgrade your enterprise technology company-wide policies and guidelines.
architecture to integrate and manage
generative AI models and orchestrate how Once policies are clearly defined, leaders should
they operate with each other and existing AI and communicate them to the business, with the CIO
machine learning (ML) models, applications, and and CTO providing the organization with appropriate
data sources. access and user-friendly guidelines. Some
companies have rolled out firmwide communications
6. Develop a data architecture to enable access about generative AI, provided broad access to
to quality data by processing both structured generative AI for specific user groups, created pop-
and unstructured data sources. ups that warn users any time they input internal
data into a model, and built a guidelines page that
7. Create a centralized, cross-functional appears each time users access a publicly available
generative AI platform team to provide generative AI service.
approved models to product and application
teams on demand.
2. Identify use cases that build value
8. Invest in upskilling key roles—software through improved productivity,
developers, data engineers, MLOps engineers, growth, and new business models
and security experts—as well as the broader
CIOs and CTOs should be the antidote to the “death
nontech workforce. But you need to tailor the
by use case” frenzy that we already see in many
training programs by roles and proficiency
companies. They can be most helpful by working
levels due to the varying impact of generative AI.
with the CEO, CFO, and other business leaders
to think through how generative AI challenges
9. Evaluate the new risk landscape and
existing business models, opens doors to new ones,
establish ongoing mitigation practices to
and creates new sources of value. With a deep
address models, data, and policies.
understanding of the technical possibilities, the
Technology’s generational moment with generative AI: A CIO and CTO guide 3
CIO and CTO should identify the most valuable — Software development: McKinsey research
opportunities and issues across the company that shows generative AI coding support can help
can benefit from generative AI—and those that software engineers develop code 35 to 45
can’t. In some cases, generative AI is not the best percent faster, refactor code 20 to 30 percent
option. faster, and perform code documentation 45
to 50 percent faster.³ Generative AI can also
McKinsey research, for example, shows generative automate the testing process and simulate
AI can lift productivity for certain marketing use edge cases, allowing teams to develop
cases (for example, by analyzing unstructured more-resilient software prior to release, and
and abstract data for customer preference) by accelerate the onboarding of new developers
roughly 10 percent and customer support (for (for example, by asking generative AI questions
example, through intelligent bots) by up to 40 about a code base). Capturing these benefits
percent.² The CIO and CTO can be particularly will require extensive training (see more in
helpful in developing a perspective on how best action 8) and automation of integration and
to cluster use cases either by domain (such as deployment pipelines through DevSecOps
customer journey or business process) or use case practices to manage the surge in code volume.
type (such as creative content creation or virtual
agents) so that generative AI will have the most — Technical debt: Technical debt can account for
value. Identifying opportunities won’t be the most 20 to 40 percent of technology budgets and
strategic task—there are many generative AI use significantly slow the pace of development.⁴
cases out there—but, given initial limitations of CIOs and CTOs should review their tech-debt
talent and capabilities, the CIO and CTO will need balance sheets to determine how generative
to provide feasibility and resource estimates to AI capabilities such as code refactoring,
help the business sequence generative AI priorities. code translation, and automated test-case
generation can accelerate the reduction of
Providing this level of counsel requires tech leaders technical debt.
to work with the business to develop a FinAI
capability to estimate the true costs and returns on — IT operations (ITOps): CIOs and CTOs will
generative AI initiatives. Cost calculations can be need to review their ITOps productivity efforts
particularly complex because the unit economics to determine how generative AI can accelerate
must account for multiple model and vendor costs, processes. Generative AI’s capabilities are
model interactions (where a query might require particularly helpful in automating such tasks
input from multiple models, each with its own fee), as password resets, status requests, or
ongoing usage fees, and human oversight costs. basic diagnostics through self-serve agents;
accelerating triage and resolution through
improved routing; surfacing useful context,
3. Reimagine the technology function such as topic or priority, and generating
Generative AI has the potential to completely suggested responses; improving observability
remake how the tech function works. CIOs and through analysis of vast streams of logs to
CTOs need to make a comprehensive review of identify events that truly require attention; and
the potential impact of generative AI on all areas developing documentation, such as standard
of tech, but it’s important to take action quickly to operating procedures, incident postmortems,
build experience and expertise. There are three or performance reports.
areas where they can focus their initial energies:
2 Ibid.
3 Begum Karaci Deniz, Martin Harrysson, Alharith Hussin, and Shivam Srivastava, “Unleashing developer productivity with generative AI,”
McKinsey, June 27, 2023.
4 Vishal Dalal, Krish Krishnakanthan, Björn Münstermann, and Rob Patenge, “Tech debt: Reclaiming tech equity,” McKinsey, October 6, 2020.
4 Technology’s generational moment with generative AI: A CIO and CTO guide
4. Take advantage of existing services infrastructure, reducing the need for data
or adapt open-source generative AI transfers. The other approach is to “bring
models data to the model,” where an organization can
A variation of the classic “rent, buy, or build” decision aggregate its data and deploy a copy of the large
exists when it comes to strategies for developing model on cloud infrastructure. Both approaches
generative AI capabilities. The basic rule holds true: achieve the goal of providing access to the
a company should invest in a generative AI capability foundation models, and choosing between them
where it can create a proprietary advantage for the will come down to the organization’s workload
business and access existing services for those that footprint.
are more like commodities.
— Maker—builds a foundation model to address
The CIO and CTO can think through the implications a discrete business case. Building a foundation
of these options as three archetypes: model is expensive and complex, requiring
huge volumes of data, deep expertise, and
— Taker—uses publicly available models through massive compute power. This option requires
a chat interface or an API, with little or no a substantial one-off investment—tens or
customization. Good examples include off- even hundreds of millions of dollars—to build
the-shelf solutions to generate code (such the model and train it. The cost depends on
as GitHub Copilot) or to assist designers with various factors, such as training infrastructure,
image generation and editing (such as Adobe model architecture choice, number of model
Firefly). This is the simplest archetype in terms of parameters, data size, and expert resources.
both engineering and infrastructure needs and
is generally the fastest to get up and running. Each archetype has its own costs that tech
These models are essentially commodities that leaders will need to consider (Exhibit 1). While new
rely on feeding data in the form of prompts to the developments, such as efficient model training
public model. approaches and lower graphics processing unit
(GPU) compute costs over time, are driving costs
— Shaper—integrates models with internal data down, the inherent complexity of the Maker
and systems to generate more customized archetype means that few organizations will adopt
results. One example is a model that supports it in the short term. Instead, most will turn to some
sales deals by connecting generative AI combination of Taker, to quickly access a commodity
tools to customer relationship management service, and Shaper, to build a proprietary capability
(CRM) and financial systems to incorporate on top of foundation models.
customers’ prior sales and engagement
history. Another is fine-tuning the model with
5. Upgrade your enterprise technology
internal company documents and chat history
to act as an assistant to a customer support architecture to integrate and manage
agent. For companies that are looking to generative AI models
scale generative AI capabilities, develop more Organizations will use many generative AI models
proprietary capabilities, or meet higher security of varying size, complexity, and capability. To
or compliance needs, the Shaper archetype is generate value, these models need to be able to
appropriate. work both together and with the business’s existing
systems or applications. For this reason, building a
There are two common approaches for separate tech stack for generative AI creates more
integrating data with generative AI models in complexities than it solves. As an example, we can
this archetype. One is to “bring the model to look at a consumer querying customer service at a
the data,” where the model is hosted on the travel company to resolve a booking issue (Exhibit
organization’s infrastructure, either on-premises 2). In interacting with the customer, the generative
or in the cloud environment. Cohere, for example, AI model needs to access multiple applications and
deploys foundation models on clients’ cloud data sources.
Technology’s generational moment with generative AI: A CIO and CTO guide 5
Exhibit 1
Each archetype has its own costs.
Archetype Example use Estimated total cost of ownership
cases
— Off-the-shelf ~ $0.5 million to $2.0 million, one-time
coding assistant
— Off-the-shelf coding assistant: ~$0.5 million for integration. Costs include a team of 6 working for 3 to
for software
4 months.
developers
— General-purpose customer service chatbot: ~$2.0 million for building plug-in layer on top of 3rd-party
model API. Costs include a team of 8 working for 9 months.
— General-purpose
Taker customer ~ $0.5 million, recurring annually
service chatbot
— Model inference:
with prompt
engineering only • Off-the-shelf coding assistant: ~$0.2 million annually per 1,000 daily users
and text chat only
• General-purpose customer service chatbot: ~$0.2 million annually, assuming 1,000 customer chats
per day and 10,000 tokens per chat
— Plug-in-layer maintenance: up to ~$0.2 million annually, assuming 10% of development cost.
— Customer ~ $2.0 million to $10.0 million, one-time unless model is fine-tuned further
service chatbot
— Data and model pipeline building: ~$0.5 million. Costs include 5 to 6 machine learning engineers and
fine-tuned with
data engineers working for 16 to 20 weeks to collect and label data and perform data ETL.¹
sector-specific
knowledge and — Model fine-tuning²: ~$0.1 million to $6.0 million per training run³
chat history
• Lower end: costs include compute and 2 data scientists working for 2 months
Shaper
• Upper end: compute based on public closed-source model fine-tuning cost
— Plug-in-layer building: ~$1.0 million to $3.0 million. Costs include a team of 6 to 8 working for 6 to 12
months.
~ 0.5 million to $1.0 million, recurring annually
— Model inference: up to ~$0.5 million recurring annually. Assume 1,000 chats daily with both audio and
texts.
— Model maintenance: ~$0.5 million. Assume $100,000 to $250,000 annually for MLOps platform⁴ and
1 machine learning engineer spending 50% to 100% of their time monitoring model performance.
— Plug-in-layer maintenance: up to ~$0.3 million recurring annually, assuming 10% of development cost.
— Foundation ~ $5.0 million to $200.0 million, one-time unless model is fine-tuned or retrained
model trained
— Model development: ~$0.5 million. Costs include 4 data scientists spending 3 to 4 months on model
for assisting in
design, development, and evaluation leveraging existing research.
patient diagnosis
— Data and model pipeline: ~$0.5 million to $1.0 million. Costs include 6 to 8 machine learning engineers
and data engineers working for ~12 weeks to collect data and perform data ETL.¹
Maker — Model training⁵: ~$4.0 million to $200.0 million per training run.³ Costs include compute and labor cost
of 4 to 6 data scientists working for 3 to 6 months.
— Plug-in-layer building: ~$1.0 million to $3.0 million. Costs include a team of 6 to 8 working 6 to 12
months.
~ $1.0 million to $5.0 million, recurring annually
— Model inference: ~$0.1 million to $1.0 million annually per 1,000 users. Assume each physician sees 20
to 25 patients per day and patient speaks for 6 to 25 minutes per visit.
— Model maintenance: ~$1.0 million to $4.0 million recurring annually. Assume $250,000 annually for
MLOps platform⁴ and 3 to 5 machine learning engineers to monitor model performance.
— Plug-in-layer maintenance: up to ~$0.3 million recurring annually, assuming 10% of development cost.
Note: Through engineering optimizations, the economics of generative AI are evolving rapidly, and these are high-level estimates based on total cost of ownership
(resources, model training, etc) as of mid-2023.
1 Extract, transform, and load.
² Model is fine-tuned on data set consisting of ~100,000 pages of sector-specific documents and 5 years of chat history from ~1,000 customer representatives, which is
~48 billion tokens. Lower end cost consists of 1% parameters retrained on open-source models (eg, LLaMA) and upper end on closed-source models. Chatbot can be
accessed via both text and audio.
³ Model is optimized after each training run based on use of hyperparameters, data set, and model architecture. Model may be refreshed periodically when needed (eg, with
fresh data).
⁴ Gilad Shaham, “Build or buy your MLOps platform: Main considerations,” LinkedIn, November 3, 2021.
5 Model is trained on 65 billion to 1 trillion parameters and data set of 1.2 to 2.4 trillion tokens. The tool can be accessed via both text and audio.
6 Technology’s generational moment with generative AI: A CIO and CTO guide
Exhibit 2
Generative AI is integrated at key touchpoints to enable a tailored
Generative AI is integrated at key touchpoints to enable a tailored
ccuussttoommere jro juorunrenye.y.
Illustrative customer journey using travel agent bot API calls
Cus- Customer logs in and requests Customer reviews Customer requests Customer completes book-
tomer to change booking options live agent ing change and drops off
Disagrees
Inter- Chatbot Chatbot Chatbot Chatbot Agent Agent
action activated communi- re- pings picks up inputs
cates sponds cus- case new solu-
message tomer and tion for
Selects
and support provides review/
option
options new feedback
solution to model
Genera- Model Model Model Model Model
tive AI receives checks explains instructs instructs
model user booking issue and booking customer
request policy and gives system to support
and pulls sees cus- alternate complete system to
user info in tomer can- options task assign
prompt not make agent
change
Back- Log-in authentifi- Booking Workflow Workflow
end apps cation, model/cus- modification management management for
tomer info access policy for booking live agent
authorization management assignment
Data
source
Customer ID data Customer history Policy data Booking system Agent assignment
data data data
Infra-
structure Cloud/on-premises infrastructure and compute
and
compute
McKinsey & Company
Technology’s generational moment with generative AI: A CIO and CTO guide 7
For the Taker archetype, this level of coordination — Model hub, which contains trained and
isn’t necessary. But for companies looking to approved models that can be provisioned on
scale the advantages of generative AI as Shapers demand and acts as a repository for model
or Makers, CIOs and CTOs need to upgrade their checkpoints, weights, and parameters.
technology architecture. The prime goal is to
integrate generative AI models into internal systems — Prompt library, which contains optimized
and enterprise applications and to build pipelines to instructions for the generative AI models,
various data sources. Ultimately, it’s the maturity of including prompt versioning as models are
the business’s enterprise technology architecture updated.
that allows it to integrate and scale its generative AI
capabilities. — MLOps platform, including upgraded MLOps
capabilities, to account for the complexity of
Recent advances in integration and orchestration generative AI models. MLOps pipelines, for
frameworks, such as LangChain and LlamaIndex, example, will need to include instrumentation
have significantly reduced the effort required to to measure task-specific performance, such
connect different generative AI models with other as measuring a model’s ability to retrieve the
applications and data sources. Several integration right knowledge.
patterns are also emerging, including those that
enable models to call APIs when responding to In evolving the architecture, CIOs and CTOs will
a user query—GPT-4, for example, can invoke need to navigate a rapidly growing ecosystem
functions—and provide contextual data from an of generative AI providers and tooling. Cloud
external data set as part of a user query, a technique providers provide extensive access to at-scale
known as retrieval augmented generation. Tech hardware and foundation models, as well as a
leaders will need to define reference architectures proliferating set of services. MLOps and model
and standard integration patterns for their hub providers, meanwhile, offer the tools,
organization (such as standard API formats and technologies, and practices to adapt a foundation
parameters that identify the user and the model model and deploy it into production, while
invoking the API). other companies provide applications directly
accessed by users built on top of foundation
There are five key elements that need to be models to perform specific tasks. CIOs and CTOs
incorporated into the technology architecture to will need to assess how these various capabilities
integrate generative AI effectively (Exhibit 3): are assembled and integrated to deploy and
operate generative AI models.
— Context management and caching to
provide models with relevant information from
enterprise data sources. Access to relevant 6. Develop a data architecture to
data at the right time is what allows the model to enable access to quality data
understand the context and produce compelling The ability of a business to generate and
outputs. Caching stores results to frequently scale value, including cost reductions and
asked questions to enable faster and cheaper improved data and knowledge protections, from
responses. generative AI models will depend on how well it
takes advantage of its own data. Creating that
— Policy management to ensure appropriate advantage relies on a data architecture that
access to enterprise data assets. This control connects generative AI models to internal data
ensures that HR’s generative AI models that sources, which provide context or help fine-tune
include employee compensation details, for the models to create more relevant outputs.
example, cannot be accessed by the rest of the
organization.
8 Technology’s generational moment with generative AI: A CIO and CTO guide
Exhibit 3
The tech stack for generative AI is emerging.
The tech stack for generative AI is emerging.
Illustrative generative AI tech stack
Users
Apps Models Data
Tooling Infrastructure
Apps-as-a- Data sources Experience layer Policy
service with Embeddings, DTC² or B2B applications (eg, Jasper) management
embedded unstructured
Role-based
foundation data,
access
models analytical
API gateway control and
End-user- data, trans- content-
facing actional data based policies
applications to secure
and founda- Context management and caching enterprise
tion models data assets
User and task context retrieved from enterprise data
accessed
sources to prompt generative AI models, cache for
through a
common requests
browser
interface as
SaaS¹ (eg,
Midjourney)
Data Model hub Prompt
platforms library
Platforms that allow users to share
Vector models and data sets (eg, Hugging Face)
databases,
data
warehouse,
data lake Closed-source Open-/closed-source
foundation foundation models
models Trained model that is made
API-based, pre- accessible (eg, BLOOM)
trained models
(eg, GPT-4)
MLOps platform
Existing enterprise platforms
(eg, ERP,³ CRM⁴)
Cloud or on-premises infrastructure QA and
and compute hardware observability
QA model
outputs (eg,
checks for bias)
1Software as a service.
2Direct to consumer.
3Enterprise resource planning.
4Customer relationship management.
McKinsey & Company
Technology’s generational moment with generative AI: A CIO and CTO guide 9
In this context, CIOs, CTOs, and chief data officers with internal systems, enterprise applications,
need to work closely together to do the following: and tools, and also develops and implements
standardized approaches to manage risk, such as
— Categorize and organize data so it can be used responsible AI frameworks.
by generative AI models. Tech leaders will need
to develop a comprehensive data architecture CIOs and CTOs need to ensure that the platform
that encompasses both structured and team is staffed with people who have the right
unstructured data sources. This requires putting skills. This team requires a senior technical leader
in place standards and guidelines to optimize who acts as the general manager. Key roles include
data for generative AI use—for example, by software engineers to integrate generative AI
augmenting training data with synthetic samples models into existing systems, applications, and
to improve diversity and size; converting media tools; data engineers to build pipelines that
types into standardized data formats; adding connect models to various systems of record and
metadata to improve traceability and data data sources; data scientists to select models and
quality; and updating data. engineer prompts; MLOps engineers to manage
deployment and monitoring of multiple models and
— Ensure existing infrastructure or cloud services model versions; ML engineers to fine-tune models
can support the storage and handling of the with new data sources; and risk experts to manage
vast volumes of data needed for generative AI security issues such as data leakage, access
applications. controls, output accuracy, and bias. The exact
composition of the platform team will depend on
— Prioritize the development of data pipelines to the use cases being served across the enterprise. In
connect generative AI models to relevant data some instances, such as creating a customer-facing
sources that provide “contextual understanding.” chatbot, strong product management and user
Emerging approaches include the use of vector experience (UX) resources will be required.
databases to store and retrieve embeddings
(specially formatted knowledge) as input for Realistically, the platform team will need to work
generative AI models as well as in-context initially on a narrow set of priority use cases,
learning approaches, such as “few shot gradually expanding the scope of their work as they
prompting,” where models are provided with build reusable capabilities and learn what works
examples of good answers. best. Technology leaders should work closely with
business leads to evaluate which business cases to
fund and support.
7. Create a centralized, cross-functional
generative AI platform team
8. Tailor upskilling programs by roles
Most tech organizations are on a journey to a
product and platform operating model. CIOs and and proficiency levels
CTOs need to integrate generative AI capabilities Generative AI has the potential to massively
into this operating model to build on the existing lift employees’ productivity and augment their
infrastructure and help to rapidly scale adoption capabilities. But the benefits are unevenly
of generative AI. The first step is setting up a distributed depending on roles and skill levels,
generative AI platform team whose core focus is requiring leaders to rethink how to build the actual
developing and maintaining a platform service skills people need.
where approved generative AI models can be
provisioned on demand for use by product and Our latest empirical research using the generative
application teams. The platform team also defines AI tool GitHub Copilot, for example, helped software
protocols for how generative AI models integrate engineers write code 35 to 45 percent faster.⁵ The
5 “Unleashing developer productivity with generative AI,” June 27, 2023.
10 Technology’s generational moment with generative AI: A CIO and CTO guide
benefits, however, varied. Highly skilled developers Beyond training up tech talent, the CIO and CTO
saw gains of up to 50 to 80 percent, while junior can play an important role in building generative
developers experienced a 7 to 10 percent decline in AI skills among nontech talent as well. Besides
speed. That’s because the output of the generative understanding how to use generative AI tools for
AI tools requires engineers to critique, validate, such basic tasks as email generation and task
and improve the code, which inexperienced management, people across the business will
software engineers struggle to do. Conversely, in need to become comfortable using an array of
less technical roles, such as customer service, capabilities to improve performance and outputs.
generative AI helps low-skill workers significantly, The CIO and CTO can help adapt academy models
with productivity increasing by 14 percent and staff to provide this training and corresponding
turnover dropping as well, according to one study.⁶ certifications.
These disparities underscore the need for The decreasing value of inexperienced engineers
technology leaders, working with the chief human should accelerate the move away from a classic
resources officer (CHRO), to rethink their talent talent pyramid, where the greatest number of
management strategy to build the workforce of the people are at a junior level, to a structure more
future. Hiring a core set of top generative AI talent like a diamond, where the bulk of the technical
will be important, and, given the increasing scarcity workforce is made up of experienced people.
and strategic importance of that talent, tech Practically speaking, that will mean building the
leaders should put in place retention mechanisms, skills of junior employees as quickly as possible
such as competitive salaries and opportunities while reducing roles dedicated to low-complexity
to be involved in important strategic work for the manual tasks (such as writing unit tests).
business.
Tech leaders, however, cannot stop at hiring. 9. Evaluate the new risk landscape
Because nearly every existing role will be affected and establish ongoing mitigation
by generative AI, a crucial focus should be on practices
upskilling people based on a clear view of what Generative AI presents a fresh set of ethica"
252,mckinsey,generative-ai-and-the-future-of-hr_final.pdf,"People & Organizational Performance Practice
Generative AI and
the future of HR
A chatbot may not take your job—but it will almost certainly change it.
Here’s how to start thinking about putting gen AI to work for you.
June 2023
Generative AI: It’s powerful. It’s accessible. And in. Lean forward and figure out how to use it in a way
it’s poised to change the way we work. On this that’s productive and safe.
episode of the McKinsey Talks Talent podcast,
talent leaders Bryan Hancock and Bill Schaninger Lucia Rahilly: The immediacy of the use cases
talk with McKinsey Technology Council chair feels so novel and so lightning fast. Explain what
Lareina Yee and global editorial director Lucia generative AI is, so we’re working from a common
Rahilly about the promise and pitfalls of using gen AI definition of that term.
in HR—from recruiting to performance management
to chatbot-enabled professional growth. An edited Lareina Yee: Generative AI is a technology that
version of their discussion follows. prompts the next best answer. A lot of people have
used ChatGPT to summarize information, to draft
a response to something, by pulling together an
What’s so different—and so disruptive enormous amount of public data. But there’s also
Lucia Rahilly: There has been so much buzz in amazing imaging. I might want a song, audio, video,
recent months about generative AI and tools like or code. Code is a huge example. It’s amazing the
ChatGPT. Many people seem to be ricocheting range of things that generative AI can do in the
between wonder at the potential of these tools world, and it’s just getting started.
and fear of their inherent risks. Lareina, what’s
different about generative AI, and what’s behind its Bryan Hancock: I asked ChatGPT about myself,
disruptive potential? and it accurately reported that I do a lot of work
on talent. However, it inaccurately reported that I
Lareina Yee: A couple of things stand out about went to Cornell because it assumed that Cornell
generative AI. In November 2022, OpenAI released was the most appropriate answer based on my
ChatGPT 3.5, and within five days, there were a background instead of the University of Virginia—
million users. So the speed of adoption was unlike where I did go. I thought it was very interesting that
anything we’ve seen. you don’t necessarily get what’s right but rather
what’s logical.
For me, what was most profound about that moment
was that anyone—of any age, any education level, Lareina Yee: In some ways, that emulates how
any country—could go onto GPT, query a question we think. I’m not suggesting it’s thinking the way
or two, and find something practical or fun, like a humans do, but in many ways, we use shortcuts
poem or an essay. There was an experience there and cues to make assumptions. That is kind of why
that was accessible to everybody. We’ve seen a lot people say, “Gosh, it feels really clever.” But to your
of advancement in the technology since then, and point, Bryan, it’s not 100 percent accurate. There’s a
it’s only been a couple of months. great term for that: “hallucinating.”
A second super-interesting thing is you don’t
What gen AI means for recruiters . . .
need to be a computer scientist to leverage the
technology—it can be used in all types of jobs. Lucia Rahilly: We’ll talk more about some of the
OpenAI’s research estimates that 80 percent of risks, but let’s turn to what these kinds of generative
jobs can incorporate generative AI technology and AI capabilities mean for talent in particular. Do
capabilities into activities that happen today in work. you expect generative AI to reshape or alter the
That is a profound impact on talent and jobs, and it’s recruiting process in any meaningful way?
different than how we’ve talked about it before.
Bryan Hancock: I think it’ll reshape recruiting in two
In some ways, the genie is out of the bottle. It’s meaningful ways. The first is helping managers write
probably not the best strategy to try to put it back better job requirements. Generative technology
2 Generative AI and the future of HR
can actually pull on the skills that are required to be in the recruiting process. Does generative AI have
successful in the job. That’s not to say managers a role in accelerating that shift from credentials like
don’t need to check the end product. They’ll need college degrees to the skills that candidates are
to be that human in the loop to make sure the actually capable of contributing to the workplace?
job requirement is a good one. But gen AI can
dramatically improve speed and quality. Lareina Yee: I’m optimistic it can. One thing this
technology does extremely well is tagging—the
The other application in recruiting is candidate ability to tag unstructured data for words. There are
personalization. Right now, if you’re an organization a lot of businesses that are thinking about applying
with tens of thousands of applicants, you may or that to e-commerce, to different types of retail
may not have super customized ways of reaching experiences. But you could also apply it to talent
out to the people who have applied. With generative acquisition or looking for capabilities. Now you don’t
AI, you can include much more personalization need to look for a credential or a degree. You could
about the candidate, the job, and what other jobs look for keywords in terms of capabilities and skills.
may be available if there’s a reason the applicant
isn’t a fit. All those things are made immensely Looking at social media, how do people talk about
easier and faster through generative AI. certain capabilities? You may find there are better
words to associate with those who have those skills.
Bill Schaninger: The best application of gen AI Think of a world where you want to be able to find
is in large skill pools where you’re trying to fill candidates who have amazing experience from
a reasonably well-known job. We need a more learning on the job but don’t have PhDs or college
productive and efficient way to navigate all the degrees. I’m optimistic that this could open more
profiles coming through. Where it makes me a little doors for folks like that.
anxious is anytime it’s a novel job—a new role—or
even, in US law, a job that’s changed more than 25 Bill Schaninger: This is an interesting trade-off in
percent or 33 percent. In those cases, you have to the business world, which likes proprietary data sets
go back and revalidate the criterion by which you and grouping of profiles. The real power might be,
would judge people in or out of the pool. “How much can you get in the public domain until
you start bumping up against paywalls?”
The challenge with validation is you need a
performance criterion to regress against and Long ago, when LinkedIn was bought, the APIs got
say, “What’s the difference?” In some cases, that limited to job titles—not necessarily all the spec
means figuring out how to get that criterion out of that was underneath it. There is power in these
a data lake without encroaching on other people’s pools—in particular, in profiles of jobs—because
proprietary performance data. If you say, “Well, then you can go look at tasks and skills. I’d imagine
we’re only going to use our data as the employer,” there’s going to be a race here toward figuring
then you are only basing the criterion off people out how we can piece these together to form the
you’ve already hired. And to validate, you have to ontological cloud, if you will, of “these 17 things
look at the people you didn’t hire. describe this skill.” Because it really is about skills
and not credentials.
So it doesn’t mean the technology can’t be used. It
just means there’s probably a little bit more front-
end work on applying it to novel jobs and a wide- . . . And what it means for
open opportunity for the big skill pools. professional growth
Bryan Hancock: You can also think about this
Lucia Rahilly: We talk a lot about having over- as aiding a skill-based transition not just from
indexed on credentials and under-indexed on skills the employer’s perspective but from the
Generative AI and the future of HR 3
candidate’s or employee’s perspective. In the provide much more transparency; you can actually
current world, if you’re somebody who may have see how close you are to a lot of things. I love it for
some skills but don’t have a very clear view of what the employee experience part. I get anxious about
your career opportunities might be, you are highly the selection part just because we’re still not sure
dependent on a manager or somebody taking about what’s in the data lake and how good people
an interest in you and helping to navigate you to are at prompting the AI.
“nontraditional” paths.
Lareina Yee: Right. It’s great to give you some
But in a world of generative AI, you could have a options, but it’s not an answer or a recommendation
conversation with a very intelligent chatbot and engine. Your judgment matters.
say, “Hey, here are my skills and experiences. What
jobs could be open to me?” And it could come back Bryan Hancock: Another thing we’re seeing is that
and say, “Well, most people with your skill profile do ChatGPT—and generative AI more broadly—can
these things, but some do A, B, C,” with “C” being be particularly good at getting new workers more
coding. And then, you could say, “Tell me what quickly up to speed.
these jobs in coding would be,” and it could pull a
job description for a coder that is not just geared There’s interesting research that Erik Brynjolfsson
toward an IT person but translated into words you at Stanford, along with others from MIT, have
understand. Then you could say, “OK, this is great. recently come out with, which looks at call-center
I’m interested. What learning experiences do I workers. They found that generative AI functionality
need?” And generative AI could tell you what those wasn’t all that helpful for the most experienced
learning experiences are. representatives. It was incredibly helpful with
new folks because they were able to get that
So for somebody who has the innate ability but not institutional knowledge much more quickly. It was
the visibility, generative AI can illuminate a range of at their fingertips. They could ask a question and
career paths and start helping people understand get the answer. So the productivity of new folks was
how to get there. dramatically higher. Generative AI really gets you
80–90 percent of the way to full proficiency.
Lareina Yee: Imagine I’m ten years into my
career and I’m feeling a little stuck. What if I had a Lareina Yee: Bryan, I love that, and I share
professional development AI assistant that helped the optimism.
me think through questions like, “What type of job
should I seek? What are the types of roles within my
company? How do I think about them?” and “What What’s new for the performance review
classes would I take?” as opposed to waiting for
Bryan Hancock: One of my personal favorite uses
someone to reskill me—which sounds awful. How
for generative AI on the people front is actually for
do I take the initiative ten years into my career to
performance reviews. Hear me out: I don’t want
build the skill sets and understand the range of jobs
generative AI actually generating somebody’s
available for my capabilities? That would be so cool.
performance review. That needs the human in the
loop, needs human judgment, needs empathy.
Bill Schaninger: Depending on the regulatory
environment you’re in, you’re not allowed to make
But let me use this example of what I do as a
any selection decision without a human being
McKinsey evaluator: I get written feedback from
involved. This is particularly true in the EU. It’s a nice
15 to 20 individuals. They enter it into a digital
way of augmenting human work but not cutting out
system. I’ve got long-form feedback. I look at
the decision making. On the employee side, it should
upward feedback scores that include written
4 Generative AI and the future of HR
commentary as well as specific number-based that. I think there’s a lot that enhances what we’ve
scores. I look at how often people were actually been trying to do so laboriously for years.
deployed on engagements. I look at compliance-
related measures. Did they turn in their stuff Bill Schaninger: We talk about putting the manager
on time? A whole range of things. For me, as an back in performance management. Every time you
evaluator, getting to a first draft is an incredibly talk to somebody about something good or bad, log
arduous process. I take pride in the time and the it away. That way, at the end of the year, it’s more of
thoughtfulness that goes into it. an aggregation and synthesis, and it’s not a surprise
to anyone. But that requires regular entry. So while
But what if I could hit a button and get a draft? I love what you’re describing, it’s not the tech that
When I have each of the conversations with the does that; it’s the people committing to the common
15 people that best know the person I’m evaluating, data capture and the common approaches that
what if I had a draft I was already working from? It’s enable it.
not a replacement for going through everything, but
that initial synthesis would help me get more quickly Bryan Hancock: Your point is well-taken. Then, as
to what I really need to probe for that person’s an evaluator, I apply my human judgment.
development and growth.
Bill Schaninger: The normative data is nice.
I’m excited about that use case because it When we get our sponsorship and mentorship
eliminates a lot of work. At first, many people would data at McKinsey, we see how we compare to
think, “I’d never want generative AI anywhere near other partners in a given region. If you don’t have a
performance reviews.” But it’s exciting if we think reference point, though, how would you know what
of this as a productivity aid or as something that “good” actually is? When you get the normative
helps us be even better. data, you can start getting some guidance. I like all
that, and it’s all enabled by huge amounts of data.
Lareina Yee: Now let’s talk about the employee
he’s evaluating. The employee gets the feedback, If this enables a more robust and wholesome view
and Bryan probably wrote it clearly, and he of actual performance, it makes it a whole lot easier
delivered it with empathy, so the person is feeling, to have a difficult performance conversation. We
“OK, I’ve got some strengths, and I’ve got some need to put the manager back in performance
development needs.” management. But can we make it easier on
managers so they can spend the time managing
But what if I, as the employee, can query, instead of scribbling out a schedule or knitting
“Who are five success models with my strengths together 15 data points?
and weaknesses, and what have they gone on to
do? How can I visualize my career development?
How can I continue to work on it?” I could also Bias and other risks
have an assistant that helps me map my Lucia Rahilly: Let’s talk a bit more about some of
professional development. In that way, when we the risks. Generative AI learns based on historical
check in a year later, I’ve really improved and data, and historical patterns of data reflect historical
increased my aspirations. biases. By relying on generative-AI-driven tools,
what’s the risk we are inadvertently propagating
What if Bill is someone I should model myself on? these inherited biases?
Instead of Bryan having to introduce me to Bill,
generative AI helps me realize that I’ve got the Lareina Yee: Certainly, today, generative AI can
makings of a Bill Schaninger. I can be inspired by amplify bias.
Generative AI and the future of HR 5
Let’s say I’m recruiting, and I describe some The other thing is there’s a real opportunity for what
different qualifications. I’m looking at urban we typically call “change management.” If you don’t
centers of talent, and I decide I’d like to look for think through how the technology changes the job,
basketball captains; or perhaps, instead, I say that workflow, or collaboration model, then you’re not
lacrosse captains are desirable. These are team necessarily directing that additional time toward
sports with captains and leadership, so in some something that’s more value added. You need to
way that makes sense. think about how it affects the rest of the workday
and workweek.
But if you look at demographics, who plays
basketball in cities is very different from who plays Bill Schaninger: In many cases, we’d like to blame
lacrosse. And so, by emphasizing lacrosse, you the technology and not highlight the poor problem
will typically get more young White male leaders, solving that happened just before implementing
whereas if you chose basketball, you might find it. Getting a better, shinier tool that’s faster and
more African Americans or Latinos. What about more expansive doesn’t relieve you of the burden of
softball, where we see women? What happens if, thinking things through.
instead, we select a whole set of sports? Even then,
just the selection of the sports as a filter could Lareina Yee: The bigger thing to call out here is that
amplify bias in the questioning. I think the power of three of us have spent this time thinking about all
the question is on us as humans. the positive intentions and the ways we can use this
for good. But there are probably people who are
Bryan Hancock: Of course there are also thinking about this technology and asking, “How
intellectual property concerns. can I use this for harm?” Traditionally, this is why
government regulation, policy, and international
But I also think there’s a risk of us all becoming less standards play a fundamental role in our society. I
interesting. If you are somebody in a creative field don’t think you can completely leave it to the private
and you leverage generative AI to get your output sector to self-regulate.
up from six articles a week to 12, you’re spending
less time per article. You may need to do that to get
to publication in time, but that also means you’re Preparing for the inevitable
not spending as much time in the shower, on a Lucia Rahilly: A big concern for people is that these
run, or in the car thinking about the articles. Your kinds of tools will eliminate their job or—potentially
productivity will go up, but you may not necessarily even worse—become their bosses. What do you
have as much time for creative thinking. We think people can do now to prepare for the changes
know that the most creative thoughts come from that are coming with generative AI?
downtime—when you’re doing something else and
letting your mind wander. Bill Schaninger: I would try to make it easier for
them to learn and play with it. This is better than
This risk of being less interesting is important, and continuing to try to resist it. I don’t think we should
one that we may not have fully thought through yet. become beholden to these fears.
Lareina Yee: Precisely. There are a lot of risks. Let’s Lucia Rahilly: And assuming HR and talent
also think about leaders who are implementing processes become increasingly automated,
this technology. Often people had a workflow how can leaders ensure that generative AI doesn’t
where they would think about a technology and the get in the way of what Bryan called “the human in
business return on investment, and only at the end the loop?”
would ask, “Are there any risks we should worry
about?” I would strongly recommend that you think Lareina Yee: Leaders have a huge role to play in
about risk up front in the workflow design. two ways. One is to modernize and leapfrog their
6 Generative AI and the future of HR
own talent capabilities within their functions. And get managers more consistently up to the level
Find more content like this on the
second, if 80 percent of their workforce is shifting, of performance that HR leaders have always
McKinsey Insights App
they play a huge role in how that happens and how wanted them to achieve instead of working on
it affects employees at their companies. I think administrative tasks. I hope that HR would view
leaders have a huge voice at the table. this as an opportunity to routinize and get rid of
the work that they don’t have to do. Then for the
Bryan Hancock: It’s a tremendous opportunity for work that they do have to do, they can use this
HR to increase access to opportunities for huge technology to find a way to get better answers
swaths of their workforce. It’s an opportunity to more quickly.
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Bryan Hancock is a partner in McKinsey’s Washington, DC, office; Bill Schaninger is a senior partner emeritus in the
Philadelphia office; and Lareina Yee is a senior partner in the Bay Area office. Lucia Rahilly is the global editorial director and
deputy publisher of McKinsey Global Publishing and is based in the New York office.
Designed by McKinsey Global Publishing
Copyright © 2023 McKinsey & Company. All rights reserved.
Generative AI and the future of HR 7"
253,mckinsey,whats-the-future-of-generative-ai-an-early-view-in-15-charts.pdf,"McKinsey Explainers
What’s the future
of generative AI?
An early view in
15 charts
Generative AI has hit the ground running—so fast that it
can feel hard to keep up. Here’s a quick take pulled from our
top articles and reports on the subject.
August 2023
Since the release of ChatGPT in November 2022, it’s been all over the McKinsey research has found that
headlines, and businesses are racing to capture its value. Within the
technology’s first few months, McKinsey research found that generative generative AI features stand to add
AI (gen AI) features stand to add up to $4.4 trillion to the global
economy—annually. up to $4.4 trillion to the global
The articles and reports we’ve published in this time frame examine
economy—annually.
questions such as these:
— What will the technology be good at, and how quickly?
— What types of jobs will gen AI most affect?
— Which industries stand to gain the most?
— What activities will deliver the most value for organizations?
— How do—and will—workers feel about the technology?
— What safeguards are needed to ensure responsible use of gen AI?
In this visual Explainer, we’ve compiled all the answers we have so far—
in 15 McKinsey charts. We expect this space to evolve rapidly and will
continue to roll out our research as that happens. To stay up to date on
this topic, register for our email alerts on “artificial intelligence” here.
What’s the future of generative AI? An early view in 15 charts 2
Web <2023>
<Future of GenAI>
Exhibit <1> of <15>
Gen AI finds its legs Generative AI has been evolving at a rapid pace.
The advanced machine learning that powers Timeline of major large language model (LLM) developments following ChatGPT’s launch
gen AI–enabled products has been decades
Nov 2022 Dec Jan 2023 Feb Mar Apr
in the making. But since ChatGPT came off the
starting block in late 2022, new iterations of gen
1 2 3 4 5 6 7 8 9 10 11 12 13
AI technology have been released several times
a month. In March 2023 alone, there were six 1 Nov 30, 2022: OpenAI’s 4 Feb 2, 2023: Amazon’s 7 Mar 7: Salesforce 10 Mar 16: Microsoft
major steps forward, including new customer ChatGPT, powered by multimodal-CoT model announces Einstein GPT announces the integration
GPT-3.5 (an improved incorporates “chain-of- (leveraging OpenAI’s of GPT-4 into its
relationship management solutions and support version of its 2020 GPT-3 thought prompting,” in models), the first Office 365 suite,
for the financial services industry. release), becomes the first which the model explains generative AI technology potentially enabling broad
widely used text- its reasoning, and for customer relationship productivity increases
generating product, outperforms GPT-3.5 on management
Source: What every CEO should know about gaining a record several benchmarks 11 Mar 21: Google releases
100 million users in 8 Mar 13: OpenAI releases Bard, an AI chatbot based
generative AI
2 months 5 Feb 24: As a smaller GPT-4, which offers on the LaMDA family
model, Meta’s LLaMA is significant improvements of LLMs
2 Dec 12: Cohere releases more efficient to use than in accuracy and
the first LLM that some other models but hallucinations mitigation, 12 Mar 30: Bloomberg
supports more than continues to perform claiming 40% announces an LLM
100 languages, making it well on some tasks com- improvement vs GPT-3.5 trained on financial data
available on its enterprise pared with other models to support natural-
AI platform 9 Mar 14: Anthropic language tasks in the
6 Feb 27: Microsoft introduces Claude, an AI financial industry
3 Dec 26: LLMs such as introduces Kosmos-1, assistant trained using a
Google’s Med-PaLM are a multimodal LLM that method called 13 Apr 13: Amazon
trained for specific use can respond to image and “constitutional AI,” which announces Bedrock, the
cases and domains, such audio prompts in addition aims to reduce the first fully managed service
as clinical knowledge to natural language likelihood of harmful that makes models
outputs available via API from
multiple providers in
addition to Amazon’s own
Titan LLMs
McKinsey & Company
What’s the future of generative AI? An early view in 15 charts 3
Web <2023>
<Future of GenAI>
Exhibit <2> of <15>
The road to human-level Due to generative AI, experts assess that technology could achieve human-
performance just got shorter level performance in some capabilities sooner than previously thought.
For most of the technical capabilities shown Estimated range for technology to achieve human-level performance, by technical capability
in this chart, gen AI will perform at a median
Post-recent generative AI developments (2023)¹ Pre-generative AI (2017)¹
level of human performance by the end of this
Median Top quartile Median Top quartile
decade. And its performance will compete with
the top 25 percent of people completing any 2010 2020 2030 2040 2050 2060 2070 2080
and all of these tasks before 2040. In some
Coordination with multiple agents
cases, that’s 40 years faster than experts
Creativity
previously thought.
Logical reasoning and problem solving
Source: The economic potential of generative
Natural-language generation
AI: The next productivity frontier
Natural-language understanding
Output articulation and presentation
Generating novel patterns and categories
Sensory perception
Social and emotional output
Social and emotional reasoning
Social and emotional sensing
¹Comparison made on the business-related tasks required from human workers.
Source: McKinsey Global Institute occupation database; McKinsey analysis
McKinsey & Company
What’s the future of generative AI? An early view in 15 charts 4
Web <2023>
<Future of GenAI>
Exhibit <3> of <15>
And automation of knowledge Advances in technical capabilities could have the most impact on activities
work is now in sight performed by educators, professionals, and creatives.
Impact of generative AI on technical automation Without generative AI¹
Previous waves of automation technology
potential in midpoint scenario, 2023 With generative AI
mostly affected physical work activities, but
gen AI is likely to have the biggest impact Overall technical automation potential, Share of global
Occupation group comparison in midpoint scenarios, 2023, % employment,2 %
on knowledge work—especially activities
involving decision making and collaboration. Educator and workforce training 15 54 4
Professionals in fields such as education, law,
Business and legal 32
technology, and the arts are likely to see parts professionals 62 5
of their jobs automated sooner than previously 28
STEM professionals 57 3
expected. This is because of generative AI’s
39
ability to predict patterns in natural language Community services 65 3
and use it dynamically.
28
Creatives and arts management 53 1
Source: The economic potential of generative 66
Office support 87 9
AI: The next productivity frontier
27
Managers 44 3
29
Health professionals 43 2
45
Customer service and sales 57 10
29
Property maintenance 38 4
Health aides, technicians, 34
and wellness 43 3
73
Production work 82 12
70
Food services 78 5
42
Transportation services 49 3
Mechanical installation 61
and repair 67 4
59
Agriculture 63 21
49
Builders 53 7
51
Overall 100
63
Note: Figures may not sum, because of rounding.
¹Previous assessment of work automation before the rise of generative AI.
2Includes data from 47 countries, representing about 80% of employment across the world.
Source: McKinsey Global Institute analysis
McKinsey & Company
What’s the future of generative AI? An early view in 15 charts 5
Web <2023>
<Future of GenAI>
Exhibit <4> of <15>
Apps keep proliferating to There are many applications of generative AI across modalities.
address specific use cases
Generative AI use cases, nonexhaustive
Gen AI tools can already create most types of Modality Application Example use cases
written, image, video, audio, and coded content.
Text Content writing
And businesses are developing applications to
address use cases across all these areas. In the Chatbots or assistants
near future, we expect applications that target Search
specific industries and functions will provide
Analysis and synthesis
more value than those that are more general.
Code Code generation
Source: Exploring opportunities in the
generative AI value chain Application prototype
and design
Data set generation
Image Stock image generator
Image editor
Audio Text to voice generation
Sound creation
Audio editing
3-D 3-D object generation
or other
Product design and
discovery
Video Video creation
Video editing
Voice translation
and adjustments
Face swaps and
adjustments
McKinsey & Company What’s the future of generative AI? An early view in 15 charts 6
Generative AI will affect business functions differently across industries.
Some industries will gain more
Generative AI productivity
t G
o
dh
f
ie
f
f
fn aa
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cA rn etI o’ ns ro
t
sp b,t r useh
u
sc ice i ns
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a
s
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s
s
t
fp
h
ua nec cmt
t
w
ii
oxi
n
l al
s
nd
,
de ap
sim
e wn
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ed
o
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a
s
n
a
tc
h
v
e
ea or si
f
ce aty
le
i Lm owp ia mc pt a b cty business functio Hn igs h¹ impact MarketingC au ns dto sm ae ler
s
operatiP or nS o so df utS cwu ta p Rrp e &l y e
D
nc gh ia ni en
e
a rn ind
g
operatiR onis skS t ar nat de lg ey
g
a an ld finaC ncT oa erl pe on rt
a
a ten d
IT
o 2rganization
Total, % of
of an industry’s revenue. Nearly all industries industry Total, 760– 340– 230– 580– 290– 180– 120– 40– 60–
revenue $ billion 1,200 470 420 1,200 550 260 260 50 90
will see the most significant gains from
Administrative and
deployment of the technology in their marketing professional services 0.9–1.4 150–250
and sales functions. But high tech and banking Advanced electronics
and semiconductors 1.3–2.3 100–170
will see even more impact via gen AI’s potential
to accelerate software development. Advanced manufacturing3 1.4–2.4 170–290
Agriculture 0.6–1.0 40–70
Source: The economic potential of generative
Banking 2.8–4.7 200–340
AI: The next productivity frontier
Basic materials 0.7– 1.2 120–200
Chemical 0.8–1.3 80–140
Construction 0.7–1.2 90–150
Consumer packaged goods 1.4–2.3 160–270
Education 2.2–4.0 120–230
Energy 1.0– 1.6 150–240
Healthcare 1.8–3.2 150–260
High tech 4.8–9.3 240–460
Insurance 1.8– 2.8 50–70
Media and entertainment 1.8– 3.1 80–130
Pharmaceuticals and
2.6–4.5 60–110
medical products
Public and social sector 0.5–0.9 70–110
Real estate 1.0–1.7 110–180
Retail4 1.2–1.9 240–390
Telecommunications 2.3–3.7 60–100
Travel, transport, and logistics 1.2–2.0 180–300
2,600–4,400
Note: Figures may not sum to 100%, because of rounding. 1Excludes implementation costs (eg, training, licenses). 2Excluding software engineering. 3Includes aero-
space, defense, and auto manufacturing. 4Including auto retail. Source: Comparative Industry Service (CIS), IHS Markit; Oxford Economics; McKinsey Corporate and
Business Functions database; McKinsey Manufacturing and Supply Chain 360; McKinsey Sales Navigator; Ignite, a McKinsey database; McKinsey analysis
McKinsey & Company What’s the future of generative AI? An early view in 15 charts 7
Web <2023>
<Future of GenAI>
Exhibit <6> of <15>
So understanding the use Generative AI could deliver significant value when deployed in some use
cases that will deliver the most cases across a selection of top industries.
value to your industry is key
Selected examples of key use cases for main Value potential High
of function for
functional value drivers (nonexhaustive)
the industry Low
Our report, The economic potential of
generative AI: The next productivity frontier, Total value
potential Value
contains spotlight sections detailing how to per industry, potential,
identify the use cases with the highest value $ billion (% as % of Product R&D,
of industry operating software Customer Marketing Other
potential in the banking, life sciences, and retail revenue) profits1 engineering operations and sales functions
and consumer-packaged-goods industries.
Banking 200–340 9–15 Legacy code Customer Custom retail Risk model
These provide a good framework for assessing (3–5%) conversion emergency banking offers documentation
interactive voice
your own industry. Optimize migration Push personalized Create model
response (IVR)
of legacy marketing and sales documentation,
frameworks with Partially automate, content tailored for and scan for
Source: The economic potential of generative natural-language accelerate, and each client of the missing
translation enhance resolution bank based on documentation
AI: The next productivity frontier
capabilities rate of customer profile and history and relevant
emergencies through (eg, personalized regulatory
generative nudges), and updates
AI–enhanced IVR generate alternatives
interactions (eg, for for A/B testing
credit card losses)
Retail 400–660 27–44 Consumer research Augmented Assist copy writing Procurement
and (1–2%) reality–assisted for marketing suppliers
Accelerate consumer
consumer customer support content creation process
research by testing
packaged enhancement
scenarios, and Rapidly inform the Accelerate writing of
goods2
enhance customer workforce in real copy for marketing Draft playbooks
targeting by creating time about the status content and for negotiating
“synthetic customers” of products and advertising scripts with suppliers
to practice with consumer
preferences
Pharma 60–110 15–25 Research and Customer Generate content Contract
and (3–5%) drug discovery documentation for commercial generation
medical generation representatives
Accelerate the Draft legal
products
selection of proteins Draft medication Prepare scripts for documents
and molecules best instructions and risk interactions with incorporating
suited as candidates notices for drug physicians specific
for new drug resale regulatory
formulation requirements
¹Operating profit based on average profitability of selected industries in the 2020–22 period.
2Includes auto retail.
McKinsey & Company
What’s the future of generative AI? An early view in 15 charts 8
Web <2023>
<Future of GenAI>
Exhibit <7> of <15>
Despite gen AI’s commercial Commercial leaders are already leveraging generative AI use cases—but
promise, most organizations most feel the technology is underutilized.
aren’t using it yet
Reported use of technology at organization¹ and level at which respondents think it should be used,2
% of respondents at commercially leading organizations
When we asked marketing and sales leaders
Machine learning Generative AI
how much they thought their organization
should be using gen AI or machine learning Almost always
for commercial activities, 90 percent thought 15 Often
20
it should be at least “often.” That’s hardly 25 Sometimes
surprising, given that marketing and sales is Rarely
40
Almost never
the area with the most potential for impact, 20
as we saw earlier. But 60 percent said their 20
organizations rarely or never do this.
Source: AI-powered marketing and sales reach
new heights with generative AI
40 65
50 55
25
10 10
5
Currently Should Currently Should
use use use use
1Senior executives in significant global B2B and B2C sales and marketing organizations across a wide range of industries and company maturity levels were
asked: To what extent is your organization using machine learning/generative AI solutions?
2Q: How much do you think your organization should be using machine learning/generative AI solutions?
McKinsey & Company
What’s the future of generative AI? An early view in 15 charts 9
Web <2023>
<Future of GenAI>
Exhibit <8> of <15>
Marketing and sales leaders Commercial leaders are cautiously optimistic about generative AI use
are most enthusiastic about cases, anticipating moderate to significant impact.
three use cases
Share of respondents estimating the impact of generative AI on use case as “significant” or
“very significant,”1 % of respondents at commercially leading organizations
Our research found that marketing and sales
leaders anticipated at least moderate impact Lead identification
(real time, based on customer trends) 60
from each gen AI use case we suggested.
They were most enthusiastic about lead Marketing optimization
(A/B testing, SEO strategies) 55
identification, marketing optimization, and
personalized outreach. Personalized outreach
(chatbots, virtual assistants) 53
Source: AI-powered marketing and sales reach Dynamic content
(websites, marketing collateral) 50
new heights with generative AI
Up-/cross-selling recs
(via usage patterns, support tickets) 50
Success analytics
(continuous churn modeling) 45
Marketing analytics
(dynamic audience targeting) 45
Dynamic customer-journey mapping
(identifying critical touchpoints) 45
Automated marketing workflows
(nurturing campaigns) 35
Sales analytics
(predictive pricing, negotiation) 30
Sales coaching
(hyperpersonalized training) 25
1Senior executives in significant global B2B and B2C sales and marketing organizations across a wide range of industries and company maturity levels were
asked: Please share your estimated ROI/impact these tools would have if implemented in your organization.
Source: McKinsey analysis
McKinsey & Company
What’s the future of generative AI? An early view in 15 charts 10
Web <2023>
<Future of GenAI>
Exhibit <9> of <15>
Software engineering, the Generative AI can increase developer speed, but less so for complex tasks.
other big value driver for many
Reduction in task completion Code Code Code High-complexity
industries, could get much
time using generative AI,1 % documentation generation refactoring tasks
more efficient
When we had 40 of McKinsey’s own
<10
developers test generative AI–based tools,
we found impressive speed gains for many
common developer tasks. Documenting
code functionality for maintainability (which
considers how easily code can be improved) can
be completed in half the time, writing new code
in nearly half the time, and optimizing existing 20–30
code (called code refactoring) in nearly two-
thirds the time.
Source: Unleashing developer productivity with 35–45
generative AI
45–50
1Compared with task completion without the use of generative AI.
Source: McKinsey analysis
McKinsey & Company
What’s the future of generative AI? An early view in 15 charts 11
Web <2023>
<Future of GenAI>
Exhibit <10> of <15>
And gen AI assistance could Generative AI tools have potential to improve the developer experience.
make for happier developers
Developer sentiments, Strongly Somewhat Neither agree Somewhat Strongly
% of respondents agree agree nor disagree disagree disagree
Our research found that equipping developers
with the tools they need to be their most Able to focus on satisfying
productive also significantly improved Felt happy and meaningful work Was in a ‘flow’ state
their experience, which in turn could help
Without With Without With Without With
companies retain their best talent. Developers generative AI generative AI generative AI generative AI generative AI generative AI
using generative AI–based tools were more
than twice as likely to report overall happiness,
fulfillment, and a state of flow. They attributed
44
this to the tools’ ability to automate grunt work 50 31
that kept them from more satisfying tasks
and to put information at their fingertips faster 20 25
15
than a search for solutions across different
online platforms.
30 38 30 56 30 50
Source: Unleashing developer productivity with
generative AI
POSITIVE POSITIVE
25 13 30 20
6
NEGATIVE 13 NEGATIVE
20
15
30
5 5
Note: Figures may not sum to 100%, because of rounding.
McKinsey & Company
What’s the future of generative AI? An early view in 15 charts 12
Web <2023>
<State of AI 2023>
Exhibit <1PDF> of <11>
Momentum among workers for Respondents across regions, industries, and seniority levels say they are
using gen AI tools is building already using generative AI tools.
A new McKinsey survey shows that the vast Reported exposure to generative AI tools, % of respondents
majority of workers—in a variety of industries
Regularly use Regularly use for work Regularly use Have tried at No Don’t
and geographic locations—have tried for work and outside of work outside of work least once exposure know
generative AI tools at least once, whether in or
outside work. That’s pretty rapid adoption less By office location Asia–Pacific 4 18 19 36 19 3
than one year in. One surprising result is that Developing markets 9 11 20 34 23 3
baby boomers report using gen AI tools for work Europe 10 14 11 45 15 6
more than millennials.
Greater China 9 10 18 46 14 3
North America 6 22 13 38 19 3
Source: The state of AI in 2023: Generative AI’s
By industry Advanced industries 5 11 16 47 15 5
breakout year
Business, legal, and professional services 7 16 13 41 21 2
Consumer goods/retail 7 11 12 40 26 4
Energy and materials 6 8 15 50 19 3
Financial services 8 16 18 41 14 4
Healthcare, pharma, and medical products 6 10 17 44 15 7
Technology, media, and telecom 14 19 17 37 9 3
By job title C-suite executives 8 16 13 42 18 2
Senior managers 10 14 16 42 15 3
Midlevel managers 7 16 20 35 19 4
By age Born in 1964 or earlier 6 17 21 30 18 9
Born 1965–80 7 18 18 37 17 3
Born 1981–96 5 22 24 36 11 3
By gender identity Men 8 16 16 37 19 4
Women 12 15 6 46 18 3
Note: Figures may not sum to 100%, because of rounding. In Asia–Pacific, n = 164; in Europe, n = 515; in North America, n = 392; in Greater China (includes
Hong Kong and Taiwan), n = 337; and in developing markets (includes India, Latin America, and Middle East and North Africa), n = 276. For advanced industries
(includes automotive and assembly, aerospace and defense, advanced electronics, and semiconductors), n = 96; for business, legal, and professional services,
n = 215; for consumer goods and retail, n = 128; for energy and materials, n = 96; for financial services, n = 248; for healthcare, pharma, and medical products,
n = 130; and for technology, media, and telecom, n = 244. For C-suite respondents, n = 541; for senior managers, n = 437; and for middle managers, n = 339.
For respondents born in 1964 or earlier, n = 143; for respondents born between 1965 and 1980, n = 268; and for respondents born between 1981 and 1996,
n = 80. Age details were not available for all respondents. For respondents identifying as men, n = 1,025; for respondents identifying as women, n = 156.
The survey sample also included respondents who identified as “nonbinary” or “other” but not a large enough number to be statistically meaningful.
Source: McKinsey Global Survey on AI, 1,684 participants at all levels of the organization, April 11–21, 2023
McKinsey & Company
What’s the future of generative AI? An early view in 15 charts 13
Web <2023>
<Future of GenAI>
Exhibit <12> of <15>
But organizations still Job postings for fields related to tech trends grew by 400,000 between
need more gen AI–literate 2021 and 2022, with generative AI growing the fastest.
employees
Tech trend job postings, 2021–22,1 thousands
As organizations begin to set gen AI goals, 700
they’re also developing the need for more 600
+6%
gen AI–literate workers. As generative and
500
other applied AI tools begin delivering value to +29%
400
early adopters, the gap between supply and +12%
demand for skilled workers remains wide. To 300
+16% +15%
stay on top of the talent market, organizations 200
should develop excellent talent management
100
capabilities, delivering rewarding working 2021 2022
0
experiences to the gen AI–literate workers they Applied AI Next-generation Cloud and edge Trust architectures Future of
hire and hope to retain. software development computing and digital identity mobility
300
Source: McKinsey Technology Trends Outlook
2023 200 +27%
100 +8% +7%
+10% +23%
0
Electrification and Climate tech beyond Advanced Immersive-reality Industrializing
renewables electrification and connectivity technologies machine learning
renewables
200
+40% +16% +44% +12%
100
–19%
0
Web3 Future of Future of space Generative AI Quantum
bioengineering technologies technologies
1Out of 150 million surveyed job postings. Job postings are not directly equivalent to numbers of new or existing jobs.
Source: McKinsey’s proprietary Organizational Data Platform, which draws on licensed, de-identified public professional profile data
McKinsey & Company
What’s the future of generative AI? An early view in 15 charts 14
Web <2023>
<Future of GenAI>
Exhibit <13> of <15>
Organizations should proceed Inaccuracy, cybersecurity, and intellectual property infringement are the
with caution most-cited risks of generative AI adoption.
Generative AI–related risks that organizations consider relevant and are working to mitigate,
The possibilities of gen AI are thrilling to many.
% of respondents1
But like any new technology, gen AI doesn’t
come without potential risks. For one thing, gen Organization considers risk relevant Organization working to mitigate risk
AI has been known to produce content that’s Inaccuracy 56 32
biased, factually wrong, or illegally scraped from
Cybersecurity 53 38
a copyrighted source. Before adopting gen AI
Intellectual property infringement 46 25
tools wholesale, organizations should reckon
Regulatory compliance 45 28
with the reputational and legal risks to which
Explainability 39 18
they may become exposed. One way to mitigate
Personal/individual privacy 39 20
the risk? Keep a human in the loop; that is, make
Workforce/labor displacement 34 13
sure a real human checks any gen AI output
Equity and fairness 31 16
before it’s published or used.
Organizational reputation 29 16
National security 14 4
Source: The state of AI in 2023: Generative AI’s
Physical safety 11 6
breakout year
Environmental impact 11 5
Political stability 10 2
None of the above 1 8
1Asked only of respondents whose organizations have adopted Al in at least 1 function. For both risks considered relevant and risks mitigated, n = 913.
Source: McKinsey Global Survey on AI, 1,684 participants at all levels of the organization, April 11–21, 2023
McKinsey & Company
What’s the future of generative AI? An early view in 15 charts 15
Web <2023>
<Future of GenAI>
Exhibit <14> of <15>
Gen AI could ultimately boost Generative AI could contribute to productivity growth if labor hours can be
global GDP redeployed effectively.
McKinsey has found that gen AI could Productivity impact from automation by scenario, 2022–40, CAGR,¹ %
substantially increase labor productivity
Without generative AI² Additional with generative AI
across the economy. To reap the benefits
of this productivity boost, however, workers Global³ Developed economies
whose jobs are affected will need to shift to Japan Germany France United States
other work activities that allow them to at least 4.2
3.9
match their 2022 productivity levels. If workers 0.6 3.7 3.6
0.6
3.3
are supported in learning new skills and, in 0.7 0.7
0.6
some cases, changing occupations, stronger
global GDP growth could translate to a more
sustainable, inclusive world. 2.6 3.7 1.6 0.1 3.4 1.3 0.2 3.0 2.9
0.8 0.6
0.2 1.4 1.1 0.2 0.3
Source: The economic potential of generative 0.1 0.6 0.4
AI: The next productivity frontier Early Late Early Late Early Late Early Late Early Late
Emerging economies
China Mexico India South Africa
3.8
0.6
2.9
0.6 2.3 2.3
0.5 0.5
3.2
0.8 2.3
0.1 1.8 1.7
0.7 0.0 0.0 0.0
Early Late Early Late Early Late Early Late
Note: Figures may not sum, because of rounding.
1Based on the assumption that automated work hours are reintegrated in work at productivity level of today.
2Previous assessment of work automation before the rise of generative AI.
3Based on 47 countries, representing about 80% of world employment.
Source: Conference Board Total Economy database; Oxford Economics; McKinsey Global Institute analysis
McKinsey & Company
What’s the future of generative AI? An early view in 15 charts 16
Web <2023>
<Future of GenAI>
Exhibit <15> of <15>
Gen AI represents just a small Generative AI could create additional value potential above what could be
piece of the value potential unlocked by other AI and analytics.
from AI
AI’s potential impact on the global economy, $ trillion
Gen AI is a big step forward, but traditional 17.1–25.6
advanced analytics and machine learning
13.6–22.1
continue to account for the lion’s share of
6.1–7.9
task optimization, and they continue to find 2.6–4.4
11.0–17.7
new applications in a wide variety of sectors.
~15–40% ~35–70%
Organizations undergoing digital and AI
incremental incremental
transformations would do well to keep an eye on
economic impact economic impact
gen AI, but not to the exclusion of other AI tools.
Just because they’re not making headlines
doesn’t mean they can’t be put to work to deliver
increased productivity—and, ultimately, value.
Source: The economic potential of generative Advanced analytics, New generative Total use All worker productivity Total AI
traditional machine AI use cases case–driven enabled by generative AI, economic
AI: The next productivity frontier
learning, and deep potential including in use cases2 potential2
learning1
1Updated use case estimates from ""Notes from the AI frontier: Applications and value of deep learning,” McKinsey Global Institute, April 17, 2018.
2The range of potential value from the combined impact of new generative AI use cases and the increased worker productivity they could enable is $6.1 trillion
to $7.9 trillion, including revenue impacts conservatively translated into productivity impact as difference between total impact and cost-isolated impact.
McKinsey & Company
What’s the future of generative AI? An early view in 15 charts 17
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255,mckinsey,how-ai-is-transforming-strategy-development_final.pdf,"Strategy & Corporate Finance Practice
How AI is transforming
strategy development
Artificial intelligence is set to revolutionize strategy activities. But as AI
adoption spreads, strategists will need proprietary data, creativity, and new
skills to develop unique options.
This article is a collaborative effort by Alexander D’Amico, Bruce Delteil, and Eric Hazan, with Andrea Tricoli
and Antoine Montard, representing views from McKinsey’s Strategy & Corporate Finance Practice.
February 2025
At its core, strategy entails deriving insights from facts and data, developing real options based
on those insights, making hard-to-reverse choices, and executing initiatives that convert those
choices into value. Data analytics has assisted in this work for several decades, but never before
has technology been able to not only augment and partially automate inputs into strategy but
also combine them into complex analyses. In time, it may even recommend viable strategies.
Artificial intelligence and generative AI have the potential to transform how strategists work
by strengthening and accelerating activities such as analysis and insight generation while
mitigating challenges posed by human biases and the social side of strategy. Building on the
recent explosion in data and earlier AI advances that produced dramatic improvements in
forecasting accuracy, the latest tools are making deriving insights much easier and cheaper.
The impact we are seeing in client organizations and in our own work as strategists leads us
to view this moment as a new inflection point in strategy design—potentially on par with the
creation of core strategic frameworks in the 1970s and ’80s.
While AI won’t change the need for leaders to demonstrate strategic courage by committing
to big moves, we expect that the technology will, in time, enhance every phase of strategy
development, from design through mobilization and execution. Today, the technology delivers
the greatest benefits in the design phase, by helping organizations assess their starting point in
the context of industry and market dynamics. They can use it to size potential markets, analyze
competitor moves, and estimate the value of different strategic initiatives across multiple
scenarios. But that’s just the beginning: Strategy requires mobilizing the organization, ensuring
the right allocation of resources, and monitoring execution. In all these tasks, AI can play a role.
Emerging roles for AI in strategy
Human judgment remains essential to crafting the strategic vision, which combines the
organization’s ambition with a view of how to realize it. However, AI can accelerate and bring
greater rigor to the work of strategy teams. Even in these early days, we see five roles for AI:
researcher, interpreter, thought partner, simulator, and communicator. Each of these roles can
come into play at various steps across the different phases of strategy development (table):
We expect that AI will, in time,
enhance every phase of strategy
development, from design through
mobilization and execution.
How AI is transforming strategy development 2
Table
AI can assist strategists in all stages of the strategy development process.
Illustrative use cases
Core use case Supplementary use case
Thought
Researcher Interpreter partner Simulator Communicator
Design the Align on the strategic
strategy challenge
Assess through multiple
lenses
Explore value-creating
big moves
Commit to a bold
strategy
Mobilize the Empower and engage
organization
Translate strategy into
concrete initiatives
Prioritize and reallocate
resources
Govern and rewire
plans and budgets
Execute, Force execution
monitor, momentum
and review
Drive and support
performance
Test assumptions
and adapt
Launch the next S-curve
— Researcher. Strategists spend significant time gathering and enriching data from numerous
sources. AI’s ability to summarize and create meaningful connections across all data sets
can significantly enhance these efforts. For example, an AI-powered engine that identifies
potential M&A targets can pinpoint under-the-radar assets that fit a company’s strategic
thesis, enhancing what today is often a serendipitous process relying on executives’ and their
intermediaries’ market knowledge. One such tool can scan public information on more than
40 million companies across various languages and create a short list of relevant targets in
How AI is transforming strategy development 3
minutes. While AI is more thorough and faster than humans, strategists still need to pose the
right questions to generate the distinctive insights they seek.
— Interpreter. To turn data analytics into useful insights, strategists need to interpret how
the findings can advance their goals. For example, a search for growth opportunities often
entails looking into adjacencies. Those expansion ideas can come from many places, such
as reviews of competitors’ moves or a deep understanding of customers’ emerging needs. AI
tools can facilitate this discovery process by converting data from a disparate set of inputs—
such as annual reports, patents, customer reviews, and purchasing data—into “growth scans.”
These scans summarize the most frequently pursued adjacencies and then interpret and
score their fit with the company’s strategy. The resulting perspective can help strategists
narrow down options, find precedents or benchmarks for actions under consideration, and
uncover fresh ideas.
Another area where AI is already acting as an interpreter is trend monitoring. Strategists
need to keep tabs on changes in major trends when developing their options and reviewing
their assumptions. A gen-AI-powered engine can read massive amounts of information
and disaggregate trends into their component patterns and then interpret whether those
patterns suggest a trend is accelerating, maturing, or subsiding. For example, an organization
seeking to understand the demand for sustainable building materials can monitor interest
from architects, patent volumes, and competitors’ mentions long before those signals
translate into sales volumes.
— Thought partner. AI can also serve as a brainstorming partner, speeding up idea generation
and countering business leaders’ potential biases or blind spots. Gen AI in particular
can help strategists avoid common pitfalls by assessing their plans against established
frameworks. For example, a team can pressure test a strategy—both before and during its
execution—by leveraging gen AI to play a challenger role to highlight potential hidden pitfalls
or management blind spots.
— Simulator. Before committing to a strategic course, strategists consider the impact of
multiple market scenarios based on macroeconomic conditions, potential competitor moves,
and stakeholder reactions, among other factors. AI can make this scenario analysis much
more rigorous through advanced modeling capabilities and tactical game and simulation
applications. This capability can also be valuable during the strategy’s execution, with AI
monitoring early signals from the market, simulating their impact, and alerting the team when
it might be prudent to change course.
— Communicator. A clear narrative of the strategic path and objective and their implications
for the organization and its stakeholders is essential to mobilizing action. Gen AI’s ability to
summarize concepts in different formats has been among the technology’s most popular
applications since ChatGPT was launched. Strategists can use gen AI tools to make their
narratives more compelling to different audiences with different levels of expertise (such
as regional markets, regulators, or analysts) and in different formats (briefs, talking points,
or, most recently, podcasts1). AI can also monitor whether external communications are
consistent across different channels.
1 Google Labs, “NotebookLM now lets you listen to a conversation about your sources,” blog entry by Biao Wang,
September 11, 2024.
How AI is transforming strategy development 4
To see how these five applications can work in practice, consider the case of a Southeast Asian
regional bank that wanted to expand to a new segment or geography. The strategy team used
its AI model to analyze the business context and promising trends in the industry and region.
The tool generated interactive reports that allowed the strategists to fine-tune their follow-up
research. Based on this work, the strategy team decided to focus on opportunities in the digital
financial ecosystem (particularly peer-to-peer payments) and microcredit.
Next, the team asked AI to provide recommendations on the most promising adjacencies for
growth investments. Based on an analysis of information from banks around the world, the
tool created a graph of close and synergistic business segments. Management prioritized a
few for deeper analysis—for example, a cross-border digital offering across the region or the
microcredit segment in Vietnam—and built hypotheses on their potential growth trajectories.
To learn more about each segment, they asked AI, “Who are my competitors in each market,
and what are their value propositions?” Some of the markets were unfamiliar to the leaders,
so the strategy team posed questions such as, “We are considering entering the Vietnamese
banking market. What are the risks that have emerged in the past? Are there examples of failed
attempts (with sources)?”
The team also considered inorganic options such as partnerships and M&A. Based on an AI scan,
they short-listed a few small and medium-size businesses with the technology the company
needed to support its digital ambition. Gen AI also helped them build initial due diligence profiles
to support potential outreach.
Finally, as hypotheses solidified into concrete strategic options, AI helped the strategists
simulate the resulting P&L and growth projections. Additionally, the tool utilized internal data,
such as management reports on the bank’s earlier expansion into another country, to help
management understand the strengths and weaknesses of their execution capabilities.
Numerous organizations have started building tools to make such scenarios a reality, with some
developing proprietary AI agents to simulate reasoning or perform complex research tasks.
However, even those earlier in their AI journey can start exploring some of the roles that AI can
play. As technology advances, strategists who build the skills to develop unique applications for
AI models will gain a critical insights edge over competitors.
Considerations for strategy leaders deploying AI
While the journey of the Southeast Asian bank is compelling, strategists should be mindful of
several challenges in deploying AI. Generative AI presents well-documented risks, ranging from
model bias (historical training data can lead AI to overemphasize certain types of customers,
for example) to reduced explainability (failure to offer a logical foundation for the analysis) to
hallucinations (constructing credible-sounding but false content). The good news is that each of
these pitfalls is being addressed. For example, AI can help police itself: A “critic agent” can check
the work done by other AI applications and flag when the content might be incorrect or directly
instruct a reworking of the task in question.
Beyond these well-understood risks, gen AI presents five additional considerations for
strategists. First, it elevates the importance of access to proprietary data. Gen AI is accelerating
a long-term trend: the democratization of insights. It has never been easier to leverage off-
the-shelf tools to rapidly generate insights that are the building blocks of any strategy. As the
adoption of AI models spreads, so do the consequences of relying on commoditized insights.
How AI is transforming strategy development 5
After all, companies that use generic inputs will produce generic outputs, which lead to generic
strategies that, almost by definition, lead to generic performance or worse. As a result, the
importance of curating proprietary data ecosystems (more on these below) that incorporate
quantitative and qualitative inputs will only increase.
Second, the proliferation of data and insights elevates the importance of separating signal from
noise. This has long been a challenge, but gen AI compounds it. We believe that as the technology
matures, it will be able to effectively pull out the signals that matter, but it is not there yet.
Third, as the ease of insight generation grows, so does the value of executive-level synthesis.
Business leaders—particularly those charged with making strategic decisions—cannot operate
effectively if they are buried in data, even if that data is nothing but signal. As with gen AI’s
growing ability to separate signal from noise, the technology is getting better at synthesis, but
in the near term, strategy leaders need to own that task.
Fourth, AI reinforces the importance of the processes that organizations follow to develop their
strategies. Our research shows that the quality of the process is far more important to strategies’
success than the quality of insights. High-quality processes include, but are not limited to, the
development and examination of strategic alternatives, properly accounting for uncertainty,
pushing to make bold commitments, and, most importantly, taking steps to remove bias from
decisions. Fortunately, as gen AI speeds up the development of insights, it leaves more time for
strategy teams to hone best-in-class processes.
Finally, to successfully leverage gen AI, the strategy function needs to invest in technology
for creating and accessing ecosystems of proprietary data sources. The ecosystem approach
removes the need for companies to internally generate or own the full gamut of proprietary data.
Instead, they build networks of sources that they can seamlessly tap into using technology. In
addition, strategists will need to identify (and often customize) gen AI tools that can effectively
serve as researchers, simulators, interpreters, thought partners, and communicators.
Moving forward
So where do you begin? We recommend three near-term steps:
— Get smart. The strategist of tomorrow needs to understand how AI works. How does a
word prediction engine manipulate complex concepts and information? How are insights
generated from the information included in models and prompts? Those who gain this
expertise will be able to contribute to creating the tools their work requires, such as running
complex simulations on how markets and competitive landscapes will evolve. Individuals with
such skills will be highly sought after, making their retention a management priority.
— Start building today. AI is here to stay, and finding the right way to apply it to strategy
development is essential. Strategy teams should familiarize themselves with the possibilities
AI offers, from helping in their research and insight generation to identifying potential risks.
Teams that explore how the available tools can assist in these tasks will better understand
what other tools they will need to build or invest in to meet their specific needs. From an
organizational perspective, leaders need to help strategy teams gain access to expertise in
data science, data engineering, and large language models. This can be done by embedding
technology experts into strategy teams or by providing strategists access to them through
centers of excellence.
How AI is transforming strategy development 6
— Develop your proprietary insights ecosystem. Even with state-of-the-art capabilities, AI
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models will be limited to interpreting existing data—they cannot generate new signals.
McKinsey Insights App
For example, AI won’t replace the insights from ethnographic research or the direct input
from customers. Indeed, such proprietary information will become even more critical to
generating unique insights as external data grows more affordable and accessible to all
market participants. To gain an edge, strategists will need to expand their exposure to
different domains by connecting with innovators and stakeholders within and outside their
organizations. Strategists’ core focus will increasingly become developing hypotheses,
testing and learning from them, and maintaining the AI and data infrastructure that enable
the conversion of insights into a competitive advantage.
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Artificial intelligence can’t—and, we believe, won’t—replace human logic and interpretation in a
complex domain, such as strategy. However, the technology can provide faster, more objective
answers that can significantly augment our decision prowess. Through the various roles AI can
already play, from researcher to thought partner to simulator, we are starting to see how these
tools may, in time, redefine strategists’ roles and help companies make strategic decisions. By
making the strategy development process more efficient while allowing the space for creativity
and breakthrough ideas that help leaders define the consequent bold moves, AI can deliver the
competitive edge needed to beat the market.
Alexander D’Amico is a senior partner in McKinsey’s Connecticut office, Bruce Delteil is a partner in the Hanoi
office, Eric Hazan is a senior partner emeritus in the Paris office, Andrea Tricoli is an associate partner in the
London office, and Antoine Montard is a client capabilities director in the Lisbon office.
This article was edited by Joanna Pachner, an executive editor in the Toronto office.
Designed by McKinsey Global Publishing
Copyright © 2025 McKinsey & Company. All rights reserved.
How AI is transforming strategy development 7"
258,mckinsey,capturing-the-generative-ai-opportunity-for-the-dutch-labor-market.pdf,"Capturing the generative
AI opportunity for the
Dutch labor market
Dutch businesses can embrace generative AI to speed up automation,
increase productivity, and ease labor market tightness. What is needed
to accelerate adoption and capture the benefits?
This article is a collaborative effort by Ashley van Heteren, Eva Beekman, and Ferry Grijpink, with Just van
der Wolf and Wouter Kokx, representing views from McKinsey’s Amsterdam office.
November 2024
The Dutch labor market is strong and evolving: work in Europe, we estimate that automation will likely
labor force participation1 is notably high, and affect 15 percent of total full-time-equivalent work hours
unemployment levels are historically low.2 But in the Netherlands by 2030—a 50 percent increase over
ongoing trends, including an aging population a scenario without gen AI.6 This represents a significant
and declining productivity growth, are putting the opportunity for Dutch businesses to tackle the
labor market under pressure.3 Recently, McKinsey challenges posed by the evolving and tight labor market,
projected that labor market tightness could triple by especially in areas in which increased automation
2030 if the Netherlands maintains current levels of can help relieve labor shortages. However, a wide
growth in GDP (1.6 percent CAGR) and productivity deployment of gen AI is expected to require extensive
(0.4 percent CAGR).4 re- and upskilling programs, including for jobs that were
previously unaffected by automation. It may also require
While traditional automation solutions to increase regulation7 to mitigate its potential risks,8 such as data
labor productivity, such as document processing privacy, intellectual property risk, and fairness.
systems, have played a significant role in addressing
this challenge, most have been limited to processing In this article, we focus on how gen AI can
structured data. Generative AI (gen AI), however, influence the future of work in the Netherlands
unlocks a new domain of automation with its ability to across sectors and organizations. We discuss how
process unstructured data such as natural language expected adoption speed varies by sector and by
and images.5 It therefore broadens the spectrum of sector composition in terms of shares of small and
automation potential to more occupations, including medium-size enterprises (SMEs), independents,
knowledge work and customer service, and holds the and corporations. We also explore actions public
potential to boost productivity and economic growth and private leaders could take to accelerate gen AI
for the Netherlands. adoption in the Netherlands. This perspective takes a
longer-term view of the singular effects that might be
Following the base case scenario of automation of directly brought about by gen AI; it does not forecast
current activities (“slower scenario”) from a recent aggregated employment effects that can be brought
McKinsey Global Institute (MGI) report on the future of about by the business cycle in the short term.
1 Share of employed people to the total population aged 20 to 64.
2 “Tension in the labor market,” Centraal Bureau voor de Statistiek (Statistics Netherlands), accessed August 18, 2024.
3 “Dashboard spanningsindicator” (“Dashboard voltage indicator”), UWV, accessed October 25, 2024.
4 Netherlands advanced: Building a future labor market that works, McKinsey, June 18, 2024.
5 “A new future of work: The race to deploy AI and raise skills in Europe and beyond,” McKinsey Global Institute (MGI), May 21, 2024.
6 Ibid.
7 “As gen AI advances, regulators—and risk functions—rush to keep pace,” McKinsey, December 21, 2023.
8 “Implementing generative AI with speed and safety,” McKinsey Quarterly, March 13, 2024.
Methodology
The following data used in this article is as a third “slower” scenario, the 25 economic potential of generative AI: The
directly sourced from a 2024 McKinsey percent point between the early and next productivity frontier and Generative AI
report, Netherlands advanced: Building a late scenarios and the future of work in America—as well as
future labor market that works1: a 2024 report, A new future of work: The race
— impact of automation on productivity to deploy AI and raise skills in Europe and
— employment projections
beyond. A full description of the methodolo-
Both this article and the Netherlands ad- gy and data used is included in the technical
— scenarios for automation adoption;
vanced report draw on the methodology and appendixes of those reports.2
two scenarios to bookend the model,
findings of two 2023 McKinsey reports—The
the “late” and “early” scenarios as well
1 Netherlands advanced: Building a future labor market that works, McKinsey, June 18, 2024.
2 The economic potential of generative AI: The next productivity frontier, McKinsey, June 14, 2023; “Generative AI and the future of work in America,” MGI, July 26, 2023; “A
new future of work: The race to deploy AI and raise skills in Europe and beyond,” MGI, May 21, 2024.
Capturing the generative AI opportunity for the Dutch labor market 2
Gen AI creates new opportunities sidebar, “Methodology”). Of course, the exact
spanning sectors and organizations automation path for gen AI is subject to considerable
uncertainty. The extent to which this potential is
The introduction of gen AI and the public
realized will depend on the ability of Dutch companies
breakthrough of OpenAI’s ChatGPT in late 2022 have
to innovate, the capability of workers to re- and
accelerated the automation potential of activities that
upskill, and the support of policy makers.
involve communication, documentation, or interaction
with people. Gen AI—in particular, applications that
In the rest of this section, we dig into the precise
are open or that use publicly available data—has also
factors affecting uptake as well as the sectors in
become available to a wider group of organizations.
which gen AI has the most promise for addressing
The range of work activities suitable for automation
labor market challenges.
has expanded to include those requiring subject
matter expertise, interpersonal interaction, and
Impact from gen AI will depend
creativity. Consequently, the timeline for automation
on sector composition
adoption could accelerate significantly, reaching
While our models reveal potential for gen AI to
previously unaffected jobs, such as those in
relieve labor market tensions, we also acknowledge
professional services.
the challenges involved in gen AI reaching its full
potential in the Netherlands.
Our modeling suggests that even in the “slower
scenario,” adoption of gen AI could reduce the
Not all Dutch businesses are preparing to adopt
total number of hours needed to perform current
analytical AI (that is, AI methods preceding generative
workforce activities by 15 percent (Exhibit 1; see
AI). AWVN (General Employers Association of the
Web <2024>
E<MxChKib24it9 1087_Gen AI impact on NL labor>
Exhibit <1> of <4>
Generative AI is expected to accelerate automation adoption in 2030 by 50
percent in all scenarios.
Automation of current work activities, % of full-time-equivalent hours expected to be automated
2030 automation potential, Netherlands, %
Description1 Without generative AI With generative AI
Fastest pace of automation
Early scenario development and adoption 35 50
25% point between early
Slower scenario and late scenarios 10 15
Slowest pace of automation
Late scenario development and adoption 2 3
Note: The range of scenarios represents uncertainty regarding the availability of technical capabilities, based on interviews with experts and survey responses.
1Scenarios for productivity growth in the Netherlands based on “A new future of work: The race to deploy AI and raise skills in Europe and beyond,"" McKinsey
Global Institute (MGI), May 21, 2024.
Source: MGI analysis
McKinsey & Company
Capturing the generative AI opportunity for the Dutch labor market 3
Netherlands) reported that 40 percent of Dutch and 46 percent in the United States.10 Sectors
companies are not yet using AI in their businesses dominated by small companies might be slower
because of a lack of knowledge, safety and privacy to embrace new automation opportunities. For
concerns, or perceived irrelevance.9 This finding example, when it came to adoption of digital sales
illustrates the types of challenges the Netherlands technologies, in 2019, the top 10 percent of largest
may face in rapidly adopting gen AI technologies. companies captured 60 to 95 percent of digital
revenues.11 This is expected to affect the rate and
The speed of gen AI adoption at a country level speed at which gen AI might accelerate automation in
is determined by multiple factors, including the the Netherlands.
economic maturity of a country, overall sector
readiness to embrace new technologies, and Large companies in the Netherlands are adopting
crucially, sector composition in terms of shares of analytical AI technologies faster than smaller
SMEs, independents, and corporations (Exhibit companies. Centraal Bureau voor de Statistiek (CBS)
2). A relatively high percentage of workers in the reported in 2020 that 48 percent of companies with
Netherlands (about 65 percent) are employed by 500 or more employees were using one or more AI
SMEs, compared with 57 percent in Germany, 54 technologies, compared with only 8 to 13 percent for
percent in the United Kingdom, 52 percent in France, companies with ten to 50 employees.12
9 Sandra Olsthoorn, “Werkgevers nog huiverig voor inzet AI” (“Employers are still hesitant about using AI”), Het Financieele Dagblad, June 10, 2024.
10 Statista defines SMEs as companies with fewer than 250 employees; US County Business Pattern 2021 defines SMEs as companies with
fewer than 500 employees; “Enterprises by business size,” OECD, accessed August 18, 2024.
11 F or more, see Jacques Bughin, Tanguy Catlin, and James Manyika, “Twenty-five years of digitization: Ten insights into how to play it right,” MGI,
May 21, 2019.
12 “ICT use in companies; company size, 2020,” CBS, updated April 22, 2022.
Web <2024>
<MCK249087_Gen AI impact on NL labor>
Exhibit 2
Exhibit <2> of <4>
Small companies are expected to adopt automation more slowly than larger
organizations.
Potential High Medium Low
Existing Expected
Up- and
Investment availability automation
reskilling
Archetypes Workers in Netherlands, million capacity + + of data and = adoption by
capabilities
infrastructure 2030
CCoorrppoorraattiioonnss 3.3 Corporations
((>>550000 ppeerrssoonnss)) (>500 persons)
LLaarrggeerr SSMMEEss1¹ Larger SMEs1
1.6 (101–500
((110011––550000 ppeerrssoonnss))
persons)
Small SMEs1
SSmmaallll SSMMEEss1¹
2.3 (2–100
((22––110000 ppeerrssoonnss))
persons)
IInnddeeppeennddeennttss Independents (1
1.2
((11 ppeerrssoonn)) person)
PPuubblliicc sseeccttoorr 0.6 Public sector
Note: Intensity based on qualitative scoring criteria.
¹Small and medium-size enterprises.
Source: Centraal Bureau voor Statistiek (CBS); McKinsey analysis
McKinsey & Company
Capturing the generative AI opportunity for the Dutch labor market 4
As with the adoption of analytical AI, the smaller- Challenges to automation adoption are not equal
company archetype could capture impact from gen for all sectors in the Netherlands, because the
AI more slowly in the next five years for the following share of SMEs varies widely across sectors (Exhibit
three reasons: 3). For example, the agriculture sector could
potentially benefit from using analytical AI and gen
Limited investment capacity. Smaller companies usually AI to improve efficiency for farms and agriculture
have lower investment capacity, constraining their companies, such as by improving on-farm decision
ability to acquire new capabilities, tools, or resources. making with camera images.15 However, the high
For example, Dutch SMEs invest about 1 percent of percentage of independent and smaller companies
profits in R&D compared with about 5 percent for larger in the sector along with factors such as plot size or
companies.13 They might lack the scale to manage these specific legislation has thus far led to lower adoption
solutions systematically, such as keeping marketing of automation. In fact, McKinsey research shows
content generator inputs up to date. However, many that only 33 percent of Dutch farmers, who farm on
gen AI solutions—especially those integrated in existing smaller plots than farmers in neighboring countries,
software packages such as Adobe and Microsoft use at least one agricultural technology, compared
Office—are already available for many SMEs. with 45 percent in Germany and 51 percent in France.
Lower up- and reskilling capabilities. Introducing Additionally, we expect that the public sector will
gen AI tools into existing operating models requires experience slower adoption of gen AI. While most
significant change management of IT-related subsectors in the public sector have a high share of
processes, including upskilling and reskilling enterprises with more than 500 employees (about
programs to help employees use gen AI tools 50 to 80 percent in government, healthcare, and
effectively. Smaller companies often lack the scale to education) and may have more scale to enable gen AI
benefit from designing and running such programs upskilling, public acceptance and different regulatory
and are half as likely to provide formal upskilling requirements can potentially slow the adoption of
programs compared with large corporates.14 gen AI in this domain. McKinsey research also shows
Rather than formal in-person training programs, that the overall investment in analytical and gen AI is
these organizations might rely more on digital and lower in public sectors than in other sectors.16
self-organized training, which could provide a less
effective learning environment.
Gen AI holds the greatest potential
Less robust existing data and infrastructure. The to address Dutch labor market
low-complexity technological landscapes of challenges in five sectors
smaller companies typically do not justify extensive Although various interventions can address labor
investment. Consequently, the technology is generally market challenges, our models show that gen AI
less mature, and existing data and infrastructure are can be a major productivity booster, particularly in a
generally less available. SMEs may, for example, use handful of sectors (Exhibit 4). A previous McKinsey
customer databases manually and not directly link report, Netherlands advanced: Building a future labor
databases to marketing and sales systems. All of market that works,17 estimated that roughly one-
this limits the pace at which gen AI solutions can be third of the necessary productivity improvements to
integrated with existing systems and data. address labor market tightness in the Netherlands can
be achieved through automation powered by gen AI.
13 R&D expenses versus profits for companies with fewer than 250 employees and for companies with more than 250 employees. See “Bedrijven;
arbeid, financiele gegevens, bedrijfsgrootte, bedrijfstak” (“Companies; employment, financials, company size, industry”), CBS, October 10, 2024;
“Research en development; personeel, uitgaven, bedrijfsgrootte, bedrijfstak” (“Research and development; personnel, expenditure, company
size, industry”), CBS, August 30, 2024.
14 For more, see “A microscope on small businesses: Spotting opportunities to boost productivity,” McKinsey Global Institute, May 2, 2024.
15 “From bytes to bushels: How gen AI can shape the future of agriculture,” McKinsey, June 20, 2024.
16 “The state of AI in early 2024: Gen AI adoption spikes and starts to generate value,” McKinsey, May 30, 2024.
17 Netherlands advanced: Building a future labor market that works, McKinsey, June 18, 2024.
Capturing the generative AI opportunity for the Dutch labor market 5
Web <2024>
<MCK249087_Gen AI impact on NL labor>
Exhibit 3
Exhibit <3> of <4>
Expected automation adoption varies across sectors based on business
archetypes.
Employment per business archetype, %
Expected automation
adoption
Business archetypes:
due to distribution of
Independents Small SMEs1 Larger SMEs1 Corporations ⚫ Public sector business archetypes
Real estate and rental
7 21 19 53
and leasing
Transportation and 10 25 21 44 Medium to(cid:22)
warehousing
high
Finance and insurance 8 40 12 40
Medium-to-high share of
Wholesale trade 6 43 17 33
large corporations
expected to positively
Manufacturing 5 34 32 29 contribute to automation
adoption
Information 23 28 23 26
Professional, scientific,
43 20 17 20
and technical services
Construction 31 29 19 20
Arts, entertainment, and
57 13 17 13
recreation
Other services 46 27 15 13 Low
Accommodation and food
7 74 10 10
services Public sector affiliation or
low share of large
Agriculture, forestry,
fishing, and hunting 52 32 10 5 corporations expected to
negatively contribute to
⚫ Administrative and
1 17 82 automation adoption
support and government
⚫ Healthcare and social
9 10 14 67
assistance
⚫ Educational services 13 1 32 54
⚫ Utilities 3 26 34 37
Note: Figures may not sum to 100%, because of rounding.
1Small and medium-size enterprises.
Source: Centraal Bureau voor de Statistiek (CBS); McKinsey analysis
McKinsey & Company
Capturing the generative AI opportunity for the Dutch labor market 6
Web <2024>
<MCK249087_Gen AI impact on NL labor>
Exhibit <4> of <4>
Exhibit 4
Generative AI can boost productivity across sectors in the Netherlands.
Expected automation adoption by 2030¹ Jobs, 2022
Medium to high; less orchestration required 1 million
Low; more orchestration required
100,000
Labor market tension per sector, Q4 2023
14
Below average Above average
12 Finance and
insurance
10
Very tight
8
Manufacturing IT
Health care and
social assistance Professional, scientific,
Accommodation and and technical services
6 food services
Wholesale Construction
trade Educational services
4 Transportation
and warehousing Administrative
and support and
Tight
Agriculture, forestry, government
fishing and hunting
2
Not tight
0
0 2 4 6 8 10
Automation accelerated with generative AI,² % of current FTE³ hours in 2030
1Based on business archetype and share of employment in large organizations.
2Slower scenario: the 25% point between the early and late “bookend” scenarios, assuming 10% automation by 2030 without generative AI.
3Full-time equivalent.
Source: Centraal Bureau voor de Statistiek (CBS); UWV; McKinsey analysis
McKinsey & Company
Capturing the generative AI opportunity for the Dutch labor market 7
While gen AI is a game changer in speeding up benefit significantly from gen AI tools, particularly
automation for some sectors, implementing this in automating daily tasks, and its high level of
technology may face delays due to sector nature digitalization can facilitate the integration of gen AI
and composition. Sectors may require varying levels into primary processes and technology development.
of innovation orchestration and support to see For example, ING partnered with McKinsey to
positive results. develop and deploy a gen AI–powered chatbot.19
Gen AI can also speed up credit and underwriting
This orchestration could include four elements. applications by automating document and image
The first, raising awareness, could involve helping processing and generating credit memos.
sectors understand relevant high-impact gen AI
use cases and their feasibility. Next, technology The IT sector can benefit greatly from gen AI’s ability
such as cost-effective, scalable, and secure gen AI to automate and accelerate software development
solutions should be available, along with support processes.20 Gen AI–powered coding assistance,
for AI literacy training. Third, support to establish automated testing, and intelligent architecture-
pilots and measure impact on labor tightness could design recommendations help IT companies speed
serve as proof points for broader adoption and up feature delivery and reduce time to market. AFAS,
scaling. Last, cross-sector partnerships could be for example, has developed an API integration with
established to scale impact, pool funding, and share OpenAI, giving companies access to advanced AI
knowledge—for instance, across public sector, models for generating business information.21 This
industry, and educational institutions. Below, we helps automate tasks such as reporting, business
explore five sectors with high potential for gen data analysis, and document creation. Gen AI can
AI automation impact: finance and insurance; help minimize incident and problem resolution times
IT; administrative and support and government; by creating tighter feedback loops, linking user
educational services; and professional, scientific, behaviors to feature changes, and collecting real-
and technical services. time feedback. Delft University of Technology’s AI for
Software Engineering Lab (AI4SE) explores these
Two sectors likely to self-propel opportunities.22
adoption of gen AI
The finance and insurance sector and the IT sector Orchestration can accelerate gen AI
have the highest potential for gen AI automation adoption in three additional sectors
and some of the highest labor market tension. Gen Our models suggest that most sectors would benefit
AI innovation in these sectors will enhance their from orchestration to accelerate gen AI adoption,
productivity.18 This, combined with their higher share including the public sector and sectors with a higher
of large corporations and large SMEs (approximately share of smaller companies. Here we explore three
50 percent of all businesses), provides business sectors that will likely require orchestration for
incentives and scale to encourage adoption without gen AI adoption and where the potential for gen AI
significant orchestration. impact is highest—including the administrative and
support and government sector, the educational
The finance and insurance sector is expected services sector, and the professional, scientific, and
to adopt gen AI because of strong commercial technical services sector.
incentives to substantially automate operations
and address labor shortages. This sector stands to In the administrative and support and government
sector, many traditional tasks are highly suited for
18 Our insights are based solely on sector composition; other factors may influence the actual potential for gen AI adoption.
19 “Banking on innovation: How ING uses generative AI to put people first,” McKinsey, accessed October 25, 2024.
20 “Unleashing developer productivity with generative AI,” McKinsey, June 27, 2023.
21 “AFAS & AI: hoe gaan we bij AFAS om met AI?” (“AFAS and AI: How do we deal with AI at AFAS?”), AFAS, accessed November 26, 2024.
22 “TU Delft and JetBrains are launching new ICAI lab AI for Software Engineering,” TU Delft, October 12, 2023.
Capturing the generative AI opportunity for the Dutch labor market 8
As gen AI becomes increasingly
integrated into various sectors, the
demand for specialized skills and
expertise in AI is expected to grow.
automation using gen AI, such as case handling Gen AI will create new roles and
and call center operation. In educational services, occupational categories
early gen AI applications already focus on adaptive As gen AI becomes increasingly integrated into
tutoring and service chatbots. These tools can various sectors, the demand for specialized
enhance learning experiences and alleviate labor skills and expertise in AI is expected to grow. For
shortages by supplementing teacher-led instruction. example, we expect increased demand in three
However, both of these subsectors are in the public occupational categories in the Dutch labor market:
sector and are expected to adopt gen AI more slowly,
as discussed above. In some cases, however, the Gen AI practitioners. Gen AI specialists—including
government has played a leading role in accelerating prompt and agent engineers or AI content
adoption to enhance service delivery and support. auditors—form a new subexpertise within the AI
For example, in the administrative and support and playing field. Globally, these roles have grown
government sector, Rijksdienst voor Ondernemend rapidly24 across sectors that implement gen AI
Nederland (RVO) launched AskSenna,23 an AI-driven in their daily practice, especially in IT functions.
tool designed to assist start-ups and early-stage Additionally, the surge in gen AI will drive demand
companies by providing instant answers to regulatory for related software and data engineering support.
and business-related queries. For example, the Dutch company Weaviate helps
companies structure their data to facilitate the
In the professional, scientific, and technical services development of gen AI use cases.25
sector, including consulting, solutions are being
developed to use gen AI for advanced search, Gen AI researchers. Gen AI has created new
synthesis tasks, and virtual coaching. However, given opportunities in research positions, within both
the high percentage of independents and SMEs in academia and enterprises. For instance, Philips
the sector (80 percent), we expect slower holistic is developing gen AI applications to improve
adoption of gen AI solutions and therefore a slower clinical decisions, diagnosis, and workflow.26
impact on labor tightness. This sector may benefit The Dutch start-up Cradle uses gen AI to predict
from orchestration that is particularly targeted to protein properties that could accelerate vaccine
help smaller enterprises understand the specific development.27
applications of gen AI relevant to their business,
learn approaches to successfully implement those Semiconductor, software, and other engineers. As
applications, and improve AI literacy. gen AI continues to grow, semiconductor-related
23 For more, see the AskSenna website.
24 “Generative AI demand soars 1,800% for US employers,” Lightcast, October 19, 2023; Ted Liu and Kelly Monahan, “2024 in-demand skills:
Unprecedented growth in AI and emergent skills for uniquely human work,” Upwork, March 19, 2024.
25 For more, see the Weaviate website.
26 Vidya Sagar, “Philips partners with AWS to develop generative AI applications,” NS Medical Devices, April 18, 2023.
27 For more, see the Cradle website.
28 “Mogelijke uitbreiding ASML op Brainport Industries Campus in Eindhoven” (“Possible expansion of ASML at Brainport Industries Campus in
Eindhoven”), Eindhoven, April 22, 2024; 2023 annual report, ASML, February 14, 2024.
Capturing the generative AI opportunity for the Dutch labor market 9
jobs that enable this technology will likewise expand, sector could facilitate this process—for example, as
including semiconductor engineers to provide Techniek Nederland has been doing since 2013 by
computing power, software engineers to build front- reskilling individuals from various backgrounds to
end solutions, and a wide range of other roles. This become installation engineers.30
presents an opportunity for the Dutch semiconductor
industry, and companies such as ASML, ASM, Besi, Granular insights such as regional job gain and loss
and NXP Semiconductors are positioned to grow analysis are crucial to understand a company’s
substantially because of the expected increase in reskilling needs and make informed decisions.
semiconductor demand driven by the growth of end To assist Dutch businesses in identifying their
applications including gen AI. For example, in April automation potential and its workforce impact,
2024, ASML and the local municipality Eindhoven public sector agencies and businesses could
signed a letter of intent to expand ASML’s facilities to consider developing tools that map different job
accommodate an additional 20,000 employees—a types to their expected automation potential.
50 percent increase in growth, which could be partly Such tools could provide businesses and local
driven by gen AI.28 governments with valuable insights and guidance
for transitioning to gen AI–driven processes,
We have previously emphasized the importance helping them make informed decisions and support
of developing soft and hard skills to keep pace employees throughout their journey.
and enable career advancement.29 This ongoing
development can enhance productivity in current Orchestrating sectors with high degrees
positions and prepare individuals for the high- of SMEs and labor tightness
demand jobs created by and for gen AI. In private sectors such as the professional,
scientific, and technical services sector, in which
both automation potential through gen AI and the
Three moves could accelerate proportion of SMEs are high, orchestration can
gen AI adoption and manage its accelerate gen AI implementation. Larger tech
effect on the Dutch workforce corporations, universities, public sector agencies
Three actions by public and private stakeholders could such as UWV, and sectoral employer organizations
accelerate gen AI adoption in the Netherlands and will could potentially facilitate this.
likely have a positive effect on the Dutch workforce.
Furthermore, Dutch companies of all sizes have
Preparing for granular upskilling opportunities to engage in the gen AI–based
and reskilling needs automation market. For example, banks could
Increased automation requires upskilling workers to collaborate with AI or software companies to
use new gen AI tools—monitoring service chatbots or create specific propositions or loans for gen
using copilots to write marketing content, for example. AI development and help disseminate these
Companies will need to develop training programs throughout the sector. The Nederlandse AI
as these solutions are implemented. And because Coalitie (NL AIC), a public–private partnership,
demand for some professions may decline, workers aims to promote the adoption and ethical use of AI
may need to reskill, sometimes across sectors. This technologies across sectors.31
would necessitate greater orchestration. The public
29 Netherlands advanced, June 18, 2024.
30 For more, see the Techniek Nederland, Techniekpact, and Mensen Maken de Transitie websites.
31 For more information, see the Nederlandse AI Coalitie website.
Capturing the generative AI opportunity for the Dutch labor market 10
Making bold investments to lead the education, and the Innovation Center for Artificial
Find more content like this on the
Netherlands’ gen AI transition Intelligence. In the private sector, many large
McKinsey Insights App
Strategic investments by both public and private corporations are investing significantly in developing
organizations could accelerate the adoption of gen innovative technology. For example, ASML and
AI and alleviate labor shortages in the Netherlands.32 Philips launched DeepTechXL, a private investment
Such funding for long-term innovation will be crucial, fund to finance and guide high-tech start-ups.34
especially in sectors that are strategically important
to the economy, such as manufacturing (including the
semiconductor industry), healthcare, construction,
and education. For example, NFI, SURF, and TNO By embracing collaboration on cutting-edge
Scan • Download • Personalize
have received €13.5 million to develop a Dutch gen technology opportunities such as gen AI, the
AI model that could accelerate development and Netherlands can position itself to build an
adoption across sectors.33 increasingly thriving business ecosystem. A proactive
approach can accelerate innovation and economic
The Netherlands already hosts a few AI funds, such growth as well as ensure the workforce is well
as NL AIC for responsible AI, AI growth fund AiNed, prepared to adapt to the changing landscape and
the Nationaal Onderwijslab AI (National Education flourish in a future shaped by AI.
Lab AI) established by Radboud University for AI in
32 “Time to place our bets: Europe’s AI opportunity,” MGI, October 1, 2024.
33 “The Netherlands starts construction of GPT-NL as its own AI language model,” TNO, November 2, 2023.
34 Heiko Jessayan, “DeepTechXL haalt €110 mln op door inleg van ASLM en pensioenfonds PME” (“DeepTechXL raises €110 million through
contributions from ASLM and pension fund PME”), Het Financieele Dagblad, March 14, 2024.
Ashley van Heteren, Eva Beekman, and Ferry Grijpink are partners in McKinsey’s Amsterdam office, where Just van der Wolf
is an AI expert and Wouter Kokx is an associate partner.
The authors wish to thank Alexander Veldhuijzen, Dieuwert Inia, Gurneet Singh Dandona, Hagar Heijmans, Joris van Niel,
Lex van der Vegt, Marc de Jong, Michael Chui, Reinout Goedvolk, and Sven Smit for their contributions to this article.
Copyright © 2024 McKinsey & Company. All rights reserved.
Capturing the generative AI opportunity for the Dutch labor market 11"
259,mckinsey,a-new-future-of-work-the-race-to-deploy-ai-and-raise-skills-in-europe-and-beyond.pdf,"A
new
future
of
work:
The
race
to
deploy
AI
and
raise
skills
in
Europe
and
beyond
A new future of work:
The race to deploy
AI and raise skills in
Europe and beyond
Authors
Eric Hazan
Anu Madgavkar
Michael Chui
Sven Smit
Dana Maor
Gurneet Singh Dandona
Roland Huyghues-Despointes
May 2024
About the McKinsey
Global Institute
The McKinsey Global Institute was established in 1990. Our mission is to provide a fact
base to aid decision making on the economic and business issues most critical to the world’s
companies and policy leaders. We benefit from the full range of McKinsey’s regional, sectoral,
and functional knowledge, skills, and expertise, but editorial direction and decisions are solely
the responsibility of MGI directors and partners.
Our research is grouped into five major themes:
— Productivity and prosperity: Creating and harnessing the world’s assets most productively
— Resources of the world: Building, powering, and feeding the world sustainably
— Human potential: Maximizing and achieving the potential of human talent
— Global connections: Exploring how flows of goods, services, people, capital, and ideas
shape economies
— Technologies and markets of the future: Discussing the next big arenas of value and
competition
We aim for independent and fact-based research. None of our work is commissioned or paid
for by any business, government, or other institution; we share our results publicly free of
charge; and we are entirely funded by the partners of McKinsey. While we engage multiple
distinguished external advisers to contribute to our work, the analyses presented in our
publications are MGI’s alone, and any errors are our own.
You can find out more about MGI and our research at www.mckinsey.com/mgi.
MGI Directors MGI Partners
Sven Smit (chair) Michael Chui
Chris Bradley Mekala Krishnan
Kweilin Ellingrud Anu Madgavkar
Sylvain Johansson Jan Mischke
Olivia White Jeongmin Seong
Tilman Tacke
A new future of work: The race to deploy AI and raise skills in Europe and beyond ii
Contents
At a glance Spotlight: Manufacturing
3 40
Context: Labor shortages Spotlight: Healthcare
and a slowdown in 42
productivity growth
4 Implications for
the workforce
Potential for accelerated 44
work transitions ahead
10 Enhancing productivity and
human capital in a time of
The varied geography of technological ferment
labor market disruptions 52
22
Technical appendix
New skills for a new era 60
26
Acknowledgments
Spotlight: Wholesale and 65
retail trade
36
Spotlight: Financial services
38
A new future of work: The race to deploy AI and raise skills in Europe and beyond 1
A new future of work: The race to deploy AI and raise skills in Europe and beyond 2
At a glance
Amid tightening labor markets and a slowdown in productivity growth, Europe and the
United States face shifts in labor demand, spurred by AI and automation. Our updated
modeling of the future of work finds that demand for workers in STEM-related, healthcare,
and other high-skill professions would rise while demand for occupations such as office
workers, production workers, and customer service representatives would decline. By
2030, in a midpoint adoption scenario, up to 30 percent of current hours worked could be
automated, accelerated by generative AI. Efforts to achieve net-zero emissions, an aging
workforce, and growth in e-commerce as well as infrastructure and technology spending and
overall economic growth could also shift employment demand.
By 2030, Europe could require up to 12 million occupational transitions, double the
prepandemic pace. In the United States, required transitions could reach almost 12 million,
in line with the prepandemic norm. Both regions navigated even higher levels of labor market
shifts at the height of the COVID-19 period, suggesting that they can handle this scale of
future job transitions. The pace of occupational change is broadly similar among countries in
Europe, although the specific mix reflects their economic variations.
Businesses will need a major skills upgrade. Demand for technological and social and
emotional skills could rise as demand for physical and manual and higher cognitive skills
stabilizes. Surveyed executives in Europe and the United States expressed a need not just
for advanced IT and data analytics but also for critical thinking, creativity, and teaching and
training—skills they report as currently being in short supply. Companies plan to focus on
retraining workers, in addition to hiring or subcontracting, to meet skill needs.
Workers with lower wages face challenges of redeployment as demand reweights
toward occupations with higher wages in both Europe and the United States.
Occupations with lower wages are likely to see reductions in demand, and workers will need
to acquire new skills to transition to better-paying work. If that doesn’t happen, there is a risk
of a more polarized labor market, with more higher-wage jobs than workers and too many
workers for existing lower-wage jobs.
Choices made today could revive productivity growth while creating better societal
outcomes. Embracing the path of accelerated technology adoption with proactive worker
redeployment could help Europe achieve an annual productivity growth rate of up to
3 percent through 2030. However, slow adoption and slow redeployment would limit that to
0.3 percent, closer to today’s level of productivity growth in Western Europe. Slow worker
redeployment would leave millions unable to participate productively in the future of work.
A new future of work: The race to deploy AI and raise skills in Europe and beyond 3
1
Context: Labor shortages
and a slowdown in
productivity growth
This report focuses on labor markets in Europe and the United States, looking at the
next few years to 2030. Technology and other factors will spur changes in the pattern of
labor demand, but these expected shifts need to be taken in the context of deep-seated
labor market changes already under way. Our study focuses on nine major economies in the
European Union along with the United Kingdom (which we refer to collectively in this report as
“Europe”), in comparison with the United States.
Structural shifts in labor markets have been ongoing for decades, including the very long-
term decline in the share of employment in agriculture, industry, and mining in favor of
services (Exhibit 1). More recently, labor markets were buffeted by pandemic shocks that
propelled not only faster shifts in hiring needs and more job switching but also new employee
preferences such as hybrid work. While COVID-19 exacerbated labor market tightening,
Europe’s high employment rate, a rapidly aging population, and a steady fall in working
hours make continuing shortages of workers and skills a persistent challenge for the future.
The burning question that remains is this: to what extent can the forthcoming technological
disruption solve labor market challenges in Europe?
A new future of work: The race to deploy AI and raise skills in Europe and beyond 4
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Exhibit <1> of <16>
Employment in Europe and the United States has shifted toward
service sectors.
Share of total employment by sector, Europe1 and US, 1850–2022, %
Europe
100
Construction
Transportation
Agriculture
80
Manufacturing
Mining
Utilities
Household work
60
Trade (retail and wholesale)
Professional services
40 Business and repair services
Telecommunications
Healthcare
20 Entertainment
Financial services
Education
Government
0
1850 1900 1950 2000 2022
US
100
Construction
Transportation
Agriculture
Manufacturing
80 Mining
Utilities
Household work2
Trade (retail and wholesale)
60
Professional services
40 Business and repair services
Telecommunications
Healthcare
20 Entertainment
Financial services
Education
0 Government
1850 1900 1950 2000 2022
1Includes Czech Republic, Denmark, France, Germany, Italy, Netherlands, Poland, Spain, Sweden, and United Kingdom.
2Increase from 1850 to 1860 in US primarily due to changes in how unpaid labor was tracked.
Source: Eurostat; Integrated Public Use Microdata Series USA, 2017; Ivan T. Berend, An Economic History of Twentieth-Century Europe, Cambridge University
Press, October 2016; US Bureau of Labor Statistics
McKinsey & Company
A new future of work: The race to deploy AI and raise skills in Europe and beyond 5
Europe’s future of work unfolds amid labor shortages
and a slowdown in productivity growth
In both Europe and the United States, labor market tightness has been on the rise, with
unfilled positions on the rise in both regions and unemployment at historically low levels.1 As
populations age on both sides of the Atlantic and the number of hours worked per worker
falls, particularly in Europe, labor market tightness is not likely to resolve naturally. In this
context, employers are increasingly competing for talent.
The pandemic had additional lasting impacts on workplaces, notably the increased adoption
of hybrid work. While about 90 percent of the working population was working fully on-site
in 2018, that number dropped to some 60 percent between 2021 and 2022. Since then,
the number has stabilized. However, only 40 percent of the 72 minutes saved daily from
not having to commute is allocated to work, with the rest mostly allocated to leisure and
caregiving.2 The overall impact on productivity is still being debated.3
Overall, in the global economy, productivity is crucial for remaining competitive.4 When a
company becomes more productive, it can produce more or higher-quality goods or services
with the same amount of resources. This often leads to lower production costs, allowing
companies to remain competitive or even expand. As a result, they may need to hire more
workers to meet the increased demand for their products or services. Also, increased
productivity in one sector can stimulate job growth in related industries; it boosts innovation
and leads to the creation of new job roles in areas such as research and development,
engineering, and information technology. Increased productivity would help address labor
market challenges, enabling employers to produce more even in tight talent markets, driving
economic growth, and creating better-paying jobs with opportunities to build human capital.
Yet Europe has experienced a long-term productivity slowdown, with productivity growth
almost steadily decreasing since the 1960s (Exhibit 2).5 Alongside its divergence in
productivity growth relative to the United States, Europe’s competitiveness is also waning.
The issues appear to be systemic rather than cyclical. European companies lag behind US
peers on multiple key metrics, such as return on invested capital, revenue growth, capital
expenditure, and R&D. Initial delays in Europe in technology development and adoption
help explain this gap, as Europe did not benefit from the information communications and
technology–driven productivity advancements that have occurred in the United States since
the 1990s. Our previous research indicates that Europe lags behind in eight out of ten key
cross-sector technologies where “winner takes most” effects are common, widening the gap
between the two regions.6 The two areas in which European companies still have an edge are
cleantech and next-gen materials, the research found.
1 In third quarter 2023, the unemployment rate stood at 6.0 percent in Europe and 3.7 percent in the United States,
compared with a peak of 11.5 percent in Europe in 1994 and 7.5 percent in the United States in 1992. For detailed data, see
“Unemployment Statistics,” Eurostat, March 2024; “Job Vacancies,” Eurostat, March 2024; and “Job Openings and Labor
Turnover,” US Bureau of Labor Statistics, March 2024.
2 Cevat Giray Aksoy et al., Time savings when working from home, National Bureau of Economic Research working paper,
number 30866, January 2023.
3 Several studies have associated remote work with productivity decreases ranging from 8 to 19 percent, whereas some
reports show a reduction of 4 percent for individual employees. Conversely, other research indicates productivity
improvements of 10 percent and more when switching to hybrid work. See, for example, Michael Gibbs, Friederike Mengel,
and Christoph Siemroth, Work from home & productivity: Evidence from personnel & analytics data on IT professionals,
Becker Friedman Institute for Economics at the University of Chicago working paper, number 2021-56, July 2021;
Natalia Emanuel and Emma Harrington, Working remotely? Selection, treatment, and the market provision of remote
work, Federal Reserve Bank of New York staff reports, number 1061, May 2023; Marta Angelici and Paola Profeta,
Smart-working: Work flexibility without constraints,” CESifo working paper, number 8165, March 2020.
4 Assuming constant exchange rates.
5 “Investing in productivity growth,” McKinsey Global Institute, March 1, 2024.
6 “Securing Europe’s competitiveness, addressing its technology gap,” McKinsey Global Institute, September 22, 2022.
A new future of work: The race to deploy AI and raise skills in Europe and beyond 6
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Exhibit <2> of <16>
European and US productivity growth decreased seven and three
percentage points, respectively, between 1950 and 2022.
Labor productivity growth (annual change in GDP per hours worked), % year over year
Second industrialization Post-war boom Era of contention Era of markets
Electrification, mass production, Continued urbanization Energy crises and Integration of Pre- and
and industrialization and infrastructure stagflation GVCs1; ICT2 post-GFC3
build-out revolution slowdown
8
United States
Europe
6
4
2
0
−2
−4
−6
1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010 2020
Note: Productivity is defined as GDP per hour worked, in 2010 dollars, as measured by purchasing-power parity. Calculated using a Hodrick-Prescott filter (λ =
6.25). Europe is calculated using the simple average of France, Germany, Italy, Spain, Sweden, and the United Kingdom. The remaining ten European countries
in our analysis were excluded because of data availability issues.
1Global value chains.
2Information and communication technology.
3Global financial crisis.
Source: Antonin Bergeaud, Gilbert Cette, and Rémy Lecat, “Productivity trends in advanced countries between 1890 and 2012,” The Review of Income and
Wealth, September 2016, Volume 62, Number 3; McKinsey Global Institute analysis
McKinsey & Company
Automation technology has the potential to revive productivity growth, allowing economies
to solve most of today’s labor market challenges. However, Europe and the United States are
not on the same trajectory for capturing this productivity growth: most AI-related innovations
are developed in the United States. There are fears in both regions that the adoption of
these technologies could prove disruptive to labor markets and exacerbate the challenges
of both finding requisite skills in the workforce and enabling workers to move from declining
occupations into rising ones.
Workers navigated major changes in demand for work during COVID-19, which resulted in a
temporary surge in occupational transitions—a sign that labor markets could successfully
adjust to rapid and heightened shifts in the pattern of employment demand. In Europe, some
3 percent of the working population voluntarily or involuntarily exited their occupational
categories between 2019 and 2022, more than triple the historical average. In the
period between 2019 to 2022, 5.5 percent of the US working population was affected by
occupational shifts, 1.5 times the historical average.7 The occupational shifts in both Europe
and the United States have subsequently returned to their historical rate, although some
professions continue to be affected, including food service.
7 Estimates based on US Bureau of Labor Statistics data.
A new future of work: The race to deploy AI and raise skills in Europe and beyond 7
Now, as Europe looks ahead, automation, AI, and other trends present opportunities for
higher productivity growth but with faster occupational transitions. Business leaders and
policy makers will face critical choices on how much to embrace technological change and
investment while training and redeploying workers into the jobs of the future. These choices
will determine whether Europe’s countries, companies, and labor force can derive the full
productivity and human capital benefits of the future of work.
Business leaders and policy
makers will face critical
choices on how much to
embrace technological change
and investment while training
and redeploying workers
into the jobs of the future.
A new future of work: The race to deploy AI and raise skills in Europe and beyond 8
A new future of work: The race to deploy AI and raise skills in Europe and beyond 9
2
Potential for accelerated
work transitions ahead
Demand for labor will continue to evolve over time, affected by structural trends at play in
Europe and the United States. Foremost among these is the expected advancements in
technology, especially AI, which could accelerate productivity growth and alter labor demand.
Structural factors such as the aging workforce and rising healthcare needs, particularly
pronounced in Europe, and additional priorities such as climate change will also reshape
demand for workers. Additionally, some trends that were boosted by the pandemic are likely
to endure, including the growth in e-commerce and the switch to remote work.
These trends represent opportunity for productivity growth but also underscore the need for
workers to transition from declining occupations to rising ones. In Europe, by our estimates, a
faster technology adoption scenario could be associated with productivity growth of roughly
2 to 3 percent per year, requiring some 12 million occupational transitions, or roughly double
the pace of occupational shifts in the prepandemic period. In the United States, with its
more dynamic labor market, the trend would be closer to the historical norm, but automation
adoption could accelerate further after 2030 in both regions. While the scale of occupational
transitions may appear daunting, both Europe and the United States navigated even higher
levels of labor market shifts during the pandemic, signaling the potential to handle future
transitions as well.
In this chapter, we outline how demand for labor could evolve and require accelerated
occupational transitions in the coming years, considering a range of scenarios to reflect
the uncertainties around pace of technology adoption (see Box 1, “Our methodology for
estimating occupational transitions”).
A new future of work: The race to deploy AI and raise skills in Europe and beyond 10
Box 1
Our methodology for estimating occupational transitions
We used methodology consistent with other A critical driver of occupational transitions is the
McKinsey Global Institute reports on the future rate at which automation, AI, and generative AI (gen
of work, dating back to 2017, to model trends of AI) will be adopted (exhibit). Two scenarios are used
job changes at the level of occupations, activities, to bookend the work-automation model: “late” and
and skills.1 For this report, we focused our analysis “early.” The “early” scenario flexes all parameters
on the 2022–30 period. We also considered how to the extremes of plausible assumptions, resulting
automation adoption could evolve beyond 2030 to in the fastest pace of automation development
2035.2 The drivers of the model have been updated and adoption, and the “late” scenario flexes all
accordingly. parameters in the opposite direction. The reality is
likely to fall somewhere between the two.4
Our model differentiates between employment
demand and occupational transitions. For the For this report, we have modeled region-specific
first, it estimates net changes in employment scenarios:
demand by sector and occupation; for the second,
— For Europe, we modeled two outcomes: a
it estimates the net decline in occupations across
“faster” scenario and a “slower” one. For the
sectors compared with the 2030 baseline. When
faster scenario, we use the midpoint—the
counting transitions, we do not include gains in this
arithmetical average between our late and
calculation to avoid double counting.
early scenarios. For the slower scenario, we
In this report, we focus our analysis on Europe use a “mid late” trajectory, an arithmetical
and the United States. For Europe, we included average between a late adoption scenario
ten countries: nine EU members that together and the midpoint scenario. We model this
represent 75 percent of the European working slower, mid-late scenario for Europe because
population—the Czech Republic, Denmark, France, achieving the faster, midpoint scenario by
Germany, Italy, Netherlands, Poland, Spain, and 2030 would require an occupational transition
Sweden—and the United Kingdom. In this report, rate significantly higher than seen in Europe’s
numbers referring to “Europe” correspond to the recent prepandemic past.
total estimates for these ten focus countries, which
— For the United States, we use the midpoint
were analyzed individually. Numbers have not
scenario, based on our earlier research. This
been extrapolated to the full European working
is an arithmetical average between our late
population. For the United States, we build on
and early scenarios of automation technology
estimates published in our 2023 report Generative
adoption.
AI and the future of work in America.3
We also estimate the productivity effects of
To understand the impact of automation and
automation, using GDP per full-time-equivalent
overall potential changes in demand in each
(FTE) employee as the measure of productivity. We
occupation, we included multiple drivers in our
first calculated automation displacement under
modeling: automation adoption, net-zero transition,
different scenarios by multiplying the projected
e-commerce growth, remote work adoption,
number of FTEs by the estimated automation
increases in income, aging populations, technology
adoption rate for each occupation in each country.
investments, infrastructure investments,
We considered only job activities that are available
marketization of unpaid work, new jobs, and
and well defined as of the date of this report. Also,
increased educational levels.
to be conservative, we assumed automation has a
labor substitution effect but no other performance
1 The modeling examines more than 850 unique occupations, more than 2,000 different activities, and 18 technical capabilities for each
activity. We also leveraged the framework devised in MGI’s 2018 report Skill shift: Automation and the future of the workforce. For
more detail, see the technical appendixes in A future that works: Automation, employment, productivity, McKinsey Global Institute,
January 2017.
2 For 2035, we modeled only the potential automation adoption rates for each occupation, not the occupational transitions required.
3 For more, see “Generative AI and the future of work in America,” McKinsey Global Institute, July 26, 2023.
4 “The economic potential of generative AI: The next productivity frontier,” McKinsey, June 14, 2023.
A new future of work: The race to deploy AI and raise skills in Europe and beyond 11
gains. We assumed that workers displaced which scenario evolves. Second, labor demand
by automation rejoin the workforce at 2022 could shift based on macroeconomic shifts in
productivity levels, net of automation. consumption due to changes in prices and costs,
which our model does not account for. Indeed, as
Our main sources of data are national and regional
automation increases productivity and income and
labor surveys. For the United States, we used data
lowers costs and the prices of goods and services,
from the Current Population Survey, conducted by
it could shift consumption, and thus labor demand,
the US Census Bureau for the US Bureau of Labor
in unanticipated ways. In the literature, this
Statistics. For Europe, we used data from the
specific impact of automation has been framed as
Labor Force Survey carried out by the European
the “deflationist” nature of technology adoption.
Commission and local labor agencies’ data.
Rapid adoption of technology could therefore
As described in chapter 4, we also conducted
establish a new equilibrium of demand. Third, the
a survey of more than 1,100 executives in five
shifts we model are the ones broadly anticipated
countries.
given the underlying base and current momentum
Our model has some important uncertainties of economies. We do not model changes in
and limitations. First, structural attributes— industrial production, trade, or labor migration that
such as management–employee relations, the may be driven by geopolitical, climatic, or social
regulatory and investment framework, and current factors, for example.
AI and innovation momentum—would affect
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Exhibit <B1> of <16>
Europe has varying automation adoption scenarios through 2030.
Automation of current work activities, % of working hours modeled to be automated, with generative AI
acceleration, Europe1 and the US, 2022–80
Early scenario Europe1 Late scenario Europe1 Faster scenario Europe1
Slower scenario Europe1 Midpoint scenario US
100
80
60
40
20
0
2022 2030 2040 2050 2060 2070 2080
Note: The range of scenarios represents uncertainty regarding the availability of technical capabilities, based on interviews with experts and survey responses.
The early scenario makes more-aggressive assumptions for all key model parameters (technical potential, integration timeline, economic feasibility, and regula-
tory and public adoption). The “faster” or midpoint adoption scenario is the average between the early and late scenarios. The “slower” scenario is the average
between the late scenario and the midpoint scenario.
1Includes Czech Republic, Denmark, France, Germany, Italy, Netherlands, Poland, Spain, Sweden, and United Kingdom.
Source: Eurostat; Occupational Information Network; Oxford Economics; US Bureau of Labor Statistics; national statistical agencies of the European countries
considered; McKinsey Global Institute analysis
McKinsey & Company
A new future of work: The race to deploy AI and raise skills in Europe and beyond 12
As technology reshapes work, demand is changing
for a wide range of occupations
Our analysis suggests that demand for some occupations could grow sharply by 2030. In our
faster, midpoint technology adoption scenario, demand for STEM and health professionals
would grow by 17 to 30 percent between 2022 and 2030, adding seven million positions in
Europe and an additional seven million in the United States. Despite the surge in tech sector
layoffs in 2023 and the potential of generative AI (gen AI) to augment tasks such as coding,
the broader, long-term demand for tech talent could remain robust across businesses of
every size and sector in an increasingly digital economy (Exhibit 3). Similarly, demand for
health aides, technicians, and wellness workers could continue growing by 25 to 30 percent
between 2022 and 2030, adding 3.3 million positions in Europe and 3.5 million in the
United States.
By contrast, demand for workers in food services, production work, customer services,
sales, and office support—all of which declined over the 2012–22 period—could continue to
decline until 2030.8 These jobs involve a high share of repetitive tasks, data collection, and
elementary data processing—all activities that automated systems can handle efficiently. In
all, our analysis suggests that this could lead to decreases in demand for these positions of
between 300,000 and 5.0 million positions in Europe and 0.1 million to 3.7 million positions in
the United States.
Demand for other occupations would remain in line with overall demand growth. This includes
positions for educators and workforce trainers in Europe and includes businesses and
legal professionals, as well as community services workers, in the United States. Demand
for occupations such as management, construction, creative and arts management, and
transportation services is expected to increase by about 8 to 9 percent.
Our analysis highlights some differences between Europe and the United States in the
occupations with growing or diminishing demand. Those differences are a result of the
differences in occupational composition between the two regions, as well as cultural
specificities. For example, the greater share of public employment in Europe, especially in
administrative activities, may reduce the impact of the expected disruption on these workers
for the coming years. Understanding the nuances of how this might play out and who might be
affected is critical to ensuring a smooth transition for individuals and businesses alike.
8 Examples here include cashiers, call-center representatives, tellers, and guest service agents.
A new future of work: The race to deploy AI and raise skills in Europe and beyond 13
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Exhibit <3> of <16>
Demand for healthcare and STEM roles could grow, while demand for office
support and customer service roles could decline.
Net expected change in labor demand, Europe1 and US, faster/midpoint scenario,1 2022–30
Europe2 US
Employ- Employ-
ment ment
Employment change vs 2022, change vs Employment change vs 2022, change vs
Occupational category million 2022, % million 2022, %
Health aides, techni-
3.3 25.2 3.5 29.7
cians, and wellness
STEM professionals 2.3 16.7 1.8 23.1
Health professionals 1.5 23.6 2.0 30.1
Managers 1.1 9.1 1.1 11.3
Business or legal pro-
1.0 6.9 1.1 6.6
fessionals
Builders 0.7 6.9 0.8 11.9
Transportation services 0.5 7.9 0.5 9.5
Property maintenance 0.4 5.3 0.5 10.3
Creatives and arts man-
0.4 8.6 0.2 10.7
agement
Community services 0.3 3.5 0.4 6.6
Educator and workforce
0.2 1.6 0.3 2.6
training
Mechanical installation
0.1 1.2 0.5 7.0
and repair
Agriculture −0.2 –3.8 0 2.3
Food services −0.3 –3.3 −0.3 –1.9
Production work −0.9 –5.3 −0.1 –0.7
Customer service and
−1.7 –12.1 −2.0 –13.4
sales
Office support −5.0 –18.3 −3.7 –18.5
1For Europe, we used the “faster” scenario, which corresponds to the “midpoint” scenario in the United States. The “faster” or midpoint adoption scenario is the
average between the early and late scenarios. The “slower” scenario is the average between the late scenario and the midpoint scenario.
2Includes Czech Republic, Denmark, France, Germany, Italy, Netherlands, Poland, Spain, Sweden, and United Kingdom.
Source: Eurostat; Occupational Information Network; Oxford Economics; US Bureau of Labor Statistics; national statistical agencies of the European countries
considered; McKinsey Global Institute analysis
McKinsey & Company
A new future of work: The race to deploy AI and raise skills in Europe and beyond 14
Some 12 million occupational transitions may be needed
in both Europe and the United States by 2030
Our analysis finds that in our faster automation adoption scenario, some 12.0 million
occupational transitions would be needed by 2030 in the ten European countries, affecting
6.5 percent of the current employed workforce.9 Under the slower scenario, the number of
occupational transitions needed would amount to 8.5 million in Europe, affecting 4.6 percent
of the current employed workforce. In the United States, the figures for the midpoint scenario
we use (which corresponds to the faster European scenario) are 11.8 million occupational
shifts, affecting 7.5 percent of the current employed workforce.
The range of outcomes for Europe from the two scenarios reflects different potential for the
number of work hours that could be automated, thereby affecting both potential productivity
gains and the number of occupational transitions that might be needed. A failure to achieve
the faster-paced adoption model would mean fewer occupational transitions are needed. But
it would also mean failing to achieve some significant productivity gains in the period to 2030.
Occupational transitions would need to roughly double in Europe
but return to their historical level in the United States
The pace of change in required occupational transitions is uneven between Europe and the
United States. Europe could experience a stark acceleration in the pace of occupational
change needed in both the faster and slower scenarios, with the number rising to between 1.1
million and 1.5 million occupational transitions annually between 2022 and 2030. That is 1.6
to 2.2 times the historical 2016–19 rate, before the COVID-19 pandemic, indicating a potential
doubling of this measure of change in the European employment market. By contrast, in
the United States, the number of occupational transitions needed annually between 20"
260,mckinsey,beyond-the-hype-capturing-the-potential-of-ai-and-gen-ai-in-tmt.pdf,"Beyond the hype:
Capturing the potential
of AI and gen AI in tech,
media, and telecom
February 2024
Beyond the hype:
Capturing the potential
of AI and gen AI in tech,
media, and telecom
February 2024
Contents
Introduction: The promise and the challenge of generative AI 2
State of the Art
4
The economic potential of generative AI 5
Making the most of the generative AI opportunity: Six questions for CEOs 33
Sector View: Telecom Operators
38
The AI-native telco: Radical transformation to thrive in turbulent times 39
How generative AI could revitalize profitability for telcos 48
Generative AI use cases: A guide to developing the telco of the future 60
Tech talent in transition: Seven technology trends reshaping telcos 70
Deploying Gen AI
81
The organization of the future: Enabled by gen AI, driven by people 82
The data dividend: Fueling generative AI 91
Technology’s generational moment with generative AI: A CIO and CTO guide 101
As gen AI advances, regulators—and risk functions—rush to keep pace 113
What the Future Holds
119
Six major gen AI trends that will shape 2024’s agenda 120
Appendix: Generative AI solutions in action 125
Glossary 127
Beyond the hype: Capturing the potential of AI and gen AI in TMT 1
Introduction: The promise and
the challenge of generative AI
The emergence of generative AI (gen AI) presents both a challenge and a significant opportunity for leaders looking
to steer their organizations into the future. How big is the opportunity? McKinsey research estimates that gen AI
could add to the economy between $2.6 trillion and $4.4 trillion annually while increasing the impact of all artificial
intelligence by 15 to 40 percent. In the technology, media, and telecommunications (TMT) space, new gen AI use
cases are expected to unleash between $380 billion and $690 billion in impact—$60 billion to $100 billion in
telecommunications, $80 billion to $130 billion in media, and about $240 billion to $460 billion in high tech. In
fact, it seems possible that within the next three years, anything not connected to AI will be considered obsolete or
ineffective.
Some leaders are moving to seize the moment and implement gen AI in their organizations at scale, but others remain
in the pilot stage, and some have yet to decide what to do. If companies are to remain competitive and relevant in the
coming years, it is essential that executives understand the potential impact of gen AI and develop the strategies
necessary to incorporate it into their operations. Such strategies would involve an AI-native transformation, focused
on building and managing the adoption of gen AI. McKinsey has conducted extensive research into how to embed
gen AI to ensure that the technology delivers meaningful value. We’ve also spent much of the past year working with
clients to create and then implement gen AI road maps. That combination of research and hands-on experience has
allowed us to identify more than 100 gen AI use cases in TMT across seven business domains.1
Our experience working with clients already indicates the potential for telcos to achieve significant impact with
gen AI across all key functions. The largest share of total impact will likely be in customer care and sales, which
together would account for approximately 70 percent of total impact; network operations, IT, and support functions
would round out the rest. The technology already is showing meaningful impact in enhancing interactions between
employees and customers: the personalization of products and campaigns, improvements in sales effectiveness, and
a reduction in time to market can spark a potential revenue increase of 3 to 5 percent. Customer care interactions—
where as much as 50 percent of activity could be automated—have potential for a 30 to 45 percent increase in
productivity while improving the customer experience and customer satisfaction scores. On the labor side, up to 70
percent of repetitive work activities could be automated via gen AI to improve productivity. There is also potential for
new efficiencies in knowledge search, validation, and synthesis, where some 60 percent of activity has the potential
for automation. And gen AI tools could boost developer productivity by 20 to 45 percent.
These areas provide rich soil for use cases. More challenging will be to go from sketching a road map to building
proofs of concept to scaling successfully and capturing impact. Years of experience in designing and implementing
digital transformations have taught us a lot, but gen AI’s nature and speed of disruption are creating a new layer of
uncertainty.
Becoming an AI-native organization at scale involves making the most of technology, data, and governance. Success
follows when leaders embrace an operating model that leverages the strengths of both humans and machines; is
rooted in agility, flexibility, and continuous learning; and is supported by strong data and analytics talent. Another
condition of success is to invest in data quality and quantity, focusing on the data life cycle to ensure high-quality
information for training the gen AI model. Building capabilities into the data architecture, such as vector databases
and data pre- and post-processing pipelines, will enable the development of use cases. Talent, data, technology,
governance—none of these can be an afterthought.
¹ Marketing and digital, sales and channels, customer care, customer strategy, support, additional areas, and new businesses.
Beyond the hype: Capturing the potential of AI and gen AI in TMT 2
Successful implementations share a clear vision and decisive approach. We advise that financial plans maintain or
increase gen AI budgets over the next year. These budgets should include resources dedicated to gen AI for the shaping
and crafting of bespoke solutions (for example, training large language models with telco-specific data, rather than
implementing off-the-shelf ones) or partnerships with IT vendors to accelerate the timeline for implementation.
The AI journey has been shown to contain many challenges and learning opportunities, such as preparing and shifting
an organization’s culture, finding data sets of significant size, and addressing the interpretability of the outputs provided
by models. Leaders should expect such daunting challenges as a shortage of talent, lack of organizational commitment
and prioritization (including among C-level executives), and difficulties in justifying ROI for certain business cases, all
amid a changing regulatory and ethics landscape that creates further uncertainty. But daunting does not have to mean
impossible. Developing a system of protocols and guardrails (such as building “moderation” models to check outputs
for different risks and ensure users receive consistent responses) will be a crucial step toward mitigating the new risks
introduced by gen AI. Another key will be change management—involving end users in the model development process
and deeply embedding technology into their operations.
This collection presents McKinsey’s top insights on gen AI, providing a detailed examination of this technology’s
transformative potential for organizations. It offers top management guidance on how to prepare for the implementation
of gen AI and explores the implications of gen AI’s use by the TMT industries, especially telecommunications. The
collection covers the essential requirements for deploying gen AI, including organizational readiness, data management,
and technological considerations. It also emphasizes the importance of effectively managing risks associated with gen
AI implementation. Furthermore, this compilation offers an overview of the future developments and advancements
expected in the field of generative AI.
Gen AI will continue to evolve. New capabilities, such as the ability to analyze and comprehend images or audio, and an
expanding ecosystem with marketplaces for GPT (generative pretrained transformers), are constantly emerging. For
leaders, the stakes are high. But so are the opportunities. The next move from TMT players will define how they move
from isolated cases to implementations at scale, from hype to impact.
Alex Singla Alexander Sukharevsky Brendan Gaffey Noshir Kaka
Senior Partner Senior Partner Senior Partner Senior Partner
Managing Partner Managing Partner Global Leader Global Leader
QuantumBlack QuantumBlack TMT Practice TMT Practice
AI by McKinsey AI by McKinsey
Peter Dahlström Andrea Travasoni Venkat Atluri
Senior Partner Senior Partner Senior Partner
Europe Leader Global Leader Global Leader
TMT Practice Telecom Operators Telecom Operators
TMT Practice TMT Practice
Tomás Lajous Benjamim Vieira Víctor García de la Torre
Senior Partner Senior Partner Associate Partner
AI and Gen AI Leader Digital and Analytics Leader TMT Practice
TMT Practice TMT Practice
Beyond the hype: Capturing the potential of AI and gen AI in TMT 3
1
State of
the art
Beyond the hype: Capturing the potential of AI and gen AI in TMT 4
The
economic
potential
of
generative
AI
The economic
potential of
generative AI
The next productivity frontier
June 2023
Authors
Michael Chui
Eric Hazan
Roger Roberts
Alex Singla
Kate Smaje
Alexander Sukharevsky
Lareina Yee
Rodney Zemmel
1
Generative AI as a
technology catalyst
To grasp what lies ahead requires an understanding of the breakthroughs that have enabled the rise
of generative AI, which were decades in the making. ChatGPT, GitHub Copilot, Stable Diff usion, and
other generative AI tools that have captured current public attention are the result of signifi cant levels
of investment in recent years that have helped advance machine learning and deep learning. This
investment undergirds the AI applications embedded in many of the products and services we use
every day.
But because AI has permeated our lives incrementally—through everything from the tech powering
our smartphones to autonomous-driving features on cars to the tools retailers use to surprise and
delight consumers—its progress was almost imperceptible. Clear milestones, such as when AlphaGo,
an AI-based program developed by DeepMind, defeated a world champion Go player in 2016, were
celebrated but then quickly faded from the public’s consciousness.
ChatGPT and its competitors have captured the imagination of people around the world in a way
AlphaGo did not, thanks to their broad utility—almost anyone can use them to communicate and
create—and preternatural ability to have a conversation with a user. The latest generative AI
applications can perform a range of routine tasks, such as the reorganization and classifi cation
This article is excerpted from the full McKinsey report, The economic potential of generative AI: The
next productivity frontier. To read the full report, including details about the research, appendix, and
acknowledgements, visit mck.co/genai.
The economic potential of generative AI: The next productivity frontier 6
of data. But it is their ability to write text, compose music, and create digital art that has garnered
headlines and persuaded consumers and households to experiment on their own. As a result, a
broader set of stakeholders are grappling with generative AI’s impact on business and society but
without much context to help them make sense of it.
How did we get here? Gradually, then all of a sudden
For the purposes of this report, we define generative AI as applications typically built using foundation
models. These models contain expansive artificial neural networks inspired by the billions of neurons
connected in the human brain. Foundation models are part of what is called deep learning, a term
that alludes to the many deep layers within neural networks. Deep learning has powered many of
the recent advances in AI, but the foundation models powering generative AI applications are a step
change evolution within deep learning. Unlike previous deep learning models, they can process
extremely large and varied sets of unstructured data and perform more than one task.
Foundation models have enabled new capabilities and vastly improved existing ones across a broad
range of modalities, including images, video, audio, and computer code. AI trained on these models
can perform several functions; it can classify, edit, summarize, answer questions, and draft new
content, among other tasks.
Continued innovation will also bring new challenges. For example, the computational power required
to train generative AI with hundreds of billions of parameters threatens to become a bottleneck in
development.¹ Further, there’s a significant move—spearheaded by the open-source community and
spreading to the leaders of generative AI companies themselves—to make AI more responsible, which
could increase its costs.
Nonetheless, funding for generative AI, though still a fraction of total investments in artificial
intelligence, is significant and growing rapidly—reaching a total of $12 billion in the first five months
of 2023 alone. Venture capital and other private external investments in generative AI increased by
an average compound growth rate of 74 percent annually from 2017 to 2022. During the same period,
investments in artificial intelligence overall rose annually by 29 percent, albeit from a higher base.
The rush to throw money at all things generative AI reflects how quickly its capabilities have
developed. ChatGPT was released in November 2022. Four months later, OpenAI released a new
large language model, or LLM, called GPT-4 with markedly improved capabilities.² Similarly, by May
2023, Anthropic’s generative AI, Claude, was able to process 100,000 tokens of text, equal to about
75,000 words in a minute—the length of the average novel—compared with roughly 9,000 tokens
when it was introduced in March 2023.³ And in May 2023, Google announced several new features
powered by generative AI, including Search Generative Experience and a new LLM called PaLM 2 that
will power its Bard chatbot, among other Google products.⁴
From a geographic perspective, external private investment in generative AI, mostly from tech
giants and venture capital firms, is largely concentrated in North America, reflecting the continent’s
current domination of the overall AI investment landscape. Generative AI–related companies based
in the United States raised about $8 billion from 2020 to 2022, accounting for 75 percent of total
investments in such companies during that period.⁵
Generative AI has stunned and excited the world with its potential for reshaping how knowledge work
gets done in industries and business functions across the entire economy. Across functions such
as sales and marketing, customer operations, and software development, it is poised to transform
roles and boost performance. In the process, it could unlock trillions of dollars in value across sectors
from banking to life sciences. We have used two overlapping lenses in this report to understand
The economic potential of generative AI: The next productivity frontier 7
2
Generative AI use
cases across functions
and industries
the potential for generative AI to create value for companies and alter the workforce. The
following sections share our initial findings.
The economic potential of generative AI: The next productivity frontier 8
Exhibit 1
The potential impact of generative AI can be evaluated through two lenses.
Lens 1 Lens 2
Total economic Labor productivity potential
potential of 60-plus across ~2,100 detailed work
organizational use activities performed by
cases1 global workforce
Cost impacts
of use cases
Revenue
impacts of
use cases1
1For quantitative analysis, revenue impacts were recast as productivity increases on the corresponding spend in order to maintain comparability with cost
impacts and not to assume additional growth in any particular market.
McKinsey & Company
Generative AI is a step change in the evolution of artifi cial intelligence. As companies
rush to adapt and implement it, understanding the technology’s potential to deliver value
to the economy and society at large will help shape critical decisions. We have used two
complementary lenses to determine where generative AI with its current capabilities could
deliver the biggest value and how big that value could be (Exhibit 1).
The fi rst lens scans use cases for generative AI that organizations could adopt. We defi ne
a “use case” as a targeted application of generative AI to a specifi c business challenge,
resulting in one or more measurable outcomes. For example, a use case in marketing is the
application of generative AI to generate creative content such as personalized emails, the
measurable outcomes of which potentially include reductions in the cost of generating such
content and increases in revenue from the enhanced eff ectiveness of higher-quality content
at scale. We identifi ed 63 generative AI use cases spanning 16 business functions that could
deliver total value in the range of $2.6 trillion to $4.4 trillion in economic benefi ts annually
when applied across industries.
That would add 15 to 40 percent to the $11.0 trillion to $17.7 trillion of economic value that we
now estimate nongenerative artifi cial intelligence and analytics could unlock. (Our previous
estimate from 2017 was that AI could deliver $9.5 trillion to $15.4 trillion in economic value.)
Our second lens complements the fi rst by analyzing generative AI’s potential impact on
the work activities required in some 850 occupations. We modeled scenarios to estimate
when generative AI could perform each of more than 2,100 “detailed work activities”—
The economic potential of generative AI: The next productivity frontier 9
such as “communicating with others about operational plans or activities”—that make up
those occupations across the world economy. This enables us to estimate how the current
capabilities of generative AI could aff ect labor productivity across all work currently done by
the global workforce.
Some of this impact will overlap with cost reductions in the use case analysis described
above, which we assume are the result of improved labor productivity. Netting out this
Exhibit 2
Generative AI could create additional value potential above what
could be unlocked by other AI and analytics.
AI’s potential impact on the global economy, $ trillion
17.1–25.6
13.6–22.1
6.1–7.9
2.6–4.4
11.0–17.7
~15–40% ~35–70%
incremental incremental
economic impact economic impact
Advanced analytics, New generative Total use All worker productivity Total AI
traditional machine AI use cases case-driven enabled by generative economic
learning, and deep potential AI, including in use potential
learning1 cases
1Updated use case estimates from ""Notes from the AI frontier: Applications and value of deep learning,” McKinsey Global Institute, April 17, 2018.
McKinsey & Company
The economic potential of generative AI: The next productivity frontier 10
overlap, the total economic benefits of generative AI—including the major use cases we
explored and the myriad increases in productivity that are likely to materialize when the
technology is applied across knowledge workers’ activities—amounts to $6.1 trillion to
$7.9 trillion annually (Exhibit 2).
While generative AI is an exciting and rapidly advancing technology, the other applications of
AI discussed in our previous report continue to account for the majority of the overall potential
value of AI. Traditional advanced-analytics and machine learning algorithms are highly
Box 1
How we estimated the value potential of generative AI use cases
To assess the potential value of generative AI, a customer service use case but not in a use
we updated a proprietary McKinsey database of case optimizing a logistics network, where value
potential AI use cases and drew on the experience primarily arises from quantitative analysis.
of more than 100 experts in industries and their
We then estimated the potential annual value
business functions.1 Our updates examined
of these generative AI use cases if they were
use cases of generative AI—specifically, how
adopted across the entire economy. For use
generative AI techniques (primarily transformer-
cases aimed at increasing revenue, such as some
based neural networks) can be used to solve
of those in sales and marketing, we estimated
problems not well addressed by previous
the economy-wide value generative AI could
technologies.
deliver by increasing the productivity of sales and
We analyzed only use cases for which generative marketing expenditures.
AI could deliver a significant improvement in the
Our estimates are based on the structure of the
outputs that drive key value. In particular, our
global economy in 2022 and do not consider the
estimates of the primary value the technology
value generative AI could create if it produced
could unlock do not include use cases for which
entirely new product or service categories.
the sole benefit would be its ability to use natural
language. For example, natural-language
capabilities would be the key driver of value in
1 “Notes from the AI frontier: Applications and value of deep learning,” McKinsey Global Institute, April 17, 2018.
effective at performing numerical and optimization tasks such as predictive modeling, and
they continue to find new applications in a wide range of industries. However, as generative AI
continues to develop and mature, it has the potential to open wholly new frontiers in creativity
and innovation. It has already expanded the possibilities of what AI overall can achieve (see
Box 1, “How we estimated the value potential of generative AI use cases”).
In this chapter, we highlight the value potential of generative AI across two dimensions:
business function and modality.
The economic potential of generative AI: The next productivity frontier 11
Value potential by function
While generative AI could have an impact on most business functions, a few stand out when
measured by the technology’s impact as a share of functional cost (Exhibit 3). Our analysis
of 16 business functions identifi ed just four—customer operations, marketing and sales,
software engineering, and research and development—that could account for approximately
75 percent of the total annual value from generative AI use cases.
Web <2023>
E<Vxihvaitbeicth 3 full report>
Exhibit <3> of <16>
Using generative AI in just a few functions could drive most of the technology’s
impact across potential corporate use cases.
Represent ~75% of total annual impact of generative AI
500
Sales
Software engineering
Marketing
(for corporate IT)
Software engineering
(for product development)
400
Customer operations
Product R&D1
300
Impact, $ billion
Supply chain
200
Manufacturing
Finance Risk and compliance
Talent and organization (incl HR)
100
Procurement management
Corporate IT1 Legal
Strategy
Pricing
0
0 10 20 30 40
Impact as a percentage of functional spend, %
Note: Impact is averaged.
¹Excluding software engineering.
Source: Comparative Industry Service (CIS), IHS Markit; Oxford Economics; McKinsey Corporate and Business Functions database; McKinsey Manufacturing
and Supply Chain 360; McKinsey Sales Navigator; Ignite, a McKinsey database; McKinsey analysis
McKinsey & Company
Notably, the potential value of using generative AI for several functions that were prominent
in our previous sizing of AI use cases, including manufacturing and supply chain functions,
is now much lower.⁶ This is largely explained by the nature of generative AI use cases, which
exclude most of the numerical and optimization applications that were the main value drivers
for previous applications of AI.
The economic potential of generative AI: The next productivity frontier 12
Generative AI as a virtual expert
In addition to the potential value generative AI can deliver in function-specific use cases,
the technology could drive value across an entire organization by revolutionizing internal
knowledge management systems. Generative AI’s impressive command of natural-language
processing can help employees retrieve stored internal knowledge by formulating queries
in the same way they might ask a human a question and engage in continuing dialogue. This
could empower teams to quickly access relevant information, enabling them to rapidly make
better-informed decisions and develop effective strategies.
In 2012, the McKinsey Global Institute (MGI) estimated that knowledge workers spent about
a fifth of their time, or one day each workweek, searching for and gathering information. If
generative AI could take on such tasks, increasing the efficiency and effectiveness of the
workers doing them, the benefits would be huge. Such virtual expertise could rapidly “read”
vast libraries of corporate information stored in natural language and quickly scan source
material in dialogue with a human who helps fine-tune and tailor its research, a more scalable
solution than hiring a team of human experts for the task.
Following are examples of how generative AI could produce operational benefits as a virtual
expert in a handful of use cases.
In addition to the potential
value generative AI can
deliver in function-specific
use cases, the technology
could drive value across
an entire organization
by revolutionizing
internal knowledge
management systems.
The economic potential of generative AI: The next productivity frontier 13
Customer operations
Generative AI has the potential to revolutionize the entire customer operations function,
improving the customer experience and agent productivity through digital self-service
and enhancing and augmenting agent skills. The technology has already gained traction
in customer service because of its ability to automate interactions with customers using
natural language. Research found that at one company with 5,000 customer service
agents, the application of generative AI increased issue resolution by 14 percent an hour and
reduced the time spent handling an issue by 9 percent.⁷ It also reduced agent attrition and
requests to speak to a manager by 25 percent. Crucially, productivity and quality of service
improved most among less-experienced agents, while the AI assistant did not increase—
and sometimes decreased—the productivity and quality metrics of more highly skilled
agents. This is because AI assistance helped less-experienced agents communicate using
techniques similar to those of their higher-skilled counterparts.
The following are examples of the operational improvements generative AI can have for
specific use cases:
— Customer self-service. Generative AI–fueled chatbots can give immediate and
personalized responses to complex customer inquiries regardless of the language or
location of the customer. By improving the quality and effectiveness of interactions via
automated channels, generative AI could automate responses to a higher percentage of
customer inquiries, enabling customer care teams to take on inquiries that can only be
resolved by a human agent. Our research found that roughly half of customer contacts
made by banking, telecommunications, and utilities companies in North America are
already handled by machines, including but not exclusively AI. We estimate that generative
AI could further reduce the volume of human-serviced contacts by up to 50 percent,
depending on a company’s existing level of automation.
— Resolution during initial contact. Generative AI can instantly retrieve data a company
has on a specific customer, which can help a human customer service representative more
successfully answer questions and resolve issues during an initial interaction.
— Reduced response time. Generative AI can cut the time a human sales representative
spends responding to a customer by providing assistance in real time and recommending
next steps.
— Increased sales. Because of its ability to rapidly process data on customers and their
browsing histories, the technology can identify product suggestions and deals tailored
to customer preferences. Additionally, generative AI can enhance quality assurance and
coaching by gathering insights from customer conversations, determining what could be
done better, and coaching agents.
We estimate that applying generative AI to customer care functions could increase
productivity at a value ranging from 30 to 45 percent of current function costs.
Our analysis captures only the direct impact generative AI might have on the productivity of
customer operations. It does not account for potential knock-on effects the technology may
have on customer satisfaction and retention arising from an improved experience, including
better understanding of the customer’s context that can assist human agents in providing
more personalized help and recommendations.
The economic potential of generative AI: The next productivity frontier 14
Marketing and sales
Generative AI has taken hold rapidly in marketing and sales functions, in which text-based
communications and personalization at scale are driving forces. The technology can create
personalized messages tailored to individual customer interests, preferences, and behaviors,
as well as do tasks such as producing first drafts of brand advertising, headlines, slogans,
social media posts, and product descriptions.
However, introducing generative AI to marketing functions requires careful consideration.
For one thing, using mathematical models trained on publicly available data without
sufficient safeguards against plagiarism, copyright violations, and branding recognition risks
infringing on intellectual property rights. A virtual try-on application may produce biased
representations of certain demographics because of limited or biased training data. Thus,
significant human oversight is required for conceptual and strategic thinking specific to each
company’s needs.
Potential operational benefits from using generative AI for marketing include the following:
— Efficient and effective content creation. Generative AI could significantly reduce the
time required for ideation and content drafting, saving valuable time and effort. It can also
facilitate consistency across different pieces of content, ensuring a uniform brand voice,
writing style, and format. Team members can collaborate via generative AI, which can
integrate their ideas into a single cohesive piece. This would allow teams to significantly
enhance personalization of marketing messages aimed at different customer segments,
geographies, and demographics. Mass email campaigns can be instantly translated into
as many languages as needed, with different imagery and messaging depending on the
audience. Generative AI’s ability to produce content with varying specifications could
increase customer value, attraction, conversion, and retention over a lifetime and at a
scale beyond what is currently possible through traditional techniques.
— Enhanced use of data. Generative AI could help marketing functions overcome the
challenges of unstructured, inconsistent, and disconnected data—for example, from
different databases—by interpreting abstract data sources such as text, image, and
varying structures. It can help marketers better use data such as territory performance,
synthesized customer feedback, and customer behavior to generate data-informed
marketing strategies such as targeted customer profiles and channel recommendations.
Such tools could identify and synthesize trends, key drivers, and market and product
opportunities from unstructured data such as social media, news, academic research, and
customer feedback.
— SEO optimization. Generative AI can help marketers achieve higher conversion and
lower cost through search engine optimization (SEO) for marketing and sales technical
components such as page titles, image tags, and URLs. It can synthesize key SEO tokens,
support specialists in SEO digital content creation, and distribute targeted content to
customers.
— Product discovery and search personalization. With generative AI, product discovery
and search can be personalized with multimodal inputs from text, images and speech, and
deep understanding of customer profiles. For example, technology can leverage individual
user preferences, behavior, and purchase history to help customers discover the most
The economic potential of generative AI: The next productivity frontier 15
relevant products and generate personalized product descriptions. This would allow
CPG, travel, and retail companies to improve their e-commerce sales by achieving higher
website conversion rates.
We estimate that generative AI could increase the productivity of the marketing function with
a value between 5 and 15 percent of total marketing spending.
Our analysis of the potential use of generative AI in marketing doesn’t account for knock-on
effects beyond the direct impacts on productivity. Generative AI–e"
261,mckinsey,mckinsey-technology-trends-outlook-2024.pdf,"Technology Trends
Outlook 2024
July 2024
McKinsey & Company
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institutions in the private, public, and social sectors achieve lasting success. For more than
90 years, our primary objective has been to serve as our clients’ most trusted external adviser.
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Contents
Insights across trends 4
The AI revolution 13 Cutting-edge engineering 65
Generative AI 14 Future of robotics 66
Applied AI 20 Future of mobility 71
Industrializing machine learning 25 Future of bioengineering 77
Future of space technologies 82
Building the digital future 30 A sustainable world 87
Next-generation software develo pment 31 Electrification and renewables 88
Digital trust and cybersecurity 36 Climate technologies beyond
electrification and renewables 94
Compute and connectivity frontiers 43
Advanced connectivity 44
Immersive-reality technologies 49
Cloud and edge computing 54
Quantum technologies 59
Technology Trends Outlook 2024 3
Insights across trends
Despite challenging overall market conditions in New and notable
2023, continuing investments in frontier technologies
The two trends that stood out in 2023 were gen AI and
promise substantial future growth in enterprise adoption.
electrification and renewables. Gen AI has seen a spike
Generative AI (gen AI) has been a standout trend since
of almost 700 percent in Google searches from 2022
2022, with the extraordinary uptick in interest and
to 2023, along with a notable jump in job postings and
investment in this technology unlocking innovative
investments. The pace of technology innovation has
possibilities across interconnected trends such as
been remarkable. Over the course of 2023 and 2024,
robotics and immersive reality. While the macroeconomic
the size of the prompts that large language models
environment with elevated interest rates has affected
(LLMs) can process, known as “context windows,” spiked
equity capital investment and hiring, underlying
from 100,000 to two million tokens. This is roughly the
indicators—including optimism, innovation, and longer-
difference between adding one research paper to a
term talent needs—reflect a positive long-term trajectory
model prompt and adding about 20 novels to it. And the
in the 15 technology trends we analyzed.
modalities that gen AI can process have continued to
These are among the findings in the latest McKinsey increase, from text summarization and image generation
Technology Trends Outlook, in which the McKinsey to advanced capabilities in video, images, audio, and text.
Technology Council identified the most significant This has catalyzed a surge in investments and innovation
technology trends unfolding today (to know more about aimed at advancing more powerful and efficient
the Council, see the sidebar “About the McKinsey computing systems.
Technology Council”). This research is intended to help
The large foundation models that power generative
executives plan ahead by developing an understanding
AI, such as LLMs, are being integrated into various
of potential use cases, sources of value, adoption drivers,
enterprise software tools and are also being employed
and the critical skills needed to bring these opportunities
for diverse purposes such as powering customer-facing
to fruition.
chatbots, generating ad campaigns, accelerating
Our analysis examines quantitative measures of drug discovery, and more. We expect this expansion
interest, innovation, investment, and talent to gauge the to continue, pushing the boundaries of AI capabilities.
momentum of each trend. Recognizing the long-term Senior leaders’ awareness of gen AI innovation has
nature and interdependence of these trends, we also increased interest, investment, and innovation in
delve into the underlying technologies, uncertainties, AI technologies and other trends, such as robotics,
and questions surrounding each trend. (For more about which is a new addition to our trends analysis this year.
new developments in our research, please see the Advancements in AI are ushering in a new era of more
sidebar “What’s new in this year’s analysis” on page 9; for capable robots, spurring greater innovation and a wider
more about the research itself, please see the sidebar range of deployments.
“Research methodology” on pages 10–11.)
About the McKinsey Technology Council
Technology is a catalyst for new opportunities, from inventing new products and services,
expanding the productivity frontier and capturing more value in our day-to-day work. The
McKinsey Technology Council helps business leaders understand frontier technologies and the
potential application to their businesses.
We look at a spectrum of technologies, from generative AI, machine learning, and quantum
computing to space technologies that are shaping new opportunities and applications. The McKinsey
Technology Council convenes a global group of more than 100 scientists, entrepreneurs, researchers,
and business leaders. We research, debate, and advise executives from all industries as they navigate the
fast-changing technology landscape.
—Lareina Yee, senior partner, McKinsey; chair, McKinsey Technology Council
Technology Trends Outlook 2024 4
−26% Electrification and renewables was the other learning solutions. Applied AI and
trend that bucked the economic headwinds, industrializing machine learning, boosted by
posting the highest investment and interest the widening interest in gen AI, have seen
scores among all the trends we evaluated. the most significant uptick in innovation,
tech trends job postings Job postings for this sector also showed a reflected in the surge in publications and
modest increase. patents from 2022 to 2023. Meanwhile,
from 2022 to 2023
electrification and renewable-energy
Although many trends faced declines in
technologies continue to capture high
investment and hiring in 2023, the long-term
interest, reflected in news mentions and
−17% outlook remains positive. This optimism is
web searches. Their popularity is fueled
supported by the continued longer-term
by a surge in global renewable capacity,
growth in job postings for the analyzed
their crucial roles in global decarbonization
trends (up 8 percent from 2021 to 2023)
efforts, and heightened energy security
global job postings and enterprises’ continued innovation and
needs amid geopolitical tensions and
from 2022 to 2023 heightened interest in harnessing these
energy crises.
technologies, particularly for future growth.
The talent environment largely echoed the
In 2023, technology equity investments
investment picture in tech trends in 2023.
+8% fell by 30 to 40 percent to approximately
The technology sector faced significant
$570 billion due to rising financing costs
layoffs, particularly among large technology
and a cautious near-term growth outlook,
companies, with job postings related to
prompting investors to favor technologies
the tech trends we studied declining by
tech trends job postings with strong revenue and margin potential.
26 percent—a steeper drop than the
This approach aligns with the strategic
from 2021 to 2023
17 percent decrease in global job postings
perspective leading companies are
overall. The greater decline in demand for
adopting, in which they recognize that
tech-trends-related talent may have been
fully adopting and scaling cutting-edge
fueled by technology companies’ cost
technologies is a long-term endeavor. This
reduction efforts amid decreasing revenue
recognition is evident when companies
growth projections. Despite this reduction,
diversify their investments across a
the trends with robust investment and
portfolio of several technologies, selectively
innovation, such as generative AI, not only
intensifying their focus on areas most likely
maintained but also increased their job
to push technological boundaries forward.
postings, reflecting a strong demand for
While many technologies have maintained
new and advanced skills. Electrification and
cautious investment profiles over the past
renewables was the other trend that saw
year, gen AI saw a sevenfold increase
positive job growth, partially due to public
in investments, driven by substantial
sector support for infrastructure spending.
advancements in text, image, and video
generation. Even with the short-term vicissitudes in
talent demand, our analysis of 4.3 million
Despite an overall downturn in private
job postings across our 15 tech trends
equity investment, the pace of innovation
underscored a wide skills gap. Compared
has not slowed. Innovation has accelerated
with the global average, fewer than half of
in the three trends that are part of the “AI
potential candidates have the high-demand
revolution” group: generative AI, applied AI,
tech skills specified in job postings. Despite
and industrializing machine learning. Gen
the year-on-year decreases for job postings
AI creates new content from unstructured
in many trends from 2022 to 2023, the
data (such as text and images), applied
number of tech-related job postings in 2023
AI leverages machine learning models
still represented an 8 percent increase from
for analytical and predictive tasks, and
2021, suggesting the potential for longer-
industrializing machine learning accelerates
term growth (Exhibit 1).
and derisks the development of machine
Technology Trends Outlook 2024 5
Exhibit 1
Despite a one-year drop in job postings, demand for jobs in many
technology trends has increased over two years.
Annual change in tech trend job postings, 2021–23, millions of postings¹
AI revolution Building the digital future Compute and connectivity Cutting-edge engineering A sustainable world
2021 2022 2023
1.4 1.4
1.2 1.2
1.0 1.0
0.8 +52% –37% 0.8
change
0.6 0.6
+33% –29%
0.4 0.4
+34% –11% –5%
+55% +1%
0.2 +72% 0.2
2021 2023
0 0
Next-generation Applied AI Climate technologies Future of mobility Electrification
software beyond electrification and renewables
development and renewables
0.6 0.6
0.4 0.4
++4499%% ––3344%% ++3399%% ––3388%% +32% –24% +55% –36%
0.2 ++7777%% ––3366%% 0.2
0 0
Digital trust and Cloud and edge Industrializing Advanced Immersive-reality
cybersecurity computing machine learning connectivity technologies
+6% –23% +29% –9% +110% +111% +29% –20% +44% –17%
0.2 0.2
0 0
Future of Future of space Generative Future of Quantum
bioengineering technologies AI robotics technologies
Cumulative change in tech trend job postings, 2021–23, millions of postings¹
2021 2023
1.0 1.0
–5% change
0.8 0.8
–6% +20% +48% +73%
0.6 0.6
0.4 0.4
0.2 0.2
2021 2023
0 0
Next-generation Applied AI Climate technologies Future of mobility Electrification
software beyond electrification and renewables
development and renewables
0.4 0.4
+0% –1%
0.2 –1% –14% +14% 0.2
0 0
Digital trust and Cloud and edge Industrializing Advanced Immersive-reality
cybersecurity computing machine learning connectivity technologies
–18% +18% +341% +3% +19%
0.2 0.2
0 0
Future of Future of space Generative Future of Quantum
bioengineering technologies AI robotics technologies
1Out of 130 million surveyed job postings (extrapolated Jan–Oct 2023). Job postings are not directly equivalent to numbers of new or existing jobs.
Source: McKinsey’s proprietary Organizational Data Platform, which draws on licensed, de-identified public professional profile data
McKinsey & Company
Technology Trends Outlook 2024 6
Enterprise technology
and space. Factors that could affect the adoption of
adoption momentum
these technologies include high costs, specialized
applications, and balancing the breadth of technology
The trajectory of enterprise technology adoption
investments against focusing on a select few that may
is often described as an S-curve that traces the
offer substantial first-mover advantages.
following pattern: technical innovation and exploration,
experimenting with the technology, initial pilots in As technologies gain traction and move beyond
the business, scaling the impact throughout the experimenting, adoption rates start accelerating, and
business, and eventual fully scaled adoption (Exhibit companies invest more in piloting and scaling. We see
2). This pattern is evident in this year’s survey analysis this shift in a number of trends, such as next-generation
of enterprise adoption conducted across our 15 software development and electrification. Gen AI’s rapid
technologies. Adoption levels vary across different advancement leads among trends analyzed, with about
industries and company sizes, as does the perceived a quarter of respondents self-reporting that they are
progress toward adoption. scaling its use. More mature technologies, like cloud
and edge computing and advanced connectivity,
We see that the technologies in the S-curve’s early
continued their rapid pace of adoption, serving
stages of innovation and experimenting are either
as enablers for the adoption of other emerging
on the leading edge of progress, such as quantum
technologies as well (Exhibit 3).
technologies and robotics, or are more relevant to
a specific set of industries, such as bioengineering
Web <2024>
<ETxehchibTrietn 2ds-L0>
Exhibit <3> of <3>
Technologies progress through different stages, with some at the leading
edge of innovation and others approaching large-scale adoption.
Adoption curve of technology trends, adoption score
Higher adoption
5 Fully scaled
4
Advanced connectivity
Applied AI
Cloud and edge computing
Generative AI
3
4 Scaling
Digital trust and cybersecurity
Electrification and renewables
Industrializing machine learning
Adoption
Next-gen software development
2
Climate technologies beyond
3 Piloting
electrification and renewables
Future of bioengineering¹
Future of mobility¹
2 Experimenting
1 Frontier Future of robotics¹
innovation Immersive-reality technologies
1
Future of space technologies¹
Quantum technologies
Lower adoption
¹Trend is more relevant to certain industries, resulting in lower overall adoption across industries compared with adoption within relevant industries.
Source: McKinsey technology adoption survey data; McKinsey analysis
McKinsey & Company
Technology Trends Outlook 2024 7
Web <2024>
<ETxehchibTrietn 3ds-L1>
Exhibit <2> of <3>
More-mature technologies are more widely adopted, often serving as
enablers for more-nascent technologies.
Self-reported adoption level by tech trend, 2023,1 % of respondents
Not investing Experimenting Piloting Scaling Fully scaled
Cloud and edge computing 25 14 13 26 22
Advanced connectivity 33 14 16 20 17
Generative AI2 26 18 20 26 10
Applied AI 26 18 21 24 11
Next-generation software development 37 14 18 23 8
Digital trust and cybersecurity 37 18 15 20 10
Electrification and renewables 37 17 19 20 7
Industrializing machine learning 37 16 20 19 8
Future of mobility 45 18 16 16 5
Climate technologies beyond electrification and
46 16 18 15 5
renewables
Immersive-reality technologies 43 18 20 15 4
Future of bioengineering 50 17 15 15 3
Future of robotics 41 22 19 13 5
Quantum technologies 47 18 20 15
Future of space technologies 57 15 13 12 3
1Respondents may interpret these categories differently based on their organizations. As such, the results should be considered as indicative of organizations’
self-assessments, rather than precise measurements. 2For a deeper look at our AI-related trends, see “The state of AI in early 2024: Gen AI adoption spikes
and starts to generate value,” McKinsey, May 30, 2024.
Source: McKinsey technology adoption survey data
McKinsey & Company
The process of scaling technology adoption also the external ecosystem conditions to ensure the
requires a conducive external ecosystem where user successful integration of new technologies into
trust and readiness, business model economics, their business models. Executives should monitor
regulatory environments, and talent availability play ecosystem conditions that can affect their prioritized
crucial roles. Since these ecosystem factors vary by use cases to make decisions about the appropriate
geography and industry, we see different adoption investment levels while navigating uncertainties and
scenarios playing out. For instance, while the leading budgetary constraints on the way to full adoption (see
banks in Latin America are on par with their North the “Adoption developments across the globe” sections
American counterparts in deploying gen AI use cases, within each trend that showcase examples of adoption
the adoption of robotics in manufacturing sectors varies dimensions for the trends or particular use cases therein
significantly due to differing labor costs affecting the that executives should monitor). Across the board,
business case for automation. leaders who take a long-term view—building up their
talent, testing and learning where impact can be found,
As executives navigate these complexities, they
and reimagining the businesses for the future—can
should align their long-term technology adoption
potentially break out ahead of the pack.
strategies with both their internal capacities and
Technology Trends Outlook 2024 8
The 15 tech trends
What’s new in this year’s analysis
This report lays out considerations for all 15 technology
This year, we reflected the shifts in the
trends. For easier consideration of related trends,
technology landscape with two changes on the
we grouped them into five broader categories: the AI
list of trends: digital trust and cybersecurity
revolution, building the digital future, compute and
(integrating what we had previously described
connectivity frontiers, cutting-edge engineering, and a
as Web3 and trust architectures) and the future
sustainable world. Of course, there’s significant power
of robotics. Robotics technologies’ synergy
and potential in looking across these groupings when
with AI is paving the way for groundbreaking
considering trend combinations.
innovations and operational shifts across the
To describe the state of each trend, we developed scores economic and workforce landscapes. We also
for innovation (based on patents and research) and deployed a survey to measure adoption levels
interest (based on news and web searches). We also across trends.
sized investments in relevant technologies and rated
their level of adoption by organizations (Exhibit 4).
Exhibit 4
Each trend is scored based on its level of innovation, interest, investment,
and adoption.
Innovation, interest, investment, and adoption, by technology trend, 2023
1.0
Adoption level, score
Applied AI (1 = frontier innovation;
5 = fully scaled)
0.8 1 2 3 4 5
Industrializing machine learning
Advanced connectivity
0.6
Future of bioengineering
Innovation,1 score
(0 = lower; Next-generation software development
1 = higher)
Cloud and edge computing
0.4 Immersive-reality technologies
Electrification/
Future of renewables
mobility
Climate technologies beyond electrification and renewables
0.2
Digital trust and cybersecurity Equity investment, $ billion
Future of robotics
Future of space technologies
Generative AI
Quantum technologies
250 150 75 20
0
0 1.00
0 0.2 0.4 0.6 0.8 1.0
Interest,2 score
(0 = lower; 1 = higher)
Note: Innovation and interest scores for the 15 trends are relative to one another. All 15 trends exhibit high levels of innovation and interest compared with
other topics and are also attracting significant investment.
1The innovation score combines the 0–1 scores for patents and research, which are relative to the trends studied. The patents score is based on a measure
of patent filings, and the research score is based on a measure of research publications.
2The interest score combines the 0–1 scores for news and searches, which are relative to the trends studied. The news score is based on a measure of news
publications, and the searches score is based on a measure of search engine queries.
McKinsey & Company
Technology Trends Outlook 2024 9
Research methodology
To assess the development of each technology trend, our team collected data on five tangible
measures of activity: search engine queries, news publications, patents, research publications,
and investment. For each measure, we used a defined set of data sources to find occurrences of
keywords associated with each of the 15 trends, screened those occurrences for valid mentions
of activity, and indexed the resulting numbers of mentions on a 0–1 scoring scale that is relative
to the trends studied. The innovation score combines the patents and research scores; the
interest score combines the news and search scores. (While we recognize that an interest
score can be inflated by deliberate efforts to stimulate news and search activity, we believe that
each score fairly reflects the extent of discussion and debate about a given trend.) Investment
measures the flows of funding from the capital markets into companies linked with the trend.
Data sources for the scores include the following:
— Patents. Data on patent filings are sourced from Google Patents, where the data highlight
the number of granted patents.
— Research. Data on research publications are sourced from Lens.
— News. Data on news publications are sourced from Factiva.
— Searches. Data on search engine queries are sourced from Google Trends.
— Investment. Data on private-market and public-market capital raises (venture capital and
corporate and strategic M&A, including joint ventures), private equity (including buyouts and
private investment in public equity), and public investments (including IPOs) are sourced from
PitchBook.
— Talent demand. Number of job postings is sourced from McKinsey’s proprietary
Organizational Data Platform, which stores licensed, de-identified data on professional
profiles and job postings. Data are drawn primarily from English-speaking countries.
In addition, we updated the selection and definition of trends from last year’s report to reflect
the evolution of technology trends:
— The future of robotics trend was added since last year’s publication.
— Data sources and keywords were updated. For data on the future of space technologies
investments, we used research from McKinsey’s Aerospace & Defense Practice.
Technology Trends Outlook 2024 10
Research methodology
(continued)
Finally, we used survey data to calculate the enterprise-wide adoption scores for each trend:
— Survey scope. The survey included approximately 1,000 respondents from 50 countries.
— Geographical coverage. Survey representation was balanced across Africa, Asia, Europe,
Latin America, the Middle East, and North America.
— Company size. Size categories, based on annual revenue, included small companies
($10 million to $50 million), medium-size companies ($50 million to $1 billion), and large
companies (greater than $1 billion).
— Respondent profile. The survey was targeted to senior-level professionals knowledgeable
in technology, who reported their perception of the extent to which their organizations were
using the technologies.
— Survey method. The survey was conducted online to enhance reach and accessibility.
— Question types. The survey employed multiple-choice and open-ended questions for
comprehensive insights.
— Definition of enterprise-wide adoption scores:
• 1: Frontier innovation. This technology is still nascent, with few organizations investing in or
applying it. It is largely untested and unproven in a business context.
• 2: Experimentation. Organizations are testing the functionality and viability of the
technology with a small-scale prototype, typically done without a strong focus on a near-
term ROI. Few companies are scaling or have fully scaled the technology.
• 3: Piloting. Organizations are implementing the technology for the first few business use
cases. It may be used in pilot projects or limited deployments to test its feasibility and
effectiveness.
• 4: Scaling. Organizations are in the process of scaling the deployment and adoption of the
technology across the enterprise. The technology is being scaled by a significant number
of companies.
• 5: Fully scaled. Organizations have fully deployed and integrated the technology across
the enterprise. It has become the standard and is being used at a large scale as companies
have recognized the value and benefits of the technology.
Technology Trends Outlook 2024 11
About the authors
Lareina Yee Michael Chui Roger Roberts Mena Issler
Senior partner, Bay Area; chair, McKinsey Global Institute Partner, Associate partner,
McKinsey Technology Council partner, Bay Area Bay Area Bay Area
The authors wish to thank the following McKinsey colleagues for their contributions to this research:
Aakanksha Srinivasan Carlo Giovine Joshua Katz Noah Furlonge-Walker
Ahsan Saeed Celine Crenshaw Julia Perry Obi Ezekoye
Alex Arutyunyants Daniel Herde Julian Sevillano Paolo Spranzi
Alex Singla Daniel Wallance Justin Greis Pepe Cafferata
Alex Zhang David Harvey Kersten Heineke Robin Riedel
Alizee Acket-Goemaere Delphine Zurkiya Kitti Lakner Ryan Brukardt
An Yan Diego Hernandez Diaz Kristen Jennings Samuel Musmanno
Anass Bensrhir Douglas Merrill Liz Grennan Santiago Comella-Dorda
Andrea Del Miglio Elisa Becker-Foss Luke Thomas Sebastian Mayer
Andreas Breiter Emma Parry Maria Pogosyan Shakeel Kalidas
Ani Kelkar Eric Hazan Mark Patel Sharmila Bhide
Anna Massey Erika Stanzl Martin Harrysson Stephen Xu
Anna Orthofer Everett Santana Martin Wrulich Tanmay Bhatnagar
Arjit Mehta Giacomo Gatto Martina Gschwendtner Thomas Hundertmark
Arjita Bhan Grace W Chen Massimo Mazza Tinan Goli
Asaf Somekh Hamza Khan Matej Macak Tom Brennan
Begum Ortaoglu Harshit Jain Matt Higginson Tom Levin-Reid
Benjamin Braverman Helen Wu Matt Linderman Tony Hansen
Bharat Bahl Henning Soller Matteo Cutrera Vinayak HV
Bharath Aiyer Ian de Bode Mellen Masea Yaron Haviv
Bhargs Srivathsan Jackson Pentz Michiel Nivard Yvonne Ferrier
Brian Constantine Jeffrey Caso Mike Westover Zina Cole
Brooke Stokes Jesse Klempner Musa Bilal
Bryan Richardson Jim Boehm Nicolas Bellemans
We appreciate the contributions of members of QuantumBlack, AI by McKinsey, to the insights on the AI-related trends.
They also wish to thank the external members of the McKinsey Technology Council for their insights and perspectives,
including Ajay Agrawal, Azeem Azhar, Ben Lorica, Benedict Evans, John Martinis, and Jordan Jacobs.
Technology Trends Outlook 2024 12
The AI revolution
Technology Trends Outlook 2024 13
Generative AI
The trend—and why it matters revolutionized, with models such as Suno creating original pieces
in various styles.
Generative AI (gen AI) has been making significant strides,
pushing the boundaries of machine capabilities. Gen AI models Gen AI has sparked widespread interest, with individuals and
are trained on vast, diverse data sets. They take unstructured organizations across different regions and industries exploring its
data, such as text, as inputs and produce unique outputs—also potential. According to the latest McKinsey Global Survey on the
in the form of unstructured data—ranging from text and code to state of AI, 65 percent of respondents say their organizations are
images, music, and 3D models. regularly using gen AI in at least one business function, up from
one-third last year,1 and gen AI use cases have the potential to
Over the past year, we’ve seen remarkable advancements in
generate an annual value of $2.6 trillion to $4.4 trillion.2
this field, with text generation models such as OpenAI’s GPT-4,
Anthropic’s Claude, and Google’s Gemini producing content that However, it’s important to recognize the risks that accompany the
mimics human-generated responses, as well as with image- use of this powerful technology, including bias, misinformation, and
generation tools such as DALL-E 3 and Midjourney creating deepfakes. As we progress through 2024 and beyond, we anticipate
photorealistic images from text descriptions. OpenAI’s recent organizations investing in the risk mitigation, operating model,
launch of Sora, a text-to-video generator, further showcases talent, and technological capabilities required to scale gen AI.
the technology’s potential. Even music composition is being
1 “The state of AI in early 2024: Gen AI adoption spikes and starts to generate value,” McKinsey, May 30, 2024.
2 The economic potential of generative AI: The next productivity frontier, McKinsey, June 14, 2023.
THE AI REVOLUTION Score by vector (0 = lower; 1 = higher)
Generative AI
Talent demand News
Scoring the trend
Gen AI saw a surge in 2023, driven by ChatGPT’s
late-2022 launch, alongside earlier models such as
DALL-E 2 and Stable Diffusion. Gen AI saw significant
growth from 2022 to 2023 across each quantitative
dimension, such as a sevenfold increase in the number Equity Searches
of searches and investments, reflecting a strong sense investment 0.2
0.4
of excitement about the trend.
0.6
0.8
Adoption score, 2023 1.0
Frontier Fully
innovation scaled
Patents Research
1 2 3 4 5
1.0
Equity investment, Job postings,
2023, 2022–23, 0
2019 2023
$ billion % difference
$36 +111%
Industries affected: Aerospace and defense;
Talent demand Ratio News Press reports
Agriculture; Automotive and assembly; Aviation,
of skilled people featuring trend-
travel, and logistics; Business, legal, and profes-
to job vacancies related phrases
sional services; Chemicals; Construction and
building materials; Consumer packaged goods; Equity investment Searches Search
Education; Electric power, natural gas, and utilities;
Private- and public- engine queries for
Financial services; Healthcare systems and
market capital raises for terms related to
services; Information technology and electronics;
relevant technologies trend
Media and entertainment; Metals and mining; Oil
and gas; Pharmaceuticals and medical products; Patents Patent Research Scientific
Public and social sectors; Real estate; Retail; filings for technologies publications on topics
Semiconductors; Telecommunications
related to trend associated with trend
Technology Trends Outlook 2024 14
Latest developments — LLMs are increasingly being embedded into various
enterprise tools. We are witnessing a significant uptick
Gen AI is a fast-growing and constantly innovating trend,
in the integration of LLMs into various enterprise
with recent developments including the following:
tools. This surge is fueled by the growing demand for
— Multimodal generative models are on the rise. As automation, efficiency, personalized user experiences,
gen AI continues to evolve and gain more attention in and the capacity to decipher complex patterns that
various industries, it’s becoming increasingly clear that can lead to actionable insights. Consequently, a rising
multimodality will play a pivotal role. By combining text, number of vendors are choosing to integrate LLMs into
images, sounds, and videos, AI models can generate their applications and tools. This trend is especially
outputs applicable across a wide range of industries prominent in the marketing and customer care domains,
and business functions. This pursuit of multimodality with Salesforce Einstein and ServiceNow serving as
is intensifying across leading players such as OpenAI prime examples.
and Google (with its Lumiere AI web app). For example,
— The multiagent approach has gained significant traction
Google’s Gemini showcases a powerful multimodal
with the rapid development of LLMs and continued
system capable of processing information in various
innovation. Companies now recognize the benefits
formats, including text, code, tables, images, and
of employing multiple language models that work in
even audio.
harmony rather than relying on a single model. This
— Powerful open-source models are challenging their approach offers a fresh perspective on tackling complex
closed-source counterparts in performance and challenges by leveraging the capabilities of multiple AI
developer adoption. While significant investments agents, each specializing in different domains, to solve
are encouraging the development of proprietary large a single problem collaboratively. By working together,
language models (LLMs), such as GPT-4 with vision these agents can not only accelerate problem-solving
(GPT-4V), the AI community is also witnessing a surge in but also leverage varied perspectives and expertise to
open-source models, such as Llama 3. This momentum deliver more effective and efficient solutions. Some
is fueled by the enthusiasm of developers and users of the tools using this approach tend to be unstable,
who welcome the unprecedented access to build but as models improve, their throughput s"
262,mckinsey,the-economic-potential-of-generative-ai-the-next-productivity-frontier.pdf,"The
economic
potential
of
generative
AI
The economic
potential of
generative AI
The next productivity frontier
June 2023
Authors
Michael Chui
Eric Hazan
Roger Roberts
Alex Singla
Kate Smaje
Alex Sukharevsky
Lareina Yee
Rodney Zemmel
ii The economic potential of generative AI: The next productivity frontier
Contents
Key insights Spotlight: Pharmaceuticals
3 and medical products
30
Chapter 1: Generative AI
as a technology catalyst Chapter 3: The generative
4 AI future of work: Impacts
on work activities, economic
Glossary growth, and productivity
6 32
Chapter 2: Generative AI use Chapter 4: Considerations
cases across functions and for businesses and society
industries 48
8
Appendix
Spotlight: Retail and 53
consumer packaged goods
27
Spotlight: Banking
28
The economic potential of generative AI: The next productivity frontier 1
2 The economic potential of generative AI: The next productivity frontier
Key insights
1. Generative AI’s impact on equal to an additional $200 billion 6. Generative AI can substantially
productivity could add trillions to $340 billion annually if the use increase labor productivity across
of dollars in value to the global cases were fully implemented. In the economy, but that will require
economy. Our latest research retail and consumer packaged investments to support workers
estimates that generative AI could goods, the potential impact is also as they shift work activities or
add the equivalent of $2.6 trillion significant at $400 billion to $660 change jobs. Generative AI could
to $4.4 trillion annually across the billion a year. enable labor productivity growth
63 use cases we analyzed—by of 0.1 to 0.6 percent annually
comparison, the United Kingdom’s 4. Generative AI has the potential through 2040, depending on the
entire GDP in 2021 was $3.1 trillion. to change the anatomy of work, rate of technology adoption and
This would increase the impact of augmenting the capabilities of redeployment of worker time
all artificial intelligence by 15 to individual workers by automating into other activities. Combining
40 percent. This estimate would some of their individual activities. generative AI with all other
roughly double if we include the Current generative AI and other technologies, work automation
impact of embedding generative AI technologies have the potential to could add 0.5 to 3.4 percentage
into software that is currently used automate work activities that absorb points annually to productivity
for other tasks beyond those use 60 to 70 percent of employees’ time growth. However, workers will need
cases. today. In contrast, we previously support in learning new skills, and
estimated that technology has the some will change occupations. If
2. About 75 percent of the value that potential to automate half of the worker transitions and other risks
generative AI use cases could time employees spend working.1 can be managed, generative AI
deliver falls across four areas: The acceleration in the potential for could contribute substantively to
Customer operations, marketing technical automation is largely due economic growth and support a
and sales, software engineering, to generative AI’s increased ability more sustainable, inclusive world.
and R&D. Across 16 business to understand natural language,
functions, we examined 63 use which is required for work activities 7. The era of generative AI is just
cases in which the technology that account for 25 percent of total beginning. Excitement over this
can address specific business work time. Thus, generative AI has technology is palpable, and early
challenges in ways that produce more impact on knowledge work pilots are compelling. But a full
one or more measurable outcomes. associated with occupations that realization of the technology’s
Examples include generative AI’s have higher wages and educational benefits will take time, and leaders
ability to support interactions requirements than on other types in business and society still
with customers, generate creative of work. have considerable challenges to
content for marketing and sales, address. These include managing
and draft computer code based on 5. The pace of workforce the risks inherent in generative
natural-language prompts, among transformation is likely to AI, determining what new skills
many other tasks. accelerate, given increases in the and capabilities the workforce will
potential for technical automation. need, and rethinking core business
3. Generative AI will have a significant Our updated adoption scenarios, processes such as retraining and
impact across all industry sectors. including technology development, developing new skills.
Banking, high tech, and life economic feasibility, and diffusion
sciences are among the industries timelines, lead to estimates that
that could see the biggest impact half of today’s work activities could
as a percentage of their revenues be automated between 2030 and
from generative AI. Across the 2060, with a midpoint in 2045, or
banking industry, for example, the roughly a decade earlier than in our
technology could deliver value previous estimates.
The economic potential of generative AI: The next productivity frontier 3
1
Generative AI as a
technology catalyst
To grasp what lies ahead requires an understanding of the breakthroughs that have enabled
the rise of generative AI, which were decades in the making. ChatGPT, GitHub Copilot, Stable
Diffusion, and other generative AI tools that have captured current public attention are the
result of significant levels of investment in recent years that have helped advance machine
learning and deep learning. This investment undergirds the AI applications embedded in many
of the products and services we use every day.
But because AI has permeated our lives incrementally—through everything from the tech
powering our smartphones to autonomous-driving features on cars to the tools retailers use
to surprise and delight consumers—its progress was almost imperceptible. Clear milestones,
such as when AlphaGo, an AI-based program developed by DeepMind, defeated a world
champion Go player in 2016, were celebrated but then quickly faded from the public’s
consciousness.
ChatGPT and its competitors have captured the imagination of people around the world
in a way AlphaGo did not, thanks to their broad utility—almost anyone can use them to
communicate and create—and preternatural ability to have a conversation with a user.
The latest generative AI applications can perform a range of routine tasks, such as the
reorganization and classification of data. But it is their ability to write text, compose music,
and create digital art that has garnered headlines and persuaded consumers and households
to experiment on their own. As a result, a broader set of stakeholders are grappling with
generative AI’s impact on business and society but without much context to help them make
sense of it.
4 The economic potential of generative AI: The next productivity frontier
How did we get here? Gradually, then all of a sudden
For the purposes of this report, we define generative AI as applications typically built using
foundation models. These models contain expansive artificial neural networks inspired by the
billions of neurons connected in the human brain. Foundation models are part of what is called
deep learning, a term that alludes to the many deep layers within neural networks. Deep
learning has powered many of the recent advances in AI, but the foundation models powering
generative AI applications are a step change evolution within deep learning. Unlike previous
deep learning models, they can process extremely large and varied sets of unstructured data
and perform more than one task.
Foundation models have enabled new capabilities and vastly improved existing ones across
a broad range of modalities, including images, video, audio, and computer code. AI trained
on these models can perform several functions; it can classify, edit, summarize, answer
questions, and draft new content, among other tasks.
Continued innovation will also bring new challenges. For example, the computational power
required to train generative AI with hundreds of billions of parameters threatens to become a
bottleneck in development.2 Further, there’s a significant move—spearheaded by the open-
source community and spreading to the leaders of generative AI companies themselves—to
make AI more responsible, which could increase its costs.
Nonetheless, funding for generative AI, though still a fraction of total investments in artificial
intelligence, is significant and growing rapidly—reaching a total of $12 billion in the first five
months of 2023 alone. Venture capital and other private external investments in generative
AI increased by an average compound growth rate of 74 percent annually from 2017 to 2022.
During the same period, investments in artificial intelligence overall rose annually by 29
percent, albeit from a higher base.
The rush to throw money at all things generative AI reflects how quickly its capabilities have
developed. ChatGPT was released in November 2022. Four months later, OpenAI released
a new large language model, or LLM, called GPT-4 with markedly improved capabilities.3
Similarly, by May 2023, Anthropic’s generative AI, Claude, was able to process 100,000
tokens of text, equal to about 75,000 words in a minute—the length of the average novel—
compared with roughly 9,000 tokens when it was introduced in March 2023.4 And in May
2023, Google announced several new features powered by generative AI, including Search
Generative Experience and a new LLM called PaLM 2 that will power its Bard chatbot, among
other Google products.5
From a geographic perspective, external private investment in generative AI, mostly from
tech giants and venture capital firms, is largely concentrated in North America, reflecting the
continent’s current domination of the overall AI investment landscape. Generative AI–related
companies based in the United States raised about $8 billion from 2020 to 2022, accounting
for 75 percent of total investments in such companies during that period.6
Generative AI has stunned and excited the world with its potential for reshaping how
knowledge work gets done in industries and business functions across the entire economy.
Across functions such as sales and marketing, customer operations, and software
development, it is poised to transform roles and boost performance. In the process, it could
unlock trillions of dollars in value across sectors from banking to life sciences. We have used
two overlapping lenses in this report to understand the potential for generative AI to create
value for companies and alter the workforce. The following sections share our initial findings.
The economic potential of generative AI: The next productivity frontier 5
Glossary
Application programming interface (API) is a way to programmatically access (usually
external) models, data sets, or other pieces of software.
Artificial intelligence (AI) is the ability of software to perform tasks that traditionally require
human intelligence.
Artificial neural networks (ANNs) are composed of interconnected layers of software-based
calculators known as “neurons.” These networks can absorb vast amounts of input data and
process that data through multiple layers that extract and learn the data’s features.
Deep learning is a subset of machine learning that uses deep neural networks, which are
layers of connected “neurons” whose connections have parameters or weights that can be
trained. It is especially effective at learning from unstructured data such as images, text, and
audio.
Early and late scenarios are the extreme scenarios of our work-automation model. The
“earliest” scenario flexes all parameters to the extremes of plausible assumptions, resulting in
faster automation development and adoption, and the “latest” scenario flexes all parameters
in the opposite direction. The reality is likely to fall somewhere between the two.
Fine-tuning is the process of adapting a pretrained foundation model to perform better in
a specific task. This entails a relatively short period of training on a labeled data set, which
is much smaller than the data set the model was initially trained on. This additional training
allows the model to learn and adapt to the nuances, terminology, and specific patterns found
in the smaller data set.
Foundation models (FM) are deep learning models trained on vast quantities of
unstructured, unlabeled data that can be used for a wide range of tasks out of the box or
adapted to specific tasks through fine-tuning. Examples of these models are GPT-4, PaLM,
DALL·E 2, and Stable Diffusion.
Generative AI is AI that is typically built using foundation models and has capabilities that
earlier AI did not have, such as the ability to generate content. Foundation models can also
be used for nongenerative purposes (for example, classifying user sentiment as negative or
positive based on call transcripts) while offering significant improvement over earlier models.
For simplicity, when we refer to generative AI in this article, we include all foundation model
use cases.
Graphics processing units (GPUs) are computer chips that were originally developed for
producing computer graphics (such as for video games) and are also useful for deep learning
applications. In contrast, traditional machine learning and other analyses usually run on
central processing units (CPUs), normally referred to as a computer’s “processor.”
Large language models (LLMs) make up a class of foundation models that can process
massive amounts of unstructured text and learn the relationships between words or portions
of words, known as tokens. This enables LLMs to generate natural-language text, performing
tasks such as summarization or knowledge extraction. GPT-4 (which underlies ChatGPT) and
LaMDA (the model behind Bard) are examples of LLMs.
6 The economic potential of generative AI: The next productivity frontier
Machine learning (ML) is a subset of AI in which a model gains capabilities after it is trained
on, or shown, many example data points. Machine learning algorithms detect patterns and
learn how to make predictions and recommendations by processing data and experiences,
rather than by receiving explicit programming instruction. The algorithms also adapt and can
become more effective in response to new data and experiences.
Modality is a high-level data category such as numbers, text, images, video, and audio.
Productivity from labor is the ratio of GDP to total hours worked in the economy. Labor
productivity growth comes from increases in the amount of capital available to each worker,
the education and experience of the workforce, and improvements in technology.
Prompt engineering refers to the process of designing, refining, and optimizing input
prompts to guide a generative AI model toward producing desired (that is, accurate) outputs.
Self-attention, sometimes called intra-attention, is a mechanism that aims to mimic cognitive
attention, relating different positions of a single sequence to compute a representation of the
sequence.
Structured data are tabular data (for example, organized in tables, databases, or
spreadsheets) that can be used to train some machine learning models effectively.
Transformers are a relatively new neural network architecture that relies on self-attention
mechanisms to transform a sequence of inputs into a sequence of outputs while focusing its
attention on important parts of the context around the inputs. Transformers do not rely on
convolutions or recurrent neural networks.
Technical automation potential refers to the share of the worktime that could be automated.
We assessed the technical potential for automation across the global economy through
an analysis of the component activities of each occupation. We used databases published
by institutions including the World Bank and the US Bureau of Labor Statistics to break
down about 850 occupations into approximately 2,100 activities, and we determined the
performance capabilities needed for each activity based on how humans currently perform
them.
Use cases are targeted applications to a specific business challenge that produces one
or more measurable outcomes. For example, in marketing, generative AI could be used to
generate creative content such as personalized emails.
Unstructured data lack a consistent format or structure (for example, text, images, and audio
files) and typically require more advanced techniques to extract insights.
The economic potential of generative AI: The next productivity frontier 7
2
Generative AI use
cases across functions
and industries
Generative AI is a step change in the evolution of artificial intelligence. As companies
rush to adapt and implement it, understanding the technology’s potential to deliver value
to the economy and society at large will help shape critical decisions. We have used two
complementary lenses to determine where generative AI with its current capabilities could
deliver the biggest value and how big that value could be (Exhibit 1).
8 The economic potential of generative AI: The next productivity frontier
Exhibit 1
The potential impact of generative AI can be evaluated through two lenses.
Lens 1 Lens 2
Total economic Labor productivity potential
potential of 60-plus across ~2,100 detailed work
organizational use activities performed by
cases1 global workforce
Cost impacts
of use cases
Revenue
impacts of
use cases1
1For quantitative analysis, revenue impacts were recast as productivity increases on the corresponding spend in order to maintain comparability with cost
impacts and not to assume additional growth in any particular market.
McKinsey & Company
The first lens scans use cases for generative AI that organizations could adopt. We define
a “use case” as a targeted application of generative AI to a specific business challenge,
resulting in one or more measurable outcomes. For example, a use case in marketing is the
application of generative AI to generate creative content such as personalized emails, the
measurable outcomes of which potentially include reductions in the cost of generating such
content and increases in revenue from the enhanced effectiveness of higher-quality content
at scale. We identified 63 generative AI use cases spanning 16 business functions that could
deliver total value in the range of $2.6 trillion to $4.4 trillion in economic benefits annually
when applied across industries.
That would add 15 to 40 percent to the $11.0 trillion to $17.7 trillion of economic value that we
now estimate nongenerative artificial intelligence and analytics could unlock. (Our previous
estimate from 2017 was that AI could deliver $9.5 trillion to $15.4 trillion in economic value.)
Our second lens complements the first by analyzing generative AI’s potential impact on
the work activities required in some 850 occupations. We modeled scenarios to estimate
when generative AI could perform each of more than 2,100 “detailed work activities”—
such as “communicating with others about operational plans or activities”—that make up
those occupations across the world economy. This enables us to estimate how the current
capabilities of generative AI could affect labor productivity across all work currently done by
the global workforce.
The economic potential of generative AI: The next productivity frontier 9
Some of this impact will overlap with cost reductions in the use case analysis described
above, which we assume are the result of improved labor productivity. Netting out this
overlap, the total economic benefits of generative AI—including the major use cases we
explored and the myriad increases in productivity that are likely to materialize when the
technology is applied across knowledge workers’ activities—amounts to $6.1 trillion to
$7.9 trillion annually (Exhibit 2).
Exhibit 2
Generative AI could create additional value potential above what
could be unlocked by other AI and analytics.
AI’s potential impact on the global economy, $ trillion
17.1–25.6
13.6–22.1
6.1–7.9
2.6–4.4
11.0–17.7
~15–40% ~35–70%
incremental incremental
economic impact economic impact
Advanced analytics, New generative Total use All worker productivity Total AI
traditional machine AI use cases case-driven enabled by generative economic
learning, and deep potential AI, including in use potential
learning1 cases
1Updated use case estimates from ""Notes from the AI frontier: Applications and value of deep learning,” McKinsey Global Institute, April 17, 2018.
McKinsey & Company
10 The economic potential of generative AI: The next productivity frontier
While generative AI is an exciting and rapidly advancing technology, the other applications of
AI discussed in our previous report continue to account for the majority of the overall potential
value of AI. Traditional advanced-analytics and machine learning algorithms are highly
effective at performing numerical and optimization tasks such as predictive modeling, and
they continue to find new applications in a wide range of industries. However, as generative AI
continues to develop and mature, it has the potential to open wholly new frontiers in creativity
and innovation. It has already expanded the possibilities of what AI overall can achieve (please
see Box 1, “How we estimated the value potential of generative AI use cases”).
Box 1
How we estimated the value potential of generative AI use cases
To assess the potential value of generative AI, a customer service use case but not in a use
we updated a proprietary McKinsey database of case optimizing a logistics network, where value
potential AI use cases and drew on the experience primarily arises from quantitative analysis.
of more than 100 experts in industries and their
We then estimated the potential annual value
business functions.1 Our updates examined
of these generative AI use cases if they were
use cases of generative AI—specifically, how
adopted across the entire economy. For use
generative AI techniques (primarily transformer-
cases aimed at increasing revenue, such as some
based neural networks) can be used to solve
of those in sales and marketing, we estimated
problems not well addressed by previous
the economy-wide value generative AI could
technologies.
deliver by increasing the productivity of sales and
We analyzed only use cases for which generative marketing expenditures.
AI could deliver a significant improvement in the
Our estimates are based on the structure of the
outputs that drive key value. In particular, our
global economy in 2022 and do not consider the
estimates of the primary value the technology
value generative AI could create if it produced
could unlock do not include use cases for which
entirely new product or service categories.
the sole benefit would be its ability to use natural
language. For example, natural-language
capabilities would be the key driver of value in
1 “Notes from the AI frontier: Applications and value of deep learning,” McKinsey Global Institute, April 17, 2018.
In this chapter, we highlight the value potential of generative AI across two dimensions:
business function and modality.
The economic potential of generative AI: The next productivity frontier 11
Value potential by function
While generative AI could have an impact on most business functions, a few stand out when
measured by the technology’s impact as a share of functional cost (Exhibit 3). Our analysis
of 16 business functions identified just four—customer operations, marketing and sales,
software engineering, and research and development—that could account for approximately
75 percent of the total annual value from generative AI use cases.
Web <2023>
E<Vxihvaitbeicth 3 full report>
Exhibit <3> of <16>
Using generative AI in just a few functions could drive most of the technology’s
impact across potential corporate use cases.
Represent ~75% of total annual impact of generative AI
500
Sales
Software engineering
Marketing
(for corporate IT)
Software engineering
(for product development)
400
Customer operations
Product R&D1
300
Impact, $ billion
Supply chain
200
Manufacturing
Finance Risk and compliance
Talent and organization (incl HR)
100
Procurement management
Corporate IT1 Legal
Strategy
Pricing
0
0 10 20 30 40
Impact as a percentage of functional spend, %
Note: Impact is averaged.
¹Excluding software engineering.
Source: Comparative Industry Service (CIS), IHS Markit; Oxford Economics; McKinsey Corporate and Business Functions database; McKinsey Manufacturing
and Supply Chain 360; McKinsey Sales Navigator; Ignite, a McKinsey database; McKinsey analysis
McKinsey & Company
Notably, the potential value of using generative AI for several functions that were prominent
in our previous sizing of AI use cases, including manufacturing and supply chain functions,
is now much lower.7 This is largely explained by the nature of generative AI use cases, which
exclude most of the numerical and optimization applications that were the main value drivers
for previous applications of AI.
12 The economic potential of generative AI: The next productivity frontier
Generative AI as a virtual expert
In addition to the potential value generative AI can deliver in function-specific use cases,
the technology could drive value across an entire organization by revolutionizing internal
knowledge management systems. Generative AI’s impressive command of natural-language
processing can help employees retrieve stored internal knowledge by formulating queries
in the same way they might ask a human a question and engage in continuing dialogue. This
could empower teams to quickly access relevant information, enabling them to rapidly make
better-informed decisions and develop effective strategies.
In 2012, the McKinsey Global Institute (MGI) estimated that knowledge workers spent about
a fifth of their time, or one day each work week, searching for and gathering information. If
generative AI could take on such tasks, increasing the efficiency and effectiveness of the
workers doing them, the benefits would be huge. Such virtual expertise could rapidly “read”
vast libraries of corporate information stored in natural language and quickly scan source
material in dialogue with a human who helps fine-tune and tailor its research, a more scalable
solution than hiring a team of human experts for the task.
Following are examples of how generative AI could produce operational benefits as a virtual
expert in a handful of use cases.
In addition to the potential
value generative AI can
deliver in specific use
cases, the technology
could drive value across
an entire organization
by revolutionizing
internal knowledge
management systems.
The economic potential of generative AI: The next productivity frontier 13
How customer operations
could be transformed
Customer self-service interactions
Customer interacts with a humanlike chatbot that
delivers immediate, personalized responses to
complex inquiries, ensuring a consistent brand
voice regardless of customer language or location.
Customer–agent interactions
Human agent uses AI-developed call scripts and
receives real-time assistance and suggestions for
responses during phone conversations, instantly
accessing relevant customer data for tailored and
real-time information delivery.
Agent self-improvement
Agent receives a summarization of the conversation in
a few succinct points to create a record of customer
complaints and actions taken.
Agent uses automated, personalized insights generated
by AI, including tailored follow-up messages or
personalized coaching suggestions.
14 The economic potential of generative AI: The next productivity frontier
Customer operations
Generative AI has the potential to revolutionize the entire customer operations function,
improving the customer experience and agent productivity through digital self-service
and enhancing and augmenting agent skills. The technology has already gained traction
in customer service because of its ability to automate interactions with customers using
natural language. Research found that at one company with 5,000 customer service
agents, the application of generative AI increased issue resolution by 14 percent an hour and
reduced the time spent handling an issue by 9 percent.8 It also reduced agent attrition and
requests to speak to a manager by 25 percent. Crucially, productivity and quality of service
improved most among less-experienced agents, while the AI assistant did not increase—
and sometimes decreased—the productivity and quality metrics of more highly skilled
agents. This is because AI assistance helped less-experienced agents communicate using
techniques similar to those of their higher-skilled counterparts.
The following are examples of the operational improvements generative AI can have for
specific use cases:
— Customer self-service. Generative AI–fueled chatbots can give immediate and
personalized responses to complex customer inquiries regardless of the language or
location of the customer. By improving the quality and effectiveness of interactions via
automated channels, generative AI could automate responses to a higher percentage of
customer inquiries, enabling customer care teams to take on inquiries that can only be
resolved by a human agent. Our research found that roughly half of customer contacts
made by banking, telecommunications, and utilities companies in North America are
already handled by machines, including but not exclusively AI. We estimate that generative
AI could further reduce the volume of human-serviced contacts by up to 50 percent,
depending on a company’s existing level of automation.
— Resolution during initial contact. Generative AI can instantly retrieve data a company
has on a specific customer, which can help a human customer service representative more
successfully answer questions and resolve issues during an initial interaction.
— Reduced response time. Generative AI can cut the time a human sales representative
spends responding to a customer by providing assistance in real time and recommending
next steps.
— Increased sales. Because of its ability to rapidly process data on customers and their
browsing histories, the technology can identify product suggestions and deals tailored
to customer preferences. Additionally, generative AI can enhance quality assurance and
coaching by gathering insights from customer conversations, determining what could be
done better, and coaching agents.
We estimate that applying generative AI to customer care functions could increase
productivity at a value ranging from 30 to 45 percent of current function costs.
Our analysis captures only the direct impact generative AI might have on the productivity of
customer operations. It does not account for potential knock-on effects the technology may
have on customer satisfaction and retention arising from an improved experience, including
better understanding of the customer’s context that can assist human agents in providing
more personalized help and recommendations.
The economic potential of generative AI: The next productivity frontier 15
How marketing and sales
could be transformed
Strategization
Sales and marketing professionals efficiently
gather market trends and customer information
from unstructured data sources (for example,
social media, news, research, product information,
and customer feedback) and draft effective
marketing and sales communications.
Awareness
Customers see campaigns tailored
to their segment, language, and
demographic.
Consideration
Customers can access comprehensive information,
comparisons, and dynamic recommendations, such as
personal “try ons.”
16 The economic potential of generative AI: The next productivity frontier
Conversion
Virtual sales representatives enabled by generative
AI emulate humanlike qualities—such as empathy,
personalized communication, and natural language
processing—to build trust and rapport with
customers.
Retention
Customers are more likely to be retained with
customized messages and rewards, and they can
interact with AI-powered customer-support chatbots
that manage the relationship proactively, with fewer
escalations to human agents.
Marketing and sales
Generative AI has taken hold rapidly in marketing and sales functions, in which text-based
communications and personalization at scale are driving forces. The technology can create
personalized messages tailored to individual customer interests, preferences, and behaviors,
as well as do tasks such as producing first drafts of brand advertising, headlines, slogans,
social media posts, and product descriptions.
However, introducing generative AI to marketing functions requires careful cons"
263,accenture,Accenture-Mondelez-International-Abridged-Transcript-FINAL.pdf,"HOW MONDELĒZ
INTERNATIONAL USES
DATA AND AI TO
TRANSFORM THEIR
ENTERPRISE
VIDEO TRANSCRIPT
Venky Rao (00:23): Javier Polit (01:22):
What was the impetus for Mondelēz to The starting point is really spending time with
becoming more of a data driven data led the business, Venky, and really understanding,
company? when I first joined, I was listening and learning.
I'm still learning. I've only been with the
Javier Polit (00:28): company two and a half years or so now, but
I think it all starts with our goal at Mondelēz really understanding what the pain points were.
International is to be the leader of snacking. And And, and it really wasn't about just filling one
we need an even stronger growth strategy to hole. It was really trying to understand the
keep up with the pace, and even influence, our holistic opportunities that we had. And once we
consumer demand and our consumer behavior. had that defined, it was building that vision and
So, we really started to focus on a relentless that strategy and making certain that you got
consumer centricity in making certain that we support of the strategy by the C-suite, which
started to aggregate 360-degree insights of, of we did. And the executive team and the board
our consumers. And the time was right because were all behind us, and we started
we had been preparing from a business communicating that strategy to the enterprise.
perspective and also from a technology And that required a lot of work for us to do and
perspective. We had the right foundation in say we need to start investing in our people
place. The company was on a cloud strategy elevating capabilities, looking at the strategic
when I joined here, multi-cloud strategy, we partners that we were going to use, right?
brought in the Google Cloud platform. So, we Besides the Accenture’s and, and the
had that behind us, and the team was doing Microsoft’s and the Googles of the world and
some great work before I joined, and we finished other strategic partners. How we're going to
that work and, it gave us really time to pivot and firmly have the conversation that we had with
really start focusing on data and AI. you and all the other partners, bring your best
to us as we're continuing to try to be the best
Venky Rao (01:18): that we can and leverage partners as we're
How do you start a journey like that? Where do trying to build capability inside the enterprise
you start from?
and, and driving change in the enterprise as so that the whole enterprise feels good about
well. From a behavioral and work perspective. the work that you're trying to drive. And they
understand that there's a sense of importance
Venky Rao (02:23):
and urgency to what you're doing. And, and
You know, there is this old saying, you know,
when I talk about communicating a lot of, I have
what you track and what you measure actually
about 30 touch points with my organization on
gets done. So, when you get on a
an annual basis, and we talk about these things.
transformation journey like this, especially in this
And then the last thing is making certain that
space of AI, AI enabled, which is all new, how
do you start measuring success? you have a core strategic central AI data
science team that's really helping the
Javier Polit (02:37): organization. You can't have these silos in the
Yeah. Well, you know, you can't manage what enterprise where they're going on and building
you don't measure. We've all heard that, that their own data science and data strategies
nomenclature, I can tell you that we've had
without understanding that there's a holistic data
some really, really good maturity here over the
driven strategy that all that data needs to come
last 18 to 24 months in regards to how we track,
together, and somebody needs to be the
how we measure the ROI’s on work that we're
steward of that. And monitoring is the data
delivering to the, to the business and the value
inside the enterprise is data outside the
based on the business case that we initially put
together for the business. And, and through that enterprise what data needs to ingress or egress
work, what we're seeing now in all our business from different sources? And you just can't have
reviews that we have conversation around that working in a silo. So, I would say it's
digital comes up the work that's being driven probably those three dimensions.
around digital. And with that we talk about data
science, and we talk about the AI work that
Venky Rao (04:39):
that's being done, right? You know, you set a
So Javier, how do you see talent and tech
transformation strategy and a vision, and you
working together to achieve the Mondelēz
say, okay, it's a three-year horizon. I always say
vision?
that after the second year, you start figuring out
what your next three-year horizon's going to be.
So, it's, it's something that is just never done. Javier Polit (04:45):
Venky it’s just continuous work. Yeah, there, there's a lot of dimensions to that.
And I will tell you that, you know, we win with
Venky Rao (03:25): our people. Our people are our greatest asset.
Now, having said that, what are the most And we invest in our people in many different
important factors in making a transformation
ways and our people are critical to anything we
successful?
change or anything we make, you know, our
success is possible because it's 79,000
Javier Polit (03:31):
incredible colleagues that we have around the
Well, I think when, when you think about a
world. And some of the things that we're doing
transformation in any large enterprise, and I've
right now is as we continue to drive the
had the opportunity to do this a couple times, is
you need to have the right sponsorship. You importance of being data driven enterprise and
know, once you develop that strategy and that have an innovative culture, we're able to make
vision, making certain that the board, the those pivots and become a dynamic
executive team is behind it, and then you need organization. . We talk about being a dynamic
to communicate as much as possible and learning organization, right?
communicate that strategy and what you're
trying to do, and communicate the sponsorship
Where we, we are not a knowing culture, we're a and, and a brilliant one at that. So Javier, where
learning culture and we want to continue to do you see the whole AI Adoption space
innovate and take risk. And I think, you know, all evolving to?
that's done through sound leadership, but, but
it's also having the right partners at the table, Javier Polit (07:13):
right? And we firmly encourage our partners,
When you think about the evolution of AI today,
whether it's Accenture or whether it's Google or
companies are using narrow AI, right? It's taking
whether it's Microsoft and many others, to bring
the ability to have a human process be
the best that you have. And we've had those
conducted through AI with greater efficiency.
conversations too, bring the best to us and make
And you have companies that are adopting that
certain that we could really partner and do some
really industry leading things, right? So, it's really well, that are the 12% AI achievers and those
not something that you could do on your own, that are falling, following and, and a little bit
but you have to have a pool of experts inside the behind. And then the next level of AI is general
enterprise as well as the experts that your AI or human AI, where you have artificial
partners bring as well. intelligence that can basically do what a human
thinks. And the more complicated AI that's going
Venky Rao (06:01):
to be happening in the future, and it's happening
So Javier, how would you assess, CPG industry
in different parts of the world today, is super AI,
in terms of AI maturity compared to other
where AI can now do things better and in a
industries? I mean, especially, I know that you
smarter way than humans can. So, it's going to
look at the tech sector quite a bit, and, and you
take a lot of inspiration from how some of the big be an evolving space. We'll have to see how
technology companies operate. But how do you those technologies, when they come to be
see that evolving in the CPG industry? commonplace are going to be leveraged in in
different industries.
And, and they're already starting to be used in, in
Javier Polit (06:21): certain industries.
Yeah, you know, we're continuously doing
industry sensing in that space and see how we
Venky Rao (08:05):
match up to other CPG companies or fast-
So in closing, any thoughts, Javier as we wrap
moving consumer goods companies. But I think
up.
it's fair to say that the tech sector is still far
ahead. But I would also say in the same breath
that I think that the gap is narrowing and Javier Polit (08:08):
especially I think what's, what's helped us narrow You know, there’s probably an abundance of
that gap that that gap is companies really thoughts and because I think we'd all agree that
advancing their digital roadmaps in the digital it's a complicated space, but I think there's
plans, right? So, I think, there's enormous room
maybe six pillars of an AI strategy, right? And I
for growth in AI Adoption and AI Adoption across
always say start with the business value, right?
all industries. Every company's a tech company.
Define the trap business value and recognize the
We've heard that phrase. I always try to extend it
leverage that you need to unlock that growth for
and say, every company's a tech company and if
the business. And when you think about
you don't conduct yourself as such, you're just
not going to be successful. algorithms, which are the critical algorithms that
are going to solve the business value that was
Venky Rao (07:04): defined by the business, and when you think
Absolutely. And that's such a spot-on answer about algorithms today, it's a complicated world,
right? We need to make certain that they're
designed to scale and that they're unbiased
because we hear a lot of algorithms are being
defined with bias now. And we have to be very
cautious about that. And then you have to think
about data, right? Because you understand the
business now, you, you're built, you're defining
the algorithms are going to support that
business value that you're trying to capture.
And you got to look at the data and have a clear
first, second-, and third-party data strategy,
right? And make certain that you have a life
cycle around that data that to create signals of
value for the enterprise. The fourth area that I
would say is a platform strategy, making certain
that you have the right ecosystem, and we
talked about that earlier, making certain that you
have the right foundation of capabilities to
create and be able to manage inside the
enterprise. And then the ability to execute that
strategy, right? How should our enterprises be
organized to be able to execute that strategy
across the enterprise? And that means different
teams and different responsibilities and different
ways of working and different behaviors in the
enterprise. And then the greatest investment is,
is the sixht piece of this is focus on your talent
and the culture that you're building and how
you're going to continue to retain, attract and
engage those resources that are helping you
bring this value to life and this distinctive
capability that you're building in your enterprise.
Venky Rao (09:56):
Javier, that was an outstanding response and a
very, very good framework for everybody to
follow, right? So, thank you so much.
Javier Polit (10:02):
Venky that was a pleasure, thank you for the
time. Thank you for the partnership.
Copyright © 2023 Accenture
All rights reserved.
Accenture and its logo
are registered trademarks
of Accenture."
264,accenture,Accenture-Video-Transcript-Semi-Value-Chain-New-Approach-Gen.pdf,"GENERATIVE AI’S ROLE
IN THE
SEMICONDUCTOR
VIDEO TRANSCRIPT
Speaker B: Miles, we're seeing companies, as
Speaker A: Hello everyone, my name is Padam
you mentioned, really introduced off the shelf
and I lead data and AI in high tech industry at
solutions that enable back office productivity like
Accenture. Welcome to our live discussion on
Copilot and things of that nature. But what's
transformative impact of generative AI in
really going to catapult companies into really the
semiconductor industry. I'm joined by Miles, an
competitive space getting products to market
expert in semiconductor applications, and today
faster is utilizing Gen AI functionality across the
we are driving into how generative AI is
entire product design lifecycle. So it's not only
revolutionizing the sector.
bringing products to market faster, but it's how
can we use not only LLMs but LMMs in the
Speaker B: Yeah, thanks Padam, thanks for
design process to create more efficient and
having me. I lead our Gen AI and AI for or semi
optimized products for their customers? So far, I
globally. Let's just hop right into it. Padam, could
was hoping you could talk a little bit about how
you start us off by sharing some of the insights
Gen AI is transforming supply chain in the
that you're seeing in the semiconductor space?
semiconductor industry.
Speaker A: There's a lot happening in
Speaker A: Supply chain is such an important
semiconductor industry today. We're seeing
area for high tech industry, but also a very
semiconductor industry enabling the rest of the
complex one. Generative AI can automate
world to leverage generative AI. But these
interactions with your third party suppliers. It
companies are focusing on complex challenges
enables real time substitution based on
beyond just the basic needs for their own needs.
inventory and supplier availability. Think about
There's a significant push towards enhancing
disruptions that could happen with a supplier.
productivity and speeding up market entry.
Are you able to provide new options to your
Strategic adoption of these new technologies is
customers in a much more autonomous
a key in driving new innovation. And speaking of
manner? And this automation improves
innovation, Miles, how is generative AI
procurement processes and overall supply
influencing the R & D process within this
chain.
industry?
Copyright © 2024 Accenture
All rights reserved.
Accenture, its logo, and High
Performance Delivered are
trademarks of Accenture.
GENERATIVE AI’S ROLE
IN THE
SEMICONDUCTOR
VIDEO TRANSCRIPT
Speaker A: I'll talk about three things. One of
the main challenges is skill gap and resource
Speaker B: So moving on to manufacturing.
limitations. Addressing the talent gap, especially
Padam, what advancements are being driven in
in technology engineering but also in
this area for Gen AI?
governance, is crucial for adopting next gen
capabilities. The second one is the fear of
Speaker A: A lot has happened in the
unknown, especially with hallucinations and
manufacturing space over the last two decades,
data privacy is huge as well. And the third
but I believe Gen AI is pivotal in predictive
important thing is building strong data
analytics. To improve yield and throughput even
foundation. Deploying responsible AI and
further, we can use synthetic data to improve
governance in that strong data foundation and
model accuracy and do better prediction and
strategically aligning to business value is very,
managed effects. This significantly enhances the
very important. It's clear that generative AI is a
manufacturing processes and efficiencies. So
powerful driver of innovation in semiconductor
you are producing less crap and building more
industry. For our viewers, we hope this
products and higher quality products. Miles, lets
discussion inspires you to explore the
go a step further. Do you want to talk a little bit
possibilities generative AI can offer. And thank
about the improvements Gen AI is bringing
you for joining us today.
about in testing and quality assurance.
Speaker B: The fabs across the world are
collecting a whole host of metrology data.
Metrology data is limited in terms of the quantity
that we're getting, but what companies can do is
they can create synthetic data in order to
leverage more efficient ways to look at defects in
product nonconformity for things that they're
producing now. Taking that metrology data that
exists, we can use that as input into future
capabilities or future LMM capabilities that I
mentioned earlier in order to design new and
more efficient products and to make sure that
those products are reaching the market not only
faster, but they're able to meet new customer
demands. Before we wrap up, Padam, could you
highlight some of the challenges that the
semiconductor industry face in Gen AI adoption?"
268,accenture,Accenture-Accelerating-The-UKs-Generative-AI-Reinvention.pdf,"Contents
Executive summary 4
The generative AI opportunity 7
The UK’s progress 18
The five imperatives to accelerate the UK’s reinvention through generative AI 24
Imperative 1: Lead with value 27
Imperative 2: Understand and develop an AI-enabled, secure digital core 31
Imperative 3: Reinvent talent and ways of working 36
Imperative 4: Close the gap on responsible AI 40
Imperative 5: Drive continuous reinvention 44
Generating growth: How generative AI can power the UK’s reinvention Copyright © 2024 Accenture. All rights reserved. 2
Preface
The UK is now a nation of potential Realising the value of generative AI (gen AI) will not come too quickly to job displacement, rather than how to use the
easy. When I speak to CEOs and leaders across industries, it’s technology to amplify human abilities and brilliance. Creating
innovators. Large Language Models
clear progress has been made. But getting to scale is proving an environment for humans and machine to best work
(LLMs), like ChatGPT, put advanced
a challenge. Based on current executive choices, the UK could together is no longer a choice; it is a responsibility. A landmark
leave nearly half a trillion pounds in economic value on the shift in digital training will be crucial to achieving this, with
skills at everyone’s fingertips. If
table in the next 15 years. executives anticipating the need to reskill 20 million people.
government and business leaders
At Accenture, we’ve been working with clients to navigate In the right hands, gen AI is a catalyst for reinvention. So,
can harness this, it could lead to a
this complex terrain—and of course, Accenture is on its own whether you’re just starting out or already on your AI journey,
new era of growth. journey of reinvention with AI, too. Though each organisation’s this report offers the formula to deploy gen AI successfully,
journey is unique, one thing is clear: those at the forefront are responsibly and with real impact.
shifting from productivity-focused use cases to positioning
We’ve already had a glimpse of how gen AI may change how
gen AI at the heart of their growth strategies.
we live and work: a future reimagined. Now all of us—from
The good news is this: The foundations clients have been employees to business leaders to government—hold the
building through their digital transformations are exactly responsibility to translate the promise into reality and deliver
what is needed to scale gen AI. With this technology, we can broad-based growth for the UK.
now complete the sentence of the digital age. Organisations
that can connect modernised tech foundations to a forward-
thinking vision for the future will emerge as true leaders.
Shaheen Sayed
CEO, Accenture, UK,
At the heart of this is taking a people-centric approach.
Ireland and Africa
Our analysis shows this will lead to greater economic upside.
We need to shift our thinking from AI to IA—Intelligence
Augmentation. The conversation on talent often moves
Generating growth: How generative AI can power the UK’s reinvention Copyright © 2024 Accenture. All rights reserved. 3
Executive
Fixing the triple fracture Make AI a multiplier Based on delivering more than 1,000 global
gen AI projects, we see a formula for success
The UK has built strong foundations for the age Closing these gaps requires a people-centric
summary
emerging. In this report, we outline the
of AI—but the cracks are beginning to show. approach. Three quarters of the nation’s
five imperatives behind that formula and
workforce could see at least a third of their
A delivery gap is opening as organisations
how it can accelerate the UK’s AI-powered
working hours enhanced by the current
struggle to move their use of gen AI beyond
reinvention: lead with value; understand
Gen AI presents a bigger state of the technology. Our economic
proofs of concept. Of the organisations that
and develop an AI-enabled, secure digital
modelling forecasts that when employees
opportunity for the UK than
have piloted gen AI, most (89%) have not
core; reinvent talent and ways of working;
are empowered to innovate and identify new
any other G7 country. scaled its use across their business. close the gap on responsible AI; and, drive
opportunities, financial gains are greatest. Yet,
continuous reinvention.
Many workers still lack even basic digital skills three out of five executives are prioritising
The technology could almost double the
and access to the training needed to develop investments in process automation that
The elements of the formula are mutually
UK’s long-term growth rate over the next 15
them, signalling an inhibiting skills gap. cut costs in the short-term over ones that
reinforcing, so shouldn’t be applied in
years (to 2038) and deliver a larger economic
Around 20 million people—62% of today’s transform people’s roles for the long-term.
isolation. Strategic alignment between
impact compared to the other
workforce—need reskilling. Executives report
technology, talent, governance and value
There is a real optimism among UK workers
22 countries we analysed.
that less than half (43%) of their workforce
roadmaps is essential. Our modelling
about the impact of AI. Three times as
is confident in the digital fundamentals
But there is no guarantee the full potential estimates an organisation is four times more
many people think gen AI will accelerate,
required for work. A surge in digital skills
for productivity and growth will be realised. likely to succeed in scaling the use of gen AI
rather than decelerate, their career
training is needed, and urgently.
A people-centric approach is required that if coordinated action is taken towards the five
progression. Many are moving ahead of their
puts the emphasis on using gen AI to amplify imperatives simultaneously.
Finally, a trust gap is emerging between organisations: half of the people using gen AI
human abilities. Too few organisations are
employees and executives, impeding at work are self-starters who are using tools
taking this approach today. Without strategic
adoption. Only a third (33%) of people expect they procured themselves. More needs to be
intervention, £485 billion in economic value
business leaders to be responsible and make done to harness this enthusiasm. Over the past 18 months,
could be left untapped by 2038—an amount
the right decisions to ensure gen AI has a gen AI has captured
A formula for success
equivalent to double the country’s current
positive impact on the UK, and even fewer
imaginations; now, with this
Nearly one in 10 (9%) organisations are using
annual healthcare spending.1
(27%) trust the government to do so.
formula, it can deliver results
gen AI at scale, so we know it can be done.
What should public and private sector
leaders do over the next 12 months to put
their organisations—and the UK economy—at
the forefront?
Generating growth: How generative AI can power the UK’s reinvention Copyright © 2024 Accenture. All rights reserved. 4
Authors
This report was a
collaborative effort
between our Data and
AI team based in the
UK, supported by our
Chris Lane Mark Farbrace Joe Hildebrand Suhail Kapoor
research team: Data & AI Lead—UK, Gen AI Lead—UK, Managing Director, Manging Director, Data
Ireland and Africa, Ireland and Africa, Gen AI for Human & AI—UK & Ireland,
Accenture Accenture Potential—UK & Ireland, Accenture
Accenture
Nitya Langley Kayur Rughani Ali Shah Bella Thornely
Managing Director, Managing Director, Responsible AI Senior Manager, Data
Data & AI—UK & Data & AI—UK & Lead—UK & Ireland, & AI—UK & Ireland,
Ireland, Accenture Ireland, Accenture Accenture Accenture
Generating growth: How generative AI can power the UK’s reinvention Copyright © 2024 Accenture. All rights reserved. 5
About the research
We took a multi-pronged approach to researching the UK’s
gen AI-powered reinvention. The report is based on:
Economic modelling to forecast the potential impact of gen AI on productivity and growth for the
economy, organisations and people. We mapped out the future growth trajectories under three different
AI deployment scenarios: aggressive, cautious and our proposed people-centric approach.
Surveys conducted with 3,752 employees and 1,085 executives from public and private sector
organisations in the UK. The samples covered 19 industries and included different demographic
groups by geography, company size and socioeconomic background. The employee survey looked
at UK workers’ experiences with gen AI. The executive survey looked at leaders’ perceptions of the AI
ecosystem, their investments in gen AI and their AI strategy. The surveys were conducted in July and
August 2024.
Interviews, client experience and case studies, drawing on insights with leaders from across the AI
ecosystem, including large technology providers, industry, government and civil society.
Further details on the research approach can be found at the end of the report.
Generating growth: How generative AI can power the UK’s reinvention Copyright © 2024 Accenture. All rights reserved. 6
The generative AI
opportunity: For people,
organisations and the
economy
Generating growth: How generative AI can power the UK’s reinvention Copyright © 2024 Accenture. All rights reserved. 7
The gen AI state of play
Figure 1. Welcome to the age of generative AI
Analyse Simulate
Scenario Optimise
Segment Recommend
Generating growth: How generative AI can power the UK’s reinvention Copyright © 2024 Accenture. All rights reserved. 8
Right here, right now
This is a pivotal moment. Gen AI is going to have a 140 languages.3 Wayve, a company developing
profound impact on how we work and live, more so autonomous driving technology, has pioneered a
than any other recent technological advancement. vision-language-action model (VLAM) that explains
It has the power to reshape industries and multiply to passengers how its AI ‘thinks’ and makes driving
workforce capabilities. The steps individuals, business decisions, increasing transparency and user trust.4
leaders and policy makers take now, will set the
These startups are not examples from Silicon Valley
trajectory for the UK in the years—and even decades—
or Shenzhen—they’re based here in the UK. In fact,
to come.
around one in four gen AI startups in Europe are based
We are only at the start of the S-curve (a model in London.5 These organisations—alongside the almost
showing a technology’s adoption from slow growth to 200,000 UK residents we estimate have AI skills—form
rapid rise and eventual saturation), but the potential part of an AI ecosystem that most executives (68%)
is already evident. Drug design and development surveyed describe as advanced or world-leading (see
company Exscientia has cut drug discovery times by Figure 2).
70%.2 AI video communications platform Synthesia
enables anyone to change written content into
studio-quality videos, voiced by AI avatars in over
Generating growth: How generative AI can power the UK’s reinvention Copyright © 2024 Accenture. All rights reserved. 9
Figure 2. The UK’s AI ecosystem has strong foundations
State of the UK’s AI ecosystem Strengths of the UK’s AI ecosystem Net Availability of AI skills in the UK
% respondents1 % respondents2 +/- # people reporting skills on LinkedIn3
Research
13 World leading 5 71 +66
institutions
Computing
10 61 +51
infrastructure
Talent pool 13 57 +44
55 Advanced 193,146
Regulatory
18 47 +29
environment
2.8x
Access to
21 45 +24
funding 69,156
10 Somewhat developed
Cost of doing
25 41 +16
5 Underdeveloped business
16 Don’t know
Weakness Strength
2023 2024
(As of July) (As August)
1. Respondents were asked: How would you describe the UK’s AI ecosystem? AI ecosystem was defined as: the network of organisations, resources and stakeholders involved in
the development of AI technologies, including government entities, companies, research institutions and support structures such as funding infrastructure, regulatory
frameworks and talent pools that collectively contribute to the growth and development of AI. Accenture UK AI business leader survey, fielded July-August 2024.
2. Respondents were asked: Would you consider each of the following as either a strength or a weakness of the UK's AI ecosystem? Data for “neither strength nor weakness” is
not shown. Accenture UK AI business leader survey, fielded July-August 2024.
3. Accenture UK Tech Talent Tracker based on data from LinkedIn Professional Network.
Generating growth: How generative AI can power the UK’s reinvention Copyright © 2024 Accenture. All rights reserved. 10
Impact from
self to society
The UK’s strong foundations position it to become a global leader in
the gen AI era. With a high share of services and knowledge-based
industries—sectors where the technology can have the greatest
impact—the potential benefits for the UK’s economy, organisations and
people are significant.
Generating growth: How generative AI can power the UK’s reinvention Copyright © 2024 Accenture. All rights reserved. 11
Our research brings into
view the size of the prize:
For the economy Figure 3. T he UK has more to gain from generative AI than any other G7 nation
We model that gen AI could:
Annual GDP gains in 2038 compared with non gen AI baseline
• Add up to £736 billion to annual UK GDP
%
in 2038—this amounts to a 23% increase
to the baseline forecast for 2038.
+23% +22%
• Shift average annual real GDP growth for
+21% +21% +20% +20%
2023–2038 from a baseline forecast of +18%
1.6% to 3.0%, representing an 89% boost
to the UK’s long-term growth rate.
Gen AI is forecast to benefit the UK economy
UK Germany France Canada Italy Japan USA
more than that of any other G7 nation
(see Figure 3).
Source: Accenture Research, simulated GDP growth under three scenarios. GDP gains shown
for People-Centric scenario. Oxford Economics GDP forecast used as the baseline.
Generating growth: How generative AI can power the UK’s reinvention Copyright © 2024 Accenture. All rights reserved. 12
For organisations
The more than 5 million businesses that make up UK plc, alongside public sector organisations
that deliver citizen services and shape the corporate environment, are the agents for creating
the conditions for growth.6
A double-digit productivity uplift could be achieved across the private and public sectors,
based on the current state of the technology. The sectors that are amongst the most important
to the UK’s economy, such as financial services, have the most to gain (see Figure 4).
If the productivity benefits are translated into cost savings, the gains could be substantial.
Across all industries analysed, total annual savings could reach £166.7 billion if the full potential
of today’s technology to automate and augment work is realised.
We estimate that
Nowhere is this opportunity bigger than in the public sector. We estimate that 47% of working
hours in the UK public sector (excluding healthcare) could be enhanced by gen AI (either
47
through automation or augmentation). This translates into a potential productivity gain of 14–
%
20% that, if realised, could result in £34.4 billion in annual savings, equivalent to more than the
annual expenditure on primary school education.7
of working hours in the UK public sector
(excluding healthcare) could be enhanced
by gen AI (either through automation
or augmentation).
Generating growth: How generative AI can power the UK’s reinvention Copyright © 2024 Accenture. All rights reserved. 13
Figure 4. Potentialproductivitygainscouldbe30%+acrossthefinancialservicesandtechsectors
Productivity gains from gen AI exposure %
Modelled range*
Software & Platforms £17.6
Capital Markets £9.7
Banking £12.7
Insurance £3.4
High Tech £1.9
Communications & Media £13.0
Life Sciences £1.3
Public Service £34.4
Travel £2.0
Retail £11.3
Aerospace & Defence £3.2
Industrial £25.4
Natural Resources £2.0
Consumer Goods & Services £5.4
Utilities £4.0
Health £13.1
Automotive £5.1
Chemicals £0.9
Energy £0.3
5% 10% 15% 20% 25% 30% 35% Mid-point
cost savings (£bn)
Source: Accenture Research based on ONS and US O*net. Lower and upper bound based on potential hours saved by occupation valued at annual occupation headcount.
Generating growth: How generative AI can power the UK’s reinvention Copyright © 2024 Accenture. All rights reserved. 14
Gen AI isn’t just a productivity play—it creates new avenues for using gen AI. Our analysis revealed that companies actively Gen AI can close the capability gaps that typically favour
growth. In the second quarter of 2024, AI startups attracted pursuing this strategy delivered a 10.7 percentage point total large companies. Today, platforms like Jasper enable SMBs
31% of all venture funding in the UK. This is nearly three times return to shareholder (TRS) premium in 2023 compared to to access services such as marketing content creation at low
the amount compared to the same period in 2022, before those that did not, even after controlling for company size, cost. In the future, agentic AI—autonomous systems that make
ChatGPT’s public release.8 headquarters location and industry.10 independent decisions and take actions to achieve specific
goals—may enable SMBs to autonomously run entire business
A significant proportion of the growth opportunity comes Gen AI’s performance premium offers both opportunities and
processes. Startups are already developing customisable AI
from the build out of AI’s foundations. In the race for AI risks for incumbent organisations. Leveraging it effectively—
agents capable of handling customer inquiries, managing
supremacy, leading technology companies are building by tapping into unique data sets from existing customer
workflows and resolving issues across multiple channels.
infrastructure akin to the expansion of the electric grid in the relationships, for instance—can provide an ‘intelligence
early 20th century. Just as electricity transformed industries advantage’ that boosts returns. Failing to do so, however,
and powered global economies, gen AI is poised to drive could leave you vulnerable to disruption from a new
the next wave of innovation. Analysts estimate over a trillion generation of AI-first companies. Survey responses from small
dollars will be spent globally on AI infrastructure over the next and medium-sized businesses (SMBs) hint at future trends.
five years, as companies compete to ‘own the grid’ of this new While SMBs only slightly trail large companies in AI adoption,
technological era.9 their satisfaction levels are notably higher. In fact, 86% of
SMB executives are satisfied with their return on investment
Over time, the effective use of gen AI will become an
(ROI), compared to 75% of executives from large corporations.
increasingly important source of competitive advantage.
This may reflect SMBs’ agility and fewer legacy constraints.
We analysed earnings calls from 1,300 global companies
For instance, SMB leaders are less likely than those in large
with revenues exceeding £750 million to assess the extent
multinationals to cite their technology platform as a significant
to which they cited efforts to build competitive advantage
barrier to scaling gen AI.
Generating growth: How generative AI can power the UK’s reinvention Copyright © 2024 Accenture. All rights reserved. 15
For people
By harnessing individual human potential, organisations will realise the most benefits. Gen AI could also help address talent gaps. Investment in vocational training in the UK is 20%
below the peak seen in the early 2000s.13 This is contributing to a shortage of people in areas
No current technology has the potential to have a bigger impact on our working lives than AI.
such as health and social work.14 Gen AI can help address these skill shortages quickly. One
Three in four people in the UK could have at least a third of their working hours enabled by the
interviewee described how a gen AI tool streamlined onboarding for new carers, enabling them
technology, either through automation or augmentation.
to reach the top 20% of performers within six weeks. Workers recognise this potential—over
Automation will save people time, taking tedious tasks off human three times as many survey respondents expect gen AI to accelerate rather than slow their
hands. Our modelling suggests the average UK worker could save 18% of their working career progression.
hours spent on routine activities. A doctor, for example, could save five hours a week while a
As people spend more time doing work they enjoy and doing it well, gen AI could help in a
commercial sales rep could save twelve hours a week.
more profound sense by improving the overall experience of work. In an experiment with our
The time saved could be reinvested in the higher-value work people enjoy doing. Creativity own sales team, we found that gen AI didn’t just result in marked increases in productivity but
is the most underutilised skill in the UK: 26% of people we surveyed say they aren’t currently also grew peoples’ confidence (+34%) and their belief they were making a meaningful impact
applying their creativity at work. With gen AI, the average UK worker could increase the time (+31%).15 Gen AI added to their job satisfaction rather than subtracted.
they spend on creative tasks by 13%.11
We see similar findings in our survey. UK workers recognise gen AI will be important to their
The benefit of augmentation will be accrued not just in time but in quality. Imaging tools, for productivity and problem-solving. But they also anticipate the technology will benefit their
example, could help medical teams make quicker and more accurate diagnoses. Early pilots autonomy and sense of purpose (see Figure 5). Familiarity with the tools reduces anxiety, as
shows that AI could help the National Health Service (NHS) almost halve diagnosis times for employees recognise how the technology complements their existing skills and helps them
stroke patients.12 perform tasks more effectively. Daily ‘power users’ of the technology were more than twice as
likely to expect gen AI to be important to both their creativity and fulfilment from work, relative
to irregular users.
Generating growth: How generative AI can power the UK’s reinvention Copyright © 2024 Accenture. All rights reserved. 16
Figure 5. PeopleanticipatebroadbenefitsfromgenAI—theirexpectationsincreaseastheyusethetoolsmore
Workers’ level of gen AI use (of those with access to the tools), % respondents1
31 56 13
Irregular users Light users Power users
Share of workers that anticipate gen AI will be important to their work experience,
% respondents by level of gen AI use1
90 90
84 84
81
80 79
74
71
68
66
64 64 63
59
56
54
53
50
40
35
33
21 21
Productivity Problem-solving Learning Creativity Autonomy Well-being Fulfillment Purpose
1. Irregular users are respondents who never or rarely use the gen AI tools available to them. Light users use the tools often (at least once a week) or sometimes (once a month). Power users use the tools every day.
Source: Accenture UK AI employee survey, fielded July-August 2024.
Generating growth: How generative AI can power the UK’s reinvention Copyright © 2024 Accenture. All rights reserved. 17
The UK’s
progress
Generating growth: How generative AI can power the UK’s reinvention Copyright © 2024 Accenture. All rights reserved. 18
Mind the gap
While the UK has laid strong foundations for the AI Figure 6. B ased on the decisions being made today, the UK is running closest to our
opportunity, it is not yet positioned to fully realise low-end economic scenario, potentially leaving £485bn in value on the table
its potential.
Our survey of business leaders examined UK economic growth simulation, 2023-38
GDP in £ billions (constant prices)
which of the three economic growth
Scenario GDP gain GDP CAGR GDP gain
scenarios we modelled aligns most closely
vs. baseline premium as a share
with the UK’s current trajectory. by 2038 vs. 1.58% of baseline
baseline
4,000
In our most optimistic, ‘people-centric’ scenario, People-centric £736bn +1.4pp +22.8%
organisations harness gen AI to automate
£485bn
routine tasks, redirecting the time saved into Cautious £561bn +1.1pp +17.4%
higher-value activities. In contrast, in our
3,500
‘aggressive’ scenario, companies prioritise Aggressive £251bn +0.5pp +7.8%
cost-cutting, with workers finding themselves
in less dynamic roles (or unemployed) after Baseline
being displaced, which stifles growth and
3,000
exacerbates inequality (see ‘Further details on
the research’ for more on these scenarios).
On current trends, the UK is leaning toward the
lower end of the growth spectrum—closest to our 2,500
2023 2026 2029 2032 2035 2038
‘aggressive’ scenario—potentially leaving £485
billion in economic value untapped (see Figure 6).
Source: Accenture Research, simulated GDP growth under three scenarios. Oxford Economics GDP forecast used as the baseline.
Exchange rate is based on the period average (USD per Pound), Oxford Economics
Generating growth: How generative AI can power the UK’s reinvention Copyright © 2024 Accenture. All rights reserved. 19
Triple fracture
What is contributing to the lost potential? These trends are mirrored among workers. Although 43% of
UK employees have access to gen AI tools to support their
63%
We identified three points of tension where gen AI
work, only 19% use them at least once a week. Even fewer
deployment is strained:
(7%) are applying the tools to critical decision-making or high-
impact analysis.
A delivery gap
of London-based employees
In 2024, survey respondents expect gen AI to account for We see regional disparity in levels of gen AI deployment.
10% of their technology spend, rising to 15% in 2025. That Organisations outside London plan to invest a third less in have access to gen AI tools;
investment has yet to translate into scaled deployment. While the technology. While 63% of London-based employees have
only 38% of employees
79% of executives report their organisations have at least access to gen AI tools, only 38% of employees elsewhere in
elsewhere in the UK do.
piloted gen AI in one or more parts of their business, only 9% the UK do. And business leaders in the capital report using the
have scaled the technology (with use cases in production technology in around 50% more of their business operations.
in more than half of their business functions). Many lack the
Given gen AI’s potential to drive productivity and growth,
foundations needed to scale. Fewer than one in four (24%)
there is an urgent need to level up its adoption nationwide.
executives, for example, feel confident that their organisation’s
technology capabilities meet the requirements to successfully
A skills gap
scale gen AI.
A landmark shift in digital skills training is essential to unlock
Where gen AI is being implemented, the focus tends to be on the benefits of gen AI. The executives we surveyed estimate
the bottom line. Three out of five executives are prioritising that 62% of their workforces will require reskilling—equivalent
investments in process automation that cut costs over to roughly 20 million people (see Figure 7). For some, this will
initiatives that augment people’s roles and transform how involve developing technical skills such as AI engineering. For
they work. most, it will focus on training to collaborate with AI systems.
Generating growth: How generative AI can power the UK’s reinvention Copyright © 2024 Accenture. All rights reserved. 20
Figure 7. Executivesestimatethat62%oftheirworkforceswillrequirereskillingduetogenAI
Expectations for how gen AI will change roles at organisations by the UK, by select region
% of current job roles1
Jobs to be transitioned: Requiring reskilling / upskilling for new roles Jobs with significant enhancement: requiring substantial reskilling/ upskilling
Jobs with some enhancement: requiring some reskilling / upskilling Jobs not impacted: No reskilling/ upskilling required
22 18 18 15 16 15 15 13 13 17
17
20 21 22 20 24
62%
23 22
23
23
23
20
17
24 23 20
23
22
24 25
30 34 38 41 41 43 46 46 48 38
London North-West West Midlands East of England South-East East Midlands Scotland South-West Yorkshire UK
and the Humber
4.78 3.57 2.85 3.12 4.77 2.41 2.89 2.60 2.64 33.09
Employment mn
1. Respondents were asked: How, if at all, do you expect generative AI to change job roles at your organisation? (Please estimate what proportion of current job roles you expect to fall into the
following categories by distributing 100% points across the options listed. Not all regions are included due to insufficient sample size.
Source: Accenture UK AI employee survey, fielded July-August 2024. ONS current employment levels. Apr-Jun 2024.
Generating growth: How generative AI can power the UK’s reinvention Copyright © 2024 Accenture. All rights reserved. 21
Despite leaders expecting gen AI Many (40%) say they are pushed to use new
deployment to require a significant technology they haven’t been trained on.
uplift in skills, less than half (45%) report
Regional disparities can be seen here too.
that their organisations have increased
Londoners are both more likely to have
training on either gen AI fundamentals
access to training opportunities and are
or technical skills in the past year.
more willing to pursue them. Over the past
Many workers still lack even basic digital skills 12 months, more than 60% of organisations
or access to the training needed to develop in London have increased training on gen AI
them. Executives estimate that less than skills, compared to only 40% in other regions.
half (43%) of their workforce is confident And when considering the potential impact
in the digital fundamentals required for of gen AI on their work, 64% of London-
work. At the same time, nearly one in five based employees are likely to consider
workers (17%) report not having received reskilling, compared to less than half (46%)
any digital skills training in the last two years. of those based outside the capital.
Generating growth: How generative AI can power the UK’s reinvention Copyright © 2024 Accenture. All rights reserved. 22
A trust gap
As we highlighted in our previous (27%) or business leaders (33%) will make Figure 8. Employeesandexecutivesarenotaligned
research report, Work, workforce, the right decisions to ensure gen AI has a on the long-term societal impact of gen AI
workers: Reinvented in the age of positive impact on the UK. Trust levels in the
generative AI, transparency and trust government range from as low as 17% in the
Expectations about the outcomes of the
are required for people to adopt gen South West to as high as 42% in London.
widespread use of gen AI in the UK
AI tools. That research revealed a trust
Expectations around the value gen AI can
gap between workers and leaders. % of executive and employee respondents
deliver—whether in boosting economic
Decrease Increase Net +ve Net -ve
Almost a year later, we find the trust gap growth, equality or employment—differ
Productivity
persists. Trust and user acceptance was significantly between employees and
Executives 10% 67% +56
the fourth most common barrier cited by leadership (see Figure 8). This disparity Employees 12% 47% +35
organisations to scaling the use of the highlights concerns about social inclusion
Economic growth
technology, behind data security and privacy and employee rights, underscoring the trust
Executives 11% 60% +49
concerns, quality and accuracy concerns gap. If not addressed, these issues could Employees 15% 30% +15
and the cost of implementation. Few undermine the potential benefits of gen AI.
Digital inclusion
workers have confidence that government
Executives 17% 51% +33
Employees 24% 30% +5
Economic equality
Executives 24% 39% +15
Employees 34% 16% -18
Social mobility
Executives 24% 39% +15
Employees 30% 17% -13
Source: Accenture UK AI business leader and employee surveys, fielded July-August 2024.
Generating growth: How generative AI can power the UK’s reinvention Copyright © 2024 Accenture. All rights reserved. 23
The five imperatives
to accelerate the UK’s
reinvention through
generative AI
Generating growth: How generative AI can power the UK’s reinvention Copyright © 2024 Accenture. All rights reserved. 24
A formula
for success
What could be done to get
the UK’s gen AI-led
reinvention back on track?
Based on our experience of delivering over
1,000 gen AI projects globally, we see a
formula emerging for how organisations can
responsibly scale the use of gen AI:
’Moving from discrete interventions
Imperative 1: Imperative 2: Imperative 3: Imperative 4: Imperative 5:
to real innovation requires a much
bolder and more holistic approach. Lead with value Understand and Reinvent talent Close the gap on Drive continuous
While many early adopters are develop an AI- and ways of responsible AI: reinvention:
Shift the focus from
focusing on building a technol"
269,accenture,Accenture-Competitive-Switzerland-2024-Report.pdf,"Playing the Long Game
Can Switzerland lead
the way in generative AI?
Competitive Switzerland Innovation. Insights. Actions
Contents
Executive Summary 3
01 | Generative AI Can Have a Profound Impact on 5
the Swiss Workforce and Economy
02 | Barriers to Unlocking the Full Potential of generative AI 16
03 | Final Considerations 37
Methodology Appendix 45
References 49
Playing the Long Game Can Switzerland lead the way in generative AI? 2
Executive Summary
Disruption provides opportunity
Half of Swiss executives believe their companies are well-prepared to harness
the opportunities presented by technological disruptions, with generative AI
(gen AI) being a primary driver today. Our research indicates that Switzerland
is third worldwide in terms of the impact of generative AI on work time.
This technology could significantly boost the Swiss economy, potentially
unlocking an additional CHF 92 billion of economic value by 2030 under
what we refer to as a “people-centric” scenario.
Switzerland will need to take a people-centric approach
To seize the economic opportunity, the Swiss workforce needs to be prepared.
Accenture’s analysis of the Swiss workforce and its tasks reveals that 45% of
work time in Switzerland is highly likely to be impacted by gen AI. This transition,
however, is viewed not as a threat, but as an opportunity to enhance productivity,
particularly within financial services.
Revenue opportunities, rather than mere productivity increases
Generative AI has the potential to do more than just boost productivity.
In Switzerland, 91% of executives believe gen AI will have a greater impact
on revenue growth than reducing costs. The optimistic outlook bodes well
for Swiss companies, as evidenced by the proactive steps taken by Helvetia,
Roche, Novartis, Givaudan, ABB, and Swisscom.
Switzerland is a top innovative country, having been ranked first in the WIPO
Global Innovation Index for the last 13 years. Its talent is recognized, too:
Switzerland has ranked first in the INSEAD Global Talent Competitiveness
Index for the last ten years.
Playing the Long Game Can Switzerland lead the way in generative AI? 3
Challenges remain
To become a global leader in generative AI, Switzerland needs to address
key challenges in three areas: enterprise, workforce, and regulatory readiness.
Top Swiss companies have room to increase their use of AI. Only a small
portion of Swiss companies are currently scaling gen AI initiatives and expect
to take longer compared to what global peers believe. Swiss workers,
meanwhile, are highly open to gen AI. They recognize its value and are willing
to acquire new skills. Still, while their optimism is evident, they maintain
a cautious stance on job security, work quality, and overall well-being.
The regulatory focus on AI has dramatically increased globally in the last
decade. Swiss policies largely align with the OECD principles, although not
completely. This has led to an ongoing and dynamic public debate within
the federal parliament, as shown by how frequently generative AI was
discussed between 2019 and February 2024.
Clear action points for Swiss companies
Five imperatives for Swiss companies to scale gen AI throughout their
organization are: leading with value; understanding and developing an
AI-enabled secure digital core; reinventing talent and ways of working;
Switzerland
closing the gap on responsible AI; and driving continuous reinvention.
truly has
Ideas for Swiss policymakers
Starting from a position of strength, there are some further ideas to allow a world of
the country to capture the full benefits of gen AI, including defining a
strategic vision for gen AI, fostering international collaboration, enhancing opportunity
role transition mechanisms, strengthening the dialogs and oversight on
ahead!
the AI/gen AI revolution, and supporting gen AI literacy in society.
Playing the Long Game Can Switzerland lead the way in generative AI? 4
01
Generative AI
Can Have a Profound
Impact on the
Swiss Workforce
and Economy
Playing the Long Game Can Switzerland lead the way in generative AI? 5
Disruption is on the rise Disruption has become a prominent force in today’s business landscape,
compelling organizations to adapt and reinvent themselves to stay competitive.
The Accenture Pulse of Change Index measures disruption across various
and is driving the need for
categories, including consumer, social, geopolitical, economic, climate, talent,
and technology categories. The indexed scores from 2019 to 2023 reveal a
reinvention
significant increase in technology disruption, primarily driven by advancements in
generative AI. This highlights the urgency for businesses to embrace innovation
and reinvent their strategies to navigate the evolving landscape effectively.
Accenture Pulse of Change Index
Indexed score, 2019–23
Overall Consumer Geopolitical Economic Climate Talent Technology
and Social
+17%
+34% +41%
+33% +88%
+13%
+20%
19 23 19 23 19 23 19 23 19 23 19 23 19 23
Source: Accenture Pulse of Change Index 2024. Overall measure of disruption is based on the average of the six sub-components, each of which are based on a set of indexed scores of a set of indicators.
Playing the Long Game Can Switzerland lead the way in generative AI? 6
Preparing for disruption is crucial for organizations to thrive in an ever-
changing environment. The data shows that only about half of Swiss
Only one in two Swiss executives feels prepared for
executives feel “very prepared” to tackle the multifaceted disruption ahead,
the multifaceted disruption ahead, with technology
particularly in technology. This indicates a need for organizations to enhance
readiness trailing behind.
their readiness by investing in technology adoption, upskilling employees,
and fostering a culture of innovation.
Level of preparation of companies for different types of disruption
(% of respondents saying they are “very prepared”)
Consumer Geopolitical Economic Climate Talent Technology
and Social
+2 pp –11 pp
–6 pp –2 pp –1 pp +7 pp
55%
51% 53% 53%
49%
47% 46%
43% 45% 43% 47% 42%
Global Switzerland Global Switzerland Global Switzerland Global Switzerland Global Switzerland Global Switzerland
Source: Accenture Pulse of Change 2024. Global N = 3450; Switzerland N = 100
Playing the Long Game Can Switzerland lead the way in generative AI? 7
Proportion of working hours in scope to be either automated or augmented by gen AI
Working hours in scope for automation augmentation
Switzerland is
UK 24.0 23.4 47.5
the third most
Canada 22.8 22.9 45.7
exposed country
Switzerland 23.2 22.0 45.2
to generative AI,
Germany 23.6 21.5 45.1
with 45% of work
Australia 23.2 21.7 44.9
time highly likely
Japan 24.9 19.4 44.4
France 22.5 21.5 44.1 to be impacted.
US 22.8 21.2 44.0
Sweden 21.3 22.2 43.5
Italy 22.3 20.3 42.6
Norway 21.2 21.4 42.6
Finland 21.3 21.2 42.5
Denmark 21.1 21.0 42.1
Argentina 23.7 18.3 42.0
Spain 21.5 20.4 41.9
Mexico 23.1 18.0 41.2
Chile 21.7 18.7 40.5
Brazil 21.8 17.4 39.2
Colombia 21.4 17.6 39.0
South Africa 21.4 17.3 38.7
Saudi Arabia 21.6 15.5 37.2
China 17.6 15.0 32.5
India 16.5 15.2 31.7
Note: Estimates are based on Human+Machine identification of work tasks exposure to impact of generative AI.
For details see the methodological notes in the appendix.
Source: Accenture Research based on National Statistical Institutes and O*Net.
Playing the Long Game Can Switzerland lead the way in generative AI? 8
Work roles will be transformed
in different ways and to different
degrees, depending on the specific
nature of tasks and the time that
each task takes.
Playing the Long Game Can Switzerland lead the way in generative AI? 9
)krow
egdelwonk(
laitnetop
noitatnemgua
rehgiH
Exposure to generative AI for top 20 occupations in Switzerland
Percentage of working time by role
45%
Software and
Applications
40% Developers
and Analysts
Shop
Administrative
35% and Specialized
Salespersons
Nursing and Secretaries
Midwifery Sales, Marketing,
30% Profes s ionals AO st sh oe cr iH ate ealth Managing Directors a Mn ad n aD ge ev re slopment a MB nu ads ni n aAe gds ems
r
siS ne isr tv ri ac te ios n
Professionals and Chief Executives
25% Physical and
Science
E Tn ecg hin ne ice ir ain ng
s
Other
S
C ul pe pric oa rtl G Oe ffn ice er a Cl
lerks
20% Domestic, Workers
Hotel, and Office
Cleaners and Helpers
15% Personal Ca re Waiters and
Worke rs in Primary Bartenders Manufacturing, Material Recording
Health Services School and Early Mining, Construction, and Transport Clerks
10% Childhood Teachers and Distribution
Managers
Building Finishers
Building and
5% and Related Trades
Housekeeping Building Frame
Workers
Supervisors and Related
0% Trades Workers
5% 10% 15% 20% 25% 30% 35% 40% 45% 50%
Higher automation potential (process work)
150K
Bubble size
75K
Number of employees
Note: Estimates are based on Human+Machine identification of work tasks exposure to the impact of generative AI. For details, see the methodology notes in the appendix. 10K in Switzerland
Source: Accenture, Federal Statistical Office of Switzerland, and O*Net.
Playing the Long Game Can Switzerland lead the way in generative AI? 10
Case Study
The office clerk’s apprenticeship
Focusing on the worker of the future
Switzerland revamps its commercial apprenticeship program
In the wake of rapidly advancing artificial intelligence, traditional models of tests are still necessary, that knowledge is immediately taken further, interwoven
vocational training face unprecedented challenges, with companies potentially into professional situations, practiced with fictitious customer conversations, and
preferring to turn to generative AI rather than hiring apprentices. consolidated in training units and practical assignments.
The concept of apprenticeship needs to undergo a profound transformation, Emphasis is put on imbuing a digital mindset and shifting towards skills machines
as this development risks marginalizing the human element of learning and cannot easily replace, such as critical thinking, creativity, social skills, and working
innovation. In the long term, this shift could lead to a widening skills gap, where independently. Moreover, trainees are prepared for the modern agile business
the workforce lacks essential problem-solving and creative thinking skills. environment, learning to adapt to changes quickly and fluently.
Switzerland has taken a proactive stance to address this problem by rejuvenating Final examinations are taken by future learners through hands-on tasks that
the basic vocational training programs such as the “Kauffrau/Kaufmann EFZ” reflect the most essential professional situations and combine basic knowledge
and “Kauffrau/Kaufmann EBA” programs. Both have been updated to optimally in German, English, IT, and economics, including accounting, general education,
prepare young businesspeople for the changing world of work and the and education in professional practice.3
future of commerce.1 The commercial apprenticeship (KV) in Switzerland is
notably popular, with 12,000 apprenticeships annually accounting for almost This initiative is just an example, but as generative AI evolves and matures, it’s
a fifth of all young professionals starting their training across roughly 250 critical to keep updating apprenticeship programs at scale, to keep them relevant
recognized professions.2 and to adequately prepare the workforce to work effectively alongside AI,
leveraging these technologies to augment human capabilities rather than replace
Schools and companies are equally involved, with trainees benefiting from them. In doing so, society can foster a more adaptable, skilled, and resilient
learning at school, at companies, and through inter-company courses. What’s workforce that is prepared to meet the challenges of the future.
more, teaching centers on a hands-on approach. For example, while vocabulary
Playing the Long Game Can Switzerland lead the way in generative AI? 11
Productivity improvement driven by gen AI by industry in Switzerland
% of time
0% 5% 10% 15% 20% 25% 30% 35%
Gen AI has the potential to
Banking 22.3% 30.4%
increase productivity by double
Insurance 19.9% 28.5%
digits in all industries, with
Capital Markets 20.4% 28.3% financial services leading
Software & Platform 20.7% 28% the pack. Additionally, Swiss
High Tech 17.3% 24.4% executives see it as a driver
of new revenues.
Comms & Media 16.8% 23.6%
Life Sciences 13.8% 20.5%
While industries in the financial services
Automotive 14.3% 20.3%
sector – capital markets, insurance, and
Retail 13.3% 19.7% banking – have the highest exposure to
gen AI, no Swiss industry is likely to remain
Public Service 13.1% 19.1%
untouched.
Energy 13.0% 19.1%
Chemicals 12.4% 18.0% To put this into context, this is a similar
increase in productivity to what Switzerland
Consumer Goods & Services 11.8% 17.9%
witnessed in the aftermath of the Internet
Natural Resources 12.3% 17.6%
revolution, from 2000 to 2006–09, and the
Utilities 11.8% 17.5% same increase seen in the last 11–16 years.
Industrial 12.2% 17.5%
Travel 11.2% 16.4%
Health 9.4% 15.5%
13% 19%
Note: Estimates are based on Human+Machine identification of work tasks exposure to impact of generative AI.
For details see the methodological notes in the appendix.
Source: Accenture Research based on National Statistical Institutes and O*Net.
Playing the Long Game Can Switzerland lead the way in generative AI? 12
Do you view generative AI as being more beneficial to
revenue growth or cost reduction for your organization?
91% of Swiss executives
% of Swiss companies’ respondents
(76% globally) believe generative AI
will be more beneficial for revenue
growth than cost reduction. This
Cost reduction
highlights a significant optimism
9%
about leveraging generative AI
for strategic advantages, beyond
automation.
Swiss executives
agree that
91%
generative AI will
be a key driver
Revenue growth
for revenue
growth over cost
reduction.
Source: Accenture Pulse of Change, Nov. 2023; n (Switzerland) = 100; companies’ press releases
Playing the Long Game Can Switzerland lead the way in generative AI? 13
Examples of top
Novartis
Swiss players turning
launched the Generative Chemistry (GenChem) initiative, revolutionizing
to generative AI to drug discovery. Using advanced AI, GenChem designs molecule structures
increase top line growth and identifies potential new medicines. This approach speeds up the
discovery of top-quality molecules and enhances their developmental
success rates. With the support of over 250 data scientists, key research
areas, from target identification to predictive biomarkers, are optimized.
Givaudan
has launched a generative AI creation assistant, a proprietary AI model,
trained on the company’s knowledge and data to support the creativity of
perfumers and flavorists.
Swisscom and NVIDIA
have joined forces with an investment of CHF 100 million to spearhead the
development of generative AI supercomputers in Switzerland and Italy. This
collaboration aims to establish a Trusted AI Factory, focusing on creating
secure, sovereign gen AI solutions.
Playing the Long Game Can Switzerland lead the way in generative AI? 14
Economic growth simulation, Switzerland 2023–2038 Impact of generative AI on Swiss GDP, by scenario
GDP in billion CHF (2015 constant prices and exchange rate) GDP added against baseline by 2030 and annual growth 2023–2030,
Baseline: Oxford Economics. Simulations for three scenarios. billion CHF and %
1,100 Annual growth rate 3.9% 2.5% 2.3%
2023-30
1,000 131
900
CHF 92 billion
additional value
800 unlocked by 2030
48
39
700
600
22 24 26 28 30 32 34 36 38 People-centric Cautious Aggressive
People-centric Cautious Aggressive Baseline
Note: Higher-quality jobs defined as those with higher Net Better Off score.
Source: Accenture Research analysis. See methodology slide for further details.
By adopting generative AI in a people-centric and responsible manner across
the Swiss economy, significant benefits are anticipated by 2030. Focusing By embracing a responsible, people-centric approach
on enhancing rather than replacing jobs could yield an extra CHF 92 billion
to generative AI, the Swiss economy stands to unlock
in economic value, contributing to an additional annual growth rate of 1.6%.
additional economic value of CHF 92 billion by 2030.
The people-centric scenario assumes a moderate adoption pace, achieving
full integration within ten years with no effects on unemployment. It also
emphasizes active support for workers transitioning to higher-quality jobs,
ensuring that the adoption of generative AI enhances, rather than undermines,
employment quality.
Playing the Long Game Can Switzerland lead the way in generative AI? 15
02
Barriers to Unlocking
the Full Potential of
generative AI
Playing the Long Game Can Switzerland lead the way in generative AI? 16
Switzerland’s tech competitive advantage
By several measures, Switzerland is in an excellent
position to be leading the generative AI wave
1st 1st 5th
In the WIPO Global Innovation In the INSEAD Global Talent In the IMD World Digital
Index in the last 13 years4 Competitiveness Index in Competitive Ranking in 20236
the last 10 years5
Playing the Long Game Can Switzerland lead the way in generative AI? 17
Large existing talent pool Strong tech infrastructure
Switzerland is recognized for its robust talent pool, particularly in tech. The Swiss National Supercomputing Centre (CSCS) provides strong
Almost 6% of its employment base are ICT specialists7, 1.5 times the computing capacity via several supercomputers (e.g., Piz Daint exceeding
European average. 25 petaflops) for detailed and complex simulation across various fields11.
CSCS will also house the new Alps supercomputer (10,000 GPUs NVIDIA),
According to the latest Global Talent Competitiveness Index, Switzerland, which will be launched in spring 2024 thanks to the efforts of ETH Zurich and
along with the US and Singapore, is at the top of the ranking in attracting EPFL to develop open-source models for generative AI12.
and retaining skilled professionals.
High focus on R&D expenditure
Switzerland has consistently been among the countries with the highest R&D
expenditure as a proportion of GDP. The latest data indicates that the private
sector plays a significant role, contributing to over two-thirds of the R&D
expenditure, which amounts to over 3% of the GDP8, more than 1% higher
than the European average. Switzerland’s leadership in patents also highlights
the commitment to innovation. In 2023, it led the world in patent applications
per million residents, filing twice as many as Sweden, the second-ranked
country, and nearly eight times more than the United States9, with significant
contributions across various industries such as pharmaceuticals, consumer
goods, and high tech.
World-class research institutions
Nine Swiss institutions appear in the latest ranking of the 500 best global
universities, with ETH named the top university in continental Europe10. In 2023,
approximately 2,000 patents were published in Switzerland, with around
1,200 patents granted. It ranked first in applications per million residents, with
a large margin over other innovative countries. Swiss companies filed almost
seven times as many patent applications per million inhabitants last year as
companies in the United States, with significant contributions across a range
of industries such as pharmaceuticals, consumer goods, and high tech.
Playing the Long Game Can Switzerland lead the way in generative AI? 18
Enterprise
readiness
To seize the full
potential of generative
AI and capitalize on its
Regulatory
opportunities, Switzerland readiness
needs to address
three key challenges
Workforce
readiness
Playing the Long Game Can Switzerland lead the way in generative AI? 19
Room for improvement in terms of AI readiness
Our AI index reveals that top Swiss companies have the potential to enhance
their AI readiness. While there are some pioneers in different aspects of
AI, many companies have room for improvement, particularly when it comes
to AI talent and responsible AI.
Difficulty in scaling
A survey13 reveals that 62% of Swiss companies have implemented AI to some
degree. However, the challenge lies in expanding the technology throughout
Enterprise
the entire organization. Our pulse survey14 indicates that only 2% of Swiss
companies are currently scaling gen AI initiatives and expect to take longer
readiness
compared to what global peers believe. Also, only 7%, half the global average,
are “extremely confident” they have the right data strategy and core digital
capabilities in place to effectively leverage generative AI.
Difficult AI governance
To leverage gen AI for specific use cases, businesses might need to feed
sensitive data into these models. This requires businesses to implement strong
safeguards to protect sensitive information and prevent unauthorized access or
breaches that could compromise privacy and trust. 52% of Swiss organizations
lack clear AI workplace policies, indicating a critical need for guidelines15,
and only 4% have progressed from designing or initiating the scaling up of a
responsible data and AI model to fully integrating one into their enterprise16.
Playing the Long Game Can Switzerland lead the way in generative AI? 20
Lack of digital skills in the workforce
An Adecco survey shows that Swiss workers are trailing their peers in the acquisition
of digital skills17 (e.g., artificial intelligence, machine learning, data analytics,
data mining, design thinking, digital design, digital marketing, programming,
data analysis), and instead are more focused on job-specific and functional skills
(e.g., accounting, marketing, finance, human resources, analysis, IT).
Unseen use of AI
Our Accenture global workforce survey shows that 85% of Swiss workers
Workforce
already utilize generative AI in their jobs in various ways18. This trend reflects
the increasing integration of generative AI into everyday tasks and highlights a
readiness
possible disconnect in formal training and understanding of these technologies’
capabilities and ethical implications among the workforce.
Trust deficit
Based on our workforce survey, 50% of Swiss workers are concerned about the
quality of gen AI output, in line with the global sample19. Additionally, 48% fear job
displacement due to generative AI. This skepticism extends beyond the workplace
into other aspects of Swiss daily life. A study by the University of Zurich’s Research
Center for the Public Sphere and Society (fög) showed that merely 29% of
participants would engage with news articles authored entirely by AI, in stark
contrast to the 84% who would opt for content crafted by journalists20.
Playing the Long Game Can Switzerland lead the way in generative AI? 21
Rapid technological advancement
The pace at which AI and gen AI technologies evolve far exceeds the speed
at which regulatory frameworks can be developed and implemented, leading
to a perpetual catch-up scenario for regulators.
Social expectations and ethical implications
Gen AI raises complex ethical and social questions, connected to bias,
privacy, and the potential for job displacement. Developing regulations
that effectively address these concerns without stiffing innovation is a
Regulatory
delicate balance. In particular, the expected workforce shift will likely lead
to substantial changes in job roles within the next few years, adding societal
readiness pressure regarding skill training and policy development.
International coordination
The global nature of gen AI development and deployment necessitates
international collaboration and harmonization of regulatory standards.
Switzerland’s active participation in international discussions and bodies, such
as the Council of Europe’s Committee on Artificial Intelligence, highlights the
importance of global cooperation. However, aligning international norms with
national regulations presents a challenge.
Playing the Long Game Can Switzerland lead the way in generative AI? 22
Enterprise readiness Workforce readiness Regulatory readiness
Our outside-in analysis indicates that top
Swiss companies have room to bring their
use of AI to a higher level in some areas.
Playing the Long Game Can Switzerland lead the way in generative AI? 23
Enterprise readiness Workforce readiness Regulatory readiness
Swiss companies AI index
Our AI index, an outside-in analysis that measures a company’s
(Average percentile rank vs global industry peers, 23 Swiss players, 2023) level of advancement in its AI journey, highlights the presence of
pioneering companies that excel. However, a wide interquartile range
100 across several indices suggests that many firms still have significant
untapped potential to harness. The companies analyzed exhibit a
90
robust tech foundation with a significant median score, indicating
a well-established IT infrastructure and a willingness to embrace
80
emerging technologies, which is critical for AI development. In
strategic communications, Swiss companies demonstrate a proactive
70
approach to AI, with 35% mentioning AI at least once in
a strategic context during their earnings calls.
60
50 In responsible AI, Swiss companies exhibit a dynamic range, with
some demonstrating commendable ethical AI practices while others
40 have yet to reach such standards. This variance presents a dual
challenge and opportunity – encouraging a universal commitment
30 to ethical AI can propel Swiss firms to the forefront of responsible
innovation and serve as a beacon for global standards. The index
20 highlights a critical gap in AI talent. While there are standout players,
the data reveals that on average, only 4% of job postings mention AI
10
skills. This indicates a potential shortfall in the required
skill sets for advancing AI technology.
0
Tech Responsible AI Board tech
foundation AI patents quotient
AI strategic AI AI Workforce
This graph shows how Swiss companies compare with global
mentions talents VC – M&A quality
industry peers across various AI index pillars. For example, Swiss
companies have a median score of 43.5 in the AI talents pillar,
Top quartile Bottom quartile Median indicating they surpass 43.5% of their worldwide competitors in
Note: Strategic mention is defined as any reference to AI-related terminology during earnings calls that link AI their ability to attract and retain AI talents.
to one or more specific categories: future trends, strategy, investment, use cases, risk, and human capital.
Source: AI Index, Accenture Research.
Playing the Long Game Can Switzerland lead the way in generative AI? 24
Enterprise readiness Workforce readiness Regulatory readiness
Expected / actual timing to fully scale up generative AI enterprise-wide
% of respondents
C-suite leaders are less confident and
Global 9% 69% 20% 3% more conservative in their timelines for
scaling generative AI enterprise-wide
compared to their global counterparts.
Switzerland 2% 61% 36% 1%
Only 2% of Swiss companies say they are currently
scaling up generative AI enterprise-wide and a
significantly higher percentage of Swiss leaders
The organization has the right data strategy and core digital capabilities than those globally expect this integration to take
(e.g., the use of structured, unstructured, and synthetic data) in place to between 12 and 18 months. Furthermore, fewer
effectively leverage generative AI Swiss leaders are “extremely confident” in their
data strategy and digital capabilities to effectively
% of respondents saying “extremely confident”
leverage generative AI than leaders globally.
Global 13%
Switzerland 7%
Currently scaling up generative AI enterprise-wide Within 12 months
12 to 18 months Longer than 18 months
Source: Accenture Pulse of Change, March 2024. n (Global) = 2,800, n (Switzerland) = 100
Playing the Long Game Can Switzerland lead the way in generative AI? 25
Enterprise readiness Workforce readiness Regulatory readiness
Swiss employees’ view on generative AI
% of respondents
Swiss workers
Swiss employees are highly receptive to generative AI ... but while their optimism is evident, they maintain a
are highly open
technology, recognizing its value and showing a cautious stance on job security, work quality, and overall
to generative AI,
willingness to acquire new skills ... well-being.
but companies
should address
their remaining
see value in working say it could add to their concerns: stress,
92% 54%
with gen AI stress and burnout
job displacement,
and output
accuracy.
want to learn new are concerned about job
93% 48%
gen AI skills displacement
are already using gen AI at work are concerned about accuracy
85% 50%
in some fashion of tool output
… and while only 30% of Swiss organizations are currently reskilling their workforce to meet growth goals,
91% recognize the necessity to revise their reskilling strategies in response to generative AI.
Source: Accenture Change workforce survey, Oct.–Nov. 2023, n (Switzerland) = 250; Accenture Pulse of Change, March 2024. n (Global) = 2800, n (Switzerland ) = 100
Playing the Long Game Can Switzerland lead the way in generative AI? 26
Enterprise readiness Workforce readiness Regulatory readiness
Number of global AI-related policy initiatives over time
0 50 100 150 200 250
The regulatory focus on AI has dramatically
2005 or
27 increased at a global level in the last decade.
before
06 1
As of 2023, the OECD counted more than 1,000 AI-related policies
07 1
globally, reaching a notable peak in 2019. Since 2019, there has
10 2 been a slight slowdown, but still a high level of activity in AI policy
introductions, indicating sustained interest and investment.
11 5
12 4
National AI policies are the most widespread type of AI-related policies
13 8 (70%), with all the 71 countries analyzed placing a strong emphasis on
developing national policies addressing AI, directly or indirectly.
14 13
Over
1,000 15 12 27% of all policies focus on trustworthy, human-centric AI, with greater
AI-related 16 37 emphasis in APAC and North America on this theme.
policies
17 57
Only 10% of overall policies focus on AI coordination and monitoring,
18 141
suggesting that some countries might be weaving these efforts into
19 220 broader digital governance strategies, indicating a holistic approach
to technology policy, or that some countries may have a decentralized
20 191
approach, focusing on individual policy initiatives without an overarching
21 136 coordination mechanism.
22 85
23 64
European Lighthouse on Swiss
secure and safe AI Supercomputer
Source: Accenture Research on OECD AI Policy observatory.
Data related to 71 countries analyzed (including European Union)
Digital society
initiative
Interdepartmental
working group on AI
Guidelines
on AI
Digital
Switzerland
Playing the Long Game Can Switzerland lead the way in generative AI? 27
Countries have diverse approaches to regulating AI:
some approach this horizontally, while others see more
benefit in industry-specific regulation
Market-driven approach
A regulatory strategy emphasizing innovation and economic growth by minimizing
government intervention in the development and application of AI technologies.
Risk-based approach
A regulatory strategy that focuses on identifying, assessing, and mitigating
potential risks associated with AI technologies to protect consumers and society.
Horizontal approach
The regulatory framework covers a broad range of issues, from AI development
to economic impact, in one document.
Vertical approach
The approach implements various regulations focused on different aspects,
or types of AI.
nevird-tekraM
hcaorppa
Vertical
approach
desab-ksiR hcaorppa
Enterprise readiness Workforce readiness Regulatory readiness
Horizontal
Source: Accenture Research on HSBC, AI Regulation, Assessing impact on companies, Feb. 2024; Accenture Research analysis approach
Playing the Long Game Can Switzerland lead the way in generative AI? 28
Enterprise readiness Workforce readiness Regulatory readiness
Qualitative classification of selected countries’ AI-related policies approach
Final text of the EU AI Act approved Bill No. 2338/2023 has the goal 2023 pro-innovation approach to
in March to provide risk-based of establishing detailed rules, AI regulation white paper 2023.
classification to ensure safety and principles, and guidelines for the
compliance with fundamental rights. development and application of AI Five cross-sectoral principles for
in the country. regulating AI on a non-statutory basis
The AI Act also applies to providers and to be applied by different sector
developers outside of the EU whose AI regulators.
systems affect EU individuals. AI laws are distributed across federal
agencies and state-level regulations
(e.g., California on gen AI). AI policy in 2019 and the Model AI
Artif"
271,accenture,Accenture-Banking-Top-10-Trends-2024.pdf,"Banking on AI
Banking Top 10 Trends for 2024
Introduction
The Digital Age revolutionized
banking; expect even more
from the Age of AI
A quarter of a century ago we stood on the interactions, today deal with only a tiny As we enter the Age of AI,
threshold of the Digital Age. Amazon had just proportion. The use of cash declined as new
many bankers feel the
made the bold decision to broaden its sales ways of paying emerged. With technology
same sense of awe that
catalog beyond books, Google was launched having become a critical differentiator, and
to help us find our way around a rapidly with almost $550 billion2 invested in the their counterparts did a
expanding internet, and we were blissfully fintech sector since 2010 alone, the industry quarter of a century ago.
unaware that the dot-com bubble was about experienced an influx of digital-native
to burst. A few years earlier, expecting digital to competitors. These included both agile
displace our industry’s incumbents, Bill Gates start-ups and bigtechs with deep pockets,
famously declared: “The world needs banking, huge customer bases, troves of data and
but it does not need banks.”1 unmatched technological expertise.
Digital didn’t disappoint us. The past 25 years Yet despite their best efforts, no fintech has
saw a revolution in how companies work and managed to break into the global top-250 list
the products and services they offer. Banks of banks by assets.3 It appears the world does
changed fundamentally. Their branches, need banks after all.
which used to handle virtually all customer
Banking on AI | Banking Top 10 Trends for 2024 2
Introduction
Today, we again stand on the verge of transformational change.
The ability to process and analyze vast stores of data, the enabling
power of cloud, and the rapid maturation of artificial intelligence
are combining to create a wealth of opportunities for enhancement
and innovation across organizations’ operations, workforce,
products and experiences.
As we enter the Age of AI, many bankers feel the same sense of
awe that their counterparts did a quarter of a century ago. They
know that, as with digitalization, very little will remain untouched.
These technologies are unlikely to change what banking does,
but they will dramatically transform how it does it.
Each of the trends we describe in this report is either caused or
amplified by AI. We, together with most bankers today, are peering
into the future: trying to figure out what this technology holds for
the industry. We are confident the Age of AI will change banking
and many other industries; exactly how, we will only know in
retrospect. However, it is we who get to choose where and how
we will use AI. The challenge is to ensure it’s a force for good that
benefits all humankind.
Banking on AI | Banking Top 10 Trends for 2024 3
Introduction
Our Top 10 Banking Trends.
01 02 03 04 05
The rise of Capturing All the risk A whole The power
gen AI the digital we cannot see new way of of pricing
dividend working
06 07 08 09 10
Time Regulation From The key Beyond
to think recalibrated technology to to the core Six Sigma
cloud first engineering
Banking on AI | Banking Top 10 Trends for 2024 4
Trend: 1
The rise
of gen AI
Banks are likely to benefit more from generative
AI than any other industry. Our analysis of operational
efficiency indicates a potential to boost productivity by
22-30%,4 while a further study found that revenue could be
increased by 6%.5 To achieve these improvements, however,
it will be necessary not only to utilize the cloud and data
effectively, but also to fundamentally rethink work and talent.
Trend 1 | The rise of gen AI
“AI will fundamentally
transform everything, from Sweeping statements like this are usually given little credence in the sober
world of banking. But that was before generative AI came along. Suddenly
business to science to
all bets are off, and bankers throughout the industry are wondering
6
society itself.” whether there is any part of the business that won’t sooner or later be
affected, if not actually transformed.
With good reason. We recently analyzed 19,265 tasks across 900 job
families in 19 industries, using data from the US Bureau of Labor Statistics
and others. The study included a breakdown of the time spent on each task
and an assessment of the potential for automation and augmentation by
generative AI. We concluded that banking is likely to be more extensively
impacted than any other industry, with almost three-quarters of all work
being well-suited to automation or augmentation (Figure 1).
Banking on AI | Banking Top 10 Trends for 2024 6
Trend 1 | The rise of gen AI
Figure 1. Banking is likely to be more profoundly impacted by gen AI than any other industry.
Work time distribution by industry and potential impact of LLMs.
Higher potential Higher potential Low potential for
for automation for augmentation automation or augmentation
Banking 39% 34% 27%
Insurance 33% 37% 30%
Capital Markets 32% 37% 31%
Software & Platforms 31% 37% 32%
Health 42% 25% 33%
Communications & Media 34% 31% 35%
Retail 36% 28% 36%
Life Sciences 34% 29% 37%
High Tech 31% 31% 38%
Travel 35% 27% 38%
Automotive 34% 27% 39%
Public Service 34% 26% 40%
Energy 35% 23% 42%
Utilities 34% 23% 43%
Industrial 33% 24% 43%
Consumer Goods & Services 32% 24% 44% Note: Weighted by employment levels
in the US in 2022. Estimates are based
Aerospace & Defense 30% 26% 44% on human + machine identification
of the exposure of work tasks to the
Chemicals 31% 22% 47% impact of generative AI.
Source: Accenture Research based
Natural Resources 31% 19% 50%
on US BLS and O*Net.
Banking on AI | Banking Top 10 Trends for 2024 7
Trend 1 | The rise of gen AI
AI has of course been around for a long time; most tech
Banking 22% 30%
historians credit the English mathematician and cryptanalyst
Insurance 20% 28%
Alan Turing with having developed the concept in 1950. What is
new is that cloud-based generative AI engines have reached the Capital Markets 19% 28%
point where they are surpassing human capabilities in important
Software & Platforms 19% 27%
respects. These progressively adaptive engines are advancing
Communications & Media 14% 20%
at an unprecedented speed, arousing both wonder and alarm
in most parts of business and society. Life Sciences 14% 20%
High Tech 14% 20%
Within months of the launch of ChatGPT at the end of 2022, early Figure 2. Banks can improve
Retail 13% 19%
adopters in the banking industry were exploring the most promising their productivity by up to 30%
use cases. Today, little more than a year later, virtually every bank Public Service 13% 18% by adopting generative AI.
has a generative AI strategy of some description and is running a
Travel 12% 17%
Potential hours saved by industry,
variety of proofs of concept. Many are reporting impressive results.
Energy 12% 16% valuated at US annual occupation
The next 12 months will see scaled adoption across multiple parts
of the organization, with the more ambitious banks using it as the Utilities 11% 16% headcount and wages of 2022.
US value only.
foundation for what we call Total Enterprise Reinvention.
Aerospace & Defense 11% 16%
Note: Estimates are based on human
Our analysis indicates that there are hundreds of use cases for Health 10% 16% + machine identification of work tasks
exposure to the impact of generative AI.
generative AI in banking. Productivity is the most obvious benefit. Industrial 11% 15%
Source: Accenture Research based on
As Figure 2 shows, there is greater potential to boost output in US BLS and O*Net data.
Automotive 11% 15%
banking than in any other industry.
Chemicals 10% 14%
Consumer Goods & Services 9% 13%
Natural Resources 9% 12%
Banking on AI | Banking Top 10 Trends for 2024 8
Trend 1 | The rise of gen AI
These gains are being realized in a wide Functions other than sales, marketing and
variety of areas, from due diligence and risk
customer interaction that are likely to receive
and compliance to legal contract generation
and code writing. However, we believe early attention are risk management and
the most significant financial impact will
compliance, technology, HR and legal.
be in helping banks increase revenue. Our
models show that by pairing AI with people
to offer personalized wealth advisory, guide Generative AI offers CEOs the chance to reshape their
commercial relationship conversations, tailor bank, empower their people, amplify their productivity and
products for individual customers, enhance increase profitability. But most executives recognize that it
the quality of contact center interactions, cannot do this on its own; to realize its full potential it needs
and streamline their product application and to work in tandem with human ingenuity. For this reason
onboarding processes, banks can improve alone, any AI strategy needs to have the workforce at its
their revenue by 6% or more within three core. The successful deployment of AI not only demands
years.7 a set of skills that few banks have in sufficient numbers,
but also requires significant changes in what people do
and how they do their work. Banks that manage this aspect
effectively will have a big advantage as they explore and
unravel the exciting possibilities of AI.
Banking on AI | Banking Top 10 Trends for 2024 9
Trend: 2
Capturing the
digital dividend
While most banks have mastered digital, its focus—more
often than not—has been on servicing. Turning even a
modest number of digital interactions into opportunities
holds immense potential. To do that, banks will need to find
ways to have meaningful conversations with customers
across digital channels. AI may hold the key.
Trend 2 | Capturing the digital dividend
After a quarter of a century of digitalizing their operations, channels,
and experiences, with a strong focus on servicing journeys, banks
can congratulate themselves for having mastered digital.
Virtually every bank has a mobile app that half of which are from their primary bank— BBVA is one bank that has succeeded at this.
works effectively: it manages the majority of 73% acquired at least one financial services By 2017 it was using its digital channels for most
customer interactions, is typically rated well product from a new provider in the past of its customer servicing, but for only 25% of
over 4 out of 5 by customers and—together 12 months.9 product sales.* Five years later the picture had
with digital enhancements elsewhere in the changed: 61% of its sales were closed on the
organization—continues to deliver big efficiency Digitalization has improved banks’ ability bank’s digital channels, and its cost-to-income
gains and convenience for customers. to solve customers’ most basic needs, but ratio had fallen from approximately 50% to
conversations about their financial aspirations 43% (see also page 13).
Yet there have been unwelcome side-effects. and how the bank can help them achieve their
By shifting customer engagement out of the goals have become increasingly rare. Yet the To increase their percentage of digital sales,
branch and onto their digital channels, banks’ goal of increasing the proportion of digital banks are getting better at personalizing their
experiences have become functionally correct sales depends on it. interactions. Like many service providers, Bank
but emotionally void. And at the same time as of America asks customers for feedback every
their personal connection with customers has The good news is that customers still trust time they engage with the organization. It now
weakened, so has banks’ ability to differentiate banks and are sending them clear signals of has more than 50 million responses. But instead
themselves: Accenture’s Life Trends 2024 what they want. To capture the full potential of just aggregating that data to gain a better
survey8 found that 42% of consumers find it of digital, banks need to improve their ability understanding of its customer base as a whole,
hard to distinguish between financial services to respond to these signals. This includes the bank’s primary aim is to focus on individual
brands. In the process, customer loyalty shifting their thinking about digital from customers: how they feel, what they want, and
has weakened. The average consumer “servicing” to “conversations”. how their experiences could be improved.10
has 6.3 financial services products, only
* M easured by the percentage of total lifetime economic value of all products sold. Banking on AI | Banking Top 10 Trends for 2024 11
Trend 2 | Capturing the digital dividend
Currently, as our 2022 analysis of 41 leading banks to gain a better understanding of each Bank customers, in the
banks across ten markets shows, less than customer’s circumstances, and to reach out
past 12 months, used
15% of them provide comprehensive rewards proactively with empathy, timely advice and
branches more than any
for customers who increase the number relevant offers. We call this approach ‘life-
of products and services they use or the centricity’. When you feel recognized and other channel to open
transactions they conduct with the bank. appreciated, why would you buy elsewhere?
accounts, get advice and
The ability to treat each customer as an
acquire new products.
individual can make a big difference to As banks commit to having conversations
both the customer and the bank, but too with customers, the logic of life-centricity Almost 2 out of 3 turn
often personalization goes little further rather than product-centricity becomes more to branches to solve
than delivering banner advertisements. compelling, and we expect to see corporate
specific and complicated
structures changing to reflect this. This will
problems.
In 2024, a growing number of banks will have many benefits, for both parties. When
seek to realize a greater return on their the banking app—consumers’ second-most
Source: Accenture Global Banking
investment in digital by using their vast important consumer technology after their
Consumer Study, 2023.
stores of customer data and advanced car8—becomes more than just a means of
analytics and AI capabilities to move beyond checking account balances and making
basic demographic segmentation and start payments but provides a steady flow of
treating customers as individuals. This will valuable, tailored advice and propositions,
not only make customers feel more special, the relationship between the two becomes
increasing their loyalty. It will also allow these more trusting, durable and productive.
Banking on AI | Banking Top 10 Trends for 2024 12
Trend 2 | Capturing the digital dividend
BBVA is a good example of a bank that has transformed its
operating model to (among other things) develop an end-to-end
personalization capability, optimize its customer experiences,
and improve the effectiveness of its customer acquisition and
cross-selling. Just one of the metrics it has announced is a 30%
improvement in its conversion rate for auto-loan sales.11
The ultimate objective is to offer the same authentic, personal
experience through digital channels as banks have always done
face-to-face in their branches. Commerzbank believes its new
mobile virtual assistant will do this, enabling private and small-
business customers to have natural and engaging conversations
on general topics as well as for financial advice.12 By combining the
convenience and efficiency of digital with the contextual relevance
that comes from a deeper and more timely understanding of each
customer, banks will be able to shift a growing proportion of their
sales to digital while simultaneously reinforcing trust and loyalty.
This is the digital dividend they have been
pursuing for so long.
Banking on AI | Banking Top 10 Trends for 2024 13
Trend: 3
All the risk
we cannot see
In 2024, banks will be confronted by a variety of
risks: some familiar, others less predictable. We
have identified five that we think deserve attention.
Planning for the unplanned will pay dividends.
Trend 3 | All the risk we cannot see
With hindsight, all risks are obvious.
Yet as we entered 2023, no one foresaw
that a bank failure in California would
escalate into a regional banking panic
and ultimately lead to the merger of
Switzerland’s last two major banks.
Given the far-reaching consequences of events such as these,
banks need to improve their planning for risks we cannot always
see. This is especially true as stability continues to elude the
industry and the markets it serves. In our latest Risk Survey,
72% of senior banking risk professionals said their organization’s
risk management capabilities and processes have failed to
keep pace with the rapidly changing risk landscape.13
Banking on AI | Banking Top 10 Trends for 2024 15
Trend 3 | All the risk we cannot see
It’s obviously impossible to know 01
exactly what risks 2024 will bring,
but here are a few ideas to get the Banks have invested vast amounts
in bolstering their cyber defences.
conversation started:
However, in November last year, a ransomware attack on the US
subsidiary of the Chinese bank ICBC nearly crashed the US 30-year
Treasury auction and forced participants to trade by using USB pen
drives.14 The advent of generative AI has handed hackers another potent
weapon, enabling them to attack all of banks’ surfaces with deep fakes
that can deceive voice analysis and other defences, amplify phishing
attacks, and create much more complex and elusive viruses. In 2024, as
the likelihood of such attacks succeeding edges toward the inevitable,
banks will shift the focus of their strategies from prevention to resilience.
They too will use generative AI—not only to detect attacks but also to
increase the frequency, depth and scope of their scenario planning,
and to look not only at the immediate implications of a cyber breach
but also the second- and third-order effects—and how they should
prepare and respond.
Banking on AI | Banking Top 10 Trends for 2024 16
Trend 3 | All the risk we cannot see
02
Almost 17 years of near-zero rates has
Figure 3. The average house price has risen more than
caused house prices to rise strongly.
personal disposable income.
There is a growing risk of stressed customers defaulting on their
160
mortgages as rates remain high and salary increases fail to offset Evolution of house prices and personal disposable
consumer price inflation. In a sample of Western markets, the rise in income across selected major economies*
150
the price of houses has significantly exceeded the average growth in Indexed: 2013 Q1 = 100
household disposable income since 2015 (Figure 3). As rates remain House price
140 index
elevated and low pre-Covid mortgages roll off, the risk of stressed
consumers defaulting rises, even where unemployment is low.
130
The question then is: will governments allow large numbers of
120
employed but hard-pressed home-owners to lose their properties or
will we see some interesting public/private partnerships—the Canadian Personal disposable
income index
government is already talking about interventions to help citizens 110
crushed by rising rates.15 In our 2023 Global Risk Survey, only 35% of
172 banking executives said their organization is fully able to assess 100
2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023
the risks associated with interest rate increases.16 This alone suggests
a low level of readiness to intervene if the situation turns ugly. *Overall indices calculated as simple averages of house price and personal disposable
income indices for: Australia, Belgium, Canada, Germany, Spain, France, UK, Italy,
Switzerland, Netherlands and US
Source: Accenture Research based on Federal Reserve Bank of Dallas
Banking on AI | Banking Top 10 Trends for 2024 17
Trend 3 | All the risk we cannot see
03
Figure 4. Commercial real estate exposure constitutes a significant share of
GDP and of banks’ and other financial institutions’ balance sheets.
The status of commercial real estate (CRE)
CRE debt as % of GDP Bank loan exposure to CRE
is similarly precarious. % of total assets, Dec 2022
18%
A lot has been written about it recently, and the bankruptcies 12% 0% 2% 4% 6% 8% 10% 12%
of Signa Development17 and WeWork have highlighted
Sweden
what may be the most publicized risk in waiting. As with
mortgages, 15 years of near-zero rates followed by a sudden USA
rise, combined with a shift to work-from-home, has left many US Europe
Norway
commercial property developers and real-estate owners
Owners of Netherlands
in a perilous position. It is a global risk, and CRE debt and
CRE debt Others
equity are held not only by banks but also by other players US only Germany
throughout the financial industry—often beyond the scope 13%
Belgium
of regulators (see Figure 4). Commercial
mortage-backed 14% 38% Banks Australia
securities
Italy
15%
Insurance Spain
21%
France
Agencies and government-
sponsored entities
Source: Accenture Research based on IMF: Global Financial
Stability Report, October 2023, and Reserve Bank of Australia:
Financial Stability Risks from Commercial Real Estate.
Banking on AI | Banking Top 10 Trends for 2024 18
Trend 3 | All the risk we cannot see
04
Figure 5. Non-bank financial institutions hold nearly
60% of the private sector’s total global financial assets.
$ trillions. Financial assets held by central banks and $422
The rise in shadow banking.
$396
public financial institutions are excluded.
$361
In the aftermath of the 2008 Financial Crisis, off-balance-sheet
$332 $335
lending became a priority for regulators, who introduced $315
waves of Basel regulations as well as many local measures. $294
$284
This caused banks to dial back their risk. But the question is: $263
$251
has that risk gone, or have we just moved it out of sight? Banks $224 $236
$209 $210
hold less than 50% of financial assets (Figure 5) and the share 57%
56%
of US non-bank mortgage origination has ballooned from
56%
9% in 2010 to 62% in 2022.18 Is anyone monitoring that risk, 55% 56% 55%
54%
54%
and what would the inevitable fallout be for banks, insurance
53%
51%
companies and pension funds should this turn bad? 50% 49%
48% 50% Banks
Non-bank
financial
institutions
2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021
Source: Accenture Research analysis based on Financial Stability Board, “Global Monitoring Report
on Non-Bank Financial Intermediation 2022”
Banking on AI | Banking Top 10 Trends for 2024 19
Trend 3 | All the risk we cannot see
05
Our aim is not to be a banking Nostradamus,
implying that we can see and evaluate all major
China’s growing involvement in the
risks. We’re simply making the point that banks
economies of most countries, and its
face a large and varied array of risks, some of
concerted effort to attract foreign
which have been publicly scrutinized while
investors, is another risk that
others are hidden in plain sight. Many have the
warrants scrutiny.
potential to cause extensive damage. To protect
The government has worked hard in recent years to
themselves and their customers, banks need to
strengthen its regulatory regime, but the fact that its
improve the frequency, depth and scope of their
residential property sector in particular is so heavily
leveraged, and that developers like Evergrande were scenario planning, using real-time data.
allowed to run up liabilities of approximately US$300
billion,19 show that the risk is very real. If the mounting
We believe that in 2024, these scenarios
debt burden is a bubble, and if the authorities fail to deal
will inform more board conversations and
with the threat, the fall-out for global banks as well as
economies worldwide could be severe. guide more strategic decisions.
Banking on AI | Banking Top 10 Trends for 2024 20
Trend: 4
A whole new
way of working
The way banks work is about to change radically. New
skills, approaches and mindsets will be needed, not only
in IT but—more critically—in every function and level of the
bank. The challenge is way bigger than recruitment alone
can solve. An entirely new strategy is called for.
Trend 4 | A whole new way of working
The digitalization of banks’ operations over the
past 25 years caused an escalation in what was
commonly dubbed ‘the war on talent’.
There is no doubt competition for The challenge goes beyond this, however, and of possibilities for banks to generate new
high-end technical skills will intensify in is different than during the Digital Age. With value for customers, more rewarding
2024 as every financial institution, and digital, banks hired specialist teams to develop work for employees, and growth for the
indeed every organization on the planet, their online and mobile banking applications. organization. To seize this opportunity,
advances its strategy to capitalize on AI, Because AI will impact nearly every job in leaders need to reimagine the future of
cloud, and data analytics. every bank, recruitment simply won’t work. human + machine work, starting with a
Banks will need to create a culture of curiosity, blank slate. They are starting to think about
Some leading banks, including Lloyds Banking receptiveness to change and continuous how generative AI should be integrated
Group20 and Banco Santander,21 are investing development—one that encourages and into every role and function, and how their
heavily in their captive IT organizations. They enables all employees to reinvent their workforces and culture will change as
are recruiting and training aggressively to roles and, indeed, themselves. the technology automates much of the
acquire the experts they need as they scale necessary work and elevates human skills
the roll-out of AI. However, demand is likely The Digital Age saw IT teams designing and such as strategic and creative thinking,
to greatly exceed their availability. In addition, building websites and mobile apps, but it judgement and relationship building.
the most talented among them will prefer to barely changed the work that most banking
work for firms that can offer careers leading professionals did. Generative AI, on the other
to leadership roles. Most banks will therefore hand, will change what people do and how
need an alternative approach. they do it. In the process it will open a world
Banking on AI | Banking Top 10 Trends for 2024 22
Trend 4 | A whole new way of working
Our 2022 Future of Work survey22 found new human roles that include the introduction, Only
that only 26% of bank CEOs had a future- management and governance of this innovation.
ready strategy that was holistically focused Less obvious, but just as important, is how 26%
on changing how, why and where their people will work alongside the machines to
people work. This is sure to change swiftly preserve the human face of the bank: be
as organizations develop ambitious plans available to customers, maintain relationships,
around AI. It is important that this strategy and show genuine empathy as they help
of bank CEOs have a
concentrates not only on the necessary to address their concerns.
future-ready strategy.
changes in roles, tasks and skills, but also
on how generative AI is likely to change It is only when the human + machine workforce
the soul of the organization. is expanded and enhanced in such a holistic and
human-centric way, and when HR and change
We have been warning for years that banks, professionals are fully involved in shaping the
in their well-intentioned drive to digitalize, transformation, that the full potential of
have become remote, impersonal and generative AI will be within banks’ reach.
undifferentiated. Generative AI could
exacerbate that. As banks define the
objectives of their generative AI
transformation, they are envisaging
Banking on AI | Banking Top 10 Trends for 2024 23
Trend 4 | A whole new way of working
OCBC putting gen AI to work
Singapore’s OCBC Bank, a generative AI trailblazer, has
completed a six-month trial of an intelligent chatbot and is
now rolling it out to all its 30,000 employees to help them
write, translate, research and innovate. Participants said
they were able, on average, to do their work 50% faster—
which included the time taken to check the accuracy of the
bot’s output. An earlier trial, to develop code, summarize
documents, transcribe calls and create an internal
knowledge base, boosted productivity by a similar amount.
The bank currently uses AI to make more than four million
decisions daily in risk management, customer services and
sales—and expects this to increase to 10 million by 2025.23
Banking on AI | Banking Top 10 Trends for 2024 24
Trend: 5
The power
of pricing
Banks have always known that optimized pricing can have
a huge impact on their top and bottom lines. This year, they
are starting to combine intuition with generative AI and more
current and comprehensive data to turbo-charge scenario
planning and move closer to personalized pricing.
Trend 5 | The power of pricing
Every businessperson knows that a small
change in price can have an oversized effect
on demand, revenue and income.
In banking, all things being equal, a Despite years of talk about “hyper- smaller and smaller groups to find the perfect
1% increase in revenue translates into a personalization”, banks’ pricing has always solution—similar to how Isaac Newton used
~40 bps improvement in pre-tax ROE. A been characterized more by consistency and calculus to measure the area under a curve.
1% improvement in cost, however, only simplicity than the ability and willingness of Unfortunately, until now, banks haven’t been
improves ROE by ~25 bps.24 individual customers to pay. What’s more, able to approximate Newton’s precision as he
with interest rates having been stuck virtually conceived of infinitely smaller spatial figures.
The challenge, however, has always been at zero for the past 15 years, there was little This has meant that, for many customers, their
to predict the impact of a price change on benefit to be gained by improving prices were wide of the mark.
revenue. Economists can plot graphs showing the sensitivity of pricing.
the price elasticity of demand, but they In the future, AI will play a major role in bringing
can seldom take account of all the relevant In 2024 we will see the beginnings of a pricing to perfection. It will consider thousands
variables and offer more than an averaged change in all this; a different approach to of variables to rapidly come up with a perfect
view of a customer base or market. Which pricing and sales that could be one of the price for retail and commercial customers—
means that a banker who sets a price will hope most important contributions of generative either individuals or small segments with very
that it works for most customers but will know AI to corporate profitability—as well as similar needs. It will measure the outcome,
that for a significant proportion it is too high, customer value. In theory there is a perfect feed it back into its calculations along with
and there’s a risk of attrition, while for another price for each combination of customer, competitive data and other changes, and
group it is less than they would be willing to product, and channel. Ideally, banks would adjust in real time.
pay, which represents a revenue forfeit. like to price customers in increasingly
Banking on AI | Banking Top 10 Trends for 2024 26
Trend 5 | The power of pricing
The new prices can be delivered health, and then shares the value this creates is that the benefits are mostly passed back to the
automatically to all customers, together through personalized interest rates and other customer. In this case, the race to perfection will
with tailored incentives for saving more or rewards. “It’s simple,” the bank states. “We initially advantage the early adopters and ultimately
subscribing to more products. These could believe that we’ll do well when our clients the banking customers. However this plays out,
be promoted through personalized marketing do well, and society will benefit too.”25 pricing is likely to receive a lot more attention
scripts, also crafted by generative AI. With as generative AI matures.
millions of iterations, and the ability to learn Dynamic pricing has always been possible,
from each, banks should soon be able to but it has mostly depended on intuition.
zero in on the perfect price. In the future, banks will price their services
with a greater understanding of how each
They will also be able to execute their business variable affects the outcome in relation to
strategies with more precision: set prices that each customer. Some may use the ability to
find the ideal balance between profit, growth maximize short-term profits, while others will Dynamic pricing has
and customer value, and between short-term test innovations and drive growth; another always been possible, but
and longer-term objectives. B"
272,accenture,Accenture-POV-Reinventing-Life-Sciences-Age-of-Gen-AI-28Aug2024.pdf,"Reinventing life
sciences in the age
of generative AI
Contents
04 07 10 41
19
Executive Supercharging AI is revolutionizing Five C-suite Start your
summary science with intelligent the pharmaceutical imperatives reinvention journey
technologies: value chain
This is not your typical
technology upgrade
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Contents Executive summary Supercharging science Revolutionizing the value chain Five C-Suite imperatives Start your reinvention journey 2
AAuutthhoorrss
Petra Jantzer Selen Karaca-Griffin Kailash Swarna Tracy Ring Jen Spada
Senior Managing Director – Thought Leadership Principal Director – Managing Director – Chief Data Officer and Global Generative Managing Director –
Global Life Sciences Lead, Products and Life Sciences, Global Clinical Lead, AI Lead – Applied Intelligence Products, Global Generative AI Strategy Lead,
Accenture Accenture Research Accenture Life Sciences Accenture Life Sciences Accenture
Petra Jantzer is the Global Lead for Selen Karaca-Griffin is the Global Research Kailash is the Global Life Sciences Clinical Tracy Ring serves as the Chief Data Jennifer is a Managing Director in
Accenture Life Sciences and the senior Lead for Accenture Products and Life Development Lead at Accenture, driving Officer and Global Generative AI Lead Accenture's Life Sciences practice,
Client Account Lead for one of the world’s Sciences, leading a team of 30+ researchers digital transformation strategies to boost for Accenture's Data & AI Life Sciences spearheading the life sciences innovation
leading pharmaceutical companies. globally. She is responsible for developing R&D productivity for global life sciences division. With more than two decades of team to develop cutting-edge solutions
She was previously the industry leader for the industry’s thought leadership agenda, clients. With over 20 years in the field, experience, she has crafted AI strategies that advance client initiatives. With a robust
R&D and Europe Life Sciences and a former which includes scientific innovation, science he has extensive experience across for numerous organizations, orchestrated background in the industry, her expertise
partner at McKinsey. Petra has 20+ years and technology convergence, digital health, drug discovery, translational sciences, large-scale transformation deals, and spans commercial transformation, strategy,
of industry experience, holds a Ph.D. market disruptions and their impact on the clinical development, pharmacovigilance, facilitated extensive platform ecosystem operations, patient services and digital
in tumor immunology and specializes in future of industries. She is based in Boston, regulatory affairs and commercialization. partnerships. In her current role, Tracy marketing. Additionally, Jennifer directs
cross-functional transformation programs. Massachusetts. Selen holds two BS degrees Kailash also engages in research at MIT's leverages generative AI to revolutionize the Accenture’s patient research activities.
She is president and co-founder of in molecular biology and chemistry, an Sloan School of Management, focusing life sciences sector, providing guidance to She earned her Bachelor of Science in
Advance – a cross-industry association in MS degree in biotechnology, and an MBA on financial engineering to enhance R&D CDOs and Chief Analytics/AI Officers on biological engineering and a MBA from
Switzerland dedicated to driving gender from Babson College. She also serves as decisions. He holds an MBA from MIT and a maximizing strategic value and impact in Cornell University.
equality in business. a Biotechnology Industrial Advisory Board Ph.D. in physical chemistry from Oklahoma commercial, R&D, supply chain and other
Member for Northeastern University. State University. enabling domains through a Responsible AI
framework. She specializes in implementing
AI solutions for regulatory submissions,
early-stage drug development, and
advancing the future of commercial and
intelligent supply chains.
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Contents Executive summary Supercharging science Revolutionizing the value chain Five C-Suite imperatives Start your reinvention journey 3
EXECUTIVE SUMMARY
Where could reinvention take your business?
What if your What if your company What if your company What if your company What if you could
company could could dramatically could rapidly optimize could dynamically use everything you
develop novel compress R&D manufacturing anticipate market know about all of your
medicines for timelines and recipes and facilitate shocks and customers — from
previously reduce the cost agile, resilient black-swan events patients to providers
undruggable targets of developing a and sustainable and respond with to payers — to truly
and address currently medicine from end-to-end supply minimal disruptions meet them where
untreatable illnesses? billions to millions chains of new to patients? they are with speed
of dollars? modalities for and efficiency?
better competitive
advantage?
Organizations are achieving exactly these kinds of breakthroughs by using intelligent
technologies such as classical and generative artificial intelligence (AI) reinventing themselves.
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Contents Executive summary Supercharging science Revolutionizing the value chain Five C-Suite imperatives Start your reinvention journey 4
In 2023, we presented Total Enterprise Reinvention for Biopharma, The Why
a strategy of changing every part of a business, adopting change
AI is supercharging science and reinventing
at scale and generating innovation, resilience and value. When business — this isn’t your typical technology upgrade.
companies engage in Total Enterprise Reinvention, we wrote,
they commit to creating a strong digital core on which they
can essentially turn “change” into a capability, such that any
transformative effort in any area of the business builds on and
The What
contributes to other efforts. The result — demonstrated by the few
AI is revolutionizing the value chain, offering strategic
leading companies we identified as “Reinventors” — is sustainable,
opportunities to generate significant value if workflows and
accelerated and efficient growth.
processes are consistently reinvented end to end.
At that time, we forecast that companies embracing the
transformative power of technology, data and AI to drive reinvention
would be ahead of the curve in the next decade and beyond.
The How
This year, the growing impact of disruptive technologies such as
Five C-suite imperatives will help you reinvent
generative AI has made it even clearer that continuous reinvention is
your business and pull ahead of the pack.
becoming the default strategy for the world's leading organizations.
In fact, our research found that the competitive edge belongs to
Reinventors, who not only define the new performance frontier
for their industries but also enjoy the largest financial benefits. In
this report, we present our recommended approach to continuous
reinvention in the era of generative AI: The Why, What and How.
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Contents Executive summary Supercharging science Revolutionizing the value chain Five C-Suite imperatives Start your reinvention journey 5
About the research
We took a comprehensive approach to study the topic of Total Enterprise Reinvention.
This report is based on:
• A multi-year survey of over 3,000 • Our annual life sciences CEO Imperatives
executives across 19 industries and 10 Research, which identifies critical
countries. Respondents were asked about disruptions and key priorities based on
their organization’s approach to business qualitative interviews with the CEOs of
transformation and reinvention strategy, the top 40 life sciences companies by
as well as about their specific programs revenue. We validate these trends in our
and success factors. The surveys were annual CEO roundtable, where industry
conducted in November 2022 and CEOs gather to discuss the industry’s
October to November 2023. In this report, most pressing issues and promising
we provide comparisons between the two, opportunities. The CEO roundtable was
focusing on new insights gained from the held at the 2024 meeting of the World
most recent responses. Economic Forum in Davos, where we
gathered industry C-suite leaders to
• The annual Pulse of Change Index that
discuss the impact of classical and
quantifies the level of change affecting
generative AI on the life sciences industry.
businesses globally, caused by six major
factors: technology, talent, economic, • Collaboration with our innovation strategy
geopolitical, climate and consumer experts and subject matter advisors to
and social. The index provides context ideate, shape and push the boundaries
supporting the need for reinvention. of our thinking on reinvention in the age
of generative AI. We then tested our
approach with multiple clients.
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Supercharging science with
intelligent technologies:
This is not your typical technology upgrade
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The biopharma industry is on the brink of a Consider these challenges at a high level:
groundbreaking revolution, propelled by the
• Lengthy and costly drug development: • Low growth due to patent expirations as well as
remarkable potential of intelligent technologies
The average time to bring a new medicine to market government and private market pressures:
such as classical and generative AI and next
is 10–12 years, with costs exceeding $2.6 billion. The top 20 biopharma companies (with some
generation computing. These technology
Approximately 90% of drug candidates fail during exceptions) are experiencing a low-growth period, with
advancements promise to deliver breakthrough
discovery and development, and R&D productivity has an average revenue CAGR of 4% over the next five years.2
treatments and life-changing medicines at an
remained stagnant over the past decade.1 This anemic growth is attributed to factors such as
unparalleled pace, addressing the industry’s
patent expirations, pricing pressures from governments
most pressing challenges head-on.
• Increasing complexity in manufacturing and
(e.g., Inflation Reduction Act in the US) and private
commercialization:
market forces leading to net price decreases.
As scientific progress leads to new modalities
and personalized treatments, the complexity of • High cost of capital:
manufacturing and commercializing these therapies The persistent high cost of capital is compelling CFOs
increases. Many new modalities are launched with to explore ways to enhance profitability. In addition, it
unsustainable supply chains that require years puts pressure on leaders to invest in programs that can
or even decades to optimize. generate returns faster.
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Contents Executive summary Supercharging science Revolutionizing the value chain Five C-Suite imperatives Start your reinvention journey 8
Intelligent technologies are set to transform every aspect
of the life sciences industry, from drug discovery, clinical
development and patient care to manufacturing and the reliable
supply of complex medicines. This shift promises to usher in an
era of unprecedented innovation and efficiency and will drive
better outcomes for patients.
But this opportunity hinges on a critical caveat: Unlike previous
technology transformations, a purposeful shift to intelligent
technologies requires companies to embrace and deeply embed a
culture of continuous reinvention across the enterprise.
According to our research, life sciences is one of the top two
industries most actively pursuing reinvention, the other being
software and platforms.3 Our annual CEO priorities research confirms
that harnessing intelligent technologies for business transformation
is the top priority for CEOs in 2024. This marks the first time in a
decade that technology has been identified as a standalone priority,
underscoring its pivotal role in addressing the industry’s challenges.4
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AI is revolutionizing the
pharmaceutical value chain
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To start, consider the effect intelligent technologies
are already having along the biopharma value chain:
• More than 50 drug candidates discovered • Intelligent technologies are helping leaders
with AI are now progressing through clinical better allocate capital for manufacturing,
pipelines. Molecules are being designed at supply chain and commercialization.
a fraction of the time previously required, Historical sales data, prescription patterns,
and for several targets once considered epidemiology and target population
undruggable.5 information improve forecasting. This
can inform commercial and medical team
• The analysis of historical data, literature,
positioning as well as the timing and
real-world evidence and simulations of
location of new manufacturing sites.
multiple trial scenarios all enable companies
to optimize clinical trial protocols and These are just a few examples of how intelligent
resource allocation. This ultimately shortens technologies are driving meaningful and
trial duration and reduces costs. positive changes in the biopharma industry.
A more comprehensive view of the “strategic
• Companies are leveraging advanced
bets” across the biopharma value chain is
analytics of complex chemistry and biology
shown in Figure 1. These strategic bets confer a
in recipes to achieve up to a 90% decrease
significant competitive advantage at each step
in waste production and energy and water
of the value chain. In the following section, we
consumption, while improving consistency
explore each possibility in turn.
and speed in the manufacturing of life-
saving drugs.6
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Figure 1
Illustrative strategic bets in biopharmaceutical value chain
Process development,
Value Pre-discovery Discovery research Preclinical Clinical, Commercialization Enterprise functions
manufacturing,
chain & translational Regulatory, Safety
quality & supply chain
Basic research into Novel target discovery Prediction & optimization Optimize trial & protocol Accelerated & accurate Predictive brand & Dynamic portfolio
disease biology & new approaches to of PKPD/ADME properties design with simulation product launch portfolio strategy management and
corporate strategy
structural biology
Basic research into Site enablement Predictive manufacturing Dynamic access
Discovery, refinement &
treatment modalities and optimization process robustness optimization Proactive risk
Accelerated target development of novel
management and
validation through biomarkers
crisis mitigation
optimized pharmacology
Basic research into safety Clinical data Autonomous Real-time content
and efficacy in humans management demand sensing & supply chain & review
Expanding biobanking
SC orchestration Enhanced corporate
Modality selection
to leverage emerging
brand and reputation
& optimization
Systems approach to technology for multi-omics
Hyper personalized
Regulatory
disease and target
submissions Predictive asset engagement
modeling
Design & synthesis of maintenance Strategic location
clinic-ready molecules Integrating internal & planning
Strategic external clinical data Real-time data analysis Democratized insights
bets into early discovery & and safety monitoring Optimized quality & & recommendations
translational science real-time batch release Strategic E2E employee
Optimize developability
value planning
& manufacturability
CMC regulatory filing
Improved customer
Optimize discovery and patient experience Optimized knowledge
portfolio for PTRS management & learning
Recipe development,
scaling & optimization
Resilient, sustainable,
agile supply networks
for all modalities
Accelerated post-
approval process &
product optimization
Source: Accenture 2024.
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Contents Executive summary Supercharging science Revolutionizing the value chain Five C-Suite imperatives Start your reinvention journey 12
Research and Development However, the industry struggles with fragmentation
(Pre-discovery, discovery research, across different functional areas of the value chain,
pre-clinical & translational, clinical, resulting in numerous handoffs, non-standard
regulatory, safety) application of technology solutions and persistent
data silos. To overcome these inefficiencies, there is
The future of R&D hinges on using intelligent
a critical need to establish a common language for
technologies to dramatically improve cycle times,
effective collaboration among scientists, engineers and
success rates and efficiency. By accelerating discovery
marketers.
through in silico methods and using AI for tasks
By optimizing processes and responding more adeptly
ranging from molecule generation, optimization of
to market demands, companies can make full use
lead compounds, biomarker discovery and patient
of their organizational capabilities. Take the recent
stratification, companies can enhance their clinical
repurposing of diabetes drugs for obesity treatment.
success rates and expedite the entire R&D process.
Companies used extensive safety data collected over
AI’s ability to predict off-target effects, optimize drug
a decade for one disease and efficiently used market
safety profiles and incorporate digital tools for remote
signals to enhance drug development for a different
monitoring and patient retention presents a significant
disease. Such strategic shifts promise to mitigate high
leap towards increasing investigational new drug
attrition rates and reduce lengthy, costly processes
approval rates and reducing trial durations.
currently burdening the industry.
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Process Development, Manufacturing, Quality
• Companies can demonstrate a deep understanding
After a drug has been discovered and moved into clinical
and control over complex drug production through
trials, companies should develop a consistent and
a combination of scientific data and AI, expediting
scalable recipe to support supply as quickly as possible.
regulatory filing and approval.
However, R&D efforts are surfacing increasingly specific
and complex drugs that require increasingly complicated,
• Recipe tech transfer and knowledge management
biology-based recipes. Faster clinical trials mean less
between sites in the supply chain (internal sites or
time for recipe development teams to optimize these
external contract manufacturers) will be accelerated
complex recipes and scale them to the appropriate level.
and less subject to the risk of unexpected quality issues,
Intelligent technologies will help move some of this
ensuring supply chain “resilience by design.”
recipe development from slow and repetitive wet lab
experimentation to the in silico space, ensuring that:
• All markets rapidly adopt significant post-approval
recipe improvements, like new automation or process
• Drugs progress through clinical trials to commercial
sensing tech.
production without being hindered by recipe
development and scaling issues.
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Supply Chain
of supply nodes — can have extremely long-term This in turn can lead to complex post-approval change
Classical and generative AI give companies the
impacts on commercial supply chain agility, controls that must be managed by regulatory affairs for
opportunity to redesign their supply chain and operations
sustainability and resilience. all markets as well as a complex proliferation of product
end-to-end, enhancing resilience, agility and sustainability.
variants that must be managed by supply chain planners.
When combined with both internal and third-party data,
Even in the actual manufacturing of the product,
With a coordinated, connected data fabric and improved
AI can give companies a unified view of demand. This
complexity increases depending on how many sites are
standardization, all parties — from commercial supply
allows them to not only understand but control the supply-
involved in the process and how well they stay harmonized
chain to recipe-development teams — can work together
side complexity of novel treatments. AI can also generate
with each other and their colleagues in R&D who are
more effectively. Such integration also better enables AI
scenarios and automate responses to many potential
developing new drugs that will move to those nodes over
tools to support collaboration, oversight and enterprise-
disruptions. This approach helps improve production
time. Each site manages its own production process,
wide continuous improvement. Without such coordination,
processes for maximum yield and highest quality.
equipment, asset management, quality control, operations
fragmentation occurs and opportunities are missed.
technology (and sometimes IT) system landscape and
Traditionally, companies complete the design of their
continuous improvement programs at individual nodes.
supply chain and operations in silos long before the
Local site level continuous improvements or corrective and
product enters the commercial supply chain. Decisions
preventative actions can lead to divergent evolutions of
during development — regarding manufacturing recipes
the recipes for a product.
(bill of materials, equipment), formulation, packaging,
release, shipping methods, CMC filing strategy and choice
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Contents Executive summary Supercharging science Revolutionizing the value chain Five C-Suite imperatives Start your reinvention journey 15
Commercialization
AI is transforming commercialization, offering significant
In marketing, ongoing advancements powered by AI
improvements across access strategies, marketing and
will accelerate original and derivative content creation
customer engagement.
including images, copy and animation. These technologies
can facilitate dynamic marketing material assembly within
For example, AI significantly bolsters access strategies by
regulatory constraints. Early applications of generative AI to
using advanced data for deal modeling and enhanced payer
medical loss ratio processes are already bolstering human
contract negotiations. Generative AI can help simulate
reviewers’ work without taking them out of the loop.
complex payer negotiations and streamline decision-making
processes. Integrating disparate data sources facilitates
Generative AI is also transforming customer engagement.
contract performance monitoring. Using AI can enhance
Using personal large language models like assistants
oversight and minimize rebate leakage, improving revenue.
better prepares field teams for customer interactions.
These “assistants” access data, provide insights, simulate
conversations and analyze customer contexts for more
productive engagement. The era of the “bionic rep” is here.
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Figure 2
Opportunities are evident in each area of the value chain. Based on our research and client experience, these strategic bets represent considerable potential value if
the workflows and processes are reinvented end-to-end.
The size of the opportunity if the workflows and processes are reinvented end-to-end.
Pre-discovery & Preclinical, Product development, Commercialization Enterprise
discovery research translational, clinical, manufacturing, quality functions
regulatory & safety & supply chain
Accelerate timelines by Accelerate timelines Lower supply chain risk Optimize patient and Lower costs and
almost 3 years per by 1.5 years per and get critical customer engagement to increase efficiency
successful drug successful drug medicines into the accelerate time to peak
hands of patients faster sales while effectively
managing costs
Discover better drug
candidates (e.g., for
undruggable targets)
1-3%
10 - 30%
Revenue uplift
(product availability) Acceleration in time
to peak sales
3 to 5%
$0.3 - 1.5B $0.2 - 0.8B
10 to 15%
Production &
Revenue upside per Revenue upside per Fulfillment costs Commercial costs
successful drug successful drug
10 to 15% 20 to 25%
$600 - 800M $300 - 400M
30% +
Working capital Script conversion
Costs per successful drug Costs per successful drug reduction (inventory) and adherence Corporate function cost
Source: Accenture Research, 2024. See methodology section.
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Contents Executive summary Supercharging science Revolutionizing the value chain Five C-Suite imperatives Start your reinvention journey 17
The full potential lies in connection.
Leaders need to think in terms of value streams.
The transformative power of intelligent technologies Understanding that opportunity is critical. Yet many life It’s time to think in terms of value streams — such as
such as generative AI on individual parts of the sciences C-suite executives in our recent pulse survey7 accelerating time to clinic or accelerating time to market
biopharma value chain are undeniable. However, remain focused on individual use cases rather than — rather than small pilot projects. (We will cover this idea
while those effects are exciting in and of themselves, end-to-end processes and capabilities. Consider how: in more detail in the section titled “lead with value”).
leaders will need to bridge functional silos to reap the
It’s time to capture the benefits of connecting deep
full benefits of these technologies.
2/3 2/3
functional areas of expertise. All functions should align
Fundamentally, generative AI empowers by know which have outlined potential their reinvention efforts to these cross-functional value
democratizing access to information, accelerating areas they want impacts of generative streams to ensure that their reinvention is comprehensive
its flow throughout the organization. Adopting to prioritize but AI but say that further and delivers value to patients, the entire enterprise and the
generative AI thus presents an opportunity to foster do not have an analysis is required healthcare system.
better collaboration and ultimately deliver value implementation plan to fully articulate
It’s time to develop end-to-end capabilities. This means
across the entire value chain — where the impact on business value
rethinking many processes and integrating intelligent
the whole is greater than the sum of its parts.
technologies into all aspects of the workflows of that
capability. It also means developing the skills needed to
use AI effectively.
Five C-suite
imperatives to help
reinvent your business
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01/
Five C-suite
Lead with value
imperatives
02/
Over the past several months, Accenture has
engaged in numerous discussions with clients
Reinvent talent and ways of working
regarding the impact of generative AI. We have
also undertaken more than 1,000 generative
AI-focused projects, many in collaboration with
03/
leading biopharmaceutical companies. Drawing
from these experiences and our analysis of
Understand and develop an AI-enabled secure digital core
industry leaders, we have identified five key
imperatives for CEOs who are committed to
capitalizing on the opportunities presented by
04/
intelligent technologies.
Close the gap on responsible AI
05/
Drive and support continuous reinvention
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Contents Executive summary Supercharging science Revolutionizing the value chain Five C-Suite imperatives Start your reinvention journey 20
01/
Lead with value
Rather than focusing solely on technology, companies should
prioritize efforts to understand how intelligent technologies
can fundamentally redefine processes and capabilities. Leading
with value means not only seeking cost saving opportunities
but also driving systematic acceleration, innovation and
growth. By taking a strategic view, companies can move
away from low-value proofs-of-concept and embrace the full
potential of intelligent technologies.
To be able to do that, biopharma companies must focus on five
large investment areas to create value at scale. We call these
investment areas “value streams.”
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Contents Executive summary Supercharging science Revolutionizing the value chain Five C-Suite imperatives Start your reinvention journey 21
Accelerating time to clinic Making medicines more accessible
Fundamentally, these value streams represent
the company’s objectives and are inherently
Select and validate novel targets and deliver human- Improve physical access to and affordability of
cross-functional. For instance, making medicines
ready molecules to the clinic at twice the speed and novel medicines to address unmet medical needs
more accessible requires early discussions during
half the cost by using in silico methods, reducing globally. One aspect of this includes supporting
clinical trial design, among clinical, market access
wet-lab experiments and using AI for predicting off novel complex modalities (for example, cell and
and manufacturing teams. These discussions
target effects, thereby optimizing differentiated gene therapies, antibody drug conjugates and
should cover cost of goods sold implications,
efficacy and reducing safety liabilities. messenger RNA) by lowering cost of goods sold
potential manufacturing challenges and impacts
and capital investment in the supply chain while
on reimbursement. Finally, enhancing patient
maximizing agility and quality. Another aspect
access to therapy necessitates collaborative
Accelerating time to market includes an improved financial coverage and more
efforts from manufacturing, supply chain and
affordable pricing to ensure more populations can
commercial teams. Design and execute patient-centric trials and efficient,
afford therapy.
well-controlled and well-understood manufacturing
The integration of value streams across
processes that maximize efficacy, safety and
different biopharmaceutical functions is
consistent sustainable quality to drive differentiated
Establishing end-to-end
illustrated in Figure 3.
regulatory approvals in a third less time.
insights and feedback loops
Share insights across the organization to enable
Maximizing the value faster information flow, better planning and reporting
proposition of medicines — and keep all stakeholders in the loop. Creating and
sharing actionable insights across the value chain for
Prove the health and economic outcomes to
the life of a molecule will allow teams to anticipate
maximize patient benefit and make the case for
and solve for bottlenecks at every step through
physicians to prescribe and for payers to cover
dynamic planning, portfolio prioritization, patient
the medicine globally.
impact, capital allocation and reporting to investors.
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Contents Executive summary Supercharging science Revolutionizing the value chain Five C-Suite imperatives Start your reinvention journey 22
Figure 3
Illustrative value streams in biopharmaceutival value chain:
* Value streams. Source: Accenture 2024.
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Contents Executive summary Supercharging science Revolutionizing the value chain Five C-Suite imperatives Start your reinvention journey 23
Case study
Lead with value: A modern approach to commercialization
As companies go through their reinvention journey, it is operational appro"
273,accenture,Accenture-UNGC-GenAI-Global-Goals-Report.pdf,"GEN AI FOR THE
GLOBAL GOALS
The Private Sector’s Guide to Accelerating Sustainable
Development with Technology
The 2030 Agenda — our global blueprint
for peace and prosperity on a healthy
planet — is in deep trouble. AI could help
to turn that around. It could supercharge
climate action and efforts to achieve
the 17 Sustainable Development Goals
by 2030. But all this depends on AI
technologies being harnessed responsibly
and made accessible to all.""
António Guterres
United Nations
Secretary-General
2 United Nations Global Compact | Accenture Gen AI for the Global Goals 3
CONTENTS
Welcome 6
Introduction 11
What is Gen AI? 15
Why is Gen AI exciting for business? 18
Looking Forward 47
What’s the catch? 21
Resource 1: Playbook for implementing
The private sector’s leading role in Gen AI responsibly 54
sustainable development 22
Resource 2: Using Gen AI to support
Using Gen AI to advance the your sustainable development ambition 56
Sustainable Development Goals 25
Resource 3: The role of each
Operational Efficiency 28 business function 58
Sustainable Value Chain 30 Resource 4: Broader ecosystem
advocacy recommendations 60
Innovation 32
Appendix 65
Communication and Reporting 34
References 69
Mitigating the sustainable
development risks of Gen AI 37 Acknowledgments 72
4 United Nations Global Compact | Accenture
FOREWORD: UNITED NATIONS
THE TEN PRINCIPLES OF THE
GLOBAL COMPACT
UNITED NATIONS GLOBAL COMPACT
HUMAN RIGHTS
Sanda Ojiambo
1. Businesses should support and respect the protection of
Assistant Secretary General and CEO,
internationally proclaimed human rights; and
United Nations Global Compact
2. make sure that they are not complicit in human rights abuses.
The world is wavering on the 2030 Agenda for Anchored in international standards, the Ten
Sustainable Development and achieving the Principles of the UN Global Compact are the guiding
LABOUR
Sustainable Development Goals (SDGs or Global framework to ensure core business models are
Goals). Increased geopolitical tensions, inequalities, principles-based, and they can also be applied to
and climate change impacts have hindered progress guide companies towards responsible AI models as 3. Businesses should uphold the freedom of association and the
and added to the complexity of the sustainability the private sector makes this technological leap. effective recognition of the right to collective bargaining;
landscape.
This report acknowledges and complements 4. the elimination of all forms of forced and compulsory labour;
Gen AI for the Global Goals outlines the private existing UN-level efforts towards in-depth analysis
5. the effective abolition of child labour; and
sector’s opportunity to use Generative AI (Gen and recommendations on the governance of AI for
AI) as an accelerator for SDG action. The private humanity and the Global Digital Compact. Drawn
6. the elimination of discrimination in respect of employment
sector’s access to capital, wealth of data, and ability from a multi-stakeholder consultative process, the
and occupation.
to act quickly across geographies creates a unique report highlights examples of tangible actions being
opportunity for impact. Yet, the private sector taken today to help private sector leaders consider
must pay special attention to the unique risks how they can support the advancement of the SDGs
ENVIRONMENT
of an explosion in Gen AI usage. with Gen AI in their strategies and operations.
Indeed, the UN Secretary General’s high-level In compiling this report, we are grateful to many 7. Businesses should support a precautionary approach to
multistakeholder advisory body on AI interim report colleagues at the UN Global Compact and our environmental challenges;
notes that AI applications could potentially be a collaborators at Accenture for their insights and
game changer in helping to meet the SDGs, but also contributions. We would also like to express our 8. undertake initiatives to promote greater environmental
that AI poses risks to cyber security, privacy, and appreciation to the business leaders and other responsibility; and
cultural diversity. stakeholders who were critical to the development
of this report. 9. encourage the development and diffusion of environmentally
To this end, several global initiatives are underway friendly technologies.
that aim to provide the necessary frameworks As we approach 2030, the stakes are high if we
for responsible investment, development and want to secure a prosperous future for people and
deployment of AI models, including Gen AI. the planet outlined in the SDGs. It is time for the ANTI-CORRUPTION
Collectively, these initiatives call on stakeholders private sector to take bold, ambitious action to
across the AI value chain to adhere to long-standing, move us forward faster. 10. Businesses should work against corruption in all its forms,
internationally agreed principles and standards for including extortion and bribery.
responsible, rights-based conduct.
For nearly 25 years, the UN Global Compact has
been the call to companies to align their operations
and strategies with its Ten Principles covering human
The Ten Principles of the United Nations Global Compact are derived from: the Universal Declaration of Human Rights,
rights, labour, environment, and anti-corruption. the International Labour Organization’s Declaration on Fundamental Principles and Rights at Work, the Rio Declaration
on Environment and Development, and the United Nations Convention Against Corruption
6 United Nations Global Compact | Accenture Gen AI for the Global Goals 7
FOREWORD: ACCENTURE
The promise of technology to unlock change continues
to inspire the private sector towards groundbreaking
innovation, and the monumental advancements brought
to us through the revolutionary technology of Gen AI
are no different. Gen AI is rapidly transforming daily
operations and productivity across the private sector.
Stephanie Jamison Accenture research shows that 97% of executives
Global Resources Industry believe Gen AI will transform their industry over the
Practice Chair and Global
next three to five years. Despite exciting growth, we
Sustainability Services
Lead, Accenture are still at the early stages of this technology; we must
continue to learn and evolve our approach to mitigate
risk, starting with intentional design when identifying
and refining use cases.
Gen AI isn’t just about increasing productivity. It has the
potential to revolutionize how we approach sustainable
development and offers new opportunities to drive
Arnab Chakraborty progress forward. At this early stage, business leaders
Chief Responsible AI have a unique opportunity to chart the course for Gen
Officer, Accenture
AI’s impact on people and the planet. With the SDGs
as our North Star, we can consider how the private
sector can use Gen AI to support our global push for
sustainable development.
This report shares key use cases across Gen AI for
sustainable development—empowering teams towards
operational efficiency, sustainable supply chains,
Louise James
innovation, and clear communication and reporting.
Global Co-Lead, Accenture
However, private sector leaders must balance the
Development partnerships
upside against the unique risks Gen AI introduces.
This report outlines findings and best practices from
our extensive experience developing and deploying
Gen AI both internally and with our clients. By following
this guidance, we can achieve the promise of Gen AI to
accelerate progress towards the SDGs.
We are grateful to the UN Global Compact for our
long-standing partnership and to its teams for their
insightful collaboration throughout this exciting and
critical work. We look forward to our continued work
together as we tackle the global issues behind the SDGs.
8 United Nations Global Compact | Accenture Gen AI for the Global Goals 9
INTRODUCTION
Gen AI for the Global Goals 11
INTRODUCTION
INTRODUCTION
Global challenges, including ongoing and reignited geo-political The reason for this interest? Gen AI can facilitate unprecedented
conflicts, the climate crisis, high inflation, and lingering effects of access to hyper-specific, tailored information, accelerate innovation
“We believe in the potential The value of data became
the COVID-19 pandemic, have converged to significantly hinder through cross-disciplinary thinking, and increase productivity
of this technology and think relevant even before we
progress on sustainable development. We are currently on track to help businesses overcome the converging headwinds and
if it’s implemented with started talking about Gen AI.
the appropriate guardrails to meet only 17% of the Sustainable Development Goals (SDG)1 complex problems which make sustainable development Early on, with machine
and principles, Gen AI can targets by 2030 . [1] All 17 SDGs, such as Gender Equality and progress so challenging. learning, we saw how data
directly impact sustainable Climate Action, are complex and require multiple stakeholder could improve our service
development in a range of collaboration. As global leaders juggle multiple issues concurrently, Imagine tackling multiple SDGs by applying Gen AI through and delivery times. Now,
areas, including increasing progress on sustainable development is becoming even more targeted actions across the agricultural value chain. At the start, leveraging AI with all this
access to clean water and challenging, widening the gap between action and goals. Gen AI can help farmers better forecast weather and crop yield, standardized and codified
sanitation, reducing hunger develop and optimize biological pest control methods, predict data brings significantly
and poverty, enabling At the same time, advances in technology across fields ranging soil erosion and suggest mitigation measures, and help with enhanced value. We have a
affordable clean energy, from computing to medicine and beyond are transforming our sustainable crop breeding. Next, Gen AI can help with the robust data storage network
building sustainable cities and societies and economies. The rise of Artificial Intelligence has agricultural supply chain, from optimizing supply chain logistics and history to leverage.”
communities, and addressing
had a particularly wide impact, with machine learning powering to forecasting demand to better manage food spoilage, helping Beatriz Tumoine, Global Social
overall climate action.”
analysis, decision making, and resource optimization across bridge the gap between the one billion meals of edible food Impact Director, Cemex
Greg Ulrich, Chief AI and Data sectors and company sizes. In fact, nearly 75% of large companies wasted each day and the 783 million people affected by hunger
Officer, Mastercard have already integrated AI into their business strategies .[2] each year .[6] Gen AI can also help workers along the agricultural
supply chains by identifying high risks for human rights violations,
Generative AI (Gen AI) in particular has captured the attention providing tailored educational and training programs, and acting
of the private sector due to its potential to unlock new business as a sustainability knowledge disseminator. Finally, Gen AI can
models and technologies. An overwhelming majority (97%) of help consumers better manage food waste, helping divert from
executives believe Gen AI will transform their industry and play landfills worldwide and promoting circular economy practices.
a major role in their strategies over the next three to five years .[3] Each of these applications represent an opportunity to create
Of these executives, 31% have already made significant investments business value while acting as an accelerator for impact across
in related initiatives, and 99% plan to amplify their investments .[3] the SDGs.
As a result, global investments in AI are projected to reach $200
billion by 2025 [4] , while the market for Gen AI could reach $1.3 Yet, we are still in the nascent stages of the Gen AI revolution,
trillion by 2032. [5] ironing out wrinkles in the technology and increasing our
understanding of the related environmental and social risks it
brings. Failing to manage these tradeoffs of Gen AI use could lead
to the technology causing more harm than good.
The world is at a critical juncture. Gen AI, if implemented
responsibly, has the potential to accelerate the private sector’s
progress on sustainable development and help bridge the gap
to 2030. With this report, the UN Global Compact gives private
sector leaders the tools to develop and deploy Gen AI responsibly
and to use Gen AI to advance sustainable development.
1. The SDGs are a set of 17 global objectives that aim
to end poverty, protect the planet, and ensure peace
and prosperity for all.
12 United Nations Global Compact | Accenture Gen AI for the Global Goals 13
INTRODUCTION
WHAT IS GEN AI?
Artificial Intelligence is a machine-based system that can
replicate human thinking, converting various inputs into outputs
ranging from predictions or recommendations to content.[ 7]
Gen AI is a type of artificial intelligence which can generate new
content beyond what it has already been exposed to.[ 8] It does this
by identifying and replicating patterns in existing text, images,
or other data to create realistic new data. Common consumer
Gen AI products include GPT-4/4o, Gemini, Claude, and Midjourney.
While most of the world’s attention is currently directed at Large
Language Models (LLMs), which use large text databases to
mimic all kinds of human language, models have been created
to generate anything from protein structures to memes.
General purpose “foundation models” (trained on large and
broad data sets) are the core of the Gen AI ecosystem. These
models can be tuned and supplemented with proprietary data
to create use-specific Gen AI applications. Applications and
foundation models typically rely on cloud providers for the
computational infrastructure needed for training and inference.2
In turn, these cloud providers rely on hardware providers for the
actual computers running the calculations, especially graphics
processing units (GPUs).
GEN AI APPLICATIONS
GEN
AI ATIONS
Provide applications that customize foundation
APPLIC m spo ed ce ifils
c
u bs ui sn ig
n
ea sd sd pit rio on ba lel mda sta and tuning to solve
AL
N
DATIO FOUNDATIONAL MODELS
N
OU Provide models, trained on diverse sets of data
F DELS
(often the open web), that can be leveraged to
O
M
develop custom Gen AI applications
UCTURE
INFRASTRUCTURE
ASTR
Provide infrastructure to host, compute, and store
INFR
Gen AI workloads using purpose-built hardware
(e.g., GPUs) through cloud providers or onsite
2. Training is the set-up of a model while
Figure 1: Gen AI Tech Stack inference is the use of a finished model.
14 United Nations Global Compact | Accenture Gen AI for the Global Goals 15
INTRODUCTION
It’s hard to manage or improve what you The biggest benefit Gen AI can deliver
can’t measure. When you layer Gen AI is contextual, localized strategy. This
on top of existing data, you can unlock can help deliver contextual and specific
insights and uncover unbelievably actions and recommendations, helping
powerful opportunities.” unlock unprecedented SDG action.”
Emilio Tenuta, Senior Vice President and Gagandeep K. Bhullar, Founder and CEO,
Chief Sustainability Officer, Ecolab SuperHumanRace
For someone who has been working Gen AI models are becoming more
on income inequality for my entire life, powerful and knowledgeable, with the
seeing an opportunity to train people ability to solve tasks we previously
quickly to help them create wealth is couldn’t imagine. The speed at which
just incredibly exciting.” this technology is developing is
astonishing and incredibly exciting.”
Shamina Singh, Founder and President of Mastercard’s
Center for Inclusive Growth and EVP, Sustainability,
Hilda Kosorus, Director of Data and AI Center
Mastercard
of Excellence for EMEA, Crayon
The greatest potential of Gen AI is having
a collective intelligence just a prompt
away and embedding that in business
processes to allow companies to make
better decisions.”
Vikram Nagendra, Director of Corporate Sustainability, SAP
16 United Nations Global Compact | Accenture Gen AI for the Global Goals 17
INTRODUCTION
WHY IS GEN AI EXCITING
FOR BUSINESS?
Gen AI’s potential to create business value comes from its three
foundational capabilities: acting as a Data Miner, an Insight
“In the past 30 years, there is
Navigator, or a Knowledge Amplifier. When combined with other
no single technology except
business capabilities, Gen AI can help companies lower costs
for AI that I have been able
through increased operational efficiency, streamline management to stand up in front of CEOs
of complex value chains, increase revenue through innovative and credibly and authentically
new offerings, and simplify reporting and compliance. When say that it will have a material
companies use Gen AI responsibly to achieve these ends they can positive impact on every part
unlock business value while advancing sustainable development. of their enterprise.”
Julie Sweet, CEO and Chair,
Imagine if businesses used Gen AI to tackle the logistical and Accenture
analytical barriers to developing a truly circular economy. FOUNDATIONAL CAPABILITIES
R&D teams could use Gen AI to accelerate development of
replacements for resource intensive and environmentally
OF GEN AI
degrading materials. Design teams could use a Gen AI assistant
to help embed circular principles across product and service
systems, starting with sustainable material selection and
advancing through designing for extended product use and new
business models. Gen AI can also help logistics teams optimize
DATA MINER Gen AI surpasses traditional analytics tools by extracting valuable insights
transportation and inventories across forward distribution
from unlabeled and unstructured data such as text, images, video, or audio,
channels and manage the increased operational complexity of
with the potential to link unstructured qualitative data with structured
reverse logistics networks. Once products reach customers,
quantitative data. As an example, Gen AI could provide deeper insights into
Gen AI can improve services that facilitate asset sharing or
market sentiment and investment trends by analyzing unstructured data
help guide customers and technicians through repairs to extend
like filings, reports, news articles, or internal communications.[9]
product life. When life-extension is no longer an option, Gen AI
can help recovery and recycling vendors to more effectively
separate valuable materials from waste streams for recovery.
Gen AI can also help businesses learn from best practices,
INSIGHT Interpreting data to drive decision-making is not always intuitive,
improving communication with value chain partners, regulators,
requiring specially trained employees and a deep familiarity with the
NAVIGATOR
and consumers to drive ecosystem-wide change. By tackling
process or context of the decision in question. Gen AI can help employees
these challenges, businesses can take the next steps towards
apply technical knowledge to analyze complex data and provide
decoupling growth from resource use, creating value while
recommendations, predictions, or explanations for businesses to act
tackling SDGs like climate action, responsible consumption
upon. For example, Gen AI can support technicians during infrastructure
and production, and affordable and clean energy.
maintenance by providing interactive guidance generated from preventative
maintenance systems and the technician’s live observations.[10]
KNOWLEDGE Gen AI tools can empower the workforce by functioning as capable
and customizable search engines, communication coaches, or virtual
AMPLIFIER
assistants. For instance, Gen AI can be used to help draft memos
and presentations or generate training plans to upskill employees
for incoming regulations.
18 United Nations Global Compact | Accenture Gen AI for the Global Goals 19
INTRODUCTION
WHAT’S THE CATCH?
Gen AI is an exciting advancement, but poses a number of
user and external risks that require careful consideration and
It is important for companies
management. User risks may include biased outputs and factual
to consider how they are
errors, opaque processes, and the opportunity for misuse.
getting the best information
External risks include increased resource use across energy, out of Gen AI. What is your
water, and infrastructure and the potential to transform society by governance system to ensure
shifting the job market and spreading misinformation. The adoption you have checks and balances
of broader AI technologies has been uneven, with businesses in around unintentional outputs?
advanced economies accounting for the majority of capability Do you have transparency and
development.[11] Not all regions and countries have equal access an understanding of the data
to the infrastructure, training, and data required to take advantage being fed into the system?”
of Gen AI’s benefits, which could widen the existing digital divide.
Brigid Evans, Director of Global
The new and rapidly changing Gen AI landscape only adds Policy, Pearson
uncertainty to these risks.
The UN Global Compact has and continues to advocate for a
principles-based approach to responsible business, considering Each time we evaluate a
human rights, the environment, labour, and anti-corruption.3 use case, we consider if it’s
Given the scale of global investment in Gen AI, it is imperative necessary to use Gen AI or if
that we monitor its development and implementation to maximize a traditional digital application
benefits while avoiding further negative effects on the SDGs. or AI could suffice.”
The UN Global Compact hopes this report can serve as a guide to Giulia Brandetti, Head of Data
the private sector in how to responsibly apply Gen AI, as well as Governance and Resource
how to leverage it as a tool to accelerate sustainable development. Allocation, Enel Group
3. See The Ten Principles of the UN Global Compact
for more detail.
20 United Nations Global Compact | Accenture Gen AI for the Global Goals 21
INTRODUCTION
THE PRIVATE SECTOR’S
LEADING ROLE IN SUSTAINABLE
DEVELOPMENT
When thinking about the SDGs, we need to think
about where we can accelerate action and create
a flywheel effect, and how Gen AI can support that.”
The private sector, responsible for more than 60% While companies face pressure to move quickly with
Shamina Singh, Founder and President of Mastercard’s Center
of global GDP4, is the largest player in production of Gen AI, they also have a responsibility to start small
for Inclusive Growth and EVP, Sustainability, Mastercard
goods and services worldwide .[12] As a driving force and move safely. Gen AI should always be developed
behind innovation and the explosion of Gen AI, the with humans in the loop, meaning that people are in
private sector has a unique opportunity to lead the charge of (and accountable for) reviews to ensure the
way in harnessing this technology for sustainable safe and responsible use of this technology. Due to its
development. By prioritizing the SDGs throughout central role in sustainable development, the private
the use of Gen AI (as described in Resources 1-4), sector should go beyond responsible implementation
the private sector can drive positive impact and and leverage technologies like Gen AI to quickly close
advance the SDGs globally. the gap between intent and action on SDGs. Gen AI’s ability to scale information and analytics
can help us get farther faster on global issues.”
The UN Global Compact challenges companies Recognizing these responsibilities and the challenge
developing, deploying, and using Gen AI to work of navigating emerging technologies, this report lays Márcia Balisciano, Chief Sustainability Officer, RELX Group
towards two key objectives when it comes to its out how to achieve these two objectives through
use, shown in figure 2. actionable insights and recommendations.
SUSTAINABLE
By increasing productivity, the private sector has
GEN AI
unlocked tremendous economic growth, but this has
come at a cost. This is where AI can really step in and
MEANS ENDS play a positive role — at the intersection of maintaining
economic growth and sustainable development.""
DEVELOP AND DEPLOY USE GEN AI TO ADVANCE
Vikram Nagendra, Director of Corporate Sustainability, SAP
GEN AI RESPONSIBLY SUSTAINABLE DEVELOPMENT
Companies must ensure that the means Companies must also consider the ends
used to develop and deploy Gen AI are for which Gen AI is deployed. The private
ethical and transparent. The private sector can accelerate and amplify
sector can do this through adopting both SDG action through applying Gen AI in
responsible processes and governance. sustainable development action areas.
Providing cited sources throughout a Gen AI
response helps to increase traceability and trust.”
Emma Grande, Director of ESG Strategy and Engagement, Salesforce
Figure 2: Key Objectives for the Private Sector
4. Additionally, more than 80% of production in low and
on Gen AI and Sustainable Development middle income countries is private sector driven.[12]
22 United Nations Global Compact | Accenture Gen AI for the Global Goals 23
USING GEN AI
TO ADVANCE
THE SUSTAINABLE
DEVELOPMENT
GOALS
Gen AI for the Global Goals 25
USING GEN AI TO ADVANCE THE SUSTAINABLE DEVELOPMENT GOALS
USING GEN AI TO ADVANCE THE
SUSTAINABLE DEVELOPMENT
GOALS
• Resource Optimization • Lifecycle Assessment
Three key elements underpin the successful and responsible use
• Efficient Code • Responsible Sourcing
of Gen AI. First, companies must ensure they clearly understand
With Gen AI, the goal is not
the problem they are solving and agree that Gen AI is an appropriate • Worker Effectiveness • Supplier Engagement
to replace human work but
solution relative to the tradeoffs. Second, they must ready their to supercharge it.”
people to use Gen AI responsibly by supporting them with the
Emma Grande, Director of
appropriate digital, data, and AI literacy training. Finally, companies
ESG Strategy and Engagement,
need to set up the right governance structures to maintain safety Salesforce
and accountability. After building the foundation, Gen AI’s ability
to act as a Data Miner, Insight Navigator, and Knowledge Amplifier
OPERATIONAL SUSTAINABLE
can be unleashed to help support sustainable development action
EFFICIENCY VALUE CHAIN
and accelerate progress towards the SDGs.
These foundational capabilities can be applied to existing
FOUNDATIONAL
technologies and business operations to accelerate sustainable CAPABILITIES
In the last year, we have
development across four use case categories, shown below.
addressed a number of DATA MINER
The following examples illustrate how businesses can — and
low-hanging fruits with Gen
already are — using Gen AI to advance their sustainability AI. Moving forward, from a INSIGHT NAVIGATOR
journeys. As the technology is so new to business, existing maturity cycle perspective,
KNOWLEDGE
case studies largely represent the low-hanging fruit, offloading we will see more high-value-
administrative work and democratizing access to information. added cases.” AMPLIFIER
Yet even these initial examples can have significant positive
Vikram Nagendra, Director of
effects on the private sector’s ability to make progress on Corporate Sustainability, SAP
sustainable development. As Gen AI improves, we can expect COMMUNICATION
INNOVATION
radical changes in the pace of innovation and level of impact of
AND REPORTING
this transformative technology, potentially impacting sustainable
development in ways that are yet to be imagined.5
The use cases outlined here are just the beginning of Gen AI’s
ability to reshape the way businesses operate globally. Gen AI
is positioned to play a pivotal role in advancing sustainable
development towards the SDGs. By responsibly integrating Gen AI • Sustainability Reporting • Green Finance
into daily operations, companies can drive positive change and
• Marketing Sustainability • Sustainable Product and Service Design
progress towards their SDGs while achieving their business goals.
• Boosting Collaboration • Cutting Edge Research
5. At the time of writing, business use of Gen AI is Figure 3: Use Cases of Gen AI for Sustainable Development
so early stage that most companies are working to
validate the exact operational impacts before public
disclosure. Several business leaders we interviewed
indicated promising initial results. Also note that the
rapid and concentrated development of Gen AI in a few
countries means that these case studies skew towards
large companies in the Global North.
26 United Nations Global Compact | Accenture Gen AI for the Global Goals 27
USING GEN AI TO ADVANCE THE SUSTAINABLE DEVELOPMENT GOALS
OPERATIONAL EFFICIENCY
Companies need to manage a finite number of resources efficiently to operate within financial and
planetary boundaries to drive consistent and sustainable returns. Opportunities for Gen AI to increase
efficiencies exist across a variety of operational capabilities, such as resource optimization, worker
CASE STUDIES
effectiveness, and code efficiency. Of course, businesses must consider the resource costs of Gen AI
adoption and usage.
SUPERHUMANRACE SIEMENS
Resource Optimization: Minimizing the resource requirements
to achieve business outcomes represents a dual opportunity SuperHumanRace set out to improve maternal Siemens deployed the Siemens Industrial Copilot,
Our goal is to increase our
for companies to lower both costs and environmental impact. health in India, prioritizing the states with the a Gen AI solution developed in partnership with
use of technologies that
The private sector can layer Gen AI’s foundational capabilities augment the capabilities of poorest outcomes. The company developed an Microsoft, on a Schaeffler manufacturing line,
on top of existing analytics and AI technologies to help employees our colleagues, enhancing app designed to provide doctors with personalized showcasing the power of Gen AI to increase
optimize the use of resources from computing power to shipping our efficiency and productivity recommendations for maternal health. Utilizing industrial efficiency and operations solutions.
networks. For example, companies can use Gen AI to upgrade while ensuring a human is Gen AI alongside machine modeling, the
a machine-learning-powered predictive analytics system into always the final decision-maker.” app leverages a large data set on maternal The Siemens Industrial Copilot has been
a prescriptive maintenance system that generates instructions health trends, interventions, and permutations instrumental in assisting Schaeffler’s automation
Michela Buzzichelli, Head of Data
and recommendations for workers .[10] Science and AI at Enel Global ICT, of high-risk pregnancies to deliver tailored engineers in generating code for programmable
Enel Group recommendations to each patient. logic controllers (PLCs). PLCs are the brains
Worker Effectiveness: Effective training and tools are critical which control factory machines; one in three
to supporting employees across their roles. Traditional training The app generates questionnaires for doctors runs on a Siemens device. By using natural
methods often fall short in providing effective, individually tailored that are tailored to the pregnancy stage, medical language inputs to develop code, the time, effort,
learning environments, while inflexible tools lack the adaptability condition, and risk factors of each patient. By and probability of errors in the coding process
to support decision-making across an employee’s responsibilities. integrating existing machine learning models with have been significantly reduced. This has not
With Gen AI, we could
Workers can use Gen AI as a powerful professional educational Gen AI, the app identifies patterns in patient data only decreased human effort on repetitive tasks
maximize the productivity
tool, personalizing learning on sustainable development topics time we’re getting back from to provide contextual descriptions of risk factors but also allowed engineering resources to focus
to each employee’s role, native language, and region-specific our workers and partners to tailored to patients. SuperHumanRace offers on higher-value work. In addition, it has the
regulations or policies. Furthermore, Gen AI can support create more opportunities in AI-enabled suggestions that link information potential to empower less-experienced shop-floor
identifying and designing specific sustainable development service of people on the planet.” with specific actions, such as recommended employees to transition into engineering roles,
training or courses relevant to a co"
274,Autres,Artificial intelligence index report.pdf,"Artificial
Intelligence
Index Report
2023
Artificial Intelligence
Index Report 2023
Introduction to the
AI Index Report 2023
Welcome to the sixth edition of the AI Index Report! This year, the report introduces more original data than any
previous edition, including a new chapter on AI public opinion, a more thorough technical performance chapter,
original analysis about large language and multimodal models, detailed trends in global AI legislation records,
a study of the environmental impact of AI systems, and more.
The AI Index Report tracks, collates, distills, and visualizes data related to artificial intelligence. Our mission is
to provide unbiased, rigorously vetted, broadly sourced data in order for policymakers, researchers, executives,
journalists, and the general public to develop a more thorough and nuanced understanding of the complex field of
AI. The report aims to be the world’s most credible and authoritative source for data and insights about AI.
From the Co-Directors
AI has moved into its era of deployment; throughout 2022 and the beginning of 2023, new large-scale AI models
have been released every month. These models, such as ChatGPT, Stable Diffusion, Whisper, and DALL-E 2, are
capable of an increasingly broad range of tasks, from text manipulation and analysis, to image generation, to
unprecedentedly good speech recognition. These systems demonstrate capabilities in question answering and the
generation of text, image, and code unimagined a decade ago, and they outperform the state of the art on many
benchmarks, old and new. However, they are prone to hallucination, routinely biased, and can be tricked into
serving nefarious aims, highlighting the complicated ethical challenges associated with their deployment.
Although 2022 was the first year in a decade where private AI investment decreased, AI is still a topic of great
interest to policymakers, industry leaders, researchers, and the public. Policymakers are talking about AI more
than ever before. Industry leaders that have integrated AI into their businesses are seeing tangible cost and
revenue benefits. The number of AI publications and collaborations continues to increase. And the public is
forming sharper opinions about AI and which elements they like or dislike.
AI will continue to improve and, as such, become a greater part of all our lives. Given the increased presence of
this technology and its potential for massive disruption, we should all begin thinking more critically about how
exactly we want AI to be developed and deployed. We should also ask questions about who is deploying it—as
our analysis shows, AI is increasingly defined by the actions of a small set of private sector actors, rather than a
broader range of societal actors. This year’s AI Index paints a picture of where we are so far with AI, in order to
highlight what might await us in the future.
Jack Clark and Ray Perrault
Artificial Intelligence
Index Report 2023
Top Ten Takeaways
1 I ndustry races ahead of academia. 4 The world’s best new scientist … AI?
Until 2014, most significant machine learning AI models are starting to rapidly accelerate
models were released by academia. Since then, scientific progress and in 2022 were used to aid
industry has taken over. In 2022, there were 32 hydrogen fusion, improve the efficiency of matrix
significant industry-produced machine learning manipulation, and generate new antibodies.
models compared to just three produced by
5 The number of incidents concerning
academia. Building state-of-the-art AI systems
the misuse of AI is rapidly rising.
increasingly requires large amounts of data, computer
According to the AIAAIC database, which tracks
power, and money—resources that industry actors
incidents related to the ethical misuse of AI, the
inherently possess in greater amounts compared to
number of AI incidents and controversies has
nonprofits and academia.
increased 26 times since 2012. Some notable incidents
2 Performance saturation on in 2022 included a deepfake video of Ukrainian
traditional benchmarks. President Volodymyr Zelenskyy surrendering and
AI continued to post state-of-the-art results, but U.S. prisons using call-monitoring technology on their
year-over-year improvement on many benchmarks inmates. This growth is evidence of both greater use of
continues to be marginal. Moreover, the speed at AI technologies and awareness of misuse possibilities.
which benchmark saturation is being reached is
6 The demand for AI-related
increasing. However, new, more comprehensive
professional skills is increasing across
benchmarking suites such as BIG-bench and HELM
virtually every American industrial sector.
are being released.
Across every sector in the United States for which
3 AI is both helping and there is data (with the exception of agriculture,
harming the environment.
forestry, fishing, and hunting), the number of AI-
New research suggests that AI systems can have related job postings has increased on average from
serious environmental impacts. According to
1.7% in 2021 to 1.9% in 2022. Employers in the United
Luccioni et al., 2022, BLOOM’s training run
States are increasingly looking for workers with AI-
emitted 25 times more carbon than a single air
related skills.
traveler on a one-way trip from New York to
San Francisco. Still, new reinforcement learning
models like BCOOLER show that AI systems
can be used to optimize energy usage.
Artificial Intelligence
Index Report 2023
Top Ten Takeaways (cont’d)
7 For the first time in the last decade, 10 Chinese citizens are among those
year-over-year private investment who feel the most positively about
in AI decreased. AI products and services. Americans …
Global AI private investment was $91.9 billion in not so much.
2022, which represented a 26.7% decrease since In a 2022 IPSOS survey, 78% of Chinese respondents
2021. The total number of AI-related funding events (the highest proportion of surveyed countries) agreed
as well as the number of newly funded AI companies with the statement that products and services using
likewise decreased. Still, during the last decade as a AI have more benefits than drawbacks. After Chinese
whole, AI investment has significantly increased. In
respondents, those from Saudi Arabia (76%) and India
2022 the amount of private investment in AI was 18
(71%) felt the most positive about AI products. Only
times greater than it was in 2013.
35% of sampled Americans (among the lowest of
8 While the proportion of companies surveyed countries) agreed that products and services
adopting AI has plateaued, the using AI had more benefits than drawbacks.
companies that have adopted AI
continue to pull ahead.
The proportion of companies adopting AI in 2022
has more than doubled since 2017, though it has
plateaued in recent years between 50% and 60%,
according to the results of McKinsey’s annual
research survey. Organizations that have adopted
AI report realizing meaningful cost decreases and
revenue increases.
9 Policymaker interest in AI
is on the rise.
An AI Index analysis of the legislative records of 127
countries shows that the number of bills containing
“artificial intelligence” that were passed into law
grew from just 1 in 2016 to 37 in 2022. An analysis
of the parliamentary records on AI in 81 countries
likewise shows that mentions of AI in global
legislative proceedings have increased nearly
6.5 times since 2016.
Artificial Intelligence
Index Report 2023
Steering Committee
Co-directors
Jack Clark Raymond Perrault
Anthropic, OECD SRI International
Members
Erik Brynjolfsson Katrina Ligett Juan Carlos Niebles Yoav Shoham
Stanford University Hebrew University Stanford University, (Founding Director)
Salesforce Stanford University,
John Etchemendy Terah Lyons AI21 Labs
Stanford University Vanessa Parli
James Manyika
Stanford University Russell Wald
Google,
Stanford University
University of Oxford
Staff and Researchers
Research Manager and Editor in Chief Research Associate
Nestor Maslej Loredana Fattorini
Stanford University Stanford University
Affiliated Researchers
Elif Kiesow Cortez Helen Ngo Robi Rahman Alexandra Rome
Stanford Law School Hugging Face Data Scientist Freelance Researcher
Research Fellow
Graduate Researcher
Han Bai
Stanford University
Undergraduate Researchers
Vania Siddhartha Mena Naima Sukrut Stone Lucy Elizabeth
Chow Javvaji Hassan Patel Oak Yang Zimmerman Zhu
Stanford Stanford Stanford Stanford Stanford Stanford Stanford Stanford
University University University University University University University University
Artificial Intelligence
Index Report 2023
How to Cite This Report
Nestor Maslej, Loredana Fattorini, Erik Brynjolfsson, John Etchemendy, Katrina Ligett, Terah Lyons,
James Manyika, Helen Ngo, Juan Carlos Niebles, Vanessa Parli, Yoav Shoham, Russell Wald, Jack Clark,
and Raymond Perrault, “The AI Index 2023 Annual Report,” AI Index Steering Committee,
Institute for Human-Centered AI, Stanford University, Stanford, CA, April 2023.
The AI Index 2023 Annual Report by Stanford University is licensed under
Attribution-NoDerivatives 4.0 International.
Public Data and Tools
The AI Index 2023 Report is supplemented by raw data and an interactive tool.
We invite each reader to use the data and the tool in a way most relevant to their work and interests.
Raw data and charts: The public data and Global AI Vibrancy Tool: Compare up to
high-resolution images of all the charts 30 countries across 21 indicators. The Global AI
in the report are available on Google Drive. Vibrancy tool will be updated in the latter half of 2023.
AI Index and Stanford HAI
The AI Index is an independent initiative at the
Stanford Institute for Human-Centered Artificial Intelligence (HAI).
The AI Index was conceived within the One Hundred Year Study on AI (AI100).
We welcome feedback and new ideas for next year.
Contact us at AI-Index-Report@stanford.edu.
Artificial Intelligence
Index Report 2023
Supporting Partners
Analytics and Research Partners
Artificial Intelligence
Index Report 2023
Contributors
We want to acknowledge the following individuals by chapter and section for their contributions of data,
analysis, advice, and expert commentary included in the AI Index 2023 Report:
Research and Development
Sara Abdulla, Catherine Aiken, Luis Aranda, Peter Cihon, Jack Clark, Loredana Fattorini, Nestor Maslej,
Besher Massri, Vanessa Parli, Naima Patel, Ray Perrault, Robi Rahman, Alexandra Rome, Kevin Xu
Technical Performance
Jack Clark, Loredana Fattorini, Siddhartha Javvaji, Katrina Ligett, Nestor Maslej, Juan Carlos Niebles,
Sukrut Oak, Vanessa Parli, Ray Perrault, Robi Rahman, Alexandra Rome, Yoav Shoham, Elizabeth Zhu
Technical AI Ethics
Jack Clark, Loredana Fattorini, Katrina Ligett, Nestor Maslej, Helen Ngo, Sukrut Oak, Vanessa Parli,
Ray Perrault, Alexandra Rome, Elizabeth Zhu, Lucy Zimmerman
Economy
Susanne Bieller, Erik Brynjolfsson, Vania Chow, Jack Clark, Natalia Dorogi, Murat Erer, Loredana Fattorini,
Akash Kaura, James Manyika, Nestor Maslej, Layla O’Kane, Vanessa Parli, Ray Perrault, Brittany Presten,
Alexandra Rome, Nicole Seredenko, Bledi Taska, Bill Valle, Casey Weston
Education
Han Bai, Betsy Bizot, Jack Clark, John Etchemendy, Loredana Fattorini, Katrina Ligett, Nestor Maslej,
Vanessa Parli, Ray Perrault, Sean Roberts, Alexandra Rome
Policy and Governance
Meghan Anand, Han Bai, Vania Chow, Jack Clark, Elif Kiesow Cortez, Rebecca DeCrescenzo, Loredana Fattorini,
Taehwa Hong, Joe Hsu, Kai Kato, Terah Lyons, Nestor Maslej, Alistair Murray, Vanessa Parli, Ray Perrault, Alexandra Rome,
Sarah Smedley, Russell Wald, Brian Williams, Catherina Xu, Stone Yang, Katie Yoon, Daniel Zhang
Diversity
Han Bai, Betsy Bizot, Jack Clark, Loredana Fattorini, Nezihe Merve Gürel, Mena Hassan, Katrina Ligett,
Nestor Maslej, Vanessa Parli, Ray Perrault, Sean Roberts, Alexandra Rome, Sarah Tan, Lucy Zimmerman
Public Opinion
Jack Clark, Loredana Fattorini, Mena Hassan, Nestor Maslej, Vanessa Parli, Ray Perrault,
Alexandra Rome, Nicole Seredenko, Bill Valle, Lucy Zimmerman
Conference Attendance
Terri Auricchio (ICML), Lee Campbell (ICLR), Cassio de Campos (UAI), Meredith Ellison (AAAI), Nicole Finn (CVPR),
Vasant Gajanan (AAAI), Katja Hofmann (ICLR), Gerhard Lakemeyer (KR), Seth Lazar (FAccT), Shugen Ma (IROS),
Becky Obbema (NeurIPS), Vesna Sabljakovic-Fritz (IJCAI), Csaba Szepesvari (ICML), Matthew Taylor (AAMAS),
Sylvie Thiebaux (ICAPS), Pradeep Varakantham (ICAPS)
Artificial Intelligence
Index Report 2023
We thank the following organizations and individuals who provided
data for inclusion in the AI Index 2023 Report:
Organizations
Code.org Lightcast
Sean Roberts Layla O’Kane, Bledi Taska
Center for Security and LinkedIn
Emerging Technology, Murat Erer, Akash Kaura,
Georgetown University Casey Weston
Sara Abdulla, Catherine Aiken
McKinsey & Company
Computing Research Natalia Dorogi, Brittany Presten
Association
Betsy Bizot NetBase Quid
Nicole Seredenko, Bill Valle
GitHub
Peter Cihon, Kevin Xu OECD.AI Policy Observatory
Luis Aranda, Besher Massri
Govini
Rebecca DeCrescenzo, Women in Machine Learning
Joe Hsu, Sarah Smedley Nezihe Merve Gürel, Sarah Tan
We also would like to thank Jeanina Casusi, Nancy King, Shana Lynch, Jonathan Mindes,
Michi Turner, and Madeleine Wright for their help in preparing this report, and Joe Hinman and
Santanu Mukherjee for their help in maintaining the AI Index website.
Artificial Intelligence
Index Report 2023
Table of Contents
Report Highlights 11
Chapter 1 Research and Development 20
Chapter 2 Technical Performance 69
Chapter 3 Technical AI Ethics 125
Chapter 4 The Economy 168
Chapter 5 Education 234
Chapter 6 Policy and Governance 263
Chapter 7 Diversity 296
Chapter 8 Public Opinion 319
Appendix 344
ACCESS THE PUBLIC DATA
Artificial Intelligence
Index Report 2023
Report Highlights
Chapter 1: Research and Development
The United States and China had the greatest number of cross-country collaborations in AI
publications from 2010 to 2021, although the pace of collaboration has slowed. The number of AI
research collaborations between the United States and China increased roughly 4 times since 2010,
and was 2.5 times greater than the collaboration totals of the next nearest country pair, the United
Kingdom and China. However the total number of U.S.-China collaborations only increased by 2.1%
from 2020 to 2021, the smallest year-over-year growth rate since 2010.
AI research is on the rise, across the board. The total number of AI publications has more than
doubled since 2010. The specific AI topics that continue dominating research include pattern
recognition, machine learning, and computer vision.
China continues to lead in total AI journal, conference, and repository publications.
The United States is still ahead in terms of AI conference and repository citations, but those
leads are slowly eroding. Still, the majority of the world’s large language and multimodal models
(54% in 2022) are produced by American institutions.
Industry races ahead of academia. Until 2014, most significant machine learning models were
released by academia. Since then, industry has taken over. In 2022, there were 32 significant
industry-produced machine learning models compared to just three produced by academia.
Building state-of-the-art AI systems increasingly requires large amounts of data, computer power,
and money—resources that industry actors inherently possess in greater amounts compared to
nonprofits and academia.
Large language models are getting bigger and more expensive. GPT-2, released in 2019,
considered by many to be the first large language model, had 1.5 billion parameters and cost an
estimated $50,000 USD to train. PaLM, one of the flagship large language models launched in 2022,
had 540 billion parameters and cost an estimated $8 million USD—PaLM was around 360 times
larger than GPT-2 and cost 160 times more. It’s not just PaLM: Across the board, large language and
multimodal models are becoming larger and pricier.
Artificial Intelligence
Index Report 2023
Chapter 2: Technical Performance
Performance saturation on traditional benchmarks. AI continued to post state-of-the-art results,
but year-over-year improvement on many benchmarks continues to be marginal. Moreover,
the speed at which benchmark saturation is being reached is increasing. However, new, more
comprehensive benchmarking suites such as BIG-bench and HELM are being released.
Generative AI breaks into the public consciousness. 2022 saw the release of text-to-image
models like DALL-E 2 and Stable Diffusion, text-to-video systems like Make-A-Video, and chatbots
like ChatGPT. Still, these systems can be prone to hallucination, confidently outputting incoherent or
untrue responses, making it hard to rely on them for critical applications.
AI systems become more flexible. Traditionally AI systems have performed well on narrow tasks
but have struggled across broader tasks. Recently released models challenge that trend; BEiT-3,
PaLI, and Gato, among others, are single AI systems increasingly capable of navigating multiple tasks
(for example, vision, language).
Capable language models still struggle with reasoning. Language models continued to improve
their generative capabilities, but new research suggests that they still struggle with complex
planning tasks.
AI is both helping and harming the environment. New research suggests that AI systems can have
serious environmental impacts. According to Luccioni et al., 2022, BLOOM’s training run emitted 25
times more carbon than a single air traveler on a one-way trip from New York to San Francisco. Still,
new reinforcement learning models like BCOOLER show that AI systems can be used to optimize
energy usage.
The world’s best new scientist … AI? AI models are starting to rapidly accelerate scientific
progress and in 2022 were used to aid hydrogen fusion, improve the efficiency of matrix
manipulation, and generate new antibodies.
AI starts to build better AI. Nvidia used an AI reinforcement learning agent to improve the design
of the chips that power AI systems. Similarly, Google recently used one of its language models,
PaLM, to suggest ways to improve the very same model. Self-improving AI learning will accelerate
AI progress.
Artificial Intelligence
Index Report 2023
Chapter 3: Technical AI Ethics
The effects of model scale on bias and toxicity are confounded by training data and mitigation
methods. In the past year, several institutions have built their own large models trained on
proprietary data—and while large models are still toxic and biased, new evidence suggests that
these issues can be somewhat mitigated after training larger models with instruction-tuning.
Generative models have arrived and so have their ethical problems. In 2022, generative models
became part of the zeitgeist. These models are capable but also come with ethical challenges. Text-
to-image generators are routinely biased along gender dimensions, and chatbots like ChatGPT can
be tricked into serving nefarious aims.
The number of incidents concerning the misuse of AI is rapidly rising. According to the AIAAIC
database, which tracks incidents related to the ethical misuse of AI, the number of AI incidents
and controversies has increased 26 times since 2012. Some notable incidents in 2022 included a
deepfake video of Ukrainian President Volodymyr Zelenskyy surrendering and U.S. prisons using
call-monitoring technology on their inmates. This growth is evidence of both greater use of AI
technologies and awareness of misuse possibilities.
Fairer models may not be less biased. Extensive analysis of language models suggests that while there
is a clear correlation between performance and fairness, fairness and bias can be at odds: Language
models which perform better on certain fairness benchmarks tend to have worse gender bias.
Interest in AI ethics continues to skyrocket. The number of accepted submissions to FAccT, a
leading AI ethics conference, has more than doubled since 2021 and increased by a factor of 10 since
2018. 2022 also saw more submissions than ever from industry actors.
Automated fact-checking with natural language processing isn’t so straightforward after all.
While several benchmarks have been developed for automated fact-checking, researchers find that
11 of 16 of such datasets rely on evidence “leaked” from fact-checking reports which did not exist at
the time of the claim surfacing.
Artificial Intelligence
Index Report 2023
Chapter 4: The Economy
The demand for AI-related professional skills is increasing across virtually every American
industrial sector. Across every sector in the United States for which there is data (with the exception
of agriculture, forestry, fishing, and hunting), the number of AI-related job postings has increased on
average from 1.7% in 2021 to 1.9% in 2022. Employers in the United States are increasingly looking for
workers with AI-related skills.
For the first time in the last decade, year-over-year private investment in AI decreased.
Global AI private investment was $91.9 billion in 2022, which represented a 26.7% decrease since 2021.
The total number of AI-related funding events as well as the number of newly funded AI companies
likewise decreased. Still, during the last decade as a whole, AI investment has significantly increased.
In 2022 the amount of private investment in AI was 18 times greater than it was in 2013.
Once again, the United States leads in investment in AI. The U.S. led the world in terms of total
amount of AI private investment. In 2022, the $47.4 billion invested in the U.S. was roughly 3.5 times
the amount invested in the next highest country, China ($13.4 billion). The U.S. also continues to lead in
terms of total number of newly funded AI companies, seeing 1.9 times more than the European Union
and the United Kingdom combined, and 3.4 times more than China.
In 2022, the AI focus area with the most investment was medical and healthcare ($6.1 billion);
followed by data management, processing, and cloud ($5.9 billion); and Fintech ($5.5 billion).
However, mirroring the broader trend in AI private investment, most AI focus areas saw less
investment in 2022 than in 2021. In the last year, the three largest AI private investment events were:
(1) a $2.5 billion funding event for GAC Aion New Energy Automobile, a Chinese manufacturer of
electric vehicles; (2) a $1.5 billion Series E funding round for Anduril Industries, a U.S. defense products
company that builds technology for military agencies and border surveillance; and (3) a $1.2 billion
investment in Celonis, a business-data consulting company based in Germany.
While the proportion of companies adopting AI has plateaued, the companies that have adopted
AI continue to pull ahead. The proportion of companies adopting AI in 2022 has more than doubled
since 2017, though it has plateaued in recent years between 50% and 60%, according to the results of
McKinsey’s annual research survey. Organizations that have adopted AI report realizing meaningful
cost decreases and revenue increases.
Artificial Intelligence
Index Report 2023
Chapter 4: The Economy (cont’d)
AI is being deployed by businesses in multifaceted ways. The AI capabilities most likely to have
been embedded in businesses include robotic process automation (39%), computer vision (34%), NL
text understanding (33%), and virtual agents (33%). Moreover, the most commonly adopted AI use
case in 2022 was service operations optimization (24%), followed by the creation of new AI-based
products (20%), customer segmentation (19%), customer service analytics (19%), and new AI-based
enhancement of products (19%).
AI tools like Copilot are tangibly helping workers. Results of a GitHub survey on the use of Copilot,
a text-to-code AI system, find that 88% of surveyed respondents feel more productive when using
the system, 74% feel they are able to focus on more satisfying work, and 88% feel they are able to
complete tasks more quickly.
China dominates industrial robot installations. In 2013, China overtook Japan as the nation installing
the most industrial robots. Since then, the gap between the total number of industrial robots installed
by China and the next-nearest nation has widened. In 2021, China installed more industrial robots than
the rest of the world combined.
Artificial Intelligence
Index Report 2023
Chapter 5: Education
More and more AI specialization. The proportion of new computer science PhD graduates from
U.S. universities who specialized in AI jumped to 19.1% in 2021, from 14.9% in 2020 and 10.2% in 2010.
New AI PhDs increasingly head to industry. In 2011, roughly the same proportion of new AI PhD
graduates took jobs in industry (40.9%) as opposed to academia (41.6%). Since then, however, a
majority of AI PhDs have headed to industry. In 2021, 65.4% of AI PhDs took jobs in industry, more
than double the 28.2% who took jobs in academia.
New North American CS, CE, and information faculty hires stayed flat. In the last decade,
the total number of new North American computer science (CS), computer engineering (CE),
and information faculty hires has decreased: There were 710 total hires in 2021 compared to
733 in 2012. Similarly, the total number of tenure-track hires peaked in 2019 at 422 and then
dropped to 324 in 2021.
The gap in external research funding for private versus public American CS departments
continues to widen. In 2011, the median amount of total expenditure from external sources for
computing research was roughly the same for private and public CS departments in the United
States. Since then, the gap has widened, with private U.S. CS departments receiving millions more
in additional funding than public universities. In 2021, the median expenditure for private universities
was $9.7 million, compared to $5.7 million for public universities.
Interest in K–12 AI and computer science education grows in both the United States and the
rest of the world. In 2021, a total of 181,040 AP computer science exams were taken by American
students, a 1.0% increase from the previous year. Since 2007, the number of AP computer science
exams has increased ninefold. As of 2021, 11 countries, including Belgium, China, and South Korea,
have officially endorsed and implemented a K–12 AI curriculum.
Artificial Intelligence
Index Report 2023
Chapter 6: Policy and Governance
Policymaker interest in AI is on the rise. An AI Index analysis of the legislative records of 127
countries shows that the number of bills containing “artificial intelligence” that were passed into law
grew from just 1 in 2016 to 37 in 2022. An analysis of the parliamentary records on AI in 81 countries
likewise shows that mentions of AI in global legislative proceedings have increased nearly 6.5 times
since 2016.
From talk to enactment—the U.S. passed more AI bills than ever before. In 2021, only 2% of
all federal AI bills in the United States were passed into law. This number jumped to 10% in 2022.
Similarly, last year 35% of all state-level AI bills were passed into law.
When it comes to AI, policymakers have a lot of thoughts. A qualitative analysis of the
parliamentary proceedings of a diverse group of nations reveals that policymakers think about
AI from a wide range of perspectives. For example, in 2022, legislators in the United Kingdom
discussed the risks of AI-led automation; those in Japan considered the necessity of safeguarding
human rights in the face of AI; and those in Zambia looked at the possibility of using AI for
weather forecasting.
The U.S. government continues to increase spending on AI. Since 2017, the amount of U.S.
government AI-related contract spending has increased roughly 2.5 times.
The legal world is waking up to AI. In 2022, there were 110 AI-related legal cases in United
States state and federal courts, roughly seven times more than in 2016. The majority of these cases
originated in California, New York, and Illinois, and concerned issues relating to civil, intellectual
property, and contract law.
Artificial Intelligence
Index Report 2023
Chapter 7: Diversity
North American bachelor’s, master’s, and PhD-level computer science students are becoming
more ethnically diverse. Although white students are still the most represented ethnicity among
new resident bachelor’s, master’s, and PhD-level computer science graduates, students from other
ethnic backgrounds (for example, Asian, Hispanic, and Black or African American) are becoming
increasingly more represented. For example, in 2011, 71.9% of new resident CS bachelor’s graduates
were white. In 2021, that number dropped to 46.7%.
New AI PhDs are still overwhelmingly male. In 2021, 78.7% of new AI PhDs were male.
Only 21.3% were female, a 3.2 percentage point increase from 2011. There continues to be a gender
imbalance in higher-level AI education.
Women make up an increasingly greater share of CS, CE, and information faculty hires.
Since 2017, the proportion of new female CS, CE, and information faculty hires has increased from
24.9% to 30.2%. Still, most CS, CE, and information faculty in North American universities are male
(75.9%). As of 2021, only 0.1% of CS, CE, and information faculty identify as nonbinary.
American K–12 computer science education has become more diverse, in terms of both gender
and ethnicity. The share of AP computer science exams taken by female students increased from
16.8% in 2007 to 30.6% in 2021. Year over year, the share of Asian, Hispanic/Latino/Latina, and
Black/African American students taking AP computer science has likewise increased.
Artificial Intelligence
Index Report 2023
Chapter 8: Public Opinion
Chinese citizens are among those who feel the most positively about AI products and services.
Americans … not so much. In a 2022 IPSOS survey, 78% of Chinese respondents (the highest
proportion of surveyed countries) agreed with the statement that products and services using AI
have more benefits than drawbacks. After Chinese respondents, those from Saudi Arabia (76%) and
India (71%) felt the most positive about AI products. Only 35% of sampled Americans (among the
lowest of surveyed countries) agreed that products and services using AI had more benefits than
drawbacks.
Men tend to feel more positively about AI products and services than women. Men are also
more likely than women to believe that AI will mostly help rather than harm. According to the
2022 IPSOS survey, men are more likely than women to report that AI products and services make
their lives easier, trust companies that use AI, and feel that AI products and services have more
benefits than drawbacks. A 2021 survey by Gallup and Lloyd’s Register Foundation likewise revealed
that men are more likely than women to agree with the statement that AI will mostly help rather than
harm their country in the next 20 years.
People across the world and especially America remain unconvinced by self-driving cars. In
a global survey, only 27% of respondents reported feeling safe in a self-driving car. Similarly, Pew
Research suggests that only 26% of Americans feel that driverless passenger vehicles are a good
idea for society.
Different causes for excitement and concern. Among a sample of surveyed Americans, those
who report feeling excited about AI are most excited about the potential to make life and society
better (31%) and to save time and make things more efficient (13%). Those who report feeling more
concerned worry about the loss of human jobs (19%); surveillance, hacking, and digital privacy (16%);
and the lack of human connection (12%).
NLP researchers … have some strong opinions as well. According to a survey widely distributed to
NLP researchers, 77% either agreed or weakly agreed that private AI firms have too much influence,
41% said that NLP should be regulated, and 73% felt that AI could soon lead to revolutionary societal
change. These were some of the many strong opinions held by the NLP research community.
Artificial Intelligence
Index Report 2023
Artificial Intelligence
Index Report 2023
CHAPTER 1:
Research and
Development
Table of Contents Chapter 1 Preview 20
Artificial Intelligence
Index Report 2023
CHAPTER 1 PREVIEW:
Research and Development
Overview 22 Computer Vision 46
Chapter Highlights 23 Natural Language Processing 47
Speech Recognition 48
1.1 Publications 24
Overview 24 1.2 Trends in Significant
Machine Learning Systems 49
Total Number of AI Publications 24
General Machine Learning Systems 49
By Type of Publication 25
System Types 49
By Field of Study 26
Sector Analysis 50
By Sector 27
National Affiliation 51
Cross-Country Collaboration 29
Systems 51
Cross-Sector Collaboration 31
Authorship 53
AI Journal Publications 32
Parameter Trends 54
Overview 32
Compute Trends 56
By Region 33
La"
275,bcg,the-c-suites-ai-agenda-slideshow-jan-2024-new.pdf,"BCG AI RADAR
From Potential to Profit with GenAI
JANUARY 2024
Survey of 1,406 executives provides insights into AI and GenAI sentiment in 2024
Executive roles Respondents from 50 markets (the 13 markets in green have >25 respondents) Industries and key functions
Norway Sweden TMT 252
14% CEO Netherlands
Canada Denmark
UK
Germany
Belgium Austria Consumer 177
France
Switzerland
Spain Azerbaijan
US Italy
Turkey Japan
14% CFO Portugal Malta Greece Israel Pakistan Industrial goods 169
Morocco Egypt UAE Bangladesh
Mexico
Saudi Qatar India Hong Kong Manufacturing 164
Arabia Thailand
Nigeria Philippines
14% CIO Togo Ethiopia
Colombia Sri Lanka Malaysia Financial institutions 156
Kenya
Singapore
Tanzania Indonesia
10% COO Brazil Angola Health care 138
Botswana
Australia
Chile Energy 81
10% CTO South Africa
Argentina
Transportation 68
18% CXO1
Public sector 63
Company revenue
Insurance 59
9% 24% 18% 18% 32%
20% Other2 Travel/tourism 41
$101M–$500M $501M–$1B $1B–$2B $2B–$5B >$5B Marketing 38
Source: BCG AI Radar (2024); n = 1,406 in 50 markets.
Note: Because of rounding, not all percentage totals add up to 100%. TMT = technology, media, and telecommunucations.
1“CxO” represents executives who directly report to the CEO (e.g., CMO, CSO, CISO).
2“Other” executive titles include chair of the board and president.
71%
of executives surveyed say that they plan
to increase tech investments in 2024—an
11-point jump from 2023
Generative AI
will revolutionize
the world—and
executives want
to capitalize
89%
rank AI and GenAI as a top-three tech
priority for 2024, and 51% put it at the top of
their list (cybersecurity and cloud computing
are the other two top priorities)
Source: BCG AI Radar (2024); n = 1,406 in 50 markets.
A global wave of rising tech and AI/GenAI investment
Executives planning to increase Executives planning to increase
71% 85%
their tech investment in 2024 their AI/GenAI investment in 2024
overall overall
Middle East 85% Middle East 93%
Asia-Pacific 80% Europe 86%
Africa 77% Asia-Pacific 85%
Europe 68% North America 85%
North America 65% Africa 82%
South America 63% South America 75%
Source: : BCG AI Radar (2024); n = 1,406 in 50 markets.
Note: In Asia-Pacific, n = 308; in North America, n = 303; in Europe, n = 647; in the Middle East, n = 28; in South America, n = 51; in Africa, n = 69.
However, most organizations are Top three reasons for dissatisfaction
not doing enough to realize the
benefits of the technology.
1
Lack of talent and skills
66%
Unclear AI and GenAI roadmap
2
and investment priorities
of executives are ambivalent or
outright dissatisfied with their
organization’s progress on AI and
No strategy for responsible
3
generative AI so far.
AI and GenAI
Source: BCG AI Radar (2024); n = 1,406 in 50 markets. For executives reporting dissatisfaction, n = 310.
62%
46%
say their firms are
still waiting to see
how AI-specific
regulations develop
Executives
of their workforce,
on average, will need
across the board to undergo upskilling
in the next three
years due to GenAI
face pressing
challenges
6%
of companies have
managed to train
more than 25% of
their people on
GenAI tools so far
Source: BCG AI Radar (2024); n = 1,406 in 50 markets.
Executives who report that more than 25% of
their workers have trained on GenAI tools
Middle East 11%
North America 8%
Executives worldwide must
boost upskilling, as Europe,
Africa, and South America Asia-Pacific 7%
are falling behind.
Europe 5%
Africa 3%
South
2%
America 6%
Source: BCG AI Radar (2024); n = 1,406 in 50 markets.
overall
Note: In Asia-Pacific, n = 308; in North America, n = 303; in Europe, n = 647;
in the Middle East, n = 28; in South America, n = 51; in Africa, n = 69.
9 0%
are either waiting for These are the observers. They
GenAI to move beyond the are opting for a wait-and-see
hype or experimenting in approach.
small ways.
That’s not an option with
generative AI.
Source: BCG AI Radar (2024); n = 1,406 in 50 markets.
Winners invest for productivity and topline growth.
1
They target 10%+ productivity gains and reinvest for revenue uplift.
Winners are upskilling systematically.
2
They are scaling their learning muscle—and that extends to
Winners are
executives as well.
acting now—
Winners are vigilant about cost of use.
here’s how they’re 3
They understand that cost of use has long-term implications and
must command attention now.
staying ahead
Winners build strategic relationships.
4
They develop an ecosystem of partners to manage complex and rapidly
evolving challenges.
Winners implement responsible AI (RAI) principles.
5
They put RAI on the CEO agenda and proactively plan for emerging
Source: BCG AI Radar (2024); n = 1,406 in 50 markets. policies and regulations.
Percentage of companies expecting cost savings
in 2024
One of the biggest benefits
that GenAI promises is
1.3x 1.5x
productivity gains.
The potential benefit is
even greater for companies
that invest more—they’re 70%
67%
54%
46%
1.5x more likely to
anticipate upward of 10%
in cost savings.
Any cost savings More than 10% cost savings1
All companies Companies expecting to invest more than
$50 million in AI/GenAI in 20242
Source: BCG AI Radar (2024); n = 1,406 in 50 markets.
1Of companies expecting cost savings.
2For companies expecting to invest more than $50 million, n = 122.
Key goals for growth with AI and GenAI investments
1.3x
1.4x
The key is to invest
in productivity—and
topline growth. 80%
66%
62%
48%
Expand market access Build business adjacencies
All companies Companies expecting to invest more than
$50 million in AI/GenAI in 20241
Source: BCG AI Radar (2024); n = 1,406 in 50 markets.
1For companies expecting to invest more than $50 million, n = 122.
The imperative to
provide GenAI training
is clear.
Executives believe that
46%
of workers, on average, Overwhelming majorities
will need to be reskilled believe that GenAI will
in the next three years. create new roles (81%) and
require significant change
management (74%).
Source: BCG AI Radar (2024); n = 1,406 in 50 markets.
Companies with more than 25% of their
workforce trained on GenAI tools
3.5x
Companies that invest
more are ahead on
reskilling their workers—
and on building their
learning muscle at scale.
21%
6%
All companies Companies expecting
to invest more than
$50 million in
Source: BCG AI Radar (2024); n = 1,406 in 50 markets. AI/GenAI in 20241
1For companies expecting to invest more than $50 million, n = 122.
Confidence in the executive team’s GenAI proficiency
The need to upskill
extends to the C-suite.
Completely confident 1%
59%
Very confident 11%
Confident 29%
of leaders surveyed say
they have limited or no
Limited confidence 40%
confidence in their
executive team’s
No confidence 19%
proficiency in GenAI.
Source: BCG AI Radar (2024); n = 1,406 in 50 markets.
Most important consideration when choosing
an AI and GenAI solution
Cost of use, which
IP and data protection 39%
has serious long-term
implications, is not
commanding the
Quality and performance 32%
attention it should
Cost 19%
Source: BCG AI Radar (2024); n = 1,406 in 50 markets.
Potential partners seen as a trusted source
of information
Winners are
Big tech platforms 71%
building strategic
relationships
with an evolving
Software providers 49%
ecosystem of
partners
GenAI companies 38%
Source: BCG AI Radar (2024); n = 1,406 in 50 markets.
Companies investing more are getting a head start
Company is already
preparing for AI-specific 38% 72%
regulations
The sheer speed of
GenAI adoption makes
RAI more important than
Company has
ever, and organizations guardrails in place for 50% 68%
using AI/GenAI at work
must be proactive in
addressing this.
14% 27%
CEO is in charge of RAI
All companies Companies expecting to invest more than $50 million in AI/GenAI in 20241
Source: BCG AI Radar (2024); n = 1,406 in 50 markets.
1For companies expecting to invest more than $50 million, n = 122.
Deploy GenAI in everyday
tasks to realize 10% to 20%
productivity potential.
Three value plays
Reshape critical functions for
to maximize GenAI’s
30% to 50% enhancement in
efficiency and effectiveness.
potential
Invent new GenAI business
models to build a long-term
competitive advantage."
276,bcg,BCG-Executive-Perspectives-Future-of-Data-Management-with-AI-EP9-10Dec2024.pdf,"Executive
Perspectives
The Future of Data Management
with AI
December 2024
Introduction In this BCG
Executive Perspective,
We meet often with CEOs to discuss AI---a topic that is both captivating and rapidly
we articulate the vision
changing. After working with over 1,000 clients in the past year, we are sharing our
most recent learning in a new series designed to help CEOs navigate AI. With and value of
AI at an inflection point, the focus in 2024 is on turning AI’s potential into
the future of data
real profit.
management with AI
In this edition, we discuss the future of data management, and the role AI will play in
fundamentally transforming the function. We address key questions on the minds of
leaders:
• How do I keep pace with growing data regulations?
• How can I unlock cost advantages while improving my data quality?
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• How can I improve my data team’s experience and generate more enthusiasm
se
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around data management? ir
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• How do I get started…and how do I get this right? o rG
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This document is a guide for CEOs and technology leaders to cut through o ts
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the hype around AI in data management and understand what creates value b
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now and in the future. 2 ©
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Executive summary | GenAI will industrialize the use of data, improving quality,
expanding and simplifying access, and increasing productivity
Data management, a manual and tedious job, is overwhelmed with growing unstructured data
Acting fast is key
(>10x in 10yrs), higher quality bars, and tighter regulatory oversight
to tackle rising
complexity and
Economics are turning out to be even more challenging, with data costs projected to grow 80% from
costs
2023 to 2028, and with hidden costs further fueling data cost growth
GenAI can support GenAI simplifies and augments data management tasks, accelerating time to value. Coupled
the needs of your with the right tooling, GenAI offers the potential to automate and expedite key data tasks, improving
data team… data quality and unlocking efficiencies
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AI vision for data management is to drive competitive advantage through improved data quality, re
se
expanded coverage, self-service analytics, and automated workflows, transforming roles and
r
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democratizing data access with scalable, secure, and compliant solutions
ir
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…by reshaping o rG
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your data function Five key drivers ensure a successful transformation: itlu
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for innovation and • Adopt an AI vision to drive sequential building of your data capability o
C
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growth • Rewire your end-to-end data management workflows to unlock efficiency gains ts
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• Employ a product-centric data operating model b
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• Ensure a robust data governance across the life cycle 2
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• Invest in key partnerships to accelerate capability builds g iry
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Have we started leveraging AI to reshape our
data function?
Do we have a refreshed data strategy, enabled by
GenAI, that is aligned with business outcomes?
How is our data function lined up to respond to the
growing data needs of the business?
2
Key questions
How did our direct and indirect data costs change
CDO/CIOs
in the past years?
should answer
How are we embedding GenAI data considerations
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in technology and operating model priorities? se
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How did we adapt our data-related people, processes, u
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and organization to expanding needs of AI? itlu
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How do we manage data-related risks and ensure o B
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adherence to evolving regulatory requirements? 0
2
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Today's data management is already burdened
by three key areas of friction along the data journey
Accessing data Understanding data Governing & monitoring data
• Processes today are more difficult to • Data annotation is a labor-intensive • Data usage monitoring is not actively
manage due to a significant increase in process and, even if automated, requires performed
access groups human-in-the-loop
• Regulations frequently change, making
• Organizations include multiple levels of • Data annotation is the primary enabler compliance challenging
approval - delays of up to six weeks to the remainder of the data journey
• Setting policies requires alignment
• Changing policies and numerous rules • Data stewards are either not qualified or across stakeholders with conflicting
require consistent management not accountable priorities
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• Concerns are increasing around vv rree
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Intellectual Property (IP) rights and
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In addition, exponential growth in the volume of unstructured and multimodal
data in the past decade has further raised the bar on data management
Data is growing exponentially; This growing volume with AI will increase demand for
~90% unstructured data by 2025 data management activities
Global data in • Identification of source and history of data
181 Data provenance
zettabytes1 • Authenticity of model data for intended use
147 10% Where did data come from?
~14x • Third-party training data underlying models
Structured
64 90% • Training data accuracy for desired output
Data classification
• Quality and consistency of labeled data
Unstructured2 How is the data labeled?
13 • Reduced training, improved model performance
2014 2020 2023 2025E
Data lineage • Traceability of data transformation
What is the sequence of processing • Reproduction of calculated results .d
Mobile, real-time data and IOT sensors steps? • Interpretation of the data used for model e v re
creating large amounts of data
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Data quality, metadata • Greater quality of model outputs
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Synthetic data generation via GenAI .p
will drive strong growth of data volumes completeness • Higher model performance without bias u o rG
How accurate is my data? • Management of data drift and concept drift g n
itlu
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Decisions enabled by AI drive companies C
Regulatory compliance • Multiple data regulations globally (e.g., EU AI Act) n
to collect more data than ever o ts
Is this usage ethical & within • Higher quality bar imposed by regulators o B
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regulations? • Vagueness of regulations on unstructured data 4
2
With AI, data inputs becoming 0 2
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Source: IDC; Seagate; Statista estimates
Higher standards imply increased data management direct
and hidden costs, which are forecast to rise in the coming years
Around 80% growth estimated for data management Also, hidden costs of data management will drive
costs in the next 5 years1 further increases across the board
2023-28
CAGR Manual interventions driving human cost
181
(e.g., BU analyst effort on data modifications)
CAGR
13% 42 Software 12%
Delayed analytics, use-case time to value
(e.g., opportunity cost of delayed business decisions)
25 Hardware 9%
Indexed to
100 100 Remediation to regulatory inspection .d
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24 47 Internal people 15% (e.g., issue identifications and corrections) se r sth
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Fines and security breaches (e.g., due to
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24 noncompliance with regulatory guidelines) g
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68 Services 14% s n
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Overspending on technology and engineering ts
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(e.g., overlapping data management tools) y b
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2023 2028E Overall: 13% 0 2
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1. Incurred by IT function, with existing processes & tech iry
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Source: IDC Semiannual Software Tracker; IDC Worldwide ICT Spending Guide; WEF Future of Jobs Report; Economic Intelligence Unit, Gartner Forecast Analysis: Data and Analytics Services; 6 o C
BCG publication ‘A New Architecture to Manage Data Costs and Complexity’
In this challenging environment, GenAI can help simplify data management
and accelerate time to value across the data management value chain
CLEAN MATCH
and refine data data through identifying similar
or related data
GenAI can interpret
and create new
GENERATE ENHANCE
content, implying
new data data traceability
potential to augment
.d
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or automate v re
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many key data
IDENTIFY ACCELERATE r
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types of data compliance and risk management .p
management tasks u
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and infer metadata
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AUGMENT n
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data analytics and 4 2
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insight generation ©
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Client example | A build-out of GenAI-led metadata labeling and lineage annotation
capabilities enabled significant productivity gains
A global financial We helped the client by focusing five key impact levers… …achieving tangible results
institution client aspires
• Help accelerate identification of potential gaps/risks
to accelerate its data
(e.g., code/data duplication) through lineage capturing Human acceptance
management and
70%+
of ""LLM out-of-box""
Enhance data • Enable data-related roles to focus on “value-added”
governance controls at scale tasks (e.g., review of outputs) business description
transformation journey
• Streamline continued monitoring and refresh of
data estate, with less manual intervention
Accuracy
•1 Current data governance
in PII1 tagging and data
only covers small portion 90%+
Improve accuracy & • Improve accuracy and ensure comprehensive coverage lineage captured post-
of data estate (only
focusing on critical data coverage of output of lineage and business metadata human validation
assets)
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•2 Heavily manual Drive efficiency to • Augment productivity for data-related roles Productivity boost
re
se
processes are needed to accelerate (e.g., data steward, central data governance function) to accelerate coverage
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generate business coverage • Boost productivity by up to 50% for critical manual tasks 50%+ of data under ir llA
metadata (weeks per data
governance
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source) with limited o
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alignment between Unlock additional g n
enterprise and BU
use cases and
• Create development and deployment patterns for itlu
additional use cases Reduction s n
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processes C
•3 Low efficiency of E2E in compliance timeline n
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data lineage and current 2-5 Yr. for high-impact data ts o B
tooling cannot generate Increase colleague • Minimize repetitive manual tasks and improve working assets y b 4
cross-system lineage satisfaction experience for targeted users 2 0
2
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1. PII = Personal identifiable information 8 o
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To control rising data-related challenges, it is imperative
for companies to work toward the AI reshape vision
Empower data management to drive competitive advantage through improved data quality,
AI vision for
expanded coverage, self-service analytics, and automated workflows, transforming roles
data mgmt.
and democratizing data access with scalable, secure, and compliant solutions
FROM … TO …
Data management generally considered an Empowered data management function transforming
afterthought in management priorities data as a driver of competitive advantage for organization
Data management focus restricted to critical Expanded data management coverage, to significantly
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areas (e.g., data under regulatory purview) enhance data quality and utilization across functions v
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Siloed, centralized approach to data Departments empowered with self-service analytics ir
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management hindering access to data and insights, unlocking data potential across organization
.p
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Nature of data management work perceived Reinvented roles with engaging, joyful, and strategic s
n
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as mundane and unappealing responsibilities, adding visible value to organization n
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Core data activities (collection, cleaning, etc.) Core data activities automated and streamlined with 4
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2
manual, restricted to data engineers/stewards business owner involvement ©
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Five key drivers can ensure that companies are on a path
in line with the vision for data management
Translate your AI-driven data management
vision to drive sequential build
Support your AI-enabled vision for data by starting
Accelerate
with building a platform and foundational capabilities,
ensuring business alignment and enabling more AI data governance
advanced capabilities
Fundamentally transform AI data governance from the
sidelines of IT into a core, daily business practice —
Rewire your E2E data
GenAI-led embedding standards, control, and governance culture
management workflows
across business units
“RESHAPE”
Fully rewire data management workflows,
minimizing manual iterative loops via of data
.d
automated processes and AI interfaces to management e v re
accelerate time to value se
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Employ a product-centric model
Invest in key partnerships to
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address capability gaps rG
to data, as part of org-wide
g
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platform operating model Identify and drive partnerships with technology
itlu
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providers to address gaps and accelerate capability C
n
Establish efficient and effective ways of working to o
builds across the data value chain ts o
drive faster throughput via transforming from a B
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reactive, service-based data model to a more proactive 4 2
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‘business partnership’ product model ©
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Value-centric approach starts and delivers value early from AI,
and matures organizational capabilities and data/tech foundations as you go
Plan your journey Experiment with AI Mature your foundations Consolidate and scale
Define vision, business Resolve initial issues and Establish teams, expand data Accelerate delivery of
opportunity, and key pain points, train core platforms, achieve economies next wave of capability
workflows impacted teams, activate key squads of scale builds
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Value creation through successive waves of capabilities g
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Governance coverage of data domains n o
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Democratizing access and ease of data use across organization 0 2
©
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But to unlock value from data as capabilities are deployed, it’s key to sequence
initiatives by building capabilities in waves
Start with fundamental Build advanced capabilities Activate ‘insight generating’
1 2 3
‘no-regret’ initiatives for regulatory compliance capabilities for business
Metadata and lineage annotation are High-quality metadata and lineage High-quality metadata, lineage, MDM,
logical starting points, to accurately enable consistent tagging of data sets, and synthetic data will better support
describe/catalog data consistently, and implying better discoverability and discovery and utilization, and
are critical for other GenAI capabilities to improved document cataloging & generate better insights for business
be effective (e.g., data quality related) mgmt. operations for better compliance (e.g., through augmented analytics)
Policy compliance mgmt.
Augmented analytics
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Document creation & mgmt. Document creation & mgmt. v re
se
MDM augmentation MDM augmentation
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Enhanced data mining Enhanced data mining u
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Synthetic data generation Synthetic data generation itlu
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Data quality management Data quality management Data quality management n o
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Metadata labeling Metadata labeling Metadata labeling b
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Lineage annotation Lineage annotation Lineage annotation th
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Directly enable Augment
AI is anticipated to have the largest impact on data analytics and
data management workflows, necessitating focus across people and processes
In which of the following IT processes and workflows do you anticipate
AI technologies having a transformative impact? [Multi-select]
70%
% of respondents
> 40%
30-40% of AI
< 30%
transformation
effort should be
Application performance invested in
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monitoring se
people and r sth
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Project and portfolio Software development life
Data analytics
processes
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management cycle o
Drive change management
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and other processes related itlu
IT infrastructure (data center, Data management s n
to people o
IT service management C
networks) operations workflows n
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Example to follow
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IT asset management 4 2
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2
and maintenance ©
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Source: BCG Build for the Future 2024 survey (n=1,000 respondents)
TODAY: Enrich metadata manually TOMORROW: ~50% effort reduction
against registry in metadata labeling with GenAI
Human intensive
Illustrative example Input data & associated Input data & associated
metadata info metadata info
Automated
Human to identify data domain GenAI to identify data domain
and business process associated and business process associated
Human to identify proper data GenAI to route to proper data
registry to look up registry and validate
Data registry
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Look up in registry, identify GenAI to create anomaly list v re
Data registry se
anomalies, and gaps of metadata and recommended changes
r
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Human to remediate identified Human to review and g n
itlu
anomalies and enrich metadata accept/reject s n
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Data storage Data storage 4 2 0
2
©
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GenAI covers steps that take ~50% of the effort
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In addition to process, key data management roles are being transformed
to be more interesting, interactive, and productive with GenAI
i ii iii iv v
Chief data Data governance Data domain Data Data
officer (CDO) office owner (DDO) steward custodian
GenAI
Low Medium Medium Very High High
impact
Drives enterprise-wide data Operationally supports rollout of Is responsible for a specific data Operationally supports DDOs Determines strategic direction
strategy and culture, champions enterprise-level data governance domain (global/ regional) with set of data families for data platforms
data governance and and data culture
evangelizes org on data ​ Refines data domain taxonomy Proposes remediation actions Ensures implementation of data
Prepares requirements to roll- and glossary and roadmap strategy on data platforms and
Typical
Coordinates prioritization of out governance of data other relevant IT systems
response
actions / data quality domains, including taxonomy, Produces data domain Implements data governance
remediation plans identifying roles in organization heatmap, defines data quality policies and processes Manages IT architecture for
targets and measures of data platforms
.d
Trains employees on data data quality Aligns with data custodian on IT e v
re
management roles needs
se
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How Augments and facilitates Allows employees to explore Augments data domain creation Augments data glossary, Automates tagging and labeling, rG
g
GenAI improvements to data supply policies and data management, and maintenance of taxonomy, dictionary, ontology generation monitors compliance of data n itlu
chain (e.g., data quality through chatbots, improving ontology, and glossary; and coherence; automates data strategy on data platform and s n
drives o
C
assessment and efficiency and reducing ad hoc automates reporting (e.g., map creation and data glossary other systems n
value o
remediation planning) support and training heatmap and data quality) updates
ts
o
B
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4
2
0
2
©
As technology enables bionic features, the burden of these roles will be reduced, enabling them to scale to higher-value tasks
th
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15 o
Source: BCG Marketing Org & Op Benchmarks C
A move from today's siloed structure toward a product-centric approach
will improve data accessibility and consumption
TODAY TOMORROW
Business communicates consistent Data product manager understands
Business Business
needs to internal data scientist needs of the business
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scientist Data product team
Data scientist needs data engineer
to construct models
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in data lakehouse
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1. DDP = data and digital platform. Please refer to ""The Future of the AI-Driven Tech and People Stack"" Executive Perspective for more details C
Establish a centralized system to fundamentally transform AI data governance
through integrated standards and control across business units
Federated data governance organization to ensure
standards, control, and governance culture across BUs
Governance council, formed of data domain owners,
1
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steers strategic priorities and establishes global policies
1 Governance council
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implementation of data governance capabilities
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Partnerships with technology providers are key to address gaps
and accelerate capability development across the data value chain
Sample processes Partners/Tools1
Data quality automation
AI tools identify and correct errors, inconsistencies, and inaccuracies in data, as well as enrich data
with additional information
CLEAN
Data anonymization
Anonymize data to protect privacy while preserving utility and integrity
Synthetic data generation
Generate synthetic data that resembles real data to protect privacy and facilitate safe data sharing
Code generation
GENERATE
Automatic functional code generation, optimization, and rewriting using trained LLMs results in
rapid prototyping and speeds up development cycles
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Source: IDC; Gartner; secondary research
How to get started | Four key activities can activate
the building of strong foundations and accelerate time to value
Develop core capabilities & strategies Extend capabilities across data Fully integrate AI into data
to effect a data-driven transformation management opportunities management at org-wide level
1 Develop your data strategy: Adopt a value-centric strategy to prioritize opportunities and align business outcomes,
data platforms, and assets to unlock outcomes over time
Assess existing setup: Review how your data function is positioned along the five drivers, understanding impact on
2
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NAMR
BCG experts |
Key contacts Dylan Vladimir David Amanda Matthew
Bolden Lukic Martin Luther Kropp
for AI data
Sesh Julie Beth Djon Steve
management
Iyer Bedard Viner Kleine Mills
transformation
Benjamin Renee Helen
Daniel Bo
Rehberg Laverdiere Han
Martines Xu
Vikram Tauseef
Sivakumar Charanya
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Sack Levin Wittig King Kapoor g
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277,bcg,BCG-Executive-Perspectives-Unlocking-Impact-from-AI-HR-EP1-30July2024.pdf,"Executive
Perspectives
Unlocking Impact from GenAI
Human Resources
July 2024
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Introduction In this BCG
Executive Perspective,
we show you how to
As part of our ongoing series of C-suite conversations on AI, we are sharing our most
recent learnings in a series designed to help navigate the rapidly changing world of
leverage AI to transform
AI. After working with over 1,000 clients in the past year, we've found that AI is at an
and create value in HR
inflection point: in 2024, the focus is on turning AI's potential into real profit.
In this edition, we discuss the future of human resources (HR) and the role AI
will play in turbocharging the function's capacity to deliver on unprecedented
demands.We address key questions on the minds of HR leaders, including:
• What will my HR organization look like – both how we are structured and what
tools and skillsets are required?
• How can we achieve near-term performance gains with AI and GenAI while
building the necessary capabilities?
• Given the sensitivity of our work, how can we proactively address ethical and
employee/candidate experience risks?
• How can we drive adoption, engagement, and adherence to capture value?
This document is a guide for CEOs and CHROs to cut through the hype
around AI in HR and understand what creates value now and in the future.
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Executive summary | Unlocking impact from GenAI in human resources
The changing nature of work is placing unprecedented demands on HR, e.g., organization-wide upskilling and
GenAI will behavioral change
enable HR to To meet these demands, HR can leverage Generative AI (GenAI) to become more productive, effective, and engaged
deliver against (e.g., ~20-40% productivity improvement)
new demands
In the near term, many leaders are starting with significant opportunities inr ecruiting (e.g., 20-25% near-term cost
reduction) and admin
HR organizations are investing in key enablers to re-imagine the function, including:
Foundational • Org and op model: Reorienting around employee experience, with new roles to shape and govern GenAI
investments will • Talent and skills: Up/re-skilling and hiring specialists to address 55-75% skill disruption in key HR roles
be required to • Data, tech, and partnerships: Preparing data and infrastructure, partnering to assemble portfolio of tools
capture value • Risk and responsible AI: Addressing potential bias and ensuring compliance with regulatory requirements
• Change management: Taking a science-backed approach to change behaviors and drive adoption
Most leaders are already taking action with GenAI in HR (e.g., among enterprises already deploying GenAI,
70-80% are using it in HR)
HR leaders must
act now
To get started, HR leaders must build integrated implementation roadmaps, upskill fellow leaders, and
prepare HR data and guardrails to ensure reliability, compliance, and sustained value capture
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HR organizations face unprecedented demands
as the future of work unfolds
Key trends in the future of work Requirements of HR
Personalized strategies to
• Dynamic, more competitive rewards/benefits
attract and retain top talent in • Personalized L&D1 pathways and career journeys
a cost-constrained environment • Deep focus on DE&I2 – on people and tech fronts
• Up-/re-skilling of non-tech talent to address GenAI
Skill disruption and human- disruption (e.g., 86% expect need in near-term)
machine teaming • Continual re-design of ways of working, teams, roles
• Behavioral change and human-machine trust
• Dynamic talent planning to address technical skill
Rising need for – and lagging gaps via hiring and re-skilling (e.g., to address >3x
supply of – tech talent increase in demand for data scientists in past 5 years;
future demand may vary with GenAI)
1. Learning & Development; 2. Diversity, equity & inclusion; Source: BCG skills disruption index; How to Attract, Develop, and Retain AI Talent (2023); BCG experience
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To deliver against these demands, HR of the …leveraging GenAI for
future must be fundamentally different… step-changes in…
Business
partners
Productivity
20-40% 90%+
Increase for HRBPs and Boost for some
HRBP
CHRO recruiters administrative workflows
Talent ecosystem
Strategic consultants and
The CEO’s strategic
The skills portfolio manager
organization architects
partner for a future-
ready organization
Speed and effectiveness
10X 50%
HR
function
Work rhythms
of the Product owners
The conductors of Faster content Decreased time to hire
learning and innovation future Guardians of AI- creation
enabled employee
experience
Engagement
HRIS
3X 25%
HR-IT experts of the new
Shared services
strategy and digital function
Ethics and bias
Short-term heroes of Growth in employee Rise in HR retention
Strategy and digital The champions of integrity standardization, long-
engagement
4
function of the future and tech bias prevention term role divergence
Source: BCG workforce diagnostic; BCG 'toil v. joy' diagnostic; BCG experience
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E2E process re-imagination is critical for HR to break the historical
compromise between productivity and employee engagement
Anticipate Attract Develop Engage
HR strategy Recruiting HR admin/ Compensation Learning and Performance and Employee Employee
and planning and resourcing shared services and benefits development career mgmt. engagement relations
From: Manual data Losing top Siloed teams Information stored Time-intensive Highly reliant on Reliant on Manual and
producing static candidates due to providing in different places, content creation, human opinion, employees to time-intensive
results lengthy process fragmented time-intensive to fragmented often providing raise issues/ documentation
and human bias service and get answers employee feedback too late concerns and reporting
long wait times experience
To: Dynamic Humans Streamlined, Chatbots and self- Personalized Delivering real- Predicting issues Automating
forecasting of augmented with faster support service tools to L&D journey and time feedback by monitoring and admin to enable
future workforce automated from 'hire to retire' help employees clear career path and objective analyzing time for human
needs and sourcing support, find information to increase performance employee engagement
re-designing freeing up time quickly engagement and insights analysis sentiment around inquiries
workflows retention
Productivity
increase
Engagement
increase
Productivity increase: <10% ~10-25% ~25-50% >50%
Engagement increase: Low Medium High
Source: BCG workforce diagnostic; BCG 'toil vs. joy' diagnostic; BCG experience
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Sensitivity and risk
Productivity impact
Talent/L&D
Early adopters of Recruiting Interactive Engagement
Content Admin workforce Employee
creation HR chatbot planning listening
GenAI in HR are
5%
12%
starting with low-
28%
25%
18% 40%
hanging fruits that
present lower risk and
44%
offer higher near-term 36%
70% 70%
productivity gains
28%
24%
Examples follow
We are not considering it
We are considering it
We are implementing, piloting, or scaling
1. Have you started experimenting on the following applications?
Source: BCG survey of CHROs or direct reports to CHRO, n=64 6
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Example 1 | Recruiting - Multi-year journey re-imagines recruitment
as part of broader professional services HR transformation
Where did they start? What are they doing? Value identified
Professional services firm facing significant From 2020-2022: 75%+ solution adoption to date driving
changes in demand for talent, including: • Diagnostic including identification of impact at scale, including:
• Greater quantity pain points and differentiators
• Broader range of skillsets • Partner diligence and selection
Decrease in time and
20-25%
• Increased diversity • Future architecture definition
expense
• Experimentation and pilots, including
In parallel, candidates and recruiters workflow re-design and standardization,
Overall recruiting cost
dissatisfied with talent acquisition tools, training, RAI guardrails, change mgmt 20-25%
reduction
putting brand and ability to capture top-tier
talent at risk From 2023-2024:
Lift in offer conversion
• E2E scaling using agile approach (e.g., 10-15%
per recruiter
Firm had formulated an organization-wide 2-week sprints, dedicated product owners)
recruiting vision and made progress on – Consolidation from many complex
quick wins, e.g., virtual recruiting tools workflows to three global standards Plus higher data fidelity, global KPI
– Streamlining from 5x ATS tools to 1 standardization, and better recruiter +
As next step, desire to pursue E2E digital • Continued RAI guardrail refinement candidate experience
transformation featuring GenAI to • Iteration of candidate communications to
accelerate performance, unlock time for ensure ongoing transparency Lessons learned informing broader go-
deeper human thinking and engagement forward HR opportunities
Source: BCG experience
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Example 1 | Recruiting – E2E re-design unlocks time
for more strategic, engaging work
Recruitment and Plan hiring Attract and source Screen Facilitate Hire and onboard Avg. duration and
resourcing journey needs candidates candidates interviews candidates engagement1
Manually assess Manually review Negotiate and Illustrative
Lead interviews for
and forecast talent Consult hiring resumes and cover extend offers
each candidate
needs manager and other letters
stakeholders on 25-30%
Select priority Summarize and Iterate onboarding
Current profile requirements
Align stakeholders candidates score each process as needed
state on forecast candidate post- 30-35%
Schedule interviews interview
Manually develop,
Oversee onboarding
review, and post Conduct phone Send candidate 35-45%
for new hires
job ads screenings follow-ups
Up to 50% reduced
GenAI enables GenAI generates
GenAI automates time to hire
GenAI creates video/text-based personalized
AI predicts resume synthesis,
targeted job interviews compensation and
workforce need with highlighting
postings onboarding plans
human involvement competency based
Future state HR selects
on specific skills
HR review and candidates from HR welcomes, 10-15%
with GenAI HR verifies with HR develops
approves posts shortlist, meets oversees 35-45%
stakeholders shortlist based on
priority candidates onboarding
GenAI output 45-50%
Engagement level: Low Medium High
1. Based on BCG 'toil v. joy' diagnostic responses to: How much do you agree with this statement ""I enjoy this task""? From (1) - Strongly disagree to (5) - Strongly
agree; Source: BCG workforce diagnostic; BCG 'toil v. joy' diagnostic; BCG experience
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Global airline carrier enhances
Example 2 | HR admin -
service speed and quality with GenAI in shared services
Where did they start? What are they doing? Value identified
Global airline carrier running organization- Conducted holistic assessment across In HR, two priority opportunities
wide improvement program as part of path corporate functions including: • Shared services center (focus of this
to recovery from COVID-19, which significantly • Activity and time allocation example)
impacted flight crew staff: • Potential GenAI impact, including • Recruiting and hiring
productivity, cost, engagement, and skills
Impact on morale disruption Of requests eliminated
55-60% (e.g., due to errors,
Aligned focus opportunities: Finance and HR
missing information)
Prioritized shortlist of workflows based on
High 20-40% Faster response time for
pain points, and assessed data capabilities
demands High remaining requests
for transversal use across organization
on employees attrition
Detailed target state and business cases for < 1 yr Breakeven point
priority opportunities, including cost savings,
Seeking to break this cycle by re-
investment, and skill requirements
establishing employee satisfaction and
operational stability, exploring how GenAI … Plus more efficient, consistent, and
Developed implementation roadmap
can help achieve these strategic goals higher-quality responses
including quick wins to fund the journey and
long-term investments
Source: BCG experience
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Streamlined shared services workflow
Example 2 | HR admin -
enables faster, higher-quality service
Shared services Initiate Validate Gather Create Send Avgerage response
request process requests requests information responses responses time
Employees send From From HR admin
question through original other personalizes
Up to 3
email requester employees template and
manually writes up days
Current
a response, HR admin sends
state HR admin HR admin sometimes response
Ask for
conducts a looks up resulting in
input if
discrepancies
completeness relevant
needed between different
check policies
employees 20-40% reduction
in response time
with current tech
Employee sends Employee interacts
question through with chatbot in real-
GenAI chatbot Chatbot engages time; chatbot redirects HR admin creates
with questioner, to HR admin as response using
Future state HR admin sends
requests missing needed for complex chatbot inputs and
response
with GenAI info or redirects to inquiries checks for ~2 days
correct completeness
HR admin human
department as
validation
needed
(during training only)
1 Source: BCG workforce diagnostic; BCG experience
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To unlock value, HR leaders are investing in foundational enablers
Org design Talent Data, tech, and Risk and Change
and op model and skills partnerships responsible AI management
Restructuring HR, Developing Evolving data to fully Ensuring all Taking a science-
including new roles professional skillsets leverage GenAI and solutions are backed approach to
to address in HR to execute expanding compliant with change behaviors
governance, data against new roles ecosystem of GenAI regulations, and drive adoption,
maintenance, and and requirements partners to assemble especially those that engagement, and
bias tool portfolio are HR-focused adherence
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Today's siloed HR organization
Org design and op model |
will evolve to re-orient around the employee experience
CHRO
Co-leads transformation office to define case for change,
drive organization-wide initiatives, and ensure ROI
North
Star HR
Talent Work HRBP Product HR digital Ethics and
Strategic
ecosystem rhythms owners and data bias
alignment
Unified E2E talent Owners of highly Managerial Specialized, tech- Strategic, data- Governing roles that
around acquisition and personalized, coaches working oriented oriented professionals work across HR,
management behavioral science- shoulder-to- professionals who who link data flows legal, and IT to
employee
professionals informed, ""in the shoulder with shape solutions to of business strategy proactively identify
experience, trained in dynamic workflow"" L&D business to drive increase to dynamic strategic and mitigate risk,
""segment-of-one"" journeys and change, continually engagement and workforce planning oversee compliance,
including
skills analysis human/GenAI redesign work and retention and advise on HR and monitor models/
new roles collaboration team structures for (e.g. well-being, technology, including algorithms for bias
models optimal compensation) data maintenance
that shape
performance
and govern
GenAI
Shared services
Critical forcing mechanism for short-term process/tool standardization, mid-to long-term evolution based on organizational strategy
Existing HR role New HR role Degree of change: Low Med-low Medium High
Source: BCG experience
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Up/re-skilling and new hires will be required
Talent and skills |
to meet the demands of these highly evolved and net new roles
Across three HR roles with the highest business impact,
deep up/re-skilling required amid 55-75% skill disruption …plus hiring of new profiles
Not exhaustive
Extent of skill disruption and example upskilling required for top 3 roles by business impact For new roles to govern,
shape, and build GenAI…
HRBP L&D Recruiter
• RAI/ethics experts
• HR/IT experts
45% 45%
65%
10% 10% For evolved roles requiring
more specialization,
10%
45% 45%
advanced degree holders in
25%
topics including…
• Behavioral science
Example • Interpersonal • Behavioral • Talent sourcing
upskilling communication science • Relationship • Data science
required • Problem solving • L&D strategy building • Programming
High: Upskilling required Moderate: Less important Low: Keep and adapt
Source: BCG analysis LightCast skill taxonomy; BCG experience 13
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For HR organizations lagging in data foundations,
Data, tech, & partnerships |
critical to address upfront and factor into GenAI investment priorities
HR organizations commonly need to Opportunity to invest in data readiness
overcome 3 barriers to prepare for GenAI and GenAI solutions in parallel
Data silos
1 Example GenAI opportunities to pursue in
HR data often housed across different, siloed parallel to HR data preparation:
systems – need to centralize in unified platform
• Recruiting and onboarding content: Writing
job descriptions, marketing emails, personalized
Data quality and inconsistency
2 onboarding; relies on existing job descriptions/
materials
Disparate systems often lead to inconsistent
data entries and formats – critical to standardize • Recruiting admin: Automating scheduling, and
and establish data governance generating reminders and follow-up
communications; does not require employee data
3 Legacy systems • L&D recommendations and content: Suggesting
trainings and developing content; requires basic
HR often using legacy systems that do not
employee data, training records, and existing
integrate with GenAI tools – upgrades required
content that is easier to clean and maintain
to enable data integration and security
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~80-85% of HR organizations are exclusively
Data, tech, & partnerships |
buying or assembling built + bought solutions
Spectrum of partner solutions available based on Five guiding questions for HR
organizational needs and priorities GenAI partnership decisions
Core systems (e.g., CRM) Point solutions Compatibility
How well does the solution integrate with existing
Description Integrated platforms with GenAI in Specialized GenAI-powered tools HR systems and data?
their product roadmaps designed for specific HR activities
Functionality
Can I afford to wait for my core system provider(s)
Advantages • Enterprise-grade technologies • Leveraging pockets of innovation
to add GenAI functionalities? Or do I need to act
• Streamlined integration that are fast to market
now (i.e., build)?
• Able to achieve step changes in • Typically, flexible pricing
capabilities (e.g., via acquisition) • Often faster to implement
Data security/compliance
Does the solution provide adequate protections
Disadvantages • Typically, higher cost and longer • Across E2E HR requirements, may for sensitive HR data? Comply with data privacy
time to implement not be cost-effective
and security regulations?
• Risk of lower quality in some • Risk of data fragmentation
capabilities vs. point solutions • Risk of partner being acquired by
Scalability
• May ""lock"" into one partnership lean core players
Can the solution scale with our evolving needs?
Examples • Human capital management • Skills-based talent development Cost/ROI
platform tools What are the initial and ongoing costs, and
expected ROI?
Source: BCG survey of CHROs or direct reports to CHRO (n=64); BCG experience 15
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Many emerging GenAI regulations explicitly
Risk and responsible AI |
address HR given the high sensitivity around its data and responsibilities
Not exhaustive
July 2023 May 2024 May 2024 Expected 2024
Geo
NYC AI Regulations EU Artificial Colorado Artificial EU, US,
Regulation (Local Law 144 Intelligence Act3 Intelligence Act Cross-border
of 2021)1 (CAIA)4
• Regulates how GenAI is • Given that GenAI tech for HR orgs will be required to: • More regulations are
used in hiring and HR falls into highest risk expected targeting HR
• Enact and report on risk-
promotion decisions category, HR orgs will • HR must monitor ongoing
management policy to • HR must conduct annual need to meet stringent commissions & regulations on
Potential
govern GenAI tool use
audit for potential bias requirements (e.g., data the horizon including
implications
• Conduct annual impact
in automated employment governance, transparent No Robot Bosses Act
for HR
assessments to identify
decision tools and provide candidate comms, (prohibits sole reliance on
algorithmic bias
subsequent notice2 compliance obligations, automated hiring decisions),
• Alert candidates of AI
incident reporting) AI Pact (network to support
use in hiring decisions
organizations’ AI compliance)
Disclaimer: For informational purposes only - does not constitute legal advice; 1. In effect as of Jan 1, 2023; 2. Defined as computer-based tools that use AI, machine learning, statistical modeling, or
data analytics to help employers make employment decisions; 3. Passed March, 2024, going into effect Summer 2024; 4. Signed May 2024, going into effect February 2026;
Source: City of New York; State of Colorado; European Commission; U.S. Congress; BCG analysis
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…Underscoring the importance of HR compliance
and proactive risk mitigation
Not exhaustive
Change Governance Data security Regulatory Candidate
management vs. speed and model bias conformity experience
Addressing resistance Balancing compliance Avoiding exposure of Setting up to comply Minimizing
HR must to changes in ways with momentum sensitive data and with new HR-focused performance issues
proactively of working biased outputs laws/regulations during rollout
anticipate
many
• Upskill early and • Provide clear • Create new role(s) • Ensure proactive • Start small with
potential continuously, starting decision-making responsible for monitoring of GenAI- pilots to minimize the
with leadership model on E2E work systematic model specific regulations to impact of the first risks,
re-design while oversight, including inform partnerships, rollout
• Build personalized
including… anticipating tradeoffs bias prevention models in use, and change journeys with • Prioritize
solution design
tailored behavioral • Establish clear, • Prioritize red- transparency where
science interventions streamlined teaming and other • Develop ongoing possible during pilot
governance processes stress tests to regulatory training and rollout, fostering
• Identify and measure
with a focus on actively address risk to ensure leadership spirit of co-creation
adoption and value to
prioritizing security, and workforce are and ongoing
track progress, inform • Consider dedicated
action, and informed communication
solution iteration HR-IT roles for data
productivity gains
oversight
Additional detail follows
Source: BCG experience
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To ensure adoption, engagement, and adherence,
Change management |
a proactive change program grounded in behavioral science is required
Four levers to catalyze the flywheel of behavioral change
Personalized change journeys Closed feedback loops
2x adoption rate with personalized Always-on change monitoring, e.g., to
user journeys that leverage inform feedback loops and respond to
interventions based on behavioral 90% of employees seeking regular
insights, pain points, and psychological leadership communication
Accuracy Trust
traits
GenAI
Nudges Co-creation
adoption
Subtly guiding behavior without For example, collaborating with
forbidding options or significantly recruiters to re-design talent
changing economic incentives; e.g., acquisition processes and develop
changing default options, highlighting supporting set of GenAI solutions
Usage
peer benchmarks to boost desired
behavior 55-60%
Source: Behavioral Science Lab; BCG workforce diagnostic; BCG experience
Three steps for HR to begin the GenAI journey
Build an integrated GenAI roadmap, grounded in
strategic HR goals, and collaborate with leadership to
chart the enterprise-wide transformation journey
Upskill leaders in HR and across the organization,
including ongoing hands-on experimentation (e.g., everyday
tools, custom GPTs, and agents)
Prepare data (e.g., mapping data sources, implementing
centralized HRIS platform, standardizing formats) and
develop guardrails to ensure reliability and compliance
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Key contacts for HR AI transformation
BCG Experts |
NAMR
Allison David Julie Renee Dylan Bill
Bailey Martin Bedard Laverdiere Bolden Beaver
Frank Tauseef Julia Kristy Sesh Matthew
Breitling Charanya Dhar Ellmer Iyer Kropp
Juliana Vladimir Rajiv Nithya Charles
Lisi Lukic Shenoy Vaduganathan Westrin
EMESA
Vinciane Jens Jaap Nicolas Nina Erik
Beauchene Baier Backx de Bellefonds Kataeva Lenhard
Julia Tom Dan Ben Marc Sebastian
Madden Martin Sack Shuttleworth Schuuring Ullrich
APAC
Ashish Sreyssha Sagar Chris Fang Jeffrey
Garg George Goel Mattey Ruan Walters"
278,bcg,ai-radar-2025-slideshow-jan-2025-r.pdf,"BCG AI RADAR
From Potential to Profit:
Closing the AI Impact Gap
JANUARY 2025
SURVEY METHODOLOGY
Global research of 1,803 C-level executives on AI in 2025
Executive roles Company revenue Markets Industries and key functions
US 214
TMT 323
14% 14% India 200
Financial institutions 256
Germany 198
5%
UK 182
Consumer/retail 250
France 171
31%
Italy 102 Healthcare/medical 192
CEO
30%
Singapore 101
CSO
<$500M Transportation/travel/tourism 145
Brazil 87
CxO1
17% $500M–<$1B
Japan 82
CTO Energy 144
$1B–<$2B
CIO UAE 81
8% $2B–<$5B
Manufacturing 142
CDO2 Spain 79
>$5B
16%
9% C-suite3 Nigeria 65
Industrial goods 110
Indonesia 64
7%
Australia 46 Public sector 110
Saudi Arabia 45
Insurance 49
22% Greater China 38
27%
South Africa 28 Marketing 41
Morocco 12
Real estate 41
Qatar 8
Source: BCG AI Radar 2025 Survey
Note: Revenue thresholds for survey inclusion: $500+ million USD (US, Europe, Japan, Australia); $100+ million USD (rest of APAC, Middle East, Africa). Survey conducted September to December 2024.
1CxO represents other executives who directly report to the CEO (e.g., CMO, CFO, COO, etc.). 2Includes CDO and CAIO. 3C-suite includes EVP, SVP, VP, Chairman, President.
Where is the value in AI?
GenAI investments are projected to increase by
60% in the next 3 years
+60%
AI ambitions are
growing alongside +30%
investments
2023 2024 2027
Source: BCG IT Spend Survey 2024.
One in three companies across all markets are planning to spend
$25 million+ on AI in 2025
How much are you planning to invest in AI in 2025?
Japan 53% 26% 10% 11%
US 59% 23% 9% 9%
Singapore 63% 25% 6% 6%
UK 65% 18% 10% 7%
France 69% 17% 6% 8%
Up to $25M
Germany 69% 18% 9% 4% $26M–$50M
$51M–$100M
India 71% 15% 8% 6%
>$100M
UAE 78% 15% 6% 1%
Spain 81% 6% 5% 8%
Italy 83% 12% 3% 2%
Brazil 86% 12% 2%
Global 69% 18% 7% 6% One in three companies
Source: BCG AI Radar 2025 Survey (n=1,803).
… but, only
75% 25%
of executives rank AI/GenAI as a top three strategic priority … of executives are
seeing significant
value from AI
Source: BCG Radar 2025 Survery (n=1,803).
What are leading companies doing differently?
Deploy AI in everyday tasks
to realize 10% to 20%
productivity potential
Three value plays to
Reshape critical functions for
30% to 50% enhancement in
maximize AI potential
efficiency and effectiveness
Invent new products and
services to build long-term
competitive advantage
Source: BCG analysis.
They focus +80% of their AI investments in reshaping
critical functions and inventing new products and services
Leading
18%
Deploy
companies go
Individual-productivity focused
well beyond 40%
Reshape
Process-level productivity aimed
deploy …
at reshaping critical functions
Invent
Company-level innovation
42%
core to the business
+80%
Source: BCG Build for the Future 2024 Global Study (merged with Digital
Acceleration Index), (n=1,000).
Share of AI investments in Deploy, Reshape,
and Invent initiatives
… but most
companies aren’t
yet prioritizing 27% Deploy
Individual-productivity focused
investments in
44%
Reshape
higher-impact Process-level productivity aimed
at reshaping critical functions
56%
plays
Invent
Company-level innovation
core to the business
29%
Source: BCG AI Radar 2025 Survey (n=1,803).
Leading companies extract greater value by
focusing their AI investments
40%
In reality, most
6.1
companies go broad
2.1x and dilute efforts
3.5
across multiple
pilots, seeing lower
ROI as a result
AI use cases More ROI for AI
prioritized anticipated
Leading companies
Other companies
Source: BCG Build for the Future 2024 Global Study (merged with Digital Acceleration Index), (n=1,000).
Yet 60% of companies are failing to define and monitor any
Leading
financial KPIs related to AI value creation
companies set
How is your organization tracking value creation from AI?
clear goals and
track top- and
32% 28% 16% 24%
bottom-line
impact
60%
Not tracking yet Operational only Financial only Operational and financial
Source: BCG AI Radar 2025 Survey (n=1,803).
Leaders follow the 10-20-70 principle to create value
10%
… but
Algorithms
2 in 3
20%
Technology
companies struggle to:
· Reimagine workflows and drive
incentives, culture, and change
70%
People and processes
· Hire AI talent and upskill workforce
Source: BCG AI Radar 2025 Survey (n=1,803).
Note: AI talent refers to AI specialists (i.e., data scientists, ML ops engineers) and non–specialists (i.e., upskilled talent leveraging AI tools).
Data privacy and security 66%
AI risks that
companies must
Lack of control or understanding
48%
of AI decisions
navigate
Regulatory challenges
44%
and compliance
Source: BCG AI Radar 2025 Survey (n=1,803).
Note: Percentages correspond to share of executives ranking risk in their top 3.
76%
Cybersecurity
remains critical
Recognize that their AI cybersecurity
measures need further improvements
Source: BCG AI Radar 2025 Survey (n=1,803).
2025: The year of AI agents?
What an agent is
What is an
agent?
Memory Reasoning Systems
Remembering across tasks Decomposing a problem Accessing external
Simply put,
and changing states and planning actions systems on your behalf
it’s an AI that
What an agent does
has learned to
use tools
Observes Plans Acts
Collect and process data Evaluate possible actions Execute by leveraging internal
from environment and prioritize toward a goal or external tools/systems
Source: BCG Analysis
67% However, agents require
deep reimagination of
work and are not a silver
bullet for impact
15%
are considering autonomous agents as
part of their AI transformation
Source: BCG AI Radar 2025 Survey (n=1,803).
Optimism around AI agents is consistent across geographies
Role that companies see for AI agents moving into 2025
74%
US 37% 37%
72%
Japan 26% 46%
UAE 32% 40% 72%
Singapore 31% 40% 71%
70%
India 34% 36%
68%
UK 27% 41%
France 34% 33% 67%
63%
Germany 30% 33%
Central or
complementary role
Spain 38% 24% 62%
Exploring
Brazil 31% 30% 61%
Italy 18% 38% 56%
Global 32% 35% 67%
Source: BCG AI Radar 2025 Survey (n=1,803).
Unlocking new potential to reshape processes and services
Agents deliver up to 3x more productivity and speed benefits compared to
traditional assistants
Breaking down silos
The biggest opportunity is seamless enterprise collaboration through zero-touch
services, advanced planning, and automated customer 360 activation
AI agents:
Key leadership
Managing the risks of more complexity
AI agents are more complex than assistants, requiring robust testing and
priorities optimization to manage operational and cyber risks effectively
Cutting through the hype
Mislabeling and overhype of “agents” will dilute trust and lead to unmet
expectations; leaders must clarify capabilities and set realistic goals
Moving forward
Success lies in targeted, high-impact applications, focused on practical design,
Source: BCG analysis.
seamless integration, and data quality over hype
With AI agents on the rise, who will hold
the power: humans or AI?
Executives see talent and AI as complementary
Human-centered AI-focused
14% 64% 22%
Prioritizing AI and humans working AI taking the lead,
human talent, side by side but humans retaining
using AI only oversight
when necessary
Source: BCG AI Radar 2025 Survey (n=1,803).
How do you expect the workforce in your organization to change?
Increase headcount,
More FTEs 8%
adding new skills
Less than 10% of
executives expect
a decrease in More productivity and
Existing workforce 68% upskilling of existing talent
to meet AI needs
headcount due
to AI automation
Restructure workforce
with new roles to replace
Net neutral 17%
redundant ones
Decrease headcount
Fewer FTEs 7%
due to AI automation
Source: BCG AI Radar 2025 (n=1,803).
~70% of the companies have trained
less than 1 in 4 of their workforce
AI upskilling is
accelerating,
but the work is
71%
94%
not over
Companies with more
than 25% of their workforce
trained on AI/GenAI tools
29%
6%
2023 2024
Source: BCG AI Radar 2025 Survey (n=1,803).
Companies with more than one-quarter of their workforce trained
on AI/GenAI tools
Singapore 44%
Singapore and
Japan 38%
Japan lead in
Germany 30%
AI/GenAI
Spain 29%
upskilling; Brazil France 29%
UK 29%
and Italy are
US 29%
falling behind
UAE 27%
India 26%
Italy 20%
29%
Brazil 20%
globally
Source: BCG AI Radar 2025 Survey (n=1,803).
Strategic playbook for CEOs
Breaking through AI’s imagination gap
Rethink what is possible with AI and business transformation
Targeting and prioritizing AI efforts
Focus on a few transformative opportunities in core functions
Leading companies
maximize value
Putting AI at the service of enterprise ambition
Define and track clear KPIs
creation by...
Leading the cultural and organizational change
Lean in personally and drive the change
Preparing for what’s next
Anticipate AI’s next value play and accompanying risks
Source: BCG analysis."
279,bcg,24-013_d9b45b68-9e74-42d6-a1c6-c72fb70c7282.pdf,"Working Paper 24-013
Navigating the Jagged Technological
Frontier: Field Experimental Evidence
of the Effects of AI on Knowledge
Worker Productivity and Quality
Fabrizio Dell'Acqua Saran Rajendran
Edward McFowland III Lisa Krayer
Ethan Mollick François Candelon
Hila Lifshitz-Assaf Karim R. Lakhani
Katherine C. Kellogg
Navigating the Jagged Technological
Frontier: Field Experimental Evidence
of the Effects of AI on Knowledge
Worker Productivity and Quality
Fabrizio Dell'Acqua Saran Rajendran
Harvard Business School Boston Consulting Group
Edward McFowland III Lisa Krayer
Harvard Business School Boston Consulting Group
Ethan Mollick François Candelon
The Wharton School Boston Consulting Group
Hila Lifshitz-Assaf Karim R. Lakhani
Warwick Business School Harvard Business School
Katherine C. Kellogg
MIT Sloan School of Management
Working Paper 24-013
Copyright © 2023 by Fabrizio Dell’Acqua, Edward McFowland III, Ethan Mollick, Hila Lifshitz-Assaf, Katherine
C. Kellogg, Saran Rajendran, Lisa Krayer, François Candelon, and Karim R. Lakhani.
Working papers are in draft form. This working paper is distributed for purposes of comment and discussion only.
It may not be reproduced without permission of the copyright holder. Copies of working papers are available from
the author.
We thank Michael Bervell, John Cheng, Pallavi Deshpande, Maxim Ledovskiy, John Kalil, Kelly Kung, Rick
Lacerda, MarcAntonio Awada, Paula Marin Sariego, Rafael Noriega, Alejandro Ortega, Rahul Phanse, Quoc-Anh
Nguyen, Nitya Rajgopal, Ogbemi Rewane, Kyle Schirmann, Andrew Seo, Tanay Tiwari, Elliot Tobin, Lebo
Nthoiwa, Patrick Healy, Saud Almutairi, Steven Randazzo, Anahita Sahu, Aaron Zheng, and Yogesh Kumaar for
helpful research assistance. We thank Kevin Dai for outstanding support with data and visualizations. For helpful
feedback, we thank Maxime Courtaux, Clement Dumas, Gaurav Jha, Jesse Li, Max Männig, Michael Menietti,
Rachel Mural, Zahra Rasouli, Esther Yoon, Leonid Zhukov, and David Zuluaga Martínez. Lakhani would like to
thank Martha Wells, Anne Leckie, Iain Banks, and Alastair Reynolds for inspiring AI futures. We used Poe,
Claude, and ChatGPT for light copyediting and graphics creations. Lakhani is an advisor to Boston Consulting
Group on AI Strategy and learning engagement.
Funding for this research was provided in part by Harvard Business School.
Navigating the Jagged Technological Frontier:
Field Experimental Evidence of the Effects of AI on
Knowledge Worker Productivity and Quality*
Fabrizio Dell’Acqua1, Edward McFowland III1, Ethan Mollick2, Hila
Lifshitz-Assaf1,3, Katherine C. Kellogg4, Saran Rajendran5, Lisa Krayer5,
François Candelon5, and Karim R. Lakhani1
1Digital Data Design Institute, Harvard Business School; 2The Wharton
School, University of Pennsylvania; 3Warwick Business School, Artificial
Intelligence Innovation Network; 4MIT Sloan School of Management;
5Boston Consulting Group, Henderson Institute
September 22, 2023
*Fabrizio Dell’Acqua (fdellacqua@hbs.edu), Edward McFowland III (emcfowland@hbs.edu),
Ethan Mollick (emollick@wharton.upenn.edu), Hila Lifshitz-Assaf (hila.lifshitz-assaf@wbs.ac.uk),
Katherine C. Kellogg (kkellogg@mit.edu), Saran Rajendran (rajendran.saran@bcg.com), Lisa
Krayer (krayer.lisa@bcg.com), François Candelon (candelon.francois@bcg.com), Karim R. Lakhani
(klakhani@hbs.edu). We thank Michael Bervell, John Cheng, Pallavi Deshpande, Maxim Ledovskiy, John
Kalil, Kelly Kung, Rick Lacerda, MarcAntonio Awada, Paula Marin Sariego, Rafael Noriega, Alejandro
Ortega,RahulPhanse,Quoc-AnhNguyen,NityaRajgopal,OgbemiRewane,KyleSchirmann,AndrewSeo,
TanayTiwari,ElliotTobin,LeboNthoiwa,PatrickHealy,SaudAlmutairi,StevenRandazzo,AnahitaSahu,
Aaron Zheng, and Yogesh Kumaar for helpful research assistance. We thank Kevin Dai for outstanding
supportwithdataandvisualizations. Forhelpfulfeedback,wethankMaximeCourtaux,ClementDumas,
Gaurav Jha, Jesse Li, Max Männig, Michael Menietti, Rachel Mural, Zahra Rasouli, Esther Yoon, Leonid
Zhukov, and David Zuluaga Martínez. Lakhani would like to thank Martha Wells, Anne Leckie, Iain
Banks, and Alastair Reynolds for inspiring AI futures. We used Poe, Claude, and ChatGPT for light
copyeditingandgraphicscreations. LakhaniisanadvisortoBostonConsultingGrouponAIStrategyand
learningengagement. Allerrorsareourown.
1
Abstract
The public release of Large Language Models (LLMs) has sparked tremendous
interestinhowhumanswilluseArtificialIntelligence(AI)toaccomplishavarietyof
tasks. In our study conducted with Boston Consulting Group, a global management
consulting firm, we examine the performance implications of AI on realistic,
complex, and knowledge-intensive tasks. The pre-registered experiment involved
758 consultants comprising about 7% of the individual contributor-level consultants
at the company. After establishing a performance baseline on a similar task, subjects
were randomly assigned to one of three conditions: no AI access, GPT-4 AI access,
or GPT-4 AI access with a prompt engineering overview. We suggest that the
capabilities of AI create a “jagged technological frontier” where some tasks are
easily done by AI, while others, though seemingly similar in difficulty level, are
outside the current capability of AI. For each one of a set of 18 realistic consulting
tasks within the frontier of AI capabilities, consultants using AI were significantly
moreproductive(theycompleted12.2%moretasksonaverage,andcompletedtasks
25.1% more quickly), and produced significantly higher quality results (more than
40% higher quality compared to a control group). Consultants across the skills
distribution benefited significantly from having AI augmentation, with those below
theaverageperformancethreshold increasingby43%andthose above increasingby
17% compared to their own scores. For a task selected to be outside the frontier,
however,consultantsusingAIwere19percentagepointslesslikelytoproducecorrect
solutionscomparedtothosewithoutAI.Further,ouranalysisshowstheemergenceof
twodistinctivepatternsofsuccessfulAIusebyhumansalongaspectrumofhuman-
AI integration. One set of consultants acted as “Centaurs,” like the mythical half-
horse/half-humancreature,dividinganddelegatingtheirsolution-creationactivities
to the AI or to themselves. Another set of consultants acted more like “Cyborgs,”
completely integrating their task flow with the AI and continually interacting with
thetechnology.
2
1 Introduction
The capabilities of Artificial Intelligence to produce human-like work have improved
rapidly,especiallysincethereleaseofOpenAI’sChatGPT,oneofseveralLargeLanguage
Models (LLMs) that are widely available for public use. As AI capabilities overlap more
with those of humans, the integration of human work with AI poses new fundamental
challenges and opportunities, in particular in knowledge-intensive domains. In this
paper, we examine this issue using randomized controlled field experiments with highly
skilled professional workers. Our results demonstrate that AI capabilities cover an
expanding, but uneven, set of knowledge work we call a ""jagged technological frontier.”
Within this growing frontier, AI can complement or even displace human work; outside
of the frontier, AI output is inaccurate, less useful, and degrades human performance.
However, because the capabilities of AI are rapidly evolving and poorly understood,
it can be hard for professionals to grasp exactly what the boundary of this frontier
might be at a given. We find that professionals who skillfully navigate this frontier gain
large productivity benefits when working with the AI, while AI can actually decrease
performancewhenusedforworkoutsideofthefrontier.
Though LLMs are new, the impact of other, earlier forms of AI have been the subject
ofconsiderablescholarlydiscussion(e.g.,Brynjolfssonetal.(2018);FurmanandSeamans
(2019);Puranam(2021)). BecauseofthelimitationsoftheseearlierformsofAI,nonroutine
tasks that were difficult to codify seemed protected from automation (Autor et al., 2003;
Acemoglu and Restrepo, 2019), especially as previous waves of technology had mostly
automatedlower-skilledoccupations(GoldinandKatz,1998). ThereleaseofChatGPTin
November, 2022 changed both the nature and urgency of the discussion. LLMs proved
unexpectedly capable at creative, analytical, and writing tasks, including scoring at top
levelsatgraduateandprofessionalexaminations(Girotraetal.,2023;Geerlingetal.,2023;
Kung et al., 2023; Boussioux et al., 2023). This represented an entirely new category of
automation, one whose abilities overlapped with the most creative, most educated, and
mosthighlypaidworkers(Eloundouetal.,2023).
Studies of previous generations of AI (Brynjolfsson et al., 2023) and controlled
1
experiments on the impact of recently released LLMs (e.g., Noy and Zhang (2023); Choi
and Schwarcz (2023)) suggest that these systems can have a large impact on worker
performance. In our study, we focus on complex tasks, selected by industry experts to
replicate real-world workflows as experienced by knowledge workers. Most knowledge
work includes this sort of flow, a set of interdependent tasks, some of which may be
good fit for current AI, while some are not. We examine both kinds of tasks, and build
on recent studies to suggest ways of understanding the rapidly evolving impact of AI on
knowledgeworkers,underwhichcircumstancesorganizationsmaybenefit,andhowthis
mightchangeasthetechnologyadvances.
This is important because understanding the implications of LLMs for the work
of organizations and individuals has taken on urgency among scholars, workers,
companies, and even governments (Agrawal et al., 2018; Iansiti and Lakhani, 2020; Berg
etal.,2023). PreviousformsofAIledtoconsiderabledebateintheliteraturearoundhow
and whether professionals should adopt AI for knowledge work (Anthony et al., 2023)
and the potential impact this might have on organizations (Raisch and Krakowski, 2021;
Glaeser et al., 2021; Brynjolfsson et al., 2021). Some scholars focused on the potential
for AI to help professionals improve their effectiveness and efficiency (DeStefano et al.,
2022;Balakrishnanetal.,2022;ValentineandHinds,2023). Othersdemonstratedthat,for
critical tasks, it can be risky for professionals to use AI (Lebovitz et al., 2021), especially
black-boxed (e.g., Lebovitz et al. (2022); Waardenburg et al. (2022)), and showed how
professionals are struggling to use it effectively (Pachidi et al., 2021; Van den Broek et al.,
2021). Finally, another group of researchers argued that the “algorithmic management”
affordedbyAIcancreatenegativepersonalimpactsforprofessionals(Kelloggetal.,2020;
Möhlmann et al., 2021; Tong et al., 2021) and raise accountability and ethical questions
(Choudhuryetal.,2020;Cowgilletal.,2020;Rahmanetal.,2024). Yet,mostofthestudies
predate ChatGPT, and investigate forms of AI designed to produce discrete predictions
basedonpastdata. ThesesystemsarequitedifferentfromLLMs.
Specifically, outside of their technical differences from previous forms of machine
learning, there are three aspects of LLMs that suggest they will have a much more
rapid, and widespread, impact on work. The first is that LLMs have surprising
2
capabilities that they were not specifically created to have, and ones that are growing
rapidly over time as model size and quality improve. Trained as general models,
LLMsnonethelessdemonstratespecialistknowledgeandabilitiesaspartoftheirtraining
processandduringnormaluse(Singhaletal.,2022;Boikoetal.,2023). Whileconsiderable
debate remains on the concept of emergent capabilities from a technological perspective
(Schaeffer et al., 2023), the effective capabilities of AIs are novel and unexpected, widely
applicable, and are increasing greatly in short time spans. Recent work has shown
that AI performs at a high level in professional contexts ranging from medicine to law
(Ali et al., 2023; Lee et al., 2023), and beats humans on many measures of innovation
(Boussioux et al., 2023; Girotra et al., 2023). And, while score performance on various
standardized academic tests is an imperfect measure of LLM capabilities, it has been
increasingsubstantiallywitheachgenerationofAImodels(OpenAI,2023).
The general ability of LLMs to solve domain-specific problems leads to the second
differentiating factor of LLMs compared to previous approaches to AI: their ability to
directly increase the performance of workers who use these systems, without the need
for substantial organizational or technological investment. Early studies of the new
generation of LLMs suggest direct performance increases from using AI, especially for
writing tasks (Noy and Zhang, 2023) and programming (Peng et al., 2023), as well as
for ideation and creative work (Boussioux et al., 2023; Girotra et al., 2023). As a result,
the effects of AI are expected to be higher on the most creative, highly paid, and highly
educatedworkers(Eloundouetal.,2023;Feltenetal.,2023)
The final relevant characteristic of LLMs is their relative opacity. This extends to the
failurepointsofAImodels,whichincludeatendencytoproduceincorrect,butplausible,
results (hallucinations or confabulations), and to make other types of errors, including in
mathandwhenprovidingcitations. TheadvantagesofAI,whilesubstantial,aresimilarly
unclear to users. It performs well at some jobs, and fails in other circumstances in ways
thataredifficulttopredictinadvance. Contributingfurthertotheopacityisthatthebest
ways to use these AI systems are not provided by their developers and appear to be best
learnedviaongoingusertrial-and-errorandthesharingofexperiencesandheuristicsvia
variousonlineforumslikeusergroups,hackathons,TwitterfeedsandYouTubechannels.
3
Taken together, these three factors – the surprising abilities of LLMs, their ability to
do real work with virtually no technical skill required of the user, and their opacity and
unclear failure points – suggest that the value and downsides of AI may be difficult for
workers and organizations to grasp. Some unexpected tasks (like idea generation) are
easy for AIs, while other tasks that seem to be easy for machines to do (like basic math)
are challenges for some LLMs. This creates a “jagged Frontier,” where tasks that appear
to be of similar difficulty may either be performed better or worse by humans using
AI. Due to the “jagged” nature of the frontier, the same knowledge workflow of tasks
can have tasks on both sides of the frontier, see Figure 1. The future of understanding
how AI impacts work involves understanding how human interaction with AI changes
depending on where tasks are placed on this frontier, and how the frontier will change
over time. Investigating how humans navigate this jagged frontier, and the subsequent
performanceimplications,isthefocusofourwork.
Wecollaboratedwithaglobalmanagementconsultingfirm(BostonConsultingGroup
- BCG) and advised them on designing, developing, and executing two pre-registered
randomized experiments to assess the impact of AI on high humancapital professionals.
Subsequently, the author team received the data that the company collected for the
purpose of this experiment and conducted the analysis presented in this paper. The
studywasstructuredinthreephases: aninitialdemographicandpsychologicalprofiling,
a main experimental phase involving multiple task completions, and a concluding
interview segment. We tested two distinct tasks: one situated outside the frontier of
AI capabilities and the other within its bounds. The experiment aimed to understand
how AI integration might reshape the traditional workflows of these high human capital
professionals.
OurresultsshowthatthisgenerationofLLMsarehighlycapableofcausingsignificant
increases in quality and productivity, or even completely automating some tasks, but the
actual tasks that AI can do are surprising and not immediately obvious to individuals or
even to producers of LLMs themselves. Because this frontier is expanding and changing,
the overall results suggest that AI will have a large impact on work, one which will
increasewithLLMcapabilities,butwheretheimpactsoccurwillbeuneven.
4
2 Methods
We collected data from two randomized experiments to assess the causal impact of AI,
specifically GPT-4 – the most capable of the AI models at the time of the experiments
(Spring 2023) – on high human capital professionals working traditionally without AI.1
We pre-registered our study, detailing the design structure, the experimental conditions,
thedependentvariables,andourmainanalyticalapproaches.2 Ouraimwastodetermine
how introducing this AI into the tasks of highly-skilled knowledge workers might
augment,disrupt,orinfluencetheirtraditionalworkflows.
BCG individual contributor consultants around the world were offered the
opportunity to spend 5 hours working on this experiment to evaluate the impact of AI
ontheiractivities. Approximately7%ofBCG’sglobalindividualcontributorconsultants’
cohortengagedinandcompletedtheexperiment.
Theexperimentwasstructuredintothreedistinctphases. Initially,consultantsentered
the study by completing a survey that captured their demographic and psychological
profiles, as well as details about their role within the company. A few weeks after
enrolling, participants received a link to complete the main experimental phase. This
phase commenced with a pre-task survey, followed by the tasks detailed subsequently,
and concluded with a post-task survey. In the final phase, participants were interviewed
tosharetheirexperiencesandperspectivesontheroleofAIintheirprofession.
In the first phase, we administered an enrollment survey to gather information about
potential participants.3 This survey captured details such as office location, internal
affiliation, and tenure at BCG. Participants also completed psychological assessments,
specifically providing insights into their Big 5 personality traits (Soto and John, 2017),
innovativeness (Agarwal and Prasad, 1998), self-perceived creativity (Miron-Spektor
et al., 2004), and paradox mindset (Miron-Spektor et al., 2018). Furthermore, the
survey included a short section on their reading habits (including their familiarity with
1TheprojecthasreceivedIRBapproval,IRB23-0392.
2Pre-registrationcompletedonOpenScienceFoundation,osf.io/ytaev. Thepre-registrationisavailable
fromtheauthorsuponrequestandwillbemadepubliclyavailableafterarticleacceptanceoraftertheOSF
embargoperiodhaspassed,whichevercomesfirst
3Outofthe852consultantswhorespondedtothesurvey,758-about89%-completedtheexperiment.
5
AI characters in fiction), and demographic details like gender, native language, and
educational background. We utilized these data for stratified random assignment and
ascontrolsinourregressionmodels,asdescribedbelow.
The study encompassed 758 strategy consultants, each of whom completed the initial
survey and experimental tasks. Each participant was assigned to one of two distinct
experiments. Stratification of participants was based on multiple criteria, both between
experimentsandacrossexperimentaltreatments. Thesecriteriaincludedgender,location,
tenure at BCG, individual openness to innovation, and native English-speaking status.
This information was collected with the survey administered during phase one, a few
weeksbeforethemainexperiment.
In order to ensure genuine engagement and effort from participants, we incentivized
their performance in the experiment. Participants who diligently participated in all
aspects of the experiment were honored with an ""office contribution"" recognition,
carryingfinancialimplicationsrelatedtotheirannualbonuses. Furthermore,torecognize
and reward excellence, the top 20% of participants received additional recognition, and
the top 5% was also awarded with a small gift. Executives at BCG reported that the
recognition received by top participants was important because it was shared with the
committeethatoverseestheircareerdevelopmentandperformanceassessments.
Subjects were allocated to one of two distinct experiments, each involving a
unique type of task, with no overlap between the groups. Both tasks were designed
in collaboration with multiple people at BCG to represent some of the typical job
activities encountered by individual contributor consultants. Approximately half of the
participants (385 consultants) tackled a series of tasks where they were prompted to
conceptualize and develop new product ideas, focusing on aspects such as creativity,
analytical skills, persuasiveness and writing skills. The other half (373 consultants)
engaged in business problem-solving tasks using quantitative data, customer and
company interviews, and including a persuasive writing component. Both sets of tasks
were developed to be realistic, and were designed with the input of professionals in
the respective sectors. A senior level executive at the company commented on these
tasks being “very much in line with part of the daily activities” of the subjects involved.
6
Notably,someformsofthesetasksarealsousedbythecompanytoscreenjobapplicants,
typically from elite academic backgrounds (including Ph.D.s), for their highly-coveted
positions.
Both experiments followed a consistent structure. Initially, participants undertook a
task without the aid of AI, establishing a baseline for performance and enabling within-
subject analyses. Following this, participants were randomly assigned to one of three
conditions to assess the influence of AI on their tasks, with these conditions being
consistent across both experiments. The first group (a control condition) proceeded
without AI support; the second (“GPT Only”) had the assistance of an AI tool based
on GPT-4; and the third (“GPT + Overview”) not only utilized the same AI tool but
also benefited from supplementary prompt engineering overview, which increased their
familiarity with AI. These materials included instructional videos and documents that
outlinedandillustratedeffectiveusagestrategies.
Ratherthanrelyingonself-reportedmetricsorindirectindicators,wedirectlyassessed
participants’ skills through a task that closely mirrored the main experiment. In both
experiments, we employ an assessment task that, while different from the experimental
task, is highly comparable, ensuring a precise evaluation of skills for this specific task
type.4 Our findings indicate that performance in the assessment task is a predictor of
performance in the experimental task, allowing us to study the differential effects of
introducingAItoparticipantsofdifferentskilllevels.
Each task assigned to participants came with a specific time allocation. In the
experiment using a task inside the frontier, the assessment task duration was set for 30
minutes, while the subsequent one was allotted 90 minutes. Conversely, in the outside-
the-frontierexperiment,boththefirstandsecondtasksweredesignated60minuteseach,
though participants could complete them earlier if they finished ahead of time. It is
importanttonotethatforthetaskinsidethefrontier,participantswererequiredtoremain
on the task’s page for the entire duration of the task, and could not complete the exercise
earlier. This approach ensured that our analysis for the inside-the-frontier tasks focused
4Dell’Acqua et al. (2023) adopts a comparable experimental framework to evaluate subjects’
competencies.
7
chiefly on the qualitative differences, rather than any timing improvements brought
aboutbyusingAI.Thesetimeframeswereautomaticallyenforced,withtheexperimental
systemadvancingtothenextquestiononcethestipulatedtimeforataskelapsed.
In every experimental task, subjects assigned to the AI conditions had access to
a company platform. This platform, developed using the OpenAI API, facilitated an
interactive experience with OpenAI’s GPT-4, mirroring the dynamics of ChatGPT. It
enabled the collection of all participants’ prompts and AI’s corresponding responses,
providing a comprehensive view into the collaborative behaviors between subjects and
AI.Allsubjectsusedthesameversionofthetool,accessingGPT-4asavailableattheend
ofApril,2023,andusingdefaultsystempromptsandtemperature.
Asidefromthethematicdifferences,thetasksdifferedinanotherkeyway. Whileboth
were designed to be comparably complex and realistic, the first task was selected to be
withinthepotentialtechnologicalfrontierofGPT-4. Thesecondexperimentwasdesigned
sothatGPT-4wouldmakeanerrorwhenconductingtheanalysis,ensuringtheworkfell
justoutsidethefrontier.
3 Results
3.1 Quality and Productivity Booster - Inside the Frontier
The inside-the-frontier experiment focused on creative product innovation and
development. The initial assessment task asked participants to brainstorm innovative
beverage concepts. From their set of ideas, they identified the most viable option and
devisedacomprehensiveplanforitsmarketdebut. Afterthistask,subjectsmovedtothe
mainexperimentalphaseandthecontexttransitionedtothemainexperimentaltask.
In this experimental task, participants were tasked with conceptualizing a footwear
idea for niche markets and delineating every step involved, from prototype description
to market segmentation to entering the market. An executive from a leading global
footwearcompanyverifiedthatthetaskdesigncoveredtheentireprocesstheircompany
8
typically goes through, from ideation to product launch.5 Participants responded to
a total of 18 tasks (or as many as they could within the given time frame). These
tasks spanned various domains. Specifically, they can be categorized into four types:
creativity (e.g., “Propose at least 10 ideas for a new shoe targeting an underserved
market or sport.”), analytical thinking (e.g., “Segment the footwear industry market
based on users.”), writing proficiency (e.g., “Draft a press release marketing copy for
your product.”), and persuasiveness (e.g., “Pen an inspirational memo to employees
detailing why your product would outshine competitors.”). This allowed us to collect
comprehensiveassessmentsofquality. AlltasksanddetailsarereportedinAppendixA.
In the experiment, the primary outcome variable is the quality of the subjects’
responses. To quantify this quality, we employed a set of human graders to evaluate
each question that participants didn’t leave unanswered.6 Each response was evaluated
by two human graders. We then calculated the mean grade assigned by humans to each
question. This gave us 18 dependent variables (one per each question). We subsequently
averaged these scores across all questions to derive a composite “Quality” score, which
we use in our main analyses. As an additional assessment, we also utilized GPT-4, to
independentlyscorethesubjects’responses. Similarlytothehumangrades,weproduced
ascoreforeachoneofthe18questions,andthenacomposite“Quality(GPT)”score.
Figure 2 uses the composite human grader score and visually represents the
performance distribution across the three experimental groups, with the average score
plotted on the y-axis. A comparison of the dashed lines and the overall distributions of
the experimental conditions clearly illustrates the significant performance enhancements
associatedwiththeuseofGPT-4. BothAIconditionsshowclearsuperiorperformanceto
thecontrolgroupnotusingGPT-4.
Table 1 presents the results of the analyses using response quality as the dependent
variable and highlights the performance implications of using AI. Columns 1, 2, and
3 utilize human-generated grades as the dependent variable, while Column 4 uses the
5Theexecutivenotedtheonlystepmissingfromthisexercisewasanevaluationofhowthenewproduct
integrateswiththecompany’sexistingproductlines. Asourexperimentusedafictionalcompany,wedid
notrequireparticipantstopresenttheirproductsuggestionsinrelationtoexistingones.
6GraderswerefromBCG,orMBAstudentsatatopprogram.
9
composite grades generated by GPT-4. Across all specifications, both treatments — GPT
+OverviewandGPTOnly—demonstratepositiveeffects. InColumn1,GPT+Overview
leads to a 1.75 increase in scores over the control mean of 4.1, which is a 42.5% increase;
GPT Only led to a 1.56, or 38% increase. Notably, Columns 2, 3, and 4 incorporate
performance metrics from the assessment task and the treatment coefficients they report
remainveryconsistent. Column4usesGPTscoresasthedependentvariable,andshows
coefficientsof1.34fortheGPT+Overviewtreatmentand1.21fortheGPTOnlytreatment
over the control group, which are equal respectively to 18.6% and 16.8% increases in
performance.7
The beneficial impacts of using AI remain consistent across all our specifications. We
mergedourAItreatmentsandusedallourpre-registeredqualityvariablesasdependent
variables. This included individual grades for each question as evaluated by humans, as
wellasgradesevaluatedbyGPT-4,basedonthethreespecificationsoutlinedinColumns
1-3 of Table 1. This resulted in a comprehensive set of 108 regressions. All of these
regressions showed a significant effect of introducing AI on consultants’ performance.
Figures3and4show54oftheseregressionseach. Additionally,threedashedlinesreport
the average effects of each regression. The mean effect size when comparing subjects
using AI with a control with no GPT-4 access is 1.69 (a 46.6% increase over the control
mean)whenusinghumanevaluationsand1.36(20.2%)whenusingGPT-4evaluations.
Another key observation from the table is the differential impact of the two AI
treatments. Specifically, the GPT + Overview treatment consistently exhibits a more
pronounced positive effect compared to the GPT Only treatment. The bottom of the
table displays a p-value that tests whether the effects of receiving GPT + Overview were
equivalent to those of being assigned to GPT Only, showing this value to be below
or around the conventional 5% threshold in all specifications. This underscores the
importance of the added overview in enhancing the efficacy of AI assistance. However,
we should note that the overview increased “retainment” (i.e., copying and pasting the
GPT-4 output), and retainment itself was associated with better performance.8 The table
7These percentage improvements are relatively lower also because GPT-4 tends to be a more lenient
graderandscoresourcontrolbaselinehigher.
8AppendixCprovidesfurtherdetails.
10
also highlights various other factors, such as gender, native English proficiency, tenure,
location,andtechopenness,andtheirinfluenceontheoutcomes.9
Table 2 presents the results related to the percentage of task completion by subjects,
which is the dependent variable in this analysis. Across Columns 1, 2, and 3, both
treatments — GPT + Overview and GPT Only — demonstrate a positive effect on task
completion. Onaverage,thesecoefficientsindicatea12.2%increaseincompletionrates.10
The control group completed on average 82% of their tasks, while the GPT + Overview
condition completed about 93% and GPT Only about 91%. Column 2 incorporates the
performance metric from the assessment, and Column 3 further extends the analysis
by including the same set of controls as in Table 1. The coefficients suggest that the
integrationofAItoolsenhancestherateoftaskcompletionverysignificantly,atthesame
timeasitincreasesquality.
Figure 5 presents an important trend: the most significant beneficiaries of using AI
are the bottom-half-skill subjects, consistent with findings from Noy and Zhang (2023)
and Choi and Schwarcz (2023).11 By segmenting subjects exposed to one of the two AI
conditions into two distinct categories — top-half-skill performers (those ranking in the
top 50% on the assessment task) and bottom-half-skill performers (those in the bottom
50%)—weobservedperformanceenhancementsintheexperimentaltaskforbothgroups
when leveraging GPT-4. When comparing the two groups, though, we see the bottom-
half-skillperformersexhibitedthemostsubstantialsurgeinperformance,43%,compared
to the top-half-skill subjects, 17%. Note that the top-half-skill performers also receive a
significantboost,althoughnotasmuchasthebottom-half-skillperformers.
For the task inside the frontier, we did not allow any subjects to complete the task
before the allotted time was over. Instead, their final question was an especially long
9Weemploybinaryvariablesforallthesefactors. ""Female""issetto1ifasubjectidentifiesasfemaleand
0otherwise. ""EnglishNative""is1ifasubjectclaimsnativeproficiencyinEnglishand0otherwise(nearly
everysubjectindicateseitherNativeorAdvancedproficiencyinEnglish). ""LowTenure""is1ifasubjecthas
beenwithBCGforayearorless,and0otherwise. ""Location""is1ifasubject’sofficeislocatedinEuropeor
theMiddleEast,and0otherwise. Lastly,""TechOpenness""is1ifthesubjectexpressedahigherreceptivity
totechnologyintheirenrollmentsurvey,and0otherwise.
10When directly comparing the two AI treatments at the bottom of the table, the difference in their
impactsisnotstatisticallysignificant.
11Itisimportanttonotethat""higher-skill""and""lower-skill""herearerelative. Alltheseconsu"
280,bcg,24-013_d9b45b68-9e74-42d6-a1c6-c72fb70c7282.pdf,"Working Paper 24-013
Navigating the Jagged Technological
Frontier: Field Experimental Evidence
of the Effects of AI on Knowledge
Worker Productivity and Quality
Fabrizio Dell'Acqua Saran Rajendran
Edward McFowland III Lisa Krayer
Ethan Mollick François Candelon
Hila Lifshitz-Assaf Karim R. Lakhani
Katherine C. Kellogg
Navigating the Jagged Technological
Frontier: Field Experimental Evidence
of the Effects of AI on Knowledge
Worker Productivity and Quality
Fabrizio Dell'Acqua Saran Rajendran
Harvard Business School Boston Consulting Group
Edward McFowland III Lisa Krayer
Harvard Business School Boston Consulting Group
Ethan Mollick François Candelon
The Wharton School Boston Consulting Group
Hila Lifshitz-Assaf Karim R. Lakhani
Warwick Business School Harvard Business School
Katherine C. Kellogg
MIT Sloan School of Management
Working Paper 24-013
Copyright © 2023 by Fabrizio Dell’Acqua, Edward McFowland III, Ethan Mollick, Hila Lifshitz-Assaf, Katherine
C. Kellogg, Saran Rajendran, Lisa Krayer, François Candelon, and Karim R. Lakhani.
Working papers are in draft form. This working paper is distributed for purposes of comment and discussion only.
It may not be reproduced without permission of the copyright holder. Copies of working papers are available from
the author.
We thank Michael Bervell, John Cheng, Pallavi Deshpande, Maxim Ledovskiy, John Kalil, Kelly Kung, Rick
Lacerda, MarcAntonio Awada, Paula Marin Sariego, Rafael Noriega, Alejandro Ortega, Rahul Phanse, Quoc-Anh
Nguyen, Nitya Rajgopal, Ogbemi Rewane, Kyle Schirmann, Andrew Seo, Tanay Tiwari, Elliot Tobin, Lebo
Nthoiwa, Patrick Healy, Saud Almutairi, Steven Randazzo, Anahita Sahu, Aaron Zheng, and Yogesh Kumaar for
helpful research assistance. We thank Kevin Dai for outstanding support with data and visualizations. For helpful
feedback, we thank Maxime Courtaux, Clement Dumas, Gaurav Jha, Jesse Li, Max Männig, Michael Menietti,
Rachel Mural, Zahra Rasouli, Esther Yoon, Leonid Zhukov, and David Zuluaga Martínez. Lakhani would like to
thank Martha Wells, Anne Leckie, Iain Banks, and Alastair Reynolds for inspiring AI futures. We used Poe,
Claude, and ChatGPT for light copyediting and graphics creations. Lakhani is an advisor to Boston Consulting
Group on AI Strategy and learning engagement.
Funding for this research was provided in part by Harvard Business School.
Navigating the Jagged Technological Frontier:
Field Experimental Evidence of the Effects of AI on
Knowledge Worker Productivity and Quality*
Fabrizio Dell’Acqua1, Edward McFowland III1, Ethan Mollick2, Hila
Lifshitz-Assaf1,3, Katherine C. Kellogg4, Saran Rajendran5, Lisa Krayer5,
François Candelon5, and Karim R. Lakhani1
1Digital Data Design Institute, Harvard Business School; 2The Wharton
School, University of Pennsylvania; 3Warwick Business School, Artificial
Intelligence Innovation Network; 4MIT Sloan School of Management;
5Boston Consulting Group, Henderson Institute
September 22, 2023
*Fabrizio Dell’Acqua (fdellacqua@hbs.edu), Edward McFowland III (emcfowland@hbs.edu),
Ethan Mollick (emollick@wharton.upenn.edu), Hila Lifshitz-Assaf (hila.lifshitz-assaf@wbs.ac.uk),
Katherine C. Kellogg (kkellogg@mit.edu), Saran Rajendran (rajendran.saran@bcg.com), Lisa
Krayer (krayer.lisa@bcg.com), François Candelon (candelon.francois@bcg.com), Karim R. Lakhani
(klakhani@hbs.edu). We thank Michael Bervell, John Cheng, Pallavi Deshpande, Maxim Ledovskiy, John
Kalil, Kelly Kung, Rick Lacerda, MarcAntonio Awada, Paula Marin Sariego, Rafael Noriega, Alejandro
Ortega,RahulPhanse,Quoc-AnhNguyen,NityaRajgopal,OgbemiRewane,KyleSchirmann,AndrewSeo,
TanayTiwari,ElliotTobin,LeboNthoiwa,PatrickHealy,SaudAlmutairi,StevenRandazzo,AnahitaSahu,
Aaron Zheng, and Yogesh Kumaar for helpful research assistance. We thank Kevin Dai for outstanding
supportwithdataandvisualizations. Forhelpfulfeedback,wethankMaximeCourtaux,ClementDumas,
Gaurav Jha, Jesse Li, Max Männig, Michael Menietti, Rachel Mural, Zahra Rasouli, Esther Yoon, Leonid
Zhukov, and David Zuluaga Martínez. Lakhani would like to thank Martha Wells, Anne Leckie, Iain
Banks, and Alastair Reynolds for inspiring AI futures. We used Poe, Claude, and ChatGPT for light
copyeditingandgraphicscreations. LakhaniisanadvisortoBostonConsultingGrouponAIStrategyand
learningengagement. Allerrorsareourown.
1
Abstract
The public release of Large Language Models (LLMs) has sparked tremendous
interestinhowhumanswilluseArtificialIntelligence(AI)toaccomplishavarietyof
tasks. In our study conducted with Boston Consulting Group, a global management
consulting firm, we examine the performance implications of AI on realistic,
complex, and knowledge-intensive tasks. The pre-registered experiment involved
758 consultants comprising about 7% of the individual contributor-level consultants
at the company. After establishing a performance baseline on a similar task, subjects
were randomly assigned to one of three conditions: no AI access, GPT-4 AI access,
or GPT-4 AI access with a prompt engineering overview. We suggest that the
capabilities of AI create a “jagged technological frontier” where some tasks are
easily done by AI, while others, though seemingly similar in difficulty level, are
outside the current capability of AI. For each one of a set of 18 realistic consulting
tasks within the frontier of AI capabilities, consultants using AI were significantly
moreproductive(theycompleted12.2%moretasksonaverage,andcompletedtasks
25.1% more quickly), and produced significantly higher quality results (more than
40% higher quality compared to a control group). Consultants across the skills
distribution benefited significantly from having AI augmentation, with those below
theaverageperformancethreshold increasingby43%andthose above increasingby
17% compared to their own scores. For a task selected to be outside the frontier,
however,consultantsusingAIwere19percentagepointslesslikelytoproducecorrect
solutionscomparedtothosewithoutAI.Further,ouranalysisshowstheemergenceof
twodistinctivepatternsofsuccessfulAIusebyhumansalongaspectrumofhuman-
AI integration. One set of consultants acted as “Centaurs,” like the mythical half-
horse/half-humancreature,dividinganddelegatingtheirsolution-creationactivities
to the AI or to themselves. Another set of consultants acted more like “Cyborgs,”
completely integrating their task flow with the AI and continually interacting with
thetechnology.
2
1 Introduction
The capabilities of Artificial Intelligence to produce human-like work have improved
rapidly,especiallysincethereleaseofOpenAI’sChatGPT,oneofseveralLargeLanguage
Models (LLMs) that are widely available for public use. As AI capabilities overlap more
with those of humans, the integration of human work with AI poses new fundamental
challenges and opportunities, in particular in knowledge-intensive domains. In this
paper, we examine this issue using randomized controlled field experiments with highly
skilled professional workers. Our results demonstrate that AI capabilities cover an
expanding, but uneven, set of knowledge work we call a ""jagged technological frontier.”
Within this growing frontier, AI can complement or even displace human work; outside
of the frontier, AI output is inaccurate, less useful, and degrades human performance.
However, because the capabilities of AI are rapidly evolving and poorly understood,
it can be hard for professionals to grasp exactly what the boundary of this frontier
might be at a given. We find that professionals who skillfully navigate this frontier gain
large productivity benefits when working with the AI, while AI can actually decrease
performancewhenusedforworkoutsideofthefrontier.
Though LLMs are new, the impact of other, earlier forms of AI have been the subject
ofconsiderablescholarlydiscussion(e.g.,Brynjolfssonetal.(2018);FurmanandSeamans
(2019);Puranam(2021)). BecauseofthelimitationsoftheseearlierformsofAI,nonroutine
tasks that were difficult to codify seemed protected from automation (Autor et al., 2003;
Acemoglu and Restrepo, 2019), especially as previous waves of technology had mostly
automatedlower-skilledoccupations(GoldinandKatz,1998). ThereleaseofChatGPTin
November, 2022 changed both the nature and urgency of the discussion. LLMs proved
unexpectedly capable at creative, analytical, and writing tasks, including scoring at top
levelsatgraduateandprofessionalexaminations(Girotraetal.,2023;Geerlingetal.,2023;
Kung et al., 2023; Boussioux et al., 2023). This represented an entirely new category of
automation, one whose abilities overlapped with the most creative, most educated, and
mosthighlypaidworkers(Eloundouetal.,2023).
Studies of previous generations of AI (Brynjolfsson et al., 2023) and controlled
1
experiments on the impact of recently released LLMs (e.g., Noy and Zhang (2023); Choi
and Schwarcz (2023)) suggest that these systems can have a large impact on worker
performance. In our study, we focus on complex tasks, selected by industry experts to
replicate real-world workflows as experienced by knowledge workers. Most knowledge
work includes this sort of flow, a set of interdependent tasks, some of which may be
good fit for current AI, while some are not. We examine both kinds of tasks, and build
on recent studies to suggest ways of understanding the rapidly evolving impact of AI on
knowledgeworkers,underwhichcircumstancesorganizationsmaybenefit,andhowthis
mightchangeasthetechnologyadvances.
This is important because understanding the implications of LLMs for the work
of organizations and individuals has taken on urgency among scholars, workers,
companies, and even governments (Agrawal et al., 2018; Iansiti and Lakhani, 2020; Berg
etal.,2023). PreviousformsofAIledtoconsiderabledebateintheliteraturearoundhow
and whether professionals should adopt AI for knowledge work (Anthony et al., 2023)
and the potential impact this might have on organizations (Raisch and Krakowski, 2021;
Glaeser et al., 2021; Brynjolfsson et al., 2021). Some scholars focused on the potential
for AI to help professionals improve their effectiveness and efficiency (DeStefano et al.,
2022;Balakrishnanetal.,2022;ValentineandHinds,2023). Othersdemonstratedthat,for
critical tasks, it can be risky for professionals to use AI (Lebovitz et al., 2021), especially
black-boxed (e.g., Lebovitz et al. (2022); Waardenburg et al. (2022)), and showed how
professionals are struggling to use it effectively (Pachidi et al., 2021; Van den Broek et al.,
2021). Finally, another group of researchers argued that the “algorithmic management”
affordedbyAIcancreatenegativepersonalimpactsforprofessionals(Kelloggetal.,2020;
Möhlmann et al., 2021; Tong et al., 2021) and raise accountability and ethical questions
(Choudhuryetal.,2020;Cowgilletal.,2020;Rahmanetal.,2024). Yet,mostofthestudies
predate ChatGPT, and investigate forms of AI designed to produce discrete predictions
basedonpastdata. ThesesystemsarequitedifferentfromLLMs.
Specifically, outside of their technical differences from previous forms of machine
learning, there are three aspects of LLMs that suggest they will have a much more
rapid, and widespread, impact on work. The first is that LLMs have surprising
2
capabilities that they were not specifically created to have, and ones that are growing
rapidly over time as model size and quality improve. Trained as general models,
LLMsnonethelessdemonstratespecialistknowledgeandabilitiesaspartoftheirtraining
processandduringnormaluse(Singhaletal.,2022;Boikoetal.,2023). Whileconsiderable
debate remains on the concept of emergent capabilities from a technological perspective
(Schaeffer et al., 2023), the effective capabilities of AIs are novel and unexpected, widely
applicable, and are increasing greatly in short time spans. Recent work has shown
that AI performs at a high level in professional contexts ranging from medicine to law
(Ali et al., 2023; Lee et al., 2023), and beats humans on many measures of innovation
(Boussioux et al., 2023; Girotra et al., 2023). And, while score performance on various
standardized academic tests is an imperfect measure of LLM capabilities, it has been
increasingsubstantiallywitheachgenerationofAImodels(OpenAI,2023).
The general ability of LLMs to solve domain-specific problems leads to the second
differentiating factor of LLMs compared to previous approaches to AI: their ability to
directly increase the performance of workers who use these systems, without the need
for substantial organizational or technological investment. Early studies of the new
generation of LLMs suggest direct performance increases from using AI, especially for
writing tasks (Noy and Zhang, 2023) and programming (Peng et al., 2023), as well as
for ideation and creative work (Boussioux et al., 2023; Girotra et al., 2023). As a result,
the effects of AI are expected to be higher on the most creative, highly paid, and highly
educatedworkers(Eloundouetal.,2023;Feltenetal.,2023)
The final relevant characteristic of LLMs is their relative opacity. This extends to the
failurepointsofAImodels,whichincludeatendencytoproduceincorrect,butplausible,
results (hallucinations or confabulations), and to make other types of errors, including in
mathandwhenprovidingcitations. TheadvantagesofAI,whilesubstantial,aresimilarly
unclear to users. It performs well at some jobs, and fails in other circumstances in ways
thataredifficulttopredictinadvance. Contributingfurthertotheopacityisthatthebest
ways to use these AI systems are not provided by their developers and appear to be best
learnedviaongoingusertrial-and-errorandthesharingofexperiencesandheuristicsvia
variousonlineforumslikeusergroups,hackathons,TwitterfeedsandYouTubechannels.
3
Taken together, these three factors – the surprising abilities of LLMs, their ability to
do real work with virtually no technical skill required of the user, and their opacity and
unclear failure points – suggest that the value and downsides of AI may be difficult for
workers and organizations to grasp. Some unexpected tasks (like idea generation) are
easy for AIs, while other tasks that seem to be easy for machines to do (like basic math)
are challenges for some LLMs. This creates a “jagged Frontier,” where tasks that appear
to be of similar difficulty may either be performed better or worse by humans using
AI. Due to the “jagged” nature of the frontier, the same knowledge workflow of tasks
can have tasks on both sides of the frontier, see Figure 1. The future of understanding
how AI impacts work involves understanding how human interaction with AI changes
depending on where tasks are placed on this frontier, and how the frontier will change
over time. Investigating how humans navigate this jagged frontier, and the subsequent
performanceimplications,isthefocusofourwork.
Wecollaboratedwithaglobalmanagementconsultingfirm(BostonConsultingGroup
- BCG) and advised them on designing, developing, and executing two pre-registered
randomized experiments to assess the impact of AI on high humancapital professionals.
Subsequently, the author team received the data that the company collected for the
purpose of this experiment and conducted the analysis presented in this paper. The
studywasstructuredinthreephases: aninitialdemographicandpsychologicalprofiling,
a main experimental phase involving multiple task completions, and a concluding
interview segment. We tested two distinct tasks: one situated outside the frontier of
AI capabilities and the other within its bounds. The experiment aimed to understand
how AI integration might reshape the traditional workflows of these high human capital
professionals.
OurresultsshowthatthisgenerationofLLMsarehighlycapableofcausingsignificant
increases in quality and productivity, or even completely automating some tasks, but the
actual tasks that AI can do are surprising and not immediately obvious to individuals or
even to producers of LLMs themselves. Because this frontier is expanding and changing,
the overall results suggest that AI will have a large impact on work, one which will
increasewithLLMcapabilities,butwheretheimpactsoccurwillbeuneven.
4
2 Methods
We collected data from two randomized experiments to assess the causal impact of AI,
specifically GPT-4 – the most capable of the AI models at the time of the experiments
(Spring 2023) – on high human capital professionals working traditionally without AI.1
We pre-registered our study, detailing the design structure, the experimental conditions,
thedependentvariables,andourmainanalyticalapproaches.2 Ouraimwastodetermine
how introducing this AI into the tasks of highly-skilled knowledge workers might
augment,disrupt,orinfluencetheirtraditionalworkflows.
BCG individual contributor consultants around the world were offered the
opportunity to spend 5 hours working on this experiment to evaluate the impact of AI
ontheiractivities. Approximately7%ofBCG’sglobalindividualcontributorconsultants’
cohortengagedinandcompletedtheexperiment.
Theexperimentwasstructuredintothreedistinctphases. Initially,consultantsentered
the study by completing a survey that captured their demographic and psychological
profiles, as well as details about their role within the company. A few weeks after
enrolling, participants received a link to complete the main experimental phase. This
phase commenced with a pre-task survey, followed by the tasks detailed subsequently,
and concluded with a post-task survey. In the final phase, participants were interviewed
tosharetheirexperiencesandperspectivesontheroleofAIintheirprofession.
In the first phase, we administered an enrollment survey to gather information about
potential participants.3 This survey captured details such as office location, internal
affiliation, and tenure at BCG. Participants also completed psychological assessments,
specifically providing insights into their Big 5 personality traits (Soto and John, 2017),
innovativeness (Agarwal and Prasad, 1998), self-perceived creativity (Miron-Spektor
et al., 2004), and paradox mindset (Miron-Spektor et al., 2018). Furthermore, the
survey included a short section on their reading habits (including their familiarity with
1TheprojecthasreceivedIRBapproval,IRB23-0392.
2Pre-registrationcompletedonOpenScienceFoundation,osf.io/ytaev. Thepre-registrationisavailable
fromtheauthorsuponrequestandwillbemadepubliclyavailableafterarticleacceptanceoraftertheOSF
embargoperiodhaspassed,whichevercomesfirst
3Outofthe852consultantswhorespondedtothesurvey,758-about89%-completedtheexperiment.
5
AI characters in fiction), and demographic details like gender, native language, and
educational background. We utilized these data for stratified random assignment and
ascontrolsinourregressionmodels,asdescribedbelow.
The study encompassed 758 strategy consultants, each of whom completed the initial
survey and experimental tasks. Each participant was assigned to one of two distinct
experiments. Stratification of participants was based on multiple criteria, both between
experimentsandacrossexperimentaltreatments. Thesecriteriaincludedgender,location,
tenure at BCG, individual openness to innovation, and native English-speaking status.
This information was collected with the survey administered during phase one, a few
weeksbeforethemainexperiment.
In order to ensure genuine engagement and effort from participants, we incentivized
their performance in the experiment. Participants who diligently participated in all
aspects of the experiment were honored with an ""office contribution"" recognition,
carryingfinancialimplicationsrelatedtotheirannualbonuses. Furthermore,torecognize
and reward excellence, the top 20% of participants received additional recognition, and
the top 5% was also awarded with a small gift. Executives at BCG reported that the
recognition received by top participants was important because it was shared with the
committeethatoverseestheircareerdevelopmentandperformanceassessments.
Subjects were allocated to one of two distinct experiments, each involving a
unique type of task, with no overlap between the groups. Both tasks were designed
in collaboration with multiple people at BCG to represent some of the typical job
activities encountered by individual contributor consultants. Approximately half of the
participants (385 consultants) tackled a series of tasks where they were prompted to
conceptualize and develop new product ideas, focusing on aspects such as creativity,
analytical skills, persuasiveness and writing skills. The other half (373 consultants)
engaged in business problem-solving tasks using quantitative data, customer and
company interviews, and including a persuasive writing component. Both sets of tasks
were developed to be realistic, and were designed with the input of professionals in
the respective sectors. A senior level executive at the company commented on these
tasks being “very much in line with part of the daily activities” of the subjects involved.
6
Notably,someformsofthesetasksarealsousedbythecompanytoscreenjobapplicants,
typically from elite academic backgrounds (including Ph.D.s), for their highly-coveted
positions.
Both experiments followed a consistent structure. Initially, participants undertook a
task without the aid of AI, establishing a baseline for performance and enabling within-
subject analyses. Following this, participants were randomly assigned to one of three
conditions to assess the influence of AI on their tasks, with these conditions being
consistent across both experiments. The first group (a control condition) proceeded
without AI support; the second (“GPT Only”) had the assistance of an AI tool based
on GPT-4; and the third (“GPT + Overview”) not only utilized the same AI tool but
also benefited from supplementary prompt engineering overview, which increased their
familiarity with AI. These materials included instructional videos and documents that
outlinedandillustratedeffectiveusagestrategies.
Ratherthanrelyingonself-reportedmetricsorindirectindicators,wedirectlyassessed
participants’ skills through a task that closely mirrored the main experiment. In both
experiments, we employ an assessment task that, while different from the experimental
task, is highly comparable, ensuring a precise evaluation of skills for this specific task
type.4 Our findings indicate that performance in the assessment task is a predictor of
performance in the experimental task, allowing us to study the differential effects of
introducingAItoparticipantsofdifferentskilllevels.
Each task assigned to participants came with a specific time allocation. In the
experiment using a task inside the frontier, the assessment task duration was set for 30
minutes, while the subsequent one was allotted 90 minutes. Conversely, in the outside-
the-frontierexperiment,boththefirstandsecondtasksweredesignated60minuteseach,
though participants could complete them earlier if they finished ahead of time. It is
importanttonotethatforthetaskinsidethefrontier,participantswererequiredtoremain
on the task’s page for the entire duration of the task, and could not complete the exercise
earlier. This approach ensured that our analysis for the inside-the-frontier tasks focused
4Dell’Acqua et al. (2023) adopts a comparable experimental framework to evaluate subjects’
competencies.
7
chiefly on the qualitative differences, rather than any timing improvements brought
aboutbyusingAI.Thesetimeframeswereautomaticallyenforced,withtheexperimental
systemadvancingtothenextquestiononcethestipulatedtimeforataskelapsed.
In every experimental task, subjects assigned to the AI conditions had access to
a company platform. This platform, developed using the OpenAI API, facilitated an
interactive experience with OpenAI’s GPT-4, mirroring the dynamics of ChatGPT. It
enabled the collection of all participants’ prompts and AI’s corresponding responses,
providing a comprehensive view into the collaborative behaviors between subjects and
AI.Allsubjectsusedthesameversionofthetool,accessingGPT-4asavailableattheend
ofApril,2023,andusingdefaultsystempromptsandtemperature.
Asidefromthethematicdifferences,thetasksdifferedinanotherkeyway. Whileboth
were designed to be comparably complex and realistic, the first task was selected to be
withinthepotentialtechnologicalfrontierofGPT-4. Thesecondexperimentwasdesigned
sothatGPT-4wouldmakeanerrorwhenconductingtheanalysis,ensuringtheworkfell
justoutsidethefrontier.
3 Results
3.1 Quality and Productivity Booster - Inside the Frontier
The inside-the-frontier experiment focused on creative product innovation and
development. The initial assessment task asked participants to brainstorm innovative
beverage concepts. From their set of ideas, they identified the most viable option and
devisedacomprehensiveplanforitsmarketdebut. Afterthistask,subjectsmovedtothe
mainexperimentalphaseandthecontexttransitionedtothemainexperimentaltask.
In this experimental task, participants were tasked with conceptualizing a footwear
idea for niche markets and delineating every step involved, from prototype description
to market segmentation to entering the market. An executive from a leading global
footwearcompanyverifiedthatthetaskdesigncoveredtheentireprocesstheircompany
8
typically goes through, from ideation to product launch.5 Participants responded to
a total of 18 tasks (or as many as they could within the given time frame). These
tasks spanned various domains. Specifically, they can be categorized into four types:
creativity (e.g., “Propose at least 10 ideas for a new shoe targeting an underserved
market or sport.”), analytical thinking (e.g., “Segment the footwear industry market
based on users.”), writing proficiency (e.g., “Draft a press release marketing copy for
your product.”), and persuasiveness (e.g., “Pen an inspirational memo to employees
detailing why your product would outshine competitors.”). This allowed us to collect
comprehensiveassessmentsofquality. AlltasksanddetailsarereportedinAppendixA.
In the experiment, the primary outcome variable is the quality of the subjects’
responses. To quantify this quality, we employed a set of human graders to evaluate
each question that participants didn’t leave unanswered.6 Each response was evaluated
by two human graders. We then calculated the mean grade assigned by humans to each
question. This gave us 18 dependent variables (one per each question). We subsequently
averaged these scores across all questions to derive a composite “Quality” score, which
we use in our main analyses. As an additional assessment, we also utilized GPT-4, to
independentlyscorethesubjects’responses. Similarlytothehumangrades,weproduced
ascoreforeachoneofthe18questions,andthenacomposite“Quality(GPT)”score.
Figure 2 uses the composite human grader score and visually represents the
performance distribution across the three experimental groups, with the average score
plotted on the y-axis. A comparison of the dashed lines and the overall distributions of
the experimental conditions clearly illustrates the significant performance enhancements
associatedwiththeuseofGPT-4. BothAIconditionsshowclearsuperiorperformanceto
thecontrolgroupnotusingGPT-4.
Table 1 presents the results of the analyses using response quality as the dependent
variable and highlights the performance implications of using AI. Columns 1, 2, and
3 utilize human-generated grades as the dependent variable, while Column 4 uses the
5Theexecutivenotedtheonlystepmissingfromthisexercisewasanevaluationofhowthenewproduct
integrateswiththecompany’sexistingproductlines. Asourexperimentusedafictionalcompany,wedid
notrequireparticipantstopresenttheirproductsuggestionsinrelationtoexistingones.
6GraderswerefromBCG,orMBAstudentsatatopprogram.
9
composite grades generated by GPT-4. Across all specifications, both treatments — GPT
+OverviewandGPTOnly—demonstratepositiveeffects. InColumn1,GPT+Overview
leads to a 1.75 increase in scores over the control mean of 4.1, which is a 42.5% increase;
GPT Only led to a 1.56, or 38% increase. Notably, Columns 2, 3, and 4 incorporate
performance metrics from the assessment task and the treatment coefficients they report
remainveryconsistent. Column4usesGPTscoresasthedependentvariable,andshows
coefficientsof1.34fortheGPT+Overviewtreatmentand1.21fortheGPTOnlytreatment
over the control group, which are equal respectively to 18.6% and 16.8% increases in
performance.7
The beneficial impacts of using AI remain consistent across all our specifications. We
mergedourAItreatmentsandusedallourpre-registeredqualityvariablesasdependent
variables. This included individual grades for each question as evaluated by humans, as
wellasgradesevaluatedbyGPT-4,basedonthethreespecificationsoutlinedinColumns
1-3 of Table 1. This resulted in a comprehensive set of 108 regressions. All of these
regressions showed a significant effect of introducing AI on consultants’ performance.
Figures3and4show54oftheseregressionseach. Additionally,threedashedlinesreport
the average effects of each regression. The mean effect size when comparing subjects
using AI with a control with no GPT-4 access is 1.69 (a 46.6% increase over the control
mean)whenusinghumanevaluationsand1.36(20.2%)whenusingGPT-4evaluations.
Another key observation from the table is the differential impact of the two AI
treatments. Specifically, the GPT + Overview treatment consistently exhibits a more
pronounced positive effect compared to the GPT Only treatment. The bottom of the
table displays a p-value that tests whether the effects of receiving GPT + Overview were
equivalent to those of being assigned to GPT Only, showing this value to be below
or around the conventional 5% threshold in all specifications. This underscores the
importance of the added overview in enhancing the efficacy of AI assistance. However,
we should note that the overview increased “retainment” (i.e., copying and pasting the
GPT-4 output), and retainment itself was associated with better performance.8 The table
7These percentage improvements are relatively lower also because GPT-4 tends to be a more lenient
graderandscoresourcontrolbaselinehigher.
8AppendixCprovidesfurtherdetails.
10
also highlights various other factors, such as gender, native English proficiency, tenure,
location,andtechopenness,andtheirinfluenceontheoutcomes.9
Table 2 presents the results related to the percentage of task completion by subjects,
which is the dependent variable in this analysis. Across Columns 1, 2, and 3, both
treatments — GPT + Overview and GPT Only — demonstrate a positive effect on task
completion. Onaverage,thesecoefficientsindicatea12.2%increaseincompletionrates.10
The control group completed on average 82% of their tasks, while the GPT + Overview
condition completed about 93% and GPT Only about 91%. Column 2 incorporates the
performance metric from the assessment, and Column 3 further extends the analysis
by including the same set of controls as in Table 1. The coefficients suggest that the
integrationofAItoolsenhancestherateoftaskcompletionverysignificantly,atthesame
timeasitincreasesquality.
Figure 5 presents an important trend: the most significant beneficiaries of using AI
are the bottom-half-skill subjects, consistent with findings from Noy and Zhang (2023)
and Choi and Schwarcz (2023).11 By segmenting subjects exposed to one of the two AI
conditions into two distinct categories — top-half-skill performers (those ranking in the
top 50% on the assessment task) and bottom-half-skill performers (those in the bottom
50%)—weobservedperformanceenhancementsintheexperimentaltaskforbothgroups
when leveraging GPT-4. When comparing the two groups, though, we see the bottom-
half-skillperformersexhibitedthemostsubstantialsurgeinperformance,43%,compared
to the top-half-skill subjects, 17%. Note that the top-half-skill performers also receive a
significantboost,althoughnotasmuchasthebottom-half-skillperformers.
For the task inside the frontier, we did not allow any subjects to complete the task
before the allotted time was over. Instead, their final question was an especially long
9Weemploybinaryvariablesforallthesefactors. ""Female""issetto1ifasubjectidentifiesasfemaleand
0otherwise. ""EnglishNative""is1ifasubjectclaimsnativeproficiencyinEnglishand0otherwise(nearly
everysubjectindicateseitherNativeorAdvancedproficiencyinEnglish). ""LowTenure""is1ifasubjecthas
beenwithBCGforayearorless,and0otherwise. ""Location""is1ifasubject’sofficeislocatedinEuropeor
theMiddleEast,and0otherwise. Lastly,""TechOpenness""is1ifthesubjectexpressedahigherreceptivity
totechnologyintheirenrollmentsurvey,and0otherwise.
10When directly comparing the two AI treatments at the bottom of the table, the difference in their
impactsisnotstatisticallysignificant.
11Itisimportanttonotethat""higher-skill""and""lower-skill""herearerelative. Alltheseconsu"
281,bcg,bcg-accelerating-climate-action-with-ai-nov-2023-rev.pdf,"Accelerating Climate
Action with AI
November 2023
By Amane Dannouni, Stefan A. Deutscher, Ghita Dezzaz, Adam Elman, Antonia Gawel,
Marsden Hanna, Andrew Hyland, Amjad Kharij, Hamid Maher, David Patterson,
Edmond Rhys Jones, Juliet Rothenberg, Hamza Tber, Maud Texier, and Ali Ziat
Boston Consulting Group partners with leaders
in business and society to tackle their most
important challenges and capture their greatest
opportunities. BCG was the pioneer in business
strategy when it was founded in 1963. Today,
we work closely with clients to embrace a
transformational approach aimed at benefiting all
stakeholders—empowering organizations to grow,
build sustainable competitive advantage, and
drive positive societal impact.
Our diverse, global teams bring deep industry and
functional expertise and a range of perspectives
that question the status quo and spark change.
BCG delivers solutions through leading-edge
management consulting, technology and design,
and corporate and digital ventures. We work in a
uniquely collaborative model across the firm and
throughout all levels of the client organization,
fueled by the goal of helping our clients thrive and
enabling them to make the world a better place.
CCoonntteennttss
01 Foreword 28 AI for Climate:
A Summary of Critical Policy
Outcomes
02 E xecutive Summary
41 Endnotes
05 The Climate Action Imperative
and the Promise of AI
43 About the Authors
09 How AI Can Help
Accelerate Climate Action 45 Acknowledgements
22 Navigating AI’s Potential Risks 47 References
Foreword
This report aims to provide policymakers, corporate decision
makers, and climate leaders with a clear and concise understanding
of the role that artificial intelligence (AI) can play in climate action.
More specifically, its goals are to highlight AI’s significant This work draws on interviews with a range of climate
potential to help address our environmental challenges, change and AI experts, builds on previous research from
to shed light on climate-relevant AI risks, and to offer organizations including Climate Change AI and the AI for
policymakers a streamlined framework for desirable the Planet Alliance, and leverages BCG’s analysis and client
policy outcomes. experience as well as Google’s technical and operational
expertise—and its experience in developing solutions.
Throughout the report, we share examples of successful
early applications of AI for climate and of instances in
which policymakers have already taken the initiative to
enable, promote, or guide the use of AI for climate action
across sectors.
1 ACCELERATING CLIMATE ACTION WITH AI
Executive Summary
Accelerating climate action is imperative, as we are While AI is only just starting to be applied to climate
on a path to fall short of the Paris Agreement’s goal challenges, leading-edge organizations and use cases
to keep warming under 1.5° Celsius. are already delivering results—and demonstrating the
promise of AI for climate—along three dimensions.
• The United Nations Intergovernmental Panel on Climate
Change (IPCC) estimates that, based on action to date, • Information. AI-curated information sources are aiding
the world will likely see warming of 2.8°C with cata- nations in shaping their climate strategy—and in re-
strophic consequences. sponding to emergencies such as wildfires.
• The IPCC forecasts that in order to meet the 1.5°C goal, • Prediction. AI’s predictive power is helping save lives by
the world will need to reduce emissions—from the base- offering advance warning of floods.
line of 2010 levels—by 43% by 2030.
• Optimization. AI applications are enabling organiza-
By scaling currently proven applications and tions to understand and reduce their Scope 1, 2, and 3
technology, artificial intelligence (AI) has the carbon footprints.1
potential to unlock insights that could help mitigate
5% to 10% of global greenhouse gas (GHG) emissions AI also poses risks that must be considered and
by 2030—and significantly bolster climate-related managed thoughtfully to ensure its use has a net
adaptation and resilience initiatives. positive impact on climate.
• 87% of executives view AI as having the potential to • Energy-Related GHG Emissions. A 2022 paper in
address climate issues. Nature Climate Change estimates that cloud and
hyperscale data centers are responsible for 0.1%–0.2%
• AI’s positive impact will be multiplied should it contrib- of global GHG emissions and that roughly 25% of data
ute to scientific breakthroughs that open new pathways center workloads are related to machine learning (ML).
for climate action. Yet, newer and more complex AI models may require
more energy. At present, robust forecasts for AI’s future
AI can contribute to climate action by reducing energy requirements remain elusive given uncertain
emissions, guiding adaptations to unavoidable adoption rates and the broad spectrum of potential
climate change impacts, and providing foundational technical advancements with the potential to decrease
capabilities that enable climate action. AI’s energy intensity. Nonetheless, AI providers are
already striving to enhance energy efficiency and
• Mitigation. Helping with both the reduction and remov- integrate clean energy sources.
al of emissions—and with the underlying measurement
needed to size the challenge and track progress • Water Use. Water-based cooling remains the most
energy-efficient option for data centers, and its overall
• Adaptation and Resilience. Aiding countries, regions, impact on water consumption is low. In 2016 in the US,
cities, citizens, and businesses in forecasting climate- data centers were estimated to have used less than
related hazards, developing plans to address them, and 0.02% of the country’s water consumption for cooling.
responding in real time to crises Nevertheless, in some cases, water-based cooling can
put pressure on local water resources. Data center oper-
• Foundational Capabilities. Enabling climate-related ators have begun to address this issue by providing more
modeling, research into climate economics, and new disclosure, exploring new cooling techniques, and invest-
approaches to climate education and supporting break- ing in replenishment initiatives.
throughs in fundamental research
BOSTON CONSULTING GROUP GOOGLE 2
• Waste. While data centers currently account for only a Policymakers have a critical oversight role to play in
small fraction of the world’s e-waste challenge, there is maximizing the benefits from AI-driven climate
an opportunity for tech firms to build on early circularity action while minimizing its risks. Critical policy
successes and take a more thoughtful approach embrac- outcomes to pursue include the following:
ing more recycling and reuse.
• Enabling AI for climate progress by encouraging data
• Other Potential Risks. AI applications should be sharing, ensuring affordable technology access, building
sustainable and equitable by intention. AI can be applied awareness, and investing in talent
to both climate-friendly and climate-unfriendly appli-
cations, can narrow or widen disparities between the • Accelerating the deployment of AI for climate by defining
Global North and the Global South, and can be trained public and private sector priorities, delivering on public
on data sets that reflect the world’s diversity. Leaders sector use cases, and encouraging private sector action
and model builders need to be mindful in their design
choices. • Promoting environmentally and socially responsible
deployment of AI
3 ACCELERATING CLIMATE ACTION WITH AI
How We Define Artificial Intelligence
According to the Massachusetts Institute of Technology, • delivering improved predictions (predictive use cases),
AI is defined as the ability of computers to imitate human and
cognitive functions such as learning and problem-solving,
using math and logic to simulate the process of reasoning • suggesting optimization moves and recommendations
that helps humans learn from new information and make to reach targets (prescriptive use cases).
decisions.
These goals can be attained by applying wide range of
For the purposes of this report, we are using a broader techniques including those in the table below--all of which
definition of AI that comprises a set of mathematical and we include in this report’s definition of AI.
computer science techniques aimed at analyzing data to
help understand and navigate real-world phenomena Applying AI to real-world problems is common practice
through: today. The technology has proven its ability to help public
and private organizations have a better understanding of
• providing better information (descriptive use cases), their context, provide better services, and improve their
operational performance.
Technology General Example Climate-Related Example
Advanced Analytics Supermarket Inventory Energy Consumption
Management. Advanced analytics Optimization. Advanced analytics
The use of advanced mathematical and
can identify best sellers and demand can optimize a building’s carbon
statistical techniques to develop insights
dynamics, enabling more efficient footprint by adjusting heating,
from structured and unstructured data.
shelving and restocking strategies, cooling, and lighting systems in
thereby reducing waste and ensuring response to real-time data from
popular items are always in stock. sensors and weather forecasts.
Machine Learning Credit Card Fraud Detection. Predicting Wildfires. Machine
Machine learning helps banks and learning models can analyze weather
Training computers to learn and make
credit card companies detect data, satellite imagery, and terrain
predictions from data. Historical data
unusual transactions, enabling them information to predict the likelihood
constitutes the inputs, while predictions
to alert card holders and minimize of wildfires, helping authorities take
based on new or unseen data are the
fraud losses. preventive measures and optimize
outputs.
resource allocation.
Deep Learning Medical Image Evaluation. Extreme Weather Prediction.
Applied to the analysis of medical Deep learning can analyze vast
A specialized form of machine learning
images such as X-rays and MRIs, amounts of historical and real-time
that uses artificial neural networks to
deep learning helps doctors diagnose meteorological and satellite data,
generate hierarchical insights from
diseases and other abnormalities leading to more accurate forecasts
diverse data sets, such as images, text, or
more accurately, enabling more for hurricanes, tornadoes, and
audio. These models are able to recognize
timely and effective treatments. typhoons.
patterns or features within the data, for
example, by identifying objects in images.
Large Language Models Customer Service Chatbots. Green Technology Innovation.
Large language models enable Large language models can
Advanced AI models trained on vast
companies to automate the process accelerate innovation by digesting
amounts of text data—and able to
of answering customer questions research papers and patent
generate human-like text as output, such
and helping troubleshoot issues, applications and rapidly surfacing
as for Generative AI use cases.
enhancing the efficiency of, and ideas and identifying knowledge gaps.
satisfaction with, customer service
operations.
BOSTON CONSULTING GROUP GOOGLE 4
The Climate Action Imperative
and the Promise of AI
D
espite significant progress over the last several years Even if the world succeeds in limiting warming to 1.5°C,
in mobilizing the global community to intensify its there will still be adverse impacts. Already today at 1.1°C,
climate actions and ambitions, the world is not on the IPCC reports that over 3 billion people live in areas
track to meet the Paris Agreement’s target to limit tem- highly vulnerable to climate impacts. We are already
perature rise to 1.5°C. This target was selected because seeing the impact on weather, agriculture, water security,
scientists believe that above that level, the effects would and migration. If we overshoot the target, the picture
be catastrophic and potentially irreversible. At present— becomes increasingly dire: seas will rise further, droughts
based on updated national pledges since COP26 in 2021— will be worse, and extreme weather events will be more
the United Nations Environment Programme currently common.
estimates that we are on a path to warming by 2.8°C.2
5 ACCELERATING CLIMATE ACTION WITH AI
In a 1.5°C world, the IPCC forecasts that 48% of the world’s Climate Action Has an Analytical Challenge—
population will be exposed to deadly heat levels for more and AI Can Help
than 20 days a year. In a 3° to 4°C world, that number
increases to 74%. If we stay on our current trajectory, the Leaders increasingly understand the urgency. So far, 194
World Bank estimates an additional 143 million people— parties to the Paris Agreement have developed Nationally
more than the combined populations of the United King- Determined Contributions (NDCs)—each representing
dom, Morocco, and Malaysia—could be displaced.3 And, detailed commitments for how their country will help the
absent significant investments in resilience, major global world meet the Paris Agreement’s 1.5°C goal—up from 75
cities—for example, Tokyo, Osaka, Mumbai, Bangkok, parties in February 2021.4
New York, London, and Lagos—will find themselves partly
under water. But avoiding the most catastrophic impacts of warming
requires more than political will. To achieve real progress,
We urgently need new tools to accelerate the reduction we need to develop a much richer analytical understanding
and removal of GHG emissions—and to help citizens, of a complex system comprising many variables and feed-
cities, regions, countries, and businesses make plans to back loops. (See Exhibit 1.)
adapt to the inevitable impacts of warming. AI offers much
promise.
Exhibit 1 - Climate is an interlinked, multi-parameter system
Core climate characteristics
Water Emissions have varying
Changes in tempera- impacts on core
precipitation ture
climate characteristics,
Salinity
and changes in these
Ice cap
Human activities, such as melting Ocean processes can worsen
fossil fuel burning and land Clouds circulation the greenhouse
use changes, create significant upheaval gas effect.
volumes of greenhouse gas.
Climate change processes
Human activities
Carbon Average Gulf Stream
cycle temperature modification
disturbance rise
Global
imIn pc er re mas ee a i bn l e warming Abrupt
surfaces (enhanced) climate Europe
Greenhouse change cooling
effect
Urbanization
Land use Sea level
changes CO rise
2 NO Fluctuations in
2
Deforestation CH climate characteristics
4 Cyclones drive major
impacts—natural,
Food physical, and
Greenhouse Heat Loss of socioeconomic—at
traditional
Transport gas emissions waves lifestyles both local and global
Droughts scales.
Disease
spread
Fossil fuel
burning Disasters
Heating Biodiversity
losses
Agriculture
Casualties
Industry
Economic
losses
Famines
Major impacts
Source: Philippe Rekacewicz, Emmanuelle Bournay, UNEP/GRID-Arendal; BCG analysis.
BOSTON CONSULTING GROUP GOOGLE 6
Developing models is essential to understanding the rela- Estimating AI’s Potential Contribution
tionships among variables—and to anticipating the likely
impact of different strategies and choices. But modeling Based on our research and experience, the three broad
these complex interconnections on a local and global scale areas in which AI can accelerate climate progress are the
is a huge challenge. It requires assembling massive, longi- following:
tudinal, and real-time global data sets. Information is need-
ed on climate (for example, temperatures, ocean process- • Mitigation. Helping with both the reduction and remov-
es, and meteorological phenomena) and on human al of emissions—and with the underlying measurement
activities (for example, emissions, and land use changes). needed to size the challenge and track progress
And not all the necessary data is even available.
• Adaptation and Resilience. Aiding citizens, countries,
But understanding the complex systems that drive regions, cities, and businesses to prepare for and
climate-relevant outcomes is exactly the kind of challenge respond to the inevitable impacts of a warming planet
at which AI excels. By amalgamating and processing
massive data sets, AI can reveal elusive patterns and • Foundational Capabilities. Enabling climate action
valuable insights, facilitate scenario development and via improvements in climate modeling, climate eco-
prediction, accelerate the evaluation of multiple courses nomics, and climate education, as well as accelerating
of action, enable operational optimizations, and help breakthrough innovations that will open new horizons
monitor progress toward predefined goals. for climate action
Business leaders agree. In a 2022 BCG survey of senior
executives with leadership roles related to climate or AI
(see AI is Essential for Solving the Climate Crisis), 87%
viewed AI as a helpful unlock for climate issues. They saw
supporting emissions reduction as the top climate use
case for AI in their organizations, but expressed interest in
other applications as well. (See Exhibit 2.)
Exhibit 2 - Leaders believe AI can play a role in climate action, especially
in helping to reduce emissions
In which areas of climate-related advanced analytics and AI do you see the
greatest business value for your organization? (%)
Reducing emissions 61%
Measuring emissions 57%
Predicting hazards 44%
87%
of respondents say that Managing vulnerabilities 42%
AI is a helpful tool to
address climate change
Removing emissions 37%
Facilitating climate research,
28%
climate economics, and education
Mitigation Adaptation & Resilience Foundational Capabilities
Source: BCG Climate AI survey 2022. All respondents have decision-making authority over climate or AI topics at their organizations. Respondents
were permitted to give more than one answer.
7 ACCELERATING CLIMATE ACTION WITH AI
Regarding emissions reduction potential, a 2021 BCG And AI offers many foundational capabilities that sup-
study (see Reduce Carbon and Costs with the Power of AI) port both short-term and long-term climate action. For
estimates that currently proven AI-enabled use cases could example, it can support today’s climate research with
reduce emissions by 5% to 10% by 2030. If that potential is higher-fidelity climate change simulations. But it also has
fully realized, AI-driven applications would be responsible the potential to accelerate breakthrough innovations in
for achieving roughly between 10% and 20% of the IPCC’s domains such as physics, chemistry, biology, and material
2030 interim emissions-reduction target for the world to science that could “bend the curve” on climate progress.
achieve net zero by 2050.5 Similarly, a Microsoft/PwC study
looking at four sectors (agriculture, energy, transport, and All of our estimates are based on the current state of AI
water) estimates that AI has the potential to reduce global technology—and thus speak primarily to AI’s potential in
GHG emissions by 4%.6 Further, respondents in a Capgemi- currently proven applications. Today, we are in the early
ni survey of companies that had leveraged AI for climate stages of the adoption curve. Transforming potential to
action reported that their efforts to date had achieved GHG achievement will require that all organizations fully em-
reductions of between 11.3% and 14.3% depending on the brace AI as an essential enabler of their climate actions.
sector—and these executives believe that AI could reduce And it is important to note that our assessment does not
overall GHG emissions by 15.9% in the next three to five encompass major AI-driven disruptions and break-
years.7 throughs—for example, new materials for batteries, new
drought-resistant crops, novel carbon removal technolo-
On adaptation and resilience, AI can help cities forecast gies, and scalable approaches to nuclear fusion—that
their climate vulnerability, develop estimates of the cost of could unlock massive positive impact.
inaction, and model the impact of different climate inter-
ventions. These insights can aid them in identifying the The promise of AI is real. While we are already seeing
actions with the greatest benefit, generating private-sector benefits, we need to accelerate its contribution to
enthusiasm for funding investable projects, and securing planet-saving climate impact. The next chapter offers a
public and philanthropic support for essential, but deeper dive into the primary known climate-related use
non-bankable, adaptations. It also can help guide real-time cases for AI—and highlights some examples of how and
decision-making in agriculture—for example, increasing where AI is already making a positive difference.
crop production through intelligent irrigation systems—or
in fast-moving crises such as wildfires.
BOSTON CONSULTING GROUP GOOGLE 8
How AI Can Help Accelerate
Climate Action
A I has demonstrated the potential to enable and AI’s Role in Emissions Mitigation
catalyze climate progress in three broad areas:
taking emissions mitigation to the next level, shap- Getting smarter on reducing and removing emissions is
ing strategies for adaptation and resilience, and supporting essential. And AI is already delivering significant wins that
both climate research and reinforcing technologies. Some need to be scaled. Its contributions fall into two broad
AI applications are in early stages, some are being tested, areas: measurement and monitoring, and reduction and
and others are already being scaled. But all will need to be removal.8
embraced more broadly if we are to fulfill the promise of AI
to limit warming to less than 1.5°C. Measurement and Monitoring
Without reliable, clean, and independently verifiable data,
Exhibit 3 summarizes the most promising of the currently effective climate action is difficult. Countries and compa-
known AI use cases for climate. The rest of this chapter will nies need to know their baselines and track their progress,
offer more detail on each, along the way highlighting inspir- both at the macro level (“What are our total GHG emis-
ing examples of how AI is helping unlock and accelerate sions?”) and the micro level (“Which aspects of our opera-
climate progress. tions and broader supply chain are the big drivers? Are our
efforts at reduction or removal delivering the expected
results?”).
9 ACCELERATING CLIMATE ACTION WITH AI
Exhibit 3 - Key AI applications to accelerate climate progress
Mitigation Adaptation and Resilience
Measurement Reduction Hazard Vulnerability
& Monitoring & Removal Prediction Management
Macro-level measurement Enabling emissions reduction Building early warning systems Responding to crises
e.g., calculating carbon footprint e.g., integrating renewable energy e.g., predicting near-term e.g., monitoring drought and
at the country level into smart grids, optimizing extreme events such as flooding, wildfire spread
transportation of goods drought, and cyclones
Micro-level measurement Supporting nature-based & Projecting long-term trends Building resilient infrastructure
e.g., calculating carbon technological removal e.g., modeling localized sea-level & protecting biodiversity
footprints of individual products e.g., assessing natural carbon rise and drought frequency e.g., intelligent irrigation,
stocks monitoring of endangered species
Foundational Capabilities
Climate modeling
e.g., monitoring drought and wildfire spread
Climate economics
e.g., developing cost of inaction assessments
Education & behavioral change
e.g., developing recommendations for climate-friendly consumption
Innovation & breakthroughs
e.g., supporting research on fusion
Source: BCG, AI for the Planet Alliance.
Effective measurement and monitoring solutions leverage Solutions are emerging for micro-level measurement as
AI to process and analyze data from multiple sources such well. Google’s Environmental Insights Explorer (EIE) uses
as satellite data, weather data, sensors, and other heavy machine learning to offer city planners annual estimates
data sets—which can, for example, help an organization of emissions from buildings and transportation, tree
develop a baseline for its Scope 1, 2, and 3 emissions. AI canopy status, and emissions reduction opportunities
can also deliver insights, revealing patterns in emissions such as the potential for expanded rooftop solar. Houston,
and suggesting the best ways to prioritize abatement Texas, used EIE to perform a solar assessment and inform
efforts. the development of its 5 million MWh Solar Energy Target
Plan. Similarly, CO2 AI, a novel SaaS platform, enables
In the domain of macro-level measurement, Climate business leaders—together with their value chain
TRACE has been an early mover. This nonprofit offers free partners—to develop an accurate estimate of their
emissions data for more than 80,000 individual sources organizations’ Scope 1, 2, and 3 emissions down to the
and facilities around the globe, providing a data foundation product level. It also helps them to model and evaluate
to help organizations get started with mitigation plans. Its emissions reduction opportunities. (See the sidebar
data could, for example, assist countries seeking to transi- CO2 AI: Helping Business Ecosystems Reduce their
tion away from coal and other fossil-fuel based electricity Carbon Footprints.)
generation by pinpointing the largest emitters and reveal-
ing the mix of power sources by region. (See the sidebar
Climate TRACE: Providing Timely, Independent Emissions
Data—for Free.)
BOSTON CONSULTING GROUP GOOGLE 10
Climate TRACE: Providing Timely,
Independent Emissions Data—for Free
Making real progress on climate requires timely and accu- Supported by Google.org, among others, Climate TRACE
rate data on emissions to inform government policy and uses AI and machine learning to calculate GHG emissions
business action. But historically, emissions data has been on a global scale, with the goal of moving toward real-time
based on self-reporting, calculated using varying algo- precision. To achieve this, its model analyzes more than 59
rithms, and submitted years after the fact. Climate terabytes of data from over 300 satellites and more than
TRACE—a global coalition of nonprofits, tech startups, and 11,000 sensors to create highly granular emissions data for
researchers—offers a powerful, free, and independent over 80,000 sources globally. That number is expected to
alternative: the first comprehensive source-level global grow to more than 70 million sources by the end of 2023.
inventory of GHG emissions.
Application area: Macro-Level Measurement
Climate TRACE tracks global emissions
Source: Climate TRACE. Used with permission.
11 ACCELERATING CLIMATE ACTION WITH AI
CO2 AI: Helping Business Ecosystems
Reduce their Carbon Footprints
In order to make real progress on decarbonization, organi- In one example, a global health care company seeking to
zations need a more granular and actionable view of their reduce its Scope 3 emissions by 20% by 2030 embraced
carbon footprints, both across their Scope 1, 2, and 3 emis- CO2 AI. The platform enabled it to incorporate 50 times
sions and at the level of individual product areas. Until more factors into its calculations and to develop an
now, that kind of single source of truth has not been avail- activity based emissions baseline that was 20% more
able to help operations leaders understand emissions hot precise. And CO2 AI’s simulation and roadmapping tools
spots and explore potential solutions. enabled it to identify decarbonization opportunities that
would deliver 120% of its emission reduction target.
CO2 AI, an innovative SaaS platform, helps organizations
seamlessly map emissions across their value chains and Application area: Micro-Level Measurement
leverage those insights to drive climate action. AI plays a
central role in both assembling emissions data and match-
ing it to activities and products—and in simulating solu-
tions and building decarbonization roadmaps.
Measuring and managing emissions with CO2 AI
Source: CO2 AI. Used with permission.
BOSTON CONSULTING GROUP GOOGLE 12
Reduction and Removal In the realm of agriculture, the integration of AI tools with
AI has the potential to aid organizations in reducing and technologies such as drones can help farmers monitor
removing emissions in two ways: enabling emissions their crops in real time for better field management, thus
reduction and supporting nature-based and technology- enhancing agricultural productivity while minimizing GHG
based carbon removal. emissions. Moreover, AI-driven precision farming helps
empower farmers to make well-informed, data-driven
Enabling Emissions Reduction. AI can contribute to the decisions regarding farming practices, crop selection,
creation of more efficient and cleaner energy systems in irrigation, fertilizing, pest management, and harvesting.
multiple ways. It can, by consolidating information from This approach streamlines resource utilization and, if done
dozens of different organizations and grid components, purposefully, can minimize the environmental impact
provide insights on how to optimize electric grid opera- associated with farming practices. For example, Alphabet’s
tions—and support informed decision-making on grid project Mineral is using robotics, AI, and computer vision
planning. It can also help speed transition from fossil fuels to create a more sustainable food production system. It is
to alternative energy sources through better supply and developing perception-powered solutions with partners
demand forecasts that reduce the need for battery storage across the agriculture value chain—from grocery retailers
and standby power and enable more efficient real-time and enterprise farms to equipment manufacturers and
balancing of electric grids. crop protection companies—to develop a better under-
standing of the complex interactions of plants, their grow-
For example, Tapestry, an Alphabet project, is creating a ing environments, and farm management practices.12
single virtualized view of the electricity system with the
goal of lowering emissions, minimizing outages, shortening Another interesting use case involves using AI to reduce
interconnection queues, and integrating more renewables contrails. Contrails, the white clouds that sometimes form
into the grid. AI is at the heart of its computational and behind airplanes when they fly, are responsible for about
simulation tools. Relatedly, on the subject of renewables, 35% of the aviation sectors’ global warming impact. AI
France’s Engie has partnered with Google Cloud to develop solutions developed by Google Research in partnership
and pilot an AI-powered tool that can provide grid opera- with Breakthrough Energy have enabled airline pilots in
tors with more accurate real-time forecasts of wind energy trial studies to reduce contrails by up to 54%.13 (See the
supplies.9 sidebar The Contrails Impact Task Force: Addressing Avia-
tion’s Other Contribution to Warming.)
In Africa and India, Husk Power Systems provides “pay-as-
you-go” 100% renewable power to off-grid and weak-grid Supporting Nature-Based and Technology-Based
communities that is 30% cheaper than the alternative: Removal. According to the IPCC, limiting warming to
diesel generation. Husk estimates that its AI model en- 1.5°C by 2100 will require an extensive deployment of CO2
ables it to predict user demand with 80% accuracy across removal measures, of which there are two broad types:
its microgrids, thereby improving capacity utilization, re- nature-based removal in which carbon is removed by and
ducing costs, delivering lower prices, and guiding capital stored in natural sinks such as forests, algae, and wetlands,
investment in additional capacity. and technology-based removal via approaches such as
direct air capture (DAC).14 AI can play a supporting role in
Moreover, AI-driven insights can also enable people and both types of removal.
organizations to make smarter decisions that decrease
emissions. For instance, as a result of using AI to improve In nature-based removal, AI-based solutions can help
demand forecasting, manufacturers can avoid both over- quantify and verify the level of carbon sequestration
production and the emissions those unsold goods would achieved in an ecosystem, enabling public and private
produce. Similarly, AI-optimized transportation can reduce sector leaders to make informed decisions regarding the
emissions by identifying and directing drivers to the most deployment of natural solutions, including land manage-
efficient routes. As of September 2023, Google Maps’ ment and reforestation efforts. One actor in this space is
"
282,bcg,ai-in-india-a-strategic-necessity.pdf,"AI in India -
A Strategic Necessity
A pragmatic playbook for Indian
organization to leapfrog on AI maturity
July 2023
Boston Consulting Group partners with leaders The Brij Disa Centre for Data Science and Artificial
in business and society to tackle their most Intelligence (CDSA) is a research centre at the
important challenges and capture their greatest Indian Institute of Management Ahmedabad
opportunities. BCG was the pioneer in business (IIMA). It offers a platform for faculty, scholars
strategy when it was founded in 1963. Today, and practitioners to conduct cutting-edge research
we work closely with clients to embrace a on data analytics and artificial intelligence,
transformational approach aimed at benefiting all providing solutions for businesses, governments
stakeholders—empowering organizations to grow, and policymakers. Besides generating action-
build sustainable competitive advantage, and oriented insights, CDSA conducts seminars,
drive positive societal impact. workshops, and conferences to disseminate
knowledge on artificial intelligence and analytics
Our diverse, global teams bring deep industry and
to a wider audience across the world.
functional expertise and a range of perspectives
that question the status quo and spark change. The Indian Institute of Management Ahmedabad
BCG delivers solutions through leading-edge (IIMA) is a premier, global management Institute
management consulting, technology and design, that is at the forefront of promoting excellence in
and corporate and digital ventures. We work in a the field of management education. Over the 60
uniquely collaborative model across the firm and years of its existence, it has been acknowledged
throughout all levels of the client organization, for its exemplary contributions to scholarship,
fueled by the goal of helping our clients thrive and practice and policy through its distinctive teaching,
enabling them to make the world a better place. high-quality research, nurturing future leaders,
supporting industry, government, social enterprise
and creating a progressive impact on society.
Contents
04 | Foreward 39 | Strategic Planning
in the Era of AI
06 | Executive 42 | The Road Ahead
Summary Adoption Process
of Analytics in
Organizations
09 | The Evolving Global 45 | Responsible AI: A
Perspectives on AI foundational pillar
in India’s growth
16 | The AI Maturity 48 | India’s AI Policy:
Survey The Current
Position and the
Way Forward
Maturity1, because their AI-derived benefits are marginal high AI Maturity, at par with global benchmarks. However,
at best. They have not fully harnessed AI to redesign their the survey also finds that around three out of four compa-
offerings or processes to achieve a sustainable competitive nies in CG and IG are classified as ‘Laggards’ in AI Matu-
advantage or higher margins. Indeed, our findings rity. Given that AI adoption will be a driver of competitive-
indicate that the margins of AI ‘Maturity Leaders’ ness, the low AI maturity of majority of CG and IG
are 3-5 percentage points above their Laggard peers. companies may constrain their global ambitions
if the issue is not addressed soon.
We find that AI delivers its best results when AI-driven
transformation is a strategic priority. Therefore, this study The report discusses the roadmap for improving AI
is designed to inform senior decision-makers in Indian maturity focusing on the organisation’s current maturity
organizations on the state of AI in India, and the way level and industry. Specifically, it defines a roadmap for
forward. It is based on a structured survey and discussion laggards in AI adoption to kickstart their AI transforma-
with CXO-level leaders in Technology, Data analytics, tion, build up their AI Maturity and ultimately achieve
Digital Transformation and Business Heads from 130 success in AI adoption. Further along the spectrum,
organizations across Banking, Financial Services and it also offers roadmaps for players with mid-level AI
Insurance (BFSI), Consumer Goods (CG) and Industrial Maturity (the so-called ‘Steady Followers’ and
Goods (IG). ‘Leapfroggers’) to graduate to global best-in-class AI
Maturity levels. For ‘Leaders’ the report focusses on
The study brings the latest research on impact of AI on the next frontiers of AI excellence to be conquered.
organisations along with the best on-ground AI-led trans-
formation experience. It unearths several encouraging This report is a joint initiative of IIM Ahmedabad’s Brij
findings—for instance, a significant number of Banking Disa Centre for Data Science and Artificial Intelligence
sector participants, and a smaller number of corporates and BCG X, the AI and Digital Transformation unit of BCG.
in Consumer Goods (CG) and Industrial Goods (IG) have
Foreword
A
rtificial Intelligence (AI) has evolved remarkably zational productivity and efficiency, changing the competi-
since its genesis in the 1950s. Today, it permeates tive landscape. The success of a country’s businesses in
every aspect of our daily lives—from the phones adopting AI will be an increasingly crucial determinant
in our hands, to the products on our supermarket shelves; of its competitiveness.
from selecting the route for our commute, to suggesting
the next movie on our entertainment platforms. It is In light of the high stakes involved, this study aims to
equally pervasive at the macro level, assisting in tasks gauge the status of AI adoption in Indian organizations,
as varied as studying the impact of weather on crops, and their success in translating it into business perfor-
optimizing supply chain risk and determining the best mance. To this end, it examines the AI Maturity of these
drug molecule for diseases. organizations. Many have already dipped their toes in
AI—for instance, the larger players in most sectors apply
Sentient AI robots may be a while away, but AI today has machine learning algorithms to make predictions on select
the potential to transform entire industries, by redefining business metrics. However, such behavior by itself does not
products, services and reshaping supply chains. Successful imply high AI maturity for such organisations. In fact, some
AI adoption is already having a profound impact on organi- of these organisations may still end up as Laggards in AI
1. We have classified companies into four groups based on their AI Maturity – Leaders (most mature), Steady Followers (less mature but steadily
catching up), Leapfroggers (less mature but recently made rapid strides) and Laggards (least mature). See pg 16 for more.
4 AI IN INDIA - A STRATEGY NECESSITY BOSTON CONSULTING GROUP + INDIAN INSTITUTE OF MANAGEMENT AHMEDABAD 5
data maturity. It draws on the concepts of the BCG AI For legacy companies, size and scale alone offers little
Iceberg and academic literature, which assert that a protection against deft small competitors who are master-
successful implementation of AI is one that impacts ing AI usage. We are seeing a rapid rise in mid- and small-
the revenues, margins and sustainability of the business. size players (as many as 16% of the companies studied)
Among the key contributors of organizational success which are well-positioned to capture greater market share.
from AI adoption, algorithms drive approximately 10% Unencumbered by legacy issues, they have thrown down
of the success, while data and technology infrastructure the gauntlet, not only to larger more established players
adds a further 20%. The remaining 70% hinges on people, in their industry but also to AI maturity Leaders.
processes and business transformation.
Leaders and Leapfroggers tend to adopt a ‘use case-first’
This study particularly draws on the views of Chief Data approach to AI adoption. They take time to identify use
Officers (CDO), Chief Analytics Officers (CAO), Chief Tech- cases which will have a palpable impact on the balance
nology Officers (CTO) and Chief Digital Transformation sheet. They then deploy technology, people and processes
Officers from leading organizations across the BFSI, CG to support those use cases. Laggards, on the other hand,
and IG sectors. It also draws on interviews with Business take a technology-first approach. They often end up with
Unit heads to gauge their views on the impact of AI on white elephant technologies which have limited impact on
business outcomes. The result is a detailed, calibrated business outcomes. Steady Followers lie in between these
understanding of a) these organizations’ plans with respect two groups. They tend to choose use cases that are tentative
to AI; b) the investments and measures taken to operation- and small-scale, and thus rarely transform the organization
alize those plans; c) the changes underway across technol- to the extent required to let AI play out at scale.
ogy, organization, people and procedures; and d) the
observed outcome. The country’s ecosystem plays a vital role in this endeavor,
as both a supplier and enabler of essential talent. If the
The study reveals that select Indian BFSI companies top 500 listed companies in India made AI a strategic prior-
(particularly banks and new-age NBFCs) have very high AI ity, they would need at least 25,000 to 30,000 advanced
Maturity, on par with global frontrunners. We have divided practitioners of AI-ML in the next 3-5 years. This covers
companies into four groups based on their maturity level— the entire gamut of AI professionals, from data scientists
Leaders, Steady Followers, Leapfroggers and Laggards. 11% and data engineers to enterprise architects. But it does
of companies in the set were adjudged Leaders. Their lead- not include managerial and leadership talent, nor the
ership position is facing a stiff challenge from the Leapfrog- workforce in AI vendor ecosystems and support infrastruc-
gers, who make up 9% of the companies. Leapfroggers ture which must enable these AI initiatives. Even with
started their AI-driven transformation journey late but India’s engineering and science talent, the quest for higher
have improved sharply in AI Maturity in the last three years, AI Maturity requires significant training and upskilling
converging with the Leaders on most aspects of AI Maturity. across data engineering, enterprise architecture, product
Executive Summary management, design thinking, domain knowledge, Agile
However, the concern is that 2/3rds of the companies in working and management of digital organizations. Finding
the set remain Laggards. These are companies with some and training talent in requisite numbers will be a critical
exposure and investment in AI in their Technology, Data determinant of whether India gains competitiveness in AI.
and Analytical capabilities. But AI is not a strategic priority
for them. Three out of four companies in Consumer Goods Research shows that AI investments augmenting end-user
and Industrial Goods are Laggards by this assessment. value and topline growth could drive significant economic
Just 5% of IG and CG organizations surveyed are AI Maturi- and wage expansion. The opportunity is India’s for the
T
he age of AI is upon us. As with previous General must invest in significant upskilling of mid- and senior-level ty leaders. The AI laggardness could have severe implica- taking—the challenge is now to turn the enormous poten-
Purpose Technologies like the steam engine and management on the business aspects of AI, digital trans- tions for the competitiveness of Indian manufacturing if it tial of AI into reality.
the internal combustion engine, or more recently, formation, ‘Agile’ ways of working and more. This study remains unaddressed.
computers and the internet, AI will have a transformative estimates that just the top 500 Indian companies would
impact on economies, societies and civilization at large. require at least one million hours of training.
In India alone, successful adoption of AI could add up to
1.4 percentage points annually to real GDP growth. From Companies cannot assume that benefits of AI will accrue
the perspective of corporates, successful adoption of AI is to them in due course. Companies have a choice to priori-
expected to add over a five year period, INR 1.5-2.5 trillion tise AI and adopt it or perish—and the nature of this tech-
in incremental pre-tax profit for the top 500 Indian compa- nology is such that either scenario would come about very
nies alone. quickly. The key to success in AI is achieving an advanced
level of AI maturity—the core theme of this report.
Investments into AI could deliver extraordinary returns
but success hinges on deploying AI at scale, as opposed AI maturity captures the overall ability of a company to
to restrictive incrementalism. Senior leaders must develop leverage AI to drive its strategic objectives and enhance
a more granular and precise understanding of the implica- its financial and operational performance. AI maturity
tions of AI for their business. For starters, organizations goes well beyond the existing measures of analytical or
6 AI IN INDIA - A STRATEGY NECESSITY BOSTON CONSULTING GROUP + INDIAN INSTITUTE OF MANAGEMENT AHMEDABAD 7
Key Highlights of the Report
AI benefit to Indian Economy AI benefit to Indian companies
Successful AI adoption by Indian businesses Successful AI adoption can add INR 1.5-2.5 trillion
could consistently add ~1.4 percentage point to real in incremental pre-tax profit, for the top 500 Indian
GDP growth. companies, over following 5 years.
AI Maturity: key to successful AI adoption India has exemplars in AI Maturity
Measures the ability of a company to leverage AI to Select Indian BFSI companies (particularly banks and
drive its strategic objectives and enhance its financial new-age NBFCs) have very high AI Maturity, on par with
and operational performance. global frontrunners.
AI Maturity level worrisome for most Future competitiveness may be impacted
The study finds overall 2 out of 3 Indian companies are In consumer goods and industrial goods sectors, 3 out
laggards in terms of AI adoption and maturity. of 4 companies are AI maturity laggards. Companies
The Evolving Global Perspective on AI
who strive for global competitiveness need to address
their low AI maturity quickly.
A
I may have started out as a research concept eight expensive computational setups. This period may be
decades ago, but it has since grown profoundly in its called the Pre-Democratization era of AI (PD-AI).
Use-case first vs technology first Other differentiators between leaders and laggards
scope and power, moving out of the laboratory into
Even laggards invest in data and technology. However Leaders tend to be aware that for AI adoption success:
everyday life. The last three decades have seen specialized Since then, three factors have lowered the entry barriers
laggards take a technology first approach and oen the algorithms drive 10% of the success, technology and
usage of increasing intensity. Today, its potential applications in AI adoption. Firstly, rapid fall in cost of data storage and
use-cases are not detailed out. Leaders first prioritise data infrastructure drive another 20%. 70% of the
cover every area of human activity, and no company can computational power. Secondly, cost effective cloud-based
the use-cases and then decide the optimal choice of success is driven by people, organisations and processes.
afford to ignore it. We have identified five factors that explain data and computational architecture which converts high
technology, algorithms, people and processes to make
the increasing pervasiveness of AI in recent decades. upfront technology CapEx to more manageable and
the use-case successful.
scalable OpEx. Lastly, coding platforms with low code or
The democratization of AI: Originally funded for military no code environment allowing companies to get started
purposes, AI was subsequently nurtured in research labs on basic AI use-cases. The last 5-7 years have thus been
and universities. Since the early 2000s, it has increasingly a period of Democratization of AI (D-AI). It is likely that
been deployed in industrial and real-world applications. this democratization is in its early phase and
But till as recently as the last decade, AI implementation improvements in technology will further reduce costs and
and adoption was limited to organizations with advanced increase AI deployment—but this alone will not ensure
Requirement of AI specialist Massive managerial upskilling required resources, high investment in data infrastructure and success in AI.
Just the top 500 Indian companies they would need at Just the top 500 Indian companies would require at
least 25,000 to 30,000 advanced practitioners of AIML least 1 Million hours of training in upskilling mid and
in the next 3-5 years. senior level management on the business aspects of AI,
digital transformation, Agile ways of working and more.
8 AI IN INDIA - A STRATEGY NECESSITY BOSTON CONSULTING GROUP + INDIAN INSTITUTE OF MANAGEMENT AHMEDABAD 9
Data Source and Analytics in the Pre-Democratization era of AI
Generative AI- A brief
PRE-DEMOCRATIZATION AI
B2C B2B
What is Generative AI
from patterns and structures present in large datasets,
Auto, Consumer Durable:
Generative AI refers to a subset of artificial intelligence enabling it to generate novel outputs that closely resemble
Data source: Sales & Distribution data; Industrial & Engineering Goods:
that focuses on creating new content rather than solely the original data. This technology draws inspiration from
Production Data from ERP Data source: Order details & production
Goods analyzing or predicting existing data. This new content fields such as deep learning, neural networks, and
& Supply Chain data from ERP & Supply Chain
ranges from music to art to text to software code. At its probabilistic modeling to mimic human creativity in
Analytics: MIS, Trend Analysis, Root Cause Basic descriptive analytics, MIS; Trend core, generative AI operates on the principle of learning a machine-driven manner.
Analysis, Linear Extraploation, Regres- Analytics of production data, ERP
sions for prediction
BFSI, Healthcare: Data Source: customer Engineering & Construction Services;
A View of Gen AI Apps
level data Data Source: Logistics, Cargo Shipping,
Services
Order data & service delivery level data
Analysis: Descriptive and trend analysis;
Customer specific and transaction specific Basic descriptive analytics, MIS; Trend
predictive models Analytics of production data, ERP
The data deluge: Data analysis has always facilitated AI deployable at scale: AI is transforming the way
decision-making at the transactional, operational and business is conducted across industries. Companies are CHATGPT Lexica Mage.space Jasper
strategic level. In the pre-AI era, companies with rigor investing heavily in AI solutions in the hope of substantial Natural text generation Browse AI generated Prompt-based Creative writing
on decision quality invested heavily in extensive setups returns. The growing prevalence of industrial robots, images and the prompts image generation (ads/ blog articles,
for data analysis. These efforts focused on descriptive computerized production equipment, marketing chatbots that have been used product descriptions)
analysis to understand the drivers and causality of past and machine learning investment algorithms is constantly
performance, with limited predictive analysis to gauge expanding the range of tasks that machines can perform.
future trends and transactional events.
AI’s inflection point- Generative AI: The sheer public
But the D-AI period is driven by cheap and plentiful excitement generated by a Generative AI app- ChatGPT
computing power, enabling easier execution of AI at its launch and afterwards is unprecedented. ChatGPT
algorithms. The increasing ability to capture and store reached 1 million users in just 5 days after its launch.
large amounts of data from communication devices, ERP In comparion, Instagram and Spotify took an estimated
Synthesia.io Midjourney Runway KAEDIM
systems or satellite data has provided the ideal setup for 75 days and 150 days respectively. If one goes by Google
Convert text to Prompt-based Image/video editing Convert 2D images to
such algorithms to run. This has opened up new use cases search count as a measure of interest, interest in ChatGPT
speaking avatar image generation and enhancement 3D objects
and created an enabling environment for new business is 7 to 8 times higher than the peak interest in Metaverse.
models on the lines of ‘X-as-a-service’, where X assumes The hype around currently available generative AI
various forms—banking, payment, logistics, manufacturing applications could be due to their ease of access and
and infrastructure management—limited only by market simple yet intuitive user interface. By formulating the
size and cost-effective execution. appropriate question in English, one can effortlessly and
quickly access information derived from a vast dataset,
Search for sustainability during uncertain times: in a user-friendly format.
Stakeholders are increasingly demanding that businesses
deliver sustainable profitability with social responsibility— Organisations in industries ranging from BFSI, Healthcare,
even in a period of economic volatility and uncertainty. Consumer Goods and Services, Technology to name a few,
This has added to the challenge of operational planning are finding powerful use-cases based on Gen AI.
and strategic decision-making. AI is well-placed to help
businesses balance these difficult imperatives.
10 AI IN INDIA - A STRATEGY NECESSITY BOSTON CONSULTING GROUP + INDIAN INSTITUTE OF MANAGEMENT AHMEDABAD 11
India’s current AI strides
The global AI market is estimated to reach US$450 billion Management services to global clients. Today, the IT-BPM
in 2022, growing at a rate of more than 20%.3 In India, AI industry is India’s largest private sector employer, account-
expenditure reached US$665 million in 2018 and is expect- ing for ~11% of the urban workforce.
ed to reach US$11.78 billion by 2025, with a CAGR of 39%
from 2019-2025.4 With AI, a growing body of evidence suggests that the
automation of repetitive tasks has led to the disappear-
As with most new technologies, there has been concern ance of middle-skilled jobs and increased wage inequality.
about the impact of AI on labor markets. While these On the other hand, there is also growing demand for labor
concerns are understandable, large scale job losses due trained in advanced technology and adept in socio-behav-
to technological innovation can be averted. A case in point ioral skills. Experts suggest that emerging technologies
is India’s thriving Information Technology sector and the may increase the productivity of existing jobs as well as
opportunities it has created. As recently as the 1990s, there create new roles which are difficult to envisage today.
were fears of computers replacing humans – yet the sector These new roles may require a combination of skills such
ended up creating large numbers of new jobs. India has as higher technological acumen, better empathy, people
become a major offshoring hub for the global software connect and critical thinking.
industry providing Business Process Outsourcing and
Exhibit 2 - Global Research and Patenting in AI by Country
1,000,000
100,000
10,000
1,000
100
10
1
3. IDC: Worldwide Semiannual Artificial Intelligence Tracker
4. https://www.ibef.org/download/AI-Revolution.pdf
Source: Exhibit 2 - Emerging Technology Observatory’s Country Activity Tracker: Artificial Intelligence: https://cat.eto.tech/
12 AI IN INDIA - A STRATEGY NECESSITY BOSTON CONSULTING GROUP + INDIAN INSTITUTE OF MANAGEMENT AHMEDABAD 13
selcitrA
latoT
)elacs
laitnenopxe(
snoitacilppA
tnetaP
Exhibit X
Exhibit 1 - How are companies leveraging GEN AI
Model topology Pre-trained model Organisation Specific
!
(Outcome of academic (E.g., GPT-3 Davinci) use cases supported
& industrial research)
Question answering
Sentiment analysis
Training data
! Training Fine-tuning Information extraction
(Text, images,
Publicly Little amounts
code & other data)
available (MBs) of specific
information from domain data or Fine-tuning Image labeling
huge dataset. inhouse data-low data
High computa- resource and time (Text, images, Object recognition
tional power, requirement code & other data)
time
Content creation
On average, 1 in 3 respondents in various roles predict Ethical Considerations and Future Prospects
a 25-50% gain in productivity from using these tools.2
The tasks which are currently getting revolutionised by As generative AI continues to evolve and permeate various
GEN AI are: aspects of society, it is crucial to address the ethical consid-
erations surrounding its use. Questions arise regarding the 1,400,000
279,962
• Writing blogs/ posts/ mails ownership and authenticity of generated content, the
potential for misuse or manipulation, and the impact 1,200,000 77,635
• Marketing material on employment and creativity. Striking a balance between
16,499
innovation and responsible implementation is essential
1,000,000
• Structured information extraction for harnessing the full potential of generative AI.
2,184 4,160 1,113 3,282 3,082
• Client outreach 800,000 983
• Writing code 600,000 176
• Answering customer queries
400,000
• Project management
200,000
0
China US India UK Germany Japan France Australia Canada Italy
Articles PatentApplications
2. https://www.sortlist.com/datahub/reports/chat-gpt-statistics/
Source: Exhibit 1 - “On the Opportunities and Risks of Foundation Models”, Center for Research on Foundation Models, arXiv, 2021; BCG analysis
Exhibit 4 - Private investment in AI and R&D
AI Investments: Companies and Amount
• Government Intervention: Arguably the biggest and government could nudge companies through suitable
most successful digital pioneer in India is the Govern- tax incentives for AI research and innovation. It should
ment of India. Its innovations at scale include Aadhaar strive to upgrade the curriculum and boost the resource
(universal biometric ID), the Unified Payments Inter- of India’s top institutions to focus on advanced technol-
face and the Open Network for Digital Commerce. The ogy education, especially in AI.
Source: “Emerging Technology Observatory’s Country Activity Tracker: Artificial Intelligence: https://cat.eto.tech/
14 AI IN INDIA - A STRATEGY NECESSITY BOSTON CONSULTING GROUP + INDIAN INSTITUTE OF MANAGEMENT AHMEDABAD 15
DSU
snoillim
ni
stnemtsevni
IA
tnemtsevnI
IA
htiw
seinapmoC
fo
rebmuN
Ultimately, India’s success in leveraging AI will be shaped • R&D and Intellectual Property: While India ranks
by four key factors: in the global top 10 for AI research and patents, the
associated value being captured is relatively miniscule.
• Nurturing AI talent: India only has around 4.5%5 of the The pay-off in terms of patents, products and profits
world’s AI professionals, and the talent crunch will get remains low relative to the volume of research conduct- 600,000 8000
more acute. 76% of India’s data talent is currently hired ed. The reasons for this are manifold. Research in latest 6,773
by the IT Services industry. However, companies are technologies is often limited to incrementalism in most 7000
500,000
struggling to find AI talent with the requisite business firms. Additionally, a fledgling collaboration between
and sector understanding. As a result, direct hire of AI industry-academia limits monetizable research and IP 6000
talent remains low despite high demand. A NASSCOM creation. This scenario further discourages advanced tal- 400,000
5000
report6 also projects that the demand-supply gap for dig- ent development. Currently, less than 3% of graduates
ital technology talent will grow 3.5x+ by 2026 to 1.4-1.8 pursue a PhD in the field.
300,000 4000 million. The current study estimates that core AI talent-
data scientist, data engineer, enterprise architect would • Investments in AI: The last decade has seen a rapid
3000
be 15% to 20% of the headline number. Further just the uptick in the number of Indian startups. Indian orga- 200,000
top 500 corporates (listed corporates by revenue) would nizations have also accelerated their adoption of da- 1,422 2000
1,198
need at least 25,000 to 30,000 advanced practitioners of ta-driven use cases. However, when it comes to private
100,000 711 642 AIML in the next 3-5 years. To handle AI driven transfor- investment in AI, the US and China lead the rest of the 557 418 294 186 127 1000
mations, the existing senior and middle managements world by a huge margin.
of these 500 companies would require a minimum a 0 0
million hours of training! US China UK Canada India Germany France Japan Australia Italy
Disclosed Investments Estimated total investments Companies
Exhibit 3 - Published AI articles by category and application (India)
India: Published AI articles by Category
Computer 1395
SpeechRecognition 4698
Simulation 656
RealTimeComputing 3540
PatternRecognition 39446
OtherAI
NaturalLanguageProcessing
MathematicsEducation 256
MathematicalOptimization
MachineLearning 15281
Lingusitics 575
Informationretreival 4690
Human-ComputerInteraction
DataScience
DataMining
ControlTheory
ControlEngineering 1118
ComputerVision
CognitivePsychology 542
Algorithm 6045
0 5000 10000 15000 20000 25000 30000 35000 40000 45000
Source: Emerging Technology Observatory’s Country Activity Tracker: Artificial Intelligence: https://cat.eto.tech/
Note: Chart shows the number of AI articles published by authors from the country. Author countries are inferred from where their institutions
are located.
5. OECD.AI (2023), visualisations powered by JSI using data from LinkedIn, accessed on 06/2/2023
6. https://community.nasscom.in/communities/emerging-tech/indias-tech-industry-talent-demand-supply-analysis
seirogetaC
elcitrA
33351
7692
2551
3739
3681
8003
2942
14024
AI Maturity goes beyond the existing measures of benefits will accrue only through fundamental changes
analytical and data maturity. Instead, it draws on the to the policy, operating processes, and behavior of the
concept of the BCG AI Iceberg and academic literature. entire loan origination setup to enable digital workflow
The Iceberg asserts that a successful implementation and data driven decision making. In the absence of such
of AI is one that impacts the revenues and margins of transformations, even organizations with a bespoke
the business. Approximately 10% of this success can AI model will be deemed to have low AI Maturity.
be attributed to algorithms, and another 20% may be
attributed to enabling data and technology capability. On the other hand, consider a Consumer Goods company
But the bulk of the success, that is 70%, hinges on that has begun to embrace AI by adopting a reasonable
people, processes and business transformation. (though not cutting-edge) demand forecasting model for
its products. The organization has expedited the adoption
This means that simply acquiring technology or using the of this model at the district level to ensure consistency
latest Machine Learning tools would not confer a strategic in supplies. It closely tracks any divergence of the actual
benefit by itself. For instance, a bank may have a well-built demand from predicted demand. These gaps are then
ML-driven risk score. But this will not transform the loan fed back into the system to improve the predictions.
acquisition and underwriting process if the model scores The front-end decision makers are trained to identify
are regularly overwritten by subjective judgment and the 5-10% instances where they will overwrite the model
ineffective credit policies. results, but these human interventions are also tracked
for quality of decision making. An organization like this is
Such an implementation of the risk model may offer exhibiting higher AI Maturity than the financial institution
limited benefits to the organization. However, large-scale in the previous example.
Exhibit 5 - How we measure AI Maturity—the seven components
VISION
STRATEGY
The AI Maturity Survey
ANALYTICS
Industries and companies across the board are Understanding AI Maturity
increasingly looking at AI to deliver a long-term
competitive advantage. AI is seen as critical not just AI Maturity measures the overall capability of a company
ETHICS & GOVERNANCE
to their growth, but to their very survival in the medium to leverage AI to drive the strategic objectives and
to long run. With the stakes this high, it is vital for the ensuing operational performance of the company.
companies to assess their AI capabilities and build These objectives may include (inter alia) sustained
a robust plan to harness the value of AI. growth in revenues and consistent margins to enhance
shareholder value. In other words, AI Mat"
283,bcg,gen-ai-increases-productivity-and-expands-capabilities.pdf,"GenAI Doesn’t Just Increase
Productivity. It Expands
Capabilities.
SEPTEMBER 05, 2024
By Daniel Sack, Lisa Krayer, Emma Wiles, Mohamed Abbadi, Urvi
Awasthi, Ryan Kennedy, Cristián Arnolds, and François Candelon
READING TIME: 12 MIN
This is the second major field experiment led by the BCG Henderson Institute designed to help business
leaders understand how humans and GenAI should collaborate in the workplace. Our previous study
assessed the value created—and destroyed—by GenAI when used by workers for tasks they had the
© 2024 Boston Consulting Group 1
capabilities to complete on their own. Our latest experiment tests how workers can use GenAI to complete
tasks that are beyond their current capabilities.
A new type of knowledge worker is entering the global talent pool. This employee, augmented with
generative AI, can write code faster, create personalized marketing content with a single prompt, and
summarize hundreds of documents in seconds.
These are impressive productivity gains. But as the nature of many jobs and the skills required to do
them evolve, workers will need to expand their current capabilities. Can GenAI be a solution there as
well?
Based on the results of a new experiment conducted by the BCG Henderson Institute and scholars
from Boston University and OpenAI’s Economic Impacts research team, the answer is an
unequivocal yes. We’ve now found that it’s possible for employees who didn’t have the full know-how
to perform a particular task yesterday to use GenAI to complete the same task today.
METHODOLOGY 
Our research involved a carefully structured experimental design to evaluate the
impact of generative AI on the ability of nontechnical knowledge workers to perform
technical, data-science tasks.
In total, 480 BCG consultants and 44 BCG data-scientist volunteers completed this
controlled study. The study participants were general consultants, for whom data-
science expertise is not typically required. This expertise exists among the data
scientists of BCG X who also volunteered to support the study by establishing
benchmarks. The performance of general consultants was evaluated by comparing
their output to that of BCG data scientists who completed the same tasks.
General consultants were randomly assigned to either a GenAI-augmented group,
which received interactive training on using Enterprise ChatGPT-4 with the Advanced
Data Analysis Feature for data science tasks, or a control group, which was asked not
to use GenAI and received interactive training on traditional resources like Stack
Overflow. The tasks assigned to participants included coding, statistical
understanding, and predictive modeling, all of which required skills that are typically
outside the expertise of nontechnical workers but within the day-to-day expertise of
BCG’s data scientists.
© 2024 Boston Consulting Group 2
Data collection was carried out in four phases: a pre-experiment survey to assess
baseline skills and attitudes, a tailored training session for each group, the
completion of two out of three randomly assigned data-science tasks, and a post-
experiment survey to measure knowledge retention without the use of AI tools.
The tasks were designed by BCG data scientists to ensure that they were challenging
enough that AI could not solve them independently. Analysis focused on comparing
the performance of the treatment and control groups against the benchmarks set by
BCG data scientists, examining the completion rates, time taken, and correctness of
the responses.
Similar to our first GenAI study, we “put our feet to the fire”—with a goal of deeply
understanding GenAI’s impact on ways of working.
With that in mind, leaders should embrace GenAI not only as a tool for increasing productivity, but as
a technology that equips the workforce to meet the changing job demands of today, tomorrow, and
beyond. They should consider generative AI an exoskeleton: a tool that empowers workers to
perform better, and do more, than either the human or GenAI can on their own.
Of course, there are important caveats—for example, employees may not have the requisite
knowledge to check their work, and therefore may not know when the tool has gotten it wrong. Or
they may become less attentive in situations where they should be more discriminating.
But leaders who effectively manage the risks can reap significant rewards. The ability to rapidly take
on new types of work with GenAI—particularly tasks that traditionally require niche skills that are
harder to find, such as data science—can be a game-changer for individuals and companies alike.
How GenAI Can Equip Knowledge Workers
In the previous experiment, we measured performance on tasks that were within the realm of the
1
participants’ capabilities. (See top row of Exhibit 1.) For tasks where GenAI is highly capable, we
found that augmented workers perform significantly better than humans working without the
technology. However, when the technology is not capable of performing the task at expert level,
humans tend to over-rely on GenAI and perform worse than if they had completed the task on their
own.
© 2024 Boston Consulting Group 3

But what happens when, instead of using GenAI to improve performance within their current skillset,
people use GenAI to complete tasks that are outside their own capabilities? Does being augmented
with GenAI expand the breadth of tasks people can perform?
For our latest experiment, more than 480 BCG consultants performed three short tasks that mimic a
common data-science pipeline: writing Python code to merge and clean two data sets; building a
predictive model for sports investing using analytics best practices (e.g. machine learning); and
validating and correcting statistical analysis outputs generated by ChatGPT and applying statistical
2
metrics to determine if reported findings were meaningful.
While these tasks don’t capture the entirety of advanced data scientists’ workload, they are
sufficiently representative. They were designed to present a significant challenge for any consultant
3
and could not be fully automated by the GenAI tool.
To help evaluate the performance impact of GenAI, only half of the participants were given access to
the GenAI tool, and we compared their results to those of 44 data scientists who worked without the
assistance of GenAI. When we dive deeper into the results, three critical findings emerge.
The Immediate Aptitude-Expansion Effect
When using GenAI, the consultants in our study were able to instantly expand their aptitude for new
tasks. Even when they had no experience in coding or statistics, consultants with access to GenAI
were able to write code, appropriately apply machine learning models, and correct erroneous
4
statistical processes. (See Exhibit 2.)
© 2024 Boston Consulting Group 4

We observed the biggest aptitude-expansion effect for coding, a task at which GenAI is highly adept.
Participants were asked to write code that would clean two sales data sets by correcting missing or
invalid data points, merging the data sets, and filtering to identify the top five customers in a
specified month.
Participants who used GenAI achieved an average score equivalent to 86% of the benchmark set by
data scientists. This is a 49-percentage-point improvement over participants not using GenAI. The
GenAI-augmented group also finished the task roughly 10% faster than the data scientists.
Even those consultants who had never written code before reached 84% of the data scientists’
benchmark when using GenAI. One participant who had no coding experience told us: “I feel that I’ve
become a coder now and I don’t know how to code! Yet, I can reach an outcome that I wouldn’t have
been able to otherwise.” Those working without GenAI, on the other hand, oen did not get much
further than opening the files and cleaning up the first “messy” data fields; they achieved just 29% of
the data-scientist benchmark.
It’s important to note that most consultants are expected to know the basics of data cleaning and
oen perform data-cleaning tasks using no-code tools such as Alteryx. Therefore, while they did not
have experience doing the coding task in Python, they knew what to expect from a correct output.
This is critical for any GenAI-augmented worker—if they don’t have enough knowledge to supervise
the output of the tool, they will not know when it is making obvious errors.
A Powerful Brainstorming Partner
For the task that involved predictive analytics, our participants faced a challenging scenario: neither
they nor the GenAI tool were highly adept at that task. Here, the technology was still valuable as a
brainstorming partner.
© 2024 Boston Consulting Group 5
While all the tasks in our experiment were designed such that the GenAI could not independently
solve them, the predictive-analytics task required the most engagement from participants. They were
asked to create a predictive model, using historical data on international soccer matches, to develop
an investment strategy. Their ultimate goal was to assess how predictable, or reliable, their model
would be for making investment decisions.
Many participants used GenAI to brainstorm, combining their knowledge

with the tool’s knowledge to discover new modeling and problem-
solving techniques.
As shown in Exhibit 2, this was the task on which the GenAI-augmented consultant was least likely to
perform on par with a data scientist, regardless of previous experience in coding or statistics. This is
because the GenAI tool is likely to misunderstand the ultimate goal of the prompt if the entire task is
copied and pasted directly into the tool without breaking the question into parts or clarifying the
goals. As a result, participants with access to GenAI were more likely to be led astray than their
nonaugmented counterparts.
Even so, we found that, with the support of GenAI, many participants were able to step outside their
comfort zone. They brainstormed with the tool, combining their knowledge with GenAI’s knowledge
to discover new modeling techniques and identify the correct steps to solve the problem successfully.
The GenAI-augmented participants were 15 percentage points more likely to select and appropriately
apply machine-learning methods than their counterparts who did not have access to GenAI.
Reskilled, but Only When Augmented
Participants’ aptitude for completing new and challenging tasks was immediately boosted when
using GenAI, but were they reskilled? Reskilling is defined as an individual gaining new capabilities or
knowledge that enables him or her to move into a new job or industry. We found in our study that
GenAI-augmented workers were in a sense “reskilled,” in that they gained new capabilities that were
beyond what either the human or GenAI could do on their own. But GenAI was only an exoskeleton;
the participants were not intrinsically reskilled, because “doing” with GenAI does not immediately
nor inherently mean “learning to do.”
While each participant was assigned just two of the three tasks in the experiment, we gave everyone
a final assessment with questions related to all three tasks to test how much they actually learned.
For example, we asked a coding syntax question even though not everyone did the coding task—and
therefore not everyone would have had a chance to “learn” syntax. Yet the people who participated
© 2024 Boston Consulting Group 6
in the coding task scored the same on the assessment as people who didn’t do the coding task.
Performing the data-science tasks in our experiment thus did not increase participants’ knowledge.
Of course, participants only had 90 minutes to complete the task. With repetition, more learning
might have occurred. We also didn’t inform participants that they would be tested at the end, so
incentivizing learning might also have helped. This is important, because we found that having at
least some background knowledge of a given subject matters.
We found that coding experience is a key success factor for workers who use

GenAI—even for tasks that don’t involve coding.
GenAI-augmented participants with moderate coding experience performed 10 to 20 percentage
points better on all three tasks than their peers who self-identified as novices, even when coding was
5
not involved. In fact, those with moderate coding experience were fully on par with data scientists
for two of the three tasks—one of which had zero coding involved.
Based on this, we posit that it is the engineering mindset that coding helps develop—for example,
having the ability to break a problem down into subcomponents that can be effectively checked and
corrected—that ultimately matters, more so than the coding experience itself.
The risk of fully automating code, then, is that people don’t form this mindset—because how do you
maintain this skill when the source of its development is no longer needed? This is part of a larger
discussion: What other seemingly automatable skills have such importance? Will these skills become
the new Latin, taught mostly to cultivate a particular mindset?
Managing the Transition
While we have used data science as a case study, we believe that our finding—that augmented
workers can skillfully perform new tasks—can be applied to any field that is within the tool’s
capabilities. We’ve identified five core implications for company leaders. (See Exhibit 3.)
© 2024 Boston Consulting Group 7

Talent Acquisition and Internal Mobility. The results across our workforce experiments have
shown that what an individual can perform on his or her own by no means approaches what can be
accomplished when augmented by GenAI. This suggests that the talent pool for skilled knowledge
work is expanding.
Recruiters should therefore incorporate GenAI into the interview process to get a more complete
picture of what a prospective employee might be capable of when augmented by the technology.
Leaders may also find that an unlikely person inside their organization can fill an open role. We’re
not suggesting that nontechnical generalists can immediately become data scientists. But a
generalist marketer could, for example, take on marketing analyst tasks or roles.
Learning and Development. What does this mean for employees seeking paths to senior roles
and/or leadership? How should members of the GenAI-augmented workforce, who can flexibly take
on various roles, cultivate the right skills for career advancement—and what are the most important
skills for them to retain long term?
While GenAI has an immediate aptitude-expansion effect, learning and development remain the
most import lever for cultivating advanced skills and supporting each employee’s professional
trajectory. Leaders therefore must ensure that employees have incentivized and protected time to
learn. Other research has shown that when specifically used for learning (and, unlike our
participants, people are generally incentivized to learn in their jobs), GenAI is an effective
personalized training tool.
© 2024 Boston Consulting Group 8
Leaders should ensure that future implementations of GenAI tools include

the functionality to inform the user if a task is outside the technology’s
capability set.
Our analysis also suggests that developing some technical skills leads to greater performance, even
for nontechnical workers. Regardless of the training employees receive, company leaders should
ensure their future implementations of GenAI tools include the functionality to inform the user if a
task is outside the technology’s capability set—information that should be compiled from regular
benchmarking.
Companies are likely to find competitive advantage from developing tools and processes that
precisely assess the capabilities of GenAI models for their use cases. As shown in Exhibit 1, how a
worker should use GenAI greatly depends on understanding where a task lies within their own skill
set and within the capabilities of the technology.
Teaming and Performance Management. Although our results show it is possible for a generalist
to take on more complex knowledge work, it will be crucial to manage their performance and ensure
the quality of their output. This could mean designing cross-functional teams to provide generalists
with easy access to an expert when they need help and establishing regular output-review
checkpoints—because an overconfident generalist may not always know when to ask for support.
Leaders will need to run pilots to ensure their teaming configurations lead to the best outcomes. This
may be an opportunity to break silos and integrate teams of generalists with experts from various
centers of excellence.
Strategic Workforce Planning. Given the implications for talent and teaming, how should
organizations think about specialized expert tracks and the structure of their workforce? What does
strategic workforce planning for knowledge work mean in a world of constant job transformation
and technological advancement? We don’t have all the answers. But we do see that the skills needed
for a given role are blurring, and workforce planning will no longer be solely focused on finding a
certain number of people with a specific knowledge skill, such as coding.
Instead, planning should include a focus on behavioral skills and enablers that will support a more
flexible workforce. While knowledge workers may be technically capable of taking on new roles with
the help of GenAI, not everyone is equally adept at embracing change.
Professional Identity. The impact of GenAI on professional identity is an important and
contentious topic. But a recent survey suggests that negative impacts can be mitigated when
employees feel supported by their employers.
© 2024 Boston Consulting Group 9
In fact, in our study, we found that 82% of consultants who regularly use GenAI for work agree with
the statements “Generative AI helps me feel confident in my role” and “I think my coworkers enjoy
using GenAI for their work,” compared to 67% of workers who don’t use it on a weekly basis. More
than 80% of participants agreed that GenAI enhances their problem-solving skills and helps them
achieve faster outputs.
This suggests that highly skilled knowledge workers genuinely enjoy using the tool when it allows
them to feel more confident in their role—which aligns with our previous findings that mandating
the use of AI can actually improve employee perception of AI. However, this is only true if employees
believe that AI is being deployed to their benefit.
We are only at the beginning of the GenAI transformation journey, and the technology’s capabilities
will continue to expand. Executives need to be thinking critically about how to plan for this future,
including how to redefine expertise and what skills to retain in the long term.
But they are not alone: Skill development is a collaborative effort that includes education systems,
corporate efforts, and enablement platforms such as Udemy and Coursera. Even the providers of
GenAI models should be thinking about how their tools can further enable learning and
development. Preparing for the GenAI-augmented workforce must be a collective endeavor—
because our collective future depends on it.
bhi-logo-image-gallery-2-tcm9-239323.jpg
The BCG Henderson Institute is Boston Consulting Group’s strategy think tank, dedicated to
exploring and developing valuable new insights from business, technology, and science by embracing
the powerful technology of ideas. The Institute engages leaders in provocative discussion and
experimentation to expand the boundaries of business theory and practice and to translate
innovative ideas from within and beyond business. For more ideas and inspiration from the Institute,
please visit our website and follow us on LinkedIn and X (formerly Twitter).
© 2024 Boston Consulting Group 10
Authors
Daniel Sack
MANAGING DIRECTOR & PARTNER
Stockholm
Lisa Krayer
PRINCIPAL
Washington, DC
Emma Wiles
ASSISTANT PROFESSOR OF INFORMATION SYSTEMS,
BOSTON UNIVERSITY’S QUESTROM SCHOOL OF BUSINESS
Mohamed Abbadi
CONSULTANT
Washington, DC
Urvi Awasthi
DATA SCIENTIST
New York
Ryan Kennedy
AI ENGINEER
Boston
Cristián Arnolds
CONSULTANT
New York
François Candelon
ALUMNUS
© 2024 Boston Consulting Group 11
1 That experiment was conducted using the first version of GPT-4.
2 Of the consultants who originally signed up to participate, 480
completed the experiment. Participants were randomly split into a
control group that was not allowed to use GenAI for the tasks and a
“treatment” group that was asked to use GenAI. Each participant was
randomly assigned two of the three tasks; each task was timeboxed for
90 minutes. It is important to note that we did not test a full end-to-end
data science workflow from ideation to delivery or advanced topics such
as deep learning.
3 We used Enterprise ChatGPT with GPT-4 and its Advanced Data
Analysis feature.
4 The findings for the statistical understanding task were consistent with
those of the coding and predictive analytics tasks; as a result, we
focused on the first two tasks in this article.
5 Coding experience was based on a self-assessment. We define moderate
experience by those who selected “I know how to code but am not an
expert” and novices as those who selected “I only know the basics of
coding” and “I don’t know how to code.”
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284,bcg,bcg-how-digital-and-ai-solutions-will-reshape-health-care-in-2025.pdf,"How Digital and AI Will
Reshape Health Care
in 2025
January 2025
Boston Consulting Group BCG X
Boston Consulting Group partners with leaders BCG X is the tech build & design unit of BCG.
in business and society to tackle their most
Turbocharging BCG’s deep industry and
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functional expertise, BCG X brings together
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advanced tech knowledge and ambitious
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entrepreneurship to help organizations enable
we work closely with clients to embrace a
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the world’s most important challenges and
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How Digital and AI Will Reshape
Health Care in 2025
T
he definition of digital health is evolving. The era A growing number of individually tailored apps and digital
spurred on by the Covid-19 pandemic—think tele- platforms will give patients more control over their medical
medicine and digital therapeutics, which have strug- conditions, predict flare-ups, and suggest real-time interven-
gled to scale—is giving way to one defined by artificial tions. We expect consumers to increasingly rely on AI chat-
intelligence (AI) and solutions that strengthen the bond bots and virtual assistants for answers to health questions.
between health care professionals and patients in an
integrated manner, with appropriate economics to support Digital health will continue to offer solutions to address
them. gaps in women’s health care, including femtech innova-
tions to redesign traditional “hardware” used for women’s
We see this shift reflected in trends that experts across health (such as the speculum), with the female experience
BCG and BCG X anticipate will shape digital health in at the center. It’s a needed shift: A recent BCG X survey
2025. As AI matures, it is rapidly expanding possibilities for found that fewer than half of women respondents across
patients, providers, and health care organizations alike. the globe (41%) agreed that there are sufficient services to
New digital solutions are being leveraged to address gaps address their specific health concerns.
in care for chronic conditions such as heart failure, diabe-
tes, and mental health. And the growing influence of gener- We are also beginning to see a maturing of partnerships
ative AI (GenAI) on every aspect of health care—from between femtech health and wellness brands that can lead
personalized care to automated workflows—is a key theme to interoperable ecosystems that pool women’s health
for the upcoming year, as it was in 2024. data and ultimately drive improved health outcomes.
Let’s dive deeper into how we expect digital and AI solu- Provider Empowerment
tions to reshape health care in 2025. Providers will be empowered and enabled by digital tech-
nology as well. AI can provide the analytical muscle to
Patient Support process vast quantities of personal patient data, powering
This year, digital health tools will continue to transform highly personalized medical treatment tailored to individu-
patient care, improving their support and access. Smart als based on their unique health data from continuous
implants and wearable devices that allow providers to monitoring devices, lifestyle inputs, and individual genetics.
monitor patients’ cardiac activity, blood sugar levels, and This enables providers to adjust treatment dynamically
other biological functions in real time from remote loca- based on feedback in real time.
tions will enable better chronic disease management and
improve patients’ quality of life. As sleep continues to gain Artificial intelligence decision-making tools will become
attention as a crucial biomarker for overall well-being, mainstream in 2025, giving doctors immediate access to
health tech companies are creating more advanced, accu- evidence-based research and treatment guidelines. GenAI
rate sleep-tracking tools. applications will accelerate diagnoses and minimize diag-
nostic errors, while speeding the delivery of patient care
and more accurately predicting patient outcomes.
BOSTON CONSULTING GROUP 1
Emergence of Ecosystems While GenAI continues to generate tremendous excite-
At the organizational level, our experts anticipate that the ment in the digital health care space, it’s not a panacea.
coming year will see an expansion of the use of AI to orga- Our experts recognize that some of these programs won’t
nize and automate entire workflows instead of just specific deliver anticipated results in 2025. When that happens, we
tasks. For example, rather than an AI tool that facilitates emphasize the importance of going back to the basics:
physician note-taking or scheduling, intelligent agents will focusing on business outcomes and tracking key perfor-
automate an entire patient episode of care, from intake mance indicators. In this way, AI failures can drive more
through treatment plan. Working across departments, AI focused, sustainable transformation in the long term.
programs will learn as they go, improving efficiency and
outcomes at both the patient and system level. Health Clearly, 2025 promises to be a transformative year.
systems will benefit, but so will other types of health care We’re excited to see how AI and more digital solutions
organizations such as pharmaceutical companies, where reshape health care.
GenAI can transform key activities such as clinical trials
and regulatory submissions.
AI-driven data processing will also allow access to data that
has until now been considered too disorganized to be useful,
such as medical records, clinical notes, and physician/
patient interaction information. Clinicians, payers, and
drug companies alike will be able to draw out actionable
insights from these data sets to improve patient care and
outcomes. At the same time, expanded access will en-
hance different systems’ ability to interact with one anoth-
er, facilitating more seamless collaboration.
2 HOW DIGITAL AND AI WILL RESHAPE HEALTH CARE IN 2025
“With AI-driven solutions, wearable devices, and
digital triaging tools, patients are no longer passive
participants in their care but active managers of their
health journey.”
Ashkan Afkhami
Managing Director and
Senior Partner
Every patient will soon have the tools to find the right By capturing, analyzing, and applying data to drive better
care, support, and treatment tailored to their unique treatments, therapies, and operational efficiency, health
needs. We’re at a tipping point in patient care. With AI-driven care organizations will realize their true potential.
platforms, wearable devices, and digital triaging tools, patients By leveraging real-time insights and advanced analytics,
are no longer passive participants in their care but active man- organizations can detect diseases earlier, streamline care
agers of their health journey. Technology is closing critical gaps pathways, and optimize operations. Interoperable systems
in care, particularly in underserved communities, by enabling and secure data-sharing frameworks are critical for achieving
timely guidance, remote consultations, and personalized care these outcomes, ensuring data can flow seamlessly across
plans. These tools empower patients to take charge of their stakeholders. As we move forward, ethical AI frameworks and
health and promote a seamless, personalized experience that integrated data strategies will be the catalysts for change,
meets them wherever they are—whether at home, in the clinic, transforming health care into a precision-driven, efficient,
or on the go. and impactful ecosystem.
Digital health solutions that simplify workflows,
optimize resources, and improve patient monitoring
will enable clinicians to deliver continuous,
high-quality care. AI-assisted technologies are helping to
address capacity challenges, reduce diagnostic turnaround
times, and improve treatment accuracy. Similarly, deci-
sion-support tools and real-time analytics are enabling
smarter, safer care delivery. By integrating remote patient
monitoring, automation, and predictive analytics, health
care professionals can focus on what matters most: treat-
ing and supporting patients beyond the clinic walls. This
shift will improve efficiency and empower clinicians to
deliver proactive, patient-centered care.
BOSTON CONSULTING GROUP 3
“Digital tools can help bridge R&D and access gaps,
driving equity in health care for women.”
Johanna Benesty
Managing Director and
Senior Partner
Women continue to face barriers to accessing health To fully harness digital health’s potential in low- and
care. Many factors contribute to lack of access, even in middle-income countries (LMICs), the health care
high-income countries—including economic disparities, ecosystem must overcome several challenges. Scaling
limited R&D on women-specific health, and systemic bias- digital health initiatives effectively remains a primary hurdle
es. For instance, 26% of US women delay care due to cost. because many digital solutions that show promise in pilot
For many low-income women, this limits access to essen- stages struggle with long-term sustainability across diverse
tial reproductive health services. Additionally, lack of R&D regions. Investment in digital infrastructure, talent develop-
on women’s conditions like endometriosis or menopause ment, and skills training is essential, as health care workers in
leads to delayed diagnoses and inadequate treatment. LMICs often lack the technical training needed to operate and
Digital health offers solutions to address these gaps. support digital health tools. Moreover, ethical considerations,
Telehealth has expanded access to underserved areas, and especially regarding AI use, are paramount. Without clear
virtual consultations are proven to support women in rural guidelines and regulatory frameworks, AI risks exacerbating
areas who need mental health services. Health apps such health inequities rather than reducing them. Cultural adapta-
as Eve and Flo collect critical health data about women’s tion and community trust in these technologies are also critical,
menstrual cycles and ovulation, advancing research on requiring a user-centered approach that aligns digital health
women’s health and creating awareness. And AI can help solutions with local values and health care practices. Address-
reduce the cost of R&D on target groups. These digital ing these challenges will be key to advancing equitable access
tools can help bridge R&D and access gaps, driving equity to health care in LMICs through digital innovation.
in health care for women.
4 HOW DIGITAL AND AI WILL RESHAPE HEALTH CARE IN 2025
“As smart implants become more sophisticated,
they will enable more proactive and tailored
health care.”
Diego Bernardo
Principal
Smart implants will play an increasingly significant AI-powered N-of-one studies will expand, providing
role in patient-centered care. Smart implants are ad- the computational power and advanced analytics
vanced medical devices that integrate with the body’s needed to make personalized medicine feasible. AI
biological systems to monitor, diagnose, or treat various and machine learning are key enablers of N-of-one research.
conditions in real time. These implants are equipped with In N-of-one studies, a single patient’s unique data—such as
sensors, microprocessors, and wireless communication genetic information, lifestyle habits, and continuous health
technologies, enabling them to gather critical health data monitoring—is collected and analyzed in real time. AI and
and adjust their function based on patient needs. For machine learning algorithms can process this vast array of
example, smart cardiac implants can regulate heart individual data, identifying patterns, predicting health out-
rhythms or detect arrhythmias, while glucose-monitoring comes, and optimizing treatments specific to the patient.
implants continuously track blood sugar levels for diabet- These tools allow continuous learning from a patient’s
ics. Neuro-prosthetics and brain-computer interfaces evolving responses to interventions, enabling dynamic
(BCIs) are also part of this revolution, allowing patients to adjustments to therapies based on real-time feedback.
control prosthetic limbs with their minds or even restore This personalized, data-driven approach can lead to more
motor functions in cases of paralysis. These devices offer effective treatments and improved patient outcomes,
continuous, real-time monitoring and treatment, reducing making AI and machine learning critical to the future of
the need for frequent medical interventions and improving precision medicine.
the quality of life for patients with chronic conditions. As
smart implants become more sophisticated, they will
enable more proactive and tailored health care.
BOSTON CONSULTING GROUP 5
Vocal biomarkers that detect early signs of disease
have the potential to speed interventions and im-
prove patient outcomes. Vocal biomarkers represent a
cutting-edge trend in digital health, where subtle changes in
voice patterns are analyzed to diagnose and monitor vari-
ous health conditions. By using artificial intelligence and
machine learning algorithms, vocal biomarkers can detect
early signs of diseases such as Parkinson’s, Alzheimer’s,
respiratory infections, and even mental health disorders like
depression and anxiety. These tools analyze factors such as
tone, pitch, cadence, and even micro-tremors in the voice,
offering a non-invasive, scalable method for continuous
health monitoring. Vocal biomarkers hold particular prom-
ise in telemedicine, where remote assessment is critical.
As the technology advances, voice analysis could become a
routine tool in both preventive care and chronic disease
management, offering early intervention possibilities and
improving patient outcomes through real-time data collec-
tion. This approach also aligns with the growing trend of
passive health monitoring using everyday interactions.
As AI matures, it is rapidly
expanding possibilities for
patients, providers, and health
care organizations.
6 HOW DIGITAL AND AI WILL RESHAPE HEALTH CARE IN 2025
“Resilient manufacturing processes will look to AI as a
strategic enabler, helping pharma manufacturers meet
growing demand with improved accuracy.”
Satty
Chandrashekhar
Managing Director and Partner
Drug discovery and development continue to accelerate. Quality control comes of age in pharma manufacturing.
Forward-thinking pharma and biotech organizations will Generative AI (GenAI) has started to play a transformative
continue to reshape their R&D agendas, leveraging cus- role in ensuring higher standards of quality control in the
tomized language models to improve understanding of manufacturing of pharmaceutical and medical device prod-
disease biology and accelerating processes to identify ucts. Leveraging this capability to enhance the detection and
promising compounds. Models, both commercial and mitigation of deviations in manufacturing processes, AI will
open, already present the potential to analyze vast bio- help organizations address quality issues in more standard
medical data sets to suggest novel molecular structures or ways across manufacturing facilities, many of them global,
predict drug interactions. Combined with causal modeling by analyzing vast operational data streams from production
approaches, the ability to identify clues previously undis- environments. This approach to quality control will allow
covered or underrepresented in clinical data will continue manufacturers to adjust processes, reduce waste, improve
to evolve. And in 2025, this trend will further shorten dis- yield, and increase product quality. An issue-resolution
covery cycles and reveal more promising candidates to test GenAI solution trained with historical data, for example,
in clinical settings. Clinical development will also continue has the potential to help organizations assess the effects of
to accelerate. By using AI to improve data quality, better minor changes on product outcomes, enabling companies
understand data lineage, and enable evolved uses of oper- to reimagine processes without extensive and often manual
ational and patient data to find the right sites and more trial-and-error tests. Enhancing safety while staying compli-
precise populations for clinical studies, the industry will ant with regulations is critical to this effort—and at-scale
force a reckoning with operational data readiness. can accelerate the speed at which new treatments reach the
market. Resilient manufacturing processes will look to AI as
a strategic enabler, helping pharma manufacturers meet
growing demand with improved accuracy and lower produc-
tion costs.
BOSTON CONSULTING GROUP 7
Pharma commercial enterprises will reimagine how
they make data-driven decisions. Many pharma compa-
nies continue to transform how they generate insights and
make strategic decisions. With the proliferation of real-world
data from sources like electronic health records, patient
registries, data related to social determinants of health, and
other non-traditional sources, companies are harnessing AI
to derive actionable insights at unprecedented speed and
scale. AI platforms will synthesize complex data sets into
clear insights that inform everything from market access
strategies to patient engagement and salesforce optimiza-
tion. Commercial teams will increasingly rely on predictive
models to forecast trends, identify emerging therapeutic
needs, and optimize pricing strategies in business-time.
These models will allow mature companies to pivot quickly
based on evolving global dynamics, competitor actions, and
regulatory shifts. This will lead to new operating models and
new ways of working to harness this data across the “extended
enterprise” with speed and precision—to move beyond
traditional silos and integrate information to drive more
cohesive decisions in the organization and anticipate and
respond to trends more effectively.
The growing influence of GenAI on
every aspect of health care—from
personalized care to automated
workflows—is a key theme.
8 HOW DIGITAL AND AI WILL RESHAPE HEALTH CARE IN 2025
“Consumer behavior patterns will likely force
established online health information gateways to offer
their own bespoke AI tools.”
Nick Cristea
Vice President,
Experience Design
Consumers will increasingly use AI chatbots for At-home diagnostic solutions will become a more
health questions, and a growing number of people ingrained part of how patients and providers interact.
will see them as trusted resources. A KFF poll from These tools should help simplify logistics for patients, espe-
August 2024 found that one in six adults say they use AI cially those in more rural settings. One example is TytoCare,
chatbots at least once a month to find health information a portable medical exam kit with an app that sends informa-
or advice, rising to 25% for adults under 30 years old. As tion to providers. The company’s recent announcement of a
the technology improves, these consumer behavior pat- new integration with Epic’s MyChart, in collaboration with
terns will likely force established online health information the University of Miami Health System, will leverage Tyto-
gateways to offer their own bespoke AI tools or risk losing Care for medical exams and enable asynchronous workflows
web traffic. Once providers move past their risk-averse for remote patient monitoring and primary care. With big
strategies, they will be able to start realizing significant hardware companies continuing to invest in consumer
operational efficiencies and competitive advantage by health care solutions, there will be an increasing expectation
leveraging their “clinical expert” brands to attract patients of preventive care benefits rather than simply vitals monitor-
to their AI services, while also reducing the burden on ing, and more formalized partnerships will emerge. As
humans who staff the 24/7 triaging capabilities that they clinical researchers discover new ways of detecting the early
offer. Deploying these tools first as co-pilots for their “nurse onset of disease through measurable biomarkers, patients
line” staff provides an early stepping stone for building and with a history of chronic conditions will be able to sign up
testing their capabilities and ensuring that they do not get with remote monitoring programs, which in turn will feed
bypassed in the future as patients seek greater access to more data back to the research teams. And, with the colla-
immediate answers and strategies to relieve their symp- tion of richer personalized health data, providers will gain a
toms. The providers who architect these solutions most deeper understanding of how best to automate solutions for
effectively will be able to realize a host of downstream patients or escalate to the right people at the right time,
opportunities in attracting patients, collecting data, and improving their operational efficiency while also decreasing
increasing effectiveness in triaging and routing patients to time to treatment.
the most appropriate sources of care.
BOSTON CONSULTING GROUP 9
“By interpreting and synthesizing unstructured
clinical data into actionable insights, GenAI will
streamline workflows and improve efficiency.”
Andre Heeg
Managing Director and Partner
The combination of AI, genomics, and wearable tech GenAI will revolutionize health administration by
is paving the way for highly personalized treatments. automating the creation and updating of medical
Digital therapeutics will evolve to offer precision care based records, reducing physicians’ time spent doing
on general population data and individual genetics, lifestyle, paperwork. By interpreting and synthesizing unstructured
and real-time health data. For instance, we’ll see more apps clinical data into actionable insights, GenAI will streamline
and platforms tailored to individuals managing chronic workflows and improve efficiency. This will free up more
diseases that can predict flare-ups and suggest real-time time for health care providers to focus on patient care
interventions based on continuous health monitoring. while ensuring that health records are more accurate
and comprehensive.
AI-powered decision-making tools will become
mainstream, improving diagnostics, treatment plans,
and patient outcomes. In particular, GenAI will give physi-
cians near-instant access to research insights, treatment
guidelines, and real-world evidence, allowing for more in-
formed decisions. This will significantly reduce diagnostic
errors and speed up patient care delivery.
10 HOW DIGITAL AND AI WILL RESHAPE HEALTH CARE IN 2025
“Medicine 3.0 prioritizes healthspan driven by improved
prevention, personalization, and participation.”
Iana Kouris
Managing Director
The distinction between longevity and healthspan is The concept of Medicine 3.0 and shifting to proactive
becoming more and more important. According to The approaches in health care is gaining momentum.
Longevity Imperative, by Andrew Scott of the London Business While Medicine 2.0 focuses mainly on extending lifespan
School, “In the UK in 1965, the most common age of death and disease treatment, Medicine 3.0 expands this and
was in the first year of life. Today the most common age to prioritizes healthspan driven by improved prevention, per-
die is 87 years old.” This sounds like great news. However, a sonalization, and participation, as noted in Outlive by Peter
longer life doesn’t always mean a healthy life. While the Attia. This drives demand for regular heath checks, popular-
global life expectancy has continued to rise (from 66.8 years izes prevention approaches, and leads to the emergence of
in 2000 to 72.5 years in 2020) and the healthy life expectan- new businesses in these areas.
cy (HALE or healthspan) has also increased (from 58.1 in
2000 to 62.8 in 2020), the gap between life span and Generative AI in health care is driving better access,
healthspan has actually increased by 1 year during that time personalization, and quality. In particular, GenAI can
(from 8.7 to 9.7 years). Hence, on average, we spend one support health care in the area of Medicine 3.0. For instance,
more year with disability/in poor health than we used to, it can drive personalization through chatbots, virtual assis-
according to the World Health Organization. This results in tance, and more precise analysis of health-related data. New
additional medical costs, challenges for our insurance sys- GenAI models focused on medical applications will be devel-
tems, and increased years spent with suffering/discomfort. oped that can, for example, interpret data from medical
imaging, lab results, and electronic health records to produce
written or spoken recommendations.
BOSTON CONSULTING GROUP 11
“If 2023 was about GenAI experimentation and 2024
was about point solutions, 2025 will be about value
delivery through end-to-end transformation.”
Julius Neiser
Managing Director and Partner
More than a third of ongoing GenAI programs will physician note taking or scheduling, we will witness inte-
fail to deliver value in 2025, and some health care grated systems that automate entire workflows, for exam-
players will draw the wrong conclusions from that. ple, from patient intake to treatment plans. These intelli-
Many GenAI solutions are delivering true impact by, for gent agents will coordinate across departments, learning
example, reducing medical/regulatory writing effort by 50% from each interaction to improve efficiency and outcomes.
and shaving valuable months off drug launch timelines. For example, in pharma, key processes that will be trans-
But more than a third of programs fail. The takeaway from formed with GenAI include clinical trials, regulatory sub-
these failures should not be to reduce funding, but rather missions, medical legal regulatory review, and omnichan-
to build on the lessons learned: obsess about business nel engagement.
outcomes, rigorously track key performance indicators, and
concentrate on the important people aspect of GenAI Unstructured health care data will become the new
transformations. The failure of some programs will ulti- structured health care data. Advances in AI-driven data
mately pave the way for more sustainable and impactful processing will allow systems to analyze and organize vast
transformations, driving a sharper focus on integrating amounts of medical records, clinical notes, and physician/
GenAI into existing health care workflows. patient interactions previously considered too disordered to
leverage. This shift will enable health care professionals,
We will see an evolution from health care GenAI pharma companies, payers, and providers to extract action-
point solutions to agentic end-to-end process able insights from a much larger data set, improving patient
transformations. If 2023 was about GenAI experimenta- care and outcomes. This transformation will enhance in-
tion and 2024 was about point solutions, 2025 will be teroperability across different systems, facilitating seamless
about value delivery through end-to-end transformation. collaboration between providers and empowering more
Instead of isolated AI tools focused on specific tasks like personalized and precise medical treatments for patients.
12 HOW DIGITAL AND AI WILL RESHAPE HEALTH CARE IN 2025
“GenAI chatbots will reshape the cost profile of contact
centers and drive significant efficiencies.”
Etugo Nwokah
Managing Director and Partner
GenAI chatbots will define a new table-stakes for Further innovation around clinical specialties will
payers and providers to deliver an omni-channel drive increased value-based payment arrangements
customer experience that patients and members will and end-to-end management for specific diseases.
expect. These capabilities will reshape the cost profile of Specialties such as oncology, orthopedics, and behavioral
contact centers and drive significant efficiencies. Health health will continue to see higher spending by payers,
plans will see a significant improvement in the adoption whose ability to influence and measure outcomes, well-
of self-service capabilities by their members that will drive defined populations, and episodes of care will be crucial to
major cost efficiencies, which will be reinvested to support leveraging new technology enablers such as large language
ambitious growth agendas and M&A. models (LLMs).
Organizational models that focus on digital products
and services will further evolve in hospital systems
and payer organizations of all sizes. As Chief Digital
Officer or Chief Product Officer roles become more preva-
lent in these organizations, principles around building
technology with measurable outcomes and customer-
centric experiences will overcome historical challenges of
IT functions becoming “digital feature factories” building a
lot of capabilities that no one uses.
BOSTON CONSULTING GROUP 13
“Forward-thinking health care companies will focus on
driving AI adoption internally to accelerate scaling.”
Sid Thekkepat
Managing Director and Partner
Many health care AI use cases will be slow to scale Sleep monitoring is emerging as a mainstream bio-
and drive value, leading to increased pressure on IT marker that consumers are increasingly prioritizing
and change management teams. Winning health care in their health-tracking routines; providers and life
companies will experiment with new approaches to sciences will pay more attention. As awareness grows
drive adoption. By fostering a culture of experimentation, around the critical role sleep plays in overall well-being—
collaborating with frontline staff, and prioritizing impacting everything from mental health to chronic disease
user-friendly AI solutions, these organizations can enhance prevention—health tech companies are responding by
adoption rates and realize AI’s full potential. Health care focusing on developing more advanced, accurate
companies that thrive will be those that treat adoption as sleep-tracking tools. This trend is driving innovations in
a critical aspect of AI implementation, leveraging iterative wearables, apps, and even non-invasive monitoring devices
learning and adaptive frameworks to drive sustainable value. designed to provide deeper insights into sleep patterns,
quality, and its correlation with other health metrics. Expect
As valuations drop, consolidation and M&A activity sleep tracking to become a cornerstone of personalized
will intensify in the health tech space, leading to the health solutions as tech continues to refine its capabilities.
emergence of more scaled, sustainable platforms.
Larger strategic players will seize this opportunity to
strengthen their positions by acquiring or merging with
innovative but financially constrained startups. Private
equity and venture capital firms, facing market uncertainty,
may hold back for now, allowing established companies to
lead the charge with bold and creative deals.
14 HOW DIGITAL AND AI WILL RESHAPE HEALTH CARE IN 2025
“The focus on tech-enabled mental health will continue
to grow, notably by further integrating mental health
services into primary care.”
Gunnar Trommer
Managing Director and Partner
More providers/hospitals will develop their own Products and solutions that are enabled by machine
workflow efficiency solutions leveraging GenAI, learning—and soon, GenAI—will see accelerated
mainly aiming at reducing the administrative burden adoption in diagnosing diseases, analyzing medical
on clinicians and improving workflows. As long as imaging, and predicting patient outcomes. AI-powered
providers develop tech-enabled solutions for their own "
285,bcg,digital-government-in-the-age-of-ai-championing-gcc-next-gen-citizen-services.pdf,"Digital Government in the
Age of AI: Championing GCC
Next-Gen Citizen Services
November 2024
By Rami Mourtada, Dr. Lars Littig, Miguel Carrasco, Semyon Schetinin, Akshara Baru
Contents
01 GCC digital government services 03 Citizen sentiment remains
lead globally while citizen the key to digital government
expectations are heightened service adoption
• What usage and satisfaction
levels reveal about GCC digital
government services
04 Government AI: A clear
path forward
02 AI and GenAI present • From promise to leadership: How GCC
governments can capitalize on progress
new opportunities for
GCC governments
• GCC public investment as a launchpad
for AI leadership
• Government investment in AI
and GenAI aligns with widespread
citizen adoption
• Seizing the GenAI opportunity while
addressing potential challenges
About the Digital Government
Citizen Survey in GCC
Conducted every other year, the Digital with The Kingdom of Saudi Arabia (KSA)
Government Citizen Survey (DGCS) and the United Arab Emirates (UAE)
is the most comprehensive and long- participating in the survey since its
running survey of global citizens on inception and Qatar joining in 2020.
digital government, with data spanning
Digital government services include 27
a decade from 2014 to 2024. The 2024
priority high-touch and citizen-facing
study surveyed 41,600 regular internet
online services across social services,
users (respondents) across 48 global
taxation, housing, health, education,
countries, representing 73% of the
transport, and immigration.
world’s population.
This report focuses on digital
government services in the GCC region,
Introduction
T
he significance of digital government services in
addressing citizens’ needs globally cannot be
overstated. These services are vital to support
individuals through key life events, from registering a
birth to healthcare and education, to public safety and
job support, social assistance and pensions, and more.
Digital government services also streamline critical
business processes like registering a company, filing
taxes, and ensuring regulatory compliance. They are
integral to the broader functioning of a nation and its
economy, acting as the backbone that ensures
societal and economic systems operate smoothly,
quickly, and efficiently.
Citizens’ satisfaction with their government services
and their experiences accessing them translate quickly
into positive overall perceptions of government
effectiveness and support. As such, building and
operating seamless digital offerings to better serve
citizens’ evolving needs should be a key priority for
governments and recognized as a core driver of
socioeconomic development. Innovation and
technological advancements, in this regard, provide
governments with vital opportunities to do even better.
This report showcases citizens’ experiences with digital
government services within the Gulf Cooperation Council
(GCC) region, based on data from BCG’s flagship 2024
Global Digital Government Citizen Survey (DGCS). A
particular focus in this edition was on citizens’ attitudes
towards government use of Artificial Intelligence (AI), and
especially Generative AI (GenAI).
1. “Citizen/s” refers to respondents who are nationals or international residents living within each country
1 DIGITALGOVERNMENTINTHEAGEOFAI:CHAMPIONINGNEXT-GENCITIZENSERVICESINTHEGCC
01. GCC digital government
services lead globally while citizen
expectations are heightened
P
revious surveys have shown that GCC governments technologies like AI and GenAI (discussed further
receive strong citizen approval for their digital below) are increasing citizens’ expectations in terms
services. This positive trend continued in 2024, of personalization and improved user experience.
as GCC countries maintained their global lead in This year’s data also highlights a strong link between
satisfaction with a net approval score of 81%2 [Exhibit 1], satisfaction levels and the perception that government
significantly higher than the global net average of 65%. digital services are on par with those of the private sector,
which are typically best-in-class. This underscores the
While this year’s survey has reconfirmed cumulative trends need for government services to match the efficiency
from past editions, it has also shown an evolution in citizen and effectiveness of the private sector while fostering
expectations. Accelerated digitalization and newer continued innovation in the future.
2. “Netexperience”ispositiveexperienceminusnegativeexperience.BCGAIRadar,FromPotentialtoProfitwithGenAI,2024
2 DIGITALGOVERNMENTINTHEAGEOFAI:CHAMPIONINGNEXT-GENCITIZENSERVICESINTHEGCC
Exhibit 1 | GCC countries lead globally in citizen satisfaction with
government digital services, especially when they match or exceed
private sector standards
Q. How satisfied are you with the use of the internet in delivering various types
of government services?
Indonesia
UAE
75 Thailand New Zealand Estonia Egypt Singapore
Australia Netherlands Italy Philippines Hong Kong-China Vietnam
Kenya Norway Canada Greece
Brazil Sri Lanka
Ukraine Spain Nigeria Mexico Bangladesh
Chile Turkey
50 Argentina
Morocco Switzerland
Malaysia
Laggards Emerging Leaders
30
0 35 70
Perception of government digital services compared to private-sector services2 (%)
Q. Are government online services better
than those offered by the private sector?
1. Net satisfaction is the percentage of satisfied respondents minus the percentage of dissatisfied respondents.
2. Respondents that agree government online services are better compared to those offered by private sector.
Source: 2024 BCG Digital Government Citizen Survey.
3 DIGITALGOVERNMENTINTHEAGEOFAI:CHAMPIONINGNEXT-GENCITIZENSERVICESINTHEGCC
latigid
htiw
1noitcafsitas
teN
)%(
secivres
tnemnrevog
KSA Qatar
Cambodia
Kazakhstan
This year’s survey shows citizens’ increased interest and What usage and satisfaction levels reveal
comparison to leading players in both private and public about GCC digital government services
sectors. For instance, while 42% of GCC respondents (a
steady proportion since 2022) expect quality standards It is important to note that while citizens expect
similar to those of global digital leaders, including top increasingly higher standards for digital government
high-tech private-sector companies. Addiotionally, 23% of services, the consistent growth in satisfaction levels in the
GCC respondents (4% higher than 2022) expect quality GCC is reflected in continued widespread usage. Trending
standards to match those of the best online government upward since 2022, the GCC records the highest usage
services globally, indicating citizens are rates of digital government services globally in 2024
more aware of global best practices across both private [Exhibit 2]. This overall usage level is a remarkably positive
and public sectors. indicator for governments in the GCC. However, examining
how both the frequently and less frequently used services
perform reveals what is going well and what still
needs improvement.
Exhibit 2 | Government service usage in GCC countries is well above
global average (+22%) with an upward uptake trend since 2022
Q. How often do you access government services online?
% of respondents across regions (2022 to 2024)
+4%
+2%
-2%
+22%
+5%
45%
Global
63 67 Average
54 56
46 44
33 38
Asia-Pacific Europe NorthAmerica GCC
2022 2024 Global Average
1. Respondents using government services at least once a week or more.
Source: 2024 BCG Digital Government Citizen Survey.
Overall, satisfaction scores of high-usage digital services frequently used services highlights ongoing or new user
(see service ranking in exhibit slides) are higher than the experience challenges that require attention. Addressing
average satisfaction across all services in the GCC. This these gaps, especially in less-used services, will reduce the
indicates that governments are focusing on improving risk of eroding satisfaction and trust (more on this year’s
these more frequently used services with higher repeat UI/UX challenges below).
value. However, the below-average satisfaction with less
4 DIGITALGOVERNMENTINTHEAGEOFAI:CHAMPIONINGNEXT-GENCITIZENSERVICESINTHEGCC
02. AI and GenAI present new
opportunities for GCC governments
T
he next generation of digital services for governments AI has rapidly become a global game changer, rapidly
will be driven by the newest emerging technologies, expanding the scale and scope of relevant use cases,
including AI and GenAI. These technologies will and accelerating adoption across organizations. Most
enable a whole new range of delivery methods and a interestingly, GenAI has emerged as an important
variety of high-value service use cases, from personalized transformational AI technology for direct citizen services.
recommendations and proactive nudges to more advanced Intuitive GenAI tools like OpenAI’s ChatGPT and Google’s
chatbots that will make usage easier and more productive, Gemini have seen rapid and widespread adoption around
reducing the burden on the citizen. the globe and across industries and users. And just as
GenAI is driving productivity and competitive advantage
• AI: The use of computer systems to perform tasks traditionally in private-sector customer service, it is also starting to
requiring human intelligence, such as learning, reasoning, transform digital government service quality and
problem-solving, and language. citizens’ experiences.
• GenAI: A type of AI that uses foundational, multi-modal
models to generate novel content, including text, images,
and audio/video, and supports interaction using natural
language prompts.
5 DIGITALGOVERNMENTINTHEAGEOFAI:CHAMPIONINGNEXT-GENCITIZENSERVICESINTHEGCC
GCC public investment as a launchpad development. Governments are integrating advanced
for AI leadership technologies like AI and IoT into public services while
also investing in AI-driven economic development. Saudi
A recent global BCG report estimates that GenAI has Arabia’s National Strategy for Data and AI, which aims to
the potential to automate 10–20%3 of an employee’s contribute SAR 500 billion ($133.3 billion) to GDP by 2030,
everyday tasks, freeing up time for more strategic work. is focused on developing AI capabilities, data governance,
In this context, it can also help make government services and analytics5,6. Recent developments include Saudi
simpler, more accessible, and more personalized. In the Arabia’s early adoption of UNESCO’s recommendations on
public sector, BCG estimates a $1.75 trillion4 annual AI ethics and the establishment of the International Center
productivity opportunity over the next decade. By delivering for Artificial Intelligence Research & Ethics (ICAIRE), which
services more efficiently and effectively, GenAI allows aims to foster ethical AI practices, support policy
governments to address backlogs, allocate further resources development, and ensure responsible AI implementation
to human-centric support, and maximize the value and across sectors. Qatar is driving its digital growth through
impact of public funds. However, appropriate AI safeguards collaboration with institutions like Qatar University and
are still needed to safely achieve these benefits, reduce tech providers to upskill ICT professionals in AI, 5G, and
potential risks (discussed below), and retain citizen trust. cloud computing7. With the Falcon LLM’s open-source
nature and cutting-edge performance, the United Arab
GCC countries have undertaken extensive investment, Emirates is positioning itself as a global AI leader; further
partnership, and upskilling efforts to leverage this bolstering its position by forming strategic alliances with
opportunity for government advancement, citizen tech giants to establish secure data centers and leverage
satisfaction, private-sector growth, and national the immense potential of data8.
Exhibit 3 | GCC countries have highest overall usage of AI and GenAI tools
compared to other regions globally
Q. How frequently1 do you use AI/GenAI2 tools ?
% of respondents by regions (2024)
79%
75%
64%
50% 50%
36%
37%
33%
27% 30%
24%
20%
Asia-Pacific Europe NorthAmerica GCC
Regional avg. AI/GenAI usage range within the region
1. Accessing digital government service and GenAI tools at least once a week or more.
Source: 2024 BCG Digital Government Citizen Survey.
3. BCGAIRadar,FromPotentialtoProfitwithGenAI,2024
4. BCG Article, Generative AI for the Public Sector: From Opportunities to Value, 2023
5. https://oxfordbusinessgroup.com/reports/saudi-arabia/2023-report/ict/digital-drive-strong-government-support-and-foreign-investment-are-
6. https://www.idc.com/getdoc.jsp?containerId=prMETA51181123
7. https://oxfordbusinessgroup.com/reports/qatar/2022-report/economy/digital-drive-both-the-public-and-private-sectors-turn-to-online-solutions-
while-the-country-taps-emerging-segments-such-as-e-sports
8. https://www.reuters.com/technology/uae-us-see-more-ai-partnerships-uae-minister-says-2024-05-21/#:~:text=The%20UAE%2C%20led%20by%20
government,activity%20outside%20the%20United%20States.
6 DIGITALGOVERNMENTINTHEAGEOFAI:CHAMPIONINGNEXT-GENCITIZENSERVICESINTHEGCC
Government investment in AI and GenAI government services, overall citizen satisfaction, and
aligns with widespread citizen adoption citizens’ trust in their government to use AI responsibly
(see slide 5 in exhibit slides).
GCC citizens are significantly more familiar with GenAI
than those in other regions, as reflected in their higher In GCC countries, which have high citizen satisfaction
general usage of GenAI tools [Exhibit 3]. This creates a scores for digital government services, citizens trust their
strong opportunity for GCC governments to accelerate governments even more than private sector entities to
the integration of AI and GenAI into their digital services. use AI responsibly. The net average trust in governments’
responsible use of AI is 71% across GCC countries – 49%
Citizen trust is imperative for governments to successfully higher than the global average – compared to 52% for
leverage AI and GenAI in digital services, especially given the private sector [Exhibit 4]. This trust gives GCC
GCC citizens’ heightened familiarity with GenAI tools. governments the opportunity to rapidly yet safely
Without trust, people are less likely to engage with AI- deploy GenAI to further enhance service efficiency,
driven solutions or features, squandering many related accessibility, and personalization, thereby fostering
service improvement opportunities. Interestingly, the data even greater trust and satisfaction.
shows a positive link between the quality of digital
Exhibit 4 | Respondents trust governments more to use AI/GenAI
responsibly than private sector, with trust in GCC countries exceeding
the global average
Q. To what extent do you trust organizations to use AI/GenAI responsibly?
Distrust Trust Net Difference to
trust1 global average
Overall
Regional 4 9 49 35 71 % +49 %pts
Government
National 4 8 32 54 74 % +51 %pts
State 5 10 37 44 +19%pts 66 % +15 %pts
Local 3 9 37 49 74 % +12 %pts
Regional 5 17 45 29 52 % +22 %pts
Private Sector
Highly distrust Somewhat distrust Somewhat trust Highly trust
1. Net trust is the percentage of positive trust minus the percentage of negative trust among respondents regarding the government's use of AI/GenAI.
Note: Responses with option ""I don't know"" are not represented.
Source: 2024 BCG Digital Government Citizen Survey
7 DIGITALGOVERNMENTINTHEAGEOFAI:CHAMPIONINGNEXT-GENCITIZENSERVICESINTHEGCC
Seizing the GenAI opportunity while GenAI for innovation, most are still in the early stages
addressing potential challenges of their journey, with only 8% applying it at scale.
The lightning speed of GenAI’s technological advancement Second, governments face citizen concerns as they assess
and its widespread global adoption come with several their pilot AI and GenAI use cases. In this year’s survey,
challenges. First, governments need to move fast enough citizens shared a number of these concerns [Exhibit 5],
to keep up in an effective and responsible way, despite including potential economic and legal issues. Citizens
rarely innovating this quickly. For example, BCG’s latest also cited the need to address technology adoption-related
Most Innovative Companies report9 ,which also assessed limitations like user capabilities and service accuracy
the public sector’s readiness to leverage GenAI, reveals to ensure high-quality outcomes. A further area of
it to score among the lowest on innovation readiness notable concern relates to ethical and social issues,
compared to other industries, and overall readiness has including the potential for service bias or discrimination
declined noticeably over the past two years. Globally, while and lack of transparency.
83% of public sector organizations are beginning to use
Exhibit 5 | While GCC respondents have high AI/GenAI usage, they have
material concerns across multiple important areas related to
Responsible AI
Q. What concerns you the most about the use of AI/GenAI?
Category Key concerns % of Respondents
Economic The potential loss of jobs and impact to the economy
andlegal
concerns The potential intellectual property risks
The capability of individuals to use AI
Technical
The accuracy of the results and analysis
concerns
The large volumes of data needed
The moral or ethical issues have not been resolved
Ethicaland
social The potential for bias or discrimination
concerns
The lack of transparency
Noconcerns I don't have any concerns
00 10 20 30 40
GCC Global range and average
Note: The data represents the top two concerns selected by respondents.
Source: 2024 BCG Digital Government Citizen Survey
It is therefore important for governments to be discerning capabilities across government organizations. Safeguards,
in how they use GenAI for productivity and efficiency including increased regulation and transparency and
gains, applying careful consideration and a robust, focused skill and talent development are especially
responsible AI framework10. They need to address the relevant in GCC countries, where governments are often
risks when integrating GenAI into their services or when major employers at the national level.
setting overall national AI strategies and building GenAI
9. BCG Research, Innovation Systems Need a Reboot, 2024
10. BCG Experience, Responsible AI | Strategic RAI Implementation | BCG
8 DIGITALGOVERNMENTINTHEAGEOFAI:CHAMPIONINGNEXT-GENCITIZENSERVICESINTHEGCC
03. Citizen sentiment remains
the key to digital government
service adoption
O
ver the past decade, GCC governments have First, governments must focus on maintaining high levels
successfully delivered impactful digital services of citizen satisfaction and trust, finetuning their service
to serve citizen needs, as evidenced by high levels features, functionality, and ease of use to enhance citizens’
of adoption and satisfaction. Meanwhile, citizens’ digital experiences. A comparison with 2022 data reveals
expectations continue to increase as the global technology a 5% improvement in the proportion of respondents who
landscape rapidly advances. As with any successful digital experienced no problems while using digital government
product, governments must continue to evolve their services. However, 72% of GCC survey respondents still
services to keep pace with demands for customer- encounter issues—for example, 26% experienced technical
centricity, personalization, and other enhancements difficulties completing requests, and 24% deemed the
enabled by emerging technologies. This presents overall process too long or difficult [Exhibit 6].
governments with a twofold challenge.
9 DIGITALGOVERNMENTINTHEAGEOFAI:CHAMPIONINGNEXT-GENCITIZENSERVICESINTHEGCC
Exhibit 6 | Percentage of survey respondents experiencing no issues
increased by %5 since 2022, albeit challenges remain across digital
government services
Q. Which of the following problems have you encountered while using digital government services?
Problems Encountered by % of Respondents (2022-2024)
I experienced technical difficulties or issues completing my request 3
+ %
The overall process took too long or was too difficult
share of
I couldn’t remember my username or password
respondents
who found their
I needed help but none was available at the time
service online
I didn’t have all the information or paperwork
15% 5
The service I needed is not available online + %
18%
I didn’t understand the instructions or didn’t know what to do share of
respondents
I could not find what I needed who experienced
no issues
28%
I have experienced no problem or issue 23%
0 10 20 30
2022 2024
Source: 2024 BCG Digital Government Citizen Survey.
These concerns can be addressed through strategic GenAI solutions. A crucial enabler for GCC governments
interventions. For example, further expanding Digital becoming global leaders in the AI-enabled digital service
IDentity (DID) efforts simplifies access, eliminates issues space is that citizens have shown a globally leading
like forgotten passwords, and eases navigation between (by a wide margin) level of trust, and they are comfortable
services. GenAI solutions, such as proactive service with a range of AI use cases in digital government
suggestions, can enhance overall service adoption, services [Exhibit 7].
while cutting-edge virtual chatbots and dynamic
assistance allow for personalized support and effective For example, GCC respondents are most comfortable
troubleshooting with 24/7 citizen accessibility. with customer support and engagement use cases, with
an average comfort level of 83%, which is 16% higher than
Distinguishing services based on frequency of use can help the global average of 67%. GCC respondents are similarly
governments make informed decisions about where to comfortable with government use of GenAI for tech
focus resources for improvement. This strategic approach development and operational efficiency, reporting a 19%
ensures that governments prioritize areas with the highest higher comfort level than the global average, at 80%
impact on citizen satisfaction and service efficiency while compared to 61%. For public relations and
ensuring improvement across their entire service portfolio. communications, the comfort level is 79%, also 19% higher
than the global average of 60%. These findings consistently
Perhaps most importantly going forward, governments express GCC citizens’ greater comfort with AI and GenAI
should identify top-priority use cases for deploying AI and use across multiple areas than their global counterparts.
10 DIGITALGOVERNMENTINTHEAGEOFAI:CHAMPIONINGNEXT-GENCITIZENSERVICESINTHEGCC
Exhibit 7 | GCC respondents are comfortable with a wide range of
AI/GenAI use cases, at a level significantly higher than the global average
Q. How comfortable you are with the government using AI/GenAI across use cases?
Categories AI/GenAI use cases Net comfort1 level % (GCC) Comfort GCC Vs Global
Difference in avg com level %
Multilingual citizen communication -15 82 67%
Customer Personalization of citizen services -16 81 65%
service and 16
24/7 chatbot access -13 84 71% + %pts
support
Virtual agent support -13 84 71% 67% 83% Avg Difference
Difference in avg com level %
Tech Software development -16 81 65%
development
Administrative tasks -16 81 65% 19
and operational + %pts
efficiency Service decision support -20 77 57% 80% Avg Difference
61%
Difference in avg com level %
Public
engagement Public information campaigns -17 80 63%
19
and + %pts
Social sentiment analysis -20 77 57%
communications 79% Avg Difference
60%
- Not Comfortable - GCC Average
- Comfortable - Global Average
1. Net comfort is the percentage of respondents who feel comfortable minus the respondents who feel uncomfortable with the government's use of AI/GenAI across the use cases.
Note: Responses with option ""I don't know"" are not represented.
Source: 2024 BCG Digital Government Citizen Survey
Governments should also assess pilot use cases for the governments optimize resources through intelligent,
greatest benefit to citizens’ experience and thus the focused staffing, where reducing manual intervention in
highest return on investment. Behind the scenes, GenAI repetitive tasks, for example, can facilitate the swift and
drives faster code development for technology solutions, effective reassignment of roles. BCG estimates that by
enabling quicker updates to meet changing user needs 2033, GenAI in the public sector workforce could yield
and resulting in more efficient service delivery and more than $65 billion in annual productivity gains
reduced administrative wait times. GenAI will also help across the GCC11.
11. BCG Article, Generative AI for the Public Sector: From Opportunities to Value, 2023
11 DIGITALGOVERNMENTINTHEAGEOFAI:CHAMPIONINGNEXT-GENCITIZENSERVICESINTHEGCC
04. Government AI:
A clear path forward
W
hile GCC citizens already report high levels of The “black box” nature of many GenAI models
trust in their governments’ use of AI, there are makes it difficult to understand how decisions are
still risks that need to be addressed. Ongoing made, reducing transparency and accountability. In
efforts to maintain and enhance safeguards and providing government services, current GenAI models
transparency should further bolster citizens’ confidence can struggle with language nuances and context,
in AI applications. The survey suggests that there is no potentially leading to misunderstandings.
single key factor that will drive a significant increase in
citizens’ trust in government use of AI, but rather a holistic To address these, specific laws and regulations
and studied set of actions, regulations, and initiatives. governing AI use can be implemented to provide
a clear legal framework that assures citizens of the
As a fairly recent technology, GenAI brings several ethical deployment of AI technologies. When asked
adoption- and design-related risks. Bias in AI algorithms which AI regulations and policy considerations would
can perpetuate and even amplify existing inequalities, increase their trust, GCC respondents were generally
leading to unfair outcomes for citizens. AI systems can on par with the overall global sentiment.
also produce “hallucinations,” generating incorrect or
nonsensical information, which can mislead users.
12 DIGITALGOVERNMENTINTHEAGEOFAI:CHAMPIONINGNEXT-GENCITIZENSERVICESINTHEGCC
It is interesting to note that introducing specific laws and communication on the benefits and risks of AI (30%),
regulations on government use of AI, and establishing mandatory reporting of adverse AI-related incidents (27%),
clear rules on how Personally Identifiable Information and disclosure of AI use in government process (27%), were
(PII) is used and protected, were slightly higher on the also important to citizens in the GCC.
priority agenda for citizens globally, while gaining 37%
and 32% average support respectively from citizens On a closing note, governments today enjoy strong
in the GCC [Exhibit 8]. support for responsible AI adoption. Ninety-four percent
of respondents in the GCC and 90% of respondents
Nonetheless, 30% of GCC citizens believe that applying globally believe that implementing at least one of the
fairness and safety measures through rigorous testing, and proposed safety and regulatory measures could increase
regular AI systems audits, are critical to build trust. Other their trust in government AI adoption.
initiatives to increase transparency, such as open
Exhibit 8 | 94% of GCC respondents say government can build trust in
AI/GenAI with key actions related to regulation, communication,
and transparency
Q. Which of the following would increase your trust in the use of AI/GenAI
by governments?
% of respondents in GCC countries (2024)
48% 47%
42% 40% 20%
34% 33%
% of GCC
37%
respondents
32% 32%
30% 29%
27%
26%
20%
28% 18% 20% 10%
15%
6%
3%
Factors that Specific laws and Rules on how Communication The application Mandatory reporting Disclosures if AI has Nothing would
increase trust regulations on how personally identifiable about the of fairness and of AI-related adverse been used in a influence my trust
AI can be used by information must be potential benefits safety measures events, breaches or government process in government to
the government safeguarded and and risks of AI incidents or decision making use AI responsibly
protected
Global average Global range Regional average
Source: 2024 BCG Digital Government Citizen Survey.
From promise to leadership: How GCC in the GCC. However, with accelerated global
governments can capitalize on progress advancements in digital services, especially in AI and
GenAI, GCC citizens’ expectations are evolving rapidly.
GCC governments have already made remarkable progress While they continue to benchmark their government
in delivering digital services with world-leading levels of services against global private-sector leaders, a notable
adoption and citizen satisfaction. They are well prepared— shift indicates that 98% now expect government digital
through supportive national strategies, matching services to rival the best private and public sector
investments, and citizen trust in their ability to do so platforms worldwide. This points to a new era of
responsibly—to expand their adoption of AI and GenAI. heightened expectations, which governments must
adapt to quickly.
Over the years, effective digital service delivery strategies
have achieved an 81% net approval score of from citizens
13 DIGITALGOVERNMENTINTHEAGEOFAI:CHAMPIONINGNEXT-GENCITIZENSERVICESINTHEGCC
Moreover, the trust GCC citizens place in their 3 Accelerate: the identification and adoption of
governments regarding AI remains an unparalleled winning AI and GenAI use cases where citizens
opportunity. Their 71% trust level in governments’ are most comfortable and where adoption can
responsible use of AI far exceeds global averages. This generate the highest impact on citizen experience
paves the way for GCC governments to further integrate and satisfaction.
AI into service delivery, leveraging this trust to introduce
transformative next-generation solutions. The challenge 4 Focus: on the trust-building actions citizens favor by
now is for governments to act swiftly and responsibly, setting a balanced strategic course, collaborating with
capitalizing on citizens’ expectations while ensuring the all relevant stakeholders to implement responsible AI
necessary safeguards to maintain and build upon their frameworks, and quickly introducing comprehensive
trust. By doing so, GCC governments can secure their guardrails to advance AI adoption.
leadership position in the digital public sector landscape
in a new responsible AI-enabled era. The time to take action is now. Tremendous opportunities
await the GCC governments that can capitalize on their
We see four steps to guide governments’ path forward: current digital service momentum, keep pace with AI
and other fast-moving technology, to exceed citizen
1 Innovate: continuously to keep pace with heightened expectations and maintain their trust and confidence.
citizen expectations and a rapidly advancing global
technology landscape.
2 Prioritize: addressing lingering service usability
and user experience issues to maintain high levels
of satisfaction. This includes diversifying investment
and improvement efforts across their entire portfolio
of digital services.
14 DIGITALGOVERNMENTINTHEAGEOFAI:CHAMPIONINGNEXT-GENCITIZENSERVICESINTHEGCC
About the Authors
Rami Mourtada is a Partner and Director in the firm’s Dr. Lars Littig is a Managing Director & Partner at the
Middle East offices. He leads BCG’s digital transformation firm’s Middle East offices. He is the EMESA Leader of
topic in the Middle East and is part of BCG’s Center for BCG’s Center for Digital Government. You may contact
Digital Government. You may contact him by email at him by email at Littig.Lars@bcg.com.
Mourtada.Rami@bcg.com.
Semyon Schetinin is a Managing Director and Partner in
Miguel Carrasco is a Managing Director and Senior BCG’s Middle East offices. He has vast experience in
Partner in BCG’s Sydney office. He is the global leader and technology & digital topics in Public Sector, TMT and other
founder of BCG’s Center for Digital Government and can industries. You may contact him by email at
be reached by email at Carrasco.Miguel@bcg.com. Schetinin.Semyon@bcg.com.
Akshara Baru is a Senior Knowledge Analyst in BCG’s
London office and provides worldwide support for the
Digital Government practice.
For Further Contact Acknowledgments
If you would like to discuss this report, please contact The authors are grateful to their BCG colleagues, whose
the authors. insights and experience contributed to this report. In
"
286,bcg,BCG_Most-Innovative-Companies-2023_Reaching-New-Heights-in-Uncertain-Times_May-2023.pdf,"Most Innovative Companies 2023
Reaching New Heights
in Uncertain Times
May 2023
Contents
01 T he Formula for Innovation from 14 How Early Winners Are
Leading Companies Unlocking AI’s Potential
• What Winners Are Doing • From Implementation to Impact
• Bosch: A Culture of Innovation • H&M: Leveraging AI and Human Input
for Amplified Intelligence
• The 50 Most Innovative Companies
of 2023 • Four Impact Success Factors
• Innovation and Performance • Moderna: Pioneering AI-Driven
Innovation in the Fight Against Cancer
• Samsung: Leading the
Commercialization of Consumer Tech
19 Methodology
07 A Downturn Ups the Stakes in
Innovation
24 About the Authors
• A New Outlook?
• Investing with Focus
• McDonald’s: Driving Growth with 25 Acknowledgments
Digital Innovation
• Good Advice (Updated) Stands
the Test of Time
The Formula for Innovation from
Leading Companies
By Justin Manly, Michael Ringel, Amy MacDougall, Will Cornock, Johann Harnoss, Konstantinos Apostolatos,
Ramón Baeza, Ryoji Kimura, Michael Ward, Beth Viner, and Jean-Manuel Izaret
F
or the third straight year, the evidence is mounting: In this year’s Most Innovative Companies report, we
companies that both prioritize innovation and make examine what innovation-ready leaders (those that are ready
sure that they are ready to act are widening the gap to develop product, process, and business model innovations
over less capable competitors. The leaders at these firms that can deliver sustainable impact) are doing to pull ahead
are consistently delivering new products, entering new and how innovation is building their resilience to economic
markets, and establishing new revenue streams. The uncertainty and fueling their pursuit of lower emissions. In
laggards struggle to make headway beyond incremental “A Downturn Ups the Stakes in Innovation,” we explore how a
improvements. potential downturn in 2023 is evoking a much different
response than did the 2009 financial crisis, especially among
This year, the findings from our global innovation survey leading firms. In “How Early Winners Are Unlocking AI’s
dovetail with other new BCG research showing that compa- Potential,” we dig into the critical role of artificial intelligence
nies built for the future share a common set of attributes (AI) in innovation as in many other areas of business today.
that enable them to exhibit superior performance, be more
resilient to shocks and disruptions, and exploit innovation
faster for value-creating growth. In addition to people and
technology capabilities (including, importantly, AI), one of
these attributes is an innovation-driven culture.
BOSTON CONSULTING GROUP 1
What Winners Are Doing ambitions. Last year, we examined companies’ readiness in the
context of climate and sustainability (C&S), which two-thirds of
Despite global economic uncertainty, innovation rose as a top companies ranked as a top corporate priority. Only about one
corporate priority in 2023, with 79% of companies ranking it in five companies was ready to take effective action.
among their top three goals. (See Exhibit 1.) This is up from
75% in 2022 and close to 2019’s high of 82%. The top areas of This year, two out of three ready companies rank innovation as
innovation emphasis are new products and exploring adja- their top priority, and 90% expect to increase spending—
cent business models. Cost is a key driver for 62% of respon- almost all by more than 10%. (See Exhibit 2.) Moreover, while
dents and a top reason for innovation. Companies remain all companies on average expect to allocate more money
bullish on their innovation prospects: 42% expect to signifi- toward incremental innovations close to the core (an under-
cantly increase spending this year, a jump of 16 percentage standably conservative approach in uncertain times), ready
points over the last economic downturn in 2009. companies are allocating fully one-third of spending toward
developing breakthrough innovations. Expanding into adjacent
These are impressive figures, especially in the current macroeco- business models is also a priority in 2023. And 89% of ready
nomic and geopolitical environment. But there is also an emerg- companies prioritize C&S compared with 58% of all companies.
ing group of companies that is going much further and putting
innovation front and center in their future growth strategies. Ready companies use a wide array of strategic tools to
strengthen their innovation platforms and practices. They
Two years ago, as the world began to emerge from the access capabilities and expertise from outside their own walls,
pandemic, we observed that successful innovation takes three and they have systems in place to leverage these tools for
things: making innovation a priority, committing investment value. These companies are much more aggressive in their
and talent to it, and being ready to transform investment into use of M&A, for example, targeting innovative technologies or
results. We found that only about one company in four was processes or acquiring leaders and employees with a demon-
“innovation ready”—that is, it met all three criteria, particu- strated ability to innovate. They are also more likely to involve
larly possessing the elements of leadership and teaming that innovation experts in target analysis and selection.
enable effective execution of a company’s innovation
Exhibit 1 - Nearly 80% of Respondents Named Innovation as a the Top-Three
Priority, While Two-Thirds of Ready Companies Ranked It as Top Priority
Where does innovation, R&D, and product development rank among your Respondents who cite innovation,
company’s priorities? (%) R&D, and product development as
their company’s top priority (%)
35
82
76 77 75 79 76 77 75 75 79
72 71
66 66 66 64 65
47
32 47 42 42 46
45 52 53 53 57 53
47 43 43 39 42 65
19 40 23 23 25 26 24 24 22 22 23 30 35 23 33 33 33 30
2005 2006 2007 2008 2009 2010 2012 2013 2014 2015 2016 2018 2019 2020 2021 2022 2023 Unready Ready
Top 3 Priority Top Priority
Sources: BCG Global Innovation Survey 2023; BCG analysis.
Note: n = 1,023 for global respondents. No data for 2011 and 2017 available. Totals may not sum due to rounding. “Ready” companies are those that
are ready to develop product, process, and business model innovations that can deliver sustainable impact.
2 MOST INNOVATIVE COMPANIES 2023
Exhibit 2 - Nearly 90% of Ready Companies Plan to Increase Innovation
Spending
How will your company's innovation, R&D, and product development spending change this year in response to
macroeconomic factors? (%)
32
30
26
23
22
16
15
11
10
7
6
3
Increase Increase by Increase by Increase slightly No change Decrease
by >30% 21% to 30% 11% to 20% (5% to 10%) (<5% change)
Increase No Change Decrease
Ready Unready
Sources: BCG Global Innovation Survey 2023; BCG analysis.
Note: n = 1,023 for global respondents. Totals may not sum due to rounding. “Ready” companies are those that are ready to develop product,
process, and business model innovations that can deliver sustainable impact.
For similar reasons, they also are more likely to orchestrate or strategy is its centralized Bosch Research unit. With 1,800
participate in ecosystems, engaging with external partners, highly specialized employees, this unit generates about a
even competitors, on innovations. They determine what they quarter of all Bosch patents. Bosch Research focuses on
need, whether it’s technology, data, or something else, and enabling technologies that can be applied across The Bosch
then work out the most effective way to access it. Group, such as AIoT, which combines AI and the Internet of
Things, to move from fundamental research to actual
They drive digital innovation with a clear bias towards new product innovation and large-scale commercialization.
digital products, agile teaming, and improving customer and Bosch builds on a broad ecosystem of internal business
marketing insights. They regularly review the performance of units and external partners to generate innovation ideas.
innovation units or vehicles (such as venture capital funds,
accelerators, incubators, and R&D) and shift resources toward While three-quarters of R&D spending has been devoted to
centers of success. And they understand that effective portfolio the company’s Mobility Solutions business and topics such
governance and management, especially with respect to data as electrification, driver assistance systems, semiconductors,
transparency, are key to driving impact. and sensors, Bosch supplements internal R&D investments
with targeted acquisitions to support high-priority areas, such
as its automated driving product portfolio.
Bosch: A Culture of Innovation
In 2022 alone, the company made three investments to
The Bosch Group (number 37 on the 2023 Most Innovative acquire IP for the next generation of mobility, consistent
Companies list) states in its annual report that “the basis with its goal of making Bosch a one-stop shop for “all the
for the company’s future growth is its innovative strength.” necessary building blocks of automated driving—from
While Bosch has a special ownership structure that actuators and sensors to software and maps,” according to
facilitates long-term planning and up-front investments, Mathias Pillin, president of the Cross-Domain Computing
it is a strong culture of innovation that underpins. Solutions division.
Bosch has a global R&D organization of about 84,800 For example, Bosch’s Semiconductor Ideas to the Market
employees, 44,000 of whom are software developers, in 130 team specializes in high-frequency-processing “System-on-
locations. From 2018 through 2021, the company has Chips” used in control units for the automotive industry. Its
maintained steady R&D spending as share of sales at FiveAI unit provides a modular cloud platform designed for
between 7.6% and 8.2%. A core pillar of Bosch’s innovation building software components and development platforms
BOSTON CONSULTING GROUP 3
for safe automated driving systems, particularly supporting These new bases of advantage are rooted in superior capa-
solutions used in complex urban environments. “We want bilities, especially those related to digital, AI, and innovation.
Five to give an extra boost to our work in software develop- These capabilities are more difficult to establish but much
ment for safe automated driving,” said Markus Heyn, more enduring for two reasons. First, technology is evolving
member of the Bosch board of management and chair- rapidly, and proficiency in a technology today, such as
man of the Mobility Solutions business. Bosch’s Atlatec AI, means that as the technology grows more powerful,
team, meanwhile, creates high-resolution digital maps that a company can be faster at deploying it. Second, compa-
are critical to automated driving functionality. nies that have these capabilities benefit from a flywheel
effect: they can invent, deploy, adapt, and reinvent more
The 50 Most Innovative Companies of 2023 quickly and with greater impact than their competitors
can. They also get better at co-creating with customers
The 50 most innovative companies for 2023 are a and ecosystem partners and at democratizing the use of
geographically diverse group, roughly evenly split between data throughout their organization.
North America and the rest of the world. Europe and Asia
are well represented, and the Middle East joins the list for Samsung: Leading the Commercialization of
the first time with Saudi Aramco at number 41. (See Exhibit 3.) Consumer Tech
Auto companies held multiple positions in the 2022 list;
international energy companies hold five spots this year. Consumer electronics giant Samsung is an example of a
This may be a sign of respondents’ concerns over climate company that uses all the tools available to drive perfor-
change and the fact that they are looking to the energy indus- mance by innovating at multiple stages of the value chain.
try to be a large and creative part of the solution. In spite of the Samsung regularly brings new technology to the mass
market headwinds that they experienced in 2022, tech compa- market through a focus on component-level technology
nies continue to dominate the top 50, including the top ten. innovations and advances in scaled manufacturing. Over the
years, as its core products and markets (such as smart-
The big story, though—this year and for most of the past phones and TVs) have matured, Samsung, known for its
decade—is the ability of innovation to drive performance. dizzying array of products, has proved adept at pushing into
Since 2005, our portfolio of the 50 most innovative adjacent markets and developing new business models.
companies has outpaced the broader market in sharehold-
er returns by a significant margin—an average of 3.3 Samsung innovates along two dimensions: component-
percentage points per year. (See Exhibit 4.) level advances (improving existing technologies with inno-
vations, such as foldable phones), and adoption (increasing
Innovation and Performance accessibility to products through mass production, lower
costs, and technological advancements). The company is a
The consistent outperformance of BCG’s most innovative global innovation leader across R&D, patents, and innova-
companies compared with the broader market is one tion vehicles such as labs and incubators. It invests heavily
indication of the correlation between superior innovation in R&D, spending more than $17 billion (9% of sales) in
capabilities and performance. There are others. 2021 alone, making it the largest non-US R&D spender.
Boasting about 10,000 researchers and developers dedicat-
New BCG research into the shifting drivers of performance ed to the development of future tech, the company has
and sustainable competitive advantage show that the developed a robust patent portfolio: it was granted 6,300
ability to innovate consistently over time is fast rising in patents in 2022, the most in the US.
importance. Traditional markets have plateaued, and
growth, which accounts for 60% to 70% of shareholder As Samsung has developed new products and sought out
returns in the medium term, is found primarily in new new markets, it has moved from displays and electronic
markets, including those created by technology disruption, components into robotics, smart home products, connected
such as e-commerce, streaming media, cloud-based inter- cars, medical equipment, virtual assistants, and 5G
actions, mobility solutions, and smart energy solutions. A connectivity. The company has captured significant shares
small number of companies that have embedded the of the global market for smartphones, QLED TVs, and
capabilities that enable them to exploit innovation for IoT products.
value-creating growth are widening the performance gap
with their competitors and generating shareholder returns The connection between innovation and both growth
almost three times greater than those of the S&P 1200. and advantage is becoming stronger than ever. Winners
understand this and invest in their innovation engines
accordingly. They will continue to widen their lead over
others until the laggards reset their own priorities and
investments for the future.
4 MOST INNOVATIVE COMPANIES 2023
Exhibit 3
The 50 Most Innovative Companies of 2023
Ranking
1–10 1 Apple 2 Tesla (+3) 3 Amazon 4 Alphabet 5 Microsoft 6 Moderna 7 Samsung 8 Huawei 9 BYD 10 Siemens
(-3) (+1) (-1) Company (+10)
11–20 Pfizer (+7) J&J (+15) SpaceX Nvidia (+1) ExxonMobil Meta (-5) Nike (-5) IBM (-8) 3M (+18) Tata Group
21–30 Roche Oracle (-3) BioNTech Shell Schneider P&G (+8) Nestlé (+22) General Xiaomi (+2) Honeywell
Electric Electric (+1)
31–40 Sony (-22) Sinopec Hitachi (+6) McDonald's Merck ByteDance Bosch (-11) Dell (-24) Glencore Stripe
41–50 Saudi Coca-Cola Mercedes- Alibaba Walmart PetroChina NTT Lenovo (-24) BMW Unilever
Aramco (-6) Benz Group1 (-22) (-32)
xxx - Returnee xxx - New entrant
Sources: BCG Global Innovation Survey 2023; BCG analysis.
Note: +/- indicates change from 2022 MIC ranking.
1Mercedes-Benz Group was previously identified as Daimler.
BOSTON CONSULTING GROUP 5
Exhibit 4
Innovators Create Value
Total shareholder return
2005 = index 100
482
+3.3
percentage
points
Annual TSR
287
100
2004 2007 2010 2013 2016 2019 2022
BCG MIC 50 MSCI World Index
Sources: BCG Innovation Journey Analytics Database; CapitalIQ.
Note: Total shareholder return (TSR) performance of publicly listed MIC top 50 companies. Chart compares their one-year TSR performance for
that year against global performance index (MSCI World). We reweight the MIC 50 basket annually to reflect changes to the list. BCG MIC 50 has
outperformed the index in 12 of 17 years.
6 MOST INNOVATIVE COMPANIES 2023
A Downturn Ups the Stakes in
Innovation
By Justin Manly, Michael Ringel, Amy MacDougall, Will Cornock, Johann Harnoss, Konstantinos Apostolatos,
Ramón Baeza, Ryoji Kimura, Michael Ward, Beth Viner, and Jean-Manuel Izaret
F rom an innovation perspective, this downturn (if it A New Outlook?
occurs) looks to be very different from others. During
the last recession, in 2009, companies reined in What’s going on in 2023? For one thing, as growth has
spending. The innovation plans we reported on in that slowed in core markets, the importance of being able to
year’s Most Innovative Companies report reflected that innovate new products and services that carry companies
belt tightening. Less than two-thirds of companies ranked into new markets with new business models has increased.
innovation as a top-three priority that year, and only 58% We reported in 2021 that most companies are at least
planned to increase spending. Nearly 15% expected to cut gradually or partially altering their core business models,
innovation investment. This year, by contrast, 79% of com- with digital opportunities and the sustainability imperative
panies see innovation as a top-three priority—15 points as two key forces driving the change. This dynamic
more than in 2009—and 66% plan to increase spending, continues today.
42% by more than 10%. (See Exhibit 5.)
More companies are building adjacent and new-
frontier business models to serve as growth engines. Many
firms are also planning to increase their spending on such
tools as M&A, innovation labs, and open innovation ecosys-
tems, despite the possible downturn. (See Exhibit 6.)
BOSTON CONSULTING GROUP 7
Exhibit 5 - Innovation Priority and Spending Plans Are Much Stronger in
2023 Than They Were in 2009
Where does innovation, R&D, and product development rank How will your company's innovation, R&D, and product
among your company’s priorities? (%) development spending change this year in response to
macroeconomic factors? (%)
100 10 3 100 5 6
18 9 7
80 80
26 20
28
60 +15pp 60
46 24
39
40 40 32
20 +8pp 20 +16pp 42
33
25 26
0 0
2009 2023 2009 2023
Increase significantly (>10%) Decrease slightly (-1% to -10%)
Top priority Top 3 Top 10 Not on the list
Increase slightly (1% to 10%) Decrease significantly (> -10%)
Stay roughly the same
Sources: BCG Global Innovation Survey 2023; BCG analysis.
Note: n = 1,023 for global respondents. pp = percentage points.
Companies are raising their commitment to innovation, In addition, the old adage about downturns separating
although many are not improving their capabilities as fast as winners and losers may be gaining real traction this time
they would like. Last year, for example, nearly 80% (39) of around. Top performers realize that playing a game of wait
BCG’s 50 most innovative companies ranked among the top and see can easily backfire in unstable times, as it gives
climate and sustainability (C&S) innovators, according to forward-looking competitors more time to position them-
global peer votes. Fully 60% of high-emitting companies were selves to win. For example, leaders know that building
targeting deep-tech innovation, and deep tech was the medium- to long-term resilience requires ongoing focus on
number one or number two innovation focus for those firms. C&S: 89% of innovation leaders cite it as a top-three priori-
In addition, many more committed C&S innovators are ty, and 49% of all companies have confidence in their C&S
leveraging external innovation vehicles that are typically used investment decisions (up from just 23% last year). Among
for longer term or more technologically advanced solutions. those prioritizing C&S, average “C&S readiness” increased
to 37% in 2023 from 28% in 2022, based on BCG’s innova-
Separate BCG research has shown that a group of companies tion to impact (i2i) parameters.
representing about 25% of the S&P 1200 have put in place
the capabilities that enable them to pivot from shoring up the
digital basics of their value chains to focusing on growth from
innovation. Some are on the leading edge of disruption in
their sectors and demonstrating considerable resilience in the
face of uncertainty. These companies are delivering impres-
sive results, far outpacing their peers on such key metrics as
shareholder returns and revenue and earnings growth.
8 MOST INNOVATIVE COMPANIES 2023
Exhibit 6
Companies Expect to Increase Spending On Key
Innovation Enablers, Even in the Face of a Downturn
How do you anticipate the use of innovation vehicles to change in response to macroeconomic factors such as a potential
downturn, inflation, or uncertainty? (%)
19 14 13 15 15 22 18
46 53 55 52
58
55 60
35 33 32 33
26 23 22
M&A Digital and Open innovation R&D Accelerator CVC fund Incubator
innovation lab ecosystem labs
Increase spending Maintain spending Decrease spending
Sources: BCG Global Innovation Survey 2023; BCG analysis.
Note: n = 1,023 for global respondents.
BOSTON CONSULTING GROUP 9
Investing with Focus Microsoft (which has ranked among the top five innovative
companies since the first edition of this report in 2005) is famous
This year’s survey found a significant percentage of companies for using M&A as well as partnerships and alliances to fill strate-
that are not only prioritizing innovation but investing in it. They gic needs that further its innovation agenda. The most recent
are also focusing their investments for greater impact and example is the company’s investments in OpenAI and the
managing their portfolios for improved results, perhaps directly integration of ChatGPT into multiple Microsoft product offerings.
in response to economic uncertainty or the prospect of turmoil.
They understand that innovation leads to advantage, or as Eric Another example is Siemens (number 10 this year and in the top
Schmidt, former chairman and CEO of Google, recently 50 for 13 of the previous 16 years), which uses M&A in various
observed in Foreign Affairs, “The main reason innovation now ways. In 2018, it spun off 25% of its Siemens Healthineers medi-
lends such a massive advantage is that it begets more innovation.” cal device business to spur entrepreneurial independence.
Siemens Healthineers has taken advantage of the flexibility to
Innovating through uncertainty requires tough prioritization pursue big bets in health care, continuing through the pandemic.
around portfolio management, rigorous governance, investment In August 2020, it announced the acquisition of longtime partner
in M&A opportunities, and the continuous building of talent and Varian Medical Systems for $16.4 billion. The Varian acquisition
internal capabilities. In practice, this means companies should positioned Siemens Healthineers as the player with the most
focus on five things. comprehensive integrated cancer care portfolio, across screening,
diagnostics, and treatment. It has allowed the company to
M&A. Leaders look to acquire missing technologies, capabilities, realize innovation synergies, combining Siemens’ imaging tech-
and talent. Our research found that innovation-ready companies nology with Varian’s therapeutic technology and AI to enhance
(those that are ready to develop product, process, and business existing products and create new ones. For example, Siemens
model innovations that can deliver sustainable impact) use a Healthineers’ new radiotherapy product combines imaging
wide array of strategic tools to strengthen their platforms and capability with AI to do rapid assessments and real-time optimi-
practices. They are much more aggressive in their use of M&A to zation of treatment while patients are receiving therapy.
further objectives by accessing new technologies and processes
or acquiring leaders and employees with a demonstrated ability
to innovate. (See Exhibit 7.) They are also more likely to involve
innovation experts in target analysis and selection.
Exhibit 7 - Companies Use M&A to Access Innovative Technologies and
Processes, and They Involve Innovation Experts in Target Selection
What role do M&As play in your company's innovation strategy during times of downturn,
inflation, or uncertainty (select one)? (%)
+3
+23
-20
-14
+7
36
33 32
26
22
17
9 10 8
6
Acquire innovative Include innovation Acquire leaders M&A does not Access to new markets
technologies or experts in our target and employees with play a significant role
processes screening and due demonstrated ability
diligence process to innovate
All companies Ready companies
Sources: BCG Global Innovation Survey 2023; BCG analysis.
Note: n = 1,023 for global respondents. ”Ready” companies are those that are ready to develop product, process, and business model innovations
that can deliver sustainable impact.
10 MOST INNOVATIVE COMPANIES 2023
Exhibit 8 - Ready Companies Lean Away from the Core Toward
Breakthrough Innovations
All companies Ready companies
100%
47 41 49 45 38 33 35 33
32 32 32 31 31
30 29 29
23 28 22 26 30 35 34 36
Past year (2022) Expected allocation in Past year (2022) Expected allocation in
uncertainty/downturn uncertainty/downturn
Distance from core: Near-in/extension New to company (adjacencies) New to world (breakthrough)
Level of advantage: Near-in/sustaining Incremental Distruptive
Source: BCG Global Innovation Survey 2023; BCG analysis.
Note: n = 1,023 for global respondents. Totals may not sum due to rounding. “Ready” companies are those that are ready to develop product,
process, and business model innovations that can deliver sustainable impact.
Portfolio Prioritization. Innovation-ready companies Portfolio Management. Embedding effective portfolio
emphasize breakthrough or disruptive innovations, while less- management and governance helps ensure ROI. The availabili-
sophisticated innovators allocate resources more heavily to ty of end-to-end tracking tools for innovation portfolios actually
“near-in” or sustaining innovation. Significant percentages of declined from 2022 to 2023, and only 38% of companies
all the companies in our 2023 survey are increasing their report a strong reliance on metrics to inform decision making
focus on digital product innovation (34%), adjacent new and governance. Less than a quarter of survey respondents
business models (30%), lowering costs (23%), and new ways of said they have successfully implemented clear KPI and deci-
working (30%). Innovation-ready companies are shifting their sion-making criteria to make portfolio decisions, and only 23%
allocation of resources away from incremental innovations use stage-gate processes with clearly defined decision criteria,
that sustain current positions or advantages toward break- reporting requirements, and performance metrics. By
through or disruptive innovations that create new markets or contrast, almost all innovation-ready companies employ
revenue streams. (See Exhibit 8.) In the event of a downturn, end-to-end tracking to assess progress and make informed
ready companies expect to allocate 50% more investment decisions about an initiative’s value. (See Exhibit 9.)
toward breakthrough innovations (34% versus 22% for other
companies) and almost that much more toward disruptive Overall revenue growth and customer satisfaction remain
innovations (36% versus 26%). Ready companies are planning the top metrics for innovation success, used by 41% and
to increase downturn spending in both areas while others 35% of companies, respectively. These are the same met-
tread water or retrench. rics, used by roughly similar percentage of companies, as
we recorded during the last recession in 2009. Impact on
environmental, social, and governance goals jumped into
the top-five success metrics tracked this year and is the
most common among ready companies.
BOSTON CONSULTING GROUP 11
Exhibit 9 - Nearly All Ready Companies Have Implemented End-to-End
Portfolio Tracking
Have you implemented a holistic view of the portfolio What metrics does your company use in innovation, R&D,
with end-to-end tracking available centrally as a source and product development (select up to three)? (%)
of truth to guide portfolio decisions? (%)
41
Overall revenue growth
33
35
Customer satisfaction
38
94
26
Impact on ESG goals
43
42 43
26
14 Return on innovation spending
23
Implemented Implemented 22
with impact1 Margin accretion
25
All companies Ready companies
Sources: BCG Global Innovation Survey 2023; BCG analysis.
Note: n = 1,023 for global respondents. “Ready” companies are those that are ready to develop product, process, and business model innovations
that can deliver sustainable impact.
1 “Implemented with impact” includes the subset of companies implementing portfolio transparency that also saw impact from those efforts.
Data, Targets, and Collaboration. Ready companies The war for talent is a perennial issue. The job market is
emphasize use of fundamental tools and ensure greater still strong, but it has softened in many parts of the world,
data transparency, clearer portfolio targets, and more meaning it may be easier now to build or strengthen
collaboration. Three-quarters have full data transparency internal teams. The same conditions that provide
to support decision making, compared with only 35% of opportunities for M&A also offer the opportunity to
not-ready companies. Almost 60% have clear portfolio acquire qualified, innovation-focused talent.
targets, and more than half use regular portfolio meetings
to assess process. (See Exhibit 10.)
McDonald’s: Driving Growth with Digital
Innovation-Focused Talent and Culture. For many Innovation
“almost ready” companies, talent and culture is the dimen-
sion holding them back from realizing the full potential of Consider the case of McDonald’s (number 34 on the 2023
their innovation function. In fact, companies that are top 50 list), a restaurant industry frontrunner in technology
almost ready (according to BCG’s i2i assessment) lag ready innovation and investment, which has combined many of
companies more on talent and culture than on any other these practices to bolster its leading position in the indus-
dimension. Ready companies focus on an innovation- try. McDonald’s spends heavily on innovation through
focused culture and talent pipeline. They are three to four partnerships, labs, digital tools, and acquisitions. It was
times as likely as their almost-ready counterparts to have investing in AI (through M&A) as early as 2019 when it
successfully implemented a strong innovation-focused acquired Apprente, which develops voice-based, conversa-
recruiting and talent acquisition foundation across all tional technology, and personalization startup Dynamic
stages of the talent pipeline. And, as we will see in “How Yield to better customize the drive-through experience.
Early Winners Are Unlocking AI’s Potential,” the third
article in this year’s report, companies that realize impact
from AI have more than three times as many people
dedicated to innovation as those who don’t.
12 MOST INNOVATIVE COMPANIES 2023
Exhibit 10 - Ready Companies Rely on Full Data Transparency, Clear
Portfolio Targets, and Regular Portfolio Meetings
How do you support effective decision making on your innovation portfolio? (%)
+37 +20 +13
72
58 51
35 38 38
Full data transparency Clear portfolio targets Regular portfolio meetings
Ready companies Unready companies
Sources: BCG Global Innovation Survey 2023; BCG analysis.
Note: n = 1,023 for global respondents. “Ready” companies are those that are ready to develop product, process, and business model innovations
that can deliver sustainable impact.
When the onset of the pandemic threated its in-restaurant • Go bargain hunting. Find attractive acquisitions and
business, McDonald’s launched the “Accelerating the build innovation into the M&A process by including
Arches” growth strategy, doubling down on digital, drive- innovation expertise on M&A teams and targeting new
through, and delivery. Digital innovations cut 30 seconds technologies and talent.
off drive-through ordering times during the pandemic,
enabling the restaurant chain to move more cars through • Acquire intellectual property (I"
287,bcg,ceo-guide-to-ai-revolution.pdf,"The CEO’s Guide to the Generative AI
Revolution
MARCH 07, 2023
By François Candelon, Abhishek Gupta, Lisa Krayer, and Leonid Zhukov
READING TIME: 15 MIN
The release of ChatGPT in late 2022 created a groundswell of interest in generative AI. Within hours,
users experimenting with this new technology had discovered and shared myriad productivity hacks.
In the weeks and months since, organizations have scrambled to keep pace—and to defend against
unforeseen complications. Some organizations have already adopted a more formal approach,
creating dedicated teams to explore how generative AI can unlock hidden value and improve
efficiency.
© 2025 Boston Consulting Group 1
For CEOs, however, generative AI poses a much bigger challenge. Today’s focus might be on
productivity gains and technical limitations, but a revolution in business-model innovation is coming.
Much as Mosaic, the world’s first free web browser, ushered in the internet era and upended the way
we work and live, generative AI has the potential to disrupt nearly every industry—promising both
competitive advantage and creative destruction. The implication for leaders is clear: today’s
breathless activity needs to evolve into a generative AI strategy owned by the C-suite.
This is no small task, and CEOs—who are likely several steps removed from the technology itself—
may feel uncertain about their next move. But from our perspective, the priority for CEOs isn’t to fully
immerse themselves in the technology; instead, they should focus on how generative AI will impact
their organizations and their industries, and what strategic choices will enable them to exploit
opportunities and manage challenges. These choices are centered on three key pillars:
Each pillar raises an urgent question for CEOs. What innovations become possible when every
employee has access to the seemingly infinite memory generative AI offers? How will this technology
change how employees’ roles are defined and how they are managed? How do leaders contend with
the fact that generative AI models may produce false or biased output?
Clearly, generative AI is a rapidly evolving space, and each of the pillars above involves short- and
long-term considerations—and many other unanswered questions. But CEOs need to prepare for
the moment when their current business models become obsolete. Here’s how to strategize for that
future.
Potential: Discover Your Strategic Advantage
AI has never been so accessible. Tools such as ChatGPT, DALL-E 2, Midjourney, and Stable Diffusion
allow anyone to create websites, generate advertising strategies, and produce videos—the
possibilities are limitless. This “low-code, no-code” quality will also make it easier for organizations
to adopt AI capabilities at scale. (See “The Functional Characteristics of Generative AI.”)
THE FUNCTIONAL CHARACTERISTICS OF GENERATIVE AI 
The transformative potential of generative AI can be summed up by three key
functional characteristics.
© 2025 Boston Consulting Group 2
Seemingly “Infinite” Memory and Pattern Recognition. Because generative AI is
trained on huge amounts of data, its memory can appear infinite. For example,
ChatGPT has been trained on a massive portion of publicly available information on
the internet. To put this in context, as of 2018 the internet generated 2.5 quintillion
bytes of new data daily, according to Domo—the equivalent of 1.2 quintillion words.
That number is likely much higher today. Generative AI can also create connections
(or recognize patterns) between distant concepts in an almost human-like manner.
Low-Code, No-Code Properties. When describing the impact of ChatGPT, Andrej
Karpathy, a founding member of OpenAI, said “the hottest new programming
language is English.” That’s because generative AI’s natural-language-processing
interface allows nonexperts to create applications with little or no coding required.
By contrast, coding assistant systems such as Github Copilot still require competent
programmers to operate them.
Lack of a Credible Truth Function. Generative AI’s “infinite” memory can become
an infinite hallucination. In reality, the level of error in today’s generative AI systems
is an expected characteristic that makes it useful for generating new ideas and
content. But because generative AI does not use logic or intelligent thought, instead
predicting the most probable next word based on its training data, it should only be
used to generate first dras of content.
Companies are working to make generative AI’s output significantly more reliable by
using an approach known as reinforcement learning with human feedback; other
approaches that combine generative AI with traditional AI and machine learning
have also been considered. Improvements to generative AI are expected soon, with
some predicting that it will be able to produce final-dra content by 2030.
The immediate productivity gains can greatly reduce costs. Generative AI can summarize documents
in a matter of seconds with impressive accuracy, for example, whereas a researcher might spend
hours on the task (at an estimated $30 to $50 per hour).
But generative AI’s democratizing power also means, by definition, that a company’s competitors will
have the same access and capabilities. Many use cases that rely on existing large language model
1
(LLM) applications—such as productivity improvements for programmers who use Github Copilot
© 2025 Boston Consulting Group 3
and for marketing content developers who use Jasper.ai—will be needed just to keep pace with other
organizations. But they won’t offer differentiation, because the only variability created will result
from users’ ability to prompt the system.
Identify the Right Use Cases
For the CEO, the key is to identify the company’s “golden” use cases—those that bring true
competitive advantage and create the largest impact relative to existing, best-in-class solutions.
Such use cases can come from any point along the value chain. Some companies will be able to drive
growth through improved offerings; Intercom, a provider of customer-service solutions, is running
pilots that integrate generative AI into its customer-engagement tool in a move toward automation-
first service. Growth can also be found in reduced time-to-market and cost savings—as well as in the
ability to stimulate the imagination and create new ideas. In biopharma, for example, much of
today’s 20-year patent time is consumed by R&D; accelerating this process can significantly increase
a patent’s value. In February 2021, biotech company Insilico Medicine announced that its AI-
generated antifibrotic drug had moved from conceptualization to Phase 1 clinical trials in less than
30 months, for around $2.6 million—several orders of magnitude faster and cheaper than traditional
drug discovery.
Once leaders identify their golden use cases, they will need to work with their technology teams to
make strategic choices about whether to fine-tune existing LLMs or to train a custom model. (See
Exhibit 1.)
© 2025 Boston Consulting Group 4

Fine-Tuning an Existing Model. Adapting existing open-source or paid models is cost effective—in
a 2022 experiment, Snorkel AI found that it cost between $1,915 and $7,418 to fine-tune a LLM
model to complete a complex legal classification. Such an application could save hours of a lawyer’s
time, which can cost up to $500 per hour.
Fine-tuning can also jumpstart experimentation, whereas using in-house capabilities will siphon off
time, talent, and investment. And it will prepare companies for the future, when generative AI is likely
to evolve into a model like cloud services: a company purchases the solution with the expectation of
achieving quality at scale from the cloud provider’s standardization and reliability.
But there are downsides to this approach. Such models are completely dependent on the
functionality and domain knowledge of the core model’s training data; they are also restricted to
available modalities, which today are comprised mostly of language models. And they offer limited
options for protecting proprietary data—for example, fine-tuning LLMs that are stored fully on
premises.
Training a New or Existing Model. Training a custom LLM will offer greater flexibility, but it comes
with high costs and capability requirements: an estimated $1.6 million to train a 1.5-billion-
parameter model with two configurations and 10 runs per configuration, according to AI21 Labs. To
put this investment in context, AI21 Labs estimated that Google spent approximately $10 million for
© 2025 Boston Consulting Group 5
2
training BERT and OpenAI spent $12 million on a single training run for GPT-3. (Note that it takes
multiple rounds of training for a successful LLM.)
These costs—as well as data center, computing, and talent requirements—are significantly higher
than those associated with other AI models, even when managed through a partnership. The bar to
justify this investment is high, but for a truly differentiated use case, the value generated from the
model could offset the cost.
Plan Your Investment
Leaders will need to carefully assess the timing of such an investment, weighing the potential costs of
moving too soon on a complex project for which the talent and technology aren’t yet ready against
the risks of falling behind. Today’s generative AI is still limited by its propensity for error and should
primarily be implemented for use cases with a high tolerance for variability. CEOs will also need to
consider new funding mechanisms for data and infrastructure—whether, for example, the budget
should come from IT, R&D, or another source—if they determine that custom development is a
critical and time-sensitive need.
The “fine-tune versus train” debate has other implications when it comes to long-term competitive
advantage. Previously, most research on generative AI was public and models were provided through
open-source channels. Because this research is now moving behind closed doors, open-source
models are already falling far behind state-of-the-art solutions. In other words, we’re on the brink of
a generative AI arms race. (See “The Future State of the LLM Market.”)
THE FUTURE OF THE LLM MARKET 
Until recently, most generative AI research has been publicly accessible. But many
companies are choosing to stop or delay publishing their research findings and are
keeping model architectures as proprietary knowledge. (For example, GPT-2 was
open-source but GPT-3 is proprietary.)
The next improvements to generative models with vast number of users will likely
come from logs of their user interaction, giving these models a significant
competitive advantage over new entrants. This reality, combined with the heavy data,
infrastructure, and talent costs required to train LLMs, means that the LLM market
has both economy and quality of scale. Advances in generative AI therefore might be
limited to large companies, while the democratization of AI development for small
and medium-sized enterprises could be limited to nondifferentiated use cases.
© 2025 Boston Consulting Group 6
The jury is still out, but this dynamic appears comparable to the “search-engine
wars.” Several large companies invested heavily in search solutions, but Google’s
user-friendliness and accuracy helped set it apart from competitors. Once users
preferred Google, other engines could not keep up—because every search request
Google received made it better and smarter. Soon, all other B2C solutions faded
away. A similar winner-take-all situation could play out in the LLM market, with the
big, early entrants eventually owning the models and having full control over access.
A winner-take-all situation could play out in the LLM market.

It’s worth noting, however, that Google did not achieve the same level of success in
the enterprise search market, which has unique requirements and challenges
compared to B2C. At the enterprise level, search-engines lack the scale to build
domain expertise and lack the volume of user data to build that capability. Similarly,
businesses will get the most value out of LLMs that are trained on their proprietary
data and that have modalities that drive unique use cases. This could make it
difficult for any single player to dominate the B2B market.
There is also the potential for companies and governments to fund open-source
models to keep them state of the art—similar to how IBM funded Linux.
These market dynamics have key implications for CEOs as they make customization
and implementation decisions:
• It is unlikely that any single LLM provider will dominate the B2B market; the key
for companies is to find large models with the modality and functionality that
match their golden use cases or use cases that require sensitive data.
• While training LLMs is an option for large businesses, the quality of scale could
make purchasing solutions more reliable (similar to cloud).
© 2025 Boston Consulting Group 7
• If choosing to train in-house, be wary of relying too much on individual
researchers. If only a small number of people have the expertise to advance and
maintain the model, this will cause a single point of failure if those researchers
choose to leave.
As research accelerates and becomes more and more proprietary, and as the algorithms become
increasingly complex, it will be challenging to keep up with state-of-the-art models. Data scientists will
need special training, advanced skills, and deep expertise to understand how the models work—their
capabilities, limitations, and utility for new business use cases. Large players that want to remain
independent while using the latest AI technology will need to build strong internal tech teams.
People: Prepare Your Workforce
Like existing forms of artificial intelligence, generative AI is a disruptive force for humans. In the near
term, CEOs need to work with their leadership teams as well as HR leaders to determine how this
transformation should unfold within their organizations—redefining employees’ roles and
responsibilities and adjusting operating models accordingly.
Redefine Roles and Responsibilities
Some AI-related shis have already occurred. Traditional AI and machine-learning algorithms
(sometimes incorrectly referred to as analytical AI), which use powerful logic or statistics to analyze
data and automate or augment decision making, have enabled people to work more autonomously
and managers to increasingly focus on team dynamics and goal setting.
Now generative AI, in its capacity as a first-dra content generator, will augment many roles by
increasing productivity, performance, and creativity. Employees in more clerical roles, such as
paralegals and marketers, can use generative AI to create first dras, allowing them to spend more
of their time refining content and identifying new solutions. Coders will be able to focus on activities
such as improving code quality on tight timelines and ensuring compliance with security
requirements.
Of course, these changes cannot (and should not) happen in a vacuum. CEOs need to be aware of
the effect that AI has on employees’ emotional well-being and professional identity. Productivity
improvements are oen conflated with reduction in overall staff, and AI has already stoked concern
among employees; many college graduates believe AI will make their job irrelevant in a few years.
But it’s also possible that AI will create as many jobs as it will displace.
The impact of AI is thus a critical culture and workforce issue, and CEOs should work with HR to
understand how roles will evolve. As AI initiatives roll out, regular pulse checks should be conducted
© 2025 Boston Consulting Group 8
to track employee sentiment; CEOs will also need to develop a transparent change-management
initiative that will both help employees embrace their new AI coworkers and ensure employees retain
autonomy. The message should be that humans aren’t going anywhere—and in fact are needed to
deploy AI effectively and ethically. (See Exhibit 2.)

As AI adoption accelerates, CEOs need to learn as they go and use those lessons to develop a
strategic workforce plan—in fact, they should start creating this plan now and adapt it as the
technology evolves. This is about more than determining how certain job descriptions will change—
it’s about ensuring that the company has the right people and management in place to stay
competitive and make the most out of their AI investments. Among the questions CEOs should ask
as they assess their company’s strengths, weaknesses, and priorities are:
• What competencies will project leaders need to ensure that individual contributors’ work is of
sufficient quality?
• How can CEOs create the optimal experience curve to produce the right future talent pipeline—
ensuring, for example, that employees at a more junior level are upskilled in AI augmentation
and that supervisors are prepared to lead an AI-augmented workforce?
• How should training and recruiting be adjusted to build a high-performing workforce now and in
the future?
Adjust Your Operating Model
© 2025 Boston Consulting Group 9
We expect that agile (or bionic) models will remain the most effective and scalable in the long term,
but with centralized IT and R&D departments staffed with experts who can train and customize
LLMs. This centralization should ensure that employees who work with similar types of data have
access to the same data sets. When data is siloed within individual departments—an all-too-
common occurrence—companies will struggle to realize generative AI’s true potential. But under the
right conditions, generative AI has the power to eliminate the compromise between agility and scale.
Because of the increased importance of data science and engineering, many companies will benefit
from having a senior executive role (for example, a chief AI officer) oversee the business and
technical requirements for AI initiatives. This executive should place small data-science or
engineering teams within each business unit to adapt models for specific tasks or applications.
Technical teams will thus have the domain expertise and direct contact to support individual
contributors, ideally limiting the distance between the platform or tech leaders and individual
contributors to one layer.
Structurally, this could involve department-focused teams with cross-functional members (for
example, sales teams with sales reps and dedicated technical support) or, preferably, cross-
departmental and cross-functional teams aligned to the business and technical platforms.
Policies: Protect Your Business
Generative AI lacks a credible truth function, meaning that it doesn’t know when information is
factually incorrect. The implications of this characteristic, also referred to as “hallucination,” can
range from humorous foibles to damaging or dangerous errors. But generative AI also presents
other critical risks for companies, including copyright infringement; leaks of proprietary data; and
unplanned functionality that is discovered aer a product release, also known as capability
overhang. (See Exhibit 3.) For example, Riffusion used a text-to-image model, Stable Diffusion, to
create new music by converting music data into spectrograms.
© 2025 Boston Consulting Group 10

Prepare for Risk
Companies need policies that help employees use generative AI safely and that limit its use to cases
for which its performance is within well-established guardrails. Experimentation should be
encouraged; however, it is important to track all experiments across the organization and avoid
“shadow experiments” that risk exposing sensitive information. These policies should also guarantee
clear data ownership, establish review processes to prevent incorrect or harmful content from being
published, and protect the proprietary data of the company and its clients.
Another near-term imperative is to train employees how to use generative AI within the scope of their
expertise. Generative AI’s low-code, no-code properties may make employees feel overconfident in
their ability to complete a task for which they lack the requisite background or skills; marketing staff,
for example, might be tempted to bypass corporate IT rules and write code to build a new marketing
tool. About 40% of code generated by AI is insecure, according to NYU’s Center for Cybersecurity—
and because most employees are not qualified to assess code vulnerabilities, this creates a
significant security risk. AI assistance in writing code also creates a quality risk, according to a
Stanford University study, because coders can become overconfident in AI’s ability to avoid
vulnerabilities.
Leaders therefore need to encourage all employees, especially coders, to retain a healthy skepticism
of AI-generated content. Company policy should dictate that employees only use data they fully
understand and that all content generated by AI is thoroughly reviewed by data owners. Generative
AI applications (such as Bing Chat) have already started implementing the ability to reference source
data, and this function can be expanded to identify data owners.
© 2025 Boston Consulting Group 11
Ensure Quality and Security
Leaders can adapt existing recommendations regarding responsible publication to guide releases of
generative AI content and code. They should mandate robust documentation and set up an
institutional review board to review a priori considerations of impact, akin to the processes for
publishing scientific research. Licensing for downstream uses, such as the Responsible AI License
(RAIL), presents another mechanism for managing the generative AI’s lack of a truth function.
Finally, leaders should caution employees against using public chatbots for sensitive information. All
information typed into generative AI tools will be stored and used to continue training the model;
even Microso, which has made significant investments in generative AI, has warned its employees
not to share sensitive data with ChatGPT.
Today, companies have few ways to leverage LLMs without disclosing data. One option for data
privacy is to store the full model on premises or on a dedicated server. (BLOOM, an open-source
model from Hugging Face’s BigScience group, is the size of GPT-3 but only requires roughly 512
gigabytes of storage.) This may limit the ability to use state-of-the-art solutions, however. Beyond
sharing proprietary data, there are other data concerns when using LLMs, including protecting
personally identifiable information. Leaders should consider leveraging cleaning techniques such as
named entity recognition to remove person, place, and organization names. As LLMs mature,
solutions to protect sensitive information will also gain sophistication—and CEOs should regularly
update their security protocols and policies.
Generative AI presents unprecedented opportunities. But it also forces CEOs to grapple with
towering unknowns, and to do so in a space that may feel unfamiliar or uncomfortable. Craing an
effective strategic approach to generative AI can help distinguish the signal from the noise. Leaders
who are prepared to reimagine their business models—identifying the right opportunities, organizing
their workforce and operating models to support generative AI innovation, and ensuring that
experimentation doesn’t come at the expense of security and ethics—can create long-term
competitive advantage.
ABOUT BOSTON CONSULTING GROUP
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important challenges and capture their greatest opportunities. BCG was the pioneer in business
strategy when it was founded in 1963. Today, we work closely with clients to embrace a
transformational approach aimed at benefiting all stakeholders—empowering organizations to
grow, build sustainable competitive advantage, and drive positive societal impact.
Our diverse, global teams bring deep industry and functional expertise and a range of perspectives
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© 2025 Boston Consulting Group 12
uniquely collaborative model across the firm and throughout all levels of the client organization,
fueled by the goal of helping our clients thrive and enabling them to make the world a better place.
© Boston Consulting Group 2025. All rights reserved.
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1 Large language models, also known as foundation models, are deep- learning algorithms that can
recognize, summarize, translate, predict, and generate content based on its training data. Today these
models are mostly trained on text, images, and audio, but they can also go beyond language and
images into signals, biological data, and more. Models trained on data beyond language are called
multimodal models.
2 “How Generative AI Is Changing Creative Work,” Harvard Business Review, November 14, 2022.
Authors
François Abhishek Gupta
Candelon
SENIOR SOLUTION
MANAGING DELIVERY
DIRECTOR & MANAGER,
SENIOR PARTNER; RESPONSIBLE AI
GLOBAL
Montreal
DIRECTOR, BCG
HENDERSON
INSTITUTE
Lisa Krayer Leonid Zhukov
PRINCIPAL VICE PRESIDENT,
DATA SCIENCE
Washington, DC
New York
© 2025 Boston Consulting Group 13"
288,bcg,leading-with-genaiai-how-vietnamese-companies-can-stay-ahead-pdf.pdf,"Leading with GenAI/AI:
How Vietnamese Companies
Can Stay Ahead
November 2024
By Il-Dong Kwon, Arnaud Ginolin, Hanno Stegmann
Boston Consulting Group partners with leaders BCG X is the tech build & design unit of BCG.
in business and society to tackle their most Turbocharging BCG’s deep industry and
important challenges and capture their greatest functional expertise, BCG X brings together
opportunities. BCG was the pioneer in business advanced tech knowledge and ambitious
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we work closely with clients to embrace a enable innovation at scale. With nearly 3,000
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BCG delivers solutions through leading-edge
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Table of contents
Case Study: Transforming a
01 16
Executive Summary Leading Southeast
Page Page
Asian Bank with GenAI
Build for the Future -Establishing
03 17
Advice for Business Leaders Solid Enterprise Foundations for
Page Page
Long-term Success
04 GenAI/AI is Already 20
Where Next for AI in Vietnam?
Page Transforming Businesses Page
GenAI/AI is a Top Strategic
07 21
Priority, yet Many Companies Appendix
Page Page
are Falling Behind
GenAI/AI Landscape in
09 22
Vietnam -Challenges and About the Authors
Page Page
Opportunities Await
From Hesitation to Execution –
14
Practical Recommendations for
Page
Beginning Your GenAI/AI Journey
Executive Summary
Artificial Intelligence (AI) is unlocking new opportunities AI and GenAI are rapidly reshaping industries worldwide,
for businesses through the complementary capabilities of and have become a top priority for executives in Vietnam
traditional predictive AI and emerging generative AI and beyond. [Exhibit 1.] GenAI/AI’s potential to reshape
(GenAI). Predictive AI focuses on decision-making and business processes, enhance productivity, and create new
optimization within defined problem areas, while GenAI business models offers immense opportunities for
generates new, context-aware content and solutions, companies aiming to stay competitive. However, while the
offering a more flexible and expansive range of technology is powerful, it must ultimately be treated the
applications. same as any other tool—requiring a strategic and well-
founded approach to leverage its benefits fully.
Exhibit 1 - Generative AI will revolutionize the world – and executives want to capitalize
Source: BCG AI Radar (2024); n = 1,406 in 50 markets.
BOSTON CONSULTING GROUP 1
Despite the promise of GenAI/AI, most Vietnamese Activating this opportunity involves securing strong
companies are still in the early stages of adoption, alignment and commitment from leadership, focusing on
grappling with significant internal challenges. These high-impact use cases, setting up robust governance,
hurdles range from strategic issues like insufficient GenAI/ upskilling the workforce, ensuring robust data and security
AI expertise among leadership and unclear strategies, to practices, and leveraging external expertise and technology
implementation barriers such as data limitations and partnerships to accelerate progress and deliver immediate
talent gaps. These challenges are further compounded by impact.
external factors like inadequate computing resources and
regulatory uncertainty. Nevertheless, Vietnam’s growing However, to truly capitalize on GenAI/AI, organizations
support ecosystem—bolstered by enhanced GenAI/AI must move beyond these initial steps and embark on a
education, localized models, and a burgeoning GenAI comprehensive transformation journey. This
startup scene—provides a strong foundation for transformation requires prioritizing a mix of strategic value
overcoming these obstacles and accelerating GenAI/AI plays—Deploy, Reshape, and Invent—to maximize GenAI/
adoption in the near future. Given the potential of GenAI/ AI’s benefit. [Exhibit 2.] Additionally, building strong
AI, and the rapid pace of advancements, companies must enterprise foundations by transforming and enhancing
act now to embark on their GenAI/AI transformation technology, people, operating models, processes and risk
journey. management is crucial to fully capture the benefits of
GenAI/AI, ensure responsible and compliant AI use and
enable scalable deployment across the enterprise.
Exhibit 2 - Three strategic plays for AI integration
RESHAPE
critical functions end to INVENT
DEPLOY
end for radical efficiency new experiences,
GenAIin everyday tasks
and effectiveness offerings and business
for broad enterprise
models powered by
productivity
GenAI
In summary, GenAI/AI presents a transformative [Exhibit 3.] The time to act is now—those companies who
opportunity for Vietnamese companies, but success will embrace GenAI/AI will secure a lasting competitive edge in
hinge on a thoughtful, strategic approach that integrates the market.
technology with business goals, people and processes.
Exhibit 3 - A holistic approach integrates strategy, pilots, and multiple functional
transformation while continuously building foundations
​GENAI/AI SETUP DEPLOY ​GenAI/AI in everyday tasks across the enterprise
​Define value pools
​In every business function:
​Articulate visionand strategy RESHAPE • Build MVPs and re-design workflows ​Transformation of function 1
critical to prove value ​Transformation of function 2
​Select priority opportunities
functions • Design future state & set targets
across Deploy, Reshape, Invent
• Cascade changes & scale E2E
​Transformation of function 3
PLAYS
​Build the business case INVENT ​New business models and products
​E2E CHANGE MANAGEMENT & DELIVERY
​Enable leaders and upskill
​Drive adoption, engagement & culture change, leveraging behavioral science
​Steer through central governance and measure impact via AI delivery office
​ENTERPRISE FOUNDATIONS
​Build capabilities to drive & sustain transformation
​Make coordinated investments in core tech & data, people,and responsible AI
2 LEADING WITH GENAI/AI: HOW VIETNAMESE COMPANIES CAN STAY AHEAD
Advice for Business Leaders
The potential of AI and GenAI in Vietnam’s business While the journey may seem daunting, business
landscape is immense—these solutions can leaders can initiate action today by leveraging
revolutionize operations, drive innovation, and create internal and external expertise—starting with high-
substantial new avenues for growth. However, impact, quick-win initiatives and laying the key
Vietnamese companies are still in the early stages of foundations of governance, data and people.
adoption, with many GenAI/AI initiatives still at pilot Alongside these efforts, companies need a strategic
stages and primarily focused on internal use cases to approach that integrates GenAI/AI with clear
tactically improve efficiency. business objectives, robust governance, and strong
data, technology, and security frameworks. These
Realizing the full potential of GenAI/AI requires more efforts should include a special focus on talent
than just technology upgrades; it demands a holistic, development, process optimization, and
end-to-end transformation of the business and organizational redesign to achieve long-term,
organization. sustainable competitive advantage.
BOSTON CONSULTING GROUP 3
GenAI/AI is Already Transforming Businesses
GenAI represents a cutting-edge advancement in artificial algorithms are built on foundation models, trained with
intelligence, designed to create seemingly new and realistic self-supervised methods to uncover underlying patterns
content—whether it’s text, images or audio—based on across a wide array of tasks. GenAI’s capabilities can be
vast amounts of training data. [Exhibit 4.] These powerful broadly categorized into three areas:
1 Generating content and ideas. GenAIcan produce unique outputs across a variety of formats,
from crafting compelling video advertisements to discovering new proteins with antimicrobial properties.
2 Improving efficiency. GenAIstreamlines manual and repetitive tasks, such as writing emails, coding or
summarizing extensive documents
Personalizing experiences. GenAItailorscontent and information to meet specific audience needs,
3
enabling personalized customer interactions through chatbots and delivering targeted advertisements
based on individual behaviors.
Exhibit 4 - Main capabilities of GenAI that drive value
Content
Tech
generation/ Knowledge Problem solving
capabilities
Conversation transcription Summarization Ideation extraction & Insights AI agents
Interactive The creation Summarization Generation Extraction of Logical & The solving of
& dynamic of specific of large of new & structured reasoning complex tasks
engagement types of amounts of Innovation knowledge process to by planning
of information, content (e.g., information ideas, concepts from make and executing a
ideas, or text, images, or text into or designs unstructured or inferences, set of actions
questions videos, audio, shorter, more (e.g., unique semi-structured draw using a suite of
between humans codea) concise versions, product solutions, data sources conclusions, tools
Description
& AI systems, that capture the exploration of make informed
responding to key points of uncharted judgements,
questions and the content territories in and derive new
generating scientific fields) insights based on
appropriate available
responses information, data,
or knowledge
4 LEADING WITH GENAI/AI: HOW VIETNAMESE COMPANIES CAN STAY AHEAD
GenAI complements traditional Predictive AI, which excels experiences. When integrated, GenAI and Predictive AI
in decision-making tasks like descriptive analytics, unlock unprecedented opportunities for businesses,
recommendation engines, and fraud detection. While allowing them to rethink operational processes and
Predictive AI provides the analytical backbone for informed revolutionize their business models, ultimately driving
decisions, GenAI takes creativity and engagement to new greater value and innovation.
heights, automating content creation and enhancing user
Exhibit 5 - GenAI complements and co-exists with previous efforts on predictive AI,
enabling new and broader applications
Predictive AI GenAI
“Left brain” “Right brain”
• Decision making and • Content creation, qualitative
optimization reasoning, orchestration of
other systems
• Each algo constrained to
specific problem space • Multi-applications
• Limited range of • Unlimited range of
possible outputs possible outputs
​Impact: 5% of employee tasks and workflows; ​Impact: 50% of employee tasks and workflows;
focuses on key decisions complemented with impact by Predictive AI
Technology for business has been advancing for decades, • High-value applications. Over 50% of executives
and many companies have worked to digitize their identify marketing and sales, customer operations,
processes and functions— but GenAI/AI is different. In our R&D and IT/software engineering as key areas where
research, roughly two-thirds (65%) of senior executives say GenAI/AI provides the most value. Additionally, sector-
it has the most significant disruptive potential of any specific applications offer further opportunities.
technology over the next five years. One-third of
respondents have already increased investments despite • New revenue streams. Initial GenAI/AI deployments
the challenging cost environment. Why are executives so typically focus on boosting productivity or enhancing
bullish on GenAI/AI? customer service. However, GenAI/AI also opens up
new revenue streams, with approximately 60% of
• Rapid time to value. GenAI/AI solutions can be initiatives aimed at cost reduction and 40% focused on
implemented quickly, delivering benefits within as revenue growth through increased engagement and
little as three months, particularly when using plug- customer satisfaction.
and-play applications. Basic tasks can see productivity
increases of 10% to 20%.
• Larger impact from ambitious applications. While
more complex applications—such as reshaping
business functions or inventing new models—may
take one to three years to implement, they can deliver
significantly larger benefits.
BOSTON CONSULTING GROUP 5
Several leading companies have already demonstrated the solutions. This resulted in a 5 to10% productivity
transformative power of GenAI/AI in complex scenarios. increase, a 15% to 20% reduction in job duration and
rework, and a 0.5% to 1% decrease in attrition,
• A global payment technology company enhanced fraud enhancing asset uptime and revenue.
detection by integrating GenAI/AI, improving real-time
fraud identification and reducing false positives. This • A financial information company transformed its core
allowed it to strengthen customer security and service by integrating GenAI, turning data and analysis
reinforce its reputation for innovation. into a conversational insight platform. It is projected to
generate up to US$100 million in additional revenue
• A leading global consumer goods organization used and significantly boost its financial profile.
GenAI to gain a competitive edge in consumer
engagement. The AI-driven marketing engine • An industrial goods client diversified revenue by
identified trends, automated actions and optimized commercializing its AI-powered image analytics model.
campaigns, leading to a 40% to 60% increase in Integrating AI and GenAI into inspections increased
engagement, up to 80% in cost savings and a three- defect detection efficiency 10-fold, improved data
month acceleration in campaign creation. ingestion by 160 times and boosted workflow efficiency
by 62%. Tasks are now completed 50% faster and
• A renewable energy developer improved the employee reporting is 67% quicker.
value proposition and boosted productivity by
reimagining maintenance processes with AI and GenAI
6 LEADING WITH GENAI/AI: HOW VIETNAMESE COMPANIES CAN STAY AHEAD
GenAI/AI is a Top Strategic Priority, yet Many
Companies are Falling Behind
AI has rapidly ascended to the top of the executive • 89% of executives rank AI and GenAI in their top three
agenda, with GenAI opening up a new world of business technology priorities for 2024.
opportunities that leaders are eager to capitalize on.
[Exhibit 6.] According to BCG’s survey of over 1,400 C-suite • 54% of leaders expect AI and GenAI to deliver cost
executives: savings in 2024, with roughly half of them anticipating
savings of more than 10%—primarily driven by
• 71% of leaders plan to increase their company’s tech productivity gains in operations, customer service and
investments in 2024, up from 60% in 2023. An even IT.
higher percentage (85%) plan to boost spending
specifically on AI and GenAI.
Exhibit 6 - A global wave of rising tech and GenAI/AI Investment
Executivesplanningtoincrease their 71% Executives planning to increase their 85%
techinvestmentin2024 overall AI/GenAIinvestment in 2024 overall
MiddleEast 85% MiddleEast 93%
Asia-Pacific 80% Europe 86%
Africa 77% Asia-Pacific 85%
Europe 68% North America 85%
NorthAmerica 65% Africa 82%
SouthAmerica 63% South America 75%
Source: : BCG AI Radar (2024); n = 1,406 in 50 markets.
Note: In Asia-Pacific, n = 308; in North America, n = 303; in Europe, n = 647; in the Middle East, n = 28; in South America, n = 51; in Africa, n = 69.
Despite the enthusiasm, many organizations still investment priorities (47%), and the absence of a
hesitate to embrace GenAI/AI fully. [Exhibit 7.] BCG’s strategy for responsible AI (42%).
findings highlight a significant gap between ambition and
action: • Only 6% of companies have managed to train
more than 25% of their people on GenAI tools
• 90% of leaders are either cautiously waiting for AI and so far.
GenAI to prove itself beyond the hype or are limiting
their efforts to small-scale experiments. • 45% of leaders say that they don’t yet have
guidance or restrictions on AI and GenAI use
• 66% of executives are ambivalent or outright at work.
dissatisfied with their organization’s progress on AI
and GenAI. They highlighted several challenges,
including a shortage of talent and skills (62%), unclear
Exhibit 7 - Executives worldwide must boost upskilling
Executives who report that more than 25%
of their workers have trained on GenAItools ​6%
​Midle East 11
​North America 8
​Asia-Pacific 7
​Europe 5
​Africa 3
​South America 2
Source: BCG AI Radar (2024); n = 1,406 in 50 markets.
Note: In Asia-Pacific, n = 308; in North America, n = 303; in Europe, n = 647; in the Middle East, n = 28; in South America, n = 51; in Africa, n = 69.
BOSTON CONSULTING GROUP 7
It’s clear that the gap between AI leaders and • 10% of companies are actively scaling GenAI, with 61%
laggards is widening. Companies with the highest AI of these having already scaled multiple predictive AI
maturity are further extending their lead by scaling GenAI use cases and realizing substantial benefits.
applications. [Exhibit 8.] According to BCG’s Build for the
Future C-level GenAI Survey in 2023/2024: • Meanwhile, 50% are still in the phase of limited
experimentation and small-scale pilots, while 40%
have yet to take significant action.
Exhibit 8 - 10% of companies already scaling GenAI; building on their higher predictive
AI maturity and widening the gaps
~40% ~50% ~10%
SCALING
companies companies companies
Are scaling
1 or more GenAI
PILOTING applications
across functions/
enterprise
Developing few focused MVPs
NO-ACTION YET to test value from GenAI
Taking no action
on GenAIyet Majority have
historically
scaled several
Majority have historically Majority have historically piloted a number of predictive AI
lacked predictive AI project predictive AI projects, but few have initiatives in a
execution capability successfully scaled few functions
% high AI maturity 25% 46% 61%
% who have
realized significant
7% 10% 44%
value from scaled
predictive AI cases
1. % who have scaled numerous predictive AI cases
8 LEADING WITH GENAI/AI: HOW VIETNAMESE COMPANIES CAN STAY AHEAD
GenAI/AI Landscape in Vietnam - Challenges and
Opportunities Await
AI has been making waves in Vietnam for the past five recognition, electronic know-your-customer (eKYC), optical
years, with both government and private sectors character recognition (OCR), and call or voice bots
increasingly recognizing its potential to drive innovation deployed in contact centers, collections and credit card
and digital transformation. activation. In other industries, some advanced applications
are emerging, such as image diagnostics in healthcare and
In 2021, the Government of Vietnam took a significant step AI/ML-driven analytics in some global CPG/FMCG
forward by issuing Decision 127/QD-TTg, launching the companies.
National Strategy on Research, Development and
Application of AI until 2030. This strategy aims to position Late 2023 and early 2024 marked a turning point for AI in
Vietnam as a leading AI hub in Southeast Asia by focusing Vietnam, driven by the rapid rise of the ChatGPT
on five key areas: (1) establishing a robust legal framework, phenomenon. As a result, Vietnamese companies began
(2) building data and computational infrastructure, (3) increasing their focus on AI, with a growing interest in
developing a thriving AI ecosystem, (4) promoting AI GenAI. However, as GenAI is still a relatively new
applications, and (5) enhancing international cooperation. technology, its adoption remains in the early stages. Some
This strategic direction has spurred AI research and large companies have started piloting GenAI, mainly for
application at both central and local government levels, internal purposes such as document and knowledge
with a focus on enhancing public services and addressing navigation, HR tasks like automated CV scanning and
social challenges. Vietnam currently ranks 59th out of 193 profiling, staff training, and IT support through code co-
countries in the Government AI Readiness Index report by pilots. External-facing use cases are still largely focused on
Oxford Insights (UK), surpassing the global average and GenAI-powered chatbots and voice bots.
improving by one position regionally to rank 5th out of 10
countries in Southeast Asia. GenAI/AI is still in its early stages in Vietnam, but this
presents significant opportunities for companies to fully
In the private sector, AI has gradually expanded across harness its potential. While many Vietnamese companies
industries in Vietnam since 2019, including banking, are initially focusing on the productivity and efficiency
insurance, consumer finance, retail, healthcare, consumer benefits of GenAI/AI—the most tangible and easiest to
packaged goods (CPG) and manufacturing. However, the AI implement—the real power of GenAI/AI is to
use cases currently being implemented remain relatively fundamentally reshape how entire functions operate,
nascent compared to those in other countries. For driving deeper productivity gains and reducing costs, and
example, in the banking industry—one of the leading even inventing new business models to create new sources
sectors in AI adoption—AI is primarily used for facial of competitive advantage to improve top-line growth.
BOSTON CONSULTING GROUP 9
Taking a broader view, there are three value plays to maximize GenAI/AI’s potential:
​Deploy GenAI/AI in everyday tasks to realize 10% to 20% productivity potential. Select and test
GenAI/AI tools, deliver massive upskilling, roll out solutions to support workers in day-to-day tasks and
carefully evaluate the costs of deployment.
​Reshape critical functions for 30% to 50% enhancement in efficiency and effectiveness.
Anticipate the impact of GenAI/AI on your workforce and core functions, create new roles, reallocate
budgets and guide a series of pilots that can reliably scale up.
​Invent new GenAI/AI business models to build a long-term competitive advantage. Develop a
strong customer-centric approach, and leverage first-party data and intellectual property to create
interactions that customers can't find anywhere else.
No single approach can capture the full scope of what This is particularly crucial for companies in Vietnam, where
GenAI/AI offers. Instead, companies should evaluate their lower labor costs can make it challenging to justify the
opportunities across three broad value plays. Ideally, these value of GenAI/AI if efforts are limited to isolated
aren’t just small-scale pilots to appease investors or productivity improvements. For GenAI/AI to truly deliver its
boards—they require a deep, organization-wide potential, it must be embedded in a way that drives
commitment to embedding GenAI/AI into every aspect of comprehensive transformation rather than just
the business from budgets and processes to roles and incremental automation. [Exhibit 9.]
culture, all while adhering to responsible AI principles.
Exhibit 9 - Three strategic plays to leverage when considering adopting and
implementing GenAI/AI
DEPLOY RESHAPE INVENT
​Deploy GenAIsolutions to augment the ​GenAIserves as an enabler to ​GenAIsolutions reimagine business
productivity of everyday tasks transform an entire function(s) and models and/or create new sources of
enhance its productivity competitive advantage
​Improve effectiveness of ​Re-engineer one or multiple ​Reimagine the org. business
existing tasks within a role e.g., critical functions e.g., Marketing models e.g., reinventing CX or
meeting summary or code or Customer Service insights and innovation platforms
development
​Difficult to directly measure ​Direct capturable impact on bottom ​Potential for significant long-term
bottom-line impact but has great line, therebyenhancing company impact on bottom line, offering a
potential to familiarize the org. productivity competitive advantage
with GenAI
​Change focus on driving adoption ​Reshapes how employees work ​Reimagines what employees do in
and improving effectiveness in a specific function(s), the organization, necessitating work
within existing setup necessitating work with change, with change, new skillsets, WoW
new skillsets, WoW and and potentially org setup
potentially org setup
​Increasing business value
Note: The change effort will depend on the organization’s starting point in data, people, and process readiness
10 LEADING WITH GENAI/AI: HOW VIETNAMESE COMPANIES CAN STAY AHEAD
Implementation challenges
• Data challenges. Fine-tuning models is difficult due
to the lack of structured, high-quality and diverse data.
Additionally, the Personal Data Protection Law
imposes strict limitations on how companies can store
and process data, further complicating efforts in
certain use cases.
• Talent gaps. There is a shortage of skilled GenAI/AI
professionals at both leadership and operational
levels, hampering effective implementation.
• Security and privacy concerns. There are significant
concerns about data leakage when sharing
information with third parties, as well as concerns
about potential misuse of data for training models
• Change management. The lack of a robust
governance structure for GenAI/AI projects, along with
resistance to new processes, cultural shifts, and
changes in mindsets and ways of working, significantly
impacts implementation.
Despite the potential, Vietnamese companies starting to Beyond that, there are two fundamental external
adopt and pilot GenAI/AI typically face several common challenges that affects the ability of Vietnam companies
internal challenges: to scale and capture the full potential of GenAI/AI:
Strategic challenges • Insufficient computing resources and
infrastructure. Securing the necessary computing
• Insufficient GenAI/AI proficiency among the resources, particularly graphics processing units
executive team. This expertise gap hinders the (GPUs), and setting up the infrastructure for GenAI/AI
ability to drive and adapt to change, limits the inference is both challenging and costly. This issue is
recognition of GenAI/AI’s transformative potential for further compounded for companies seeking to deploy
the business and results in a lack of top-down these solutions on private infrastructure.
strategic direction for GenAI/AI adoption. [Exhibit 10.]
• Regulatory uncertainty. The absence of legally
• Lack of clear AI strategy: Many GenAI/AI initiatives binding regulations for GenAI/AI, along with limited
are currently embedded within broader guidance on ethics and responsible AI (RAI), poses
transformation efforts or are focused on solving significant challenges to broader adoption.
specific, discrete problems, leading to a fragmented
approach.
Exhibit 10 - The need to upskill extends to the C-suite
CoCnofindfiednecneceininthteheexeexceuctuivtievetetaemam’s’GseGneAnIApIrporfiocfiiceinecnycy
CoCmomplpetleetlye lcyo cnofindfiednetnt 1%1%
5599%%
VeVreyr cyo cnofindfiednetnt 111%1%
CoCnofindfiednetnt 292%9%
ofo lfe laedaedresr ssu sruvrevyeeyde dsa sya tyh tehyey
hahvaev lei mlimiteitde do ro nr on o
LimLimiteitde dco cnofindfiednecnece 404%0%
cocnofindfiednecneceini nth tehire ier xeexceuctuivteiv e
tetaemam’s ’ps rporfiocfiiecniecnyc iyn i nG eGneAnIA.I.
NoN coo cnofindfiednecnece 191%9%
Source: BCG AI Radar (2024); n = 1,406 in 50 markets.
BOSTON CONSULTING GROUP 11
While these challenges exist, Vietnam also has unique improving university training, specialized programs
strengths and a rapidly evolving support ecosystem that is with global experts and corporations are expected to
poised to accelerate GenAI adoption in large enterprises: help build a skilled AI workforce. Recently, a global
tech giant partnered with the Ministry of Planning and
• Building talent foundations. The number of Investment to offer 40,000 AI scholarships, while
educational programs specifically focused on AI and another leading tech company launched AI training for
GenAI is increasing, especially in leading institutions. university lecturers to strengthen Vietnam’s AI
For example, Hanoi University of Science and capabilities. Combined with the unique strength of
Technology has launched Vietnam’s first specialized Vietnam’s young, tech-savvy, low-cost workforce, these
engineering program in GenAI. Vietnam National efforts are expected to produce a high-quality AI talent
University, Ho Chi Minh City is also focusing on AI, pool in the coming years. [Exhibit 11.]
and, with 6,000 undergraduates, it aims to provide the
nation with highly qualified AI talent. Beyond
Today’s students are remarkably advanced in AI and GenAI. Through our
engagement with these students in GenAI competitions, we’ve been impressed
by how many undergraduate students can solve complex problems and develop
prototypes in a short period of time.
AI Expert at a Leading Commercial Bank
Exhibit 11 - Vietnam’s affordable talent advantages
Average monthly IT Engineer salary in 2024, in dollars
Singapore 5,627
Taiwan 3,782
South Korea 2,826
Thailand 2,174
Malaysia 1,313
Vietnam 665
0 2,000 4,000 6,000
Source: Salary Explorer, Nikkei Asia, BCG Estimation
12 LEADING WITH GENAI/AI: HOW VIETNAMESE COMPANIES CAN STAY AHEAD
• Localized AI models. More than 20 large language
models (LLMs) developed or fine-tuned for the
Vietnamese language and context have been
introduced recently, offering tailored options beyond
models from abroad. This development has attracted
a diverse range of contributors, from large technology
companies to startups, independent research groups,
and universities. While these models are still in
development, and face challenges such as limited
computing power and data resources, they are
expected to meet the needs of Vietnamese companies
within the next one to two years.
• Growing GenAI startup ecosystem. The rise of new
GenAI startups in Vietnam is accelerating adoption by
creating competitive pressure and providing proven
use cases for large enterprises. These startups also
offer GenAI/AI applications and services that can be
integrated into larger enterprise GenAI/AI systems.
We’re witnessing a surge in GenAI
startups in Vietnam, which now
ranks #2 in the region for number
of GenAI startups. This marks a
significant milestone, as Vietnam
is emerging as a strong contender
in this field, closing the gap with
Singapore for the first time.
Business Development
Manager for Startups at
Global Hyperscaler
Given the rapid advancements in GenAI/AI and the
significant opportunities it presents, companies need to
act now and prepare for the future:
Start now with small but high-
1 impact use cases to quickly adopt
and realize GenAI/AI benefits and
integrate GenAI/AI into daily operation.
In parallel, prepare for business and
2 organizational transformation to
scale and fully capture the potential of
GenAI/AI in the long term.
BOSTON CONSULTING GROUP 13
From Hesitation to Execution - Practical
Recommendations for Beginning Your
GenAI/AI Journey
For companies that have yet to embark on their GenAI/AI
journey or still at the early stage, moving forward can seem
daunting. However, the rewards of successfully adopting
and scaling GenAI/AI are immense. Here’s how to take
those critical first steps and move from hesitation to
execution.
14 LEADING WITH GENAI/AI: HOW VIETNAMESE COMPANIES CAN STAY AHEAD
Turn Leadership into Informed Upskill Your Workforce
1 4
Advocates
Empowering your team to work effectively
GenAI/AI adoption starts at the top. Leaders with GenAI/AI is crucial for success:
must become well-informed advocates • Develop training programs.
for GenAI/AI, understanding its potential Implement training initiatives and
value and pushing the organization to communication strategies that shift
embrace the technology. This is particularly mindsets and equip users with the
crucial because piloting GenAI/AI use skills to maximize GenAI/AI’s value.
cases can sometimes be challenging, with • Provide hands-on support. During
results that may fall short of expectations. implementation, offer necessary
However, GenAI/AI is a long-term journey, handholding to hel"
289,bcg,mitsmr-bcg-ai-report-november-2024.pdf,"In collaboration with
November 2024
Learning
to Manage
Uncertainty,
With AI
by Sam Ransbotham, David Kiron,
Shervin Khodabandeh, Michael Chu,
and Leonid Zhukhov
AUTHORS
Sam Ransbotham is a professor of analytics at Michael Chu is a vice president of data science
the Carroll School of Management at Boston at BCG, where he focuses on applying AI and
College, as well as guest editor for MIT Sloan machine learning to business problems in
Management Review’s Artificial Intelligence commercial functions, including optimizing
and Business Strategy Big Ideas initiative. pricing, promotions, sales, and marketing. He
can be reached at chu.michael@bcg.com.
David Kiron is the editorial director, research,
of MIT Sloan Management Review and program Leonid Zhukhov is a vice president of data
lead for its Big Ideas research initiatives. science at BCG and leads the Tech & Biz
Lab at the BCG Henderson Institute. He
Shervin Khodabandeh is a senior partner and
leads the design and build of AI and machine
managing director at Boston Consulting Group
learning solutions for BCG clients across
(BCG) and the coleader of its AI business
a range of sectors. He can be reached at
in North America. He is a leader in BCG X
zhukov.leonid@bcg.com.
and has over 20 years of experience driving
business impact from AI and digital. He can be
contacted at shervin@bcg.com.
CONTRIBUTORS
François Candelon, Todd Fitz, Kevin Foley, Sarah Johnson, Michele Lee DeFilippo, Meenal Pore,
Namrata Rajagopal, Allison Ryder, Barbara Spindel, and David Zuluaga Martínez
The research and analysis for this report was conducted under the direction of the authors as part of
an MIT Sloan Management Review research initiative in collaboration with and sponsored by Boston
Consulting Group.
To cite this report, please use:
S. Ransbotham, D. Kiron, S. Khodabandeh, M. Chu, and L. Zhukov, “Learning to Manage Uncertainty,
With AI,” MIT Sloan Management Review and Boston Consulting Group, November 2024.
SUPPORTING SPONSORS
Copyright © Massachusetts Institute of Technology, 2024. All rights reserved. REPRINT #: 66262
CONTENTS
1 Uncertainty Abounds
2 Combining Organizational Learning and AI-specific
Learning Leads to Augmented Learning
4 Augmented Learners Are Better Prepared for
Many Types of Uncertainty
8 Three Ways to Enhance Organizational
Learning With AI
11 Developing Augmented Learning Capabilities
13 Learning With AI Is Key to Navigating Uncertainty
14 Appendix: The State of AI in Business
Uncertainty Abounds
Uncertainty is all about the unknown. The less an organi- ELC is not alone: The company is among the 15% of orga-
zation knows, the greater its uncertainty and the less able nizations that integrate AI into their learning capabilities.
it is to manage resources effectively. Managing uncertainty, These organizations — what we refer to as Augmented
therefore, requires learning. Companies need to learn Learners — are 1.6 times more likely than those with lim-
more, and more quickly, to manage uncertainty. ited learning capabilities to manage various environmental
and company-specific uncertainties, including unexpected
Addressing uncertainty constitutes a pressing challenge
technological, regulatory, and workforce changes. These
for leadership, especially today, when geopolitical tensions,
companies are twice as likely to be prepared to manage
fast-moving consumer preferences, talent disruptions,
talent-related disruptions compared with organizations
shifting regulations, and rapidly evolving technologies
that have limited learning capabilities. What’s more, these
complicate the business environment. Companies need
organizations are 60%-80% more likely to be effective at
better tools and perspectives for learning to manage
managing uncertainties in their external environments
uncertainty arising from these and other business disrup-
than Limited Learners — companies with limited learning
tions. Our research finds that a major source of uncertainty,
capabilities. By doing so, they reap advantages with AI well
artificial intelligence, is also critical to meeting this chal-
beyond direct financial benefits.
lenge. Specifically:
Based on a global survey of 3,467 respondents and inter-
Companies that boost their learning capabilities with AI views with nine executives, our research quantitatively and
are significantly better equipped to handle uncertainty qualitatively establishes a relationship between organiza-
from technological, regulatory, and talent-related dis- tional learning, learning with AI, and the ability to manage
ruptions compared with companies that have limited rapidly changing business environments. Organizational
learning capabilities. learning itself has long been associated with improved per-
formance. Integrating AI with an organization’s learning
The Estée Lauder Companies (ELC) offers a case in point.
capabilities significantly improves corporate responses to
The cosmetics company has a strategic need to anticipate
uncertainties from talent mobility, new technology, and
consumer trends ahead of its competitors. In earlier times,
related regulations. This report defines an AI-enhanced
consumer preferences might have shifted seasonally. Now,
organizational learning capability (augmented learning),
preferences are less certain; shifts happen more quickly due
explains its use in reducing the considerable uncertainty
to social media and digital influencers. Fashion trends can
managers face today, and offers key takeaways for exploit-
change by the week. If the color peach suddenly captures
ing these new abilities.
the public’s interest, the company needs to discern that
trend as quickly as possible. It uses AI to detect and rap-
idly respond to consumer trends. Sowmya Gottipati, vice
president of global supply chain technology at ELC, reports
that the company, which carries products across more than
20 brands and “hundreds of different shades,” uses fuzzy
matching to figure out which products can meet the demand
and delight consumers. “We are looking to AI to discover
Companies need to learn
consumer trends and then match up our existing products to
the trends so that we can repackage them and position them
more, and more quickly,
in the market for that trend,” Gottipati explains. ELC uses
AI to detect sudden changes and have a market response to manage uncertainty.
ready so it can redeploy inventory and supply chain pro-
cesses to meet demand efficiently. Companies can’t control
the changes but can use AI to manage their responses.
Learning to Manage Uncertainty, With AI 1
ABOUT THE RESEARCH
This report presents findings from the eighth annual Combining Organizational Learning
global research study on artificial intelligence and busi-
and AI-specific Learning Leads to
ness strategy by MIT Sloan Management Review and
Boston Consulting Group. In spring 2024, we fielded a Augmented Learning
global survey and subsequently analyzed records from
3,467 respondents representing more than 21 industries Organizational learning is an organization’s capability to
and 136 countries. We also interviewed nine executives change its knowledge through experience.1 Organizations
leading AI initiatives in a broad range of companies and that learn from mistakes, tolerate failure, capture best
industries, including financial services, technology, retail, practices, and support new ideas have an advantage over
travel and transportation, and health care. organizations that don’t: They learn to get better. Those
Our research connects organizational learning, learning that struggle to learn will struggle to navigate increasing
with AI, and the ability to manage rapidly changing environ- uncertainties. Extensive past research demonstrates the
ments. This report defines an AI-enhanced organizational benefits of general organizational learning.
learning capability, explains its use in reducing several types
General organizational learning capabilities don’t neces-
of uncertainty managers face today, and offers key leader-
sarily depend on AI; organizations can have strong organi-
ship takeaways for exploiting these new abilities.
zational learning capabilities without using the technology.
To assess whether organizations have “high” or “low”
Conversely, organizations can use AI to learn even if they
organizational and AI-specific learning capabilities, we
don’t otherwise have strong organizational learning capa-
analyzed survey responses to these statements using an
bilities. Managers can learn from generative AI tools, use
agree-disagree Likert scale:
AI to deepen their understanding of performance, and iter-
ate with AI to develop new insights and processes. These
›
My organization learns through experiments. (organi-
individual learning experiences create value from AI but
zational learning)
may not constitute an organizational learning capability.
›
My organization tolerates failures in experiments.
Our research finds that organizations that combine organi-
(organizational learning)
zational learning with AI-specific learning — Augmented
›
My organization learns from postmortems on both Learners — outperform organizations that employ either
successful and failed projects. (organizational learning) approach in isolation. As businesses adopt AI and embrace
› successively more powerful AI tools in various contexts, they
My organization codifies its learning from initiatives.
have new opportunities to strengthen their learning capa-
(organizational learning)
bilities — for both human workers and their machines. Our
›
My organization gathers and shares information that prior research, “Expanding AI’s Impact With Organizational
employees learn. (organizational learning) Learning,” found that organizations with superior learning
› capabilities are more likely to obtain significant financial ben-
My organization’s use of AI leads to new learning.
efits from their AI use.2 In our latest research, we find that
(AI-specific learning)
the reverse is also true: Using AI can improve organizational
›
My organization uses AI to learn from performance. learning capabilities, and these learning improvements are
(AI-specific learning) tied to not only enhanced financial results but also the ability
› to manage strategy-related uncertainties.
My organization builds AI solutions with human feed-
back loops. (AI-specific learning)
Assessing Learning Capabilities
›
Employees in my organization learn from AI solutions.
Our survey instrument measured each enterprise’s orga-
(AI-specific learning)
nizational learning capability using five questions. We
also assessed how individuals and systems learn with AI
We then grouped respondents into four categories:
through a different set of four questions. Together, these
Limited Learners, Organizational Learners, AI-specific
questions probe several aspects of organizational learning
Learners, and Augmented Learners. (See Figure 2, page 3
and AI-specific learning: knowledge capture, synthesis,
For theSe breakdownS.)
and dissemination. (see figure 1, page 3.)
2 MIT SLOAN MANAGEMENT REVIEW • BCG
Becoming adept at these learning activities — which rep-
Organizational learning AI-specific learning
resent only a slice of an organization’s overall learning
capability — significantly improves a company’s ability to
• Learns through experiments and • Uses AI to lead to new learning
manage uncertainty. tolerates failure
• Uses AI to learn from
• Supports employees presenting performance
Most Companies Have Limited new ideas
• Builds AI solutions with human
Learning Capabilities • Learns from postmortems on feedback loops
successful and failed projects
Given the uncertainties facing many companies, it’s strik- • Enables employees to learn
• Codifies learning from initiatives from AI solutions
ing that most organizations have limited learning capabili-
• Gathers and shares information
ties; 59% of all companies represented in our sample report
that employees learn
low levels of both organizational learning and AI-specific
learning. Only 29% of respondents agree or strongly agree
that their enterprise has organizational learning capabili-
ties. While 27% of organizations report learning with AI, FIGURE 1
only 15% combine AI-specific learning with organizational Characteristics of Organizational
Learning and AI-specific Learning
learning capabilities. These Augmented Learners are the
focus of this report. We outline characteristics of organizational and
AI-specific learning based on nine survey questions.
12 15
% %
59 14
% %
In our global survey, we assessed an organization as having “high”
or “low” organizational and AI learning capabilities. For more detail,
see “About the Research,” page 2.
gninraeL
cfiiceps-IA
AI-specific Learners Augmented Learners
Limited Learners Organizational Learners
Low High
Organizational Learning
hgiH
woL
12 15
% % OrganizatiOnal learning
An organization’s capability to change its
knowledge through experience.
ai-specific learning
The measure of organizations’ use of
AI for learning.
59 14
augmented lear%ners %
Organizations that score high on organizational
learning and AI-specific learning.
limited learners
Organizations that score low on organizational
learning and AI-specific learning.
In our global survey, we assessed an organization as having “high”
or “low” organizational and AI learning capabilities. For more detail,
see “About the Research,” page 2.
FIGURE 2
Learning Capabilities Vary
Only 15% of organizations are Augmented Learners —
organizations that enhance organizational learning with AI.
gninraeL
cfiiceps-IA
AI-specific Learners Augmented Learners
Limited Learners Organizational Learners
Low High
Organizational Learning
hgiH
woL
Fig. 2
Title: Learning Capabilities Vary
Caption: Only 15% of organizations are Augmented Learners —
organizations that enhance organizational learning with AI.
Learning to Manage Uncertainty, With AI 3
Limited learning capabilities constrain opportunities and disruptions from talent mobility, changing technology,
undermine organizations’ ability to manage uncertainty. and evolving regulatory and legal requirements. (see fig-
ure 5, page 6.)
Augmented Learners Are Better
at Managing Uncertainty Disruptions From Talent Mobility
Among our sample, 15% of organizations report high lev- Elevated rates of workers quitting, retiring, being laid off,
els of both organizational learning and AI-specific learning. or even ghosting employers create risks and ambiguities
These Augmented Learners display abilities and advantages for organizations striving to compete. Shilpa Prasad is head
that lead to better outcomes than organizations with limited of incubation, AI Ventures at LG Nova, the subsidiary of
LG Electronics that works with startups to fuel innova-
capabilities. They are more likely to improve financial out-
tion for the company. She observes that “60% of the work-
comes with AI than Limited Learners: 99% of Augmented
force will likely hit the age of 65 by the year 2028 or 2030,
Learners report annualized revenue benefits from AI. (see
which means that a lot of knowledge will go out from the
sidebar, “enhancing OrganizatiOnal learning With
workforce because they’ll retire, not because they’re going
ai imprOves financial OutcOmes,” page 5.) What’s more,
somewhere else to work.” When employees leave organi-
they are much more likely to be prepared to deal with uncer-
zations, their knowledge can leave with them unless the
tainty from talent, technology, and legal disruptions.
company has effective organizational learning capabilities.
Figure 3 shows that organizational learning alone or
These problems are not new for organizations. In indus-
AI-specific learning alone offers some benefits, but their
tries like chemicals, aerospace, and oil and gas, retirement
combination represents the most powerful hedge against
rates have been an increasing cause for alarm for years.
multiple types of uncertainty. Organizational learning with
However, companies have new resources to address these
AI may well prove to be a source of resilience against other
challenges. Augmented learning is a valuable resource for
forms of disruptions or uncertainty.
addressing disruptions from talent mobility. Only 39% of
organizations with limited learning feel prepared to han-
dle the disruption in knowledge from departing employ-
Augmented Learners Are
ees, but this readiness increases to 64% if the companies
Better Prepared for Many have organizational learning capabilities. Using AI can
Types of Uncertainty further contribute to this readiness: Eighty-three percent
of Augmented Learners are prepared to deal with the
Combining organizational learning and AI-specific learn- uncertainty of knowledge disruption from talent mobil-
ing capabilities helps enterprises manage uncertainty and ity — twice as much as Limited Learners.
FIGURE 3
Learning With AI Helps
AI will allow us to manage uncertainty Organizations Manage
in our industry. 1.6× Uncertainty
Organizations that combine
organizational and AI-specific
Limited Learners 53% learning (Augmented Learners)
are 1.6 times more likely to feel
Organizational Learners 58%
prepared to manage uncertainty
AI-specific Learners 76% than organizations with limited
learning capabilities.
Augmented Learners 82%
Percentage of respondents in each learning category who strongly agree or agree with the above statement.
4 MIT SLOAN MANAGEMENT REVIEW • BCG
SIDEBAR
ENHANCING ORGANIZATIONAL LEARNING
WITH AI IMPROVES FINANCIAL OUTCOMES
Numerous studies now show the direct financial benefits of to recognize some revenue benefits from AI compared with
AI adoption. Clearly, organizations are finding ways to extract organizations with limited learning capabilities. Indeed, virtually
financial benefits through AI, even if many such efforts fail all of these organizations (99%) recognize or observe some
or their costs exceed revenues. Extensive past research also revenue benefits from AI. What’s more, organizations that
surfaces the general benefits of organizational learning for combine AI and organizational learning are significantly more
companies. In prior research, we found that organizations with likely to have realized revenue benefits from AI compared with
superior learning capabilities are more likely to obtain signifi- companies that excel at organizational learning but not learning
cant financial benefits from their AI use than organizations with with AI, and with companies that excel at AI-specific learning
lesser learning capabilities. but not organizational learning. That is, combining organiza-
tional learning and AI-specific learning enables organizations to
In this study, we find that using AI can improve organizational
cross a revenue benefit threshold that neither type of learning
learning capabilities and that these learning improvements are
alone can generate.
similarly tied to improved financial results. Organizations using
AI to improve organizational learning are 1.4 times more likely
Over the past three years, AI has
1.4×
created additional business value.
Limited Learners 66%
Organizational Learners 76%
AI-specific Learners 89%
Augmented Learners 95%
Percentage of respondents who strongly agree or agree that AI has created additional business value over
the past three years.
Our organization has realized revenue
1.4× FIGURE 4
benefits from AI on an annualized basis. Enhancing Organizational
Learning With AI Leads to
Financial Benefits
Limited Learners 71%
Organizations that combine
Organizational Learners 72% organizational learning and
AI-specific learning (Augmented
AI-specific Learners 79%
Learners) are 1.4 times as likely
Augmented Learners 99% to realize additional business
value and annualized revenue
Percentage of respondents who report revenue benefits from AI. benefits from AI.
Learning to Manage Uncertainty, With AI 5
As more and more workplace communications occur Generative AI tools can help synthesize and disseminate
via digital channels, emerging AI capabilities can make personalized knowledge. “GenAI helps you get more value
this raw data sensible, and tacit knowledge accessible, out of this knowledge so that you can find what you’re
on demand. Jackie Rocca, former vice president of prod- looking for and be more effective in using all that data that
uct at Slack, describes how AI can surface and distill the has been available to you but hasn’t been very easy for you
trove of information from past conversations in a platform to access and use,” Rocca says. While tools like wikis make
like Slack when people need it. “People can get context it easier for people to record knowledge, AI capabilities can
from coworkers who left the company months or years bolster organizational learning about what workers know.
ago and still learn from that knowledge,” she points out. That enables organizations to better handle knowledge
My organization is prepared to deal with uncertainty from …
Talent disruptions 2.2×
Limited Learners 39%
Organizational Learners 64%
AI-specific Learners 58%
Augmented Learners 83%
Technology disruptions 1.8×
Limited Learners 49%
Organizational Learners 71%
AI-specific Learners 68%
Augmented Learners 86%
Legal disruptions 1.6×
Limited Learners 48%
FIGURE 5
Organizational Learners 61%
Combining Organizational
AI-specific Learners 68% Learning With AI Learning Helps
With Many Types of Uncertainty
Augmented Learners 79%
Organizations that combine
Percentage of respondents in each learning category who strongly agree or agree that their organization is organizational and AI-specific learning
prepared to deal with each type of uncertainty. Some values calculated with rounding. (Augmented Learners) are more likely
to manage talent, technology, and
legal disruptions.
6 MIT SLOAN MANAGEMENT REVIEW • BCG
loss from talent mobility, reducing uncertainty around of Excellence at Novo Nordisk, notes that “technology is
how and when to capture tacit knowledge. evolving faster than organizations can address. Combining
that with the hype around technology’s possible effects pulls
One cloud services provider wasn’t preparing for a poten-
the organization to do something.” Emerging technologies
tial pandemic when it developed its learning tool, but
become “a propeller for the organization,” she observes,
when in-person meetings were no longer possible due
even if it’s initially unclear what the business case is or where
to COVID-19, its platform and micro-learning content
investments should go. Reassessing technology investments
enabled the company to sustain and even enhance mean-
can be beneficial, even if organizations don’t end up adjusting
ingful educational experiences. The company’s responsible
their strategies but, rather, reinforce them to work within the
AI lead explains how an innovative learning tool turned into
new technological landscape.
a powerful tool for managing uncertainties wrought by the
pandemic. Before the pandemic, the company had begun What’s more, technology adoption can lead to more, and
shifting its learning modules to shorter, AI-supported more complex, regulatory scrutiny and compliance issues,
“micro-adaptive” approaches suitable for a “TikTok world.” raising difficult questions about how to navigate increas-
The pandemic necessitated a remote work environment ingly uncertain legal environments. Surprisingly, using AI
that changed what employees needed to know and, further- to amplify organizational learning dramatically improves a
more, made it more difficult for the company’s educational company’s ability to manage uncertainty from both tech-
content providers to determine what employees knew and nology and regulatory disruptions. Compared with orga-
didn’t know on an ongoing basis. nizations with limited learning capabilities, Augmented
Learners are significantly more likely to be prepared to deal
The adaptive modules tailored content recommendations
with uncertainty from technology disruptions (86% versus
to each individual as the system assessed individual users’
49%) and regulatory disruptions (79% versus 48%). (see
learning capabilities. “AI became a huge part of that,” this
figure 5, page 6.)
executive says. “We monitored users’ self-reporting and
skills self-assessments in their profiles and from the learning Learning to manage uncertainty that comes from a depen-
platform.” By analyzing skills and competency proficiency dence on older technology and from future waves of tech-
across systems throughout the organization, the company nology is a growing opportunity for Augmented Learners.
identified what its employees were learning and needed Shelia Anderson, CIO of Aflac U.S., shares how the insurer
to learn. She adds, “The AI-enabled modules did not just uses generative AI to reverse-engineer code in certain leg-
enable a different delivery of content; the platform helped acy systems. This approach is projected to boost current
people better understand what they knew and how that levels of system productivity by five to 10 times by revealing
intersected with what they needed to know.” Drawing on hidden complexities. “We have built in approaches to learn-
ing that leverage AI and actually help to inform our organi-
the learning approaches and habits of many of the company’s
zation on how AI can be used as well,” Anderson says. She
workers, the learning modules made tailored recommenda-
notes that Aflac also has a technology incubator that uses
tions based on individual needs that reduced uncertainty
AI to evaluate new technologies and rapidly prototype lead-
about what an individual needed to learn next. Enhancing
ing candidates to prove out concepts for the business. If a
organizational learning with AI provided flexibility to man-
prototype appeared to be viable for the business, Anderson
age necessary changes during an unanticipated crisis.
says, “we would use AI to build a full business model with
Technological and Regulatory Uncertainty the return on investment or productivity savings or what-
ever business value metric we’re looking to achieve.”
Increasingly frequent technology innovations lead to signifi-
cant strategic and operational uncertainty. Adapting systems On the regulatory front, large organizations with global
over and over again can be exhausting and disruptive to tech- operations can use AI to navigate complex, uncertain reg-
nologists and business users alike. Just when companies had ulatory frameworks that vary from one country to the next.
begun to understand how to incorporate AI use into their For example, ELC’s Gottipati observes, “From a company
business strategies, generative tools introduced changes that point of view, you make one product and distribute it. But
required a reassessment. (see “the state Of ai in business,” then, if the requirements are different for different countries,
page 14.) Tonia Sideri, director of the AI and Analytics Center and also certain ingredients are limited in certain countries,
Learning to Manage Uncertainty, With AI 7
it becomes time intensive to keep track of all these changing Surman also sees open source as critical for managing that
regulations and at the scale at which we operate.” The com- uncertainty. He expects that “open source will play a huge
binatorial explosion of products in a large number of mar- role in the organizational learning and corporate AI space
kets is difficult to keep up with. But Gottipati sees potential because it lends itself to on-premises privacy, respecting
in using AI to help manage the myriad combinations. “That’s customized models. It creates market demand for open-
where I think AI can play a huge role: to identify the right source models that you can fine-tune on your own data.”
combination of products before we ship anything, or send- However, taking full advantage of open source requires
ing us alerts and assisting with compliance,” she notes. Using organizational learning.
AI can offset growing legal complexity.
Openness isn’t restricted to the models themselves. For
Technology and regulatory uncertainty are inherently example, federated learning allows multiple organizations
intertwined. As difficult as technological disruptions can to train models collaboratively while keeping their data pri-
be, legal disruptions can exacerbate technological uncer- vate. Surman believes that “private AI — using open-source
tainty in addition to creating uncertainty on their own. models — becomes a hedge against regulatory uncertainty.
Mark Surman, president of the Mozilla Foundation, notes Federated learning, in which I benefit from my data in the
that the software company is still very early in the process partnership, you benefit from your data in the partnership,
of figuring out the legal implications of AI. He says, “The and there’s some area where we collectively benefit or at
core piece is, there’s just so many questions about copyright
least we can operationalize on each other’s data, is super
and what it means to own knowledge. Maybe the copyright
juicy.” While standards for federated learning are still being
law we have just needs to be interpreted for the AI era. Or
worked out, Augmented Learners are better able to manage
maybe we need new copyright law.” Within and beyond the
regulatory and legal uncertainties like these.
boundaries of organizations, AI has turned the question of
knowledge ownership upside down because, as Surman
points out, “many of the main large language models are Three Ways to Enhance
built on stuff that, arguably, doesn’t belong to them.”
Organizational Learning With AI
Augmented Learners have an advantage here because they
have abilities that those unable to learn with AI lack. For While it may be tempting to identify organizational learn-
example, knowing how to build your own AI tools could ing as — or, more pointedly, reduce it to — knowledge
hedge against uncertainty from third-party solutions sub- management or learning and development, organizational
ject to upcoming copyright regulation. Surman explains learning involves far more than these important activities.
that this knowledge can help an organization navigate It encompasses whether organizations view unsuccess-
current legal uncertainties: “The one thing that is known ful experiments as failures or as sources of learning; how
and safe is [what’s] inside the organization … to the degree organizations develop, not just manage, knowledge; and
that you have good practices that information is clean and how organizations anticipate the unknown rather than
belongs to you. So if you train a large language model on merely capture what is known. It occasionally requires
your company’s information, it’s yours.” setting aside old ways of working to make learning new
Combining organizational learning with
AI-specific learning yields more benefits
than taking either approach alone.
8 MIT SLOAN MANAGEMENT REVIEW • BCG
capabilities possible.3 What’s more, organizational learn- AI technologies represent new capabilities for capturing
ing encompasses synthesizing and analyzing information existing tacit knowledge. In a more down-to-earth context,
to glean what is and is not working in the enterprise. It LG Nova’s Prasad observes that AI-based augmented reality
also involves optimizing metrics, not merely maximizing (AR) glasses have the potential to capture the tacit knowl-
performance on existing metrics. Finally, organizational edge of factory workers on the shop floor who have mastered
learning addresses the communication, dissemination, and a certain way of working with machines. “If they’re doing a
accessibility of knowledge. technique on the shop floor that only they know, AR glasses
can allow real-time content creation,” she says. While AR
Combining organizational learning with AI-specific learn-
use is not common today, Prasad states this use case has the
ing yields more benefits than taking either approach alone.
potential to become a more significant approach to capturing
AI-specific learning can significantly enhance (at least)
tacit knowledge as the technology/hardware matures.
three areas of organizational learning: knowledge capture,
knowledge synthesis, and knowledge dissemination. These Using AI to distill information at scale enables the capture
are not incremental additions; Augmented Learners multi- of salient information that would otherwise be impossible
ply their abilities in these areas. for humans to discern. Since 2021, LG Nova’s mandate has
been to work, develop, and collaborate with startups to build
Knowledge Capture
new business ventures — a typically daunting task, given the
Adopting generative AI and embracing developments in tra- sheer number of potential targets worldwide. Prasad summa-
ditional AI can expand an organization’s ability to capture rizes the question "
290,bcg,June 2023 - BCG Perspectives on GenAI_s Impact on Private Equity - Summary.pdf,"Generative AI:
Implications for PE Investors
Operational and Portfolio Overview
June 2023
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Table of contents | GenAI perspectives for private equity
Scope of the assessment
Overview of foundation models, the tech stack, why now, what the
1 What is GenAI, why now, and why it matters
current capabilities are, and what the expected impact is
2 GenAI's impact on Private Equity
Framework for how to evaluate GenAI's impact on portfolio
2a Impact on Portfolio and new deals
industries, functions and targets
GenAI use cases in fund mgmt. from fundraising to investing to
2b Impact on PE operations
portfolio mgmt.
Immediate next steps; Introduction to GenAI control tower to
3 How to proceed with Gen AI deployment
coordinate fund-wide efforts
4 Why BCG for the GenAI journey Why BCG is a thought leader in GenAI
5 How to partner going forward Areas where BCG can support, our approach and commercial offer
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GenAI is already proving to be a game-changer: Significant productivity increases
1 have already been reported and companies' value propositions are being challenged
with significant impact on enterprise value
GenAI's impact on an industry basis should be analyzed from two complementary
2 lenses: Productivity gain potential (cost and/or effectiveness improvements) and its
Value proposition change (new product & business models) distinctly impact businesses
Actions required depend on type of impact: Funds should scan portfolios for horizontal
Key Messages
3 productivity gain potential and PortCo-specific value proposition changes. Productivity
gain potential implies org changes, value prop changes require strategic reviews
People and process change are the most critical factors to succeed: Whilst data and
4
technology are important, organizational transformation through GenAI is 70% about
People, 20% about Processes, and only 10% about Tech
As an immediate step, funds need to start assessing impact across its portfolio, in
5 fund operations and its investments strategy; as efforts develop, a GenAI control
tower setup can serve as a focal point of efforts and drive coordination
In addition to risks, GenAI presents meaningful investments opportunities across industries. These
requires assessments on a sub-sector basis, and will be the focus of upcoming perspectives
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What is GenAI, Why now, and Why it matters
GenAI's impact on Private Equity
Impact on portfolio and new deals
Impact on PE operations
Agenda
How to proceed with GenAI deployment
Why BCG for the GenAI journey
How to partner going forward
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5
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Generative AI (Gen AI) refers to the application of foundation models in order
to create original content across various modalities
Input Activity Outputs
Pretraining Finetuning
• Large datasets (TBs) • Little amounts data
Data needed (MBs) of specific
• No labels needed domain data needed
• Labels may be needed
Question
Answering
Text
Sentiment
Analysis
Images
Information
Extraction
Speech
Foundation
Content
model
generation
Structured Data
Object
Recognition
Signals
Instruction
Following
Generation Task
Traditional AI Task
Source: “On the Opportunities and Risks of Foundation Models”, Centerfor Research on Foundation Models, arXiv, 2021; BCG Analysis
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The GenAI tech stack builds from data, foundation models, & infrastructure
layers to support end-applications
Data modality
Text Code Image Video Speech Other
Vertical AI applications
Vertical applications
Generative AI applications that are tailored to solve vertical-
End- specific use cases
Horizontal applications
applications
Horizontal AI applications
General productivity applications Generative AI applications that solve cross-cutting functional
department use cases such as sales & marketing
MLOPs
General productivity applications
Model Generative AI applications that solve cross-cutting non-functional
Model distribution and non-vertical specific use cases that improve productivity,
infrastructure
such as software development
Model tools & updating
Model infrastructure
Generative AI platforms and tools that aid deployment and
Domain adapted models
integration, improve model performance, expand model
distribution, and improve model training and experimentation
Foundation models
Model APIs
Model APIs
Proprietary foundational models
Model providers who develop foundation models to support
generative AI end-applications and use cases (going forward,
Open-source foundation models domain-adapted models could also emerge, building from
foundation models)
Horizontal, vertical & synthetic
data Data
Enablers
Cloud platforms and specialized Providers of input data used to train and fine-tune foundational
hardware models
Source: Expert interviews; BCG analysis 6
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GenAI is already proving to be a game-changer
Productivity gains are Companies' value prop Barriers to AI are Widely expected to
real and proven are being challenged lower than ever create outsized value
55% -49% Conversational UX eases ~$20B+
human adoption
Same model can handle
faster completion of
drop in Chegg's stock share
multiple downstream of committed VC funding for
coding tasks with higher
price after CEO attributed
tasks Generative AI in the last
success rate using GitHub
the slowdown in three years alone5
CoPilot1 Robust against
subscriptions to Chat GPT
unstructured, unlabeled
messy data
37%
2 months
faster completion of
to 100M users for OpenAI
knowledge work with
ChatGPT,
comparable quality results
the fastest product on
using OpenAI ChatGPT2
record4
1. https://github.blog/2022-09-07-research-quantifying-github-copilots-impact-on-developer-productivity-and-happiness/
2. https://joshbersin.com/2023/03/new-mit-research-shows-spectacular-increase-in-white-collar-productivity-from-chatgpt
3. https://www.cnbc.com/2023/05/02/chegg-drops-more-than-40percent-after-saying-chatgpt-is-killing-its-business.html
77
4. Reuters, Yahoo! Finance, OpenAI
5. Crunchbase, Pitchbook, BCG Analysis
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What is GenAI, Why now, and Why it matters
GenAI's impact on Private Equity
Impact on portfolio and new deals
Impact on PE operations
Agenda
How to proceed with GenAI deployment
Why BCG for the GenAI journey
How to partner going forward
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Early adopters and uses cases likely to emerge in more error-tolerant or easily
audited use cases
Timing of impact depends on creative requirements and error
tolerance… …with uses cases falling into 3 key categories
Error tolerance Nature of use case / task Near-term | Creative, repetitive, highly error-tolerant or easily-
1
Requirement that GenAI output be Degree of flexibility in type of work audited use cases
factually accurate and precise (e.g., creative vs. execution-based)
• Use cases for frequently generating new material, and that do not vary widely
between verticals (e.g., draft marketing copy vs. research report)
• Use cases involving time-consuming, repetitive workflows, and that would
Adoption Illustrative
create competitive or cost advantages for mature companies
• Examples: draft marketing copy, logo creation, ad text/image generation, art
media, grading for short-answer questions, audio/video/image correction,
product review summarization / aggregation
2 Mid-term | Explorative, error-tolerant, moderately auditable use cases
Creative,
Repetitive, • Use cases for more complex, vertical-specific creative tasks, targeting
high-/lower identification of potential new solutions to complex problems
Explorative, error tolerance Long runway for • Examples: Biopharma drug discovery; chemical synthesis pathways; CO2 generative AI adoption
high error (0-3 yrs.) alternative material discovery
Operational, tolerance
no error (3-5 yrs.)
tolerance
(5+ yrs.) 3 Long-term | Operational, no error tolerance, limited auditability
• Use cases for generating, execution, or facilitating complex manual processes
1
with highly specific requirements
3 2
• Examples: Industrial facility design; Machinery part design and tooling / 3D
Industry maturity
printing
Source: Industry interviews; BCG analysis Legend: Adoption range due to variety of underlying use cases within category 9
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GenAI has implications on three key dimensions for PEs
Focus of following sections Focus of upcoming perspectives
Impact on portfolio Impact on fund Impact on
and new investments operations Investment Strategy
GenAI impact on key portfolio Use cases in fund mgmt. from Investment themes emerging from
industries, impact time horizon fundraising to investing to GenAI, Competitive landscape and
and how to prepare portfolio mgmt.; shortlist dynamics
'transformative' use-cases
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What is GenAI, Why now, and Why it matters
GenAI's impact on Private Equity
Impact on portfolio and new deals
Impact on PE operations
Agenda
How to proceed with GenAI deployment
Why BCG for the GenAI journey
How to partner going forward
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GenAI will impact industries through both improvements in productivity, and
fundamental changes to customer value proposition
1 Productivity 2 Value proposition
gains Benefits of GenAI change
are expected to be
By automating or augmenting By synthesizing large volumes of
two-fold
repetitive tasks, GenAI models can complex data, GenAI models can
unlock cost benefits in back office enable new offerings; while some
processes, and/or revenue growth industries likely to remain
via improvements in service unaffected, many will benefit from
delivery, personalization, or complementary offerings and others
customer experience may be exposed to new competition
Need to urge portfolio companies to Need to help portfolio companies
understand and adopt available GenAI tools to understand potential
and evolve their org & op model to realize opportunities and risks and
productivity gains navigate strategic changes
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Initial view – list is dynamic as new use-cases emerge
Productivity gains | Most impacted corporate roles expected to be within
1
marketing, customer service, legal, SW and knowledge management
Non-exhaustive
Degree of generative AI impact on roles
High impact Medium impact Low impact
Functions that will likely see extensive or Functions likely to be benefited by Functions with less direct impact from GenAI,
eventually full automation, resulting in one or automation of certain tasks, with potential but likely some benefit from general
more of cost reductions, demand generation cost reduction or per-head productivity productivity applications
due to via higher quality service, or ability to benefits • E.g. email generation
focus resources on higher-value tasks • E.g. generation of finance reports
• E.g., GenAI customer service chatbots to
support call center efficiency
Marketing & advertising Finance & Administration Operations
Human resources Management
Customer service
Product development Community & social services
Legal
Engineering Manufacturing workers
Software development
Business development & Sales Construction workers
Research services & knowledge management IT Support Transportation
Research Mining & extraction labor
Healthcare Services Maintenance and repair
Key use-cases per role covered ahead 13
Source: Expert interviews, LinkedIn Sales Insight, BCG analysis, GPTs are GPTs: An Early Look at the Labor Market Impact Potential of Large Language Models ( arXiv:2303.10130)
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Initial view – list is dynamic as new use-cases emerge
Value proposition impact | Most industries will see either competitive shifts
2
from new services, or little change; few expected to be completely automated
Non-exhaustive
Degree of generative AI impact on value proposition
Significant impact Industry disruption Limited impact
Businesses whose offerings can be entirely Industries where players can gain competitive Businesses predominantly revolving around
replaced with GenAI services, being both advantage / customer benefits by automating providing or tracking physical goods and
undifferentiated / commoditized and not workflows, but differentiate on existing services, where the core value proposition
dependent on making or tracking a change in a product, services and non-public data. Fast- relates to a change in the customer's physical
customer's physical environment adapting incumbents can retain share against environment
• e.g., translation services, copywriting new entrants, but could still lose revenue • e.g., dentists, food delivery, mining
due to GenAI impact on profit pool.
• e.g., legal services, medical diagnostics
Translation services Capital markets and institutions Consumer and business products
Media Retail & apparel
Personal assistants
Software Restaurants, hotels, leisure
Chatbots
Biopharma Tech hardware and networking
Public information aggregators
Healthcare diagnostics Transportation
Fact-based reporting (e.g., sports results) White-collar services Insurance
Education Healthcare delivery
Proprietary data vendors / aggregators Manufacturing
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In aggregate, GenAI's impact on value proposition and productivity will most
disrupt knowledge & content industries; less-digital sectors less impacted (1/2)
Greater productivity
per head, driving
reduced costs or
improved quality of
outputs & delivery
Require adoption of
available GenAI tools
and redesigning org &
op model accordingly
to unlock productivity
Limited Value proposition impact Significant
Significant value prop impact requires deep understanding of
sector/portco-specific dynamics to make strategic choices
Source: BCG Analysis, Expert Interviews, GPTs are GPTs: An Early Look at the Labor Market Impact Potential of Large Language Models (arXiv:2303.10130)
hgiH
laitnetop
niag
ytivitcudorP
woL
Highly-impacted industries focused on knowledge or content
generation, where GenAI can replace / automate large portions
of operations and service
Industries with significant
changes to value proposition,
Moderately-impacted industries, where many tasks can be leading to turbulence
automated, but value is provided in the form of a product or manifested through new
face-to-face service products / services emerging,
changes in customer demand,
changes in competitive
Low / no impact due to high dynamics, pricing evolution,
dependence on physical and changes in employee bases
products, infrastructure, or
face-to-face service delivery
16
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In aggregate, GenAI's impact on value proposition and productivity will most
disrupt knowledge & content industries; less-digital sectors less impacted (2/2)
Highly-impacted Potentially disrupted
Accounting Newspaper
Legal services Proofreading & copying
& payroll publishing
services
Translation services
Moderately-impacted
Hospitals & Museums &
Travel & reservation
Utilities
clinics historic sites services
Scientific Clothing
research retailers Graphic design
Digital health
Low / no impact Tax preparation services
Financial
Food & Oil & gas
vehicles Motion picture / sound
beverage extraction
recording
Libraries &
Telecom. Real estate Web search / information
archives
services
Limited Value proposition impact Significant
Non-exhaustive
Low / no impact due to high Moderately-impacted due to task Highly-impacted as Gen AI can Fundamentally impacted ted as the
dependence on physical products automation, but value provided in replace / automate knowledge & value proposition has changed & new
/ services product / personal service content generation products / services have emerged
Source: BCG Analysis, Expert Interviews, GPTs are GPTs: An Early Look at the Labor Market Impact Potential of Large Language Models (arXiv:2303.10130) 16
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GenAIimpact on PE Portfolio and new deals Impacted industries
Directional
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A set of consideration factors requires assessment to gauge the productivity
gain potential and value proposition impact for portfolio and new investments
GenAI impact consideration factors
Illustrative
GenAI impact Consideration factors Questions to determine GenAI impact
Bespoke assessments of
1 consideration factors
How much of companies' total cost basis can be reduced by adopting GenAI-enabled
conducted to determine
Efficiency gains
software solutions?
1. Productivity gain potential
and 2. Value prop. impact for
a given sector or company
Effectiveness gains How can GenAI impact the quality of outputs?
driven by GenAI technology
Productivity
To what degree will companies' operating models (e.g., workforce composition,
Operating model shifts
gain potential
workflow, job responsibilities) change due to AI?
To what degree do sector-specific barriers exist (e.g., regulation, legal threats) and
Adoption barriers
what is their impact (e.g., require legislation, customer preference shift)?
2
To what degree does GenAI enable new product offerings, which may either reset
Competitive impact
competitive dynamics through substitution or induce greater competitive intensity?
To what degree will GenAI erode or increase demand? Is the mix shift favorable or
Demand impact
Value unfavorable?
proposition
Impact Price and margin
To what degree will GenAI drive a commoditization of prices or margin?
impact
Alternatively, would companies be able to improve margin given productivity gain?
Assessment of each criteria includes evaluation of current GenAI technology, and how it is expected to evolve
Source: BCG analysis 17
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Illustrative &
Not exhaustive
GenAI capabilities are continuously developing; as such, assessing GenAI's
impact requires considering current as well as potential future capabilities
High degree of feasibility Unclear degree of feasibility
GenAI capabilities with path to GenAI capabilities requiring
Current GenAI capabilities
development in near future substantial incremental R&D
• Generate 'second drafts' of long-form responses • Use of e.g. LoRa for organization specific fine tuning • Ability to reliably assess factual accuracy of output
to general prompts based on learned patterns to increase accuracy of output (e.g., scientific • Move beyond token window of LLMs to work on large
Text from training data papers) bodies of text or extended conversations
• Enhance search capabilities through • Agent-based capabilities to generate and perform • Architecture to hold long conversations without
information aggregation and extraction lists of tasks ""drifting"" from initial parameters
• Generate basic code from natural language • Expansion of support to more programming • Interpret intended use case and generate context-
Code/ prompts and autocomplete existing base code languages and frameworks driven code
Data • Code translation from one language to another • Integration with other platforms for specific tasks • Ability to train on self-generated data via
(e.g., Java to Python) (e.g., Wolfram Alpha for mathematical calculations) unsupervised learning
• Generate unique art, logos and photographs • Ability to tweak generated output via iterative • Ability to create 3d models requiring internal
Images
from natural language feedback from user in natural language interface consistency (e.g., architectural models, CAD renders)
• Short clips from natural language prompts; first • Generate video 'first drafts' from natural language • Using natural language to create complex video
attempts at 3D renders and video models • Generate high quality 3D scenes with consistency scenes/models that are fully 3D modeled, resulting
Video/
• Advanced speech recognition, transcribing, between frames in highly personalized media
Speech
highlighting and outlining meeting notes • Ability to tweak generated output via iterative
feedback from user in natural language interface
Current-state Future-state
Source: BCG analysis, Generative AI: A Creative New World | Sequoia Capital US/Europe
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GenAIimpact on PE Portfolio and new deals Impacted industries
40-50% of avg PE portfolio within industries expected to have high impact from
GenAI – though sub-sector & company specific analyses are warranted
Example of Industries1 - Average global top 20 PE funds portfolio (% of total)
Professional Services
Software
Biotech
30% Media
Retail & Apparel
IT Services
Capital Markets
Healthcare Services
Insurance
Consumer Products
Chemical & Gases
Commercial Products
Energy Equipment
Source: BCG analysis; Notes: (1) example set and not exhaustive of all industries; penetration is estimation 19
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Directional
5% 10%
3%
Whilst general patterns
3% exists, GenAI's impact
5%
will be vastly different
4%
5% for sub-sectors and
3%
companies within
industries
5%
4%
Size of bubble = Approx.
Limited Value proposition impact Significant
portfolio share of PE funds
20
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GenAI impact on PE Portfolio and new deals Implications for PE
What does this
mean for PE?
21
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GenAIimpact on PE Portfolio and new deals
PortCo implications | Org. changes required in PortCos exposed to high
productivity gain potential, whilst value prop changes require strategic review
Highly-impacted industries | ""Organizational Re-haul"" Fundamentally impacted
industries| ""Moments of Truth""
Significant organizational implications: Significant workforce changes
required to adapt to GenAI – e.g. re-skilling, hiring, and operating model Strategic shifts: Radical change
(positive or negative) in
Limited strategic implications: Limited effects on strategic positioning and
competitive differentiation strategic positioning of
competitors
Potential market share capture: Players with strong data governance
Demand impact: Change in
frameworks and tech org hold an initial speed-to-adoption advantage
demand for core
Low/Moderately-impacted industries | ""Fast following"" products/services
Negligible short-term impact: Limited (short-term) effects on productivity Intensified competition:
and value proposition Heightened competition with
new players and improved
""Second-mover"" advantage: Oppt'y to adopt GenAI at lower cost/risk later in
offerings
time through monitoring and gaining familiarity with GenAI developments
21
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Implications for PE
Directional
Limited Value proposition impact Significant
22
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GenAIimpact on PE Portfolio and new deals Implications for PE
Fund actions | Identify cross-portfolio productivity gain potential and assess
strategic implications of value proposition changes for impacted PortCos
Productivity gain
Stand up Prepare for
potential:
Set objectives Size the prize
functional CoEs1 implementation
Identify and • Determine sub-set of • Aggregate headcount by • Identify & drive best • Assess implications on
implement PortCos and functions to (sub-) function across practices across PortCo people, processes & tech
be evaluated portfolio
operational best • Set up GenAI focused • Consider extent which
practices across • Consider the end-state • Estimate the productivity teams across key productivity translates to
portfolio, function goal of the evaluation improvement potential impacted functions (e.g. cost take out, workstream
(e.g, cost take out vs. by (sub-) function call centers) reinvention or op model
by function
quality improvement) enhancement
Value proposition
Identify high Assess scenarios Develop option Set up war room
impact:
impact-sectors sets
• Screen the portfolio for • Initiate deep-dive • Evaluate options: e.g. • Assemble war room
Screen for highly
high impact industries analysis for prioritied product dev., M&A, involving mgmt & board
impacted industries
PortCos to estimate size partnerships
• Look for anticipated • Develop action plan and
and assess strategic
and scope of impact
changes in core • Estimate execute with high
implications PortCo-
offerings, customer • Assess PortCo's positions costs/investments urgency
by-PortCo
demand, competitive vs. key competitors required and potential
dynamics outcome
1. Center of excellence 22
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What is GenAI, Why now, and Why it matters
GenAI's impact on Private Equity
Impact on portfolio and new deals
Impact on PE operations
Agenda
How to proceed with GenAI deployment
Why BCG for the GenAI journey
How to partner going forward
23
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Non-exhaustive
Four main GenAI capabilities that can be leveraged across PE operations
Portfolio
Fund operations Deal activity
operations
Fund support Assessment & inv. Exit & re- Portfolio mgmt. &
Fund strategy Deal sourcing
functions decision investment monitoring
Automating/Assisting analysis: e.g, Sentiment analysis, data cleaning, market dynamics and trends, areas of concern / uncertainty
Content summarization & synthesis: e.g., summarization of expert interviews, long documents, publications,. (IR updates/ portfolio developments)
Knowledge mgmt. & access: Increased self-service / access to internal data, policies, training and advanced applications
Content creation: E.g., presentations (pitch decks etc.) and documents (emails, marketing material, legal contract drafting, etc.)
24
Source: Expert interviews; BCG analysis
seitilibapaC
View as of April 2023
25
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View aBs aocfk Mupay 2023
Non-exhaustive
Indicative
Potential products for private equity operations enabled by GenAI (1/2)
quantification
Adoption Value &
Product Description timeline impact
Investment themes Early identification of emerging investment themes based on e.g. social media, news
identification discussions, market publications and developments, investments and/or expert interviews
Investment success factor Identifying and detailing investment criteria for target screening based on past successful
identification investments, market developments and desired investment themes to invest behind
Investor relations (Fundraising Identifying new potential investors across data sources and internet outside of traditional
analytics, relationships) databases, and based on their lifecycle tailor products and timing to their emerging needs
Product development assistant General product development, incl. e.g., code writing assistance for IT department to
(e.g. coding) accelerate IT developments and data science efforts across the company
Risk monitoring (LP financing, Risk and compliance monitoring across support functions, incl. KYC/AML, invoicing and
and compliance) expense compliance, LP agreement and regulatory / legal compliance monitoring
Talent mgmt. (marketing, Create job descriptions and personalized outreach, and by analyzing missing team
interview assessment, capabilities using employee data, such as performance reviews and job history, it can help
recruiting) suggest applicants to target or review, as well as to help interview process
Landscape assessment
Analyze large volumes of data from various sources and generate insights that aid
(dynamics in particular
landscape assessment ahead of specific target identification
segments)
Target identification, filtering Identify potential targets based on investment criteria, competitive environment and
and comparison investment thesis
Note: Adoption and impact quantification is to be considered relative to one another (what will be the first, middle and last group of applications emerging)
Source: Desktop research; BCG analysis 25
Short- Long-
Degree of impact Wide adoption in: Short-term <3yrs Long-term >5yrs
term term
26
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View aBs aocfk Mupay 2023
Non-exhaustive
Indicative
Potential products for private equity operations enabled by GenAI (2/2)
quantification
Adoption Value &
Product Description timeline impact
Generative AI creates research and outline content of (pre-) due diligence (incl. market
Due diligence assistance
dynamics, landscaping etc.) in addition to identifying key analysis questions
Tool to draft deal structure and align incentives efficiently across stakeholders to create
Deal structuring tool
customized offer to decision makers
Market survey creation and Creation of market survey questions in research process, e.g., customer sentiment or
analysis customer KSC, and preliminary analysis of survey results
VDD & Sales material Draft first version of VDD and sales prospectus including company profile, historical
assistance financial information etc., and facilitate data collection, research as part of prospectus
Automated exit assistant Generative AI powered assistant to manage and co-ordinate process stakeholders with
(process mgmt.) scheduling, automated responses and transaction marketing to potential buyers
Continuous portfolio monitoring of emerging risks and performance based on updating
Portfolio monitoring (risks and
market conditions and developments with potential to synthesize data for outlook and
performance etc.)
sensitivity assessments
Value creation plans and Creating creative value creation plans and ideas through analysis of current events and
Variable
assistance market dynamics, to best help portfolio companies grow their business
In addition, range of horizontal Range of horizontal GenAI applications for HR processes (recruiting, interviewing, etc),
Variable Variable
applications non-PE specific legal (contracting, compliance reviewing, etc.), finance (invoicing, expensing), marketing (
Note: Adoption and impact quantification is to be considered relative to one another (what will be the first, middle and last group of applications emerging)
Source: Desktop research; BCG analysis 26
Short- Long-
Degree of impact Wide adoption in: Short-term <3yrs Long-term >5yrs
term term
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What is GenAI, Why now, and Why it matters
GenAI's impact on Private Equity
Impact on portfolio and new deals
Impact on PE operations
Agenda
How to proceed with GenAI deployment
Why BCG for the GenAI journey
How to partner going forward
27
28
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Portfolio Actions
• Assess entire portfolio and identify opportunities,
efficiencies and risks arising from GenAI, analyzing its
impact on productivity and value proposition
• Start identifying 'transformative use cases' for
internal operations, partner up to experiment with
Several
technology
immediate steps
• Start evaluating 'Responsible AI' policies for the
for your GenAI organization to manage potential risks
roadmap
Investment Actions
• Review investment strategy, including identifying
investment themes in GenAI
Setup GenAI control tower to coordinate efforts across
the organization and within the portfolio
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Generative AI does still
come with risks...
… but many of
Intellectual property and these can
copyright infringement
eventually be
mitigated with
Biased outputs
the right
Responsible AI
Cybersecurity and
data privacy
approach
Hallucination / confidently
wrong answers
Midjourney | ""leaping between two boulders""
29
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What is GenAI, Why now, and Why it matters
GenAI's impact on Private Equity
Impact on portfolio and new deals
Impact on PE operations
Agenda
How to proceed with GenAI deployment
Why BCG for the GenAI journey
How to partner going forward
30
31
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Cross-functional teams with specialized
1 skillsets across the AI/ML stack with proven
3,000+
track record of enabling applications in GenAI
tech build & design team
Advisor to
Deep understanding of how investors are
2
world leading PE Funds
assessing the GenAI landscape globally
What makes and Software Investors
BCG unique
in this space?
Prioritized ac"
291,bcg,harnessing-the-power-of-genai-in-indonesian-financial-services.pdf,"+ +
Harnessing

the Power of (Gen)AI
in Indonesian
Financial Services
August 2024
Table of Contents
02 Foreword: Unlocking New Ventures & Financial
Empowerment in a Booming Economy

03
Executive Summary
06
The Role of AI & GenAI in Transforming
Indonesian Financial Services
11
3 Strategic Plays for GenAI Integration
13
Themes Defining the Opportunity for AI & GenAI
23
Inherent Risks & New Challenges in the Age of GenAI
27
Enterprise Foundations Framework for (Gen)AI
Success
32
Authors
Harnessing the Power of (Gen)AI in Indonesian Financial Services
Foreword
Unlocking New Ventures &
Financial Empowerment

in a Booming Economy
When we set out to build this report, we is leading to fast-paced investment
sought to explore the strategic impact of discussions about how to multiply and
Generative AI (GenAI) on the financial bolster local data centers for domestic
services sector in Indonesia. We wanted to GenAI development. Because data centers
understand its potential to drive significant consume so much power, public and
sector-wide transformation and deliver a private sector stakeholders are having
new generation of investment and venture serious discussions related to Indonesia’s
opportunities in an economy growing at 5% energy infrastructure, the clear role that
annually and projected to reach US$1.47 renewables must play, and how to finance it
trillion this year.

 all.
The report was motivated by a global shift This report provides an in-depth analysis of
toward GenAI, which is rapidly altering the Predictive AI and GenAI adoption within
competitive landscape across many Indonesian financial services. It outlines
industries. In local financial services, this emerging opportunities, highlights
technology offers not just incremental fascinating case studies, and discusses
improvements, but foundational shifts that ways to address key challenges. It also
enable new business models and enhance offers actionable insights for companies
operational efficiencies.

 seeking to enter the market as well as
established entities aiming to integrate new
AI has the potential to increase access to technologies.


financial services across broader
demographics and underserved markets. In As we release this report, we urge
particular, it can help rural and remote policymakers, business leaders, and
communities access services such as investors to consider its contents and
credit, insurance, and savings, thus insights. The integration of GenAI presents a
stimulating economic engagement at unique opportunity to redefine financial
multiple levels.

 services in Indonesia, enhancing both
economic efficiency and empowerment.
For global institutional investors, there is a
robust and multifaceted opportunity in
Pandu Sjahrir
Indonesia as we speak. AI and GenAI
promise great applicability in sectors such Founding Partner, AC Ventures
as healthcare and education, two spaces Head of Technology & Digital
that tie directly into financial services via Finance, Kadin Indonesia

insurance and credit.


Meanwhile, as the government seeks to
establish a regulatory framework for this Andy Lees
new tech, the concept of “Sovereign AI” has Managing Director & Partner,
become top-of-mind for the incoming BCG X
administration. In a cascading fashion, this
Harnessing the Power of (Gen)AI in Indonesian Financial Services 2
Executive Summary
AI and GenAI are transforming the financial sector, reducing barriers to entry and enabling new
players to compete in the market against established institutions. In Indonesia, these technologies
are enhancing financial education, credit scoring, customer service, and more.
How Financial Institutions View GenAI:
85% of global financial institutions view GenAI as highly disruptive or
transformational, yet only 18% have a clear strategy and are implementing it.
58% of GenAI users globally are saving at least five hours a week, shifting focus to
using this extra time for value creation and customer joy.
Global worker confidence in GenAI has increased by 16 percentage points to 42%
since 2023, but fear of job loss has also risen by 5 percentage points to 17%.
51% of Indonesian financial institutions are focusing on deploying GenAI for
everyday tasks, and an additional 27% see great opportunity in inventing new
products and services.
49% of business leaders in Indonesia's financial sector prioritize GenAI to enhance
customer service, with 34% already seeing tangible benefits from its deployment.
61% of Indonesian financial institutions are confident they have strong
technological readiness with established data and tech stacks for GenAI.
3 Strategic Plays for AI Integration
Deploy Reshape Invent
GenAI in everyday critical functions end-to- new experiences, offerings,
tasks for broad end for radical efficiency and business models
enterprise productivity and effectiveness powered by GenAI
Financial institutions in Indonesia can GenAI pilots are in progress across all major
successfully integrate AI and GenAI into their Indonesian financial institutions, with many
operations using the proven strategic plays of already transitioning these initiatives into
Deploy, Reshape, and Invent.

 scalable projects. These efforts aim to
democratize financial access and inclusion,
This involves deploying enhancements to especially for underserved communities,
everyday use cases, both internally and on the aligning with the stringent compliance
customer-facing side; reshaping in-house requirements of Indonesia’s Personal Data
processes and skills for greater efficiency; and Protection (PDP) Law. The government is also
inventing completely new products to achieve enhancing AI and GenAI regulatory frameworks.
greater savings and create new revenue
streams.
Harnessing the Power of (Gen)AI in Indonesian Financial Services 3
Advice for Business Leaders
The potential of GenAI in
Indonesia's financial sector is
evident—it can expand financial
access, enhance customer
experiences, enable rapid
service scaling, and more.


Our research indicates that both
major financial institutions and
fintech startups have quickly
adopted this technology. But
many of these initiatives are still
in the pilot stage and have not
yet delivered substantial
business value at scale.
Financial institutions need a strategic
framework for AI that includes governance,
operations, and talent management to align
with business objectives. This report reveals
that neglecting the business perspective can
cause AI implementations to fail. It is crucial
to ensure AI projects match actual business
needs, emphasizing data security and
regulatory compliance, while also focusing
on goal setting, responsible AI development,
and staff training. This approach balances
technological readiness with business and
ethical considerations, integrating AI
effectively within organizational practices.


Harnessing the Power of (Gen)AI in Indonesian Financial Services 4
Kadin Indonesia’s Advice

to Policymakers
AI and GenAI have clear potential to boost To realize this, Indonesia may strategically invest in
Indonesia's economic growth and global robust, sustainable data center infrastructure
competitiveness by transforming the way state- enhanced by renewable energy sources, and
owned enterprises and government agencies underpinned by stringent legal frameworks for data
operate. With this in mind, the local administration privacy and autonomy. Emphasizing public-private
could prioritize the creation of trusted AI within its partnerships and collaborations could accelerate
overarching strategy.

 the process, while strict cybersecurity measures can
safeguard the country’s critical data assets.


The strategic development of sovereign AI offers
promise across various sectors, from financial While this report highlights private-sector strategies
services to clean energy, commerce, and beyond. for AI and GenAI implementation, government
Developing this technology under the oversight of leaders may want to observe, adapt, and modify
the incoming administration has the potential to these approaches for their own initiatives while also
bolster national security and ensure Indonesia's considering three core strategies:

technological independence for future generations.
1
Establishing a National AI Research & Development Fund
Central to a potential sovereign AI initiative could be Cost-Benefit Outlook: The primary expenditures
the creation of a National AI Research and would involve initial funding for research and key
Development Fund. This fund would focus on infrastructure, such as data centers. The future
spurring AI innovation in critical economic sectors benefits may include but are not necessarily limited to
such as healthcare, agriculture, and manufacturing. enhanced sector efficiencies, job creation in AI and
Government agencies would need to allocate GenAI development, and improved international
appropriate resources to this fund, sourced or business competitiveness, all of which are relevant to
redirected from Indonesia's existing technology- annual GDP growth due to productivity improvements
related investment funds and programs. and new business opportunities.

2
Implementing AI Education & Training Programs
Integrating AI and GenAI into Indonesia’s state- Cost-Benefit Outlook: Costs on this frontier involve
owned digital ecosystem—and society at large— the operational expenses of education programs.
would require a strong emphasis on education and The anticipated benefits are a skilled workforce,
training. These initiatives could span all reduced unemployment, and increased average
educational levels, incorporating academic and income levels. Such educational initiatives may
vocational training to prepare a workforce skilled in reduce skill gaps relative to Indonesia’s global
AI technologies. Significant annual investments peers, enhance employment rates in the
may need to be made to develop curricula, train technology sector, and boost median salaries due
educators, and establish AI-equipped learning to an influx of skilled labor.
centers for government agencies, as well as
Indonesia’s secondary and tertiary schools.

3
Incentivizing AI Startups & Foreign Investment
For a thriving sovereign AI ecosystem, the Cost-Benefit Outlook: While administrative costs of
Indonesian government could also support AI managing these drives may be clear and present,
startups and attract direct foreign investment with such incentives could spur more domestic and
favorable policies and incentives. A specific portion international investments in Indonesia, driving
of the annual budget could be allocated for tax technological advancements and further economic
incentives, direct subsidies, and support services expansion. A few key potential impacts may include
for AI companies licensed to operate in Indonesia.
 an increase in foreign direct investment, the creation
of high-tech jobs, and a more competitive market
environment driven by innovation.

Harnessing the Power of (Gen)AI in Indonesian Financial Services 5
The Role of

AI & GenAI

in Transforming
Indonesian
Financial
Services
Harnessing the Power of (Gen)AI in Indonesian Financial Services 6
The Basics:

Predictive AI vs GenAI
In the context of financial services, the use of AI is compliance checks, but also at making consistent
not a particularly new trend. However, it is crucial to and accurate inferences—often surpassing human
differentiate between Predictive AI and GenAI. capability—from vast data sets, particularly in
Predictive AI excels not just at automating routine areas like risk and fraud detection. [Exhibit 1]
tasks such as transaction processing and
GenAI complements and co-exists with previous
Predictive AI efforts, enabling new and broader
applications
[Exhibit 1]

Predictive AI Generative AI
“Left brain” “Right brain”
Decision-making and Content creation,
optimization

 qualitative reasoning,
orchestration of other
Each algorithm systems


constrained to specific
problem space

 Multi-applications


Limited range of possible Unlimited range of
outputs possible outputs
Impact: 5% of employee Impact: 50% of employee
tasks and workflows; tasks and workflows;
focuses on key decisions complemented with impact
by Predictive AI
Exhibit 1
In contrast, GenAI, as a newer technology, takes a This capability not only saves
significant leap forward by not only understanding
the user's context—including who they are and why time and energy but also
they are requesting information—but also by
broadens the use cases from
communicating in a more flexible, fluid, and natural
manner compared to the more algorithmic, rules- very specific applications with
based interactions typical of Predictive AI. This
capability allows GenAI to generate new, context- Predictive AI to a wider range of
aware content on demand, rather than simply
applications with GenAI.
retrieving existing information.
Harnessing the Power of (Gen)AI in Indonesian Financial Services 7
GenAI's conversation, summarization, and knowledge extraction capabilities may help create broader
access to loans and working capital for Indonesians. This could noticeably influence the nation's economic
landscape and potentially support financial inclusion. [Exhibit 2]
Main capabilities of GenAI that drive value
[Exhibit 2]

Content Problem
Tech Knowledge
Conversation generation/ Summarization Ideation solving
 AI agents
capabilities extraction
transcription & Insights
I​nteractive T​he creation of S​ummarization G​ eneration of E​xtraction of L​ogical & The solving of
& dynamic specific types of of large new
 structured reasoning complex tasks
engagement of content (e.g., text, amounts of ​& innovative knowledge from process to by planning and
information, ideas, images, videos, information or ideas, concepts unstructured or make executing a set
or questions audio, code) text into shorter, or designs (e.g., semi-structured
 inferences, draw of actions using
Description between humans more concise unique product d​ata sources conclusions, a suite of tools
& AI systems, versions, that solutions, make informed
responding to capture the key exploration of judgments, and
questions and points of the uncharted derive new
generating content territories in insights based
appropriate scientific fields) on available
responses information,
data, or
knowledge
Exhibit 2
BCG and ACV conducted a survey of 41 business
leaders from traditional financial institutions in Organizational perspectives
 Exhibit 3
on Predictive vs GenAI
Indonesia for this report. The majority of leaders
view Predictive AI and GenAI as complementary
technologies. However, nearly a quarter of the
leaders consider Predictive AI more crucial,
particularly due to its role in core financial products
like credit scoring and credit limit calculations. 66%
Despite this, it is evident that GenAI has quickly
established itself as a legitimate and valuable tool
for the financial sector within just two years.
[Exhibit 3]
A majority of business 22%
12%
leaders see Predictive AI
and GenAI as
They are Predictive AI is GenAI is more
complementary more important important
complementary
[Exhibit 3]

Harnessing the Power of (Gen)AI in Indonesian Financial Services 8
Economic Impact & Growth

Projections
The economic impact of AI and GenAI in Southeast Asia is poised to be significant, with projections
indicating a substantial US$1 trillion GDP uplift by 2030.


In our survey, we found that business leaders' top three expectations from GenAI include improving
operational efficiency within their organizations, enhancing customer experience, and spurring innovation in
financial products and services. [Exhibit 4].
The most significant
Anticipated benefits of Integrating GenAI Exhibit 4
anticipated benefit
85%
of GenAI is the
76%
improvement of 66%
56%
operational
41%
efficiency, such as 32%
the automation of
basic tasks.
[Exhibit 4]

Improved Enhanced Innovation in Improved Cost Advanced
operational customer product/ employee reduction risk
efficiency experience services productivity management
Harnessing the Power of (Gen)AI in Indonesian Financial Services 9
Impact on Traditional Financial
Institutions & Fintech Startups
Traditional Financial Institutions
GenAI adoption by banks has been surprisingly rapid. All mid to All mid to large-sized
large-sized financial institutions surveyed in Indonesia are at financial institutions
the minimum running pilots and proof of concepts with GenAI, surveyed are at least
with a majority having implemented a few use cases at scale
piloting GenAI, with a
for customers or employees. Considering GenAI has only been
majority having
on the market for two years, its rapid adoption by traditional
financial institutions, even within a highly regulated industry, implemented a few use
underscores how seriously these businesses are taking the cases at scale for customers
technology. [Exhibit 5] or employees.

[Exhibit 5]

Exhibit 5
GenAI use in traditional financial institutions %
Piloting the technology and running 41%
proof of concepts
Some instances of implementation at 39%
scale for employees or customers
Several instances of implementation at
20%
scale for employees or customers
Fintech Startups
The AI and GenAI boom is lowering barriers to entry The rise of fintech companies, empowered by AI
in Indonesia's financial services sector, enabling and GenAI, is transforming Indonesia's credit
fintech startups to challenge traditional banks services sector by offering personalized, accessible,
more effectively. This shift also encourages and efficient credit solutions—tasks that previously
strategic partnerships that merge fintech required large departments and specialized
innovation with the robust infrastructure and expertise. These AI-driven innovations enable
operations of conventional banks, enhancing fintech firms to gain significant market share,
operational efficiencies and expanding service challenging the dominance of traditional financial
offerings.
 institutions and forcing them to innovate.


Many of Indonesia's large banks have introduced
Innovative Dynamics in the signature digital banking apps, allowing users to
Financial Landscape
 manage their financial lives fully online—from
opening accounts to making investments—without
AI and GenAI are blurring the lines within the fintech ever visiting a bank branch.
ecosystem, enabling SaaS companies to move into
fintech, fostering partnerships between fintechs Mid to large-sized banks are increasingly exploring
and banks, and pushing banks to adopt fintech-like microfinance. Our data shows that 51% of surveyed
strategies.

 entities are testing or have implemented GenAI to
simplify lending and loan disbursement in this area,
For example, ESB has transitioned from a restaurant but long-term benefits have yet to be realized
SaaS platform to facilitating loans by leveraging (Exhibit 9). 12% of respondents see significant
transaction data to assess SME credit risks more potential in GenAI for microfinance, as it enhances
effectively.
 credit assessment with unstructured data sources.
Harnessing the Power of (Gen)AI in Indonesian Financial Services 10
3 Strategic
Plays for GenAI
Integration
Harnessing the Power of (Gen)AI in Indonesian Financial Services 11
Both established institutions and early-stage Through our survey results, we found that financial
fintech startups recognize the importance of a institutions are prioritizing the integration of GenAI
diversified set of strategic plays when integrating AI into everyday tasks (Deploy) to enhance specific
and GenAI into their existing technology stacks and operations, such as customer service. This is
operational procedures. Based on our findings, followed by a focus on the creation of new products
companies successfully capturing outsized upside and services (Invent), indicating that companies
engage in one or more of the following strategic are addressing immediate needs while also aiming
plays. We’ve broken them down into three key to leverage GenAI for the development of entirely
categories: Deploy, Reshape, and Invent. new products and services. [Exhibit 7]
There are 3 strategic plays to leverage when
considering integrating GenAI into products, services,
and systems
[Exhibit 6]

Deploy Reshape Invent
GenAI in everyday critical functions end-to- new experiences,
tasks for broad end for radical efficiency offerings, and business
enterprise productivity and effectiveness models powered by GenAI
This refers to the use of AI and Reshape involves the Invent is the practice of
GenAI for everyday tasks, reimagination of specific in-house leveraging GenAI to pioneer new
typically involving an existing processes, for radical operational experiences, offerings, and
product. Examples include efficiency changes. Examples business models. Firms create
employees using GenAI to could include GenAI-powered hyper-personalized financial
summarize meetings and calls chatbots that handle customer products and services that meet
in seconds, highlight key inquiries with speed and precision, the unique needs of Indonesia's
takeaways from lengthy reports, tools that detect fraudulent diverse population. GenAI also
and quickly create first drafts of activities by analyzing patterns in facilitates the rapid testing and
communications. Additionally, real time. This requires a holistic, deployment of innovative
GenAI can assist with more centrally coordinated effort to business models, such as
technical and specific aspects transform work and workforce mobile-first banking solutions
of workflows, such as data dynamics with responsible AI and micro-financing tailored to
labeling and analysis. This enablers where possible, rather rural communities. New products
approach dramatically saves than a set of isolated use cases enhance customer engagement
time and also increases the with limited scalability and but also expand financial
quality of outputs.
 decentralized rollout.
 inclusion across the archipelago.

Traditional financial Exhibit 7
GenAI strategic priorities for
institutions are focusing on
financial institutions in Indonesia
deploying GenAI for
51%
everyday tasks, and see
great opportunity in
27%
22%
inventing entirely new
products and services.
[Exhibit 7]
 Deploy Reshape Invent
Harnessing the Power of (Gen)AI in Indonesian Financial Services 12
Themes
Defining the
Opportunity for
AI & GenAI
Harnessing the Power of (Gen)AI in Indonesian Financial Services 13
In addition to surveying 41 business leaders from relationship managers (RM) to better engage
traditional financial institutions in Indonesia, we with customers. About one-fifth (22%) of
also conducted interviews with five fintech startups companies have introduced such RM tools and
in the country’s financial services sector. These are now seeing positive outcomes.


executives are integrating AI and GenAI into their
operations, and we’ve identified six recurring Hyper-personalization and fraud management
themes of opportunities across a range of are identified as areas with significant potential,
company types, from emerging micro-fintech firms though they are still in the nascent stages of
to well-established financial institutions.

 implementation, with only 7% of companies
having launched related products and seen
Per our survey, nearly half (49%) of business leaders gains. There is a notable opportunity for
see customer service as the primary application for organizations to explore hyper-personalization
GenAI, with a third (34%) of companies already further, which, if effectively implemented, can
reaping benefits from its implementation. The next lead to successful upselling and cross-selling on
promising area for is enhancing tools for financial platforms. [Exhibit 9]
GenAI leads in customer service among business leaders, with 49%
identifying it as the primary application for the technology. 34% are
already seeing benefits, while relationship management, hyper-
personalization, and fraud prevention show potential.
[Exhibit 9]

Where financial institutions see the most future potential for GenAI Exhibit 9
5% 7%
7% 15% 12%
10% 15% 29%
7% 15% 22% 37%
12%
22% 15% 20% 22%
24% 17%
15%
37% 37% 15% 20%
49% 15%
20% 12%
7% 12% 2%
15%
7% 7% 7% 12%
Customer service Tools for Relationship Fraud management Hyper-personalization Front and back office Easier lending and loan
agents to improve Managers to engage with Generative AI to engage and upsell employee productivity disbursement in
customer experience customers detection and customers leveraging Generative microfinance
management AI tools
Most potential High Potential Moderate Potential Low Potential Least potential Not Ranked
Progress of GenAI implementation & realization of benefits Exhibit 9
0%
5% 7% 9% 10%
22% 17% 20%
20%
37% 39%
29%
34%
49%
51%
10% 24%
37% 39%
7%
34% 15% 10% 5% 22%
22%
7% 7% 7% 5%
Customer service Tools for Relationship Fraud management Hyper-personalization Front and back office Easier lending and loan
agents to improve Managers to engage with Generative AI to engage and upsell employee productivity disbursement in
customer experience customers detection and customers leveraging Generative microfinance
management AI tools
GenAI rolled out and benefits are being realized GenAI rolled out, but benefits are not yet being realized
Piloting GenAI Planning to implement GenAI, but not yet in pilot phase No plans for GenAI
Harnessing the Power of (Gen)AI in Indonesian Financial Services 14
1
Customer
AI-driven customer service improves
Success
 customer experiences by efficiently handling
inquiries through natural language
of the Future processing, enhancing both customer
satisfaction and operational efficiency.
Fintech Startups Traditional Financial Institutions
Customer service is pivotal in helping fintech Banks are not just focusing on enhancing customer
startups scale by reducing costs and maintaining service with GenAI but are aiming for a
the necessary 24/7 support for sensitive products comprehensive service transformation across
like credit cards. With government caps on credit multiple touchpoints. They are employing GenAI-
lending profits, minimizing non-core operating enabled tools and processes to quickly respond to
expenses is essential. Utilizing GenAI as the primary customer inquiries, build stronger relationships
interface in customer service ensures consistent between clients and relationship managers, and
support at significantly reduced staffing costs. This shift conversations from service to sales.
strategy not only enhances customer experience
but also lowers non-core expenses.
Fintech Case Study
SkorLife, an Indonesian credit builder,
aimed to reduce customer service
costs by 50%.
As customer service constitutes 40% of its operating expenses, the
team implemented GenAI assistance to provide consistent, 24/7
support. Recognizing that such use cases are applicable across
industries, SkorLife chose to partner with a vendor to develop its
GenAI customer service capabilities instead of building them in- Ongki Kurniawan

house. Co-founder & CEO
44% of business leaders stated that their primary
Exhibit 10
Largest benefits of using goal for implementing GenAI in customer service
GenAI customer service is to establish 24/7 support, crucial for addressing
urgent issues like credit card fraud and loss that
demand immediate action. Additionally, 37% aim
to use GenAI to enhance the quality of customer
44%
service. Only 5% of traditional financial institution
37% respondents consider cost reduction as a goal for
adopting GenAI, highlighting a significant
difference from the priorities of smaller fintech
companies. [Exhibit 10]
Prioritizing 24/7 customer
12%
support with GenAI in
5% traditional financial
2%
institutions: A key strategy
24/7 Higher quality Reduced Lower cost to Improved
customer customer waiting serve rates of debt
support service period for customers collection beyond cost reduction.
customers [Exhibit 10]

Harnessing the Power of (Gen)AI in Indonesian Financial Services 15
2
Productivity GenAI is transforming employee productivity. A
recent BCG survey on AI usage in the workplace
Co-Pilot
identified Asia Pacific (APAC) as one of only two
regions globally where a majority of frontline
workers utilize GenAI tools. Furthermore, nearly a
third of APAC employees have received training
on how GenAI will impact their roles, a figure that
surpasses the global average.

Fintech Startups Traditional Financial Institutions
Enterprise-grade instances of GenAI The potential productivity gains for banks in Indonesia are
remain too costly to deploy across entire immense at scale. With thousands of employees spread
companies. Instead of adopting a broad across various branches, the implementation of GenAI
use case approach, leaders are first could revolutionize operations, streamlining tasks that
assessing the costs and benefits to ensure previously took hours into mere minutes. This results in
that their employees can effectively utilize significant time savings across the board. GenAI users
the technology. This strategy focuses on report saving at least five hours a week using these tools.
addressing time-intensive, low-value tasks Additionally, there is a significant opportunity to invest in
within specific teams and processes. upskilling employees to effectively interact with GenAI tools.

Fintech Case Study
JULO, a credit lending app, has its data
analytics team utilizing GitHub Co-Pilot to
boost coding productivity, resulting in a 2x
increase in engineer efficiency.
For data warehouse queries in BigQuery, the team employs Gemini, which
accelerates query responses by 2x to 3x, significantly reducing the time to
gain insights. Additionally, mundane tasks like data classification are
managed by GenAI, which automates the tagging and labeling of data, Martijn Wieriks

minimizing the need for manual intervention. Chief Data Officer
Exhibit 11
37% of business leaders are leveraging GenAI to
Largest benefits of using
improve the quality of their employees' work,
while 27% are prioritizing its use to increase GenAI for employee productivity
employee productivity through capabilities in
content generation, summarization, and flexible
task completion. Notably, leaders aim for
37%
employees to enhance the quality and speed of
their core tasks rather than freeing up time for
more strategic work. [Exhibit 11] 27%
24%
Business leaders are looking
to improve the quality and
10%
speed of employees' core work
rather than to free up time for
more strategic work. Improve quality Increase output Reduce time Free up time for
[Exhibit 11]
 of employee and speed of spent on more strategic
work employees administrative work
tasks
Harnessing the Power of (Gen)AI in Indonesian Financial Services 16
3
Rapid Lending Predictive AI and machine learning have been crucial
in analyzing borrower profiles, accelerating decision-
with Reduced making, and reducing non-performing loans. Among
other things, Predictive AI can deliver deterministic
outcomes with explainability for the underlying
Risk
rationale—a capability that GenAI has yet to fully
develop. However, GenAI excels at utilizing
unstructured data sources that contain quality data,
providing additional data points to enhance models.
Fintech Startups Traditional Financial Institutions
While GenAI shows promise, leaders are not With the abundance of unstructured data in large
yet prepared to replace Predictive AI for companies, it can be easy to overlook valuable data
mission-critical tasks. Instead, they are using sources that have not yet been integrated into models.
it to enhance supporting functions with GenAI has the ability to enhance Predictive AI credit
capabilities where it excels. models by incorporating supplementary information
from unstructured data sources, such as rejected
applications, to improve the models' understanding.
Fintech Case Study
Broom is a company that provides an
end-to-end financial solution for auto
dealer inventories in Indonesia. It
leverages AI to expedite credit
approvals.
Users may submit their applications before mid-day and receive
funds within a few hours. AI reduces underwriting time from an
hour to just 10 minutes. An employee then double-checks to
ensure 100% accuracy, serving as the final checkpoint and
demonstrating the effectiveness of a bionic process. Pandu Adi Laras

Co-founder & CEO, Broom
Finku, a personal finance
application that helps Indonesian
consumers manage their finances
and provides them loans, initially
underwrote loans using credit
bureau data.
Over time, the firm incorporated deep learning and machine
learning into its underwriting process as the basis of its credit
Reinaldo Tendean
 lending model, significantly cutting down non-performing
Co-Founder, Finku
 l"
292,bcg,The-GenAI-Imperative-for-Telco-B2B-Sales-Teams.pdf,"WHITE PAPER
The GenAI Imperative for
Telco B2B Sales Teams
February 2024
By Bryan Gauch, Alexa Vignone, Adolfo Magan, Jean-Marie Pierron, Johannes Goltsche, Basir Mustaghni,
Phillip Andersen, Alfonso Abella, and Ignacio Hafner
Boston Consulting Group partners with leaders Salesforce is the #1 AI CRM for Communications.
in business and society to tackle their most Salesforce for Communications enables service
important challenges and capture their greatest providers to find more prospects, close more
opportunities. BCG was the pioneer in business deals, deliver services more rapidly, and serve
strategy when it was founded in 1963. Today, customers more efficiently by connecting with
we work closely with clients to embrace a customers in a whole new way.
transformational approach aimed at benefiting all
Salesforce brings together all your data, from any
stakeholders—empowering organizations to grow,
source. Salesforce for Communications, powered
build sustainable competitive advantage, and
by Einstein 1, unites your marketing, sales,
drive positive societal impact.
commerce, delivery, service, and IT teams with a
Our diverse, global teams bring deep industry and single, shared view of customer information - at
functional expertise and a range of perspectives scale. With Communications Cloud, an asset-
that question the status quo and spark change. based, catalog-driven, modular solution built on
BCG delivers solutions through leading-edge industry standards, you can further spur growth
management consulting, technology and design, and reduce costs with industry specific functions
and corporate and digital ventures. We work in a like churn predictions, order management and
uniquely collaborative model across the firm and pricing and product designer. With artificial
throughout all levels of the client organization, intelligence embedded within our platform and
fueled by the goal of helping our clients thrive and apps, Salesforce helps augment everyone in your
enabling them to make the world a better place. company to work more productively and better
deliver the personalized experiences customers
love.
The GenAI Imperative for Telco B2B
Sales Teams
T
he future—in the form of both predictive and generative artificial intelligence (AI)—is
calling communications service providers. In every conversation with customers,
partners, and industry analysts, we have heard how excited telco B2B sales teams are
to embrace the new era of data, predictive AI, and now generative AI. They’re so eager that
sales ops teams are even reinventing their role as AI ops. It’s clear that AI could energize
service providers and help them deliver the true potential for their customers and experience
accelerated growth themselves. Harnessing the technology’s power, however, is a journey
that requires a progressive approach that generates value at each incremental step. Many
have started that journey but have yet to harness its full potential.
Transforming an organization to take advantage of AI/GenAI will leverage data, advanced
technology, infrastructure, and human interaction to create powerful end-to-end sales
processes. Harnessing this, we see a total value potential for telco operators of 40 percent to
70 percent uplift in EBITDA from driving both top-line growth and bottom-line efficiencies.
The telco industry, like utilities and other mature industries, faces a fundamental challenge:
growing their business while accelerating cost optimization and initiatives to find economies
of scale. This is particularly true in the B2B sector, where administrative tasks take up the
bulk of sales representatives’ time, the catalog of products is increasingly complex, and
legacy applications are clogging the system—frustrating reps and customers alike. In BCG’s
view, transforming the organization and its ways of working, supported strongly by developing
and deploying AI and GenAI, presents the best, perhaps the only, viable path for
organizations that want to break out of their stasis and generate positive momentum.
Even if organizations are beginning to employ AI/GenAI in some of their operations, its
deployment among sales professionals is often very much suboptimal (see Exhibit 1).
Exhibit 1 - How sales reps spend their time
9.2% Prioritizing leads/ opportunities 10.4% Meeting in person with customers
9.3% Researching prospects
9.4% Connecting virtually with customers
28%
9.0% Preperation and planning 8.7% Prospecting
Selling
72%
9.4% Generating quotes/
proposals and gaining approvals
Non-selling 8.8% Internal meetings and trainings
8.8% Manually entering customer
and sales information
8.3% Downtime
8.8% Administrative tasks
Source: State of Sales, 5th ed. report, Salesforce.com 2022
BOSTON CONSULTING GROUP + SALESFORCE 1
Sales reps spend 72 percent of their time on administrative and non-selling tasks, including
prioritizing leads, researching prospects, and planning. BCG and Salesforce have designed
and deployed several scenarios demonstrating how combining different types of AI can
transform B2B sales for telcos.
Leveraging these technologies and properly integrating them into sales processes will drive
productivity by automating or accelerating many steps. Predictive AI analyzes and evaluates
information, and GenAI synthesizes information and relays original output in natural
language—creating, for instance, an interactive engine to understand solution options for
specific customer needs (see Exhibit 2).
The unique characteristic of GenAI, which is based on powerful large language models
(LLM), is its ability to synthesize data that was input or extracted from unstructured data -i.e.
data that is typically categorized as qualitative data, does not have a predefined data model,
and cannot be processed and analyzed via conventional data tools and methods. GenAI can
then generate original data in different formats—text, images, sound, etc. GenAI platforms
trained in text, such as ChatGPT, Cohere, Anthropic, etc. can “understand” conversational
prompts and create original text, a complementary genre of output than, say, predictive
forecasting or recommendations based on crunching years of behavioral data (see Exhibit 3).
Our vision for AI-assisted sales
How well organizations integrate such AI models in the day-to-day of their employees and
their customer engagements is the key to success. Most clients we have worked with over-
index on the wrong elements, severely underestimating the change effort required. Based on
numerous deployments in different industries, we are convinced that three factors are
critical to success:
Exhibit 2 - PredAI and GenAI need to be combined to drive new frontiers
and accelerate existing applications
Not exhaustive
PredAI/ML GenAI
Use Predictive AI for decision-making Use Generative AI for content generation
Unstructured data
Dynamic pricing engines
ingesting & interpretation
Lead scoring and Synthesize findings in
prioritization large datasets
Demand
forecasting
Cross-sell / upsell Write and debug code
Protein
Churn prevention design & Creative content generation
selection
Other PredAI/ML applications Other GenAI applications
Use the combination of PredAI &
GenAI to maximize impact
generation
Source: BCG Analysis
2 THE GenAI IMPERATIVE FOR TELCO B2B SALES TEAMS
• 70 percent of the success revolves around processes and people: business process
reinvention, adoption at scale within the organization (which increases dramatically if the
“why” is clear behind the prediction), change (which requires the sponsorship and buy-in
of leaders), and rewiring the operating model. Equally important are well-defined business
objectives. Introducing new platforms without prioritizing the desired high-value, scalable
business outcomes, or simply using them for isolated applications, would vastly under-
exploit their potential for business transformation.
• A much smaller amount—20 percent—will be directly related to the technology stack and
foundations to make it work: model infrastructure, machine learning operations (MLOps),
data quality assurance, architecture design, app integration, and leveraging digital
platforms in the cloud, especially for their business support system (BSS) stack.
• Finally, 10 percent can be linked to the most disruptive technological advancements:
GenAI and AI/ML models (see Exhibit 4).
How to realize the value of AI
To make this tech-enabled organizational transformation a reality, organizations need to
actively consider their readiness and willingness to embark on this journey.
We understand that companies have different horizons in terms of their readiness to deploy
AI tools. We see three distinct horizons to choose from, advancing in maturity while implying
a greater need for transformational change and shift in the go-to-market strategy and
customer engagement.
1. Task automation and augmentation. Easier to realize, enhancing the business as usual
with tools, often out of the box from vendors, that speed and improve the process, such as
automating call summaries, generating targeted customer insights and emails, updating
CRM records, etc. Leveraging just these capabilities would allow sales teams to gain
substantial productivity already, addressing mostly their non-selling time.
Exhibit 3 - GenAI will not replace AI, but rather seamlessly incorporate it
to improve enterprise capabilities across the value chain…
Generative AI is complementary to AI & ML offerings Implications
Generative AI
• Step-change in ability of models to summarize, Gen AI extends AI capabilities…
categorize and generate language
• …by simplifying user interfaces, embedding in
• Generation of novel examples by learning workflows and enabling efficiency and
patterns in the data it is trained on and ability effectiveness across industries
to work with other media than just text/language
• Creation of true conversational user interfaces,
giving rise to new class of applications
AI AI use cases will persist….
• ...such as attribution modelling, budget
• Human-led programming sometimes constrained by allocation, personalization, and forecasting.
predefined rules
• Performance restricted to tasks within the programming
scope
ML
• Algorithms designed to analyze vast amounts of data and infer
ML allows AI to learn and improve accuracy…
correlations and causations
• …by allowing AI to find and learn patterns in data
• Provides the fuel to enable AI to autonomously improve outcomes
without being explicitly programmed
• …by helping AI make informed decisions based on
data with high degrees of accuracy
Source: Forbes; BCG analysis
BOSTON CONSULTING GROUP + SALESFORCE 3
2. Reimagined individual workflows. Reshape end-to-end solutions for sales agents.
Typical outcomes include lead outreach and qualification, quote updates, approval
processes, or the automated creation of proposals, hence unlocking revenue upsides and
competitive advantages from time to market.
3. Transformational change. This represents a fundamental shift in sales motions and
how the telco interacts with its customers. It requires the organization to adopt new
ways of working, undertake complete end-to-end redesign of processes crossing different
departments, and orchestrating across solutions that span many systems in their
architectural landscape, leveraging both structured and unstructured data (see Exhibit 5).
The outcome will enable processes to be executed at hyper speed and will create a new type
of empowered sales agent. An end-state vision is a no-touch sales process with minimal
human oversight, digitized end-to-end and driving quality engagements across the funnel:
the GenAI seller.
Managing the change effort across all of these horizons unlocks the true value of AI/GenAI.
To guide this effort, the following key principles have helped organizations embark on this
journey and make it a success:
• Set the top-down vision and ambition
• Simplification. The goal is to create simple, consistent, and seamless experiences for the
clients, the employees, and the partner ecosystem
• Choose a “lighthouse” use case. Stronger focus on value creation from day one
• Drive rapid organizational change while nurturing a sense of opportunity
• Embed analytics into the operating model and incentivize adoption
• Human-Centered AI (creating AI systems that amplify and augment rather than displace
human abilities)
Exhibit 4 - We can help you to deliver…
10% Disruptive 20% Technology 70% Business
Models Stack Transformation
Define the most strategic models Collaborate to define necessary Deliver lasting capabilities and
based on what is already available tech capabilities, developing and assets, reimagining business
and what we can build together deploying on the existing processes while ensuring fully
infrastructure enabaled teams
Guiding • What platform investments have • What capabilities does the tech • What is the vision for an AI-powered
questions been made? stack already have? sales organization?
• What models are already • What additional capabilities will • What traditional AI and Gen AI use
available? it need to support each model? cases support the desired business
• What combination of traditional • How can we equalize the risks of outcomes?
AI and Generative AI models is existing applications? • How do we adapt our ways of
best suited to the strategic goals? working to make change sustainable?
• How do we bring our teams along the
journey?
• How should we approach training to
ensure independence?
Source: BCG Analysis
4 THE GenAI IMPERATIVE FOR TELCO B2B SALES TEAMS
Salesforce and BCG customer stories
BCG and Salesforce have launched numerous AI/GenAI initiatives and projects for customers
in every industry and in all domains, with a goal to experiment, bring value, and augment
sales and customer operations. We are starting to collect great stories and lessons learned,
as illustrated in these four B2B comms, media, and high-tech examples.
Salesforce reference clients AI transformation project Metrics
Tier-1 Telco operator in EMEA; Einstein Copilot in local language of FAQ for • Deployed in few weeks
comms industry, B2C & B2B employees, including 1,800 articles • Time-saving
• Value enhancement
• Quality enhancement
Business information services Main objective: to mine sales interactions to Improved monetization of
leader; media industry, B2Ba increase sales efficiency and revenue with Einstein, existing customer base
including the following business capabilities: using predictive cross-sell,
up-sell models, pricing
• Summarize insights from customer calls to train
signals, and next best
reps and share feedback with the product team
action
• Mine all sales rep interactions with the
customer & summarize into key insights
• Build a recommendation engine for the print
business to identify customers ready for
upgrades
World leader in artificial This company relies on MuleSoft and Salesforce to • 40% reuse rate
intelligence computing; high- combine the power of APIs and AI to drive employee • Developer time + asset
tech industry, B2B productivity. It can connect back-office systems and reuse, multimillions
AI to build an intuitive chatbot to allow employees savings
to self-serve customer information for faster support.
By leveraging MuleSoft for AI-related projects like
the chatbot, employees can focus their energy on
tasks that require more hands-on attention.
BCG reference clients AI transformation project Metrics
US-based provider for • 3-year bot program, targeting comprehensive • >6% of net recurring
collaboration and capability uplift across the organization; revenue uplift, across
communication tools; comms/ leveraging the best of BCG across while aligning all customer segments
hi-tech, B2B incentives and putting skin in the game from SMEs to large B2B
• Inserting data-centricity into every customer enterprises
interaction, enhancing the technology stack e2e • Uplift x-sell and upsell by
while upskilling >500 sales agents +95%
• Churn -40%
• Price realization by +10%
Integrated US-based provider Sales acceleration through operating model • 2x lead conversion rates
for telecommunication transformation and AI-driven models’ deployment • Reduced Priority 1 stalled
services to drive execution velocity and sales engagement deals by 70%
in high-value deals
Integrated European-based Pipeline push focused on cross-selling enabled >4% of revenue uplift
provider for through win-rooms and AI-driven models’ to drive
telecommunication services opportunity identification and prioritization
BOSTON CONSULTING GROUP + SALESFORCE 5
“
“We are seeing different levels of AI
maturity in telco and recognize the
importance of trust & security and real
ROI in this cost conscious market.
Our customers are excited about the
tangible business results they are
seeing with our unified & composable
architecture that gets enterprise AI
solutions into the hands of the people
who need it, right in the flow of work.”
Alexa Vignone,
Executive Vice President, Salesforce
BCG’s sales transformation client story
The BCG client story began when we were approached by a B2B SaaS provider with annual
revenue between $1-5B. The company’s desire was to become the market leader in their
industry. It was suffering high monthly loses from churn and down-selling, so one goal was
clearly to do better at retaining customers. We undertook an initial diagnostic and noted that
the approach had room for improvement. The sales agents had only adopted existing tools in
a limited way and they lacked a clear understanding of how these could deliver value for
them.
The initial focus was on building the required capabilities—enhancing the platform and
building AI into it, while accelerating the launch of initiatives. To implement, BCG integrated
selected assets with the Salesforce.com platform’s capabilities in a modular approach.
Specifically, the BCG team:
• Developed the base data and infrastructure layer to support personalized account
management and unlocked access to additional data sources and facilitated ingestion.
• Managed campaign and experimentation enabled by a campaign manager-optimizer—
responsible for AI-based action codification, audience selection, and monitoring and
measurement. In addition, this campaign manager allowed us to launch targeted
experiments with new actions and enable an efficient test-learn cycle for the AI models.
• Gathered account intelligence, by building more than 15 different use cases on top of this
enhanced platform, leveraging AI from cross-sell to churn prevention.
• Developed more than 10 AI targeting models whose adoption was facilitated by translating
the models into natural language to build trust and improve decision-making.
Exhibit 5 - We help you to create value in the short- and long-term by iden-
tifying quick wins and by creating a roadmap to re-define workflows and
business models
Source: Forbes; BCG analysis
BOSTON CONSULTING GROUP + SALESFORCE 7
ytivitcudorP
Augment & automate tasks Reimagine individual workflows Drive transformation change
Do what is done today, but faster Change the way work gets done with with a fundamental shift in sales
and better new and redefined workflows motions & customer engagement
• Data curation to help sellers sell more • Advanced automation to take on significant • Execute back-office processes at hyperspeed
effectively (e.g., customer insights) work from sales (e.g., RFP response creation, (e.g., sales ops, deal desk)
• Task level automation to help sellers get quote updates, approval triggers) • Enable direct engagement with customers
more done faster (e.g., CRM updates, call • Precise prioritization (e.g., rethink demand and partners with GenAI 'sellers' and
summaries, tailored emails) gen with GenAI powered lead qualification) QA teams (e.g., RFP response teams
• Advanced automation (e.g., quote updates, to autonomously bid on work)
trigger approvals)
Deploy out of the Configure, build and integrate Invent new, cross-
box capabilities solutions within platform architecture
solutions
Time
• Activated channels by integrating target audiences and activating them in online channels
with Salesforce Sales Cloud for Contact Centers, further enhanced by customization with
BCG’s Agent desktop solution.
BGC teams provided support in all domains, combining classic strategic capabilities and
tech, including consultants, data scientists, engineers, and developers, among others.
From the beginning, we set a clear ambition to enable sustainable value delivery.
Consequently, towards the end of our program, we shifted the joint efforts to ensure
capabilities and ownership could be transferred effectively to the client’s teams across its AI,
tech, campaign management, and execution functions. Overall, the transformation delivered
significant impact, uplifting revenue by 5-8% annually and the effective transfer allowed the
client teams to maintain the performance and continue to iteratively enhance its
capabilities.
Talking about the tech stack
The common thread across all discussions we have with clients, partners, and industry
analysts about the new era of data and AI is concern about tech stack consolidation—not
just about trimming costs, but about accelerating productivity and unleashing growth.
As the case studies above show, the list of ingredients necessary for successful
transformation starts with data. We know that AI is only as knowledgeable as the data it’s
grounded on. Many customers tell us that to fully leverage AI, they need and want a single
platform. Point solutions atop the CRM create siloed data pockets that increase risk,
duplicate capabilities, reduce seller productivity, and increase costs.
AI is the next ingredient. A solid data foundation built in one CRM ensures that predictive
and generative AI bring real productivity gains. AI can automate emails, take actions, and
create account summaries based on CRM context—or tell your sales agents which products
are ripe for cross-selling opportunities. The possibilities are just beginning to be understood.
The number one goal is securing the AI architecture. That requires a trust layer, natively built
into the platform, with strong components to support data residency and compliance. As an
example, the Salesforce Einstein Trust Layer is equipped with security guardrails (see Exhibit 6).
Exhibit 6 - Salesforce’s Trust layer incorporates guardrails
Models
Customer, company, and outcome data
Promt Secure Data Dynamic Data Prompt Hosted Models
Retrieval Grounding Masking Defense
in Salesforce
Zero Trust Boundary
Retention
Audit Trail Data Toxicity Response Bring your own
Demasking Detection
CRM Einstein models, your own
Infrastructure
apps Copilot
Secure Gateway
Einstein Trust Layer External models
with Shared
Trust Boundary
Source: Einstein Trust Layer, Salesforce.com 2023
8 THE GenAI IMPERATIVE FOR TELCO B2B SALES TEAMS
As Exhibit 6 illustrates, when you generate a prompt that is built in Prompt Builder, the prompt is
sent to the Einstein Trust Layer, which masks any sensitive data before sending it to the LLM.
When prompts are sent to external models through the shared trust boundary, your data is
encrypted to ensure its security in transit. Additionally, any sensitive information within the
prompts is masked.
Another great concern is risk mitigation, both tactical and strategic. In a recent survey,1 73
percent of employees believe GenAI introduces new security risks, underscoring the need for
organizations to leverage GenAI technologies built with trust first. In this regard, the zero
retention of the Salesforce Trust Layer ensures that no information is stored or remembered by
large language models, prioritizing user privacy and data security.
There are indeed many types of risks related to AI, including trust, data property, data quality,
biases, hallucinations, costs of AI, value for money, human-centric vs. Full-bot approaches, business
deployment, and so forth. These risks are recognized by dynamic, new regulations and frameworks
that companies will have to carefully investigate. Our vision at BCG and Salesforce is to join forces
to propose a strategic enterprise approach for AI implementation, taking into consideration all
these dimensions, and not simply proposing to launch Proof of Concepts or a pilot.
To set the stage, Salesforce and BCG have developed a framework of AI-driven Sales-related use
cases matching the needs of companies in the Telco sector as illustrated in Exhibit 7.
We observed from most Salesforce customers that use cases relied on a blend of predictive and
generative AI, as well as analytics and automation.
Customer use cases also relied on knowledge extraction across a mix of unstructured and
structured data from multiple applications. This could include text documents, chats, audio, or
video. The importance of unstructured data should not be underestimated.
These varied data sources and data types come with different data latency requirements that must
be considered and coordinated for different use cases (e.g. real-time data streams or near real-time
data latency requirements in addition to batch data from different systems and schedules).
1. FY23 Salesforce Customer Success Metrics
Exhibit 7 - Trusted AI, Built Into the Flow of Work
Accelerate pipeline + Supercharge productivity + Unlock revenue
SALES MARKETING SERVICE
Close better Create more Elevate Customer
deals faster resonant content Engagement
Product Finder / Proactive retention
Sales Copilot Campaign Copilot Bahavior scoring Service Copilot
FAQ Search Engine campaign
Lead / Opportunity ""Where is my order"" Order Management
Call Summaries Content creation Engagement scoring
scoring Assistant Insights
Personalized Attrition Reduction Customer & Network
Activity 360 Content tagging Engagement frequency
conversations Assistant Serviceability Insights
Account /
Next Best Products Segment creation Spend Time optimization Renewal Assistant Next Best Actions
Opportunity overview
CPQ Assistant Pipeline inspection Smart promotion creation Smart return analysis Conversations Catch Up Next Best Products
Proposal / Contract Product Product Finder /
Forecasting Simulation Demo creation
Generation recommendations FAQ Search Engine
Source: Salesforce Industry Advisory 2024
BOSTON CONSULTING GROUP + SALESFORCE 9
“
“The building narrative we are
hearing across our Telco clients
is clear – GenAI’s imperative for
sales is to create smarter and
more impactful customer
interactions.”
Bryan Gauch,
Managing Director and Partner, Global Salesforce Offering,
Boston Consulting Group
The majority of these use cases and features are already available. They can be mapped to
service provider needs and help create a value-based transformation journey.
The business outcomes are already impressive. While security risks are a continuing concern
for employees we have surveyed, 68 percent say that GenAI will help them better serve
customers and save them an estimated five hours on average each week. Recent benchmarks
on ongoing Sales AI pilots have shown a potential of 29 percent increase in productivity.
How to move from strategy to execution at scale
BCG and Salesforce have teamed up and designed two engagement archetypes that can be
leveraged to kickstart the AI/GenAI journey for B2B sales organizations in the telco sector
and create an aligned starting point and vision:
1. Value Assurance. This is directed to customers that want to maximize the value from
their technology investment. To achieve this, Salesforce and BCG undertake a tailored
diagnostic to pinpoint concrete opportunities for value enhancement, delivered by a joint
team from BCG, Salesforce, and the client.
2. AI/GenAI Exploration. Also delivered jointly, in the format of a workshop, it will leverage
best-in-class GenAI strategy and capabilities to drive sustained success. The outcome will
be an industry point of view on the relevant use cases and will enable our clients to pilot
and scale their AI/GenAI practice, providing the basis for a smooth implementation and
transformation—faster and at a lower cost.
Following the initial vision and design, different approaches are available to realize the
potential, depending on the value at stake and the readiness—from further opportunity
exploration and detailing to full-scale transformation and support.
BCG and Salesforce teams have seen the potential of a predictive AI/GenAI future for service
providers. Now is the time to make that future happen and embark jointly on this exciting
journey.
Exhibit 8 - BCG x Salesforce AI/GenAI combination enables accelerated
value generation of GenAI while de-risking the implementation
• Value-driven strategies & transformations paired with • Integrated CRM platform with industry and functional
strong CRM transformation management capabilities specific clouds
• Deep industry & functional expertise, best-in-class Go-to- • Strong GenAI solutions and capabilities as well as an
Market frameworks and toolkits, optimized for CRM extensive marketplace with third party apps
• Sales, marketing and service persona-specific change • Product and platform expertise to support deployment
management approach of high-value CRM capabilities
• Trusted GenAI leader with purpose-built AI assets • Platform and product roadmap insights
(e.g., Deep.AI) integrated into Salesforce
• Best in class GenAI strategy & capabilities to drive sustained success
• Deliver platform and GenAI implementations faster and at lower cost
Source: BCG
BOSTON CONSULTING GROUP + SALESFORCE 11
12 THE GENAI IMPERATIVE FOR TELCO B2B SALES TEAMS
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2/24"
293,bcg,unlocking-the-genai-opportunity-in-latin-america.pdf,"Unlocking the Gen AI
Opportunity in Latin America
Insights Generated from the 2024 Latam Tech Forum (LTF), an Invitation-Only Private Gathering of
the CEOs and Founders of the Largest and Leading Tech Companies from Across Latin America
July 2024
By Lucas Frenay, Julian Herman, David Marín, Federico Muxi and Joan Viñals
Since its inception in 2011, The Latin America Tech Forum
(LTF) has become a prestigious private gathering for
CEOs and founders of Latin America’s largest and leading
technology companies. The forum brings together founders
and c-suite executives from Latin America, alongside Latin
America heads of global technology companies, global
luminaries, and a small sub-set of investors and technology
advisors. The mission of LTF is to provide a platform for
leaders to collaborate, build trust and develop long standing
relationships across the technology ecosystem in Latin
America, which helps further economic development and
prosperity across the region.
Private, off-the-record, and by invitation-only, the forum
is held once annually and includes thought-provoking
interactive Executive Sessions, Fireside Chats with global
business leaders and renowned personalities, and other
activities relevant to this peer group across several days.
Attendance is limited to ensure the right environment for
developing new and meaningful connections.
Organized by Riverwood Capital, LTF is an industry initiative
supported by several leading institutions with the objective
of expanding the Latin America technology ecosystem.
TABLE OF CONTENT
Key Survey Insights .................................................................... 3
Introduction ............................................................................... 4
Chapter 1 | The Case for Gen AI at Scale .................................. 5
Chapter 2 | Use Case Deployment and Success Cases ............ 8
Chapter 3 | Key Challenges and How to Overcome Them ....... 11
Acknowledgements .................................................................... 14
About the Authors ...................................................................... 14
LTF 2024 17
Key Survey Insights
170
~100 ~70
Respondents
Tech company Company executives, tech investors,
Founders/CEOs and other industry leaders from
across Latin America
95 20 50
% % %
of top leaders say Gen AI consider they have a have passed the
will have a great impact well-defined Gen AI “Quick Win stage”and are
or be transformative for vision and ambition. now reshaping their core
their organizations. functions with the
use of Gen AI.
3 most relevant use cases stated: Main challenges perceived:
• Product enhancement (42%) • Talent (70%)
• New product development (29%) • Responsible Gen AI and data privacy (50%)
• Code generation assistance (26%) • Data and tech readiness (42%)
Note: The data set from Latam tech executives is displayed alongside responses from a BCG survey of global large traditional companies for context
in some areas of the report.
LTF 2024 3
Introduction
The Latam Tech Forum (LTF) 2024 yielded significant insights on
companies at the forefront of innovation in the region.
During the Forum, and to provide an in-depth reduction in customer inquiry costs, a 25% reduction in
understanding of how tech leaders perceive one of this marketing content creation time, and a 30% boost in
decade’s most pivotal technologies, BCG conducted a content production efficiency—all contributing to improved
survey on Gen AI with the participation of more than customer satisfaction and faster issue resolution. These
170 CEOs and C-level executives of the most prominent successes illustrate the substantial impacts achievable
tech companies in Latam. when Gen AI is deployed effectively.
BCG worked together with Riverwood Capital to plan and The report also serves as a strategic tool for decision-
execute Executive Sessions on Gen AI. This report makers to evaluate their Gen AI progress and understand
synthesizes key learnings from these discussions, together the evolving landscape, informing their strategic
with the insight of BCG’s experience across over 200 global engagements moving forward.
client cases.
It sheds light on the ongoing Gen AI adoption level within
the Latam tech sector, highlighting both achievements and
areas for improvement, and reveals how Gen AI has
demonstrated significant benefits, such as a tenfold
4 UNLOCKING THE GEN AI OPPORTUNITY FOR TECH PLAYERS IN LATIN AMERICA
Chapter 1 | The Case for Gen AI at Scale
GEN AI: TRANSFORMATIVE POTENTIAL FOR COMPANIES AND INDUSTRIES
One of the most spread beliefs gathered during the Forum is the recognition of Gen AI’s exponential growth and impact.
Over 90% of the participating C-level executives see Gen AI as a high-impact, transformative force within their sectors and
the competitive landscape (Exhibit 1). Gen AI is poised to revolutionize competitive dynamics and operational strategies in
the business world.
.
Selected examples of impact delivered:
30-40 95 10-15 20
% % x %
increase in reduction in fraud gains in marketing increase in
service desk prevention content generation developer
productivity manual tasks productivity productivity
Exhibit 1 | Almost every company perceives high value in Gen AI
What impact do you believe Gen AI will have in your business/industry?
% of total responses
85
63
32
13
5
2
Moderate impact High impact Completely transform
Global large traditional companies (BCG Build for the Future Survey)1 Latam tech companies (LTF Survey)
1. Traditional companies are based on a 2023 survey of 159 large companies (>USD 25 billion value) in North America, Europe and Asia-pacific –
covering various sectors.
Source: LTF 2024, BCG Survey; BCG Build for the Future C-level Gen AI survey
LTF 2024 5
TRADITIONAL COMPANIES’ GEN AI APPROACH TECH COMPANIES’ GEN AI APPROACH
Recognizing the impact and potential of Gen AI, traditional Top Latam tech companies are taking a different approach
companies are strategically harnessing its power. More to harnessing Gen AI. They have adopted Gen AI quickly
than 50% of these companies have adopted a well-defined and efforts are proliferating across functions and use cases,
strategic approach to Gen AI, emphasizing clear ambitions not only in the form of quick wins but also by reshaping
and prioritizing scalable use cases based on impact and their core businesses. Yet, just 21% of these companies
feasibility (Exhibit 2). declare having set an ambition (North Star), a Gen AI
strategy, and a clear implementation plan (Exhibit 2).
Moreover, more than 75% of companies scaling Gen AI are
actively engaged in shaping strategies, defining roadmaps, Only companies whose business models are
and discussing proactive investment plans of Gen AI. fundamentally based on Gen AI largely state to have a
well-defined ambition and a strategic approach to Gen AI.
Traditional companies are focusing on envisioning a
sustainable end-state operating model, despite the nascent Despite lesser planning, tech companies have adopted
stage of this technology and the lack of consensus on the Gen AI more rapidly than traditional businesses. At
optimal approach. Current models vary from decentralized LTF 2024, examples of quick wins through operational
structures, where AI engineers or specialists are distributed efficiencies, such as automating customer service with
across teams, to centralized or federated models. chatbots and optimizing data management, were
prevalent, as well as examples of product enhancements
Highlighting the importance of responsible AI together to reduce costs and drive top-line growth.
with creativity, an increasing number of organizations
see the federated model as the right landing point. This This is also reflected in the limited progress in defining and
model not only helps set and enforce clear policies but implementing a Gen AI operating model, with only 5% of
also ensures rigorous prioritization based on return and companies stating to have defined and implemented
the widespread dissemination of Gen AI resources delivery Gen AI teams with clear structure and KPIs.
throughout the company, promoting broad accessibility
and integration.
However, establishing specialized teams with clear roles
Start the journey collecting data and
and governance across the company remains a significant
challenge, with only 28% of traditional companies defining your strategy, and only then
successfully implementing such teams so far (Exhibit 3).
connect to specific capabilities and
use cases to continue evolving.
Leading Tech Executive
and LTF 2024 Participant
Exhibit 2 | Tech companies are far behind in defining strategy
Do you have an ambition (North Star), strategy and implementation plan underway?
% of total responses
61
51
36
21
17
13
Not defined Partially defined Well-defined
Latam tech companies (LTF Survey) Global large traditional companies (BCG Build for the Future Survey)1
1. Traditional companies are based on a 2023 survey of 159 large companies (>USD 25 billion value) in North America, Europe and Asia-pacific –
covering various sectors.
Source: LTF 2024, BCG Survey; BCG Build for the Future C-level Gen AI survey
6 UNLOCKING THE GEN AI OPPORTUNITY FOR TECH PLAYERS IN LATIN AMERICA
Exhibit 3 | Only 28% of traditional companies have AI dedicated teams
at scale
Have you defined and implemented Gen AI delivery teams/squads with clear structure and KPIs?1
% of total responses
43
33 33
32
28
19
6 5
No delivery teams Partially in some areas1 At scale in some areas1 At scale company wide
Latam tech companies (LTF Survey) Global large traditional companies (BCG Build for the Future Survey)2
1. These options were consolidated into one in the BFF survey (answered by Global large traditional companies): “Scale at pockets, traditional
governance.”
2. Traditional companies are based on a 2023 survey of 159 large companies (>USD 25 billion value) in North America, Europe and Asia-pacific –
covering various sectors.
Source: LTF 2024, BCG Survey; BCG Build for the Future C-level Gen AI survey
Key Takeaways
In the early stages of adoption, business leaders widely recognize the potential of Gen AI. Traditional companies are
proactively setting ambitions and developing new operating models for the Gen AI era.
Meanwhile, the Latam tech sector, eager to rapidly advance in experimentation across functions and use case
escalation, shows room for more defined strategy and governance.
Fast and nimble deployment and innovation is inherent to tech companies. Nevertheless, a scattergun approach could
lead to operational inefficiencies, and without a clear strategy across the organization, businesses face a significant
challenge in identifying and measuring potential value. This in turn could impact the correct allocation of efforts and
resources, delaying investment decision and stalling execution capabilities altogether.
In our opinion, developing a structured Gen AI strategy and establishing clear ambitions is essential, a stance
supported by numerous tech leaders at LTF 2024.
LTF 2024 7
Chapter 2 | Use Case Deployment and Success Cases
Latam’s dynamic tech ecosystem is on the cusp of a 3. Inventing new Gen AI-driven business models:
major shift, driven by the integration of Gen AI into the New value proposition and revenue streams
business landscape.
To navigate this transformative era, companies can deploy The boldest players in the Latam tech scene are exploring
Gen AI to capture quick wins, reshape critical functions new horizons by leveraging Gen AI to create innovative
through Gen AI or invent new Gen AI driven business business models and long-term competitive advantages.
models (Exhibit 4). These examples were rarer in the discussions at LTF 2024,
since only 24% of companies surveyed were inventing new
1. Deploying Gen AI in everyday tasks: Broad Gen AI-driven business models. However, the ones that do,
enterprise-wide productivity enhancement have the potential to disrupt their industries and establish
and quick wins entirely new market spaces.
A common starting point is to capture quick wins. Several companies have not only embraced Gen AI but
According to the survey, 55% of the companies and CEOs also made significant strides in their application. Some
surveyed reported that they are already doing so. success cases are illustrated on the next two pages.
2. Reshaping critical functions: Radical productivity,
speed and quality improvements
The natural next stage is to integrate Gen AI deeply within
core functions.
In comparison with most traditional companies, that
are still at the Gen AI proof-of-concept stage, over half
of tech businesses in Latam are reshaping their core
functions like marketing, sales, and HR, with Gen AI to
increase both efficiency and effectiveness (with impacts
in the +50% zone).
Currently, tech companies’ main area of focus in Latam
is improving the product portfolio, 42% mostly enhancing
core products, while new products or services positions
as the second use case with ~30% of respondents
(Exhibit 5).
Exhibit 4 | Focus and Gen AI adoption in Tech companies in Latam
Q: Which of following statements best describes the focus and degree of Gen AI adoption in
your company?
55%
50%
24%
4%
No action on Deploy: Capturing Reshape: Embedding Invent: Innovating
Gen AI yet quick wins Gen AI in core functions new business models
1. Requested answer: select all that apply
Source: LTF 2024, Participants Survey
8 UNLOCKING THE GEN AI OPPORTUNITY FOR TECH PLAYERS IN LATIN AMERICA
Exhibit 5 | Most relevant use cases in tech companies in Latam
Q: What are the 3-5 most relevant use cases you are currently implementing/discussing
in your company?
Standard use cases
Product Core product enhacement 42%
New products or services 29%
Code generation 26%
Tech Code review/auditing 14%
Code documentation 9%
Chatbot for sales/support 15%
Copilot for customer support 8%
Sales and CX Personalized add creation/marketing 7%
Copilot for salesforce 3%
Claims/complaint evaluation 2%
Finance 9%
Procurement 9%
Support functions
HR 9%
Legal 7%
Knowledge management 9%
General productivity Document/meeting summaries 9%
Copilot for office tools (ppt, e-mail, ...) 5%
1. Requested answer: select 3-5 options
Source: LTF 2024, Participants Survey
INVGATE
InvGate is an IT Management software company with a in proprietary models and talent. They have implemented
focus on AI-enabled Enterprise Service Management and a strategic investment plan focusing on an internal AI
large-scale IT device inventory and configuration Service alongside internal models and engineering
management. It enables drastically lowered time-to-value capabilities, asserting that flexibility will be crucial in
in all of the categories it focuses on, by leveraging no-code navigating this dynamic landscape.
implementations that are an order of magnitude shorter
than competing offerings. InvGate has 1000+ customers in SENSEDIA
over 50 countries, including NASA, Arcos Dorados, Telekom
Malasia, and Collins Aerospace among others. A leading Brazilian company specializing in API
management and integration strategy, enhancing digital
Key use cases implemented | InvGate has embedded connectivity and open technology ecosystems. The
GenAI capabilities across its solutions for Service company offers solutions for integrating diverse digital
Management and Asset Management. These include: channels and adopting more modern architectures, like
microservices, APIs, events and service mesh.
• Resolution Recommendation: This feature automatically
suggests a possible solution to a ticket based on not Key use cases implemented | Three innovative Gen AI use
only knowledge-base articles but also previously cases enhancing API design and client services:
resolved tickets.
• API Copilot: boosts API design productivity by gener-
• AI-Knowledge Article Generation: Uses resolved ticket ating new documentation and improving existing ones
information to create knowledge base articles that can be based on business context.
later referenced by agents or Invgate’s AI agent.
• API Simplification: Leverages Gen Al to identify duplicat-
• Ticket Summarization: This feature reduces the time ed APIs across an organization and propose simplifica-
needed to manage IT incidents, speeds up new team tion, enabling companies to reduce costs.
members onboarding, and enhances overall support
efficiency. • API Consumption: Supports API portfolio management
by assessing individual API performance metrics and
Impact | 30%-40% increase in productivity and a noticeable defining best prioritization model for a given objective.
increase in MTTR (mean time to resolution) due to its
GenAI capabilities. Impact | Although comprehensive metrics are not yet
developed, the API Copilot shows potential to enhance API
Key learnings | The company emphasizes being vendor- design productivity by up to fivefold (5x).
agnostic when it comes to GenAI capabilities and investing
LTF 2024 9
Key learnings | Talent acquisition was the biggest hurdle, RAPPI
so the company formed a small agile squad to reduce the
learning curve and accelerate time-to-market, quickly Rappi is a leading on-demand delivery superapp. It was
sharing insights to enhance overall capabilities. founded in 2015 in Bogotá, has operations in 9 countries in
Latin America and a network of more than 300 thousand
DLOCAL businesses across multiple segments (restaurants,
groceries shops, pharmacies, and more).
Founded in 2016, the company became Uruguay’s first
unicorn and went public in 2021 with a $9.5 billion Key use cases implemented | Rappi is fully embracing
valuation. The company is known for its API-based Gen Al, being able to deliver several use cases at scale
payment solutions, serving over 330 merchants in across multiple business functions and verticals:
29 emerging markets, and supports various local
payment methods. • Customer Support Sidekick: Empowers customer
support by providing best recommended response
Key use cases implemented | Dlocal has advanced its core and retrieving relevant policy documents, and boosts
functions with productive Gen AI use cases at scale: productivity by summarizing conversations.
• Smart Router: an AI-based routing solution that selects • Account Managers Sidekick: Enables sales teams to
payment processors based on variables like payment add more value to their clients preparing them with
method, industry, country, and merchant-specific factors prioritized insights and proposed action items with
to improve conversion rates and cost. higher sales impact.
• Fraud Prevention: Gen AI automates the examination of • Developer Copilot: Supports developers to increase
merchant websites for risk assessment and formulates coding and development productivity. Supports code
questions to help prevent fraud. documentation/writing.
• Support Cloud Engineer: The GenAI Copilot has • Merchandising Content Generation: Enables
significantly transformed CI/CD pipeline management. It merchandising teams to significantly improve digital
detects errors and automatically suggests fixes, reducing storefront customization and creative content
human support from the Cloud Platform team by 90%. development. Same team is able to create 10X more
This allows the team to focus on platform development, seasonal merchandising events.
while engineering teams receive immediate answers,
accelerating their delivery speed and enhancing overall • Product search/personalization improvement: Using
satisfaction. Future plans include enabling the Copilot the same underlying Transformers technology as used
to directly fix issues in the code, further increasing the in GPT models, Rappi’s in-house recommendation
speed of error resolution. systems predict the likelihood of click or conversion for
a customer at a given time, location, product and search
Additionally, Dlocal has launched Smart Request to terms, to create a better search/browse experience.
optimize payment option selection and a company-wide
chatbot powered by OpenAI, to boost productivity by • Back Office Automation: Multiple AI-powered
assisting with various inquiries. applications, including purchase order and invoice data
extraction to drastically reduce cost, error rate and
Impact | The copilot has already reduced the need for accelerate identification of discrepancies.
human intervention in support tasks by 90%, and the fraud
prevention tool has cut manual tasks by 95%. However, Impact | Significant impact across use cases. A few
some of these metrics are still preliminary, and further examples are 10-15x productivity gains in site
operation is needed to develop a comprehensive set of merchandising quantity of events and 20% increase in
KPIs for Gen AI use cases. developer productivity.
Key learnings | Dlocal has not established a defined Gen Key Learnings | Don’t be fixated on developing the most
AI North Star per se but acknowledges the need for a sophisticated or complex AI technologies in-house.
prioritization methodology. A dedicated team reviews Prioritize tech solutions with the most direct path for
inputs from different BUs to identify and rank use cases impactful adoption and likely this involves leveraging
based on impact, acting as gatekeepers to filter, prioritize commercial/industry tech offerings. Develop a central
and allocate resources. architecture that enables whole organization to get easy
access to Gen Al and invest in upskilling - enabling teams
Dlocal recognizes the importance of adopting a new to produce solutions in a decentralized way and focusing
mindset to fully leverage Gen AI potential, and despite on delivering client value. Deliver solutions that will
progress, also acknowledges certain resistance to change. empower humans - not displace them - and pass the value
generated to customers.
10 UNLOCKING THE GEN AI OPPORTUNITY FOR TECH PLAYERS IN LATIN AMERICA
Chapter 3 | Key Challenges and How to Overcome Them
Adopting Gen AI within the dynamic Latam tech ecosystem Talent scarcity: Digital transformation has significantly
presents a unique set of challenges. This chapter increased the need for Gen AI expertise. Traditionally, tech
synthesizes the common roadblocks as expressed by companies would tackle challenges by hiring the most
industry leaders during LTF 2024. experienced talent. However, with current scarcity of
Gen AI professionals, simply hiring the top candidate no
MAIN CHALLENGES TO OVERCOME longer suffices, and leaders know it. They stated that
acquiring new skilled professionals and training existing
The three primary challenges perceived by Latam tech employees in Gen AI is a considerable task.
companies are related to Talent, including the difficulty
in recruiting new skilled personnel, training existing Talent training: Businesses now heavily rely on AI’s
employees, and ensuring leadership readiness; scalability, underscoring the urgent need to train
Responsible AI, involving data privacy, transparency, employees for AI-driven processes. Traditional problem-
policies, and regulation; and Data mastery and tech solving methods are inadequate in the face of growing
readiness, including tech integration, data governance, and complexity and the unique challenges brought by Gen AI.
model training (Exhibit 6). To effectively equip teams, an innovative training approach
incorporating external expertise and new educational
In addition, during the LTF sessions, executives highlighted methods is essential.
that infrastructure and cost management pose significant
barriers to the deployment and scaling of Gen AI. Companies are contemplating how to best allocate their
resources for talent development in AI, weighing whether
to develop and train in-house AI expertise or wait for AI
1. Talent transformation: Elevating AI competence platforms to evolve and become more user-friendly.
and empowerment
AI adoption: Leaders are aware of the talent and cultural
gap, particularly in specialized AI knowledge and usage.
There are four specific They expressed the need to set a culture of AI use and
Hiring the most topics related to Talent and understanding across all teams, not just within
experienced person to AI that need to be engineering or product development.
solve the problem will addressed: Scarcity, training,
not be the solution in adoption and leadership.
this occasion.
Leading Tech Executive
and LTF 2024 Participant
Exhibit 6 | Challenges around talent and data are the major concerns in the
implementation of AI
Which are the following dimensions that will pose the biggest challenge moving forward?
Talent/Resources 71%
Responsible AI/Data privacy 50%
Data mastery/Tech readiness 42%
Main challenges
Governance/Working model 22%
Regulation 22%
Other1 5%
0% 20% 40% 60% 80%
% of total responses: >70% 40%-30% 25%-10% >10%
1. Including: Investment, competition, mindset change, output accuracy
Source: LTF 2024, BCG Survey
LTF 2024 11
Leadership readiness: Leadership these bills will be as stringent in compliance as the EU
is crucial in the AI transformation legislation is.
journey. The challenge is to equip
leaders with an AI-ready mindset Start small, Companies need to carefully adhere to intellectual
to effectively identify and leverage conquer early wins property and copyright norms within the evolving AI
Gen AI capabilities. Focusing on to incorporate legislation to stay competitive without trespassing
leadership readiness is vital to capabilities and legal boundaries.
align the organization with funding for larger
AI capabilities and technologies. deployments! Data mastery and tech readiness
Leading Tech Executive
and LTF 2024 Participant Data mastery: When it comes to data mastery, challenges
lay in three main pillars: data capabilities, data design and
2. Responsible AI: Forging the path to accountability data governance.
and integrity in technology
First, datasets for Gen AI (foundation) models are
Gen AI will only amplify existing and new risks associated becoming ‘multimodal’, so there is a rising need to
with AI, namely litigation around copyright infringement, incorporate a much broader range of data inputs.
data and financial loss, reputational damage and
regulatory compliance, as well as new risks to consider, like Second, design considerations must be observed for
accuracy, ownership and bias or harm of outputs produced. multimodal processing, ranging from data provenance,
metadata, data lineage, output data quality and
Data privacy: In an age where data equates to currency, regulatory compliance.
privacy concerns are of utmost importance. Companies
must navigate the complexities of protecting individual Third, the incorporation of large amounts of unstructured
privacy while leveraging data for AI innovation. This data also introduces new risks (data usage outside of
balance is critical, especially for U.S.-based operations, given purpose, unauthorised data usage, output reliability,
where regulations are stringent, and the cost of non- computing cost when using unstructured data), which
compliance is high. require new approaches and governance to mitigate and
control data.
Ethical AI and transparency: Discussions from LTF 2024
reveal a trend towards establishing ethical AI frameworks Tech readiness: Tech challenges with regards to Gen AI is
and practices, addressing concerns such as models’ twofold, CIOs and CTOs will have to manage both “Gen AI
hallucination and bias, to ensure AI’s decisions are in Tech” (i.e. transform the IT organization) as well as
transparent and accountable. This commitment to ethical “Tech in Gen AI” (enable business transformation). Also,
AI extends to maintaining regulatory compliance, CIOs and CTOs will have a growing role as orchestrators to
particularly in data-sensitive areas. help businesses navigate the landscape, deliver value, and
ensure responsible use of AI while keeping the pace
The opacity of AI decision-making processes — the ‘black with Gen AI evolution.
box’ issue — requires a push for greater transparency and
understanding of AI’s internal workings. This transparency Companies, with the help of their tech leaders, will have to
is crucial for building trust among users and stakeholders. decide which model archetype to adopt. There are 4-four
main archetypes: Public, Managed Secured, Hybrid Private,
Human control: As AI technology advances, maintaining Fully Private. Each of the four archetypes has different data
human oversight is critical. Without it, AI models can and training characteristics and choosing the right model
produce harmful behaviors. Ensuring AI systems have deployment archetype will depend on business use cases.
robust human-in-the-loop mechanisms is essential to Hybrid Private is the most common and widely spread
prevent these issues and control risks associated to archetype, only to be questioned if cost of API is prohibitive
accuracy, ownership, and bias from the outputs produced or there is very high domain specific complexity.
using Gen AI models. This is even more important for
those companies in sectors with strict ethical standards Also, the platform and model partnership selection will
and regulations, making human supervision a moral and need to be carefully assessed. Partner preference,
regulatory imperative. geographical presence, hosting, portability, and security are
among the main selection criteria for platform provider
Regulatory compliance: AI regulatory compliance is selection. While performance, capabilities and complexity,
complex, requiring businesses to balance innovation with ability to fine-tune, and cost and compatibility with
legal constraints. EU is one of the few regions globally that platform are the key criteria to select the model.
has “passed” a legislation on Gen AI. Most of the Latam
countries have taken the “risk-based approach” set up in Finaly, when referring to the tech stack and architecture
the EU legislation, as the basis for their bills. If passed, new capabilities will be required, essentially in the AI Layer
12 UNLOCKING THE GEN AI OPPORTUNITY FOR TECH PLAYERS IN LATIN AMERICA
(to manage AI products and platforms), the Model Layer model and Gen AI data storage), refer to Exhibit 7 for
(to build, operate and maintain Gen AI models), the Agent more details.
Layer (to manage prompting and agents) and in the
Central Data Layer (additional storage on prompt, Gen AI
Exhibit 7 | Gen AI tech stack will require new technology capabilities
Tech stack Gen AI evolution
Modern data architecture blueprint Gen AI Simplified
Smart Business Layer Smart Business Layer
Impacted Cognitive Apps
Omnichannel App
Chat Image Video Music
builder
Cross-channel mechanisms and business components
AI Layer
AI products
Data Layer AI guardrails
New Content moderation Observability Operational
Repository & storage
Operational AI platforms AI services
data services Agent
Ingestion & distribution
Model
Data Layer
Core Transaction Layer Impacted
Data Repository Ingestion & Operational
products & storage distribution data services
ERP Other systems
Core Transaction Layer
Infrastructure/Cloud Infrastructure/Cloud
On-prem Cloud Hybrid On-prem Cloud Hybrid TPU/GPU
Key Takeaways
To overcome AI talent shortages, companies should benefiting the company overall. Identifying and
collaborate with specialized recruitment firms and categorizing the risks associated with AI systems through
create tailored AI training programs incorporating a well-defined risk taxonomy is crucial. Additionally,
external expertise. A new educational approach is companies need to create a clear governance structure
essential to upskill existing employees, while also with teams specialized in international and local AI laws,
implementing a comprehensive workstream to develop, dedicated to ensuring the continuous responsible use of
engage, anticipate, and attract skilled professionals, as AI, monitoring evolving regulations, and adapting their
well as fostering adoption and change management. policies accordingly (central small but rather senior
Additionally, investing in leadership programs focused teams). Finally, the key principles for Responsible AI,
on AI readiness and forming partnerships with peers including accountability for model outcomes,
and external entities to share insights can significantly transparency, fairness and equity promotion, safety and
enhance organizational capabilities and spur innovation. risk reduction, adverse effects avoidance and human-
Finally, they should consider allocating resources machine collaboration, must be embedded in existing
disproportionately towards the human aspects of the company policies centrally managed.
Gen AI transformation, including change management
and skill development to ensure adoption. Leaders To master Data, companies should in"
295,bcg,unlocking-potential-strategies-driving-gccs-digital-ai-maturity.pdf,"Unlocking Potential: Strategies Driving
GCC’s Digital & AI Maturity
DECEMBER 20, 2024
By Rami Mourtada, David Panhans, Lars Littig, and Hassen Benothman
READING TIME: 5 MIN
Emerging technologies are reshaping the world at an accelerating pace. As the GCC races ahead with
its ambitious economic development plans, the region has already well progressed on its technology
infrastructure and has provided many needed legislative, investment, and entrepreneurial
environments for digital-first leading organizations to emerge within the region.
To gauge this digital and AI readiness, BCG’s 2024 Build for the Future (BFF) study examined the
digital maturity of organizations in the GCC with a special focus on AI as a most transformative
© 2024 Boston Consulting Group 1
emerging technology. The study surveyed C-suite executives and senior leaders from 200+
organizations across eight sectors in Qatar, Saudi Arabia, and the United Arab Emirates.
Upon evaluating organizations across 53 core capabilities pertaining to digital maturity and AI
readiness, the BFF study methodology categorizes each surveyed organization into one of four
categories representing their stage of digital transformation, from least to most mature: Stagnating,
Emerging, Scaling, and Future-Built.
 
Core Capabilities: GCC Organizations to Catchup
with Global Digital and AI Maturity Levels
GCC organizations are presented with a unique opportunity and a matching challenge to build on
their capabilities to leap into global-level digital and AI maturity. In 2024, GCC organizations show
higher maturity around customer journey and digital operations capabilities, however, have yet to
fully possess many of the critical enabler capabilities that would allow them to fully deploy their
digital and (Gen)AI strategies, and will need a step-change, particularly in their data and technology
capabilities.
© 2024 Boston Consulting Group 2
Where fast-changing technology landscapes and rapid AI adoption are paramount to future success,
GCC organizations have a tangible opportunity to catch up with their global counterparts at the
overall digital maturity level. While 25% of GCC organizations fall into the top two scaling or future-
built maturity levels the global share is 31%. At a sector level, the Public Sector in the GCC exhibits
key areas at world-class digital maturity levels, while Financial Institutions and Tech companies
exhibited the highest digital and AI maturity scores across the GCC. Overall, however, digital and AI
maturity in the GCC in 2024 fell behind the global average.
Challenges & Opportunities: GCC Organizations on
the Road to AI Value Delivery
In 2024, 17% of organizations in the GCC scored into the top two AI maturity levels (AI-scaling & AI-
Future-Built). In this regard, the Financial Institutions sector had the highest share of top-level AI-
1
maturity organizations or “AI leaders” where 29% of financial institutions scored in the top two
levels in 2024, followed by Healthcare sector (23%) and the Public Sector (20%).
Additionally, our study found that the highest AI maturity organizations have three times the rate of
success extracting value from GenAI (more on this below). Yet, the GCC remains at the early stages
of (Gen)AI adoption, with only 9% of organizations surveyed at this level of value delivery. Overall half
of all organizations surveyed (53%) are either still experimenting with GenAI with no official policies
set in place, or not actively using it at all. The remaining (38%) recognize the value of adoption and
are planning to scale up with guardrails. Similar to the overall digital and AI maturity trend in the
GCC, sectors with highest share of organizations generating value with (Gen)AI are the Public Sector,
as well as the Tech and Telco sectors.
While every AI journey is tailored to each organization, we found common challenges in the region.
For instance, 6% of surveyed organizations have expressed not fully understanding GenAI, which lies
in the critical need for leadership initiatives to educate and upskill while setting an AI-and People,
Org, and Process-first strategy. For organizations further down the line of (Gen)AI adoption, several
challenges have been marked across BCG’s. “10-20-70” Algorithm- Technology- and People, Org, and
2
Processes framework.
In fact, the highest share of GCC organizations observed gaps in the people, processes, and
organizational dimension as the biggest barriers to AI maturity. This includes limited specialized
talent, a gap in overall AI literacy, and a lack of sufficient incentives for innovation and GenAI
adoption in working processes. Difficulty integrating AI within exiting IT systems and lack of access to
unified and high-quality data further hinders progress.
On the other hand, compared to stagnating and emerging organizations, AI leaders have been
successful in embedding AI for process-level productivity aimed at reshaping critical business and
© 2024 Boston Consulting Group 3
customer-facing functions as well as at integrating innovation in core corporate functions. To do so,
AI leaders in the GCC have focused on key enablers including increased investment and focused
budget allocation, as well as digital-first resource planning. High-maturity organizations allocated
2.4x more funding to AI initiatives as well as a 2.3x higher share of FTEs were dedicated to digital &
AI transformation, achieving a 1.7x higher share of (Gen)AI products scaled organization-wide and
reflecting a long-term commitment to embedding innovation.
Value Makers: Digital & AI-First Strategy for Future-
built Organizations
The 2024 BFF study highlights the need for most GCC organizations to progress beyond incremental
moves and embrace comprehensive digital and AI strategies to unlock transformative value across
sectors. While GCC organizations have made impressive progress in digital and AI capabilities, there
remain opportunities to further enhance their maturity levels in critical areas and continue building
on their strengths to lead globally in digital transformation and AI adoption.
To bridge the global maturity gap and accelerate impact, GCC organizations must embrace a bold,
digital & AI-first strategy, across 5 key recommendations:
1. Re-align organizational strategy with a digital-first vision to overcome structural barriers
like operational agility and talent development.
2. Set a bold strategic ambition for AI adoption focused on clear value pathways and
guardrails for responsible AI adoption.
3. Boost viable people and org capabilities and underlying technology platforms to support
ambition and invest in parallel to scale up.
4. Maintain a pipeline of continuing innovation pilots to rapidly and effectively adapt to
changing landscape of emerging technologies.
5. Prioritize high-profile cross-cutting lighthouse initiatives with high ROI to fund the journey
and build momentum for transformational org-wide change.
The GCC stands at a crossroads where technological advancements intersect with the region's
aspirations to lead in digital and AI innovation. By addressing these priorities, GCC organizations
can unlock transformative potential, enabling them to capitalize on emerging opportunities, catch-up
to global peers, and earn their position as future-ready pioneers in an increasingly digital world.
© 2024 Boston Consulting Group 4
Authors
Rami Mourtada
PARTNER & DIRECTOR, DIGITAL TRANSFORMATION
Dubai
David Panhans
MANAGING DIRECTOR & SENIOR PARTNER
Dubai
Lars Littig
MANAGING DIRECTOR & PARTNER
Dubai
Hassen Benothman
MANAGING DIRECTOR, BCG PLATINION
Dubai
1 Top-level AI-Maturity organizations are organizations who scored over 50
on 30 digital capabilities examined.
2 The BCG 10-20-70 digital and AI transformation model is “Focus 10% of
your efforts on algorithms, 20% on the underlying technology and data,
and 70% on people, org, and processes”
ABOUT BOSTON CONSULTING GROUP
Boston Consulting Group partners with leaders in business and society to tackle their most
important challenges and capture their greatest opportunities. BCG was the pioneer in business
strategy when it was founded in 1963. Today, we work closely with clients to embrace a
transformational approach aimed at benefiting all stakeholders—empowering organizations to
grow, build sustainable competitive advantage, and drive positive societal impact.
Our diverse, global teams bring deep industry and functional expertise and a range of perspectives
that question the status quo and spark change. BCG delivers solutions through leading-edge
management consulting, technology and design, and corporate and digital ventures. We work in a
© 2024 Boston Consulting Group 5
uniquely collaborative model across the firm and throughout all levels of the client organization,
fueled by the goal of helping our clients thrive and enabling them to make the world a better place.
© Boston Consulting Group 2024. All rights reserved.
For information or permission to reprint, please contact BCG at permissions@bcg.com. To find the
latest BCG content and register to receive e-alerts on this topic or others, please visit bcg.com. Follow
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© 2024 Boston Consulting Group 6"
296,gartner,gpc-genai-ocsummaryv2-content.pdf,"Generative
AI Surveys
Overview Barriers, Benefits, Use Cases Open-ended Insights Peer Data & Insights Additional Insights
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BY THE 7 PARTICIPANTS BREAKDOWN
NUMBERS
BUSINESS FUNCTION
SPECIFIC SURVEYS
833
In a few weeks' time, we
completed 7 business
LEADERS
function specific surveys
3
with a total response of 833
North America
leaders, across 3
51%
CONTINENTS
continents, representing 21
industries about their 21 APAC
29%
impressions of generative AI
programs, and the EMEA
INDUSTRIES
19%
associated opportunities,
risks, and use cases. JOB LEVEL COMPANY SIZE INDUSTRY
30% 10,0001+ <1,001
While it remains early days employees employees Software 14%
25%
for many respondents, their 24%
21% Professional
feedback gives significant 12%
Services
insight into the potential
Finance, Banking
12%
future attitudes of about and & Insurance
applications of these tools.
5,001 – 10,000 1,001 – 5,000
C-suite VP Director Manager employees employees
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Generative
AI Surveys
Overview Barriers, Benefits, Use Cases Open-ended Insights Peer Data & Insights Additional Insights
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Top of mind risks for IT & INFOSEC LEADERS
Barriers to
Potential for vulnerabilities or
58% 57% Potential for generating
leaked secrets in AI-generated
incorrect or biased outputs
code
Generative AI
Adoption
Biggest challenges cited by SOFTWARE ENGINEERING LEADERS using Generative AI
66%
Undesirable results
43%
Lack of corporate governance policies
38%
Pushback from leadership
Reasons shared by SOFTWARE ENGINEERING respondents whose departments have not adopted Generative AI
71%
76%
Security Inaccurate or biased results
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Challenges of D&A LEADERS who have AI-generated synthetic data
Barriers to
51%
Generative AI
46% Not having
enough real-world
Inherited bias in source data
41%
synthetic data
Adoption
Inaccuracy caused
by statistical noise
34%
Inaccuracy
caused by
statistical noise
The top selected adoption barriers among SALES LEADERS
51%
49% 38%
Lack of widespread adoption Integrations with existing technology Availability and quality of data
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Generative AI adoption barriers for MARKETING LEADERS
Barriers to
55%
Skills Gaps
Generative AI
42%
Integrations with existing technology
Adoption
38%
Unforeseen security threats
Top adoption barriers submitted by SUPPLY CHAIN LEADERS
58% Integrations with existing 57%
Unforeseen security threats
technology – or a lack thereof
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IT & INFOSEC LEADERS expect the following for Generative AI
Identifying
66%
Tech leaders predict positive Positive impact on the bottom-line
financial performance
bottom-line impacts from
Generative AI’s large language models
(LLMs) and generative AI
apps; slightly fewer expect
59% Improve top-line financial
Benefits top-line impacts.
performance
D&A LEADERS realized benefits of synthetic data
60%
Improved model
accuracy
56%
45%
Mitigated data
Improved model
privacy concerns
efficiency
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SOFTWARE ENGINEERING LEADERS believe that
Identifying
Generative AI will have a
70% 23% It will have a very positive
Generative AI’s
somewhat positive impact on
impact
software engineering
Benefits
SUPPLY CHAIN LEADERS identify as expected benefits
49%
Improved agility
48%
Improved productivity
48%
Improved cybersecurity
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SALES LEADERS believe that Generative AI would
Identifying
Allow them to completely 37%
Generative AI’s replace a person
Benefits
MARKETING LEADERS top selected benefits
57%
43% 38%
Improved speed to market Improved productivity Improved ROI
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IT & INFOSEC LEADERS cite the following top use cases
Pinpointing
Use Cases
53%
30%
Marketing and advertising Research and
development
56%
Data analysis and prediction
32%
34% Fraud detection &
cybersecurity
Operations and
logistics
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SOFTWARE ENGINEERING LEADERS are excited about using Generative AI in
Pinpointing
Use Cases
61%
55% 48%
Code generation AI-assisted pair programming Technical document generation
SUPPLY CHAIN LEADERS are planning to put Generative AI to use for
48%
Internal knowledge base enhancement
44%
Problem resolution management
42%
Generating interactive predictive models
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MARKETING LEADERS selected
Pinpointing
Use Cases
62%
Content production
52%
38%
Generating ad copy
Generating product
copy
SALES LEADERS most common use cases for Generative AI
44%
48%
Create sales enablement materials Create L&D or training content
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Generative
AI Surveys
Overview Barriers, Benefits, Use Cases Open-ended Insights Peer Data & Insights Additional Insights
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IT Leaders are saying…
“
“
“We are behind on embracing generative AI for
security purposes, which is regrettable, because,
“I think there is a general nervousness about
predictably, malicious actors are not as behind.”
jumping in too soon here. I think in the next
DIRECTOR
6-12 months we will all get a better
Arts and Entertainment Industry | 10,000+ Employees
understanding of what and how we can
leverage this to our advantage as
“
businesses and as society.”
“We see the huge benefits of generative AI but
are taking baby steps with Chat GPT.”
C-SUITE
Consumer Goods Industry | 1,000 – 5,000 Employees
DIRECTOR
Professional Services Industry | 5,000 – 10,000 Employees
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IT Leaders are saying…
“ “
“Definitely using but cautiously and “Generative AI has to be seriously
primarily for data analysis and business considered despite its limitations and
planning and forecasting at this point. regulatory challenges, especially for
people in high-regulated industries.”
Not using clinically.”
C-SUITE C-SUITE
Healthcare Industry | 1,000 – 5,000 Employees Finance Industry | 1,000 – 5,000 Employees
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InfoSec Leaders are saying…
“
“
“This is a new area and all our decisions are
being questioned constantly.”
“Loss of internal IP is rising to the top of our
list as the number 1 risk for ChatGPT use
C-SUITE
within our organization with the potential for
Professional Services Industry | 1,000 – 5,000 Employees
developers to feed it source code to help
improve quality.”
“
“It's not 100% fool-proof and still benefits from
VICE PRESIDENT
Natural Resource Extraction Industry | 10,000+ Employees
human intervention.”
DIRECTOR
Healthcare Industry | < 1,000 Employees
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InfoSec Leaders are saying…
“ “
“We are currently assessing
“There is still no transparency about
compliance aspects [and] static
data models are training on, so the risk
analysis tool capabilities to continuously
associated with bias, and privacy is
scan AI generated code, and also
very difficult to understand and
forming guidelines for aware and ethical
estimate.”
use of generative AI tools by
engineers.”
C-SUITE
Finance Industry | <1,000 Employees C-SUITE
Finance Industry | <1,000 Employees
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Software Engineering Leaders are saying…
“
“
“Low-level software engineering jobs will be
replaced by AI.” “[Generative AI will] increase productivity
to a large extent, [and] create a lot of
jobs for software engineers. The
DIRECTOR
Telecommunication Services Industry | 5,000 – 10,000 Employees
department will take a more strategic
tack. More jobs will be created to
“
develop a new set of human work tasks
— many of them of higher value.”
“It will create more volume of new code than we
have resources to keep in check.”
DIRECTOR
Telecommunication Services Industry | 10,000+ Employees
DIRECTOR
Manufacturing Industry | 1,000 – 5,000 Employees
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Software Engineering Leaders are saying…
“ “
“[Generative AI] is going to change the
overall TAT [turnaround time] for “[Generative AI] will help speed up coding —
producing quality code. [It] may eradicate with human intervention after the main work
a lot of jobs especially at the junior is done by the AI.”
software developer level.”
DIRECTOR
VICE PRESIDENT Natural Resource Extraction Industry | 10,000+ Employees
Software Industry | 1,000 – 5,000 Employees
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D&A Leaders are saying…
“
“
“AI generated synthetic data is quite sensitive
and needs to be handled securely.”
“It is in [an] early stage and will be tough
to adopt across [the] entire organization
MANAGER
Finance Industry | 5,000 – 10,000 Employees and also ROI cannot be [easily]
calculated. Regulatory issues are a
“
major concern.”
“AI generated [techniques have] a high level of
myopic bias, selecting the right vendor for data
C-SUITE
remains a challenge.” Finance Industry | 10,000+ Employees
MANAGER
Finance Industry | 1,000 – 5,000 Employees
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D&A Leaders are saying…
“ “
“There has to be [an] integration of Human “It's difficult to reduce bias while also
Resource insights along with AI generated improving accuracy for healthcare data.
synthetic data to improve the utmost So far the only way is to tokenize real-
world data to reduce risk while
effectiveness.”
preserving data accuracy and quality.”
MANAGER DIRECTOR
Professional Services Industry | 5,000 – 10,000 Employees Finance Industry | 10,000+ Employees
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Supply Chain Leaders are saying…
“ “
”Generative AI can be employed to design and manage
“Ethical implications are humongous while working with
warehouse operations more effectively, optimizing space
AI/ML in supply chain industry. The AI disruptions leading to
utilization, labor allocation, and material handling
elimination of supply chain manpower from various critical
processes. By automating these tasks, logistics companies
stages of business is posing issue for businesses and
can significantly reduce their operational costs and improve
professionals globally.”
overall efficiency.”
DIRECTOR
MANAGER
Education Services | APAC | 501 – 1,000 Employees
Manufacturing | APAC | 10,001+ Employees
“ “
“[Generative AI] will be a part of the supply chain
“[Generative AI has] very bright future for accurate
technology ecosystem, and will be used to
modelling of tasks and find fastest route possible
predict outcomes, prevent issues from occurring,
and inventory replenishment.”
and prescribe actions.”
DIRECTOR VICE PRESIDENT
Consumer Goods | North America | 10,001+ Employees Consumer Goods | APAC | 51 - 200 Employees
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Marketing Leaders are saying…
“
“
""Once generative AI is integrated with most marketing
technology systems, I foresee prompt based images,
videos and copy being widespread. Imagine creating
""By utilizing the power of generative AI,
multivariate tests using multiple assets in multiple
languages with multiple landing pages.""​ marketing teams can enhance customer
experience and boost sales by creating tailored
MARKETING VP
Hospitality | APAC | <1,000 Employees
content, evaluating customer feedback,
implementing precise pricing strategies,
“
launching focused marketing campaigns, and
automating customer service processes.""
""[Marketing teams] should be using generative AI in
all aspects of marketing. Content, digital ad copies,
SEO suggestions, brand video and infographics.""
C-SUITE
Finance Industry | 10,000+ Employees
MARKETING DIRECTOR
Finance & Banking | APAC | 1,001 – 5,000 Employees
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Sales Leaders are saying…
“ “
""Don't rely on it completely so that your ""It can serve as a useful outline,
customers will easily find out that you however it lacks innovative thinking.
have used generative AI tool."" It reports from past data.""
SALES MANAGER SALES DIRECTOR
Professional Services Industry | APAC | 1,001 – 5,000 Employees Telecommunication Services | North America | 1,001 - 5,000 Employees
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Generative
AI Surveys
Overview Barriers, Benefits, Use Cases Open-ended Insights Peer Data & Insights Additional Insights
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IT –
ChatGPT policies under development, risk convos make it to the
boardroom
Currently don’t have an acceptable use policy in place for
79%
ChatGPT
In the process of
32%
developing one
69% Use ChatGPT for business purposes
21% Use paid
subscription
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Additional Results: IT
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Source: Generative AI and ChatGPT: Adoption and Usage
InfoSec –
AI working groups, data guidelines and humans in the
loop for risk mitigation
Their organization has or will establish new
44%
working groups to manage generative AI security
and risks.
61%
use or plan to usedata guidelines
55% associated with generative AI tools
orfoundational models
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Additional Results: InfoSec
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D&A –
AI-generated synthetic data can overcome real-world data
shortfalls but is not infallible
51%
57%
availability
complexity
60%
56%
adopted AI-generated
improved model
synthetic data because
of challenges with real- efficiency
60%
world data accessibility
improved model
accuracy
45%
mitigated data
privacy
concerns
Source: Generative AI for Synthetic Data
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Additional Results: D&A
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Source: Generative AI for Synthetic Data
Software Engineering
– using generative AI, but many lack
governance
More than half of respondents say generative AI
is currently used in their software engineering
department.
60% of those use it forAI-assisted pair
programming
t
78% of those respondents use
ChatGPT.
55% do not have governance policies
in place.
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Additional Results: Software Engineering
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Source: Generative AI for Software Engineering Teams
Supply Chain*-
leaders are looking to AI to address the corporate
brain drain and increasing unpredictability
40% of respondents are already using Generative AI
as a part of their supply chain strategy
45%
plan to deploy it soon
t
71% expect that generative AI will become a standard
in supply chain within 4 years.
Nearly half of surveyed supply chain leaders are using or plan to
use generative AI to enhance internal knowledge bases
42%
plan to use it to generate predictive models
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*Survey still in collection phase. Results are preliminary
Marketing* –
expects generative AI to become a mainstay in the
MarTech stack, and many are already using it
100%
t
reportedthat they
believe generative AI will be a
regular aspect of marketing
team's tech stacks within6 years
76%
of marketers report their
content marketing teams are
already using generative AI,
the top choice among
respondents.
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*Survey still in collection phase. Results are preliminary
Sales* -
Some believe they can completely replace a team member
with generative AI, with sales ops being most common
believe generative AI tools would allow them to
37%
completely replace a person on their team while
still producing the same results.
t
74% believe sales operations roles could be
replaced.
say they would be extremely or moderately
55%
concernedif a customer discovered their content
was AI generated.
Source: Generative AI Sales Tools
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Generative
AI Surveys
Overview Barriers, Benefits, Use Cases Open-ended Insights Peer Data & Insights Additional Insights
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One-Minute Insights
Individual Reports for Deeper Dives
1 2 3 4
BENCHMARK YOUR RESPONSES BENCHMARK YOUR RESPONSES BENCHMARK YOUR RESPONSES BENCHMARK YOUR RESPONSES
GET FULL REPORT GET FULL REPORT GET FULL REPORT GET FULL REPORT
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JOIN THE CONVERSATONS!
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297,ibm,ibm_2024-sustainability-readiness-report.pdf,"The State of Sustainability Readiness 2024
How do we close
the gap between
ambition and action?
think report
Foreword
In the broadest sense, sustainability is New data from The State of Sustainability operational budget. At the same time,
about working to preserve continuous Readiness 2024 report shows only half of surveyed leaders feel prepared
operations over time; work that never ends organizations are well underway in to deal with increasingly disruptive
in an ever-shifting landscape of challenges grappling with this task. 9 out of 10 climate risks.
and opportunities. respondents believe in the potential
of AI to contribute to sustainability The potential and consequences of AI
Today, business leaders understand outcomes. 61% of respondents view do not stop at the lines drawn on any
AI reflects both a challenge and an investments in information technology organizational chart, and so this report is Christina Shim
opportunity. AI is accelerating the (IT) for sustainability from the for CEOs, CSOs, CIOs, COOs and more. Chief Sustainability Officer, IBM
discovery of lifesaving drugs and perspective of opportunity and growth Successfully operationalizing sustainability
sustainable materials, optimizing supply rather than cost mitigation. And almost is not about an annual report, it is about
chains and mining efforts, and supporting 90% plan to increase investments in using data and technology to tackle an
the transition to more renewable, IT for sustainability. This data signals organization’s core mission with a smart,
decentralized electric grids. Yet AI organizations understand the enormous strategic, long-term approach. The data
adoption has also driven higher energy opportunity for AI, if implemented shows that more than ever businesses
use and costs for many organizations, correctly, to drive both organizational and are starting to, and must, approach
and even forced some to reevaluate their environmental sustainability. sustainability in its broadest sense—using
sustainability targets. every tool at their disposal to mitigate
At the same time, the report shows there climate threats, support more streamlined
For business leaders, the task ahead is huge room—and need—for growth. More and cost-efficient operations, and stay
is how to maximize the business value than half of organizations (56%) are not yet competitive with their peers.
of AI—delivering the results clients need actively using AI for sustainability, and 48%
better, faster and with higher quality— say investments in IT for sustainability are
while minimizing its costs and “one-off” rather than coming from a regular
environmental impacts.
Introduction
Global industry leaders see the opportunities in The State of Sustainability Readiness 2024 The research found most organizations
report was conducted independently by understand the necessity of climate
using IT—and most prominently, AI—to elevate
Morning Consult and sponsored, analyzed risk mitigation to protect assets and
and published by IBM. Interviews were advance operations. Many acknowledge
sustainability and their companies. However, they
conducted between April and May of the importance of investment in IT,
also see gaps: outmoded policies, confidence in 2024 with 2,790 business leaders and infrastructure and human capital. Candid
decision-makers, across 15 industries, responses revealed perceptions of
tracking progress, energy consumption and lack
in 9 countries. More than 30 survey readiness and progress differ between
questions correlated to climate risk and leadership levels. But almost all the
of expertise, especially in employee skills relating
corporate responsibilities, and covered surveyed C-level executives expect AI to be
to AI and generative AI. In this report, we’ll show strategic, financial, regulatory and a change agent in furthering their business
compliance concerns. while growing climate resiliency.
you how key players are investing in sustainability
through the opportunity of technology.
3
Chapter 1 Chapter 2 Chapter 3 Chapter 4
Stronger IT, greater sustainability The urgency to think ahead The AI sustainability dilemma From risk to resilience
Chapter 5 Chapter 6 Chapter 7
Top challenges: Budget, The perception gap problem Recommendations for readiness
measurement and skills
1. Stronger IT,
greater sustainability
Investing in IT for sustainability isn’t just a matter of doing
the right thing—it can have a net positive impact on your
organization’s current and future success. Along with practical
goals, such as reducing risk to business assets and lowering
energy costs, investing in IT for sustainability can satisfy
stakeholders, attract principled employees and position their
organization for AI in the future.
88
%
of business leaders intend to
increase investments in IT to
advance sustainability efforts.
Chapter 1
Brand reputation
57% 55% 53% 70% 57% 49% 68% 64% 40% 57%
Energy cost
54% 52% 50% 63% 56% 58% 64% 55% 38% 51%
Business resilience
52% 51% 51% 51% 54% 54% 60% 61% 38% 49%
Regulator y pressure
42% 43% 42% 37% 45% 41% 48% 47% 34% 45%
Global US Canada Brazil UK Germany UAE India Japan Australia
Figure 1.
Top factors in increasing IT sustainability investments energy costs and long-term business resilience. Regulatory pressure holds less importance.
Decision-makers cite brand reputation as a key reason for investing in IT and related services for The findings suggest the importance of strategic alignment of organizational objectives and
sustainability. Globally, this reason accounts for 57% of positive responses, followed closely by desired outcomes when it comes to IT investments for sustainability.
Chapter 1
16%
61%
43%
higher rate of revenue growth
is seen by organizations that
embed sustainability.
52%
11%
7%
Global US Global US more of businesses integrating
sustainability outperform their
Cost driven Opportunity driven
peers on profitability.
Figure 2.
What drives sustainability investment? Opportunity.
The top leaders surveyed said they invest in IT sustainability initiatives based on the perceived embed sustainability are 52% more likely to outperform their peers on sustainability
opportunity rather than cost mitigation. And this opportunity mindset is paying off. The drive when compared with those that do not, according to research conducted by the
to invest in such initiatives is benefiting organizations using this approach. Organizations that IBM Institute for Business Value.1
2. The urgency
to think ahead
From Kerala to Sao Paulo, Auckland to Kyoto, global leaders
are increasingly investing more in sustainability IT—but not all
with the same urgency. Economic factors, household income,
government policy and geographic differences undoubtably
affect these investment decisions. But citizens and businesses
around the globe have one thing in common: they need their
leaders to think ahead.
Chapter 2
34%
Germany
52%
UAE
Countries are investing more
in IT for sustainability
Investing in IT for sustainability is
anticipated to grow across global markets
41%
over the next 12 months. Countries, such
Canada 41%
as India, Brazil and Australia, which have
UK
faced recent extreme climate issues, plan
24%
to invest even more than the global average
Japan
of 88%. Within this global average, 44% of
markets plan significant IT investment.
The global cost of climate change damage
is estimated to be USD 143 billion per
year.2 This cost is expected to increase
over time as the impacts of climate change
become more severe. As a result, countries
with lower incomes are at a higher risk from
the adverse economic impacts of climate
37%
change, which is why they might be 70%
US
likelier to invest in sustainability-related India
IT. Leaders in Japan, on the other hand,
answered much more conservatively across
the report’s key metrics.
58%
43%
Brazil
Australia
Figure 3.
India and Brazil plan to
invest 15% over the global
average in IT sustainability
3. The AI sustainability
dilemma
AI is a hot topic in the sustainability community, and it’s
easy to see why. It can help streamline data collection from
various sources, aid sustainability leaders in understanding
environmental risks, and assist in navigating the regulatory
compliance process and making informed decisions.
Additionally, AI can help organizations adapt their operations
to the changing climate, and better maintain their assets and
operations in response.
Chapter 3
90% 96% 95% 32%
Global Brazil United Arab Emirates United Kingdom
Very positive and positive opinions
Very positive opinions
Figure 4.
Leaders agree AI will have a positive influence in achieving sustainability goals
Almost universally, respondents had an overwhelmingly positive take on the influence of AI in responding with very positive opinions about the impact of AI on their organization’s sustainability
their organizations, especially surveyed leaders in developing markets, such as India, Brazil efforts. Whether it’s innate cultural skepticism, policy or geography, it’s tough to measure. But as
and United Arab Emirates. However, leaders in the UK were more ambivalent, with only 32% sustainability increasingly becomes a business imperative, minds may change.
“W e have a commitment to reach net zero
by 2035, and we’ve used that commitment
to influence our recently enacted
Evergreen IT strategy.”
Steve Elliott
Head of IT Services, Water Corporation
Organizations are already seeing results in applying AI to This strategy saved Water Corporation roughly 1,500 hours of
their sustainability efforts. Water Corporation, a Western manual labor annually associated with infrastructure support
Australia state-owned entity, employed AI to help migrate and cut development efforts and associated costs by 30%.
its back-office services to a responsible cloud-first approach. And all these savings helped them offset the cost of running
The team used generative AI to convert plain English into their cloud environment by more than 40%.
code recommendations for the automation functions that
Read the case study on IBM.com ↗ were targeted for the migration and upkeep of the new
environment. Additionally, they automated common, often-
repeated support tasks to be performed automatically.
Chapter 3
64%
56%
44%
40%
33%
32%
29%
28%
27%
of leaders are not using
AI for sustainability efforts
despite widespread positive
sentiments regarding AI.
Global US UK India Global US UK India
Plan to implement AI solution for Actively using AI for
sustainability soon sustainability now
Figure 5.
AI adoption for sustainability purposes shows promise percentage could improve in the near future, however, as 32% of respondents claimed they plan
The report revealed that despite the widespread positive sentiments connected with AI, to implement an AI solution for sustainability soon. India leads all surveyed countries in actively
more than half of organizations studied are not using AI for their sustainability efforts. That using AI at 64%, which is in stark contrast to the UK, where AI adoption is at just 27%.
Chapter 3
2030
AI is powerful, but at what ultimate cost?
For all the power AI can deliver,
The power demand of AI is
organizations must still account for the
expected to rise by 160%.3
energy use it demands—something leaders
are trying hard to mitigate. The good news?
This new adoption of AI is galvanizing
organizations to employ more sustainable
practices, such as utilizing foundation
models, optimizing data processing
locations, investing in energy-efficient
processors and leveraging open-source
collaborations. These strategies not only
reduce the environmental footprint of AI,
but also enhance operational efficiency and
cost-effectiveness, balancing innovation
with sustainability.
2024
Figure 6.
4. From risk to resilience
Climate resilience―anticipating, adapting to and recovering
from the impacts of climate change―is a necessary goal for any
organization. From responsibly managing natural resources to
finding skilled employees to devise solutions to environmental
challenges, business leaders are investing in climate resilience
by thinking of their IT as an engine for sustainability—
and profitability.
Climate resilience is the most
critical sustainability issue
Chapter 4
Climate risk 31%
1/2
Energy use 31%
Availability of skilled staff 30%
Measurement and analytics 26% of leaders surveyed feel fully
prepared to deal with these
aspects of climate risk.
Water use 26%
Figure 7.
A ranking of the most challenging sustainability issues that companies must solve
On average globally, organizations cited climate risk to operations and assets, energy use, and asset life. Leaders in India and Germany are particularly proactive in addressing these issues,
skilled staff availability as their top 3 sustainability challenges. By implementing a strategy that especially the urgent need to address challenges related to water use. Even so, only half of global
prioritizes repairs and replacements, they can improve asset health, predict failures and extend leaders surveyed feel confident in their readiness for climate risks.
“W e’re the largest provider of through-life
support asset management services for
passenger rollingstock in Australia.”
Adam Williams
Head of Growth, Rail and Transit Systems at Downer Group
Harnessing infrastructure data offers a great opportunity. Their AI-powered platform harnesses complex analytics and
With the correct data and proper analysis, companies can near real-time data to support predictive maintenance efforts
identify and fix early problems, extending the life of critical for more than 200 trains.
machinery and reducing maintenance and material waste.
The Downer Group turned to IBM asset management to Downer effectively doubled the number of trains it could
monitor, measure and maintain the trains in its critical maintain from one maintenance center alone—all while netting
Read the case study on IBM.com ↗ transportation infrastructure. a 20% improvement in efficiency.
5. Top challenges: Budget,
measurement and skills
The big 3 challenges facing business leaders are: how much to
allocate to sustainability efforts, how to measure sustainability
key performance indicators (KPIs) and how to stay staffed with
experienced workers amid current labor shortages.
Chapter 5
48%
Global allocations for sustainability
The first big challenge organizations face
Exceptional budget created
when it comes to investing in sustainability is
specifically for IT and aligned
financial planning. One telling statistic reveals
services sustainabilty
whether a company considers sustainability
measures as part of their operating budget or
relegates it to the lower-priority exceptional
budget—or even figures it in at all. Responses
27%
showed only 26% said IT sustainability is
part of their regular operational budget,
Dedicated sustainability budget
signaling that IT sustainability is not a priority
within the overall strategy of organizations.
What accounts for the gap between
dedicated and exceptional budgets? Quite
26%
likely, opportunity. With many sustainability
issues tied to energy consumption and
Regular operational budget
IT and the rising investment in AI, an
increased IT budget could signal that
organizations are beginning to see the
benefits and how to operationalize
sustainability through new technology.
Figure 8.
50
%
of business leaders believe their
data to measure sustainability KPIs
isn’t very mature.
Chapter 5
Total energy Renewable energy Recycling
consumption consumption
79%
72%
52%
49% 50%
48%
29% 42%
51%
35%
38%
32%
32%
27%
20%
40%
37%
14%
42%
7%
1% 1%
Plastic use GHG emissions GHG emissions Supplier metrics
Scope 2 Scope 1
Global US Brazil India Global US Brazil India Global US Brazil India
Water use Waste GHG emissions
generation Scope 3
Growing up Hitting our stride Walking confidently
Figure 9. Figure 10.
Top KPIs used to measure sustainability outcomes How mature are organizations in using data to track progress?
The second challenge is knowing where to begin. Leaders in most surveyed countries looked to Reliable data plays a significant role in improving and tracking progress on sustainability
resource efficiency, citing renewable energy consumption, total energy consumption and recycling goals. Metrics are inconsistent across markets in their ability to track sustainability KPIs.
as their top 3 KPIs for sustainability outcomes. Renewable energy consumption is cited as the top India and Brazil’s strong reliance on brand reputation, elevated expectations with AI
KPI in Brazil, UAE and the UK. Over 80% of global energy production comes from fossil fuels, which integration, relatively advanced stages of digital transformation preparedness and
are nonrenewable resources, such as coal, oil and gas.2 However, cleaner, renewable sources of commitments to skill enhancement through targeted investments may contribute to
energy—solar, wind, geothermal, hydropower, ocean energy and bioenergy—are gaining ground. this confidence level.
Chapter 5
The third pressing need for executives is
expertise in AI and generative AI, sustainable
business strategies, and renewable and clean
energy. Responses showed skills in AI were
most desired in Brazil, at 53%, and the US,
at 47%, with a strong appreciation for
sustainable business strategies in UAE at 44%,
Australia at 40% and Canada at 39%.
“T he supply chain for renewable
products was in many ways a new
kind of business, and we needed a
new foundation to build it on.”
Marko Mäki-Ullakko
Head of Integrated ERP, Neste
Along with measuring and finding efficiencies, technological As a catalyst to achieving this goal, they needed a truly global
breakthroughs also present new sustainability opportunities. supply chain strategy to effectively manage their network of
Neste, the world’s leading producer of sustainable aviation advanced renewable refineries and technologies.
fuel and renewable diesel, aims to help customers reduce
their greenhouse gas (GHG) emissions by at least 20 million IBM Consulting® provided Neste with the process design
tons annually by 2030. support it needed to optimize its enterprise resource planning
Read the case study on IBM.com ↗ (ERP) investments.
6. The perception
gap problem
There’s a real chasm in the ways organizations view the promise
of AI and the way they actually use it. For instance, many
industry leaders report a desire to bolster the resilience of their
assets, infrastructure and supply chains in the face of potential
climate hazards, but only half of respondents believe their data
is mature enough to measure sustainability KPIs. Perhaps at
the core of these disconnections is the perception gap that
exists between C-suite executives and those more likely to
implement operational decisions, with the former generally
having a rosier perception than the reality.
Different perceptions between leadership levels can signal a
divergence in direction or, perhaps worse, no direction at all.
But it presents an opportunity for dialogue. In their responses,
C-suite executives revealed a more optimistic outlook than the
vice presidents and directors who work for them—and are more
likely to implement operational decisions. The study revealed
that while reducing potential damage from climate risk is a
common goal, division can and does creep in when ambition
(what we should do) meets action (how we can achieve it).
Chapter 6
11% gap
67%
Business leaders’ perceptions differ on
approach to climate resilience efforts
Surveyed business leaders showed a
marked divergence in their perceptions
56%
of readiness and active defense against
climate risks. Top-level executives felt
more proactive when addressing and
acting on climate resilience efforts than
lower-level decision-makers.
Percentage describing their organization’s climate resilience efforts as proactive
C-suite executives
Vice presidents and directors Figure 12.
Chapter 6
13% gap 7% gap 9% gap
Confidence levels about readiness
for climate risk factors diverge
In their responses, C-suite executives
revealed more confidence than vice
55%
presidents and directors that their
52%
51%
organization was prepared to handle
different aspects of climate risks.
45%
Financial risks represented the largest
42% 42%
gap between the groups at 13%.
Financial risks Physical infrastructure risks Supply chain risks
C-suite executives
Vice presidents and directors Figure 13.
Different leadership
perceptions present an
opportunity for alignment
Chapter 6
12% gap 10% gap
51%
47%
39%
37%
Percentage claiming AI will very positively impact achieving sustainability goals Percentage claiming AI is actively used for sustainability efforts
Figure 14a. Figure 14b.
Business leaders have gaps regarding the impact of and use for AI in sustainability
As AI becomes more prevalent in operations, C-level executives tended to show a more positive
outlook than vice presidents and directors about the impact that AI could have on achieving their C-suite executives
sustainability goals and in the way their organizations put AI to use in sustainability efforts.
Vice presidents and directors
Chapter 6
32% gap 13% gap 6% gap 15% gap
57%
51%
47%
44%
42%
36%
32%
19%
Banking and financial services Telecommunications Retail Manufacturing
Figure 15.
Industries reveal perception gaps on the use of AI for sustainability efforts
When considering the impact of AI and its use for sustainability progress, leaders in the banking
and finance industry presented the strongest divergence between C-suite and lower-level
decision-makers. But responses from the retail industry showed the opposite, with 42% of
vice presidents and directors stating their organizations were actively using AI for sustainability C-suite executives
initiatives, as opposed to the C-suite executives’ response of 36%.
Vice presidents and directors
7. Recommendations
for readiness
Sustainability challenges tend to carry over from year to year,
but leaders said they feel those challenges more strongly
this year than before. Even with the optimistic attitudes cited
by C-suite executives, they accept they need to alleviate
challenges as a whole organization. Here’s how to address
notable issues head-on.
1 2
Mind the gap Invest in upskilling
Organizations should use data to obtain a To address the growing, seemingly infinite
more holistic view of their operations and need for digital skills related to climate
understand where the different perceptions risk mitigation, the workforce must make
between C-suite and other decision-makers an even stronger turn toward technology
originate. To keep an eye on changes training. One way to start is with online
and blind spots, use a data analysis and skills-based courses that offer free training
reporting tool to help maintain a state of and reskilling with purpose-designed
readiness visible to individuals across the curricula for all skill levels. Organizations
organization, so they can proactively come can also identify skills needs and bridge any
up with and implement solutions. existing and anticipated future skills gaps
by tapping their ecosystem of partners.
Chapter 7
3 4
Invest in IT for smarter assets Invest in AI
Developing predictive maintenance Trusted AI tools can help save time
practices can facilitate more efficient and money in sustainability efforts. For
resource allocation and business example, generative AI can provide insights
operations. Consider investing in an that help identify opportunities to reduce
application suite with intelligent asset carbon emissions, create scenarios
management, monitoring, predictive and algorithms for better practices,
maintenance and reliability planning in and simulate risk scenarios, including
a single platform. This investment can ephemeral details, such as weather or
enable your organization to optimize asset local disasters. An investment in AI can
performance, extend asset lifecycles and also help reduce the workforce skills talent
reduce downtime and costs. shortage many executives identified in
this report. The desire for expertise in AI
and generative AI is documented, and an
investment in that area can aid in filling the
need. Explore use cases to increase your
portfolio of ideas.
Methodology
2,790 15
This poll was conducted online by Morning
Consult among a sample of 2,790 business
leaders from each of these markets: the
US, Canada, Brazil, the UK, Germany, UAE,
India, Japan and Australia.
This report highlights global leaders overall
and by market, along with the C-suite
executives (high-level decision-makers) executives industries
and vice presidents and directors
(lowerlevel decision-makers).
30 9
To qualify as a vice president or director,
respondents must be employed at a
company with more than 1,000 employees
and work as a director or vice president.
To qualify as a C-suite executive,
respondents must be employed at a
company with more than 100 employees
and must be the owner or work in the
questions countries
C-suite. A significant proportion of the
C-suite audience includes executives
working at companies with more than
1,000 employees.
About us
About Morning Consult About IBM
Morning Consult provides global survey IBM can help you plan a profitable
research tools, data services and news We’re doing business in an path forward with open, AI-powered
to organizations in business, marketing, unpredictable world. solutions and platforms and deep industry
economics and politics. Morning Consult expertise that address your goals in
surveys thousands of people around the Success requires new levels of resilience 5 key areas: climate risk management;
world every day, pairing that exclusive, and agility rooted in responsible practices infrastructure and operations; supply chain;
forward-looking survey data with that preserve our planet for future electrification, energy, and emissions
analytical applications, to offer a distinct, generations. Sustainability is now a management; and a sustainability strategy.
competitive advantage for our users and strategic business imperative. For more information, visit
cement our leadership in the decision ibm.com/sustainability or subscribe
intelligence category. to receive sustainability updates.
1. Beyond checking the box: How to create business © Copyright IBM Corporation 2024 THE INFORMATION IN THIS DOCUMENT IS
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38"
298,ibm,6-power-moves-cfos-must-make.pdf,"IBM Institute for Business Value
Global
C-suite
Series
31st Edition
CFO Study
6 power moves
CFOs must make
Tackling hard truths
in the generative AI era
Contents
2
Introduction
The imperative
of bold leadership
6
What sets leading
About the study CFOs apart?
In Q1 2024, in cooperation with Oxford Economics, the IBM Institute
for Business Value surveyed 2,000 Chief Financial Officers (CFOs). 9
Separately, a small group of executives was engaged for in-depth The six power moves Champion tech Make execution the Show me the ROI.
as core. yin to strategy’s yang.
qualitative interviews. These discussions focused on key insights from
the study and the CFOs’ on-the-ground experience leading their finance
organizations and interacting across their organizations on developing
and executing strategy in the new era of AI. Respondents span
10 18 26
26 industries and 34 locations worldwide. For more details,
Strategy Execution Investment
see “Research methodology and analysis” on page 60.
The cover concept and individual patterns in
this report were developed using generative AI.
IBM IBV designers translated each of the “power moves” into prompts, Determine your risk Make data your Ignite your talent 59
and then used these prompts within Adobe Firefly to generate tolerance, then place AI’s oxygen. revolution. Conclusion
vector-based imagery that inspired the basis and structure your big bets. Leading through
for each pattern. Similarly, the photos that appear in this report technology-driven
change and uncertainty
were identified using AI-assisted, natural-language search,
using the generated patterns as reference images.
Overall, the efficiency gained by integrating these
34 42 50 60
tools into the design process is as follows: Risk Data Talent Research
methodology
Concept—3 weeks to 1.5 days
and analysis
Patterns—2 weeks to 2 days
Photography—1 week to 2 hours
The
imperative
of bold
leadership
It’s the generative AI era. Will your Nearly two-thirds of CEOs in our 2024 CEO Study as accelerating technological modernization,
organization disrupt—or be disrupted? say they need to rewrite their organizational creating economies of scale, differentiating
playbook to remain competitive.1 You can’t products and services, and building strategic
It depends on how your organization
run the business of tomorrow with today’s skills, alliances (see Figure 1) to help them deliver this
reacts today and prepares for tomorrow.
technology, or operating models. And with 72% advantage. Yet, our study reveals some CFOs are
of top CEOs saying competitive advantage depends leaping forward while others are being cautious:
Generative AI is a once-in-a-generation
on who has the most advanced generative AI,2
opportunity to drive radical productivity – 72% of leading CFOs identify their CTO as highly
the tensions finance leaders already face are
and create new avenues for explosive important or critical to their success, more than
compounded: risk versus reward, short-term versus
“The role of the CFO has migrated any other role.
growth—but only for leaders who make long-term, optimization versus constraint, agility
the right moves. CFOs must use from a more traditional financial versus discipline, and innovation versus financial – 65% of all CFOs say their organization is under
perspective to one with a really stability. So, what’s a CFO to do? pressure to accelerate ROI across their
their influence to advance their
strong understanding of the technology portfolio.
organization in innovative ways. CFOs must be both guardians of stability and agents
business and a repository – Only 35% of finance organizations engage
of transformation.3 At the epicenter of strategy
Execution is essential. for insights.” development and execution, CFOs, working hand in early IT planning with tech leaders to set
expectations on how technology advances
Complacency is not an option. in glove with tech leaders, supercharge technology
Diana Vuong enterprise strategy.
as the transformative force in their organization.
VP, Finance & CFO, Vancouver Airport Authority
In this high-stakes environment, CFOs must make
CFOs play a crucial role in driving competitive
power moves that confront hard truths about
advantage and creating value for their organizations.
transformation, talent, innovation, and more.
They are looking to change-heavy initiatives such
2 Introduction 3
Figure 1 The CFO leaders
check once in layout
The stuff leading CFOs are made of
Competitive advantage and barriers to overcome
CFOs expect competitive advantage coming Perspective
from change-heavy initiatives and innovation
requiring tough choices. How are leading A sharp focus on a strategic future
1 Leading CFOs articulate vision and
CFOs preparing
strategy to optimize long-term
for an uncertain future? investment value. They not only
Most important enablers of competitive Greatest barriers to innovation
control and manage risk, but also
advantage over the next 3 years in the organization
finesse it with financial effectiveness.
#1 Accelerated technology #1 Management resistance
modernization to change
We’ve identified a group of leading CFOs,
representing 9% of our global data set,
#2 Scale advantages #2 Aversion to risk/ that is outperforming the competition.
(economies of scale) disruption
tie
Here are the four powerful CFO
An adeptness at strategy execution
characteristics that enable tackling 2
#2 New differentiated products #3 Limited budget/financial Leading CFOs translate decisions into
the hard truths of the generative AI era. financially astute actions—as well as
and services resources
tie tie embrace cloud-based enterprise
performance management.
#2 Strategic alliances #3 Lack of clear innovation
and partnerships strategy
tie tie
Qs. What are the most important enablers of your competitive advantage over the next three years?
Which factors present the greatest barriers to innovation in your organization?
An agile responsiveness to changing market
3 conditions and new opportunities
Leading CFOs are decisive, making decisions
with more speed and financial effectiveness than
their competitors. They’re enabling better, faster
62% decisions across their enterprises.
72%
of CEOs say they
must rewrite their
organizational playbook.4 A keen eye for technology that drives
of top CEOs say competitive 4 competitive advantage
advantage depends on who has Leading CFOs connect tech investments to
quantifiable business outcomes. They create
the most advanced generative AI.5
cross-functional teams to forecast and allocate
technology costs and manage tech budgets.
4 IntroSdtruactteiogny 55
The CFO leaders The CFO leaders
What sets leading High performance starts with financial metrics. Leading CFOs And their success transcends financials. “It is important for not only
CFOs apart? drive significantly higher annual revenue growth and operating Leading CFOs say their organizations the CFO but also management
margins than their peers. In 2023, they outperformed their outperform in several key areas that
to have a broad perspective
competition by 39% in revenue growth. In 2023, leading CFOs deliver a competitive edge.
in order to see the bigger picture.”
achieved nearly 11% greater operating margins.
Leading CFOs Leading CFOs Iwaaki Taniguchi
Director, Executive Vice President & CFO
All others All others
Chugai Pharmaceutical Co., Ltd.
Talent development
Annual revenue/budget growth Annual operating margin and retention
80
+6.7%
60
36%
Developing more Executing
enterprise
19%
40 enterprise
16% strategy more strategy
20 32%
+9.1%
more
+10.6%
20%
more
+9.1%
24%
12%
Technological more Brand
+3.8%
maturity
22%
reputation
more
+39.3% Cyber risk and
cybersecurity
8%
Qs. Rate the effectiveness of your organization in the following areas: Developing enterprise strategy, executing enterprise strategy.
2020-2022 2023 2024-2026 2020-2022 2023 2024-2026
Percentage reflects “effective” and “highly effective.” How does your organization’s performance compare to similar organizations over
Estimated Estimated
the past three years? Percentage reflects “outperformed” and “significantly outperformed.” How would your closest competitor rate your
organization’s performance compared to similar organizations? Percentage reflects “leading” and “significantly leading.”
6 Introduction 7
The six
power moves
“You have to be able to understand the
levers of your business and how each
Here are six power moves that CFOs 1
Champion tech
must make—from managing risk tolerance
and every decision impacts your
as core.
to strengthening tech partnerships
to embracing generative AI—that
business—and then potentially which
successfully use technology to propel
their organizations forward. 2 Make execution the
levers need to be pulled in order
yin to strategy’s yang.
to achieve the desired result.”
3
Show me
Martin Günther
CFO, smart Europe GmbH the ROI.
4 Determine your risk
tolerance, then place
your big bets.
5
Make data your
AI’s oxygen.
6
Ignite your talent
revolution.
8 Introduction 9
Every product is becoming a digital product. Yet when organizations see technology
as an enabler, they treat it like a toolbox. They wait for problems tech can solve rather
than exploring what new opportunities it creates. Only when they recognize technology
as the transformative force at the core of innovation can organizations seize first-mover
advantages, define markets, and gain economies of scale.
Tech strategy and business strategy cannot be separated, and the CFO plays as important
Champion tech a role—if not more—in this integration as anyone. To drive innovation, competitiveness,
and sustainable growth, the two must align.
CFOs must seize this opportunity to transform their role: this requires shifting from
as core.
a financial overseer, approving or rejecting technology project funding, to a strategic ally.
In this new role, CFOs align technology initiatives with advancement of enterprise strategy
and promote technologies that drive transformation. They need to evolve from evaluating
tech investments via traditional business cases to using a holistic value assessment that
With technology becoming core to the enterprise—not just an enabler— includes strategic, operational, customer, and financial impacts.
CEOs recognize that collaboration between finance and tech is crucial
With time of the essence, and detailed business cases too often yesterday’s “nice to have,”
to success. Leading CFOs say CTOs are their most important relationship,
it is on CFOs to make sure that tech is at the table, providing invaluable context and
with 72% identifying them as highly important or critical. Now is the time knowledge. One telling trait of a leading CFO? They recognize that, when pursuing
for CFOs to supercharge technology as the transformative force at the enterprise success, the most important relationships are technology related.
core of innovation. CFOs need to advocate for tech leaders’ expertise
Leading CFOs are diplomats, with the CTO, CISO, and CIO topping their list of CxO
in the boardroom by integrating evaluation, investment, and planning, relationships needed to be successful. With no single tech role being the lone expert,
forming a powerful coalition that drives strategic growth. leading CFOs recognize that each tech leader brings their unique strengths to form
a high-powered coalition necessary to drive critical technology transformation. Leading
CFOs say CTOs are their most important relationship, with 72% identifying them as highly
important or critical. And 63% of leading CFOs value relationships with their CISO. Given
“The real question is: Who will
their longstanding influence, CFOs are in the best position to actually help build the long
adapt and ride this wave of change?
overdue bridge between business strategy and technology.
Those who do will thrive, while
others risk being left behind.”
Fabio Martinez “I work closely with our CIO to make sure that we make
CFO, Alstom Brazil
the right decisions. We evaluate trade-offs.”
Yoshito Murakami
CFO Power & Gas Power, GE Vernova
1100 Strategy 11
CEOs rely heavily on CFOs and tech leaders for their assessment of technology. Nearly half of these high performers engage in early IT planning to set
Where is the greatest potential? What captures value? CEOs need boots on the expectations on how technology advances enterprise strategy in driving
ground to gather intelligence yet only 50% of CFOs say that finance connects innovation, efficiency, and competitive advantage. 115% more leading CFOs
technology investments to quantifiable enterprise business outcomes. embed nonfinancial technology metrics (for example, user engagement and
speed to market) into business cases, providing a more comprehensive view of
When compared to all other CFOs, leading CFOs blaze the trail to help ensure
technology investment impact on the overall business. Over half meticulously
that technology investments are aligned with financial goals and overall
measure the performance of technology investments, helping ensure that each
business strategy (see Figure 2).
dollar allocated to technology drives tangible business outcomes.
By nurturing tech relationships and engaging in the strategies above, all CFOs
Bringing tech to the table
can harness tech’s potential, inform strategy, and capture value—and educate
Only 50% of all CFOs
their CEOs accordingly.
say that finance connects technology investments
to quantifiable enterprise business outcomes.
Figure 2
Bringing tech to the table
Leading CFOs Engage in early IT planning to set Embed technology metrics
expectations on how technology (such as user engagement, speed
All others
advances enterprise strategy to market) into business cases
48%
56%
72% of leading CFOs
say CTOs are a highly important or critical
34%
relationship, more than any other role.
26%
Q. To what extent has finance collaborated with your technology leaders to do
the above? Percentage reflects “to a large extent” and “to a very large extent.”
12 Strategy 13
What to do
“I see IT and the CIO as my strongest counterparts.
The CIO has to understand the whole end-to-end
process, translating business requirements—and
then implementing them for an IT solution.”
Martin Günther
CFO, smart Europe GmbH
Forge a tech-finance alliance.
– Refocus funding discussions around long-term value creation instead
of traditional “CAPEX versus OPEX” debates.
– Redefine technology investment success metrics to capture indirect benefits
such as user engagement.
– Work with tech leaders at the beginning of technology planning processes
to create board presentations with financial analyses and strategic
considerations related to technology initiatives.
Apply FinOps across the enterprise to make technology
more valuable.
– Set up/refine your organizational home for FinOps with a responsibility
assignment matrix with resources from finance, IT, and the business.
– Implement a cost estimation and tracking framework that can help your
team understand the costs associated with technology projects.
– Modernize budgeting, forecasting, and chargebacks to reflect costing,
agile scenario planning, and incentives for shared objectives.
Decode tech value.
– Track and report post-implementation value creation. Quantify tangible and
intangible benefits, including revenue enhancement, productivity, customer
satisfaction, insights for decision-making, and brand impact.
– Articulate the opportunity or pain point that the technology can address
and its impact on KPIs.
– Assess the risks of not making technology investments. Not investing could
contribute to inefficiencies, limit the ability to scale operations, lead to falling
behind competitors, result in missed opportunities, hinder service delivery,
cause customer dissatisfaction, yield higher costs, and increase risks.
14 Strategy 15
Case study
The Standard: Rationalizing cloud
costs by aligning IT spend with
key business priorities6
Successful FinOps practices combined with Technology Business Management
(TBM) exemplify the budding synergy between finance and technology. The
disciplines of FinOps and TBM foster a collaborative culture that breaks down silos
so organizations can translate cloud and other technology investments into value.
The Standard, a leading provider of financial products and services, is realizing
the benefits of adopting these practices. Facing a lack of transparency on key
drivers of technology spending, the organization’s business and IT teams were not “Our approach for digitalization
working together efficiently. The company was relying on a legacy ERP system and
spreadsheets to prepare the budget, analyze financial data, and make decisions is the operational iron triangle,
about technology investments—a manual and time-consuming process that was
prone to error.
where finance, IT, and operations
The Standard implemented an IBM Apptio® solution to build cost transparency,
management come together
provide actionable insights, and enable faster decision-making. Adopting FinOps
and cloud governance practices alongside the Cloudability product gave the
company insights into its cloud spending—allowing it to drive greater accountability to form the driving engine.
by enhancing cloud procurement and provisioning decisions. In addition,
the Target process product helped the company improve its resource and program IT represents technology,
management—aligning team workstreams to business priorities, gaining greater
visibility into consolidated workflows, and tracking dynamic variables like status, operations represents process,
stakeholders, dependencies, and progress.
and finance represents data.”
The Standard has realized significant benefits. It has increased business/IT
alignment and financial agility, with the IT Finance team now able to focus 80%
of its time on analysis, decision support, forecasting, and insights. The company
FuShan Hu
has also gained more control over cloud spend, with projected savings of 10%
CIO, CHINT Group Co., Ltd.
in 2023 and even more in 2024. Additionally, the company improved its say:do ratio
by 20%—a measure of the gap between what the IT organization says it will
do and what it actually delivers. The company plans to continue investments
in cloud governance to drive similar business results across the organization.
1166 Strategy 17
Executing strategy requires a coordinated dance. It’s choreographing strategy into
actionable steps, optimizing resource constraints, and aligning and communicating
priorities across the enterprise. Executing strategy is complicated, and less than
half (46%) of all CFOs say their finance functions are effective at strategy planning
Make execution the and execution—even though that’s up from 38% in 2022.8
The strategic direction of the enterprise can only be steered by thoughtful metrics
to drive behaviors needed to achieve the organization’s objectives. CFOs have
yin to strategy’s yang.
shifted from evaluating strategy performance using siloed views of financial and
operational metrics toward a broader outlook that’s more suitable to their role as
cross-organizational leaders.
That perspective considers a combination of financial and operational KPIs,
CEOs are accelerating transformational change in 2024, with 77%
including customer satisfaction, product quality, employees, and project delivery,
maintaining or increasing their pace.7 With decision-making processes
all holistic indicators of an organization’s future health and productivity (see
and performance management intertwined, CFOs are poised
Figure 3). This expansive approach empowers all CFOs to more easily and
to be agents of change. To succeed, CFOs must balance precision effectively fine-tune strategies.
and agility while navigating new factors influencing strategies,
Succeeding in finance requires purposeful decision-making agility. To implement
requiring a harmonious “dance” of strategic planning and execution.
strategies, CFOs must move away from structured, methodical processes such as
In fact, 36% more leading CFOs are able to respond with agility to hierarchical, fixed-planning cycles and periodic reviews with minimal feedback
changes in strategy than their peers. loops. Leading CFOs are engaging robust monitoring and reporting mechanisms—
using shorter planning cycles and insights to quickly adapt to changes.
“Executives are asking us how
“The CFO is the architect responsible for making
non-financial and sustainability
things happen by translating and facilitating
factors impact our financials.”
the company’s mission through culture.”
Masakatsu Sato
Júlio Ponte
Director, Managing Executive Officer (CFO)
CFO, Terral Agricultura e Pecuária S.A.
Sumitomo Mitsui Trust Holdings, Inc.
18 Execution 19
Leading CFOs measure the contributions of critical processes. 54% of them Figure 3
(26% more than peers) foster adaptability by forecasting and communicating
Establishing a foundation
business environment changes. They act—sometimes with ruthless precision.
for future health and profitability
When it comes to redeploying capital from underperforming projects, they report
Most important non-financial metrics
cutting their losses and redirecting resources to areas that will drive growth
to help predict an organization’s
29% more often than their peers. As a result, they can adeptly respond to shifts Esftuatbulriseh hinega lat hfo aunndd aptrioonfi tfaobr ifluittyure
and changes in strategies and business models. health and profitability
Most important non-financial metrics to help predict
For all CFOs, a combination of agile performance management and a broader set organization’s future health and profitability
of KPIs beyond financial metrics is critical to orchestrate business evolution. 43% 41% 40% 38%
Execute with purpose.
Only
Customer satisfaction/ Net retention rate Product quality/percent Average time
46%
net promoter score defect percentage to hire
36% 36% 36% 31%
of all CFOs say their finance
functions are effective at strategy
planning and execution.
Customer lifetime value On-time rate Employee Return on security
turnover rates investment
30% 30% 28%
36%
Overdue project Employee Innovation
percentage engagement quotient
more leading CFOs are able
to respond with agility to changes
in strategy than their peers.
Q. Which of the above non-financial metrics are most important to
Chheelp cprked icwt yohure ornga niiznat iolna’sy fuoturue htealth and profitability?
20 Execution 21
What to do
“If you look at the role of finance, it’s not only the
accounting and the controlling and looking into the
numbers, but also making the business model happen.”
Martin Günther
CFO, smart Europe GmbH
– Partner across business units to translate high-level strategic goals into
financial targets and metrics.
– Enforce financial discipline by monitoring performance of critical processes
against goals and benchmarks.
– Promote a culture of accountability where leaders take ownership of their
decisions and outcomes.
Foresee possibilities.
– Assess, forecast, and communicate economic factors, industry-specific
competitor actions and geopolitical trends that could impact the organization.
– Adapt financial strategies in response to changes in the external environment
or shifts in company priorities. Embrace agile processes that allow teams
to seize new opportunities and adapt to threats.
– Anticipate various future scenarios and their potential implications
on the organization’s financial health and strategic objectives.
Achieve impact.
– Set and monitor outcome indicators that serve as early warnings for potential
opportunities or risks associated with customers, employees, product quality,
security, and innovation.
– Determine the data sources and leverage financial management systems,
analytics tools, and business intelligence platforms to track your
outcome indicators.
– Prove business value during regular reviews and report progress on financial
metrics, operational efficiency indicators, and market performance measures.
22 Execution 23
Case study
Ikano Group: Preparing your
organization to meet sustainability
reporting requirements9
“Because you are seeing the
organization a lot more broadly
The Ikano Group, a multinational conglomerate, has sustainability embedded into
its DNA as a key driver for its business strategy. The group recognized the need and from a different lens at
for an ESG reporting and data management solution to support each of its
six subsidiaries’ work toward individual sustainability targets.
times, you help put in place
The group is implementing the IBM Envizi™ ESG Suite to simplify complex data
plans to execute strategy which
capture and reporting required by the EU’s Corporate Sustainability Reporting
Directive (CSRD). The platform is being configured for each Ikano business to
streamline the reporting and disclosure process. There are already 15,685 data is often directly tied to your
types being captured in the platform across all businesses. Envizi allows Ikano
Group to trace data to source, maintain change records, and provide direct capital allocation.”
auditor access, making all businesses audit-ready.
The implementation has enabled Ikano Group to establish aggregated KPIs Diana Vuong
across all businesses, increasing confidence in their sustainability data VP, Finance & CFO, Vancouver Airport Authority
foundation and accuracy of emissions calculations. The Envizi ESG Suite
provides a reliable accounting system for ESG performance metrics, such as
energy, water, materials, and recycling data, making it robust and accessible.
Within Ikano Group, the CFO is accountable for the CSRD reporting process.
A Steering Committee at the Group level meets monthly to review project
progress, discuss challenges, track advancements across the businesses,
and make necessary decisions. Regular updates are also shared and discussed
across the C-suite and in management meetings at the Group level. Additionally,
meetings are held regularly with all the business CSRD teams, which include the
CFO, CSRD project coordinator, and function managers from all departments
(finance, sustainability, risk/legal, HR, and operations).
2244 Execution 25
CFOs understand that tech investments must be made, and tech improvements
that support long-term business strategies need to be prioritized. However, less
than half say their finance functions are intimately involved in the development
of technology business cases. Neglecting prioritization for exciting new use cases
Show me that come into view every day will constrain future growth.
With nearly two-thirds saying they are under pressure to accelerate the value
across the technology portfolio, CFOs can be compelled to pursue short-term
the ROI.
investments. But these choices can ultimately hinder digital transformation by
compromising long-term strategy, undermining employee morale, and sacrificing
critical needs in technology and innovation.
While their peers sometimes get mired in short-termism, leading CFOs play the
CEOs sacrifice long-term innovation for short-term gains, citing
long game. They’re not sacrificing growth for short-term gains; instead, they strike
short-termism as their biggest hurdle.10 57% of CFOs succumb to
a balance between efficiency and innovation. 30% more of these top-of-game
prioritizing short-term targets over long-term investments—often
CFOs hit that sweet spot, balancing both cost reduction/efficiencies and growth
increasing technical debt, for example, by sacrificing long-term opportunities (see Figure 4).
maintainability for short-term functionality. Opportunities in tech,
sustainability, and emerging markets demand a departure from
traditional investment strategies. With their strategic oversight Figure 4
Balance investments focused on efficiency with
and financial acumen, CFOs must guide investment decisions that gBraowlatnhc oep ipnovretusntmitieesnts focused on
capture value over short-, mid-, and long-term horizons. efficiency with growth opportunities.
Leading CFOs 52%
All others
40%
“We can no longer assume static environment
assumptions when we make investment decisions.
Q. To what extent does your finance function do the above to manage your organization’s
We’re going to have to revisit, validate, test, and pivot,
investment priorities? Percentage reflects “to a large extent” and “to a very large extent.”
and that requires us to change the way we manage
check how it works in layout
the process.”
Yoshito Murakami
CFO Power & Gas Power, GE Vernova
26 Investment 27
These CFOs are also tech-savvy trailblazers, intimately involved in developing 65% of CFOs say their organizations are
business cases that connect tech spend to real business value. A substantial under pressure to accelerate ROI
20% more of these leading CFOs drive digital transformation through rigorous across their technology portfolio.
involvement in the development of technology business cases.
How do leading CFOs manage such a nuanced approach? What sets them apart
is an agile multihorizon investment strategy. Over half of them are not just focused
on quick wins or distant dreams; they’re allocating investments across the short,
medium, and long term. This pragmaticism helps ensure they’re servicing
immediate needs—but not at the expense of future ambitions.
Tightrope walking is tricky, especially when it comes to investments.
Wobbles happen, risk is incurred. And risk is necessary—if CFOs can manage
it more adeptly than their competitors, their organizations can ultimately
benefit over the long haul. Forward-thinking CFOs provide the guidance needed
in the organization’s strategic investment process. They’re visionaries who help
the entire organization find ways to effectively balance short-term pressures
with long-term value.
Over
“We’ve got five lenses, and those lenses are
half
expected to be used through every single decision
we make. You put it through the financial lens,
the reconciliation lens, the climate lens,
of leading CFOs allocate
the digital lens, and the customer lens.”
their organization’s investments
across the short, medium,
Diana Vuong and long term.
VP, Finance & CFO, Vancouver Airport Authority
28 Investment 29
What to do
“We must be mindful of allocating resources to balance
investments in growth areas, ensuring that we don’t
miss out on opportunities for expansion.”
Masakatsu Sato
Director, Managing Executive Officer (CFO), Sumitomo Mitsui Trust Holdings, Inc.
Cultivate a future-focused perspective.
– Provide long-term guidance to your stakeholders and share the progress
toward achieving long-term goals.
– Structure executive compensation that’s tied to long-term performance
of your enterprise.
– Create dynamic, longer-term forecasts on future cash flows and educate
employees on how the market recognizes value creation over time.
View spending through a wide-angle lens.
– Invest in the initiatives (for example sustainability, generative AI) that align
with your long-term goals.
– Prioritize technology applications that accelerate the transition from piloting
to gaining efficiency to driving new growth.
– Evaluate and quantify the opportunity cost of borrowing from tomorrow to pay
for today.
Fund the future—flexibly.
– Introduce option theory into investment opportunities with contributions over
different future time periods.
– Avoid static capital allocation. Use a fluid portfolio with each investment
focused on outcomes.
– Conduct regular investment evaluations to drive capital redeployments
and resource reallocations.
30 IInnvveessttmmeenntt 3311
Case study
Edger Finance: Accelerating the
“So, it’s always a weighing up,
collection and analysis of investment
information with generative AI11
but if we can demonstrate that
a certain activity would bring
Edger, a fintech company, partnered with IBM to develop a pilot project using
value and massive efficiencies
generative AI to improve investment analysis and reporting for retail investors.
The pilot aimed to create a more efficient and personalized experience for
investors by collecting and reviewing investment data. in the mid to long run, then this
The tests conducted during the pilot demonstrated clear results and great
is the way to go. We would fight
potential for generative AI at Edger:
– 90% improvement in the turnaround time for quarterly report data extracts. for it.”
Whereas previously the process could take up to a week, the pilot
demonstrated that it could be accomplished in just four hours.
Martin Günther
– Approximately 96% improvement in the time it takes to summarize the main
CFO, smart Europe GmbH
points of a 30+ page report. Whereas previously it could take an analyst up
to half an hour to complete this task, the pilot demonstrated that it could
be accomplished in a matter of sec"
299,ibm,6-blind-spots-tech-leaders-must-reveal.pdf,"IBM Institute for Business Value Global
C-suite
Series
30th Edition
Technology Leaders Study
6 blind spots tech
leaders must reveal
How to drive growth
in the generative AI era
Contents
2
Introduction
The end of business
as usual
7
Tech leaders outlook
About the study
In Q1 2024, in cooperation with Oxford Economics, the IBM Institute 11
We treat tech We say we are working We hope it will
for Business Value (IBM IBV) surveyed 2,500 C-suite technology leaders, The six blind spots
as an enabler but... together but... be a magic wand but...
including Chief Technology Officers (CTOs), Chief Information Officers
(CIOs), and Chief Data Officers (CDOs). Separately, a small group of Tech must be the core Our collaboration Generative AI could
executives was engaged for in-depth, qualitative interviews. These of everything we do. is only skin-deep. break our organization.
discussions focused on key insights from the study and the executives’
on-the-ground experience leading technology for organizations in the
new era of AI. With respondents spanning 26 industries and 34 locations
12 20 28
worldwide, this study marks a significant first look at a new technology
Innovation Leadership Infrastructure
coalition that is managing the enablement and delivery of AI capabilities
across the business. For more details, see “Research methodology and
analysis” on page 62.
We want it to be We talk about data We think our team
The cover concept and individual patterns in
trustworthy but... as currency but... is strong but...
this report were developed using generative AI.
Our AI may be Our data could We’re still fighting 61
IBM IBV designers translated each of the “blind spots” into prompts,
irresponsible. be a liability. yesterday’s talent Conclusion
and then used these prompts within Adobe Firefly to generate
battle.
vector-based imagery that inspired the basis and structure
62
for each pattern. Similarly, the photos that appear in this report
Research
were identified using AI-assisted, natural-language search,
methodology
using the generated patterns as reference images. 36 44 52 and analysis
AI Data Talent
Overall, the efficiency gained by integrating these
tools into the design process is as follows:
Concept—3 weeks to 1.5 days
Patterns—2 weeks to 2 days
Photography—1 week to 2 hours
Introduction
The end
of business
as usual
Elevating tech leadership
IT as a standalone function is dead. CEOs who say technology officers will be crucial decision-makers over the next
The rapid ascent of generative AI three years increased 50% since 2023.2 CFOs cite CTOs as the partners most
important to their success.3 To meet these expectations will demand a new
delivered the death knell.
approach to tech leadership.
Technology is the business.
And 72% of top-performing For technology to deliver enterprise-wide business outcomes, tech leaders must
“Business leaders are becoming be part mastermind, part maestro. They must architect technology strategy
CEOs say competitive advantage
across data, security, operations, and infrastructure, teaming with business
more tech savvy. When you have
depends on who has the most
leaders—speaking their language, not tech jargon—to understand needs, imagine
a discussion, they have a very good
advanced generative AI.1 possibilities, identify risks, and coordinate investments. They must build
understanding of what technology
That means organizations multidisciplinary teams to bring the strategy to life, encouraging the
can do. You have to be empathetic
experimentation and fresh ideas that inspire employees and delight customers.
are counting on tech leaders
to what they understand. It involves
as never before. It’s an enormous responsibility and one that many tech leaders have struggled
being much more versatile.”
to meet. As the scope of “technology” has expanded over the past two decades,
new roles have been added. But despite a growing team of technology leaders,
Bernd Bucher
Global Head Data, Digital, & IT/CIO, Novartis “technology” has not consistently and effectively been integrated into strategic
decision-making for the business (see Perspective, “Beyond the org chart:
A high-powered tech coalition” on page 6).
2 Introduction 3
Figure 1
Slippery slope
Our 2024 survey of 2,500 CIOs, CTOs, and CDOs suggests they are still being C-suite leaders agree that IT has become
left out of critical conversations. Their absence or ineffective participation has less effective at basic technology services
resulted in organizational blind spots in areas such as data, infrastructure, over the last 10 years.
talent, and innovation. While 43% of CEOs say they intend to increase the pace
of change for their organization this year,4 these blind spots are making it
difficult for organizations to seize today’s opportunities in artificial intelligence
in all its guises—traditional AI, gen AI, machine learning, and automation. Percent saying the IT organization is effective
at providing basic technology services
Our study also reveals that tech leaders are straining under the pressure. More
than half say they’re struggling to balance growth and productivity, and juggling 2013 Today 2013 Today 2013 Today
tasks is taking a toll on internal operations. Notably, the percentage of C-suite
69%
leaders who say their IT function is effective in delivering even basic services
has plummeted over the last decade (see Figure 1).
64%
We see in our results that when tech claims an equal seat at the C-suite table,
they can indeed steer significant outcomes (see “Tech outperformers crack the
60%
code to success”). But just as CEOs must face the hard truths outlined in our
2024 CEO study, tech leaders must courageously expose the blind spots that
are preventing their organizations from achieving AI advantage. In this report,
we discuss how these impediments can be overcome if tech executives
command the honest, must-have discussions about the readiness of their
50%
organization to deliver breakthrough innovation and business outcomes. The
47%
future is on the line. Tech leaders’ ability to insert their essential expertise into
enterprise decisions will ultimately determine their organizations’ success in
the AI era.
36%
“I do believe that technology teams are called to have CEOs CFOs Tech leaders
greater symbiosis with our business. The boundaries
between business and technology have become
increasingly blurred.” Source: Rate the effectiveness of your IT organization in providing basic technology services. Percentages
represent those CEOs, CFOs, and tech executives who responded effective or highly effective in IBM IBV 2013
and 2024 C-suite surveys. 2013 tech leaders is CIO-only data.
Alberto Rosa
CTO, CaixaBank
4 Introduction 5
Perspective Tech leaders outlook
Beyond the org chart: Tech outperformers crack
A high-powered tech coalition the code to success
Tech leaders juggle strategy, delivery, and support across
As technology permeates organizations, tech leadership roles have
data, security, operations, and infrastructure—all aimed
evolved, and new ones have emerged. But in an increasingly complex
at optimizing efficiency and competitiveness. Our research
operating environment where data, security, operations, and infrastructure
identified a high-performing group, comprising nearly 20%
are more integrated, business and technology teams must come together to
of our global sample, that excels in this mission.
deliver a cohesive set of experiences, capabilities, and outcomes. Remaining
in functional silos is no longer an option. CIOs, CTOs, and CDOs need to
reinvent how they work together toward their organizations’ shared business
goals, building bridges in support of shared ownership and accountability.
Four critical capabilities and characteristics
At the same time, tech executives still need to divide and conquer, set tech outperformers apart
focusing on their areas of expertise.
1 2 3 4
Chief Technology Officers Chief Information Officers Chief Data Officers
CTOs continue to battle the balance Amid shifting responsibilities, Data is no longer a domain unto
between security and innovation. CIOs question the effectiveness itself but the nerve center that Effective strategy Cross-functional A commitment A sharp eye on tech
Generative AI complicates an of the IT function. A remarkable connects technology to the broader development and collaboration to to measuring at all levels throughout
execution support tech outcomes and value the organization
already complex cyber threat 63% admit their tech organizations business and propels innovation.
investments
landscape, exacerbating tension are not very effective at leveraging For most organizations, a robust
between protecting what has been workflows and automation to drive data culture that can enable and Enabling a compelling Working with business Partnering with finance Maintaining keen
strategic vision that lines and finance to understand digital visibility into all
built and pushing the boundaries business strategy. But therein lies support AI operations is still
drives business to manage technology initiative value and IT at decentralized,
of what’s possible. Indeed, the opportunity. Winners are a work-in-progress. But taking
outcomes costs and budgets alignment with line-of-business,
cybersecurity ranks second transforming in-house functions an enterprise-wide view of the enterprise strategy geography, and
on CTOs’ priority list, behind product with the help of an augmented relationship between data and AI function levels
and service innovation. workforce where employees and AI operations is essential. “Classifying
The good news: core security combine to work smarter and faster. the data problem as a technology
practices—zero trust, secure problem is a bit unjust,” says
by design, DevSecOps—are still FuShan Hu, CIO at CHINT Group
the best defense. Co., Ltd. “It’s a comprehensive
problem and that’s why data
governance is so difficult.”
6 Introduction 7
Tech leaders outlook
Where do high-performing
tech leaders excel? “Deploying a generative AI Perspective
capability has to be done in
Going all in on cloud and AI
conjunction with complete
High-performing tech leaders
have significantly outpaced their Operating wholesale business
Today, tech leaders are prioritizing infrastructure
peers in annual revenue growth margin transformation … generative AI
+10% +16% investments, spending nearly one-third more on
and operating margin since 2020. alone won’t deliver the outcomes
hybrid cloud than AI. Looking ahead, they are fully
that a lot of CEOs are expecting.”
committed to the power of cloud and AI together.
2020–2022 2023
Annual revenue/ Over the next two years, tech leaders expect to
budget growth Mark Breslin
+21% +52% spend half their budget on the two combined.
Chief AI Officer, Informa PLC
Current Projected spend
Compared to peers, the spend over next 2 years
74%
high-performing tech leaders are
significantly more effective across
several key operational areas.
High-performing
“Technology today as a stand-alone
65%
tech leaders
function does not make sense;
All others technology is there to reimagine and
29%
power the business. And this requires
57% a much closer integration and 24%
56% collaboration with business leaders.”
Hybrid cloud
1. Rate the effectiveness of your
Mohammed Rafee Tarafdar
organization in delivering outcomes
CTO, Infosys
for productivity, cybersecurity and data
privacy, and product and service
innovation. Percentage reflects those 49%
who responded “effective” and 14%
“highly effective.” 12%
2. Extent you agree with statement: 45% Traditional AI
We have clear alignment with the enterprise
strategy across data, operations, 43%
technology, and security. Percentage 42% 6%
5%
reflects those who responded “to a large
extent” and “to a very large extent.”
Generative AI
Productivity1 Cybersecurity Product Alignment of tech
and data and service with enterprise
privacy1 innovation1 strategy2
8 Introduction 9
The six
Just as drivers are taught to identify blind spots to avoid crashes, tech leaders must recognize
blind spots
both when “objects in mirror are closer than they appear” and when risks may be hidden from
view entirely. Executives adept at navigating hazards safely, and at speed, can be the difference
between making technology the core of an organization’s competitive advantage and becoming
a wreck on the side of the road.
These six blind spots challenge 1 We treat tech as an enabler but...
longstanding assumptions about the Tech must be the core
relationship of technology and the business.
of everything we do.
Some risks may be closer than they appear,
even for the most sophisticated executives.
2
Tech leaders will need to look in their We say we are working together but...
mirrors and make a compelling case Our collaboration
to their C-suite peers for why these blind
is only skin-deep.
spots are holding their organizations back
in the quest for AI advantage.
3
We hope it will be a magic wand but...
Generative AI could break
our organization.
4 We want it to be trustworthy but...
Our AI may be
irresponsible.
5
We talk about data as currency but...
Our data could
be a liability.
6
We think our team is strong but...
We’re still fighting
yesterday’s talent battle.
10 11
We treat tech as an enabler but... CEOs have spoken: product and service innovation is their top priority
over the next three years. And 62% are willing to take more risks than
Tech must competitors to maintain an advantage.5
But tech leaders have a confession: only 43% say their technology
organizations are effective at delivering differentiated products
be the core of and services (see Figure 2). And to add salt to the wound, 53% say
other execs in their organization view tech as no more than moderately
important to product and service innovation. This disconnect between
technology and business suggests a massive change is needed.
everything we do.
It starts with tech leaders positioning technology as essential
to business outcomes. They say resistance to change among
management and employees are top barriers to innovation,
so tech leaders must amp up their outreach to the organization
When organizations see technology as an enabler, they treat
on what and how technology can deliver.
it like a toolbox. They wait for problems tech can solve rather
than exploring what new opportunities it creates. Only when More importantly, organizations need a fresh, bold approach
to innovation. A staggering 70% of tech executives say their
they recognize technology as the transformative force
organization is taking a fast-follower approach, adapting others’ ideas
at the core of innovation can organizations seize first-mover
or rolling out fixes rather than pioneering something radically new.
advantages, define markets, and gain economies of scale.
Shayan Hazir, Chief Digital Officer of HSBC Singapore, observes, “We
as technologists in financial services have tried to find problems for
technology to solve, but I don’t think we’re spending enough time
addressing what emerging technology can enable meaningfully for
“The biggest secret to digital transformation
customers, communities, and economies.”
is to change your perspective. It’s not about what
you can do, it’s about whether you can deliver value
to your customers in a rapidly changing environment.”
XiaoLong HE
CIO, VP of Digitalization, Tianshan Material Co., Ltd.
12 Innovation 13
Figure 2
Business awaits
Tech leaders are struggling to deliver
CEOs’ number one objective.
CEOs say product and To re-energize innovation for competitive gains, tech leaders must
service innovation is their look ahead for technology-fueled big bets. They need to shift from
top priority a project emphasis to a customer focus, prioritizing outcomes rather
than features as well as execution accompanied by customer
over the next 3 years
validation.6 They will need to avoid the ideation trap where many
get caught: 73% of business executives say their greatest strength
is researching customer needs or ideation, but only 27% say their
forte is executing or scaling product plans.7 Tech leaders must
quickly bring the ideas to life.
That requires them to evangelize a culture for innovation—one based
on pragmatic experimentation of high-potential ideas—and then
work to bring the rest of the C-suite on board. They can call on CFOs
to help define the most promising possibilities and to join them in
leading C-suite conversations about the importance of innovation to
the organization’s broader strategy. They need to encourage senior
leaders to look beyond near-term concerns such as efficiency, cost
but only 43%
takeout, and modest incremental gains.
of tech leaders say they are effective
or highly effective at delivering
product and service innovation
“We have this concept that we call open
innovation because we cannot do all
the innovation alone. Part of the work
is finding the right partners.”
Iosu Ibarbia
Technology Director, CAF (Construcciones y Auxiliar de Ferrocarriles)
111444 Innovation 15
“How do we leverage what’s good enough and push forward with What to do
it and then scale it? Traditional large organizations try to plan,
strategize, and build a solution. And by the time you finish it,
the technology and the landscape has changed.”
Escape the fast-follower treadmill by embracing
revolution, not perfection.
Jimmy Yeoh
CIO, DHL Express APEC
Jump from the treadmill onto the launchpad.
– Create urgency for meaningful action that disrupts the impulse
for incrementalism; pinpoint prudent precautions that encourage
more confident risk-taking.
– Identify critical business problems to be solved by blending tech and business
expertise on product and service development teams.
– Do the due diligence necessary to make leading practices real for your
organization and define an investment strategy that takes necessary resource
tradeoffs into account.
Break your analysis paralysis with generative AI.
– Use generative AI to synthesize customer feedback and analyze product usage
insights to accelerate meaningful iteration.
– Establish a framework for evaluating and ranking potential solutions with
generative AI. Ruthlessly cull efforts that don’t support your objectives.
– Develop KPIs to measure solution success and use generative AI to predict
outcomes and simulate scenarios.
Embrace a digital product innovation approach.
– Establish a digital product innovation framework for ideation, prototyping,
testing, and launch. Incorporate security and governance as design
considerations from the outset.
– Break down silos between tech and the business to enable rapid iteration that
delivers timely experiences and products to customers.
– Create incentives that reward experimentation and smart risk-taking for
solutions that improve productivity and innovation.
16 Innovation 17
Case study
IBM Software embraces
“We are now studying what kind of gen AI
gen AI for design8
use cases can have the greatest value
to customers. Once we figure out the
framework, and when we start to actually
develop something, then we can invite
IBM Software has defined an initiative around identifying the “top 10” some of our customers into the process.”
set of workflows in which it is actively embedding generative AI. The organization
Hiroshi Okuyama
is incorporating generative AI into products and processes, automating
Director and Member of the Board, Chief Digital Officer
workflows, improving output, and accelerating design.
Group Divisional Manager, Yanmar Holdings Co., Ltd.
IBM Software is also training 100% of their designers in AI. In general,
the designers find it invigorating to learn new skills and keep current with
cutting-edge AI technology—and they love the prospect of spending more time
on the creative aspects of their job that they’re passionate about.
“I’ve spent the last 12 to 18 months building an
In terms of synthesizing insights and crafting compelling content, IBM Design
enterprise-wide digital brain trust across our
has seen a 12% average daily time savings for content designers. In addition to
content design, the organization is investigating how to incorporate generative AI organization, bringing together multifaceted
across product management, UX design, content design, and research. teams that have been exceptional within their
own product category or technology area but
are creatives at heart. These people are now the
catalysts within their own business areas—
they’re the ambassadors of change. When they
go back to their day jobs, they infiltrate the
mindsets of their teams.”
Shayan Hazir
CDO, HSBC Singapore
18 Innovation 19
The AI race is just beginning—and while it may not be won over the next
two to three years, it can be lost over the next two to three quarters if
finance and tech executives fall out of sync. While CFOs complain that
tech decisions made in isolation by IT can lead to unsustainable costs,
We say we are working together but... tech leaders know that shortsighted technology decisions can wreak
long-term havoc. Their insights on technology are integral to their
Our collaboration organization’s strategic and financial decisions, while finance’s input
is critical to prioritizing technology investments.
A historically tense relationship must become more collaborative—not
is only skin-deep.
just through words but in deeds.9 Two-thirds of CEOs say that
a strong partnership between tech executives and CFOs is critical to
their organization’s success.10 Technology leaders agree—CIOs, CTOs,
and CDOs each rank the CFO as either the first or second most important
relationship for driving their individual success. But the tech-finance
While finance and technology have a history of working together,
relationship is still evolving from intention to practice. Only 39% of tech
that history masks critical gaps in planning processes
execs say they collaborate with finance to embed tech metrics into
and decisions that are disjointed or ill-informed. Only when business cases. Similarly, only 35% of CFOs say they’ve been engaged
the finance-tech relationship evolves from siloed to inseparable early in IT planning to set expectations on how technology advances
enterprise strategy.11
will they drive smarter decisions linking technology investments
to quantifiable business outcomes and improving ROI.
“There’s no such thing as the business
and IT. We’re all one team.”
Julia Knox
“We believe in cooperative leadership. Chief Technology and Analytics Officer, Sobeys
We build a leadership mindset that relies
on the collective intelligence of the team
rather than individuals.”
Moritz Hartmann
Global Head Roche Information Solutions, Roche Diagnostics
20 Leadership 21
“Technology decisions should be analyzed Figure 3
from a value perspective; what value
The tech-finance tango
will this decision bring to the business,
High-performing tech executives
the organization, and our clients.” are partnering with their finance peers
to align strategies and capture value.
Alberto Rosa
CTO, CaixaBank
However, our high-performing tech executives demonstrate the value All others Tech high performers
of building a strong rapport between tech and finance leaders.
They report notably stronger collaboration across key operational
practices (see Figure 3). Our analysis also shows that when
Apply learnings to improve
finance connects technology investments to quantifiable business 59%
future digital investments
outcomes, the high-performing group reports higher revenue growth.
57% Embed technology metrics
To drive organizational results like our top performers,
into business cases
tech leaders must pivot from informing to collaborating with
finance—recognizing how finance can supercharge tech’s influence
across the C-suite. They need to make themselves indispensable Engage early in IT planning to set
53%
to finance and demonstrate their commitment to fiscal responsibility. expectations on how technology
advances enterprise strategy
At the same time, finance leaders need to meet tech halfway,
looking beyond return on investment to understand how technology
contributes to operational outcomes. Both sides should see the
relationship as symbiotic, reinforcing mutual strengths so that
it’s greater than the sum of its parts.
46%
42%
35%
22 Leadership 23
“You need to be able to…collaborate What to do
more on the mid- and long-term
objectives and stick to the strategy.”
Align with finance to elevate your role as a strategic
Kristian Åkerström
xCIO/Head of IT & Digital, smart Europe collaborator and advisor.
Engage in aggressive diplomacy across the C-suite.
– Develop a deep understanding of the organization’s financial drivers and
leverage this knowledge to inform IT investment decisions.
– Identify and pursue ROI everywhere, including the financial and non-financial
measures that are essential to tracking business objectives.
– Agree on a shared approach to creating and evaluating new technology
investments for competitive advantage.
Make yourself indispensable to critical enterprise decisions.
– Seek opportunities to demonstrate the value of technical expertise
in enterprise decision-making processes and engage allies to ensure
your voice is recognized.
– Model your financial stewardship with a clear commitment to financial
transparency and accountability. Seek ways to recapture costs to fund
innovation efforts.
– Lead challenging organizational conversations, such as balancing
the intense energy consumption of AI against organizational sustainability
goals and commitments.
Show your work to build credibility.
– Frame technical discussions in financial terms, using data and analytics to
demonstrate the value of IT investments and drive strategic decision-making.
– Quantify operational metrics in monetary terms. Gain greater fluency in
financial performance metrics.
– Create a finance-facing dashboard that translates technology KPIs into
financial measures (such as cost per user, revenue per customer, ROI).
24 LLeeaaddeerrsshhiipp 2255
Case study
The Standard rationalizes cloud costs by aligning
IT spend with key business priorities12
Successful FinOps practices combined with Technology Business Management
(TBM) exemplify the budding synergy between finance and technology. The
disciplines of FinOps and TBM foster a collaborative culture that breaks down silos
so organizations can translate cloud and other technology investments into value.
The Standard, a leading provider of financial products and services, is realizing
the benefits of adopting these practices. Facing a lack of transparency on key
drivers of technology spending, the organization’s business and IT teams were
not working together efficiently. The company was relying on a legacy ERP
“I think that in the future, there will
system and spreadsheets to prepare the budget, analyze financial data, and make
decisions about technology investments—a manual and time-consuming process
be no essential contradiction between
that was prone to error.
The Standard implemented an IBM Apptio® solution to build cost transparency, Chief Technology Officers and
provide actionable insights, and enable faster decision-making. Adopting FinOps
and cloud governance practices alongside the Cloudability product gave the CFOs because they will both focus
company insights into its cloud spending—allowing it to drive greater accountability
by enhancing cloud procurement and provisioning decisions. In addition, on a common goal of the company’s
the Target process product helped the company improve its resource and program
management—aligning team workstreams to business priorities, gaining greater
successful future. I think they are,
visibility into consolidated workflows, and tracking dynamic variables like status,
stakeholders, dependencies, and progress.
for the most part, mutually supportive
The Standard has realized significant benefits. It has increased business/IT
and cooperative relationships.”
alignment and financial agility, with the IT Finance team now able to focus
80% of its time on analysis, decision support, forecasting, and insights.
The company has also gained more control over cloud spend, with projected
WeiWei Zhang
savings of 10% in 2023 and even more in 2024. Additionally, the company
CDO, Tianshan Material Co., Ltd.
improved its say:do ratio by 20%—a measure of the gap between what the IT
organization says it will do and what it actually delivers. The company plans
to continue investments in cloud governance to drive similar business results
across the organization.
2266 LLeeaaddeerrsshhiipp 2277
We hope it will be a magic wand but... Nearly three in four CEOs say their organizations’ digital infrastructure
enables new investments to efficiently scale and deliver value.13
Generative AI could But tech leaders have a different view. The scale and complexity of AI
demands an infrastructure that supports its voracious appetite for data,
compute, and storage. Only 16% of tech executives say they’re very
confident their current cloud and data capabilities are fully ready
break our organization.
to support generative AI.14 And 43% say their concerns about their
technology infrastructure have increased over the past six months
because of gen AI (see Figure 4).
Even more concerning: other IBM IBV research reveals that only 29%
Because organizations hope generative AI will solve all their
of cloud IT assets and services are performing as required.
problems, they ignore the added stress it places on their existing The remaining 71% is essentially tech debt accumulated over years
infrastructure, among other things. Only when they address their of piecemeal technology implementations.15 This burden is forcing
organizations to divert energy and resources toward maintaining
technical debt and transition from a patchwork of systems to
and troubleshooting outdated, disparate systems—not executing
a purpose-built technology foundation can organizations fully
bold ideas and future-focused initiatives.
embrace the shift from +AI to AI+.
“When you talk about the hardware and “When something suddenly becomes very
software stack, you are running into the issue important, but the foundation is not
of legacy things that you have to maintain. in place, then there’s a lot of internal
If you want to modernize it, it’s easy to say, but transformation we need to do to catch up.”
on the implementation side, it’s really difficult.”
Pochara Vanaratseath
Head of Information Technology Group, Krungsri Bank
Tawatchai Cheevanon
Chief Product and Business Solutions, Krung Thai Bank
28 Infrastructure 29
Figure 4 Tech leaders must tackle this weakness head-on, starting with a reality check
for other C-suite leaders. To catalyze AI transformation, organizations need
Unfit for AI
a thoughtful infrastructure renovation, repurposing what’s useful but also
Many organizations don’t
investing for the future. They need an architectural framework that helps
have an AI-ready technology
intentionally optimize business value through technology while addressing
infrastructure.
the entire technology estate: platforms, security, AI, cloud, and data. The goal
is to build a launchpad that brings together disparate technologies and can
support the business for years to come.16
Nearly
Daimler Trucks Group CIO Marcus Claesson recognizes the value of modernizing
three
architectures and operating models. Since Daimler Trucks spun off from
Mercedes-Benz, Claesson’s team has been rigorously rethinking and replacing
outdated technology and redefining how work gets done—not an easy
in four undertaking. “It’s like going to the gym. It’s difficult and painful,” he says.
“But we come out in better shape with a better foundation for the future
of the company.”
CEOs say their organizations’ digital As tech leaders ready for gen AI, infrastructure is wisely their top priority
infrastructure enables new investments investment. In fact, organizations are actually allocating more toward hybrid
to efficiently scale and deliver value. cloud than AI itself: 24% of their current spend versus 18% for traditional and
generative AI. As part of this focus, careful selection of cloud partners becomes
crucial to avoid risks such as vendor lock-in—a concern shared by two in three
But
tech leaders who are proactively identifying partner risks.
43%
An AI-optimized infrastructure isn’t a one-and-done proposition. Tech leaders
need to put in the work to align investments to business outcomes—with an eye
to minimizing the overhead associated with current technical debt and optimizing
of technology executives say generative AI existing resources and capacity to free up funds for AI innovation.
has increased their infrastructure concerns.
“You may deliver the technology, but if the business
is not ready or the business is "
300,ibm,ceo-6-hard-truths-ceos-must-face.pdf,"IBM Institute for Business Value
Global
C-suite
Series
29th Edition
CEO Study
6 hard truths
CEOs must face
How to leap forward with
courage and conviction
in the generative AI era
Contents
3
Introduction
The opportunity
paradox
7
The CEO outlook
About the study
This study represents the 29th edition of the IBM Institute for Business
Value (IBM IBV) C-suite Study series. For the 2024 CEO Study, IBM IBV, 13
in cooperation with Oxford Economics, conducted two rounds of survey- The six hard truths Your team isn’t as The customer Sentimentality is
strong as you think. isn’t always right. a weakness when
based interviews with more than 2,500 CEOs from 30+ countries and
expertise is in
26 industries. Conducted from December 2023 through April 2024, these
short supply.
conversations focused on business priorities, leadership, technology, talent,
partnering, regulation, industry disruption, and enterprise transformation.
14 20 26
Additional insights were drawn from ongoing IBM IBV research related to
Talent and skills Innovation Ecosystem
evolving technologies, including generative AI and hybrid cloud, and various partnerships
industries. Findings were also derived from numerous client interactions,
including more than two dozen deep-dive interviews with CEOs conducted
from July 2023 through April 2024.
Sparring partners People hate Tech short-cuts
The cover concept and individual patterns in
make the best progress. are a dead end.
this report were developed using generative AI.
leaders.
IBM IBV designers translated each of the “hard truths” into prompts,
and then used these prompts within Adobe Firefly to generate
vector-based imagery that inspired the basis and structure for
32 38 44 51
each pattern. Similarly, the photos that appear in this report were
Decision-making Vision and culture Transformation Conclusion
identified using AI-assisted, natural-language search, using the
When you’re on
generated patterns as reference images. a burning platform,
big risks are just
Overall, the efficiency gained by integrating these tools into
good business
the design process is as follows:
Concept—3 weeks to 1.5 days
52
Patterns—2 weeks to 2 days
Research and
Photography—1 week to 2 hours methodology
Introduction
The
opportunity
paradox
Is generative AI your wildest dream The risk is real, but sticking to the status quo isn’t any safer. As generative AI
or your worst nightmare? It depends throws everything into question, CEOs understand that they can’t stay the course
and stay in the race. More than two-thirds say the potential productivity gains
on how your organization reacts
from automation are so great that they must accept significant risk to remain
today—and prepares for tomorrow.
competitive—and 62% say they’ll take more risk than the competition to maintain
their competitive edge (see Figure 1).
Generative AI has the potential to
And it doesn’t stop there. Our 2024 CEO study reveals that:
shake up the way your business
has always worked, driving – 59% of all CEOs surveyed—and 72% of top-performing CEOs—agree that
unprecedented productivity and competitive advantage depends on who has the most advanced generative AI.
revealing new avenues for growth. – 72% of all CEOs see industry disruption as a risk rather than an opportunity.
“The more uncertainty you face, the
But those tremors could also crack – 62% say they will need to rewrite their business playbook to win in the future,
the foundation—and send everything more opportunities you have. In the rather than play to existing strengths.
past, eight out of 10 CEOs could get it
you’ve built crashing to the floor.
In this high-stakes environment, CEOs must strike the right balance between
right, but now only two CEOs can get
caution and courage—while moving faster than ever before. 43% say they’ll
it right. For the two CEOs who do it
increase the tempo of their organization’s transformational change in 2024,
right, the benefits are even greater.”
compared to just 19% that expect to slow down. As top leaders pick up the pace,
they need to unite disparate teams to deliver growth while also managing data
Chairperson, Industrial Manufacturing, China privacy concerns, legal liabilities, and technical complexity.
2 Introduction 3
Figure 1
Big risks, big rewards
The promise of generative AI
inspires CEOs to step out of
their comfort zones
Even if they don’t know exactly where they’re headed, CEOs have to
push their teams forward faster. Productivity gains and other quick wins
67%
are fueling this acceleration, but that’s just the beginning. CEOs that stop
here will miss out on the biggest part of the generative AI opportunity:
top-line growth.
say the potential productivity gains
Yet, 59% say they aren’t willing to sacrifice operational efficiency today to drive
from automation are so great that
greater innovation. CEOs also say focusing on short-term outcomes is the top
they must accept significant risk
to stay competitive. barrier to innovation. This suggests that many could fall into the trap of making
incremental improvements, instead of transforming critical operations. But if
CEOs open the aperture, generative AI can be the springboard they’re searching for.
They’ll have to make some important trade-offs. As the shelf-life of successful
business strategies continues to shrink, they’ll need to question old assumptions.
62%
That may mean exploring new business models, developing entirely new product
lines, bringing new partners into the fold—or saying good-bye to business
relationships that can’t drive new strategies forward.
say they will take more To make their wildest generative AI dreams reality, CEOs need to let go of
risk than the competition “what has always worked” and start tackling the hard truths holding them back.
to maintain their
For technology to transform the business, first the business must evolve.
competitive edge.
CEOs that settle for productivity
gains will miss out on the
biggest part of the generative
AI opportunity: top-line growth.
4 Introduction 5
The CEO outlook
How are leading
CEOs preparing for
an uncertain future?
CEOs across the board expect their investments to drive growth and
profitability. But those results don’t always materialize. So, what are
leading CEOs doing differently?
We’ve identified a group of CEOs, representing roughly 10% of our
global dataset, that are outperforming the competition despite global
disruption. Here are six critical capabilities and characteristics that
allow them to act with conviction even in the face of uncertainty.
“If someone else destroys our
old business model, we will be
Effective strategy Expertise-led Robust technology
development differentiation foundation
ruined. But if we destroy our old The executive leadership team Expertise informs business Digital infrastructure enables
crafts a compelling strategic vision decisions and gives the new investments to efficiently
to drive business outcomes. organization a competitive edge. scale and deliver value.
business model, we will survive.”
Nobuhiro Tsunoda
Chairperson, Ernst & Young Tax Co., Japan
Outcomes-focused Active ecosystem Actionable
investment engagement enterprise metrics
Technology leaders deliver The organization engages Enterprise data and KPIs
capabilities aligned with partners by delivering effectively set a clear bar for
enterprise strategy and priorities. industry-specific solutions success, which helps teams
beyond enterprise borders. achieve business objectives.
6 The CEO outlook 7
The CEO outlook
What sets High performance starts with financial metrics. CEOs in our leader group run
top-performing organizations that have outperformed the competition in annual revenue growth
and operating margin since 2020.
CEOs apart?
2020-2022 Outperformance metrics 2023
+ 16.4% Annual revenue/ + 17.7%
budget growth
20.4% 19.8%
+ Operating margin +
Leading CEOs also say their organization
outperforms in several key areas that Executing enterprise strategy1
deliver a competitive edge. 80
“As AI develops, there will
Leading CEOs
60 45% be three types of people:
All others
more
17% Talent
Innovation3
40 development those who create AI, those
more
and retention2
who use AI, and those who
20 43%
more
are used by AI.”
19%
more
Kazuhiro Nishiyama
President, Kansai Mirai Bank, Limited
23%
Partner/
ecosystem 22% more Cyber risk
development3 more and cybersecurity2
Technological maturity3
1. Rate the effectiveness of your organization in the following areas: Executing the enterprise strategy. Percentage reflects “effective” and
“highly effective.” 2. How does your organization’s performance compare to similar organizations over the past three years? Percentage
reflects “outperformed” and “significantly outperformed.” 3. How would your closest competitor rate your organization’s performance
compared to similar organizations? Percentage reflects “leading” and “significantly leading.”
8 The CEO outlook 9
The CEO outlook
CEO priorities Disruption is demanding CEOs to shift their focus. As new challenges come to the fore, CEOs have CEOs seek a rapid transition with generative AI, from piloting projects to increasing
and challenges are CEOs are prioritizing different strategic objectives and tapping quickly evolving technologies, big plans for efficiency to driving growth. Those who aren’t planning to transform quickly risk
changing rapidly including new forms of AI, to deliver business results. generative AI being left behind.
2023 2024
Not Piloting and Efficiency and Growth and
investing experimentation cost savings expansion
Top priorities Productivity or profitability 1 Product and service innovation
Tech modernization 2 Tech modernization Today
24% 47% 26% 3%
Customer experience 3 Cybersecurity and data privacy
2025
10% 20% 52% 18%
Cybersecurity and data privacy 4 Forecast accuracy
Environmental sustainability 5 Productivity or profitability 2026 13% 38% 49%
Product and service innovation 6 Customer experience
2029 3% 30% 67%
Note: Not all lines add up to 100% due to rounding.
Top challenges Environmental sustainability 1 Business model innovation
Cybersecurity and data privacy 2 Productivity or profitability
CEO concerns about generative AI
Tech modernization 3 Scalability of service delivery adoption are also changing as
capabilities mature.
More
Talent recruiting and retention 4 Marketing and sales effectiveness
concerned
Diversity and inclusion 5 Forecast accuracy
Data privacy
Less
Business model innovation 10
Data lineage concerned
Regulation Insufficient
Top technologies Cloud computing 1 Generative AI proprietary data
Improper use of
IoT, mobile, and connected devices 2 IoT, mobile, and connected devices intellectual property
Irrelevant use
Machine learning 3 Advanced analytics
“A good CEO can read the market and grasp cases
Advanced analytics 4 Data architecture the degree of tension, just like flying a kite,
loosening it when there is wind and pulling
Automation 5 Traditional AI
it when there is no wind.”
AI chatbots and natural 6 Hybrid cloud
language processing Chairperson, Industrial Manufacturing, China
10 The CEO outlook 11
The six
hard truths
“CEOs continue to manage that
creative tension between having
1
Here are six difficult realities CEOs must face— Your team isn’t as
a vision for the organization of the
from people challenges to operations hurdles to
strong as you think.
data and technology limitations—to outcompete
future while still being grounded in the age of generative AI.
2
in the realities of today.” The customer
isn’t always right.
Ngiam Siew Ying
CEO, Synapxe
3
Sentimentality is a
weakness when expertise
is in short supply.
4
Sparring partners
make the best
leaders.
5
People hate
progress.
6
Tech short-cuts
are a dead end.
12 13
Your team isn’t as CEOs understand that their people will make all the difference.
Already, 51% are hiring for generative AI-related roles that didn’t
exist last year. Yet, most say their organizations are straining under
the pressure. More than half say they’re already struggling to fill key
strong as you think.
technology roles—and it’s unlikely this task will get easier any time
soon. Overall, CEOs say 35% of their workforce will require retraining
and reskilling over the next three years—up from just 6% in 2021.
In a world where generative AI separates the Yet, they aren’t sure exactly what should change. Nearly two-thirds
say their teams have the skills and knowledge to incorporate
winners from the losers, people are a CEO’s
generative AI—and 67% say their recruiting and retention efforts
biggest technology problem. No matter how deliver the skills and expertise they need to achieve business
objectives, even as they face a talent shortage. A lack of data may be
good a team is today, it isn’t good enough to
causing this disconnect, as only 44% of CEOs say they’ve assessed
the impact of generative AI on their workforce (see Figure 2).
compete tomorrow.
Look for the people doing
tomorrow’s jobs today to
redefine how work should
be done.
“Talent is key to resilience. If I don’t have
talent that can anticipate and adapt,
absolutely nothing is going to happen.”
Fabián Hernández
CEO, Movistar Colombia
14 Talent and skills 15
Figure 2 “We must change our business model to
benefit from AI—and in the future, quantum
Connect the dots
computing—to recruit the best talent.”
Most CEOs are acting fast
on generative AI—but fewer Nobuhiro Tsunoda
understand its workforce Chairperson, Ernst & Young Tax Co., Japan
implications
Connecting the dots will be crucial in the coming year, given that
40% of CEOs plan to add staff because of generative AI. A larger
51%
portion (47%) expect to reduce their workforce because of
of CEOs say they’re generative AI, but they say the number of jobs created will exceed
currently hiring for generative AI-related the number of jobs lost overall. On average, they plan to increase the
roles that didn’t exist last year. workforce by nearly 6% over the next three years. As generative AI
continues to shake up how work is done, CEOs will need to rethink
how skills, experience, and job roles relate to each other to make the
most of this talent investment.
But only
44% The augmented workforce of the future promises to create more
value than people or machines can deliver alone, but you can’t plug
tomorrow’s talent into yesterday’s operating model. CEOs must
of CEOs have assessed the
identify the people doing tomorrow’s jobs today and tap their
impact of generative AI on
experience to define how work should be done in the future.1
their workforce.
“We have to have the best team for
today—but will it be the right team
for the future? We cannot be sure.
That’s why we need to reskill,
retool, and get people ready for
what is coming.”
Ngiam Siew Ying
CEO, Synapxe
16 Talent and skills 17
Reimagine how humans and What to do
machines can share the load.
Look beyond initial productivity gains to see how a new
division of labor—and an entirely new operating model—
could drive innovation and transformative growth.
Take a fresh look at your talent.
– Adopt a “day 1” mindset. If you wouldn’t hire your people today, identify what’s
missing and whether training can get them where they need to be.
– Identify forward-thinking talent that’s leading the change. Give these people
a platform to teach others.
– Accurately assess the cost associated with replacing talent that can’t adapt.
Compare this against the opportunity cost of stagnation—and act as quickly
as budgets will allow.
Boost creativity with a culture of curiosity.
– Cultivate human-tech chemistry by pairing people from different parts
of the organization to drive transformation initiatives.
– Redefine ways of working. Encourage experimentation with generative AI
tools and build in time for teams to share their learnings.
– Reward thoughtful risk-taking to set the tone. Use incentives to show
that, win or lose, experimenting with generative AI delivers value for
the organization.
Make people your most important tech investment.
– Analyze workforce data to determine where your organization has skills gaps
and define a timeline for closing them.
– Know when to buy, build, borrow, or bot. Assess where it makes sense to fill
the gap with employee training, targeted automation, or partner resources.
– Be prepared to spend more than you have in the past to hire for in-demand
skills.
18 Talent and skills 19
Generative AI can help companies tap into vast stores of customer data, from in-depth
market research to individual device metrics, to come up with paradigm-busting product
ideas. It can even validate far-out concepts against real-world business criteria, letting
employees focus on the creative work required to bring the best ideas to life. With these
The customer game-changing capabilities on the table, 86% of global digital product leaders say
generative AI is now a critical part of digital product design and development.2
However, this is only the starting point for true product innovation. Hitting the right mark
isn’t always right.
in a hyper-competitive consumer landscape will require more co-creation than companies
are used to. Rather than spending months designing and developing the perfect product or
experience, companies will need to prioritize speed to market—and fast feedback loops
that give customers a voice.
Customers don’t know what they’ll want tomorrow.
Generative AI can take some of the guesswork out of this process by making customer
It’s not that they’re indecisive—it’s that the next big feedback more accessible to product teams. According to recent IBM Institute for Business
Value (IBM IBV) research, only 30% of organizations are harnessing generative AI to quickly
thing could change everything.
analyze and summarize customer feedback to inform product design and development
today. But these early adopters already have an edge: they’re 86% more likely to be
Just as connected mobile devices have introduced
creating hyper-personalized experiences than their counterparts.3
must-have products that didn’t exist a decade ago,
Until recently, hyper-personalization at scale seemed like a pipe dream. But it’s quickly
generative AI could open the door to a new universe becoming reality with the help of generative AI. While only a quarter of organizations are
using generative AI to create hyper-personalized digital product experiences today, that
of opportunity. This may be why CEOs say product
figure is expected to more than double to 64% by the end of 2024.4
and service innovation is their top priority for the
next three years—up from sixth place in 2023.
“At smart Europe, we are super-fast, we are super
agile, we listen, and we change. As long as we show
“AI has a role in helping us advance to
customers that we’re taking their issues seriously and
provide better service to our customers.”
fix them quickly, they’re happy. People appreciate our
Javier Tamargo co-creation approach.”
CEO, 407 ETR
Dirk Adelmann
CEO, smart Europe GmbH
20 Innovation 21
Figure 3
Product wizardry
In this way, generative AI can make customer experiences magical. It can give
versus privacy invasion
customers exactly what they want before they’ve even thought to ask for it.
This instant gratification could be very addictive—as long as technology respects Open communication with customers
is essential to successfully deliver
people’s boundaries.
hyper-personalized products
To walk the line between product wizardry and privacy invasion, companies must
use customer data ethically and responsibly. Customers are willing to be wowed 80%
by hyper-personalization, but they want to know what’s happening behind the
curtain. For instance, a 2024 IBM IBV consumer study found that more than
half of consumers want to receive personalized information, advertisements, and
offerings from retailers, but roughly four in 10 want information about and control of CEOs say transparency around the
over how that data is being used.5 organization’s adoption of new technologies
is critical to fostering customer trust.
As hyper-personalized experiences become less fiction, more reality, CEOs know
they need to protect customer trust. Almost three in four agree that establishing
and maintaining customer trust will have a greater impact on their organization’s
success than any specific product or service features. And four in five say
transparency around the organization’s adoption of new technologies is
critical to fostering that trust (see Figure 3).
71%
“We tend to start with the business problem and what
we’re trying to accomplish, and then we look for say establishing and maintaining customer
technologies or innovations that can help us do that.” trust will have a greater impact on their
organization’s success than any specific
Judy McReynolds product or service.
CEO, ArcBest
22 Innovation 23
Design holistic experiences and What to do
hyper-personalize product development
while keeping an eye to the future.
Create dynamic experiences that incorporate continual
customer feedback and build trust through transparency.
Get more from your systems.
– Use technology to deliver superior experiences—but think beyond current
customer sentiment and expectations.
– Look beyond what customers say they want today to design the breakthrough
innovations of tomorrow.
– Use data and generative AI to identify new opportunities to move forward
rather than perfect the present.
Be transparent about how you use customer data.
– Make trusted data the backbone of your organization. Be upfront about what
data you’re collecting, how you’re using it, and why.
– Let customers share their data on their own terms. Explain how their data will
improve their experience and let them opt-in based on their personal priorities.
– Stay ahead of customers’ ethical expectations. Go beyond what’s required by
regulation to cultivate customer trust in your data policies.
Co-create products and experiences to increase
customer engagement.
– Set expectations up front for every interaction to make customers feel like
they’re being catered to, rather than spied on.
– Lead with design thinking. Use customer feedback to inform rapid iteration,
with generative AI suggesting and validating potential improvements.
– Use large language models to power hyper-personalized experiences, such
as curated product recommendations, tailored marketing messages, and
customized content.
24 IInnnnoovvaattiioonn 2255
Looking to the future, CEOs know they need to be selective about
which partners they prioritize. Nearly two-thirds say their
organization’s strategy is to concentrate on fewer high-quality
partners. This is perhaps to keep key vendors close at hand,
Sentimentality is as 60% expect critical expertise and capabilities to be increasingly
concentrated in a small cluster of organizations.
Striking the right mix between familiar faces and fresh ideas will be
a weakness when
crucial as CEOs push their teams to innovate. Today, more than half
say changing strategic priorities demand reconfiguring core business
partnerships. Yet, in the same breath, 76% say they have the right
expertise is in network of partners to execute their strategy today (see Figure 4).
short supply.
CEOs need to trust the partners they bring
to the table—and that trust can take years to
build. But valuing connections over capabilities
could be kryptonite for business leaders as they “It’s dangerous if we can’t have
heart-to-heart discussions with
jockey for a competitive edge with generative AI.
our partners about how we’re
positioned to navigate change—
and what will happen if things
“An enterprise must look at who it walks with. are left as they are.”
In the business ecosystem, you must work with
Kazuhiro Nishiyama
the best—otherwise you will be left behind.”
President, Kansai Mirai Bank, Limited
Chairperson, Industrial Manufacturing, China
26 Ecosystem partnerships 27
Figure 4
Recalibrating
relationships
CEOs expect to pivot their
partnerships as priorities change
While trust and shared values are central to
successful partnerships, CEOs must resist the
urge to cling to what’s comfortable as they
76%
navigate the winds of change. They won’t be able
to accelerate transformation if they keep investing
in an unproductive status quo.
of CEOs say they have the By assessing their organization’s strengths—and
right network of partners to deciding what must be done in-house—leaders
execute their strategy. can determine where to get external support.
While it may seem unnatural at first, CEOs will
need to cede control over non-essential aspects
of the business to focus more attention on what
matters most. With the right partners in the right
seats, CEOs can tap capabilities that were
previously out of reach.
But “You can’t be good at
55% everything. That’s why you
have to find partners—and
find a model that makes you
say changing strategic priorities comfortable working with
demand reconfiguring core
these partners.”
business partnerships.
Mikkel Hemmingsen
CEO, Sund & Bælt Holding A/S
28 Ecosystem partnerships 29
Ask for what you need—and don’t settle for less. What to do
Clearly define the outcomes you need from your partnerships
and what matters most to each player. Access relevant,
high-demand skills through ecosystem partnerships to
supplement the core capabilities you build in-house.
Ruthlessly cut dead weight to make room for new growth.
– Know what you value most. Don’t continue to invest in long-term partnerships
that are no longer producing results.
– Surround yourself with the best. Build a new relationship checklist and move
on from partners that don’t meet your standards.
– Ensure that your partners are aligned with your approach to AI ethics and
the guardrails that are in place.
Decide when and how you will let others take the wheel.
– Define—then clearly communicate—how much control you’re willing to cede,
as well as which capabilities you must keep in-house to control essential
operations.
– Trust the experts. You can’t be the best at everything, but you can benefit from
collaborating with specialists.
– Engage your ecosystem partners as full participants in technology innovation
and adoption.
Build symbiotic relationships.
– Cultivate the give-and-take. Create mutual dependency with your best
partners by investing time and resources to support their strategic goals.
– Take advantage of complementary strengths and perspectives to boost
foresight and resilience in the event of change.
– Clearly communicate what you need, what’s a deal-breaker, and what you’re
willing to compromise on.
30 Ecosystem partnerships 31
Just as sparring strengthens fighting skills, emphatic discussion leads to better
decisions, especially in times of uncertainty. But CEOs need to set clear ground
rules to keep these conversations constructive. If leaders believe no holds are
barred, debates can devolve into all-out brawls. These melees tend to be
Sparring partners counter-productive, with nearly half of top leaders saying competition among
their C-suite execs impedes collaboration from time to time.
However, conflict can also increase creativity, as clashes help leaders
make the best
find common ground. When leaders learn to speak each other’s languages—and
co-create shared strategies—they find inspired solutions to interconnected
business challenges. This will be crucial as technology transforms the business
leaders. landscape, with nearly two-thirds (65%) of CEOs saying their organization’s
success is directly tied to the quality of collaboration between finance and
technology functions (see Figure 5).
Over the next three years, CEOs will lean on COOs, CFOs, and CTOs to make
The C-suite shouldn’t always agree. Each officer
pivotal decisions. Technology leaders will need to set the bar for tech
comes to the table with their own perspective capabilities across the business, COOs must advise where technology can
make the biggest day-to-day impact, and CFOs will need to advise where finite
and area of expertise. No individual view offers
budgets should be spent. To make sure the organization benefits from the
objective truth. Rather, it’s the full picture they expertise of all its leaders, not just the ones who shout the loudest, CEOs will
need to set clear cultural parameters around how decisions are made.
paint together that helps CEOs decide which
direction the organization should take.
When leaders learn to speak
each other’s languages, they find
“If a senior management team completely
inspired solutions to interconnected
excludes the exchange and collision of views
and opinions, the team is not creative.” business challenges.
Chairperson, Industrial Manufacturing, China
32 Decision-making 33
Perspective
Figure 5 Different corners,
different views
Rules of engagement
C-suite officers have different perspectives on how
CEOs must foster a culture that
to measure progress—and what’s holding innovation
encourages emphatic debate and
back—based on where they sit in the organization.
constructive collaboration
CEOs CFOs Tech CxOs
65%
Barriers to Short-term Management Regulatory
of CEOs say their organization’s
focus resistance to change constraints
innovation
success is directly tied to the quality
of collaboration between finance and Regulatory Aversion to risk Inadequate
constraints technology or data
technology functions.
Employee resistance Limited budget Management
48% to change resistance to change
Measures Organizational Financial benefits Innovation
say competition within their
digital maturity maturity
of enterprise
C-suite sometimes
impedes collaboration. transformation Cybersecurity Risk exposure Cybersecurity
maturity maturity
Technology Project progress Customer
adoption experience
“The more you specialize and
divide a process into parts, the
more you have to create some kind
of dependency between the parts.”
Mikkel Hemmingsen
CEO, Sund & Bælt Holding A/S
34 Decision-making 35
Build a C-suite that can lead with conviction. What to do
Generative AI changes what you can do—but it shouldn’t
change who you are. Reinforce a clear and compelling vision
to prioritize new opportunities and align transformation efforts
across the organization.
Define rules of engagement and emphasize expertise.
– Use consistent data, establish clear governance, and define desired outcomes.
– Set ground rules for healthy debate and build constructive tension to spark
growth and innovation.
– Highlight where it’s critical to speak a common language and where individual
expertise is essential.
Break down barriers between IT and the business.
– Surface conflicting expectations around critical paths and timelines.
– Stop measuring business and IT goals separately.
– Prioritize IT projects with the strongest links to business value.
Restructure the C-suite for success.
– Create a clear decision-making matrix. Give leaders clear guidance about who
has authority in which area.
– Align rewards and incentives to encourage debate on the right topics.
– Actively encourage the inclusion of different expert opinions while clearly
defining when a decision has been made or where you need quick consensus.
36 Decision-making 37
CEOs see the people problem that generative AI is creating. Nearly
two-thirds (64%) say their organization must take advantage of
technologies that are changing faster than employees can adapt—and
61% say they’re pushing their organization to adopt generative AI
People hate more quickly than some people are comfortable with.
Part of the issue is that many people think they’re training their
replacement. Despite the fact that business leaders consistently say
progress.
this technology will support human employees—not replace them—
employees remain skeptical. Until they’re convinced, they won’t take
the initiative to rethink how work is done.
To get people on board, organizations will have to invest in training
Generative AI promises to bring opportunities that
that will help them see generative AI in a new light. If they
were once pure fantasy into the realm of possibility. understand how this technology can make their jobs easier—and
more rewarding—organizations could see a major uptick in adoption.
But moving beyond productivity gains to business
Most CEOs know that making the most of generative AI will require
model innovation will require buy-in at all levels developing technology and people in equal portion, with nearly
two-thirds saying success will depend more on people’s adoption
of the organization—and many employees see
than the technology itself (see Figure 6).
generative AI as something that’s happening
CEOs also need to help people connect the dots between strategy,
TO them, not a tool that works FOR them. governance, and security as transformation continues to accelerate.
They’ll need to create thoughtful guardrails—not processes "
302,ibm,IBM_Annual_Report_2023.pdf,"Let’s Create
2023 Annual Report
Dear IBM Investor:
In 2023, we made significant
progress in our journey to become
a more innovative and focused
company, built around the two most
transformational technologies of
our time: hybrid cloud and AI. We
executed against a proven strategy,
refined our portfolio, expanded our
ecosystem of partners, and enhanced
Arvind Krishna productivity throughout IBM.
Chairman and Chief Executive Officer
We also continued to address the evolving needs of our clients.
As AI becomes a top priority, our clients are using watsonx
– IBM’s flagship AI and data platform – to help revolutionize
customer service, modernize countless lines of code, and
automate enterprise tasks to boost employee productivity.
I have never been more confident in IBM’s direction. Today’s
IBM is more capable and more productive. We have a strong
portfolio and a solid foundation to support sustainable growth.
And we are delivering on our promise to be the catalyst that
makes the world work better.
2023 performance
For the year, IBM generated $61.9 billion in revenue, up 3%
at constant currency, and $11.2 billion of free cash flow, up
$1.9 billion year-over-year. We experienced growing demand
for our new watsonx platform, marked by thousands of client
interactions. This demand contributed to roughly doubling the
book of business for watsonx and generative AI from the third
to the fourth quarter.
IBM 2023 Annual Report 1
Software Consulting Infrastructure
We also expanded profit margins by emphasizing high- Technology and expertise
value offerings in Consulting and Software and by digitally AI and hybrid cloud continue to drive value creation, allowing
transforming our processes and scaling AI to enhance businesses to scale, increase productivity, and seize new
productivity within IBM. market opportunities. IBM has built two powerful platforms
to capitalize on the strong demand for both technologies:
Software revenues were up more than 5% at constant watsonx for AI, and Red Hat OpenShift for hybrid cloud.
currency, as clients turned to our advanced software
capabilities across hybrid cloud, data & AI, automation, Watsonx is our comprehensive AI and data platform, built to
transactions processing, and security. Our performance was deliver AI models and give our clients the ability to manage
led by Red Hat, and we had solid growth in our recurring the entire lifecycle of AI for business, including the training,
revenue base. tuning, deployment, and ongoing governance of those models.
As clients shift from experimenting with generative AI to
Consulting revenues were up 6% at constant currency. building and deploying it throughout their enterprises, we are
We capitalized on the growing need for expertise in focused on practical and urgent business use cases, including
digital transformation and AI deployment, leveraging our code modernization, customer service, and digital labor.
consulting services in data and technology consulting,
cloud modernization, application operations, and business Financial institutions like Citi, Bradesco, and NatWest
transformation. are using watsonx to help increase productivity, improve
code quality, and enhance customer experiences. Our
Infrastructure revenues decreased by 4% at constant enterprise-ready AI capabilities are being embedded into
currency, in line with the typical product cycle dynamics in SAP solutions. EY launched EY.ai Workforce, a new solution
this segment. IBM z16 is significantly outperforming previous that will use watsonx Orchestrate to automate HR tasks and
cycles, demonstrating the enduring value this platform processes. Service partners such as NTT Data Business
provides to our clients. Solutions, Wipro, and TCS are launching watsonx Centers
of Excellence to scale AI-powered client innovations. And
IBM’s revenue growth and cash generation enabled us to generative AI from watsonx, combined with expertise from
make substantial investments in the business and deliver Consulting, is enhancing the digital experiences of the U.S.
value to our shareholders. In 2023, IBM spent nearly $7 Open, the Masters, Wimbledon, the GRAMMYs, and ESPN
billion on research and development, more than $5 billion to Fantasy Football.
acquire nine companies, and returned more than $6 billion to
stockholders through dividends.
2
IBMers are also embracing watsonx to unleash greater along with new machine learning, intelligence, and operational
productivity, eliminate complexity, simplify workflows, and improvements for z/OS.
automate manual tasks. Examples include processing HR and
IT tasks more easily, generating code up to 60% faster, and In addition, we enhanced IBM’s portfolio with nine
answering client inquiries more quickly. acquisitions in 2023, including Apptio, a suite of software to
help our clients better understand their technology investment
Hybrid cloud architectures have seen massive adoption, with and the business value it delivers.
nearly 80% of IT decision makers operating hybrid cloud
environments. But nearly two thirds of companies report Client engagement and partnership
difficulty managing these complex environments, a challenge IBM’s success is directly tied to the success of our clients.
that will grow as businesses deploy generative AI across Their problems are our problems. And their opportunities
multiple clouds. IBM’s industry-leading hybrid cloud platform, are our opportunities. That is why we developed a more
based on Red Hat OpenShift, can solve this problem. It collaborative, experience-based approach that allows us to
helps our clients move from architectures that are hybrid by respond effectively to their needs.
default to architectures that are hybrid by design. It enables
companies to run workloads seamlessly across multiple The IBM Garage Method, now integrated across our business,
clouds, both public and private, to simplify operations, unify combines agile development and design thinking to facilitate
data and applications, and accelerate new innovations. And co-creation with our clients. Clients have embraced this highly
it complements our watsonx platform, allowing clients the collaborative way of working with IBM, turning ideas into
flexibility to manage multi-model AI across complex, multi- outcomes with thousands of Garage engagements throughout
cloud environments. the year.
Virgin Money is harnessing IBM’s hybrid cloud to enable new Our approach to client engagement allows us to meet clients
digital customer experiences and improve their credit card where they are, bringing together whatever technology
services. Red Hat OpenShift is now the preferred platform and expertise are needed across our expanding partner
provider to Nokia’s core network applications business. ecosystem. That is why we strengthened our strategic
And the Boston Red Sox are leveraging our hybrid cloud partnerships with key industry players like Adobe, AWS,
technologies to improve the club’s operations. Microsoft, SAP, Salesforce, Samsung, and others. Strategic
partnerships now make up more than 40% of our Consulting
Experts from Consulting provide differentiated value as we revenue and delivered double-digit growth in both signings
establish IBM as a leader in AI for business, just as they did and revenue for the year.
with our hybrid cloud business. Our extensive network of
data and AI consultants has already facilitated thousands of Research and development
hands-on client interactions. IBM combines technology with In 2023, IBM Research advanced the fundamental science
consulting services to deliver the data architecture, security, of several critical technologies, including AI, quantum
and governance our clients need to adopt trusted AI solutions. computing, and semiconductors.
IBM consultants are working with Riyadh Air on mission- In AI, we demonstrated our ability to quickly transform
critical technology and business capabilities to support the research into commercial applications. We launched the
path to their first flight. NATO chose IBM to help detect and watsonx AI and data platform, introduced the groundbreaking
respond to cyber threats with greater speed. And Diageo Granite AI foundational model, and developed new AI-
partnered with Consulting and SAP on an ambitious five-year optimized hardware.
business transformation and cloud migration.
We have IBM Quantum System One engagements with several
Throughout 2023, clients modernized their infrastructure with leading organizations, including Cleveland Clinic, the Platform
the z16 platform in alignment with their hybrid cloud and AI for Digital and Quantum Innovation of Quebec, Rensselaer
strategies. IBM launched a new suite of AI offerings for IBM Z Polytechnic Institute, and the University of Tokyo. We also
IBM 2023 Annual Report 3
unveiled our 133-qubit Quantum Heron processor, which technology ethics by 2025. And IBM committed to training
enhanced the performance, efficiency, and scalability of the two million learners in AI by the end of 2026 to address the
newly deployed IBM Quantum System Two. And our work on technology skills gap.
error correction and mitigation is helping to lay the foundation
for a new era of quantum utility. But IBM’s commitment to trust goes beyond our citizenship,
products, and policies. We earn trust by delivering on our
Research also pushed the limits of semiconductor design and promises.
packaging, building on recent innovations such as the 2nm
node chip, hybrid bonding, and vertical transistors. We are We articulated a clear vision for the future of IBM in the
working with Rapidus to propel Japan’s push for leadership spring of 2020. We promised a more focused company
in semiconductor research and manufacturing, and we are built around two powerful technologies: hybrid cloud and
participating in an initiative with New York State, Micron, and AI. We promised fundamental changes to our go-to-market
others to jointly invest $10 billion in semiconductor R&D. strategy, putting clients at the center of everything we do and
transforming competitors into partners. And we promised
The promise of IBM operational changes to simplify our internal processes and
IBM is in the business of shaping the future for our clients. increase our productivity. As this report details, we are
That future must be built on trust. fulfilling those promises.
IBM is at the forefront of technologies, like AI and quantum As we look ahead, we renew our commitment to the journey
computing, which will fundamentally change the way we work we began in 2020. We will continue to innovate, to execute
and live. We bear significant responsibility to develop those with speed and purpose, find more opportunities for
technologies ethically and deploy them with transparency and operational efficiency, and further enhance our productivity
trust. That is why we built powerful AI governance into our by employing the same technologies we use to drive growth
watsonx platform and developed quantum-safe cryptography for our clients. And as always, we will be the catalyst that
to secure sensitive data. It is why we advocate for smart AI makes the world work better, bringing together our colleagues,
regulation, including holding those who develop and deploy clients, and partners with a simple invitation: Let’s Create.
AI accountable for fraudulent, discriminatory, and harmful
activity. And it is why IBM and Meta announced the formation This is the promise of IBM.
of the AI Alliance, a group of more than 70 organizations
dedicated to advancing open, safe, and responsible AI.
We also earn trust by operating with integrity, staying true
to our values, and addressing the needs of all stakeholders.
We continue to advance our efforts on the environment, Arvind Krishna
ethics, and education. IBM has achieved a 63% reduction Chairman and Chief Executive Officer
in greenhouse gas emissions against base year 2010.
We announced a new program to train 1,000 suppliers in
In an effort to provide additional and useful information regarding the company’s financial results and other financial information, as determined by generally
accepted accounting principles (GAAP), these materials contain non-GAAP financial measures on a continuing operations basis, including revenue growth
rates adjusted for constant currency and free cash flow. The rationale for management’s use of this non-GAAP information is included on page 6 and 31 of
the company’s 2023 Annual Report, which is Exhibit 13 to the Form 10-K submitted with the SEC on February 26, 2024. For reconciliation of these non-GAAP
financial measures to GAAP and other information, please refer to pages 17 and 31 of the company’s 2023 Annual Report. For watsonx and generative AI, book of
business includes Software transactional revenue, SaaS Annual Contract Value and Consulting signings.
4
Report of Financials 5
International Business Machines Corporation and Subsidiary Companies
MANAGEMENT DISCUSSION NOTES TO CONSOLIDATED FINANCIAL STATEMENTS
Overview 6 Basis & Policies
Forward-Looking and Cautionary Statements 7 A Significant Accounting Policies 50
Management Discussion Snapshot 8 B Accounting Changes 63
Description of Business 11 Performance & Operations
Year in Review 17 C Revenue Recognition 64
Prior Year in Review 28 D Segments 66
Other Information 29 E Acquisitions & Divestitures 71
Looking Forward 29 F Other (Income) and Expense 78
Liquidity and Capital Resources 30 G Research, Development & Engineering 78
Critical Accounting Estimates 33 H Taxes 78
Currency Rate Fluctuations 36 I Earnings Per Share 82
Market Risk 36 Balance Sheet & Liquidity
Financing 38 J Financial Assets & Liabilities 83
K Inventory 84
Report of Management 41 L Financing Receivables 84
Report of Independent Registered M Property, Plant & Equipment 87
Public Accounting Firm 42 N Leases 87
O Intangible Assets Including Goodwill 90
CONSOLIDATED FINANCIAL STATEMENTS P Borrowings 91
Income Statement 44 Q Other Liabilities 94
Comprehensive Income 45 R Commitments & Contingencies 95
Balance Sheet 46 S Equity Activity 97
Cash Flows 47 Risk Management, Compensation/Benefits & Other
Equity 48 T Derivative Financial Instruments 100
U Stock-Based Compensation 104
V Retirement-Related Benefits 107
W Subsequent Events 121
Performance Graphs 122
Stockholder Information 123
Board of Directors and Senior Leadership 124
6 Management Discussion
International Business Machines Corporation and Subsidiary Companies
OVERVIEW
The financial section of the International Business Machines Corporation (IBM or the company) 2023 Annual Report includes the
Management Discussion, the Consolidated Financial Statements and the Notes to Consolidated Financial Statements. This Overview
is designed to provide the reader with some perspective regarding the information contained in the financial section.
Organization of Information
• The Management Discussion is designed to provide readers with an overview of the business and a narrative on our financial
results and certain factors that may affect our future prospects from the perspective of management. The “Management
Discussion Snapshot” presents an overview of the key performance drivers in 2023.
• Beginning with the ""Year in Review,"" the Management Discussion contains the results of operations for each reportable segment
of the business, a discussion of our financial position and a discussion of cash flows as reflected in the Consolidated Statement of
Cash Flows. Other key sections within the Management Discussion include: ""Looking Forward"" and ""Liquidity and Capital
Resources,"" the latter of which includes a description of management's definition and use of free cash flow.
• The Consolidated Financial Statements provide an overview of income and cash flow performance and financial position.
• The Notes follow the Consolidated Financial Statements. Among other items, the Notes contain our accounting policies, revenue
information, acquisitions and divestitures, certain commitments and contingencies and retirement-related plans information.
• On November 3, 2021 we completed the separation of our managed infrastructure services unit into a new public company,
Kyndryl. The accounting requirements for reporting the separation of Kyndryl as a discontinued operation were met when the
separation was completed. Accordingly, the historical results of Kyndryl are presented as discontinued operations and, as such,
have been excluded from continuing operations and segment results for all periods presented. Refer to note E, “Acquisitions &
Divestitures,” for additional information.
• In September 2022, the IBM Qualified Personal Pension Plan (Qualified PPP) purchased two separate nonparticipating single
premium group annuity contracts from The Prudential Insurance Company of America and Metropolitan Life Insurance Company
(collectively, the Insurers) and irrevocably transferred to the Insurers approximately $16 billion of the Qualified PPP’s defined
benefit pension obligations and related plan assets, thereby reducing our pension obligations and assets by the same amount.
The group annuity contracts were purchased using assets of the Qualified PPP and no additional funding contribution was
required from IBM. The transaction resulted in no changes to the benefits to be received by the plan participants. As a result of
this transaction we recognized a one-time, non-cash, pre-tax pension settlement charge of $5.9 billion ($4.4 billion net of tax) in
the third quarter of 2022, primarily related to the accelerated recognition of accumulated actuarial losses of the Qualified PPP.
Refer to note V, “Retirement-Related Benefits,” for additional information.
• Effective January 1, 2023, due to advances in technology, we increased the estimated useful lives of our server and network
equipment from five to six years for new assets and from three to four years for used assets. Based on the carrying amount of
server and network equipment included in property, plant and equipment-net in our Consolidated Balance Sheet as of
December 31, 2022, the effect of this change in accounting estimate was an increase in income from continuing operations
before income taxes of $208 million or $0.18 per basic and diluted share for the year ended December 31, 2023.
• In 2023, we executed workforce rebalancing actions to address remaining stranded costs from portfolio actions over the last
couple of years resulting in charges to pre-tax income from continuing operations of $438 million. In addition, beginning in the
first quarter of 2023, we updated our measure of segment pre-tax income to no longer allocate workforce rebalancing actions to
our reportable segments, consistent with our management system. Workforce rebalancing charges in 2022 and 2021 of
$40 million and $182 million, respectively, were included in the segments.
• The references to “adjusted for currency” or “at constant currency” in the Management Discussion do not include operational
impacts that could result from fluctuations in foreign currency rates. When we refer to growth rates at constant currency or adjust
such growth rates for currency, it is done so that certain financial results can be viewed without the impact of fluctuations in
foreign currency exchange rates, thereby facilitating period-to-period comparisons of business performance. Financial results
adjusted for currency are calculated by translating current period activity in local currency using the comparable prior-year
period’s currency conversion rate. This approach is used for countries where the functional currency is the local currency.
Generally, when the dollar either strengthens or weakens against other currencies, the growth at constant currency rates or
adjusting for currency will be higher or lower than growth reported at actual exchange rates. Refer to “Currency Rate
Fluctuations” for additional information.
• Within the financial statements and tables in this Annual Report, certain columns and rows may not add due to the use of
rounded numbers for disclosure purposes. Percentages presented are calculated from the underlying whole-dollar numbers.
Certain prior-year amounts have been reclassified to conform to the change in current year presentation. This is annotated where
applicable.
Management Discussion 7
International Business Machines Corporation and Subsidiary Companies
Operating (non-GAAP) Earnings
In an effort to provide better transparency into the operational results of the business, supplementally, management separates
business results into operating and non-operating categories. Operating earnings from continuing operations is a non-GAAP
measure that excludes the effects of certain acquisition-related charges, intangible asset amortization, expense resulting from basis
differences on equity method investments, retirement-related costs, certain impacts from the Kyndryl separation and their related
tax impacts. Due to the unique, non-recurring nature of the enactment of the U.S. Tax Cuts and Jobs Act (U.S. tax reform),
management characterizes the one-time provisional charge recorded in the fourth quarter of 2017 and adjustments to that charge
as non-operating. Adjustments primarily include true-ups, accounting elections and any changes to regulations, laws, audit
adjustments that affect the recorded one-time charge. Management characterizes direct and incremental charges incurred related
to the Kyndryl separation as non-operating given their unique and non-recurring nature. In 2022, these charges primarily related to
any net gains or losses on the Kyndryl common stock and the related cash-settled swap with a third-party financial institution,
which were recorded in other (income) and expense in the Consolidated Income Statement. As of November 2, 2022, the company
no longer held an ownership interest in Kyndryl. For acquisitions, operating (non-GAAP) earnings exclude the amortization of
purchased intangible assets and acquisition-related charges such as in-process research and development, transaction costs,
applicable retention, restructuring and related expenses, tax charges related to acquisition integration and pre-closing charges,
such as financing costs. These charges are excluded as they may be inconsistent in amount and timing from period to period and are
significantly impacted by the size, type and frequency of our acquisitions. Given its unique and temporary nature, management has
also characterized as non-operating expense, the mark-to-market impact on the foreign exchange call option contracts to
economically hedge the foreign currency exposure related to the purchase price of our announced acquisition of StreamSets and
webMethods from Software AG. The mark-to-market impact is recorded in other (income) and expense in the Consolidated Income
Statement and reflects the fair value changes in the derivative contracts. All other spending for acquired companies is included in
both earnings from continuing operations and in operating (non-GAAP) earnings. For retirement-related costs, management
characterizes certain items as operating and others as non-operating, consistent with GAAP. We include defined benefit plan and
nonpension postretirement benefit plan service costs, multi-employer plan costs and the cost of defined contribution plans in
operating earnings. Non-operating retirement-related costs include defined benefit plan and nonpension postretirement benefit
plan amortization of prior service costs, interest cost, expected return on plan assets, amortized actuarial gains/losses, the impacts
of any plan curtailments/settlements including the one-time, non-cash, pre-tax settlement charge of $5.9 billion ($4.4 billion, net of
tax) in the third quarter of 2022 and pension insolvency costs and other costs. Non-operating retirement-related costs are primarily
related to changes in pension plan assets and liabilities which are tied to financial market performance, and we consider these costs
to be outside of the operational performance of the business.
Overall, management believes that supplementally providing investors with a view of operating earnings as described above
provides increased transparency and clarity into both the operational results of the business and the performance of our pension
plans; improves visibility to management decisions and their impacts on operational performance; enables better comparison to
peer companies; and allows us to provide a long-term strategic view of the business going forward. In addition, these non-GAAP
measures provide a perspective consistent with areas of interest we routinely receive from investors and analysts. Our reportable
segment financial results reflect pre-tax operating earnings from continuing operations, consistent with our management and
measurement system.
FORWARD-LOOKING AND CAUTIONARY STATEMENTS
Certain statements contained in this Annual Report may constitute forward-looking statements within the meaning of the Private
Securities Litigation Reform Act of 1995. Any forward-looking statement in this Annual Report speaks only as of the date on which it
is made; IBM assumes no obligation to update or revise any such statements except as required by law. Forward-looking
statements are based on IBM’s current assumptions regarding future business and financial performance; these statements, by
their nature, address matters that are uncertain to different degrees. Forward-looking statements involve a number of risks,
uncertainties and other factors that could cause actual results to be materially different, as discussed more fully elsewhere in this
Annual Report and in the company’s filings with the Securities and Exchange Commission (SEC), including IBM’s 2023 Form 10-K
filed on February 26, 2024.
8 Management Discussion
International Business Machines Corporation and Subsidiary Companies
MANAGEMENT DISCUSSION SNAPSHOT
($ and shares in millions except per share amounts)
Yr.-to-Yr.
Percent/Margin
For year ended December 31: 2023 2022 (1) Change
Revenue (2) $ 61,860 $ 60,530 2.2 %
Gross profit margin 55.4 % 54.0 % 1.4 pts.
Total expense and other (income) $ 25,610 $ 31,531 (18.8) %
Income from continuing operations before income taxes $ 8,690 $ 1,156 NM
Provision for/(benefit from) income taxes from continuing operations $ 1,176 $ (626) NM
Income from continuing operations $ 7,514 $ 1,783 NM
Income from continuing operations margin 12.1 % 2.9 % 9.2 pts.
Loss from discontinued operations, net of tax $ (12) $ (143) (91.8) %
Net income $ 7,502 $ 1,639 NM
Earnings per share from continuing operations–assuming dilution $ 8.15 $ 1.95 NM
Consolidated earnings per share–assuming dilution $ 8.14 $ 1.80 NM
Weighted-average shares outstanding–assuming dilution 922.1 912.3 1.1 %
Assets (3) $ 135,241 $ 127,243 6.3 %
Liabilities (3) $ 112,628 $ 105,222 7.0 %
Equity (3) $ 22,613 $ 22,021 2.7 %
(1)Includes a one-time, non-cash, pre-tax pension settlement charge of $5.9 billion ($4.4 billion net of tax) resulting in an impact of ($4.84) to diluted
earnings per share from continuing operations and an impact of ($4.83) to consolidated diluted earnings per share. Refer to note V, “Retirement-
Related Benefits,” for additional information.
(2)Year-to-year revenue growth of 2.9 percent adjusted for currency.
(3)At December 31.
NM–Not meaningful
The following table provides the company’s operating (non-GAAP) earnings for 2023 and 2022. Refer to page 28 for additional
information.
($ in millions except per share amounts)
Yr.-to-Yr.
For year ended December 31: 2023 2022 Percent Change
Net income as reported (1) $ 7,502 $ 1,639 NM
Loss from discontinued operations, net of tax (12) (143) (91.8) %
Income from continuing operations (1) $ 7,514 $ 1,783 NM
Non-operating adjustments (net of tax)
Acquisition-related charges 1,292 1,329 (2.8) %
Non-operating retirement-related costs/(income) (1) (30) 4,933 NM
U.S. tax reform impacts 95 (70) NM
Kyndryl-related impacts — 351 (100.0) %
Operating (non-GAAP) earnings $ 8,870 $ 8,326 6.5 %
Diluted operating (non-GAAP) earnings per share $ 9.62 $ 9.13 5.4 %
(1)2022 includes a one-time, non-cash pension settlement charge of $4.4 billion net of tax.
NM–Not meaningful
Management Discussion 9
International Business Machines Corporation and Subsidiary Companies
Macroeconomic Environment
Our business profile positions us well in challenging macroeconomic times. Our diversification across geographies, industries,
clients and business mix and our recurring revenue base provides some stability in revenue, profit and cash generation. In the
current environment, technology demand continues to be a major driving force behind global economic and business growth.
Businesses and governments around the world are looking for opportunities to scale, offer better services, drive efficiencies and
seize new market opportunities. More recently, geopolitical events and the interest rate environment are adding to the uncertainty.
In response, clients are leveraging technologies like hybrid cloud and artificial intelligence (AI) that boost productivity and
competitiveness.
For the year ended December 31, 2023, movements in global currencies continued to impact our reported year-to-year revenue and
profit. We execute hedging programs which defer, but do not eliminate, the impact of currency. The (gains)/losses from these
hedging programs are reflected primarily in other (income) and expense. Refer to “Currency Rate Fluctuations,” for additional
information. We saw progress from the actions we have taken to mitigate the impacts of escalating labor and component costs and
a strong U.S. dollar (USD).
Financial Performance Summary
In 2023, we reported $61.9 billion in revenue, income from continuing operations of $7.5 billion, and operating (non-GAAP)
earnings of $8.9 billion. Diluted earnings per share from continuing operations was $8.15 as reported and diluted earnings per share
was $9.62 on an operating (non-GAAP) basis. We generated $13.9 billion in cash from operations and $11.2 billion in free cash flow,
and returned $6.0 billion to shareholders in dividends. We are pleased with the fundamentals of our business and progress we have
made in executing our strategy. Our 2023 performance demonstrates the strength of our diversified portfolio and sustainability of
our revenue growth. We increased our investment in innovation and talent and completed nine acquisitions in 2023, strengthening
our hybrid cloud and AI capabilities, all while continuing to return value to shareholders through our dividend.
Total revenue grew 2.2 percent year to year as reported and 3 percent adjusted for currency compared to the prior year, led by
Software and Consulting. Software revenue increased 5.1 percent as reported and 5 percent adjusted for currency, with growth in
Hybrid Platform & Solutions and Transaction Processing. Hybrid Platform & Solutions increased 4.6 percent as reported and 5
percent adjusted for currency, with growth across Red Hat, Automation and Data & AI. Transaction Processing increased 6.2
percent as reported and 6 percent adjusted for currency, reflecting the success of our zSystems platform which continued to drive
client demand. Consulting revenue increased 4.6 percent as reported and 6 percent adjusted for currency with growth across all
lines of business, highlighting the solid demand for data and technology transformation and application modernization projects.
Infrastructure decreased 4.5 percent year to year as reported and 4 percent adjusted for currency, reflecting product cycle
dynamics.
From a geographic perspective, Americas revenue grew 2.0 percent year to year as reported (2 percent adjusted for currency).
Europe/Middle East/Africa (EMEA) increased 3.0 percent as reported (1 percent adjusted for currency). Asia Pacific grew 1.6
percent as reported (7 percent adjusted for currency).
Gross margin of 55.4 percent increased 1.4 points year to year, with continued margin expansion across all reportable segments
driven by revenue growth, improving portfolio mix and productivity actions. Operating (non-GAAP) gross margin of 56.5 percent
increased 1.3 points versus the prior year, due to the same dynamics.
Total expense and other (income) decreased 18.8 percent in 2023 versus the prior year primarily driven by the one-time, non-cash
pension settlement charge of $5.9 billion in 2022 and the benefits from productivity actions we have taken; partially offset by the
effects of currency, higher workforce rebalancing charges to address remaining stranded cost from portfolio actions, and higher
spending reflecting our continued focus on talent and portfolio innovation to drive our strategy. Total operating (non-GAAP) expense
and other (income) increased 4.5 percent year to year, d"
303,ibm,redp5695.pdf,"Front cover
IBM Cloud Pak for Data on
IBM Z
Jasmeet Bhatia
Ravi Gummadi
Chandra Shekhar Reddy Potula
Srirama Sharma
Data and AI
Redguide
Executive overview
Most industries are susceptible to fraud, which poses a risk to both businesses and
consumers. According to The National Health Care Anti-Fraud Association, health care fraud
alone causes the nation around $68 billion annually.1 This statistic does not include the
numerous other industries where fraudulent activities occur daily. In addition, the growing
amount of data that enterprises own makes it difficult for them to detect fraud. Businesses
can benefit by using an analytical platform to fully integrate their data with artificial intelligence
(AI) technology.
With IBM Cloud Pak® for Data on IBM Z, enterprises can modernize their data infrastructure,
develop, and deploy machine learning (ML) and AI models, and instantiate highly efficient
analytics deployment on IBM LinuxONE. Enterprises can create cutting-edge, intelligent, and
interactive applications with embedded AI, colocate data with commercial applications, and
use AI to make inferences.
This IBM Redguide publication presents a high-level overview of IBM Z. It describes IBM
Cloud Pak for Data (CP4D) on IBM Z and IBM LinuxONE, the different features that are
supported on the platform, and how the associated features can help enterprise customers in
building AI and ML models by using core transactional data, which results in decreased
latency and increased throughput.
This publication highlights real-time CP4D on IBM Z use cases. Real-time Clearing and
Settlement Transactions, Trustworthy AI and its Role in Day-To-Day Monitoring, and the
Prevention of Retail Crimes are use cases that are described in this publication. Using CP4D
on IBM Z and LinuxONE, this publication shows how businesses can implement a highly
efficient analytics deployment that minimizes latency, cost inefficiencies, and potential
security exposures that are connected with data transportation.
1 https://www.bcbsm.com/health-care-fraud/fraud-statistics.html
© Copyright IBM Corp. 2023. 1
IBM Z: An overview
Ever wonder how many transactions a bank processes per day? What about the pace at
which these transactions happen? According to an IBM® report, 44 of 50 of the world's top
banks use IBM Z mainframes for these daily transactions.2 IBM Z is a platform that is
designed for voluminous data, maximum security, real-time transaction analysis, and cost
efficiency.
The most recent platform for IBM Z is IBM z16™. The IBM z16 supports the following
features:
(cid:2) On-chip AI acceleration
(cid:2) Quantum-safe crypto discovery
(cid:2) Simplified compliance
(cid:2) Flexible capacity
(cid:2) Modernization of applications
(cid:2) Sustainability
With these features, enterprises can upgrade applications while preserving secure and
resilient data.
To learn more about these features, see the IBM z16 product page.
Figure1 on page3 shows a picture of the IBM z16 mainframe.
2 https://www.ibm.com/case-studies/bankwest/
2 IBM Cloud Pak for Data on IBM zSystems
Figure 1 IBM z16
IBM z16 and IBM LinuxONE Emperor 4 features
IBM Z are based on enterprise mainframe technology. Starting with transaction-based
workloads and databases, IBM Z has undergone tremendous transformations in its system
design for many generations to build servers that cater to Linux-based workloads and security
with a cyberresilient system, and support quantum computing and modernization by using a
hybrid cloud with a focus on data and AI.
3
Figure2 provides a snapshot of the IBM Z processor roadmap, which depicts the journey of
transformation and improvement.
Figure 2 IBM Z: Processor roadmap
The IBM z16 and IBM LinuxONE Emperor 4 are the latest of the IBM Z, and they are
developed with a ‘built to build’ focus to provide a powerful, cyberresilient, open, and secure
platform for business with an extra focus on sustainability to help build sustainable data
centers. Although the z16 server can host both IBM z/OS® and Linux workloads, LinuxONE
Emperor 4 is built to host Linux only workloads with a focus on consolidation and resiliency.
Depending on the workload, consolidation from numerous x86 servers into a LinuxONE
Emperor 4 can help reduce energy consumption by 75% and data center floor space by 50%,
which helps to achieve the sustainability goals of the organization.
Figure3 on page5 shows a summary of the system design of IBM LinuxONE Emperor 4 with
the IBM Telum™ processor. The IBM Telum processor chip is designed to run enterprise
applications efficiently where their data resides to embed AI with super low latency. The
support for higher bandwidth and I/O rates is supported through FCP Express cards with an
endpoint security solution. The memory subsystem supports up to 40 TB of memory.
4 IBM Cloud Pak for Data on IBM zSystems
Figure 3 System design of IBM z16 LinuxONE Emperor 4
The IBM z16 and IBM LinuxONE Emperor 4 servers are built with 7-nm technology at a
5.2 GHz speed. They consist of four dual-chip modules (DCMs) per central processor
complex (CPC) drawer, each of which is built with two 8-core Telum processor chips that has
“first in the industry” on-chip acceleration for mid-transaction, real-time AI inferencing, which
supports many different use cases, including fraud detection.
Each core has access to a huge private 32 MB L2 cache where up to 16 MB of the L2 cache
of an inactive core can be used as virtual cache (L3 / L4) by neighboring active cores on the
chip. This cache helps address translation and access checking by prefetching the same
virtual cache into the L2 cache. The virtual cache also includes Neural Network Processing
Assist instructions and direct memory access with protection, and per chip GZIP
compression.
5
Figure4 provides more information about the features of AI Accelerator integration with the
IBM Z processor cores.
Figure 4 IBM z16 on-chip AI Accelerator integration with IBM Z processor cores
The IBM z16 and IBM LinuxONE Emperor 4 server platforms are built with the hardware
features that are shown in Figure4 with addressing data and AI workloads in mind.
Regardless of where the ML and deep learning (DL) frameworks are used to build and train
data and AI models, the inferencing on existing enterprise application data can happen along
currently running enterprise business applications. CP4D 4.6 supports Tensorflow and IBM
Snap ML frameworks, which are optimized to use the on-chip AI Accelerator during
inferencing. Support for various other frameworks is planned for future releases.
Figure5 on page7 shows the seamless integration of AI into existing enterprises workloads
on the IBM z16 while leveraging the underlying hardware capabilities.
6 IBM Cloud Pak for Data on IBM zSystems
Figure 5 Seamless integration
What is Cloud Pak for Data on IBM Z
IBM Cloud Pak for Data allows enterprises to simplify, unify, and automate the delivery of data
and AI. It categorizes the activities within the journey to AI as four rungs of the AI Ladder:
Collect, Organize, Analyze, and Infuse. For more information about each of the AI Ladder
rungs, see Become Data Driven with IBM Z Infused Data Fabric, REDP-5680.
CP4D on IBM Z provides enterprises with a resilient and secure private cloud platform. You
can use it to create ML and AI models that may be included into modern intelligent
applications. You also can use it to use and construct applications for mission-critical data.
With CP4D on IBM Z, enterprises can lower data movement latency, cost inefficiencies, and
potential security exposures. Enterprises can safely store and access their most important
company data, and leverage their current infrastructure by using cutting-edge hybrid cloud
applications. Enterprises can combine their current database applications without any
rewrites, which results in reduced cost and complexity. Lastly, by using CP4D on IBM Z,
enterprises can update their database infrastructure to benefit from easier management, a
quicker time to value, and lower operating expenses.
7
Figure6 shows a solution overview of CP4D. The infrastructure alternatives are shown at the
bottom, and they include IBM Z and LinuxONE. They all leverage Red Hat OpenShift.
Common Foundational Services come next, which offer clarity throughout the data and AI
lifecycle, that is, from user access management to monitoring and service provisioning. A
high-level view of the services is shown in the middle section. The services have several
different capabilities that span the AI hierarchy. The platform can be expanded, and it offers a
seamless user experience for all distinct personas across the AI lifecycle, from data gathering
through AI infusion.
Figure 6 Solution overview of Cloud Pak for Data
We highlight the four main pillars that make IBM Z the correct infrastructure for CP4D:
(cid:2) Performance and Scale
(cid:2) Embedded Accelerators
(cid:2) Reliability and Availability
(cid:2) Security and Governance.
From a performance perspective, CP4D on IBM Z provides your data and AI with high
transaction processing and a powerful infrastructure. From the embedded accelerators
perspective, CP4D on IBM Z can investigate each transaction thanks to a cutting-edge DL
inference technology even in the most demanding, sensitive, and latency-prone real-time
workloads. From a reliability perspective, CP4D on IBM Z provides high availability and
resiliency. Lastly from the security perspective, CP4D on IBM Z is suitable for protecting
sensitive data and AI models for enterprises in highly regulated industries or those industries
that are worried about security.
8 IBM Cloud Pak for Data on IBM zSystems
Cloud Pak for Data capabilities on IBM Z and IBM LinuxONE
With CP4D on IBM Z and IBM LinuxONE, users can develop, train, and deploy AI and ML
models. Users can accomplish this task by using the CP4D IBM Watson® Studio and IBM
Watson Machine Learning (WLM) services. By using these two fundamental services, users
can accomplish the following tasks:
(cid:2) Provision various containerized databases.
(cid:2) Explore, clean, shape, and alter data by using Data Refinery.
(cid:2) Use project-specific data that is uploaded, or connect to distant data.
(cid:2) Create Spark run times and applications.
(cid:2) Create, build, evaluate, and deploy analytics and ML models with trust and transparency.
(cid:2) Leverage the AI Integrated Accelerator for TensorFlow 2.7.2 and Snap ML 1.9.
For more information about the specifics of these capabilities, see Capabilities on Linux on
IBM Z and IBM LinuxONE.
Open-source ecosystem
These days, innovation and product development are not limited to closed doors within an
organization. In any industry sector, the solutions include a mix of proprietary code
addressing the core business solution that is supported or integrated into other software
components from open source. In some cases, enterprises business solutions also are built
from open-source community offerings. Thus, open-source software becomes an important
ingredient in modern-day solution building.
IBM actively participates in various open-source communities as part of steering boards
defining the roadmap of the community, and also in contributing code to make the community
a better place for everyone to participate. Red Hat also actively participates in various
open-source communities and makes extensive contributions. In open-source communities,
although most open-source development happens on x86 / amd64 or the Intel architecture,
the same open-source software is used by other architectures, such as IBM Power (ppc64le),
IBM Z and IBM LInuxONE (s390x), ARM, and Sparc. So, the availability of an open-source
ecosystem on any architecture is key and critical to business.
On IBM Z and IBM LinuxONE (s390x) architecture, there is a huge open-source support
ecosystem that ranges from operating systems such as Linux; application run times; cloud
and container services; DevOps and automation; big data; observability; analytics;
databases; and storage. The ecosystem on IBM Z and IBM LinuxONE is growing.
IBM Z and IBM LinuxONE include much open-source software in their ecosystem. You can
see the growing list of open-source software for IBM Z and LinuxONE at The Growing
Ecosystem of Open-Source Software for IBM Z and LinuxONE.
IBM Z and IBM LinuxONE are available to various communities to include support for s390x
builds as part of their community’s continuous integration and continuous delivery (CI/CD).
Also, for open-source community developers, infrastructure resources are available on a
no-charge basis through the IBM LinuxONE community cloud.
9
CP4D includes a mix of open-source and proprietary data and AI runtime databases;
open-source run times like Python; open-source data platforms like Anaconda; ML and DL
frameworks like Pytorch and Tensorflow; and thousands of reusable Python packages. All of
them are available and supported on s390x architecture to provide seamless parity with x86
architecture and a seamless experience for enterprise data scientists, architects, and data
and AI solution developers on IBM Z and IBM LinuxONE platforms.
Anaconda is one of the open-source data platforms that provide Python and R based data
science ML frameworks; analytics and data visualization tools; and open-source data science
tools and libraries like Conda, XGBoost, and SciKit-Learn. Anaconda runs natively on Linux
on IBM Z and IBM LinuxONE, and on IBM z/OS Container Extensions (zcX) on z/OS. For
more information, see Announcing Anaconda for Linux on IBM Z and LinuxONE.
In addition to strong, open-source ecosystem support for application development on Linux
and enterprise operating systems, a new generation of IBM Z and IBM LinuxONE servers
(IBM z16™) also have strong platform support, and AI acceleration capabilities that can be
leveraged by open-source software to perform better on the server infrastructure. For
example, the recently released CP4D 4.6 has Tensorflow and IBM SnapML frameworks that
leverage the AI accelerators when running on an IBM z16 server.
So, to summarize, there is a huge, growing data and AI open source ecosystem that is
supported and optimized on IBM Z and IBM LinuxONE servers.
Why AI on IBM Z
Data and AI playing a major role in the modernization story to enable the digital
transformation journey of every organization. Many organizations recognize the business
value of infusing AI into their infrastructure. CP4D provides the cloud-native solution to put
your data to work. With CP4D, all your data users can collaborate from a single, unified
interface that supports many services that work together, including collecting data, organizing
the data, analyzing the data, and infusing AI.
Traditional ML models' power most of today's ML applications in business and among AI
practitioners. CP4D supports traditional ML frameworks for training and inferencing, such as
Scikit-learn, Snap ML, and XGBoost. Snap ML is a library that provides high-speed training
and inferencing of ML models that leverage the AI accelerator while running on an IBM z16
(Linux on IBM Z). CP4D supports DL frameworks such as TensorFlow and PyTorch.
TensorFlow is a DL framework that leverages the AI accelerator while running on an IBM z16
(Linux on IBM Z).
Figure7 on page11 provides an overview of the components that are supported on
CP4D on IBM Z. You can leverage Watson Studio for model building, training, and validation,
and WML for deployment of the model. Eventually, applications can use the AI inference
endpoint to score the model.
10 IBM Cloud Pak for Data on IBM zSystems
Figure 7 Developing, training, and deploying an AI model on Cloud Pak for Data on IBM Z and IBM LinuxONE
In summary, here are some of the reasons why you should choose AI on IBM Z:
(cid:2) World-class AI inference platform for enterprise workloads:
– Embedded accelerators: A centralized on-chip AI accelerator that is shared by all
cores.
– Industry standard AI ecosystem: Many industry open-source data science frameworks
are available on the platform.
– Seamlessly integrate AI into existing enterprise workload stacks: Train anywhere, and
then deploy on IBM Z.
(cid:2) Security: Encrypted memory, and improved trusted execution environments.
(cid:2) Sustainability: Reduce your energy consumption with real-time monitoring tools about the
energy consumption of the system.
AI use cases
With billions of transactions per day in many of today’s industries, it is key to get real-time
insights about what is happening in your data. AI on the IBM Z stack understands these
situations, and it delivers in-transaction inference in real time and at scale.
Core banking solutions running on IBM Z that are involved in processing inbound transactions
need real-time fraud detection to prevent fraud. Other types of possible use cases might be
credit risk analysis, anti-money laundering, loan approval, fraud detection in payments, and
instant payments.
For insurance companies, a pressing use case would be claims processing. For markets and
trading, clearing and settlement use cases are paramount.
11
For the health care industry, medical image processing (such as MRIs and x-rays), skin
cancer detection, and patient monitoring activities such as infant motion analysis, is
important.
For the airline industry, processes such as air traffic management, flight management
systems, and flight maintenance predictions are use cases that are ideal candidates for using
AI on IBM Z.
In the following sections, we describe the following use cases:
(cid:2) “Use case 1: Responsible AI augmented with risk and regulatory compliance” on page12
AI model lifecycle governance, risk management, and regulatory compliance are key to
the success of the enterprises. It is imperative to adopt a typical AI model lifecycle to
protect new end-to-end risks.
(cid:2) “Use case 2: Credit default risk assessment” on page22
Core banking solutions running on IBM Z that are involved in processing inbound
transactions need real-time fraud detection to prevent fraud. Other types of possible use
cases might be credit risk analysis, anti-money laundering, loan approval, fraud detection
in payments, and instant payments.
(cid:2) “Use case 3: Clearing and settlement” on page25
The use of AI can help to predict which trades or transactions have high risk exposures,
and propose solutions for a more efficient settlement process.
(cid:2) “Use case 4: Remaining Useful Life of an aircraft engine” on page27
We describe how AI can help to avoid unplanned aircraft downtime by determining the
remaining time or cycles that an aircraft engine is likely to operate before failure.
(cid:2) “Use case 5: AI-powered video analytics on an infant's motions for health prediction” on
page30
In this section, we describe how AI can predict an infant’s health conditions by monitoring
real-time body movements.
Use case 1: Responsible AI augmented with risk and regulatory
compliance
Advancement in AI is changing the world, and organizations must adopt AI to embrace new
challenges daily. Many enterprises see tremendous value in adopting AI and ML technologies
while establishing organization trust in the models, underlying data, and the process to be
followed. An AI model lifecycle can be a daunting task.
How mature is your AI governance? In this section, we provide a use case demonstrating the
trustworthiness of AI and its importance in daily monitoring.
Industry challenges
Here are the three main reasons why organizations struggle with the adoption of AI:
(cid:2) Scaling with growing regulations
(cid:2) Lack of confidence in operationalized AI (making responsible AI)
(cid:2) Challenges around managing the risk throughout the entire AI workflow
12 IBM Cloud Pak for Data on IBM zSystems
Scaling with growing regulations
Laws and regulations in the data and AI space are accelerating, and many countries are
proposing strict AI policies. Countries are monitoring adherence of these policies by the
enterprises and imposing fines for any violations. Responding to these regulations are
challenging global organizations where multiple regulations apply. For enterprises, it is
important to adopt AI policies when there is change, and to validate explainable models to
protect against discrimination.
Responsible AI
Responsible AI protects against loss of data privacy, and reduced customer loyalty and trust.
A data scientist cannot maximize accuracy and model performance above all other concerns.
Practicing responsible AI is a best practice, and you must establish protection and validation
to ensure that any models that are placed into production are fair and explainable.
Risks throughout the entire AI workflow
Organizations need to mitigate risk of the following items:
(cid:2) Deciding not to use certain technologies or practices
(cid:2) Using personal information when needed and with a user's consent
(cid:2) Ensuring automated decisions are free from bias
(cid:2) Customer confidence by providing explanations for business decisions
(cid:2) Fraud to the organization and to customer's accounts
(cid:2) Delays in putting models into production
In fact, in a recent survey, these concerns were echoed by real AI adopters when asked what
aspects of trust are most important to them. Although explaining how AI decides is the
primary concern, all of these concerns are important.
The key point here is that risk exists throughout the entire AI lifecycle starting with the
underlying data and the business justification behind the “why” of the project and continuing
into production. Without a formalized process, there is no way to mitigate these risks to unlock
the scale that is required to make automated decisions profitable. With these decisions, the
business can operate proactively instead of reactively.
13
For example, a business can start testing a model before production for fairness metrics. For
this task, enterprises need an end-to-end workflow with approvals to mitigate these risks and
increase the scale of AI investments, as shown in Figure8, which presents a typical AI model
lifecycle in an enterprise.
Figure 8 Typical AI model lifecycle
Due to regulations, more stakeholders adopt the typical AI model lifecycle to protect their
brand from new end-to-end risks. To ensure various aspects of both regulatory compliance
and security, the personas that must be involved include the chief financial officer (CFO),
chief marketing officer (CMO), chief data officer (CDO), HR, and chief regulatory officer
(CRO), along with the data engineers, data scientists, and business analysts, who build AI
workflows.
IBM governance solution for IBM Z
AI model lifecycle governance, risk management, and regulatory compliance are key to the
success of enterprises.
AI governance is a comprehensive framework that uses a set of automated processes,
methodologies, and tools to manage an organization's use of AI. Consistent principles
guiding the design, development, deployment, and monitoring of models are critical in driving
responsible and trustworthy AI. AI governance includes processes that trace and record the
origin of data, models (including associated metadata), and pipelines for audits. The details of
entry should include the techniques that trained each model, the hyperparameters that were
used, and the metrics from testing phases. These details provide increased transparency into
the model's behavior throughout the lifecycle, the data that was influential in its development,
and the possible risks.
In a world where trust, transparency and explainable AI matters, every organization wants
compliance along with the comfort of understanding how analytic insights and decisions are
made. The following sections describe some of the principles and organizational
requirements for AI governance.
14 IBM Cloud Pak for Data on IBM zSystems
Lifecycle governance
Lifecycle governance helps you manage your business information throughout its lifecycle,
that is, from creation to deletion. IBM AI governance addresses the problems that challenge
records managements:
(cid:2) Monitor, catalog, and govern AI models from anywhere throughout the AI lifecycle.
(cid:2) Automate the capture of model metadata for report generation.
(cid:2) Drive transparent and explainable AI at scale.
(cid:2) Increase accuracy of predictions by identifying how AI is used and where it is lagging.
Risk management
Risk management is used in IBM AI governance to identify, manage, monitor, and report on
risk and compliance initiatives at scale:
(cid:2) Automate facts and workflow management to comply with business standards.
(cid:2) Use dynamic dashboards for clear and concise customizable results.
(cid:2) Enhanced collaboration across multiple regions and geographies.
Regulatory compliance
Regulatory compliance is a set of rules that organizations must follow to protect sensitive
information and ensure human safety. Any business that works with digital assets, consumer
data, health regulations, employee safety, and private communications is subject to regulatory
compliance.3 The IBM AI governance solution for IBM Z includes the following tasks:
(cid:2) Help adhere to external AI regulations for audit and compliance.
(cid:2) Convert external AI regulations into policies for automatic enforcement.
(cid:2) Use dynamic dashboards for compliance status across policies and regulations.
Enterprises can develop AI models and deploy them by using IBM Watson Studio or WML on
CP4D on Red Hat OpenShift on a virtual machine that is based on IBM z/VM or Red Hat
Enterprise Linux KVM on IBM Z. AI governance on IBM LinuxONE is supported in the
following two ways:
(cid:2) Monitor the AI models with Watson OpenScale on CP4D on Red Hat OpenShift on a
virtual machine on IBM Z.
(cid:2) Enterprises can develop AI models by creating and training models by using Watson
Studio and development tools such as Jupyter Notebook or JupyterLab, and then
deploying the model onto WML on CP4D on Red Hat OpenShift on a virtual machine on
IBM Z. Then, these enterprises can achieve end-end AI governance by running AI
Factsheets, IBM Watson OpenScale, and IBM Watson OpenPages® on CP4D on x86.
Figure9 on page16 shows the end-to-end flow for a remote AI governance solution.
3 https://www.proofpoint.com/us/threat-reference/regulatory-compliance
15
Figure 9 Remote AI governance solution end-to-end flow
To achieve end-to-end AI governance, complete the following steps:
1. Create a model entry in IBM OpenPages by using CP4D on a x86 platform, as shown in
Figure10.
Figure 10 Creating a model entry in IBM OpenPages
16 IBM Cloud Pak for Data on IBM zSystems
2. Train a model by using Watson Studio and by using development tools such as Jupyter
Notebook or JupyterLab on CP4D on Red Hat OpenShift on a virtual machine on IBM Z,
as shown in Figure11.
Figure 11 Training an AI model by using Watson Studio
3. Deploy the model by using WML on CP4D on Red Hat OpenShift on a virtual machine on
IBM Z, as shown in Figure12.
Figure 12 Deploying an AI model by using WML on Cloud Pak for Data
17
4. Track the external model lifecycle by browsing through the Catalogs/Platform assets
catalog by using AI Factsheets and OpenPages while using CP4D on an x86 platform, as
shown in Figure13. The external model (deployed on CP4D on Red Hat OpenShift on a
virtual machine on IBM Z) is saved as a platform asset catalog on the x86 platform.
Figure 13 External model
You can track the model through each stage of the model lifecycle, as shown in Figure14,
by using AI Factsheets and OpenPages.
Figure 14 Tracking the model
18 IBM Cloud Pak for Data on IBM zSystems
You can see that the model facts are tracked and synchronized to IBM OpenPages for risk
management, as shown in Figure15.
Figure 15 Model facts that are tracked and synchronized to IBM OpenPages on an x86 platform
19
5. Create an external model by using IBM OpenScale on the x86 platform, as shown in
Figure16.
Figure 16 Creating an external model on an x86 platform
IBM OpenScale provides a comprehensive dashboard that tracks fairness, quality monitoring,
drift, and explainability of a model. Fairness determines whether your model produces biased
outcomes. Quality determines how well your model predicts outcomes. Drift is the
degradation of predictive performance over time. A sample is shown in Figure17 on page21.
20 IBM Cloud Pak for Data on IBM zSystems
Figure 17 IBM OpenScale dashboard that is used to monitor the external model
You developed and deployed the AI model by using Watson Studio, WML on CP4D on Red
Hat OpenShift on a virtual machine on IBM Z, and end-to-end AI model governance by
leveraging AI Factsheets, OpenScale, and OpenPages on CP4D on a x86 platform. Figure18
shows end-to-end AI governance when using IBM OpenPages, AI Factsheets, and
OpenScale.
Figure 18 Final result: End-to-end AI governance when using IBM OpenPages, AI Factsheets, and OpenScale
21
Use case 2: Credit default risk assessment
In today’s world, many individuals or businesses seeking loans to meet their growing business
needs often look to financial institutions. Financial institutions can offer loans to individuals or
businesses and charge interest based on the current market situations.
Industry challenges
Financial institutions must make an accurate decision about whether to sanction a loan or not,
and judging the likelihood of default is the difference between a successful and unsuccessful
loan portfolio. In a traditional scenario, an experienced banker can judge someone’s likelihood
of default, but that is not an efficient method for judgment as a business grows.
Predictions of credit default risk assessment
In the modern world, growing business institutions can no longer rely on only experienced
bankers to decide whether to sanction a loan knowing that there is a probability that the
borrower might default on their loans. A better choice is to rely on technological
advancements that can help with reasoning based on facts, such as leveraging credit risk
modeling techniques to process the historical data of past borrowers to understand their
credit behavior and make a more informed decision about whether to lend money, how much
money, and decide on the tenure to close the loan.
Financial institutions can leverage AI solutions by using ML techniques to predict the credit
risk. Applying AI to credit risk modeling techniques can benefit institutions in decision-making,
and thus can help better manage the exposure to credit risk.
Figure19 on page23 shows a sample architecture about how to design and develop an AI
model for credit risk assessment on IBM Z. An IBM WebSphere® Application Server is used
for handling in-bound transactions, and CP4D is used for AI model lifecycle management that
includes building, training, and deploying the model.
22 IBM Cloud Pak for Data on IBM zSystems
Figure 19 Architecture for credit risk prediction by using an ML AI model on IBM Z
A data scientist can leverage Watson Studio to develop and train an AI model and WML to
deploy and score the model. In this sample architecture, the WML Python run time leverages
the ML framework, IBM Snap Machine Learning (Snap ML), for scoring, can leverage an
integrated AI accelerator at the time of model import.
Then, the banking loan approval team can send a loan applicant request to the IBM
WebSphere Application Server, which can make a request to the AI inference endpoint. The
AI inference engine scores the transaction and sends the result back to the loan approval
team. Based on the results, the approval team can decide on whether to approve a loan or
not, and also decide how much they can lend, timelines, and other factors.
The transaction system that is shown in Figure19 uses IBM WebSphere Liberty as an
application server, but you also can use an IBM Open Liberty® application server or any
application server that can send RESTful API communications.
Models are frequently developed and tested in many platforms and languages, such as
Python, Scala, R, and Go. Models can leverage ML frameworks like scikit-learn, Snap ML, or
XGBoost, or DL frameworks like TensorFlow or PyTorch. Training a model can be done on
any platform if you have enough computing power for complex models, but moving that model
into production requires careful testing to ensure that transactions are not delayed, especially
if you plan to run the model within a transaction.
We showed how IBM Z enable customers to use AI frameworks to detect credit risk. Now, we
look at how you can leverage CP4D and TensorFlow on IBM Z to detect the credit risk.
23
Figure20 shows an architecture for predicting credit risk by using DL on IBM Z.
Figure 20 Architecture for credit risk prediction by using DL on IBM Z
Data scientists can star"
306,ibm,IBM-ETAB-Report-white-paper-DIGITAL-20241212_5B30_5D.pdf,"New York State
Emerging Technology
Advisory Board
Recommendations for making
NY a leader in responsible AI
New York State Emerging Technology Advisory Board
Table of contents
3 Letter from the co-chairs
4 The Emerging Technology Advisory Board
5 Abstract
7 New York’s AI landscape
15 Inspirational AI stories from external
stakeholders
16 Responsible AI
18 Vision and ambitions
Adoption at scale
Democratization of AI
Resilience and equity within the workforce
20 Recommendations
40 Next steps
42 Acknowledgements
44 Endnotes
2
New York State Emerging Technology Advisory Board
Letter from the co-chairs
New York has a long history of capitalizing on major technological The ETAB is proud to provide Governor Hochul with this report,
breakthroughs and economic shifts. Few other states have which sets forth bold, ambitious, and powerful recommendations
adapted as successfully to these profound changes, which have based on the latest research and AI developments. These
shaped workers’ lives and industries’ fate. Today, New York State recommendations reflect the diversity of thought and experience
has, once again, a unique opportunity to pioneer and embrace provided by the Board and other stakeholders, shaping the
emerging technologies. To do this, New York must build a thriving future of emerging technologies. They build on the Governor’s
ecosystem that supports innovation, deploys innovations at existing, substantial efforts and New York’s global reputation
scale, and provides equitable opportunities for its people and as a place where businesses come to grow, innovate, and create
its workforce. future technologies. The recommendations are designed to guide
both the State and organizations across New York in driving an
In March 2024, Governor Hochul asked us to establish and lead innovative AI ecosystem, ensuring responsible AI deployment
an independent advisory board. This board aims to develop at scale, fostering a resilient workforce, and empowering all New
recommendations for how New York State can best support Yorkers with equitable access to the benefits of AI.
and grow a thriving ecosystem for emerging technologies. The
Emerging Technology Advisory Board (ETAB) comprises private Together, we can secure New York’s position at the forefront of
sector leaders, and leaders from globally renowned nonprofit this transformative era.
and foundation organizations (see here for Governor Hochul’s
June 13, 2024 press release). These board members are actively Sincerely,
involved in civic life and duty. Through their work on the ETAB,
they are dedicating their expertise and contributions to advancing
New York’s interests. The ETAB dedicated the first six months
to developing recommendations that achieve one unified vision:
Elevate New York as an AI leader. Arvind Krishna Dr. Tarika Barrett
Chairman and CEO, IBM CEO, Girls Who Code
AI development is fast-moving and exciting; however, the ETAB Co-Chair, ETAB Co-Chair, ETAB
acknowledges there remains uncertainty about the scale, speed
of adoption, and consequent impacts on the workforce. Being
a successful leader in AI will require agility and adaptability,
and the State should frequently reassess its approach, and the
recommendations in this report, as AI continues to be deployed
and the landscape evolves.
3 Whitepaper | Template Month Year
New York State Emerging Technology Advisory Board
The Emerging Together, the Advisory Board’s immense contributions and
dedicated efforts reflect the critical need to thoughtfully
Technology and boldly elevate New York as a leader in this pivotal moment.
The report’s ambitious, independent recommendations
are a testament to the insightful perspectives and inspiring
Advisory Board
collaboration of the Advisory Board members. As such, this
report is a culmination of contributions from the Board as
a whole; each item included may not directly reflect every
member’s point of view nor should they be read as mandates
for organizations.
Albert Bourla René F. Jones
CEO CEO
Pfizer M&T Bank
Richard Buery Lynn Martin
CEO President
Robin Hood The New York Stock Exchange
Bertina Ceccarelli Sanjay Mehrotra
CEO CEO
NPower Micron Technology
Somak Chattopadhyay Aparna Pappu
Managing Partner Vice President and General Manager
Armory Square Ventures Google Workspace
Mario Cilento Julie Samuels
President CEO
New York State AFL-CIO Tech:NYC
Dev Ittycheria Lisa Sobierajski Avila
CEO CEO
MongoDB Kitware
Joanna Geraghty Pat Wang
CEO CEO
JetBlue Healthfirst
Lyndie Hice-Dunton Darren Walker
Executive Director President
National Offshore Wind Research & Ford Foundation
Development Consortium
4 Whitepaper | Template Month Year
Abstract
5 Whitepaper | Template Month Year
Abstract
Making New York a Grounded in these learnings, the Advisory Board engaged over 40
external stakeholders and experts to gain a deeper understanding
leader in advancing of the nuances and complexities in the challenges identified, how
those challenges manifest across New York organizations and
communities, ongoing initiatives aiming to address the potential
responsible AI
challenges, and others aiming to build on New York’s position
of strength. The stakeholder interviews also revealed bold ideas
and perspectives on potential recommendations to include
in this report.
The Advisory Board reflected on the insights and ultimately
aligned on three ambitions for the State of New York to pursue:
– Enable all New York businesses to responsibly deploy
In 2022, the widespread introduction of generative AI (gen AI at scale
AI) rapidly transformed the technology landscape, creating – Commit to AI literacy for at least 15 million
unprecedented global opportunities. For New York, gen AI New Yorkers by 2030, democratizing AI in the process
could mean an economic expansion of up to $100 billion from – Ensure every worker in New York can thrive in the new
productivity improvements alone.1 Given the potential for AI landscape
significant disruption in this critical moment, Governor Hochul
asked the Emerging Technology Advisory Board (ETAB) to These ambitions are supported by 9 recommendations that
develop a plan for a thriving emerging technology ecosystem foster public-private partnerships and balance the priorities
in New York. The first six months of their effort outlined of timely impact and sufficient scale.
recommendations that could make New York the leader
in advancing responsible AI.
The Advisory Board took a comprehensive approach to
developing the recommendations outlined in this report. First,
the Advisory Board reviewed New York’s AI landscape. The
effort validated the state’s foundational position of strength.
New York’s robust economy, extensive tech talent pool,
academic excellence, and legacy for innovation underpin the
state’s promising potential to be a leader in AI. The assessment
also identified challenges the state may face, primarily related
to supporting and empowering its workforce to thrive in the AI
transition, and ensuring equitable access to resources to enable
all New Yorkers to leverage and benefit from the opportunities
AI offers.
6 Whitepaper | Template Month Year
New York’s
AI landscape
7 Whitepaper | Template Month Year
New York’s AI landscape
AI presents AI has rapidly transformed the technology landscape, creating
unprecedented opportunities. The McKinsey Global Institute
a tremendous estimates AI could contribute up to $17–26 trillion to
the global economy annually.2 As an example, by improving
worker productivity through technology and better use of
opportunity globally
time, AI could add ~$3.5 trillion (approximately 4%) to the
global economy. This implies a productivity impact for the US
and in New York
estimated at $1 trillion, of which New York is expected to make
up a disproportionate share of up to $100 billion. New York
is uniquely positioned due to its high-productivity industries,
which are digitally mature and ripe for AI adoption.3 AI presents
opportunities worth seizing.
Beyond the economic impact, AI has enormous potential to
improve our lives in ways both subtle and surprising. Machine
learning tools can analyze medical images, such as X-rays
and MRIs, to help medical professionals diagnose diseases
faster and more accurately. AI algorithms can monitor driving
patterns and modify traffic signals to reduce congestion,
commute times, and emissions. AI-powered analysis enables
universities to assess student needs and offer better targeted
support to address them.4 To make these and many other
benefits a reality, AI technologies will need to be part of a whole
ecosystem that includes innovations in technology, education,
business, and society.
To chart the course to a thriving AI ecosystem in New York,
the Emerging Technology Advisory Board (ETAB) examined
New York’s current AI landscape, including New York’s ability
to realize the potential of AI; the groundbreaking investments
Projected economic benefit from AI-augmented productivity New York has made in AI and AI-adjacent industries; and the
opportunities and challenges that AI will present for New York’s
businesses, people, and infrastructure.
$$33..55TT $3.5T
gglloobbaall economy global economy
$1T
$1T $1T
US
US US
$100B $100B
$100B
New York State New York State
New York State
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New York’s AI landscape
New York is poised With its deep-rooted financial infrastructure and expertise,
New York holds a strategic advantage in driving AI growth.
to capitalize on the Its leadership in private investment markets positions the state
to significantly enhance its AI investment landscape. Over
the years, the state’s commitment to innovation has sparked
AI opportunity
a remarkable surge in AI VC funding, far outpacing investment
growth in other areas.6
The state’s robust economy is expected to experience growing
labor demand, with a net gain of more than 200,000 jobs by
the end of the decade.7 This robust job growth could ease the
potential pressures of the job shifts, creating more opportunities
for reskilling and upskilling.
A robust economy
New York is an economic leader in the US, ranking 3rd nationally
in labor productivity.5 Over half of the state’s sectors surpass the
national productivity average and have experienced remarkable
growth over the past three decades. This surge in productivity
can largely be attributed to their embrace of digital technologies.
Consequently, these high productivity, digitally mature sectors,
such as retail, financial services, and advanced manufacturing,
are primed for the adoption of artificial intelligence, positioning
them—and New York—ideally for the next wave of technological
advancement.
The global economic potential of gen AI, $ billion8
240–
460
240–
390 200–
340 180–
170–
300 160–
290 270 1 25 60 0– 150– 150–
250 240 120– 120–
230 200 110–
180 100– 90–
170 80–
150 140 70– 60– 60–
110 60–
110 110
100
50+
40+
9 Whitepaper | Template Month Year
Tech Retail Banking logistics Travel
&
manufacturing Advanced packaged
goods
Consumer Healthcare services Professional Energy Education Basic
materials
Real
estate
semiconductors Electronics
&
Construction Chemical social
sector
Public
&
Media Life
science
Telecom Insurance Agriculture
Extensive talent pool
New York has long been a beacon for tech talent. Not only is the
state a top tech talent destination—ranking 3rd nationally—
New York City has also become a magnet for tech talent
relocations, outpacing all other cities.9 This influx stands
in stark contrast to San Francisco, which saw a net loss during
the same period. New York’s leading position is driven by
its ability both to develop talent in its premier institutions
and to attract tech talent to relocate.
Top 5 sectors in NY
New York’s AI landscape
Academic excellence
Micron: Harnessing AI for advanced manufacturing
New York has long been a leader in innovation and academic
As semiconductor manufacturing becomes more excellence, with 3 of the top 20 US universities for R&D funding
complex and AI increasingly powers the growing in engineering research.12 Complementing its academic strength,
demand for semiconductors, Micron Technology New York hosts leading research labs, such as NYU’s CILVR, and
has internally deployed AI extensively through its private tech companies such as IBM, MongoDB, and DeepMind.
manufacturing processes to ensure that Micron and
its future New York workforce remains at the cutting
edge. Semiconductor manufacturing involves more
than 1,500 individual steps to turn mined silicon into 3 of the top 20 US universities for
the Micron memory and storage chips that store the R&D funding in engineering research
data for smartphones, the automotive sector, and
other key industries, and Micron uses AI to support
a variety of manufacturing processes, including:
image analytics, acoustic listening, and thermal A legacy for innovation and adaptability
imaging. As a result of these AI innovations, Micron
has improved worker safety and kept its operations New York has a long legacy of pioneering advancements and
competitive: between 2016 and 2020, worker evolving its economy to lead in emerging technology. The state
productivity rose 18%, time to resolve quality issues has birthed countless innovations—from the telegraph (invented
fell by 50%, time to market for new chips fell 50%, by NYU professor Samuel Morse) to photographic film rolls
and product scrap production fell 22%. (invented and popularized by Rochester-based Kodak) to Gorilla
Glass (invented and commercialized by Corning). But beyond
this inventiveness, the state has also successfully navigated
numerous economic shifts, transitioning from a manufacturing-
1st ↑200K based economy to one dominated by financial services and
beyond. Today, New York’s economy is strategically diversified
across technology, media, healthcare, and education. This rich
in tech talent relocations, net job gain by 2030 dynamic not only shields it from economic downturns but also
claiming 15% of all tech sets the stage for a future where new technologies such as
talent relocations10 AI can be harnessed to their fullest potential.
2nd ↑32%
nationally in science rise in AI VC funding over
and engineering degrees the last 9 years, outpacing
conferred11 the 2% growth in all other
VC deals
3rd
nationally in productivity
10 Whitepaper | Template Month Year
New York’s AI landscape
New York is making New York is continuing its longstanding investments in emerging
technology, with a bold vision to advance semiconductors,
groundbreaking quantum computing, and AI. Accordingly, the state has
implemented strategic programs, policies, and commitment to
funding that prioritizes productivity, equity, and sustainability.
investments in AI and
For example, New York’s landmark $400 million investment to
establish Empire AI, a consortium of seven leading universities
AI-adjacent industries
and research institutions that will collaborate in a state-of-the-
art AI computing center, will unlock university research critical
to accelerating AI use cases for public good.13
New York’s simultaneous investments in AI-adjacent industries
are key to building a thriving AI technology ecosystem, as
semiconductors provide the essential processing power that
enables efficiency of AI deployments and quantum computers
NY SMART I-Corridor: could accelerate the speed of AI algorithms. Central in the state’s
Developing a semiconductor cluster investments are:
In July 2024, the Economic Development Administration Semiconductors14
designated the New York Semiconductor Manufacturing – $100 billion commitment from Micron Technology to create
and Research Technology Innovation Corridor (SMART 9,000 Micron jobs, 4,500 construction jobs, and 40,000
I-Corridor) as a Tech Hub. The NY SMART I-Corridor indirect jobs
is a consortium of over 100 institutions in the Buffalo, – $11.6 billion commitment from Global Foundries to generate
Rochester, Ithaca, and Syracuse Metropolitan Statistical over 1,500 jobs
Areas (MSAs) that aims to build a globally leading – $10 billion partnership to advance next-generation chips
semiconductor cluster in Upstate New York. Leading research at NY CREATES Albany
partners include Micron Technology, CenterState – $40 million of federal funding from the CHIPS Act to the
Corporation for Economic Opportunity, and the University NY SMART I-Corridor Tech Hub
at Buffalo. The Tech Hub applies a comprehensive
approach to developing the semiconductor cluster. The Quantum computing
$40 million in federal funding will be used to implement – IBM’s first ever IBM Quantum System One on a university
four projects around supply chain expansion, workforce campus (Rensselaer Polytechnic Institute [RPI]) to accelerate
development, commercialization, and governance.18 quantum computing research15
– $6.5 million public investment to construct a Quantum
Internet Test Bed at Stony Brook University16
$400M Innovation
– $100 million investment from JMA Wireless to relocate its 5G
headquarters—the only US-owned 5G campus—to Syracuse17
landmark public-private – $2.5 million annually to NYSTAR Innovation Hot Spots, which
investment to establish serve as startup incubators and regional hubs connecting
Empire AI technology initiatives across their region
The Emerging Technology Advisory Board aims to build on
these longstanding investments, a testament to Governor
Hochul’s relentless commitment to lead at the forefront of
emerging technology.
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New York’s AI landscape
As New York continues Strengthening workforce and talent
development
to embrace AI, the state
AI is likely to transform the employment landscape. McKinsey
could face challenges
estimates occupational shifts may be required in the New York
Combined Statistical Area (CSA) by 2030 as the evolving nature
of work likely shifts the mix of jobs in the region.19 Some of the
occupational categories that may face the most shifts are office
support workers, customer service and sales, food services,
production work, and business and legal professionals.20 It must
be a priority to responsibly support the workforce through this
transition, for example, by providing upskilling and reskilling
opportunities, creating high-quality, family-sustaining jobs,
connecting workers to employment opportunities, or increasing
Ensuring workers can thrive benefits. By providing resources to support the workforce through
this transition, New York could help workers reap the benefits and
To ensure workers thrive, in addition to initiatives advantages of AI (e.g. by moving to new in-demand industries).
mentioned in this report, including education, training,
and job placement, the State could take additional Employers could disclose the use of AI when it is used in
steps consistent with its labor and employment policy for connection with employees’ substantive work and to make
other industries and new technologies. These include: or assist in labor and employment decisions. There is an
– Labor peace, prevailing rate, and domestic opportunity for New York to monitor the impact of AI on the
content preferences employment landscape to understand and respond to any
– Disclosure and bargaining of AI use in the workplace negative impact on workers in an agile manner.
– Robust worker data privacy, bias, whistleblower,
and discrimination protections New York, along with other traditional tech hubs, faces rising
– Prioritizing employee retention competition for AI talent nationally as the geographic dispersion
– Providing for direct support for displaced workers, of AI roles increases. Between 2018 and 2023, New York saw
including enhanced UI benefits and COBRA a 1.7 percentage point decline in its share of AI job postings.21
premium assistance If domestic and international talent is not retained, that loss
– Ensuring that public spending, investments, and could put New York at a disadvantage.
subsidies only go to applicants who develop or
implement AI to create additional jobs or support With a longer-term view of its talent pipeline, New York has the
existing ones, as opposed to displacing workers opportunity to ensure AI literacy is embedded in the education
of its 2.4 million K–12 public school students.22 Students are
well-positioned to learn from and with AI—early exposure could
encourage safe adoption as AI becomes mainstream.
Change in each state’s share of AI job postings between 2018 and 2023
+1.3 +1.2
+0.8 +0.7 +0.7 +0.7 +0.6 +0.5 +0.5 +0.5
0.0
-0.2 -0.4 -0.4 -0.5 -0.6 -0.6
-1.7
-2.5
-6.0
MD TX KS VA IL AL FL ID MA OK WI CO MI PA NC MN NM NY WA CA
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New York’s AI landscape
Ensuring equitable representation
Girls Who Code: Closing the gender gap in tech
and access to resources
Girls Who Code (GWC), an international nonprofit
working to close the gender gap in tech, is leading the All too often, technological transitions leave underrepresented
movement to inspire, educate and champion girls, groups behind. These groups often lack the resources, such
women and non-binary people, with a special focus as higher education and financial funding, required to succeed
on historically underrepresented groups, to become in transitions. For example, there has been a long history of
changemakers in tech. In 2024, an independent study inequitable distribution of VC funding. In 2022, only ~2% of VC
found that high school students who participate in funding in the US was committed to female-founded companies,
GWC’s summer programs are more likely than their ~1% committed to Black-founded companies, and ~1.5%
peers to major in computer science-related fields committed to Latino-founded companies.24 The inequity in
in college. The impacts of their holistic approach to access to resources could lead to disproportionate impacts
computer science education, grounded in project-based of AI across the state’s workforce. Experts estimate the effects
learning, community, and real-world applications of of labor market churn could have uneven distributional impacts,
emerging technology, are consistently demonstrated which could manifest as a higher risk of impact for low-wage
among students historically underrepresented in workers (4.2X) than high-wage workers, women (1.3X) than
computing, including Black and Hispanic or Latino/a men, non-college-educated workers (1.6X) than those with at
students. These efforts highlight the crucial role of least bachelor’s degrees, and Hispanic workers (1.2x) than white
targeted educational initiatives in fostering gender workers.25 Although AI has the potential to follow the same
diversity in tech.23 inequitable path as other technological transitions, New York can
make proactive, intentional interventions to bridge the equity gap.
Equitable representation in AI is not just about achieving better
outcomes for the individuals, but also for the technology itself.
For example, Dr. Joy Buolamwini has shown that because many
of the datasets that were originally used to train AI models
were not representative of the world at large, many AI tools have
higher misidentification rates for people of color, which can have
devastating effects.26 Equitable representation in AI development,
along with widely prescribed norms, can help improve the
technology’s abilities so it can be deployed responsibly and avoid
biased outcomes for consumers, workers, and the public at large.
Low-wage workers are 4.2x more likely Non-college educated workers are 1.6x
to be affected than high-wage workers. more likely to be affected than those with
bachelor’s degrees.
Women are 1.3x more likely to be affected Black, Hispanic/Latino, American Indian,
than men. and Alaskan Native graduates in higher
education represented less than 25% of
graduates27 while making up ~32% of the
US population.28
Hispanic workers are 1.2x more likely
to be affected than white workers.
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New York’s AI landscape
Building trust in AI Continuing to lead in productivity
AI can’t be successfully deployed at scale if it’s not trusted. Despite being a national leader in overall productivity, New
Currently, 50% of New York constituents fear AI, expressing York’s productivity growth lags the national average and states
concerns about the lack of transparency in its deployment including California, Washington, and Massachusetts.32 AI-driven
and the potential for bad actors to exploit its use cases.29 automation and workforce reskilling could substantially boost
A recent study also found that the less people know about the state’s productivity growth, creating more and higher paying
AI, the more they worry about it.30 Building public awareness jobs for New Yorkers.
of AI’s applications and limitations is a prerequisite to
building New Yorkers’ trust of AI, a challenge the state
could be well-positioned to take on.
New York’s labor productivity growth
American attitudes about increased use of AI:31 from 2019–23 (0.9%) lags states like
California (2.4%), Washington (3.3%),
and Massachusetts (2.3%).
38% 15%
The ETAB supports NYS’s efforts to
address broader AI-related challenges
The ETAB aims to address these challenges and strengthen the
foundation for New York’s leadership in AI. We also acknowledge
there must be broader efforts to address the complexity
More concerned than excited More excited than concerned of some challenges beyond the scope of the Board’s charter.
The state’s energy infrastructure capacity is a prime example.
Electricity consumption by AI data centers is expected to
increase from 2.8% of New York’s supply today to 3-7% by
2030.33 This is an additional pressure on the grid, at a time
when the state is actively pursuing its ambitious clean energy
transition.34 In stakeholder interviews, leaders also highlighted
their growing concern about losing international talent after
significant investment in their development. They continue to
face difficulty in attracting and retaining talent in areas with
a high cost of living. More must be done to fortify the grid with
sufficient carbon-free, reliable, and affordable energy; attract
and retain the talent the state seeks; and to support that talent
with affordable housing and necessary resources to thrive. The
ETAB fully supports the state’s ongoing, comprehensive efforts
to address these challenges and others.
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New York’s AI landscape
Inspirational AI stories The Advisory Board engaged over 40 cross-sector stakeholders
and experts to get their thoughts about AI—what the challenges
from external stakeholders and opportunities are, how they manifest in NY, and how they
can be remediated or seized. Stakeholder insights surfaced
powerful proof points about how AI can transform education,
the arts, research, and creative economies.
TeachAI: Educating the AI generation NYSCA: Finding the intersection of art and AI
TeachAI is an initiative led by Code.org, ETS, ISTE, Khan New York State Council on the Arts (NYSCA) provides
Academy, and the World Economic Forum. It brings grants and other support to advance their mission to
together public and private education leaders and “foster and advance the full breadth of New York State’s
technology experts to help create policy guidance arts, culture, and creativity for all.”37 As one of NYSCA’s
and resources about the safe, effective, and responsible values is “the constant evolution of artmaking and creative
usage and teaching of AI in schools.35 The resulting practice,” NYSCA is interested in the way AI is shaping
AI Guidance for Schools Toolkit helps education system the landscape of art and artists in New York State. NYSCA
leaders create guidance, includes seven principles highlights the complex impact AI can have on artists and
for AI in education, and recommends strategies for the importance of having artists be a part of conversations
engaging parents, staff, and student stakeholders. about AI development and policy. NYSCA also supports
organizations at the intersection of technology and art,
Pat Yongpradit, Chief Academic Officer of Code.org which can be a part of educating artists about AI, such as
and Lead of TeachAI has said “My sincere hope is that the Buffalo Center for Arts and Technology, which provides
teachers feel guided and supported by their leaders mentorship, tutoring, and workforce development.
as we all adapt to the changes AI brings to education.”36
TeachAI is an example of convening many thought
partners and stakeholders together to advance
change, as they have brought together private sector
companies, national and state government agencies, Etsy: Keeping commerce human
and policy groups to advance guidance and frameworks.
Etsy has long been a leader in leveraging AI and machine
learning to craft a uniquely human shopping experience
that connects its community of creative entrepreneurs
with tens of millions of passionate buyers around the
Empire AI: Investing in research for public good world. To further its mission to Keep Commerce Human
while embracing cutting edge technology, Etsy created
Empire AI, a consortium of seven New York-based world- a Responsible AI Working Group to govern its exploration
class research institutions, is making strides to secure of AI. Etsy leverages AI to help sellers more effectively
New York’s place at the forefront of AI research. The grow their businesses, surface more relevant and inspiring
consortium will create a first-in-the-nation, research- items to buyers that help drive more sales for sellers,
focused AI computing center, powered by clean and improve the shopping and selling experience for the
hydropower. The center will provide grant researchers Etsy community. Etsy’s goal in this work is to leverage
across the state access to essential computing AI to Keep Commerce Human, while upholding the values
resources, catalyzing innovation, fostering recruitment of of respect, fairness, reliability, transparency, privacy, and
global tech talent, and advancing AI for the public good. security when advancing the adoption of AI.
15 Whitepaper | Template Month Year
Responsible AI
16 Whitepaper | Template Month Year
Responsible AI
Responsible AI is For the purpose of this report, the ETAB defines AI as the
simulation of human intelligence processes by machines.
the throughline of While AI offers incredible potential, it could pose a risk if not
pursued responsibly. Deploying AI responsibly across New
York State is critical to the safety of all New Yorkers and New
the Advisory Board’s
York businesses. Responsible AI is defined as an approach
to designing, developing, assessing, and deploying AI in a safe,
recommendations
trustworthy, and ethical way. Responsible AI encompasses
the following principles:
1. Fairness and inclusiveness
AI systems avoid bias, treat everyone fairly, and
avoid affecting distinct groups differently, with an
emphasis on ensuring that community voice actively
contributes to AI creation to ensure it addresses,
IBM: Implementing ethics standards and does not exacerbate, the most pressing challenges.
IBM’s AI Ethics Board is the lynchpin of its responsible 2. Transparency and traceability
technology efforts and infuses IBM’s principles into Users understand how and why AI systems function
business and product decision-making. The AI Ethics the way they do so they can determine appropriate
Board is steered by senior leaders from across the use cases and identify potential limitations, which
company, supported by a strong advocacy network and can include an emphasis on “human-in-the-loop”
AI Ethics Focal Points within various business units. In (HIL) design.
addition to actively supporting the principles, the AI
Ethics Board shares thought leadership around emerging 3. Reliability and safety
issues, and in 2023, published various white papers, AI systems operate reliably, safely, and consistently,
including “Augmenting Human Intelligence–the IBM handling exceptional conditions.
Point of View” and “Foundation Models: Opportunities,
Risks and Mitigations.” The AI Ethics Board is one 4. Governance and accountability
component of IBM’s Integrated Governance Program, Developers, organizations, and policymakers take
which allows the organization to adapt many existing ownership of responsible deployment of AI.
processes to address new AI requirements and obligations.
5. Privacy and security
AI systems are continually updated to comply with
data protection protocols about the collection, use,
storage, and disclosure of data.
Kitware: Advancing explainable AI 6. Sustainability
AI systems achieve beneficial outcomes for people
Kitware is at the forefront of ethical AI research, and the planet.
developing methods and leading studies on how AI can
be trusted and how it can be harnessed to benefit society The Advi"
308,mit_edu,The_20Simple_20Macroeconomics_20of_20AI.pdf,"∗
The Simple Macroeconomics of AI
Daron Acemoglu
Massachusetts Institute of Technology
April 5, 2024
Abstract
This paper evaluates claims about the large macroeconomic implications of new advances in AI. It
starts from a task-based model of AI’s effects, working through automation and task complementarities. It
establishesthat,solongasAI’smicroeconomiceffectsaredrivenbycostsavings/productivityimprovements
at the task level, its macroeconomic consequences will be given by a version of Hulten’s theorem: GDP and
aggregateproductivitygainscanbeestimatedbywhatfractionoftasksareimpactedandaveragetask-level
cost savings. Using existing estimates on exposure to AI and productivity improvements at the task level,
these macroeconomic effects appear nontrivial but modest—no more than a 0.71% increase in total factor
productivity over 10 years. The paper then argues that even these estimates could be exaggerated, because
early evidence is from easy-to-learn tasks, whereas some of the future effects will come from hard-to-learn
tasks, where there are many context-dependent factors affecting decision-making and no objective outcome
measures from which to learn successful performance. Consequently, predicted TFP gains over the next 10
yearsareevenmoremodestandarepredictedtobelessthan0.55%. IalsoexploreAI’swageandinequality
effects. IshowtheoreticallythatevenwhenAIimprovestheproductivityoflow-skillworkersincertaintasks
(without creating new tasks for them), this may increase rather than reduce inequality. Empirically, I find
that AI advances are unlikely to increase inequality as much as previous automation technologies because
their impact is more equally distributed across demographic groups, but there is also no evidence that AI
willreducelaborincomeinequality. AIisalsopredictedtowidenthegapbetweencapitalandlaborincome.
Finally, some of the new tasks created by AI may have negative social value (such as design of algorithms
for online manipulation), and I discuss how to incorporate the macroeconomic effects of new tasks that may
have negative social value.
JEL Classification: E24, J24, O30, O33.
Keywords: Artificial Intelligence, automation, ChatGPT, inequality, productivity, technology adop-
tion, wage.
∗PaperpreparedforEconomic Policy. IamgratefultoCanYe¸sildereforphenomenalresearchassistance,
to Leonardo Bursztyn, Mert Demirer, Lauren Fahey, Shakked Noy, Sida Peng, Julia Regier, and Whitney
Zhangforusefulcomments,andtoparticipantsintheEconomicPolicy conferenceandmydiscussantsthere,
DavidH´emousandBenoˆıtCoeur´e,forcommentsandsuggestions. IthankPamelaMishkinandDanielRock
for generously sharing their data on AI exposure. I am also heavily indebted to my collaborators on several
projects related to these topics, David Autor, Simon Johnson and Pascual Restrepo, from whom I learned a
great deal and who have also given me very useful comments on the current draft. All remaining errors are
mine. The online Appendix is available upon request.
1 Introduction
Artificial intelligence (AI) has captured imaginations. Promises of rapid, even unparalleled,
productivity growth as well as new pathways for complementing humans have become com-
monplace. There is no doubt that recent developments in generative AI and large language
modelsthatproducetext, informationandimages—andShakespeareansonnets—inresponse
to simple user prompts are impressive and even spellbinding. ChatGPT, originally released
on November 30, 2022, soon became the fastest spreading tech platform in history, with an
estimated 100 million monthly users only two months after launch.
AI will have implications for the macroeconomy, productivity, wages and inequality, but
all of them are very hard to predict. This has not stopped a series of forecasts over the last
year, often centering on the productivity gains that AI will trigger. Some experts believe
thattrulytransformativeimplications, includingartificialgeneralintelligence(AGI)enabling
AI to perform essentially all human tasks, could be around the corner.1 Other forecasters
are more grounded, but still predict big effects on output. Goldman Sachs (2023) predicts
a 7% increase in global GDP, equivalent to $7 trillion, and a 1.5% per annum increase in
US productivity growth over a 10-year period. Recent McKinsey Global Institute (2023)
forecasts suggest that generative AI could offer a boost as large as $17.1 to $25.6 trillion to
the global economy, on top of the earlier estimates of economic growth from increased work
automation. They reckon that the total impact of AI and other automation technologies
could produce up to a 1.5 − 3.4 percentage point rise in average annual GDP growth in
advanced economies over the coming decade.2
Are such large effects plausible? And if there are going to be productivity gains, who will
be their beneficiary? With previous automation technologies, such as robotics, most gains
1Korinek and Suh (2024) predict a “baseline” GDP growth of 100% over the next 10 years, and also
entertain the possibility of much higher “aggressive” AGI growth rates, such as a 300% increase in GDP.
Many others are seeing recent developments as a confirmation of the forecasts in Kurzweil (2005) about the
impending arrival of “singularity” and “explosive” economic growth (Davidson, 2021).
2Three caveats are in order. First, although most recent advances are in generative artificial intelligence,
the economic forces explored here apply to other types of AI, and estimates of exposed tasks I use come on
the basis of anticipated improvements in a range of AI-related technologies, including computer vision and
software building on large language models. Hence, I consider the numbers here to apply to all of artificial
intelligence and thus typically refer to “AI”, unless there is a reason to emphasize generative AI.
Second, I focus on the US economy because much of the existing evidence on microeconomic effects of
AI and prevalence of exposed tasks is from the United States. The impact on other industrialized nations
should be similar, whereas the consequences for the developing world are harder to ascertain and require
much more in-depth research.
Third,somecommentatorsuse“productivity”torefertooutputperworker(oraveragelaborproductivity),
while others mean total factor productivity (TFP). Throughout, I distinguish between aggregate TFP and
GDP effects, and I use productivity improvement at the micro/task level as synonymous to cost savings.
1
accrued to firm owners and managers, while workers in impacted occupations experienced
negative outcomes (e.g., Acemoglu and Restrepo, 2020a). Could it be different this time?
Some experts and commentators are more optimistic. A few “proof-of-concept” experimen-
tal studies document nontrivial productivity gains from generative AI, largely driven by
improvements for less productive or lower-performing workers (e.g., Peng et al., 2023; Noy
and Zhang, 2023; Brynjolfsson et al., 2023), and this has prompted some experts to be cau-
tiously optimistic (Autor, 2024), while others are forecasting a “blue-collar bonanza” (The
Economist, 2023).
This paper uses the framework from Acemoglu and Restrepo (2018, 2019b, 2022) to pro-
vide some insights for these debates, especially relevant for the medium-term (about 10-year)
macroeconomic effects of AI. I build a task-based model, where the production of a unique
final good requires a series of tasks to be performed, and these tasks can be allocated to ei-
ther capital or labor, which have different comparative advantages. Automation corresponds
to the expansion of the set of tasks that are produced by capital (including digital tools and
algorithms). In this framework, AI-based productivity gains—measured either as growth of
average output per worker or as total factor productivity growth—can come from a number
of distinct channels (see Acemoglu and Restrepo, 2019a):
• Automation (or more precisely extensive-margin automation) involves AI models tak-
ing over and reducing costs in certain tasks. In the case of generative AI, various
mid-level clerical functions, text summary, data classification, advanced pattern recog-
nition, and computer vision tasks are among those that can be profitably automated.
• Task complementarity can increase the productivity in tasks that are not fully au-
tomated and may even raise the marginal product of labor. For example, workers
performing certain tasks may have better information or access to other complemen-
tary inputs. Alternately, AI may automate some subtasks, while at the same time
enabling workers to specialize and raise their productivity in other aspects of their job.
• Deepening of automation can take place, increasing the productivity of capital in tasks
that have already been automated. For example, an already-automated IT security
task may be performed more successfully by generative AI.
• New tasks may be created thanks to AI and these tasks may impact the productivity
of the whole production process.3
3Newtasksinthisframeworkalsocapturethepossibilityofproductivity-enhancingreorganizingproduc-
2
In this paper, I focus on the first two channels, though I also discuss how new tasks en-
abled by AI can have positive or negative effects. I do not dwell on deepening of automation,
because the tasks impacted by (generative) AI are quite different than those automated by
the previous wave of digital technologies, such as robotics, advanced manufacturing equip-
ment and software systems.4 I also do not discuss how AI can have revolutionary effects by
changing the process of science (a possibility illustrated by new crystal structures discovered
by the Google subsidiary DeepMind and recent neural network-enabled advances in protein
folding), because large-scale advances of this sort do not seem likely within the 10-year time
frame and many current discussions focus on automation and task complementarities.
I show that when AI’s microeconomic effects are driven by cost savings (equiva-
lently, productivity improvements) at the task level—due to either automation or task
complementarities—its macroeconomic consequences will be given by a version of Hulten’s
theorem: GDP and aggregate productivity gains can be estimated by what fraction of tasks
are impacted and average task-level cost savings. This equation disciplines any GDP and
productivity effects from AI. Despite its simplicity, applying this equation is far from trivial,
because there is huge uncertainty about which tasks will be automated or complemented,
and what the cost savings will be.
Nevertheless, as an illustrative exercise, I use data from a number of recent studies, in
particular, Eloundou et al. (2023) and Svanberg et al. (2024), as well as the experimental
studies mentioned above, to obtain some back-of-the-envelope numbers. Eloundou et al.
(2023) provide the first systematic estimates of what tasks will be impacted by generative
AI and computer vision technologies. Their methodology does not fully distinguish whether
the impact will take the form of automation or task complementarities, and does not provide
information on when we expect these impacts to be realized and how large their cost savings
will be.5 For computer vision technologies, Svanberg et al. (2024) provide estimates of what
fraction of tasks that are potentially exposed to AI can be feasibly automated in different
tion. The role of AI in enabling such reorganization is emphasized by, among others, Bresnahan (2019) and
Agrawal et al. (2023).
4Eloundou et al. (2023) report negative statistical associations between their measure of exposure to AI,
which I use below, and measures of exposure to robots and manual routine tasks.
5Morespecifically,IusethemostgranularinformationthatEloundouetal.(2023)present,whichistheir
“automation index”, coded with help from GPT-4. This index provides information on how much of the
activities involved in a task/occupation can be performed by AI. Although this index has somewhat greater
emphasis on automation, it does not systematically distinguish between automation and task complemen-
tarities. As I discuss further below and Eloundou et al. (2023) themselves note, their exposure measure
often captures the possibility that generative AI and related digital technologies can perform some of the
subtasks in an occupation, providing more time for workers to focus on other activities, and thus contains
both automation and task complementarity elements.
3
time frames.
I take Eloundou et al.’s estimates of tasks that are exposed to AI (without distinguishing
automation vs. task complementarities). I then aggregate this to the occupational level and
weight the importance of each occupation by its wage bill share in the US economy. This
calculation implies that 19.9% of US labor tasks are exposed to AI. I then use Svanberg et
al.’s estimate for computer vision tasks that, among all exposed tasks, 23% can be profitably
performed by AI (for the rest, the authors estimate that the costs would exceed the benefits).
I take the average labor cost savings to be 27%—the average of the estimates in Noy and
Zhang (2023) and Brynjolfsson et al. (2023)—and turn this into total cost savings using
industry labor shares, which imply an average total cost savings of 15.4%.
This calculation implies that total factor productivity (TFP) effects within the next 10
years should be no more than 0.71% in total—or approximately a 0.07% increase in TFP
growth annually. If we add bigger productivity gains from Peng et al. (2023), which are less
likely to be broadly applicable, or incorporate further declines in GPU costs, this number
still remains around 1%.
To turn these numbers into GDP estimates, we need to know how much the capital stock
will increase due to AI. I start with the benchmark of a rise in the capital stock proportional
to the increase in TFP. This benchmark is consistent with the fact that generative AI does
not seem to require huge investments by users (beyond those made by designers and trainers
of the models). With these investment effects incorporated, GDP is also estimated to grow
by around 1.1% over the next 10 years. When I assume that investments will be similar
to those for earlier automation technologies and use the full framework from Acemoglu and
Restrepo (2022) to estimate the increase in the capital stock, the upper bound on GDP
effects rises to 1.6 − 1.8%. Nevertheless, my framework also clarifies that what is relevant
for consumer welfare is TFP, rather than GDP, since the additional investment comes out
of consumption.6
I then argue that the numbers above may be overestimates of the aggregate productivity
benefits from AI, because existing estimates of productivity gains and cost savings are in
tasks that are “easy-to-learn”, which then makes them easy for AI. In contrast, some of
the future effects will come from “hard-to-learn” and hard for AI tasks, where there are
many context-dependent factors affecting decision-making, and most learning is based on the
behaviorofhumanagentsperformingsimilartasks(ratherthanobjectiveoutcomemeasures).
6For example, if AI models continue to increase their energy requirements, this would contribute to
measured GDP, but would not be a beneficial change for welfare.
4
Productivity gains in these hard tasks will be less—though, of course, it is challenging to
determine exactly how much less. Using a range of (speculative) assumptions, I estimate
an upper bound of 74% easy tasks among Eloundou et al.’s exposed tasks. I suppose that
productivity gains in hard tasks will be approximately one quarter of the easy ones. This
leads to an updated, more modest increase in TFP and GDP in the next 10 years that can
be upper bounded by 0.55% and 0.90%, respectively.
New tasks created with AI can more significantly boost productivity. However, some of
the new AI-generated tasks are manipulative and may have negative social value, such as
deepfakes, misleading digital advertisements, addictive social media or AI-powered malicious
computer attacks. While it is difficult to put numbers on good and bad new tasks, based
on recent research I suggest that the negative effects from new bad tasks could be sizable.
I make a very speculative attempt using numbers on the negative welfare effects of social
mediafromarecentpaperbyBursztynetal.(2023). Theseauthorsfindthatconsumershave
positive willingness to pay for using social media (in particular Instagram and TikTok) when
others are using it, but they would prefer that neither themselves nor others use it. Roughly
speaking, their estimates imply that revenue can increase by about $53 per user-month,
but this has a negative impact on total GDP/welfare equivalent to $19 per user-month.
Combining these numbers with an estimate of the fraction of activities that may generate
negative social value (in practice, revenues from social media and spending on attack-defense
arms races in IT security), I suggest that with more intensive use of AI, it is possible to have
nontrivial increases in GDP. For example, AI may appear to increase GDP by 2%, while in
reality reducing welfare by −0.72%.
I also explore AI’s wage and inequality effects. My framework implies that productivity
gains from AI are unlikely to lead to sizable wage rises. Moreover, even if AI improves
the productivity of low- and middle-performing workers (or workers with limited expertise
in complex tasks), I argue that this may not translate into lower inequality. In fact, I
show by means of a simple example how an increase in the productivity of low-skill workers
in certain tasks can lead to higher rather than lower inequality. Adapting the general
equilibrium estimates from Acemoglu and Restrepo (2022) to the setting of AI, I find that
the more intensive use of AI is unlikely to lead to substantial wage declines for affected
groups, because AI-exposed tasks are more evenly distributed across demographic groups
than were the tasks exposed to earlier waves of automation. Nevertheless, I estimate that
AI will not reduce inequality and is likely to have a negative effect on the real earnings of
5
low-education women (especially white, native-born women). My findings also suggest that
AI will further expand the gap between capital and labor income as a whole.
Finally, I argue that as originally suggested in Acemoglu and Restrepo (2018), more
favorable wage and inequality effects, as well as more sizable productivity benefits, will
likely depend on the creation of new tasks for workers in general and especially for middle-
and low-pay workers. While this is feasible in theory and I have argued elsewhere how it
could be achieved (Acemoglu, 2021 and Acemoglu et al., 2023), I also discuss why this does
not seem to be the focus of artificial intelligence research at the moment.
In sum, it should be clear that forecasting AI’s effects on the macroeconomy is extremely
difficult and will have to be based on a number of speculative assumptions. Nevertheless,
the gist of this paper is that a simple framework can discipline our thinking and forecasts,
and if we take this framework and existing estimates seriously, it is difficult to arrive at very
large macroeconomic gains.
The rest of the paper is organized as follows. The next section outlines the concep-
tual framework I use throughout the paper and derives a number of theoretical insights
on aggregate productivity gains, investment responses, and wage and inequality effects. It
also discusses the crucial distinction between easy-to-learn and hard-to-learn tasks and their
productivity implications, and introduces the contrast between good and bad new tasks.
Section 3 provides a preliminary quantitative analysis of new AI breakthroughs within this
framework. It first presents a baseline (upper bound) estimate on the basis of the fraction
of existing tasks that are likely to be impacted by AI within the next 10 years and existing
estimates of cost savings (productivity improvements) from AI. It then refines this estimate
by introducing the distinction between easy-to-learn and hard-to-learn tasks and undertakes
a preliminary classification of AI-exposed tasks into the easy and hard categories. I then
make an even more speculative attempt at incorporating the macroeconomic implications of
bad new tasks into this framework. Finally, I report estimates on the wage and inequality
implications of recent AI advances. Section 4 concludes with a general discussion, while the
Appendix, which is available upon request, includes additional information on how tasks are
classified into exposed and non-exposed and easy-to-learn and hard-to-learn categories.
2 Conceptual Framework
The model here builds on Acemoglu and Autor (2011) and Acemoglu and Restrepo (2018,
2019b, 2022), and I focus on the main elements of the framework, referring the reader to
6
these papers for further details and refinements. The economy is static and involves the
production of a unique final good, and all markets are competitive.7
The production of a unique final good takes place by combining a set of tasks, with
measure N, using the following production function
(cid:18)(cid:90) N (cid:19) σ−σ 1
σ−1
Y = B(N) y(z) dz , (1)
σ
0
where Y(z) denotes the output of task z for z ∈ [0,N], σ ≥ 0 is the elasticity of substitution
between tasks and the parameter B(N) depends on N to capture the possible system-wide
effects of new tasks, though in what follows I will suppress this dependence to simplify the
notation. For now, the elasticity σ can take any value, but it is reasonable to presume σ ≤ 1,
so that tasks are gross complements. I later set the elasticity of substitution between tasks
to σ (cid:39) 0.5, as estimated by Humlum (2023) and also imposed in Acemoglu and Restrepo
(2022).
Tasks can be produced using capital or labor according to the production function
y(z) = A γ (z)l(z)+A γ (z)k(z) for any z ∈ [0,N],
L L K K
where A and A are labor-augmenting and capital-augmenting productivity terms, γ (z)
L K L
and γ (z) are labor’s and capital’s task-specific productivity schedules, and l(z) and k(z)
K
denote labor and capital allocated to performing task z. This task production function
implies that capital and labor have different productivities in different tasks, but within a
task they are perfect substitutes.8
Throughout, Iassumethatγ (z)/γ (z)isincreasinginz, sothatlaborhasacomparative
L K
advantage in higher-indexed tasks. This implies that there exists some threshold I such that
tasks z ≤ I are produced with capital and those above this threshold are produced with
labor.
7Acemoglu and Restrepo (2018) provide a dynamic version of this economy with capital accumulation
and endogenous technological choices, while Acemoglu and Restrepo (2022) provide a generalization with
multiple types of labor and multiple sectors, and Acemoglu and Restrepo (2023) consider a non-competitive
version of this economy. Extending the framework in any of these directions has no effect on the results and
implications I explore here.
8One important simplification is to assume that tasks assigned to labor do not require any capital or
tools, which is clearly unrealistic. The online Appendix of Acemoglu and Restrepo (2018) shows that the
results are very similar if the task production function is modified such that:
y(z)=A γ (z)(cid:2) l(z)1−κk (z)κ(cid:3) +A γ (z)k(z),
L L C K K
where κ ∈ (0,1) and k (z) is labor-complementary capital in task z (while k(z) denotes capital used for
C
automating task z). Because κ < 1, tasks assigned to labor are still less intensive in capital than are
fully-automated tasks.
7
I normalize the total population to 1 and assume that different workers have different
unitsofeffectivelabor. Tosimplifythediscussion, Iassumethattherearetwotypesoflabor,
high-skill and low-skill, and there is no comparative advantage difference between these two
typesoflabor(Ireturntocomparativeadvantagelater). Theonlydifferenceisthathigh-skill
labor, which makes up a fraction φH of the population, has λH units of effective labor, while
the remaining φL = 1−φH low-skill labor has only λU < λH units of effective labor. This
specification ensures that both high-skill and low-skill workers could be performing some of
the same tasks. It also implies that wage inequality is pinned down by λH/λU—a feature I
relax later.
I also assume that all labor is supplied inelastically, so I write the total supply of labor
as
φUλU +φHλH = L.
The labor market-clearing condition is
(cid:90) N
L = l(z)dz, (2)
0
and I denote the wage rate by w.
Capital is specialized for the tasks in which it is used, and I assume that capital of type
z is produced linearly from the final good with unit cost
R(z) = R(K)ρ(z), (3)
where
(cid:90) N
K = k(z)dz
0
is the overall capital stock of the economy. All firms take the cost of capital for task z, R(z),
as given. The first term in (3) implies that the required rate of return on overall capital can
increase when the capital stock of the economy is larger and the second term is task-specific,
representing the possibility that different types of capital could have different costs. For
tasks that are not yet technologically automated—meaning that they cannot be produced
by capital—we can either set γ (z) = 0 or take ρ(z) to be very large.
K
Finally, I assume that there exists a (non-satiated) representative household that con-
sumes the final good (net of capital expenditures).
2.1 Equilibrium
I focus on a competitive equilibrium, which satisfies the following usual conditions:
8
• The allocation of tasks z ∈ [0,N] is cost-minimizing. That is, task z ∈ [0,N] is
produced by labor if and only if
w R(z)
< .
A γ (z) A γ (z)
L L K K
• The amount of capital k(z) is chosen to maximize Y −R(z)k(z), where Y is given as
in (1) and the overall capital stock of the economy K is taken as given.
• The labor market clears. That is, (2) holds.
Notice that the first condition imposes an innocuous tie-breaking rule that when indiffer-
ent, firms use capital for performing a task. Given this tie-breaking rule, all tasks z > I will
be performed by labor (i.e., l(z) = 0 for all z ≤ I and k(z) = 0 for all z > I). Whether this
is high- or low-skill labor is indeterminate in the baseline model, so I focus on the overall
amount of effective labor units.
In a competitive equilibrium, all tasks performed by labor must have
Bσ− σ1 A Lσ− σ1 γ L(z)σ− σ1 l(z)− σ1 Y σ1 = w. (4)
This implies that for any two tasks z > I and z(cid:48) > I,
l(z) γ (z)σ−1
L
= . (5)
l(z(cid:48)) γ (z(cid:48))σ−1
L
Notice that when σ < 1, less labor is allocated to tasks in which labor’s productivity is
higher—a feature whose implications I will emphasize later. Equation (5), combined with
the labor market-clearing condition (2), implies
γ (z)σ−1
L
l(z) = L. (6)
(cid:82)N
γ (z)σ−1dz
I L
Moreover, with a similar reasoning for any task z < I, only capital is used, and the
first-order condition for capital is simply
Bσ− σ1 A Kσ− σ1 γ K(z)σ− σ1 k(z)− σ1 Y σ1 = R(K)ρ(z). (7)
Combining (6) and (7) with (1), GDP or total output can be written as
  σ
(cid:16) (cid:17)1 σ−1
(cid:82)N γ (z)σ−1dz σ (BA L)σ−1
 I L L σ 
Y =  (cid:18) (cid:19)  . (8)
 1− (cid:82)I (cid:16) γK(z) (cid:17)σ−1 dz Aσ−1Bσ2−1
0 R(K)ρ(z) K σ
9
The denominator here is due to the roundabout nature of production, and I assume that
(cid:32) (cid:33)
(cid:90) I (cid:18) γ K(z) (cid:19)σ−1
dz
Aσ−1Bσ2−1
< 1 (9)
σ
R(K)ρ(z) K
0
to ensure that output is finite in this economy. (Otherwise, because output linearly produces
machines, which then produce output, overall output can reach infinity). With an identical
argument to that in Acemoglu and Restrepo (2022), an equilibrium exists and is unique,
provided that (9) is satisfied.
2.2 How AI Could Affect Production
Before completing the characterization of equilibrium, I discuss how AI could affect produc-
tion in this economy.
1. AIenablesfurther(extensive-margin)automation,increasingI. Suchautomationcould
be triggered either because AI reduces the cost of capital for some marginal tasks
(i.e., tasks slightly above I) or increases the effectiveness of machinery or algorithms
performing some marginal tasks, thus raising γ (z) for some z above I. Obvious
K
examples of this type of automation include generative AI tools such as large language
models (LLMs) taking over simple writing, translation and classification tasks as well
assomewhatmorecomplextasksrelatedtocustomerserviceandinformationprovision,
or computer vision technologies taking over image recognition and classification tasks.
2. AI can generate new task complementarities, raising the productivity of labor in tasks
it is performing. For example, AI could provide better information to workers, directly
increasing their productivity. This possibility could be modeled as AI reducing the cost
of complementary capital k (z) in some tasks z > I in the more general formulation in
C
footnote 8. Alternatively, AI could automate some subtasks (such as providing ready-
made subroutines to computer programmers) and simultaneously enable humans to
specialize in other subtasks, where their performance improves. This channel would
requiretheexplicitmodelingoftherangeofsubtasksmakingupeachtask. Inthiscase,
new AI technologies would perform some of these subtasks and do so with sufficiently
high productivity, so that the subtask-level displacement would be weaker than the
productivity gains, expanding the demand for labor and the marginal productivity
of labor in these tasks. The logic of the productivity effect being larger than the
displacement effect is the same as in the basic models of automation, as exposited
10
in Acemoglu and Restrepo (2018, 2019b). Even more interestingly, AI may enable
workers to specialize in the non-automated subtasks and raise their expertise in these
activities (e.g., when humans spend less time in writing standard subroutines, they
can become better at other parts of programming). I represent task complementarities
by an increase in γ (z) in some tasks z ≤ I, or when they happen in all tasks, by an
L
increase in A .
L
3. AI could induce deepening of automation—meaning improving performance, γ (z), or
K
reducingcosts, ρ(z), insomepreviouslycapital-intensivetasks(tasksz ≤ I). Examples
include IT security, automated control of inventories, and better automated quality
control (see Acemoglu and Restrepo, 2019a).
4. AIcangeneratenew labor-intensive products or tasks, whichcorrespondstoanincrease
in N. As argued in Acemoglu and Restrepo (2020b), Acemoglu (2021) and Acemoglu
et al. (2023), there are many pathways for such new tasks. Later I discuss the case
where some of these new products and tasks can be manipulative and have negative
social value.
The effects of new AI tools will depend on the extent of each one of these effects, and I
will try to provide more specificity on these possibilities later. In the rest of this section, I
will derive the consequences of different effects of AI.
2.3 Equilibrium Wages and Comparative Statics
As a first step, let us combine (4) and (6), so that the equilibrium wage can be expressed as
w =
(cid:18) Y (cid:19) σ1
(BA
)σ−1
(cid:18)(cid:90) N
γ
(z)σ−1dz(cid:19) σ1
. (10)
L σ L
L
I
This equation is intuitive. The first term shows that the wage is proportional to labor
productivity (raised to the power 1/σ), and the second term captures the contribution to
the marginal productivity of labor coming from Hicks-neutral and labor-augmenting tech-
nologies, while the third term represents the contribution of the allocation of tasks to the
marginal productivity of labor. The effect of any small technological change (potentially
altering multiple dimensions of the production technology, such as B; A and A ; γ (z) and
L K L
γ (z); and I and N) can then be written as:
K
1 (cid:18) Y (cid:19) σ −1 1 (cid:18)(cid:90) N (cid:19)
dlnw = dln + (dlnB +dlnA )+ dln γ (z)σ−1dz . (11)
L L
σ L σ σ
I
11
The effect of an extensive-margin automation—an increase in I—is given by
dlnw 1 dlnY 1 γ (I)σ−1
L
= − .
(cid:16) (cid:17)
dI σ dI σ (cid:82)N
γ (z)σ−1dz
I L
Ingeneral, thisexpressionhasambiguoussign, soautomationcanreducewages. Morespecif-
ically, there are two opposing effects (Acemoglu and Restrepo, 2018, 2019b): (a) automation
always produces a positive effect on wages (and labor demand) because it increases produc-
tivity (or equivalently, reduces costs). This positive productivity effect is represented by
the first term; (b) simultaneously, automation displaces workers from the tasks they used to
perform. The negative displacement effect is represented by the second term. In the special
case where R(K) is constant, it can be verified that automation increases wages. This is not
the case, in general, when R(K) is increasing, "
309,mit_edu,pdf.pdf,"Harvard Data Science Review • Special Issue 5: Grappling With the
Generative AI Revolution
Institutional Eorts to
Help Academic
Researchers Implement
Generative AI in Research
1 1,2
Jing Liu H. V. Jagadish
1Michigan Institute for Data Science, University of Michigan, Ann Arbor, Michigan, United States of
America,
2Division of Computer Science and Engineering, College of Engineering, University of Michigan,
Ann Arbor, Michigan, United States of America
Published on: Feb 26, 2024
DOI: https://doi.org/10.1162/99608f92.2c8e7e81
License: Creative Commons Attribution 4.0 International License (CC-BY 4.0)
Harvard Data Science Review • Special Issue 5: Grappling With the Institutional Eorts to Help Academic Researchers Implement
Generative AI Revolution Generative AI in Research
ABSTRACT
The scale and speed of the generative AI (artificial intelligence) revolution, while offering unprecedented
opportunities to advance science, is also challenging the traditional academic research model in fundamental
ways. The academic research model and academic institutions are not set up to be nimble in the face of rapidly
advancing technologies, and the task of adopting such new technologies usually falls on individual researchers.
Excitement about the opportunities that generative AI brings is leading to a rush of researchers with various
levels of technical expertise and access to resources to adopt this new technology, which could lead to many
researchers ‘reinventing the wheel’ and research outcomes lacking in ethics, rigor, and reproducibility. This
problem not only applies to generative AI, but could also be true for other upcoming and similarly disruptive
technologies. We argue that the current norm of relying on individual researchers for new technology adoption
is no longer adequate. It is time that academic institutions and their research organizations such as our own (the
Michigan Institute for Data Science) develop new mechanisms to help researchers adopt new technologies,
especially those that cause major seismic shifts such as generative AI. We believe this is essential for helping
academic researchers stay at the forefront of research and discovery, while preserving the validity and
trustworthiness of science.
Keywords: institutional transformation, best practices, training, academic researcher, rigor and reproducibility,
institutional support
1. Problem Statement and Solution Proposition
Generative AI (artificial intelligence) is a type of AI algorithm that can generate new content (such as text,
images, audio, video, and other modalities) that is statistically probable based on the data that the algorithm is
trained on (Bommasani et al., 2021; Cao et al., 2023; Dwivedi et al., 2023; Gozalo-Brizuela & Garrido-
Merchan, 2023; Vaswani et al., 2017). Compared to other types of AI technology, such as natural language
processing, generative AI is based on newer AI architectures, most notably transformers and diffusion models,
trained on enormous volumes of (sometimes multimodal) data in their natural forms (such as raw texts and
images from the internet) without the need of labeling the training data. Generative AI thus opens up enormous
possibilities to revolutionize how AI assists humans in all types of activities that involve interacting with a
computer.
1.1. Opportunities with Generative AI
The emergence of generative AI has tantalized academic researchers with its potential to vastly accelerate
research, and even to enable new research, in multiple ways (Boyko et al., 2023; Dwivedi et al., 2023;
Microsoft Research AI4Science and Microsoft Azure Quantum, 2023; Morris, 2023; Wang et al., 2023).
2
Harvard Data Science Review • Special Issue 5: Grappling With the Institutional Eorts to Help Academic Researchers Implement
Generative AI Revolution Generative AI in Research
1. The use of domain-agnostic generative tools (such as text and image generation) to improve research
productivity, by assisting with routine tasks such as drafting and editing emails and manuscripts, checking
for compliance, and facilitating the communication with the lay audience.
2. The use of domain-agnostic generative AI to enhance the research expertise of individual researchers and
research teams. This includes summarizing and representing knowledge within disciplines, gathering
interdisciplinary insights, and supporting communication for interdisciplinary collaboration.
3. The use of domain-agnostic and domain-specific generative AI to accelerate and automate the research
process, such as data cleaning, formatting, and imputation; suggesting research hypotheses and selecting
experimental parameters; coding, data analysis, and visualization.
4. The use of domain-specific generative models, such as for aerospace engineering or protein structure
models, to enable new paths for research discovery.
Such possibilities are fueling researchers’ enthusiasm for incorporating generative AI in research, even though
most of generative AI’s potential benefits for research remain to be tested and validated. Of the four types of
generative AI use that we mention, the use of domain-specific models has been reported extensively (as
examples, see Andrade & Walsh, 2023; Chenthamarakshan et al., 2023; Grisoni et al., 2021; Gu et al., 2023;
Hie et al., 2023; Madani et al., 2023; Zeng et al., 2022). But successes of the first three types of generative AI
use in research are only beginning to be reported (see the following examples: Boiko et al., 2023; Ciucă &
Ting, 2023; Jablonka et al., 2023; Lyu et al., 2023; Mahjour et al., 2023). This enthusiasm is also accompanied
by a lack of preparedness among researchers. In the academic research environment, many faculty members
have no concrete idea about how to implement generative AI in their research, or even how to work with
generative AI at all, including simply using prompts to query information.
Many also do not know a good starting point because new generative AI tools emerge almost daily and there is
not an obvious path of skills progression. A survey that we conducted in November of 2023 of 60 faculty
affiliates of the Michigan Institute for Data Science (MIDAS) (Table 1) gives us a glimpse of this picture. Only
12% of the respondents have the expertise to train their own generative AI models; fewer than one-third can
run existing models or fine-tune models. Even after ChatGPT, which is supposed to be an easy-to-use tool,
became available for almost a year, half of all respondents are not able to use prompts with ChatGPT to obtain
good results. The faculty members’ biggest need is to develop skills through training and learning from peers.
This closely mirrors a brief survey that we conducted in the summer of 2023 with MIDAS faculty, in which
70% of the 92 respondents indicated that they had no knowledge or only conceptual understanding (as opposed
to hands-on practice) with generative AI. We believe this is representative of the academic research scene at
this moment across institutions.
Table 1. Faculty researchers’ expectations and current preparedness for Generative AI to aid
academic research (n = 60).
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How do you want to use generative AI in your research? Improving productivity (drafting documents, summarizing
documents, etc): 72%
Coding: 63%
Data analysis and modeling: 55%
Communication (email, presentation, etc.): 45%
Helping with data generation, processing, and documentation: 38%
What is the skill level in your research group with regard to Can use things like ChatGPT with prompts, but not using them
generative AI? well yet: 47%
Can use things like ChatGPT with prompts and can get some good
results: 48%
Can run existing models: 28%
Can fine tune existing models: 22%
Can train models: 12%
What support is important for you to use generative AI in your Technical tutorials: 68%
research?
Connecting with other researchers exploring GenAI to learn from
each other: 60%
Brainstorming sessions to develop project / grant ideas: 51%
Finding collaborators on grants and projects: 42%
Finding students: 42%
1.2. Concerns about Generative AI
The enthusiasm and the unpreparedness are naturally accompanied by researchers’ concerns about using
generative AI. Some concerns are common among generative AI users in many lines of work, and include
issues such as data privacy and confidentiality, the biases that the models inherit from the training data, the AI
confabulation or hallucination, the opacity of data and training algorithms to the users of generative AI models,
thus the inability to assess whether a model is appropriate for a certain type of use (Birhane et al., 2023;
Liebrenz et al., 2023; Ray, 2023; Zhuo et al., 2023). In addition, there are also concerns specific to using
generative AI for scientific research. The rigor and reproducibility of research with generative AI in the
workflow has already become a major consideration. Any research with AI models that are not developed
locally, and without transparency of data and algorithms, poses fundamental challenges throughout the research
workflow, from study design and data query all the way to results validation (Li et al., 2023; Sohn, 2023;
Spirling, 2023). Many researchers are already aware of such issues to various degrees. For example, stories
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about generative AI hallucinating citations are shared widely. But there is very little discussion yet of how well
generative AI systems do in coming up with research hypotheses that are creative, testable, and of practical
value. So it remains to be seen how the benefits of generative AI in research weigh against the negative
consequences.
None of these concerns are new to academic researchers. There are always hopes and fears when a new
technology emerges with the promise of transforming research and people wonder how best to adopt it for
research innovation while upholding research integrity. These concerns, however, are amplified in the case of
generative AI because of how quickly new AI systems are developed, while our understanding of the functions
and limitations of these systems is still very limited (Bengio et al., 2023; Bommasani et al., 2023). These issues
are further exacerbated when researchers at all skill levels rush to adopt generative AI methods in their
research and there is not a standard or process for model selection or for quality control of the model use. What
we will almost surely witness, then, will be a flood of research outcomes and publications of uncertain quality
using generative AI, which will likely distract scientists from doing good research in the short term and may
even have long-term impacts. Academic researchers are quite aware of these challenges. In fact, at a generative
AI faculty workshop in the summer of 2023 (see more description in Section 2.2), the concerns of the attendees
were reflected in the following specific topics:
A. Understanding model output, upholding research rigor and reproducibility.
How to think about research rigor and reproducibility when there is lack of transparency of generative AI
models, and when the model output depends on the specific prompts.
How to assess the novelty of the model’s output.
How to identify and correct bias, misinformation, or erroneous training data and in model outputs.
How to think about data provenance and governance with generative AI models.
How to quantify uncertainty of model outputs.
B. Understanding issues of ethics, authorship, copyright, and privacy.
How to cite, acknowledge, and report generative AI in research work.
How to assess issues related to copyrighted training data, and model outputs based on copyrighted training
data.
How to assess data privacy and confidentiality issues when researchers have little knowledge about the
training data.
How to assess the balance between privacy / confidentiality and the need for data and model transparency.
Patent issues if a research idea is first suggested by generative AI.
C. Technical and infrastructure considerations with the use of generative AI in research.
Choosing a model and comparing models for a particular research question.
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Fine-tuning models locally and the local resources needed for this.
Keeping up-to-date knowledge of generative AI models.
1.3. We Need a New Model for Institutional Transformation
It is obvious to us that it is not feasible, or at least highly inefficient, if individual researchers are expected to
address such issues themselves not only because most lack the expertise, effort, and resources, but also because
they would each be reinventing the wheel. The typical researcher learns to use a new research method on their
own or through their collaborators, and gets pointers to resources from someone they happen to interact with.
This, somewhat random, social diffusion will not be sufficient when they need to acquire skills with a new
technology overnight and put it to immediate research use, and also goes against the nationwide drive to ensure
equitable access to AI technologies (National Artificial Intelligence Research Resource Task Force, 2023). It is
also virtually impossible for researchers to individually assess model quality, validity, and reliability, leading to
at least some guesswork in adoption and implementation choices.
We believe a new model of enabling the adoption of rapidly emerging technologies is sorely needed at this
point, and we believe academic institutions and their research centers should play a critical role. Universities
are already responsible for providing the research infrastructure, such as computing centers and research cores
for scientific instrumentation, and supporting resource-intensive, large-scale, and high-throughput research.
They should also be responsible for enabling the adoption of new technologies in research. Indeed, many
universities are already keenly aware of the importance of generative AI and are already developing capacity,
such as computing resources. The University of Michigan, for example, has just launched UMGPT, which
provides a relatively secure environment for campus use, including research use. Some institutions are also
training domain-specific generative AI models for academic research such as OLMo (Open Language Model)
and the GatorTron (Yang et al., 2022).
However, these are not enough. We believe that the emergence of generative AI is a call for universities, as the
home of new knowledge and the home of academic researchers, to play a much more active role in enabling
academic researchers to develop new skills and adopt new research methods in ethical, responsible, and
effective ways. This will likely have long-lasting benefits to research and discovery. Universities, however, are
not set up to be nimble in ways that some businesses can be in response to new technology developments and
‘market trends.’ So what can be done?
We advocate for university-level research institutes to fill this need and help complete a solution–
implementation–outcome process that will help academic researchers adopt new technologies or research
standards (solutions) to achieve better research innovation and outcomes (Figure 1). While it is difficult to
imagine an entire university being nimble in the face of an emerging technology, an organization within a
university can be so. Universities often set up a research institute to advance a research area of importance.
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Indeed, there are many examples of institutes that have spearheaded research in a ‘hot’ area and risen to well-
deserved prominence for their work in advancing the frontier, particularly in interdisciplinary areas.
But we believe there can be a very different role for a research institute at a university, which can have an even
greater impact on science: to serve as a knowledge base and facilitator for the adoption of new methods that
have the potential to transform research across a range of disciplines. Such methods frequently arise in fields
such as data science and AI. generative AI is perhaps the best example because of its applicability in almost
every line of work and its fast pace of advancement. But it surely is only one of the very first technologies that
could bring sweeping changes. Hence, what we advocate for, supporting the adoption of generative AI in
research, will be equally relevant for future waves of new technologies. In other words, academic research
institutes can play a significant role in institutional transformation by developing and disseminating tools,
training researchers, and establishing best practices, all of which are essential for researchers to swiftly adopt
new technologies to stay at the forefront of research and innovation.
Figure 1. Universities should play a significant role to help researchers adopt new
technologies and guidelines for research innovation.
In the next section, we describe some of the work that we have already started to develop in this new role for
our institute. The work is still very preliminary, given that we and the researchers that we support are still at the
initial stage of understanding myriad considerations associated with generative AI. But it provides a starting
point for further discussion on the institutional effort needed for adopting new technologies in academic
research.
2. Supporting Academic Researchers Through Research
Incubation, Training, and Best Practices
The Michigan Institute for Data Science (MIDAS) at the University of Michigan (U-M) has been investing
effort for institutional transformation over the past few years, with an initial focus on technical skill
development and rigor and reproducibility in data-intensive research. As U-M’s focal point of data science and
AI research, the central goal of MIDAS is to enable the transformative use of data science and AI methods for
both scientific and societal impact, across an enormous array of disciplines with wildly different
epistemological approaches and data use practices.
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Among its many threads of work in enabling research, providing training, and building research collaboration,
one component is to teach new research methodology to faculty and staff researchers through a set of summer
academies that introduce data science and AI research skills from the beginning level to advanced topics.
These summer academies started as an experiment because we were uncertain of the needs; but in the past
three years, the offering expanded from one week-long bootcamp per year to multiple week-long sessions, and
has trained nearly 300 faculty and staff researchers. Our experience demonstrates that faculty and staff
researchers need such opportunities to systematically learn new research methodologies.
MIDAS’s effort to improve rigor and reproducibility focuses on filling another important gap (Liu et al., 2022).
Many journals, funding agencies, and professional societies have developed clear guidelines, requirements, and
incentives for research rigor and reproducibility. Many researchers have a reasonable understanding of the
issue and know what outcomes are expected from them. But the reproducibility problem remains serious,
especially for data-intensive research that has a long and complex workflow (Hardwicke et al., 2021;
Laurinavichyute et al., 2022; Stodden et al., 2018). Through collaboration with the university’s research
community, MIDAS has coordinated grassroots efforts and developed online resources and training to enable
rigor and reproducibility in data-intensive research. The MIDAS reproducibility online resource hub has had
more than 10,000 visits. MIDAS is now developing a nationwide training program for faculty and staff
scientists, funded by the National Institutes of Health, on improving the rigor and reproducibility of data-
intensive research.
More importantly, through this work we have come to realize that a major gap in the researchers’ efforts to
improve reproducibility is that they often lack the means or the expertise to translate guidelines into outcomes.
In other words, researchers need to be handed validated methods/tools and know how to use them in order to
complete the solution–implementation–outcome process (Figure 1). In this case, the solution is the
reproducible research guidelines; the outcome is more reproducible research; and the implementation is the
phase where researchers are equipped with appropriate tools and processes.
Such previous work has developed the mindset at MIDAS that allowed the team to plunge into action when
generative AI ‘stormed’ the world stage. Since early 2023, MIDAS has started developing best practice
guidelines, coordinating the exploration of generative AI for research, and providing training for researchers.
2.1. Developing Guidelines
Just like the researchers themselves, almost all research organizations are scrambling to cope with generative
AI and its regulation, which changes quickly. Guidelines in addition to researchers’ discretion are essential
because generative AI’s use in research is fraught with issues every step of the way, from whether the training
data is appropriate for a particular type of research to the validation of output. Its use to improve productivity
can also be tangled with additional issues such as confidentiality and copyright. The National Institutes of
Health and the National Science Foundation, for example, have already formally forbidden the use of
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generative AI in grant proposal review (National Institutes of Health, 2023; National Science Foundation,
2023). Many journals, such as Nature and Science, also prohibit certain types of usage of text and images
created by generative AI (Flanagin et al., 2023; Harker, 2023). Understanding what they are or are not allowed
to do is an additional challenge for researchers. We expect many such guidelines and that they will evolve
quickly with time.
To provide a starting point for researchers, MIDAS compiled a set of guidelines that include the following
topics:
Writing with generative AI
Can I use generative AI to write research papers?
Can I use generative AI to write grants?
Can I use generative AI to help me when I write a literature review section for my paper?
Can I use generative AI to write nontechnical summaries, create presentations, and translate my work?
Using generative AI to improve productivity
Can I use generative AI to review grant proposals or papers?
Can I use generative AI to write letters of support?
How can I use generative AI as a brainstorming partner in my research?
Using generative AI for data generation and analysis
Can I use generative AI to write code?
Can I use generative AI for data analysis and visualization?
Can I use generative AI as a substitute for human participants in surveys?
Can I use generative AI to label data?
Can I use generative AI to review data for errors and biases?
Reporting the use of generative AI
How do I cite contents created or assisted by generative AI?
How do I report the use of generative AI models in a paper?
Considerations for choosing generative AI models
How do I decide which generative AI to use in research?
Open source
Accuracy and precision
Cost
What uniquely generative AI issues should I consider when I adopt generative AI in my research?
Ethical issues
Bias in data
AI hallucination
Plagiarism
Prompt engineering
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Knowledge cutoff data
Model continuity
Security
We selected these topics based on our discussions with researchers in our community. We are updating the
guide several times a month as new guidelines are published from federal agencies, funding agencies,
professional societies, and journals.
2.2. Demonstrating the Use of Generative AI in Research and Exploring Possibilities
Many academic researchers may have only tried using ChatGPT to draft an email or to edit some texts, but
most of them are aware of the possibility of using generative AI to do much more and to accelerate research
and enable new research ideas in many other ways. However, how this can be done is still elusive. For
example, many have heard that generative AI can be used to summarize research literature. However,
successful implementations are still very few, and researchers are concerned with many issues associated with
such use, such as the indiscriminate inclusion of published work with poor quality or that is irreproducible, and
bias against work in non-English languages. Many researchers are also aware that generative AI can help with
data analysis, but what skills researchers need to have in order to ensure that the analysis is correct is also
unclear to many. Domain-specific generative AI models have been used for protein structure research, drug
design, material science, and many other fields of inquiry, yet many researchers are unclear what special skills
and data are needed to train and deploy such models. Exposing researchers to successful examples, therefore,
has been one of our top priorities.
MIDAS organized a faculty workshop in the summer of 2023 with 92 U-M faculty attendees. Twelve speakers
demonstrated how they incorporated generative AI in research (health care research, chemistry, social science,
arts and design), and discussed ethical and technical considerations as well as infrastructure challenges. The
attendees participated in a few rounds of breakout discussions focusing on how generative AI can be used in
research to improve productivity, significant research questions that can be boosted with generative AI, and
ethical and technical challenges. The attendees came from 45 academic units at the university, with a diverse
range of research areas (Table 2). Such diverse participation is a strong indicator of the widespread interest in
generative AI.
Table 2. Research fields represented by the attendees of the faculty workshop on how to use
generative AI in research.
Arts and Design Biological Science Engineering
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Architecture and Urban Planning Biostatistics Aerospace Engineering
Arts and Design Physiology Chemical Engineering
Civil and Environmental Engineering
Electrical Engineering and Computer
Science
Industrial and Operations Engineering
Mechanical Engineering
Nuclear Engineering and Radiological
Sciences
Robotics
Environmental and Earth Sciences Medical Science Math and Physical Science
Environment and Sustainability Anesthesiology Chemistry
Climate and Space Science and Cardiac Surgery Mathematics
Engineering
Computational Medicine and Physics
Bioinformatics
Statistics
Internal Medicine
Kinesiology
Learning Health Sciences
Ophthalmology
Pediatrics
Pharmacy
Psychiatry
Radiation Oncology
Social Science and Business
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Business
Communications and Media
Information
Political Science
Public Policy
Also in summer, 2023, MIDAS organized a webinar series, Generative AI Coast-to-Coast (C2C), together with
Johns Hopkins University, Rice University, the International Computer Science Institute, The Ohio State
University, and University of Washington. The webinars featured eight speakers from the six institutions on
“Generative AI in Healthcare,” “Generative AI in the Lab,” “Policy, Ethics and Generative AI,” and “An
Under the Hood Look at Generative AI: Potentials and Pitfalls.” The goal of the series was also to demonstrate
the successful implementation of generative AI in research, build collaboration, and point out the cautions to
take.
2.3. Developing Technical Skills
Based on our faculty survey, receiving training is their top priority regarding generative AI. Thus, MIDAS has
offered a series of hands-on tutorials as the starting point for academic researchers. The focus was not domain-
specific generative AI models trained on technical data, such as protein structures; instead, the focus was on
using generative AI, including large language models (LLM), in domain-agnostic ways. The topics included:
Writing, planning, and literature review: enhancing professional productivity with generative AI
Code smarter, not harder: harnessing generative AI for research programming efficiency
Integrating generative AI into your research workflow: using image generation as the example
Making generative AI better for you: fine-tuning and experimentation for custom research solutions
However, feedback from workshop attendees and researchers in our community indicated that we should have
started from an even more basic place and be as hands-on as possible. As shown in Table 1, the vast majority
of researchers would like to use generative AI to improve productivity, but only half are getting reasonable
results even when using ChatGPT with prompts. Therefore, we are planning a few new tutorial sessions,
making them more hands-on, and focusing on more basic tasks. The topics will include:
Improving general productivity with ChatGPT: non-research writing (emails, posters and presentations,
checking for compliance, letters of recommendation, translation)
Finding, synthesizing, and summarizing literature with LLM
Generating simulated data with LLM
Data analysis and visualization with ChatGPT (text, image, and numeric data)
Drafting research articles with ChatGPT (drafting, writing, and formatting bibliographies)
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2.4. What Is Next
During this set of work in the past few months, and through discussions with our research community, it is
increasingly obvious to us that the support MIDAS is providing to academic researchers for generative AI is, in
essence, filling the same ‘implementation’ gap that we identified in the solution–implementation–outcome
model for research reproducibility (Table 3). In both cases, researchers know that there is a solution for them to
do better research: incorporating generative AI in research, or following guidelines for research reproducibility.
They also know in both cases what the ideal outcome should be: accelerated research and innovation, and more
rigorous and reproducible research. However, in both cases the gap is that researchers are left to their own
devices to implement the solution to achieve the outcome. In both cases, the effort from MIDAS can have a
significant impact to fill the implementation gap.
Table 3. The role of academic research institutes to fill the implementation gap to achieve
institutional transformation.
Adopting Generative AI for Research Improving Research Reproducibility
Solution Generative AI as a powerful tool to Guidelines for improving research
accelerate the research process, and develop reproducibility.
previously infeasible research.
Implementation gap Most researchers do not have skills and Most researchers do not have methods/tools
resources to swiftly and responsibly adopt at their disposal, and do not have the skills
generative AI in research. and resources to develop their own tools to
improve research reproducibility.
The role of academic research institutes Identify researchers’ needs, develop Identify researchers’ needs, develop
guidelines, standard processes and tools to standard processes and tools to improve
adopt generative AI in research, and enable research reproducibility and enable the wide
the wide adoption of such processes and adoption of such processes an"
310,mit_edu,pdf.pdf,"Forrester’s Top 10 Emerging Technologies For 2024: GenAI, TuringBots, And IoT Security Poised To
Deliver The Fastest ROI
June 25, 2024
The rapid acceleration of AI innovation has sparked a surge in advancement in other emerging technologies
CAMBRIDGE, Mass.--(BUSINESS WIRE)--Jun. 25, 2024-- According to Forrester’s (Nasdaq: FORR) The Top 10 Emerging Technologies In 2024
report, generative AI (genAI) for visual content, genAI for language, TuringBots, and IoT security are the top emerging technologies that will deliver the
most immediate ROI for businesses in 2024 and beyond.
With new technologies emerging seemingly every day, business and technology leaders need to time those investments based on value, risk, and
potential payout timelines. Forrester organizes its top emerging technologies by benefit horizon to help with these decisions.
Emerging technologies that will offer significant benefits within the next two years:
GenAI for visual content. Advanced machine learning models that generate images or video from text, audio, or video
prompts, this technology will help firms generate visual content for marketing, experiences, and products.
GenAI for language. GenAI for language is already delivering value in customer support and content creation but
continues to advance at a blinding pace. It is accelerating many other technologies as it goes.
TuringBots. Accelerated by advancements in genAI for language, these AI-powered software robots help developers build
applications that deliver more than just code generation.
IoT security. The proliferation of devices has led to an exponential explosion in security attacks, raising the importance of
security for IoT devices. Vendors are competing and colliding in a rush to offer capabilities.
Midterm emerging technologies that will deliver benefits in the next two to five years:
AI agents. The role of autonomous workplace assistants or AI agents has expanded beyond the back office and employee
assistance to customer-facing automation. These AI agents will grow increasingly sophisticated to better understand and
respond to nuance and context.
Autonomous mobility. This technology will accelerate commercial and urban transportation ecosystem collaborations to
orchestrate personalized mobility experiences for both customers and businesses.
Edge intelligence. Advanced edge intelligence capabilities such as edge machine learning are still not yet common, even
though many foundational elements like Apple foundation models are becoming available.
Quantum security. This technology will overhaul security systems for on-premises and cloud compute, storage and
network infrastructure, commercial off-the-shelf software, commercial software-as-a-service offerings, and in-house built
software.
Emerging technologies that will take at least five more years to deliver tangible value for most firms and use cases:
Extended reality (XR). Only 8% of US online adults own a virtual-reality headset, and just 16% have used an augmented-
reality device or app. While XR is advancing in training and onboarding, companies are resisting investing in tools like
these until they see broad adoption.
Zero Trust edge (ZTE). ZTE technology has the potential to protect remote workers, retail outlets, and branch offices with
embedded local security, but only a handful of true ZTE solutions exist today, and legacy devices add additional
management complexity.
“Tech leaders must be able to identify the right use cases and quantify potential benefits, costs, and risks across multiple horizons,” says Brian
Hopkins, Forrester VP, emerging tech portfolio. “They need to spread investments out, with shorter-term technologies delivering quick returns and
longer-term bets requiring more effort, more foundational investment, and the capacity to manage more risk.”
Resources:
Learn more about the top emerging technologies that will deliver ROI for enterprises.
Read The Top 10 Emerging Technologies In 2024 report to gain insight into maturity trends, business value, important use
cases, and risks associated with each technology (client access required).
Register for a complimentary webinar that takes a deep dive into the 2024 list of emerging technologies and their use
cases and benefits.
Download Forrester’s complimentary guide for tech leaders to identify use cases and gain support for emerging
technologies.
Tech and data leaders can attend Forrester’s 2024 Technology & Innovation Summits in North America, EMEA, and Asia
Pacific to learn how to align their IT strategy to their business goals to accelerate growth.
About Forrester
Forrester (Nasdaq: FORR) is one of the most influential research and advisory firms in the world. We help leaders across technology, customer
experience, digital, marketing, sales, and product functions use customer obsession to accelerate growth. Through Forrester’s proprietary research,
consulting, and events, leaders from around the globe are empowered to be bold at work — to navigate change and put their customers at the center
of their leadership, strategy, and operations. Our unique insights are grounded in annual surveys of more than 700,000 consumers, business leaders,
and technology leaders worldwide; rigorous and objective research methodologies, including Forrester Wave™ evaluations; more than 100 million
real-time feedback votes; and the shared wisdom of our clients. To learn more, visit Forrester.com.
View source version on businesswire.com: https://www.businesswire.com/news/home/20240625049417/en/
Press:
Hannah Segvich
hsegvich@forrester.com
Source: Forrester"
311,mit_edu,Information_20Systems.pdf,"Rising Scholars Conference
Information Systems Student Research Presentations
Jonathan Gomez Martinez Emory University
jonathan.gomez.martinez@emory.edu Goizueta Business School
Jonathan Gomez Martinez is a PhD candidate in Information Systems and Operations
Management at the Goizueta Business School. Informed by his background as a Mexican
immigrant and first-generation college student, Jonathan’s research highlights the unintended
consequences of technology and technology policy. His ongoing projects evaluate the role of AI,
privacy policy, and digital platforms on censoring minority voices and complicating the
operations of small and midsized businesses. To learn more about Jonathan, visit
www.jgomezm.com.
Abstract:
Platform Policy Changes: Impact of Auto-Moderation on Minority Community Rights
User-generated content on social media platforms has always been moderated as
advertisers on these platforms require interactions to be safe, non-abusive, and generally in
compliance with regulations such as those dealing with intellectual property rights. Even if
assisted by algorithms to filter content, human reviewers had always made the final call, until
recently where unprecedented volume and other factors have forced platforms to rely fully on
automated, Artificial Intelligence based (AI-based) content moderation. Cognizant of unintended
consequences of technology usage, our research exploits a natural experiment wherein Twitter
had resorted to auto-moderation in 2020. Our investigation reveals the dramatic impact of such
technologies, often context-blind, on the interactions of a minority group of users such as the
LGBTQ+ community. Through a rigorous empirical approach, our findings show that interactions
within this community reflect a heavily censored language after auto-moderation deployment by
Twitter. In the absence of any explicitly LGBTQ+ related policy changes on Twitter, our work
underscores the inadvertent harm that ensues when context-less AI technologies are adopted.
Farnam Mohebi Univeristy of California, Berkeley
farnam.mohebi@gmail.com Haas School of Business
I am currently a management PhD student at the Haas School of Business and a data science
fellow at the Dlab, UC Berkeley, having previously completed my MD-MPH. I focus on the
intersection of healthcare and management, driven by a deep interest in understanding the
multi-faceted role of physicians in the AI world. I am interested in physicians' perception and
experience with clinical AI and physician-scientists' narratives of it. Additionally, I study the
impact of management practices on physicians. My work is guided by my background in
healthcare and a commitment to improving organizational practices within the field.
Abstract:
Assessing the Multifaceted Role of Physicians in the AI Landscape.
1 Research Question
The central research question of this study is multi-faceted, exploring how physicians navigate
the rapidly evolving landscape of Artificial Intelligence (AI) in healthcare. Specifically, the
question aims to unpack the complexities of physicians’ diverse roles as developers, adopters,
evaluators, and managers of AI technologies in medical settings.
As Developers: Why are Physicians Becoming Developers? How do physicians influence the
trajectory of medical AI science development? Beyond their influence on the trajectory of AI,
how does the professional standing of physicians contribute to their credibility as developers?
Do non-financial incentives, such as academic recognition or potential for societal impact, also
play a role? What issues of legitimacy arise when physicians act as developers? Are there
elements of elitism and prestige that attract physicians to the field of AI development? How do
these factors interact with other motivations and constraints?
As Adopters: How do elements like professionalism and hierarchy affect physician adoption
rates at various levels?How do age, gender, and other demographic characteristics of
physicians influence their willingness to adopt AI technolo-gies? Does a younger generation of
physicians, for example, show less openness to incorporating AI into their practices compared
to their older counterparts? How do the level of training and the years of clinical experience
impact a physician’s propensity to adopt AI? Do physicians with more advanced training or
specialization show different patterns of adoption?What role does exposure to AI in medical
education play in facilitating or hindering adoption? How is the legitimacy of the technology
assessed before adoption? Are there non-financial incentives that significantly impact adoption,
such as the prospect of improved patient outcomes, peer recognition, or professional
development opportunities?
As Evaluators: In what ways do physicians’ professionalism ensure a more rigorous and
ethically sound evaluation of AI tools? Does the commitment of physicians to professional ethics
and existing medical practices make them more resistant to adopting innovative AI technologies
that challenge traditional healthcare paradigms? Are senior physicians or those higher up in the
medical hierarchy more likely to maintain the status quo, thereby hindering the adoption of
transformative AI technologies?
As Managers: How does a physician’s role as a manager facilitate the integration of AI
technology into clinical settings, particularly in terms of operational efficiency and patient care?
In what ways does the managerial role of physicians contribute to fostering an organizational
culture that is more receptive to AI innovations? How does a physician-manager’s clinical
background influence the prioritization of AI projects that have the most direct impact on patient
care?Does a physician’s managerial role lead to conflicts of interest when deciding on AI
projects, perhaps prioritizing those that align with their own clinical specializations or authority
over others that may benefit the healthcare system more broadly? How might the dual
responsibilities of physician managers contribute to potential burnout, thereby affecting their
capacity to evaluate and implement AI technologies effectively?
2 Methods
My research adopts a full-cycle approach. In the initial qualitative stage, I will employ
ethnography and content analysis to delve into various social media, press releases, scholarly
publications, and other publicly available data that provide insights into physicians’ perspectives
on AI. The focus will be on their roles as developers, adopters, evaluators, ethicists, and
managers.
The qualitative insights will then inform the design of lab or field experiments, administrative
data analysis, and surveys. These will focus on physicians’ decision-making patterns, adoption
rates, ethical considerations, and managerial choices in the context of AI integration into
healthcare.
3 Implications
Understanding the economic and social dynamics that influence AI adoption is crucial for
policymakers and industry stakeholders. This research could contribute valuable insights into
how to navigate the conflicting interests between organizational efficiencies and end-user
acceptance. It also opens up discussions on the economic implications of technology adoption
in critical sectors like healthcare.
Carolina Reis Virginia Tech
creis2@vt.edu Pamplin College of Business
Carolina Reis is a fourth-year Information Systems PhD student at Virginia Tech. Broadly, her
research focuses on the hybrid human-machine behavior. In particular, her research
investigates: (1) how the introduction of AI systems into social and organizational ecosystems
alters human beliefs and behaviors, and (2) how people themselves also shape AI systems
through the training of these systems using active human input.
Abstract:
Outsourcing Morality: The Hidden Path to Machine Ethics
Authors: Carolina Reis, Virginia Tech; Nicholas Brown, Indiana University
Artificial intelligence (AI) technologies rely on large language models (LLMs) trained on easily
accessible information online, such as content from the “darkest recesses of the internet”
(Perrigo, 2023). To remove toxicity (e.g., sexism, racism, xenophobia, hate speech, calls for
violence) from the training sets, companies hire human agents to perform content moderation—
the process of reviewing and monitoring digital toxicity. Currently, a large share of this content
moderation process is outsourced to companies and workers in developing nations, where the
work is often unregulated, leaving content moderators to label and annotate toxic content on
their own. These subjective labels then serve as the ground truth for LLM technologies (Perrigo,
2023). However, the appropriateness of language use varies among cultures, contexts, and
people, and what is morally acceptable depends on where the person lives. A paradox thus
ensues: AI technologies are used worldwide, especially in technologically advanced countries,
but their ingrained morality is determined in foreign countries that do not necessarily hold similar
moral values.
In this research, we intend to investigate the differences in cultural perspectives that influence
content moderation and whether these differences perpetuate harmful AI biases. To begin, we
conducted a pilot study, using the leaked Facebook documents on hate speech content
moderation (“Hate Speech and Anti-Migrant Posts: Facebook’s Rules,” 2017), to assess
whether individuals from different countries vary in their moral predisposition. Preliminary results
confirm this hypothesis, and show that (1) a model powered by content moderated by
individuals from the United States (n = 391) would have a significantly higher accuracy rate,
precision rate, recall rate, and F1 score than a model powered by content moderated by
individuals from India (n = 286), and (2) a model powered by content moderated by individuals
from India would have a significantly higher accuracy rate, precision rate, recall rate, and F1
score than a model powered by content moderated by individuals from Brazil (n = 32), when the
Facebook guidelines are used as ground truth. These results indicate cross-cultural ethical
variation and raise potential concerns with current machine ethics practices.
In our forthcoming studies, we will employ a mixed-method design. In Study 1, we will interview
content moderators located in different countries to grasp the practices adopted in the content
moderation process. In Study 2, we will launch an online experimental platform (similar to Awad
et al., 2018) where we will explore different content moderation scenarios and collect data from
people in multiple countries to assess cultural differences in ethical tendencies. In Study 3, we
will develop algorithmic models powered by the labeled data from different cultures and show
the differences in algorithmic output and performance. Ultimately, our aim is to help advance
potential solutions for the problem of universal machine ethics.
Kai-Cheng Yang Northeastern University
yang3kc@gmail.com Network Science Institute
Kai-Cheng Yang is a postdoctoral researcher in the Lazer Lab at Northeastern University's
Network Science Institute. He obtained his Ph.D. in Informatics from the Luddy School of
Informatics, Computing, and Engineering at Indiana University Bloomington. He is interested in
computational social science. His research aims to uncover how technologies like generative AI
are used for deceptive and disruptive purposes, study how humans react to these abuses, and
develop countermeasures. Specifically, he focuses on bad actors like malicious social bots and
misinformation on social media. He built popular tools, such as Botometer, that have served
tens of thousands of users. He also acted as the social bot expert in the trial of Twitter vs. Elon
Musk. His work has been covered by CNN, BBC, The New York Times, and many other popular
news outlets.
Abstract:
Large language models and cyber social threats: Good, bad, and ugly
Large language models (LLMs) may profoundly impact our information ecosystem. On the one
hand, they exhibit impressive capabilities in generating realistic text across diverse subjects and
show great potential in many applications. On the other hand, concerns have been raised that
they could be utilized to produce fake content with deceptive intentions. In this talk, I will present
three studies to demonstrate how LLMs can be abused by bad actors and leveraged by users
for self-protection. In the first study, I will introduce a Twitter botnet that appears to employ
ChatGPT to generate human-like content. These accounts form a dense cluster of fake
personas that exhibit similar behaviors, including posting machine-generated content and stolen
images, and engage with each other through replies and retweets. ChatGPT-generated content
promotes suspicious websites and spreads harmful comments. While the accounts in the botnet
can be detected through their coordination patterns, current state-of-the-art LLM content
classifiers fail to discriminate between them and human accounts in the wild. In the other two
studies, I will talk about using LLMs to counter the spread of misinformation. Through extensive
experiments, I find that ChatGPT, a prominent LLM, can evaluate the credibility of news outlets.
This suggests that LLMs could be an affordable reference for credibility ratings in fact-checking
applications. Then, I further test the feasibility of using ChatGPT as a fact-checking tool in a
human-subject experiment. Although ChatGPT performs reasonably well in debunking false
headlines, it does not significantly affect participants' ability to discern headline accuracy or
share accurate news. In certain cases, ChatGPT might even be harmful. The findings
underscore the importance of accounting for human factors when incorporating AI models into
our information ecosystem."
312,mit_edu,Paper_Artificial-Intelligence-and-Jobs-Evidence-from-Online-Vacancies.pdf,"fi
Arti cial Intelligence and Jobs:
Evidence from Online Vacancies
Daron Acemoglu, MassachusettsInstituteofTechnology(MIT)
andNationalBureauofEconomicResearch(NBER)
David Autor, MITandNBER
Jonathon Hazell, LondonSchoolofEconomics
Pascual Restrepo, BostonUniversityandNBER
Westudytheimpactofartificialintelligence(AI)onlabormarkets
usingestablishment-leveldataonthenearuniverseofonlinevacan-
ciesintheUnitedStatesfrom2010onward.Thereisrapidgrowth
in AI-related vacancies over 2010–18 that is driven by establish-
mentswhoseworkersengageintaskscompatiblewithAI’scurrent
capabilities.AstheseAI-exposedestablishmentsadoptAI,theysi-
multaneously reduce hiring in non-AI positions and change the
skillrequirementsofremainingpostings.Whilevisibleattheestab-
lishment level, the aggregate impacts of AI-labor substitution on
employmentandwagegrowthinmoreexposedoccupationsandin-
dustriesiscurrentlytoosmalltobedetectable.
WethankBlediTaskafordetailedcommentsandprovidingaccesstoBurningGlass
data;JoshuaAngrist,AndreasMueller,RobSeamans,andBetseyStevensonforvery
usefulcommentsandsuggestions;JoseVelardeandZheFredricKongforexpertre-
searchassistance;andDavidDemingandKadeemNorayforsharingtheircodeand
data.AcemogluandAutoracknowledgesupportfromAccentureLLP,IBMGlobal
Universities, Schmidt Futures, and the Smith Richardson Foundation. Acemoglu
SubmittedDecember15,2020;AcceptedNovember24,2021.
JournalofLaborEconomics,volume40,numberS1,April2022.
©2022TheUniversityofChicago.Allrightsreserved.PublishedbyTheUniversityofChicagoPressin
associationwithTheSocietyofLaborEconomistsandTheNationalOpinionResearchCenter.https://
doi.org/10.1086/718327
S293
S294 Acemogluetal.
I. Introduction
Thepastdecadehaswitnessedrapidadvancesinartificialintelligence(AI)
based on new machine learning techniques and the availability of massive
datasets.1Thischangeisexpectedtoaccelerateintheyearstocome(e.g.,Ne-
apolitan and Jiang 2018; Russell 2019), and AI applications have already
startedtoimpactbusinesses(e.g.,Agarwal,Gans,andGoldfarb2018).Some
commentators see this as a harbinger of a jobless future (e.g., Ford 2015;
West2018;Susskind2020),whileothersconsidertheoncomingAIrevolu-
tionasenrichinghumanproductivityandworkexperience(e.g.,McKinsey
GlobalInstitute2017).Thepersistenceofthesecontrastingvisionsisunsur-
prisinggiventhelimitedevidencetodateonthelabormarketconsequences
ofAI.Datacollectioneffortshaveonlyrecentlycommencedtodetermine
theprevalenceofcommercialAIuse,andwelacksystematicevidenceeven
onwhethertherehasbeenamajorincreaseinAIadoption—asopposedto
justextensivemediacoverage.
ThispaperstudiesAIadoptionintheUnitedStatesanditsimplications.
OurstartingpointisthatAIadoptioncanbepartiallyidentifiedfromthe
footprintsitleavesatadoptingestablishmentsastheyhireworkersspecial-
izinginAI-relatedactivities,suchassupervisedandunsupervisedlearning,
naturallanguageprocessing,machinetranslation,orimagerecognition.To
putthisideaintopractice,webuildanestablishment-leveldatasetofAIac-
tivitybasedonthenearuniverseofUSonlinejobvacancypostingsandtheir
detailed skill requirements from Burning Glass Technologies (hereafter,
BurningGlassorBG)fortheyears2007and2010through2018.2
Westartwithatask-basedperspective,linkingtheadoptionofAIandits
possibleimplicationstothetaskstructureofanestablishment.Thisperspec-
tive emphasizes that current applications of AI are capable of performing
specifictasksandpredictsthatfirmsengagedinthosetaskswillbetheones
acknowledges support from Google, the National Science Foundation, the Sloan
Foundation, and the Toulouse Network on Information Technology, and Autor
thankstheCarnegieFellowsProgram,theHeinzFamilyFoundation,andtheWash-
ington Center for Equitable Growth. Contact the corresponding author, David
Autor,atdautor@mit.edu.Informationconcerningaccesstothedatausedinthispa-
perisavailableassupplementalmaterialonline.
1AIisacollectionofalgorithmsthatactintelligentlybyrecognizingandrespond-
ingtotheenvironmenttoachievespecifiedgoals.AIalgorithmsprocess,identify,
andactonpatternsinunstructureddata(e.g.,speechdata,text,orimages)toachieve
specifiedgoals.
2TheBGdatahavebeenusedinseveralrecentpapers.Alekseevaetal.(2021)and
Babina et al. (2020), discussed below, use BGdata to study AIuse and its conse-
quences. Papers using BG data to explore other questions include Hershbein and
Kahn(2018),Azaretal.(2020),Modestino,Shoag,andBallance(2020),Hazelland
Taska(2019),andDemingandNoray(2020).
ArtificialIntelligenceandJobs S295
thatadoptAItechnologies.3Toidentifythetaskscompatiblewithcurrent
AItechnologies,weusethreedifferentbutcomplementarymeasures:Felten,
Raj,andSeamans’s(2018,2019)AIoccupationalimpactmeasure;Brynjolfs-
son,MitchellandRock’s(2018,2019)suitabilityformachinelearning(SML)
index;andWebb’s(2020)AIexposurescore.Theseindicesallidentifysets
oftasksandoccupationsthataremostimpactedbyAItechnologies,buteach
iscomputedonthebasisofdifferentassumptionsaboutAIcapabilities.We
constructanestablishment’sAIexposurefromitsbaseline(2010–12)occu-
pationalstructureaccordingtoeachoneoftheseindicesandusethesebase-
linemeasuresasproxiesforAIexposurethroughoutouranalysis.4Sinceour
goalistostudytheimpactofAIonAI-usingfirmsratherthanAI-producing
firms,weexcludefirmsintheprofessionalandbusinessservicesandinfor-
mationtechnologysectors(NorthAmericanIndustryClassificationSystem
[NAICS]51and54),bothofwhichareprimarysuppliersofAIservices.
OurfirstresultisthatthereisarapidtakeoffinAIvacancypostingsstart-
ingin2010andsignificantlyacceleratingaround2015–16.Consistentwith
atask-basedviewofAI,thisactivityisdrivenbyestablishmentswithtask
structures that are compatiblewith current AI capabilities. For instance, a
1standarddeviationincreaseinourbaselinemeasureofAIexposurebased
onFeltenetal.—approximatelythedifferenceintheaverageAIexposurebe-
tweenfinanceandminingandoilextraction—isassociatedwith15%more
AIvacancyposting.ThestrongassociationbetweenAIexposureandsub-
sequentAIactivityisrobusttonumerouscontrolsandspecificationchecks
whenusingtheFeltenetal.andtheWebbmeasures,butthisislessapparent
withtheSMLindex.ThisleadsustoplacegreateremphasisontheFelten
etal.andWebbmeasureswhenexploringtheeffectsofAIexposureonthe
demandfordifferenttypesofskillsandnon-AIhiring.
OursecondresultestablishesastrongassociationbetweenAIexposure
and changes in the types of skills demanded by establishments. With the
Feltenetal.andWebbmeasures(and,toalesserextent,withSML),wefind
thatAIexposureisassociatedwithbothasignificantdeclineinsomeofthe
skillspreviouslysoughtinpostedvacanciesandtheemergenceofnewskills.
ThisevidencebolstersthecasethatAIisalteringthetaskstructureofjobs,
3SeeAcemogluandAutor(2011)andAcemogluandRestrepo(2018,2019).This
isnottheonlypossibleapproachtoAI.OnecouldalsothinkofAIascomplement-
ingsomebusinessmodels(ratherthanperformingspecifictaskswithinthosemod-
els)orasallowingfirmstogenerateandcommercializenewproducts(seeAgarwal,
Gans,andGoldfarb2018;Bresnahan2019).Weexplainbelowwhythetask-based
approachisparticularlywellsuitedtoourempiricalapproachandhowitreceives
supportfromourfindings.
4Figure4belowshowsthattherelationshipbetweenthemeanwageofanoccu-
pation and the three AI exposure measures is distinct, which is the basis of our
claimthateachoneoftheseindicescapturesadifferentaspectofAIexposure.
S296 Acemogluetal.
replacing some human-performed tasks while simultaneously generating
newtasksaccompaniedbynewskilldemands.
ThefindingthatestablishmentswithAI-suitabletaskshireworkersinto
AIpositionsandchangetheirdemandforcertaintypesofskillsdoesnot,of
course,telluswhetherAIisincreasingorreducing overallnon-AIhiring
inexposedestablishments.Inprinciple,AI-exposedestablishmentsmaysee
anincreasein(non-AI)hiringifeitherAIdirectlycomplementsworkersin
sometasks,increasingtheirproductivityandencouragingmorehiring,or
AIsubstitutesforworkersinsometasksbutincreasestotalfactorproduc-
tivitysufficientlytoraisedemandinnonautomatedtasksviaaproductivity
effect(AcemogluandRestrepo2019).Alternatively,AIadoptionmayre-
duce hiring if AI technologies are replacing many tasks previously per-
formed by workers and the additional hiring they spur in nonautomated
tasksdoesnotmakeupforthisdisplacement.
Our third main result shows that AI exposure is associated with lower
(non-AI and overall) hiring. These results are robust in all of our specifi-
cations using the Felten et al. measure and in most specifications with the
Webbmeasurebut,asanticipated,notwithSML.Thetimingoftheserela-
tionshipsisalsoplausible:substantialdeclinesinhiringtakeplaceinthetime
window during which AI activity surged—between 2014 and 2018. This
patternofresults,combinedwiththeconcentrationofAIactivityinmore
AI-exposedtasks,suggeststhattherecentAIsurgeisdriveninpartbythe
automationofsomeofthetasksformerlyperformedbylabor.Wefindno
evidenceforeithertheviewthattherearemajorhuman-AIcomplementar-
ities in these establishments ortheexpectation that AI will increase hiring
becauseofitslargeproductivityeffects—althoughwecannotruleoutthat
otherapplicationsofAIthatarenotcapturedherecouldhavesucheffects.
Incontrasttotheestablishment-levelpatterns,wedonotdetectanyre-
lationshipbetweenAIexposureandoverallemploymentorwagesatthein-
dustryoroccupationlevel.Therearenosignificantemploymentimpactson
industrieswithgreaterexposuretoAI,andtherearealsonoemploymentor
wages effects for occupations that are more exposed to AI. We conclude
thatdespitethenotablesurgeinAIadoption,theimpactofthisnewtech-
nologyisstilltoosmallrelativetothescaleoftheUSlabormarkettohave
hadfirst-orderimpactsonemploymentpatternsoutsideofAIhiringitself.
Nevertheless,ourmainfindings—thatAIadoptionisdrivenbyestablish-
ments that have a task structure that is suitable for AI use and that this
hasbeenassociatedwithsignificantdeclinesinestablishmenthiring—imply
thatanypositiveproductivityandcomplementarityeffectsfromAIareat
presentsmallcomparedwithitsdisplacementconsequences.
OurpaperbuildsonAlanKrueger’sseminalworkontheeffectsofnew
digitaltechnologiesonworkersandwages(Krueger1993;Autor,Katz,and
Krueger 1998). Subsequent literature has investigated the implications of
automationtechnologies,focusingonwages,employmentpolarization,and
ArtificialIntelligenceandJobs S297
wageinequality(e.g.,Autor,Levy,andMurnane2003;GoosandManning
2007;AutorandDorn2013;Goos,Manning,andSalomons2014;Michaels,
Natraj,andVanReenen2014;Gregory,Salomons,andZierahn,forthcom-
ing).Recentworkhasstudiedtheimpactofspecificautomationtechnolo-
gies, especially industrial robots, on employment and wages, focusing on
industry-levelvariation(GraetzandMichaels2018),locallabormarketeffects
(AcemogluandRestrepo2020),orfirm-levelvariation(DinlersozandWolf
2018;Bessenetal.2019;Bonfigliolietal.2019;Humlum2019;Acemoglu,
Lelarge,andRestrepo2020;Dixon,Hong,andWu2021;Koch,Manuylov,
andSmolka2021).
TherearefewerstudiesoftheeffectsofAIspecifically,althoughthisbody
of work is growing rapidly. Bessen et al. (2018) conduct a survey of AI
startupsandfindthatabout75%ofAIstartupsreportthattheirproducts
helpclientsmakebetterpredictions,managedatabetter,orprovidehigher
quality.Only50%ofstartupsreportthattheirproductshelpcustomersau-
tomateroutinetasksandreducelaborcosts.GrennanandMichaely(2019)
studyhowAIalgorithmshaveaffectedsecurityanalystsandfindevidence
of task substitution: analysts are more likely to leave the profession when
they cover stocks for which there are abundant data available. Differently
fromthesepapers’focusonAI-producingsectorsandspecificapplications
ofAI,suchasfinance,westudythistechnology’seffectsonAI-usingestab-
lishmentsandnon-AIworkersthroughouttheeconomy.
Mostcloselyrelatedtoourpaperareafewrecentworksalsoinvestigating
theeffectsofAIonfirm-leveloutcomes.Babinaetal.(2020)studythere-
lationshipbetweenAIadoptionandemploymentandsalesatboththefirm
andtheindustrylevel.Theydocumentthat,consistentwithAlekseevaetal.
(2021), AI investment is stronger among firms with higher cash reserves,
highermarkups,andhigherR&Dintensityand,moreover,thatthesefirms
growmorethannonadopters.AcontrastbetweenourapproachandBabina
etal.’sisthatwefocusonAIsuitabilitybasedonestablishments’occupa-
tional structures rather than observed AI adoption, and this may explain
why we arrive at different results for hiring. Also related is Deming and
Noray (2020), who use Burning Glass data to study the relationship be-
tweenwages,technicalskills,andskillsobsolescence.Althoughtheirfocus
isnotAI,theirworkdemonstratesthatBurningGlassdataaresuitablefor
detectingchangesinjobskillrequirements,anangleofinquirywepursue
below.
Asnotedabove,ourworkexploitsmeasuresofAIsuitabilitydeveloped
byFelten,Raj,andSeamans(2018,2019),Brynjolfsson,Mitchell,andRock
(2018,2019),andWebb(2020).OurresultsareconsistentwithFelten,Raj,
andSeamans(2019),whofindapositiverelationshipbetweenAIsuitability
andAIvacancyposting,butnorelationshipwithemploymentgrowth,at
theoccupationallevel.WeconfirmthatAIsuitabilityisnotatpresentasso-
ciatedwithgreaterhiringinmorehighlyexposedoccupationsorindustries,
S298 Acemogluetal.
butwefindrobusteffectsonskilldemandandanegativeimpactonestab-
lishmenthiring.
Therestofthepaperisorganizedasfollows.SectionIIpresentsamodel
motivatingourempiricalstrategyandinterpretation.SectionIIIdescribes
thedata,andsectionIVpresentsourempiricalstrategy.SectionVpresents
ourmain results on AI exposureand AI hiring, while section VI looksat
changes in the types of skills AI-exposed establishments are looking for.
SectionVIIexplorestheeffectsofAIonhiringattheestablishment,indus-
try, and occupation levels. Section VIII concludes. Appendix A contains
additional material on our model, and additional robustness checks and
empirical results are presented in appendix B (appendixes are available
online).
II. Theory
Inthissection,weprovideamodelthatmotivatesourempiricalapproach
andinterpretation.
A. Tasks,Algorithms,andProduction
Establishmente’soutput,y,isproducedbycombiningtheservices,y(x),
e e
oftasksx ∈ T ⊂ T withunitelasticity(i.e.,aCobb-Douglasaggregator):
e
ð
lny 5 lnA 1 aðxÞlnyðxÞdx, (1)
e e e
T
e
whereT isthesetoffeasibletasks,asubsetT ofwhichisusedinthepro-
e
ductionprocessofestablishmente,andaðxÞ ≥ 0designatestheimportance
orqualityoftaskxinthepÐroductionprocess,whichiscommonacrosses-
tablishments. We impose aðxÞdx 5 1 for all feasible T, which ensures
T e
e
thatallestablishmentshaveconstantreturnstoscale.
EstablishmentsdifferintheirproductivitytermA and,moreimportantly,
e
inthesetoftaskstheyperform(e.g.,becausetheyproducedifferentgoods
andservicesorusedistinctproductionprocesses).Wealsoassumethateach
establishmentfacesadownward-slopingdemandcurveforitsproductand
will set its price p to maximize profits (and its problem is separable from
e
theprofit-maximizationproblemofthefirm’sotherestablishmentsincase
of multiestablishment firms). In this profit-maximization problem, we as-
sume that each establishment is small in the labor market and takes other
pricesandaggregateoutputasgiven.
Tasksareproducedbyhumanlabor,ℓ(x),orbyservicesfromAI-powered
e
algorithms,a(x):
e
(cid:2) (cid:3)
y eðxÞ 5 ðg ‘ðxÞ‘ eðxÞÞðj21Þ=j 1ðg aðxÞa eðxÞÞðj21Þ=j j=ðj21Þ , (2)
wherejistheelasticity ofsubstitutionbetweenlaborandalgorithms and
g(x) and g (x) are assumed to be common across establishments. We
ℓ a
ArtificialIntelligenceandJobs S299
assumethatAIservicesareprovidedbycombiningAIcapital(machineryor
algorithms)purchasedfromtheoutside,k(x),andin-houseworkersoper-
e
ating,programming,ormaintainingthiscapital,‘AIðxÞ,withthefollowing
e
technology:
(cid:4) (cid:5)
aðxÞ 5 min kðxÞ,‘AIðxÞ , (3)
e e e
whichimpliesthatin-houseAIworkersneedtobecombinedwithcapitalin
fixedproportions.5Weassumethroughoutthatallestablishmentsareprice
takersforproductionworkers,AIworkers,andAIcapital,whoserespec-
tivepricesarew,wAI,andR.
WeviewrecentadvancesinAIasincreasingtheabilityofalgorithmsto
perform certain tasks—corresponding to an increase g (x) for some x. In
a
whatfollows,wedenotebyTAthesubsetoftasksthat,duetotheseadvances,
can now be profitably performed by algorithms/AI. These advances in AI
technology will have heterogeneous impacts on establishments depending
ontheirtaskstructure.Forexample,anincreaseing (x)fortextrecogni-
a
tionwillimpactestablishmentsinwhichworkersperformsignificanttext
recognition tasks and will change the factor demands of these “exposed
establishments.”
Tomaketheseideasprecise,wedefineestablishmente’sexposuretoAIas
∫ ‘ ðxÞdx
exposure to AI 5 x∈T e\TA e , (4)
e ∫ ‘ ðxÞdx
x∈T e e
wheretheemploymentsharesaremeasuredbeforetheadvancesinAItake
place.Thismeasurerepresentstheshareoftasksperformedinanestablish-
mentthatcannowbeperformedbyAI-poweredalgorithms.6
WenextexplorehowadvancesinAIimpactAIactivityandthedemand
for(non-AI)workers.
5Thisassumptioncanberelaxedinvariousways. First,thetechnologycanbe
moregeneralthanLeontief,sothatfactorpricesaffecthowintensivelyAIworkers
areused.Second,establishmentsmaybeallowedtosubstituteoutsourcedAIwork-
ersforin-houseservices.Thefirstmodificationwouldnothaveanymajoreffecton
ourresults,whilethesecondwouldimplythatourproxyforAIactivityatthees-
tablishmentlevelmayunderstatetheextentofAI,potentiallyleadingtoattenuation
ofourestimates.Thecommontechnologyassumptionineqq.(2)and(3)canalso
berelaxedbutisusefulforsimplifyingtheexpositionbyensuringthatdifferences
infactordemandsacrossestablishmentsaredrivenentirelybytaskstructures,mak-
ingthelinkbetweenthemodelandourempiricalapproachmoretransparent.
6Whenj 5∞,asinpropositions1anÐd2belowandtheshareofAIalgorithmsin
costisinitiallysmall,exposuretoAIis aðxÞdx,whichgivestheshareoftasks
thatcannowbecompletedwithAIintx∈ oT te\ aT lA
costs.
S300 Acemogluetal.
B. TaskStructureandAIAdoption
ToillustratehowthetaskstructuredeterminesAIadoption,wefollow
Acemoglu and Autor (2011) and Acemoglu and Restrepo (2018, 2019)
andassumethatj 5 ∞,sothatalgorithmsandlaborareperfectlysubstitut-
ablewithinatask.Wealsofocusontherealisticcaseinwhichtheinitialcost
share of AI, denoted by sA (5ðRk ðxÞ1wAI‘AIðxÞÞ=total costs), is small.
e e e
Additionally, we consider the problem of a single establishment, holding
thepricesofotherestablishmentsinthemarketasgiven.
PROPOSITION1.Supposethatj 5 ∞andtheinitialcostshareofAI,sA e,is
small.ConsideranimprovementinAItechnologiesthatincreasesg (x)
a
inTAandleadstotheuseofAIalgorithmsinthesetasks.Thentheeffects
onthecostshareofAIandin-houseAIemploymentaregivenby
dsA 5 exposure to AI ≥ 0
e e
and
(cid:6) (cid:7)
12sA
dln‘AI5 e 1ðε (cid:2)r 21Þ(cid:2)ð12sAÞ(cid:2)p (cid:2)exposure to AI ≥ 0,
e sA e e e e e
e
whereε > 1isthedemandelasticityfacedbytheestablishment,r > 0is
e e
theestablishment’spass-throughrate,andp ≥ 0istheaveragepercent-
e
agecostreductionintasksperformedbyAI.
TheproofofthispropositionisprovidedinappendixA,wherewealso
providetheexpressionsforthepass-throughrate,r,andaveragecostsav-
e
ingsfromtheuseofAIalgorithms,p.
e
ThepropositionshowsthatchangesinAIactivityandhiringofAIwork-
ersarebothproportionaltoexposuretoAI.Motivatedbytheseresults,in
ourempiricalworkweuseexposuretoAIasthekeyright-handsidevari-
ableandidentifygreateruseofAIwiththepostingofmorevacanciesforin-
houseAIworkers.
Althoughinthispropositionwefocusedonthecasewherej 5 ∞,asim-
ilar logic applies when j > 1 and AI does not fully replace workers in the
tasks it is used. In this case, AI advances still increase the cost share of AI
andthehiringofAIworkersinexposedestablishments.Whenj < 1,how-
ever, technological advances will not raise the cost share of AI because of
strongcomplementaritiesbetweentasksproducedbyalgorithmsandhumans.
C. AI,TaskDisplacement,andHiring
ThenextpropositioncharacterizestheeffectsofAIadvancesonhiringof
(non-AI)workers.ItsproofisalsoinappendixA.
ArtificialIntelligenceandJobs S301
PROPOSITION2.Supposethatj 5 ∞andtheinitialAIshareofcosts,sA e,is
small.ConsideranimprovementinAItechnologiesthatincreasesg (x)
a
inTAandleadstotheuseofAIalgorithmsinthesetasks.Theeffectson
non-AIemployment,ℓ,are
e
dln‘ 5 ð211ðε (cid:2)r 21Þ(cid:2)p Þ(cid:2)exposure to AI , (5)
e e e e e
whereε > 1isthedemandelasticityfacedbytheestablishment,r > 0is
e e
theestablishment’spass-throughrate,andp ≥ 0istheaveragepercent-
e
agecostreductionintasksperformedbyAI.
Proposition2showsthattheeffectsofAIadvancesonlabordemandare
proportionaltoourexposuremeasure.Morecentrally,itclarifiestheeffects
ofAIadvancesonlabordemand.Thedirectconsequenceofsuchadvances
istoexpandthesetoftasksperformedbyalgorithms,TA,andtoshrinkthe
setoftasksallocatedtoworkersinexposedestablishments.Becausej 5 ∞,
this technological improvement displaces workers from tasks in TA. This
displacement effect is captured by the “21” in the parentheses on the
right-hand side of equation (5). In addition, as emphasized in Acemoglu
andRestrepo(2018),thereallocationoftasksfromworkerstoalgorithms
reduces costs and expands establishment output, y (and this output re-
e
sponsedependsonthedemandelasticity andthepass-throughrate).This
“productivity effect,”themagnitude ofwhichisproportionaltothecost
reductions due to AI, p ≥ 0, increases hiring in nonautomated tasks. If
e
thesecondtermontheright-handsideofequation(5),ðε (cid:2)r 21Þ(cid:2)p ,ex-
e e e
ceeds 21, the productivity effect dominates and AI technologies increase
hiring.7 Otherwise, AI advances will reduce (non-AI) hiring in exposed
establishments.
Wemaketwoadditionalremarks.First,aswiththeresultsonAIactivity,
the main conclusions of proposition 2 can be generalized to the case in
whichj > 1.Inthiscase,notallworkerspreviouslyemployedinAIexpost
tasks would be displaced, but the substitution away from them to algo-
rithmswouldcreateanegativedisplacementandapositiveproductivityef-
fect,similartothoseintheproposition.
Second,ifdifferenttasksrequiredifferentskills,thentheadoptionofAI
technologies may also change the set of skills that exposed establishments
demand(andlistintheirvacancies).Skillsrelevantfortasksnowperformed
byalgorithmswillbedemandedlessfrequently,andnewskillsnecessaryfor
workingalongsideAIalgorithmsmayalsostartbeingincludedinvacancies.
Our empirical work will be based on equation (5). We will explore the
relationshipbetweenAIexposure,asdefinedinequation(4),andchanges
7Thisexpressionalsoclarifiesthatwhenthepass-throughrateislessthan1=ε,
e
the establishment’s price increases sufficiently that output does not expand and
thusemploymentalwaysdeclines.
S302 Acemogluetal.
inthenumberandskillcontentofthevacanciesanestablishmentposts.Spe-
cifically, we will look at whether exposed establishments hire more AI
workers,demanddifferentsetsofskills,andincreaseorreducetheirhiring
ofnon-AIworkers.
D. Human-ComplementaryAI
Wehavesofarnotconsideredhuman-complementaryeffectsofAI.The
possibilitythatAIwillcomplementworkersengagedinexposedtaskscan
becapturedbyassumingthata(x)increasesforexposedtasks(seeeq.[1])
or,alternatively,thatj < 1,sothatalgorithmsandhumanlaborarecomple-
mentary within a task (or both). This type of human-complementary AI
mayincreaselabordemandbecausealgorithmsraisehumanproductivityin
exactlythetasksinwhichAIisbeingadopted.
Evidence that AI is associated with greater establishment-level employ-
mentwouldbeconsistentwiththehuman-complementaryviewbutcould
alsobeconsistentwithtasksubstitutionassociatedwithlargeproductivity
gainsthatnonethelessincreasehiringatexposedestablishments.Conversely,
evidenceofnegative,orevenzero,effectswouldweighagainstboththe
human-complementaryviewandthepossibilityoflargeproductivitygains
fromAI—sincebothAI-humancomplementarityandlargeproductivity
effectsboostingemploymentinnonautomatedtaskscouldgenerateapos-
itiverelationshipbetweenAIexposureandestablishmentshiring.Ourev-
idencebelowfindsnegativeeffectsofAIexposureon(non-AI)hiringand
thussuggeststhatthecurrentgenerationofAItechnologiesispredominantly
taskreplacingandgeneratesonlymodestproductivitygains.8Itremainspos-
sible that other AI technologies than the ones we are proxying here could
havedifferenteffects.
E. MeasuringExposuretoAI
Propositions1and2showthatweshouldseetheeffectsofadvancesinAI
inestablishmentswithtaskstructuresthatmakethemhighlyexposedtoAI.
Differencesinexposureare,inturn,drivenbythedifferenttaskstructures
acrossestablishments.Inourempiricalexercise,wewillusetheoccupational
mixofanestablishmentpriortothemajoradvancesinAItoinferitstask
structure and compute its exposure to AI. Formally, we assume that the
setoftasksintheeconomy,T,ispartitionedintotasksperformedbyaset
ofdistinctoccupationsanddenotethesetoftasksperformedinoccupation
o ∈ ObyTo.Eachestablishmente’staskstructureisthusrepresentedbythe
setofoccupationsthattheestablishmentemploys,denotedbyO ⊂ O,and
e
8Orthatproductivitygains,ifpresent,havelittleeffectondemand,potentially
becauseoflowpass-throughrates.
ArtificialIntelligenceandJobs S303
so T
e
5 [ o∈OeTo. For example, some establishments will employ accoun-
tantsandtheirproductionwillusethesetoftasksaccountantsperform,while
others require the tasks performed bysecurity analysts orretailclerks and
thus hire workers into these occupations. In our empirical work, we will
use the occupational indices provided by Felten, Raj, and Seamans (2018,
2019), Webb (2020) and Brynjolfsson, Mitchell, and Rock (2018, 2019) to
identify the set of occupations involving tasks where AI can (or could) be
deployed.WewillthencomputemeasuresofAIexposurebasedontheoc-
cupationalstructureofanestablishment.9
III. Data
WenextdescribetheBGdata,documentthatitisbroadlyrepresentative
ofemploymentandhiringtrendsacrossoccupationsandindustries,present
our AI exposure indices, and document their distribution across occupa-
tionsandtheirevolutionovertime.
A. BurningGlassData
BurningGlasscollectsdatafromroughly40,000companywebsitesand
onlinejobboards,withnomorethan5%ofvacanciesfromanyonesource.
BGappliesadeduplicationalgorithmandconvertsthevacanciesintoaform
amenabletodataanalysis.Thecoverageisthenearuniverseofonlinevacan-
cies from 2010 onward in the United States, with somewhat more limited
coverage in 2007. Our primary sample comprises data from the start of
2010untilOctober2018,althoughwealsomakeuseofthe2007data.The
vacancydataenumerateoccupation,industry,andregioninformation;firm
identifiers;anddetailedinformationonoccupationsandskillsrequiredby
vacancies,garneredfromthetextofjobpostings.
AkeyquestionconcernstherepresentativenessofBGdatagiventhatthe
sourceofthevacanciesisonlinejobpostings.Figure1showsthatBGdata
9Formally,theseAIindicesaretheempiricalanalogofourtheoreticalexposure
toAImeasureineq.(4).Toseethis,notethat
AIindexo 5
∫ x∈To\TA‘ðxÞdx
,
∫ x∈To‘ðxÞdx
where‘(x)isaverageeÐmploymentintaskxandwedenoteaverageemploymentin
occupationoby‘o 5 ‘ðxÞdx.When‘ðxÞ5 ‘ðxÞ,whichfollowsfromourcom-
x∈To e
montechnologyassumption,theexposuretoAImeasureisequaltotheemploy-
mentweightedaverageoftheoccupationAIexposuremeasure:
∫
o o∈O oeA oI ∈Oin e‘d oexo‘o 5 o o∈Oe ox∫∈T x o∈ ∈o T\ OoT ‘ eA ð ‘‘ xð oÞx dÞ xdx‘o 5 ∫ x ∫∈ xT ∈e\
T
eT ‘A ð‘ xð Þx dÞ xdx 5 exposure to AI e:
S304 Acemogluetal.
FIG.1.—VacanciesinBurningGlassandJOLTS.Thisfigureplotsthetotalnum-
berofvacanciesinJOLTSandthetotalnumberofvacanciesinBurningGlassbyyear.
We multiply the number of job openings in JOLTS by a constant factor of 0.65
toarriveatanumberofvacanciesthatmatchestheconceptofavacancyinBurning
Glass.ThismethodfollowsCarnevale,Jayasundera,andRepnikov(2014).Acolor
versionofthisfigureisavailableonline.
closely track the evolution of overall vacancies in the US economy as re-
corded by the nationally representative Bureau of Labor Statistics (BLS)
JobOpeningsandLaborTurnoverSurvey(JOLTS).Theexceptionisthe
downturninBGpostingsdatabetween2015and2017.10FigureA1(avail-
ableonline)showsthatoverthe2010–18period,theoccupationalandin-
dustrycompositioninBGiscloselyalignedwithbothoveralloccupation
employmentsharesfromOccupationalEmployment Statistics(OES)and
withindustryvacancysharesfromJOLTS.11
10WhileJOLTSmeasuresasnapshotofopenvacanciespostedbyestablishments
duringthelastbusinessdayofthemonth,BGcountsnewvacanciespostedbythe
establishmentduringtheentiremonth.Weadjustthenumbersofjobopeningsin
JOLTStomatchBG’sconceptofvacancies,usingtheapproachdevelopedbyCar-
nevale,Jayasundera,andRepnikov(2014).ThedifferenceinconceptbetweenJOLTS
andBurningGlassvacancieslikelyaccountsforthedownturninBGpostingsdatabe-
tween2015and2017.
11WenotethatBGdatarepresentvacancyflowswhiletheOESreportsemploy-
mentstocks;thus,wedonotexpectthetwodatasourcestoalignperfectly.More-
over,onlinevacancypostingstendtooverrepresenttechnicalandprofessionaljobs
ArtificialIntelligenceandJobs S305
WemakeuseofBurningGlass’sdetailedindustryandestablishmentdata.
Whenthisinformation isavailablefrom thetextofpostings,vacanciesare
assigned a firm name and a location, typically at the city level, as well as
anindustrycode.Weclassifyeachfirmasbelongingtotheindustryinwhich
itpoststhemostvacanciesoveroursampleperiod.Wedefineanestablish-
mentofafirmasthecollectionofvacanciespertainingtoafirmandcommut-
ingzone(CZ).CZsaregroupsofcountiesthat,becauseoftheirstrongcom-
mutingties,approximatealocallabormarket(TolbertandSizer1996).
OfparticularimportanceforourpaperareBG’sdetailedskillandoccu-
pation coding. Vacancies in BGdata containinformation on skillrequire-
ments,scrapedfromthetextofthevacancy.Theskillsareorganizedaccord-
ing to several thousand standardized fields. Groups of related skills are
collectedtogetherinto“skillclusters.”Morethan95%ofvacanciesareas-
signedasix-digit(StandardOccupationalClassification[SOC])occupation
code.12
WeusetheseskilldatatoconstructtwomeasuresofAIvacancies,narrow
and broad. The narrow category includes a selection of skills relating to
AI.13ThebroadmeasureofAIincludesskillsbelongingtothebroaderskill
clustersofmachinelearning,AI,naturallanguageprocessing,anddatasci-
ence.AconcernwithourbroadAImeasureisthatitmayincludevarious
IT functions that are separate from core AI activities. For this reason, we
focusonthenarrowAImeasureinthetextandshowtherobustnessofour
main results with the broad occupation measure in appendix B. Figure 2
shows the evolution of postings of narrow and broad AI vacancies in the
BGdata,highlightingtherapidtakeoffofAIvacanciesafter2015,asnoted
intheintroduction.Whileasharpuptickisvisibleinallindustries,theright
panel of figure 2 shows that the takeoff is particularly pronounced in the
information,professionalandbusinessservices,finance,andmanufacturing
sectors.
Inwhatfollows,ourprimaryfocusisonAI-usingsectors,andwedrop
establishments belonging to sectors that are likely to be producing AI-
related products, namely, the information sector (NAICS sector 51) and
relativetobluecollarandpersonalservicejobs(Carnevale,Jayasundera,andRep-
nikov2014).
12Six-digitoccupationcodesarehighlygranular,includingoccupationssuchas
pestcontrolworker,collegeprofessorinphysics,andhomehealthaide.
13Theskillsaremachinelearning,computervision,machinevision,deeplearning,
virtualagents,imagerecognition,naturallanguageprocessing,speechrecognition,pat-
ternrecognition,objectrecognition,neuralnetworks,AIchatbot,supervisedlearn-
ing,textmining,unsupervisedlearning,imageprocessing,Mahout,recommendersys-
tems, support vector machines, random forests, latent semantic analysis, sentiment
analysis/opinionmining,latentDirichletallocation,predictivemodels,kernelmeth-
ods,Keras,gradientboosting,OpenCV,XGBoost,Libsvm,Word2vec,machinetrans-
lation,andsentimentclassification.
S306 Acemogluetal.
FIG.2.—ShareofAIvacanciesinBurningGlass.Theleftpanelplotstheshareof
vacanciesinBurningGlassthatpostaskillinthebroadornarrowAIcategories,as
definedinthemaintext.TherightpanelplotstheshareofnarrowAIvacanciesin
BurningGlass,byyear,ineachindustrysector.pp5percentagepoint. Acolorver-
sionofthisfigureisavailableonline.
theprofessionalandbusinessservicessector(NAICSsector54).Theformer
includes variousinformationtechnology industries,likely tobeselling AI
products, while the latter contains industries such as management consul-
tancy,likelytobeintegratingAIintootherindustries’productionprocesses.
B. AIIndices
WestudythreemeasuresofAIexposure.Eachisassignedatthesix-digit
SOC occupation level, and each is designed to capture occupations con-
centrating in tasks that are compatible with the current capabilities of AI
technologies.
ThefirstmeasureisfromFelten,Raj,andSeamans(2019).Itisbasedon
datafromtheAIProgressMeasurementproject,fromtheElectronicFron-
tier Foundation. The Electronic Frontier data identify a set of nine appli-
cationareasinwhichAIhasmadeprogresssince2010,suchasimagerec-
ognitionorlanguagemodeling.Feltenetal.useAmazonMTurktocollect
crowdsourcedassessmentsoftherelevanceofeachoftheseapplicationareas
tothe52O*NETabilityscales(e.g.,depthperception,numberfacility,and
ArtificialIntelligenceandJobs S307
written comprehension). The authors then construct the AI occupational
impact for each O*NET occupation as the weighted sum of the 52 AI
application-abilityscores,whereweightsareequaltotheO*NET-reported
prevalenceandimportanceofeachabilityin"
313,mit_edu,AI-K-12_final-V3.pdf,"TOPICAL POLICY BRIEF
Labeling AI-Generated Content:
How Policy Can
Help Ensure the
Proper Use of AI in
K-12 Education
MIT Responsible AI for Social Empowerment
and Education (RAISE) Initiative
Daniella DiPaola
Andrés F. Salazar-Gómez
Hal Abelson
Eric Klopfer
David Goldston
Cynthia Breazeal July 19, 2024
aipolicy.mit.edu
AI Policy Brief: K12 Education
MIT Responsible AI for Social Empowerment and Education (RAISE) Initiative
Daniella DiPaola, Andrés F. Salazar-Gómez, Hal Abelson, Eric Klopfer,
David Goldston, and Cynthia Breazeal
I. Introduction – Promise and Perils
Artificial intelligence (AI) has the potential to significantly improve K-12 education if
implemented appropriately – in ways that ensure its safe and equitable use. We use the
term “AI” broadly to mean any technology that uses data to make predictions and
decisions, or creates new content. AI offers great promise for students, teachers, and
school administrators. Examples include: the personalization of learning through virtual
chatbots (e.g., a ChatGPT-powered system such as Khanmigo) that provide K-12
students hints and clues tailored to where the child is in the learning process1; social
robots that support children learning to read through predictive algorithms of vocabulary
knowledge2; automated grading systems that allow teachers to use their time more
effectively by providing detailed feedback and scoring based on a student’s answers3;
and predictive systems for school administrators that identify high school students at
risk of dropping out4.
Children have unique developmental needs and vulnerabilities, and AI should be
integrated into schools in a way that enables kids to flourish while keeping them safe.
Doing so should not displace or reduce the role of teachers, who play a critical role in
students’ education and social development.
There is little or no federal guidance on AI for K-12 education. Little systematic research
at scale exists on how and when students learn better with AI, and states and school
districts are left on their own in a “Wild West” of competing claims, with AI offering
unverifiable allure and unknown risks. Nonetheless, states are creating an array of AI
implementation recommendations and guidelines for K-12 education, with no consensus
among them. Different states have different definitions and recommendations for topics
as important as plagiarism and AI literacy5. Federal policies based on state-of-the-art
research can help guide states and localities, while still leaving room for states and
school districts to use AI in a way that meets their particular needs.
1Bidarian, N. (2023, August 21). Meet Khan Academy’s Chatbot Tutor | CNN business. CNN.
2Zhang, X., Breazeal, C., & Park, H. W. (2023, March). A Social Robot Reading Partner for Explorative Guidance. In
Proceedings of the 2023 ACM/IEEE International Conference on Human-Robot Interaction (pp. 341-349).
3Metz, C. (2021, July 20). Can A.I. grade your next test?. The New York Times.
4Page, L., & Gehlbach, H. (2018, January 16). How Georgia State University used an algorithm to help students
navigate the road to college. Harvard Business Review.
5Dusseault, B. (2024, March). New state AI policies released: Signs point to inconsistency and fragmentation | Center
on Reinventing Public Education (CRPE), Arizona State University.
POLICY RECOMMENDATIONS FOR THE USE OF AI IN EDUCATION
2
This table enumerates some of the major potential benefits and challenges of using AI
in the classroom. Successful federal policy would make the benefits more likely and the
harms less so.
Potential Benefits Potential Harms
● Increase students’ learning gains ● Enable the collection of vast amounts of
through personalized learning personal data, compromising privacy.
experiences. ● Produce inaccurate, inappropriate, or harmful
● Complement instruction by outputs.
teachers. ● Favor certain learning approaches or abilities.
● Promote creative learning, ● Exhibit bias.
designing, and making. ● Exacerbate inequities among school districts.
● Reduce barriers to access to ● Undermine the development of basic skills
advanced knowledge. such as writing.
Beyond concerns related to weaknesses inherent in AI systems themselves, harms
could result from the way AI is deployed. Are there ways AI can be used to maximize
educational benefits and minimize potential detrimental effects, like the potential loss of
needed basic, age-appropriate skills? How can educators and parents keep adapting as
AI systems continue to improve, and as society and regulation adjust to AI’s new
capabilities?
II. The Federal Role
While pre-college education is primarily a state and local responsibility, the federal
government has a critical role in shepherding AI into the classroom and ensuring its
appropriate use. Many states and localities lack the expertise and capacity to design or
enforce technical requirements for AI systems, even more so if AI systems are offered
by just a small number of national suppliers. The federal government is more likely to
have the wherewithal, both financial and human, to fund research, set technical
standards, assist procurement, promote transparency, and provide guidance on many
crucial aspects of AI in education. If the federal government plays its role properly,
states and school districts will have the information and the funding they need to make
their own decisions on exactly how to integrate AI into teaching and the curriculum.
We discuss five areas where increased federal activity is needed: research; standards
development and auditing; procurement assistance; educational guidance; and AI
literacy. We describe the federal role in fulfilling each of these needs and then offer
steps the federal government could take to meet the challenge.
Research on AI in K-12 Education
The federal government has long been an important funder of research in critical areas
likely to be neglected or underfunded by the corporate sector or others. Two broad
research areas need much greater focus if AI is to be used safely and effectively in the
POLICY RECOMMENDATIONS FOR THE USE OF AI IN EDUCATION
3
K-12 classroom. The first is research on AI systems themselves. AI systems need to be
more accurate, less biased, less likely to output dangerous or offensive material, and
less likely to compromise privacy, among other deficiencies, if they are to be entrusted
with supporting the education of minors. [See issue brief on LLMs.]
Second, and equally important, much more research is needed to understand how the
use of AI might affect teachers and students, and how to use AI to maximize its
educational benefits. We understand too little to begin with about how students learn
best, and far less about the impact of a new technology. AI is not a magical elixir that
can be added to education with guaranteed positive results. Ongoing research,
monitoring, and evaluation will be needed to understand how to use AI optimally.
Research on how to most effectively deploy AI is needed in education as it is in other
fields. The research, piloting, and monitoring will have to be done in a way that does not
sacrifice children and their teachers as guinea pigs who are handed AI tools before we
know how they would work best. One area of research should try to figure out how to
help any children whose education has been harmed by using AI before its implications
are sufficiently understood.
RECOMMENDATIONS: The Department of Education (ED) and the National Science
Foundation (NSF) should create programs specifically to address the research issues
discussed above. Funding for this research should be a budget priority, including
funding for field research, especially at scale, to see how AI is actually being used in a
diverse range of classrooms and the impacts of that use. The National AI Research
Resource (NAIRR), now being piloted, should make computer time available for the
research described above. However, it should be recognized that the NAIRR, even if
fully funded, would not have adequate resources to help carry out major research
projects.
ED and NSF will also need to take steps to ensure that the results of the research they
fund are broadly disseminated and that their implications are clear to state education
officials, school administrators, and teachers.
Technical Standards and Auditing
As in many areas of technology, the federal government has a key role to play in
developing technical standards (and conducting the research needed to do so), even
though private entities or other levels of government can decide which standards to
adopt. Standards development is especially important in a field like AI – which is rapidly
changing and largely dominated at the moment by a handful of companies (at least in
terms of the broad platforms on which more targeted systems are built). And it is even
more important when what’s at stake is education – a largely public undertaking with
enormous societal impacts. As noted in the section above, many of the AI “safety”
issues that are central to education are of concern to AI users more broadly and are
already a focus of federal standards-development work. As AI is incorporated into more
systems, standards also will be needed to describe which AI tools should require
auditing.
POLICY RECOMMENDATIONS FOR THE USE OF AI IN EDUCATION
4
Standards can be effective only if they are actually followed; those using AI in K-12
education should not be left to rely on chance when it comes to how AI systems actually
function in practice. An auditing ecosystem will need to be developed to ensure that AI
systems perform as advertised, especially in terms of concerns such as accuracy,
fairness, and privacy. [See main AI Policy Brief.] This is even more important for AI
systems that will be interacting with children. Auditing itself requires the creation of
technical standards – on what to test for, and how – and then trustworthy entities are
needed to conduct the audits. Audits can be done before an AI system being deployed,
or after, or both – with each type of audit having its strengths and weaknesses.
One possible approach would be for the National Institute of Standards and Technology
(NIST), in collaboration with the ED and NSF, to develop standards and guidelines for
auditing and red-teaming of AI designed to be used in K-12 education. States and
localities could then decide whether to require that AI systems used in their school
systems be audited, and that could be done in accordance with the federal standards
(or something based on them). A federal, state, and/or local system – or a non-profit
one – would need to be set up to certify auditors. We believe that auditing AI systems
for pre-college education must be performed by third parties, not by those developing or
selling the AI systems, to avoid conflicts of interest inherent in self-audits.
Privacy should be one aspect of AI that is audited. The issue is not only whether
individuals, institutions, or companies could get access to identifiable data about an
individual using the AI, but also whether data could be aggregated across different
platforms. Third-party actors could also collect school data and combine it with other
behavioral data from children’s online presence. For example, an AI-driven tutor created
by a large tech company might collect the full history of a child’s interaction with the
system to develop more effective personalized tutoring responses, then sell the
information to a company focused on toy advertising. NIST already has a mandate to
create AI privacy standards, but more attention is needed specifically about the privacy
of minor children.
Regarding privacy, we can learn from existing federal initiatives designed to protect
privacy, including the Children’s Online Privacy Protection Act (COPPA) and the
Children’s Internet Protection Act (CIPA) – administered by the Federal Trade
Commission and the Federal Communications Commission, respectively. These laws
provide authority to issue guidelines or regulations for AI, but that has not happened.
The Kids Online Safety Act (KOSA) could also be used as the basis for privacy
regulations.
Developing an effective and reliable auditing ecosystem may be one of the most difficult
and critical steps in ensuring that AI systems are appropriate for K-12 education. [See
main AI Policy Brief.]
RECOMMENDATIONS: The White House has rightly made NIST the lead government
agency for setting technical standards for AI. NIST needs more funding to carry out this
POLICY RECOMMENDATIONS FOR THE USE OF AI IN EDUCATION
5
vital work. NIST should coordinate with ED and NSF to determine what specific
standards may be needed for AI systems used in education.
There are a range of options the federal government could then use to try to ensure that
audits are conducted, including (from strictest to more lenient):
● Prohibiting states and school districts that receive federal funding from buying or
using AI systems that have not been audited in accordance with federal
standards.
● Prohibiting federal funds from being used to buy or deploy AI systems that have
not been audited in accordance with federal standards and/or requiring post-hoc
audits as a condition of federal funding to buy or deploy AI systems.
● Providing additional funds when entities buy or deploy an AI system that has
been audited in accordance with federal standards.
● Creating “safe harbor” legal standards for AI systems used in education that have
been audited in accordance with federal standards. (This will be meaningful only
if there is a functional liability regime to begin with – an issue that goes beyond
education.)
● Withholding funds from school districts that have problems with AI systems that
were not audited before purchase or deployment. (This may, though, create
incentives not to report problems, or may penalize schools that are already
financially strapped.)
● Providing funding for post-hoc audits after an AI system has been deployed.
We believe that there should be some federal requirements that set a minimum
standard for auditing and safety for AI that could be used for K-12 education.
At the very least, ED should make sure states and school districts have access to any
NIST auditing standards and results with information on the implications for education.
Schools should not be using unaudited AI systems.
Procurement Assistance
Also, AI systems can be expensive, and poorer school districts in particular may not be
able to afford them. The federal government should ensure that AI does not create a
new “digital divide” because only the wealthiest districts can afford AI or appropriate AI.
(As discussed in the next section, the federal government should also take steps to
ensure that school districts, especially poorer ones, do not weaken education by over-
reliance on AI or use AI as a way to displace teachers.) The federal government should
also make sure that poorer school districts are not turned into guinea pigs, using
donated equipment without adequate testing or thinking about its optimal use.
RECOMMENDATIONS: ED could establish a program (or use a current program) for
competitive grants to states and/or school districts to fund the use of AI systems that
meet adequate standards. (See section above on standards.) There is a clear
POLICY RECOMMENDATIONS FOR THE USE OF AI IN EDUCATION
6
precedent for the federal government providing equitable access to technology for
education, the universal service Schools and Libraries Program (E-rate)6.
Those seeking grants should have to describe not only the kind(s) of AI systems that
the money would be used for but also how that AI would be deployed and monitored.
Any federal agency providing funding for AI should take steps to ensure that AI is not
used to displace teachers. AI should be used to enhance teaching, not to reduce the
number of teachers. ED and other agencies could require that the size of the teaching
staff not be reduced at least during the life of the grant.
Educational Guidance
Drawing on the research discussed above, the federal government should offer
guidance on the use of AI in education, teacher training and certification, and other
aspects of AI in education (even though final decisions on when, where, and how to
deploy AI will remain with states and localities). The federal government should also act
as a convener, by itself or with associations of education professionals or others,
bringing together school officials and teachers from around the country to discuss AI
issues.
One key issue is determining when using AI constitutes a breach of academic integrity –
when it is just “cheating.” We do not think that AI use should be subject to broad bans,
but rather that students should be taught when and how it is appropriate to use it as a
tool. That will, of course, differ by grade level and subject. Generative AI such as
ChatGPT can be a creative partner and help students better utilize time on their
assignments. When students enter the workforce, using these tools will be
commonplace.
However, there are cases in which AI should not be used. For example, in early
education (typically defined as Pre-K to 3rd grade), while students are learning how to
read and write, they should not be allowed to use AI in a manner that will interfere with
them developing their own skills. As AI is introduced and permitted, students should
practice using the tool in contexts when it is appropriate to do so, based on age-
appropriate guidelines and the teacher’s discretion, and understand the consequences
if they misuse it.
Educators should clearly state when students can and cannot use AI for their
assignments. We suggest that teachers not rely on AI tools to detect AI use on
assignments, as these tools currently have a high rate of false positives.
RECOMMENDATIONS: ED and NSF should begin preparing materials that can guide
states, school districts and teachers on AI education (see more on that below) and the
use of AI in education. Any guidance should be updated regularly.
6https://www2.ed.gov/about/inits/ed/non-public-education/other-federal-programs/fcc.html
POLICY RECOMMENDATIONS FOR THE USE OF AI IN EDUCATION
7
ED should consider creating a new office devoted solely to AI. While AI education and
the use of AI in education need to be integrated with all other aspects of primary and
secondary education, a separate office would not only highlight the importance and
uniqueness of the AI issues, but might help attract a cadre of relevant technical experts
to the department. ED and NSF should run grant programs to help fund the
development of curriculum, educational materials, and teacher training programs related
to AI.
ED and NSF, in collaboration with states and school districts, should develop metrics
that could help assess the impact of AI in the classroom on learning gains and skills
development. These should include metrics on the level of student engagement, and
the impact on struggling and disenfranchised students.
AI Literacy
The federal government should develop guidelines not only for how AI should be used
in K-12 education but also for what students should learn about AI itself. The goal
should be to help school districts create an AI-literate generation so students can
become effective citizens and productive workers in a world where AI will play a
prominent role.
We define “AI literacy” to include educating students on the appropriate and productive
use of AI. This encompasses gaining an understanding of how AI-powered technologies
operate, their applications, how to create with AI and work with AI effectively and
responsibly, and drawbacks and possibilities. AI literacy is relevant to many different
course areas and AI literacy should be infused across the curriculum, instead of just in
the computer science classroom.
For example, the appropriate use of generative AI could be addressed in arts
classrooms, and the impact of AI on society could be taught in civics classrooms. The
curriculum should clearly connect AI’s capabilities and these different subject areas.
K12 students should learn about AI through learning-by-making experiences and
responsible design practices7, where appropriate.
For students to be AI literate, they need AI-literate teachers. Teachers should receive
training on how to promote AI literacy and how best to use AI. The federal government,
along with states, localities, and non-profits, should develop ways to certify qualified AI
teachers.
The federal government should also issue guidelines on what students should know to
be considered AI literate and should help create curriculum and assessment tools on AI
literacy. Federal involvement in developing AI literacy standards and curricula for
students is needed because states are setting curriculum regulations that often vary
7https://dayofai.org/
POLICY RECOMMENDATIONS FOR THE USE OF AI IN EDUCATION
8
widely from each other. For example, West Virginia’s guidelines8 suggest that AI literacy
is only a part of computer science and technical courses, while North Carolina9
emphasizes AI literacy across all curriculum areas. U.S. students won’t be equally
trained in AI without a more cohesive approach across states.
RECOMMENDATIONS: ED and NSF should fund curriculum development on AI literacy
– both for teacher training and for students. ED and NSF should also fund teacher
training on AI literacy.
ED should update the national common core standards in digital literacy to encompass
AI and create a new category of common core standards specific to AI literacy. Grants
should encourage collaboration among non-profits, universities, teachers, and others in
developing AI literacy curricula and professional development materials.
III. Concluding Thoughts
The beneficial use of AI in K-12 education will not happen automatically and should not
be left to chance. The advent of AI raises, among other things, difficult questions about
the pace at which AI should be introduced in the classroom that education officials at all
levels will have to answer. How can a school find the proper balance – not holding back
on AI so much that teachers and students are deprived, but not racing ahead so fast as
to be saddled with AI systems that either become quickly obsolete, have serious flaws,
or are used in ways that do more harm than good (before it’s even clear how to evaluate
that)?
Finally, while K-12 education raises some very specific issues about AI, education will
be greatly affected by larger trends in AI and AI policy. Many concerns about AI
systems in education – accuracy, bias, etc. – apply to many uses of AI to at least some
degree. Steps needed to facilitate the proper use of AI, like the development of auditing
and liability regimes, are especially important for education.
K-12 education should prepare children to be successful adults in their personal and
professional lives. They should be prepared to be informed citizens, creative makers,
and critical thinkers. AI's role in the education and training of students (and teachers)
needs to be carefully considered and repeatedly iterated if we are to best serve the
future of our children, their teachers, and our nation.
Authors
Daniella DiPaola is a Ph.D. student in the Personal Robotics Group at the MIT Media Lab;
Andres F. Salazar-Gomez is a research scientist at MIT Open Learning; Hal Abelson is Class of
1922 Professor of Computer Science and Engineering; Eric Klopfer is Professor and Director of
8West Virginia Board of Education. (2024, January). Guidance, Considerations, & Intentions for the Use of Artificial
Intelligence in West Virginia Schools | Virginia Department of Education.
9North Carolina Department of Public Instruction. (2024, January). North Carolina Generative AI Implementation
Recommendations and Considerations for PK-13 Public Schools
POLICY RECOMMENDATIONS FOR THE USE OF AI IN EDUCATION
9
the Scheller Teacher Education Program and The Education Arcade at MIT; David Goldston is
Director of the MIT Washington Office; Cynthia Breazeal is the Dean for Digital Learning and a
professor of media arts and sciences at the MIT Media Lab.
Acknowledgments
The authors would like to thank Marc Aidinoff, Safinah Ali, Bill Bonvillian, Kate Darling,
Tom Giancola, Prerna Ravi, and Andrew Whitacre for their insights and advice. They
have not reviewed the paper, though, and are not responsible for its content."
316,deloitte,ca-the-state-of-generative-ai-in-the-enterprise-a-canadian-perspective-aoda-en.pdf,"The State of Generative AI
in the Enterprise
A Canadian perspective
to scaling GenAI solutions
Q3 Insights
Contents
+ Closing the productivity gap ................................. 4
+ Adopting a balanced approach
that considers trust................................................. 5
+ Prioritizing key actions to
derive value from GenAI ........................................ 6
+ Measuring value with
specific KPIs ............................................................... 7
+ Conclusion ................................................................. 9
2
2024 has been a pivotal year for Generative AI (GenAI). surveyed 2,770 global respondents between May
GenAI experimentation has shown promising results, and June 2024 across several industries from all
2,770
which has led to growing investments, soaring levels, including board and C-suite members, as well
expectations, and emerging challenges. C-suites and as those at the president, vice president and director
boards are beginning to ask for measurable returns on levels. In this wave of the series, the focus was on AI Global respondents
investment – but can GenAI deliver? If projected returns governance, risk and compliance, data foundations,
Survey conducted between
are not met, interest in GenAI will fold as quickly as it and identifying how organizations are measuring
May and June 2024 across
has emerged. and communicating value.
several industries from
all levels
Our quarterly survey series, The State of Generative AI This article examines how Canadian businesses are
in the Enterprise, tracks global trends in decisions and adopting GenAI to boost productivity, build trust, capture
actions by leading organizations who are deploying value quickly, and measure impact effectively.
GenAI solutions. In Wave 3 of this report, Deloitte
3
+
Closing the
productivity gap
Canadian organizations are seeking clear
measures of productivity & efficiency
Survey respondents expressed that productivity is still the Improving Canada’s productivity has become an urgent
Productivity is the most sought after benefit
to realize the full potential of GenAI
number one most sought-after benefit for organizations priority. Though there is no consensus regarding the
looking to realize the full potential of GenAI globally (54%) cause for Canada’s limited productivity growth, there
and in Canada (53%). Of the Canadian organizations that have been signs that the downturn has been linked to the
have implemented AI solutions, 29% of respondents have structure of the business sector, regulatory bottlenecks
54%
noted improved productivity and efficiency as the most like interprovincial trade barriers, slow permitting
important benefit realized. processes to a lack of business investment. Globally,
Canadian companies are investing significantly less than
Global
While productivity remains the top priority for their peers – the CD Howe Institute estimates that for
organizations adopting GenAI, Canada’s stagnant each dollar a US company invests per worker, a Canadian
productivity performance has become front-page news, counterpart invests just 52 cents.4 It is no wonder that 29%
of Canadian
with an annual average rate increase of just 0.9%.1 Canada’s productivity is running 30% below the US.5 There respondents have noted
53%
improved productivity
Canada now stands as the second least productive is scope for Canada to take advantage of its fast-growing
and efficiency as the most
country in the G7.2 The downward trend was amplified AI ecosystem to reset. With rapid growth in AI talent, the
important benefit realized
following the 2020 pandemic leading policymakers, like breadth of venture capital funding, and large-scale increase
Bank of Canada Senior Deputy Governor Carolyn Rogers, in patent filings, Canadian organizations have a unique
Canada
to label it a “productivity emergency”.3 opportunity to leverage GenAI technologies to close the
productivity gap and meet their efficiency goals.6
4
+
Adopting a balanced approach
that considers trust
While trust is improving, it is still a concern for Canadian organizations
Globally, trust in GenAI is on the rise. Based on our Exercising caution must not be an excuse to delay Trust in GenAI
survey, 89% of respondents indicate that they moderately innovation. Rather, organizations should leverage this
trust (54%) or highly trust (35%) GenAI. In Canada, those opportunity to role model how to effectively balance risk
numbers resemble global metrics where 88% say they management while encouraging innovation. To achieve
35%
Highly trust
moderately trust (51%) or highly trust (37%) GenAI. this balance, it is crucial to establish guardrails for the
89%
54%
Moderately trust
responsible deployment of Generative AI solutions while
This growing confidence underscores a critical responsibility prioritizing upskilling to ensure that people understand the
for Canadian enterprises: to ensure that trust is not taken technology and know how to use it effectively.
Global
for granted but actively nurtured through transparency
and collaboration. It is essential that all parties are
engaged, and that governance is embedded into the
design, development, and implementation of AI solutions.
37%
Highly trust
88%
51%
Moderately trust
Canada
5
+
Prioritizing key actions to
derive value from GenAI
Canadian organizations are focusing on embedding
GenAI into functions and processes and managing risk
Respondents both globally and in Canada believe that To fully realize these benefits, the identification, Many organizations are now assigning dedicated individuals
embedding GenAI into organizational functions and prioritization, and design of specific use cases must be or teams to oversee AI implementation and establish
processes is the primary mechanism to extract maximal a collaborative effort between business functions and IT responsible AI practices.
value from GenAI solutions. From our survey, 23% of teams, ensuring alignment with broader organizational
Canadian respondents agree that learning to infuse GenAI goals. This approach ensures that GenAI initiatives are not Having accountable teams in place ensures that protective
into the DNA of the organization is essential. managed as isolated projects by the CIO, but are integrated measures are thorough and transparent, which in turn
into the core business strategy, and championed by builds trust within the organization. As more companies
What does this look like? Consider accountants that business leaders. This helps to ensure that the GenAI tool commit to ethical practices and strong risk management
can leverage GenAI to convert PDF invoices into excel or process is not only effective at its intended purpose, but principles, trust in GenAI will continue to grow.
spreadsheets, lawyers using Generative AI for lease also sustainable and ideally, scalable.
abstraction, case workers leveraging intelligent AI query
chatbots to gain access to the most relevant information Among Canadian survey respondents, 18% identified 23%
across an enterprise. Across all these roles, GenAI effective risk management as the second most important
embedded into day-to-day processes fundamentally factor for successfully scaling GenAI solutions, just behind
Of Canadian respondents agree that
transforms roles and responsibilities, automates tedious integrating GenAI into organizational functions and
learning to infuse GenAI into the DNA
work and allows humans to focus more on human- processes. But with the technology evolving so rapidly, how
of the organization is essential
centered tasks. can risk management be effectively implemented?
6
+
Measuring value
with specific KPIs
Canadian organizations are using specific KPIs to
measure and communicate the value of GenAI
To fully unlock the benefits of GenAI, organizations must the technology. By tracking and analyzing these metrics, Participants using GenAI-specific key
performance indicators (KPIs) to evaluate
effectively measure the productivity and efficiency gains organizations can demonstrate the tangible value of
the success of their investments
from new implementations. In our global report, we GenAI and make data-driven decisions to optimize future
asked how respondents are tracking and communicating investments. Measuring productivity gains accurately is
the value created by GenAI. Globally, 48% reported critical to maximizing GenAI’s value and ensuring its long-
using GenAI-specific key performance indicators (KPIs) term integration into business operations.
48%
to evaluate the success of their investments. In Canada,
that number was even higher, with 57% of participants
leveraging specific KPIs.
Global
These targeted KPIs not only offer a clear, quantifiable
view of how GenAI is impacting business processes but
also serve as a strategic guide for further investments in
57%
Canada
7
+
Measuring value
with specific KPIs (cont’d)
Direct/
KPI indirect Description
KPIs can be measured in two ways: as direct or Response time Direct Measures the time to coherent and accurate natural
language response
indirect indicators (i.e., primary and secondary impacts).
Sample KPI metrics:
User satisfaction Indirect Analyzes quality of user experience
Resource utilization Direct Evaluates worker utilization and machine downtime
Cost reduction Direct Measures the reduction in operational costs achieved through
GenAI implementation (e.g., reduced human labor hours, lower
processing costs)
Inventory optimization Indirect Measures throughput of inventory
Error rate Direct Tracks the frequency of incorrect or nonsensical outputs, providing
insights into the reliability of GenAI outputs
Adoption rate Indirect The percentage of employees or users regularly utilizing GenAI in
their day-to-day tasks after implementation
8
Conclusion
Approach to scaling Generative AI
successfully across your enterprise
To maximize the effective implementation of GenAI, Canadian
organizations must prioritize education, strengthen data
foundations, create an environment that champions robust AI
governance and continuously monitor for risk and compliance.
As many organizations are seeking tangible benefits from
GenAI, understanding how to communicate and measure value
regarding these solutions will be vital in the coming months.
Organizations should consider the following approaches:
9
Conclusion (cont’d)
1 2
Closing the productivity gap Adopting a balanced approach that
considers trust
GenAI offers a crucial opportunity to close the
productivity gap, especially for small and medium-
Balancing risk management with the encouragement of
sized enterprises, which dominate Canada’s business
innovation can enhance public trust. AI governance must be
landscape. By integrating GenAI into their operations,
embedded in the design, development, and implementation
SMEs can boost efficiency, accelerate growth, and
of AI solutions to ensure transparency and build confidence.
contribute to improving national productivity.
There is inherent risk in adopting GenAI solutions, but it’s
worth noting that there is also risk of complacency—trying
to be too risk averse is a risk in and of itself.
10
Conclusion (cont’d)
3 4
Prioritizing key actions to drive Measuring value from specific KPIs
value from GenAI
Accurately measuring productivity gains (direct or
To maximize the value of GenAI, it is crucial to deeply indirect) through specific KPIs is essential for maximizing
embed it into various functions and processes. the value of GenAI. By identifying and tracking these
Collaborating with business functions and IT teams metrics, organizations can demonstrate the tangible
to identify, prioritize, and design use cases will benefits of GenAI and make informed decisions about
ensure that GenAI becomes a core component of the their investments.
business strategy. Don’t feel you need to solve every
problem yourself, leverage ecosystem partners and
alliances to realize solutions.
Leaders must champion these initiatives to ensure Canada Access the Q3
report here.
remains competitive globally as we embrace the potential of AI.
11
Endnotes
1 Prompting Productivity: Generative AI Adoption by Canadian Businesses; Canadian Chamber of Commerce
(2024) ) https://bdl-lde.ca/wp-content/uploads/2024/05/Prompting_Productivity_Report_May_2024.pdf
2 Ibid
3 https://globalnews.ca/news/10384078/bank-of-canada-productivity-emergency/
4 Opinion: The Budget got one thing right-living standards are slipping. Then it made things worse. Financial
post: https://financialpost.com/opinion/budget-admits-living-standards-slipping-makes-things-worse
5 Canada’s Growth Challenge: Why the economy is stuck in neutral. RBC: Canada’s Growth Challenge: Why
the economy is stuck in neutral - RBC Thought Leadership
6 Impact and opportunities: Canada’s AI Ecosystem – 2023. Deloitte: https://www2.deloitte.com/content/
dam/Deloitte/ca/Documents/press-releases/ca-national-ai-report-2023-aoda-en.pdf
1122
Contact
Audrey Ancion
Partner, AI & Data
aancion@deloitte.ca
Aisha Greene
Director, Office of Generative AI
aigreene@deloitte.ca
Contributors
Jas Jaaj Bram Judd
Managing Partner, Global AI Ecosystems Senior Consultant,
and Alliance Leader, Deloitte Global Office of Generative AI
Nihar Dalmia Andrew Klein
Partner, AI and Data, Consultant,
Consulting Practice Office of Generative AI
1133
About Deloitte
Deloitte provides audit and assurance, consulting, financial advisory, risk advisory, tax, and related services to public and private clients spanning multiple industries. Deloitte
serves four out of five Fortune Global 500® companies through a globally connected network of member firms in more than 150 countries and territories bringing world-class
capabilities, insights, and service to address clients’ most complex business challenges. Deloitte LLP, an Ontario limited liability partnership, is the Canadian member firm of Deloitte
Touche Tohmatsu Limited. Deloitte refers to one or more of Deloitte Touche Tohmatsu Limited, a UK private company limited by guarantee, and its network of member firms, each
of which is a legally separate and independent entity. Please see www.deloitte.com/about for a detailed description of the legal structure of Deloitte Touche Tohmatsu Limited and
its member firms.
Our global Purpose is making an impact that matters. At Deloitte Canada, that translates into building a better future by accelerating and expanding access to knowledge. We
believe we can achieve this Purpose by living our Shared Values to lead the way, serve with integrity, take care of each other, foster inclusion, and collaborate for measurable
impact.
To learn more about Deloitte’s approximately 412,000 professionals, over 14,000 of whom are part of the Canadian firm, please connect with us on LinkedIn, Twitter, Instagram,
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© Deloitte LLP and affiliated entities.
Designed and produced by the Agency | Deloitte Canada. 24-9715586"
317,deloitte,DI_Governance-of-AI_A-critical-imperative-for-todays-boards.pdf,"i
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Governance of AI: A critical In a new Deloitte Global survey of board
directors and executives, almost 50% say AI is
imperative for today’s boards not yet on the board agenda. Is it time to step
up AI oversight in the boardroom?
Deloitte Global Boardroom Program
About the Frontier series
This report is the latest in Deloitte’s Frontier series, a set of research initiatives from the Deloitte Global Boardroom Program that
explores critical topics boards now face. Launched in 2021, the Frontier series has covered topics such as climate change, digital
transformation, trust, and talent. Learn more about The Deloitte Global Boardroom Program.
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02 . . . Foreword
03 . . . Introduction
04 . . . Scaling up board engagement to bolster oversight
06 . . . Boards are eager to devote more time to AI-related
discussions
08 . . . AI adoption is a journey, not an instant solution
09 . . . Many boards are still getting up to speed on AI
11 . . . Near-term AI use in organizations is primarily focused on
productivity and efficiency
15 . . . Building a board governance model for AI
17 . . . Steps boards can take now to bolster AI oversight
19 . . . Endnotes
Foreword
W e are at an inflection point, not As the following research shows, it’s complicated. What
only for business and industry, is resoundingly clear, though, is that boards are eager
but for society at large. Board to spend more time on AI and gen AI, enhance their
members and executives alike are knowledge and experience, and accelerate the pace of
excited at the chance to shape a adoption in their organizations.
future powered by the latest tech-
nologies of the day, including artificial intelligence and This is a pivotal moment in the history of human inven-
generative AI. But this does not come without risk and tion—a moment future generations will certainly look
responsibility. The decisions leaders make today will back on. It’s imperative we reflect on the legacy we are
have pervasive impacts on both the organizations they creating as we navigate the path forward. We hope the
lead and societies around the world. Infusing a mindset insights from this Deloitte Global survey can spark and
of trust and ethics from the start will be vital to shaping inform meaningful conversations in your boardrooms
short-term and long-term adoption. While AI is not new, and with your management teams—inspiring a fresh
its scaled use in the enterprise and by employees brings look at whether and how AI and gen AI can play a
the question of governance and oversight of AI and gen role in your organization, all while keeping trust at the
AI into sharp focus. forefront.
So how are boards navigating these opportunities and –Lara Abrash, chair, Deloitte US
challenges? How are they balancing their time to help
ensure all pressing boardroom topics get the time and Arno Probst, Global Boardroom Program leader,
attention they deserve? And how are they confident that Deloitte Global
AI implementation is transparent, safe, and responsible
with the appropriate guardrails?
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Introduction
W hen the gen AI tool ChatGPT teams balance the wide array of opportunities and risks
exploded onto the global market that AI can introduce?
in November 2022, it democ-
ratized access to the newest AI In June 2024, the Deloitte Global Boardroom Program
capabilities within a matter of surveyed nearly 500 board members and C-suite exec-
days.1 Now, nearly two years utives across 57 countries to understand how involved
later, the growth in AI investment continues to rise: boards have been in AI governance. The survey explored
Gartner forecasts that worldwide IT spending will total sentiments about the current pace of adoption and the
US$5.26 trillion in 2024, an increase of 7.5% from 2023, board’s role in strategic oversight of this emerging
and points to generative-AI-related investments as the technology (see “Methodology”). We also spoke with
main reason behind this growth.2 board directors and Deloitte subject matter specialists
to understand how AI stewardship is evolving in board-
As organizations prepare to move past the piloting stage rooms around the world. Of note, while the survey asked
to integrate AI more broadly into strategy and opera- respondents about both generative AI and artificial intel-
tions, how active are boards in overseeing their organiza- ligence more broadly, our interviews revealed that many
tions’ approach to AI? Are they providing the right level business leaders are primarily focused on gen AI adop-
of stewardship to help the organizations’ management tion right now.
Scaling up board engagement to
bolster oversight
T he survey reveals that, so far, board-level at every meeting, 25% say it’s on the agenda twice a
engagement with AI has been limited: year, and 16% say AI is discussed annually. Nearly half
Across industries and geographies, AI is (45%) of respondents say AI hasn’t yet made it onto their
not a topic of discussion that comes up board’s agenda at all (figure 1).
often at board meetings. Only 14% of
respondents say their board discusses AI
Figure 1
In many organizations, AI is rarely discussed at the board level
How frequently AI is a topic on respondents’ board agendas
Not yet on the agenda
45%
Semiannually
25%
Once a year
Every meeting
16%
14%
Note: n = 468.
Source: Deloitte Global Boardroom Program AI/gen AI board governance survey, June 2024.
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Alongside this board research, each quarter, Deloitte US’s AI-related matters, it’s most frequently delegated to the
State of Generative AI in the Enterprise report3 surveys committee responsible for risk: either the risk and regu-
C-suite executives and board members from organiza- latory committee (25%) or the audit committee (22%).
tions that are actively implementing gen AI technologies
to continuously track progress and challenges leaders However, where broader AI oversight or specific gen
face. Its third-quarter 2024 report, which surveyed over AI oversight may ultimately land is still an open ques-
2,500 respondents, finds that while “promising pilots tion, Hodo says: “It’s still unknown whether governance
have led to more investments . . . many generative AI of gen AI should be a matter for the entire board of
efforts are still at the pilot or proof-of-concept stage.” directors or its audit committee, or [how the board will
Much like the use cases many organizations are experi- oversee] management.” Some aspects of AI oversight and
menting with, the survey shows that it’s still early stages governance might be relevant for the full board—those
for AI board governance, too. topics that are generally more pervasive—while some
might be more appropriate for a committee to handle.
While AI may not be on the board agenda itself, some Boards may also need to consider how oversight will
boards are talking about AI as part of the broader tech- be shared when some topics transcend committees, and
nology discussions they’re having with management. some may choose to establish an AI-specific committee.
“Rather than AI specifically, boards often see digital
transformation on their agenda, of which AI is a part,” We also asked respondents to comment on which C-suite
says Chikatomo Hodo, external director on the board of roles are primarily responsible for engaging with the
directors at ORIX Corporation, KONICA MINOLTA board about AI and gen AI. Most (69%) say they’re
Inc., Mitsubishi Chemical Group Corporation, and engaging with technology leaders, such as the chief
Sumitomo Mitsui Banking. information officer or chief technology officer. Half of
respondents say they are talking with their CEO about
When AI is on the board agenda, nearly half of respon- these topics, while about a quarter (26%) of respondents
dents (46%) say it is discussed at the full board level. say they are engaging with the chief financial officer.
Among those who say a committee has been tasked with
Boards are eager to devote more
time to AI-related discussions
M any respondents are cognizant that either they’re not satisfied with or they are concerned
their board’s current level of engage- about the amount of time devoted to discussions on AI
ment may not be enough to over- (figure 2).
see the opportunities and risks that
could manifest by using AI, particu-
larly gen AI. Nearly half (46%) say
Figure 2
Almost half of respondents would like their boards to be devoting more time to AI oversight
How do you feel about the amount of time your board spends on AI topics?
Not satisfied
34%
Neutral
31%
Somewhat satisfied
16%
Concerned
12%
Very satisfied
7%
Note: n = 468.
Source: Deloitte Global Boardroom Program AI/gen AI board governance survey, June 2024.
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Figure 3
Nearly half of respondents say their organizations need to accelerate progress on AI
implementation
How would you characterize the pace of adoption at your organization?
AI/gen AI is not considered
5%
relevant for our organization
Have yet to start AI/ gen
AI adoption throughout 35%
our organization
Need to accelerate 44%
Satisfied 14%
Very satisfied 2%
Note: n = 468.
Source: Deloitte Global Boardroom Program AI/gen AI board governance survey, June 2024.
However, most respondents do not believe their orga- Many respondents would like to see quicker progress:
nization is ready for broader AI deployment. Only Only 16% say they’re satisfied or very satisfied with the
3% of respondents think their organizations are very current pace of adoption (figure 3), and 44% say the
ready, while 41% say their organizations are not ready. pace needs to accelerate. These findings, combined with
This perception of a low state of readiness could also the lack of time devoted to AI on board agendas thus far,
be responsible for the growing sense of urgency as AI emphasize the opportunity boards have to contemplate,
capabilities continue to be developed. define, and scale AI oversight.
AI adoption is a journey, not an
instant solution
W hile many are eager to implement important,” Weber-Rey says. “You need the employees
AI, some boards and non-tech to be willing to adopt it because many of the areas in
leaders may not fully appreciate which you employ AI or gen AI are where there are a
how difficult it can be to scale lot of employees, like marketing, sales, risk, audit, and
AI across the enterprise. Deloitte financial reporting.”
US’s State of Generative AI in the
Enterprise Q3 2024 report explains how this challenge Deloitte US’s State of Generative AI in the Enterprise
is currently playing out: “Leaders grasp how essential report further emphasizes that both data and people
governance, risk, and compliance are for responsible are essential elements for scaling gen AI initiatives from
generative AI adoption. However, there still seems to be pilot to production.4 They are part of a critical suite of
a ‘knowing’ versus ‘doing’ gap for most organizations.” foundational elements including strategy, processes, risk
management, and technology. Getting all of these right
Daniela Weber-Rey, independent director at Fnac Darty as part of an organization’s strategy will be necessary in
and, until recently, HSBC Trinkaus & Burkhardt AG, the major transformations that AI and gen AI will likely
explains that organizations need to ensure they have a enable over the next few years and beyond.
strong foundation in place to support AI implementa-
tions. “If you don’t have the proper data management The challenge of scaling the use of AI, while remain-
system in place, you cannot really make full use of AI ing aligned to the organization’s integrated strategy, is
or gen AI. The data infrastructure must be established an area boards will need to deeply understand. Jean-
and there must be a proper data management system in Dominique Senard, chairman of the board of directors
the company.” at Renault, emphasizes, “There should be a close link
between the board and management—one that is trans-
Harder still, there’s also the challenge of getting parent, candid, and open.” Having a clear ambition for
employees to buy in and actually use the tools once AI and an understanding of the intended value it can
they are available. “Adoption by the employees is really create will be key to realizing long-term value.
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Many boards are still getting up
to speed on AI
B alancing the desire for rapid progress with the “art of the possible” in the use of AI in the enterprise.
the patience to scale effectively will be an Reflecting on the current level of understanding of AI
important line for organizations to walk. in the boardroom, over three-quarters of respondents
But to do that, their boards will need to (79%) say their boards have limited, minimal, or no
stay up to speed. According to the survey, knowledge or experience with AI (figure 4). Just 2% said
most boards have limited understanding of their boards were highly knowledgeable and experienced.
Figure 4
Nearly 80% of respondents say their boards have limited to no knowledge or experience with AI
How much does your board know about AI and how it works?
Highly knowledgeable and experienced 2%
Moderate knowledge and experience 19%
Limited knowledge and experience 41%
Minimal knowledge and experience 30%
No knowledge or experience 8%
Note: n = 468.
Source: Deloitte Global Boardroom Program AI/gen AI board governance survey, June 2024.
Figure 5
Boards are exploring various avenues to enhance AI fluency
Actions respondents say their boards are taking (multiple answers allowed)
Board members independently seeking to
57%
enhance their respective knowledge
Providing foundational education
to the board 40%
Bringing in external specialists to
discuss AI/gen AI on a regular cadence 37%
Adding AI/gen AI specialists through
new board directors 8%
Note: n = 468.
Source: Deloitte Global Boardroom Program AI/gen AI board governance survey, June 2024.
“Digital literacy needs to be elevated both within the In many countries, board education is recommended or
board and management, but we need to consider that mandated in corporate governance codes or other related
there should be a division of roles,” Chikatomo Hodo regulations or guidelines. “There is a binding recommen-
says. “In reality, there are not many external direc- dation in the French and German corporate governance
tors with an IT background, and many companies are codes that board members need to educate themselves.
prioritizing bringing a diverse range of skills and back- But there is also an obligation for the company to assist
grounds to their boards, rather than solely focusing on them in such training,” Weber-Rey explains. “This
bringing digital transformation and AI knowledge and certainly applies to gen AI or any technological changes,
experience.” such as digitalization. In the past, perhaps board direc-
tors could have gained a lot of knowledge of opera-
When considering board composition, our interviews tions by walking through the factory floors in certain
highlighted the importance of making sure the board has companies. Nowadays, you definitely need to have a
the right mix of skill sets, which could include skills in classroom-type training for gen AI, even just to get a
AI. Some boards are turning to external experts to add high-level understanding.”
to their AI literacy and fluency. Others are referring more
to operational teams in their business to understand the One approach to help boards achieve AI fluency is for
potential opportunities and challenges presented by AI. participants to use and experience AI—to “show rather
than tell.” Digital avatars, demos, and hands-on expe-
This survey shows boards are aware of the need to riences can be used as learning tools to help boards
upskill and are taking action to increase their knowledge to understand “the art of the possible” for AI in their
of AI in the boardroom (figure 5). organizations.5 These experiences can also be tailored to
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industry or sector, allowing an organization to mirror growing recognition that while board education is essen-
the most relevant opportunities and challenges in their tial, having bona fide digital expertise, particularly in AI,
operating environment.6 is increasingly seen as critical for effective governance in
today’s business landscape.
While these immersive experiences can play a critical
role in helping boards build AI fluency, some organiza- Regardless of the approaches boards pursue, it will be
tions might also wonder if education alone will ever be vital to continuously educate board members by bring-
enough. Perhaps the composition of the board should ing multidisciplinary and cross-industry perspectives to
change? Notably, 8% of respondents indicated their inform decision-making.
boards are starting to include AI specialists among their
new board directors (figure 5). This may highlight a
Near-term AI use in organizations
is primarily focused on
productivity and efficiency
T he board survey asked respondents the to incorporate AI in certain areas; 33% say they’re exper-
degree to which their organizations have imenting; and another 32% say AI hasn’t yet been incor-
been incorporating AI into their business porated into their organization’s business and operating
and operating plan. For their plans over plan over the next 12 months. Only 4% say AI is incor-
the next 12 months, about a third of porated throughout their near-term (next 12 months)
respondents (31%) have focused efforts business and operating plan (figure 6).
Figure 6
How is AI being incorporated into your organization’s business and operating plan over the
next 12 months?
Not Focused
incorporated Incorporated efforts in
at this time throughout certain areas Experimenting
32% 4% 31% 33%
Note: n = 468.
Source: Deloitte Global Boardroom Program AI/gen AI board governance survey, June 2024.
Among organizations that have started to use AI in some experience (46%), and core operations (40%) (figure 8).
capacity, respondents point to a number of strategic
areas aligned with these investments (figure 7). Jean-Dominique Senard says the evolution to AI at
Renault was a natural one, and they have already seen
Perhaps not surprisingly, enhancing productivity and its benefits as a tool for productivity and quality. “AI is
efficiency is the top strategic area (66%), followed by everywhere in the company, and it’s quite visible when
improving the customer experience (50%) and develop- you go across a Renault plant. We leverage it in the
ing new products or other innovations (46%). design department, engineering, customer relationships,
and, of course, in our vehicles. It was a normal evolution
These strategic priorities largely align with the planned for us and has proven to be incredibly powerful.”
areas for future AI investment. Top areas for future
investment include technology (63%), customer
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Figure 7
Productivity and efficiency enhancements, customer experience improvements, and
developing new products or other innovations are the main strategic goals aligned to
current AI adoption
Top reasons respondent organizations are leveraging AI (multiple answers allowed)
Enhancing
productivity and 66%
efficiency
Improving the
customer 50%
experience
Developing new
products or other 46%
innovations
Cost optimization
37%
New business
15%
expansion
Investment review,
including mergers 8%
and acquisitions
Note: n = 468.
Source: Deloitte Global Boardroom Program AI/gen AI board governance survey, June 2024.
Figure 8
Management at respondents’ organizations say advancements to technology, customer
experience, and core operations are top reasons to spend more on AI
Planned areas of focus for future AI investment (multiple answers allowed)
Technology (for example, tools, data) 63%
Customer experience 46%
Core operations 40%
Risk and regulatory 25%
Finance 23%
Talent 20%
No current investments or plans 12%
Note: n = 468.
Source: Deloitte Global Boardroom Program AI/gen AI board governance survey, June 2024.
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Building a board governance
model for AI
I n a complex environment in which opportuni- a few considerations to keep top of mind as boards
ties, challenges, and priorities frequently emerge, govern at scale: identifying and engaging with relevant
it’s critical for organizations to govern at scale. stakeholders, refining the board’s responsibilities, and
Whether related to AI oversight or any other managing risk through appropriate guardrails.
emerging issue, this means challenging ortho-
doxies while implementing balanced processes As organizations consider how boards can approach
that allow the board to operate efficiently, transpar- their AI-related responsibilities, it’s vital to first under-
ently, and in the best interests of the organization as a stand the organization’s key stakeholders. Right now,
whole—supporting growth, creating long-term value, respondents regard customers and employees as their top
and sustaining the organization. two stakeholders to consider in AI governance (figure 9).
But as AI scales, other stakeholder groups will become
Given this pivotal phase in gen AI experimentation and more of a factor in board decision-making.
adoption, what kind of role should the board play as they
build their governance models? This research showed
Figure 9
AI governance: Most respondents say customers and employees are the most important
stakeholder groups to consider
Percentage of respondents who identify the following as key stakeholders (multiple answers allowed)
Shareholders/
investors
Customers 73% Employees 69% 41% Regulators 39% Suppliers 34% General public 14%
Note: n = 468.
Source: Deloitte Global Boardroom Program AI/gen AI board governance survey, June 2024.
Daniela Weber-Rey explains, “Regulators are interested But while boards develop their oversight models, they
particularly around the risk and control aspects. But shouldn’t wait to start putting guardrails around risk
recent EU regulations on AI are new, and have not come management of AI. There is a danger people might not
fully into effect yet.” Interviewees echoed that as the understand the risks associated with gen AI. Close to
regulatory landscape expands, particularly in heavily three-fourths (72%) of respondents from Deloitte’s
regulated industries like financial services, regulators State of Generative AI in the Enterprise Q3 report esti-
will likely become an even more important stakeholder mate that less than 40% of their overall workforce has
for boards in the future. access to their organization’s approved gen AI tools.
Considering that employees may still be able to access
Which governance areas will be within the board’s some other tool on their own, the organization’s data
purview moving forward? Respondents to this board may be at risk if employees are using unsanctioned tools.
survey pointed to several areas they believe will be criti- As a result, the organization may have less control over
cal tenets of effective board oversight in the near future. how the company is integrating gen AI.
These include overall governance and oversight, includ-
ing ethics (57%); strategy development, including policy Daniela Weber-Rey agrees: “I love this phrase: ‘The
on AI (44%); risk and opportunity management (35%); biggest risk of gen AI is not taking the risk of gen AI.’”
and oversight of implementation (14%) (figure 10).
Figure 10
Respondents point to governance and ethical use, policy and strategy development, risk
management, and implementation as key tenets of AI board oversight
Percentage of respondents who say the following should be board responsibilities
Overall governance and oversight
57% 11% Staying informed of AI and its development
including ethical use
44% Strategy development including policy on AI 7% Developing and nurturing talent
35% Risk and opportunity management 3% Investment and resource allocation
14% Oversight of AI application and implementation
Notes: Based on analysis of open-ended answers; multiple choices were allowed; n = 407 (excludes vague or incomplete responses).
Source: Deloitte Global Boardroom Program AI/gen AI board governance survey, June 2024.
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Steps boards can take now to
bolster AI oversight
T he data shows that boards are eager – Regulatory scanning: How will the organiza-
to spend more time on AI and gen AI, tion review AI’s regulatory and compliance land-
enhance their knowledge and experience, scapes across the geographies and jurisdictions
and accelerate the pace of adoption in in which it operates?
their organizations. But how can boards
best navigate these opportunities and – Measurement: When and how will the organi-
challenges? The following are a few immediate actions zation review the measurement of the progress
boards can consider taking to bolster AI governance. and benefits of using AI in a way that ensures
robust oversight of investments without stifling
1. Put AI on the board agenda—and make it strategic. innovation?
Boards that aren’t yet discussing AI should consider
adding it as an agenda item. Areas to consider 2. Define the governance structure. To effectively
include: exercise oversight, boards will likely also need to
delineate and assign AI-related responsibilities.
– Cadence of discussions: How often should AI Considerations include:
be on the board agenda?
– Ownership of AI on the board: Which matters
– Special sessions: Would the board benefit from a should be discussed as a full board? Can some be
special session on AI or a board strategy retreat? delegated to a committee—and if so, which one?
– Strategy and scenario planning: Has the board – Receiving robust and beneficial information
scheduled an initial discussion with management from management: Is the board getting sufficient
to hear their analysis on risks and opportunities and appropriate information from management
related to AI and the AI ambition of the organ- about AI-related matters, including risk manage-
ization? ment and internal controls, to exercise oversight?
– Management oversight: How will the board – Having access to more leaders: Given the wide
assess, support, and, if necessary, challenge range of impacts across all areas of the business,
management’s point of view? is the board connecting to other key members
of the C-suite and business leaders beyond the
– Risk appetite: Has the board had a discussion CEO or CTO?
about risk appetite, both for the use of AI and,
more broadly, for the organization, given the
more uncertain environment that AI creates?
– Striking the right balance: Is board involvement – Revamping succession plans to be more tech-for-
too high-level to effectively govern the use of AI? ward: Have succession plans for the board and
Will deeper board education and engagement management been updated to focus on leaders
result in too much oversight? who have experience with emerging technolo-
gies, including AI? Have learning opportunities
been developed to help the pipeline of future
3. Evaluate and enhance AI literacy. To effectively
leaders expand their skills and expertise in these
oversee the opportunities and threats AI can intro-
technologies?
duce, boards should ensure they and their manage-
ment teams are AI literate. They may consider:
– Staying in the flow of action: How can the board
ensure it remains actively engaged in the evolving
– Finding opportunities for education to fill gaps
landscape of AI, guarding against complacency
in knowledge: What training and educational
and outdated perspectives and remaining agile
opportunities are available to help the board
and responsive to AI’s evolving capabilities?
upskill on AI and emerging technologies? Would
the board benefit from bringing in internal or
external experts to inform discussions?
– Reevaluating the skills matrix: Does board
composition need to be adjusted to recruit
board members with more experience with AI
and emerging technologies? What about in the
C-suite?
METHODOLOGY
The Deloitte Global Boardroom Program surveyed Industries represented include financial services those with values of US$10 billion or more (17%). (Note:
468 board members (86%) and C-suite executives (25%); manufacturing (16%); energy and resources percentages do not equal 100% due to rounding.)
(14%) in 57 countries from May to July 2024. Some (9%); business and professional services (8%); retail
respondents may serve at multiple organizations and wholesale (7%); technology (7%); health care About the Frontier series
as both executives and board members. and pharmaceuticals (5%); telecommunications,
media, and entertainment (3%); and various other This report is the latest in Deloitte’s Frontier series,
Responses were distributed across the Americas industries (20%). a set of research initiatives from the Deloitte Global
(42%), Asia Pacific (20%), and Europe, the Middle East, Boardroom Program that explores critical topics
and Africa (EMEA) (38%). Among the respondents, The survey includes respondents across a range of boards now face. Launched in 2021, the Frontier
43% serve at publicly listed companies, while 39% company sizes: Fifty-five percent of respondents series has covered topics such as climate change,
serve at privately owned companies, including represent organizations with equity market values of digital transformation, trust, and talent. Learn more
family-owned businesses. The rest came from a less than US$1 billion, followed by those with values about The Deloitte Global Boardroom Program.
mix of government and state-owned enterprises, between US$1 billion and US$10 billion (29%), and
as well as nonprofits.
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Endnotes
1. Kevin Roose, “How ChatGPT kicked off an AI arms race,” The 4. Ibid.
New York Times, February 3, 2023. 5. Deloitte, “The Generative AI dossier: A selection of high-impact
2. Gartner, “Gartner forecasts worldwide IT spending to grow use cases across six major industries,” accessed October 4,
7.5% in 2024,” press release, July 16, 2024. 2024.
3. Deloitte, “The state of generative AI in the enterprise: Moving 6. Ibid.
from potential to performance—Q3 report,” August 2024.
About the authors
Lara Abrash Karen Edelman
labrash@deloitte.com kedelman@deloitte.com
Lara Abrash is the chair of Deloitte US. Abrash stepped into this Karen Edelman is a senior editor at Deloitte Insights, where she
role in June 2023 after serving for four years as the chair and chief leads content strategy for the Deloitte Center for Financial Services
executive officer of Deloitte & Touche LLP, where she was respon- and the Global Boardroom Program. She also serves as a talent
sible for overseeing the US Audit & Assurance business. She is a adviser for Deloitte’s Research and Insights team.
member of Deloitte Global’s Board of Directors and chair of the
Deloitte Foundation.
Arno Probst
aprobst@deloitte.de
Prof. Dr. Arno Probst is the leader of the Deloitte Global Boardroom
Program. In addition to his global role, Dr. Probst leads Deloitte
Germany’s Executive & Board Program and the Center for
Corporate Governance. He is a partner in Deloitte Germany’s Audit
& Assurance practice.
Acknowledgments
The Deloitte Global Boardroom Program would like to thank participating boardroom programs around the world who supported
this project. A special thanks to our designers Meena Sonar, Natalie Pfaff, and Harry Wedel, and our editorial team, Karen Edelman,
Elisabeth Sullivan, and Annalyn Kurtz from Deloitte Insights.
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About the Deloitte Global Boardroom Program
The Deloitte Global Boardroom Program brings together the knowledge and experience of Deloitte member firms around the world to
address critical topics of universal interest to company boards and management. Supplementing country programs, its mission is to promote
dialogue between corporations and their boards and management, investors, the accounting profession, academia, and government. In
addition to the publication of thought-pieces on critical topics, the Deloitte Global Boardroom Program hosts a series of must-see webinar
discussions with eminent panelists to help boards and management of global companies to stay current and challenge perceived wisdom.
To become a member "
318,deloitte,in-ra-deloitte-ciso-guide-to-gen-ai-ap-noexp.pdf,"The CISO’s Guide
to Generative AI
Opportunities, outcomes, and the urgency of now
2
True or false? Generative AI can help:
• Unlock new opportunity and value in an organization’s cybersecurity approach.
• Reduce costs and supercharge generation of reporting and intelligence products.
• Rapidly protect against sophisticated phishing attacks.
• Guide organizations in identifying critical information based on past actions.
• Make sense of regulatory and compliance guidance.
• Build a cybersecurity road map for now and the future.
If your answers were all true, then you’re thinking correctly
about this powerful technology.
Read on for more on how Generative AI can help transform
your organization’s cybersecurity approach.
© 2024. For information, contact Deloitte Asia Pacific Limited.
33
Generative AI is here.
What can it do for you?
The Generative AI (Gen AI) buzz is everywhere. People are
wondering what this new artificial intelligence (AI) can do for their
organizations, their data, and their security. It’s a complex question
that defies an easy answer.
Gen AI is a subset of AI in which machines create But while cyber events have long since eclipsed
new content in the form of text, code, voice, the capabilities of a traditional human security
images, videos, and processes. The technology may operations center, AI is deeply impactful in
truly revolutionize work and life. When it comes enhancements to cyber infrastructure and
to cybersecurity, Gen AI holds promise for both detect-and-respond capabilities. Deep learning
organizations and governments that need to protect models are well suited to detecting attacks.
themselves, create tools to automate reporting and
intelligence, reduce costs, grow more efficiently, sort But cyber leaders may still wonder: While AI has
through the varied and ever-changing regulatory increased our defense capabilities and postures,
atmosphere, and so much more. could Gen AI take us even further? How could
it be used to limit blast radiuses of attacks,
Gen AI can also provide new tools for bad actors protect against data loss, and expand our threat
who want nothing more than to leverage these response capabilities within budget and on time?
powerful technologies for negative outcomes In other words, can it help us get ahead—
and their own gain. Cyberattacks continue to and stay ahead—of attackers?
increase in both volume and tactics: in fact, more
than 90% of respondents to the Deloitte Global
2023 Future of Cyber survey reported at least
one compromise.
© 2024. For information, contact Deloitte Asia Pacific Limited.
4
GENERATIVE AI IS HERE. WHAT CAN IT DO FOR YOU?
Gen AI can do each of those things—and it holds To unlock the potential of Gen AI, cyber leaders
so much promise for better cyber outcomes for should first understand where it can help, the
organizations seeking out and defending against types of data it needs, and how to develop a plan
breaches. It’s fast and reasoned and can process of action that includes considerations for safety,
more knowledge than any one human can. It resilience, and trustworthiness.
has the potential to reduce costs, supercharge
security investigations, and speed up third-party Two things to remember: This is an evolution
risk assessments. of AI, not a net-new concept, and adoption plans
and risk management constructs can be
While more established AI capabilities (such as evolved accordingly. And like any true evolution,
machine and deep learning) can identify patterns these are long-term transformation efforts.
and make inferences, Gen AI can put it together Adopting Gen AI for cybersecurity is a capability-
while generating human-like responses and building effort. Treat it that way.
working at extraordinarily high speeds.
It can create a new type of threat intelligence that
empowers security analysts with near real-time
incident analysis to identify and help contain
threats before they spread.
Cyber leaders are right to be concerned about
how bad actors may use Gen AI—but they should
be optimistic that, with the right approach
and governance in place, Gen AI can help an This paper will explore how Gen AI can help and what the
organization harden its cyber posture, overcome cyber considerations may be. It’s important to remember
challenges in talent, and build new road maps for
that an organization’s success with using Gen AI to
threat detection and response.
drive better outcomes rests on its ability to imagine a
collaborative intelligence between humans and machines
and to ask the right questions. Trusting that Gen AI can
make a true impact in your organization means first
understanding its power and potential.
So let’s get started.
© 2024. For information, contact Deloitte Asia Pacific Limited.
5
Gen AI’s immense Here’s what we mean:
Predict: Analyze asset inventories, security logs, threat intelligence,
value for cybersecurity etc., to help predict risk scores and recommend preventive measures.
Interpret: Summarize and process large volumes of textual data
into coherent, actionable summaries; alert reception; and parsing.
Machine learning has long been used to detect
Generate logical analysis (inference, deduction, and/or explanation)
Gen AI is a force multiplier of cyber vulnerabilities and perform threat monitoring
given context or knowledge base.
at scale, but it takes a high degree of technical
value because it can do human-
proficiency and investment to train an organization’s
like work at hyper speeds that model to understand patterns and detect anomalies Simulate: Extract information from a knowledge base to help generate
in the data. Rules-based AI, in other words, can find responses to natural language questions; create test cases and
no human can match.
only known attacks and work in specific use cases. sample scenarios.
But with Gen AI and large language models (LLMs),
Automate: Create incident response activities, including triaging
the game changes. Gen AI uses foundational neural
alerts, correlating events, and guiding incident handlers with
network models that are powered by and trained
response playbooks.
on vast amounts of data, working across data silos
and acting as a bridge between data sets. This can
give analysts a more natural method for identifying,
Detect: Identify connections between alert data and threat intelligence
synthesizing, and summarizing insights.
reports to help determine the impact on infrastructure. Update specific
responses that can guide security analysts in remediation and recovery
activities.
Interact: Analyze governing documents, laws and regulations, data,
and standards to quickly inform actions. Deliver personalized and
targeted threat and crisis response trainings to employees based
on roles, responsibilities, and job requirements.
Create: Generate content by converting it to a new format or style and
for a variety of modalities based on a set of input data, examples, or
specific themes or topics.
© 2024. For information, contact Deloitte Asia Pacific Limited.
6
Cyber risk management Threat detection Vulnerability management
Looking for Others
and compliance and response and security testing
specifics? Gen AI
Risk scoring and prioritization Actionable and precise Controls testing and automation Role mining
can help transform
Analyze asset inventories, security threat intelligence Create test cases/sample scenarios; Use Gen AI to recommend role
cybersecurity logs, and threat intelligence to Generate summarized reports/ expected outcomes; develop assignments based on user attributes
predict risk scores and recommend executive briefings for active threats supporting documentation to ensure adaptive access control
activities like these.
preventative measures from historic trends or publicly
available data Secure code generation Data classification and monitoring
Third Party Risk Management Develop application code and Classify and monitor unstructured
Analyze data in vendor submitted Threat correlation and detection relevant supplementary test cases text-based data, which enables better
and external documentation to Identify correlation between alert in line with the latest security protection against exfiltration
evaluate the security posture of data and threat intelligence reports considerations (backward integration
third-party providers to determine impact of secure coding guidelines) Training and awareness
on infrastructure Deliver personalized and targeted
Automated policy Enhanced vulnerability scanning threat/crisis response trainings
review & orchestration Security incident response Correlate vulnerability data (scan to employees based on roles,
Map current policies, standards and Automate incident response activities, data, external information and responsibilities, and job requirements
procedures against standard industry including triaging alerts, correlating remediation plans) to prioritize
and regulatory frameworks to meet events, and guiding incident handlers action plans
compliance requirements with response playbooks
Enhanced systems
Cybersecurity Enhanced recovery design/configuration
maturity assessments and remediation Augment system/security architecture
Self-assess the organization’s cyber Create specific responses that design by drafting preliminary technical
risk maturity; identify gaps in cyber can guide security analysts in specification and/or recommending
strategy and generate relevant remediation and recovery activities optimal configuration
improvement recommendations
Gen AI-enabled phishing detection
Use Gen AI to detect threats and/or
phishing attempts created by LLMs
Note: This is not an exhaustive list. Feasibility of some of these use cases must be evaluated based on data availability and other constraints.
© 2024. For information, contact Deloitte Asia Pacific Limited.
Draft requirements
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The power of pairing AI and Gen AI
An organization using Indicators of Co-pilot for
Response process
AI to detect and combat Compromise (IOCs) incident response
automation
cyberthreats is already ahead signature generation and SOC automations
of the game. Layering on Gen
AI can add further complexity
and power to its models.
Simplify requirements-gathering phase Classify IoCs (e.g., information about a Detect hidden patterns, harden Automate cyber defense strategies,
by developing prototypes of complex specific security breach that notifies defenses, and respond to incidents industry notifications, future mitigation
While a traditional AI model
applications. Provide more intuitive security teams if an attack has taken faster with triage signals and predictive strategies, etc., as part of the response
can detect threats, adding Gen engagement between the analyst and the place) using distinct signature guidance. Quickly synthesize data from process.
AI could allow it to summarize customer to better inform development. generation. multiple sources to provide actionable
the incident, prepare insights.
documentation, and create a
response action plan.
Gen AI can help an Reduces the risks of Improves visibility of cyber attacks and Introduces robust and reliable approach Improves organizational compliance
organization move beyond miscommunication (i.e., the analyst and streamlines the security team’s response to incident response, threat hunting, and with incident response plans and
customer are able to align on the with expedited identification and triage. security reporting contingency plans through automation.
rules-based analysis and
prototype before proceeding to the In doing so, improves efficacy and
expand into outputs of higher
build phase). streamlines execution.
complexity and capabilities.
• Customer engagement • Information gathering • SOC • Cybersecurity SOC
(e.g., review cycles) • Mission expertise/security • Threat detection and response • Threat response
• Storyboarding clearances
Data science, AI/ML engineering, deep learning, UI/UX design, high performance computing, prompt engineering,
Core AI skills
digital operations & delivery, multidisciplinary collaboration, computer vision, NLP
© 2024. For information, contact Deloitte Asia Pacific Limited.
88
The cyberthreat
considerations for Gen AI
To understand Gen AI’s power, an organization should be fully aware
of the considerations inherent to the technologies.
As we’ve said, Gen AI opens new opportunities for
Growing concerns and global action
organizations to prepare for and defend against
cyberattacks. But as with any new technology, Gen AI In the fall of 2023, the Biden administration
comes with risks and the potential to amplify existing announced an executive order on the safe
ones as well. and trustworthy use of AI that will likely create
downstream effects for new regulations and
Earlier AI systems were traceable, and it was standards, further complicating the regulatory
possible to understand certain outputs via its atmosphere.
data. But Gen AI is a different game with multiple
Meanwhile, the European Union (EU) is moving
parameters that can make it more challenging to
toward stringent rules around AI, even moving
trace output. Gen AI is also trained on much larger
to ban its use in some cases. As the use of
data sets than traditional AI, which can make it more
Gen AI becomes more prevalent, we expect
difficult to know where and how the data may have
governments to take more action to mitigate
been altered or where quality concerns may exist.
potential risks.
Constantly evolving risk profiles demand a
new perspective.
© 2024. For information, contact Deloitte Asia Pacific Limited.
9
There’s a lot to consider.
We’ve broken out some
current cyber risks for Gen AI:
Data breach Reputational risk
Don’t just take our word for it.
Sharing sensitive data with external Gen Bad actors can leverage Gen AI tools to
The Open Worldwide Application Security Project
AI vendors for model training or through widely and rapidly spread misinformation (OWASP) has published its Top 10 risks for large
prompts may lead to leakage of confidential and deepfakes, which can adversely influence language model applications, including trained
and/or personal information. Adversarial public opinion, trust, and/or security. data poisoning and supply chain vulnerabilities.3
attacks can also be used to deceive the ML
model by changing input data.
Unsecured integration Regulatory risk
Improper integration of Gen AI tools with Organizations using Gen AI may need to meet
other organizational systems may lead to new compliance requirements as growing
potential vulnerabilities (e.g., unsecured data concerns influence new laws, regulations,
channels) and back doors. and guidelines, such as National Institute of
Standards and Technology’s (NIST) proposed
AI Risk Management Framework1 and new EU
regulations for General Purpose AI Systems2.
(Read more)
© 2024. For information, contact Deloitte Asia Pacific Limited.
10
A framework for risks and limitations associated with Gen AI
While emerging tech has inherent risks, Deloitte’s Technology Trust Ethics (TTE) framework can be leveraged to build, deploy, and commercialize AI applications
Deloitte’s TTE framework Foundational capabilities Gen AI-specific capabilities
AI strategy Management of hallucinations and
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Deloitte’s TTE framework may be leveraged for
for specific technology threats (malicious and • Derive viable methods of accountability, trust,
foundational and Gen AI specific capabilities
environmental) that are targeted at AI models and ethics when using Gen AI and underlying
and underlying technology technology
© 2024. For information, contact Deloitte Asia Pacific Limited.
1111
How to prepare
Example checklist • Establish secure channels and
mechanisms to transfer data between
• Update policies and controls for new enterprise and cloud-hosted Gen AI
types of bias, legal, regulatory, privacy, tools.
intellectual property, and data risks of
• Review third-party controls and
With forethought and deliberation, cyber leaders can ready Gen AI.
establish contractual obligations to help
their organizations for the capabilities and risks of Gen AI. • Identify new compliance requirements protect sensitive data shared with Gen
and impacts on compliance activities AI vendors.
with existing laws and regulations.
• Monitor for novel attacks (e.g., prompt
Regardless of an organization’s particular The key is to evolve those constructs to answer • Closely evaluate use cases for Gen AI injection) and help ensure appropriate
needs, defining key outcomes and instituting the nuanced risks and threats that may be for the organization to help ensure usage of Gen AI tools to prevent
guardrails can help leaders improve risk targeted toward Gen AI or AI systems. impactful outcomes and overcome any vulnerabilities.
preparedness, promote resilience, and unlock An organization’s specific risks may depend resistance to adoption.
• Define boundaries of where and when
new business opportunities around Gen AI. on what adoption model it chooses, such as
• Implement appropriate contractual Gen AI technologies can be used within
software-as-a-service or private LLMs.
obligations for Gen AI vendors around the organization.
Leaders should recognize that Gen AI requires
security and usage of any information
new approaches to technology, training, and When choosing adoption strategies, an • Integrate secure-by-design
shared, and monitor the data sharing
processes—but that said, this is an evolution of organization should recognize the power and principles during integration of
channels used by them.
existing risks. Gen AI may not require net-new necessity of end-to-end transformation rather Gen AI applications into enterprise
road maps and trainings. An organization’s than automating one or two activities. • Implement privacy and data protection architecture.
standards and controls when
risk management and cyber constructs may • Help protect the organization’s brand
developing and training models for Gen
still work. by monitoring for misinformation,
AI tools.
and define communication strategies
• Enhance existing code review processes to counteract and decrease impact of
to help test code created by Gen AI for disinformation campaigns.
back doors and vulnerabilities.
• Take action immediately on the risks
• Implement access controls and monitor from adversarial and malicious Gen AI
use of Gen AI tools to help limit risks usage.
from inadvertent or inappropriate use.
© 2024. For information, contact Deloitte Asia Pacific Limited.
12
HOW TO PREPARE
Above all, remember: A road map for Gen AI adoption
Adoption of Gen AI by organizations will depend on six factors
should include close, constant collaboration for risk
stakeholders, including cyber leaders, chief resource
Cost and efficiency: Ability to assess whether benefits of using Gen AI-
officers, an organization’s legal team, and more, to 1
based systems outweigh the associated expenses, as handling and storing
help understand and anticipate the risks. (And don’t large datasets can result in increased expenses related to infrastructure and
forget to include testing and monitoring.) computational resources.
Knowledge and process-based work: High degree of knowledge and
2
process-based work vs. only field and physical work.
High cloud adoption: Medium-to-high level of cloud adoption, given
3
infrastructure requirements.
Low regulatory and privacy burden: Functions or industries with high
4
regulatory scrutiny, data privacy concerns, or ethics bias.
Specialized talent: Strong talent with technical knowledge and new capabilities,
5
and ability to help transform workforce to adapt quickly.
Intellectual property and licensing and usage agreements: Ability to assess
6
licensing/usage agreements and restrictions, establish and monitor related
compliance requirements, and negotiate customized agreements
with relevant vendors.
© 2024. For information, contact Deloitte Asia Pacific Limited.
13
Cyberattacks won’t stop.
The good news is, Gen AI
progress won’t either.
Gen AI could accelerate both cyberattacks and
threat response capabilities. Organizations
need to recognize both sides of that equation.
The question is, how can cyber leaders steer their teams and
organizations through the disruption while harnessing the capabilities
of what is, to date, the most powerful artificial intelligence ever created?
Many organizations are so busy fighting today’s battle that it’s hard
to conceive of creating a new Gen AI ecosystem that may require
development, operations, new talent, and evolved processes.
For any cyber leader, it’s important to start the journey toward Gen AI
with questions specific to the organization. Gen AI is an unprecedented
opportunity for a new kind of collaborative intelligence, one that can
provide increased security and next-level collaboration. So where does
a leader start? With our deep bench of cyber experience, alliance relationships,
and pragmatic perspective on the future, Deloitte can help
With one question: “What if?” From there, it’s all a new frontier.
organizations address their most pressing cybersecurity
challenges—now, and for whatever is around the bend.
Reach out to learn more.
© 2024. For information, contact Deloitte Asia Pacific Limited.
14
Endnotes
Get started
1. AI Risk Management Framework | NIST
2. https://www.europarl.europa.eu/news/en/press-room/20231206IPR15699/
artificial-intelligence-act-deal-on-comprehensive-rules-for-trustworthy-ai Authors
David Caswell, Sabthagiri Saravanan Chandramohan, Deborshi Dutt, Chris Knackstedt,
3. OWASP Top 10 for LLM Applications Version 1.1, October 16, 2023
Vikram Reddy Kunchala, David Mapgaonkar, Mike Morris, Abdul Rahman,
Kate Fusillo Schmidt, Niels van de Vorle
Contributors
Sanmitra Bhattacharya, Edward Bowen, Ben Bressler, Suzanne Denton,
Eric Dull, Lena La, Sajin Mathew, Nirmala Pudota, Stephanie Salih, Colin Soutar
Contacts – Asia Pacific Cyber leaders
Ian Blatchford Steven Feng Yuichiro Kirihara
Asia Pacific/Australia China Japan
iblatchford@deloitte.com.au stefeng@deloitte.com.cn ykirihara@tohmatsu.co.jp
Youngsoo Seo Anu Nayar Tarun Kaura
Korea New Zealand South Asia
youngseo@deloitte.com anayar@deloitte.co.nz tkaura@deloitte.com
Tse Gan Thio Max Y. Lin
Southeast Asia Taiwan
tgthio@deloitte.com maxylin@deloitte.com.tw
© 2024. For information, contact Deloitte Asia Pacific Limited.
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© 2024. For information, contact Deloitte Asia Pacific Limited."
323,deloitte,DI_Intelligent-drug-supply-chain.pdf,"Intelligent drug supply chain
Creating value from AI
About the Deloitte Centre for Health Solutions
The Deloitte Centre for Health Solutions (CfHS) is the research arm of Deloitte’s Life Sciences and Health
Care practices. We combine creative thinking, robust research and our industry experience to develop
evidence-based perspectives on some of the biggest and most challenging issues to help our clients
transform themselves and, importantly, benefit the patient. At a pivotal and challenging time for the
industry, we use our research to encourage collaboration across all stakeholders, from pharmaceuticals
and medical innovation, health care management and reform, to the patient and health care consumer.
Connect
To learn more about the CfHS and our research, please visit
www.deloitte.co.uk/centreforhealthsolutions
Subscribe
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please visit https://www.deloitte.co.uk/aem/centre-for-health-solutions.cfm
To subscribe to our blog, please visit https://blogs.deloitte.co.uk/health/
Life sciences companies continue to respond to a changing global landscape and strive to pursue
innovative solutions to address today’s challenges. Deloitte understands the complexity of these
challenges and works with clients worldwide to drive progress and bring discoveries to life.
Contents
The rationale for transforming the biopharma supply chain 2
How AI can augment supply chain transformation 6
AI’s role in helping supply chains respond, recover and thrive after
COVID-19 16
A roadmap for implementing an intelligent supply chain 21
Endnotes 32
Intelligent drug supply chain
The rationale for transforming
the biopharma supply chain
The Intelligent biopharma series explores the ways artifi cial intelligence
(AI) can impact the biopharma value chain. The fi rst two reports, Intelligent
drug discovery1 and Intelligent clinical trials,2 highlight the potential of AI to
accelerate the development of new drugs. This report explores the poten-
tial for AI technologies to improve the value of the biopharma supply chain
and manage risks more eff ectively. Evidence shows that the need for dig-
ital transformation of the supply chain has never been more pressing.
THE BIOPHARMA SUPPLY chain involves a Protecting biopharma supply chains is a priority
complex set of steps that are required to pro- not only for companies but also for all governments,
duce a drug, from sourcing and supply of given the importance of ensuring access to the
materials, through manufacturing and distribution, lifesaving and life-enhancing products that are
to delivery to the consumer. This forms a golden vital for the health and well-being of their popu-
thread between the discovery of new therapies and lations. The COVID-19 pandemic has highlighted
patients receiving them (fi gure 1).3 the importance of biopharma supply chains in
meeting the demand for leading-edge products.4
FIGURE 1
The different steps in the biopharma supply chain
Post-market
Research & Clinical Manufacturing Launch &
surveillance &
discovery development & supply chain commercial
patient support
SOURCING MANUFACTURING DISTRIBUTION DELIVERY PATIENTS
APIs and other Processing, testing Wholesale distributors, Integrated delivery Providing effective
materials and packaging labelling and networks of retail and safe treatments
serialisation pharmacies, hospitals
and clinics
PEOPLE, SKILLS AND INFORMATION SYSTEMS
REGULATORY RISK AND COMPLIANCE
TRANSPORT AND LOGISTICS
Source: Deloitte analysis.
Deloitte Insights | deloitte.com/insights
2
Creating value from AI
Biopharma supply chains create inherent resilience play in its digital transformation (Part 2). Given
risks for corporations and governments alike. the unprecedented challenges to the health
Supply chains must also meet the expectations of a care ecosystem resulting from the COVID-19
complex range of stakeholders, comprising multiple pandemic, the report also considers the role
payers, health care providers and patients with that AI can play in helping the supply chain to
complex and varied needs, both within and respond, recover and thrive (Part 3). Finally, the
across diff erent countries. Consequently, report provides a strategic roadmap for imple-
intelligent and insightful monitoring and man- menting an AI-enabled supply chain (Part 4).
agement of the supply chain is an imperative.
The risk landscape for biopharma supply
This report examines the rationale for transforming chains comprises internal, external
the supply chain (Part 1) and the role that AI can and macro risk factors (fi gure 2).
FIGURE 2
The complexity and risks affecting the globally distributed biopharma supply chain
Macro risk External supply chain Internal risk
Negative impact to Risks in upstream and Potential risks from
entire industrial chain downstream supply internal operation
due to changes in chain processes
macro environment
Geopolitical Environmental
change protection policies
Compliance Insufficient/excess
Exchange capacity Trade
rate Operational incidents barrier
fluctuation change
Production safety
Quality Technical
risks Inaccurate demand forecast bottlenecks
Product development delay
Trade secret High transportation cost
leaks Unstable
Long delivery cycle IT system
Labour High
law defect Equipment malfunction Large-scale
strikes
rate
Cold chain logistics Competition
Bankruptcy/ Infringement
Waste financial risks of IP rights Epidemic COVID-19
management outbreak
Cybersecurity
Terrorism/large Extreme weather
scale civil strife conditions
Source: Deloitte analysis.
Deloitte Insights | deloitte.com/insights
3
Intelligent drug supply chain
The complexity of
Given the above complexity and risks, governments
have established an evolving and complex frame- biologics manufacturing
work of local, regional and international regulatory and supply chains
bodies. There are also global bodies, such as the
World Health Organisation (WHO) and Interna- In comparison to ‘traditional’ small molecules,
tional Council for Harmonisation of Technical biologics have more complex supply chains (fi gure
Requirements for Pharmaceuticals for Human Use, 3). A 2019 survey of 151 experts to understand
aimed at improving regulatory collaboration.5 more about manufacturing practices and trends
identifi ed that the biggest challenges of biologics
FIGURE 3
The different complexities affecting the three main biologics manufacturing processes
RECOMBINANT PRODUCTS – VIRUS, BACTERIA, CULTURED CELLS
Proteins (recombinant
antibodies, cytotokines,
enzymes, etc.)
Antisense RNAs
In vivo gene therapies Initial batch Harvest and Inactivation Formulation Batch quality Transport/
Vaccines (DNA, RNA and thawing purification of and assembly and filling for control, distribution to
proteins for prevention and cell/ antigens or (vaccines) cryopreservation validation and hospital and
and therapy) microorganism active packaging pharmacies
growth molecules (cold chain)
HUMAN BLOOD PRODUCTS – BLOOD DONORS
Whole blood,
blood derivatives and
blood components
Tissue Transport to Tissue processing Transport to
collection processing • Testing hospital for
facilities or • Centrifugation patient treatment
storage unit • Component separation or to storage unit
(cold chain) (cold chain)
GENE AND CELL THERAPIES – PATIENTS OR DONORS
Gene and cell therapies
Autologous, including
CAR-T therapies, and
allogenic, including
stem cell therapies
Apheresis or Transport to Personalised Cell Batch quality Transport to
other tissue manufacturing bioprocessing concentration, control and hospital (cold
collection facilities or • Cells separation formulation validation chain) for
storage unit • Gene editing and filling for therapy
(cold chain) • Reprogramming cryopreservation application or
• Cell expansion to storage unit
BIOLOGIC PRODUCTION TAKES FROM SIX MONTHS TO THREE YEARS
70% OF THE TIME IS REQUIRED FOR QUALITY CONTROL
COLD CHAIN TRANSPORT AND STEPS INVOLVING CELL CULTURING
ARE KEY TO MAINTAINING YIELD AND QUALITY
Source: Deloitte analysis.
Deloitte Insights | deloitte.com/insights
4
Creating value from AI
manufacturing are process robustness (59 per requires a more agile supply chain structure. This
cent of respondents), process reproducibility (56 is particularly the case for cell therapies (known as
per cent), product yield optimisation (46 per cent) ex vivo); for example, chimeric antigen receptor
and product characterisation (42 per cent).6 T-cell (CAR-T) therapies. By the end of 2019, the
FDA had approved two CAR-T therapies; however,
Deloitte research demonstrates a continued there were some 600 clinical trials involving
growth in biologics and estimates an equal split CAR-T therapies in the biopharma pipeline.10
with small molecules in worldwide sales by 2024.7
However, large-scale production of biologics is at
The need for a new supply
present seen as one of the main challenges of the
biopharma industry, mostly due to the inherent chain model is driving the
variability of biological systems and the instability digital transformation of most
of finished products. Ascertaining the yields and
industries, supply chains
quality of these types of drugs is crucial in ensuring
that they are reproduced effectively and maintained In the past few years, manufacturing companies
until they reach patients, with regulatory bodies ap- across all industries have initiated digital trans-
plying strict inspection and reporting requirements. formation of the different steps in the supply
These need additional analytical methods that chain. Big tech giants such as Amazon, Apple
measure specific physical and biochemical prop- Inc. and Google have led the way in the early
erties to ensure these therapies remain safe and adoption of end-to-end digital supply chains. The
have not lost their activity during manufacturing.8 opportunity was created by the availability of
large amounts of reliable and relatively untapped
NEXT-GEN THERAPIES BRING data at the same time as technological break-
A NEW LEVEL OF COMPLEXITY throughs were developing, such as advanced
TO SUPPLY CHAINS analytics and AI, blockchain, digital twins and
Responses to a 2019 survey indicate that the most the Internet of Things (IoT), intelligent auto-
commercially important biopharma therapeutic mation and virtual and augmented reality.11
products currently available are monoclonal
antibodies (80 per cent), followed by vaccines (51 While many life sciences companies have been
per cent) and other recombinant proteins (36 per exploring the opportunities that digital tech-
cent). Fewer respondents mentioned cell therapies nologies offer, many are yet to make consistent,
(18 per cent), gene therapies (18 per cent) and sustained and bold moves to take advantage of
RNA-based therapies (9 per cent). When the same the new capabilities.12 Companies able to make
question was asked concerning the next five to ten the transition could rocket ahead of competitors
years, the answers were substantially different. and fend off intruders from outside the industry
Respondents put gene therapies in first place (66 trying to enter biopharma’s orbit. In 2019, the
per cent), followed by monoclonal antibodies Deloitte US Center for Health Solutions led a
(58 per cent) and cell therapies (52 per cent).9 four-day online crowdsourcing simulation with
biopharma leaders and found that companies
The high sensitivity and more precise targeting are getting closer to incorporating digital tech-
of biologics requires a direct connection between nologies more broadly in everything, from R&D,
pharma companies, the health care system and to supply chain, to patient engagement.13
even individual patients; consequently, this
5
Intelligent drug supply chain
How AI can augment supply
chain transformation
A huge amount of internal and external data is generated routinely
across the biopharma supply chain, but historically these data have
been underutilised. Simply capturing data fails to provide actionable
insights. Using AI technologies to process these data will be critical to
orchestrating operational efficiency and, ultimately, to creating a cost-ef-
fective, near autonomous and thriving biopharma supply chain.
AI TECHNOLOGIES ARE poised to transform WHAT IS AI?
supply chain and manufacturing through
AI refers to any computer programme or
real-time data processing and decision mak-
system that does something we would think
ing to make supply chains truly data-driven, of as intelligent in humans. AI technologies
reducing human subjectivity and bias. AI tools have extract concepts and relationships from
the potential to unlock commercial, regulatory and data and learn independently from data
operational data to find non-linear and complex patterns, augmenting what humans can
relationships that would otherwise be missed and do. These technologies include computer
to deliver powerful strategic insights. AI algorithms vision, deep learning (DL), machine
learning (ML), natural language processing
have the potential to deliver significant improve-
(NLP), speech, supervised learning and
ment in productivity and gross margins and
unsupervised learning.14
contribute to the sustainability of the biopharma
industry. In particular, AI algorithms can improve
end-to-end visibility, leading to more efficient Deloitte has identified five critical areas and
demand forecasting, inventory management, logis- processes of the biopharma supply chain where AI
tics optimisation, procurement, supply chain is likely to have the highest impact (figure 4). This
planning and workforce planning. is based on our research, including comprehensive
literature reviews, interviews and workshops
AI algorithms have the with colleagues working on supply chain proj-
ects, analysis of the relevant findings from the
potential to deliver
US Deloitte Center for Health Solutions’ online
significant improvement crowdsourcing simulation with biopharma leaders,
and discussions with digital technology companies.
in productivity and gross
margins and contribute to
the sustainability of the
biopharma industry.
6
Creating value from AI
FIGURE 4
Applications of AI-powered technologies in the biopharma supply chain
END-TO-END VISIBILITY
Point-to-point visibility across the whole supply chain will enable companies to become
more efficient by rapidly responding to and mitigating disruptions. AI-augmented
control towers provide advanced decision-making systems, by efficiently collecting
and managing data in real-time and generating actionable insights.
DEMAND FORECASTING, LOGISTICS AND INVENTORY MANAGEMENT
AI tools can mine and analyse data from multiple sources to detect patterns and
potential anomalies to generate accurate demand forecasts and help companies
efficiently manage their inventory levels.
INTELLIGENT AUTOMATION ENABLING INDUSTRY 4.0 AND THE INTERNET OF THINGS
Adoption of AI tools, such as ML, NLP and computer vision, into an Industry 4.0 and
IoT platform will be the key to minimising human error and leveraging operational data
to generate strategic insights and improve productivity and accuracy of processes.
OPTIMISING PREDICTIVE MAINTENANCE
AI technologies can find patterns and interdependencies between variables that
would otherwise be missed by traditional methods. Leveraging AI through real-time
performance monitoring will optimise maintenance, minimise downtime and,
ultimately, maximise productivity.
PROTECTING THE INTEGRITY OF THE SUPPLY CHAIN
Combining AI with other advanced technologies, such as blockchain, can create a
system that is immutable, transparent, secure, and shielded from counterfeit and
substandard drugs.
Source: Deloitte analysis.
Deloitte Insights | deloitte.com/insights
interconnected supply chain will allow data to
End-to-end visibility:
be safely extracted in real-time using AI tools
the holy grail of supply to generate actionable insights, consequently
chain management improving decision-making. This can help
companies mitigate disruptions and become
In biopharma’s hyper-connected globally complex more agile, effi cient and responsive (fi gure 5).
supply chain, companies need to be able to
respond rapidly to any supply chain event that The concept of end-to-end visibility can be realised
impacts outcomes. End-to-end visibility is the through supply chain control towers, which provide
foundation for quickly making the right decisions a holistic view across all supply chain functions.
to mitigate risk and deliver required outcomes. Control towers function as centralised hubs that
Supply chain visibility means having access to collect information from disparate systems to
data relating to every transaction and demand be used for monitoring, auditing and generating
trigger, across every step and tier of the supply insights.16 For control towers to be successful in
chain and all the logistics movements in between. supply chain management, companies need to
incorporate AI capabilities, such as ML, to help
Digital technologies that improve visibility across orchestrate operations and, ultimately, have
the supply chain, such as AI and blockchain, can a near autonomous and self-learning supply
create a dynamic, interoperable system where chain. In October 2019, IBM launched Sterling
transactions are transparent and traceable.15 Supply Chain Insights, an AI-enabled control
Disruptions in specifi c parts of the supply chain tower that allows for comprehensive, real-time
can impact subsequent steps, creating a cascade visibility of the supply chain by providing rapid
of ineffi ciencies. End-to-end visibility across the integration and interpretation of structured and
unstructured data at scale (case study 1).17,18
7
Intelligent drug supply chain
FIGURE 5
End-to-end visibility of the supply chain
Enhanced Real-time traceability Optimised inventory
supplier connectivity and lower time-to-value levels
Suppliers Manufacturing facilities Warehouses/ Pharmacies/hospitals/ Patients
distribution centres clinics
End-to-end visibility
Control tower
Actionable insights and powerful decision-making capabilities
Source: Deloitte analysis.
Deloitte Insights | deloitte.com/insights
CASE STUDY 1 – IBM STERLING WANTS TO UNTANGLE COMPLEXITY WITH SMARTER SUPPLY CHAINS
Tech giant IBM created Sterling Supply Chain Insights with Watson to help companies achieve end-
to-end visibility with a control tower that leverages AI to connect data across siloed systems. Watson
AI correlates data from both internal and external sources, enabling analysis of 80 per cent of
unstructured data, including digital media and weather reports. These capabilities allow companies
to better understand and assess how these data impact their entire supply chain.
When disruptions occur, Sterling Supply Chain Insights aids with faster decision-making to align issue
resolution with business objectives, optimising management while responding to unplanned events.
Sterling Supply Chain Insights is a critical part of the IBM Sterling Supply Chain Suite, an integrated
suite that enables companies to connect crucial data and supply chain processes, while leveraging
AI, blockchain and IoT. Sterling Supply Chain Insights powers the IBM Sterling Supply Chain Suite’s
Intelligence Services and Control Tower capabilities.19
Lenovo, a global technology and manufacturing company, wanted to establish greater visibility
across its complex supply chain systems and data sources to minimise disruptions and improve
customer order management. Lenovo implemented IBM Watson Supply Chain Insights to optimise
the orchestration and gain end-to-end visibility of its supply chain. With this tool, Lenovo adopted
an AI-powered approach to risk management, reducing its average response time to supply chain
disruptions from days to minutes (up to 90 per cent faster than before) and gaining opportunities
to reduce costs and drive revenues. Ultimately, these innovations could enable Lenovo to generate
more precise delivery estimates for its clients in real-time, adding value to its off ering.20
8
Creating value from AI
Demand forecasting,
and sales data, market intelligence and other
inventory management external data inputs that can affect inventory levels,
and logistics such as weather and epidemiological develop-
ments (including infectious diseases outbreaks).22
Demand forecasting plays a critical role in logistics The use of AI tools, specifically DL and ML, is
and supply chain management. Accurately adjusted particularly important in demand forecasting.
inventory levels are needed if the value of the Predictive analytics techniques can mine, analyse
supply chain is to be unlocked and, importantly, and interpret data aggregated from various
patients are to obtain timely, reliable access to sources to detect patterns and certain anomalies
their therapies.21 Forecasting uses a combination and generate more accurate demand forecasts
of decision variables, including historical shipment compared to traditional methods (case study 2).23
CASE STUDY 2 – MERCK KGaA USES ML TO OPTIMISE DEMAND FORECASTING
Merck KGaA, also known as the Merck Group, a large German multinational pharmaceutical,
chemical and life sciences company headquartered in Darmstadt, has embarked on a data-driven
supply chain operation to optimise demand forecasting.24
Merck is using Aera Technology, formerly FusionOps, an ML and cloud-based software solution that
enables a holistic and actionable view of a company’s supply network to increase efficiency across its
supply chain, including demand forecasting. Aera continuously combs through enterprise systems to
collect, harmonise and refine data, and to consequently provide real-time analytics and end-to-end
visibility of the company’s supply chain operation and performance.25
By using this technology at scale, Merck improved the forecast accuracy of 90 per cent of its
products. Aera’s AI algorithms use data collected from Merck’s enterprise resource planning
software to quickly and accurately forecast the demand for its products in terms of both quantity
and location.26
9
Intelligent drug supply chain
Having robust data on the significant macro, TRANSPORTATION LOGISTICS
external and internal risks affecting the biopharma CAN BE SUPPORTED BY AI
supply chain is critical for forecasting demand. A particular challenge of biologics production
For example, weather prediction technologies and supply is their large size and structure,
using algorithms are now fairly reliable when which is difficult to keep stable. Temperature
forecasting up to two weeks ahead.27 Still, in 2017 fluctuations and contamination can impact batch
and 2018, extreme weather conditions, such quality and yield, especially during transporta-
as droughts, floods and heatwaves, resulted in tion. Therefore, maintaining biologic APIs and
economic losses of $215 billion.28 In 2016, IBM finished products at a constant low temperature
announced the launch of Deep Thunder, a research is a key requirement, which has led to stricter
project that combines big data and AI algorithms regulations that mandate rigorous, end-to-end
and a global forecasting model built from The temperature control. Cold chain transportation
Weather Company’s vast wealth of data, to provide technology needs to be integrated with tracking
accurate predictions to weather-dependent software to ensure the effectiveness and safety of
business operations, including supply chains.29,30 therapeutics when they reach patients. By 2022,
an estimated 30 of the 50 top global biopharma
In addition, as seen in the COVID-19 pandemic products will require cold chain handling and
(Part 3), infectious diseases that spread directly specialised, temperature-controlled logistics.36
or indirectly from one individual to another can
cause serious disruption to supply chains.31 Today, In addition, research has shown that temperature
vast amounts of public health surveillance data are control is not sufficient to deliver efficacious
available from multiple sources, such as academic and safe biological medicines, as other physi-
institutions, climate databases, digital media, cochemical parameters, such as humidity, light
global transportation, genome databases, human and vibration, also affect the integrity of these
demographics, official public health organisations, compounds.37 To thrive, biopharma companies
livestock reports and social media.32,33 AI tools can can leverage advanced, intelligent technologies
make sense of these data sets by generating accu- that allow for real-time, end-to-end visibility. This
rate analyses and projections of potential infectious enables biopharma and logistics companies to
diseases outbreaks. For example, the severe acute track the state of the drugs and take proactive
respiratory syndrome (SARS) outbreak in 2003 led and timely interventions when any issue arises.
to the founding of BlueDot, a company that uses
advanced analytics and AI tools for automated,
As seen in the COVID-19
real-time infectious disease surveillance.34,35 Such
information can then be used by companies to pandemic, infectious
adjust their operations accordingly. This can be
diseases that spread
vital for biopharma to optimise the manufacturing
and distribution of specific drugs to affected areas. directly or indirectly
from one individual to
another can cause serious
disruption to supply chains.
10
Creating value from AI
Intelligent automation
which can then be unlocked and optimised by
enabling Industry 4.0 and using advanced analytics with AI capabilities.
the Internet of Things
Robotic process automation (RPA) is increasingly
Industry 4.0 aims to encourage the digitalisation deployed to reduce manual eff ort in repetitive
and automation of manufacturing processes and time-consuming tasks, minimising human
(fi gure 6). This is increasingly being adopted errors, and enabling operators to focus on high-
in the biopharma sector to help overcome the er-value and more motivating work. Today, the
multiple obstacles that the industry is facing, such convergence of AI, automation and customer data
as strict regulatory and production demands.38,39 has resulted in the emergence of a new class of
In addition, embracing Industry 4.0 will enable tools, known as intelligent process automation
companies to move towards Quality by Design (IPA). IPA combines RPA and machine learning
(QbD), a data- and risk-based approach for to deliver powerful tools that can mimic human
the development and manufacturing of drugs interaction and make advanced decisions based
that the US FDA and European Medicine on the outputs of those robotic inputs.44,45 This
Agency (EMA) are actively encouraging.40,41 will be key to minimising human intervention and
leveraging operational data to generate strategic
Digitalisation and automation of operations can insights and improve performance metrics.46,47
help biopharma companies establish cost-ef- Recent research by the Everest Group estimated
fective, reliable and robust processes that are that the 2019 intelligent automation market was
coordinated across the supply chain.42 This can worth $80 million–$85 million and is expected to
be optimised through the implementation of reach $450 million–$490 million by 2023, high-
an IoT platform, which interconnects ‘digital’ lighting the strong focus biopharma companies are
and ‘physical’ assets through the use of chips, placing on their digital and automation journeys.48
sensors and networks.43 IoT connections, there-
fore, generate a vast amount of monitoring data,
FIGURE 6
Benefits from intelligent automation, Industry 4.0 and the Internet of Things
Intelligent automation Interconnected and Computer vision
sensorised devices
Advanced analytics and decision-making through ML and NLP
IMPROVED OPERATIONAL METRICS AND PROCESS ACCURACY
BETTER ADHERENCE TO REGULATORY COMPLIANCE STANDARDS
Source: Deloitte analysis.
Deloitte Insights | deloitte.com/insights
11
Intelligent drug supply chain
The integration of IPA, IoT and Industry 4.0 in how one of the most disruptive companies in this
the manufacturing step of the biologic supply sector, Moderna Therapeutics, has digitalisation
chain is one the most promising approaches as a core part of its business strategy and is
towards reducing variability and ensuring safe and applying these types of technologies for large-
reliable large-scale production of drugs derived scale production of RNA-based therapeutics.
from living organisms. Case study 3 describes
CASE STUDY 3 – MODERNA THERAPEUTICS
Moderna Therapeutics is a clinical stage biotechnology company based in Cambridge, Massachusetts
(MA), in the United States. It is pioneering the development of messenger RNA (mRNA) therapeutics
and vaccines. Their mRNA medicines are designed to instruct the body’s cells to produce proteins
that have a therapeutic or preventive effect on a broad spectrum of diseases, including cancer and
cardiovascular, infectious and rare diseases.49
Moderna is a fully digital company that has digitisation as a core attribute of its business strategy.
Their landscape has been built on the following six key building blocks:
• AI – To enable key breakthroughs in analytics and predictive modelling that will help provide critical
insights into production and research data.50
• Cloud – Built on Amazon Web Services (AWS) Cloud to provide computational power, agility to
operate, cost-effectiveness and efficient organisation and processing of data.51
• Integration – To bring data and processes together in a consistent manner, avoiding silos of
information and manual interventions.
• IoT – Based on smart, interconnected devices to generate information about their environments
and operations. This provides real-time guidance in compliance and traceability in supply
chain and manufacturing, including controlling inventory, optimising energy consumption and
tracking material.
• Automation and robotics – Use of robotics to reach an unprecedented level of automation to
increase operation accuracy, repeatability and throughput, while reducing human errors and
improving quality and compliance.
• Analytics – Use of the latest tools and analytical methods to generate scientific and business
insights for informed decision-making.
In July 2018, Moderna opened their state-of-the-art, digitally enabled Moderna Technology Center
(MTC) manufacturing facility in Norwood, MA, which was designed to Current Good Manufacturing
Practices (cGMP) specifications. The facility has three core functions:
• Pre-clinical production – To develop materials for pre-toxicology studies using integrated robotics
to produce around 1,000 mRNA per month at research scale.
• Clinical production – To run Phase I and II clinical development programs driven by real-time data
and a fully integrated manufacturing execution system.
• Personalised cancer vaccine (PCV) unit – For the fast manufacturing and supply of
individualised batches.52
Moderna’s digital strategy enables continuous exchange of data, while reducing response time and
error proofing, to integrate compliance and provide information on all the manufacturing activities.53
12
Creating value from AI
Optimising predictive
A digitised and integrated quality and compliance
maintenance function can be a competitive advantage in terms of
innovation, pricing and quality. However, manufac-
Traditionally, manufacturing facilities have oper- turers still commonly face the challenge of reducing
ated in preventive maintenance or run-to-failure maintenance costs and duration of time-sensitive
modes. Preventive maintenance normally consists repairs, while ensuring that operating units work
of scheduled procedures, such as routine asset efficiently. Estimates suggest that between 60 to
monitoring and visual inspection, to obtain regular 73 per cent of all manufacturing data is not utilised
information on the condition of the different or analysed.57,58 By using advanced technological
system components.54 In contrast, run-to-failure tools, such as AI, these data can be transformed
maintenance lets machinery run until it breaks into vital insights about operations and equipment
down before being repaired.55 These approaches performance, by identifying patterns and complex
can make operations inefficient, lead to permanent relationships between variables, as well as fore-
equipment failure and, ultimately, may result casting failures, faults or other issues before they
in unnecessary downtime of entire production happen. ML can help manufacturing assets to be
lines with serious financial consequences. ready when needed by preventing unplanned down-
time. This technology can supply information not
A 2016 Deloitte survey found that more than only to pinpoint and address the problem causing
a third ("
324,deloitte,DI_AI-readiness-for-government.pdf,"A report from the
Deloitte Center for Government Insights
AI readiness for government
Are you ready for AI?
About the authors
Ed Van Buren | emvanburen@deloitte.com
Ed Van Buren is a principal at Deloitte Consulting LLP and the leader of Deloitte’s Government & Public
Services (GPS) Strategy and Analytics (S&A) practice. With more than 20 years of consulting and public
sector experience, Van Buren has served a diverse portfolio of clients across the civilian, defense, and
national security sectors. Prior to leading the GPS S&A practice, he led Deloitte’s United States Postal
Service account, providing a range of strategy, risk, supply chain, and technology services and growing
the account to one of the largest in the GPS practice. He specializes in strategic planning and
implementation, and has extensive project experience designing and implementing enterprise, sales,
and retail strategies, performance measures, business processes, and technology programs as part of
large-scale organizational transformations.
Bruce Chew | brchew@deloitte.com
Bruce Chew is a managing director with Monitor Deloitte, Deloitte Consulting LLP’s strategy
service line. For more than 20 years, his work has focused on strategy development and
implementation and the building of organizational capabilities. Chew is a former Harvard
Business School professor and has twice served on the advisory board panel for the president’s
Federal Customer Service Awards. He has worked with the federal government, universities,
and companies across a broad range of industries. Chew is based in Kennebunkport, Maine
William D. Eggers | weggers@deloitte.com
William Eggers is the executive director of Deloitte’s Center for Government Insights, where he is
responsible for the firm’s public sector thought leadership. His most recent book is Delivering on Digital:
The Innovators and Technologies That are Transforming Government (Deloitte University Press, 2016). His
other books include The Solution Revolution, the Washington Post bestseller If We Can Put a Man on the
Moon, and Governing by Network. He coined the term Government 2.0 in a book by the same name.
His commentary has appeared in dozens of major media outlets including the New York Times, the Wall
Street Journal, and the Washington Post. He can be reached at weggers@deloitte.com or on
Twitter @wdeggers. He is based in Rosslyn, VA.
Contents
Introduction | 2
Your AI readiness depends on your destination | 5
Key milestones on the AI journey | 8
Frequently asked questions about AI readiness | 9
Endnotes | 10
AI readiness for government
Introduction
Is your agency ready for artificial intelligence (AI)? If not, what would it take to
get to a place where it can enjoy the benefits of AI?
A GOVERNMENT AGENCY’S READINESS for If an organization wishes to progress beyond pilots,
AI is not simply a question of preparing to it is helpful to consider the following distinct but
buy and install new technology. The interdependent areas in which to assess AI
transformative nature of AI typically calls for readiness: strategy; the organizational dimensions
preparation across multiple critical areas. To of people and processes; the technology-focused
capture AI’s potential to create value, government dimensions of data; technology and platforms;
organizations will need a plan to retool the relevant and the ethical implications of this
existing processes, upskill or hire key staff, refine transformative capability (figure 1).
approaches toward partnership, and develop the
necessary data and technical infrastructure to All these six areas can be important because all are
deploy AI. likely to require action and change during the AI
journey defined by your agency. They can help you
form an initial baseline as to where you are and
how ready you are to undertake the journey:
• Strategy. Because AI is a transformative
technology, alignment on direction and level of
ambition is crucial. Define an AI vision and
goals that align with organizational objectives,
and then you can devise an approach for
managing capability across the enterprise. (See
our companion piece on “Crafting an AI
strategy for government.”)
• People. Agencies may face challenges around
accessing and recruiting necessary technical
skills, as well as helping existing employees
develop and deploy AI skills.1 To address these
22
Are you ready for AI?
FIGURE 1
AI readiness can be assessed in six areas
WHY
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Source: Deloitte analysis.
Deloitte Insights | deloitte.com/insights
areas, consider integrating AI with human it is integrated with the work and processes of
workflows, redefining talent models, and getting the organization.2
stakeholder buy-in through effective
communications and change management. • Data. AI is only as good as the data upon which
it is built, and its appetite for data is voracious.
• Processes. Establish, define, and design Design a data governance system that includes
processes, controls, and governance systems to engineering and security. Data governance
enable successful AI implementation. While AI should include rules for sourcing, accessing,
pilots can serve to provide proof of AI’s and quality management.3
potential, its true value cannot be captured until
33
AI readiness for government
To capture AI’s potential to
the computing environment. A variety of
create value, government models for pursuing AI exist4 that vary in
terms of platforms and ownership of
organizations will need a plan
technology (e.g., internal or in partnership),
but, in all cases, AI requires a coherent
to retool the relevant existing
approach that considers future
processes, upskill or hire key requirements as AI scales within the
organization and its usage evolves.
staff, refine approaches toward
partnership, and develop the • Ethics. Establish mechanisms to
understand and prevent AI bias, promote
necessary data and technical
fairness and transparency, and ensure
infrastructure to deploy AI. values and integrity are embedded in
AI-driven initiatives. While any technology’s
• Technology and platforms. Procure and deployment should be ethical, AI brings
develop appropriate AI technology and issues such as transparency, privacy, and bias
platforms to operationalize AI assets, including into particular focus.5
those related to vendors, interoperability, and
4
Your AI readiness depends
on your destination
WHILE ALL SIX areas described above 2. Process- or problem-focused use cases
should be considered in all AI initiatives,
the level of effort will largely depend 3. AI-fueled transformation (which has the
both on the current state of the organization and potential to bring the greatest change and
the ambitiousness of the agency’s vision for AI highest value)
(figure 2). Broadly speaking, an agency’s level of
ambition can be categorized as: Generally speaking, the more ambitious an
agency’s goal (further to the right in figure 2), the
1. Narrow, single-point solutions greater the value, the broader the scope, and, as a
Source: Deloitte analysis.
Deloitte Insights | deloitte.com/insights
5
EULAV
LAITNETOP
Are you ready for AI?
FIGURE 2
AI’s nonlinear nature means that agencies can start their AI journey from
any point on the AI ambition curve
High
Transformative
Use case
targeted
Holistic approaches
Single-point
seeking to transform the
solutions
organization utilizing AI to
enhance speed, efficiency,
Task-based point Process- or problem-focused and productivity while
solutions typically applications involving enhancing mission success
involving robotic intelligent automation,
process automation engagement and/or insights
Low
Narrow Broad
SCOPE OF EFFORT
5
AI readiness for government
result, the greater the technical and organizational operations businesses, but they may also be
complexity. program or mission leaders.
Narrower, single-point solutions will typically Finally, truly transformational efforts seek to
demand less of a stretch by an organization. In develop AI-fueled breakthroughs in back-office
these instances, AI can generate quick efficiencies performance or mission outcomes. Leaders identify
by automating simple processes, often in back- opportunities to fundamentally change a business
office areas most prone to standardization. This process or mission area through a combination of
allows staff to refocus their time and effort on more AI technologies and organizational and process
meaningful issues. If many such single-point changes. Areas of opportunity can include
opportunities exist, collectively, they can represent reimagined clinical trial operations, AI-augmented
or autonomous security
clearances and vetting, smart
Leaders identify opportunities to
cities, and revenue service
fundamentally change a business process collections. Transformational
uses of AI can maximize the
or mission area through a combination
technology’s value as an
of AI technologies and organizational enabler of organizational
change.
and process changes.
An agency need not limit
itself to a single approach.
significant value. This path can be a relatively easy For example, consider the AI applications that the
way for an agency to start using AI, with relatively US Department of Defense (DoD) outlines in its AI
quick returns to build support for AI solutions. strategy. The DoD has identified opportunities to
deploy AI across the ambition curve. In terms of
An approach focused on use cases considers point solutions, using intelligent automation to
common processes or problems that can be reduce time spent on manual and repetitive tasks
developed, then rolled out to other parts of the may generate low value per task, but given the
agency. This approach can yield a higher value but DoD’s size, the aggregate opportunity is very high.
generally represents a greater challenge, calling for The DoD is also looking at a specific use case for AI
a higher state of readiness. It leverages similar to enable predictive maintenance, anticipating the
types of AI across the enterprise—for example, need for repairs to critical equipment in order to
intelligent chatbots in contact centers, or natural optimize inventory levels. And on the
language processing and predictive analytics for transformative end of the curve, developing a
units that deal with large amounts of data, whether solution that can help predict or prevent
structured (such as disparate HR databases) or cyberattacks or greatly advance the ability to
unstructured (such as emails, memos, and explain AI algorithms could have transformative
documents). During this time, some agency leaders effects.6
tend to emerge who are evangelists in scaling AI
across their enterprise. Where these leaders will be Many commercial and government organizations
located depends on which use cases are chosen and are pursuing an approach that carries them from
how the adoption of AI is governed: AI leaders can left to right along the ambition curve. Starting on
come from the IT, data security, finance, or the left with point solutions can give organizations
66
Are you ready for AI?
experience in implementing AI in a less complex
setting. As they install point solutions,
organizations may also overhaul their data and
technology infrastructure to create a stronger
foundation for future AI implementations.
Organizations progressing to AI for specific use
cases often find that these projects provide solid
evidence of scalable benefits, which can encourage
strong advocacy for AI and its larger potential.
Success or failure at this stage sets the tone for AI’s
further deployment: While success tends to push
an agency toward looking at more transformative
opportunities, failure could deter agencies from
fully scaling AI and may even discourage existing
AI efforts.
Assessing your agency’s readiness depends on its
current strategy, people, processes, data,
technology and platforms, ethics choices and
governance, as well as on its high-level strategic
choices concerning its level of ambition and path
forward. These strategic choices should also reflect
the agency’s goals, challenges, and available
funding. But regardless of the precise character of
your AI path and destination, there are some
universal milestones along the way.
77
AI readiness for government
Key milestones on the
AI journey
DEVELOPING A COHERENT AI strategy is the (figure 3). Note that an agency might cycle through
first step in creating a clearer set of choices this journey multiple times if it begins first with
for building and deploying AI capabilities. point solutions and takes them through to scaling
It defines an agency’s level of ambition, guides the and ongoing management, followed by broader use
prioritization of focus areas, and, along with an cases and, ultimately, AI-fueled transformation. By
understanding of the agency’s readiness, identifies assessing where they are in this journey, agencies
what critical capabilities need to be developed. can evaluate which capabilities already exist and
which need to be built from the ground up to
The AI strategy development phase sets the stage achieve their AI effort’s desired outcomes.
for the other critical milestones on the AI journey
FIGURE 3
Milestones on the AI journey
Develop Translate Implement Scale Manage
Understand AI Design and validate Undertake rapid Scale and roll out Manage AI-enabled
potential, set AI initiatives to prototyping and proven AI solutions, solutions, updates,
ambition level, confirm costs testing of the AI addressing technical and expansion;
and prioritize and benefits applications with and organizational monitor results,
applications and establish the highest value; barriers adapting as needed
governance evaluate overall
results
Source: Deloitte analysis.
8
Are you ready for AI?
Frequently asked questions
about AI readiness
SOME COMMON QUESTIONS heard from deliver worker retraining, and build new
government leaders as they evaluate their operating models.
AI readiness:
WE HAVE ALWAYS BEEN FAST
WHAT’S THE FIRST THING I SHOULD IN IDENTIFYING USE CASES FOR
DO TO BECOME AI-READY? SPECIFIC TOOLS OR IT CAPABILITIES.
Your first action should be to assess your HOW IS AI DIFFERENT?
organization in the six areas outlined in figure 1 The nature of AI—the types of insights it can
and gauge any current gaps in capabilities, deliver (including predictive insights), its potential
infrastructure, and resources relative to to enhance engagement with citizens and other
your ambition. stakeholders, and its ability to automate highly
complex processes—means that any combination of
OUR ORGANIZATION HAS BEEN AI (deep learning, computer vision, natural
DEPLOYING SIMPLE AUTOMATION. language processing, etc.) can fundamentally
SHOULD WE PUT THIS ON HOLD UNTIL transform how you work in a way not formerly
WE DEVELOP AN AI STRATEGY? possible. But the breadth and diversity of use cases
No. Even simple automation projects can be helpful for AI means that agencies should choose carefully.
in introducing new types of work into If applied around low-value processes, in silos, or
organizations, serving as a learning experience that in areas that are not meaningful, AI is not likely to
prepares people for change. That said, before you yield significant value.
move to your 15th or 50th automation, consider
whether more complex use cases can lead to higher AI has the potential to fundamentally transform
returns and mission impact. government operations. However, agencies must
be ready to take advantage of this potential. To do
IN ADDITION TO DEDICATED this, they should build a solid foundation by
DATA SCIENTISTS AND AI putting the right data and technology platforms in
PROFESSIONALS, WHAT OTHER place, while at the same time developing the talent,
PEOPLE RESOURCES ARE NEEDED TO strategy, and governance processes needed to
START REALIZING VALUE FROM AI? effectively implement and use AI solutions.
Specific IT and AI skills are critical, but they are
not sufficient for success. Also required are AI’s transformative potential is so strong that it will
individuals who can help identify which business likely eventually become ubiquitous across
and mission areas to focus on, set up governance government. If this happens, then success in
and ethics frameworks and processes, consider navigating the AI journey will play a large part in
relevant center-of-excellence models, drive culture determining how effectively government agencies
change and change management, develop and deliver on their mission.
9
AI readiness for government
Endnotes
1. William D. Eggers et al., How to redesign government work for the future, Deloitte Insights, August 5, 2019.
2. William D. Eggers, David Schatsky, and Dr. Peter Viechnicki, AI-augmented government: Using cognitive
technologies to redesign public sector work, Deloitte University Press, April 26, 2017.
3. Omer Sohail, Prakul Sharma, and Bojan Cric, Data governance for next generation platforms, Deloitte, 2018.
4. Nitin Mittal, Dave Kuder, and Samir Hans, AI-fueled organization, Deloitte Insights, January 26, 2019.
5. Nihar Dalmia and David Schatsky, The rise of data and AI ethics, Deloitte Insights, June 24, 2019.
6. US Department of Defense, “Summary of the 2018 Department of Defense artificial intelligence strategy,” 2018.
10
Are you ready for AI?
Acknowledgments
The authors would like to thank Pankaj Kishnani for his research contributions, as well as Tina
Mendelson for her review at critical junctures and contributing her ideas and insights to this project.
About the Deloitte Center for Government Insights
The Deloitte Center for Government Insights shares inspiring stories of government innovation, looking
at what’s behind the adoption of new technologies and management practices. We produce cutting-
edge research that guides public officials without burying them in jargon and minutiae, crystalizing
essential insights in an easy-to-absorb format. Through research, forums, and immersive workshops,
our goal is to provide public officials, policy professionals, and members of the media with fresh
insights that advance an understanding of what is possible in government transformation.
11
AI readiness for government
Contact us
Our insights can help you take advantage of change. If you’re looking for fresh ideas to address your
challenges, we should talk.
Industry leadership
Ed Van Buren
Principal | Deloitte Consulting LLP
+ 1 571 882 5170 | emvanburen@deloitte.com
Ed Van Buren is a principal at Deloitte Consulting LLP and the leader of Deloitte’s Government & Public
Services (GPS) Strategy and Analytics (S&A) practice. He is based in Arlington, VA.
The Deloitte Center for Government Insights
William D. Eggers
Executive director | Deloitte Center for Government Insights | Deloitte Services LP
+ 1 571 882 6585 | weggers@deloitte.com
William D. Eggers is the executive director of Deloitte’s Center for Government Insights. He is based in
Rosslyn, VA.
Bruce Chew
Managing director | Deloitte Consulting LLP
+ 1 617 437 3526 | brchew@deloitte.com
Bruce Chew is a managing director with Monitor Deloitte, Deloitte Consulting LLP’s strategy service line.
He is based in Kennebunkport, Maine.
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327,deloitte,cdo-data-foundation-insights.pdf,"The Mission-Driven CDO
Insights from the 2023 Survey of
Federal Chief Data Officers (CDOs)
In the fall of 2023, federal department-, agency-, and bureau-level CDOs and
Statistical Officers completed a survey developed by the Data Foundation and
Deloitte to understand the evolving CDO role and CDO community needs. The
insights below are based on the results of this survey, which is the fourth annual
of its kind.
CDOs are...
Catalysts Strategists
for AI adoption and innovation within their aligning data governance and equitable practices to the organization’s
organization. mission.
• 55% of CDOs already use basic or advanced AI • CDOs are supporting their organization’s mission by maximizing the
and 95% intend to adopt new AI technologies for value of their organization’s data, supporting a data community, and
their organizations in the next year. leading the development of data policies and processes.
• The 2023 Executive Order establishing the Chief • CDOs are expanding data-driven decision making, improving data
AI Officer (CAIO) role will increase the expansion infrastructure and data quality (i.e., demographic
of AI throughout all organizations. representation in data), and promoting
inclusivity in the workplace and in staffing.
CDOs will be critical partners to CAIOs,
aligning all cross-functional areas of CDOs are responsible for orienting their
their organization to strategic AI organization towards equitable and
initiatives. data-centered approaches that serve
their mission and the public.
Champions Operators
of data literacy and culture in the of shared data agendas and evolving
workforce to keep pace with emerging needs of their organizations.
technology.
• 52% of CDOs work with a host of C-Suite
• Well-trained talent specializing in the leaders, with 60% of CDOs naming CIOs as
intersection of data, AI, and industry is cited the leader they collaborate with most frequently. In
by 60% of CDOs as a key resource needed to 2023, more CDOs (55%) experienced challenges reporting up to CIOs than in
effectively carry out their missions. 2022 (34%).
• Beyond foundational data knowledge, 75% • CDOs cite funding, authority, and staffing contraints as the top three barriers
of CDOs believe their roles also influence the hindering mission success. CDOs also provided an array of additional barriers,
organization’s data culture, encouraging data indicating that each organization faces unique challenges.
professionals to value data and use it ethically
and responsibly. With the advent of the new CAIO position, it is even more crucial for CDOs to
establish shared agendas across leaders. Despite differences among organizations,
Data literacy programs can position their the key to success is that each organization’s structure and resources empowers
organization’s staff for success and boost the CDO office to achieve their data goals and mission requirements.
data-driven decisions.
The 2023 Federal CDO Survey illustrated four emerging characteristics of
CDOs: Catalyst, Strategist, Champion, and Operator. To accelerate CDO and
CAIO journeys towards these goals, below are a sample of Deloitte’s suite of
tools and services.
The Catalyst - Thinking about innovating and adopting new technology?
AI Readiness & Management Framework (aiRMF): Partner with Deloitte to assess where you are on your AI journey, define target
outcomes, and chart a path forward across 10 AI capability areas to achieve enterprise AI readiness and maturity.
Government AI Use Case Dossier: See what’s working for other agencies and consider the ways AI can advance your mission with the
Government and Public Services Sector AI Use Case Dossier.
Trustworthy AI™: Understand seven key areas of risk for AI and keep your use of AI safe and ethical with Deloitte’s Trustworthy AI™
framework in line with NIST.
The Strategist - Thinking about equity and data centered approaches?
AI & Data Strategy Services: Align on an organizational vision for AI, prioritize AI use cases, and make strategic choices about where to
invest in AI, accelerated by Playbooks and immersive Labs guided by experienced facilitators.
CDO Playbook: See the most recent thought leadership of CDOs in the government based on trends and understanding AI priorities,
strategies, and implementation of operation models.
The Champion - Thinking about data literacy, culture, and quality?
Deloitte’s POV on Data Literacy: Learn how to support members of your organization in reading, working with, analyzing, and using
data to ethically solve challenges, drive innovation, and collaboratively create value.
Trustworthy AI™: Understand seven key areas of risk for AI and keep your use of AI safe and ethical with Deloitte’s Trustworthy AI™
framework in line with NIST.
CDO Playbook: See the most recent thought leadership of CDOs in the government based on trends and understanding AI priorities,
strategies, and implementation of operation models.
The Operator - Thinking about aligning data strategy and data processes?
AI & Data Strategy Services: Align on an organizational vision for AI, prioritize AI use cases, and make strategic choices about where to
invest in AI, accelerated by Playbooks and immersive Labs guided by experienced facilitators.
Data Labs, including CDO/CAIO Transition and the Data Strategy Lab: Create organizational vision, disrupt ordinary thinking, and
learn from industry leaders how to achieve your vision.
Contact Us
Deloitte supports many Federal clients in the data and AI space. With best-in-class AI
advice and capabilities, we can help at each stage of the race, providing Chief Data Adita Karkera Lorenzo Ross
Chief Data Officer, Deloitte Technology Fellow, Deloitte
Officers with the CDO Services they need to navigate the role of the CDO.
Government and Public Services Government and Public Services
adkarkera@deloitte.com wross@deloitte.com
About Deloitte
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member firms are legally separate and independent entities. DTTL (also referred to as “Deloitte Global”) does not provide services to clients. In the United States, Deloitte refers to one or more of the US
member firms of DTTL, their related entities that operate using the “Deloitte” name in the United States and their respective affiliates. Certain services may not be available to attest clients under the rules
and regulations of public accounting. Please see www.deloitte.com/about to learn more about our global network of member firms.
Copyright © 2024 Deloitte Development LLC. All rights reserved."
328,deloitte,DI_AI-leaders-in-financial-services.pdf,"Research from the Deloitte
Center for Financial Services
AI leaders in financial services
Common traits of frontrunners in the
artificial intelligence race
About the Deloitte Center for Financial Services
The Deloitte Center for Financial Services, which supports the organization’s US Financial Services
practice, provides insight and research to assist senior-level decision-makers within banks,
capital markets firms, investment managers, insurance carriers, and real estate organizations.
The center is staffed by a group of professionals with a wide array of in-depth industry experiences
as well as cutting-edge research and analytical skills. Through our research, roundtables, and
other forms of engagement, we seek to be a trusted source for relevant, timely, and reliable
insights. Read recent publications and learn more about the center on Deloitte.com.
Deloitte Analytics and AI
We are Deloitte Analytics. Many of the world’s leading businesses count on us to deliver power-
ful outcomes, not just insights, for their toughest challenges. Fast. Our analytics practice is built
around the wide range of needs our clients bring to us. Data scientists, data architects, business
and domain specialists who bring a wealth of business-specific knowledge, visualization and
design specialists, and of course technology and application engineers. We deploy this deep tal-
ent all over the world, at scale. To learn more, visit Deloitte.com.
Contents
Running the AI leg of the digital marathon 3
Three common traits of AI frontrunners in financial services 6
Significant challenges could lie ahead 14
Getting off to a solid start 17
Appendix: The AI technology portfolio 18
Endnotes 20
AI leaders in financial services
KEY MESSAGES
• Embed AI in strategic plans: Integrating artificial intelligence (AI) into an
organization’s strategic objectives has helped many frontrunners develop an
enterprisewide strategy for AI that various business segments can follow. The greater
strategic importance accorded to AI is also leading to a higher level of investment by
these leaders.
• Apply AI to revenue and customer engagement opportunities: Most frontrunners
have started exploring the use of AI for various revenue enhancements and client
experience initiatives and have applied metrics to track their progress.
• Utilize multiple options for acquiring AI: Frontrunners seem open to employing
multiple approaches for acquiring and developing AI applications. This strategy is
helping them accelerate the adoption of AI initiatives via access to a wider pool of
talent and technology solutions.
22
Common traits of frontrunners in the artificial intelligence race
Running the AI leg of
the digital marathon
THE FINANCIAL SERVICES industry has jump-start or adapt their AI game plans to come
entered the AI phase of the digital marathon. up on top as the race heats up?
The journey for most companies, which To answer these questions, Deloitte surveyed 206
started with the internet, has taken them through US financial services executives to get a better
key stages of digitalization, such as core systems understanding of how their companies are using AI
modernization and mobile tech integration, and technologies and the impact AI is having on their
has brought them to the intelligent automation business (see sidebar, “Methodology: Identifying
stage. AI frontrunners among financial institutions”). The
report identified some of the following key charac-
Many companies have already started implement- teristics of respondents who have gotten off to a
ing intelligent solutions such as advanced analytics, good start and taken an early lead:
process automation, robo advisors, and self-learn-
ing programs. But a lot more is yet to come as Embed AI in strategic plans: Integrating AI
technologies evolve, democratize, and are put to into an organization’s strategic objectives has
innovative uses. helped many frontrunners develop an enterprise-
wide strategy for AI, which different business
To effectively capitalize on the advantages offered segments can follow. The greater strategic impor-
by AI, companies may need to fundamentally tance accorded to AI is also leading to a higher
reconsider how humans and machines interact level of investment by these leaders.
within their organizations as well as externally with
their value chain partners and customers. Rather Apply AI to revenue and customer engage-
than taking a siloed approach and having to rein- ment opportunities: Most frontrunners have
vent the wheel with each new initiative, financial started exploring the use of AI for various revenue
services executives should consider deploying AI enhancements and client experience initiatives and
tools systematically across their organizations, have applied metrics to track their progress.
encompassing every business process and function.
Utilize multiple options for acquiring AI:
As with any race, some companies are setting the Frontrunners seem open to employing multiple
pace, while others are struggling to hit their stride approaches for acquiring and developing AI appli-
after leaving the starting gate. What can those who cations. This strategy is helping them accelerate
are seemingly at the back of the pack do to keep up the adoption of AI initiatives via access to a wider
with their frontrunning competitors? How can they pool of talent and technology solutions.
33
AI leaders in financial services
METHODOLOGY: IDENTIFYING AI FRONTRUNNERS AMONG FINANCIAL INSTITUTIONS
To understand how organizations are adopting and benefiting from AI technologies, in the third quarter
of 2018 Deloitte surveyed 1,100 executives from US-based companies across different industries that are
prototyping or implementing AI.1 In this report, we focus on a sample of 206 respondents working for
financial services companies. All respondents were required to be knowledgeable about their company’s
use of AI technologies, with more than half (51 percent) working in the IT function. Sixty-five percent of
respondents were C-level executives—including CEOs (15 percent), owners (18 percent), and CIOs and
CTOs (25 percent).
All financial services respondents in the survey were required to be currently using AI technologies in
some form or another (see “Appendix: The AI technology portfolio”). The entire respondent base of
individuals working for financial institutions could thus be considered as early adopters of AI initiatives.
Within this respondent base, we wanted to identify the practices adopted by those leading the pack in
terms of AI deployment experience and tangible returns achieved from them. Using data from Deloitte’s
AI survey, we identified two quantitative criteria for further analysis: performance (financial return from AI
investments) and experience (number of fully deployed AI implementations, which represents AI projects
that are “live,” fully functional, and completely integrated into business processes, customer interactions,
products, or services).
We found that companies could be divided into three clusters based on the number of full AI
implementations and the financial return achieved from them (figure 1). Each of these clusters
represents respondents at different phases of their current AI journey.
• Frontrunners: Thirty percent of respondents worked for companies that had achieved the highest
financial returns from a significant number of AI implementations.
• Followers: Forty-three percent of respondents worked for companies in the middle ground of AI
implementations and financial returns.
• Starters: Twenty-seven percent of respondents worked for companies that were at the start of their AI
journey and/or lagging in the level of return achieved from AI implementations.
4
METHODOLOGY: IDENTIFYING AI FRONTRUNNERS AMONG FINANCIAL INSTITUTIONS, CONT.
Frontrunners: 30%
1%
11+ 12% 8% 13%
2%
6–10 11% 11% 8%
Starters: 27% Followers: 43%
3–5 7% 7%
2% 2%
3%
1–2 7% 6%
0% or lower +10% +20% +30% or
return return return higher return
Financial return on AI investments
Note: Percentages may not total 100 percent due to rounding.
Source: Deloitte Center for Financial Services analysis, based on Deloitte Services LP “State of AI in the Enterprise,”
2nd Edition survey, 2018.
Deloitte Insights | deloitte.com/insights
5
nekatrednu
snoitatnemelpmi
lluf
fo
rebmuN
Common traits of frontrunners in the artificial intelligence race
FIGURE 1
Respondent segmentation based on AI implementations and track record
AI leaders in financial services
Three common traits of AI
frontrunners in financial
services
AS FINANCIAL INSTITUTIONS look to find a reduction; and adopt a portfolio approach for
rhythm in their AI race, frontrunners could acquiring AI, where they utilize multiple develop-
provide an early-bird view into how to ment models for implementing AI solutions
effectively integrate the technology with an organi- (figure 2).
zation’s strategy, as well as which approaches
companies could adopt for implementing such ini- EMBED AI IN STRATEGIC PLANS WITH
tiatives throughout their organization. EMPHASIS ON ORGANIZATIONWIDE
IMPLEMENTATION
From the survey, we found three distinctive traits While many financial services companies agree that
that appear to separate frontrunners from the rest. AI could be critical for building a successful com-
Frontrunners are generally able to embed AI in petitive advantage, the difference in the number of
strategic plans and emphasize an organizationwide respondents in the three clusters that acknowl-
implementation plan; focus on revenue and cus- edged the critical strategic importance of AI is
tomer opportunities, rather than just cost quite telling (figure 3).
FIGURE 2
Survey spotlights key practices among AI frontrunners in financial institutions
Embed AI in strategic plans with
1
emphasis on organizationwide
implementation
Focus on applying AI to 2
revenue and customer
engagement opportunities
3 Adopt a portfolio approach
for acquiring AI
Source: Deloitte Center for Financial Services analysis, based on Deloitte Services LP “State of AI in the Enterprise,”
2nd Edition survey, 2018.
Deloitte Insights | deloitte.com/insights
6
Common traits of frontrunners in the artificial intelligence race
FIGURE 3
Frontrunners better recognize strategic importance of AI adoption
Importance of adopting or using AI to a company’s overall business success
Minimally important Somewhat important Very important Critical strategic importance
25% 6% 2%
8% 6% 6%
8% 18% 38%
Frontrunners Followers Starters
59% 71% 53%
Frontrunners recognize the critical strategic importance over four times more
than followers and over 12 times more than starters.
Source: Deloitte Center for Financial Services analysis, based on Deloitte Services LP “State of AI in the Enterprise,”
2nd Edition survey, 2018.
Deloitte Insights | deloitte.com/insights
An early recognition of the critical importance of AI in AI more heavily than other segments, while also
to an organization’s overall business success proba- accelerating their spending at a higher rate. Close
bly helped frontrunners in shaping a different AI to half of the frontrunners surveyed had invested
implementation plan—one that looks at a holistic more than US$5 million in AI projects compared
adoption of AI across the enterprise. The survey to 27 percent of followers and only 15 percent of
indicates that a sizable number of frontrunners had
launched an AI center of excellence, and had put in
FIGURE 4
place a comprehensive, companywide strategy for AI
A significant number of frontrunners
adoptions that departments had to follow (figure 4).
have a detailed organizationwide
AI strategy
For example, as part of an overall strategy to
become a “bank of the future,” Canada-based TD
Bank set up an Innovation Centre of Excellence Frontrunners 49%
Followers 41%
(CoE). Acting like an umbrella organization, the
Starters 36%
CoE connects all the innovation initiatives, includ-
ing AI, to broader bank business units. It provides Almost half of the frontrunners
have a comprehensive, detailed,
a platform for experimentation across the organi-
companywide strategy in place for
zation with the purpose of reducing operational AI adoption, which departments
complexity and improving customer experience. are expected to follow.
The CoE thus helps in testing and identifying best
practices from AI pilots before introducing them as
full-scale customer solutions.2
It is also no surprise, given the recognition of stra- Source: Deloitte Center for Financial Services analysis,
based on Deloitte Services LP “State of AI in the
tegic importance, that frontrunners are investing Enterprise,” 2nd Edition survey, 2018.
Deloitte Insights | deloitte.com/insights
7
AI leaders in financial services
FIGURE 5
Frontrunners are investing more in AI initiatives
Investments in AI/cognitive technologies/projects in the recent fiscal year
Less than $250,000 $250,000 to $499,999 $500,000 to $999,999 $1 million to $4.99 million
$5 million to 9.99 million $10 million or more
Frontrunners
11% 11% 31% 20% 25%
Followers
11% 15% 46% 15% 12%
Starters
4% 15% 34% 30% 9% 6%
Note: All dollar amounts refer to US dollars.
Source: Deloitte Center for Financial Services analysis, based on Deloitte Services LP “State of AI in the Enterprise,”
2nd Edition survey, 2018.
Deloitte Insights | deloitte.com/insights
starters (figure 5). In fact, 70 percent of frontrun- that they consider what value they are trying to
ners plan to increase their AI investments by deliver for clients using AI.
10 percent or more in the next fiscal year, com-
pared to 46 percent of followers and 38 percent of Value delivery could either include customizing
starters (figure 6). offerings to specific client preferences, or continu-
ously engaging through multiple
channels via intelligent solutions
Frontrunners are investing in AI more
such as chatbots, virtual clones,
heavily than other segments, while and digital voice assistants.
also accelerating their spending at a
For developing an organization-
higher rate. wide AI strategy, firms should keep
in mind that these might be
applied across business functions.
A major emphasis of these investments likely was Starting purposefully with small projects and learn-
to secure the talent and technologies necessary for ing from pilots can be important for building scale.
the transformational journey ahead.3
For scaling AI initiatives across business functions,
Calls to action building a governance structure and engaging the
For financial institutions early in their AI entire workforce is very important. Adding gamifi-
journey, embedding AI in strategic initia- cation elements, including idea-generation contests
tives is an important first step. Elevating the and ranking leaderboards, garners attention, gets
critical importance assigned to these initiatives, ideas flowing, and helps in enthusing the work-
along with building a long-term AI vision and strat- force. At the same time, firms should develop
egy, lays out the foundation for the strategy. As programs for upskilling and reskilling impacted
companies customize their AI strategy based on workforce, which would help garner their contin-
their scale, size, and complexity, it is important ued support to AI initiatives.
8
Common traits of frontrunners in the artificial intelligence race
FIGURE 6
Frontrunners plan to increase AI allocations at a faster clip
Predicted change in companies’ investment in AI/cognitive
Decrease by 10–20% Decrease by 1–9% Stay the same Increase by 1–9%
Increase by 10–20% Increase by more than 20% Don’t know
2% 2%
16% 10% 5% 4% 5% 9% 17%
2%
16%
Frontrunners Followers Starters
30%
41% 46%
54% 43%
More than two-thirds of frontrunners plan to
increase their AI investments by 10% or more.
Source: Deloitte Center for Financial Services analysis, based on Deloitte Services LP “State of AI in the Enterprise,”
2nd Edition survey, 2018.
Deloitte Insights | deloitte.com/insights
FOCUS ON APPLYING AI TO That said, what differentiated frontrunners (figure
REVENUE AND CUSTOMER 7) is the fact that more leading respondents are
ENGAGEMENT OPPORTUNITIES measuring and tracking metrics pertaining to rev-
Despite steady improvement in the economy fol- enue enhancement (60 percent) and customer
lowing the 2008 financial crisis, the pressure to experience (47 percent) for their AI projects. This
reduce costs at financial institutions has contin- approach helped frontrunners look at innovative
ued to increase. At the same time, rising ways to utilize AI for achieving diverse business
competition from incumbents and nontraditional opportunities, which has started to bear fruit.
entrants, as well as greater regulatory oversight
and compliance demands, are raising the cost of A good case could be how AI and predictive analyt-
doing business. The return on average equity of ics were used by UK-based Metro Bank to help
commercial banks, for example, has yet to reach customers manage their finances. Working in part-
pre-financial-crisis levels.4 nership with Personetics, the bank launched an
in-app service called Insights, which monitored
It is no surprise, then, that one in two respon- customers’ transaction data and patterns in real
dents were looking to achieve cost savings or time. The app then provided personalized prompts
productivity gains from their AI investments. to make subscription payments and be aware of
Indeed, in addition to more qualitative goals, AI unusual spending. The AI tool also provides per-
solutions are often meant to automate labor- sonalized financial advice, including savings
intensive tasks and help improve productivity. recommendations and alerts.5
Thus, cost saving is definitely a core opportunity
for companies setting expectations and measuring Frontrunners have taken an early lead in realizing
results for AI initiatives. better business outcomes (figure 8), especially in
9
AI leaders in financial services
FIGURE 7
Frontrunners focus on revenue and customer opportunities in addition to
cost reduction
Frontrunners Followers Starters
Cost reduction Revenue enhancement Customer engagement
60%
54%
49%49% 49%
47% 46% 46% 47%47%
42%
40%
Cost savings targets Productivity targets Revenue targets Customer-related
(sales, cross-selling) targets (engagement,
satisfaction, and
retention)
Source: Deloitte Center for Financial Services analysis, based on Deloitte Services LP “State of AI in the Enterprise,”
2nd Edition survey, 2018.
Deloitte Insights | deloitte.com/insights
achieving revenue enhancement goals, including relevant response. Another project uses algo-
creating new products and pursuing new markets. rithms to study central bank documents and
understand the central bank’s economic perspec-
This mindset was reflected in the overall perfor- tive. The bank is also actively evaluating
mance among respondents as well, with opportunities to deploy AI for automating claims
frontrunners reporting a companywide revenue handling, detecting fraud, and providing personal-
growth of 19 percent according to the survey, ized recommendations to clients.7
which was in stark contrast to the growth of
12 percent for followers and a decline of Calls to action
10 percent for starters. While exploring opportunities for
deploying Al initiatives, companies
Meanwhile, our research indicated that companies should explore product and service expansion
should give special emphasis to the human-cen- opportunities. This could be kick-started by mea-
tered design skills needed to develop personalized suring and tracking outcomes of AI initiatives to
user experiences.6 In fact, the survey found that the company’s top line. Adding AI adoption to
frontrunners are already starting to suffer from a sales and performance targets and providing AI
shortage of designers for AI initiatives, which indi- tools for sales and marketing personnel could also
cates the high degree of application of these skills help in this direction.
by frontrunners during AI implementations.
To boost the chances of adoption, companies
Nordic bank Nordea is using AI to lead multiple should consider incorporating behavioral science
efforts across the organization. Nova, an internally techniques while developing AI tools. Companies
developed chatbot, uses natural language process- could also identify opportunities to integrate AI
ing to interpret customers’ queries and decide the into varied user life cycle activities. While
10
Common traits of frontrunners in the artificial intelligence race
FIGURE 8
Frontrunners have achieved better business outcomes across revenue objectives
Frontrunners Followers Starters
Cost reduction Customer engagement
32%33% 32%33% 32%33%
15% 15%
6%
Reduce headcount Reduce operating Improve customer
through automation costs experience
Revenue enhancement
47%
44%
39%
33%
25% 24% 27% 24%
13%
9% 8%
6%
Optimize external Free up workers Create new products Pursue new markets
processes such as to be more creative
marketing and sales by automating tasks
Source: Deloitte Center for Financial Services analysis, based on Deloitte Services LP “State of AI in the Enterprise,”
2nd Edition survey, 2018.
Deloitte Insights | deloitte.com/insights
working on such initiatives, it is important to also From our survey, it was no surprise to see that
assign AI integration targets and collect user feed- most respondents, across all segments, acquired
back proactively. AI through enterprise software that embedded
intelligent capabilities (figure 9). With existing
Companies can also look at making best-in-class vendor relationships and technology platforms
and respected internal services available to exter- already in use, this is likely the easiest option for
nal clients for commercial use. most companies to choose.
ADOPT A PORTFOLIO APPROACH For example, Guidewire, maker of enterprise soft-
FOR ACQUIRING AI ware solutions for insurance companies, offers its
As market pressures to adopt AI increase, CIOs of users access to AI capabilities through its
financial institutions are being expected to deliver Predictive Analytics for Claims app. The app uti-
initiatives sooner rather than later. There are mul- lizes machine learning algorithms to categorize
tiple options for companies to adopt and utilize AI claims based on their severity and the potential for
in transformation projects, which generally need litigation, automatically routing any high-priority
to be customized based on the scale, talent, and claims to the correct departments.8 Similarly,
technology capability of each organization. Salesforce helps users access AI through its
11
AI leaders in financial services
Einstein program, which applies machine learning developing AI in multiple ways (figure 9)—what
to historical sales data and predicts which pros- we refer to as the portfolio approach.
pects are most likely to close.9
This portfolio approach likely enabled frontrun-
However, the survey found that frontrunners (and ners to accelerate the development of AI solutions
even followers, to some extent) were acquiring or through options such as AI-as-a-service and auto-
mated machine learning. At the same time,
FIGURE 9
Frontrunners are comfortable in developing AI through multiple options
Frontrunners Followers Starters
AI as a service
56%
55%
32%
Enterprise software with integrated AI/cognitive
61%
51%
58%
Data science modeling tools
54%
58%
34%
Automated machine learning
63%
60%
36%
Codevelopment with partners
49%
57%
42%
Open-source AI/cognitive development tools
65%
63%
40%
Crowdsourced development communities
56%
39%
32%
Note: Chart indicates percentage of respondents that are already using the above-mentioned ways to acquire AI.
Source: Deloitte Center for Financial Services analysis, based on Deloitte Services LP “State of AI in the Enterprise,”
2nd Edition survey, 2018.
Deloitte Insights | deloitte.com/insights
12
Common traits of frontrunners in the artificial intelligence race
through crowdsourced development communities, well-fitting model within a few hours. This model
they were able to tap into a wider pool of talent was converted to an application programming
from around the world. interface (API), which was combined with RPA to
automate the entire email classification, depart-
Adopting the portfolio approach could help compa- ment identification, and mail-forwarding process.10
nies preserve the legacy business process while
utilizing AI for incremental gains. American Calls to action
Fidelity Assurance, a US-based health and life Financial institutions that have never uti-
insurance company, was evaluating options to lized multiple options to access and
improve the handling of a growing volume of cus- develop AI should consider alternative sources for
tomer emails and mapping the flow to different implementation. Companies would need time to
departments. The company’s R&D team was gather the requisite experience about the benefits
exploring both robotic process automation (RPA) and challenges of each method and find the right
and machine learning applications, albeit sepa- balance for AI implementation.
rately. While RPA was a good match for
automatically sending mails to the correct depart- Once companies start implementing AI initiatives,
ment, it was providing too many rules for a mechanism for measuring and tracking the effi-
identifying the right department based on the cacy of each AI access method could be evaluated.
email’s subject and keywords. To resolve this, the Identifying the appropriate AI technology approach
team decided to explore automated machine learn- for a specific business process and then combining
ing with the help of a third-party vendor. Using the them could lead to better outcomes.
database of customer emails and eventual depart-
ment response (outcome), the company found a
13
AI leaders in financial services
Significant challenges
could lie ahead
AS FINANCIAL SERVICES companies advance Indeed, starters would likely be better served if
in their AI journey, they will likely face a they are cognizant of the risks identified by front-
number of risks and challenges in adopting runners and followers alike (figure 11) and begin
and integrating these technologies across the orga- anticipating them at the onset, giving them more
nization. But not all are facing the same set of time to plan how to mitigate them.
challenges. Our survey found that frontrunners
were more concerned about the risks of AI (figure We observed a similar pattern in terms of the skills
10) than other groups. gap identified by different segments in meeting the
needs of AI projects (figure 12). More frontrunners
With the experience of several more AI implemen- rated the skills gap as major or extreme compared
tations, frontrunners may have a more realistic to the other groups. While a higher number of
grasp on the degree of risks and challenges posed implementations undertaken could partly explain
by such technology adoptions. Starters and follow- this divergence, the learning curve of frontrunners
ers should probably brace themselves and start could give them a more pragmatic understanding
preparing for encountering such risks and chal- of the skills required for implementing AI projects.
lenges as they scale their AI implementations.
FIGURE 10
Divergence in risk estimation for different segments
Companies’ investment in AI/cognitive
No concern Minimal concern Moderate concern Major concern Extreme concern
1%
47%5% 11% 30% 11% 13% 8%
19%
Frontrunners Followers Starters
18% 19% 39% 34% 45%
Almost two-thirds of frontrunners highlight potential risks
associated with AI to be of major or extreme concern.
Source: Deloitte Center for Financial Services analysis, based on Deloitte Services LP “State of AI in the Enterprise,”
2nd Edition survey, 2018.
Deloitte Insights | deloitte.com/insights
14
Common traits of frontrunners in the artificial intelligence race
FIGURE 11
Top risks of AI that each respondent segment is most concerned about
AI risk Frontrunners Followers Starters
Cybersecurity vulnerabilities of AI/cognitive 1 2 1
Making the wrong strategic decisions based
2 1 3
on AI/cognitive recommendations
Regulatory noncompliance risk 3 3 4
Erosion of customer trust from AI/cognitive
4 5 7
failures
Ethical risks of AI/cognitive 4 4 6
Legal responsibility for decisions/actions
6 5 2
made by AI/cognitive systems
Failure of AI/cognitive system in a
7 7 5
mission-critical or life-and-death context
We have no concerns about potential risks
8 8 8
of AI/cognitive
Note: Respondents were asked to select up to three risks and rank them in order of concern, 1 being that of
highest concern.
Source: Deloitte Center for Financial Services analysis, based on Deloitte Services LP “State of AI in the Enterprise,”
2nd Edition survey, 2018.
Deloitte Insights | deloitte.com/insights
FIGURE 12
Divergence in skills estimation for different segments
No skills gap at all/We have all the skills needed Minimal skills gap Moderate skills gap
Major skills gap Extreme skills gap/We have almost no skills needed
2%
25%15% 12%4% 29% 17% 28%
25%
12%
Frontrunners Followers Starters
18%
30% 30% 53%
More than half of frontrunners believe they have a major or
extreme skills gap in meeting requirements for their AI projects.
Source: Deloitte Center for Financial Services analysis, based on Deloitte Services LP “State of AI in the Enterprise,”
2nd Edition survey, 2018.
Deloitte Insights | deloitte.com/insights
15
AI leaders in financial services
Delving deeper into the capabilities needed to fill User experience could help alleviate the “last mile”
their skills gap, more starters and followers believe challenge of getting executives to take action based
they lack subject matter experts who can infuse on the insights generated from AI. Frontrunners
their expertise into emerging AI systems, as well as seem to have realized that it does not matter how
AI researchers to identify new kinds of AI algo- good the insights generated from AI are if they do
rithms and systems. not lead to any executive action. A good user expe-
rience can get executives to take action by
While these skills are often necessary in the initial integrating the often irrational aspect of human
stages of the AI journey, starters and followers behavior into the design element.
should take note of the skill shortages identified by
frontrunners, which could help them prepare for That said, financial institutions across the board
expanding their own initiatives. Frontrunners sur- should start training their technical staff to create
veyed highlighted a shortage of specialized skill and deploy AI solutions, as well as educate their
sets required for building and rolling out AI imple- entire workforce on the benefits and basics of AI.
mentations—namely, software developers and user The good news here is that more than half of each
experience designers (figure 13). financial services respondent segment are already
undertaking training for employees to use AI in
their jobs.
FIGURE 13
Skills required for implementing AI programs
Skills for AI efforts Frontrunners Followers Starters
Software developers 34% 27% 16%
AI researchers 24% 27% 32%
Data scientists 27% 21% 30%
User experience designers 41% 23% 22%
Business leaders 15% 21% 16%
Project managers 20% 21% 16%
Subject matter experts 15% 25% 38%
Change management/transformation
22% 29% 27%
experts
Source: Deloitte Center for Financial Services analysis, based on Deloitte Services LP “State of AI in the Enterprise,”
2nd Edition survey, 2018.
Deloitte Insights | deloitte.com/insights
16
Common traits of frontrunners in the artificial intelligence race
Getting off to a solid start
AS COMPANIES PREPARE for the AI leg of playing catch-up. Build a strong AI foundation
their digital marathon by revamping their that can be ramped up quickly across
processes and working environments, it is the organization.
imperative they revisit their fundamentals—goals,
strengths, and weaknesses. This could help organi- 2. Get early wins under your belt. Aim at
zations lay a solid foundation for rethinking how generating early success by pursuing a diverse
humans and machines interact within working portfolio of achievable projects. This would help
environments. Answering the following questions generate confidence among management and
could be a good start: the broader workforce at the beginning of the
transformation process, while building momen-
• What existing business goals and missions can tum to roll out more AI initiatives for multiple
the organization achieve by deploying AI? business functions.
• How could AI be used to build a 3. Emphasize organizationwide AI imple-
competitive advantage? mentation once experience (and success)
is achieved. Develop a holistic approach by
• Does the organization have ad"
332,deloitte,2024-tmt-outlook-technology.pdf,"2024 technology
industry outlook
2024 technology industry outlook
What’s inside
Executive summary 3
Angling for a comeback, with help from cloud,
AI, and cybersecurity 4
Striking a balance between globalization and self-reliance 6
Setting the stage for growth with generative AI 7
Reckoning with regulations for the tech industry 9
Signposts for the future 11
About Deloitte’s Outlooks
Our 2024 outlook for the technology industry seeks to identify the strategic issues and opportunities for tech organizations to consider in the
coming year, including their impacts, key actions to take, and critical questions to ask. The goal is to help equip US technology organizations
with the information and foresight to better position themselves for a robust and resilient future.
2
2024 technology industry outlook
Executive summary
The technology industry flourished during the early pandemic years might shift or augment their offerings to meet that demand. While
as companies accelerated their digital transformation efforts. But the generative AI has sparked imaginations and headlines, with tech
industry has hit several speed bumps over the past two years. High giants investing billions9 and startups playing a key role,10 enterprise
inflation, elevated interest rates, and considerable macroeconomic and purchasing in this specific category isn’t expected to ramp up
global uncertainties contributed to a softening of consumer spending, until at least the second half of 2024.11 Deloitte expects nearly all
lower product demand, falling market capitalizations, and workforce enterprise software and service companies to integrate generative AI
reductions in 2022.1 Headwinds continued into 2023, with slight capabilities into at least some of their offerings in the coming year.12
weakening of global tech spending and rising layoffs. But there are now
• Striking a balance between globalization and self-reliance.
glimmers of hope that a tech comeback may be imminent: Economists
The worldwide, interconnected nature of the tech industry heightens
have lowered their assessments of recession risk, and analysts are
the risk of disruptions from geopolitical unrest, supply chain
optimistic that the tech sector could return to modest growth in 2024.2
volatility, raw material shortages, and new regulations and policies.
Leaders should diversify their supply chains and manufacturing and
As the technology market faced heightened global challenges over
development locations, spreading operations among trusted regions
the past few years—geopolitical tensions, supply chain volatility, raw
and ensuring redundancy. As governments refine trade policies, tech
material shortages, and emerging regulations—Deloitte urged tech
companies should be agile in adapting their strategies.
leaders to evaluate where manufacturing happens, to improve the
transparency and resiliency of their supply chains, and to prepare • Setting the stage for growth with generative AI. The next
proactively for future systemic risks.3 We suggested leaders use year is expected to be transitional for generative AI, with tech
technology to streamline business processes, rely more on intelligent companies experimenting and finding applications that can drive
automation, reduce tech debt by implementing leading practices efficiency and productivity. Some will likely evaluate how to speed
for software development, and modernize legacy architectures by up software development with generative AI-enabled tools. At the
migrating to cloud resources and XaaS.4 We also recommended same time, providers can determine how to best deliver generative AI
that tech companies consider how to extend their reach into other capabilities and how to monetize them. As generative AI investment
industries, using digital advancements to spur transformation.5 Finally, and experimentation accelerate in the coming months, the legal and
we advised leaders to build up talent in critical areas such as artificial regulatory landscape may evolve rapidly, setting the stage for greater
intelligence (AI), robotic process automation (RPA), and cybersecurity.6 adoption in the second half of 2024 and into 2025.
• Reckoning with regulations for the tech industry.
With global and economic uncertainties continuing into 2024, these
Governments around the world are evaluating the impacts that
recommendations remain important. But it’s likely time to refocus on
massive tech platforms and social networks have on businesses
innovation and growth as well. A Q4 2023 Deloitte survey of 122 tech
and consumers. In the coming months, regulations in the European
executives revealed an optimistic perspective: 55% of respondents
Union and the United States will likely take effect, pushing tech
rated the tech industry as “healthy” or “very healthy,” and even more
companies to prioritize data protection, harm reduction, the ethical
(62%) believe it will be at that level six months from now.7 Asked to
use of AI, and commitment to sustainability goals.13 A global minimum
choose their company’s primary area of focus, “efficiency” topped the
tax aims to close loopholes and push corporations to pay more, while
list (with 25% selecting it), while “innovation” and “productivity” tied for
new credits and incentives are designed to spur sustainable growth
second place at 21%. “Growth” was a close third, at 19%. These leaders
and job creation. With strong collaboration between business,
described the current state of the tech industry as “innovative” and
legal, accounting, and finance teams, tech companies can elevate
“evolving”—and nearly two-thirds (62%) believed it was a good time
compliance efforts into competitive advantages.
for their company to take greater risks.
Each of these themes represents a strategic opportunity for tech
Some of the strategies we expect tech leaders to focus on in 2024
companies to reduce risk and set the stage for sustainable growth
and beyond include:
in the next 12 to 18 months. Prudent investment in supply chain
• Angling for a comeback, with help from cloud, AI, and resilience and data governance may serve as a hedge against
cybersecurity. Enterprise spending on software and IT services— geopolitical and regulatory shifts, while generative AI can streamline
particularly artificial intelligence, cloud computing, and cybersecurity operations in the immediate term and accelerate longer-term
technology—is expected to enable the most growth in the tech innovation efforts.
market over the coming year.8 Tech leaders should assess how they
3
2024 technology industry outlook
Angling for a comeback, with help
from cloud, AI, and cybersecurity
High interest rates, worries about the potential for a slowing economy, continued to contend with lower VC deal activity and valuations—
and geopolitical challenges contributed to a slight weakening of global but Deloitte expects that the valuation corrections may fuel renewed
tech spending in 2023.14 Facing decelerating revenue growth, many interest from venture capitalists and corporate buyers.20 The 2023
tech companies ramped up layoffs last year, continuing to adjust their uptick in tech IPO activity—following a slump since the end of
workforces after aggressive hiring in prior years.15 Now, there may be 2021—could signal the start of a positive trend that allows more
light on the horizon: Economists are more optimistic about the US tech companies to exit successfully.21 While there are some positive
economy as a whole, lowering the risk of a recession in 2024 to below indicators, tech leaders should remain vigilant about ongoing risks
50%. Deloitte’s analysis pegs the risk at just 20%.16 and forge their own careful strategies for growth in 2024.
For the tech sector specifically, analysts are optimistic about a potential What could help drive this tech rebound? Global IT investments are
return to modest growth in 2024, with more robust prospects for 2025. expected to be fueled largely by double-digit growth in spending
Predictions for growth in global IT spending in 2024 cover a range from for software and IT services in 2024.22 Analysts estimate that public
5.7% to 8%.17 cloud spending will grow by more than 20%, and they foresee
stronger demand for cybersecurity.23 AI investment (not specifically
There are signs that aspects of a tech rebound may already be generative AI) is also seen as contributing to overall spending growth.24
underway. In 2023, the stock values of the so-called Magnificent Economists have projected that AI-related investments could reach
Seven—the seven largest US tech companies—surged, outperforming $200 billion globally by 2025, led by the United States.25 Beneficiaries
the rest of the S&P 500 Index.18 The heavily tech-weighted Nasdaq of that spending include companies that create and train AI models,
Composite index took a mere 18 months to recover 80% of its 2021 all- provide infrastructure to run AI (such as cloud services), and supply AI
time high—versus taking 14 years to regain 80% of its 2000 peak after applications or services.26
the dot-com crisis.19 At the smaller end of the spectrum, startups
44
2024 technology industry outlook
A Q4 2023 Deloitte survey of tech executives reinforces the analyst An uptick in tech mergers and acquisitions (M&A) in 2024 would be
viewpoints: The survey asked leaders which technologies they another sign of a tech recovery—but is far from certain. Leaders have
expected to enable the most growth in the tech industry in the traditionally viewed strategic tech M&A as a growth engine, but with
next 12 months.27 Artificial intelligence,28 cloud computing, and the continued high cost of financing and focus on belt-tightening, 2023
cybersecurity topped the list (with 52%, 47%, and 40% of tech proved disappointing for tech M&A. Deal volume and the total market
leaders choosing each as a top-three technology, respectively).29 value of those transactions remain well below their 2021 highs.36 On
the bright side, a handful of billion-dollar-plus enterprise tech deals has
What about generative AI, which has grabbed headlines, captured given analysts a reason to hope that the tide may turn for tech M&A
the attention of tech leaders, and fueled a notable wave of in the coming months.37 Technology geared toward productivity and
experimentation over the past year (see “Setting the stage for growth efficiency improvement—including industrial automation and decision
with generative AI”)? Deloitte expects generative AI growth in 2024 to intelligence platforms—is seen as having the potential to spark
be modest, with adoption and spending picking up in the second half, renewed M&A activity.38 AI may also prove to be a driver: Companies
followed by more robust growth in 2025.30 Tech execs seem bullish may obtain AI technology and expertise via acquisitions, rather than
about imminent generative AI spending: More than a quarter (27%) building their own capabilities.39
of respondents to the Deloitte survey selected generative AI as a
top-three industry growth driver for the coming year.31 Perhaps due
to the level of investment or effort required for full-scale generative
AI initiatives, respondents from larger companies (those with 10,000+
employees or US$10B+ in revenue) selected generative AI at a higher Strategic questions to consider:
rate than others. Notably, tech giants with plans to invest billions
in generative AI will likely play a part in the sector’s rebound.32 • How will our company navigate the evolving economic landscape,
continued high cost of borrowing, and ongoing geopolitical
challenges while still achieving our growth objectives?
Cybersecurity is also expected to play a key role in the comeback.
Analysts forecast that global spending on security and risk • Has our company evaluated how adopting AI—specifically
management will see low double-digit growth from 2023 to 2024.33 generative AI—might help us drive productivity and efficiency
gains? Have we considered how embedding generative AI
Motivators include a persistent threat landscape, ongoing cloud
capabilities into our products and services could help drive
adoption, remote work, the emergence of generative AI, and evolving
revenue and competitive advantage?
data privacy and governance regulations.34 While the rapid adoption
• Is our company continually assessing the security threat
of generative AI may expose organizations to new attack surfaces
landscape and keeping up with the latest advances in security
and techniques, AI may also play a pivotal role in speeding up
and risk management? Are we considering how AI could play
breach detection and containment.35 a role in helping us boost our defenses?
• How can we ensure that our workforce has the right mix of
skills for competitive success? Are we focused on building
expertise in growth areas, especially cloud, generative AI,
and cybersecurity?
• Are we considering how strategic acquisitions could
complement our existing capabilities, help us innovate,
expand our market reach, and even augment our talent?
5
2024 technology industry outlook
Striking a balance between
globalization and self-reliance
The worldwide, interconnected nature of the tech industry, with crises underscore the risks of over-relying on tech talent in any one
its global supply chains and international manufacturing and location.48 Leaders should consider expanding their workforce in
development centers, makes it highly vulnerable to global shocks secure regions and taking care that pivotal functions and roles are
including natural disasters, pandemics, and geopolitical tensions.40 distributed. Tech jobs have specialized training and educational
needs that continue to evolve due to advancements such as
Supply chain resilience is no longer simply prudent; it’s critical. generative AI. Redistributing talent pools likely means partnering
To help mitigate the risk of disruption, tech giants are diversifying with universities and engineering schools; working more closely with
their manufacturing and development locations and supply chains, local tech schools, vocational schools, and community colleges; and
reducing reliance on single suppliers or countries.41 Leaders often supporting national institutions that promote STEM fields.49
now view it as imperative to establish relationships with suppliers
worldwide and spread operations across various trusted regions. All Tech companies may be able to boost resilience in their operations
critical product components and elements of the value chain should and supply chains by co-investment and knowledge-sharing initiatives
have redundancies and alternate sources. Moreover, tech companies with channel partners, contract manufacturers, and suppliers. This
should work closely with suppliers to ensure resilience and flexibility could involve helping suppliers with approvals and logistics as they
throughout the production network. work to establish facilities in different regions, as well as offering
essential talent, engineering, and administrative proficiency as they
As the geopolitical landscape continues to shift, governments spin up new operations.
worldwide are redefining their trade policies. Tech companies should
monitor these changes and align their strategies accordingly. Countries Throughout 2024, tech companies will likely continue to prioritize
and trading blocs often offer incentives, subsidies, and tax credits to sustainability and resilience, aiming to strike the right balance
encourage the localization of technology supply chains and innovation between globalization and onshoring/self-reliance. Organizations
hubs.42 This trend is particularly evident in the semiconductor should continue to globalize their operations to take advantage of
industry, where the United States and Europe are making substantial lower costs, greater access to talent, and faster innovation. However,
investments to build out domestic chip fabrication capacity, especially they should also look to onshore or self-source critical components
at advanced processing nodes. They’re also ramping up assembly and and operations to reduce their risks from global disruptions.
packaging capabilities, although from a low base.43 Strategic planning
should include sustainability assessments, tracking, and reporting,
both to secure maximum credit and ensure compliance with local
and international regulations.44
Strategic questions to consider:
After severe chip shortages began in 2020, the US government
passed the CHIPS and Science Act of 2022—which provides $52 • Has our company adequately evaluated our supply chain
billion in financial assistance to spur research and manufacturing and operational vulnerabilities? Do we have a strategy for
mitigating them?
in the domestic semiconductor industry.45 Semiconductor
manufacturers are working to identify which parts of their supply • Is our company’s supply chain designed for flexibility in the
chain should be domestic (onshoring), which parts can be in short term and sustainability in the long term? Have we
implemented multi-sourcing strategies to ensure a stable
countries close to home (nearshoring), and which parts can be
supply chain?
handled in countries considered to be allies (friendshoring).46
For some tech companies, particularly hardware and electronics • Have we determined the right blend of onshoring,
nearshoring, and friendshoring?
manufacturers, full onshoring may be impractical or infeasible—
but a blend of onshoring and friendshoring could help provide • Have we assessed the stability of our onshore and global talent
a hedge against instability.47 pools, ensuring that critical functions are not concentrated in
vulnerable regions? Is there a way to distribute our tech talent
to make it more resilient to global disruptions?
As organizations identify potential choke points and determine how
to reengineer their operations and processes to improve resilience,
they may also focus on building redundancy into their research
and development operations and talent pool. Recent geopolitical
6
2024 technology industry outlook
Setting the stage for growth
with generative AI
Over the past year, generative AI sparked the public imagination, Tech leaders should consider how to best utilize and deliver this new
unleashed new avenues for creativity, fueled a surge of startups, functionality. This could involve using “off the shelf” solutions from
and became a strategic consideration for many of the world’s largest cloud and tech providers with generative AI integrated, building their
companies. The next year is poised to be a time of transition, with own proprietary solutions (which could be prohibitively expensive),
tech leaders assessing how to best deliver and monetize generative or partnering with co-developers.
AI, how to integrate the technology into their operations, and how
to address considerable challenges around data privacy, copyright, Tech companies may use all these approaches to incorporate
and emerging regulations. generative AI into existing or new offerings. One possibility is that AI
solutions will evolve into an ecosystem where large players provide
Innovating with generative AI foundational platforms and contextual models as commodities,
In the past year, US tech companies focused intensely on generative while additional parties build capabilities and functions on top to
AI, embedding it into their offerings and signaling plans to double cater to the specific needs of their customers.58
down on investments.50 Across the sector, many tech companies will
face the challenge of how to augment their products and services Focusing on productivity
with generative AI to remain competitive. On the software front, Like their counterparts in other industries, many tech leaders
Deloitte has predicted that nearly all enterprise software companies are experimenting with embedding generative AI capabilities into
will embed generative AI into at least some of their products in their workflows to assist professionals and augment business
2024 and that the revenue uplift (for these companies and for the processes.59 At this stage, many are focused on optimizing
cloud providers of generative AI processing capacity) will approach productivity and efficiency. A recent Deloitte survey of marketing
a US$10 billion run rate by the end of the year.51 Deloitte expects leaders found that 26% already use generative AI (e.g., for content
2024 to be a transition year, as generative AI-enabled software tools marketing), and another 45% expect to use it by the end of 2024.
launch and adoption and revenues start to gain traction, setting the Users estimated the technology has saved them more than 11 hours
stage for more robust potential growth in 2025.52 On the hardware per week.60
front, Deloitte expects the uplift for chips and servers that execute
generative AI to surpass US$50 billion in 2024.53 Generative AI is being used to facilitate sales—from interpreting
customer requirements documents to developing proposals and
Several tech companies associated with generative AI experienced prioritizing leads—and to improve customer service (e.g., helping
rising valuations in 2023, partially due to excitement around the human agents respond to questions and solve problems and even
technology’s potential.54 However, they’re still figuring out how anticipating customers’ future needs).61 Research has revealed
to monetize and profit from generative AI. Deploying and scaling that more than eight in 10 sales professionals surveyed feel using
generative AI involves heavy-duty servers packed with expensive, generative AI helps them speed up customer communication and
power-hungry chips, and the operational costs can range from US$0.01 increase sales, while nine in 10 service professionals believe it
to US$0.36 per generative AI query.55 Some providers who charge helps them address customer needs more quickly.62 Companies
a per user per month (PUPM) fee may be losing money currently are also driving back-office efficiency by embedding generative AI
due to those who use the service more heavily than anticipated.56 capabilities into functions such as finance and order management—
We expect that tech companies will continue to grapple with how to accelerating processes, reporting, and insights. Tech leaders should
translate generative AI into increased revenue, experimenting with consider where to adopt generative AI in their company to best
a variety of pricing models (such as consumption-based, PUPM, improve productivity and how they might use it to improve customer
or a hybrid approach).57 interactions and enhance tech support.
7
2024 technology industry outlook
Particularly important for tech companies, generative AI tools are International regulations governing privacy, potential harm, and
boosting programmer productivity and may be on the verge of ethical practices are also high on the list of concerns for generative
transforming software development.63 These tools can act as coding AI adopters. The EU’s AI Act, for instance, is expected to be adopted
and testing partners, suggesting lines of code, developing boilerplate in the second quarter of 2024, with a 24-month implementation
code, writing documentation, generating synthetic test data, and period for most obligations.72 US companies are working to comply
creating test cases.64 A survey of professional developers found that with the Biden administration’s October 2023 executive order
44% are already using AI tools in their development process, and governing the safe and secure development and use of AI.73 The
another 26% plan to do so soon.65 With productivity gains reported order will require certain developers of “very powerful” foundation
in the 10% to 30% range, tech leaders should evaluate where they models to share safety test results with the government. It will also
can bring generative AI into their development processes.66 impose requirements for federal agencies, including the use of
watermarks to identify AI-generated content, measures to protect
Adopters that are further along in their evaluations and may have user privacy, and efforts to minimize bias (see “Reckoning with
completed successful pilots with generative AI will likely focus on regulations for the tech industry”).
the challenge of scaling up and operationalizing the technology.67
Moving to production use will likely involve prioritizing highest-
value use cases, mapping them to core capabilities required for
implementation, and developing an implementation road map.
Strategic questions to consider:
Avoiding legal and regulatory pitfalls
The use of generative AI raises considerable challenges around • Have we determined which use cases and workflows could
data privacy and content use. One area of concern for tech leaders be best improved with generative AI? Have we assessed
where we could deploy generative AI in our value chain?
is whether the large language models (LLMs) used in generative
AI implementations have been trained using copyright-protected • Are we evaluating how generative AI could create
content.68 To address concerns, several leading software companies opportunities for new products, services, business models,
and, ultimately, new revenues?
have pledged to assume liability in case their tools expose customers
to IP infringement claims.69 Another misgiving is whether a company • Does our workforce have the right set of skills for upcoming
might lose control of its own data when it’s added to public models, generative AI initiatives? For example, have we considered
training existing staff to improve generative AI literacy? Are
whether through accidental data leaks or adversarial prompt
we recruiting the right talent?
engineering.70 As a result, Deloitte expects that more companies will
begin training generative AI on their private enterprise data—but • How will the changing legal and regulatory landscape affect
our generative AI plans? Are we setting the right guardrails on
this approach could raise challenges around access to talent and
our generative AI initiatives?
specialized GPUs.71 Generative AI adopters should weigh the risks of
publicly trained models and the expense and expertise required for
building proprietary models as they decide which approach is right
for their company.
8
2024 technology industry outlook
Reckoning with regulations
for the tech industry
Large online platforms built up enormous power and influence over
the past decade, and regulators are considering how to best address
the potential risks. Tech companies of every size are under pressure
to ensure data protection, harm reduction, ethical use of AI, and
commitment to sustainability goals. They’re also tasked with pivoting
to maximize tax credits and incentives while minimizing effective tax
rates in the face of new global tax regulations.
Content and corporate conduct
The largest tech companies are affected by the European Union’s
Digital Services Act (DSA) and Digital Markets Act (DMA). The DSA
includes a raft of new rules around consumer protection, holding
online platforms and service providers responsible for content
moderation, fraud, and unscrupulous uses of their technologies.74
It also imposes stringent requirements on consumer-facing tech
companies that collect customer data.75
The DMA requires platforms to eliminate practices that stifle
competition, including granting third-party businesses and
advertisers more access to data and allowing them more freedom
to engage customers outside the platform. Some of the “large
platforms” identified by the EU are challenging their designations
in court.76
AI everywhere
The proliferation of AI has also spurred a new wave of regulatory
developments.77 Expected to begin taking effect in 2024, the EU’s
AI Act—which is nearly finalized—takes a risk-based approach to
AI implementations, requiring visibility into the quality of data
sets used, technical documentation and recordkeeping, human
oversight, accuracy, and cybersecurity.78 It applies to any AI system
that outputs results used in the EU, and it is expected to impact AI
providers in the United States.79
In the United States, President Biden signed an executive order
on October 30, 2023, that seeks to promote the safe and secure
development and use of AI and creates requirements related to
the use of AI throughout the federal government. The executive
order directs the development of both voluntary and mandatory
guidance to govern the use of AI in the public and private sectors.
It includes more than 100 directives to agencies, which will mostly
be implemented over the next year. The Commerce Department
will play an important role in implementation and has formed
a US AI Safety Institute to help develop technical guidance for
other agencies.80
9
2024 technology industry outlook
Global tax equality which was signed into law in fall 2023, requires climate disclosures
Another factor that tech companies will likely encounter in 2024 is the and climate-related financial risk reporting from any company with
OECD’s Pillar Two global minimum tax (GMT). Some countries have revenues greater than US$1 billion doing business in California.85
passed legislation and many others are proposing legislation to activate
these rules. This ruleset is designed to ensure that multinational Taken together, these developments could drive increased
corporations pay a minimum of 15% regardless of location, removing investment in cybersecurity, data management, and ESG reporting
the incentive to locate headquarters in low-tax jurisdictions. solutions. Tech companies will likely benefit by working with
regulators and taking an active role in testing their products
Certain factors, including credits and incentives, may bring the effective and services for compliance.
tax rate in a country below 15%, in which case these companies will
have to pay a “top-up” tax to meet the 15% threshold. This may reduce
or eliminate the benefit of the incentive.81
For the tech industry, the way different jurisdictions operationalize Strategic questions to consider:
these rules and how they define and treat credits and incentives may
lead to operational shifts. Countries may jockey to build out incentive • How can we ensure that our AI implementations don’t expose
programs that don’t have an impact on effective tax rates. the company to potential regulatory and legal risk?
• What investments should we explore in cybersecurity and
ESG credits and compliance data governance to achieve compliance with new
No regulatory outlook would be complete without a discussion of consumer-protection regulations?
environmental, social, and governance (ESG) reporting requirements. • Can we leverage regulatory sandboxes to test our products
The EU’s Corporate Sustainability Reporting Directive (CSRD) and services?
expands the number of companies required to provide sustainability
• How can we model potential tax scenarios now to inform
disclosures from around 12,000 to more than 50,000.82 It also operational decisions for 2024–2025?
imposes requirements around double materiality; companies must
• How can we maximize ESG credits and incentives while
report the impacts that ESG efforts have on their businesses and the
preserving our effective tax rate?
impacts they’re expected to have on the environment, human rights,
social standards, and sustainability-related risk.83 These rules apply
to multinational entities (like tech giants) that meet certain revenue
criteria. European branches of these companies may have to provide
consolidated reporting on their parent company’s activities as well.
In the United States, the Federal Acquisition Regulatory Council
has proposed a rule that would require certain federal contractors
to disclose their greenhouse gas (GHG) emissions, as well as their
climate-related financial risk, and set science-based targets to
reduce their emissions.84 California’s Climate Accountability Package,
10
2024 technology industry outlook
Signposts for the future
2024 finds the technology industry preparing for a return
to growth. Tech companies may protect themselves
against future global disruptions by engineering a balance
between globalization and self-reliance, and they’re
gearing up for a raft of expected regulations. Preparations
will likely involve doubling down on data governance,
cybersecurity, and supply chain resilience. At the same
time, tech companies are looking at generative AI as a way
to achieve greater efficiency in the near term—and as a
way to fuel innovation and growth for themselves and
other industries in the long term.
In the coming year, tech companies should be on
the lookout for potential signals of change in the
market, including:
• Shifting macroeconomic co"
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Tech Trends 2025 In Deloitte’s 16th annual Tech Trends report,
AI is the common thread of nearly every
trend. Moving forward, it will be part of the
substructure of everything we do.
02 . . . Executive summary
INTRODUCTION
05 . . . AI everywhere: Like magic, but with algorithms
INTERACTION
09 . . . Spatial computing takes center stage
INFORMATION
17 . . . What’s next for AI?
COMPUTATION
27 . . . Hardware is eating the world
BUSINESS OF TECHNOLOGY
37 . . . IT, amplified: AI elevates the reach (and remit) of the tech function
CYBER AND TRUST
45 . . . The new math: Solving cryptography in an age of quantum
CORE MODERNIZATION
53 . . . The intelligent core: AI changes everything for core modernization
CONCLUSION
60 . . . Breadth is the new depth: The power of intentional intersections
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Executive summary
Tech Trends, Deloitte’s flagship technology report, as electricity to daily business and personal lives. As
explores the emergence of trends in three elevating forces our team in Deloitte’s Office of the CTO put finishing
(interaction, information, and computation) and three touches on Tech Trends 2025, we realized that AI is a
grounding forces (business of technology, cyber and common thread in nearly every trend. We expect that
trust, and core modernization)—all part of our macro going forward, AI will be so ubiquitous that it will be a
technology forces framework (figure 1). Tech Trends part of the unseen substructure of everything we do, and
2025, our 16th trip around the sun, previews a future we eventually won’t even know it’s there.
in which artificial intelligence will be as foundational
Figure 1
Six macro forces of information
technology
INFORMATION
What’s next for AI?
INTERACTION COMPUTATION
Spatial computing Hardware is
takes center stage eating the world
BUSINESS OF CORE
TECHNOLOGY MODERNIZATION
IT, amplified: The intelligent core:
AI elevates the reach CYBER AI changes
(and remit) of the AND everything for core
tech function modernization
TRUST
The new math:
Solving
cryptography in an
age of quantum
2
3
yrammus
evitucexE
Introduction efficient choice for all organizational needs. Enterprises
are now considering small language models and open-
AI everywhere: Like magic, but with algorithms source options for the ability to train LLMs on smaller,
more accurate data sets. Together with multimodal
Generative AI continues to be the buzzword of the models and AI-based simulations, these new types of
year, but Tech Trends 2025—and in fact, the future of AI are building a future where enterprises can find the
technology—is about much more than AI. This year’s right type of AI for each task. That includes AI that not
report reveals the extent to which AI is being woven only answers questions but also completes tasks. In the
into the fabric of our lives. We’ll eventually take it for coming years, a focus on execution may usher in a new
granted and think of it in the same way that we think of era of agentic AI, arming consumers and organizations
HTTP or electricity: We’ll just expect it to work. AI will with co-pilots capable of transforming how we work
perform quietly in the background, optimizing traffic and live.
in our cities, personalizing our health care, or creating
adaptative and accessible learning paths in education.
We won’t proactively use it; we’ll simply experience a Computation
world in which it makes everything work smarter, faster,
and more intuitively—like magic, but grounded in algo- Hardware is eating the world
rithms. The six chapters of Tech Trends 2025 reflect this
emerging reality. After years of software dominance, hardware is reclaim-
ing the spotlight. As AI demands specialized computing
resources, companies are turning to advanced chips to
Interaction power AI workloads. In addition, personal comput-
ers embedded with AI chips are poised to supercharge
Spatial computing takes center stage knowledge workers by providing access to offline AI
models while “future-proofing” technology infrastruc-
Spatial computing continues to spark enterprise interest ture, reducing cloud computing costs, and enhancing
because of its ability to break down information silos and data privacy. Although AI’s increased energy demands
create more natural ways for workers and customers to pose sustainability challenges, advancements in energy
interact with information. We’re already seeing enter- sources and efficiency are making AI hardware more
prises find success with use cases like advanced simula- accessible. Looking forward, AI’s continued integration
tions that allow organizations to test different scenarios into devices could revolutionize the Internet of Things
to see how various conditions will impact their oper- and robotics, transforming industries like health care
ations. With a stronger focus on effectively managing through smarter, more autonomous devices.
spatial data, organizations can drive more cutting-edge
applications. In the coming years, advancements in AI
could lead to seamless spatial computing experiences Business of technology
and improved interoperability, ultimately enabling AI
agents to anticipate and proactively meet users’ needs. IT, amplified: AI elevates the reach
(and remit) of tech talent
Information After years of progressing toward lean IT and every-
thing-as-a-service offerings, AI is sparking a shift away
What’s next for AI? from virtualization and austere budgets. Long viewed
as the lighthouse of digital transformation through-
To take advantage of the burgeoning excitement around out the enterprise, the IT function is now taking on AI
generative AI, many organizations have already adopted transformation. Because of generative AI’s applicability
large language models (LLMs), the best option for many to writing code, testing software, and augmenting tech
use cases. But some are already looking ahead. Despite talent in general, forward-thinking technology leaders
their general applicability, LLMs may not be the most are using the current moment as a once-in-a-blue-moon
opportunity to transform IT across five pillars: infra- core enterprise systems represents a significant shift in
structure, engineering, finance operations, talent, and how organizations operate and leverage technology for
innovation. As both traditional and generative AI capa- competitive advantage. This transformation is about
bilities grow, every phase of tech delivery could see a automating routine tasks and fundamentally rethinking
shift from human in charge to human in the loop. Such a and redesigning processes to be more intelligent, efficient,
move could eventually return IT to a new form of lean IT, and predictive. It requires careful planning due to inte-
leveraging citizen developers and AI-driven automation. gration complexity, strategic investment in technology
and skills, and a robust governance framework to ensure
smooth operations. But beware of the automation para-
Cyber and trust dox: The more complexity is added to a system, the more
vital human workers become. Adding AI to core systems
The new math: Solving cryptography may simplify the user experience, but it will make them
in an age of quantum more complex at an architectural level. Deep technical
skills are still critical for managing AI in core systems.
In their response to Y2K, organizations saw a loom-
ing risk and addressed it promptly. Today, IT faces a
new challenge, and it will have to respond in a similarly Conclusion
proactive manner. Experts predict that quantum comput-
ers, which could mature within five to 20 years, will Breadth is the new depth: The power
have significant implications for cybersecurity because of intentional intersections
of their ability to break existing encryption methods and
digital signatures. This poses a risk to the integrity and Organizations have long relied on innovation-driven new
authenticity of data and communications. Despite the revenue streams, synergies created through mergers and
uncertainty of the quantum computer timeline, inaction acquisitions, and strategic partnerships. But increasingly,
on post-quantum encryption is not an option. Emerging segmentation and specialization have given way to inten-
encryption standards offer a path to mitigation. Updating tional intersections of technologies and industries. For
encryption practices is fairly straightforward—but it’s a example, when two technologies intersect, they are often
lengthy process, so organizations should act now to stay complementary, but they can also augment each other so
ahead of potential threats. And while they’re at it, they that both technologies ultimately accelerate their growth
can consider tackling broader issues surrounding cyber potential. Similarly, new opportunities can emerge when
hygiene and cryptographic agility. companies aim to extend their market share by purpose-
fully partnering across seemingly disparate industries.
Core modernization
The intelligent core: AI changes
everything for core modernization
Core systems providers have invested heavily in AI,
rebuilding their offerings and capabilities around an
AI-fueled or AI-first model. The integration of AI into
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INTRODUCTION
AI everywhere: Like magic,
but with algorithms
Tech Trends 2025 reveals how much artificial intelligence is being woven into the
fabric of our lives—making everything work smarter, faster, and more intuitively
Kelly Raskovich
Two years after generative artificial intelligence staked Nowhere is this AI-infused future more evident than in
its claim as the free space on everyone’s buzzword-bingo this year’s Tech Trends report, which each year explores
cards, you’d be forgiven for imagining that the future of emerging trends across the six macro forces of informa-
technology is simply … more AI. That’s only part of the tion technology (figure 1 in the executive summary).
story, though. We propose that the future of technology Half of the trends that we’ve chronicled are elevating
isn’t so much about more AI as it is about ubiquitous forces—interaction, information, and computation—that
AI. We expect that, going forward, AI will become so underpin innovation and growth. The other half—the
fundamentally woven into the fabric of our lives that it’s grounding forces of the business of technology, cyber
everywhere, and so foundational that we stop noticing it. and trust, and core modernization—help enterprises
seamlessly operate while they grow.
Take electricity, for example. When was the last time you
actually thought about electrons? We no longer marvel As our team put the finishing touches on this year’s
that the lights turn on—we simply expect them to work. report, we realized that this sublimation and diffu-
The same goes for HTTP, the unseen thread that holds sion of AI is already afoot. Not the “only trend” nor
the internet together. We use it every day, but I’d bet “every trend,” AI is the scaffolding and common thread
most of us haven’t thought about (let alone uttered) the buttressing nearly every trend. (For those keeping a close
word “hypertext” in quite some time. eye at home, “The new math: Solving cryptography in
an age of quantum”—about the cybersecurity implica-
AI will eventually follow a similar path, becoming so tions of another game-changing technology, quantum
ubiquitous that it will be a part of the unseen substruc- computing—is the only one in which AI does not have
ture of everything we do, and we eventually won’t even a foundational role. Yet behind the scenes, AI advance-
know it’s there. It will quietly hum along in the back- ments are accelerating advances in quantum.)
ground, optimizing traffic in our cities, personalizing our
health care, and creating adaptative and accessible learn- • Spatial computing takes center stage: Future AI
ing paths in education. We won’t “use” AI. We’ll just advancements will enhance spatial-computing simu-
experience a world where things work smarter, faster, lations, eventually leading to seamless spatial-com-
and more intuitively—like magic, but grounded in algo- puting experiences integrated with AI agents.
rithms. We expect that it will provide a foundation for
business and personal growth while also adapting and • What’s next for AI?: As AI evolves, the enterprise
sustaining itself over time. focus on large language models is giving way
to small language models, multimodal models,
AI-based simulations, and agents that can execute
discrete tasks.
• Hardware is eating the world: After years of soft- Because we expect AI to become part of tomorrow’s
ware dominance, hardware is reclaiming the spot- foundational core—like electricity, HTTP, and so many
light, largely due to AI’s impact on computing chips other technologies—it’s exciting to think about how AI
and its integration into end-user devices, the Internet might evolve in the next few years as it marches toward
of Things, and robotics. ubiquity, and how we as humans may benefit. We here at
Tech Trends will be chronicling every step of the journey.
• IT, amplified: AI elevates the reach (and remit) of
tech talent: AI’s applicability to writing code, testing Until next time,
software, and augmenting tech talent is transform-
ing IT and sparking a shift away from virtualization
and austere budgets.
Kelly Raskovich
• The intelligent core: AI changes everything for Office of the CTO
core modernization: Core systems providers have Executive editor, Tech Trends
invested heavily in AI, which may simplify the user
experience and data-sharing across applications
but will make these systems more complex at an
architectural level.
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Trending the trends
INTERACTION INFORMATION COMPUTATION BUSINESS OF CYBER CORE
TECHNOLOGY AND TRUST MODERNIZATION
Spatial
Hardware
2025 computing What’s next is eating IT, amplified The new math The intelligent
takes center for AI? core
the world
stage
2024 Interfaces in Genie out of Smarter, not From DevOps Defending Core workout
new places the bottle harder to DevEx reality
2023 Through the Opening up Above the Flexibility, In us we trust Connect and
glass to AI clouds the best ability extend
Blockchain: DEI tech:
2022 Data sharing Ready for Cloud goes Tools for The tech stack Cyber AI IT, disrupt
made easy vertical goes physical thyself
business equity
Rebooting the ML Ops:
2021 digital Bespoke for Machine data Industrialized Strategy, Supply Zero trust Core revival
billions revolution engineered unchained
workplace AI
Human Finance and Ethical
2020 experience Digital twins the future of Architecture technology
awakens
platforms IT and trust
NoOps in a DevSecOps
2019 Intelligent Beyond AI-fueled serverless Connectivity and the cyber
interfaces marketing organizations of tomorrow
world imperative
Enterprise
2018 Digital reality data API Blockchain to No-collar Reengineering The new
imperative blockchains workforce technology core
sovereignty
2017 Mixed reality Dark analytics Machine Everything Trust economy IT Inevitable
intelligence as-a-service unbounded architecture
2016 Internet of AR and VR Industrialized Democratized Right speed IT Autonomic Reimagining
Things go to work analytics trust platforms core systems
Note: To learn more about past Tech Trends, go to www.deloitte.com/us/TechTrends
Source: Deloitte analysis.
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INTERACTION
Spatial computing takes center stage
What is the future of spatial computing? With real-time simulations as just the
start, new, exciting use cases can reshape industries ranging from health care
to entertainment.
Kelly Raskovich, Bill Briggs, Mike Bechtel, and Ed Burns
Today’s ways of working demand deep expertise in narrow If eye-catching virtual reality (VR) headsets are the first
skill sets. Being informed about projects often requires thing that come to mind when you think about spatial
significant specialized training and understanding of computing, you’re not alone. But spatial computing is
context, which can burden workers and keep information about more than providing a visual experience via a
siloed. This has historically been true especially for any pair of goggles. It also involves blending standard busi-
workflow involving a physical component. Specialized ness sensor data with the Internet of Things, drone, light
tasks demanded narrow training in a variety of unique detection and ranging (LIDAR), image, video, and other
systems, which made it hard to work across disciplines. three-dimensional data types to create digital representa-
tions of business operations that mirror the real world.
One example is computer-aided design (CAD) software. These models can be rendered across a range of inter-
An experienced designer or engineer can view a CAD file action media, whether a traditional two-dimensional
and glean much information about the project. But those screen, lightweight augmented reality glasses, or full-on
outside of the design and engineering realm—whether immersive VR environments.
they’re in marketing, finance, supply chain, project
management, or any other role that needs to be up to Spatial computing senses real-world, physical compo-
speed on the details of the work—will likely struggle nents; uses bridging technology to connect physical
to understand the file, which keeps essential technical and digital inputs; and overlays digital outputs onto a
details buried. blended interface (figure 1).2
Spatial computing is one approach that can aid this type Spatial computing’s current applications are as diverse
of collaboration. As discussed in Tech Trends 2024, as they are transformative. Real-time simulations have
spatial computing offers new ways to contextualize emerged as the technology’s primary use case. Looking
business data, engage customers and workers, and inter- ahead, advancements will continue to drive new and
act with digital systems. It more seamlessly blends the exciting use cases, reshaping industries such as health
physical and digital, creating an immersive technology care, manufacturing, logistics, and entertainment—
ecosystem for humans to more naturally interact with which is why the market is projected to grow at a rate
the world.1 For example, a visual interaction layer that of 18.2% between 2022 and 2033.3 The journey from
pulls together contextual data from business software the present to the future of human-computer interaction
can allow supply chain workers to identify parts that promises to fundamentally alter how we perceive and
need to be ordered and enable marketers to grasp a prod- interact with the digital and physical worlds.
uct’s overall aesthetics to help them build campaigns.
Employees across the organization can make meaning of
and, in turn, make decisions with detailed information
about a project in ways anyone can understand.
Figure 1
The possibilities of spatial operations
Physical Bridging Digital
Wearables (for example, headset, Sensors (for example, LIDAR)
Augmented reality objects
smart eyewear, and pins) and sensor fusion
Next-gen displays Computer vision Interactive digital objects
Internet of Things devices
GPS/spatial mapping software Holographic projections
(for example, biometric devices)
Sensory tech
3D design and rendering tools Audio outputs
(for example, haptic suits)
Comprehensive next-gen
Spatial audio devices Avatars
network infrastructure
Cameras Data lakes Generative AI
Next-gen batteries
Source: Abhijith Ravinutala et al., “Dichotomies spatial computing: Navigating towards a better future,” Deloitte, April 22, 2024.
Now: Filled to the rim with sims One of the primary applications unlocked by spatial
computing is advanced simulations. Think digital twins,
At its heart, spatial computing brings the digital world but rather than virtual representations that monitor
closer to lived reality. Many business processes have a physical assets, these simulations allow organizations
physical component, particularly in asset-heavy indus- to test different scenarios to see how various conditions
tries, but, too often, information about those processes will impact their operations.
is abstracted, and the essence (and insight) is lost.
Businesses can learn much about their operations from Imagine a manufacturing company where designers,
well-organized, structured business data, but adding engineers, and supply chain teams can seamlessly work
physical data can help them understand those operations from a single 3D model to craft, build, and procure all
more deeply. That’s where spatial computing comes in. the parts they need; doctors who can view true-to-life
simulations of their patients’ bodies through augmented
“This idea of being served the right information at reality displays; or an oil and gas company that can layer
the right time with the right view is the promise of detailed engineering models on top of 2D maps. The
spatial computing,” says David Randle, global head possibilities are as vast as our physical world is varied.
of go-to-market for spatial computing at Amazon Web
Services (AWS). “We believe spatial computing enables The Portuguese soccer club Benfica’s sports data science
more natural understanding and awareness of physical team uses cameras and computer vision to track players
and virtual worlds.”4
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throughout matches and develop full-scale 3D models New: Data is the differentiator
of every move its players make. The cameras collect
2,000 data points from each player, and AI helps identify Enterprise IT teams will likely need to overcome signifi-
specific players, the direction they were facing, and criti- cant hurdles to develop altogether-new spatial comput-
cal factors that fed into their decision-making. The data ing applications. They likely haven’t faced these hurdles
essentially creates a digital twin of each player, allowing when implementing more conventional software-based
the team to run simulations of how plays would have projects. While these projects have compelling busi-
worked if a player was in a different position. X’s and ness value, organizations will have to navigate some
O’s on a chalkboard are now three-dimensional models uncharted waters to achieve them.
that coaches can experiment with.5
For one thing, data isn’t always interoperable between
“There’s been a huge evolution in AI pushing these systems, which limits the ability to blend data from
models forward, and now we can use them in deci- different sources. Furthermore, the spaghetti diagrams
sion-making,” says Joao Copeto, chief information and mapping out the path that data travels in most organi-
technology officer at Sport Lisboa e Benfica.6 zations are circuitous at best, and building the data pipe-
lines to get the correct spatial data into visual systems
This isn’t only about wins and losses—it’s also about is a thorny engineering challenge. Ensuring that data is
dollars and cents. Benfica has turned player development of high quality and faithfully mirrors real-world condi-
into a profitable business by leveraging data and AI. tions may be one of the most significant barriers to using
Over the past 10 years, the team has generated some spatial computing effectively.9
of the highest player-transfer deals in Europe. Similar
approaches could also pay dividends in warehouse oper- Randle of AWS says spatial data has not historically been
ations, supply chain and logistics, or any other resource well managed at most organizations, even though it
planning process. represents some of a business’s most valuable information.
Advanced simulations are also showing up in medical “This information, because it’s quite new and diverse,
settings. For instance, virtual patient scenarios can be has few standards around it and much of it sits in silos,
simulated as a training supplement for nurses or doctors some of it’s in the cloud, most of it’s not,” says Randle.
in a more dynamic, self-paced environment than text- “This data landscape encompassing physical and digital
books would allow. This may come with several chal- assets is extremely scattered and not well managed. Our
lenges, such as patient data concerns, integration of AI customers’ first problem is managing their spatial data.”10
into existing learning materials, and the question of
realism. But AI-based simulations are poised to impact Taking a more systematic approach to ingesting, orga-
the way we learn.7 nizing, and storing this data, in turn, makes it more
available to modern AI tools, and that’s where the real
Simulations are also starting to impact health care learnings begin.
delivery. Fraser Health Authority in Canada has been a
pioneer in leveraging simulation models to improve care.8 Data pipelines deliver the fuel that drives business
By creating a first-of-its-kind system-wide digital twin,
the public health authority in British Columbia generated We’ve often heard that data is the new oil, but for an
powerful visualizations of patient movement through American oil and gas company, the metaphor is becom-
different care settings and simulations to determine the ing reality thanks to significant effort in replumbing some
impact of deploying different care models on patient of its data pipelines.
access. Although the work is ongoing, Fraser expects
improvement in appropriate, need-based access to care The energy company uses drones to conduct 3D scans of
through increased patient awareness of available services. equipment in the field and its facilities, and then applies
computer vision to the data to ensure its assets operate Next: AI is the new UI
within predefined tolerances. It’s also creating high-fi-
delity digital twins of assets based on data pulled from Many of the aforementioned challenges in spatial
engineering, operational, and enterprise resource plan- computing are related to integration. Enterprises strug-
ning systems. gle to pull disparate data sources into a visualization
platform and render that data in a way that provides
The critical piece in each example? Data integration. The value to the user in their day-to-day work. But soon, AI
energy giant built a spatial storage layer, using appli- stands to lower those hurdles.
cation program interfaces to connect to disparate data
sources and file types, including machine, drone, busi- As mentioned above, multimodal AI can take a variety
ness, and image and video data.11 of inputs and make sense of them in one platform, but
that could be only the beginning. As AI is integrated
Few organizations today have invested in this type of into more applications and interaction layers, it allows
systematic approach to ingesting and storing spatial data. services to act in concert. As mentioned in “What’s next
Still, it’s a key factor driving spatial computing capabil- for AI?” this is already giving way to agentic systems that
ities and an essential first step for delivering impactful are context-aware and capable of executing functions
use cases. proactively based on user preferences.
Multimodal AI creates the context These autonomous agents could soon support the roles
of supply chain manager, software developer, financial
In the past, businesses couldn’t merge spatial and busi- analyst, and more. What will separate tomorrow’s agents
ness data into one visualization, but that too is chang- from today’s bots will be their ability to plan ahead and
ing. As discussed in “What’s next for AI?” multimodal anticipate what the user needs without even having to
AI—AI tools that can process virtually any data type ask. Based on user preferences and historical actions,
as a prompt and return outputs in multiple formats—is they will know how to serve the right content or take
already adept at processing virtually any input, whether the right action at the right time.
text, image, audio, spatial, or structured data types.12
This capability will allow AI to serve as a bridge between When AI agents and spatial computing converge, users
different data sources, and interpret and add context won’t have to think about whether their data comes
between spatial and business data. AI can reach into from a spatial system, such as LIDAR or cameras (with
disparate data systems and extract relevant insights. the important caveat that AI systems are trained on
high-quality, well-managed, interoperable data in the
This isn’t to say multimodal AI eliminates all barriers. first place), or account for the capabilities of specific
Organizations still need to manage and govern their data applications. With intelligent agents, AI becomes the
effectively. The old saying “garbage in, garbage out” has interface, and all that’s necessary is to express a prefer-
never been more prescient. Training AI tools on disorga- ence rather than explicitly program or prompt an appli-
nized and unrepresentative data is a recipe for disaster, as cation. Imagine a bot that automatically alerts financial
AI has the power to scale errors far beyond what we’ve analysts to changing market conditions, or one that
seen with other types of software. Enterprises should crafts daily reports for the C-suite about changes in the
focus on implementing open data standards and working business environment or team morale.
with vendors to standardize data types.
All the many devices we interact with today, be they
But once they’ve addressed these concerns, IT teams phone, tablet, computer, or smart speaker, will feel
can open new doors to exciting applications. “You can downright cumbersome in a future where all we have to
shape this technology in new and creative ways,” says do is gesture toward a preference and let context-aware,
Johan Eerenstein, executive vice president of workforce AI-powered systems execute our command. Eventually,
enablement at Paramount.13 once these systems have learned our preferences, we may
not even need to gesture at all.
12
13
egats
retnec
sekat
gnitupmoc
laitapS
The full impact of agentic AI systems on spatial comput- environments are just a few ways leading enterprises
ing may be many years out, but businesses can still are making their operations more spatially aware. As
work toward reaping the benefits of spatial comput- AI continues to intersect with spatial systems, we’ll see
ing. Building the data pipelines may be one of the the emergence of revolutionary new digital frontiers,
heaviest lifts, but once built, they open up myriad use the contours of which we’re only beginning to map out.
cases. Autonomous asset inspection, smoother supply
chains, true-to-life simulations, and immersive virtual
Endnotes
1. Abhijith Ravinutala et al., “Dichotomies Spatial Computing: 9. Gokul Yenduri et al., “Spatial computing: Concept,
Navigating Towards a Better Future,” Deloitte, April 22, 2024. applications, challenges and future directions,” preprint,
2. Ibid. 10.48550/arXiv.2402.07912 (2024).
3. Future Market Insights, Spatial Computing Market Outlook 10. Randle interview.
(2022 to 2032), October 2022. 11. Deloitte internal information.
4. David Randle (global head of go-to-market, AWS), interview 12. George Lawton, “Multimodal AI,” TechTarget, accessed Oct.
with the author, Sept. 16, 2024. 29, 2024.
5. Joao Copeto, chief information and technology officer, Sport 13. Johan Eerenstein (senior vice president of workforce
Lisboa e Benfica, interview with the author, August 27, 2024. enablement, Paramount), interview with the author, July 16,
6. Ibid. 2024.
7. Isabelle Bousquette, “Companies finally find a use for virtual
reality at work,” The Wall Street Journal, Sept. 6, 2024.
8. Fraser Health, “Fraser Health Authority: System wide digital
twin,” October 2023.
14
15
egats
retnec
sekat
gnitupmoc
laitapS
Continue the conversation
Industry leadership
Frances Yu Stefan Kircher
Unlimited Reality™ GM/Business lead | Principal | Deloitte Unlimited Reality™ CTO | Managing director | Deloitte Consulting
Consulting LLP LLP
+1 312 486 2563 | francesyu@deloitte.com +1 404 631 2541 | skircher@deloitte.com
Frances Yu is a partner at Deloitte Consulting LLP, where she has Stefan Kircher is a managing director in the Products & Solutions
served in a range of global practice leadership roles. She has helped practice of Deloitte Consulting LLP and CTO for Deloitte’s
Fortune 500 clients as well as Deloitte launch several new ventures, Unlimited Reality™ Business. He has over 25 years expertise in the
evolved growth strategies, and transformed their demand value industry, technology strategy, and solution-building across various
chain. Currently, she is the US and global business lead and general industries, R&D, innovation, and partnerships with strategic tech
manager for Deloitte’s Unlimited Reality™, a multinetwork inno- partners like AWS.
vation business for the industr"
334,deloitte,govtech-trends-2025.pdf,"Tech Trends 2025 | Deloitte Insights
This report provides a government-specific take on Deloitte’s
Peering through the
Tech Trends 2025 report, spotlighting the accelerating technology
trends most likely to cause disruption in enterprise IT over the next
18-24 months. We explore which trends may be most relevant for
lens of government
governments and how ready governments are to take advantage
of them.
The technologies that enhance our Learn how governments can harness new opportunities in emerging
technologies to transform their organizations.
organizations and our lives are more
Relevance and readiness scale:
powerful and essential than ever
before. Forward-thinking governments We looked at each trend and assigned a value from one (low)
and five (high) based on the trend’s relevance and readiness of
and organizations chart upcoming
government adoption.
technological changes and look for ways
READINESS: RELEVANCE:
to utilize them for the benefit of citizens,
How ready is the government How impactful would it be if the
constituents, and employees alike. to adopt the trend? government adopted the trend?
Tech Trends 2025 | Peering through the lens of government
open-source options for the ability to train such models on smaller,
more accurate data sets. Together with multimodal models and AI-
based simulations, these new types of AI are building a future where
enterprises can find the right type of AI for each task. This includes AI
that can not only answer questions, but also complete burdensome
administrative tasks. In the years to come, a focus on execution may
usher in a new era of “agentic AI,” arming government employees
Spatial computing takes center stage with copilots capable of boosting efficiency and delivering enhanced
impact on the lives of constituents.
Exciting new use cases can reshape
industries
Trends in action
Spatial computing continues to spark enterprise interest because of Given the rate of advancement in Generative AI technologies,
its ability to break down information silos and create more natural government leaders should continually evaluate where employees
ways for government employees and the public to interact with and constituents gain the most benefits from increased adoption.
information. We’re already seeing enterprises find success with use They should balance the implications of cost against response
cases like advanced simulations that allow organizations to test quality and speed against risk. And, as we see the potential of agentic
different scenarios to see how various conditions will impact their AI, in which agents can act on behalf of humans, evaluating the
operations. With a stronger focus on effectively managing spatial implications on the skills of the workforce is even more important.
data, organizations will drive more cutting-edge applications. In the Today, AI can help social workers scan volumes of reports, help
coming years, advancements in AI could lead to seamless spatial speed hiring processes, and help perform routine constituent
computing experiences and improved interoperability, ultimately services while keeping a human in the loop. The value proposition
enabling AI agents to anticipate and proactively meet users’ needs. for AI remains robust, but government leaders should be strategic to
maximize ROI.
Trends in action
READINESS RELEVANCE
Being able to interact with information in a spatial context creates
2 4
many possible opportunities for governments, from urban planning
to emergency response to environment monitoring and much
more. Moving beyond simple data visualizations, organizations can
create immersive experiences to explore and visualize data in new
ways. Urban planners could visualize the changes to city planning
in near real time. The National Park Service could create immersive
educational experiences of the parks, blending history, science, and
wonder. Leaders should prioritize high-quality experiences over just
mediocre ones.
READINESS RELEVANCE
Hardware is eating the world
1 2
The promise of AI depends on more
than software
After years of software dominance, hardware is reclaiming the
spotlight. As AI demands specialized computing resources,
companies are turning to advanced chips to power AI workloads. In
addition, personal computers embedded with AI chips are poised
to supercharge knowledge workers by providing access to offline AI
models while “future-proofing” technology infrastructure, reducing
cloud computing costs, and enhancing data privacy. Although
What’s next for AI?
AI’s increased energy demands pose sustainability challenges,
Enterprises move beyond a
advancements in energy sources and efficiency are making
one-size-fits-all approach
AI hardware more accessible. Looking forward, AI’s continued
integration into devices could revolutionize the Internet of Things
To take advantage of the burgeoning excitement around Generative
and robotics, transforming industries like health care through
AI, many organizations have already adopted large language models
smarter, more autonomous devices.
(LLMs)—the best option for many use cases. But some are already
looking ahead. Despite their general applicability, LLMs may not
Trends in action
be the most efficient choice for all types of organizational needs.
Enterprises are now considering small language models (SLMs) and As AI and advanced computing capabilities grow in capability and
1
Tech Trends 2025 | Peering through the lens of government
scale to edge devices, government leaders should strategically
READINESS RELEVANCE
consider when, and how, to deploy specialized hardware to support
2 5
systems, data centers, and end users. The cost/performance ratio
of new technologies needs to be carefully evaluated, and leaders
may find that taking advantage of less widely available capabilities
requires workloads to run in cloud. In either case, the decisions
will be costly. Deploying AI-enabled hardware to remote areas like
research stations, forestry operations, or emergency response
zones may provide essential local computing power where internet
connectivity is unreliable. Through careful analysis and decision-
making, government agencies can enhance hardware investments to
support mission-critical functions.
The new math
READINESS RELEVANCE Solving cryptography in an age of quantum
2 3
In their response to Y2K, organizations saw a looming risk and
addressed it promptly. Today, IT faces a new challenge, and it will
have to respond in a similarly proactive manner. Experts predict that
quantum computers, which could mature within five to 20 years, will
have significant implications for cybersecurity because of their ability
to break existing encryption methods and digital signatures. This
poses a risk to the integrity and authenticity of data and
communications. Despite the uncertainty of the quantum computer
IT, amplified timeline, inaction on post-quantum encryption is not an option.
AI elevates the reach (and remit) of the Emerging encryption standards offer a path to mitigation. Updating
encryption practices is fairly straightforward—but it’s a lengthy
tech function
process, so organizations should act now to stay ahead of potential
After years of progressing toward lean IT and everything-as-a-service threats. And while they’re at it, they can consider tackling broader
offerings, AI is sparking a shift away from virtualization and austere issues surrounding cyber hygiene and cryptographic agility.
budgets. Long viewed as the lighthouse of digital transformation
Trends in action
throughout the enterprise, the IT function is now taking on AI
transformation. Because of Generative AI’s applicability to writing Future quantum computers could threaten the security of encrypted
code, testing software, and augmenting tech talent in general, data and transactional integrity. Given the volume and nature of
forward-thinking technology leaders are using the current moment information stored and processed by government organizations,
as a once-in-a-blue-moon opportunity to transform IT across five preparing for the transitions that will be required to better secure
pillars: infrastructure, engineering, finance operations, talent, and data and transactions is an imperative, not a nice to have. Agencies
innovation. As both traditional and Generative AI capabilities grow, should assess their current posture, identify the sources of
every phase of tech delivery could see a shift from human in the most sensitive or vulnerable data, and use modernizations
charge to human in the loop. Such a move could eventually return and transformation programs to upgrade their encryption and
IT to a new form of lean IT, leveraging citizen developers and cybersecurity practices. Steps taken today will help ease the burden
AI-driven automation. of steps that need to be taken in the future to address a constantly
shifting threat landscape.
Trends in action
READINESS RELEVANCE
The new trends in AI transformation allow government IT leaders
the opportunity to “lead from the front.” By incorporating AI tools 2 5
into current operations, practicing “digital transformation” inside
of IT itself, and fostering a culture of experimentation, technology
leaders can seize the moment to reboot, streamline, and accelerate
IT operations. New AI-augmented tools may empower nontechnical
users to perform increasingly sophisticated tasks such as developing
custom applications. By articulating a vision, fostering a technology-
savvy culture, and communicating the opportunities and potential
risks of AI, technology leaders can position to be enablers and critical
partners to achieving their agencies’ missions.
2
Tech Trends 2025 | Peering through the lens of government
The intelligent core
AI changes everything for the core
Core systems providers have invested heavily in AI, rebuilding their
offerings and capabilities around an AI-fueled or AI-first model. The
integration of AI into core enterprise systems represents a significant
shift in how organizations operate and leverage technology for
competitive advantage. This transformation is about automating
routine tasks and fundamentally rethinking and redesigning
processes to be more intelligent, efficient, and predictive. It requires
careful planning due to integration complexity, strategic investment
in technology and skills, and a robust governance framework to
ensure smooth operations. But beware of the automation paradox:
The more complexity is added to a system, the more vital human
workers become. Adding AI to core systems may simplify the user
experience, but it will make them more complex at an architectural
level. Deep technical skills are still critical for managing AI in core
systems.
Trends in action
Government agencies often find themselves with legacy systems
built to serve the needs of yesteryear, but poorly suited for the
changing, dynamic nature of today’s constituent expectations.
Using new tools, based on AI/machine learning and Generative AI,
are increasingly enabling the possibility of “modernizing in place” so
as to not disrupt critical operations. Today, AI can answer routine
questions accurately and accelerate the processing of standardized
forms, even if they contain large volumes of text. Investing in the
technology—and the talent to manage it—is increasingly become a
governmental imperative.
READINESS RELEVANCE
2 5
3
Tech Trends 2025 | Peering through the lens of government
Learn more
Follow
@DeloitteGov
@DeloitteOnTech
Related reports
Tech Trends 2025
www.deloitte.com/us/techtrends
Future of Government
www.deloitte.com/insights/future-of-government
Authors
For questions regarding GovTech Trends 2025, please contact:
SCOTT BUCHHOLZ
Government & Public Services CTO
Deloitte Consulting LLP
sbuchholz@deloitte.com
+1 571 814 7110
@scott_buchholz
This article contains general information only and Deloitte is not, by means of this article, rendering accounting,
business, financial, investment, legal, tax, or other professional advice or services. This article is not a substitute
for such professional advice or services, nor should it be used as a basis for any decision or action that may
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consult a qualified professional advisor. Deloitte shall not be responsible for any loss sustained by any person
who relies on this article.
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Copyright © 2025 Deloitte Development LLC. All rights reserved."
335,deloitte,us-2024-chief-strategy-officer-survey.pdf,"2024 Chief Strategy Officer
(CSO) Survey
As the world faces heightened
uncertainty, CSOs are optimistic,
resilient, and evolving
August 2024
Chief strategy officers are optimistic, resilient, and evolving
Over the last year, uncertainty has been In this year’s survey, we set out not only to Here’s what we found: CSOs surveyed offered optimism, resilience, and a
all over the news, with global markets pulse strategy leaders on their commitment to evolution.
sending mixed and occasionally outlooks, growth agendas, and focus
Optimistic outlook: Despite economic and geopolitical instability,
confusing signals. These signals speak areas but also to better understand how
most CSOs are optimistic that their organizations will successfully
to the challenges facing strategy and they are adapting their approaches to
navigate the year ahead, a sentiment that may be indicative of
business leaders today—how to create strategy in the face of these pressures. In planning for new capabilities, such as artificial intelligence (AI).
advantage and capture value in a a recent Monitor Deloitte report, “Strategy
Drastic shifts in investment areas: CSOs are investing in
landscape characterized by disruptive Now,” we explored how strategy is
emerging areas, including AI and ecosystems, for competitive
technologies, geopolitical and changing and outlined the new options,
advantage. A lag in activation may be representative of the early
economic uncertainty, changing challenges, and opportunities for strategy
stages these areas are in, which presents an opportunity for CSOs
consumer and stakeholder leaders. That report suggests strategy
to take an active role.
expectations, and complex policies should be resilient, agile, inclusive, and bold
Evolving ways of approaching strategy: CSOs report increased
and regulations around sustainability, to better match the challenges businesses
confidence in their core strategic initiatives, a shift that could be
tech, and data. In this environment, we face today. We wanted to explore this
related to changes in shaping, executing, and collaborating
conducted our fifth annual global assertion in this year’s survey data.
on strategy, consistent with traits outlined in “Strategy Now.”
survey of chief strategy officers (CSOs).
Strategy should be resilient, agile,
Obstacles to overcome: CSOs report facing real challenges as they
CSOs offer unique perspectives that
inclusive, and bold to better match navigate these forces and the evolution of their function, including
can resonate across businesses and
the challenges businesses managing across time horizons, talent shortages, and competing
industries. Often reporting directly to
strategic priorities.
face today.
the CEO, CSOs advise on special
projects, collaborate cross-functionally
on high-impact decisions, lead
corporate development, keep a pulse Read on for more highlights from the most
on markets, and are increasingly
recent global Chief Strategy Officer Survey.
responsible for executing strategies.
What’s inside
04
The outlook and priorities of CSOs in 2024
CSOs face geopolitical and economic uncertainty but remain optimistic about
their organizational performance.
05
Current investment areas and the CSO engagement gap
Investment priorities are changing but do not always align with CSOs’ roles and engagement,
which is unexpected given the importance of their leadership on topics related to
competitive advantage and growth.
07
Embracing new approaches to strategy
CSOs surveyed report new ways of leading strategy, consistent with the perspective
in “Strategy Now,” and signaling their shift toward a more resilient, agile, inclusive, and bold
approach to strategy execution.
10
What comes next for CSOs
Time, talent, and conflicting priorities pose challenges for CSOs and organizations in
achieving their strategic priorities. The ""Strategy Now"" principles may help manage these
challenges, and specific questions are offered to help guide CSOs and strategy leaders.
13
Authors
14
Survey methodology and acknowledgements
Outlook and priorities
Despite broad pessimism and uncertainty, CSOs surveyed are optimistic that their
organizations will successfully navigate the year ahead
In 2024, strategy leaders are…
Positive about their organization’s potential despite real pessimism Navigating uncertainty, focusing on core growth, and quickening their pace:
about business conditions:
Top external issues that # 1 # 2
may disrupt their
business strategy in the
Geopolitical Financial market
next 12 months
instability uncertainty
Most respondents are
focused on core growth, 91% 27%
indicating a relatively 50%
Organizational outlook Economic outlook
balanced portfolio
64% 11% Strengthening the Expanding into Pursuing
core adjacent growth transformational
growth
of respondents of respondents
reported being optimistic about reported being optimistic about Facing pressure to
say the strategy 55% say strategy
their organization’s performance the global economy in the next accelerate the pace of
development refresh
in the next 12 months. 12 months. strategy development 68% timeline is frequency is
and execution
shrinking. increasing.
For CSOs surveyed, organizational performance in the next year revolves around managing geopolitical and financial risks—risks that
are perceived to be substantial given general pessimism about the global economy and industrywide trends. To manage these risks,
CSOs are focusing on core business growth.
4 Copyright © 2024. Deloitte Development LLC. All rights reserved.
Investments and the engagement gap
Drastic shifts are happening in the percentage of respondents investing in new areas—
including AI, data, and ecosystems—to drive future growth and performance
Historical investment Future investment Delta in CSOs investing
Source of competitive advantage
AI (e.g., Generative AI, computer vision AI) 25% 88% 63%
Top
investment areas Data 61% 96% 35%
gaining focus Ecosystem business models 32% 60% 28%
Customer experience 68% 90% 22%
Sustainability, equity, and trust 61% 81% 20%
Cost structure 66% 85% 19%
Brand reputation 72% 90% 18%
Distribution channels 47% 65% 18%
Technology other than AI 78% 96% 18%
Supply chain 39% 54% 15%
Talent 82% 94% 12%
Innovation or R&D 68% 80% 12%
Economies of scale 59% 70% 11%
Intellectual property 40% 45% 5%
Product or service quality 88% 87% -1%
AI: Driving efficiency and productivity Ecosystems: Driving growth, innovation, and new business models
Potential reasons CSOs surveyed were overwhelmingly aligned on the No single benefit stood out for CSOs surveyed on the topic of
organizational benefits they anticipate for their AI efforts: ecosystems, but benefits tended to be focused on growth, with top use
for the focus on AI
80% indicated efficiency and productivity as a leading cases centering on improving customer experience, creating new
and ecosystems benefit, with top use cases of automation, business models, accessing new customers and markets, and
optimization, and customer service. facilitating innovation and product development.
5 Copyright © 2024. Deloitte Development LLC. All rights reserved.
Investments and the engagement gap
However, CSOs are not as involved in activating these efforts as expected
CSOs surveyed reported they are often not the one setting direction on issues at
the top of the strategy agenda, suggesting a gap in alignment/leadership:
The survey results identified a discrepancy between priority investment areas
and the involvement of CSOs. Given the CSOs’ critical role in detecting
industry shifts and spearheading special projects, it is surprising that they are
not more actively engaged in leading and shaping strategic investments:
Over half (54%) of CSOs surveyed reported playing a supporting (as
opposed to lead) role in shaping AI strategy. Some of this may be due to
early trials with AI being driven by tech leaders as Generative AI emerged and
companies tried to quickly understand the hype.
AI engagement gap Ecosystem engagement gap
Likewise, ecosystem strategy—an organization’s ability to work
28% 26% collaboratively within or across industries—has been an emerging priority
Only Only
for organizations for some time. However, of the CSOs surveyed, 31%
indicated they have had no role in their company’s ecosystem strategy.
reported playing a “lead role” in AI reported ecosystem strategy as a priority for
strategy development despite a 63- them despite there being a 29-percentage-
percentage-point increase in CSOs point increase in expected investment.
investing in AI.
There are many reasons strategic investments may not align with where strategy leaders are focusing in the context of a specific
company, but it is essential that CSOs are at the table for decisions in such important areas and this should be an intentional choice.
6 Copyright © 2024. Deloitte Development LLC. All rights reserved.
Adopting the traits of ‘Strategy Now’ may increase
confidence in growth outlook and a
CSO’s ability to shape key
strategic priorities
Want to learn more about the evolution of strategy?
Check out Deloitte’s perspective on “Strategy Now.”
New approaches to strategy
CSOs surveyed are evolving how they approach strategy, consistent with traits of ‘Strategy Now’
Four traits of ‘Strategy Now’ Dimensions measured in the survey Surveyed CSOs reported…
63%
 Embracing uncertainty Bolster resilience by being data-driven
Resilient and incorporating analytics and insights Agreed their strategy function is evolving
 Leveraging data and analytics
into how work gets done to use more data and analytics
82%
 Bridging the gap with “execution” Boost agility by being execution-oriented
Agile Agreed their strategy function is evolving
and closer to where the strategy is activated
 Accelerating insights to be more execution-oriented
 Incorporating new views from Create inclusiveness by being externally 61%
Inclusive within and beyond the focused and seeking outside viewpoints as
Agreed their strategy function is evolving
organization part of strategy development
to be more externally focused
59%
 Matching bold plans Pursue boldness, including being
Bold
tech-savvy in a year when AI and Agreed their strategy function is
with bold execution
disruptive tech were front and center evolving to collaborate closely with
tech leaders
While this year’s survey did not set out to directly test “Strategy Now,” it was an opportunity to find evidence of the evolution of strategy
in the market. Based on responses to the above questions, the traits of “Strategy Now” appear to reflect the experiences of strategy
leaders worldwide. Well over half the respondents agreed their function was evolving to be more in line with each dimension.
8 Copyright © 2024. Deloitte Development LLC. All rights reserved.
New approaches to strategy
Adoption of more aspects of ‘Strategy Now’ appears linked to CSO confidence and
optimism in the growth of their organizations
27%
of CSOs are activating all four dimensions: This “Strategy Now” cohort is far more confident in strong
the “Strategy Now” cohort. growth in the next year than its peers.
Two 65%
One
dimensions
dimension 23%
14%
39%
“Strategy Now”
CSOs
27% 33% Peers
All four Three
dimensions dimensions
Strong growth expectations
Count of “Strategy Now” dimensions reported by each CSO as a % of total respondents. Does not add to 100% as it shows relative % of each cohort reporting “strong
Does not add to 100% based on rounding and other respondents (<3%). growth expectations”
CSOs surveyed who are collectively more data-driven, execution-oriented, externally focused, and tech-savvy have an apparent
confidence. Their optimism resounds in a year marked by uncertainty and change.
9 Copyright © 2024. Deloitte Development LLC. All rights reserved.
What comes next
CSOs and their strategy teams hold a unique vantage point for
stewarding their organizations through today’s uncertain times. Their
cross-functional mindset and natural inclination to growth enables
them to remain optimistic despite market and geopolitical uncertainty
and volatility.
However, CSOs are not immune to these trends. While they are
optimistic about their outlook, they identified real challenges on time,
talent, and priorities that could hinder their ability to close the
“engagement gap” and achieve their ambitions more broadly and that
reinforce the need to continuously evolve their role.
The principles of “Strategy Now” can provide a framework and set of
guidelines to help CSOs think about how they adapt.
What comes next
Time, talent, and competing priorities are three of the biggest challenges facing CSOs this year
Balancing time horizons Talent constraints Competing priorities
~10% 51% #1
Only
Barrier
say they are spending enough time on long-term market cite talent and labor shortages as a key issue expected to CSOs surveyed note the top barrier to their organization
sensing—meaning ~90% were not spending enough time—despite disrupt or influence their strategies. Likewise, strategy achieving their AI and ecosystem goals is competing
68% noting that this is one of the role’s core responsibilities. functions decreased in size for the first time since 2021. strategic priorities (61% and 59%, respectively).
Organizations look to CSOs to support execution while Organizations ask more of their CSOs and on a faster Organizations rely on CSOs to help resolve strategic
advising on potential disruption. timeline, but CSOs have constraints. mis-alignment.
CSOs may need to find a way to manage the tension between CSOs may need to find a way to experiment with new CSOs may need to leverage their relationships with
the near and long term. approaches, despite the challenge. stakeholders to support emerging areas.
While these challenges impact every CSO and their organization differently, one way to navigate them, and potentially increase your
organization’s confidence, could be to adopt “Strategy Now” traits.
11 Copyright © 2024. Deloitte Development LLC. All rights reserved.
What comes next
A starting point for CSOs looking to adapt for today’s environment
Amp up your resilience
 What data, insights, and tools are you leveraging to develop, monitor, and adjust your strategy? How are you leveraging AI
to drive outcomes faster and/or manage expectations better?
 To what extent are you incorporating long-range scenario planning into your strategic planning cycle?
How are you stimulating dialogue within the organization across multiple possible futures?
Double down on agility
 How can you, as a CSO, be more engaged in the execution of the strategy and stay closer to the
business and functional leaders?
 How can you ensure insights are moving more rapidly from the businesses into the strategy and
vice versa? Have you created a feedback loop and aligned on an appropriate “burden of proof”
for accelerating decision-making?
Shift your strategy to be more inclusive
 To what extent does your organization have a way to prioritize value to other stakeholders—
employees, vendors, and communities—in its strategies?
 How are you incorporating diverse perspectives (internal and external) into your strategy, market
sensing, corporate development, or capability-building efforts?
Aspire to be bolder
 What are the steps you are taking as a CSO to understand how new technologies can help unlock growth? Can you
achieve the growth you hope for without taking more of a leading role in AI strategy development?
12 Copyright © 2024. Deloitte Development LLC. All rights reserved.
Get in touch | Start the conversation
Kristen Stuart Gagan Chawla
US Consulting Strategy Leader US Business Strategy Leader
Deloitte Consulting LLP Deloitte Consulting LLP
kstuart@deloitte.com gachawla@deloitte.com
Nick Jameson
Andrew Blau
Principal, Business Strategy
CSTrO Program Leader US Leader, Eminence and Insights
Deloitte Consulting LLP Deloitte Consulting LLP
njameson@deloitte.com ablau@deloitte.com
Want to learn more about the role of the Chief Strategy Officer? Check out Deloitte’s Chief Strategy and Transformation Officer Program
and “The making of a successful chief strategy officer: Insights from the field.”
13 Copyright © 2024. Deloitte Development LLC. All rights reserved.
Methodology and
Acknowledgements
Survey methodology and demographics
This year’s survey was fielded from November 2023 to January 2024. With 128 respondents, the fifth iteration of Monitor Deloitte’s Chief
Strategy Officer Survey covered a diverse spectrum of strategy leaders.
Respondent industry breakdown*
32%
20% 19%
4%
11%
9%
Financial Services Consumer Energy, Resources & Technology, Media & Life Sciences & Health Nonprofits & Other
Industrials Telecommunications Care Public
Respondent organizational revenue size Respondent geographical location**
North America Rest of the world
9%
5%
36% 19% 42%
23%
Less than $5B
36% 55%
72% $5B–$24.9B
Greater than $25B
Europe, Middle
North America Latin America
East and Africa
15 Copyright © 2024. Deloitte Development LLC. All rights reserved. (*) Note: Total does not equal 100% as not all CSOs identified an industry. (**) The total does not sum to 100% as Asia Pacific
(AP) is not shown; AP data was limited this year.
Acknowledgments
Recognition and appreciation
This report is brought to you by Deloitte’s Chief Strategy and Transformation Officer (CSTrO) Program
but would not be possible without the thought partnership, collaboration, and assistance of our
colleagues working with strategy leaders in the United States and abroad that helped shape this report.
Thought partners
Benjamin Finzi, Jim Rowan, Francisco Salazar, Tom Schoenwaelder
Global advisers
Kendra Bussey, Luiz Caselli, Cedric Dallemagne, Daissy Davila, Andrew Dick,
Adam Ferfoglia, Chris Forrest, Takeshi Haeno, Gianni Lanzillotti, Shohei Mabuchi,
Gavin McTavish, Wayne Nelson, Robyn Noel, Kellie Nuttall, Maria Teresa Vilches
Research and eminence adviser
Elizabeth Molacek
Chief Strategy and Transformation Officer Program collaborators
Brittany Altonji, Jessica Barzilay, Chris Coelho, Elena Crowe, Drew Dickenson,
Matt Engel, Matt Hauck, Virginie Henry, David Spivak, Maclain Thornton, Shannon Woods
Additional thanks to the marketing and design team, including Hali Austin, Vanessa Carney, Matthew
Chervenak, Serafina Gontha, Jeanie Havens, Linnea Johnson, Vijayakrishnan K M, Alyson Lee, Nina
Lukina, Melissa Newmann, Jennifer Plym, Rachel Rosenberg, Erin Shapley, Meredith Schoen, Marie
Eve Tremblay, Talia Wertico
16 Copyright © 2024. Deloitte Development LLC. All rights reserved.
This document contains general information only and Deloitte is not, by means of this article, rendering accounting, business,
financial, investment, legal, tax, or other professional advice or services. This document is not a substitute for such professional advice
or services, nor should it be used as a basis for any decision or action that may affect your business. Before making any decision or
taking any action that may affect your business, you should consult a qualified professional adviser.
Deloitte shall not be responsible for any loss sustained by any person who relies on this article.
About Deloitte
Deloitte refers to one or more of Deloitte Touche Tohmatsu Limited, a UK private company limited by guarantee (“DTTL”), its network
of member firms, and their related entities. DTTL and each of its member firms are legally separate and independent entities. DTTL
(also referred to as “Deloitte Global”) does not provide services to clients. In the United States, Deloitte refers to one or more of the US
member firms of DTTL, their related entities that operate using the “Deloitte” name in the United States and their respective affiliates.
Certain services may not be available to attest clients under the rules and regulations of public accounting. Please see
www.deloitte.com/about to learn more about our global network of member firms.
About Deloitte’s Chief Strategy and Transformation Officer Program
The role of the CSO and the role of strategy as a corporate function continues to evolve, as CSOs are getting an increasingly important
role in the C-suite and are expected to set the short- and long-term direction for the business. Whether you seek to build your strategy
function, enhance your organization’s strategic planning capability, or pursue a specific growth mandate, we can leverage our long
heritage in strategy and a range of proprietary tools to help you succeed.
Contact: CSOProgram@deloitte.com
Website: www.deloitte.com/us/chiefstrategyofficer"
336,deloitte,tte-annual-report-2024.pdf,"State of Ethics and
Trust in Technology
Annual report
Third edition
Table of contents
Foreword Ethical standards Organizational
01 04 07
8 practices
3
30
Executive summary AI implications for Role of government
02 05 08
ethical standards
4 34
15
Introduction Role of the Chief Promoting trust and
03 06 09
Ethics Officer ethics in technology:
6
27 the way forward
39
2
Foreword
01
The rapid integration of Generative Artificial Intelligence and other We are at a pivotal moment in the history of human invention. Future
emerging technologies brings unparalleled opportunities to drive generations will undoubtedly look back on the decisions we make today.
0022
efficiency, improve automation, and enhance how humans and machines As leaders, policymakers, and stakeholders, it is critical to reflect on the
work together. Many of us are energized by the chance to be on the legacy we are creating. Our decisions should not only address the
ground floor of shaping this tech-driven future. However, these immediate benefits of technological advancement but also safeguard 0033
technologies also pose complex risks with pervasive impacts to principles to uphold a sustainable and equitable future for the next
organizations and society at large. This dualism underscores the need for a generation. It is up to us to honor our collective responsibility to those who
04
balanced approach—embracing innovation while upholding an unwavering will inherit the world we shape.
commitment to ethical standards.
05
The third edition of the State of Ethics and Trust in Technology report
illustrates the vital relationship between technological transformation and
06
ethical responsibility. This report shares valuable, actionable insights for
leaders to keep trust and ethics at the forefront when building a blueprint
for the deployment and governance of technology. Leaders can, and
07
should, ask probing questions to evaluate impacts and set strategic
priorities to navigate them, which will often require an agile, Lara Abrash
08
multi-disciplinary approach guided by a diversity of experiences and Chair
perspectives. By infusing this mindset into our decision making, we lay the Deloitte US
groundwork to harness technological capabilities, deliver value, and
09
advance a trustworthy future.
3
Executive summary
01
The accelerated rise in adoption of Artificial can create social, reputational, and financial value
Intelligence (AI) technologies since the release for their organizations, which can help build
0022
of last year’s Report increases the importance the confidence of their customers and increase
for organizations to consider the ethical employee engagement.
dimensions and implications of emerging 03
technologies. In this third edition Technology Trust and Ethics
(TTE) Report, we investigate how organizations
04
Generative AI (GenAI) offers organizations set ethical standards for emerging technologies
both the opportunity to improve efficiency and the implications of GenAI for the
and transform customer engagement, and establishment of those standards. We explore 05
the potential to expose organizations to the role of Chief Ethics Officers and their potential
reputational and financial risk. Similar potential power to inspire ethical behavior at organizations.
06
for good and harm exists across all emerging We examine the possible divide between
technologies. By establishing ethical standards employers and employees on issues of ethical
for the development and use of technology, technology use and how organizational practices
07
organizations can improve their relationships can create stronger, more aligned teams. Finally,
with customers and employees, demonstrate we look at the role government regulation can
08
a commitment to trust and responsible play in promoting ethical technology standards
technology use, and differentiate themselves and supporting organizations in implementing
from competitors. Leaders who drive their their own guidelines.
09
organizations to adopt trustworthy and ethical
principles for the use of emerging technologies
4
Important takeaways
Safety first Reputation is important AI Is a powerful tool,
01
but it requires guardrails
“Safe and secure” was marked as the most important Respondents show concern for reputational damage Cognitive technologies such as AI are recognized as
0022
ethical technology principle by respondents. to an organization associated with misuse of having both the highest potential to benefit society
Organizations developing or operationalizing their technology and failure to adhere to ethical standards. and the highest risk of misuse. The accelerated
ethical technology standards may consider using More than financial penalties, respondents point to adoption of GenAI may be outpacing organizations’
safety and security as the best entry point to bring an organization’s perceived ability to honor ethical capacity to govern the technology. Companies 03
leaders and workforce on board to implement ethical commitments as important to long-term success. should prioritize both the implementation of ethical
standards at scale. standards for GenAI and meaningful selection of use
cases to which GenAI tools are applied. 04
Leaders can inspire ethical Organizations should strive Organizations should
05
engagement for consensus, followed by shore up alignment with their
enhanced processes employees on ethics
06
Chief Ethics Officer roles are increasingly common. In Increasing ethics-based trainings and issue reporting Employers face a challenge coordinating with their
most organizations the position is viewed as enforcing practices may be successful at building consensus to workforces on embedding trust in professional
compliance, driving adherence to standards, and adopting trustworthy behaviors. Organizations should ecosystems. Trust in one’s organization and its emerging 07
championing individual responsibility for ethical next consider enhancing processes and actions in technologies may be declining and more pronounced
issues. Yet, where a Chief Ethics Officer is in place, technology development to address ethical risks and in younger generations, with concerns about user data
executive leaders should be involved to help define putting knowledge from trainings to action. privacy and security alongside the current state of GenAI. 08
and implement ethical tech processes. Organizations proactive in following through on ethical
technology standards may stay aligned to employee’s
expectations, leading to higher engagement and
09
potentially better outcomes from technology adoption.
5
Introduction
01
Goals of the survey
0022
Last year’s 2023 State of Ethics and Trust in The research to support this report started by Using findings from the survey and interviews,
Technology report reflected the watershed reviewing takeaways from last year’s report and our report was designed to provide insight
03
moment of GenAI, its rapid adoption, identifying how shifts in the technology landscape into how organizations are addressing the
and the new ways in which it demanded could alter those findings. We launched a ethics of emerging technologies alongside
organizations to prepare for its safe and 61-question survey to over 1,800 business and implementation. In this report, we analyze how
04
responsible use. In this 2024 edition, the technical professionals globally. The survey organizations are changing processes to align
concerns surrounding GenAI have grown, addressed how organizations place value on with ethical standards, and how organizations
05
from its capacity to widen the digital divide ethical principles for emerging technologies, the are collaborating with the government and
to its potential to increase the spread impact of GenAI on ethical technology learning commercial entities in their establishment
of misinformation and harmful content. and process changes within organizations in the of ethical standards. The report emphasizes
06
Despite awareness of these potential harms, first full year of its larger scale adoption, and how why organizations should approach internal
the opportunities afforded by GenAI are organizations implement practices that support technology operations, strategy, and decision-
clear, compelling organizations to balance ethical use and development of technology. We making from a framework of trust, and how 07
the benefits of adopting GenAI and related also interviewed 15 specialists and leaders across organizations can benefit and derive business
emerging technologies with the need to industries and 11 Deloitte leaders to gather value from embedding trust and ethics in their
08
mitigate their potential harms. insights in support of the survey’s findings. use of emerging technologies.
09
66
Emerging technologies under consideration
01
“Emerging technologies” refers to digitally enabled tools representing new and significant developments within a field. These technologies can be
grouped into the following categories:
0022
0033
04
Cognitive Digital Reality Ambient Autonomous Quantum Distributed Robotics
Technologies Experiences Vehicles Computing Ledger
Technology (DLT)
05
including general including augmented including AI/ML including automotive, including quantum including blockchain, including robotic
Artificial Intelligence reality (AR), virtual assisted wearables, aerial, and maritime. simulation, quantum crypto, non-fungible process automation.
(AI), GenAI, Large reality (VR), mixed voice assistants, linear algebra for token (NFT), and 06
Language Models reality (MR), voice and in-environment AI/ML, quantum more.
(LLMs), machine interfaces, speech devices. optimization and
learning (ML), neural recognition, ambient search, and quantum
07
networks, bots, computing, 360° factorization.
natural language video, immersive
processing, neural technologies,
08
nets, and more. computer vision,
and more.
09
In 2023 and 2024, GenAI received substantial attention for its potential to change the very nature of work. This year, we reflect on indicators from organizations who have begun
to create value and scale GenAI to their businesses and services, and whether their governance structures and workforces are keeping pace with innovation. We also explore
the increased awareness of concerns about GenAI ranging from data security and quality, explainability of GenAI outputs, and its potential for misuse.
7
Ethical standards
01
Today, many organizations have ethical standards and are committing more resources to train
their teams to use, develop, deploy, and scale technology safely and responsibly. At the same
time, many organizations are missing out on these potential business benefits by focusing on 0022
the risk mitigation or compliance aspects of ethical technology standards.
0033
While having standards in place can reduce Technology leaders seem to respect an intrinsic
risk, businesses that invest the minimum to value in having ethical guardrails baked into
ensure legal compliance or mitigate obvious all stages of technology development—from 04
harms may not experience longer term research to product launch. Bill Briggs, Chief
benefits and advantages of richer customer Technology Officer and principal, Deloitte
05
relationships, improved market reputation, Consulting LLP, posits that organizations who
and greater employee engagement. As one apply ethical checks solely as an extra step to
executive interviewed explained, “the challenge is meet compliance requirements are missing their
06
establishing systems to make sure organizations full purpose and impact. Embedding ethical
are thinking about the long term, without taking principles early and repeatedly in the technology
07
shortcuts. It is hard, because we are bad at development life cycle can help demonstrate
estimating long-term risks as people and weighing a fuller commitment to trust in organizations
that in proportion to short-term gains. Yet even and keep ethics at the front of your workforce’s
08
in the terms of corporate self-interest, in the long priorities and processes.2
view, it is better to be ethical.”1
09
8
Ethical standards
01
Are ethical technology standards in place?
0022
Fifty-three percent of respondents answered “no” or “unsure” to whether their organization had ethical technology standards. In this context,
“unsure” responses suggest where standards do exist, insufficient communication and facilitating education may be common. Response rates
0033
are similar to previous years’ surveys, suggesting this remains an area for optimization in organizations to be addressed sooner rather than later.
Organizations should consider taking steps to Organizations without ethical technology Figure 1. Does your company have
04
increase the impact and awareness of their standards may be not have them for a variety of defined ethical standards for developing
emerging technologies?
ethical technology principles, including: reasons, including:
(Percentage)
05
• Increasing investments in technology ethics • A lack of sponsorship from leaders to
14
teams, assessments, operationalization of operationalize standards.
standards, and learning resources.
• An under-appreciation of the risk associated 06
• Dedicating resources to research emerging with emerging technologies.
47
technologies and associated risks and
• A desire to lead with technologies before
07
appropriate use cases.
ensuring readiness with sufficient research,
39
• Prioritizing communications to socialize infrastructure.
08
standards and research findings to their
• An absence of a strong business case to
workforce.
understand how investing in ethical standards
could translate into positive returns. 09
Yes No Unsure
Wave 3 – 2024 (n=1,848)
Source: 2024 Deloitte Technology Trust Ethics Survey
9
Ethical standards
01
How do ethical technology standards function within an organization?
0022
Most respondents shared their organizations use company culture (20%). Reputation and brand advancing an ethical standard. This reflects a
ethical technology policies to manage risks, follow protection (15%), adding value to society (7%) and more reactive approach to why standards are
0033
regulatory compliance, and align workforces, revenue growth (3%) followed, indicating these important. Organizations may also lack evidence
more so than as an opportunity to create direct as secondary or tertiary boons to leveraging of how ethical practices translate into positive
business value. When asked the primary reason ethical principles (percentages sum to 101 due business outcomes. Sharing leading practices
04
their organization employed ethical technology to rounding). Respondents may emphasize the and achievements between organizations may
standards, the most common responses were potential consequences to non-compliance help them learn from what others have achieved
05
compliance with regulations (34%), enforcing with policy and company conduct as more through their own adoption of ethical imperatives.
standards of conduct (22%), and supporting significant than the benefits gained by proactively
06
Figure 2. Which of the following is the most important reason for your organization to have ethical tech policies and guidelines? 07
(Percentage)
08
34 22 20 15 7 3
Compliance with regulations Standards of conduct Company culture Reputation and brand protection Value-add to society Revenue growth 09
Wave 3 – 2024 (n=1,848)
Source: 2024 Deloitte Technology Trust Ethics Survey
10
Ethical standards
01
One year-over-year indicator is explainability Figure 3. Does your organization utilize
statements continue to be utilized across Explainability Statements providing
0022
users with information on how the
organizations as a means of communicating
technology works, including when,
accountability and informed purpose for the
how, and why it is used?
use of technology to customers and internal (Percentage) 0033
end-users. Fifty-one percent of organizations
use explainability statements—non-technical
15
04
explanations of a technology’s purpose, how it
was designed, and how it operates—to support
transparency in technology deployments. 05
51
35 06
07
Yes No Unsure
Wave 3 – 2024 (n=1,848)
08
Source: 2024 Deloitte Technology Trust Ethics Survey
09
11
Trustworthy and ethical principles of emerging technologies
01
Deloitte’s proprietary Technology Trust Ethics (TTE) Framework can serve as a first step in diagnosing the ethical dimensions of a company’s
emerging technology products. Deloitte first published the TTE Framework in its 2022 report and defines technology as trustworthy and ethical
by adhering to the following principles.3 0022
Safe and secure
Reskilling & Education
Private
0033
Users of the technology are protected User privacy is respected, and data is not
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a socially responsible manner. Technology’s S
benefits (e.g., quality, speed, safety, and/or price) 09
ELBISNOPSER
are evaluated in comparison to potential misuses.
12
Ethical standards
01
Consensus on “safe and secure”
0022
The imperative for safety and security is one Executives interviewed noted cybersecurity Sean Page, Managing Director, Risk and Brand
area of consensus among most respondents and domains have well-established standards that Protection, Deloitte LLP, also explains, “We now
0033
may indicate an entry point into broader ethical most organizations follow. While maintaining and have the ability to share whatever data we want
considerations for organizations, including those monitoring the safety and security of technology across the globe, [but] in some cases controls
without established guardrails. In 2024’s survey, systems is not a new problem or unique to are not in place to manage sharing. We need
04
78% of respondents selected “safe and secure” AI, this increased focus may indicate AI made to challenge the notion that all data is freely
as one of the top 3 ethical technology principles, a wider audience aware of the importance available and to appropriately segregate internal
05
a 37% increase over the previous year’s survey. of cybersecurity and user protections. As and customer data, because the likelihood of
Isolating responses for 2024 demonstrates one executive stated, “access to advanced incidents is significant. Contractual protections
the signal on “safe and secure” as the leading technologies opened a new ballgame. It is no are needed to ensure organizations can use the
06
principle is strong, with 36% of respondents longer in the hands of experts, and we have to data. While cybersecurity and privacy measures
indicating it is their number one ethical principle, account for bad actors who can do things with as are important, those have longer standing
more than twice the next most common rank little as an internet connection.”4 regulations, policies, and standards in place. With 07
1 choice, “responsible” (Figure 4). Individuals GenAI, additional emphasis on data governance
building a case for their organizations to and on bias detection and mitigation is needed.”5
08
adopt ethical technology standards may
find “safe and secure” resonates with
stakeholders and can act as the centerpiece 09
of an ethical technology strategy.
13
Ethical standards
01
Figure 4: Using the following list of technology-focused ethical principles, rank the top 3 by their relative importance to your organization.
(Percentage)
0022
Safe and secure 18 24 36
0033
Transparent and explainable 19 18 12
04
Robust and reliable 17 16 13
05
Responsible 14 14 17
Accountable 14 10 7
06
Fair and impartial 12 9 7
07
Private 7 8 8
08
Rank 3 Rank 2 Rank 1
Wave 3 – 2024 (n=1,848) 09
Note: Percentages shown only include ethical principles included in all three years of the survey to align with Deloitte's TTE framework dimensions.
Source: 2024 Deloitte Technology Trust Ethics Survey
14
AI implications
for ethical
01
standards
0022
Use of Generative AI is growing rapidly Figure 5. Which of the following most closely aligns to your 0033
GenAI has been embraced by organizations, organization's stage of adopting Generative AI technologies?
(Percentage)
with most respondents having exposure
04
to GenAI applications. Ninety-four percent
of respondents indicated GenAI is in
6 11
use at their organizations, though most 05
12
respondents indicated their organizations 8
In development
are piloting GenAI, with 12% of respondents
In testing 06
indicating their organizations have GenAI in
In pilot phases
wide scale use.
In limited use
In wide scale use 07
30
33
Not in development or use
08
Wave 3 – 2024 (n=1,848)
Source: 2024 Deloitte Technology Trust Ethics Survey 09
15
AI implications for ethical standards
01
Adoption of GenAI is becoming universal, Internal use of GenAI increased significantly GenAI use cases for customer engagement and
with 87% of respondents indicating their from 2023. Employees have begun to adopt marketing are rising, with 47% of respondents
0022
organizations are increasing their use productivity tools, and organizations reported reporting their organizations are using GenAI
of GenAI. using GenAI to streamline processes and reduce externally, compared to 31% in the previous
the cost and effort of operations. Seventy-eight year’s survey (a 52% increase). 0033
percent of respondents reported using GenAI
internally, compared to 65% in last year’s survey
04
(a 20% increase).
Figure 6. In the past year, has your Figure 7. Is your organization using Figure 8. Is your organization using
organization increased its use of Generative AI technologies internally? Generative AI technologies for 05
Generative AI overall? (Percentage) external-facing applications?
(Percentage) (Percentage)
06
78%
2024
65%
4
9
07
47%
87 % 31%
08
87
09
2023 2024 2023 2024
Yes No Unsure Yes Yes
(n=1,848) Wave 2 – 2023 (n=1,717), Wave 3 – 2024 (n=1,848) Wave 2 – 2023 (n=1,717), Wave 3 – 2024 (n=1,848)
16
Source: 2024 Deloitte Technology Trust Ethics Survey Source: 2024 Deloitte Technology Trust Ethics Survey Source: 2024 Deloitte Technology Trust Ethics Survey
AI implications for ethical standards
01
Technology, media, and telecommunications (TMT) Figure 9: In the past year, has your organization increased its use of Generative AI overall?
companies lead the adoption of GenAI relative to (Percentage)
0022
other industries. Fifty percent of respondents from
TMT companies said their organizations increased
Consumer
4 11 50 35
their use of GenAI. (n=222) 0033
Energy, Resources & Industrials
7 11 53 29
Amidst this period of growth, executives acknowledge (n=159)
04
the potential of GenAI and the imperative to retain
Financial Services
accountability for its proper use. Chris Griffin, (n=221) 2 8 52 38
Managing Partner - Transformation & Technology, 05
Life Sciences & Health Care
Deloitte & Touch LLP, states, “GenAI is a significant 6 9 55 29
(n=241)
technological advancement, which offers generational
Technology, Media & Telecomm. 06
opportunities for innovation and efficiencies across 3 7 39 50
(n=806)
industries and organizations. However, as organizations
Other
look to harness the potential of GenAI, it’s critical that (n=119) 7 20 44 29 07
they prioritize responsible development and ethical use
to sustain trust with stakeholders. This means investing
in robust governance frameworks that provide Unsure No Yes. Somewhat. Yes. Substantially. 08
Wave 3 - 2024 (n=1,848)
transparency and foster a culture of learning—while
Source: 2024 Deloitte Technology Trust Ethics Survey
also building out the systems of controls that allow
09
organizations to mitigate risks and maximize benefits.”6
17
AI implications for ethical standards
01
Risks of increasing Generative AI adoption
0022
As more companies experiment with GenAI, pilots Debbie Rheder, Deloitte Global Chief Ethics Respondents highlighted data privacy as
will progress into real-world implementations. Officer, offers, “GenAI tools are beginning to offer the most significant concern with the use
0033
During this transition, organizations have a the ability to analyze and interpret tone, also of GenAI. Seventy-two percent of respondents
chance to assess how existing ethical technology known as sentiment analysis, with algorithms ranked data privacy as their number 1, 2, or 3
standards can be adapted to meet GenAI improving at assessing human interactions. concern, and 40% ranked it as their top concern,
04
deployments. While excitement around GenAI While such advanced capabilities offer a new over 3 times more than the next top concern
and competitive pressure accelerates the pace opportunity for insights, it is another example (data provenance, 12%, rank 1). This may indicate
of adoption, companies can potentially run the where bias may be introduced in an algorithm personal unease about the protection of one’s 05
risk of employees overstepping around the and unfairly affect the accuracy of its outputs to data as well as awareness of the potential
use of data, customer privacy, system security, certain users if they are not represented in the harms—to both individuals and organizations—
06
appropriate use of tools, and other areas. This data used in training stages.”8 from violations of customer and employee
may create risks, straining controls that may be privacy and misuse of data. Individuals today also
insufficient for emerging technologies. As Sachin have greater awareness of existing regulations 07
Kulkarni, Managing Director, Risk and Brand such as the European Union General Data
Protection, Deloitte LLP, explains, “GenAI collapses Protection Regulation (GDPR)9 or the California
08
the ‘expertise barrier’: more people can get more Consumer Privacy Act (CCPA)10 in the U.S. and
out of data, with less technical knowledge needed. how regulations can influence global economies
While a benefit, the potential for data leakage may and other pending regulations. 09
also increase as a result.”7
18
AI implications for ethical standards
01
The next highest ethical concerns respondents Figure 10: For Generative AI, which of the following do you consider the top three most
pressing ethical concerns?
reported were transparency (47%, rank 1, 2, or 3)
(Percentage) 0022
and data provenance (40%, rank 1, 2, or 3). This
suggests individuals are seeking clarity for how
Data privacy 13 19 40
GenAI operates and how it collects and manages 0033
data. Users of GenAI tools should be able to Transparency 17 19 11
trust the reliability and veracity of its outputs
04
and may harbor concerns about the potential Data provenance 15 14 12
theft, replication, or misuse of the intellectual
IP ownership 13 13 11
property and creative outputs of individuals. 05
Bagrat Bayburtian, Technology Leader, Risk
Hallucinations 9 12 11
and Financial Advisory, Deloitte Transactions and
Business Analytics LLP, suggests, “organizations Data poisoning 11 11 5 06
need to know models are being trained the way
Authentic experiences 12 6 4
they want them to be. This is not trivial, and
07
as users of their models, organizations should
Job displacement 8 5 3
understand responsibility rests squarely
with them.”11 Static data 3 2 1 08
Rank 3 Rank 2 Rank 1 09
Wave 3 – 2024 (n=1,848)
Source: 2024 Deloitte Technology Trust Ethics Survey
19
AI implications for ethical standards
01
AI risks and rewards
0022
Respondents perceive cognitive technologies and industries. Assistive tools in use today can reality technologies similarly declined from 14% to
such as AI as having the most significant benefits help analysts more quickly and accurately parse 9% since 2022. Distributed ledger technologies,
0033
and risks of any emerging technology. Fifty-four through data, prioritize and render judgment on autonomous vehicles, and digital reality devices
percent of respondents indicated cognitive outliers, and extract insights from their datasets.12 offer examples as to how enthusiasm for emerging
technologies posed the most severe ethical risks technology can decline if limited practical use cases
04
of emerging technologies, while 46% indicated Only 4% of respondents thought distributed exist. Furthermore, if prominent use cases are
they potentially offer the most social good (Figures ledger technologies would contribute the most problematic and lack ownership of issues, trust in
12 and 13). The responses indicating AI could social good, down from 9% in 2022, and digital these technologies can experience a quick decline. 05
cause severe risks are down slightly year-over-
year, while the responses indicating AI’s potential Figure 11: Emerging technologies with the most potential for ethical risk and social good
06
use for good increased. The widespread
According to survey respondents, emerging technologies According to survey respondents, emerging technologies
adoption of GenAI may have increased
with the most potential for serious ethical risk: with the most potential for social good:
respondents’ familiarity with practical 07
applications, providing positive experiences 3DOWN 54 % 5UP 16 % No 6 % 7UP 46 % 9DOWN 9 % 3DOWN 7 %
pts pts change pts pts pts
through use of the technology. Will Bible, Digital
08
Transformation and Innovation Leader, Audit &
Assurance Partner, Deloitte & Touche LLP, cites
data analytics assistive tools powered by AI as an 09
Cognitive Digital Distributed ledger Cognitive Digital Autonomous
example and potential benefit across businesses
technologies reality technology technologies reality vehicles
20
Source: 2024 Deloitte Technology Trust Ethics Survey
AI implications for ethical standards
01
Figure 12: Which of the following emerging technologies do you Figure 13: Which of the following emerging technologies do you
think could potentially pose the most severe ethical risks? think will drive the most social good?
(Percentage) (Percentage) 0022
41% 33%
Cognitive Cognitive 0033
57% 39%
Technologies Technologies
54% 46%
16% 14%
Digital Reality 11% Digital Reality 12%
04
16% 9%
Distributed 13% Distributed 11%
Ledger 6% Ledger 10%
Technology 6% Technology 7%
05
8% 11%
Autonomous Autonomous
6% 11%
Vehicles Vehicles
7% 13%
8% 11%
Quantum Quantum 06
9% 10%
Computing Computing
7% 12%
7% 11%
Robotics 5% Robotics 12%
07
5% 10%
6% 9%
Ambient Ambient
6% 6%
Experiences Experiences
5% 4%
08
1% 1%
Other 1% Other 1%
0% 0%
2022 2023 2024 09
Wave 1 – 2022 (n=1,794), Wave 2 – 2023 (n=1,717), Wave 3 – 2024 (n=1,848)
Source: 2024 Deloitte Technology Trust Ethics Survey
21
AI implications for ethical standards
01
Organizations may consider taking a more Additionally, organizations should know when
cautious, patient, and informed approach to to do nothing. As noted by Bill Briggs, Chief
0022
selecting use cases to apply AI tools to meet Technology Officer and principal, Deloitte
business needs. As one executive indicated, Consulting LLP, organizations should invest in
applying AI to every use case may expose research to understand a technology and the 0033
an organization unnecessar"
337,deloitte,the-financeai-dossier-generative-ai-use-cases-in-finance.pdf,"The FinanceAI™ Dossier
A selection of high-impact
Generative AI use cases in Finance
Table of Contents
Introduction 3 Order to Cash 15
Finance Insights Engine 7 Procure to Pay 17
Autonomous Close 9 Working Capital Optimization 19
Dynamic Risk Assessment 11 Engage with my Tax Data 21
Cash Flow Forecasting 13 Investor Communications 23
2
The FinanceAI™Dossier
Introduction
TheadventofGenerativeAIhasdelightedandsurprisedtheworld,throwingopen For each of these domains, we explore how Generative AI can address
the doortoAIcapabilitiesoncethoughttobestillfaroffinourfuture.Witha enterprisechallenges in new ways, permit more and greater capabilities,
remarkable capacitytoconsumeandgeneratenoveloutputs,GenerativeAIis anddeliver advantages in efficiency, speed, scale, and capacity across the
promptingexcitement andstimulatingideasaroundhowthistypeofAIcanbeused financeorganization.
fororganizationalbenefit. Farmorethanasophisticatedchatbot,GenerativeAIhas
thepotentialtounleash innovation,permitnewwaysofworking,amplifyotherAI As with any type of AI, there are potential risks. We use Deloitte’s Trustworthy AI™
systemsandtechnologies,and transformenterprisesacrosseveryindustry. framework to elucidate factors that contribute to trust and ethics in Generative AI
deployments, as well as some of the steps that can promote governance and risk
TheFinanceAI™Dossierisacompendiumthathighlightsa handful ofthemost mitigation. Trustworthy AI in this respect is: fair and impartial, robust and reliable,
compellinguse casesforGenerativeAIacrossthe finance organization: transparent and explainable, safe and secure, accountable and responsible, and
respectful ofprivacy.
Financial Planning & Analysis Transactional Finance
To be sure, this collection of use cases is just a sample among myriad other
applications, some of them yet to be conceived. As Generative AI matures as a
Controllership Strategic Finance
technology and organizations move forward with using it for business benefit, we
will likely see even more impressive and compelling use cases. Theapplications
Internal Audit Tax highlighted here canhelp spark ideas, reveal value-driving deployments, and set
organizations on a road to making the most valuable use of this powerful
Treasury Investor Relations newtechnology.
3
The FinanceAI™Dossier
Our Perspective
Generative AI has the potential to transform Finance. Generative AI is powered by data, and Finance creates and
relies upon mountains of data. It’s a natural fit. Generative AI might start by producing concise and coherent
summaries of text, converting existing content to new modes, or generating impact analyses from new
regulations. Producing novel content represents a definitive shift in the capabilities of AI, moving it from an
enabler of our work to a potential collaborator. Leading organizations have launched pilot programs and are
scalingfast.
Generative AI continually adapts and learns. So, too, will the leaders who leverage the technology. At first,
Generative AI might support strategic planning—analyzing reports and data to create summaries or proposals.
It might augment autonomous finance operations or detailed reporting work. It will replace labor-intensive
processes and likely accelerate its own value rapidly. Generative AI might elevate continuous controls
monitoring. It could streamline strategic stakeholder communications.
CFOs and Finance leaders should consider today how Generative AI will affect both their functions and their
businesses tomorrow. To make sound decisions, leaders must consider the use of Generative AI from an
enterprise-wide approach with a clear understanding of where the technology will have an impact on operating
expenditures, capital expenditures, market capitalization, and a lot more. The impact is unlikely to stop there,
though. With its ability to process vast amounts of data and quickly produce novel content, Generative AI holds
promise for progressive disruptions we cannot yet anticipate.
Success will require strategic collaboration among C-suite executives—and return on investment—of Generative
AI deployment and adoption. The journey should begin with a sound strategy and a few use cases to test and
learn with well-governed and accessible data. It does not have to be perfect, but it should be controlled. In this
way, Generative AI can spark the next wave of innovation in Finance.
Generative AI heralds a new frontier for efficiently leveraging data, extracting insights, and creating content that
evolves from an enabler of our work to a collaborator.
4
The FinanceAI™Dossier
Six key modalities
One of the primary differences between more traditional AI and Generative AI is that the latter can create novel output that appears to be generated by
humans. The coherent writing and hyper-realistic images that have captured public and business interest are examples of Generative AI models outputting
datainwaysonceonlypossible withhumanthought,creativity,andeffort.Today,GenerativeAImodelscancreateoutputs insixkeymodalities.
Text Code Audio Image Video 3D/Specialized
Writtenlanguageoutputs Computercodeina Muchliketextualoutputs, Textualorvisualprompts Similartoimagery, Fromtextor
presentedinanaccessible varietyofprogramming audiooutputtedin leadthemodeltocreate GenerativeAImodels can two-dimensionalinputs
toneandquality,with languageswiththe natural,conversational, imageswithvarying takeuserprompts and (e.g.,images),models
detailsandcomplexity capacitytoautonomously andevencolloquial degreesofrealism, outputvideos,with canextrapolateand
alignedwiththeuser’s summarize,document, styleswith thecapacity variability,and“creativity.” scenes,people,and generate data
needs. andannotatethecodefor torapidlyshiftamong objectsthatareentirely representing 3D objects.
humandevelopers. languages,tone,and Examplesinclude fictitiousandcreatedby
Examplesinclude degreesofcomplexity. simulatinghowaproduct themodel. Examplesincludecreating
summarizingdocuments, Examplesinclude mightlookinacustomer’s virtualrenderingsinan
writingcustomer-facing generatingcodefrom Examplesinclude homeandreconstructing Examplesinclude omniverseenvironment
materials,andexplaining naturallanguage Generative AI-powered anaccidentsceneto autonomouslygenerating andAI-assisted
complextopicsinnatural descriptionsand callcentersand assess insurance claims marketingvideosto prototyping anddesignin
language. autonomously troubleshootingsupport and liability. showcaseanewproduct apurely virtualspace.
maintaining codeacross fortechniciansinthefield. andsimulatingdangerous
different platforms. scenariosforsafety
training.
Byunderstandingthesemodalities,organizationsareempoweredtothinkthroughandbetterunderstandthekindsofbenefitsGenerativeAIcouldpermit.Foreachusecase
describedinthisdossier,theremaybemorethanonevalue-drivingmodality.Achatbottextoutputcouldbepresentedassimulatedaudio;ageneratedimagecouldbeextended
intoa video.Ultimately,theGenerativeAIusecaseandthevaluetheorganizationseekswilldeterminewhichoutputmodalitiescancontributethegreatestadvantagesand
outcomes.
5
The FinanceAI™Dossier
6
c a
ThevaluethatGenerativeAIusecasescanenablecanbeconceivedacrosssixdimensions: costreduction,processefficiency,growth,innovation,discoveryand
insights, andgovernmentcitizenservices. Tobesure,asingleuse casecandrivemorethanonevaluecapture,buttohelppaintthevision forhowGenerativeAI
canbeusedto movetheneedleoncompetitive differentiators andoperationalexcellence,theusecasesdescribedinthisdossier areeachassociated witha
primaryvaluecapture.
Costreduction Government
citizen services
Reduce cost, typically by30%
orgreater, primarilythrough
Increaseaccuracyofvarious
automatingjobfunctions
federalandlocalprograms
andthenundertakingjob
andcreateeasieraccessfor
substitutions
at-riskpopulations
Valuecapture
Processefficiency Accelerating
innovation
Createprocessefficiencies
throughautomatingstandard
Increasethepaceofnew
tasksandreducingmanual
productornewservice
Growth Newdiscovery
interventions developmentandspeedier
andinsights
go-to-market
Increaserevenuegeneration
throughhyper-personalized
Uncovernewideas,insights,
marketingfortargetcustomers
andquestionsandgenerally
unleashcreativity
dezilaicepS/D3
egamI
Broad categories of value capture from Generative AI
The FinanceAI™Dossier
Finance Insights Engine
Financial Planning and Analysis
Process efficiency and
New discovery/insight
7
dezilaicepS/D3
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How Gen AI can help
Data consumption at scale
Generative AI opens the potential for leaders to leverage data at a depth and
speed far beyond today’s possibilities. Operational data and financial data
are often inconsistent across an organization and lack a uniform structure.
Even key economic indicators like inflation, consumer spending, or interest
rates can vary substantively across geographies, sources of truth, or
Platforms powered by generative artificial Issue / opportunity interpretations. Generative AI could quickly reconcile disparatedata,
intelligence (Gen AI) can review and analyze analyze against company data,and deliver real-time, insight-rich content
Finance work often includes repetitive tasks like pulling that drives strategy.
data, identify gaps and suggest ways to fix
reports and reconciling data, much of which is manual
them, and provide leaders with on-demand and often in spreadsheets. There remain few resources Faster analysis and performance reporting
insights. and little time left to focus on the why behind the data Finance professionals could leverage a Finance Insights Engineto support,
supplement, and accelerate their work. The engine might identifyvariances
or explore multiple what-if scenarios. A generative
between plan and actuals and explain why they exist—eventually learning to
AI-powered insights platform could serve as a digital
tell more complicated stories deep into the financials.For example, when
analyst, allowing finance professionals to ask
labor expense comes in higher than forecast, generative AI can go multiple
questionsin plain language, explore unlimited
layers down in detail—considering geography, operational performance,
datasets, and receive custom reports that reveal
seasonality, special projects, and more—to identifythe root cause.
business performance.
Explanations could then be offered immediatelyin multimodal formats,
including text, graphs, charts, or video.
More productive strategy sessions
Imagine holding a planning session to identify needle-moving
opportunities for the upcoming year. Today, analyzing core financial
metrics for multiple time periods and business lines is a time-consuming
and subjective process. With generative AI-enabled technology at the table,
leaders could request and receive ad hoc analyses of operational and
financial data from the engine in real time to gain retrospective and
prospective insights.
7
The FinanceAI™Dossier
Transformation with speed and confidence
Managingriskandpromotingtrust
Reliable Transparent and explainable
The generative AI model is Confidence in generative AI
susceptible to erroneous, outputs requires stakeholders to
outputs delivered with understand how and why the
complete confidence, even with machine reached its conclusions. Human
hallucinated data points or conclusions. validation and regular audits of generative AI
Before conducting any analysis, data outputs remain essential.
sets should be confirmed and reviewed
for errors.
Potentialbenefits
Enhanced decision making Reduced latency
A Finance Insights engine powered by generative AI With its ability to analyze data instantly, generative AI
can dramatically reduce the manual effort to analyze can provide on-demand, actionable financial
data and deliver consistent, accurate, and up-to-date information to guide leaders’ business strategies.
insights for human analysts to leverage.
88
The FinanceAI™Dossier
Autonomous Close How Gen AI can help
Smart reconciliation
Generative AI could reconcile unstructured or inconsistent journal entries
or take on more complicated accounts that require supporting thoughts or
Controllership
significant estimates to reconcile. Conversational, generative AI-powered
chatbots might also enable users to input exceptions for remediation at
the source, run through next steps, update reconciliations, and consolidate
Generative AI could create a true “lights Issue / opportunity financials.
out” close process by improving leader
A consistently timely, accurate, and efficient financial Perceptive task management
visibility, minimizing rote work, and
close is a challenge. It requires a lot of human power. Generative AI could create integrated, automated closing checklists and, in
ultimately managing and completing tasks. Short bursts of activity take place throughout the year, time, it could centrally track and manage all close activities. It could also
use prior history to anticipate how journal entries impact others, recognize
but this limits visibility into the close process and often
issues to the close, and proactively reduce or eliminate delays.
prevents the finance department from focusing on
more strategic initiatives.
Improved variance analysis
Instead of relying solely on quantitative data, human analysts could
Generative AI can help eliminate the scramble to get
leverage generative AI to weave in unstructured data, like meeting notes,
the books closed on time and without errors. It can do
news stories, and interviews, to gain a deeper understanding of variances
the grunt work—categorizing transactions, making
between actuals and forecasts.
journal entries, and generating financial statements—
so that finance teams can focus on the bigger picture. Interpretative reporting
With time, it might take a bigger role in managing the Finance teams might set up templates from which generative AI could
produce initial accounting reports. As the technology develops logic to
close process and provide commentary on how the
monitor and interpret new or changing regulations, it might start to company performed.
provide impact assessments and produce more advanced accounting
treatments in response.
99
dezilaicepS/D3
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Process efficiency
The FinanceAI™Dossier
Autonomous Close
Managingriskandpromotingtrust
Robust and reliable Transparent and explainable
Generative AI is moving When it comes to the closing
from an enabler of human process, generative AI-driven
work to a potential co-pilot, processes and content must be
but work still remainsto ensure accurate, clearly understood by finance teams and decision-
reliable results. makers.
Potentialbenefits
Process efficiency Cost savings
Generative AI can accelerate the close timeline with Passing off rule-based processing of routine
reduced effort and increased transparency. In time, transactions to generative AI technology can save time
generative AI might learn to anticipate barriers to by handling repetitive tasks.
close, predict next steps, and ultimately take a larger
role in managing the close process, allowing finance
teams to focus on strategic initiatives.
10
The FinanceAI™Dossier
Dynamic Risk Assessment How Gen AI can help
Key risk indicators and continuous monitoring
Generative AI may enhance risk management processes by enabling
unlimited, simultaneous, and continuous anomaly detection and analysis.
Internal Audit, Controllership, and Compliance, Risk
The technology could analyze transactions and other enterprise-wide risk
indicators in real time and generate immediate reports and insights on
potential discrepancies and outliers, allowing for timely risk response and
AI, including generative AI, will continue to Issue / opportunity mitigation.
elevate risk assessments, driving a
Risk management is critical for an organization’s Enhancing risk interviews
streamlined and value-added integrated
success—from business transformation to ongoing Generative AI can analyze unstructured data sources, like interviews, to
risk management approach that could operations. Sophisticated approaches require extensive uncover specific takeaways, themes, and insights. Leaders can then rapidly
transform today’s periodic risk assessments analyses of processes and data, from qualitative and identify and respond to existing and emerging trends.
into a state of continuous monitoring. quantitative sources. The work can be complex, time
Cyber risk monitoring
consuming, and susceptible to human error or Organizations can leverage generative AI to develop an aggregated
unintentional bias. depiction of cyber risk. With near real-time data that ranges across various
dimensions, leaders could better align their thinking and address critical
During risk assessments, leaders in various functions
gaps, threats, and opportunities. With time and development, generative
are often interviewed to gain risk-driven insights.
AI-enabled systems might also activate security measures, such as creating
However, interview capture and reporting are often
action reports, providing recommendations, and notifying users who may
performed manually, which could lead to missed or be impacted and need to take immediate action.
misinterpreted insights and a slow process. Further,
new metrics like indicators of cyber risk are emerging External risk sensing
Predictive, AI-powered analytics could analyze massive amounts of that can be more difficult for leaders to grasp.
intelligence—from open sources such as social media, blogs, forums,
Risks are also often highly interconnected across website reviews, industry newsletters, survey data, and news sources—
organizations, which make monitoring impacts more and then formulate actionable insights. Companies could gain advanced
complex. AI, including generative AI, could help leaders notice of emerging risks, knowledge of potential loss events, and increased
awareness of potential threats to their business or industry. effectively sense and assess risks to strategy,
operations, and other areas in a more dynamic and
real-time manner.
1111
dezilaicepS/D3
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Cost reduction, Process
efficiency, New
discovery/insight, and
Accelerating innovation
The FinanceAI™Dossier
Dynamic Risk Assessment
Managingriskandpromotingtrust
Reliability Accountable Privacy Accountable
Work remains to ensure Continued risk management Interviews and surveys of WhiletheuseofGenerative
that generative AI produces requires identifying decision- business leaders may need AIcanacceleratethework
accurate, reliablecontent. makers for technology use and to be kept anonymous; ofdevelopers,withouta
Today, generative AI might confidently the decisions derived from the responses. in which case it will be crucial to ensure humanintheloop(e.g.,validatingand
produce incorrect output, known as that data privacy is maintained. debuggingcode),criticalfailuresmay
hallucinations occur.Shoringupaccountabilitymay
includedocumentingandcommunicating
standardsandexpectationsfor
employeesusingGenerativeAI.
Potentialbenefits
Value creation Process efficiency Accelerating insights New discovery
Generative AI can support an integrated Business units can receive more timely Leveraging generative AI solutions Companies can identify emerging risks and
approach to risk management, which reports that draw upon massive throughout the risk assessment life cycle predict organizational impacts in advance
includes teaming with the business to quantitative and qualitative data sets to can lead to data-powered insights of the marketplace through advanced
help maximize ROI and enabling better inform decisions and strategy. through end-to-end digital enablement capabilities of capturing and analyzing
business performance through effective and allows organizations to evolve massive internal and external data sets.
controls and governance. toward continuous assurance.
12
The FinanceAI™Dossier
Cash Flow Forecasting How Gen AI can help
Exponential data consumption
Generative AI can process and interpret data at unprecedented scale and speed.
Itcan ingest and analyze historical company data as far back as it dates and can
Treasury
also factor in external data from various sources, in multiple formats. Collectively,
richer data forms the foundation for the cash flow forecast, leading to more robust
analyses and more accurate forecasts.
Generative AI can improve the accuracy of Issue / opportunity Predictive analyses
Generative AI can identify the biggest drivers of cash flows and utilize a larger
cash flow forecasting, reduce manual
Cash flow forecasting is often a labor-intensive process. sample of parameters to forecast future cash flows more accurately.
processes, and provide greater insights to
And despite the work associated with it, many For accounts receivable, this might include factoring in customer trends, such as
business leaders. companies struggle to achieve a reliable forecast. average delay, percentage of payments delayed, average number of invoices per
payment, total open amounts, and time between payments. Additionally, it could
Thiscan lead to companies taking on higher borrowing
consider invoice factors, such as previous payment times, month due, day of the
costs for operations and potentially missing investment
week due, invoice value, and total current invoice value. It could also keep a pulse
opportunities. Generative AI offers the potential to on public data and extract economic patterns and customer activities that might
reduce the manual effort of data aggregation and impact future cash flows. This additional level of granularity and ability to predict
increase the accuracy of the forecast output— with precision can offer business leaders more confidence in their plans.
ultimately saving costs and enhancing returns. For accounts payable, this might include projecting expected trade payables
factoring in specificities related to vendors, based on importance and payment
Data sets often reside across multiple systems in terms. For larger cash outflow drivers, such as taxes or payroll, this could involve
structured and unstructured formats. A generative correlation of data from other sources (e.g., financial statements projections
fortaxes or Human Resources (HR) information for payroll) to enhance
AI-enabled solution can aggregate all sources into its
forecastaccuracy.
analyses. It might also begin to own part of the process.
Foreign exchange assessment When gaps or inconsistencies in the data arise, the
Generative AI can continually monitor international markets, factor volatility into
technology might research and resolve issues by
itsforecasting, and develop hedging strategies. Armed with this information,
following a set workflow (e.g., prompting sales leaders can gain more confidence that their associated decisions are rooted in
representatives with requests for sales forecast reliable data.
confirmation) or leveraging historical trends
Variance reduction
andprobabilities. With manual processes, forecasting relies on different perspectives to provide,
review, and analyze historical financial data. Generative AI can streamline and
Finance teams could access unlimited scenario-based standardize the process, leading to a significant reduction in potential for error
insights and predictions, allowing them to focus less variance to actual results. Forecasts could be further enhanced with integrated
time on generating reports and more time on analyzing visualizations to improve interpretation and confidence, quickly and with less
overall effort.
potential impacts.
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New discovery/insight
The FinanceAI™Dossier
Cash Flow Forecasting
Managingriskandpromotingtrust
Transparent and Safe and secure Robust and reliable
explainable The financial information that Generative AI will require
Important decisions are will form the basis of the data early manual input and
made from cash flow models for generative AI tuning of data and tools
forecasting; therefore, it is critical for must be invulnerable to unauthorized access to realize the benefits of automation.
decision-makers to have visibility and or unintended uses outside of the intended Companies will need to identify how
accountability into how generative AI purpose for which the model is built. granular to get, as well as guidelines and
works. Forecasts will also improve over guardrails.
time, as the models have more
opportunities to run larger data sets.
Potentialbenefits
Timely market analyses More accurate forecasting Reduced borrowing costs Enhanced investment returns
Generative AI can conduct real-time, The more data that generative AI can Better visibility into cash flows and more Companies with a strong cash position can
ongoing reviews of multiple media leverage, the greater the possibility for confidence in forecasts could reduce the
confidently take advantage of longer-term,
sources and internal data that inform reliable, accurate information for planning need to tap into revolving credit lines
higher-yield investment opportunities.
forecasts and potentially improve purposes. and reduce associated borrowing
accuracy and reliability. expenses.
14
The FinanceAI™Dossier
Order to Cash How Gen AI can help
Automated orders
AI and machine learning (ML) can eliminate most of the manual tasks
across the order to cash cycle. Automated data collection, collation, and
Transactional Finance
interpretation can reduce the time spent on customer onboarding, data
management, and deal closing. ML-driven smart quote generation can
significantly reduce processing time on quotes and renewals. Once a sale
A mix of AI can fundamentally transform Issue / opportunity has been approved, AI can create an invoice and order fulfillment request
traditional order to cash processes. based on customer contract terms and standard policies and procedures.
Order to cash is the backbone of a business and a
AI,generative AI, and machine learning
critical component of the working capital value chain. Customer credit risk analysis
canautomate and improve tasks and The order to cash cycle is made up of several sub- Businesses want to know who they are selling to and how likely that
workflowsacross the order to cash cycles, many of which are highly manual today. This person is to pay on time, with accuracy. Generative AI can evaluate credit
risk by analyzing customer data and credit history to help identify high-risk
cycle,resulting in cost savings and workflow is ripe for generative AI-powered
customers, improve credit decision-making, and reduce costs associated
fastercollections. transformation, through which companies can better
with bad debt. Based on the risk analysis, generative AI can tailor sales
understand customer credit risk, shorten sales cycles
offers based on the risk category of customers.
and dayssales outstanding, and increase overall
processefficiencies. Faster collections
Collections today is labor intensive—phone calls and emails with invoice
questions, overdue reminders, and other dispute intervention, often
repeatedly. Leading organizations are already leveraging AI-enabled virtual
assistants that use natural language processing (NLP) to enable self-service
customer payments and collection activities by phone and chat, in some
instances pairing it with ML-enabled recommendation engines to offer
customized offers and payment plans. Generative AI and ML are likely to
further expand the capability of these virtual assistants in the near future
by tracking collections and work lists, automating dunning letters and calls,
making and documenting collectors' calls, providing collections agents with
recommended next actions in real time, running potential discount
analyses, and automating cash postings. They could also understand
payment trends and predict exceptions to get in front of them proactively.
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reduction, and growth
The FinanceAI™Dossier
Order to Cash
Managingriskandpromotingtrust
Robust and reliable Accountable Fair and impartial
As the heart of the business Finance professionals will continue Particularly as it relates to
and cash flow generator, it is to be in the loop for reviews and credit decisions, sales terms,
important that order exception processing.Policies and discounts, the technology
to cash technology produces consistent and that determine who is responsible for the must be designed and operated inclusively
accurate outputs and withstands errors. decisions made or derived with the use of for equitable application, access, and
order to cash technology will be necessary. outcomes.
And since this technology is in front of
customers, potentially around sensitive
subjects like collections, it is important that
the agent script is carefully curated and on
brand to avoid reputational risk.
Potentialbenefits
Accelerated time-to-value Reduced collections efforts Enhanced accuracy
Integrating generative AI across the order Digitization and predictive analysis can help create a
Automating processes and operations can
to cash cycle can expedite orders by better understanding of customer credit risk, allowing
improve accuracy and help reduce the risk of
reducing processing time and improve companies to make smarter decisions around credit limits
days sales outstanding through faster and increasing the likelihood that payments will be made human errors. Humans will remain in the loop
collections. The efficiencies gained across for exception processing but can spend more
in full. This reduces the effort to collect payments or give
the cycle can improve working capital. time focused on strategic activities.
up accounts receivable in disputes.
16
The FinanceAI™Dossier
Procure to Pay How Gen AI can help
Enable efficiencies across procurement
Generative AI can enable efficiencies across procurement, with the
greatest potential in process automation, proactive risk and compliance
Transactional Finance
management, and strategic decision-making and negotiations around
suppliers and pricing. In an increasingly uncertain world, instant access
and ability to process information is vital for mitigating and managing risk
Generative AI can boost efficiencies Issue / opportunity and empowering organizations.
andunlock value across the
Despite having historically been at the forefront of Touchless invoicing and strategic supplier management
procure-to-payprocesses.
technological disruption, many sourcing and Generative AI accelerates the drive toward touchless invoice processing.
procurement functions continue to struggle to optimize Today’s automation is smart enough to process, match, and pay—acting as
a ‘digital employee.’ ‘Traditional employees’ will likely only need to
efficiency, manage risk, and manage costs. Generative
intervene upon exception and can shift their focus to more strategic,
AI can make the procure to pay process simpler,
value-adding tasks. Additionally, generative AI can help manage suppliers,
cheaper, smarter, predictive, and more accurate—
interacting directly through a chatbot feature that could, for instance,
lowering the cost of doing business and unlocking
answer questions about payment timing, or clarify disputes in payments
growth opportunities.
received. It can also develop supplier payment strategies based on things
like the likelihood the supplier to deliver on time, given any term changes.
Automated insights and growth driver
Generative AI unlocks the ability for insights, reducing the effort for
knowledge-based, value-add work. AI can now create models that are
learning and predictive in a manner that can give companies the first cut of
insights, giving employees a kickstart into their analyses, their ‘so-what’s’.
Companies can get smarter about managing inventory by leveraging
generative AI to analyze historical fulfillment rates. They can better
understand what they ordered, received and paid for to demand plan
more accurately and know when to place orders. Companies can know
when they need to have product to help generate revenue and be in a
better position to grow.
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Process efficiency
The FinanceAI™Dossier
Procure to Pay
Managingriskandpromotingtrust
Accurate Reliable
The procure-to-pay process Using a generative AI-powered
starts by initiating a financial predictive model can enable
commitment and ends with organizations to make fact-
based and data-driven
cash leaving the company. Errors in
decisions. Organizations can compare
amounts or elsewise could be detrimental
products and services and rationalize them
and, as such, it is critical that any
across their supplier base, based on factors
automation around these processes is
that drive value for the company. Supplier
accurate.
performance becomes defendable, rather
than just opinion based. The analysis can
involve complex trade-offs, strategic
considerations, and tacit knowledge that
the AI models may no"
339,deloitte,scaling-mission-driven-ai.pdf,"Scaling
mission-driven AI
The path for US health agencies and nonprofits
SSScccaaallliiinnnggg mmmiiissssssiiiooonnn---dddrrriiivvveeennn AAAIII
What can
AI achieve
for US health
organizations?
Imagine the elation when public health researchers
break the code to slow heart disease, reverse diabetes,
or treat substance abuse disorders. Imagine the
gratifi cation epidemiologists feel when they can stop
a measles outbreak by providing the at-risk population
with information that prompted them to take the right
preventive steps.
Now, visualize the data scientists at the agency that
put the right tools in their hands to make this happen.
Artifi cial intelligence (AI) and now Generative AI are
powerful tools that can help federal health agencies
and nonprofi ts achieve breakthrough research and
advancements in public health and health care
delivery. However, leading a mission-driven AI agency
goes far beyond implementing a few successful pilots.
It’s a mindset.
222
Scaling mission-driven AI
Take the lead and scale AI across the organization
AI is poised to reshape what’s achievable not only Using mission-driven AI means that an agency’s AI
within these organizations but also for the strategy cannot be a product purely of IT or technical
communities they serve. teams but should be driven by senior business leaders.1
A recent Deloitte survey found that organizations where
Embracing AI and Generative AI can fuel federal senior leaders communicate a clear vision for AI are
health agencies’ mission by improving efficiency, 50% more likely to achieve their desired outcomes
effectiveness, and equity in health care. In many with AI.2,3 The White House is taking steps to clearly
cases, the path to achieving such benefits relies on communicate sweeping action to harness the benefits
incorporating advances in computer science with of AI, while mitigating its risks in President Biden’s
all other scientific and operational disciplines of an landmark AI Executive Order.4 Federal agencies have
agency. And these new technological tools are going already reported completing all of the 150-day actions
to play a role in the work of almost every employee in tasked by it.5
some capacity.
It’s certainly a start, but for transformative change to
occur, AI has to scale across the enterprise and into the
hands of employees. Leaders should visibly commit to
an AI strategy and champion the benefits.6
3
Scaling mission-driven AI
Balancing benefits with responsible use
Federal health agencies face multiple challenges. These include growing data volumes,
the increasing complexity of administering medical benefits and claims, upholding
an array of regulatory and grant obligations, protecting patient data and privacy,
and approving drugs and devices for safe and efficacious treatment. To address
these challenges, the search for more efficient, effective, and equitable solutions is
ongoing.7 AI can help organizations tackle these challenges and meet their missions,
especially when used responsibly.
Increasing efficiency and Facilitating insights for Supporting better citizen Maintaining public trust
cost effectiveness better decisions health outcomes For federal health
As with most innovations, Toward that end, in 2023
AI can alleviate the burden Generative AI can augment Generative AI can help AI and Generative AI pose President Biden issued
of repetitive yet essential skills and knowledge of provide hyper-personalized agencies, transparent risks. The technology is new an executive order that
tasks, enabling officials to employees. For example, experiences at scale for and requires governance to instructed federal agencies
focus on higher priority it can prompt systems to patients, employees, and the make sure data is secure and to establish guidelines for
processes and
activities. It can reduce analyze policies or datasets public, putting complicated used appropriately. While safe, secure, and trustworthy
costs and improve capacity. for answers across many types regulatory information, consumers appear to be development and use of
For example, it can craft of documents and images, health recommendations, guidelines can help comfortable with their doctors AI.12 Among other things,
optimized supply chain including handwritten notes.8 and claims requirements into using Generative AI in some this comprehensive order
strategies or accelerate Generative AI can help make simpler language. It can even capacities, 4 in 5 consumers addressed Americans’
ensure responsible use
drug discovery processes, recommendations, generate help unlock cures to disease think it is important or privacy, called for consumer
thereby helping transform ideas, and improve decision faster by facilitating improved extremely important that their protections, and advocated
federal health agencies making with intelligent information sharing across of AI and build public health care provider disclose for implementing in ways that
into more efficient and semantic search.9 research groups, running when they are using it for their advance equity and civil rights.
effective organizations. simulations and selecting health needs.11 Federal health It also supported innovation
trust in the technology.
candidates for clinical trials, agencies must balance AI’s and competition as well as
and learning from vast expected benefits with ways responsible and effective
amounts of data that can lead to ensure trustworthiness.12 government use of AI.
to more effective targeted
treatments.10 Yet, it must be
done responsibly.
4
SSccaalliinngg mmiissssiioonn--ddrriivveenn AAII
Human tasks/machine tasks
Where to apply AI becomes clear when
specific tasks are examined
Leaders should be quick to address concerns and
involve employees in identifying what tasks could be
completed faster, easier, or better by a machine, and
what tasks will be better done by humans. Leaders
should also begin to consider “futureproofing” their
workforce as employees will need new skills in a
Generative-AI era.13 AI is truly effective when it is
integrated into the everyday work of employees.
For example, when a Congressional inquiry comes
into a US health agency, AI can help draft the written
report that must be generated in response. AI can
assist in reviewing grant submissions, which is time
consuming when done manually, and consolidating the
information for a human-written or edited report. For
a health nonprofit, AI can translate disease information
into language that is more understandable for lay
readers. In these cases, the technology augments
the tasks of an employee. Instead of spending time
on these lengthy processes, employees can focus on
reviewing and improving their outputs and adding their
insights where appropriate.
Identifying where AI can be most advantageous gets
easier when specific tasks are analyzed.
5
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Real-world
applications
As AI advances, humans will continue to oversee
and manage outputs for authenticity. AI can create
mission-led value that is efficient, effective, and
equitable for federal health agencies and nonprofits.
And it’s already happening. This period of innovation
is best facilitated by a multi-skilled team of business
and technology leaders who put forth solutions that
are co-created, human-centric, and mission-effective.14
Take a look at these advancements.
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Scaling mission-driven AI
MISSION-LED VALUE
Equitable: Greater fairness
Health equity in action
Health and clinical research has long underrepresented
specific populations, citing lack of participation in clinical
trials, the inability to travel to participating sites from
rural areas, and other social and economic issues.15
This challenge has limited the applicability of findings
to a subset of the population, creating an incomplete
picture of how to improve health outcomes for all.
Today, health agencies can employ AI to generate
insights to better understand and improve health
outcomes among these groups. Here’s how:
Chronic conditions prevention Maternal-child health Food insecurity
and management
AI has been used to help predict risk in pregnant The US Department of Agriculture reports that 39
AI has helped discover which populations are most mothers and design interventions tailored to who million people, including 18 million children, are food
vulnerable to certain chronic conditions like diabetes they are and where they live. Highlighting and finding insecure in America alone.19 AI has helped uncover
and hypertension. Even more encouraging for risks, especially in low-resource settings, helps to which populations face the greatest level of food
prevention and management is AI’s role in analyzing target this research and provides access to care for insecurity by locating food deserts. It can also help
social determinants of health (SDoH), which has been expectant mothers who need it. Real-time electronic predict hunger crises. When it comes to food aid,
challenging to do in the past. AI can mine electronic health recording and predictive modeling helps timing is vital.20 Insights have led to interventions like
health records and doctors’ notes, integrating SDoH clinicians monitor pregnancy, especially in mothers mobile farmers markets and transportation access to
factors like age, housing, lifestyle, and income level into who have gestational diabetes.17 AI has also been used help address food insecurity within a specific population.
more comprehensive treatment plans, recognizing that to improve prenatal diagnosis of birth defects and
health is influenced by a multitude of factors beyond prenatal genetic testing.18
the physical.16 AI can also help define which SDoH are
the greatest predictors for developing chronic conditions.
These insights have helped determine what actions
can be implemented to reduce the prevalence of
chronic conditions within a population.
7
Scaling mission-driven AI
MISSION-LED VALUE
Efficient: Time-saving
New frontiers in biomedical
research
Researchers increasingly rely on vast amounts of data
to validate their hypotheses. However, most data is
still in disparate formats, located in silos, and very
hard to find and reuse. AI is helping to consolidate this
multimodal data–including publications, images, and
multi-omics data–into a common format. Multimodal
data analysis has the potential to uncover new insights.
With access to more data, researchers can use AI to
accelerate the discovery of better treatments and
cures for diseases.21 It can help identify trends, improve
understanding, and foster better collaboration.
In addition, AI is helping researchers and physicians
communicate with patients more effectively by mapping
clinical notes into a format for an electronic health
record that patients can access and understand, which
can also be computable downstream for a researcher.
This improved management of complex information
can provide patients and care teams with more insights,
positively affecting health outcomes.
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MISSION-LED VALUE
Effective: Informed decisions
Drug discovery and availability
Research and development in biopharmaceuticals can be risky and expensive
endeavors. Only a fraction of new drug candidates survives clinical trials, demonstrates
success, and ultimately receives approval. It’s important that resources along the supply
chain are made available to researchers developing new drugs and running the clinical
trials to help them accelerate their mission-critical work. In addition, once the drug is
approved, companies need to keep up with demand.
Accelerating the process Supply chain insights
Some organizations are turning to AI and Generative Generative AI is also helping researchers organize
AI to transform many aspects of the drug discovery unstructured data about key suppliers of starting
process. Generative AI can rapidly create 3D materials to illuminate critical aspects of the drug
biomolecular structures and predict drug-to-protein supply chain. Organizations can receive millions of
binding. AI can increase the speed and efficiency of document submissions that have valuable supply chain
drug discovery, facilitating the creation of a digital twin data. This information is often reported inconsistently
for clinical drug trials that can lead to better patient and in a variety of formats, making it difficult to interpret
screening. Generative AI can help predict a clinical and detect potential pharmaceutical supply chain
trial’s probability of success, so researchers have disruptions. The use of Generative AI can help bolster
added confidence in the projected outcomes of surveillance efforts and enable researchers to better
real-world trials.22 understand the impacts of supply chain issues that
stem from starting materials.
9
Scaling mission-driven AI
MISSION-LED VALUE
Effective: Enhanced compliance
Effective grant decision making
In the world of grants management, getting necessary What if program leaders had access to a Generative AI
information that federal funding agencies can analyze tool that allows them to quickly generate a summary
quickly to monitor and support grant recipients can profile for one recipient, a set of recipients, or all
be difficult and time consuming. Agencies often have recipients? It could populate a pre-defined profile
multiple, separate systems to collect this information, template, pulling information from a variety of data
making it difficult to analyze the data and respond to sources such as budgets, work plans, progress reports,
incoming requests and inquiries. performance measures, and technical assistance data.
An organization can be called upon to generate The summaries would allow program leaders to
recipient-specific profiles and summaries of the focus more time on high-value activities to monitor
funding each recipient receives, its work, alignment and support grant recipients in working to achieve
with priorities, accomplishments, challenges, and the their goals. Generative AI’s capabilities can even help
technical assistance requested and received from the agencies inform their future funding decisions, which
funding organization. These summaries support site will lead to better and more profound public health
visits and policy or data requests from Congress, impacts in the future.
NGO partners, and other stakeholders.
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MISSION-LED VALUE
Efficient: Cost optimization
Better biosurveillance
capabilities
Biosurveillance focuses on developing effective
capabilities for detecting, monitoring, countering, and
preventing national health threats in humans, animals,
food, water, agriculture, and the environment.23 Such
threats can include supply chain disruptions like those
that occurred during the COVID-19 pandemic.
AI technologies are assisting public health agencies in
creating more resilient health care networks, helping to
ensure that much-needed materials are in place ahead
of the next public health emergency. AI can also help
agencies understand public health vulnerabilities and
emerging risks as well as assist in disease tracking.
Generative AI is helping with data gathering from a
multitude of sources, not merely text documents,
but photos, audio, and video that can be used to improve
surveillance. It has the power to detect epidemic signals
much earlier than traditional surveillance, triggering
investigation and responses at the regional level.24
The more efficient agencies and nonprofits can be
in biosurveillance efforts will not only help in cost
optimization now, but it will also result in cost savings
as future crises are averted.
11
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MISSION-LED VALUE
Equitable: Better communication
Increasing donor and
community engagement
Many health nonprofits depend on donations and
gifts. AI can be used to help health nonprofits identify
and segment potential donors and then encourage
donor actions to support their missions. AI can also
be used to help nonprofits predict outcomes, such as
analyzing a large donor dataset to identify who might
be able to financially contribute to their work to protect
and enhance public health.25
Furthermore, AI can help nonprofits target and
personalize communications to potential donors,
which can contribute to improving the effectiveness
of fundraising efforts. AI chatbots can streamline
interactions and answer donor questions.
Like donor engagement, AI is being used at some
federal health agencies to improve communications
with its intended audiences. There, AI is helping to
combine various websites and rewrite the content
to be understandable and digestible for the public.
Generative AI can help build content, reach new
audiences, and answer questions. Making resources
easier to find and digest can increase transparency in
government initiatives, building trust while engaging
communities and improving the health of Americans.
12
Scaling mission-driven AI
Accelerate
Scaling AI: An integrated three-tier approach
the AI journey
1
Set the AI Direction
Determine where and how AI can best improve an organization’s operations and achieve mission/business needs
Fast-forward deployment and use
AI Exploration AI Strategy & Governance
Identify AI Opportunities & Use Cases Defi ne Vision and Establish Governance
While AI is in action at many health agencies and
nonprofi ts, leaders are asking a lot of questions about
scaling AI eff orts to maximize benefi t. It’s not just
2
implementing a pilot case here or there; it’s making
it a part of the entire organization. In order to do so Build Core Capabilities and Deliver AI Value
consider these questions: Determine foundational capabilities across people, data, and technology to enable AI solutions and deliver value
PEOPLE DATA TECHNOLOGY
• What are the key AI use cases to drive mission impact?
Customer & User Trustworthiness,
• What’s the best way to deploy, use, and embrace AI? Experience* AI Enabled Workforce Data Readiness AI Infrastructure / Platforms Security, & Risk*
Apply Customer-Centric Prepare the Workforce Provide the Data Foundation Provide Technical Foundation Mitigate Risk and
• What key data and infrastructure decisions need to Design & Delivery Instill Confi dence
be made at the outset?
AI Apps and Solutions
• How will AI be monitored and managed? Develop AI Solutions
• What investments are the right ones?
3
Consider taking a three-step integrated approach
to AI that considers strategies, technologies, and Manage AI Holistically
components. AI readiness and management requires Continuously maintain, manage, and build upon AI capabilities
a holistic view to fast-forward widespread deployment
and use. Various elements like data, algorithms, models, AI Delivery and Operations AI Sourcing Management
Scale, Maintain, and Operate AI Solutions Streamline Procurement
governance, ethics, and human expertise should be
brought together to create a comprehensive AI program.
Maximizing the benefi ts of AI while minimizing the risks
* Trustworthiness, Security, & Risk and Customer & User Experience are core to all AI capability areas and should be considered throughout the AI Journey
is the goal.
13
Scaling mission-driven AI
Set the agency’s AI direction
1
Determine where and how AI can best improve an
organization’s operations and achieve the mission
AI Exploration
The first step on the journey is to educate relevant
stakeholders and end users about the capabilities
and benefits of AI. Investing in AI fluency efforts and
workshops will help these stakeholders understand AI’s
potential to address their agency’s needs and challenges.
As these discussions progress, potential opportunities—
or use cases—for AI can be identified. These use cases
address specific needs and challenges within the agency
where AI can bring value and help solve problems more
efficiently and effectively. This step helps agencies better
understand what business apps they can develop to
realize the value of AI in producing better outcomes or
efficiency gains.
AI Strategy and Governance
Then a vision should be developed that includes defining
goals, success criteria, and time frame with focus on
prioritizing use cases that will have the greatest impact
and value. Factors to consider include time saved, mission
impact, and cost reduction. Now is also the time for agencies
to establish clear guidelines on governance. Guardrails
should be developed to help minimize risk, improve data
accuracy, address potential bias in the data, and provide
for transparency and accountability.
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Scaling mission-driven AI
Build scalable, enterprise-wide core capabilities that deliver AI value
2
Develop foundational capabilities across people, data, and technology to enable AI solutions and deliver value
People: Prepare the workforce Technology: Provide a platform to build solutions
Every technology innovation should start with the people Once the data is free-flowing, trusted, and secure,
it’s meant to support. As agencies build AI capabilities and organizations need a platform—an innovation sandbox—
business applications, they should also build an AI-enabled in order to create AI solutions. It should be an easy-to-use
workforce. Workers should be included at the start of the platform with capabilities to quickly build, deploy, and
AI journey, so they understand the potential benefits and monitor AI solutions for desired outcomes. The platform
risks of the technology. Adoption is sure to take hold as should leverage appropriate architectural principles
they help co-create the solution they are meant to use. (e.g., Data Commons) and implement governance, security,
Developing AI fluency among workers is important as is and trustworthiness principles. This will help ensure secure
upskilling those whose jobs could be directly affected by use of the emerging AI/GenAI technology.
the AI applications. Organizations who achieve AI at scale
do not shortchange this aspect of the program. It’s important to clearly define the objectives and goals
of any AI pilot project along with the metrics that will be
Data: Create a solid foundation of readiness used to measure success. It’s also critical to understand
Sound data practices make all the difference when it how citizens or employees will engage and interact with
comes to AI. If the data are inaccurate or simply unavailable, the solution and to ensure solutions are easy to use and
the quality of the output suffers. Organizations need to compliant with regulations and policies. Pilots should
develop adequate infrastructure and capacity to sufficiently begin in a controlled environment and use synthetic data
curate agency datasets for use in training, testing, and for testing on a development platform. The performance
operating AI. A strong data foundation enables the of the AI model should be evaluated against defined
implementation of enterprise-level AI solutions, all while quality metrics, and improvements should be made until
ensuring the use of secure, precise, and trustworthy data. adequate outcomes are reached.
Sound data governance practices, particularly data curation,
labeling, and standardization, can help maximize Establishing an AI Center of Excellence can help optimize
appropriate outcomes. costs of development by creating repeatable business
applications that can be tweaked for a variety of purposes.
Most importantly, data access must be democratized, Think of a chatbot that supports multiple workflows or a
making it free flowing and accessible. Data stuck in tool that summarizes contents to inform users. The same
silos isn’t working for the organization or its mission. chatbot or tool could be used by multiple departments in
Collaborating with professionals who have experience the same organization for different purposes. A Center
in building data and AI capabilities can provide much- of Excellence can help achieve AI at scale and help instill
needed guidance. trust in the solution.
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Scaling mission-driven AI
Manage AI holistically
3
Continuously maintain, manage, and build upon
AI capabilities
AI Delivery and Operations
AI models can change and evolve over time as they
continuously learn and adapt. Health agencies must
regularly monitor and evaluate their performance. AI models
can be tested with new data to evaluate performance and
help ensure they are providing accurate, reliable results.
All in all, think like a
Incorporating feedback from users and stakeholders
helps identify areas for improvement. An interactive AIOps
process is needed to help ensure continued accuracy and researcher to find more
performance of AI solutions.
effective ways to achieve
AI Sourcing Management
AI technology is constantly evolving and it’s important
to stay abreast of the latest advancements and leading the mission. Make it easy to
practices. A sourcing strategy that enables the effective
procurement, oversight, and management of vendor-
implement AI solutions from
provided AI solutions, tools, and services can advance
mission, operations, and technology objectives. It takes
a village. Make sure to evaluate performance continuously.
an infrastructure standpoint.
Ensure the mechanics are
there for a safe, secure,
ethical experience that
includes humans in the
loop for monitoring.
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Trustworthiness,
security, and risk
Understanding the vulnerability and threat
What are the risks of AI? There are two ways to classify AI
risk: AI vulnerabilities from using the technology in an agency
program (risks to using AI) and AI threats typically from bad
actors using AI to their benefit to hurt an organization (risks
coming from AI). AI systems can be complex and opaque
and by nature are susceptible to a wide range of issues that
can limit their ability to perform consistently and accurately,
making them less reliable in dynamic, real-world scenarios.
AI system vulnerabilities can include data privacy breaches,
bias and ethical concerns, and a lack of explainable or
erroneous results.
AI threats can include malware generation, system breaches,
fictitious personas, misinformation, and social engineering.
Rapid advancements in AI and the availability of open-
source AI tools have also lowered the entry barrier for
attackers, who can automate and scale more damaging
attacks. These risks can affect an agency’s reputation,
mission, finances, and data. One recent cyberattack
threatened the security of patient information and has
disrupted patient care and access to medications.26
17
Scaling mission-driven AI
Regulations are gearing up
GGuuiiddaannccee aanndd rreegguullaattiioonnss aarree eemmeerrggiinngg ttoo aaddddrreessss tthhee
rriisskkss aassssoocciiaatteedd wwiitthh AAII’’ss ccoommpplleexx iinntteerrppllaayy ooff tteecchhnniiccaall
aanndd ssoocciieettaall ffaaccttoorrss.. FFeeddeerraall,, ssttaattee,, aanndd llooccaall aaggeenncciieess
aarree ccoonnssiiddeerriinngg oorr eennaaccttiinngg AAII gguuiiddaannccee aanndd rreegguullaattiioonnss
to account for and govern the use of the technology
across government.
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DDDeeellloooiiitttttteee’’’sss TTTrrruuussstttwwwooorrrttthhhyyy AAAIII™ can help
from the White House,27 tthhee OOffiffi ccee ooff MMaannaaggeemmeenntt aanndd
mmmiiitttiiigggaaattteee rrriiissskkk aaannnddd eeennnsssuuurrreee cccooommmpppllliiiaaannnccceee
Budget (OMB) has issued the fi rst government-wide
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OOMMBB’’ss ppoolliiccyy aaddddrreesssseess rriisskk ffrroomm AAII uussee,, eexxppaannddss tthhee EEEssstttaaabbbllliiissshhhiiinnnggg AAAIII gggooovvveeerrrnnnaaannnccceee cccaaannn hhheeelllppp iiidddeeennntttiiifffyyy,,, mmmiiitttiiigggaaattteee,,,
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AAII,, rreemmoovveess bbaarrrriieerrss ttoo rreessppoonnssiibbllee AAII iinnnnoovvaattiioonn,, ccaallllss lllaaannndddssscccaaapppeee aaannnddd rrreeeggguuulllaaatttooorrryyy eeennnvvviiirrrooonnnmmmeeennnttt... DDDeeellloooiiitttttteee’’’sss
for agencies to grow their AI workforce, and requires TTTrrruuussstttwwwooorrrttthhhyyy AAAIII™ is anchored on seven dimensions.
federal agencies to strengthen AI governance.28 AAAIII mmmuuusssttt bbbeee tttrrraaannnssspppaaarrreeennnttt aaannnddd eeexxxppplllaaaiiinnnaaabbbllleee,,, fffaaaiiirrr,,, aaannnddd
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Federal agencies may also be required to designate sssaaafffeee aaannnddd ssseeecccuuurrreee,,, aaannnddd rrreeessspppooonnnsssiiibbbllleee aaannnddd aaaccccccooouuunnntttaaabbbllleee...
an AI offi cial, conduct maturity and compliance
assessments, and develop their own AI strategy. AAAttt iiitttsss fffooouuunnndddaaatttiiiooonnn,,, AAAIII gggooovvveeerrrnnnaaannnccceee eeennncccooommmpppaaasssssseeesss aaallllll ttthhheee
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TThhee UUSS DDeeppaarrttmmeenntt ooff CCoommmmeerrccee’’ss NNaattiioonnaall IInnssttiittuuttee ppprrroooccceeesssssseeesss,,, aaannnddd pppeeeooopppllleee... TTTooogggeeettthhheeerrr,,, gggooovvveeerrrnnnaaannnccceee aaannnddd
ooff SSttaannddaarrddss aanndd TTeecchhnnoollooggyy ((NNIISSTT)) hhaass rreelleeaasseedd aann AAII cccooommmpppllliiiaaannnccceee aaarrreee hhhooowww aaannn ooorrrgggaaannniiizzzaaatttiiiooonnn aaannnddd iiitttsss ssstttaaakkkeeehhhooollldddeeerrrsss
Risk Management Framework29 to help manage risks eeennnsssuuurrreee AAAIII dddeeepppllloooyyymmmeeennntttsss aaarrreee eeettthhhiiicccaaalll aaannnddd cccaaannn bbbeee tttrrruuusssttteeeddd...
to individuals, organizations, and society associated
wwiitthh AAII.. IItt iiss iinntteennddeedd ffoorr vvoolluunnttaarryy uussee aanndd tthhee ggooaall iiss
to improve the ability to incorporate trustworthiness
considerations into the design, development, use,
aanndd eevvaalluuaattiioonn ooff AAII pprroodduuccttss,, sseerrvviicceess,, aanndd ssyysstteemmss..
There’s no doubt that specifi c frameworks and
methods for identifying, managing, and monitoring
AI risks are needed.
1188
Scaling mission-driven AI
Creating AI mission-led value
There are many potential short- and long-term benefits of AI in public health. Federal health agencies all
Efficient Effective Equitable
strive to be efficient in how they work, effective at achieving the goals of their underlying mission, and fair
and equitable in how they serve all citizens. Here’s what agency leaders can do by providing employees
with a supercharged tool that is efficient, effective, and equitable.
Time saving Informed decision- Greater fairness
making
AI can assist employees by AI solutions can be designed to
augmenting human work and AI can provide quick insights mitigate bias in data sets and
reducing manual tasks. AI and answers to questions, models, leading to more fairness
and Generative AI specifically thereby reducing the time and equity in decision making
can quickly analyze and spent on manual data analysis and more targeted outreach.
summarize large amounts and allowing health agency
of unstructured data, such employees to make more Better communication
as lengthy applications and data-informed decisions in
AI is helping to turn complicated
progress reports, to extract a timely manner.
or sophisticated language into
salient points and provide
easy-to-read text that the average
quick insights. Enhanced compliance
person can understand.
AI can assist in monitoring
Automating manual tasks and
compliance with grant policies
processes not only saves time
and requirements, which
for workers but can enable
in turn can help granting
them to focus on more high-
agencies be better financial
value activities. In total, these
stewards of funding.
Cost optimization
Improved accuracy benefits can
AI can help agencies predict
AI can deliver more accurate
which regions, applicants, or
and consistent results
recipients carry more risk or result in better
by following predefined
need monitoring assistance,
criteria and rubrics. It can
allowing employees to
also improve the quality of health outcomes
strategically allocate limited
questionnaires by flagging
resources for better outcomes.
errors or missing information,
Task automation can reduce for citizens.
providing feedback, and
costs associated with manual
reducing the number of
review processes.
ma"
340,deloitte,us-advisory-ai-systemic-risk-in-banking-june-2024.pdf,"'Weapon and tool' - systemic risk implications of AI in banking and finance
Initial perspectives related to remarks by the Acting Comptroller of the Currency, Michael J. Hsu
On June 6, 2024, Acting Comptroller of the Currency Michael J. Hsu delivered remarks at the Conference on Artificial Intelligence and Financial Stability, hosted by the Financial Stability Oversight Council (FSOC)
in partnership with the Brookings Institution, wherein he discussed systemic risk implications of artificial intelligence (AI) and offered his thoughts on approaches to AI deployment to improve its safety.1His
remarks are the latest illustration of regulators’ growing concern about AI. In its 2023 Annual Report, FSOC—for the first time—identified AI as a potential systemic risk.2
5 insights youshould know 5 considerations to evaluate
AI presents accountability challenges: AI’s ability to evolve overtime and self-learn makes it a powerful tool but Establish clear roles and responsibility: Banks should apply existing principles of risk governance and model risk
can also result in model drift, where the model’s accuracy and performance deviate from expectations. This management (see Federal Reserve Supervisory Letter 11-7, OCC Bulletin 11-12, and the Comptroller’s Handbook
may be especially true in the case of nontransparent models that are powered by third parties. Banks may 1 on Model Risk Management)3to their AI applications and across their model lifecycles. For third-party AI-tools that
struggle to identify whom to hold accountable for what or how to fix any issues, which could—ultimately— may pose particular challengesto an organization’s internal accountability framework, controls should be put in
erode trust within the banking system. place commensurate with the bank’s risk exposure and complexity and extent of the model’s usage.
Competitive pressures may cause banks to neglect controls: As competitive pressures grow within the industry Develop gates between AI development stages: Banks should identify in advance “gates” or points at which
to develop and launch AI-enabled applications, risk management and controls may be neglected by some pauses in growth and development are needed to establish controls as AI develops across the maturity spectrum.
banking organizations. As a result, risks may grow undetected and unaddressed until a critical failure or 2 Hsu stated AI applications evolve across three stages: (1) inputswhere AI provides information for humans to act
disruption occurs. It is therefore critically important for adequate initial due diligence, and risk management and upon; (2) co-pilotswhere AI enables humans to do tasks more quickly; and (3) agentswhere AI executes activities
controls to keep pace with growth in order todrive sustainable growth and stability. on behalf of humans. It’s important for banks to demonstrate to regulators a coherent AI strategy with controls.
AI-enabled fraud is a top concern: Nefarious actors are increasingly able to access and deploy AI-enabled tools Invest in customer protection and compliance: Leveling up customer security protocols and consumer compliance
for fraudulent activities. For example, AI tools—including deepfakes—may be used to impersonate an should be considered, so as tobetter align with evolving AI technologies. This may include investing in AI-enabled
individual’s voice or likeness to trick friends and family to send money to a fraudster or even bypass a bank 3 security solutions to detect and respond to AI-fraudulent activities in real-time, such as advanced behavioral
customer’s account security check. AI may be used to drive the increase in the scale and scope of fraud, which analysis and anomaly detection. Additionally, banks should proactively manage the risk of consumer compliance
could undermine trust in the payments and banking system. violations, such as prioritizing model accountability and transparency particularly for consumer-facing applications.
AI-enabled cyberattacks are a growing risk: Cybercriminals are increasingly deploying AI-enabled tools to Invest in cybersecurity and operational resilience: Strategic attention should be given to evaluating cybersecurity
launch sophisticated attacks on individuals and organizations. The frequency and scale of cybercrime, such as defenses, including technology infrastructure and endpoint detection and response (EDR) solutions, to assess their
ransomware attacks, may increase. These tools are not only being used by criminal organizations, but also 4 suitability against potential AI threat actors. Building resilient organizations involves not only building leading
nation-state actors to disrupt or disable critical infrastructure. It is therefore important for both policymakers technology systems, but also maintaining disaster recovery and business continuity plans that are regularly
and banking organizations to focus on operational resilience. updated and tested to ensure they are effective against AI-enabled threats.
Shared responsibility model for AI: TheActing Comptroller proposed a shared responsibility framework for AI, Engage with industry and public-private collaboration initiatives: Consider engaging with regulator-convened
similar tothat used in the cloud computing context, where responsibilities of customers and AI-technology forums, such as NIST’s AI Safety Institute Consortium and other collaboration efforts such as industry member
service providers are allocated depending upon the “AI stack” layer and service arrangement. One potential 5 groups. Coordination among and in between industry participants and policymakers will likely be key to developing
vehicle for facilitating this framework could be the newly established US Artificial Intelligence Safety Institute AI standards, including a potential shared responsibility framework. Participation can also help share knowledge
(AISI) within the National Institute of Standards and Technology (NIST). and leading practices between AI stakeholders and improve both the industry and banks’ AI practices.
Copyright © 2024 Deloitte Development LLC. All rights reserved.
'Weapon and tool' - systemic risk implications of AI in banking and finance
Initial perspectives related to remarks by the Acting Comptroller of the Currency, Michael J. Hsu
Acting Comptroller Hsu proposed a “shared responsibility framework” similar to what exists in the cloud computing context, whichallocates operations, maintenance, and security responsibilities to customers
and cloud service providers depending on the service a customer selects. See Figure 1 below.
Within the “AI stack,” there exists (i) an infrastructure layer, (ii) a model layer, and (iii) an application layer. But, according to Acting Comptroller Hsu, for the framework to be actionable, consensus on the sub-
components within each layer and on the types of third-party arrangements would be needed—something FSOC is uniquely positioned to contribute to, given its role and ability to coordinate among agencies,
organize research, seek industry feedback, and make recommendations to Congress.
Figure 1: Shared responsibility model in cloud computing
Source: General Services Administration (GSA), “Cloud Information Center,” accessed June 10, 2024.
Copyright © 2024 Deloitte Development LLC. All rights reserved.
Endnotes
1 Office of the Comptroller of the Currency (OCC), “Acting Comptroller of the Currency Michael J. Hsu remarks ‘AI Tools, Weapons, and Accountability: A Financial Stability Perspective,’” June 6, 2024.
2 Financial Stability Oversight Council (FSOC), “Annual Report 2023,’” December 2023.
3 Federal Reserve Board of Governors (FRB), “SR 11-7: Guidance on Model Risk Management,” April 4, 2011; OCC, “Bulletin 11-12: Supervisory Guidance on Model Risk Management,” April 4, 2011; OCC,
“Comptroller’s Handbook on Model Risk Management,” August 2021.
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341,deloitte,the-mission-driven-cdo-insights-from-the-2023-survey-of-federal-chief-data-officers.pdf,"The Mission-Driven CDO
Insights from the 2023 Survey of
Federal Chief Data Officers (CDOs)
In the fall of 2023, federal department-, agency-, and bureau-level CDOs and
Statistical Officers completed a survey developed by the Data Foundation and
Deloitte to understand the evolving CDO role and CDO community needs. The
insights below are based on the results of this survey, which is the fourth annual
of its kind.
CDOs are...
Catalysts Strategists
for AI adoption and innovation within their aligning data governance and equitable practices to the organization’s
organization. mission.
• 55% of CDOs already use basic or advanced AI • CDOs are supporting their organization’s mission by maximizing the
and 95% intend to adopt new AI technologies for value of their organization’s data, supporting a data community, and
their organizations in the next year. leading the development of data policies and processes.
• The 2023 Executive Order establishing the Chief • CDOs are expanding data-driven decision making, improving data
AI Officer (CAIO) role will increase the expansion infrastructure and data quality (i.e., demographic
of AI throughout all organizations. representation in data), and promoting
inclusivity in the workplace and in staffing.
CDOs will be critical partners to CAIOs,
aligning all cross-functional areas of CDOs are responsible for orienting their
their organization to strategic AI organization towards equitable and
initiatives. data-centered approaches that serve
their mission and the public.
Champions Operators
of data literacy and culture of shared data agendas
in the workforce to keep pace and evolving needs of their
with emerging technology. organizations.
• Well-trained talent • 52% of CDOs work with a
specializing in the intersection host of C-Suite leaders, with
of data, AI, and industry is cited 60% of CDOs naming CIOs as the
by 60% of CDOs as a key resource leader they collaborate with most
needed to effectively carry out their frequently. In 2023, more CDOs (55%)
missions. experienced challenges reporting up to
CIOs than in 2022 (34%).
• Beyond foundational data knowledge, 75%
of CDOs believe their roles also influence the • CDOs cite funding, authority, and staffing contraints as
organization’s data culture, encouraging data the top three barriers hindering mission success. CDOs also provided an array
professionals to value data and use it ethically of additional barriers, indicating that each organization faces unique challenges.
and responsibly.
With the advent of the new CAIO position, it is even more crucial for CDOs to
Data literacy programs can position their establish shared agendas across leaders. Despite differences among organizations,
organization’s staff for success and boost the key to success is that each organization’s structure and resources empowers
data-driven decisions. the CDO office to achieve their data goals and mission requirements.
Contact Us
Deloitte supports many Federal clients in the data and AI space. With best-in-class AI
advice and capabilities, we can help at each stage of the race, providing Chief Data
Adita Karkera Lorenzo Ross
Officers with the CDO Services they need to navigate the role of the CDO.
Chief Data Officer, Deloitte Technology Fellow, Deloitte
Government and Public Services Government and Public Services
adkarkera@deloitte.com wross@deloitte.com
About Deloitte
Deloitte refers to one or more of Deloitte Touche Tohmatsu Limited, a UK private company limited by guarantee (“DTTL”), its network of member firms, and their related entities. DTTL and each of its
member firms are legally separate and independent entities. DTTL (also referred to as “Deloitte Global”) does not provide services to clients. In the United States, Deloitte refers to one or more of the US
member firms of DTTL, their related entities that operate using the “Deloitte” name in the United States and their respective affiliates. Certain services may not be available to attest clients under the rules
and regulations of public accounting. Please see www.deloitte.com/about to learn more about our global network of member firms.
Copyright © 2024 Deloitte Development LLC. All rights reserved."
342,deloitte,trustworthy-ai-in-unemployment-insurance-programs.pdf,"Trustworthy AI in
Unemployment
Insurance Programs
Trustworthy AI in Unemployment Insurance Programs
Unleashing the power
of AI and automation in
unemployment insurance
As the domestic workforce experiences fluctuations, economic uncertainties,
and evolving labor market dynamics, unemployment insurance (UI) programs
face both unprecedented challenges and unparalleled opportunities. Embracing
Artificial Intelligence (AI) holds immense potential to revolutionize UI program
management by improving operational efficiency, reducing errors and
enhancing resource allocation. However, the effective integration of AI hinges
on a fundamental requirement—trustworthiness. Trustworthy and ethical AI
is essential in unemployment insurance programs to maintain fairness and
mitigate biases, promote transparency and explainability in decision-making,
and prioritize data privacy and security. By addressing these factors, AI systems
can maintain public confidence, support equitable outcomes, and safeguard
sensitive information while creating efficient and systematic claim processing,
eligibility verification, and decision-making.
Emerging regulatory landscape
As UI program leaders harness the potential of AI to provide a wide range of
benefits in terms of efficiency and effectiveness, they are faced with the critical
responsibility of navigating the ever-evolving landscape of emerging regulatory
requirements and guidelines that govern the management of AI risks. These
regulatory frameworks place a crucial emphasis on the ethical and responsible
use of AI, urging program leaders to take steps to align AI implementations
with established standards to maintain public trust and protect the well-being
of individuals.
Executive Order (EO) 13960 titled ""Promoting the Use of Trustworthy Artificial
Intelligence in the Federal Government"" aims to facilitate the trustworthiness,
security, and ethical alignment of AI technologies used by the federal
government. It emphasizes the adoption of transparent, accountable, and
unbiased AI systems within federal agencies, prioritizing privacy, civil rights, and
civil liberties. This order intends to enhance the government's effective use of AI
while safeguarding the public interest and maintaining public trust. Additionally,
the Department of Labor (DoL) has established specific requirements to
facilitate the trustworthiness of state AI systems, emphasizing equity and equal
treatment. Moreover, the White House Office of Science and Technology Policy
(OSTP) has released the AI Bill of Rights (AIBoR), providing a framework for
developing trustworthy and ethical automated systems that protect individuals'
rights and access to critical resources. These measures collectively promote
responsible AI use and uphold societal well-being.
2
Trustworthy AI in Unemployment Insurance Programs
Overall, these regulatory developments reflect a growing recognition across the
government of the imperative for AI systems to be trustworthy. By upholding
fairness, equity, and the protection of individuals' rights, program leaders
can establish a solid foundation of public confidence and integrate AI into UI
programs in a responsible and ethical manner that is in the interests of those
who rely on these vital services.
Use of AI and the ethical implications
UI program leaders are entrusted with administering measures to promote
equitable access to their UI programs and maintaining timely, accurate,
and fraud-free payments. Leveraging the power of AI will help the state
government UI agencies to efficiently address these challenges across the UI
lifecycle. Despite this, AI has the potential to place unnecessary or inequitable
burdens on legitimate claimants. Three use cases for AI and the equitable
implications are highlighted in Figure 1. Hence, state UI agencies that invest
in implementing responsible AI practices can seize the benefits of AI to help
achieve mission outcomes, improve human experience, and provide efficient
services while controlling and protecting against unintended AI risks and
non-equitable outcomes.
Figure 1: Ethical AI Use Cases Addressing UI Program Risks
AI Use Case Description Of Potential AI Solution Ethical/Trustworthy Use Case
Efficiently Address Large Train models to assist staff with claims Characteristics of certain groups may be indicative
Backlog of UI Claims by leveraging data to check eligibility and of suspicious activity causing claims to be delayed
recommend issues for staff to clear with or denied
provided files
(e.g., multigenerational households with multiple
unemployed residents may be suspicious because
they use the same address)
Prevent Hijacking of Implement models and filters trained to flag Vulnerable groups may be inherently more likely to
Legitimate Claims by Bad suspicious changes to claims after filing for be suspicious
Actors staff review
E.g., unstable living and banking situations may be
Identify fraud trends by analyzing inadvertently caught in filters
banking information to identify previously
unidentified suspicious claims for review;
Stop Improper Payments Implement behavioral nudging solutions Understanding why a model is making a
Before They Are Paid that analyze and suggest activities for recommendation of specific activities can help drive
staff to complete that reduce improper compliance
payments to claimants
3
Trustworthy AI in Unemployment Insurance Programs
By leveraging AI technologies, UI programs can enhance efficiency in claim
processing, eligibility verification, and decision-making, leading to improved
outcomes for applicants. However, the integration of AI introduces risks such as
bias, which can perpetuate inequalities and hinder fair distribution of benefits.
A Trustworthy AI framework addresses these concerns by prioritizing fairness,
transparency, and accountability. It enables scrutiny of AI algorithms to identify
and mitigate biases, while facilitating compliance with regulations and guidelines
set forth by regulatory authorities. By embracing AI through a Trustworthy
AI framework, UI programs can increase the potential benefits of AI while
mitigating risks, providing fair and equitable services to those in need while
upholding regulatory standards.
Deloitte’s Trustworthy AITM Framework & products
Deloitte’s Trustworthy AITM framework enables agencies to identify and mitigate
risks and potential ethical issues across six dimensions spanning the stages of
the AI development lifecycle. Deloitte's Trustworthy AITM Framework and suite
of product services help provide strategic and tactical solutions to enable state
workforce UI Program Leaders to continue to embrace AI while promoting
trustworthiness in its use. The framework is used to evaluate AI systems
supporting the steps in the unemployment insurance processes across its six
dimensions (Figure 2), identifying risk and recommending leading practices to
mitigate and monitor risks. This process will develop controls and mechanisms
to manage AI risks and bolster stakeholder trust in the agency operations.
Figure 2: Applying the six dimensions of Deloitte's Trustworthy AITM
Framework can help build effective and equitable AI solutions
4
Trustworthy AI in Unemployment Insurance Programs
Trustworthy AITM Framework Compatibility with
other regulations
The Trustworthy AITM framework and the associated suite of products and
offerings helps agencies comply with current and emerging regulations while
achieving agency objectives. The framework closely aligns with the White House
AIBoR (Figure 3) and EO 13960 and includes a roadmap for implementing AI-
powered systems through each phase of the AI development and maintenance
lifecycle. The framework also simultaneously addresses the equity requirements
of the DoL in administering UI benefits to claimants.
Figure 3: AIBoR mapped to Deloitte’s Trustworthy AITM Framework
Deloitte Trustworthy AITM
AI Bill of Rights Princples Description Framework
Safe and effective systems Protect against inappropriate or irrelevant • Privacy
data usage through testing, monitoring, and • Safe/Secure
engaging stakeholders, communities, and • Robust/Reliable
domain experts
Algorithmic discrimination Protect against discrimination by designing • Fair/Impartial
protections systems equitably and making system • Transparent/Explainable
evaluations understandable and readily • Robust/Reliable
available
Data privacy Protect against privacy violations by limiting Privacy
data collection and ensuring individuals
maintain control of their data and how it is
used
Notice and explanation Provide clear and timely explanations for any • Transparent/Explainable
decisions or actions taken by an automated • Privacy
system
Human alternatives, Provide opportunities to opt out of automated • Responsible/Accountable
consideration, and fallback systems and access to persons who can • Privacy
quickly remedy any problems encountered in • Robust/Reliable
the system
5
Trustworthy AI in Unemployment Insurance Programs
Impact of applying Trustworthy AI to the UI process
Applying Trustworthy AI to the state UI processes will impact three critical areas
that will accelerate AI adoption.
• Institutionalize AI governance: AI governance calls for a pan-organization
awareness of the principles and participation in its processes. Hence,
integrating it into the organization culture has the advantage of ensuring
seamless compliance. For example, an individual building using AI driven
systems should be aware of the critical importance of sound data
management principles in creating a robust and ethical AI solution. Having
a framework of trustworthy AI principles with practical guidance across
many stages of an AI build and deployment can make its governance a full
organizational responsibility rather than relying on the judgement of distinct
individuals.
• Increased stakeholder trust: When outcomes of AI systems are deemed
trustworthy there will be greater internal and external stakeholder buy-in.
A framework covering disparate dimensions of trustworthiness will increase
stakeholder trust, allowing for deeper integration, wider adoption, and better
improvements in organizational efficiency.
• Readiness for regulatory compliance: Building AI guided by a framework that
is aligned with current and emerging regulations will help agencies maintain
compliance with future regulations and reduce the need for expensive re-
work and re-development to make an AI solution compliant after regulation
finalization.
A trusted advisor
As AI technologies become increasingly powerful and the regulatory
environment continues to evolve, UI program directors need a trusted advisor
to help them navigate the dynamic landscape. The DoL’s focus on equity and
the AIBoR are just the latest governmental call to action for organizations to
proactively protect the American public as they embrace innovation through
automation and AI. The AIBoR sets the tone for future legislation and industry
regulation. State UI agencies need to not just be aware of the evolving
requirements but also have a plan of action to rapidly integrate them into their
AI-driven operations.
Deloitte has the capabilities to help state UI agencies navigate the expanding
space of AI regulations. We can leverage our subject matter experience in the UI
processes along with our experience with AI implementations governed by our
Trustworthy AITM Framework to provide insights which accomplish agency goals
while effectively managing risks.
6
Authors
Joe Conti Carol Tannous
US Risk & Financial Advisory US Risk & Financial Advisory
Government and Public Sector Government and Public Sector
Managing Director Managing Director
Deloitte & Touche LLP Deloitte Transactions and Business Analytics LLP
joconti@deloitte.com ctannous@deloitte.com
Michael Greene Tyler Ranalli
AI Data Engineering US Risk & Financial Advisory
Government and Public Sector Government and Public Sector
Technology Fellow Manager
Deloitte Consulting LLP Deloitte Financial Advisory Services LLP
migreene@deloitte.com tranalli@deloitte.com
Aritra Nath
Enterprise Performance
Government and Public Sector
Senior Solution Specialist
Deloitte Consulting LLP
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343,deloitte,us-gen-ai-dichotomies.pdf,"ISSUE 002
Generative AI
DICHOTOMIES NAVIGATING TOWARDS
A BETTER FUTURE
DICHOTOMIES
The Dichotomies series
projects the possibilities of
an emerging technology in
two divergent scenarios.
Through speculative fiction
and actionable takeaways,
we help leaders understand
the implications and risks of
the future.
02 DICHOTOMIES | GENERATIVE AI
GENERATIVE ARTIFICIAL INTELLIGENCE (AI):
GENERATIVE AI LEARNS FROM EXAMPLES TO ARTIFICIALLY
GENERATE NEW AND USEFUL OUTPUTS.
GENERATIVE AI AND CONTEXTUALIZES IT ...TO GENERATE A
TAKES AN INPUT... USING TECHNOLOGY... NOVEL RESPONSE
CODE
New code,
self learning code
CODE
TEXT
Scripts, articles, plays,
AUDIO LARGE LANGUAGE MODELS conversations
DIFFUSION NETWORKS
GANs
2/3D PHOTOS
PHOTO New visuals,
photo edits
TRANSFORMERS
NOVEL TECHNIQUES
TEXT 2/3D VIDEO
Short-clips, edited videos,
new videos
VIDEO
AUDIO
Voices, music
0033 DDIICCHHOOTTOOMMIIEESS || GGEENNEERRAATTIIVVEE AAI!
1940
—
A BRIEF
—
— 1943 | Warren McCulloch and Walter Pitts’ research
lays the foundation for computer based “neural
HISTORY OF networks” – a critical element of today’s generative AI
1970
—
GENERATIVE AI
—
— 1973 | Harold Cohen, a painter and professor,
— collaborates with a program called AARON to produce
— art autonomously. The paintings are all done in
Cohen’s style
—
—
—
—
1980
—
—
—
—
—
—
1988 | AI researchers signal the shift from rules-based
—
methods to probabilistic methods
—
—
2000
—
—
2003 | Researchers begin work on intelligent
—
voice assistants, which would go mainstream on
smartphones in the following decade —
—
—
—
—
—
2010
—
2012 | A Google Brain computer cluster trains itself to
—
recognize a cat from millions of images
—
2014 | Ian J. Goodfellow and colleagues publish the first —
paper on Generative Adversarial Networks (GANs) which
—
can determine if an image is real or fake
— 2018 | OpenAI releases GPT-1, a groundbreaking
2017 | Google releases the first Transformer model, the — advance for large language models (LLMs)
foundation for many popular generative AI tools today —
2019 | Engineer Phillip Wang uses the StyleGAN model
— to build the website ThisPersonDoesNotExist, which
2020 generates hyper-realistic portraits
2022 | Stable Diffusion launches as an open-source — 2021 | DALL-E leverages OpenAI’s GPT model and
image generation model and quickly gains traction for — Contrastive Language-Image Pre-training (CLIP) to
its differentiated ability to render images of people develop a 12-billion-parameter image creation tool that
utilizes just a single sentence to generate an image
2022 | ChatGPT brings generative AI to the masses,
reaching 100 million active monthly users just 2 months
after launch
04 DICHOTOMIES | GENERATIVE AI
2023 | Adobe unveils Firefly, a family of 2023 2023 | Google releases public access to Bard,
generative AI models tailor-made for creative — a generative AI chatbot built on 137 billion
professionals, with built-in guardrails for safety — parameters, and embeds generative AI capabilities
and copyright standards into its Workspace products
2023 | OpenAI releases GPT-4, a multimodal
generative AI model with one trillion parameters
2023 | Meta introduces LLaMA,
a 65 billion parameter LLM
A BRIEF FUTURE OF GENERATIVE AI
CONTENT GENERATION TIMES
NOW IF ARE ACCELERATED
Generative AI Businesses Major advances in language processing and multimodality
accelerates can find a way accelerate select activities, such as copywriting, UI/UX
business as to mitigate risk design, and content editing. The technology is nascent and
usual with as-of-yet still requires major human oversight. Questions on risk
unreliable and veracity require humans to double-check outputs,
technology accelerating but not automating development.
INNOVATION & INTEGRATION
NEW IF ENABLE TRUE AUTOMATION
Generative AI Questions on Continued innovation will reduce the need for human
automates accountability, oversight. AI will be able to predict human reactions
minor activities ownership, and and generate high-fidelity, verifiable, and trustworthy
security are content, and will integrate with other tools (e.g., email,
resolved calendars) to impact business as usual, as described
in The Implications of Generative AI for Businesses.
The value to businesses will be maximized when
clear regulations are set.
TRUE AUGMENTATION
NEXT IF IS THE ULTIMATE FRONTIER
Generative AI The public and Co-development of technologies such as neural
augments regulators can interfacing and quantum computing will allow
the human workforce understand the generative AI to tackle complex problems such
evolving role of as drug design, advanced simulations, and
humans in the creative automation. As more companies go All
workforce in on AI, humans will regularly rely on AI as a
virtual teammate rather than a tool, provided
there is a change in public hearts and minds.
05 DICHOTOMIES | GENERATIVE AI
FUTURE PROJECTIONS
NOW (TODAY) NEW (18-24 MONTHS) NEXT (5+ YEARS)
Improved AI alignment in natural Basic generation will be part of daily life Neuroadaptive capabilities lead to
language creates outputs that meet direct generation from brain activity
Advanced emotion alignment enables
human expectations, working towards
AI to become a reliable first point-of- Models will understand human intent
a seamless, natural-language-based
contact for customer-facing applications from context such as recent actions,
Interface computer interface
emotions, and situational awareness
Sophisticated content can be
AI systems are starting to support
generated, marked by longer duration, AI responses adapt to individual
multimodal input and output
increased complexity, and custom personalities
formats
Generative AI can logically reason, AI can generate complex prototypes, Generative AI can optimize strategic
generate code, and craft imagery on par such as an app, based on a prompt planning by providing choices, impacts,
or at better capability than humans takeaways, and recommendations
Industrial use cases, such as generating
Language generated is nearly flawless, an architecture for a bridge, will be Integration with quantum computing
Capability
with strong translation capabilities more common will enable advanced simulations (e.g.,
next-gen digital twins) and optimization
AI operates equally well across diverse AI can coordinate multiple tools to act
in engineering, design, logistics, and
industries as an agent
more
Computing power enabled recent Further tool integrations will extend Novel architectures will incorporate
models to dwarf earlier generations in current capabilities continuous, on-edge, and inexpensive
size, complexity, and cost learning, leading to higher-quality
Ingesting strategies and blueprints as
outputs
Capabilities are powered by significant training data will improve AI’s planning
Enablers
advances in model training and and coordination abilities Context limitations could reduce to
architecture, and an abundance of data near-zero as AI integrates with most
Improved training methods and self-
products and has thorough context of
improving code will lead AI to generate
user intent and trajectory
& learn “on-the-edge”
ALLURE CONCERN
Widespread adoption of generative AI Rampant misuse of generative AI
will augment human creativity, leading will lead to inaccurate or harmful
to an era of enhanced productivity, outputs, perpetuate historical
rapid scientific breakthroughs, and biases, and break down trust in
more leisure time for all. information.
Projecting future possibilities across three domains:
WORK EDUCATION SOCIETY
06 DICHOTOMIES | GENERATIVE AI
WORK
Generative AI can accelerate
the pace of creative outputs
across the enterprise, but
organizations need to closely
review their models and build in
systems of checks and balances
07 DICHOTOMIES | GENERATIVE AI
WORK
ALLURE
Aarti
Aarti’s car drives itself with ease into a “Ooh, Aarti’s in trouble,” Roger jokes. development took an entire year. This
sharp loop off the highway. She drums newest virus could have hundreds of
“Shush,” she replies.
her fingers incessantly on top of the mutating spikes or require an entirely
steering wheel — a bad habit she A hologram of their CEO Lindsay, novel method of inoculation. Whatever
picked up from her father. As the car looking distraught, appears on Aarti’s it was, Aarti was bent on advancing the
parks at her office, Aarti’s ears perk tablet. She immediately shares a video field. With the speed of AI simulations,
up to the voice of Anderson Cooper, with Aarti: a press release from the she knew her team could stop the
rendered by generative AI at her WHO alerting the world to a novel next pandemic before it affected
request. Her Monday podcast, tailored zoonotic virus identified in Zurich. millions of families like hers.
to her interests in biotech news, plays
a snippet on the latest healthcare Aarti’s eyes widen. “Lindsay, I want to Aarti snaps out of her reverie and
scandal. She cringes as she leaves —“ employs an AI marketing assistant
to draft a press release, prompting
the car.
“I know, that’s why I called. Shelve the it to talk about her past and her
Aarti strides into her lab and greets current project and give me five viable company’s desire to create the first
the tired faces of her researchers. vaccine options to move towards vaccine. Remembering the scandal
For the past few weeks, they’ve been clinical testing by the end of the week.” she heard about on her morning
assigned to a drug development Lindsay cuts the call short. Roger and podcast—about the marketing issues
project that could ease the symptoms the other researchers stare. of the Gen AI startup Deliveri, Aarti
of dementia, and their board wants makes sure to send the article to
“Let’s get started!” Aarti declares, and
results as soon as possible. Aarti and SaluTech’s PR manager for review. She
the lab springs into a frenzy. Someone
her colleague Roger study the latest also provides permission to generate
shouts out that the WHO has already
outputs of their proprietary generative a video using her face and voice, so
sequenced the virus, so others
AI program: a dozen viable, high- SaluTech’s audience could connect to
begin feeding the info into their AI to
fidelity protein structures, replete with the emotions of her father’s passing.
produce vaccine candidates.
percentages to indicate likelihood of Then, she rejoins her team — she’s
side effects. While the team scrambles, Aarti sits eager to dive into the details.
still at her desk, drumming her fingers
As Aarti guides Roger on which
nervously across the marble surface.
structures to feed into their quantum
More than a decade ago, her father
molecule simulator to forecast viability,
passed away from COVID-19 before a
she receives a call on her tablet from
vaccine was available. Even with only
the CEO of their company SaluTech.
one spike protein to address, drug
08 DICHOTOMIES | GENERATIVE AI
WORK
CONCERN
Xavier
“Only one today,” he mutters to Xavier can’t believe the contents Ajay had laid off over half the staff
himself as he sits down with his of the article. Before he can even and increased reliance on generative
morning coffee. Xavier, the marketing process, Cara alerts him that the AI vendors, which meant Xavier was
lead of Gen-AI startup Deliveri, is company’s founder, Ajay, is waiting the entire marketing department.
trying to cut back on his caffeine in his virtual meeting. Xavier knew Xavier rakes his hands through
intake after dozens of alerts from his better than to make his boss wait. his hair as if to bring some ideas
smart watch about caffeine fueling out of his head to life. He opens
Ajay is yelling at the team as Xavier
his anxiety and insomnia. He opens BrandBoost, an AI program used
joins. “What do you mean you can’t
a laptop for his Daily Download, a to build multi-modal marketing
retrain the program? Isn’t that what I
personalized report generated each campaigns. With the fear of Ajay’s
pay you engineers to do?”
morning with his daily agenda and deadline looming, he rushes to enter
relevant industry news. “Well…” Laurence, the head engineer, various prompts to produce press
hesitates to find the right words for releases and video advertisements
Expecting a leisurely read, he instead
Ajay’s temperament. “You asked us to and uploads them without review.
snaps to attention as his AI assistant
use AI-as-a-service to cut costs, and
Cara alerts him of being late to an As the afternoon passes, Xavier asks
the bias is baked into the vendor’s AI
urgent meeting. “How did I miss that?” Cara to assess engagement with
training data. It’s going to take time
he wonders. His alarm increases as the posts he previously distributed.
to rectify.”
he reads the article within the invite. “Not positive,” she declares. Xavier’s
“Ugh! Xavier, let’s eyes widen at the flurry of comments
see if you can prove pointing out the ads only include
The Irony of Deliveri: The AI That Failed To Deliver
more useful today. White mothers and infants, not the
Shocking patient testimonials reveal how the London-based Use BrandBoost Black mothers who’ve been impacted
startup perpetuated stereotypes and prejudices towards for a marketing by Deliveri.
expectant mothers in the Black community.
campaign that
“Book an urgent meeting with Ajay in
The technology, which leverages generative AI to create shows how
the next available time slot,” Xavier
virtual training scenarios for physicians, promised reduced
inclusive we are.
costs, improved bedside manner, and more. Yet, Black instructs Cara.
Send it out before
mothers claim that physicians trained by Deliveri have
lunch.” He looks at his coffee mug from this
stereotyped them and provided inappropriate dosing for
morning, pondering how much more
pain management. Says one mother, “It’s like they’ve trained Before Xavier can
caffeine he’ll need to get through
their AI on medical thinking from the 2010s.”
object, Ajay ends
what he knows will be a horrible
the meeting.
evening.
09 DICHOTOMIES | GENERATIVE AI
WORK
TAKEAWAYS
HUMAN AND MACHINE, FROM BLACK BOX MOVE FAST, BUT
BETTER TOGETHER TO GLASS BOX DON’T BREAK THINGS
As generative AI Widespread adoption Generative AI
becomes more of AI across industries technology could
accessible, reliable, and could turn algorithms eventually lead to
robust, more workers into high-level breakthroughs for
can expect to partner decision-makers. seemingly intractable
with these tools in their daily work, as While this may greatly lower costs and problems, like dementia or the next global
detailed in Deloitte's recent Benefits increase productivity, trust will be the pandemic. The computational power of AI
and Limitations of Generative AI report. differentiating factor between successful can exponentially speed the completion
Lower-order tasks such as preliminary adoption and disastrous outcomes. As of tasks that are typically inefficient or
research or drafting, content generation, detailed in Deloitte's Tech Trends 2023, time-consuming for humans, like trial-
and summarization can be delegated to deploying frameworks to make AI more and-error experimentation. Yet, as Xavier
machines, while humans focus on higher- responsible and transparent, as we finds out, the speed of generative AI
order tasks. For instance, Aarti relies would expect a human colleague to be, often needs to be tempered by human
on AI to generate options for protein can ensure that organizations maximize reviewers, as detailed in Deloitte's
structures but applies her own expertise value and mitigate risk. Otherwise, Proactive Risk Management in Generative
to determine the best options. Going Xavier’s trouble with an opaque and AI. Organizations can develop a generative
forward, organizations should be looking unreliable AI could become all AI strategy by pinpointing the areas with
to hire people with uniquely human too common. the highest potential for efficiency gains,
skills like ingenuity, adaptiveness, and and where checks and balances may be
problem-solving, while the machines do required.
what they do best.
INDUSTRY SELECTED USE CASE EMBRACING THE ALLURE MITIGATING THE CONCERN
Boost research and development Quality control and human
Life Sciences Drug discovery (R&D) processes, resulting in involvement is needed to oversee
innovative outcomes and accelerated the development and testing
time to market. process and ensure fit for purpose.
Improve onboarding and training Transparency into algorithms is
Healthcare Simulation generation processes using a wide array of required to actively root out any
scenarios (combining AR/VR + AI) discriminatory training data.
in controlled environments.
Enable hyper-personalization and Establish guardrails to mitigate
Media Press releases automation of PR content, resulting potentially discriminatory or
in more customer engagement while inappropriate content produced
reducing cost. by AI.
10 DICHOTOMIES | GENERATIVE AI
EDUCATION
Generative AI tools can unlock
a new era of adaptive learning
and emphasize skills in creative
thinking and design, provided
they protect against historical
biases
111111 DDDIIICCCHHHOOOTTTOOOMMMIIIEEESSS ||| GGGEEENNNEEERRRAAATTTIIIVVVEEE AAAIII
EDUCATION
ALLURE
Imani
“Add eggs and vanilla extract to the completed tasks on her holographic Her ADHD meant people often didn’t
dry ingredients and whip till smooth. tablet. Her smile fades when she realizes believe in her capabilities, and she
Next, sprinkle brown sugar on top to she’s forgotten the literature review due reveled in proving them wrong. She just
caramelize (BUT do not go overboard tonight. She opens the generative AI needed the right idea.
– yes, I’m talking to you Imani).” Imani research tool her professor suggested
giggles to herself as she pauses the and puts it to work, asking it to scrape The oven chimes to indicate the
audio of her mother’s famous cookie together publicly available papers and cookies are done, and the idea strikes
recipe. Back in her freshman year at synthesize the first draft of a summary. Imani. She has such fond memories of
Bergin College, she’d learned how to use She quickly sets the constraints for baking with her mom, but their family
AI to mimic her mom’s voice with just citations in MLA and the format of a restaurant was lately struggling to
a short audio clip and a block of text, bulleted memo, and then turns her compete against establishments with
and she played this recipe whenever attention back to the main more funding. What if she could develop
she baked, which she often did when capstone assignment. a generative AI program tailored to
she procrastinated. Despite her mom’s small restaurants? It could fuse existing
warning, Imani applies a heaping of Before she dictates any code recipes with global cuisines to come up
brown sugar and pops her mixture into requirements to her tablet, Imani plays with innovative weekly specials, produce
the oven. the audio of her last visit to office hours. a new website with a few clicks, and
“Generative AI outputs are everywhere even build a basic app for ordering. If
Back at her desk, Imani resumes the — it’s like the TikTok of your age,” her generative AI capstone could show
final learning module for her senior Professor Morris had said when she off her coding and creativity at the same
year capstone in Applied AI. Professor mentioned her capstone idea. time, she would be a hit with all the
Morris’s modules are practically a companies attending AI recruiting week.
lullaby, and her ADHD doesn’t make “Huh?” Imani didn’t know what to make
matters any easier. Fortunately, Imani of the reference. Imani jumps up to grab the cookies and
can feed the module into a generative calls her mom.
AI education assistant and watch an “Think outside the box. What’s a specific
avatar of her personal hero, Admiral problem that we haven’t addressed with “Baking again?” the knowing voice on the
Grace Hopper, deliver the lecture as generative AI yet? Something only you other line asks.
a conversation, which better fits her can tackle.”
learning style. “A lot more than cookies,”
Imani was initially discouraged, but she Imani responds.
Once she finishes the module, Imani appreciated that she had to go above
feels quite accomplished, ticking off and beyond what was expected of her.
12 DICHOTOMIES | GENERATIVE AI
EDUCATION
CONCERN
Elu
MOM application process, especially how While the professor scrolls through
other students could use generative his holographic tablet, looking
Osiyo Elu - Can you pick up your
AI to write essays that reflected their distracted, Elu tries to explain
brothers from practice tonight? I
background but the essay generator their concern.
have a double shift
never portrayed Cherokee culture
or two-spirit people accurately. To “In the past, I’ve not seen offensive
“Sounds good,” Types Elu, who uses satisfy Mom, Elu enters prompts content generated if prompts are
the pronoun they, and directs an AI about Cherokee people into the AI, written well,” Pardo states.
assistant to update their calendar with following the professor’s guidelines
enough time to ride the 7 train
on workarounds since Cherokee isn’t “I tried the workarounds. Do you have
to Queens.
a default option. Their eyes widen any other suggestions?” Elu pleads.
with disbelief as inaccurate and
“Less time than I thought for that
offensive avatars are generated. Their “Perhaps focus on ways to represent
assignment,” Elu mutters while
heartrate quickens and they wipe the your family without race, like abstract
sinking into a library seat. It’s the first
sweat from their palms and text a versions or symbols. Think outside
full week of classes at Bergin College,
classmate for advice. the box,” he replies, continuing to
and Elu’s already eager to score an
scroll on his tablet.
A in Avatar Generation 101 — they’ll
ETHAN
need it to major in Metaverse Design.
Elu storms out, fuming, while
Yet, between their part-time job and Yeah, I finished the homework.
Professor Pardo barely notices their
helping raise their siblings, there Few of us from my high school
exit. Walking aimlessly towards the
doesn’t seem to be enough time in shared prompts... Want me to
library, Elu sighs as their smart watch
the day. They wished they could don send them to ya?
pings with a reminder to pick up
a VR headset and play games to relax,
their siblings. They turn towards the
but instead they open up the avatar
Elu rubs their forehead, weighing the nearest subway stop as doubts creep
generation platform and smile at
options. The idea of sharing prompts into their mind. Maybe becoming a
the professor’s assignment: “Create
seems wrong. And Ethan and his first-generation metaverse designer,
a group of avatars that reflect your
classmates are white, so their prompts and the first Cherokee one they’d
family.” Elu’s sure it’ll be a breeze.
might not even generate Cherokee personally seen, was too much
features. Elu wants to be accurate, to dream. If their classmates are
As a first-generation student, Elu
but also needs an A. Feeling lost, Elu going to have such an easy time in
hears their mother’s voice in their
remembers the orientation leader comparison, it feels futile to even
head all the time. “Be proud of
advising freshmen to ask their professors try competing. As the 7 train rattles
your heritage. Never give up.” She
when in doubt. Shoving their laptop in towards Queens, Elu hangs tight to
had repeated it like a mantra when
their bag, Elu hustles out of the library to the pole and rehearses how they’ll
Elu complained about the college
catch Professor Pardo’s office hours. break the news to Mom.
13 DICHOTOMIES | GENERATIVE AI
EDUCATION
TAKEAWAYS
BRINGING EVERYONE RETHINKING BREAKING THE
ALONG INTELLIGENCE BIAS BARRIERS
Generative AI is The integration of AI Generative AI systems
likely to close some in education will likely often contain bias in
technological divides necessitate a shift in their training data that
and expand others. the way we evaluate leads to discriminatory
While neurodivergent student performance— outputs. To prevent
students like Imani can benefit from and even the concept of intelligence. further marginalizing students like Elu,
adaptive learning, those with less access Students like Imani and Elu have already it is vital to prioritize DEI during the
to AI can face new risks. For instance, begun adopting generative AI to create creation of generative models, in both
older generations may be more likely everything from art to essays for school data collection and team structure.
to be attacked by deepfakes and data assignments. As such tools become As detailed in our Trustworthy AI™
breaches, and minorities racially and more widespread, schools should grade framework, organizations can also
ethnically diverse people like Elu may students based on their ability to design, design new processes to break down
not be able to attain the same benefits rather than their ability to execute. In bias, such as conducting regular
from AI as others. Organizations should turn, organizations will likely redesign algorithm audits or embedding ethics
prioritize developing resources that workplace performance reviews to experts on coding teams. When
promote generative AI literacy and incentivize creativity over execution mistakes do occur, it’s equally important
accessible UX design, in order to unlock or efficiency, delivering a better for those building or applying generative
its potential across industries for a more customer experience. AI tools to take accountability and
equitable playing field. correct any unintended consequences.
INDUSTRY SELECTED USE CASE EMBRACING THE ALLURE MITIGATING THE CONCERN
Assist software engineers in writing Employ skilled technical reviewers
more efficient code and providing to oversee output, since code could
Hospitality Web development solutions to complex problems. Facilitate be prone to inaccuracies or user
personalized, safe user experiences experience issues.
through chatbots, optimized search
engines, and cybersecurity testing.
Create personalized and adaptive Ensure training data is diverse and
educational content, catering to inclusive, and regularly evaluate
individual student needs and learning the generated content for bias.
Education Curriculum design
styles. Supplement traditional teaching
methods rather than replacing
them, to ensure students practice
creativity and critical thinking.
14 DICHOTOMIES | GENERATIVE AI
SOCIETY
Generative AI can bring our
imaginations to life with
unprecedented speed and
convenience, but it can also
enhance the ability of bad actors
to spread misinformation
15 DICHOTOMIES | GENERATIVE AI
SOCIETY
ALLURE
Rafael
“Do you want to build a snowman? then enter the home generated by their “Do you want Elsa to sing you Happy
Come on, let’s go and play!” Candice prompt: Mid-century modern style with Birthday?” Maria asks. Since they’ve
belts out the lines as her mother two floors, home office, and kid’s room purchased a license, they can prompt
Maria stops the car outside the with a piano. Rafael immediately takes the character to generate any child-
home design store. an interest in the kitchen and asks the friendly song with just a few taps.
generative AI to place the stove in a
“Papi – I should be a singer when I grow different area, and generate the smell “I want her to sing about the pyramids!”
up!” Candice insists as she slides out of of his favorite meal, his grandmother’s Candice replies, eyeing her cupcake.
the car. ajiaco recipe, to really feel at home.
Rafael chuckles. “Let me see if she can
“Of course, hija! I bet Cairo has great Meanwhile, Maria smells the soup work the pyramids into the Happy
choirs. Maybe our new home can have a as she speeds upstairs to work on Birthday song.” As he pulls up the
piano.” Rafael smiles at Candice, hoping her perfect home office, prompting screen on the coffee table, the first
to keep her in good spirits despite this the generative AI with requests image is his tailored daily newsletter,
sudden shopping trip on her birthday. about window placement, monitor generated based on his interests.
screens, and a whiteboard. Candice He gasps at an image of his college
Yesterday, Rafael received an too, hesitantly heads up the stairs to roommate Tyler in a headline about
unexpected promotion to senior her room. Knowing she’s a child, the plagiarism. He shoots a glance at Maria
engineer, requiring him and his family generative AI begins with providing and swipes away, opening the Disney
to relocate to Egypt within the year. options for fun wall colors, and Candice application.
Since graduating with his PhD in settles on a periwinkle blue. The design
Nuclear Engineering, he’d dreamed consultant taps her on the shoulder and “Okay,” Rafael nudges Candice, “you
of commercializing fusion-produced asks if she’d like any murals on her wall. ready to sing?”
power, and this opportunity would be
a huge step forward. Still, he couldn’t “Put Elsa in Egypt,” Candice thinks
shake his nerves about 9-year-old out loud, and instantly a mural is
Candice adjusting to a new country. generated of a Disney-inspired princess
He hoped that visualizing it could get in pharaoh’s clothes. Rafael’s nerves
her excited. are calmed by the sound of Candice’s
delight as he removes his VR headset to
Rafael speaks to a design consultant watch her.
who turns his preferences into prompts
for their generative AI assistant. Donning After a long evening, the family gathers
VR headsets in an immersive media around their smart coffee table with a
room, Rafael and his family visualize cupcake for Candice.
different neighborhoods in Cairo, and
16 DICHOTOMIES | GENERATIVE AI
SOCIETY
CONCERN
TYLER
“Something is missing,” Tyler mutters his distinct style to their iconic brand. claim they’ve run an information check
to himself as he stares at the website The page begins to refresh and Tyler and found that the story was fabricated
he’s designed for his client’s new salon. clenches his fist with excitement. A using AI, but others echo the article’s
He quickly uploads his initial draft to his familiar design spreads across the sentiments and post more examples.
favorite generative AI design platform screen, and a smile spreads across Tyler feels his heart pounding as he
and uses prompts that he’s honed to Tyler’s cheeks. He scrolls down the page scrolls through pages and pages about
produce alternate designs. He picks to screenshot the prize announcement independent artists being plagiarized
the option that best represents his so he can send it to his good friend with no recourse, until he finds a forum
style: Sharp angles and gradients that Rafael. But his joy quickly fades as he that encourages creators to fight back.
produce a shimmering yet minimalist reads:
look. He sends the design mock-up Using reams of historical evidence
over to his client and leans back, feeling that seem convincing, the forum users
satisfied. Thanks to his work going viral present an argument that captivates
Congratulations to our winners
on a popular design blog, Tyler had Tyler. He follows the steps they suggest
PS Design!
turned his beloved design hobby into a to generate a deepfake video of PS
full-time job and the speed of generative Design’s CEO admitting to financial fraud
AI enabled him to take on hundreds of Tyler scrambles to call his contact Kim and posts it anonymously on his favorite
small clients in the past few years. who organized the brand competition. design blog. He shuts his laptop and
When she picks up, Tyler frantically rushes away, feeling unsure.
Suddenly remembering the date, Tyler explains that there must be a
clicks over to the page of a design mistake — the design on the page The next morning, Tyler wakes up and
competition he had entered that could is unmistakably his. can’t stop regretting his decision. He
land him a huge contract. hopes to quietly delete the deepfake,
“We went with PS Design because of but his jaw drops when he sees that it’s
their size and reputation. They use the received millions of views and several
same AI model you prefer, so perhaps hundred comments. Knowing this isn’t
Check back at 11:00 AM on it drew on your work? In either case, I’m right, Tyler reveals himself as the original
the 15th of June to see if your
afraid our decision is final.” poster. Messages from reporters start
design has been chosen to
flooding his inbox, and Tyler sighs as he
represent everyone’s favorite
burger joint! Before Tyler can reply, she hangs up, looks at the clock again — it’s about to
leaving him fuming. He paces around be an even longer day than yesterday.
his office, considering his options, but
Tyler sighs as he glances at the clock. eventually returns to his laptop and
10:58 AM. He feels optimistic: He’d comes across an article about the
impressed the f"
344,deloitte,us-driving-business-impact-through-the-data-cloud.pdf,"Many companies are moving data to the cloud, and
while doing so, they prefer modernized platforms.
This begets the question—is data modernization
driving cloud adoption, or vice versa?
“
As we help clients migrate their data
and modernize their underlying compute
infrastructure on the Data Cloud, I encourage
them to think about what is on the horizon.
What is next? And what is the business value
”
that one could be continuously gaining?
NITIN MITTAL | AI Growth Offering Leader, Principal, Deloitte Consulting LLP
According to a recent Data reason for cloud migration.1 Instead of
Modernization and Cloud Computing treating it as a straight lift and shift of
Survey, 91 percent of companies the data from a legacy environment,
surveyed are keeping their data organizations are looking at the Data
on cloud platforms and more than Cloud as a new means of modernizing
half of those companies see data their ability to manage information.
modernization as a key component or
2
“
Given that data is the linchpin of AI, analytics,
and other cognitive technologies, companies
must consider augmenting their strategies to
ensure that they’re embracing both cloud and
data simultaneously to help better position their
businesses, now and in the future.”
ASHISH VERMA Global Data Analytics and Modernization Market
Offering Leader, Principal, Deloitte Consulting LLP
What is the
Data Cloud?
In today’s world, data silos make Cloud is enabled by Snowflake’s platform
harnessing the value of data time- and is populated with data from customers
consuming and expensive. Governance and other data providers that use Snowflake
and collaboration are also often impossible to store, access, and share data.
to achieve across so many different
technologies and clouds. The Data Cloud Organizations can leverage the Data
is a network that connects customers, Cloud to help reduce silos, mitigate risk,
partners, data providers, and service and simplify cumbersome data sharing
providers—enabling them to share methods. But data modernization is not
rapidly growing data sets in secure, without its challenges.
governed, compliant ways. The Data
Potential drivers Potential benefits
What to consider
>$10k cost Up to 50% reduction of storage,
when modernizing
per terabyte for in-house data centers computing, and infrastructure costs
your data:
70% of data >75% more
goes unused elasticity and agility
>55% of organizations >50% lower
need to adapt legacy infrastructure cost of operations
and skill sets
3
AI can
enable greater
business value
With the volume, velocity, and variety of data in the Data Cloud, it is not
possible to process and analyze through sheer human effort. Through the
power of artificial intelligence, organizations can surpass previously imagined
value creation opportunities by generating value across five key levers:
Intelligent automation: Automate the
“last mile” of automation by removing
A recent Deloitte survey of
humans from low value and often repetitive
2,700+ executives uncovered
activities (often in service of machines)
that AI gives organizations a
competitive advantage and most
Hyper-intelligent insights: Improve
organizations are making plans
understanding and decision making
to harness AI more broadly.2
through analytics that are more
proactive, predictive, and able to see
64% Believe that AI enables a
patterns in increasingly complex sources
competitive advantage over their
competitors
Transformed engagement: Change
the way people interact with technology,
allowing businesses to engage on human 54% Are spending 4x more than
terms rather than forcing humans to last year on AI initiatives
engage on machine terms
74% Plan to integrate AI into
Fueled innovation: Redefine “where all enterprise applications within
to play” and “how to win” by enabling three years
creation of new products, markets, and
business models 76% Anticipate that AI will
substantially transform their
Fortified trust: Secure the franchise organization within three years
from risks such as fraud and cyber,
improve quality and consistency, and
enable greater transparency to enhance
brand trust
“
Today, every enterprise is looking to digitally engage customers,
stakeholders, suppliers, vendors, or anyone else in their value
“
chain—they can enable and fuel it with artificial intelligence.
NITIN MITTAL | AI Growth Offering Leader, Principal, Deloitte Consulting LLP
4
Human and machine
collaboration can
take organizations to
new heights
As AI technologies standardize across and applying AI and machine learning
industries, an increasing number of to solve it, rethinking the way that
companies are moving from experimentation humans and machines interact within
to AI at scale, increasing the lead versus working environments.
late adopters. Data leaders are no longer
just optimizing the data environment but Those companies that can move from
rather thinking about how to use their simply gathering and analyzing data via
data as an asset. human hypotheses to enabling proactive
AI/ML across the organization will be better
That includes a better understanding able to derive value from the Data Cloud.
of the problems they are trying to solve
AI experimentation AI at scale AI-fueled organization
• Siloed application • High impact use cases • Enterprise-wide adoption
• Building expertise • Defining ROI clarity • Insights-driven decision making
• Modernizing data • Establishing governance • Trustworthy AI
“
There is a paradigm shift from organizational
capabilities being driven by what technology allows
them to do to technology not being a limiting factor.
“
It’s the art of the possible.
CHRISTIAN KLEINERMAN | SVP Product, Snowflake
5
The Data Cloud is just
the start of the journey
to becoming an AI-
fueled organization
Around the globe, AI-fueled “
What we’ve seen over the last
organizations are progressing beyond
just experimentation, just adoption, just few years is a significant uptake
mainstreaming, and just scaling up AI—
in investments from our clients
to truly rethinking the very DNA, culture,
and fabric of their organization.
in data topics—embedding data
products and services at the heart of
their strategy, adopting cloud data
platforms, experimenting with AI—
and then finding ways to incorporate
that into their business and drive
it to scale. While these are very
powerful concepts, they also bring
complexity into the organization that
”
must be managed.
FRANK FARRALL AI & Ecosystem Leader, Principal,
Deloitte Consulting LLP
6
AI-fueled organizations
deploy AI systematically to
lead to better outcomes
An AI-fueled organization employs data as an asset to
deploy AI across the enterprise in a human-centered
and ethical way.
Deploys AI across every core business
process with a reimagined operating model
to fully capture the potential of AI
Utilizes data as an asset for
Utilizes a holistic ethical AI
autonomous decision making
framework to generate trust
through real-time processing,
across stakeholders
learning, and acting
Creates human-centered Employs a diverse talent
digital experiences, enabling ecosystem enabled by
seamless human with a culture of innovation
machine interactions that rewards ingenuity
and risk-taking to leverage
future of work insights and
Utilizes partnerships reimagine work
and ecosystems to drive
innovation and growth
POTENTIAL OUTCOMES
Rapid decision Productive and Supercharged Enhanced customer Faster
making fulfilled workforce performance experience innovation
7
Those organizations who are able to
embrace AI in a human-centered and
ethical way across the enterprise are
gaining a competitive edge. They are
leveraging data to make the human
experience simpler, faster, and more
personalized. And moving from table
stakes innovation to meaningful,
sustainable, cultural transformation.
The Snowflake
Data Cloud
Snowflake’s Data Cloud enables the Data Cloud and execute a number
organizations to pursue the frontiers of of critical workloads, including data
data modernization by reducing data engineering, data warehousing, data
silos created within organizations, and lakes, data science, data sharing, and
scattered throughout their subsidiaries, building and operating data applications.
business ecosystems, geographies, and Unlike traditional data infrastructures,
the one or more public cloud providers Snowflake’s platform scales instantly
they use. By unlocking the latent value and near-infinitely, and enables any
of data, the Data Cloud empowers organization to operate across different
organizations to capitalize on market public clouds and regions as a single
drivers; drive decision making with cloud, while helping satisfy industry and
faster, actionable insights; and create regional data privacy requirements.
new revenue streams by monetizing
previously siloed data.
With the help of Snowflake’s platform,
organizations can easily unify, integrate,
analyze, and share their data within
8
As a Snowflake Elite Services Partner, Visit www.deloitte.com/us/snowflake
our alliance combines the advanced to learn how together, Deloitte and
capabilities of Snowflake’s platform Snowflake are empowering the next
with Deloitte’s recognized leadership frontier of data modernization.
in strategy, analytics, and technology
services to help businesses speed
up their migration to the cloud while
reducing costs and increasing agility.
NITIN MITTAL
AI Growth Offering Leader, Principal
Deloitte Consulting LLP
nmittal@deloitte.com
FRANK FARRALL
AI Ecosystem Leader, Principal
Deloitte Consulting LLP
frfarrall@deloitte.com
1 Deloitte, Data Modernization and Cloud Computing Survey, 2019
2 Deloitte, State of AI in the Enterprise, 3rd Edition, 2020
This publication contains general information only, and none of the member
firms of Deloitte Touche Tohmatsu Limited, its member firms, or their related
entities (collective, the “Deloitte Network”) is, by means of this publication,
rendering professional advice or services. Before making any decision or
taking any action that may affect your business, you should consult a qualified
professional adviser. No entity in the Deloitte Network shall be responsible for
any loss whatsoever sustained by any person who relies on this publication.
As used in this document, “Deloitte” means Deloitte Consulting LLP, a
subsidiary of Deloitte LLP. Please see www.deloitte.com/us/about for a detailed
description of the legal structure of Deloitte USA LLP, Deloitte LLP and their
respective subsidiaries. Certain services may not be available to attest clients
under the rules and regulations of public accounting.
All rights reserved. Member of Deloitte Touche Tohmatsu Limited.
© 2021 Deloitte Development LLC. 9"
345,deloitte,us-from-code-to-cure-1.pdf,"From code to cure, how
Generative AI can reshape
the health frontier:
Unlocking new levels of efficiency,
effectiveness, and innovation
From code to cure, how Generative AI can reshape the health frontier | Unlocking new levels of efficiency, effectiveness, and innovation
Contents
Executive summary 3
Section 1: The shifting health care market landscape 5
Section 2: N avigating the obstacles and opportunities for
Generative AI in health care 8
Section 3: Unlocking the value of Generative AI 12
Section 4: Activating Generative AI for your organization 16
Striking the right balance for success 20
2
From code to cure, how Generative AI can reshape the health frontier | Unlocking new levels of efficiency, effectiveness, and innovation
Executive summary
Generative artificial intelligence (AI) has begun to unleash digital and management of unstructured, unlabeled data. This technology
waves across industries, but its promise to transform health is has tremendous untapped potential to deliver an immediate
only just beginning. The health care ecosystem is grappling with stepwise improvement, and exponential long-term improvement,
interlocking crises, from labor shortages and clinician burnout to to the health care ecosystem. It may help address the health
declining profitability and worsening health outcomes, particularly care industry’s greatest pain points by democratizing knowledge,
in underserved communities. The urgent need for a transformative, increasing interoperability, accelerating discovery, and enabling true
enterprise approach centers on leveraging new groundbreaking personalization.
technology while reintroducing genuine care and trust into
health care practices, both for the sustainability of health care Perhaps most importantly, Generative
organizations and the well-being of consumers.
AI can either deepen and restore trust
Generative AI technology has the potential to address these
or exacerbate mistrust and introduce
existential crises, among enterprise and direct-to-consumer
applications alike. Today, consumers are already using Generative new skepticism among consumers
AI for health care needs, and health care leaders have already
and health care stakeholders alike.
expressed activity, investment, and plans for Generative AI.
Generative AI is a solution for many of health care’s major
According to the Deloitte Center for Health Solutions,
challenges of workforce, margins, trust, and value with
and the 2024 Life Sciences and Health Care Generative
immediate opportunities in driving administrative efficiency,
AI Outlook Survey:
hyperpersonalizing the care experience, and creating digitally
75% enabled enterprise with low-code access to data and insights as
well as frictionless user interfaces. To address these challenges
of leading health care companies are already
successfully, however, Generative AI must be designed, deployed,
experimenting with Generative AI or attempting to
and scaled using a transformational approach that incorporates
scale across the enterprise
organizational change, ethics, and trust.
82%
We have predicted this seismic shift enabled by AI and radically
currently have or plan to implement governance and
interoperable data for several years, articulating that consumers
oversight structure for Generative AI
and clinicians alike are demanding new technologies to solve age-
92% old problems.1 Generative AI has the potential to catalyze trust
and power the broader Future of Health™ transformation—the
of leaders overwhelmingly see promise for Generative AI
shift from sick care and reactionary treatment to a well-being and
to improve efficiencies and
prevention focus—by helping enable radically interoperable data
65% through open, secure platforms and empowering consumers. In
of leaders see promise to enable quicker decision-making helping create this future, Generative AI can potentially eliminate
significant portions of the $1 trillion in wasted health care
spending.2 Various projections estimate that Generative AI, at large,
may contribute up to $7 trillion in global GDP over the next 10
In recent years, natural language processing (NLP) and machine years.3 As health care–specific Generative AI models and platforms
learning (ML)—subsets of AI technology—have gained traction in become more widespread, however, business leaders must identify
a host of health care use cases, ranging from clinical trial patient threats to their market position and retain a competitive edge.
recruitment to virtual physician assistants. New Generative AI Generative AI will be used in a way to disrupt today’s care models
models have demonstrated unprecedented capabilities and and create new ways to deliver medicine. These changes could
stakeholder interest as a significant expansion in natural language present challenges to incumbents, as well as current business
generation, summarization, translation, insight retrieval, reasoning, models and workflows.
3
From code to cure, how Generative AI can reshape the health frontier | Unlocking new levels of efficiency, effectiveness, and innovation
What might this disruption look like?
For health care providers, this is a golden opportunity to embrace informed decisions on traditionally complex matters including
and integrate democratized, personalized medical information into benefits, treatments, costs, prescriptions, appointments, clinical
their practice. Primary care practitioners could be equipped with trials, and wellness.
cross-disciplinary, real-time knowledge spanning medical, drivers of
Elsewhere in the health care ecosystem, life sciences companies can
health, social, and professional specialties. And modern-day doctor’s
tap into next-generation computational tools that both shave years
appointments could be streamlined, with patient information
off R&D timelines and reduce tedious commercial and regulatory
and intent gathered, analyzed, and synthesized beforehand and
barriers to entry. Furthermore, medical technology (MedTech)
integrated into their workflow, leading to tailored treatments for
companies stand to not only accelerate development, but also
each patient. Consumers can more easily access convenient,
generate some of the most meaningful, untapped multimodal data
appropriate services by having their symptoms and monitoring data
to empower longitudinal preventive care.
analyzed and triaged beforehand to be directed to the appropriate
setting of care. Generative AI holds promise across industries to streamline
operations, from discovery through commercialization—enhancing
Retail health incumbents stand to drastically improve the quality and
efficiency, compliance, and consumer-centricity. By harnessing
accessibility of care with Generative AI—leveraging a vast consumer
Generative AI, companies can achieve a competitive edge, accelerate
base, expansive and accessible footprint, and advanced analytics.
innovation, and ensure more agile and informed decision-making
These assets can fast-track an automated and interconnected
across their value chain. In the Deloitte Generative AI Dossier, we
experience, curtailing cost while uplifting care quality. With access
provide a road map for health care executives, sharing the most
to greater data, combined with the capabilities of GenAI to facilitate
compelling use cases that enhance operational performance,
navigation, the opportunity for retail health is expanding and
provide hyperpersonalized experiences, and develop enterprise
accelerating. Retail health can become a health hub, while improving
solutions while enhancing quality of care and health outcomes.
accessibility and cost of care overall.
As Generative AI advances, it will shift investments to promote and
Laboratory service businesses can extend across the value chain,
restore health, rather than simply treat sickness, by:
integrating more deeply into care delivery. Generative AI will not
only require more data, which laboratory services can feed: this
• Enabling radical interoperability
can boost their core business model in a more cost-effective,
streamlined approach leveraging “smart labs.” GenAI also offers an
• Leveling the competitive playing field
opportunity for these businesses to expand their business model
to direct patient support, second-opinion services, and provisioning
• Fostering creativity and seeding innovation
of care. These businesses can be at the forefront of clinical decision
support, where 70% of medical decisions are already anchored in
• Delivering complex reasoning
lab results.4
This new age of AI makes it even more critical for executives to
Payers and integrated payvidors (organizations that offer both
leverage Generative AI for an enterprise transformation, rather than
health insurance and health care services) can completely reshape
individual point solutions. Leaders should be asking:
their operations to lower cost and more efficiently offer services
with Generative AI powering innovative new operating platforms
• What are the long-term implications of Generative AI for my
and potent care management models. They can offer new products
business model?
and services that promote and orchestrate entirely new multimodal
care models. These organizations can become radically more
• How should my organization prepare to deploy and scale
personalized in design and administration. The basis of competition
Generative AI?
for health insurers will be reshaped, as consumers and employers
demand not only more cost-efficient services but also deeper clinical
• How can I build an enterprise transformation road map that
insights and personalized service.
encompasses the full suite of impacts, including regulatory,
Among consumers, the rising availability of digital platforms will compliance, privacy, trust, workforce transformation, and
be pivotal to bring engagement and health literacy to new heights. tax structures?
Today, consumers expect greater fulfillment across multidimensional
touch points in their care. With the advent of Generative AI–enabled
solutions, consumers will consider it table stakes for their clinicians
and insurers to provide personalized experiences informed by their
longitudinal health record and preferences. This will accelerate
shopping behaviors, as consumers are better equipped to make
4
From code to cure, how Generative AI can reshape the health frontier | The shifting health care market landscape
SECTION 1
The shifting health care
market landscape
As we navigate the complexities of the 21st century, the
health care ecosystem finds itself at a critical juncture
marked by a series of interlocking crises. The industry
has attempted to incrementally solve these issues,
and yet we have not made progress toward equitable,
quality health care delivery. We are mired in operational,
talent, financial, and value crises that demand a new
disruptive paradigm. Generative AI is the missing
element to truly drive the value, efficiency, effectiveness,
and innovation that we require.
5
From code to cure, how Generative AI can reshape the health frontier | The shifting health care market landscape
Figure 1: The 21st century interlocking healthcare crises
Labor shortages Profitability declines
Clinician burnout Value worsens
struggles
• Labor shortages: Accelerated by the COVID pandemic, health care • Value worsens: National health care expenditures have
organizations lack workers at every level. Today, hospital CEOs rank continued to rise, while the US life span has decreased to its lowest
“workforce challenges” as the top concern,5 and these shortfalls point since 1996.13 We are paying more and getting less. Health
are expected to persist with the Association of American Medical outcomes and life expectancy have significant disparities. The
Colleges forecasting a 124,000-doctor shortfall in 2034.6 Even the closure of health care facilities and the presence of provider deserts,
premier health systems and health plans are unable to stand up especially in rural areas and some urban areas, are exacerbating
operations to manage the growing demand for health care services. health care accessibility issues, affecting underserved communities
The industry has a shortage of 1.1 million nurses, forcing many the most. Consumers face a 26-day average wait time to see a
organizations to use contract labor.7 doctor,14 and place as many as 20 phone calls to find care.15
• Clinician burnout struggles: The increasing workload, emotional
We need to introduce caring back
stress, and administrative burdens have caused 81% of clinicians
to report high or modest levels of burnout.8 Clinicians cite
into health care, and humanity
administrative requirements, like paperwork and documentation,
as unnecessary and low value add. Clinicians are demanding into the experience. Trust is
technology and automation to focus on what matters most: caring
more important today than ever
for patients. Yet many clinicians also do not trust their organizations
to properly implement these innovations. Fewer than half (45%) of before—trust in clinicians, insurers,
frontline clinicians trust their organization’s leadership to do what’s
therapeutics, and institutions.
right for its patients. Even fewer, 23%, trust their leadership to do
what’s right for workers.9 We define trust holistically: as a
• Profitability declines: While consumers are concerned about series of actions, administrative
rising and unexpected health care costs,10 businesses face climbing
processes, governance, workflows,
operational costs and shrinking reimbursement rates, coupled with
an inflationary and tumultuous macroeconomic environment. As and regulations.
interest rates remain high, net working capital will remain expensive,
and payers and providers will be pressured to substantially increase
rates and cut costs, while attempting to maintain service and
experience. Health plan underwriting margins fell to a seven-year
low of 2% in 2022.11 Hospital operating margins are at just above 1%
and have been negative on average the past year.12
6
From code to cure, how Generative AI can reshape the health frontier | The shifting health care market landscape
Today’s health care enterprises each face their own challenges. A multifaceted approach to address these health care challenges
Payers are battling to streamline selling, general, and administrative requires a combination of improved efficiency, increased
costs and the cost of care.16 Providers are witnessing double-digit effectiveness, and innovation—all of which are ways in which
growth in staffing costs amid an unprecedented labor shortage.17 Generative AI can unlock new value for health care leaders
Retail health organizations are battling skyrocketing shrinkage, across efficiency, effectiveness, and innovation. According to the
increasing margin pressures, and evaporating COVID sales.18 Deloitte Health Care Generative AI Outlook Survey of 60 health
Laboratory organizations are facing stark supply chain and labor care C-suite executives in September 2023, 90% of leaders believe
cost challenges.19 Consumers are seeing their out-of-pocket health Generative AI technologies can best help their organization by
care costs continue to rise.20 improving efficiencies.
Success in health care hinges on creating and deepening trust and
innovation across the ecosystem, in providers to make sound care
decisions, in payers to cover costs and reimburse appropriately,
in pharmaceutical companies to develop efficacious treatments,
and in pharmacies to disburse and educate on medications. Yet
despite numerous health care advancements over the past 50 years,
confidence in the medical system is at all-time lows, down from 80%
to 34%. Fifty-five percent of consumers report a negative experience
causing them to lose trust in a health system, and patients with
lower trust are 19% less likely to engage in preventive care.21 Within
health care organizations, fewer than half (45%) of frontline clinicians
trust their organization’s leadership to do right by patients, and even
fewer (23%) trust their leadership to do right by workers.22 Deloitte’s
2022 TrustID Brand Index Survey—which included 25 life sciences
and health care brands—tracked similar trends: trust in both
payers and providers has dropped by 15% to 38% in humanity
and transparency.
Trust is still the key differentiator to win partners, consumers, and
talent. Generative AI has the potential to build trust and address
many of the current challenges while unlocking new value creation.
Technology, including AI, promises a similar transformative
potential as seen in other industries—from the revolution of
agriculture through automated irrigation, to the overhaul of retail
operations with inventory management systems, and the dramatic
improvement of manufacturing productivity via assembly lines.
The promise of Generative AI for
health care is the capability to tackle
greater complexity, apply more
humanlike reasoning, and interact
on a more human level than prior
AI technologies. We see intrinsic
value along dimensions of efficiency,
effectiveness, and innovation.
7
From code to cure, how Generative AI can reshape the health frontier | Navigating the obstacles and opportunities for Generative AI in health care
SECTION 2
Navigating the obstacles and
opportunities for Generative
AI in health care
Generative AI, while immensely powerful, forms just one
part of a larger, more diverse toolbox of AI solutions
available to business leaders. The foremost step in
deploying AI in an organization is a clear identification
and understanding of the problem to be solved. Based
on the need, the appropriate AI solution can be chosen
from the broader suite of tools that extend beyond
Generative AI. It’s critical to understand that Generative
AI isn’t a cure-all; it offers distinct capabilities but may
not be the right fit for every scenario.
8
From code to cure, how Generative AI can reshape the health frontier | Navigating the obstacles and opportunities for Generative AI in health care
A comprehensive AI strategy, or even an AI solution, often involves The gap of AI adoption in health care
bundling various technologies such as rule-based systems for
In Deloitte’s State of AI in the Enterprise 2022 report, we note that
processing defined business logic, robotic process automation
there is a stronger urgency, especially among biopharmaceutical
for automating repetitive tasks, discriminative AI for making
executives, to tackle the risks associated with AI technology in order
precise predictions based on a set of given inputs, and finally, the
to innovate and gain an edge over the competition.23 Yet, the adoption
indispensable human intervention for complex decision-making
of AI and all technologies in health care has consistently trailed behind
and reasoning.
other sectors, often falling behind due to cost, structural, regulatory,
Business leaders must ask the question: What is Generative AI organizational, and technical challenges.
best suited for, compared to other solutions in place today?
Health care has lagged in its AI adoption. A study conducted by
1. Generative AI technologies should be viewed as accelerants and Brookings in 2022 found that health care’s AI integration rate
supplements, not replacements, to humans. As this technology lagged all other industries outside of construction.24 Technical and
matures, we expect the sophistication and independence of the interpretability challenges, a heavy dependence on text and contextual
solutions to require less human intervention. data, and inherent biases in AI models have hindered widespread
AI acceptance in health care. Prior NLP techniques demonstrated
2. Generative AI is a powerful new technology to be embedded significant shortcomings, with false-negative rates and limited efficacy
within a suite of other AI solutions. Indeed, Generative AI is detecting contextual types of languages.26 These issues, combined
not a panacea for all solutions. It outperforms other AI models with the high-stakes nature of health care, underscore the complexity
on key dimensions but still lacks capabilities in extraction and sensitivity of implementing AI.
and computation.
Yet, there is tremendous opportunity to leverage an ample supply of
health care and real-world data. Health care has become the world’s
Generative AI should be seen as a largest data source, at 30% of annual production,27 with 80% of that
health care data being unstructured.28 The path to widespread AI
piece of the larger puzzle in the
adoption in health care is uphill, but the richness of health care data
strategic application of technologies, and ongoing advancements suggest an optimistic outlook for this next
age of Generative AI. We anticipate that Generative AI will likely make
each complementing the other to form
near-term impacts across efficiency, effectiveness, and innovation. In
our point of view, “A new frontier in artificial intelligence: Implications
a robust and comprehensive solution
of Generative AI for businesses,” we proposed a five-part functional
for diverse business challenges. framework for Generative AI use cases and value levers. Generative AI
models differ from prior AI and ML models in ways that deliver value
across activities that accelerate, automate, create, personalize,
and simulate.
Figure 2: The evolution of AI technologies
Generative AI
Artificial Intelligence
(AI) is a broad market
of which Generative AI
is one of the many
technologies that can
Conversational Autonomous disrupt how society
AI systems interacts and business
Deep is conducted...
learning
Speech
recognition Machine
learning
Artificial general
intelligence (AGI)?
Computer
Predictive vision
analytics
Intelligent 9
automation
From code to cure, how Generative AI can reshape the health frontier | Navigating the obstacles and opportunities for Generative AI in health care
Figure 3: The differentiated functions of GenAI in health care
Accelerate Automate Create Personalize Simulate
Enhance productivity by Deliver business and Push boundaries of Create familiarity and Create environments
accelerating outcomes technical workflows, creativity, leveraging personalization, in which workflows,
and offering top-tier and in some cases, prompts to develop which could take experiments, and
building blocks replace humans novel content significant effort experiences can
be simulated
Document distillation Code classification Record summarization Prompt generation Interaction visualization
Synthesizing lengthy text Processing unstructured Summarizing care Enabling information Building digital 3D models
into short-form summaries, inputs to produce a list of encounters (for HCPs) gathering across of cellular and chemical
evidence tables, or discrete alphanumeric with details about history, stakeholders in a patient- structures to aid in
dashboard/knowledge graphs codes that are used in symptoms, procedures, friendly way, through a back- discovery, development,
downstream processes diagnoses, etc. and-forth conversation and diagnosis
Component compilation Multimedia creation Jargon simplification Hypothesis validation
Integrating information from Generating interactive Explaining complex concepts Running experiments and
different source systems into materials that contain text, at an appropriate health workflows via a machine
a cohesive, ready-for-review interspersed with video literacy level through shorter- to help refine parameters
artifact with next steps and images, for education form, simplified versions before rolling out a process
and image or engagement out in practice
Translation to preference
Translating patient-facing
clinical and non-clinical
documents in real time, in a
patient’s preferred language
The promise of recent and upcoming accessibility, positioning them as a flexible and cost-effective choice
Generative AI advancements for organizations desiring domain-specific performance. These
strategic choices exert pressure on large hyperscaler market leaders,
2023 has witnessed an unprecedented level of advancement of who are now facing increasing customer demands to provide greater
Generative AI technologies. By April, ChatGPT was shown in a JAMA transparency and flexibility in their proprietary models.
study to outperform physician responses to medical questions on
Generative AI aligns well to functional needs within health care
dimensions of both quality and empathy.29 Google’s health care–
underserved by traditional AI and ML models. In certain functions,
specialized MedPaLM-2 large language model (LLM) became the first
Generative AI is positioned to potentially replace tasks and roles
to achieve an expert-level passing score on the US Medical Licensing
in data entry, classification, and generation, while supplementing
Exam,30 and the first drug completely designed with Generative
tasks requiring more empathy, innovation, and decision-making.
AI techniques was entered into human clinical trials.31 New
Today, Generative AI solutions are better fits for top-left functions
groundbreaking GH200 graphics processing units (GPUs) have been
that are lower cost and lower complexity, but as the models advance
announced with promise to precipitously drop costs for LLMs in both
and stitch together with a broader suite of AI solutions, we foresee
training and inference in 2024.32 The Deloitte Health Care Generative
potential for broad use.
AI Outlook Survey found that 72% of health plans and 80% of health
systems have already launched pilots or are actively scaling across
the enterprise highlighting potential rapid adoption. This pace of
change and uptake plots a tremendous trajectory.
The complex health care industry, we project, will likely focus on
specialized Gen AI models and heavily prompted and fine-tuned
use cases. Indeed, the investments into Generative AI this year
alone have demonstrated outcomes previously thought to be 20
to 30 years away. Competition among major technology players
is raising the bar—fueling new releases on the scale of months,
rather than years. Some notable technology organizations have
made their models open source to the public. These open-source
LLMs offer benefits in customizability for specific tasks, fine-tuning
on proprietary data, control over privacy and costs, and enhanced
10
From code to cure, how Generative AI can reshape the health frontier | Navigating the obstacles and opportunities for Generative AI in health care
Figure 4: The impact of GenAI based on task and value
Figure 5: The Future of Health™ and GenAI
11
woL
hgiH
Ability for GenAI to execute tasks in health care roles
High Low
Data entry Classification Summarization Content Visualization Prediction Optimization Decision Innovation Empathy
generation
IAneG
gnisu
eulav
ezilaer
ot
ytixelpmoc
dna tsoC
Calculation Interpretation
adH mo cls i esp s ri kit oa nl s sM pb ee il cd li in aic g la is l t tP eh ca hr nm ica iac ny Data analyst tR ea cd hi no il co iagy n Scheduler Hom ae id h eealth
recM ore dd sic ca lel rk M ce od di ec ral aR se ss oe ca ir ac th e IT technician Underwriter R te hs ep ri ara pt io stry cooP a ra c dcti iee nsn ast t or Charge nurse M aa nrk ae lyt sin tg aM sse id stic aa nl t
M sce rd ibic eal L teab cho nra ict io ar ny M we rd iti ec ral M da er sk ige nti en rg Pu eb dli uc c h ae toa rlth Sup ap nl ay l yc sh tain tP hh ey rs ai pc ia sl t aR se ss oe ca ir ac th e Counselor
reM cee pd tii oca nl i st reC vla ieim wes r Health coach cooP a ra c dcti iee nsn ast t or cor oeC s rli den a ii nc ra acl th o r Acutary Optometry prG ace tn ite iora nl e r mAc ac no au gn et r
Cl sin pi ec ca il a d lisa tta Cl min aic na al gd ea rta serP va ict eie sn rt e p. Bio-statistician Pathology Pharmacist sB tu ras tin ee gis ss t Sales rep
Re ag ffu ala irt sory asQ suu ra ali nty c e in mfoH are nma al a gt th eio rn M spa erk ce iat li in stg siM pm ee a cd g iai ic n la ig sl
t
Radiology eP nr go ic ne es es r H ao ds mpi it nal B eio nm gie nd ei ec ral
Te nle uh re sa elth Dosimetrist Epidemiologist Dermatology opC eli rn ai tc ioal n s S pp he ysc ii ca il ais nt R se cs iee na tr ic sh t Psychologist manager
Audiologist Data scientist dM ire ed ci tc oa rl Pr pim hya sr iy c ic aa nre
teM r ce he c nd o iri cc d ia asl n H ee coal nth o mca isr te Bio-informatician Surgeons
Generative AI has far exceeded previous state-of-the-art solutions. These early
successes are just the beginning as Generative AI leaves the laboratory and
integrates into products across the health care ecosystem. It is no stretch to
imagine the potential transformative applications of this technology.
Champion interoperability Level the playing field
GenAI capabilities to manage Make data usable and accessible Act as true creativity engines Evolve into reasoning engines
structured and unstructured data to nontechnical users and Accelerate discovery by reducing Personalize patient interactions by
and data labeling will enable and smaller-scale organizations the needs for hyperspecialization suggesting alternatives and making
accelerate data interoperability that historically lag and suggesting novel ideas communication more empathetic
across the industry sophisticated competition
Foundational advancements will lead to the rise of specific business models…
Science and Health product Personalized virtual
Data conveners
insights engine developers health actors
These businesses will create and operate...
Genesis of autonomous agents Constructing innovative care models New modalities of health and wellness
Autonomously analyze numerous systems and A primary care practitioner with cross-disciplinary New foundation models can embed treatment
datasets to perform tasks or identify insights, expertise to deliver personalized care with instant plans with customized music, 3D printed scans
integrating the abilities of data analysts, onboarding and complete knowledge of a and onsite-produced prosthetics, and virtual
AI/ML engineers, physicians, patient’s history appointments with physical renderings
and psychologists
From code to cure, how Generative AI can reshape the health frontier | Unlocking the value of Generative AI
SECTION 3
Unlocking the value
of Generative AI
The power of this moment, at large, is tremendous,
yet the obvious question remains: Where should I,
as a health care business leader, make immediate
investments to win in the new age of AI?
In practice, the key question becomes how to effectively
deploy these Generative AI models, both in terms of
which issues they are fit to solve and which areas of the
enterprise will likely be positioned to maximize their
value. In the Deloitte Health Forward Blog, we argue for
the value of incrementalism, where health care leaders
strike a balance between short-term demands and a
long-term vision. Business leaders must keep an eye
toward the innovation arc, while placing immediate bets
on areas within the enterprise.
12
From code to cure, how Generative AI can reshape the health frontier | Unlocking the value of Generative AI
Generative AI pre-trained models have historically used publicly In our Deloitte Generative AI Dossier, we elaborate upon high-value
available, non-industry-specific datasets. Some of these pre-trained use cases that health care leaders can pursue to create value
models have potential applicability to administrative, operational, across (1) employee productivity and operational efficiency, (2)
and back-office use cases. However, within the health care hyperpersonalized experiences, and (3) new enterprise digital and
environment, especially in the context of clinical delivery, stakes data capabilities. Below, we provide each example aligned to a
are high, and language must be precise, in addition to articulate. respective value driver.
The rise of health care–specific Generative AI LLMs in addition to
a more deliberate and experienced execution approach is breaking
ground to pursue these more sensitive and nuanced use cases that
evolve patient care.
Co-writer for denial Supply chain Personalized
appeal letter optimization service for patients
Driving administrative Supporting optimization Assisting human
cost-efficiency through by leveraging GenAI staff responding to
employee productivity and to simulate, model, patient questions
operational efficiency and generate
data-driven insights
Opportunity • There are many claims that are • Supply chains involved many • Patients often have to spend hours
denied in the US representing stakeholders and dependencies with IVRs and other systems to
billions in added costs creating complexity resolve issues
• Sixty percent of denied claims • High complexity makes • High call volumes require numerous
can be reclaimed, but only 0.2% efficiency, resilience and cost agents"
346,deloitte,us-Deloitte-MMTS-report.pdf,"2023 Mid-market technology trends report
Convergence topples industry walls and powers
growth ambitions for midsize private companies
2023 Mid-market technology trends report | Convergence topples industry walls and powers growth ambitions for midsize private companies
Table of contents
Introduction .............................................................................. 1
About the survey ...................................................................... 2
Section 1: A foundation for growth ........................................ 3
Section 2: Industry convergence: Opportunities
for growth and transformation ............................................... 6
A special message—Industry convergence:
Practical outcomes and responsible growth ..........................9
Section 3: AI adoption
and implementation ................................................................10
Section 4: Executable strategies for consideration .............13
A special message—CISO perspective:
Adapting cyber priorities to evolving threats,
new risks, and organizational changes ................................14
Conclusion .................................................................................15
Get in touch ...............................................................................16
2023 Mid-market technology trends report | Convergence topples industry walls and powers growth ambitions for midsize private companies
Introduction
In Washington state, orchardists are testing a 14-foot-tall robot with
mechanical arms that's capable of picking the ripest apples from
the tree. The robotic picker might one day help alleviate human
labor shortages for the painstaking agricultural work. Through
this mode of automation, researchers see ways to create more
sustainable systems in farming, to feed livestock, milk cows, or
navigate greenhouses under human supervision.1 This type of
precision farming is possible because of the convergence between
the high-tech industry and agriculture using technologies such as
the Internet of Things, artificial intelligence (AI), robotics, and big
Wolfe Tone
data—allowing workers to optimize the growth, harvesting, and
distribution of agricultural products.2
The rise of industry convergence—as well as The leaders who participated in our survey
the blurring of boundaries within sectors— represent predominantly privately held
is one of the key trends uncovered in this companies with annual revenue between
year’s survey of private and family-owned $250 million and $1 billion. Many of these
companies. An analysis of this year’s survey, enterprises are seeking greater returns on
our ninth assessment of the technology their technology investments and appear
priorities, investments, and challenges to be stretching their innovation muscles:
facing America’s middle market, offers a Seven out of ten respondents (70%) report
strong assessment that these companies that they have or are in the process of
are not only prioritizing technology developing assets that can be leveraged
investments that reduce time to value but and monetized outside of their own
Chris Jackson
also seeing value and innovating at a pace business for additional growth or
we haven’t historically seen in prior surveys. expansion. Many of these companies
appear to be seeking growth outside of their
traditional sector boundaries or investing
to help defend against other organizations
encroaching into their sectors.
Wolfe Tone Chris Jackson Ryan Jones
Vice Chair, US and Global Deloitte Private Deloitte Consulting
Deloitte Private Leader Technology leader Private Equity leader,
Ryan Jones
and Former Technology
Sector leader
11
2023 Mid-market technology trends report | Convergence topples industry walls and powers growth ambitions for midsize private companies
About the survey
Balancing act: Emerging opportunities and familiar challenges
Enterprises across this slice of the commercial landscape are seizing on the potential of AI to transform network operations, increase
efficiencies, and improve customer service.
In addition to navigating increasingly blurred sector boundaries, mid-market companies are confronting headwinds affecting a broad swath of
businesses, including evolving cyberthreats, changing talent configurations, and the impact of generative AI. Unlike prior surveys, this year’s
results suggest mid-market companies are doubling down on their technology investments—and proactively investing to stay ahead of these
challenges. Many of their investment priorities over time appear to be paying off, with three in four respondents reporting they have high or
very high confidence in their cybersecurity capabilities, for instance. From a human capital perspective, respondents indicated that they are
prioritizing hiring based on skills versus degrees. Furthermore, company leaders say they are leveraging talent from their ecosystem partners
and/or service providers.
To better understand what drives success more fully for these companies—and, in turn, their appetite for technology investments—we
reviewed the survey results for companies that believe they have been most successful in achieving their tech objectives. Then, we tracked
the respondents who anticipated achieving the highest return on investment (ROI) on their recent technology investments.
In these pages, we explore how private and family-owned companies continue to unleash their full potential for growth in an era where
disruption happens in real time.
Survey methodology
From May 4–24, 2023, a Deloitte survey of private and mid-market companies was conducted by a market research firm. The survey
examined technology trends taking place in this market segment to determine the role that technology plays and how it influences business
decisions. The 500 survey respondents represented companies with annual revenues ranging from $250 million to a little more than $1
billion. Firms with revenue between $250 million and $499.9 million in annual revenue comprised 10% of the sample; firms with at least $500
million to $749.9 million in annual revenue comprised 30% of the sample; firms between $750 and $1 billion in annual revenue comprised
30% of the sample; and firms more than $1 billion in annual revenue comprised 30% of the sample this year. Half of the respondents were
C-suite executives, while the remainder were non-C-suite decision-makers. Eighty percent of the respondents represented companies that
are privately held, while the rest were publicly traded firms. Among industries, 39% were from technology, media, and telecommunications
companies; 22% were from financial services companies; 21% represented consumer and industrial products companies; and the remaining
respondents were divided among energy and resources companies, and life sciences and health care (LSHC) companies. Some percentages
in the charts throughout this report may not add up to 100% due to rounding or for questions where survey participants had the option to
choose multiple responses.
2
2023 Mid-market technology trends report | Convergence topples industry walls and powers growth ambitions for midsize private companies
Section 1: A foundation for growth
Investment, spending, and new opportunities
Spotlight:
If budgets are reflections of a company’s ambitions, private
enterprises surveyed are conveying a strong desire to innovate at How companies and industries
the edges. This year’s survey reveals that overall technology
are investing
spending is at its highest level since 2019, perhaps making up for
lost ground during the pandemic. Further, of those businesses that
In the health care industry, AI could streamline and
reported spending more than 5% of their revenue on technology,
90% also reported an increase in their technology spending automate the often costly and time-consuming
compared to last year. process of appealing denied insurance claims. In
2021, more than 48 million claims were denied,
The leading areas of technology investment span a range of business
representing about 17% of all claims. AI’s ability to
needs as companies adapt to new innovations. In our prior survey in
automate this resource-intensive process could
2021, just 12% of respondents predicted AI would have a significant
potentially yield significant savings for hospitals
impact on their business within a year. In the current survey, AI has
leaped ahead of other technologies as 40% of respondents call it the and other health care providers while freeing
top tech investment priority. For many of these organizations, workers to focus on higher-value tasks.3
AI can provide value in the automation of repetitive processes,
working to create demonstrable value and savings for organizations. Security, risk, and threat monitoring come in at
a close second among the top targets for tech
investment. Cloud infrastructure and customer
resource management (CRM) investments round
out the top four areas of investment over the
past year.
This year’s results also suggest how company
ownership and industry can influence technology
investment decisions. For instance, while just over
a quarter of family-owned businesses (27%) say
they invested in AI over the past year, around half
of private equity-owned businesses (49%) say they
have pursued the technology.
More than any other sector, respondents in the
energy, resources, and industrial (ER&I) industry
report a focus on metaverse technology. As a
practical application, ER&I companies can tap
the metaverse for virtual reality and augmented
reality-enabled immersive employee training,
and externally, for virtual storefronts to promote
sustainable efforts.4
3
2023 Mid-market technology trends report | Convergence topples industry walls and powers growth ambitions for midsize private companies
Calculating technology ROI
This year, we measured a range of factors
to quantify more precisely how technology
investments are translating into the ability to
successfully achieve technology objectives.
Several factors inform our analysis, including
whether the quality of data is sufficient
for the application of AI to the business;
talent capabilities; the respondents’ vendor
and partner networks; and the ability to
expand outside of the company’s industry
and sector. We used these inputs to assess
ROI on tech investments as measured by
revenue increases for these companies.
The businesses reporting the highest ROI on
their tech objectives report being more than
twice as likely to strongly agree that their
data was sufficient for the application of AI.
Respondents at the upper end of revenue
growth report they are more likely to
increase their technology spending by more
than 20% compared to the previous year. “Especially for companies with active AI initiatives, there’s
Correspondingly, businesses at this end of
a growing level of confidence in these investments as the
the spectrum are almost one-and-a-half
times more likely to have seen an increase in businesses harness, monetize, and generate revenue
revenue of 20% or more.
from selling data and tech-enabled services.”
Our assessment also reveals that a mature
Khalid Kark, CIO research director, Deloitte LLP
cyber posture can be a critical investment.
Security, risk, and threat monitoring
software—such as risk quantification tools
Notably, respondents with active AI “Companies have matured across their
that compare the costs, benefits, and ROI
solutions are about two-and-a-half times as cyber capabilities as they’ve outsourced
of cyber investments5—are the technology
likely to have very high confidence in their the most complex parts of their cyber
investments respondents ranked as most
cybersecurity capabilities compared to functions, added additional protections,
likely to have a high or very high return
businesses not using or exploring AI at all and leveraged investments in cloud and
on investment. Meanwhile, AI was the
(32% vs. 11%). other digital infrastructure,” says Criss
technology investment that was most likely
Bradbury, Markets, Offerings, and Alliances
to have a very high ROI.
Still, relatively few C-suite executives appear leader, Deloitte LLP. “As more organizations
to be comfortable with the state of their embrace AI, I expect even more urgency to
Evolving posture toward cyber risk
cybersecurity efforts (12% report very high embrace security, ensure compliance, and
confidence) compared to leaders outside enable customer trust.”
The leaders we surveyed this year appear
of the C-suite (22%). This could suggest
to be seeing the results of a sustained and
that technology leaders have been actively
maturing focus on data security. Three in
pushing for investments to meet the array of
four respondents indicate high or very high
emerging threats because of their familiarity
confidence in their business’s cybersecurity
with the domain—and they are putting
capabilities. Consider that in our 2018
these solutions into action.
survey, fewer than half of respondents (48%)
said they had governance structures in place
concerning information security threats.6
4
2023 Mid-market technology trends report | Convergence topples industry walls and powers growth ambitions for midsize private companies
The report also
highlights a shift
to skills-based
organizations, with
forward-thinking
organizations
altering their focus
from jobs and job
titles to acquiring
specific skills.7
In our technology survey, almost a quarter
of the respondents report the use of skill-
based hiring versus degree-based hiring.
Talent strategy: A tech workforce revolution
Roughly the same share says they are using
talent and skills from ecosystem partners
Addressing the workforce crunch for jobs that require skills such as engineering and
and/or service providers as their approach
data science is another evergreen topic among private company technology executives.
to develop tech talent.
Compared to the prior 12 months, almost half of the respondents surveyed (49%)
indicate no change in their ability to retain their top technology talent. Over a quarter of
“It’s critical for leaders to convene a broad
respondents report that it has been easier to retain key technology talent compared to the
ecosystem of partners who can help
past 12 months.
navigate the speed of change and the
complexity that comes with converging
Nonetheless, one-third of businesses at the lower range of our ROI measurement for tech
technologies and industries,” says Ryan
investments say they are facing more difficulties in retaining their top tech talent compared
Jones, Deloitte Consulting Private Equity
to 12 months ago.
leader, and Former Technology Sector leader.
There is additional evidence that the nature of jobs is changing. Deloitte’s 2023 Global Human
While respondents in our survey express
Capital Trends report describes an increase in the share of workers who say they already have
an interest in acquiring skills through
switched, or are likely to switch, employment models throughout their careers—from full-
external means, they appear to be pulling
time jobs to opportunities like freelancing and gig work.
back dramatically in their own efforts to
impart these skills to their teams. In our
2018 survey, 61% of respondents said that
reskilling employees to realize the greatest
benefit from technology was the top focus
area for maintaining their workforce through
technology. This year, just 19% say upskilling
or retraining existing talent is their primary
method to developing technology talent.
According to Jones: “Rapidly emerging
technology and changing job roles have
created a reliance on tech providers that
have deep engineering skills.”
5
2023 Mid-market technology trends report | Convergence topples industry walls and powers growth ambitions for midsize private companies
Section 2: Industry convergence:
Opportunities for growth
and transformation
Crossing traditional business boundaries the toughest threats. When asked which industries outside
of their industry would pose a threat to their current position in
With new technologies disrupting business models at every turn, the marketplace in the near future, respondents across most
convergence across industries seems inevitable. Whether it’s industries said those threats would arise from companies within
investing in current capabilities, acquiring a leading-edge startup, their own sectors.
or finding new uses for existing assets, private and family-owned
companies have an array of approaches at their disposal to seek For instance, among respondents from consumer products
new opportunities as industry and sector lines converge.8 companies, financial services firms, and TMT companies,
respondents reported that adjacent businesses within those
Perceptions about the competition make up just one part of the industries pose the biggest threats in the near future.
convergence story: In our survey, half of the total respondents
(51%) see a high or very high threat to their current position in the Nonetheless, respondents are overwhelmingly confident that they
marketplace from businesses outside of their sector. have the tools in place to move into adjacent industries. More than
two-thirds of respondents (70%) believe their business has an asset
The reality may be slightly different in practice, however. An that could potentially be monetized outside of their sector. Among
industry-by-industry view shows that as companies simultaneously respondents reporting the highest ROI on their tech investments,
defend their turf, they see companies within the same sectors as the share jumps to 81% with an asset ready for an adjacent market.
70% of respondents 1/3 of respondents
believe their business has report spending more
70% 33%
an asset that could be than 5% of revenue on
monetized outside growth outside their
of their sector industry or sector
Key
findings
44% of respondents
55% of respondents
say boards should
44% say boards should focus 55%
concentrate on industry
on cybersecurity
convergence
6
2023 Mid-market technology trends report | Convergence topples industry walls and powers growth ambitions for midsize private companies
Manifesting ambitions
As we noted in our analysis of tech investment priorities, technology
spending often telegraphs strategic plans and how companies intend
to sustain their growth ambitions: Almost a third of respondents
(32%) report their companies are spending more than 5% of their
revenue on growth outside of their industry or sector. To put that
into perspective, companies with $1 billion in annual revenue report
spending a minimum of $50 million to pursue growth plans outside
of their existing area of business.
As companies focus on ethical and regulatory considerations of
rapidly evolving technologies, this year’s responses suggest where
boards should be prioritizing their energy and time: More than half
of respondents (55%) say boards should focus on cybersecurity and
regulatory matters, while 44% of respondents say they want their
board members to concentrate on industry convergence.9
“Tech is the unifying thread as
the lines between humans and
machines, traditional industries and
their competitors, and customers
and their suppliers continue to
converge,"" says Brett Davis, principal,
Deloitte Consulting LLP and Global
Assets leader and general manager
of Converge by Deloitte. “That a
significant share of companies are
devoting resources to exploring
growth in industries beyond their
own tells us that employees, leaders,
and boards see opportunity and
value through convergence.”
7
2023 Mid-market technology trends report | Convergence topples industry walls and powers growth ambitions for midsize private companies
Where do sectors perceive the biggest threat to their current
position in the marketplace?
Boundaries between sectors are increasingly diminishing, with a majority of mid-
market businesses having their footprints in a different sector within their industry.
These blurred boundaries have led mid-market companies to perceive notable threats from
Threats in the near fu-
sectors within the same industry. This speaks to a rise in sector convergence. The exception is
ture by sector (n=256)
industrial products and construction, which sees the biggest threat from outside its industry—
71%
1 Automotive Consumer Products
from technology.
60% Retail, Wholesale & Distri-
2 Consumer Products
bution
Sector perceived as
Retail, Wholesale & Distri- 33% Sector
3 Consumer Products the highest threat
bution
Transportation, Hospitali- 43% Retail, Wholesale & Distri- 1 Automotive 71% Consumer Products
4
ty & Services bution
100% Power, Utilities & Renew-
5 Mining & Metals 2 Banking & Capital Markets 48% Insurance
ables
57% Power, Utilities & Renew-
6 Energy & Chemicals
ables 3 Consumer Products 60% Retail, Wholesale & Distribution
Industrial Products & 40%
7 Technology
Construction
4 Energy & Chemicals 57% Power, Utilities & Renewables
Banking & Capital Mar- 48%
8 Insurance
kets
56% Banking & Capital Mar- 5 Health Care 55% Life Sciences
9 Insurance
kets
Industrial Products &
10 Life Sciences 38% Health Care 6 40% Technology
Construction
55%
11 Health Care Life Sciences 7 Insurance 56% Banking & Capital Markets
75% Telecommunications, Me-
12 Technology
dia & Entertainment 8 Life Sciences 38% Health Care
Telecommunications, Me- 25%
13 Technology
dia & Entertainment
9 Mining & Metals 100% Power, Utilities & Renewables
Retail, Wholesale &
10 33% Consumer Products
Distribution
Telecommunications, Media &
11 Technology 75%
Entertainment
Telecommunications,
12 24% Technology
Media & Entertainment
Transportation, Hospitality
13 43% Retail, Wholesale & Distribution
& Services
8
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A special message
Industry convergence: Practical
outcomes and responsible growth
By Brett Davis, US Consulting Chief Innovation Officer and
General Manager of Converge by Deloitte
The world is being reshaped and redefined by convergence. helped the company build and launch a digital platform, and
The lines between traditional industries, competitors and even in a highly regulated industry covering multiple jurisdictions
collaborators, and customers and suppliers are becoming and regulations, Deloitte was able to help the client launch the
increasingly blurred. Platforms, partnerships, and products are platform across select markets in just 11 months.
no longer the domain of single industries or adjacent industries.
All of these elements are converging—creating a profound We have also seen an emerging trend in which data assets have
transformation that’s creating new efficiencies, streamlined value across different use cases in adjacent sectors or markets.
processes, and novel opportunities for companies to grow. In the mid-market technology survey, 70% of respondents report
having an asset that can be leveraged for value outside of their
This year’s mid-market technology survey highlights how industry organizations. That’s a remarkable data point, owing to the rapidly
convergence is accelerating—as companies activate cloud, AI, eroding boundaries and barriers for creating cross-industry
5G, mobile, and other technologies that enable movement into solutions. Conversely, the motivations for creating them have
adjacent sectors or transform existing services in new ways. In increased—as companies realize how the data they produce
fact, more than two-thirds of respondents see a high or very high can be repackaged, used to create new value in other markets,
threat to their position in the marketplace from outside of their transformed and aggregated to deliver goods and services in
sector. And nearly a third of businesses are spending more than personalized ways across their supply chains, or even used by
5% of their revenue on growth outside of their industry/sector. other industries for additional insights.
These trends are emerging in multiple industries. In the consumer industry, companies can use third-party and
primary data to create more personalized experiences for
For instance, tech and consumer companies are offering health consumers. With this enhanced capability comes additional
care services through new digital experiences. Conversely, health responsibility in managing that data. Think of a retailer
care organizations are using consumer technologies to reach entering the health care space by tapping into consumer
and support patients in new ways. In one example, we helped a information through its network of physical stores and digital
medical school engage potential clinical trial participants directly platforms. There's an incredible opportunity to personalize care,
via a digital platform, allowing patients to participate remotely, create brand loyalty, and deliver outcomes for someone on a
and helping investigators and other stakeholders collaborate wellness journey.
more easily in the research process. Prior to these types of
innovations, a study participant would have had to be identified
and engaged at a medical center. Notwithstanding, there are important
ethical, regulatory, and security
There is also a shift in technology buying behavior among
customers who want to enter into new industries—they implications to be considered when
increasingly expect pre-built tech solutions and a trusted partner
entering new adjacent industries.
to help them enter these markets and industries.
This trend is evident in banking, where nontraditional financial
services providers are increasingly offering digital banking The lines between traditional industries and their competitors will
services to their existing set of customers and bundling these continue to converge—enabled and accelerated by technology.
products with nonfinancial service offerings. A recent example of It will be an exciting decade ahead as industries are reinvented
this is Deloitte’s engagement with a multinational consumer client because of this convergence.
that wanted to offer services in multiple global markets. We
99
2023 Mid-market technology trends report | Convergence topples industry walls and powers growth ambitions for midsize private companies
Section 3: AI adoption
and implementation
For private companies trying to determine how to turn the hype around AI into a Where are mid-market
business differentiator, one place to look for perspective is the financial services industry.
companies on the AI
For transactions such as billing, payments, or collections, the opportunities include
generative AI-powered “agents” that could deliver tailored content to customers, as well adoption curve?
as conversational Q&A using models trained on enterprise data to support new employees
through the procedures.10
There are also risks when it comes to exposure of sensitive financial data using AI tools. A 41% report
report by the World Economic Forum and Deloitte argues that by being an AI early adopter,
exploring AI
AI could expose the financial system to new hazards by triggering failures that damage brand
equity and customer trust, trigger additional regulatory scrutiny, and alienate employees.
Companies in other industries may not be far behind in having to consider such issues—if
they aren’t already doing so. Nearly all respondents in our survey report they are on the AI
30% report
adoption curve, with 41% of respondents saying they were exploring AI, 30% saying they were
piloting AI solutions, and one-quarter of respondents saying they have active AI applications. piloting AI
solutions
Among industries, technology, media, and telecommunications
(TMT) and life sciences and health care (LSHC) are the most
25% report
likely businesses to have active AI solutions, while companies
having active AI
in financial services and insurance (FSI) are the least likely to be
active in this area. applications
% of business with active AI solutions by industry
Technology, Media &
33%
Telecommunications (TMT)
Life Sciences & Health Care
32%
(LS&HC)
Consumer 20%
Energy, Resources &
20%
Industrials (ER&I)
Financial Services (FSI) 13%
10
2023 Mid-market technology trends report | Convergence topples industry walls and powers growth ambitions for midsize private companies
“There’s an infinite number
of AI use cases that
can provide value to an
organization; however,
to truly scale, companies
should start their journey
by focusing on a single
use case that’s promoted
all the way to production.
This can provide the
foundation for future AI-led AI and talent
innovation throughout the
While there's been no measurable change in businesses' overall ability to hire technology
organization.” talent, respondents are finding it difficult to build a workforce with AI expertise.
Deborshi Dutt, AI Strategic Growth
Nearly one-third from financial services companies report that AI ethics officers are in short
offering leader, Deloitte Consulting LLP
supply, which tracks with an overall labor-market squeeze and evolving regulatory pressures
that are driving demand for compliance officers.
As viewed through our ROI measurement, it
appears that businesses that have achieved
the highest success in their tech objectives
and tech ROI are more likely to have active
AI solutions in a business area. And it
turns out, AI appears to be helping these
businesses achieve an array of benefits.
40% 37% 35%
report difficulty report difficulty report difficulty
attracting AI attracting finding data and deep-
strategists engineering talent learning scientists
About nine out of ten respondents (87%)
who state their companies have active AI What did respondents say are the top worker challenges?
solutions report that those solutions are
currently generating both revenue and
saving costs.
In the past year, respondents with active AI
solutions say they’ve focused their efforts
on harnessing data, modernizing legacy
systems, and improving cybersecurity.
What’s more, respondents from companies 40% 37%
with active AI solutions are more likely
report employee report ethical
to be very confident in their companies’
cybersecurity capabilities. perception of AI use of AI
11
2023 Mid-market technology trends report | Convergence topples industry walls and powers growth ambitions for midsize private companies
Challenges to AI adoption
We asked respondents to identify the challenges their companies are facing related to AI.
Leaders who responded to our survey report different concerns depending on their industry—
which may reflect how companies are struggling to align culture, ethics, and strategy in their AI
adoption journey.
Challenges to AI adoption
We asked respondents to identify the challenges their companies are facing related to AI. Leaders who AI concerns related to talent
responded to our survey report different concerns depending on their industry — which may reflect how
companies are struggling to align culture, ethics, and strategy in their AI adoption journey.
AI concerns related to talent Employee perception of AI ER&I 56%
• Lack of available talent/Consumer and LSHC/33%
• Internal employee resistance to advanced AI/ER&I/38%
Internal employee resistance to advanced AI ER&I 38%
• Employee perception of AI/ER&I/56%
Consumer/
AI concerns related to trust Lack of available talent 33%
• Ethical use of AI/Energy/42% LSHC
• Customer concerns or perceptions of AI/TMT/38%
AI concerns related to business strategy
• No real business and tech alignment around AI/Financial Services/29%
• Lack of enterprise strategy around AI focus/TMT/29% AI concerns related to trust
• Lack of an innovative culture to take chances/ER&I and LSHC, 30% each
• Lack of business engagement/LSHC/36%
Ethical use of AI ER&I 42%
Customer concerns or perceptions of AI TMT 38%
AI concerns related to business strategy
Lack of business engagement LSHC 36%
Lack of an innovative culture to take chances ER&I/LSHC 30%
Lack of enterprise strategy around AI focus TMT 29%
No real business and tech alignment around AI FSI 29%
Key: ER&I—Energy, Resources & Industrials FSI—Financial Services LSHC—Life Sciences & Health Care TMT—Technology, Media & Telecommunications
Note: These industries represent the top responses for each of the options for this question.
12
2023 Mid-market technology trends report | Convergence topples industry walls and powers growth ambitions for midsize private companies
Section 4: Executable strategies
for consideration
A look ahead
The top three overall technology objectives from the past year (improving cybersecurity, enabling business growth, and optimizing business
operations) remain the top three technology goals in the coming year as well. Companies in life sc"
347,deloitte,us-ai-in-surveillance-POV.pdf,"Augmenting trade surveillance
programs with artificial intelligence and
machine learning: A brief overview
May 2024
“What we were looking to do here was really to
answer some of the questions that were presented
in surveillance: changing market conditions, increased
volatility, increased volumes and change in conduct. And
so, using deep learning made a lot of sense to start to
answer those challenges.”
—Susan Tibbs, Former Vice President, Market Manipulation Group,
Financial Industry Regulatory Authority (FINRA)1
Augmenting trade surveillance programs with artificial intelligence and machine learning | A brief overview | May 2024
Artificial intelligence: A brief overview
The financial markets have generally been a hotbed of With AI being considered as a key element in future innovations,
competitiveness, risk, and innovation. To uphold the economy’s the financial services industry is looking to leverage its potential
good health and to build investor confidence, it is crucial to as a transformative tool. In areas such as improved fraud
maintain integrity and stability of financial markets. In this world detection, risk management, and predictive analytics.2 Some
of fast-paced technological developments, artificial intelligence common AI use cases in the banking and financial services
(AI) is becoming a potent weapon in the field of risk monitoring sector include:
and surveillance. By looking at its diverse applications and
upcoming trends, AI may be a crucial factor in helping to protect Algorithmic trading
financial markets.
Fraud detection
AI involves the use of algorithms and analytics to enable systems
to demonstrate intelligent behavior, including learning from data,
making decisions, and solving problems, all with minimal human Regulatory compliance monitoring
intervention. Similarly, machine learning (ML) is the process of
discovering patterns in data without human intervention and Personalization of financial investment advice
using them to make predictions. Specific to trade surveillance,
systems integrated with AI and ML not only aim to uncover Risk management
suspicious trading patterns, but also to help in reducing the
volume of false alerts, thereby helping mitigate their risk to the Enhancement of customer experience
trading ecosystem.
Augmenting trade surveillance programs with artificial intelligence and machine learning | A brief overview | May 2024
Rule-based surveillance: Status quo
Market surveillance has traditionally been rule-based, generating
alerts based on pre-specified rules/static thresholds that lead to
specific actions when such conditions are breached. Due to high
interpretability, these “traditional” surveillances have the main
advantage of being simple and reliable, meaning they are easier
to understand and develop/enhance standardized rules that
would enable ongoing market surveillance. However, while rule-
based systems have been quite effective in laying a successful
foundation for trade surveillance, they have limitations due to
them not being effective for all use cases:
• Data cleansing requirements: Rule-based systems often
struggle with large and unstructured datasets that require
extensive efforts in cleansing and formatting to make it well-
structured and usable for surveillance systems.
• Tackling new threats: Rule-based systems work based on
prescribed/preconditioned directives; hence they cannot pick
up manipulative patterns that are new or even slightly modified,
resulting in possible surveillance lapses.
• Adaptability across markets: Rule-based systems require
dedicated models to cover different asset classes and markets.
While these systems currently have dynamic thresholds
that may help to an extent, they still need to account for
manageability, as firms may end up with a significant number of • Holistic surveillance is an approach that enables
models and an even higher number of thresholds/parameters simultaneous monitoring across multiple surveillance functions,
across asset classes and markets that require a larger supporting higher-quality tethering and control effectiveness
maintenance effort, making this construct susceptible to errors. between trade and communications data to help identify
false or misleading statements and potential market abuse
To tackle the limitations and challenges of rule-based
behaviors such as ""pump and dump,"" ""flying,"" and ""printing"".5
surveillance, there is widespread consensus among market
participants and regulators about the need to analyze more • Dynamic parameters can be used to determine and assess
dynamic and robust surveillance insights for the future3 AI and specific trading behaviors more accurately based on factors like
ML models are being considered by both regulatory authorities the standard deviation of client or account trading activities/
and financial institutions (FIs) as an accelerator for market patterns, market conditions, and economic indicators when
surveillance. Alternative solutions are also being explored in compared to static thresholds. This approach can help diminish
parallel for enhancing existing surveillance capabilities as well: the number of false positives, which has been a significant
drawback of rule-based surveillances.
• Quantum computing is being looked at as a potential
accelerator for AI as it could enhance the ability of AI-based • Integration of distributed ledger technology (DLT)/
models to process and analyze large datasets at faster blockchain with AI at the back end for data storage and
speeds.4 retrieval could help tackle the opaque nature of AI. The
immutability, traceability, and decentralized nature of DLT/
• Network and behavioral analysis techniques could help
blockchain enables improved security, transparency of
in revealing hidden connections/relationships/patterns to
execution, and efficiency that could be a strong fit for AI-based
identify potential coordinated market manipulation behaviors.
surveillance systems.
Deviations from normal behavior patterns could result in
identifying evidence of market abuse.
4
Augmenting trade surveillance programs with artificial intelligence and machine learning | A brief overview | May 2024
Rule-based surveillance: Status quo (cont.)
In addition to the above areas, many FIs are looking at AI and ML • Real-time identification of patterns and anomalies:
as an augmentative solution for trade surveillance. Integration AI-enhanced models can identify patterns in trading activities
of AI/ML in existing surveillances has the potential to be a great and relate them to generic or specific market events that
value add for organizations looking to enhance surveillance may indicate trading anomalies. For instance, AI-integrated
efficiency and overcome the limitations posed by traditional surveillance models designed to detect intraday market
surveillance analytics. Some of the benefits AI can provide manipulation risk can identify market volatility resulting from
around surveillance include: index rebalancing, option expiry events, or movement in a
stock’s price because of issuer-specific news and compare
• Adaptive and scalable surveillances: AI-based models
outcomes to historic situations to more effectively trigger
are capable of processing large datasets quickly and highlight
or provide supporting information. This can increase the
evolving patterns of potential market manipulation-related
effectiveness of models to proactively trigger alerts on unusual
activities. The capability of AI-based models to process
participation or movements in price that may not be attributed
large and diverse datasets could assist firms to identify
to any specific external events. The model can also aid in
and manage risk more appropriately. ML models stand out
spotting trends and abnormalities, including ones that rule-
in handling uncertainties as they can provide confidence
based systems might miss, to recognize complex patterns that
scores, or probabilities associated with their predictions,
may not be immediately evident.
which is valuable when dealing with varied trading and order
placement behaviors. The personalization feature of AI/ML • Supporting the surveillance review: The integration of AI
makes it possible to create alerts that are more pertinent in surveillance can enhance the surveillance review process,
and in line with the distinctive market dynamics of various making it more efficient and effective. By leveraging e-discovery
financial instruments. Based on their risk appetites and usecases, electronic communications can be reviewed with
trading methodologies, AI allows organizations to customize greater ease and accuracy. For instance, a four-month review
alert thresholds. For example, with the help of AI/ML models, that required one million documents and a hundred personnel
dynamic thresholds/parameters can be set for a variety of was reduced to six weeks and five personnel by utilizing
clients. Clients with low turnover/trading activity and trading large language models with search prompts. This technology
manually can be distinguished from clients trading in large enables analysts to identify the origin of trades and patterns of
volume and on low-latency/high-frequency flows using complex behavior beyond traditional rule-based surveillance, thereby
trading algorithms. With the help of AI, a clear distinction can enhancing the overall surveillance experience.6
be made while generating alerts for such activities.
• Reduction of false positives: A major concern with rule-
based systems is increasing alert volume and the time involved
in reviewing the same. Integration of AI in trade surveillance
can help to reduce false positives and alerts posing no risk, and
increase learnability and feedback loops with historical market
and surveillance data.
5
Augmenting trade surveillance programs with artificial intelligence and machine learning | A brief overview | May 2024
Feasibility of implementing AI in
surveillance
Trade surveillance requires data from multiple sources including
but not limited to exchanges, venues, trading platforms, news
feeds, and internal trading records. Accessing and integrating data
from these diverse sources can be technically complex. When
combined with surveillance data from communications, it can
lead to a more efficient and effective analysis and interpretation
of suspicious activity. This approach recognizes that different
AI models may excel in different aspects of surveillance and
combining their strengths can lead to more robust results. Various
stand-alone models can be configured and trained for specific
aspects of surveillance. Implementing AI comes with specific
requirements and prerequisites that are essential for its
successful adoption.
• Natural language processing (NLP) models for speech-to-
text data, translation engines, analyzing news, sentiment, and
textual data related to financial markets.
• Time-series models like recurrent neural networks (RNNs)
or long short-term memory (LSTM) networks for detecting
patterns and trends in historical trade data and behavior.
• Graph-based models to analyze the relationships and
connections among different entities in the financial markets,
such as traders, firms, and securities.
• Anomaly detection models to flag unusual trading behavior.
Once the technical infrastructure for AI implementation is in place,
It is vital to define clear objectives and use cases for AI in both
human expertise should be leveraged to add maximum value to
trade and communications surveillance. Whether related to risk
the efficiency of the AI-based model. Right from the thoroughness
appetite of the entities or driven by rules, guidance, or mandates
of data till the end results, human skills are pivotal to choose the
from regulators and venues, having specific goals can aid in
appropriate inputs. If the model produces below-par results, the
greater effectiveness of an AI-based model. It is essential to have
model owner is held responsible since they make all the important
appropriate infrastructure in place. This includes the hardware
decisions pertaining to developing, training, and maintaining
and software needed to collect, store, and process data efficiently.
AI models. The organization’s commitment to meet these
High-performance servers, data management tools, and storage
foundational requirements are important for the success of the
solutions are required for handling the vast amounts of data
AI implementation.
involved across surveillance purposes. Access to a substantial
amount of historical data is essential to train a robust and accurate
model. This helps the AI system learn from past events and identify
patterns of misconduct. Sufficient education across technology,
compliance, and surveillance teams enables the effective use
and feedback loops to improve the effectiveness and efficacy of
integrating AI into surveillance systems.
7
Augmenting trade surveillance programs with artificial intelligence and machine learning | A brief overview | May 2024
Opaque nature of AI
With the use of AI in surveillance, there may be a tendency to be • Assess data quality to provide confidence in the design and
wary of its opaque nature and lack of transparency of underlying operation of models used. This may include using exploratory
algorithms—how the model operates (its dependencies and data analysis as well as sandbox environments with both true-
limitations) and how its predictions or results are produced. This positive and false-positive examples, back-tested data, and
closed approach surrounding AI makes it challenging for a non- stress-tested environments.
technical audience to understand the model logic. To address
• Implement controls below the line and periodic review of
this inherent skepticism, firms can:
results to provide confidence in inputs and outputs.
• Document the design, purpose, and key features of the model
This can help to reduce ambiguity surrounding the opaque
to make clear the inputs and expected outputs.
nature of AI, and organizational personnel can get comfortable
• Build dashboards and visuals to explain the flow of with AI’s capabilities to make consequential compliance decisions.
model decision-making and, subsequently, provide detailed
explanations of alert predictions.
8
Augmenting trade surveillance programs with artificial intelligence and machine learning | A brief overview | May 2024
AI/ML in surveillance: The regulatory
perspective
The banking and financial services sector is highly regulated, • Accountability – Ensuring policies are in place to determine
constantly trying to uphold its fundamental value of data responsibility and ownership of decisions made by the AI/ML
governance/protection and customer privacy.7 However, models.
increasing adoption of AI and ML can pose a distinct challenge
• Safety and security – Ensuring controls are in place to protect
regarding model explainability.
the AI/ML models from risks that could have a significant
ML models often provide for some explainability in terms of the negative impact on the firm/stakeholders.
underlying assumptions and factors considered when making a
• Reliability and robustness – Implementing controls to
prediction. The regulators recommend improving explainability
ensure accurate outputs, withstand errors, and quickly
of the AI/ML model being used to help users and supervisors
recovery from unforeseen disruptions.
understand the functionality by breaking down the opaque
nature to provide clarity.8 To tackle the challenges stated above, At Deloitte, we provide an end-to-end framework to assist with
establishing a trustworthy AI framework that helps organizations the implementation of AI that echoes the application of all the
develop ethical safeguards to address key concerns across above-stated dimensions to build an ethically adept AI/ML
the following dimensions is crucial in managing the risks and system. Please refer to Deloitte’s Trustworthy AI™ framework
capitalizing on the returns associated with AI: to learn more.
• User privacy – Implementing controls to ensure data usage is
limited to its intended and stated use and duration, with users
having the option to share data.
• Transparency and explainability – Tackling the opaque
nature of AI to ensure that users understand how the AI/ML
models work by explaining the inputs, inherent logic involved in
decision-making and outputs such that the decision-making is
clearly understood, auditable, and open to inspection.
9
Augmenting trade surveillance programs with artificial intelligence and machine learning | A brief overview | May 2024
AI/ML in surveillance: Technical
standpoint
An AI-implemented surveillance solution is considered effective
if it is proficient in recognizing trading patterns. Market
manipulation patterns can be recognized through a
combination of data analysis, pattern recognition algorithms,
and machine learning techniques. Prior to analysis, relevant
trading data is sourced and prepared for pre-processing and
feature extraction. AI algorithms are used to identify patterns
within the pre-processed data–these could be trend-based,
reversal-based, or other trading pattern types used to detect
anomalous trading behavior. Supervised and unsupervised
learning approaches are used to train ML models so that
trading outcomes are more closely correlated to input features.
Techniques such as clustering, dimensionality reduction, time
series analysis, and deep learning can be leveraged in pattern
identification (deep learning is a method in AI that teaches
computers to process data in a way that is inspired by the
human brain). Identified trading patterns are reviewed via
model evaluation, back testing, and validation including manual
analysis wherever necessary. Since market dynamics are ever
changing, the nature of market manipulation patterns needs to
be evaluated on an ongoing basis, so that AI-based surveillance surveillance, practitioners should have a strong grip on market
models keep performing effectively. abuse/manipulation processes, models, and regulations so
that business requirements and technical specifications are
ML models score alerts, not only based on the data points
aligned. It should be noted that AI is still a developing area, hence
directly related to the alert (e.g., parameter or threshold breaches
knowledge of technologies and concepts needs to be updated on
of volumes or prices), but also on how similar alerts have been
an ongoing basis.
classified by the firm’s risk and compliance division previously.
AI-based alert scoring is particularly useful when alerts are There are multiple benefits in leveraging AI in the surveillance
generated through a traditional rule-based approach. This is world; however, having skilled resources to implement AI is
because the scoring functionality can be considered as a second essential. A group of professionals well-versed in AI technologies
layer, which is implemented on top of the regular alert-generating and concepts are more likely to effectively bring AI into practice.
process and, as such, can also optimize the outcome of legacy In this regard, surveillance professionals still have some way to
trade surveillance systems. go in being AI proficient and are currently dependent on
technical specialists for AI implementation. To bridge this gap,
Being an area with vast potential and a steep learning curve,
training on AI concepts and use cases can help traditional
experienced practitioners of AI in surveillance are in short supply.
surveillance professionals become more familiar with
Developing an understanding of AI concepts and techniques
onboarding AI solutions.
requires sound knowledge of data analysis, feature extraction,
and anomaly detection. Additionally, having working knowledge
of econometrics (regression, time series analysis, etc.) and ML
helps to develop clearer concepts of AI model development,
testing, validation, and governance. For implementing AI in
10
Emerging trends in digital assets manipulation and surveillance | April 2024
Augmenting trade surveillance programs with artificial intelligence and machine learning | A brief overview | May 2024
Conclusion and key takeaways
Financial institutions can focus on developing and improving As stated earlier, building and implementing an AI framework
surveillance models as an integral part of their journey to with governance and regulatory safeguards across key
establish an extensive surveillance program. A well-defined dimensions such as data privacy, accountability, and reliability
and understood risk framework serves as the scaffolding to is a crucial step in managing the risks and capitalizing on
construct and operationalize a trustworthy AI program. Once the the returns associated with artificial intelligence. Please
risk models to be covered under the surveillance program are refer to Deloitte’s Trustworthy AI™ framework to learn more
decided, the risk and compliance team can evaluate which alert about our end-to-end framework to help synergize ethical AI
models can benefit from implementation of AI. AI can be used to implementation and integration with organizations.
either create a new surveillance model, adjust an existing one,
Organizations should view the implementation of AI in
or improve rule-based and static surveillance models. It may
surveillance as an evolving and an ongoing process; while AI may
not always be beneficial to develop AI into simple rule-based
require resources like technology, infrastructure, and skilled
surveillances like potential wash trading and locates. Also, AI
human capital, learning, testing, enhancing, and iterating needs
should not be incorporated into surveillance programs of small
to happen on an ongoing basis to be successful. This can be
firms with manageable trading volumes as it would not have
achieved with the help of a dedicated AI center of excellence
a drastic impact on efficiency or effectiveness compared to
(CoE) within the organization. Institutions need to evaluate and
existing off-the-shelf products. However, large firms dealing with
prioritize accordingly to help them achieve their desired goals
significant trade, order, and communications data; using complex
while incorporating AI in surveillance. While the future prospect
trading mechanisms; and having sophisticated clients will likely
of AI in surveillance is exciting, it is imperative to understand
benefit more from implementing AI into their existing
that this is a long journey, and the optimal way to progress is
surveillance programs.
through an effective collaboration between firms and regulatory
authorities in taking this forward.
12
Contacts
Roy Ben-Hur Nitin B S
Managing Director Senior Consultant
Risk & Financial Advisory Risk & Financial Advisory
Deloitte & Touche LLP Deloitte & Touche Assurance & Enterprise
rbenhur@deloitte.com Risk Services India Private Limited
bnitin@deloitte.com
Adam Clarke
Director Kewal Harshad Jagani
Risk Advisory Senior Consultant
Deloitte UK Risk & Financial Advisory
adamclarke@deloitte.co.uk Deloitte & Touche Assurance & Enterprise
Risk Services India Private Limited
Niv Bodor
kjagani@deloitte.com
Senior Manager
Risk & Financial Advisory Subramanian Krishnan
Deloitte & Touche LLP Senior Consultant
nbodor@deloitte.com Risk & Financial Advisory
Deloitte & Touche Assurance & Enterprise
Anand Ananthapadmanabhan
Risk Services India Private Limited
Senior Manager
subrak@deloitte.com
Risk & Financial Advisory
Deloitte & Touche Assurance & Enterprise Anuj Khasgiwala
Risk Services India Private Limited Senior Consultant
aananthapadmanabh@deloitte.com Risk & Financial Advisory
Deloitte & Touche Assurance & Enterprise
David Isherwood
Risk Services India Private Limited
Senior Manager
akhasgiwala@deloitte.com
Risk Advisory
Deloitte UK S Goutham
davidisherwood@deloitte.co.uk Consultant
Risk & Financial Advisory
Romit Deb Mookerjea
Deloitte & Touche Assurance & Enterprise
Manager
Risk Services India Private Limited
Risk & Financial Advisory
sgoutham2@deloitte.com
Deloitte & Touche Assurance & Enterprise
Risk Services India Private Limited
rmookerjea@deloitte.com
13
Endnotes
1. Financial Industry Regulatory Authority (FINRA), “Deep learning: The future of the Market Manipulation Surveillance Program,”
FINRA Unscripted podcast (ep. 98), January 25, 2022.
2. Deloitte, “How artificial intelligence is transforming the financial services industry,” accessed April 2024.
3. FINRA, “Deep learning: The future of the Market Manipulation Surveillance Program”; FINRA, “AI applications in the securities
industry,” from Artificial intelligence (AI) in the securities industry, June 2020.
4. FINRA, “Section II: Potential applications of quantum computing in the securities industry,” from Quantum computing and the
implications for the security industry, October 2023.
5. Financial Conduct Authority (FCA), Market Watch 76, January 2024.
6. Deloitte, “Deloitte launches new Generative AI-powered solution on RelativityOne and Relativity Server to help organizations
accelerate document review, employee conduct investigations, PII identification and compliance activities,” press release, January
22, 2024.
7. FINRA, “Key challenges and regulatory considerations,” from Artificial intelligence (AI) in the securities industry, June 2020.
8. Deloitte, “Trustworthy AI™,” accessed April 2024.
14
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348,deloitte,us-ai-and-ceo-of-the-future.pdf,"The role of the CEO in
tomorrow’s Generative AI world
Leading a Generative AI-fueled enterprise:
A CEO series
Deloitte Global CEO Program
Deloitte AI InstituteTM
TThhee rroollee ooff tthhee CCEEOO iinn ttoommoorrrrooww’’ss GGeenneerraattiivvee AAII wwoorrlldd
About the Deloitte Global CEO Program
The Deloitte Global CEO Program is dedicated to advising
chief executive officers throughout their careers—from
navigating critical points of inflection, to designing a strategic
agenda, to leading through personal and organizational
change. The program offers innovative insight and immersive
experiences to help:
This paper is a work of fiction and the
• Facilitate the personal success of individual executives, new
product of the authors’ imaginations.
or tenured, throughout their life cycle.
• Elevate the relationships between them, their leadership
It presents a dramatic scenario designed to provoke thoughtful
teams, and their boards.
conversations and spur difficult questions about the kind of
AI-enabled future today’s business leaders might envision for • Support the strategic agenda for their organizations in
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22
The role of the CEO in tomorrow’s Generative AI world
The role of the CEO in
tomorrow’s Generative AI world
As recurring waves of technology disruption occur, CEOs have been called to
articulate increasingly innovative visions for their organizations. With
Generative AI, the disruption has finally reached the role of the CEO itself.
In tomorrow’s AI world, many aspects of the CEO role and the CEO experience
will change. This change will challenge CEOs well beyond the rational
dimension and into the moral, emotional, and even existential levels.
To explore these changes, we aim to help CEOs immerse themselves in
tomorrow’s AI world through fictional narratives.
Below, we present a day-in-the-life of an imaginary CEO today, along with
the same day re-imagined in 2030, where the same characters live in a
world transformed by AI.
Our intent is not to provide precise predictions or answers. Rather, our
goal is to envision a plausible future and initiate a debate on its implications
for the choice of being (and the requirements to succeed as) a CEO.
3
5:45 am 5:37 am
Beethoven’s 5th blares in the dark and Sanjay darts out The second movement of Tchaikovsky’s 4th plays in the
his hand to shut off the music. dark, nudging Sanjay out of his slumber at the most
optimal time in his REM cycle. Synched with his
“Wow,” his wife Dana mutters, groggy. “What was that?” biometrics and tailored to the day’s calendar, the music
starts to enter its first crescendo as he sweeps his legs
“Leo told me he listens to classical music every day since
off the bed. He shuts it off so his wife Dana can sleep a
becoming CEO of Q.Helix. Giving it a try,” Sanjay says,
bit longer.
rubbing his eyes.
Sanjay’s mind is already jogging: thoughts of the
“Nothing like a morning heart attack to get you going,” she
afternoon’s board meeting (they’ll want to investigate the
mocks, and goes back to bed. Sanjay’s mind is already
volatility in company stock) and the thousand other
jogging: thoughts of the afternoon’s board meeting (they’ll
things on his to do-list. The phone display reads 5:37 AM.
want to investigate the volatility in company stock) and
the thousand other things on his to do-list. The phone He settles into his seat a minute early for the 6 AM
display reads 5:45 AM. Peloton session with an old college friend. They text each
other during the water breaks to catch up, and the friend
Sanjay settles into his seat a minute early for the 6 AM
won’t stop raving about the latest VR game he plays with
Peloton session with an old college friend. They text each
his family. Sanjay promises to give the game a chance
other during the water breaks to catch up, and the friend
that night. “You can afford to play something that’s not a
won’t stop raving about the latest season of The Bear.
strategy game,” his MBA section mate jokes.
Sanjay promises to give the show a chance that night.
“You can afford to try something that’s not a business
podcast for once,” his MBA section mate jokes.
College buddy
suggests actively
playing a virtual
reality game
College buddy CEO wakes up
suggests passively at 5:37 as
watching the series, Tchaikovsky
The Bear alarm syncs to
CEO’s sleep
CEO wakes up
biometrics
at 5:45 to
and calendar
Beethoven’s 5th
2024 2030
A day in the life of a CEO: 2024 Same day reimagined: 2030
4
The role of the CEO in tomorrow’s Generative AI world
7:00 am 7:00 am
After a shower and a glance at his packed calendar, After a shower, Sanjay’s health AI recommends a protein
Sanjay eats eggs and toast alongside Dana and his two shake and fruit for his first meal. He prepares those and
teenagers, Viraj and Avani. Sanjay’s distracted from sits alongside Dana and his two teenagers, Viraj and
thoughts of the day’s meetings by Viraj’s fidgeting. He’ll Avani. Viraj signals that he’s busy preparing for an exam
need to confirm his son’s doctor appointment regarding using his smart glasses, and an intermittent buzz on his
his son’s apparent inability to concentrate on tasks. son’s wrist keeps him concentrated. He takes a quick
glance at the day’s calendar, organized by his digital Chief
Dana picks up her lecture materials for the day’s classes of Staff (DCoS), an AI agent that he’s nicknamed Erika.
and heads out the driveway with the kids, while Sanjay
greets the driver of his company car. By the time he Dana picks up her lecture materials for the day’s classes
arrives in the office, he’s read the headlines from his and heads out the driveway with the kids. Sanjay boards
favorite newspapers, digested the daily e-mail from his an autonomous car, and by the time he arrives in the
Chief of Staff (CoS) Erik, and sent the family an article on office, he’s prepared for the day. He listens to a
the benefits of waking up to classical music. The latter customized daily download of article summaries from his
elicits barf emojis from his children and silence from favorite newspapers, and then reviews a series of
Dana, no surprise. decisions Erika has prepared. Speaking into his earpiece,
he verbally approves a change to his schedule to fly to
Milwaukee for an in-person client meeting, but leaves a
voice note for Erika to ensure he doesn’t miss his tennis
tournament. As Erika continues presenting other items
that require approval, along with suggestions, Sanjay
sends the family an article on the benefits of waking up
to classical music. The latter elicits barf emojis from his
children and no response from Dana, no surprise. Erika
offers a potential “non-Dad” response to them. Sanjay
decides to pass.
CEO gets picked up
by the driver and
reviews daily email
from Chief of Staff
Health AI
recommends a
protein shake
and fruit CEO gets picked up by
Breakfast of eggs
autonomous car;
and toast selected
virtual assistant briefs
randomly
CEO on a variety of
topics and resolves
scheduling conflicts
5
2024 2030
5
The role of the CEO in tomorrow’s Generative AI world
8:00 am 8:00 am
Erik greets him at their favorite coffee shop, 2 blocks from At reception, a coffee is ready for him as he walks in.
the office, and they walk and talk, reviewing the calendar. Sitting at his smart desk, he asks the DCoS to display the
Erik breaks the news of a virtual meeting being more sensitive parts of his daily download, including
rescheduled so Sanjay can fly to Milwaukee and attend stock price movement, and sales and operational data.
in-person. Sanjay pinpoints which appointments to move Erika provides a theory about the recent stock volatility:
around so he won’t have to miss his tennis tournament. By scanning social media chatter, it has identified
growing discontent among junior staff, as a result of
At the office, they begin reviewing the weekly report changes in HR benefits, that could’ve caused the concern
about the company’s stock price movement, sales data, among investors. Sanjay directs Erika to generate and
and operational updates, but as usual, they’re not finished send a list of questions to Hector, the CHRO, inquiring as
by the half-hour mark. to the latest in employee morale.
Time is short,
unfinished work
Clear
communication
made possible
with AI assistance
Erika, the virtual saving time
assistant, identifies
employee morale
Erik, the CEO’s issue while CEO is
Chief of Staff seated at the
meets to resolve smart desk
scheduling issues
over coffee
6
2024 2030
6
The role of the CEO in tomorrow’s Generative AI world
8:30 am 8:30 am
Sanjay and Erik stroll into their kitchen cabinet meeting Sanjay strolls into his kitchen cabinet meeting around
around 8:30. Hector, the CHRO, appears on edge and 8:30. The COO, also an AI agent, agrees with Erika’s
confesses concerns about a recent benefits explanation and recommends retracting the recent
communication that has irked some employees. “I’m benefits communication. “Understood. I’m going to send
going to send a follow-up clarification,” he declares. The a follow-up clarification,” Hector declares. The group then
COO, Marcus, pipes up: “Someone always takes things the views AI simulations of their board members to
wrong way. A clarifying message may help shut down the anticipate questions and align on responses.
rumor mill.” After other bits of advice from around the
table, the group shifts to rehearsing their talking points
for the board meeting.
Virtual AI agent
recommends
a follow-up
to employees
CHRO warns
of disgruntled
employees
AI board member
simulations help
executive team
anticipate
questions and
align on
responses for
Other
board meeting
executives
chime in for a
resolution
7
2024 2030
The role of the CEO in tomorrow’s Generative AI world
9:01 am 9:01 am
At 9:01 AM, Sanjay makes it back to his office for a At 9:01 AM, Erika reminds Sanjay of a meeting with the
meeting with the head of sales, Ed. He listens to the head of sales, Ed. Sanjay deputizes it to attend the
progress of their retail stores across the southern states meeting on his behalf. He then continues rehearsing with
and fires off a few decisions on investment and location his cabinet for the board meeting, and notices that Erika
strategy. He can’t help but wonder if his time was elevates one question from Ed to his attention: Optimize
needed for this. for short-term performance (quarterly results) or
long-term enterprise value? Given the pressure on the
Soon after he’s done, Erik walks in to finish their calendar stock price, Sanjay hesitantly picks the former. Even with
review, but Sanjay raises a hand to halt him. He takes a AI assistance, he still has to make the no-win decisions,
moment, like the pause before hitting a serve, to admire he realizes.
the sun glinting on the skyscrapers. Erik waits for about
one minute and then clears his throat. “Media interview When he’s done with his cabinet meeting, Sanjay feels
in five.” better prepared and optimistic that the HR action may
improve market sentiment. He walks back to his office
and takes a long moment, like the pause before hitting a
serve, to just admire the sun glinting on the skyscrapers.
Erika reminds him in his earpiece: “Media interview in 5.”
Sanjay asks: “Context?”
“Reporter has questions about interim decarbonization
metrics in light of company commitment to net-zero
emissions by 2050.”
“See if they’ll talk to Sarah, our CSO, instead.”
“Noted.”
CEO meets with
Time freed up
head of sales on
with help of
location strategy
virtual assistant
of retail stores
that delegates
feeling others
the interview with
could have made Virtual assistant
the CSO
those decisions only brings key
CEO is feeling decisions to CEO,
rushed to media who still must
interview make them
8
2024 2030
The role of the CEO in tomorrow’s Generative AI world
9:30 am 9:30 am
The reporter peppers Sanjay with questions about Sanjay uses the freed-up time to listen and dictate
interim decarbonization metrics, given their commitment responses to urgent e-mails that Erika has surfaced as
to net-zero emissions by 2050. Erik listens and adds worth his attention. He notices his digital PR agent has
details to Sanjay’s answers as needed. Sanjay wonders if posted on his behalf about his upcoming industry panel
Erik could simply handle these calls on his own someday. on the future of fashion. It’s a picture of Sanjay from the
90s, wearing a denim jacket at college in London, and a
After the interview, they depart for an industry panel satiric message about the future being more of the past.
covering the future of fashion, where Sanjay is scheduled Not bad.
to speak and answer a few questions from the audience.
He’d planned to review Erik’s notes to prepare answers, Then, he boards a self-driving car to attend the panel.
but his attention is drawn to a few urgent emails. He also Armed with Erika’s detailed notes, Sanjay strikes gold. His
notices that Yanna, the PR consultant, has posted on his reflections on the impact of recommendation engines
social media about his upcoming appearance at the and digital filtering on clothing draw applause from the
industry panel. crowd, and several people approach him after his
remarks to ask questions and network. Erika listens and
At the panel, Sanjay doesn’t exactly strike gold, analyzes all his interactions so that it can send
meandering through answers on the impact of spatial follow-ups.
computing on brick-and-mortar stores. People approach
him after the panel to network, but Sanjay cuts the As Sanjay steps outside the convention center, the crisp
conversations short and walks outside to clear his head, air and the smell of the river reinvigorate him. He sees
taking in the crisp air and the smell of the river. The the famed Lyric Opera building just down the water— a
famed Lyric Opera building is just down the water—a good opportunity to enjoy the city as a family. He’d even
good opportunity to enjoy the city as a family. He’d even read this morning that classical music could help with
read this morning that classical music could help with focus, if he could convince Viraj to give it a try. He
focus, if he could convince Viraj to give it a try. wonders whether Erika would have advice for how to
approach the topic with Viraj.
CEO meanders
through questions
for an industry
panel with
lackluster
performance
CEO peppered
Virtual
with questions
assistant helps
from reporter
make time for
urgent tasks
9
2024 2030
With media interview
delegated, CEO is
briefed for panel by
virtual assistant
Successful panel
discussion followed by
energetic networking
The role of the CEO in tomorrow’s Generative AI world
12:00 pm 12:00 pm
At noon, he walks into Gibson’s for lunch with Elaine, the At noon, he walks into Gibson’s for lunch with Elaine, the
CEO of one of his company’s top distributors. Elaine, who CEO of one of his company‘s top distributors. Elaine, who
usually asks about his family, heads straight into the usually asks about his family, heads straight into the
subject of recent geo-political tensions and the potential subject of geo-political tensions and the potential impact
impact on manufacturing schedules. “There’ll be hardly on manufacturing schedules. “There’ll be hardly any
any impact on our deliveries,” Sanjay assures her, based impact on our deliveries,” Sanjay replies, and pulls out
on his broad knowledge of the supply chain and his views his phone. “Let’s run the scenarios.”
of the geo-political outlook. After addressing her
concerns over appetizers and resolving a pending “Has that been listening to everything I’m saying?”
agreement over entrée salads, they can finally chit-chat Elaine inquires.
over dessert. “Viraj beating you on the court yet?” Elaine
“Well, that’s the best way to get the answers you need,”
teases him.
Sanjay replies.
“I can neither confirm nor deny,” Sanjay shoots back.
Even as Erika provides reassuring numbers, Elaine crosses
“How is Dave’s hip?” he inquires about Elaine’s husband
her arms in silence. Sanjay puts his phone away as he tries
as he reaches for the bill.
to address Elaine’s concerns over appetizers and resolve a
“Well, it’s titanium now. He keeps bumping into the pending agreement over entrée salads. They finally
furniture to test it out!” As they share a laugh, Sanjay chit-chat over dessert. “How is Dave’s hip?” He asks about
knows the meeting was a success. Elaine’s husband as he reaches for the bill.
“It’s fine,” Elaine responds, not eager to start their usual
banter. With the conversation more stilted than ever
before, Sanjay leaves the meeting unsure if it’s a success
and decides to ask Erika. She confirms that, based on her
tone of voice, word selection, and unwillingness to engage
in personal chitchat, Elaine felt threatened and untrusting.
Sanjay wonders about how he can repair the damage, and
his instinct tells him not to ask Erika for advice.
Client happily
engages in
personal chitchat
Client is upset
she’s been
CEO addresses recorded without
client concerns permission so,
over lunch CEO explains and
tries to reassure
10
2024 2030 Virtual assistant
confirms client felt
threatened and
untrusting
10
The role of the CEO in tomorrow’s Generative AI world
1:20 pm 1:20 pm
As Sanjay reaches his office, he requests Erik to attend a As Sanjay reaches his office, he heads into a meeting with
strategy meeting with Sarah, the CSO, on his behalf. Erik Sarah, his CSO. They debate at length the amount of AI
hesitates. “Sarah needs an important decision on how chips they’ll need to invest in over the next five years,
many AI chips to buy.” and Sanjay ultimately throws the issue to Erika.
Sanjay loses his cool for a moment and insists, more curt Back in his office, Sanjay decides to use a digital
than usual. “Sarah will understand.” Erik acquiesces but surrogate for the 2 PM virtual townhall. He takes the
doesn’t look convinced. extra time to continue preparing for the board meeting,
and then joins the last 15 minutes of the call for live Q&A.
At 2 PM, Sanjay logs on to a webinar for a virtual town hall A few of the employees appear delighted at how effective
with employees. He delivers the updates that Erik the surrogate was, but others appear miffed, and one
prepped in a document last week and adds in personal even sends him a direct message: “You can’t delegate
stories about his recent family trip to Disney, and an culture.” The poll administered at the end proves the
anecdote from the panel he’d just attended. The poll employee right: only 59% of employees are pleased with
administered at the end empowers him to breathe a sigh the company’s direction and leadership.
of relief: 89% of employees are pleased with the
company’s direction and leadership. Sanjay strolls out of his office, hoping to make small talk
with others about what just happened, but there are
Sanjay strolls out of his corner office feeling energized and fewer employees in the office these days. He hears the
makes small talk in the halls. As he peruses the snacks in pings of their AI assistants reminding them of upcoming
the break room, an intern, who doesn’t recognize him, meetings. He strides into the board meeting a few
asserts that the fruit bars are great for staying awake in minutes early and takes his seat.
meetings. As Sanjay grabs one, another employee reveals
that he’s the CEO. Sanjay smiles warmly as the intern’s
face turns beet red.
Erika, the virtual
Personal rapport with assistant, helps to
employees, positive calculate needs for
feedback after virtual AI chips
town hall
Less than stellar
response after
surrogate takes
CEO’s place at
town hall
CEO delegates
chip decision
11
2024 2030
11
The role of the CEO in tomorrow’s Generative AI world
3:00 pm 3:00 pm
The three-hour board meeting commences with a The board meeting commences with an AI-curated
review of the minutes. Sanjay provides his executive summary of the previous meeting. The chairman
report, followed by Linh, the CFO, and Hector. Then, the explains that one of the board members has sent a
independent committees share their updates—Audit digital surrogate because she is unable to attend herself.
and Governance and the others. Finally, they begin the When it’s time for Sanjay to deliver his remarks, Erika
strategic discussions. Sanjay sits taller in his chair and generates visuals in a hologram. The CFO and others
opens up a fruit bar. Time is consumed by the opening deliver their reports, followed by updates from the
and introduction of various documents and visual aids. independent committees. Once they begin the scenario
Still, they manage to have the much-awaited discussion planning discussion, Erika also generates live meeting
on updates to the five-year strategic plan and notes and strategic scenarios. The board turns out to be
compliance issues. Sanjay relies on Erik’s notes to speak largely aligned, and Sanjay thinks the meeting might even
about the progress on AI adoption. The discussion end early. Yet, one board member complains about the
seems a success, though the stock volatility is still a surrogate in the room, and the conversation devolves.
question. Multiple members express uneasiness and the chair
proposes a thorough in-person conversation about rules
of engagement at the next meeting. Everybody, including
the Gen AI surrogate present, agree.
Visual aids
presented as
holograms
Time is consumed
by cumbersome
visual aids
Board members
discuss
uneasiness
CEO relies on about surrogate
notes of his in meeting
Chief of Staff
12
2024 2030
The role of the CEO in tomorrow’s Generative AI world
6:00 pm 6:00 pm
When Sanjay returns to his office at 6 PM, his phone rings When Sanjay returns to his office at 6 PM, his phone rings
with a call from a key investor. His tone is frantic as he with a call from a key investor. His tone is frantic.
interrogates Sanjay about the morning panel. The market Someone had apparently leaked news that their
interpreted some of Sanjay’s comments as negative, the company would be removing sustainability metrics from
investor said, and he wanted assurance that the stock its store growth in the southern states. The market might
price hit would be momentary. “Why didn’t Erik tell me like it, the investor said, but he didn’t like the implications
about the price movement?” Sanjay mutters to himself. for the company’s environmental impact. Sanjay
stammers, struggling to answer, and promises to look
Sanjay rehashes the same reassurances he’d delivered to into it. “Why didn’t you mention that this morning?”
Elaine that morning, but as soon as that call ends, the Sanjay mutters into his earpiece.
chair of the board rings, wanting to debrief about the
strategic plan updates. When Sanjay’s ready to leave the
office, Erik swings by to apologize for not mentioning the
price dip; it just slipped through all the other points of
discussion. “Never enough time,” Sanjay nods. He wants
to allay Erik’s contrition, but he’s too irritated.
13
2024
Nowhere to
channel
frustration at
misjudgment by
virtual CoS
CEO irritated
by Chief of
Staff’s error
Virtual assistant
fails to forewarn
of leaked news
CEO caught off
guard by upset
investor
2030
13
The role of the CEO in tomorrow’s Generative AI world
7:00 pm 7:00 pm
Before heading home, Sanjay has dinner with one of his Before heading home, Sanjay has dinner with one of his
mentees, Jill, who insisted on sushi. She’s climbing up the mentees, Jill, who insisted on sushi. She’s climbing up the
ranks of her private equity firm and wants his advice on a ranks of her private equity firm and wants his advice on a
decision Sanjay has faced before: stay and rise within or decision Sanjay has faced before: stay and rise within or
leave for other opportunities. After hearing her thoughts, leave for other opportunities. After hearing her thoughts,
he asks: “Have you thought about starting your own he asks “Have you thought about starting your own
business?” Jill’s face lights up. He orders plates of uni, and business?” Jill’s face lights up. He orders plates of uni, and
they get down to brainstorming—it’s the most fun he has they get down to brainstorming—it’s the most fun he has
all day. all day.
In the car on the way home, Sanjay stares at his unread In the car, traveling north to his house, Sanjay has Erika
emails, including a lengthy update from Hector that process through the e-mails in his inbox again, and the
makes him decide to procrastinate. Hector’s emails tend most urgent message is from Hector, the CHRO.
to elicit that response from him. Instead, he plays the full Apparently, his new communication had mixed results,
Beethoven symphony that had shocked him out of bed and he wonders what to do next. Sanjay dictates a
in the morning. The jarring highs and soothing lulls over nuanced reply and promises to meet with Hector
the thirty-six-minute composition, the sense of tension tomorrow. After the other minor e-mails are summarized
rising and never dissipating, make it perfectly relatable. and filed away, Sanjay asks Erika to pick a symphony
based on his day. The jarring highs and soothing lulls of
Beethoven’s 5th, the sense of tension rising and never
dissipating, make it perfectly relatable.
14
2024 2030
The virtual
assistant knows
exactly what to
play
CEO picks music
to play
Just the same,
the CEO and
his mentee
brainstorm for
The CEO and his answers
mentee
brainstorm for
answers
14
The role of the CEO in tomorrow’s Generative AI world
9:30 pm 9:30 pm
The sound of opening his front door instantly puts Sanjay The sound of opening his front door instantly puts Sanjay
at ease. It’s Avani’s turn for chores and he gives her at ease. It’s Avani’s turn for chores and he gives her
company, chopping veggies for the next day’s lunch as company, chopping veggies for the next day’s lunch as
she recounts the latest unforgivable lapses of her soccer she recounts the latest unforgivable lapses of her soccer
coach. Sanjay’s phone continues to prompt him with coach.
reminders about incoming e-mails and texts, distracting
him from what his daughter is saying. He campaigns for the family to put on their smart glasses
together and play the VR game his friend had
He campaigns for the family to watch an episode of The recommended. They have a blast fighting their way
Bear together, but within the first fifteen minutes, he through an imaginative world of zombies, each of them
admits, “This show is frantic.” Instead of being relaxed, he discovering a special skill within the team. Erika remains
can’t help but take out his phone to answer some of the silent throughout the evening: though it’s assessing
incoming pings from Erik and others. The kids pull out incoming messages and scanning newsfeeds for
their phones in quick succession. significant events; none justify interrupting Sanjay’s
family time. After spending a little too long sucked into
In bed, he’s still dealing with a batch of e-mails, staring the game, the family parts ways to their bedrooms.
into the light of the device as Dana sleeps beside him. At
midnight, he’s halfway through the inbox and decides to Under the covers, Sanjay’s tempted to turn on his device
declare victory. There will be time tomorrow, he reminds and see if Hector has replied, but he decides against it.
himself. There will be time tomorrow, he assures himself, as he
falls asleep to more gentle music.
15
2024
After time with
the family, CEO
chooses sleep
over phone
Frantic TV show Virtual assistant
stimulates filters incoming
restlessness and messages for
phone use urgency, allowing
for quality time
with family
2030
CEO uses phone
in bed to answer
emails
15
TThhee rroollee ooff tthhee CCEEOO iinn ttoommoorrrrooww’’ss GGeenneerraattiivvee AAII wwoorrlldd
The role of the CEO in
tomorrow’s Generative AI world
The series of vignettes above present plausible futures
for CEOs, touching on select tensions to be navigated.
Our hope is that they will help you reflect on the issues to
explore on your way to your preferred tomorrow. Here are
a few questions to further the conversation.
• G iven the 2030 day, would you still want to be CEO in this future world? Why?
What are the different layers of introspection (rational, moral, emotional, and
even existential) involved in answering this question?
• W hat is added, what is gained, and what is lost for you as a leader and human?
• H ow might CEOs communicate differently in the Gen AI-rich environment
of 2030?
• H ow will you use the time AI will save? (e.g., attending a live town hall
but delegating decision-making)
• W hat are the possible implications of Gen AI for Talent strategy?
What does an enterprise workforce look like in 2030?
• H ow will Gen AI affect enterprise performance?
• W hat’s the price we’ll pay for fast and convenient access to information?
• H ow will we address accountability for decisions delegated to AI?
Will it actually be possible to go against AI recommendations?
• W hat activities and elements are best left to humans?
• W hat are your observations about the interactions between humans
and AI agents in the story?
1166
The role of the CEO in tomorrow’s Generative AI world
17
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action that may affect your business. Before making any decision or taking any action
that may affect your business, you should consult a qualified professional advisor.
Deloitte shall not be responsible for any loss sustained by any person who relies on
this publication.
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Deloitte refers to one or more of Deloitte Touche Tohmatsu Limited, a UK private
company limited by guarantee, and its network of member firms, each of which is a
legally separate and independent entity. Please see www.deloitte.com/about for a
detailed description of the legal structure of Deloitte Touche Tohmatsu Limited and
its member firms. Please see www.deloitte.com/us/about for a detailed description
of the legal structure of Deloitte LLP and its subsidiaries. Certain services may not be
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This publication contains general information only and Deloitte is not, by means of
this publication, rendering accounting, business, financial, investment, legal, tax, or
other professional advice or services. This publication is not a substitute for such
professional advice or services, nor should it be used as a basis for any decision or
action that may affect your business. Before maki"
349,deloitte,us-ai-institute-ai-bill-of-rights-new.pdf,"Deloitte’s Trustworthy AI™ Framework
and the White House Blueprint
for an AI Bill of Rights
November 2022
Brochure / report title goes here | Section title goes here Deloitte’s Trustworthy AI™ Framework and the White House Blueprint for an AI Bill of Rights
The blueprint for an AI Bill of Rights
supports an evolving regulatory landscape
The Artificial Intelligence (AI) regulatory Figure 1 | Definition of Rights, Opportunities, or Access
according to the White House's Blueprint for an AI Bill of Rights.4
landscape continues to mature as government
agencies refine and build upon previous
guidance designed to manage AI risk, ensure
equality and transparency, and provide trust in Civil rights, civil liberties, and privacy,
automated systems. As American institutions including freedom of speech, voting, and
continue to innovate and embrace AI to harness protections from discrimination, excessive
its benefits; federal, state, and local agencies punishment, unlawful surveillance, and violations
of privacy and other freedoms
are increasing their regulatory efforts to protect
in both public and private sector contexts;
the American public.1 The latest iteration
of federal regulatory guidelines is The
Blueprint for an AI Bill of Rights (AIBoR).2
Equal opportunities, including equitable access
to education, housing, credit, employment, and
In October 2022, the White House Office of other programs; or,
Science and Technology Policy (OSTP) released
the AIBoR to provide additional guidance for
organizations to create trustworthy and ethical Access to critical resources or services,
such as health care, financial services, safety,
automated systems. The AIBoR provides
social services, non-deceptive information about
guidance to American innovators to harness
goods and services, and government benefits.
the extraordinary potential and benefits of
automated systems and AI while protecting
“the American public’s rights, opportunities,
or access to critical resources or services.”3
The AIBoR applies to all automated systems
that have the potential to meaningfully impact
individuals’ or communities’ exercise of rights,
opportunities, or access (FIGURE 1).
2 2
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At Deloitte, we recognized the critical importance of the need
for protections and for AI-Fueled organizations to earn trust
in the AI-enabled assets and services provided to the public.
Deloitte's Trustworthy AITM Framework and can be particularly susceptible to a wide range
AI Governance & Risk services help provide of AI-related risks through all phases of the AI
strategic and tactical solutions to enable life cycle. For example, AI-based systems may
organizations to continue to build and use introduce or reinforce a risk of perpetuating
AI-powered systems while promoting inequity and historic bias, and enforceable
Trustworthy AI (FIGURE 2). regulations to protect the American public by
ensuring equitable, ethical and transparent
Deloitte recognizes that organizations and AI may be not only critical but inevitable. An
institutions are increasingly adopting AI indication of the future AI regulatory landscape
and automated systems for their potential can be seen in recent proposed and enacted
to revolutionize significant aspects of the state and local laws governing AI in specific
American public's daily lives from health care, use cases, such as AI-based performance
to banking, to shopping, to leisure downtime, evaluation and hiring decisions, loan
to many more. However, these innovations underwriting models, etc.5
Figure 2 | Deloitte's Trustworthy AI™ Framework
3
Deloitte’s Trustworthy AI™ Framework and the White House BluepDrienlot iftotre ’asn T rAuI sBtiwllo orft hRyig AhIt™s Framework and the White House Blueprint for an AI Bill of Rights
Figure 3 | AIBoR Principles and how they map to Deloitte’s Trustworthy AI Framework
AI Bill of Rights Description Deloitte Trustworthy AI
Principles Framework
Protect against inappropriate or irrelevant data Privacy
Safe and usage through testing, monitoring, and engaging Safe & Secure
effective systems stakeholders, communities, and domain experts. Robust & Reliable
Protect against discrimination by designing Fair & Impartial
Algorithmic
systems equitably and making system evaluations Transparent & Explainable
discrimination
understandable and readily available. Robust & Reliable
protections
Protect against privacy violations by limiting data Privacy
Data privacy collection and ensuring individuals maintain control
of their data and how it is used.
Provide clear and timely explanations for any Transparent & Explainable
Notice and
decisions or actions taken by an automated system. Privacy
explanation
Human Provide opportunities to opt out of automated Responsible & Accountable
alternatives, systems and access to persons who can quickly Privacy
consideration, remedy any problems encountered in the system. Robust & Reliable
and fallback
The White House AIBoR aligns well with The AIBoR is the latest governmental call to
Deloitte's Trustworthy AITM Framework and can action for organizations to proactively protect
help enable our clients to safely and effectively the American public as they embrace innovation
operationalize automated systems while through automation and AI. Though not directly
protecting individuals and communities and enforceable, the AIBoR can set the tone for
adhering to emerging regulations (FIGURE 3). the inevitable future legislation. We recognize
Deloitte's Trustworthy AI framework includes that navigating AI implementation challenges
a roadmap for implementing AI-powered can be complex, and Deloitte is committed to
systems and is aligned with AIBoR guidelines helping our clients navigate these challenges
through each phase of the AI development and agilely while establishing trust with stakeholders
maintenance lifecycle. and regulators. Deloitte, with our suite of AI
Governance & Risk assets and services, is
The world today is experiencing enormous dedicated to assisting our clients through the
human capacity for insight, decision- changing regulatory landscape in creating Trust,
making, efficiency, and innovation—all Equity and Transparency as they serve the
dramatically expanded by cognitive American public.
systems. We are also in a period in which trust
is paramount. The tools that unleash world-
changing capabilities should be trusted to act
in line with human expectations for ethics and
appropriateness. The full potential of AI just may
hinge on that confidence.
4
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LLLLeeeet ttt's '''sss s ssst ttta aaar rrrt ttt a aaa c ccco ooon nnnv vvve eeer rrrs sssa aaat ttti iiio ooon nnn
Let's start a conversation
Beena Ammanath Ed Bowen Oz Karan Joe Conti
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Global Deloitte AI Institute Managing Director Trustworthy AI™ Trustworthy AI™ Government
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bammanath@deloitte.com AI Center of Excellence Deloitte & Touche LLP and Public Sector
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edbowen@deloitte.com ozkaran@deloitte.com Deloitte & Touche LLP
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joconti@deloitte.com
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Rasmus Nielson John Fogarty
Senior Manager Senior Manager
Trustworthy AI™ Government Trustworthy AI™
and Public Sector Deloitte & Touche LLP
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Endnotes
1 The AI Bill of Rights follows the Executive Order 13960: Promoting the Use
of Trustworthy Artificial Intelligence in the Federal Government (December
2020), Executive Order 13859: Maintaining American Leadership in Artificial
Intelligence (February 2019), Office of Management and Budget (OMB)
Memorandum M-21-06: Guidance for Regulation of Artificial Intelligence
Applications (November 2020), White House Office of Science and Technology
Policy (OSTP): American AI Initiative: Year One Annual Report (February 2020),
National AI Initiative Act of 2020 (Introduced March 2020), and the National
Institute of Standards and Technology (NIST) is expected to release the NIST AI
Risk Management Framework in January 2023. International initiatives include
the Organisation for Economic Co-operation and Development (OECD): 2019
Recommendations on Artificial Intelligence, and the European Union Artificial
Intelligence Act proposal (April 2021).
2 Blueprint for an AI Bill of Rights | OSTP | The White House
3 Ibid, White House.
4 Ibid, White House.
5 In 2022 alone, legislative bills or resolutions relating to AI were proposed in
seventeen different U.S. states and enacted in four. See Legislation Related to
Artificial Intelligence (ncsl.org) for further details.
5
This publication contains general information only and Deloitte is not, by means of
this publication, rendering accounting, business, financial, investment, legal, tax, or
other professional advice or services. This publication is not a substitute for such
professional advice or services, nor should it be used as a basis for any decision
or action that may affect your business. Before making any decision or taking any
action that may affect your business, you should consult a qualified professional
advisor. Deloitte shall not be responsible for any loss sustained by any person who
relies on this publication.
Deloitte refers to one or more of Deloitte Touche Tohmatsu Limited, a UK private
company limited by guarantee (“DTTL”), its network of member firms, and their
related entities. DTTL and each of its member firms are legally separate and
independent entities. DTTL (also referred to as “Deloitte Global”) does not provide
services to clients. In the United States, Deloitte refers to one or more of the US
member firms of DTTL, their related entities that operate using the “Deloitte” name
in the United States, and their respective affiliates. Certain services may not be
available to attest clients under the rules and regulations of public accounting.
Please see www.deloitte.com/about to learn more about our global network of
member firms.
Copyright © 2022 Deloitte Development LLC. All rights reserved."
350,deloitte,us-navigating-the-impact-of-generative-ai-on-security.pdf,"Navigating the impact of
generative AI on security
A CISO’s guide
Navigating the impact of generative AI on security | A CISO’s guide
Evolving the role of CISO with
the advent of Gen AI
Cyber has been ranked as the most important risk globally1 for the second year in succession.
Reports have shown that average costs from such incidents reached an all-time high in 2022 and
will continue to increase at a multi-fold pace in the coming years. The role of the chief information
security officer (CISO) will likely assume an even greater strategic significance within the organization’s
cybersecurity program. The insurance industry, in particular, is being targeted by a myriad of
cyberattacks as it possesses a great deal of personally identifiable information (PII) and protected health
information. Research has found that customer and employee PII is the costliest to have compromised
at $183 per record.2 As insurance companies migrate toward digital channels to create tighter customer
relationships and offer new products, a new wave of investment is directed toward advanced analytics
and generative artificial intelligence (Gen AI). Although these investments provide new strategic
capabilities, they also introduce new cyber risks and attack vectors to organizations. The challenges
are likely to become more complex as insurers prepare to leverage large language models (LLMs) for
Gen AI, which will require not only collecting and handling large amounts of sensitive data, but also
safely exposing it across multiple applications, interfaces, and cloud platforms.
As business and product teams find new ways to use AI, organizations must also ensure its safe, secure, and
ethical use. With growing digital disruption to conduct business, and diminishing trust, the need for a skilled
CISO has grown manyfold. The CISO is responsible for managing security across a distributed network to
ensure that the data remains secure; maintaining compliance with regulatory requirements; and educating
employees and informing executives about cybersecurity risks (82% of the largest insurance carriers have
been the focus of ransomware attacks from cybercriminals3). They not only have to contend with AI’s
immediate impact, but also prepare for how it will shape their responsibilities in the future. Highlighted
below are some of the ways Gen AI could add to the responsibilities of a CISO:
• Data security and privacy: Assess how models handle sensitive data and ensure they comply with
data protection laws and regulations.
• Access control: Implement robust access controls to ensure that only authorized individuals have
access to systems.
• Model integrity and security: Protect AI models from tampering and reverse engineering. This
includes ensuring that the models themselves are securely stored.
• Logging and monitoring: Establish logging and monitoring systems to detect and respond to
security incidents.
• Training and awareness: Provide training and raise awareness among employees and stakeholders.
Staying informed is the first step. A CISO should make time to be curious and continuously learn about
new developments and how they can affect insurers’ security posture. The CISO role will likely evolve from
being the “de facto” accountable person for treating cyber risks to being responsible for ensuring business
leaders have the capabilities and knowledge required to make informed, high-quality risk decisions.
2
Navigating the impact of generative AI on security | A CISO’s guide
Potential benefits of Gen AI
Gen AI has the potential to add contextual awareness and decision- • Act as application development assistant: Gen AI can also
making to enterprise workflows and can radically change act as a secure application development assistant. Many code
how we do business. The far-reaching impacts and potential value generation tools are embedding security features, and application
when deploying Gen AI are accelerating experimental, consumer, security tools are already leveraging LLM applications that can help
and (soon) enterprise use cases. According to Gartner, 68% of in common security use cases such as vulnerability detection, false-
executives believe that the benefits of Gen AI outweigh the costs, positive reduction, and mitigation suggestions.
compared with just 5% who feel the risks outweigh the benefits.4
• Mitigate social engineering attacks: Insurance companies also
The two big advantages of Gen AI today are its capacity to process
run the risk of losing money due to whaling attacks (a type of social
huge amounts of data at high speed and its ability to communicate
engineering attack where cybercriminals send executives a spoof
clearly. Still, questions remain about how insurance carriers can use
email to dupe them into authorizing massive cash transfers). LLMs
Gen AI to bring effectiveness, efficiency, and understanding, while
not only generate text, but they are also helpful in detecting and
managing the associated cybersecurity risks.
watermarking AI-generated text, which could become a common
function of email protection software. Identifying AI-generated text
Gen AI security use cases
in social engineering attacks can help to detect phishing emails and
• CISO executive reporting: Gen AI can help streamline and polymorphic code.
automate the process of drafting reports, including requirements
• Alleviate security talent and skill shortage: The sheer amount
for incident response, threat intelligence, risk assessments, audits,
and complexity of data and threats have become increasingly
and regulatory compliance. It can provide real-time insights into
difficult to tackle. The integration of Gen AI into several security
an organization’s risk profile, including its threat landscape, risk
operations tools enables cybersecurity teams to scale while
levels against critical vulnerabilities, current cybersecurity posture,
remaining lean and focused. This new interface can reduce the skill
compliance requirements, and cybersecurity performance metrics,
requirements for using the tool, shorten the learning curve, and
which can all be of aid to insurance CISOs.5
allow more users to benefit.
• Act as security assistant: A Gen AI security assistant can assist
security analysts in sifting through piles of log entries to evaluate Apart from the previously mentioned use cases, our first paper in
possible security threats by providing an assessment in seconds. the series delves into further instances across the insurance value
With a single prompt, Gen AI can scour logs and other data and chain where Gen AI is utilized, exploring its implications. You can find
report back on what may be an immediate threat and what isn’t. It more details here: Implications of Gen AI for insurance.
can also explain and add valuable context to the threat identifiers.
3
Navigating the impact of generative AI on security | A CISO’s guide
Key risks of Gen AI on CISOs’
watchlist/threat landscape
Despite numerous benefits, Gen AI was one of the top concerns In addition, a CISO should be consulted and informed about the
among security executives over the first few months of 2023.6 following risks:
CISOs may feel pressure to allow use of Gen AI broadly, but doing so
• Legal and regulatory risk: Legal and compliance risks arise from
indiscriminately could create unreasonable risk.
the fact that the legal and regulatory landscape surrounding Gen
AI is still nascent. Consequently, enterprises may not be aware of
Risks associated with Gen AI for a CISO at the enterprise level
all the legal requirements they need to comply with when using
generally stem from:
this technology. When Gen AI is used as part of a regulated use
• Data and privacy confidentiality: Enterprise use of Gen AI may case in consumer-facing communications, whether for direct
result in access and processing of sensitive information, intellectual consumer interactions or to produce consumer-facing materials
property, source code, trade secrets, and other data, through (such as consumer information notices), regulatory or private law
direct user input or an application programming interface (API). may include requirements and create liability.
Sending confidential and private data outside of the organization’s
– Mitigating measure: Comply with relevant data protection
servers could trigger legal and compliance exposure, as well as
regulations, and refrain from sharing customers’ sensitive
risks of information exposure. Such exposure can result from
information and the organization’s own sensitive data. Consider
contractual (e.g., with customers) or regulatory obligations
a platform that operates inside the secure network of the
(e.g., CCPA, GDPR, HIPAA, CPP Model law) that are in place and
organization. Obtain explicit user consent when collecting and
relevant to the organization.
using personal data for Gen AI purposes.
– Mitigating measure: Adhere to relevant regulations, such as GDPR
• Bias and discrimination: Training on biased data may lead to
or CCPA, to help safeguard sensitive information and maintain
illegal discrimination, potential damage to reputation, and possible
customer trust, and use secure Gen AI platforms.
legal repercussions for the enterprise as Gen AI may not be aware
• Data poisoning/prompt injections: Corrupt/polluted (poisoned) of potentially defamatory, discriminatory, or illegal content.
data leads to malicious or unintended outcomes and can affect
– Mitigating measure: Ensure that the training data used for Gen AI
the accuracy and reliability of the LLM. By using carefully designed
models is diverse, representative, and free from biases. Regular
inputs, attackers can manipulate LLMs, compelling them to carry
monitoring and auditing of the models’ outputs can help identify
out the attacker’s desires. This manipulation can occur by directly
and address any potential biases, promoting fairness and
altering the system prompt or manipulating external inputs, which
inclusivity in AI-generated content.
may result in serious issues like data exfiltration.
• Copyright and ownership/risk to intellectual property
– Mitigating measure: Implement robust access controls to ensure
(IP) rights: Gen AI models are trained on diverse data, which
that only authorized individuals can access systems.
might include copyrighted and proprietary material, raising
• Enterprise, SaaS, and third-party security: Due to Gen AI’s ownership and licensing concerns between the enterprise and
wide adoption and proliferation of integrations, there are other data sources used for training.
concerns that data would be shared with third parties at a much
– Mitigating measure: CISOs should seek a tool that operates end
higher frequency than earlier anticipated, posing a threat to
to end on their company’s network and does not require users
non-public enterprise data and third-party software. For example,
to send sensitive data to external servers or third parties. CISOs
third-party applications leveraging a Gen AI API, if compromised,
should also consider collaborating closely with legal teams to
could potentially provide access to email and the web browser, and
establish robust IP protection measures.
allow an attacker to take actions on behalf of a user.
– Mitigating measure: Establish comprehensive data governance
policies and procedures. This includes defining data ownership,
data classification, and data life cycle management. Clearly
define who has access to AI-generated data, how it is stored, and
for how long. Additionally, organizations must implement data
quality controls and establish mechanisms for data lineage and
audit trails. By adopting a robust data governance framework,
enterprises can help mitigate risks associated with Gen AI.
4
Navigating the impact of generative AI on security | A CISO’s guide
Gen AI security: Focus areas for CISOs
A CISO should ask questions and provide guidance to help leaders 3. External regulations8
create an organizational AI strategy. A comprehensive AI strategy
• Cross-industry: Comply with applicable external regulations.
provides guidelines for its usage and factors in legal, ethical, and
For example, recently enacted EU AI Act 20239 is a
operational considerations. If used responsibly and with proper
comprehensive guide to AI law with clearly defined
governance, Gen AI can provide businesses with many benefits across
transparency requirements and risk levels. The EU AI Act
automated processes and optimized solutions. A comprehensive AI
classifies insurance as a high-risk industry, which leads to more
strategy can help ensure privacy, security, and compliance. It should
stringent regulations and greater transparency industrywide,
consider the following key questions:
and firms have to ensure higher system compliance levels to
• Who is using the technology in the organization, and for what purpose? prevent any penalties.
• How can I protect enterprise information (data) when employees are • Insurance-specific: Focus on insurance regulations being
interacting with Gen AI? Do we have governance and contingency in established to safeguard insurance-specific risks. For example,
place (i.e., usage and controls)? Colorado’s draft AI regulation 202310 guides life insurers’ use
of external consumer data and information sources. It outlines
• How can I manage the security risks of the underlying technology?
requirements that ensure usage of algorithms and predictive
How do I balance the security trade-offs with the value the
models (i.e., AI models) do not result in unfairly discriminatory
technology offers?
insurance practices with respect to race.
Deloitte’s approach to responsible AI7—Trustworthy AI™—delivers • The National Association of Insurance Commissioners (NAIC) has
trust by design throughout the AI life cycle. It’s relevant to executives outlined a draft bulletin11 that provides guidelines for insurers
at every level: to use while utilizing AI systems (AIS) and ensuring compliance.
It emphasizes the importance of AIS programs, AI governance,
• The CEO and board set the strategy with special attention to public
and documentation.
policy developments and to corporate purpose and values.
4. Risk management
• Chief risk and compliance officers oversee control, including
governance, compliance, and risk management. • Fair/not biased: Define and measure fairness and test systems
against standards.
• Chief information and information security officers take the
lead on responsible practices, such as cybersecurity, privacy, • Transparent and explainable: Enable transparent model
and performance. decision-making.
• Data scientists and business domain specialists apply responsible • Responsible and accountable: Use policies to clearly establish
core practices as they develop use cases, formulate problems and accountability for AI outputs.
prompts, and validate and monitor outputs.
• Robust and reliable: Enable high-performing and
reliable systems.
How an insurer intends to use Gen AI and its impacts should be
thoroughly assessed across the following five key areas before • Privacy: Develop systems that preserve data privacy.
embarking on a Gen AI journey:
• Safe and secure: Design and test systems to prevent
1. Strategic considerations data harms.
• Impact of data and AI: Consider the moral implication of uses • Role-based access control: Implement robust authentication
of data and AI and codify them into your organization’s values. and authorization mechanisms to restrict access to sensitive
Gen AI systems and data.
• External policy and regulation: Understand public policy
and regulatory trends to align compliance processes. 5. Leading practices
2. Internal controls • Use-case identification: Identify the concrete problem you
are solving for and whether it needs an AI or machine
• AI governance: Enable oversight of systems across the three
learning solution.
lines of defense.
• Industry standards: Follow industry standards and
• Internal compliance: Comply with organization policies and
best practices.
industry standards.
• Continuous monitoring: Implement continuous monitoring
5
to identify drift and risks.
Navigating the impact of generative AI on security | A CISO’s guide
Path forward to balance
Gen AI’s challenges and
opportunities
Gen AI offers immense potential for innovation and creativity across
the insurance value chain and processes. In this journey, it is critical
for the office of the CISO to tackle the unique security challenges and
ethical issues and stay on top of the ever-changing regulations in this
domain. With business teams eager to leverage Gen AI at scale as
early as possible, here are a few change management guardrails to
consider in the short and medium term to mitigate risks to insurers’
security posture:
• Educate employees on the potential risks of Gen AI
usage through in-person training, online courses, and
awareness workshops.
• Communicate the importance of transparency and accountability
to prevent bias, hallucinations, and other risks.
• Identify and protect sensitive training data, enforce access
controls, and implement data loss prevention to prevent leaks.
• Establish clear usage policies, assessment frameworks, and
diligence models to evaluate the credibility of third-party AI
solutions, with the do’s and don’ts of using AI-generated content
within the organization.
• Form an approval board comprising stakeholders from different
business units to define internal policies based on a risk
assessment framework, and oversee adherence to the same
while implementing Gen AI use cases.
6
Navigating the impact of generative AI on security | A CISO’s guide
Contacts
Sandee Suhrada Vishvam Raval
Principal Senior consultant
Deloitte Consulting LLP Deloitte Consulting LLP
ssuhrada@deloitte.com viraval@deloitte.com
Sunny Aziz Sharat Viswanathan
Principal Senior consultant
Deloitte & Touche LLP Deloitte Consulting LLP
saziz@deloitte.com sharviswanathan@deloitte.com
Rohan Shinde Meenakshi Rawat
Manager Senior consultant
Deloitte Consulting LLP Deloitte Consulting LLP
roshinde@deloitte.com merawat@deloitte.com
Endnotes
1. Allianz, Allianz risk barometer 2024, January 2024.
2. IBM, Cost of a data breach report, 2023.
3. Eliot Partnership, “The evolving role of chief information security officers in the insurance industry,” April 17, 2023.
4. Gartner, “Gartner poll finds 45% of executives say ChatGPT has prompted an increase in AI investment,” press release,
May 3, 2023.
5. Michael Sentonas, “Introducing Charlotte AI, CrowdStrike’s generative AI security analyst: Ushering in the future of AI-powered
cybersecurity,” CrowdStrike, May 30, 2023.
6. Deloitte, Trustworthy AI™, accessed January 23, 2024.
7. Gartner, “Gartner survey shows generative AI has become an emerging risk for enterprises,” press release, August 8, 2023.
8. While we acknowledge that regulations pertaining to Gen AI are continuously evolving, we have outlined some of the newly
drafted regulations as of October 1, 2023, for reference.
9. European Parliament, “EU AI Act: First regulation on artificial intelligence,” updated December 19, 2023.
10. Colorado Department of Regulatory Agencies, Division of Insurance, SB21-169 – Protecting Consumers from Unfair
Discrimination in Insurance Practices, accessed January 23, 2024.
11. National Association of Insurance Commissioners (NAIC), “NAIC Model Bulletin: Use of Algorithms, Predictive Models, and
Artificial Intelligence Systems by Insurers,” exposure draft, July 17, 2023.
7
About Deloitte
Deloitte refers to one or more of Deloitte Touche Tohmatsu Limited, a UK private
company limited by guarantee, and its network of member firms, each of which is a
legally separate and independent entity. Please see www.deloitte.com/about for a
detailed description of the legal structure of Deloitte Touche Tohmatsu Limited and
its member firms. Please see www.deloitte.com/us/about for a detailed description
of the legal structure of Deloitte LLP and its subsidiaries. Certain services may not
be available to attest clients under the rules and regulations of public accounting.
This publication contains general information only and Deloitte is not, by means of
this publication, rendering accounting, business, financial, investment, legal, tax, or
other professional advice or services. This publication is not a substitute for such
professional advice or services, nor should it be used as a basis for any decision
or action that may affect your business. Before making any decision or taking any
action that may affect your business, you should consult a qualified professional
adviser. Deloitte shall not be responsible for any loss sustained by any person who
relies on this publication.
Copyright © 2024 Deloitte Development LLC. All rights reserved.
8242799"
351,deloitte,us-ssoef-2024-breakout-innovation-ai-and-innovation-market-dynamics-and-the-opportunity-for-gbs.pdf,"AI & Innovation:
Market Dynamics &
the Opportunity for
GBS
Prakul Sharma & Shri Chary, April 4th, 2024
Welcome and Introductions
Prakul Sharma Shri Chary
Managing Director In Strategy & Healthcare and Life Sciences
Analytics Practice Lead
Deloitte Consulting LLP. Deloitte Consulting LLP.
Copyright © 2024 Deloitte Development LLC. All rights reserved.
Agenda
The Evolution of GBS
1
Unleash Value through AI & Innovation
2
3 Case Studies and Lessons Learned
4 Q&A
Image generated with DALL•E 2 with prompt:
“open road cutting through a futuristic city, colorful, <Leading
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Entertainment Conglomerate #1> movie”
The GBS market has evolved over the last two decades from execution roles to mature
value-added roles through innovation and service portfolio expansion
1990-2000 2000-2010 2010 - 2019 Beyond 2020
Business •Transactional processes •Expanded functional scope •Emergence of GS and CoEs •Strategic partnership with core business
Process Strategy •Basic support system •Focused global delivery models •Increased service coverage •Transformation & innovation partner
Workforce •Isolated workforce •Standardized workforce •Focus on global workforce strategy •Global skills hub for enterprise
Strategy management strategy •Integrated governance & management •Virtual and remote working spaces
Enterprise •Standalone tools and applications •Digital platforms, integrated tech, RPA •Automation, AI/ML and cognitive hub
•Basic computing technology
Technology •Increased focus on analytics •Enterprise tech. and ERP for services •Mature enterprise tech, consolidated systems
Business
Focus
Productivity and Operations Value
Based Improvement
Perform an existing process or service faster
without increasing cost and delivery time
Cost
Single Function Multi-Function Alignment End-to-end process
CCooppyyrriigghhtt ©© 22002244 DDeellooiittttee DDeevveellooppmmeenntt LLLLCC.. AAllll rriigghhttss rreesseerrvveedd.. 44
1
nuF
1
nuF
2
noitcnuF
3
nuF
3
nuF
Business Impact
Mature into service delivery partner delivering
business impact through innovation and
automation
Moving up the Value Chain
GS Lead
Increase breadth of services with
Global Process Owners
increased sharing (tools & people) E2E
Processes
Process 1
Multi-functional lead Process 2
Global Process Owners
Service Management
Comms / Training
Service Management IT facilities & support
Comms / Training
Region 1
IT Facilities & Support
Region 2
Region 3
tcudorP tnempoleveD remotsuC ecneirepxE
Process 3
Cost Arbitrage and Scalability
Perform an existing process or service at
lower cost, retaining same quality and
delivery time
1
noitcnuF
2
noitcnuF
sUB
GS
GBS organizations are increasingly assuming responsibility for strategic and high-value
business functions, transitioning towards a multi-functional scope creation…
Functions performed by Global Services organizations
1Traditional Functions
2Emerging Functions
Finance 91%
Advanced Capabilities
Finance1
Human Resources 62% IT1
FP&A HR1
Information Technology 57%
IT Strategy
Tax
Procurement 48% Procurement1
Enterprise Talent
Treasury Architecture Strategy
Tax 43% Design
Workforce Sourcing
Supply Chain/ Mfg Support 19% AF six se ed ts MAp ap in D tee nv a. n& c Planning Strategy
Legal 18% Increase YOY R2R Infra e C Bo em nep f. i t& s MaS nu ap gp el mier e nt ChaSa nl ne es l/ M Strk atg te. gy MS aa rkle es t i& n g2
Mgmt.
Sales and Marketing 13% Decrease YOY T&E L&D Contracting Pricing/Sales SP tr ro ad teu gc yt
Execution
Engineering/R&D* 10% Same IT Ops Employee Promotions / Legal2
O2C Separation Advertising Regulatory risk and
Sourcing compliance mgmt.
End Talent Sales Monitoring
P2P User Acq. Market & Reporting
Traditional functions delivered Services Analysis Customer inveL si tt ii gg aa tt ii oo nn sa un pd p ort Para legal services
Support
New/strategic functions delivered Talent Market Contract
*increase from 2019, as data was not available for 2021
Onboarding Research Legal research Mgmt. Customer Service C Su es rt vo im cee 1r
Admin. and analysis Complaints Strategy
Services Complaint Closure
~60% Global Business Services organizations perform more than 5 functions whichinclude both traditional (IT, Complaint Investigation Customer Service
finance, etc.)and strategic functions (e.g., sales & marketing, etc.). Allocation RC eo sm op lula tii on nt Analytics
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Digital infused operations are harnessing new-age technologies like intelligent automation,
AI (& Gen AI), and process mining to drive process enhancements & efficiencies
Evolution of Process Improvement
Exponential
Shift
Shift
Process Improvement by
leveraging new age
technologies such as
RPA, Process Mining,
Business Process GenAI etc.
Management Suites
Traditional approach to
for entire process
improve individual
improvement life
processes (elimination,
cycle
reducing hand-offs)
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eulaV
% of GS implementing different transformation levers
2023 Focus area in
Automation 59% Ranking next 1-3 years
1 1
Single instance ERP 47%
2 4
Case Management/ Workflow tool 45%
Centralized analytics reporting 3 5
28%
& Performance Dashboard
4 3
Global standard processes 28%
5 2
Self service 18%
6 6
Cloud 15%
7 10
Instilling a culture of innovation 11%
8 7
Data lake 8%
9 12
Agile 8%
10 11
Capability
Source: Deloitte Research and Analysis Source: Deloitte 2023 Shared Services Outsourcing Survey
Organizations are increasingly looking at their GBS centers to drive Process Improvement initiatives
More than 50% of GBS organizations are prioritizing process transformation and improvement as a key skill to be developed within their centers
For the next 3-5 years, there is an increased interest in global standard processes, centralized analytics, AI & Data and self-service
GBS organizations are pushing the boundaries to maximize value creation
GBS should expand its functional portfolio to include new service areas, while pioneering data-driven, digitally-infused
operations and facilitating enterprise-wide adoption of emerging technologies like GenAI
High Value Centre Digitally infused operations for established functions
(5% of total centers)
Cloud PaaS, SaaS
Data, AI & Analytics Data Lifecyccle, Advanced Analytics, AI/ML
Enterprise Technology ERP, CRM, BI
Integrated Workflows API Integration, Low-code/No-code
NLP, RPA, ML, AI, GenAI
Intelligent Automation & GenAI
Scaling emerging functions @speed
R&D Regulatory, Sustenance, NPD, Clinical
Commercial Sales, Marketing, Customer Services
Supply Chain Procurement, Vendor management
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tcapmI
fo
leveL
Transactional Core Tech Digital- Operations infused
Capabilities
Degree of GBS involvement in the parent’s business functions/processes
Level of Involvement
tcapmI
fo
eergeD
fo
smret
ni tnerap
no
evah
SBG
eht
tcapmi
fo
eergeD
devas
tsoc
ro
dedda
eunever
Potential strategic levers for GBS value creation
High
Research & Analysis, Paralegal Services, Regulatory
Legal
Support
Low
Low Degree of Involvement
Life Sciences Example - Adoption of emerging technologies across the GS value chain
Illustrative Example
AI Analytics RPA IoT Blockchain
Potential impact: Low Medium High
Drug discovery Manufacturing, Pharmacovigilance /
Regulatory and
research, and Clinical trials supply chain and Marketing and sales complaints
medical affairs
pre-clinical trials distribution management
Lead optimization Patient recruitment and Resource, demand and Market analysis and Market access and HEOR ADR intake / complaint Labeling, artwork and CMC
scheduling supply planning competitive intelligence capture
Safety assessment Clinical data management Quality testing, analysis and Patient access and support Sales support and Case / complaint processing Product registration and
documentation programs salesforce effectiveness clinical trial applications
Data management Protocol development & Procurement and vendor Product support Market support and Reporting Regulatory writing, review
design management effectiveness and submission
Biostatistics % statistical Distribution and logistics Contract management Signal and risk management Regulatory information
programming support management
Site management and trial Medical affairs
monitoring
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As organizations continue to invest in its digital core capability, GBS needs to integrate
Intelligent Automation, Generative AI and Data Management to accelerate value capture
TODAY WHAT IS NEXT WHY INTEGRATING GEN AI WITH IA IS IMPORTANT
Automation has delivered solid GenAI is poised to disrupt skilled workforce GenAI unlocks use-cases too complex for traditional IA, but it requires data, new
and sustainable value in dramatic ways capabilities, and a centralized approach
Many GBS organizations have
GenAI is poised to offer 25-35%
delivered solid and sustainable additional value1 when combined FAQ chatbots Personalized
&voiceagents omni-channel
value through automation, with traditional automation and AI experience
achieving 10%-40% savings tooling
Fraud
Code-Creation Detection
Marketing
ContentCreation
GenAI Capability support requires
Capabilities are self-sustaining
new ways to deal with trust in AI, Document
IA Capabilities and goals are
Instant
data management and change Comparison
driven largely by functional and Document answerson
mgmt. retooling
Extraction enterprisedata2
enterprise goals
Document
Extraction&
Generation3
Avoiding GenAI duplication (and
PolicyDocument
Function Demand is narrowing Text
lower returns) requires
Creation
Classification
Demand support from functions is centralized and coordinated
narrowing on GenAI, GBS support to bring E2E & Cross-
Capabilities, and external thought- Functional use-cases forward Use-cases represent common solution patterns for Generative AI
2Deloitte supported clients through Search, realizing 26% efficiency gains on RAG Retrieval and Semantic Search patterns
leadership 3Deloitte has built content generation GenAI applications for rganizations in Finance, Marketing, Regulatory, Commercial, Med-Tech
R&D and Pharma R&D
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To deliver full benefits of AI/Gen AI, Shared Services must focus on foundational
capabilities
Shared Services should focus on developing
Intelligent Automation and Analytics capabilities are among
core capabilities to deliver broader digital
top capabilities delivered through Shared Service today…
transformation impact
Process Excellence and Continuous Improvement 71% 23%
Reporting & Analytics 55% 36%
1 Foundational AI & Data Capability
Intelligent Automation 53% 36%
End-to-End Process Ownership 53% 32%
Change Management and Training 50% 31%
Customer Experience & User Centric Design
2
Business Process Mining & Mapping 31% 47%
Vendor Management 48% 26%
Business Continuity Planning 51% 22%
3 End to end process ownership
Knowledge & Content Management 39% 33%
Program Mgmt. & Transition Mgmt. 49% 23%
GBS Footprint Strategy 34% 36%
Knowledge and Content Management
Customer Experience & User Centric Design 31% 37% 4
Environmental, Social & Governance (ESG) 23% 31%
M&A Integration SWAT team 17% 20%
5 Talent Upskilling and Retention
Have implemented Planning to implement
Source: Deloitte Shared Services & Outsourcing Survey 2023
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To deliver full benefits of AI & Gen AI, Shared Services must focus on building the right
foundations across 5 key pillars and enabling a “platform” for innovation
• How do we consistently ideate, prioritize and execute a high volume of concurrent use case decisions to align funding model & value capture?
• How will the breadth of stakeholders impacted be aligned to achieve a cohesive Gen AI vision and business case?
Strategy • What should be the strategy to convince the board of an investment in the Gen AI space?
• How do we identify & address new IP, legal, ethical and regulatory risk?
• What are the new data architecture, data governance and data science patterns? How do we minimize AI & data silos?
Governance • How to evaluate & adapt to a rapidly evolving tech partner landscape?
• What unstructured data is needed & how do we make it usable? What should be the approach – training on public or private data for creating
Gen AI models?
• How do we combine Gen AI, Traditional AI, and Analytics? What new data and data science tools do we need?
Technology
• Should we build, buy or adopt Gen AI solutions and models?
• What are the roles, responsibilities, skills and delivery models needed to be successful at delivering Gen AI at scale? How do we access talent?
Talent, Org & • How do we support a culture of “AI First” & ensure Gen AI adoption?
Culture
• How do we establish a consistent and repeatable approach to execute the backlog of Gen AI initiatives?
• How can we empower the business to deliver Gen AI solutions with minimal investment?
• What are the delivery best practices required to rapidly propagate Gen AI across the enterprise?
Delivery
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Case Studies
Copyright © 2024 Deloitte Development LLC. All rights reserved.
Case Studies related to IA and GenAI delivery
Case-Study STRATEGIC DRIVER APPROACH TO BUILDING MOMENTUM –Supported by Deloitte
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secneicS
efiL
egraL
ynapmoC
The CEO has tasked their leadership team to develop a multi-year • Define Gen AI strategy, operating model, and delivery approach to
Established GenAI COE to AI/Generative AI (GenAI) strategy to deliver: productionize use cases supported by Deloitte team of ~200 resources)
automate use cases across GBS,
• 50+ GenAIuse cases developed by end of year (spread across all • Establish ethical AI framework for Gen AI
Corp, Commercial, R&D &
functions)
Supply Chain functions • Establish 10+ value realization teams to identify & Qualify Gen AI use cases
• Target 25%+ uplift in productivity from each use case
• Develop an organizational specific platform leveraging OpenAIand
Business Impact: ~$3B
• 10K colleagues access to internal platform (version of chatGPT) reusable solution pattern framework
knaB
egraL
ta
tiduA
lanretnI
Internal Audit (IA) function wanted to establish capability and
• Defined the Gen AI strategy that covered the 5 critical dimensions –
platform to drive critical productivity and innovation use cases for the
Strategy, Governance, Technology, Talent and Delivery / Op Model
Establish the GenAIcapability function:
and platform for addressing • Develop and mobilize the technology architecture curated to integrate with
• Reduce labor costs associated with audit reviews and generating
Internal Audit (IA) use case existing technology landscape
required standard documentations
requirements
• Develop and publish a playbook to support Gen AI use case development
• Utilizing historical data (structured and unstructured) to augment
Business Impact: 30% that factors all leading practices around LLM models, Model Ops, Trusted AI
audit findings
etc.
improvement in productivity
• Establish a Gen AI CoEfor IA that worked closely with broader
• Develop, review, prioritize and mobilize critical use case development
organization Gen AI CoE
labolG
ta
ecnaniF
SBG
ynapmoC
GBS function looking to automate financial anomalies detection to:
• Implemented automated anomaly detection modelwithin the Azure Cloud
Automate Variance
• Reduce labor costs associated with identifying fraud and the to identify automated detection of anomalous transactions in the accounts
AnomaliesDetection &
costs of unidentified fraud receivable database
Reporting for GBS-Finance
• Generate automatic text reports from identified fraud cases • Utilized state-of-the-art advances in language modeling to generate text-
Business Impact: 90% accuracy
based reports of explanations for anomalous transactions, greatly reducing
• Increase team’s throughput to service a large number of reports
in anomaly detection
time and labor costs required to report financial anomalies
without additional headcount
ediW
esirpretnE
cificepS
esaC-esU
Key Learnings And Take Aways
Platform Centric Approach Product Mindset
Setting up of the core platform, including its components and Product-oriented mindset is essential, particularly for application
architecture development work.
Democratize AI Business Analysts’ and Tech Teams
Gen AI services should be built as a shared platform taking into Techno-functional Business Analysts should work closely with Product
consideration scalability and data security. Owners.
Prototype First
Cross-Functional Teams
Validate your hypothesis with tools like Jupiter notebooks or just GPT
directly to ensure your idea works. Building Use cases is much more than just building a model. It heavily
revolves around application development and experience.
Prompt Engineering/Validation takes time
Platform Education for Use case Teams
Buffer time for prompt engineering and testing real data.
Understanding the AI CoE's functionalities, its capabilities
And how it can be leveraged to achieve business goals.
Learn how to Consume AI
LLM Response Evaluation Framework
Team needs to understand how to build products with AI. i.e., AI fits into
A well-defined framework during the High-Level Design phase
customer journeys and not the other way around.
CCooppyyrriigghhtt ©© 22002244 DDeellooiittttee DDeevveellooppmmeenntt LLLLCC.. AAllll rriigghhttss rreesseerrvveedd.. 1144
Q&A
Copyright © 2024 Deloitte Development LLC. All rights reserved.
About Deloitte
Deloitte refers to one or more of Deloitte ToucheTohmatsu Limited, a UK private company limited by guarantee (“DTTL”), its network of member firms, and their related entities. DTTL and each of its member
firms are legally separate and independent entities. DTTL (also referred to as “Deloitte Global”) does not provide services to clients. In the United States, Deloitte refers to one or more of the US member firms of
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This presentation contains general information only and Deloitte is not, by means of this presentation, rendering accounting, business, financial, investment, legal, tax, or other professional advice or services.
This presentation is not a substitute for such professional advice or services, nor should it be used as a basis for any decision or action that may affect your business. Before making any decision or taking any
action that may affect your business, you should consult a qualified professional advisor.
Deloitte shall not be responsible for any loss sustained by any person who relies on this presentation.
Copyright © 2024Deloitte Development LLC. All rights reserved.
Designed by CoRe Creative Services. RITM1653349"
353,deloitte,us-trustworthy-ai-cdoi-grmf-summary-v4.pdf,"Colorado Artificial Intelligence
(AI) Regulations:
Summary of Governance and Risk Management
Framework (GRMF) Requirements for Life
Insurance companies
Colorado Artificial Intelligence (AI) Regulations: Summary of Governance and Risk Management Framework (GRMF) Requirements for Life Insurance companies
Overview of Colorado Division of Insurance’s
(CDOI) AI Regulations
The AI regulatory landscape continues to mature as government Risk Management Framework, issued by the National Institute of
agencies refine and build upon previous guidance designed to Standards and Technology (NIST) in January 2023. Additionally, the
manage AI risk, to reasonably ensure equality and transparency, National Association of Insurance Commissioners (NAIC) continues
and to provide trust in automated systems. This is evidenced by to prioritize responsible data and AI use by insurers as reflected in its
recent publications including the White House’s Blueprint for an AI 2023 regulatory priorities.
Bill of Rights, issued in October 2022, and the Artificial Intelligence
Figure 1 — Timeline
2019
Jan 18, 2019 — NYFDS issues circular letter on “Use of AI”1
2020
Aug 14, 2020 — NAIC publishes “Principles on Artificial Intelligence”2
2022
Oct 4, 2022 — White House released its “Blueprint for an AI Bill of Rights”3
Jan 26, 2023 — National Institute of Standards and Technology releases its “AI Risk Management Framework”4
Feb 1, 2023 — CDOI releases its draft “Algorithm and Predictive Model Governance Regulation”5
Feb 13, 2023 — NAIC 2023 regulatory priorities6 include Data and Artificial Intelligence
May 16, 2023 — United States Senate sub-committee holds hearing on “Oversight of A.I.”7
2023
May 26, 2023 — CDOI releases a revised “Algorithm and Predictive Model Governance Regulation”
June 14, 2023 — European Parliament passes the “A.I. Act”8
Sep 8, 2023 — United States Senators Sen. Richard Blumenthal and Sen. Josh Hawley propose outline for
“Bipartisan Framework for U.S. AI Act”
Sep 21, 2023 — CDOI releases “Algorithm and Predictive Model Governance Regulation”9
Sept 23, 2023 — CDOI releases its draft “Algorithm and Predictive Model Quantitative Testing Regulation” 10
Nov 14, 2023 — CDOI “Algorithm and Predictive Model Governance Regulation” becomes effective
Continuing down the path of making the governance requirements more tangible, the CDOI released the AI regulation on September 21, 2023
(initial draft issued on February 1, 2023, and revised draft on May 26, 2023), and proposed a “draft” of its “Algorithm and Predictive Model
Quantitative Testing Regulation” on September 23, 2023.
Following active engagement with industry stakeholders, the CDOI intends to establish requirements for a life insurance company’s internal AI
Governance and Risk Management Framework (GRMF) as part of its adopted AI regulation and additional requirements related to testing and
reporting as part of its proposed quantitative testing regulation.
• Algorithm and Predictive Model Governance Regulation • DRAFT’ Algorithm and Predictive Model Quantitative Testing
(effective: November 14, 2023): The regulation is designed to Regulation (effective in 2024 (tentative)): The draft regulation builds
reasonably ensure that life insurers’ use of external consumer data upon the prior legislation from Colorado designed to minimize
and information sources (ECDIS), algorithms, and predictive models unfairly discriminatory insurance practices which may result from
(i.e., AI models) do not result in unfairly discriminatory insurance algorithms, AI, and the use of ECDIS. The regulation sets out testing
practices with respect to race. This is believed to be the first such and reporting requirements for insurers who leverage ECDIS
regulation on AI targeting insurers, and specifically life insurance. either directly or as an input to algorithms or models used in the
underwriting process.
Based on recent regulatory trends, there is a possibility that other states may follow suit and/or the scope of the regulation may be expanded
to other types of insurance (e.g., auto, property) and other industries utilizing ECDIS.
2
Colorado Artificial Intelligence (AI) Regulations: Summary of Governance and Risk Management Framework (GRMF) Requirements for Life Insurance companies
Requirements per CDOI’s adopted AI regulation include –
AI Governance and Risk Management Framework (GRMF)
Insurers dealing with life insurance are responsible to ensure that the following requirements are met,
including instances where third-party vendors are engaged:
01. AI governing principles (5A1) to ensure that use of ECDIS and AI 07. An up-to-date inventory of ECDIS and AI models (5A8) in use,
models using ECDIS are designed to prevent unfair discrimination including version control, detailed descriptions, purpose, problem
02. Board or board committee & senior management their use is intended to solve, potential risks, and safeguards
oversight (5A2&3) of GRMF, and for setting and monitoring the overall 08. Documented explanation of changes to the inventory (5A9) as
strategy on the use of ECDIS and AI models and provide direction for well as rationale for the change
AI governance 09. Documented description of testing to detect unfair
03. Documented cross-functional governance group (5A4) with discrimination (5A10) from use of ECDIS and AI models and steps
representatives from key functional areas taken to address disproportionate negative outcomes
04. Documented policies and procedures (5A5) iincluding assigned 10. Documented description of ongoing monitoring (5A11) of ECDIS
roles and responsibilities to ensure that ECDIS and AI models are and AI models including accounting for model drift ECDIS and AI
documented, tested, and validated models including accounting for model drift
05. Documented processes and protocols to address consumer 11. Documented description of process of selecting external
complaints and inquiries (5A6) about use of ECDIS and AI models resources (5A12) including third-party vendors
06. A rubric for assessing and prioritizing risks (5A7) associated 12. Annual reviews of the governance structure and risk
with the use of ECDIS and AI models, including insurance practices’ management framework (5A13) and updates to required
customer impact documentation to ensure its accuracy and relevance
Reporting (Once the final AI regulation goes into effect)
Insurers using ECDIS and AI models using Insurers that do not use ECDIS and AI Insurers that do not use ECDIS and AI
ECDIS should submit the following – models using ECDIS should submit the models using ECDIS as of the effective
following – date of this regulation, but subsequently
plan to use ECDIS and/or AI models using
ECDIS should submit the following –
• On June 1, 2024 (6A) A report summarizing • In one month (6C) from the effective date • Prior to the use (6D) of ECDIS or AI
progress toward complying with GRMF, areas of this regulation and on December 1 models and annually thereafter – A report
under development, challenges encountered, annually thereafter (6C) – An attestation summarizing compliance with GRMF
and expected completion date indicating they do not use ECDIS requirements, along with the title of
each individual responsible for ensuring
• On December 1, 2024, and annually
compliance (for each requirement). This
thereafter (6B) — A report summarizing
report must be signed by an officer attesting
compliance with GRMF requirements, along
to compliance with this regulation
with the title of each individual responsible for
ensuring compliance (for each requirement).
This report must be signed by an officer
attesting to compliance with this regulation
• Insurers using ECDIS may additionally
become subject to the newly proposed
Algorithm and Predictive Model Quantitative
Testing Regulation
3
Colorado Artificial Intelligence (AI) Regulations: Summary of Governance and Risk Management Framework (GRMF) Requirements for Life Insurance companies
Additionally, when using third-party vendors or external resources:
• The insurer is accountable for providing any documents required
by the CDOI
• Insurers may fulfill requests by letting third-party vendors provide
the required materials directly to the CDOI
• All components of the GRMF must be available upon request by
the CDOI on December 1, 2024, and annually thereafter
Key Terms
“External Consumer Data and Information Source” or “ECDIS” means, for the purposes of this regulation, a data or an information
source that is used by a life insurer to supplement or supplant traditional underwriting factors or other insurance practices or to establish
lifestyle indicators that are used in insurance practices. This term includes credit scores, social media habits, locations, purchasing habits,
home ownership, educational attainment, licensures, civil judgments, court records, occupation that does not have a direct relationship to
mortality, morbidity or longevity risk, consumer-generated Internet of Things data, biometric data, and any insurance risk scores derived by
the insurer or third-party from the above listed or similar data and/or information sources.
“Unfairly discriminate” and “Unfair discrimination”11 includes the use of one or more external consumer data and information
sources, as well as algorithms or predictive models using external consumer data and information sources, that have a correlation to race,
color, national or ethnic origin, religion, sex, sexual orientation, disability, gender identity, or gender expression, and that use results in a
disproportionately negative outcome for such classification or classifications, which negative outcome exceeds the reasonable correlation to
the underlying insurance practice, including losses and costs for underwriting.
“Bayesian Improved First Name Surname Geocoding” or “BIFSG” is a statistical modeling methodology developed by the RAND
Corporation aiming to help US-based organizations identify potential racial and ethnic incongruities amongst their datasets. The approach
utilizes both geocoded address information and administrative name data to predict a racial and ethnic probability for each data point.
BIFSG has been found to be 41% more accurate than similar modeling relying solely on surname data and 108% more accurate than utilizing
only geographic data.
4
Colorado Artificial Intelligence (AI) Regulations: Summary of Governance and Risk Management Framework (GRMF) Requirements for Life Insurance companies
Proposed Algorithm and Predictive Model Quantitative Testing
Regulation
The proposed draft regulation will apply to insurers who leverage ECDIS or models that utilize ECDIS to make or support underwriting
decisions. ECDIS data can include credit information; social media or purchasing habits; education, occupation, and licensure data; or
public records including home ownership or court records. Before delving into the testing and reporting requirements, insurers should first
understand their use of ECDIS, including by third-party input providers, to determine the applicability of these requirements.
This draft regulation prescribes two types of testing:
1. Logistic regression testing for application approval decisions
2. Linear regression testing for premium rates
Insurers must apply BIFSG estimated race and ethnicity to each of their application and premium datasets to perform testing of Hispanic,
Black and Asian Pacific Islander (API) applicants and insureds relative to White applicants and insureds. Dependent on outcomes of
the application approval and premium rate testing, variable testing of both datasets may be required to isolate and identify potentially
discriminate variables.
Testing database Application approvals Premium rates
Testing Methodology Logistic Regression Further Variable Testing Linear Regression Further Variable Testing
Logistic Regression Linear Regression
Event Scope All insurers using ECDIS Insurers with approval All insurers using ECDIS Insurers with a difference
(When is the testing in the underwriting rate differences of 5% or in the underwriting in premium rates of 5% or
required?) decisioning making greater as identified in the decisioning making greater per $1,000 of face
process Applications Approvals process amount as identified in
logistic regression testing the Premium Rates
Unfairly Discriminate A difference in approval Any difference in model A difference in premium Any difference in model
Threshold rates of 5% or greater coefficient for ECDIS rates of 5% or greater per coefficient for ECDIS
between racial or ethnic variables between the $1,000 of face amount variables between the
groups using BIFSG logistic regressions between racial or ethnic linear regressions
estimated race and groups using BIFSG
ethnicity variables estimated race and
ethnicity variables
Statistically significant differences in the results of the regression
testing of the race or ethnicity groups may require additional testing
and resolution, including insurers taking steps to remediate the
discriminatory outcomes identified. The proposed regulation calls on
insurers to establish accountability for the decisions driven from AI
and ensure fairness in their use of technology.
Reporting will be required for each ECDIS, algorithm, and predictive
model which utilizes ECDIS on an annual basis to the Colorado
Division of Insurance beginning April 2024, and annually thereafter.
As this regulation builds on prior CDOI AI regulations, additional
enforceable requirements may be provided in the near future. Life
insurance companies using ECDIS, whether directly or not, may be
subjected to specific remediation requirements or other regulatory
scrutiny resulting from the reporting requirements specified herein.
5
Colorado Artificial Intelligence (AI) Regulations: Summary of Governance and Risk Management Framework (GRMF) Requirements for Life Insurance companies
Deloitte’s Trustworthy AI™ Framework
At Deloitte, we recognized the importance of protections and for AI-fueled organizations to earn trust
in the AI-enabled assets and services provided to the public.
Deloitte’s Trustworthy AITM Framework and AI Governance systems may introduce or reinforce a risk of perpetuating
& Risk services help provide strategic and tactical solutions to inequity and historic bias, and enforceable regulations to protect
enable organizations to continue to build and use AI-powered the American public by reasonably ensuring equitable, ethical,
systems while promoting Trustworthy AI (Figure 2). and transparent AI may not only be critical but inevitable.
Deloitte recognizes that organizations and institutions are An indication of the future AI regulatory landscape can be
increasingly adopting AI and automated systems for their seen in recent proposed and enacted state and local laws
potential to revolutionize significant aspects of the American governing AI in specific use cases, such as AI-based performance
public’s daily lives from health care, to banking, to shopping, to evaluation and hiring decisions and loan underwriting models.12
leisure downtime, to many more. However, these innovations
can be particularly susceptible to a wide range of AI-related risks
through all phases of the AI life cycle. For example, AI-based
Figure 2 — Deloitte’s Trustworthy AI™ Framework (Framework)
6
Colorado Artificial Intelligence (AI) Regulations: Summary of Governance and Risk Management Framework (GRMF) Requirements for Life Insurance companies
Understanding GRMF requirements through Deloitte’s
Trustworthy AITM Framework
Deloitte’s Framework has been designed to assist insurance companies in operationalizing automated
systems safely and effectively, while protecting individuals and communities and adhering to emerging
regulations, such as those provided by CDOI (Figure 3).
Deloitte TWAITM Framework
7
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Documented AI governance principals outlining unfair   
discrimination protections
Board or board committee and senior management  
oversight + clear roles and responsibilities
Documented policies, processes, and procedures for   
creation, deployment, and use of ECDIS and AI models
AI models are safe and secure 
Documented process and protocol to address consumer  
inquiries and complaints
A rubric for assessing and prioritizing risks
Up to date inventory of ECDIS and AI models, with version    
control and rationale for changes documented
Testing to detect unfair discrimination   
Ongoing monitoring
Vender selection process    
Annual review of GRMF and the accuracy and relevance of  
associated documentation
gnitropeR
stnemeriuqer
Figure 3 — CDOI’s GRMF requirements and how they map to Deloitte’s
Trustworthy AITM Framework (Framework)
Report on progress  
Report on compliance  
Regular reporting  
Colorado Artificial Intelligence (AI) Regulations: Summary of Governance and Risk Management Framework (GRMF) Requirements for Life Insurance companies
The world is experiencing significant human capacity for insight, may set the tone for industry leading practices and other regulators
decision making, efficiency, and innovation—all dramatically looking to implement similar requirements. Additionally, CDOI has
expanded by cognitive systems—during a time in which trust is also suggested similar rules may be applied to other insurance
paramount. The tools that assist in unleashing world-changing lines or other AI or algorithmic uses. We recognize that navigating
capabilities should be trusted to act in line with human expectations AI implementation challenges can be complex, and Deloitte Risk
for ethics and appropriateness. The full potential of AI may hinge on & Financial Advisory is committed to helping our clients navigate
that confidence. these challenges agilely while establishing trust with stakeholders
and regulators. Deloitte Risk & Financial Advisory, with our suite of
The CDOI’s AI regulation will provide specific enforceable AI governance and risk assets and services, is dedicated to assisting
requirements for life insurance companies using ECDIS and AI our clients through the changing regulatory landscape and helping
models using ECDIS in Colorado. CDOI has leveraged high-level clients create trust, equity, and transparency as they serve the
principles, found in places like the federal regulatory guidelines, and American people.
attempted to turn them into reporting requirements for governance,
documentation, and reporting. While it is expected to only apply to
life insurance companies doing business in Colorado, the regulation
8
Colorado Artificial Intelligence (AI) Regulations: Summary of Governance and Risk Management Framework (GRMF) Requirements for Life Insurance companies
Contact us:
Oz Karan Satish Iyengar Richard Godfrey David Sherwood
Risk & Financial Advisory, Risk & Financial Advisory, Insurance Sector Leader, Risk & Insurance Regulatory Leader,
Trustworthy AI Leader, Partner Trustworthy AI — FSI Leader, Financial Advisory, Principal Risk & Financial Advisory,
Deloitte & Touche LLP Managing Director Deloitte & Touche LLP Managing Director
okaran@deloitte.com Deloitte & Touche LLP rgodfrey@deloitte.com Deloitte & Touche LLP
siyengar@deloitte.com dsherwood@deloitte.com
Contributor:
Ajay Ravikumar Jordan Baker Tim Cercelle Jordan Kuperschmid
Risk & Financial Advisory Risk & Financial Advisory Managing Director Principal
Trustworthy AI – FSI, Senior Manager Trustworthy AI Senior Manager Deloitte & Touche LLP Deloitte & Touche LLP
Deloitte AERS India Pvt. Ltd. Deloitte & Touche LLP tcercelle@deloitte.com jkuperschmid@deloitte.com
ajr@deloitte.com jorbaker@ deloitte.com
Endnotes
1 NYFDS issues circular letter on “Use of AI”
2 NAIC publishes “Principles on Artificial Intelligence”
3 White House released its “Blueprint for an AI Bill of Rights”
4 National Institute of Standards and Technology releases its “AI Risk Management Framework”
5 CDOI releases its draft “Algorithm and Predictive Model Governance Regulation”
6 NAIC 2023 regulatory priorities include Data and Artificial Intelligence
7 United States Senate sub-committee holds hearing on “Oversight of A.I.”
8 European Parliament passes the “A.I. Act”
9 CDOI releases “Algorithm and Predictive Model Governance Regulation”
10 CDOI releases its draft “Algorithm and Predictive Model Quantitative Testing Regulation”
11 “Unfairly discriminate” and “Unfair discrimination”
12 National Conference of State Legislatures Report on Legislation Related to AI
9
This document contains general information only and Deloitte is not, by means of this document,
rendering accounting, business, financial, investment, legal, tax, or other professional advice or
services. This document is not a substitute for such professional advice or services, nor should
it be used as a basis for any decision or action that may affect your business. Before making
any decision or taking any action that may affect your business, you should consult a qualified
professional advisor.
Deloitte shall not be responsible for any loss sustained by any person who relies on this document.
As used in this document, “Deloitte Risk & Financial Advisory” means Deloitte & Touche LLP, which
provides audit, assurance, and risk and financial advisory services; Deloitte Financial Advisory
Services LLP, which provides forensic, dispute, and other consulting services; and its affiliate,
Deloitte Transactions and Business Analytics LLP, which provides a wide range of advisory and
analytics services. These entities are separate subsidiaries of Deloitte LLP.
Please see www.deloitte.com/us/about for a detailed description of our legal structure. Certain
services may not be available to attest clients under the rules and regulations of public accounting.
Copyright © 2023 Deloitte Development LLC. All rights reserved.
Design by Core Creative Services | RITM1568262"
354,deloitte,us-tte-annual-report-2023.pdf,"State of Ethics and
Trust in Technology
Annual report
Second edition
Table of contents
Executive summary How companies Immediate actions
01 05 07
define and meet companies can take
4
ethical principles for to promote ethics
emerging technologies in technology
Introduction
02
23 32
6
Trustworthy and The importance
Emerging
06 08
03 Ethical principles of of collaboration in
technologies under
emerging technologies establishing ethical
consideration
principles
7 27
38
The need for
Promoting trust and
04
ethics in emerging 09
ethics in technology:
technologies
the way forward
10
45
2
Executive summary
01
Since the publication of our first State of corporate purpose, ethical principles, and This report presents the results of a study
Ethics and Trust in Technology Report in societal values, organizations can embrace conducted to gain perspective on how ethical
0022
2022, the rapid development of Generative internal and external collaboration in co-creating principles inform the development of emerging
Artificial Intelligence (AI) tools spurred ethical standards for technology. Leaders who technologies. Our research began by reviewing
the need for even greater attention to the define trustworthy and ethical principles for key takeaways from last year’s report and 0033
ethical dimensions of emerging technologies. the use of emerging technologies can create seeing how market shifts reinforced or altered
While Generative AI tools offer significant social, reputational, and financial value for their those findings. We interviewed 26 specialists
04
possibilities, it comes with the potential to organizations, cementing consumer trust and across industries to garner insights and test
inflict great human harm and reputational attracting future generations of talent. hypotheses. With these hypotheses, we launched
or financial damage to the organizations that a 64-question survey to over 1,700 business and 05
produce and use them. And while Generative This second edition Technology Trust Ethics technical professionals. The survey addressed
AI is the focus of today, similar potentials (TTE) Report explores trends in how industry the impact of Generative AI on organizations, the
06
for good and harm exist across all emerging leaders perceive and address ethical issues value placed on ethical principles for emerging
technologies. related to emerging technologies, with a focus technologies, and mechanisms to implement
on Generative AI. We articulate the need for ethical behavior throughout organizations.
07
Organizations can proactively promote organizations to consider formulating and
trustworthy and ethical principles at every promoting ethical principles in the context of We hope this report supports your journey
08
level and focus on incorporating ethics as part new and impending regulations. We identify to increasing trust and confidence in your
of the development and implementation of actions leaders and organizations may take to operations and contributing towards a more
technological products and services. To align help embed ethical principles into technological equitable society.
09
quality product development processes with development and deployment.
3
Key takeaways to consider
01
1 2 3
0022
Organizations should develop Organizations should design Organizations should proactively
trustworthy and ethical principles and apply tailored ethical embed trustworthy and ethical
0033
for emerging technologies, as principles specific to each of principles as part of a team-based
reinforced by the rapid impacts their technological products. collaborative development process
of Generative AI in the past year. for emerging technologies. 04
4 5
05
Organizations should actively Organizations who design and
seek to collaborate with other earnestly adopt trustworthy and 06
businesses, government ethical principles should benefit
agencies, and industry leaders from mitigating reputational
07
to create uniform, ethically and financial damage and
robust regulations for emerging reinforcing trust in employees
technologies. and stakeholders.
08
09
4
Introduction
01
When we wrote the 2022 report on ethics 2023 has seen rapid, significant progress in the
and trust in emerging technologies, the field of Generative AI. With the advancement
0022
state of artificial intelligence (AI) had of chatbots and other Generative AI tools,
been relatively stable over the past five suddenly the once-familiar AI ground has shifted
years. As such, the report focused on tremendously, opening new arenas of ethical 03
emerging technologies broadly, including inquiry. From questions of how Generative AI
autonomous vehicles, blockchain, and may exacerbate the digital divide to the potential
04
quantum computing. for plagiarism, distribution of harmful content
and misinformation, and worker displacement,
organizations find themselves wrestling 05
with new ethical issues posed by wide-scale
adoption of this once-again new technology.
06
As such, our report provides insight into how
organizations are approaching the ethics
of Generative AI along with other emerging
07
technologies. Furthermore, our report highlights
why organizations should embed trust into
08
every aspect of internal operations, strategy,
and decision-making, and the benefits from
meaningfully building trust and ethics.
09
55
Emerging technologies under consideration
01
“Emerging tech” refers to digitally enabled tools representing new and significant developments within a particular field.1
These technologies can be grouped into the following categories:
0022
0033
04
Cognitive Digital Reality Ambient Autonomous Quantum Distributed Robotics
Technologies Experiences Vehicles Computing Ledger
Technology (DLT)
05
including general including augmented including AI/ML including automotive, including quantum including blockchain, including robotic
and Generative AI, reality (AR), virtual assisted wearables, aerial, and maritime simulation, quantum crypto, non-fungible process automation
machine learning reality (VR), mixed voice assistants, linear algebra for token (NFT), and more 06
(ML), neural networks, reality (MR), voice and in-environment AI/ML, quantum
bots, natural language interfaces, speech devices optimization and
processing, neural recognition, ambient search, and quantum
07
nets, and more computing, 360° factorization
video, immersive
technologies,
08
computer vision,
and more
While these technologies are already in use and rapidly evolving, Generative AI received the 09
most attention this year for its groundbreaking potential to change the very nature of work.
6
Emerging technologies under consideration
Perceptions of emerging Industry leaders shared current and potential This year, quantum computing entered the top 01
benefits and misuses of these technologies (see three for the most potential for serious ethical
technologies
Figure 1 for a subset of responses). From last year risk; however, as a leader on AI said during one
0022
the biggest shift in perception of both positive of our interviews, “Quantum is at the later part of
As the basis for this report, we surveyed business
and negative outcomes occurred within cognitive early stage, still far out from real maturity—still in
leaders and developers of emerging technology
technologies. the stage of just initially testing it in production.”2
0033
about intended uses and broader implications
Potential issues could be forthcoming but are yet
of these technologies. Through the survey and
to be realized.
interviews with specialists, we gained insight into 04
these use cases.
Figure 1: Emerging technologies with the most potential for social good and ethical risk
05
Survey respondents felt the emerging According to survey respondents, emerging According to survey respondents, emerging
technologies with the most potential for social technologies with the most potential for technologies with the most potential for serious
social good: ethical risk:
good are cognitive technologies (39%)—which
06
includes Generative AI—digital reality (12%),
Down Down
Up 2 points Up Up 5 points Up
and ambient experiences (12%). Conversely,
6 points 1 point 16 points 1 point
07
respondents identified technologies with the
39 12 12 57 11 9
most potential for serious ethical risk as cognitive % % % % % %
technologies (57%), digital reality (11%), and
08
quantum computing (9%).
09
Cognitive Digital Ambient Cognitive Digital Quantum
technologies reality experiences technologies reality computing
7
Source: 2023 Deloitte Technology Trust Ethics Survey
Emerging technologies under consideration
01
Potential benefits and misuses
of emerging technologies 0022
A longstanding school of thought in critical
0033
technology studies and computer ethics known
as Values in Design (ViD) asserts technologies
are built using assumptions that express value 04
commitments.3 Thus, technologies encode values
into societies that adopt them.4 For example,
05
common surveillance technologies (e.g., doorbell
cameras) embed the value of the right to see
anything happening in or around one’s property, 06
but they also can infringe on users’ privacy. Because
these values are often unconscious, emerging
07
technologies have a range of potential impacts,
both beneficial and harmful.
08
09
8
01
The need for
0022
ethics in emerging
0033
technologies
04
As technologies grow more powerful, so does the
05
potential for harm. And with any technology-related
ethical misstep made by organizations, trust that
took years to build can erode in an instant. Given the
06
importance reputation can have on long-term success,
organizations should prioritize ethical principles.
07
08
09
9
The need for ethics in emerging technologies
01
Ethical missteps cause multiple types of damage
0022
Ignoring or downplaying ethical issues associated with emerging technologies comes at a cost. Ordered by perceived severity of damage to the
organization by respondents, these include the following:
0033
38 27 17 9 9
% % % % %
Reputational Human Regulatory Financial Employee
04
damage damage penalties damage dissatisfaction
Ethical missteps can leave customers Implementing emerging technologies Legal experts are scrambling to keep Reputational damage leading to loss Unethical behavior or lack of visible
distrusting the organization and before they are vetted, trained, up with emerging technologies, and of sales and costly lawsuits resulting attention to ethics can decrease a 05
tarnishing an organization’s hard- and tested to understand risks lawsuits filed allege harms such as from unethical behaviors can company’s ability to attract and keep
won positive brand sentiment. can cause severe and lasting harm copyright infringement,8 privacy negatively impact an organization’s talent. One study found employees
Reputational damage especially to individuals and communities. violations,9 harm to children and bottom line.11 While the adoption of of companies involved in ethical
06
affects younger generations, who Potential harms include violations teens,10 and more. Adopting a clear ethical principles cannot guarantee breaches lost an average of 50%
tend to be values-driven; as a result, of privacy, technology-assisted set of ethical principles in addition financial solvency, research suggests in cumulative earnings over the
Organizations should be clear on discrimination, challenges to human to a thoughtful implementation plan companies that implement ethics subsequent decade compared to
permissible uses of technology.5 agency, and job displacement. The may help companies proactively as part of their business philosophy workers in other companies.13 An
07
Organizations that commit to World Health Organization warned forestall these issues before are more profitable than those that interviewee for this report suggested
ethical and responsible practices for too-speedy adoption of Generative AI regulators take action. do not.12 productivity may decline as people
emerging technologies can build trust could potentially cause a plethora of become less motivated to work in
with stakeholders and differentiate harms, including misdiagnoses and unethical environments.14 Having a
08
themselves in the market. treatment biases.6 Thus, companies compromised employee base affects
could commit to ethical principles of many aspects of a company.
emerging technologies that articulate
not just guidelines, but specific goals, 09
metrics, and an understanding of
what a failure of these principles
might look like.7
10
The need for ethics in emerging technologies
01
The damages from ethical
missteps can add up. One study 0022
estimates workplace misconduct
cost US businesses $20 billion 0033
in 2021.15 Conversely, companies
that proactively establish and 04
uphold ethical principles in
05
technology use cases help foster
trust amongst stakeholders,
06
solidify their brand reputation,
and increase profitability.
07
08
09
11
> Hey, Chatbot.
01
0022
> What’s going on
0033
with Generative AI? 04
05
06
Generative AI is a noteworthy example of how we might expect to see emerging 07
technologies affect markets moving forward. With potential impacts and risks
in areas like information services, manufacturing, sustainability, science, and 08
healthcare, AI highlights the need for ethical standards.16
09
12
>
Uses of AI in industry
01
Generative AI is predicted to “change the nature of how we interact with all software”17 and to add $4.4 trillion in value annually to the global economy.18
AI’s power and ethical concerns alike come from its ability to automate tasks previously done by humans. Though Generative AI entered the mainstream
0022
less than a year ago, it has shown its influence in areas like generative design, ad and marketing campaigns, customer assistance, personalizing customer
experiences, and more.
0033
Despite the relative nascence of Generative AI in the marketplace, most companies surveyed are already testing or using
Generative AI tools:
04
74 65 31
% % %
05
have begun
06
have begun
have begun testing using Generative
using Generative
Generative AI AI technologies
AI technologies
technologies for external
internally
consumption 07
08
Given Generative AI’s newness, most organizations have 09
work to do in adapting responsibly to this tool.
13
>
Concerns with AI use
01
For all the buzz about Generative AI’s potential for productivity and profit, respondents expressed trepidation about its potential downsides. These
concerns are ranked below in descending order by the percentage of survey respondents who selected the issue as one of their top three concerns:
0022
22 14 12 12
% % % %
0033
Data privacy Transparency Data poisoning Intellectual property
and copyright
04
Data privacy is a big concern associated with Generative AI tools. Generative AI is trained on Generative AI tools depend Some Generative AI tools
Developers and scientists acknowledge machine learning-based millions of data points and on robust data training sets are trained on data that can
language models (LLMs) can inadvertently leak information from hundreds of features, leading for their effectiveness. These include copyrighted works,
05
the data used to train them, potentially exposing sensitive data to technically complex systems sets can be deliberately putting AI-generated work in
including personally identifiable information (PII). If LLMs are that often obscure how “poisoned” or “polluted” by murky legal territory.23, 24 To
designed without addressing data protection, it risks incidents like information is produced.21 To hackers and other bad actors, minimize legal risk, companies
06
training data extraction attacks, using queries to extract specific dispel “black box” concerns, leading to the propagation using works derived from AI
pieces of data.19 Companies using Generative AI tools are providing companies should focus on of inaccurate results.22 should attend to issues of
workarounds to protect data privacy. For instance, one person creating transparent and Companies should focus on ownership in IP and copyright.
interviewed for this report remarked his company engaged a third- explainable AI solutions. safe and secure information 07
party company to provide software that takes a sample of data and sets, assuring customers
creates a dataset with no connectivity to original source data.20 of the data’s provenance in
trusted sources. 08
09
14
>
Concerns with AI use
01
12 9 8 7 3
% % % % %
Data provenance Data “hallucinations” Authentic experiences Job displacement Static data 0022
Knowing where your data comes Generative AI tools are known The sophistication of A report released in June 2023 “Legacy analytic solutions”
from and what it contains is key. to make up or “hallucinate” Generative AI tools makes suggested AI contributed to (i.e.siloed datasets) produces 0033
Without this understanding, AI data, including fabricating it difficult to distinguish nearly 4,000 job losses in the inaccurate AI results.30 Solutions
tools can extrapolate biases, information like names and between human-generated previous month.29 Companies should design, test and
leading to adverse customer dates, medical explanations, and computer-generated should consider using AI to release with current data that
04
affects, skewed outcomes, and plots of books, citations, text, images, and videos.27 offset tasks to make human provides validated answers. For
lower accuracy.25 In addition, and even historical events.26 Companies should consider work more productive and systems that do not use recent
several types of bias errors Companies should ensure adopting ethical frameworks implement job upskilling information, disclosures should
05
can be introduced from the their AI systems are sufficiently like the US government’s “AI Bill where appropriate. be pronounced and frequent.
human side, including sample/ robust in their training and of Rights,” which reserves the
selection bias, exclusion reliable in their outputs to right of users to know when
bias, measurement bias, and minimize the potential for they are interacting with a 06
association bias. hallucinations. human versus a bot.28
07
08
Companies should adopt ethical frameworks like the US government’s “AI Bill of Rights,” 09
which reserves the right of users to know when they are interacting with a human versus a bot.28
15
>
Concerns with AI use
01
Figure 2. Top pressing ethical concerns with using Generative AI
(Percentage)
0022
25
22%
0033
20
04
15
14%
05
12% 12% 12%
10
9%
06
8%
7%
5
07
3%
0 08
Data Privacy Transparency Data Poisoning IP Ownership Data Provenance Hallucinations Authentic Job Static Data
Experiences Displacement
09
Source: 2023 Deloitte Technology Trust Ethics Survey
16
>
How organizations can safely incorporate AI
01
To harness the transformative power of AI Exploration
effectively and ethically, companies should To start, companies can familiarize
0022
consider assessing and rethinking their themselves with the technology and
development strategy. Below is a multi-step development approaches. Exploring use cases
framework to assist companies in integrating can foster innovation and lay the groundwork 0033
emerging technologies. for creating road maps to incorporate
Generative AI. Exploration could consist of
04
workshops in which teams of product owners,
AI/ML practitioners, and business leaders
brainstorm, then rank by return-on- 05
investment areas in which AI/ML might create
value to the company. “Value” here consists of
06
both profits as well as brand value like reliability,
company trust, and social goodwill. Companies
can develop qualitative and quantitative cost/
07
benefit analyses, weighing the impacts of
incorporating AI against the risks.
08
Exploring use cases can foster innovation and lay the 09
groundwork for creating road maps to incorporate Generative AI.
17
>
How organizations can safely incorporate AI
01
Foundational Whether to buy or build platforms depends on Figure 3. Approaches to building
foundational Generative AI capabilities
Incorporating Generative AI into a the type of business. For instance, higher tech
(Percentage) 0022
business could require building or companies tend to build their own AI platforms.
identifying an internal data foundation for an As one person interviewed points out, companies
6
LLM. Companies can thus decide whether to who build their own platforms can more readily 5 0033
collaborate with existing platforms or hire talent write ethical standards into their specs.31 By 8
30
to build in-house. contrast, life sciences companies often do not
04
build in-house and rely on vendors for data
Among survey respondents, 30% indicated their solutions. Buying a platform or collaborating with
companies opted to use existing capabilities third parties requires extending the company’s 26 05
through major AI platforms, 24% of respondents’ trust and reliability, so companies should carefully
24
companies used private capabilities through review potential collaborators and their products
06
major platform developers, 26% created custom for ethical principles.
private tools in collaboration with major platform
Utilizing public-based capabilities through
developers, 8% built a complete platform major platform developers
07
Utilizing private-instance capabilities through
in-house, 6% were unsure, and 5% opted not to
major platform developers
use Generative AI at all.
Partnering with major platform developers
to develop custom, private instance 08
Building completely in-house
We are not planning to use Generative AI
09
Unsure
Source: 2023 Deloitte Technology Trust Ethics Survey
18
>
How organizations can safely incorporate AI
01
Governance Trainings/Education Pilots
Formulating and abiding by robust Training could encompass courses As part of introducing AI, companies
0022
standards and protocols can help in the ethical principles of the should consider proof of concepts
forestall potential risks and harms of Generative company governing AI, and technical training and pilot programs. By doing so, an interviewee
AI. Before developing a specific set of standards that focuses on the diverse LLMs and how use says, engineers and product leaders can initiate 0033
and policies governing AI, the company should cases should be enabled. With both types of experiments with different use cases and run a
first consider defining ethical principles. 56% of training, employees can feel more invested variety of product tests.34 Pilots that fail to meet
04
respondents say their company does not have or and empowered. requirements or are deemed too high-risk can be
are unsure if they have ethical principles guiding cancelled at this stage.
the use of Generative AI. 05
Additionally, another specialist suggests, pilots
For governance, companies should consider and proofs of concepts can provide time to
06
AI Centers of Excellence (CoE), comprised of discuss the ethical, legal, regulatory, risk, and
internal experts that develop, scale, and oversee operational aspects of Generative AI.35
AI strategy throughout the enterprise.32 One
07
person interviewed suggests by centralizing
the development of AI and creating an internal
08
CoE, companies may have better control over
how adoption happens.33 The CoE could lead
implementation of AI, creating standards,
09
responsibility frameworks, and guidelines, and
developing trainings and education.
19
>
How organizations can safely incorporate AI
01
Implementation Companies should consider accountability for Audit
A successful implementation strategy product implementation, establishing product Companies will likely need to scale
0022
should include roadmapping, ownership and reporting structures for failures and adjust their policies to account
assignment of accountability, and built-in plans and other issues. for the potentially harmful impacts of AI tools,
for transparency. according to one interviewee.36 Another 0033
Companies should have a transparency recommends establishing a feedback system
Product leaders, in concert with the CoE, can strategy, defining what happens with user data, to make sure products aren’t manipulated for
04
create launch plans and product roadmaps to how the model arrives at a solution, and the bad intentions.37
help bring the newly enhanced products to confidence level of the model (i.e., how likely it is
market. Once released to the public, the company to “hallucinate”). 05
should have a team of data scientists and AI/ML
experts ready to boost the product’s capabilities
06
and address issues..
07
08
Companies should assign accountability for product implementation, establishing product 09
ownership and reporting structures for failures and other issues.
20
>
How do Generative AI tools impact
human workers?
01
Job displacement was ranked low on the list As one interviewee asserts, AI is not coming Figure 4. How respondents’ companies
handle employees displaced by Generative AI
of concerns by survey respondents compared for our jobs, but rather our tasks.38 Embracing
(Percentage) 0022
to issues like data privacy and transparency. AI and automating routine tasks can allow
But still, what happens to workers when this workers to pursue higher-level activities.
technology is deployed? Another person interviewed points out 0033
27
integrating Generative AI creates new jobs
Among respondents, 49% said workers at (for example, “Prompt Engineer”).39
04
their organization displaced by AI moved to
49
different roles and retrained and upskilled. For workers displaced by tech, companies can
13% moved to different roles but not retrained invest in upskilling and retraining; some 11 05
or upskilled. 11% are terminated. And 27% of organizations have programs to pay for
respondents have not had workers displaced employee retraining.40 Thus, companies might 13
06
by AI at their organization. frame the adoption of Generative AI not as
tech replacement but an opportunity for
They are moved to different roles and are
change management. re-trained/upskilled
07
They are moved to different roles but are not
re-trained/upskilled
They are terminated
08
N/A: This does not happen within my
organization
09
Source: 2023 Deloitte Technology Trust Ethics Survey
21
How companies define and meet ethical
principles for emerging technologies
01
0022
As technology is moving faster than Companies can establish ethical principles governing emerging technologies through four approaches:
regulation, the onus for creating ethically
sound technologies is increasingly placed on 1 2 0033
companies that design and develop those
technologies.
04
By meeting compliance and regulatory Following company culture (up 7
standards (up 2 percentage points in percentage points from last year)
individual survey responses from last year)
05
This approach to instilling ethical principles relies
The focus of this approach is operating within on standards set by company culture, defined
06
legal, published guidelines minimally impacted as the sum of formal and informal systems,
by company values. behaviors, and values, all of which help create an
experience for employees and customers.
07
08
09
22
How companies define and meet ethical principles for emerging technologies
3 4 01
0022
Following standards of conduct Defining specific ethical standards Companies that create or use Generative AI
(down 4 percentage points from last year) (down 5 percentage points from last year) products need to be familiar with established
standards, internal policies, and procedures: 0033
The focus of this approach relies on standards of In this approach, companies establish ethical industry-produced documents like the Data &
conduct, defined as guiding pillars that manage standards specific to the organization and the Trust Alliance’s Algorithmic Bias Safeguards for
04
an employee’s entire professional responsibilities. products and services developed and used. Workforce41 and Responsible Data & AI Diligence
They include things like avoiding discrimination, for M&A,42 governmental regulations like the
conflicts of interest, insider trading, bribery, and 60% of respondents indicate their company European Union’s General Data Protection 05
other commonly unaccepted ethical behaviors. considers their mission, purpose, and values Regulation’s (GDPR),43 the National Institute
when navigating emerging technologies. of Standards and Technology (NIST) AI Risk
06
However, the survey indicates fewer companies Management Framework,44 and academic reports
use approach 4 (defining ethical standards like the Berkman Klein Harvard report, which
specific to technology), arguably the most puts forward eight key principles on maximizing
07
ethically robust of the four approaches. the benefits and minimizing the harms of AI.45
Applying ethical principles from one emerging
08
technologies (like quantum computing) to
another (like autonomous vehicles) is inadvisable
because each technology are different.
09
2233
How companies define and meet ethical principles for emerging technologies
01
89% (up 2% from 2022) of survey respondents Figure 5. Percentage of companies surveyed with
standards specific to given emerging technologies
said, except for AI principles, their company
(Percentage) 0022
does not have or are unsure if they have specific
trustworthy and ethical principles governing
80
emerging tech products. Among those that define 72% 0033
principles specific to certain kinds of technology,
the most common is cognitive technologies 60
04
(72%), followed by digital reality (48%), ambient
48%
45%
experiences (45%), distributed ledger (31%),
quantum computing (29%), robotics (27%), and 40 05
31%
autonomous vehicles (24%) (see Figure 5). 29%
27%
24%
20 06
07
0
Cognitive Digital Ambient Distributed Quantum Robotics Autonomous
Technologies Reality Experiences Ledger Computing Vehicles
08
Source: 2023 Deloitte Technology Trust Ethics Survey
For companies who take a robust ethical route—i.e., defining ethical principles 09
specific to each of their products—doing so requires planning.
2244
How companies define and meet ethical principles for emerging technologies
01
Ethical principles should be Figure 6: Frequency at which organizations update ethical principles
(Percentage)
updated frequently 0022
60
Companies implementing a regular ethical review 51%
0033
process for emerging technology products can
42%
build trust, create higher quality products, and be
40 38%
leaders in safeguarding a common social good. 35% 04
The survey shows a trend of companies updating
their principles frequently, moving from a longer
05
cycle to a quarterly or better (53% of companies,
20
up 10 percentage points from 2022). Those with 15%
slower review cycles may find their principles no 8% 06
6%
5%
longer apply to the products and services they
1%
0%
are meant to govern. 0
Monthly (at least) Quarterly Annually Less than annually Never 07
2022 2023
08
Source: 2023 Deloitte Technology Trust Ethics Survey
The survey shows a trend of companies updating their principles frequently, 09
moving from a longer cycle to a quarterly or better approach.
2255
Trustworthy and
Figure 7: Deloitte’s Technology Trust Ethics (TTE) Framework
Ethical Principles Reskilling & Education
01
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Trustworthy and Ethical principles of emerging technologies
The principles are ordered by their relative importance according to survey respondents:
01
0022
Responsible Safe and secure Transparent and Robust and reliable Accountable
The technology is created The technology is protected explainable The technology produces Policies in place to determine
0033
and operated in a socially from risks that may cause Users understand how consistent and accurate outputs, who is responsible for the
responsible manner. individual and / or collective technology is being leveraged, withstands errors, and recovers decisi"